The Python Podcast.__init__

**Update Your Model's View Of The World In Real Time With Streaming Machine Learning Using River** Dec 12, 2022
Preamble

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

Summary

The majority of machine learning projects that you read about or work on are built around batch processes. The model is trained, and then validated, and then deployed, with each step being a discrete and isolated task. Unfortunately, the real world is rarely static, leading to concept drift and model failures. River is a framework for building streaming machine learning projects that can constantly adapt to new information. In this episode Max Halford explains how the project works, why you might (or might not) want to consider streaming ML, and how to get started building with River.

Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.

Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!

Your host is Tobias Macey and today I’m interviewing Max Halford about River, a Python toolkit for streaming and online machine learning

Interview

Introduction

How did you get involved in machine learning?

Can you describe what River is and the story behind it?

What is "online" machine learning?

What are the practical differences with batch ML?

Why is batch learning so predominant?

What are the cases where someone would want/need to use online or streaming ML?

The prevailing pattern for batch ML model lifecycles is to train, deploy, monitor, repeat. What does the ongoing maintenance for a streaming ML model look like?

Concept drift is typically due to a discrepancy between the data used to train a model and the actual data being observed. How does the use of online learning affect the incidence of drift?

Can you describe how the River framework is implemented?

How have the design and goals of the project changed since you started working on it?

How do the internal representations of the model differ from batch learning to allow for incremental updates to the model state?

In the documentation you note the use of Python dictionaries for state management and the flexibility offered by that choice. What are the benefits and potential pitfalls of that decision?

Can you describe the process of using River to design, implement, and validate a streaming ML model?

What are the operational requirements for deploying and serving the model once it has been developed?

What are some of the challenges that users of River might run into if they are coming from a batch learning background?

What are the most interesting, innovative, or unexpected ways that you have seen River used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on River?

When is River the wrong choice?

What do you have planned for the future of River?

Contact Info

Email

@halford_max on Twitter

MaxHalford on GitHub

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

River

scikit-multiflow

Federated Machine Learning

Hogwild! Google Paper

Chip Huyen concept drift blog post

Dan Crenshaw Berkeley Clipper MLOps

Robustness Principle

NY Taxi Dataset

RiverTorch

River Public Roadmap

Beaver tool for deploying online models

Prodigy ML human in the loop labeling

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Sponsored By:
Linode: Do you want to try out some of the tools and applications that you heard about on Podcast.\_\_init\_\_? Do you have a side project that you want to share with the world? With Linode's managed Kubernetes platform it's now even easier to get started with the latest in cloud technologies. With the combined power of the leading container orchestrator and the speed and reliability of Linode's object storage, node balancers, block storage, and dedicated CPU or GPU instances, you've got everything you need to scale up. Go to [pythonpodcast.com/linode](https://www.pythonpodcast.com/linode) today and get a $100 credit to launch a new cluster, run a server, upload some data, or... And don't forget to thank them for being a long time supporter of Podcast.\_\_init\_\_!

**Declarative Machine Learning For High Performance Deep Learning Models With Predibase** Dec 05, 2022
Preamble

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

Summary

Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep learning and Horovod for horizontally distributing model training. In this episode CTO and co-founder of Predibase, Travis Addair, explains how they are reducing the burden of model development even further with their managed service for declarative and low-code ML and how they are integrating with the growing ecosystem of solutions for the full ML lifecycle.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host is Tobias Macey and today I’m interviewing Travis Addair about Predibase, a low-code platform for building ML models in a declarative format

Interview

Introduction

How did you get involved in machine learning?

Can you describe what Predibase is and the story behind it?

Who is your target audience and how does that focus influence your user experience and feature development priorities?

How would you describe the semantic differences between your chosen terminology of "declarative ML" and the "autoML" nomenclature that many projects and products have adopted?

Another platform that launched recently with a promise of "declarative ML" is Continual. How would you characterize your relative strengths?

Can you describe how the Predibase platform is implemented?

How have the design and goals of the product changed as you worked through the initial implementation and started working with early customers?

The operational aspects of the ML lifecycle are still fairly nascent. How have you thought about the boundaries for your product to avoid getting drawn into scope creep while providing a happy path to delivery?

Ludwig is a core element of your platform. What are the other capabilities that you are layering around and on top of it to build a differentiated product?

In addition to the existing interfaces for Ludwig you created a new language in the form of PQL. What was the motivation for that decision?

How did you approach the semantic and syntactic design of the dialect?

What is your vision for PQL in the space of "declarative ML" that you are working to define?

Can you describe the available workflows for an individual or team that is using Predibase for prototyping and validating an ML model?

Once a model has been deemed satisfactory, what is the path to production?

How are you approaching governance and sustainability of Ludwig and Horovod while balancing your reliance on them in Predibase?

What are some of the notable investments/improvements that you have made in Ludwig during your work of building Predibase?

What are the most interesting, innovative, or unexpected ways that you have seen Predibase used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Predibase?

When is Predibase the wrong choice?

What do you have planned for the future of Predibase?

Contact Info

LinkedIn

tgaddair on GitHub

@travisaddair on Twitter

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Predibase

Horovod

Ludwig

Podcast.__init__ Episode

Support Vector Machine

Hadoop

Tensorflow

Uber Michaelangelo

AutoML

Spark ML Lib

Deep Learning

PyTorch

Continual

Data Engineering Podcast Episode

Overton

Kubernetes

Ray

Nvidia Triton

Whylogs

Data Engineering Podcast Episode

Weights and Biases

MLFlow

Comet

Confusion Matrices

dbt

Data Engineering Podcast Episode

Torchscript

Self-supervised Learning

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

**Build Better Machine Learning Models With Confidence By Adding Validation With Deepchecks** Nov 28, 2022
Preamble

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

Summary

Machine learning has the potential to transform industries and revolutionize business capabilities, but only if the models are reliable and robust. Because of the fundamental probabilistic nature of machine learning techniques it can be challenging to test and validate the generated models. The team at Deepchecks understands the widespread need to easily and repeatably check and verify the outputs of machine learning models and the complexity involved in making it a reality. In this episode Shir Chorev and Philip Tannor explain how they are addressing the problem with their open source deepchecks library and how you can start using it today to build trust in your machine learning applications.

Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.

Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.

Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!

Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!

Your host is Tobias Macey and today I’m interviewing Shir Chorev and Philip Tannor about Deepchecks, a Python package for comprehensively validating your machine learning models and data with minimal effort.

Interview

Introduction

How did you get involved in machine learning?

Can you describe what Deepchecks is and the story behind it?

Who is the target audience for the project?

What are the biggest challenges that these users face in bringing ML models from concept to production and how does DeepChecks address those problems?

In the absence of DeepChecks how are practitioners solving the problems of model validation and comparison across iteratiosn?

What are some of the other tools in this ecosystem and what are the differentiating features of DeepChecks?

What are some examples of the kinds of tests that are useful for understanding the "correctness" of models?

What are the methods by which ML engineers/data scientists/domain experts can define what "correctness" means in a given model or subject area?

In software engineering the categories of tests are tiered as unit -> integration -> end-to-end. What are the relevant categories of tests that need to be built for validating the behavior of machine learning models?

How do model monitoring utilities overlap with the kinds of tests that you are building with deepchecks?

Can you describe how the DeepChecks package is implemented?

How have the design and goals of the project changed or evolved from when you started working on it?

What are the assumptions that you have built up from your own experiences that have been challenged by your early users and design partners?

Can you describe the workflow for an individual or team using DeepChecks as part of their model training and deployment lifecycle?

Test engineering is a deep discipline in its own right. How have you approached the user experience and API design to reduce the overhead for ML practitioners to adopt good practices?

What are the interfaces available for creating reusable tests and composing test suites together?

What are the additional services/capabilities that you are providing in your commercial offering?

How are you managing the governance and sustainability of the OSS project and balancing that against the needs/priorities of the business?

What are the most interesting, innovative, or unexpected ways that you have seen DeepChecks used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on DeepChecks?

When is DeepChecks the wrong choice?

What do you have planned for the future of DeepChecks?

Contact Info

Shir

LinkedIn

shir22 on GitHub

Philip

LinkedIn

@philiptannor on Twitter

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

DeepChecks

Random Forest

Talpiot Program

SHAP

Podcast.__init__ Episode

Airflow

Great Expectations

Data Engineering Podcast Episode

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

**Build A Full Stack ML Powered App In An Afternoon With Baseten** Nov 21, 2022
Preamble

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

Summary

Building an ML model is getting easier than ever, but it is still a challenge to get that model in front of the people that you built it for. Baseten is a platform that helps you quickly generate a full stack application powered by your model. You can easily create a web interface and APIs powered by the model you created, or a pre-trained model from their library. In this episode Tuhin Srivastava, co-founder of Basten, explains how the platform empowers data scientists and ML engineers to get their work in production without having to negotiate for help from their application development colleagues.

Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host is Tobias Macey and today I’m interviewing Tuhin Srivastava about Baseten, an ML Application Builder for data science and machine learning teams

Interview

Introduction

How did you get involved in machine learning?

Can you describe what Baseten is and the story behind it?

Who are the target users for Baseten and what problems are you solving for them?

What are some of the typical technical requirements for an application that is powered by a machine learning model?

In the absence of Baseten, what are some of the common utilities/patterns that teams might rely on?

What kinds of challenges do teams run into when serving a model in the context of an application?

There are a number of projects that aim to reduce the overhead of turning a model into a usable product (e.g. Streamlit, Hex, etc.). What is your assessment of the current ecosystem for lowering the barrier to product development for ML and data science teams?

Can you describe how the Baseten platform is designed?

How have the design and goals of the project changed or evolved since you started working on it?

How do you handle sandboxing of arbitrary user-managed code to ensure security and stability of the platform?

How did you approach the system design to allow for mapping application development paradigms into a structure that was accessible to ML professionals?

Can you describe the workflow for building an ML powered application?

What types of models do you support? (e.g. NLP, computer vision, timeseries, deep neural nets vs. linear regression, etc.)

How do the monitoring requirements shift for these different model types?

What other challenges are presented by these different model types?

What are the limitations in size/complexity/operational requirements that you have to impose to ensure a stable platform?

What is the process for deploying model updates?

For organizations that are relying on Baseten as a prototyping platform, what are the options for taking a successful application and handing it off to a product team for further customization?

What are the most interesting, innovative, or unexpected ways that you have seen Baseten used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Baseten?

When is Baseten the wrong choice?

What do you have planned for the future of Baseten?

Contact Info

@tuhinone on Twitter

LinkedIn

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Baseten

Gumroad

scikit-learn

Tensorflow

Keras

Streamlit

Podcast.__init__ Episode

Retool

Hex

Podcast.__init__ Episode

Kubernetes

React Monaco

Huggingface

Airtable

Dall-E 2

GPT-3

Weights and Biases

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

**Skip Straight To The Fun Part Of Your Project With PyScaffold** Nov 07, 2022
Summary

Starting a new project is always exciting and full of possibility, until you have to set up all of the repetitive boilerplate. Fortunately there are useful project templates that eliminate that drudgery. PyScaffold goes above and beyond simple template repositories, and gives you a toolkit for different application types that are packed with best practices to make your life easier. In this episode Florian Wilhelm shares the story behind PyScaffold, how the templates are designed to reduce friction when getting a new project off the ground, and how you can extend it to suit your needs. Stop wasting time with boring boilerplate and get straight to the fun part with PyScaffold!

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Florian Wilhelm about PyScaffold, a Python project template generator with batteries included

Interview

Introductions

How did you get introduced to Python?

Can you describe what PyScaffold is and the story behind it?

What is the main goal of the project?

There are a huge number of templates and starter projects available (both in Python and other languages). What are the aspects of PyScaffold that might encourage someone to adopt it?

What are the different types/categories of applications that you are focused on supporting with the scaffolding?

For each category, what is your selection process for which dependencies to include?

How do you approach the work of keeping the various components up to date with community "best practices"?

Can you describe how PyScaffold is implemented?

How have the design and goals of the project changed since you first started it?

What is the user experience for someone bootstrapping a project with PyScaffold?

How can you adapt an existing project into the structure of a pyscaffold template?

Are there any facilities for updating a project started with PyScaffold to include patches/changes in the source template?

What are the most interesting, innovative, or unexpected ways that you have seen PyScaffold used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on PyScaffold?

When is PyScaffold the wrong choice?

What do you have planned for the future of PyScaffold?

Keep In Touch

Website

LinkedIn

FlorianWilhelm on GitHub

@florianwilhelm on Twitter

Picks

Tobias

Daredevil TV series

Florian

The Peripheral

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

PyScaffold

Innovex

SAP

Cookiecutter

Pytest

Podcast Episode

Sphinx

pre-commit

Podcast Episode

Black

Flake8

Podcast Episode

Poetry

Setuptools

mkdocs

ReStructured Text

Markdown

Setuptools-SCM

Hatch

Flit

Versioneer

Gource git visualization

MyPy Compiler

Rust Cargo

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Add Configuration Best Practices To Your Application In An Afternoon With Dynaconf** Oct 30, 2022
Summary

Application configuration is a deceptively complex problem. Everyone who is building a project that gets used more than once will end up needing to add configuration to control aspects of behavior or manage connections to other systems and services. At first glance it seems simple, but can quickly become unwieldy. Bruno Rocha created Dynaconf in an effort to provide a simple interface with powerful capabilities for managing settings across environments with a set of strong opinions. In this episode he shares the story behind the project, how its design allows for adapting to various applications, and how you can start using it today for your own projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Bruno Rocha about Dynaconf, a powerful and flexible framework for managing your application’s configuration settings

Interview

Introductions

How did you get introduced to Python?

Can you describe what Dynaconf is and the story behind it?

What are your main goals for Dynaconf?

What kinds of projects (e.g. web, devops, ML, etc.) are you focused on supporting with Dynaconf?

Settings management is a deceptively complex and detailed aspect of software engineering, with a lot of conflicting opinions about the "right way". What are the design philosophies that you lean on for Dynaconf?

Many engineers end up building their own frameworks for managing settings as their use cases and environments get increasingly complicated. What are some of the ways that those efforts can go wrong or become unmaintainable?

Can you describe how Dynaconf is implemented?

How have the design and goals of the project evolved since you first started it?

What is the workflow for getting started with Dynaconf on a new project?

How does the usage scale with the complexity of the host project?

What are some strategies that you recommend for integrating Dynaconf into an existing project that already has complex requirements for settings across multiple environments?

Secrets management is one of the most frequently under- or over-engineered aspects of application configuration. What are some of the ways that you have worked to strike a balance of making the "right way" easy?

What are some of the more advanced or under-utilized capabilities of Dynaconf?

What are the most interesting, innovative, or unexpected ways that you have seen Dynaconf used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Dynaconf?

When is Dynaconf the wrong choice?

What do you have planned for the future of Dynaconf?

Keep In Touch

rochacbruno on GitHub

@rochacbruno on Twitter

Website

LinkedIn

Picks

Tobias

SOPS

Bruno

Severance tv series

Learn Rust

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Dynaconf

Dynaconf GitHub Org

Ansible

Bash

Perl

12 Factor Applications

TOML

Hashicorp Vault

Pydantic

Airflow

Hydroconf

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Take A Tour Of The Hidden Language Of Hardware And How It Powers Your Code** Oct 23, 2022
Summary

Software is eating the world, but that code has to have hardware to execute the instructions. Most people, and many software engineers, don’t have a proper understanding of how that hardware functions. Charles Petzold wrote the book "Code: The Hidden Language of Computer Hardware and Software" to make this a less opaque subject. In this episode he discusses what motivated him to revise that work in the second edition and the additional details that he packed in to explore the functioning of the CPU.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Charles Petzold about his work on the second edition of Code: The Hidden Language of Computer Hardware and Software

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the focus and goal of "Code" and the story behind it?

Who is the target audience for the book?

The sequencing of the topics parallels the curriculum of a computer engineering course of study. Why do you think that it is useful/important for a general audience to understand the electrical engineering principles that underly modern computers?

What was your process for determining how to segment the information that you wanted to address in the book to balance the pacing of the reader with the density of the information?

Technical books are notoriously challenging to write due to the constantly changing subject matter. What are some of the ways that the first edition of "Code" was becoming outdated?

What are the most notable changes in the foundational elements of computing that have happened in the time since the first edition was published?

One of the concepts that I have found most helpful as a software engineer is that of "mechanical sympathy". What are some of the ways that a better understanding of computer hardware and electrical signal processing can influence and improve the way that an engineer writes code?

What are some of the insights that you gained about your own use of computers and software while working on this book?

What are the most interesting, unexpected, or challenging lessons that you have learned while writing "Code" and revising it for the second edition?

Once the reader has finished with your book, what are some of the other references/resources that you recommend?

Keep In Touch

Website

Picks

Tobias

The Imitation Game movie

Charles

The Annotated Turing book by Charles Petzold

Confidence Man: The Making of Donald Trump and the Breaking of America by Maggie Haberman

Links

Code: The Hidden Language of Computer Hardware and Software

Fortran

PL/I

BASIC

C#

Z80

Intel 8080

PC Magazine

Assembly Language

Logic Gates

C Language

ASCII == American Standard Code for Information Interchange

SkiaSharp

Algol

Code first edition bibliography

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Take Control Of Your Electrical Systems With The Open Source FlexMeasures Energy Management System** Oct 16, 2022
Summary

The generation, distribution, and consumption of energy is one of the most critical pieces of infrastructure for the modern world. With the rise of renewable energy there is an accompanying need for systems that can respond in real-time to the availability and demand for electricity. FlexMeasures is an open source energy management system that is designed to integrate a variety of inputs intelligently allocate energy resources to reduce waste in your home or grid. In this episode Nicolas Höning explains how the project is implemented, how it is being used in his startup Seita, and how you can try it out for your own energy needs.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Nicolas Höning about FlexMeasures, an open source project designed to manage energy resources dynamically to improve efficiency

Interview

Introductions

How did you get introduced to Python?

Can you describe what FlexMeasures is and the story behind it?

What are the primary goals/objectives of the project?

The energy sector is huge. Where can FlexMeasures be used?

Energy systems are typically governed by a marketplace system. What are the benefits that FlexMeasures can provide for each side of that market?

How do renewable sources of energy confuse/complicate the role that the different stakeholders represent?

What are the different points of interaction that producers/consumers might have with the FlexMeasures platform?

What are some examples of the types of decisions/recommendations that FlexMeasures might generate and how to they manifest in the energy systems?

What are the types of information that FlexMeasures relies on for driving those decisions?

Can you describe how FlexMeasures is implemented?

How have the design and goals of the system changed/evolved since you started working on it?

What are the interfaces that you provide for integrating with and extending the functionality of a FlexMeasures installation?

What are the operating scales that FlexMeasures is designed for?

What are the most interesting, innovative, or unexpected ways that you have seen FlexMeasures used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexMeasures?

When is FlexMeasures the wrong choice?

What do you have planned for the future of FlexMeasures?

Keep In Touch

Website

@nhoening on Twitter

LinkedIn

Picks

Tobias

She-Hulk

Nicholas

Kleo on Netflix

Altair

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

FlexMeasures:

Github

Linux Energy Foundation

Mailing List

Twitter

EyeQuant

Energy Management System

OpenEMS

ICT == Information and Communications Technology

HomeAssistant

Podcast Episode

FlexMeasures HomeAssistant Plugin

Universal Smart Energy Framework

PostgreSQL

Data Engineering Podcast Episode

TimescaleDB

Data Engineering Podcast Episode

OpenWeatherMap

Timely-Beliefs library

Flask

Click

Pyomo

scikit-learn

sktime

LF Energy

Flake8

MyPy

Podcast Episode

Black

Arima Model

Random Forest

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**How And Why To Build Effective Teams As An Engineering Leader** Oct 10, 2022
Summary

Your ability to build and maintain a software project is tempered by the strength of the team that you are working with. If you are in a position of leadership, then you are responsible for the growth and maintenance of that team. In this episode Jigar Desai, currently the SVP of engineering at Sisu Data, shares his experience as an engineering leader over the past several years and the useful insights he has gained into how to build effective engineering teams.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with a fully automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your dbt, Snowflake, Tableau, Looker, or whatever you’re using and Select Star will set everything up in just a few hours. Go to pythonpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.

Your host as usual is Tobias Macey and today I’m interviewing Jigar Desai about building effective engineering teams

Interview

Introductions

How did you get introduced to Python?

What have you found to be the central challenges involved in building an effective engineering team?

What are the measures that you use to determine what "effective" means for a given team?

how to establish mutual trust in an engineering team

challenges introduced at different levels of team size/organizational complexity

establishing and managing career ladders

You have mostly worked in heavily tech-focused companies. How do industry verticals impact the ways that you think about formation and structure of engineering teams?

What are some of the different roles that you might focus on hiring/team compositions in industries that aren’t purely software? (e.g. fintech, logistics, etc.)

notable evolutions in engineering practices/paradigm shifts in the industry

What are some of the predictions that you have about how the future of engineering will look?

What impact do you think low-code/no-code solutions will have on the types of projects that code-first developers will be tasked with?

What are the most interesting, innovative, or unexpected ways that you have seen organizational leaders address the work of building and scaling engineering capacity?

What are the most interesting, unexpected, or challenging lessons that you have learned while working in engineering leadership?

What are the most informative mistakes that you would like to share?

What are some resources and reference material that you recommend for anyone responsible for the success of their engineering teams?

Keep In Touch

LinkedIn

Picks

Tobias

Bullet Train movie

Jigar

Top Gun Maverick movie

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Sisu Data

OpenStack

Java

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Complete Your Hardware "Weekend Projects" In An Actual Weekend With Belay** Oct 03, 2022
Summary

Working on hardware projects often has significant friction involved when compared to pure software. Brian Pugh enjoys tinkering with microcontrollers, but his "weekend projects" often took longer than a weekend to complete, so he created Belay. In this episode he explains how Belay simplifies the interactions involved in developing for MicroPython boards and how you can use it to speed up your own experimentation.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with a fully automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your dbt, Snowflake, Tableau, Looker, or whatever you’re using and Select Star will set everything up in just a few hours. Go to pythonpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.

Your host as usual is Tobias Macey and today I’m interviewing Brian Pugh about Belay, a python library that enables the rapid development of projects that interact with hardware via a micropython-compatible board.

Interview

Introductions

How did you get introduced to Python?

Can you describe what Belay is and the story behind it?

Who are the target users for Belay?

What are some of the points of friction involved in developing for hardware projects?

What are some of the features of Belay that make that a smoother process?

What are some of the ways that simplifying the develop/debug cycles can improve the overall experience of developing for hardware platforms?

What are some of the inherent limitations of constrained hardware that Belay is unable to paper over?

Can you describe how Belay is implemented?

What does the workflow look like when using Belay as compared to using MicroPython directly?

What are some of the ways that you are using Belay in your own projects?

What are the most interesting, innovative, or unexpected ways that you have seen Belay used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Belay?

When is Belay the wrong choice?

What do you have planned for the future of Belay?

Keep In Touch

BrianPugh on GitHub

LinkedIn

Picks

Tobias

Gunnar Computer Glasses

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Belay

Geomagical

PIC Microcontroller

AVR Microcontroller

Matlab

MicroPython

Podcast Episode

CircuitPython

Podcast Episode

Celery

Potentiometer

Raspberry Pi

Raspberry Pi Pico

ADC Converter

Thonny

Podcast Episode

Adafruit

Pyboard

Python Inspect Module

Python Tokenize

Magnetometer Project

Lidar

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Catching Up With Pyre, A Fast Type Checker For Python** Sep 19, 2022
Summary

Static typing versus dynamic typing is one of the oldest debates in software development. In recent years a number of dynamic languages have worked toward a middle ground by adding support for type hints. Python’s type annotations have given rise to an ecosystem of tools that use that type information to validate the correctness of programs and help identify potential bugs. At Instagram they created the Pyre project with a focus on speed to allow for scaling to huge Python projects. In this episode Shannon Zhu discusses how it is implemented, how to use it in your development process, and how it compares to other type checkers in the Python ecosystem.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Shannon Zhu about Pyre, a type checker for Python 3 built from the ground up to support gradual typing and deliver responsive incremental checks

Interview

Introductions

How did you get introduced to Python?

Can you describe what Pyre is and the story behind it?

There have been a number of tools created to support various aspects of typing for Python. How would you describe the various goals that they support and how Pyre fits in that ecosystem?

What are the core goals and notable features of Pyre?

Can you describe how Pyre is implemented?

How have the design and goals of the project changed/evolved since you started working on it?

What are the different ways that Pyre is used in the development workflow for a team or individual?

What are some of the challenges/roadblocks that people run into when adopting type definitions in their Python projects?

How has the evolution of type annotations and overall support for them affected your work on Pyre?

As someone who is working closely with type systems, what are the strongest aspects of Python’s implementation and opportunities for improvement?

What are the most interesting, innovative, or unexpected ways that you have seen Pyre used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pyre?

When is Pyre the wrong choice?

What do you have planned for the future of Pyre?

Keep In Touch

shannonzhu on GitHub

Picks

Tobias

Lord Of The Rings: The Rings of Power on Amazon Video

Shannon

King’s Dilemma board game

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

PYre

MyPy

Podcast Episode

PyRight

PyType

MonkeyType

Podcast Episode

Java

C

PEP 484

Flow

Hack

Continuous Integration

OCaml

PEP 675 – Arbitrary literal strings

Gradual Typing

AST == Abstract Syntax Tree

Language Server Protocol

Tensor

Type Arithmetic

PyCon: Securing Code With The Python Type System

PyCon: Type Checked Python In The Real World

PyCon: Łukasz Lange 2022 Keynote

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Standardizing On Python For All Software Projects At Ascend.io** Sep 13, 2022
Summary

Every software project is subject to a series of decisions and tradeoffs. One of the first decisions to make is which programming language to use. For companies where their product is software, this is a decision that can have significant impact on their overall success. In this episode Sean Knapp discusses the languages that his team at Ascend use for building a service that powers complex and business critical data workflows. He also explains his motivation to standardize on Python for all layers of their system to improve developer productivity.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Sean Knapp about his motivations and experiences standardizing on Python for development at Ascend

Interview

Introductions

How did you get introduced to Python?

Can you describe what Ascend is and the story behind it?

How many engineers work at Ascend?

What are their different areas of focus?

What are your policies for selecting which technologies (e.g. languages, frameworks, dev tooling, deployment, etc.) are supported at Ascend?

What does it mean for a technology to be supported?

You recently started standardizing on Python as the default language for development. How has Python been used up to now?

What other languages are in common use at Ascend?

What are some of the challenges/difficulties that motivated you to establish this policy?

What are some of the tradeoffs that you have seen in the adoption of Python in place of your other adopted languages?

How are you managing ongoing maintenance of projects/products that are not written in Python?

What are some of the potential pitfalls/risks that you are guarding against in your investment in Python?

What are the most interesting, innovative, or unexpected ways that you have seen Python used where it was previously a different technology?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on aligning all of your development on a single language?

When is Python the wrong choice?

What do you have planned for the future of engineering practices at Ascend?

Keep In Touch

LinkedIn

@seanknapp on Twitter

Picks

Tobias

Delver Lens app for scanning Magic: The Gathering cards

Sean

Typer

DuckDB

Amp It Up book (affiliate link)

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Ascend

Data Engineering Podcast Episode

Perl

Google Sawzall

Technical Debt

Ruby

gRPC

Go Language

Java

PySpark

Apache Arrow

Thrift

SQL

Scala

Snowflake runtime for Python Snowpark

Typer CLI framework

Pydantic

Podcast Episode

Pulumi

Podcast Episode

PyInfra

Podcast Episode

Packer

Plot.ly Dash

DuckDB

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Exploring The Process And Practice Of Building Better Software Through Code Reviews** Sep 05, 2022
Summary

Writing code is only one piece of creating good software. Code reviews are an important step in the process of building applications that are maintainable and sustainable. In this episode On Freund shares his thoughts on the myriad purposes that code reviews serve, as well as exploring some of the patterns and anti-patterns that grow up around a seemingly simple process.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing On Freund about the intricacies and importance of code reviews

Interview

Introductions

How did you get introduced to Python?

Can you start by giving us your description of what a code review is?

What is the purpose of the code review?

At face value a code review appears to be a simple task. What are some of the subtleties that become evident with time and experience?

What are some of the ways that code reviews can go wrong?

What are some common anti-patterns that get applied to code reviews?

What are the elements of code review that are useful to automate?

What are some of the risks/bad habits that can result from overdoing automated checks/fixes or over-reliance on those tools in code reviews?

identifying who can/should do a review for a piece of code

how to use code reviews as a teaching tool for new/junior engineers

how to use code reviews for avoiding siloed experience/promoting cross-training

PR templates for capturing relevant context

What are the most interesting, innovative, or unexpected ways that you have seen code reviews used?

What are the most interesting, unexpected, or challenging lessons that you have learned while leading and supporting engineering teams?

What are some resources that you recommend for anyone who wants to learn more about code review strategies and how to use them to scale their teams?

Keep In Touch

LinkedIn

@onfreund on Twitter

Picks

Tobias

The Girl Who Drank The Moon

On

Better Call Saul

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Wilco

Code Review

Home Assistant

Podcast Episode

Trunk-based Development

Git Flow

Pair Programming

Feature Flags

Podcast Episode

KPI == Key Performance Indicator

MIT Open Learning Engineering Handbook

PEP Repository

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Ship With Confidence By Automating Quality Assurance** Aug 28, 2022
Summary

Quality assurance in the software industry has become a shared responsibility in most organizations. Given the rapid pace of development and delivery it can be challenging to ensure that your application is still working the way it’s supposed to with each release. In this episode Jonathon Wright discusses the role of quality assurance in modern software teams and how automation can help.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Jonathon Wright about the role of automation in your testing and QA strategies

Interview

Introductions

How did you get introduced to Python?

Can you share your relationship with software testing/QA and automation?

What are the main categories of how companies and software teams address testing and validation of their applications?

What are some of the notable tradeoffs/challenges among those approaches?

With the increased adoption of agile practices and the "shift left" mentality of DevOps, who is responsible for software quality?

What are some of the cases where a discrete QA role or team becomes necessary? (or is it always necessary?)

With testing and validation being a shared responsibility, competing with other priorities, what role does automation play?

What are some of the ways that automation manifests in software quality and testing?

How is automation distinct from software tests and CI/CD?

For teams who are investing in automation for their applications, what are the questions they should be asking to identify what solutions to adopt? (what are the decision points in the build vs. buy equation?)

At what stage(s) of the software lifecycle does automation live?

What is the process for identifying which capabilities and interactions to target during the initial application of automation for QA and validation?

One of the perennial challenges with any software testing, particularly for anything in the UI, is that it is a constantly moving target. What are some of the patterns and techniques, both from a developer and tooling perspective, that increase the robustness of automated validation?

What are the most interesting, innovative, or unexpected ways that you have seen automation used for QA?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on QA and automation?

When is automation the wrong choice?

What are some of the resources that you recommend for anyone who wants to learn more about this topic?

Keep In Touch

LinkedIn

@Jonathon_Wright on Twitter

Website

Picks

Tobias

The Sandman Netflix series and Graphic Novels by Neil Gaimain

Jonathon

House of the Dragon HBO series

Mystic Quest TV series

It’s Always Sunny in Philadelphia

Links

Haskell

Idris

Esperanto

Klingon

Planguage

Lisp Language

TDD == Test Driven Development

BDD == Behavior Driven Development

Gherkin Format

Integration Testing

Chaos Engineering

Gremlin

Chaos Toolkit

Podcast Episode

Requirements Engineering

Keysight

QA Lead Podcast

Cognitive Learning TED Talk

OpenTelemetry

Podcast Episode

Quality Engineering

Selenium

Swagger

XPath

Regular Expression

Test Guild

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Remove Roadblocks And Let Your Developers Ship Faster With Self-Serve Infrastructure** Aug 14, 2022
Summary

The goal of every software team is to get their code into production without breaking anything. This requires establishing a repeatable process that doesn’t introduce unnecessary roadblocks and friction. In this episode Ronak Rahman discusses the challenges that development teams encounter when trying to build and maintain velocity in their work, the role that access to infrastructure plays in that process, and how to build automation and guardrails for everyone to take part in the delivery process.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Ronak Rahman about how automating the path to production helps to build and maintain development velocity

Interview

Introductions

How did you get introduced to Python?

Can you describe what Quali is and the story behind it?

What are the problems that you are trying to solve for software teams?

How does Quali help to address those challenges?

What are the bad habits that engineers fall into when they experience friction with getting their code into test and production environments?

How do those habits contribute to negative feedback loops?

What are signs that developers and managers need to watch for that signal the need for investment in developer experience improvements on the path to production?

Can you describe what you have built at Quali and how it is implemented?

How have the design and goals shifted/evolved from when you first started working on it?

What are the positive and negative impacts that you have seen from the evolving set of options for application deployments? (e.g. K8s, containers, VMs, PaaS, FaaS, etc.)

Can you describe how Quali fits into the workflow of software teams?

Once a team has established patterns for deploying their software, what are some of the disruptions to their flow that they should guard against?

What are the most interesting, innovative, or unexpected ways that you have seen Quali used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Quali?

When is Quali the wrong choice?

What do you have planned for the future of Quali?

Keep In Touch

@OfRonak on Twitter

Picks

Tobias

The Terminal List on Amazon

Ronak

Midnight Gospel on Amazon

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Quali

Torque

Visual Studio Plugin

Subversion

IaC == Infrastructure as Code

DevOps

Terraform

Pulumi

Podcast Episode

Cloudformation

Flask

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Benefits Of Python And Django For Going From Zero To MVP At Speed** Jul 31, 2022
Summary

Every startup begins with an idea, but that won’t get you very far without testing the feasibility of that idea. A common practice is to build a Minimum Viable Product (MVP) that addresses the problem that you are trying to solve and working with early customers as they engage with that MVP. In this episode Tony Pavlovych shares his thoughts on Python’s strengths when building and launching that MVP and some of the potential pitfalls that businesses can run into on that path.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Tony Pavlovych about Python’s strengths for startups and the steps to building an MVP (minimum viable product)

Interview

Introductions

How did you get introduced to Python?

Can you describe what PLANEKS is and the story behind it?

One of the services that you offer is building an MVP. What are the goals and outcomes associated with an MVP?

What is the process for identifying the product focus and feature scope?

What are some of the common misconceptions about building and launching MVPs that you have dealt with in your work with customers?

What are the common pitfalls that companies encounter when building and validating an MVP?

Can you describe the set of tools and frameworks (e.g. Django, Poetry, cookiecutter, etc.) that you have invested in to reduce the overhead of starting and maintaining velocity on multiple projects?

What are the configurations that are most critical to keep constant across projects to maintain familiarity and sanity for your developers? (e.g. linting rules, build toolchains, etc.)

What are the architectural patterns that you have found most useful to make MVPs flexible for adaptation and extension?

Once the MVP is built and launched, what are the next steps to validate the product and determine priorities?

What benefits do you get from choosing Python as your language for building an MVP/launching a startup?

What are the challenges/risks involved in that choice?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on MVPs for your clients at PLANEKS?

When is an MVP the wrong choice?

What are the developments in the Python and broader software ecosystem that you are most interested in for the work you are doing for your team and clients?

Keep In Touch

LinkedIn

Picks

Tobias

datamodel-code-generator

Tony

Screw It, Let’s Do It by Richard Branson (affiliate link)

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

PLANEKS

Minimum Viable Product

Django

Cookiecutter

Django Boilerplate

OCR == Optical Character Recognition

Tesseract OCR framework

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Powering The Next Generation Of Application Architectures With Web Assembly And The Fermyon Platform** Jul 25, 2022
Summary

Application architectures have been in a constant state of evolution as new infrastructure capabilities are introduced. Virtualization, cloud, containers, mobile, and now web assembly have each introduced new options for how to build and deploy software. Recognizing the transformative potential of web assembly, Matt Butcher and his team at Fermyon are investing in tooling and services to improve the developer experience. In this episode he explains the opportunity that web assembly offers to all language communities, what they are building to power lightweight server-side microservices, and how Python developers can get started building and contributing to this nascent ecosystem.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Need to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!

Your host as usual is Tobias Macey and today I’m interviewing Matt Butcher about Fermyon and the impact of WebAssembly on software architecture and deployment across language boundaries

Interview

Introductions

How did you get introduced to Python?

For anyone who isn’t familiar with WebAssembly can you give your elevator pitch for why it matters?

What is the current state of language support for Python in the WASM ecosystem?

Can you describe what Fermyon is and the story behind it?

What are your goals with Fermyon and what are the products that you are building to support those goals?

There has been a steady progression of technologies aimed at better ways to build, deploy, and manage software (e.g. virtualization, cloud, containers, etc.). What are the problems with the previous options and how does WASM address them?

What are some examples of the types of applications/services that work well in a WASM environment?

Can you describe how you have architected the Fermyon platform?

How did you approach the design of the interfaces and tooling to support developer ergonomics?

How have the design and goals of the platform changed or evolved since you started working on it?

Can you describe what a typical workflow is for an application team that is using Spin/Fermyon to build and deploy a service?

What are some of the architectural patterns that WASM/Fermyon encourage?

What are some of the limitations that WASM imposes on services using it as a runtime? (e.g. system access, threading/multiprocessing, library support, C extensions, etc.)

What are the new and emerging topics and capabilities in the WASM ecosystem that you are keeping track of?

With Spin as the core building block of your platform, how are you approaching governance and sustainability of the open source project?

What are your guiding principles for when a capability belongs in the OSS vs. commercial offerings?

What are the most interesting, innovative, or unexpected ways that you have seen Fermyon used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Fermyon?

When is Fermyon the wrong choice?

What do you have planned for the future of Fermyon?

Keep In Touch

LinkedIn

@technosophos on Twitter

technosophos on GitHub

Picks

Tobias

Thor: Love & Thunder movie

Matt

Remembrance of Earth’s Past trilogy ("Three Body Problem" is the first) by Cixin Liu

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Fermyon

Our Python entry for the Wasm Language Matrix

SingleStore’s WASI-Python

Great notes about Wasm support in CPyton

Pyodide for Python in the Browser

SlashDot

Web Assembly (WASM)

Rust

AssemblyScript

Grain WASM language

SingleStore

Data Engineering Podcast Episode

WASI

PyO3

PyOxidizer

RustPython

Drupal

OpenStack

Deis

Helm

RedPanda

Data Engineering Podcast Episode

Envoy Proxy

Fastly

Functions as a Service

CloudEvents

Finicky Whiskers

Fermyon Spin

Nomad

Tree Shaking

Zappa

Chalice

OpenFaaS

CNCF

Bytecode Alliance

Finicky Whiskers Minecraft

Kotlin

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Gain A Deeper Understanding Of What Your Code Is Doing And Where It Spends Its Time With VizTracer** Jul 17, 2022
Summary

As your code scales beyond a trivial level of complexity and sophistication it becomes difficult or impossible to know everything that it is doing. The flow of logic and data through your software and which parts are taking the most time are impossible to understand without help from your tools. VizTracer is the tool that you will turn to when you need to know all of the execution paths that are being exercised and which of those paths are the most expensive. In this episode Tian Gao explains why he created VizTracer and how you can use it to gain a deeper familiarity with the code that you are responsible for maintaining.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Need to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!

Your host as usual is Tobias Macey and today I’m interviewing Tian Gao about VizTracer, a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution

Interview

Introductions

How did you get introduced to Python?

Can you describe what VizTracer is and the story behind it?

What are the main goals that you are focused on with VizTracer?

What are some examples of the types of bugs that profiling can help diagnose?

How does profiling work together with other debugging approaches? (e.g. logging, breakpoint debugging, etc.)

There are a number of profiling utilities for Python. What feature or combination of features were missing that motivated you to create VizTracer?

Can you describe how VizTracer is implemented?

How have the design and goals changed since you started working on it?

There are a number of styles of profiling, what was your process for deciding which approach to use?

What are the most complex engineering tasks involved in building a profiling utility?

Can you describe the process of using VizTracer to identify and debug errors and performance issues in a project?

What are the options for using VizTracer in a production environment?

What are the interfaces and extension points that you have built in to allow developers to customize VizTracer?

What are some of the ways that you have used VizTracer while working on VizTracer?

What are the most interesting, innovative, or unexpected ways that you have seen VizTracer used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on VizTracer?

When is VizTracer the wrong choice?

What do you have planned for the future of VizTracer?

Keep In Touch

gaogaotiantian on GitHub

LinkedIn

Picks

Tobias

Travelers show on Netflix

Tian

objprint

Lincoln Lawyer

bilibili – Tian’s coding sessions in Chinese

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Viztracer

Python cProfile

Sampling Profiler

Perfetto

Coverage.py

Podcast Episode

Python setxprofile hook

Circular Buffer

Catapult Trace Viewer

py-spy

psutil

gdb

Flame graph

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Stream Processing In Real Time And At Scale In Pure Python With Bytewax** Jul 10, 2022
Summary

Analysis of streaming data in real time has long been the domain of big data frameworks, predominantly written in Java. In order to take advantage of those capabilities from Python requires using client libraries that suffer from impedance mis-matches that make the work harder than necessary. Bytewax is a new open source platform for writing stream processing applications in pure Python that don’t have to be translated into foreign idioms. In this episode Bytewax founder Zander Matheson explains how the system works and how to get started with it today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with a fully automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your dbt, Snowflake, Tableau, Looker, or whatever you’re using and Select Star will set everything up in just a few hours. Go to pythonpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.

Need to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!

Your host as usual is Tobias Macey and today I’m interviewing Zander Matheson about Bytewax, an open source Python framework for building highly scalable dataflows to process ANY data stream.

Interview

Introductions

How did you get introduced to Python?

Can you describe what Bytewax is and the story behind it?

Who are the target users for Bytewax?

What is the problem that you are trying to solve with Bytewax?

What are the alternative systems/architectures that you might replace with Bytewax?

Can you describe how Bytewax is implemented?

What are the benefits of Timely Dataflow as a core building block for a system like Bytewax?

How have the design and goals of the project changed/evolved since you first started working on it?

What are the axes available for scaling Bytewax execution?

How have you approached the design of the Bytewax API to make it accessible to a broader audience?

Can you describe what is involved in building a project with Bytewax?

What are some of the stream processing concepts that engineers are likely to run up against as they are experimenting and designing their code?

What is your motivation for providing the core technology of your business as an open source engine?

How are you approaching the balance of project governance and sustainability with opportunities for commercialization?

What are the most interesting, innovative, or unexpected ways that you have seen Bytewax used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bytewax?

When is Bytewax the wrong choice?

What do you have planned for the future of Bytewax?

Keep In Touch

Slack

Twitter

LinkedIn

Picks

Tobias

Alta Racks

Zander

Atherton Bikes

Links

Bytewax

GitHub

Flink

Data Engineering Podcast Episode

Spark Streaming

Kafka Connect

Faust

Podcast Episode

Ray

Podcast Episode

Dask

Data Engineering Podcast Episode

Timely Dataflow

PyO3

Materialize

Data Engineering Podcast Episode

HyperLogLog

Python River Library

Shannon Entropy Calculation

The blog post using incremental shannon entropy

NATS

waxctl

Prometheus

Grafana

Streamz

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Tetra: A Full Stack Web Framework That Doesn't Make You Write Everything Twice** Jul 03, 2022
Summary

Building a fully functional web application has been growing in complexity along with the growing popularity of javascript UI frameworks such as React, Vue, Angular, etc. Users have grown to expect interactive experiences with dynamic page updates, which leads to duplicated business logic and complex API contracts between the server-side application and the Javascript front-end. To reduce the friction involved in writing and maintaining a full application Sam Willis created Tetra, a framework built on top of Django that embeds the Javascript logic into the Python context where it is used. In this episode he explains his design goals for the project, how it has helped him build applications more rapidly, and how you can start using it to build your own projects today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.

Need to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!

Your host as usual is Tobias Macey and today I’m interviewing Sam Willis about Tetra, a full stack component framework for your Django applications

Interview

Introductions

How did you get introduced to Python?

Can you describe what Tetra is and the story behind it?

What are the problems that you are aiming to solve with this project?

What are some of the other ways that you have addressed those problems?

What are the shortcomings that you encountered with those solutions?

What was missing in the existing landscape of full-stack application development patterns that prompted you to build a new meta-framework?

What are some of the sources of inspiration (positive and negative) that you looked to while deciding on the component selection and implementation strategy?

Can you describe how Tetra is implemented?

What are the core principles that you are relying on to drive your design of APIs and developer experience?

What is the process for building a full component in Tetra?

What are some of the application design challenges that are introduced by Combining the javascript and Django logic and attributes? (e.g. reusing JS logic/CSS styles across components)

A perennial challenge with combining the syntax across multiple languages in a single file is editor support. How are you thinking about that with Tetra’s implementation?

What is your grand vision for Tetra and how are you working to make it sustainable?

What are the most interesting, innovative, or unexpected ways that you have seen Tetra used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Tetra?

When is Tetra the wrong choice?

What do you have planned for the future of Tetra?

Keep In Touch

@samwillis on Twitter

Website

LinkedIn

samwillis on GitHub

Picks

Tobias

The Machine Learning Podcast

Sam

Slow Horses TV Show

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Tetra Framework

Django

PHP

ASP

Alpine.js

HTMX

Ruby

Ruby on Rails

Flutterbox

Vue.js

Laravel Livewire

Python Import Hooks

python-inline-source

Tailwind CSS

PostCSS

Pickle

Fernet

esbuild

Webpack

Rich

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Design Real-World Objects In Python With CadQuery** Jun 27, 2022
Summary

Virtually everything that you interact with on a daily basis and many other things that make modern life possible were designed and modeled in software called CAD or Computer-Aided Design. These programs are advanced suites with graphical editing environments tailored to domain experts in areas such as mechanical engineering, electrical engineering, architecture, etc. While the UI-driven workflow is more accessible, it isn’t scalable which opens the door to code-driven workflows. In this episode Jeremy Wright discusses the design, uses, and benefits of the CadQuery framework for building 3D CAD models entirely in Python.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.

Need to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!

Your host as usual is Tobias Macey and today I’m interviewing Jeremy Wright about CadQuery, an easy-to-use Python module for building parametric 3D CAD models

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what CAD is and some of the real-world applications of it?

Can you describe what CadQuery is and the story behind it?

How did you get involved with it and what keeps you motivated?

What are the different methods that are in common use for building CAD models?

Are there approaches that are more common for models used in different industries?

What was missing in other projects for programmatically generating CAD models that motivated you to build CadQuery?

Can you describe how the CadQuery library is implemented?

How have the design and goals of the project changed or evolved since you started working on it?

How would you characterize the rate of change/evolution in the CAD ecosystem, and how has that factored into your work on CadQuery?

How did you approach the process of API design?

How do you balance accessibility for non-professionals with domain-related nomenclature?

Can you describe some example workflows for going from idea to finished product with CadQuery?

How are you using CadQuery in your own work?

What are the most interesting, innovative, or unexpected ways that you have seen CadQuery used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on CadQuery?

When is CadQuery the wrong choice?

What do you have planned for the future of CadQuery?

Keep In Touch

Discord

Twitter

GitHub

GitLab

Picks

Tobias

Doctor Strange: In The Multiverse of Madness

Jeremy

Star Trek: Strange New Worlds

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

CadQuery

CAD == Computer Assisted Design

3D Printer

Jeremy’s CNC Router

jQuery

Blender

Fusion 360

Open Cascade (OCCT)

Fluent API

FreeCAD

KiCAD

Semblage

cq-editor

jupyter-cadquery

cq-kit

FX Bricks

Voxels

cq_warehouse

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Intelligent Dependency Resolution For Optimal Compatibility And Security With Project Thoth** Jun 15, 2022
Summary

Building any software project is going to require relying on dependencies that you and your team didn’t write or maintain, and many of those will have dependencies of their own. This has led to a wide variety of potential and actual issues ranging from developer ergonomics to application security. In order to provide a higher degree of confidence in the optimal combinations of direct and transitive dependencies a team at Red Hat started Project Thoth. In this episode Fridolín Pokorný explains how the Thoth resolver uses multiple signals to find the best combination of dependency versions to ensure compatibility and avoid known security issues.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Need to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!

Your host as usual is Tobias Macey and today I’m interviewing Fridolín Pokorný about Project Thoth, a resolver service that computes the optimal combination of versions for your dependencies

Interview

Introductions

How did you get introduced to Python?

Can you describe what Project Thoth is and the story behind it?

What are some examples of the types of problems that can be introduced by mismanaged dependency versions?

The Python ecosystem has seen a number of dependency management tools introduced recently. What are the capabilities that Thoth offers that make it stand out?

How does it compare to e.g. pip, Poetry, pip-tools, etc.?

How do those other tools approach resolution of dependencies?

Can you describe how Thoth is implemented?

How have the scope and design of the project evolved since it was started?

What are the sources of information that it relies on for generating the possible solution space?

What are the algorithms that it relies on for finding an optimal combination of packages?

Can you describe how Thoth fits into the workflow of a developer while selecting a set of dependencies and keeping them up to date over the life of a project?

What are the opportunities for expanding Thoth’s application to other language ecosystems?

What are the interfaces available for extending or integrating with Thoth?

What are the most interesting, innovative, or unexpected ways that you have seen Thoth used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Thoth?

When is Thoth the wrong choice?

What do you have planned for the future of Thoth?

Keep In Touch

LinkedIn

Website

Picks

Tobias

Brass Against

Fridolin

micropipenv

Links

Redhat

Emerging Technologies Group

Project Thoth

Thamos CLI

PyPA Advisory Database

Project2Vec

Thoth Prescriptions

Thoth: Egyptian God

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Take A Deep Dive On How Code Completion Works And How To Customize It** May 30, 2022
Summary

Most developers have encountered code completion systems and rely on them as part of their daily work. They allow you to stay in the flow of programming, but have you ever stopped to think about how they work? In this episode Meredydd Luff takes us behind the scenes to dig into the mechanics of code completion engines and how you can customize them to fit your particular use case.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Meredydd Luff about how code completion works and what it takes to build your own

Interview

Introductions

How did you get introduced to Python?

Most programmers are familiar with the idea of code completion, but can you just give the elevator pitch to get us all on the same page?

You gave a presentation recently at PyCon about how to build a code completion system. What was your approach to identifying what fundamental concepts needed to be addressed and how to fit that lesson into the available time?

In the presentation you mentioned that you had built a more full-featured completion engine into Anvil. Can you describe what possessed you to build your own code completion tool?

What are the core components required to build a completion engine?

What are the benefits that can be realized by customizing the completion engine for a given language or task?

Can you describe the feature set and implementation details of the full-fledged completion engine that is available in Anvil?

Beyond the toy example, there are a number of considerations to address if you want to make the completion engine "production grade". Can you talk through some of the obvious edge cases and how to solve for them? (e.g. handling parsing of incomplete code)

What are the inputs that you use to build up the list of candidate tokens for completion?

Once you have a functioning baseline for offering completions, what are some of the signals that you hook into for ranking suggestions?

In your presentation you leaned on the machinery available in the Python standard library. What are some of the ways that you might think about generalizing across languages vs. coupling to a given language?

What design/architectural advice do you have for compartmentalizing logic in a full-featured completion engine?

What are some of the complexities that become a factor when you are trying to scale across an entire code base?

Beyond just being able to parse and process a body of code, there is also the question of integrating with the development environment. What are some of the challenges that get introduced when trying to access the appropriate set(s) of files and code through the editor interface(s)?

What are the most interesting, innovative, or unexpected ways that you have seen code completion applied to developer experience?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on code completion for Anvil?

When is code completion more effort than it’s worth?

What do you have planned for the future of the Anvil code completion functionality?

Keep In Touch

LinkedIn

meredydd on GitHub

@meredydd on Twitter

Picks

Tobias

"Weird Al" Yankovic

Meredydd

TimescaleDB

Data Engineering Podcast Episode

Promscale

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

PyCon presentation about building a completion engine

Anvil

Podcast Episode

Nano

Language Server Protocol

Jedi

Podcast Episode

Skulpt

Parser

Abstract Syntax Tree

OpenAPI

GitHub Copilot

Halting Problem

Parser Generator

Python Language Grammar Definition

Lezer Parser Generator

Tree-sitter

PyScript

Grafana Tempo Tracing Service

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Hunting Black Swans With Bees: Catching Up With The Inimitable Russell Keith-Magee** May 24, 2022
Summary

Russell Keith-Magee is an accomplished engineer and a fixture of the Python community. His work on the Beeware suite of projects is one of the most ambitious undertakings in the ecosystem and unfailingly forward-looking. With his recent transition to working for Anaconda he is now able to dedicate his full focus to the effort. In this episode he reflects on the journey that he has taken so far, how Beeware is helping to address some of the threats to Python’s long term viability, and how he envisions its future in light of the recent release of PyScript, an in-browser runtime for Python.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Russell Keith-Magee about the latest status of the Beeware project, the state of Python’s black swans, and how the PyScript project ties into his ambitions for world domination

Interview

Introductions

How did you get introduced to Python?

For anyone who hasn’t been graced with the BeeWare vision, can you give the elevator pitch of what it is and why it matters?

At PyCon US 2019 you presented a keynote about the various potential threats to the Python language community and its future viability. With the clarity of 3 years hindsight, how has the landscape shifted?

What is PyScript and how does it fit into the venn diagram of BeeWare’s objectives and the portents of black swan events (and what is your involvement with it)?

How does it differ from the dozens of other "Python in the browser" and "Python transpiled to Javascript" projects that have sprouted over the years?

Now that you have been granted the opportunity to dedicate your full attention to BeeWare and build a team to support it, what new potential does that unlock?

What are the current areas of focus/challenges that you are spending your time on for the BeeWare project?

What are some of the efforts in the BeeWare suite that proved to be dead-ends?

What are the most interesting, innovative, or unexpected ways that you have seen the BeeWare suite/PyScript used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on BeeWare?

When is BeeWare the wrong choice?

What do you have planned for the future of BeeWare/PyScript/Python/world domination?

Keep In Touch

LinkedIn

Website

@freakboy3742 on Twitter

Picks

Tobias

Joby Gorillapod

Russell

PyScript

The Great TV Show

Links

Black Swans Episode

BeeWare Episode

BeeWare

Django

Cordova

Black Swan

Apple II

Altair

Briefcase

Web Assembly (WASM)

Gary Bernhardt

PyScript

Pyodide

Toga

Kotlin

Swift

Gaffer Tape

Repl.it

Brython

Transcrypt

Python Anywhere

Batavia

Anaconda

Conda

Voc

Maestral

Eddington GUI

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Take Control Of Your Digital Photos By Running Your Own Smart Library Manager With LibrePhotos** May 16, 2022
Summary

Digital cameras and the widespread availability of smartphones has allowed us all to generate massive libraries of personal photographs. Unfortunately, now we are all left to our own devices of how to manage them. While cloud services such as iPhotos and Google Photos are convenient, they aren’t always affordable and they put your pictures under the control of large companies with their own agendas. LibrePhotos is an open source and self-hosted alternative to these services that puts you in control of your digital memories. In this episode the maintainer of LibrePhotos, Niaz Faridani-Rad, explains how he got involved with the project, the capabilities that it offers for managing your image library, and how to get your own instance set up to take back control of your pictures.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This episode is sponsored by Mergify. It’s an amazing tool to make you and your team way more productive with GitHub. Mergify is all about leveling up your pull requests with useful features that eliminate busy work. Automatic merges allow you define the conditions for acceptance and Mergify will take care of merging the pull request as soon as it’s ready. Automatic updates take care of merging your pull requests serially on top of each other, so there is no way to introduce a regression. With a merge queue you can merge your urgent pull request first, organize your Prs as you wish and Mergify will merge them in that order. Mergify’s backports feature will even copy the pull request into another branch once the pull request has been merged, shipping your bug fixes on multiple branches automatically. By saving time you and your team can focus on projects that matter. Mergify is coordinated with any CI and fully integrated into GitHub. They have a Startup Program that offers a 12 months credit to leverage Mergify (up to $21,000 of value). Start saving time; visit pythonpodcast.com/mergify today to sign up for a demo and get started! Or just click the link in the show notes.

Your host as usual is Tobias Macey and today I’m interviewing Niaz Faridani-Rad about LibrePhotos, an open source, self-hosted application for managing your personal photo collection

Interview

Introductions

How did you get introduced to Python?

Can you describe what LibrePhotos is and the story behind it?

What are the core objectives of the project?

What kind of users are you focused on?

What are some of the major features of LibrePhotos?

There are a number of open source and commercial options for different photo oriented use cases. What are the main capabilities that influence someone’s decision to use one over the other?

Many people’s baseline expectations will be around services such as Google Photos or iPhotos. What are some of the challenges that you face in trying to provide a comparable experience?

One of the features that users rely on with these services is backup/disaster recovery of their photo library. What is the recommended approach for users of LibrePhotos?

Can you describe how LibrePhotos is architected?

How have the design and goals evolved since you first started working on it?

How have recent advances in machine learning algorithms and related tooling improved the availability and quality of advanced features in LibrePhotos?

How much improvement of accuracy in face/object recognition do you see as users invest in cataloging and organizing their collections?

Is there a minimum quantity of images/iindividual people that are necessary to start using the ML powered features?

What kinds of storage locations are supported?

What are the interfaces available for extending/enhancing/integrating with LibrePhotos?

What are the most interesting, innovative, or unexpected ways that you have seen LibrePhotos used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on LibrePhotos?

When is LibrePhotos the wrong choice?

What do you have planned for the future of LibrePhotos?

Keep In Touch

derneuere on GitHub

@der_neuere on Twitter

Website

LinkedIn

Picks

Tobias

Uncharted movie

Niaz

Steam Deck

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

LibrePhotos

Self-hosted Sub-Reddit

OwnPhotos

Google Photos

Google Takeout

Digikam

x265

HEIC Files

RAW Image Format

ImageMagick

Panorama Photograph

Lytro light field cameras

rq asynchronous task library

Typescript

Redux Toolkit

MobileNet v3

DLib

ARM Processor

Docker Compose

LibrePhotos Comparison Page

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Making Investment Data Easy To Access And Analyze With The OpenBB Terminal** May 10, 2022
Summary

Investing effectively is largely a game of information access and analysis. This can involve a substantial amount of research and time spent on finding, validating, and acquiring different information sources. In order to reduce the barrier to entry and provide a powerful framework for amateur and professional investors alike Didier Rodrigues Lopes created the OpenBB Terminal. In this episode he explains how a pandemic project that started as an experiment has led to him founding a new company and dedicating his time to growing and improving the project and its community.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Didier Rodrigues Lopes about the OpenBB Terminal, a modern Python-based integrated environment for investment research

Interview

Introductions

How did you get introduced to Python?

Can you describe what OpenBB is and the story behind it?

What is the problem that you are trying to address by creating the OpenBB project and providing it as open source?

What are some of the use cases where someone might need to use this project?

The elephant in the room for financial data research is the Bloomberg Terminal. What are the other tools or services available for that purpose?

What are the differentiating features of the OpenBB Terminal?

Can you describe how the OpenBB Terminal is implemented?

How have the design and goals/scope of the project changed since you started working on it?

Can you describe a typical workflow for someone who is using the OpenBB Terminal?

How have you approached the user experience design, and what are you optimizing for?

What kinds of utilities do you offer beyond raw data access?

What are some examples of data sources that you rely on?

What is involved in integrating a new data source?

What are the extension points and integration capabilities for expanding the functionality of the tool?

What are the most interesting, innovative, or unexpected ways that you have seen OpenBB Terminal used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on OpenBB Terminal?

When is OpenBB Terminal the wrong choice?

What do you have planned for the future of OpenBB Terminal?

Keep In Touch

DidierRLopes on GitHub

LinkedIn

@didier_lopes on Twitter

Picks

Tobias

Vikings: Valhalla show on Netflix

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

OpenBB

Matlab

Papermill

Bloomberg Terminal

Robinhood

Coinbase

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Accelerate Your Machine Learning Experimentation With Automatic Checkpoints Using FLOR** May 02, 2022
Summary

The experimentation phase of building a machine learning model requires a lot of trial and error. One of the limiting factors of how many experiments you can try is the length of time required to train the model which can be on the order of days or weeks. To reduce the time required to test different iterations Rolando Garcia Sanchez created FLOR which is a library that automatically checkpoints training epochs and instruments your code so that you can bypass early training cycles when you want to explore a different path in your algorithm. In this episode he explains how the tool works to speed up your experimentation phase and how to get started with it.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Rolando Garcia about FLOR, a suite of machine learning tools for hindsight logging that lets you speed up model experimentation by checkpointing training data

Interview

Introductions

How did you get introduced to Python?

Can you describe what FLOR is and the story behind it?

What is the core problem that you are trying to solve for with FLOR?

What are the fundamental challenges in model training and experimentation that make it necessary?

How do machine learning reasearchers and engineers address this problem in the absence of something like FLOR?

Can you describe how FLOR is implemented?

What were the core engineering problems that you had to solve for while building it?

What is the workflow for integrating FLOR into your model development process?

What information are you capturing in the log structures and epoch checkpoints?

How does FLOR use that data to prime the model training to a given state when backtracking and trying a different approach?

How does the presence of FLOR change the costs of ML experimentation and what is the long-range impact of that shift?

Once a model has been trained and optimized, what is the long-term utility of FLOR?

What are the opportunities for supporting e.g. Horovod for distributed training of large models or with large datasets?

What does the maintenance process for research-oriented OSS projects look like?

What are the most interesting, innovative, or unexpected ways that you have seen FLOR used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on FLOR?

When is FLOR the wrong choice?

What do you have planned for the future of FLOR?

Keep In Touch

rlnsanz on GitHub

@rogarcia_sanz on Twitter

Picks

Tobias

The Batman

Rolando

Severance

GitHub Codespaces

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

FLOR

UC Berkeley

Joe Hellerstein

MLOps

Data Engineering Podcast Episode

RISE Lab

AMP Lab

Clipper Model Serving

Ground Data Context Service

Context: The Missing Piece Of The Machine Learning Lifecycle

Airflow

Copy on write

ASTor

Green Tree Snakes: Python AST Documentation

MLFlow

Amazon Sagemaker

Cloudpickle

Horovod

Podcast Episode

Ray Anyscale

PyTorch

Tensorflow

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Automatically Enforce Software Structures With Powerful Code Modifications Powered By LibCST** Apr 25, 2022
Summary

Programmers love to automate tedious processes, including refactoring your code. In order to support the creation of code modifications for your Python projects Jimmy Lai created LibCST. It provides a richly typed and high level API for creating and manipulating concrete syntax trees of your source code. In this episode Jimmy Lai and Zsolt Dollenstein explain how it works, some of the linting and automatic code modification utilities that you can build with it and how to get started with using it to maintain your own Python projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Zsolt Dollenstein and Jimmy Lai about LibCST, a concrete syntax tree parser and serializer library for Python

Interview

Introductions

How did you get introduced to Python?

Can you describe what LibCST is and the story behind it?

How does a concrete syntax tree differ from an abstract syntax tree?

What are some of the situations where the preservation of the exact structure is necessary?

There are a few other libraries in Python for creating concrete syntax trees. What was missing in the available options that made it necessary to create LibCST?

What are the use cases that LibCST is focused on supporting

Can you describe how LibCST is implemented?

How have the design and goals of the project changed or evolved since you started working on it?

How might I use LibCST for something like restructuring a set of modules to move a function definition while maintaining proper imports?

How do the capabilities of LibCST for codemodding compare to the Rope framework?

What are some other workflows that someone might build with LibCST?

What are some of the ways that LibCST is being used in your own work?

What are the most interesting, innovative, or unexpected ways that you have seen LibCST used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on LibCST?

When is LibCST the wrong choice?

What do you have planned for the future of LibCST?

Keep In Touch

Zsolt

zsol on GitHub

LinkedIn

Jimmy

jimmylai on GitHub

LinkedIn

Picks

Tobias

Osprey Manta Backpack

Zsolt

Autotransform

Glean

Jimmy

Paying down technical debt

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

LibCST

Carta

lib2to3

Abstract Syntax Tree

Concrete Syntax Tree

Pyre

Parso

Cython

Podcast Episode

mypyc

Rope

Flake8

Podcast Episode

Pylint

ESLint

Fixit

MonkeyType

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Cloud Native Networking For Developers With The Gloo Platform** Apr 19, 2022
Summary

Communication is a fundamental requirement for any program or application. As the friction involved in deploying code has gone down, the motivation for architecting your system as microservices goes up. This shifts the communication patterns in your software from function calls to network calls. In this episode Idit Levine explains how the Gloo platform that she and her team at Solo have created makes it easier for you to configure and monitor the network topologies for your microservice environments. She also discusses what developers need to know about networking in cloud native environments and how a combination of API gateways and service mesh technologies allow you to more rapidly iterate on your systems.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Idit Levine about what developers need to know about service-oriented networking and her work at Solo on the Gloo project

Interview

Introductions

How did you get introduced to Python?

Can you describe what Solo is and the story behind it?

How much should developers need to know about the ways that their applications and services are communicating?

What is the current state of networking for applications across physical, cloud, and containerized environments?

How do service mesh features influence the architectural decisions that software teams make while building their applications?

What operational capabilities do they unlock?

What are the aspects of application networking that are simplified or enhanced by service mesh platforms?

In what ways has service mesh introduced new complexity to operating software systems?

How can developers mirror the network topologies for production environments while working on new features?

What are the most interesting, innovative, or unexpected ways that you have seen Gloo used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gloo?

When is Gloo the wrong choice?

What do you have planned for the future of Gloo?

Keep In Touch

LinkedIn

@Idit_Levine on Twitter

Picks

Tobias

Shadow and Bone on Netflix

Idit

Elizabeth Holmes HBO Documentary

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Solo

Computational Biology

Microservices

Kubernetes

Service Mesh

Istio

LinkerD

Envoy Proxy

API Gateway

CRD == Custom Resource Definition

Gloo Edge

Bazel Build System

GraphQL

mTLS

GitOps

Dagger

WASM == Web Assembly

Kubernetes Gateway API

Consul Connect

eBPF

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Accelerate And Simplify Cloud Native Development For Kubernetes Environments With Gefyra** Apr 11, 2022
Summary

Cloud native architectures have been gaining prominence for the past few years due to the rising popularity of Kubernetes. This introduces new complications to development workflows due to the need to integrate with multiple services as you build new components for your production systems. In order to reduce the friction involved in developing applications for cloud native environments Michael Schilonka created Gefyra. In this episode he explains how it connects your local machine to a running Kubernetes environment so that you can rapidly iterate on your software in the context of the whole system. He also shares how the Django Hurricane plugin lets your applications work closely with the Kubernetes process model.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.

Your host as usual is Tobias Macey and today I’m interviewing Michael Schilonka about Gefyra and what is involved with developing applications for Kubernetes environments

Interview

Introductions

How did you get introduced to Python?

Can you describe what Gefyra is and the story behind it?

What are the challenges that Kubernetes introduces to the development process?

What are some of the strategies that developers might use for developing and testing applications that are deployed to Kubernetes environments?

What are the use cases that Gefyra is focused on enabling?

What are some of the other tools or platforms that Gefyra might replace or supplement?

What are the services that need to be present in the K8s cluster to enable Gefyra’s functionality?

Can you describe how Gefyra is implemented?

How have the design and goals of the project changed since you first started working on it?

What is the process for getting Gefyra set up between a K8s cluster and a developer’s laptop?

Can you describe what the developer’s workflow looks like when using Gefyra?

How do you avoid collisions/resource contention among a team of developers who are working on the same project?

What are some of the ways that developing for Kubernetes influences the architectural and design decisions for a project?

What are some of the additional practices or systems that you have found to be beneficial for accelerating development in cloud-native environments?

What are the most interesting, innovative, or unexpected ways that you have seen Gefyra used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gefyra?

When is Gefyra the wrong choice?

What do you have planned for the future of Gefyra?

Keep In Touch

LinkedIn

Schille on GitHub

Picks

Tobias

kubernetes.el – Kubernetes interface for Emacs

Michael

It’s fermentation friday, perfect for baking a sourdough bread or brewing beer

Two of my favorit YouTube channels Kurzgesagt – In a Nutshell and LockPickingLawyer

For entrepreneurial spirits: Reddit community research with (GummySearch)[https://gummysearch.com/]?utm_source=rss&utm_medium=rss

Links

Kopf framework

PyOxidizer

Tuna

Wireguard-go

https://k3d.io/?utm_source=rss&utm_medium=rss

kind

Django Hurricane

Blueshoe

Django

Kubernetes

K3d

Telepresence

Unikube

Sidecar Pattern

Docker-compose

Kubernetes Patterns book

O’Reilly Platform

Amazon (affiliate link)

CodeZero

CoreDNS

Nginx

Cookiecutter

Tornado

Podcast Episode

uWSGI

Podcast Episode

12 Factor App

Pycloak

Keycloak

Kubernetes Operator

Kubernetes CRD (Custom Resource Definition

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building A Community And Technology Stack For Scalable Big Data Geoscience At Pangeo** Mar 28, 2022
Summary

Science is founded on the collection and analysis of data. For disciplines that rely on data about the earth the ability to simulate and generate that data has been growing faster than the tools for analysis of that data can keep up with. In order to help scale that capacity for everyone working in geosciences the Pangeo project compiled a reference stack that combines powerful tools into an out-of-the-box solution for researchers to be productive in short order. In this episode Ryan Abernathy and Joe Hamman explain what the Pangeo project really is, how they have integrated a combination of XArray, Dask, and Jupyter to power these analytical workflows, and how it has helped to accelerate research on multidimensional geospatial datasets.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.

Your host as usual is Tobias Macey and today I’m interviewing Ryan Abernathy and Joe Hamman about Pangeo, a community platform for Big Data geoscience

Interview

Introductions

How did you get introduced to Python?

Can you describe what Pangeo is and the story behind it?

What is your role in the project/community and how did you get involved?

What are the goals of the project and community?

What are the areas of effort and how are they organized?

What are the scientific domains that Pangeo is focused on supporting?

What are the primary challenges associated with data management and analysis in these scientific communities?

What are the forms that these data take and how have they been evolving? (e.g. formats/sources)

What are some of the challenges introduced by the widespread adoption of cloud resources and the associated architectural patterns?

Can you describe the technical components that fall under the Pangeo umbrella?

How do they come together to form a functional workflow for geo sciences?

How has the scope of the Pangeo project changed or evolved since it started?

What are the most interesting, innovative, or unexpected ways that you have seen Pangeo used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pangeo?

When is Pangeo the wrong choice?

What do you have planned for the future of Pangeo?

Keep In Touch

Joe

@HammanHydro on Twitter

Ryan

@rabernat on Twitter

rabernat on GitHub

Website

Picks

Tobias

Mountain Biking

Ryan

Klara And The Sun by Kazuo Ishiguro

Joe

Range by David Epstein

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Pangeo

Pangeo Forge

CarbonPlan

M2LInES

LEAP

Columbia University

XArray

MIT

MatLab

PHP

Ruby

Java

NumPy

SciPy

Matplotlib

C

Fortran

Perl

Dask

Data Engineering Podcast Episode

Jupyter

IDL

HDF5

Unidata

NetCDF

CF Metadata Conventions

Intake

Podcast Episode

FSSpec

Parquet

Data Engineering Podcast Episode

Zarr

Data Engineering Podcast

Pangeo Forge

Airbyte

Data Engineering Podcast Episode

Fivetran

Data Engineering Podcast Episode

Stitch

TileDB

Data Engineering Podcast Episode

Pythia

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Automating Application Lifecycles For Developer Happiness At Wayfair** Mar 20, 2022
Summary

A common piece of advice when starting anything new is to "begin with the end in mind". In order to help the engineers at Wayfair manage the complete lifecycle of their applications Joshua Woodward runs a team that provides tooling and assistance along every step of the journey. In this episode he shares some of the lessons and tactics that they have developed while assisting other engineering teams with starting, deploying, and sunsetting projects. This is an interesting look at the inner workings of large organizations and how they invest in the scaffolding that supports their myriad efforts.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.

Your host as usual is Tobias Macey and today I’m interviewing Joshua Woodward about how the application lifecycle team at Wayfair uses Python to

Interview

Introductions

Josh Woodward, for the past year have been managing the application lifecycle team at Wayfair. Prior to that, IC on python platforms team. Embed with teams looking to decouple from monolith. See pain points first hand.

How did you get introduced to Python?

High school physics class, TI84 Calculator, friend wrote a program to solve vector problems, I thought it was amazing.

Used TI-Basic to solve specific physics problems for me. (Give fixed inputs, run through equation, get outputs)

Approaching college, thinking about student loans.

Heard about python and decided to give it a shot.

Wrote program to simulate various payback / interest scenarios.

Went to college for ME, switched to SE when I found out my dorm neighbors were using python to draw cool images with python + turtle

Can you describe what the role of the application lifecycle team is and the story behind it?

Story behind it:

Around 2018, in a state where we had deploy congestion, challenging to iterate and ship changes. tech org invested in containerization and decoupling to directly combat this problem. Teams incentiviced to decouple.

While on python platforms, the team had already been experimenting with code templating.

Standard cookiecutter template for flask apps.

Wayfair experimenting with Kubernetes late 2017.

Spent 1 year embedding with 4 different teams to help knowledge transfer re: k8s, containers, application setup, python best practices, testing, linting, etc – through that we got a lot of great feedback on our tooling.

Took senior engineers weeks to get something setup.

Know who to contact, click the right buttons, file the right ticket

Approach: Counted manual steps. Something like 60 distinct / atomic activities that had to be performed to get a "hello world" response from a basic flask app in production.

Focus on reduce manual steps

Released product (Mamba, on theme of snakes)

Initially, supporting one main user story.

User story: "As an engineer, I would like to create a production ready application in 10 minutes so that I can have a reliable and standardized application setup that follows best practices."

grew out of python platforms, created own team with own scope, that was about 1.5 years ago.

What is your team’s scope now?

Team Scope is to facilitate the creation, maintenance, and decommissioning of decoupled applications at Wayfair.

What are the interfaces that your team has to the rest of the organization?

People Interfaces:

We value getting feedback on our work to build strong products.

Make assumptions, Willing to be wrong. Validate assumptions with customers.

Software Interfaces:

for mamba, CLI at first

Backstage (open sourced from spotify)

Lots of Github

What is your method of determining what projects to work on?

(See above). Known pain points. Intuition, Free day fridays. Being comfortable taking risk (using friday time). Vet solution with customers.

How do you measure the impact of your work on the rest of the organization?

We don’t force use of our products. Adoption of tooling.

Number of microservices being spun up.

Number of automated pull requests being created, merged.

DORA metrics throughput (deployment frequency, lead time for changes) and stability (change failure rate, mean time to recovery)

What is the role of Python in your work?

we use it and love it!

existing skillset from incubation phase within python platforms

right tool for the job

lightweight automation

hitting lots of APIs

define lots of user facing specifications (json, yaml)

pydantic has been great for creating descriptive, human and machine specifications.

open source (we rely on it, we also have some presence)

cookiecutter -> columbo

gitpython -> pygitops

Can you tell me more about your application creation solution. Who can use it, and what does it actually do?

Written in python, though it templates out code for any language.

Runs automation to onboard an application to production

git repo, build pipeline, calling out to various APIs to signal a new app is present

Wayfair has a variety of applications (python, java, .net, php, javascript, some go)

Team interested in integrating with our solution will create a github repository containing 1..* cookiecutter template(s)

Provide a specification for what questions to ask users.

Limitation with cookiecutter where the approach to ask questions isn’t dynamic. lack of validation.

Pat Lannigan -> Columbo (open sourced). Python DSL to describe the set of questions to ask users.

python fastapi application will have a completely different set of questions than a java library for example.

You had mentioned that another part of your team scope is to facilitate the maintenance of applications. Can you tell me more about that?

Reduce engineering toil around keeping applications up to date.

Average engineer owns several, dozens of repos

Create automated pull requests:

Versioned dependencies (Renovate)

Propagating platform changes (Gator)

Ex1: python apps use "black" to format code and our python platform team would like to prescribe a line length. Our tooling can be used to declare desired changes. yaml specification -> pr automation at scale.

Ex2: shared library, new version released, breaking interface change. Code instructions for performing AST manipulation and resolving breaking change for people.

Shift from: "We need you to do this", "I am proactively letting you know that something needs to change, and I also made the change for you!"

How do you actually go about creating automated pull requests?

manual steps would involve cloning, checking out feature branch, applying code changes, staging / committing, pushing up branch, creating the PR

gitpython is an existing and extremely powerful tool, but its api is fairly involved and (by design) doesn’t provide the type of high level abstractions that we need.

created pygitops (open sourced), built completely on top of gitpython

high level abstractions for the workflow I described.

coolest / most pythonic part about it is the "feature branch" context manager.

code changes are made in the context of a feature branch

when you intentionally or accidentally leave the context of a feature branch, we want certain things to be true (default / main branch, clean workdir, no unstaged changes)

when writing PR automation, don’t have to worry about this!

Can you describe some of the more technical details about how your change propagation system (Gator) works?

heavily inspired by kubernetes resource model (resources are defined via a declarative specification)

Kubernetes itself ships with resources that implement behaviors of common resources (pods, services, etc)

Gator’s execution model is broken up into two parts:

what repos to act on (Source)

what are the changes that need to be applied. (Output)

Ex: Source to proxy github search. write github search query to get back list of repos

Output to scan a repo for regex pattern at specified paths and replace with some fixed term. Very popular, engineers love find and replace.

What are the most interesting, innovative, or unexpected ways that you have seen mamba / gator used?

resource model of gator supports the idea of we don’t know, what we don’t know

reference k8s, CRDs, resource model.

container execution

log4j identification and remidiation

automate some of the work for identifying vulnerabilities

java platform team was able to use java native tooling in the environment of their choosing to identify vulnerable apps.

What are the most interesting, unexpected, or challenging lessons that you have learned while working on application lifecycle concerns?

What do you have planned for the future of application lifecycle management/developer experience improvements at Wayfair?

Hope to start open sourcing interesting aspects of our change propagation tool (Gator)

As someone who maintains many open source projects, or even at the enterprise level, we think that some of our patterns and approaches can be shared! yaml -> code changes

Keep In Touch

Email

Github

Linkedin

Picks

Tobias

Nocciolata hazelnut spread

Joshua

Cities Skylines Game

Cities Skylines – Cities Planner Plays: Verde Beach

Links

pygitops

columbo

backstage

renovate

DORA metrics

TI-84 Calculator

TI BASIC

Wayfair Python Platforms Team Podcast Episode

Pydantic

Podcast Episode

Helm

PyUp

GitPython

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Run Your Applications Reliably On Kubernetes Without Losing Sleep With Robusta** Mar 14, 2022
Summary

Kubernetes is a framework that aims to simplify the work of running applications in production, but it forces you to adopt new patterns for debugging and resolving issues in your systems. Robusta is aimed at making that a more pleasant experience for developers and operators through pre-built automations, easy debugging, and a simple means of creating your own event-based workflows to find, fix, and alert on errors in production. In this episode Natan Yellin explains how the project got started, how it is architected and tested, and how you can start using it today to keep your Python projects running reliably.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.

Your host as usual is Tobias Macey and today I’m interviewing Natan Yellin about Robusta,

Interview

Introductions

How did you get introduced to Python?

Can you describe what Robusta is and the story behind it?

What are some of the challenges that teams face when running their systems in Kubernetes?

How does Robusta help address those difficulties?

How does Robusta compare to e.g. Rookout?

What are some of the ways that Robusta is able to provide specific insights for Python applications?

Can you describe how Robusta is implemented?

What are some of the most challenging engineering tasks that you have had to work through while building Robusta?

How have the capabilities and components evolved from when you started working on it?

What is the workflow for integrating Robusta into a Kubernetes environment and a team’s maintenance processes?

What are some examples of the kinds of questions that Robusta can help answer out of the box?

What are some tasks that Robusta facilitates which require manual exploration?

What are the interfaces available for customizing and extending the functionality of Robusta?

What is involved in adding a new automation capability to Robusta?

How have you approached the design of the tool to make it ergonomic and intuitive so that it doesn’t contribute to the stresses of dealing with errors in production?

Given that it is a tool to help resolve problems in production infrastructure, how have you worked to ensure its reliability and resilience?

What is the governance and sustainability model for Robusta?

What are the most interesting, innovative, or unexpected ways that you have seen Robusta used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Robusta?

When is Robusta the wrong choice?

What do you have planned for the future of Robusta?

Keep In Touch

LinkedIn

@aantn on Twitter

aantn on GitHub

Website

Picks

Tobias

Kubernetes: Up And Running (affiliate link)

Natan

Kubernetes for SysAdmins Youtube video by Kelsey Hightower

Learn to delegate

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Robusta

GHOP

Objective C

Snyk

Heroku

Google AppEngine

OOM Killer

Bin Packing/Knapsack Problem

Prometheus

Kubernetes Pods

PySpy

tracemalloc

Pyrasite

VSCode Debugger

Pydantic

Podcast Episode

Helm – Kubernetes package manager

Why Profiler

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Accelerate The Development And Delivery Of Your Machine Learning Applications Using Ray And Deploy It At Anyscale** Mar 06, 2022
Summary

Building a machine learning application is inherently complex. Once it becomes necessary to scale the operation or training of the model, or introduce online re-training the process becomes even more challenging. In order to reduce the operational burden of AI developers Robert Nishihara helped to create the Ray framework that handles the distributed computing aspects of machine learning operations. To support the ongoing development and simplify adoption of Ray he co-founded Anyscale. In this episode he re-joins the show to share how the project, its community, and the ecosystem around it have grown and evolved over the intervening two years. He also explains how the techniques and adoption of machine learning have influenced the direction of the project.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Robert Nishihara about his work at Anyscale and the Ray distributed execution framework

Interview

Introductions

How did you get introduced to Python?

Can you describe what Anyscale is and the story behind it?

How has the Ray project and ecosystem evolved since we last spoke? (2 years ago)

How has the landscape of AI/ML technologies and techniques shifted in that time?

What are the main areas where organizations are trying to apply ML/AI?

What are some of the issues that teams encounter when trying to move from prototype to production with ML/AI applications?

What are the features of Ray that help to mitigate those challenges?

With the introduction of more widely available streaming/real-time technologies the viability of reinforcement learning has increased. What new challenges does that approach introduce?

What are some of the operational complexities associated with managing a deployment of Ray?

What are some of the specialized utilities that you have had to develop to maintain a large and multi-tenant platform for your customers?

What is the governance model around the Ray project and how does the work at Anyscale influence the roadmap?

What are the most interesting, innovative, or unexpected ways that you have seen Anyscale/Ray used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ray and Anyscale?

When is Anyscale/Ray the wrong choice?

What do you have planned for the future of Anyscale/Ray?

Keep In Touch

robertnishihara on GitHub

@robertnishihara on Twitter

Website

LinkedIn

Picks

Tobias

The Edge Chronicles: Beyond The Deepwoods

Robert

Production RL Summit

Project Hail Mary by Andy Weir

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Ray

Podcast Episode

Anyscale

UC Berkeley

Matlab

Deep Learning

Pandas

NumPy

Horovod

Podcast Episode

XGBoost

Modin

Podcast Episode

Dask

Ray Datasets

Reinforcement Learning

Production Reinforcement Learning Summit

AlphaGo

Databricks

Snowflake

Data Engineering Podcast Episode

TPU == Tensor Processing Unit

Weights and Biases

MLFlow

RLLib

Ray Serve

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**See The Structure Of Your Software At A Glance With Call Graphs From Code2Flow** Feb 28, 2022
Summary

As software projects grow and change it can become difficult to keep track of all of the logical flows. By visualizing the interconnections of function definitions, classes, and their invocations you can speed up the time to comprehension for newcomers to a project, or help yourself remember what you worked on last month. In this episode Scott Rogowski shares his work on Code2Flow as a way to generate a call graph of your programs. He explains how it got started, how it works, and how you can start using it to understand your Python, Ruby, and PHP projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Subsurface Live is the cloud data lake conference, a virtual conference where data engineers, data scientists, data architects, and data analysts can gather and hear about cloud data lakes and the data ecosystem. Subsurface Live Winter 2022 includes keynote talks from Bill Inmon, the father of the data warehouse, Author of Deep Work Cal Newport, and several more from companies such as Dremio, AWS, dbt, and more. Subsurface will also have many breakout sessions featuring Pandas creator Wes McKinney, Apache Superset & Airflow creator Maxime Beauchemin, and engineers from Apple, Uber, Adobe, Bloomberg, and more. Meet other data professionals and learn about the data technologies and practices helping companies meet their current and future data needs. Register today at pythonpodcast.com/subsurface

Your host as usual is Tobias Macey and today I’m interviewing Scott Rogowski about Code2Flow, a utility for generating "pretty good" call graphs for dynamic languages

Interview

Introductions

How did you get introduced to Python?

Can you describe what Code2Flow is and the story behind it?

What are some of the ways that a program’s call graph might be used?

How does the visual representation generated by Code2Flow help with exploring the structure of a project?

What are some of the alternative approaches/tools that might be used to gain similar insights?

What do you see as the overlap in utility between Code2Flow and e.g. SourceGraph?

Can you describe how the Code2Flow project is implemented?

How have the design and goals of the project changed since you first began working on it?

Given that Code2Flow is implemented in Python, how have you managed the parsing/processing of the other languages that you support?

Visualizing a complex program can quickly become very messy. How have you approached the layout of the output to enhance comprehension?

What are some of the situations where Code2Flow will be unable to provide a full picture of a program’s call graph?

What are some of the pieces of information that are unavailable due to the static analysis approach that you have taken?

Can you describe the process of applying Code2Flow to a project?

Once the structure is on display, what are some next steps that an individual or team might take to analyze and act on the information?

Given the static nature of the output, how might Code2Flow be incorporated in a CI/CD system to provide insight into the evolution of a projects structure?

What are the most interesting, innovative, or unexpected ways that you have seen Code2Flow used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Code2Flow?

When is Code2Flow the wrong choice?

What do you have planned for the future of Code2Flow?

Keep In Touch

Website

scottrogowski on GitHub

Picks

Tobias

Taking Vacation

Universal Studios, Florida

Scott

Service work

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Code2Flow

Colombia

Mongita

TI-83

Ruby

PHP

AST == Abstract Syntax Tree

Graphviz

Pylint

Robert Frost

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Scaling Knowledge Management For Technical Teams With Knowledge Repo** Feb 21, 2022
Summary

One of the most persistent challenges faced by organizations of all sizes is the recording and distribution of institutional knowledge. In technical teams this is exacerbated by the need to incorporate technical review feedback and manage access to data before publishing. When faced with this problem as an early data scientist at AirBnB, Chetan Sharma helped create the Knowledge Repo project as a solution. In this episode he shares the story behind its creation and growth, how and why it was released as open source, and the features that make it a compelling option for your own team’s knowledge management journey.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Chetan Sharma about Knowledge Repo, an open source framework for managing documentation for technical users

Interview

Introductions

How did you get introduced to Python?

EE + CS/AI + Stats degrees

Airbnb working on ML models

Knowledge Repo itself

Can you describe what Knowledge Repo is and the story behind it?

We started seeing interviewees use ipython notebooks, thought they were great

Wanted to push more people to use notebooks, but they weren’t very shareable, vettable

Existing notebook hosting services weren’t very good, and weren’t built for people who aren’t data stakeholders. It was especially poor with images, annoying cell blocks

Made a simple post processor to remove cell blocks, push the images to s3, and host on flask

Once we were pushing notebooks into a Github repo for hosting on a flask app, so many things became possible

Review cycles

Shareability / collaboration features

Indexing / searching

Concurrently, great work was happening on developing internal R packages / python libraries to provide consistent, branded aesthetics

What are some of the approaches that teams typically take for recording and sharing institutional knowledge?

Copy and paste to google docs, slides

Facebook was using facebook photo albums

untrustworthy, not discoverable, divorced from the code

What are the unique requirements that are introduced when attempting to record and distribute learnings related to data such as A/B experiments, analytical methods, data sets, etc.?

Reproducibility is a big one

Making sure the learnings are trustworthy (good data? no bugs?)

Distributing widely, across the org and across time

Experimentation

Experimentation is at the end of a research-design-build-measure cycle, strategic analysis is often before

Capturing all of the context

Can you describe how the Knowledge Repo project is architected?

Repositories: a store of posts, most commonly a github repo

Markdown as original lingua franca, eventually a KR specific “KR post” concept (which is still basically markdown)

Post processors

Convert whatever upstream file to markdown / KR post (Jupyter notebook, R Markdown, markdown were the original ones)

Handle images and other large assets, usually pushing them to cloud storage

Evolved to handle PDFs, googledocs, keynotes

What were the motivating factors for making it available as an open source project?

It was such a common problem. Even incredibly sophisticated data teams at Uber, Facebook, etc. were begging us to share the system.

What is the workflow for creating, sharing, and discovering information in an installation of Knowledge Repo?

Create a github repo for hosting strategic analysis

Use the KR script to create a stub/template for whatever format you’re working in

Do your work in Jupyter, etc.

Instead of using github scripts (git add) use knowledge scripts (knowledge add), which is basically the github scripts with postprocessors

Do typical Github workflows

See the result in the hosted knowledge repo app

What are some of the options available for extending or customizing an installation of Knowledge Repo?

More postprocessors! google docs, presentations, UX research, anything can be done in KR with a simple postprocessor to turn it to markdown/images/PDF

Tying the system to your internal data tools. For example, an experimentation system like Eppo or whatever you use for marketing campaigns

If you were to start over today, what are some of the ways that you might approach the solution to knowledge management differently?

Think of it more holistically:

What are the most interesting, innovative, or unexpected ways that you have seen Knowledge Repo used?

UX research

Writing up guide for acquihiring

Demonstrating of capabilities, data framework

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Knowledge Repo?

Strategic analysis needs to be elevated, this leads to paradigm changes

Organization problems are helped by tools like KR: eg. promotions

Meeting people’s tools/workflows where they are is powerful

When is Knowledge Repo the wrong choice?

Keep In Touch

LinkedIn

@chesharma87

Picks

Tobias

Learning Guitar

Chetan

Underrated cooking ingredients: chickpea flour, butter fried kimchi (in grilled cheese, nachos)

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Eppo

Data Engineering Podcast Episode

Knowledge Repo

IPython

Jupyter

Flask

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Simplify And Scale Your Software Development Cycles By Putting On Pants (Build Tool)** Feb 14, 2022
Summary

Software development is a complex undertaking due to the number of options available and choices to be made in every stage of the lifecycle. In order to make it more scaleable it is necessary to establish common practices and patterns and introduce strong opinions. One area that can have a huge impact on the productivity of the engineers engaged with a project is the tooling used for building, validating, and deploying changes introduced to the software. In this episode maintainers of the Pants build tool Eric Arellano, Stu Hood, and Andreas Stenius discuss the recent updates that add support for more languages, efforts made to simplify its adoption, and the growth of the community that uses it. They also explore how using Pants as the single entry point for all of your routine tasks allows you to spend your time on the decisions that matter.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Building data integration workflows is time consuming and tedious, requiring an unpleasant amount of boilerplate code to do it right. Rivery is a managed platform for building our ELT pipelines that offers the industry’s first native integration with Python, allowing you to seamlessly load and export Pandas dataframes to and from all of your databases, services, and data warehouses with a few clicks and no extra code. Rivery is hosting a live demo of their first class Python support on February 22nd, and when you use the promo code "Python" during registration you will be entered to win a brand new series 7 apple watch. Go to pythonpodcast.com/rivery today to learn more and register.

Your host as usual is Tobias Macey and today I’m interviewing Eric Arellano, Stu Hood, and Andreas Stenius about the Pants build tool and all of the work that has gone into it recently

Interview

Introductions

How did you get introduced to Python?

Can you describe what Pants is and the story behind it?

What is the scope of concerns that Pants is focused on addressing?

What are some of the notable changes in the project and its ecosystem over the past 1 1/2 years?

How do you approach the work of defining the target scope of the Pants toolchain?

What are some of your guiding principles to decide when a feature request belongs in the core vs as a plugin?

What are some of the ergonomic improvements that you have added to simplify the work of getting started with Pants and adopting it across teams?

What are some of the challenges that teams run into as they start to scale the size of their monorepos? (e.g. project design, boilerplate reduction, etc.)

How are you managing the work of growing and supporting the community as you move beyond early adopters/experts into newcomers to Pants and programming?

How are you handling support for multiple language ecosystems?

What are some of the challenges involved with making Pants feel idiomatic for such a range of communities?

How does the use of Python as the plugin/extension syntax work for teams that don’t use it as their primary language?

What are the architectural changes that needed to be made for you to be capable of integrating with the different execution environments?

How would you characterize the level of feature coverage across the different supported languages?

Now that you have laid the foundation, how much effort is required to add new language targets?

What are the most interesting, innovative, or unexpected ways that you have seen Pants used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pants?

When is Pants the wrong choice?

What do you have planned for the future of Pants?

Keep In Touch

Eric

LinkedIn

Eric-Arellano on GitHub

@earellanoaz on Twitter

Stu

LinkedIn

@stuhood on Twitter

stuhood on GitHub

Andreas

@andreasstenius on Twitter

kaos on GitHub

Picks

Tobias

Last Kingdom on Netflix

Eric

Getting Curious

Stu

Checks and Balance Podcast

Andreas

The Pragmatic Programmer

Links

Pants

Make

Earthly

Podcast Episode

MyPy

Podcast Episode

PyRight

Pylint

Flake8

Podcast Episode

Bazel

pre-commit

Podcast Episode

Underpants library

PyOxidizer

Podcast Episode

Eric PyCon Talk

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Achieve Repeatable Builds Of Your Software On Any Machine With Earthly** Feb 06, 2022
Summary

It doesn’t matter how amazing your application is if you are unable to deliver it to your users. Frustrated with the rampant complexity involved in building and deploying software Vlad A. Ionescu created the Earthly tool to reduce the toil involved in creating repeatable software builds. In this episode he explains the complexities that are inherent to building software projects and how he designed the syntax and structure of Earthly to make it easy to adopt for developers across all language environments. By adopting Earthly you can use the same techniques for building on your laptop and in your CI/CD pipelines.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Vlad A. Ionescu about Earthly, a syntax and runtime for software builds to reduce friction between development and delivery

Interview

Introductions

How did you get introduced to Python?

Can you describe what Earthly is and the story behind it?

What are the core principles that engineers should consider when designing their build and delivery process?

What are some of the common problems that engineers run into when they are designing their build process?

What are some of the challenges that are unique to the Python ecosystem?

What is the role of Earthly in the overall software lifecycle?

What are the other tools/systems that a team is likely to use alongside Earthly?

What are the components that Earthly might replace?

How is Earthly implemented?

What were the core design requirements when you first began working on it?

How have the design and goals of Earthly changed or evolved as you have explored the problem further?

What is the workflow for a Python developer to get started with Earthly?

How can Earthly help with the challenge of managing Javascript and CSS assets for web application projects?

What are some of the challenges (technical, conceptual, or organizational) that an engineer or team might encounter when adopting Earthly?

What are some of the features or capabilities of Earthly that are overlooked or misunderstood that you think are worth exploring?

What are the most interesting, innovative, or unexpected ways that you have seen Earthly used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Earthly?

When is Earthly the wrong choice?

What do you have planned for the future of Earthly?

Keep In Touch

LinkedIn

@VladAIonescu on Twitter

Website

Picks

Tobias

Shape Up book

Vlad

High Output Management by Andy Grove

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Earthly

Bazel

Pants

Podcast Episode

ARM

AWS Graviton

Apple M1 CPU

Qemu

Phoenix web framework for Elixir language

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building A Detailed View Of Your Software Delivery Process With The Eiffel Protocol** Jan 31, 2022
Summary

The process of getting software delivered to an environment where users can interact with it requires many steps along the way. In some cases the journey can require a large number of interdependent workflows that need to be orchestrated across technical and organizational boundaries, making it difficult to know what the current status is. Faced with such a complex delivery workflow the engineers at Ericsson created a message based protocol and accompanying tooling to let the various actors in the process provide information about the events that happened across the different stages. In this episode Daniel Ståhl and Magnus Bäck explain how the Eiffel protocol allows you to build a tooling agnostic visibility layer for your software delivery process, letting you answer all of your questions about what is happening between writing a line of code and your users executing it.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Daniel Ståhl and Magnus Bäck about Eiffel, an open protocol for platform agnostic communication for CI/CD systems

Interview

Introductions

How did you get introduced to Python?

Can you describe what Eiffel is and the story behind it?

What are the goals of the Eiffel protocol and ecosystem?

What is the role of Python in the Eiffel ecosystem?

What are some of the types of questions that someone might ask about their CI/CD workflow?

How does Eiffel help to answer those questions?

Who are the personas that you would expect to interact with an Eiffel system?

Can you describe the core architectural elements required to integrate Eiffel into the software lifecycle?

How have the design and goals of the Eiffel protocol/architecture changed or evolved since you first began working on it?

What are some example workflows that an engineering/product team might build with Eiffel?

What are some of the challenges that teams encounter when integrating Eiffel into their delivery process?

What are the most interesting, innovative, or unexpected ways that you have seen Eiffel used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Eiffel?

When is Eiffel the wrong choice?

What do you have planned for the future of Eiffel?

Keep In Touch

Daniel

d-stahl-ericsson on GitHub

LinkedIn

Magnus

LinkedIn

magnusbaeck on GitHub

Picks

Tobias

Red Notice

Daniel

The Witcher

Magnus

Lego

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Eiffel

Ericsson

Axis Communications

Hudson CI framework

Spinnaker

Jenkins

Tekton

Gradle

Artifactory

JSON Schema

RabbitMQ

Prometheus

Continuous Delivery Foundation

CD Events

XKCD Competing Standards

Python Eiffel SDK

Pydantic

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Improve Your Productivity By Investing In Developer Experience Design For Your Projects** Jan 24, 2022
Summary

When we are creating applications we spend a significant amount of effort on optimizing the experience of our end users to ensure that they are able to complete the tasks that the system is intended for. A similar effort that we should all consider is optimizing the developer experience for ourselves and other engineers who contribute to the projects that we work on. Adam Johnson recently wrote a book on how to improve the developer experience for Django projects and in this episode he shares some of the insights that he has gained through that project and his work with clients to help you improve the experience that you and your team have when collaborating on software development.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Adam Johnson about optimizing your developer experience

Interview

Introductions

How did you get introduced to Python?

Can you describe what you mean by the term "developer experience"?

How does it compare to the concept of user experience design?

What are the main goals that you aim for through improving DX?

When considering DX, what are the categories of focus for improvement? (e.g. the experience of a given software project, the developer’s physical environment, their editing environment, etc.)

What are some of the most high impact optimizations that a developer can make?

What are some of the areas of focus that have the most variable impact on a developer’s experience of a project?

What are some of the most helpful tools or practices that you rely on in your own projects?

How does the size of a development team or the scale of an organization impact the decisions and benefits around DX improvements?

One of the perennial challenges with selecting a given tool or architectural pattern is the continually changing landscape of software. How have your choices for DX strategies changed or evolved over the years?

What are the most interesting, innovative, or unexpected developer experience tweaks that you have encountered?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on your book?

What are some of the potential pitfalls that individuals and teams need to guard against in their quest to improve developer experience for their projects?

What are some of the new tools or practices that you are considering incorporating into your own work?

Keep In Touch

@AdamChainz on Twitter

Website

adamchainz on GitHub

Picks

Tobias

Eternals movie

Adam

Fan of Eternals, enjoyed Neil Gaiman series

Also general MCU fan, watched it all in lockdown

Moon Knight trailer

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Boost Your Django DX

Rust

Ripgrep

Factory Boy

Mimesis

Podcast Episode

Language Server Protocol

EditorConfig

Starship Command Prompt

Pre-Commit

Podcast Episode

Flake8

Podcast Episode

DevDocs

Dash library documentation search tool

pyupgrade

StandardJS

Cython

Podcast Episode

The Phoenix Project

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**An Exploration Of Effective Pandas Practices With Matt Harrison** Jan 15, 2022
Summary

Pandas has grown to be a ubiquitous tool for working with data at every stage. It has become so well known that many people learn Python solely for the purpose of using Pandas. With all of this activity and the long history of the project it can be easy to find misleading or outdated information about how to use it. In this episode Matt Harrison shares his work on the book "Effective Pandas" and some of the best practices and potential pitfalls that you should know for applying Pandas in your own work.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Matt Harrison about best practices for using Pandas for data exploration, manipulation, and analysis

Interview

Introductions

How did you get introduced to Python?

What motivated you to write a book about Pandas?

There are a number of books available that cover some aspect of the Pandas framework or its application. What was missing from the available literature?

Who is your target audience for this book?

What are some of the most surprising things that you have learned about Pandas while working on this book?

What are the sharp edges that you see newcomers to pandas run into most frequently?

It is easy to use Pandas in a naive manner and get things done. What are some of the bad habits that you have seen people form in their work with Pandas?

How and when do those habits become harmful?

What are the most interesting, innovative, or unexpected ways that you have seen Pandas used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on this book?

What are some of the projects that you are planning to work on in the near/medium term?

Keep In Touch

Website

@__mharrison__ on Twitter

Blog

mattharrison on GitHub

Picks

Tobias

MSR Snowshoes

Matt

Telemark Skiing

22 Designs

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Effective Pandas Book (affiliate link with 20% discount code applied)

Discount code INIT

TCL

Perl

Pandas

Podcast Episode

Pandas Extension Arrays

Podcast Episode

Koalas

Dask

Data Engineering Podcast Episode

Modin

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Generate Your Text Files With Python Using Cog** Jan 13, 2022
Summary

Developers hate wasting effort on manual processes when we can write code to do it instead. Cog is a tool to manage the work of automating the creation of text inside another file by executing arbitrary Python code. In this episode Ned Batchelder shares the story of why he created Cog in the first place, some of the interesting ways that he uses it in his daily work, and the unique challenges of maintaining a project with a small audience and a well defined scope.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Ned Batchelder about Cog, a tool for generating files or text from embedded Python logic

Interview

Introductions

How did you get introduced to Python?

Can you describe what Cog is and the story behind it?

What are the use cases that you initially created Cog to address?

What were the shortcomings or extraneous overhead that you encountered in tools such as Jinja, Mako, Genshi, etc. that led you to create a new tool?

What was your path from a quick and dirty script that suited your own purposes to turning it into a niche open source project that was general and stable enough for the broader community?

One of your claims to fame is your role as the maintainer for coverage.py. How has your experience managing such a widely used project translated to the relatively small and low traffic project like Cog?

Can you describe how Cog is implemented?

How did you approach the design of the syntactic elements for embedding Python code into a host file?

What is the workflow for someone using Cog to generate all or parts of a file?

How does the introduction of third party dependencies impact the viability and utility of Cog as compared to other templating systems?

What are the most interesting, innovative, or unexpected ways that you have seen Cog used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cog?

When is Cog the wrong choice?

What do you have planned for the future of Cog?

Keep In Touch

Website

nedbat on GitHub

@nedbat on Twitter

LinkedIn

Picks

Tobias

Samson Q9U Microphone

Ned

McFly Command Line History Tool

Go for a walk

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Cog

Boston Python

Lotus

Lotus Notes

Zope

Cheetah Template Engine

Coverage.py

Podcast Episode

Unix Philosophy

Hungarian Notation

Jupyter Notebooks

GitHub Profile ReadMe

Ned’s GitHub Profile

Raw Markdown

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**A Friendly Approach To Regression Models For Programmers** Jan 02, 2022
Summary

Statistical regression models are a staple of predictive forecasts in a wide range of applications. In this episode Matthew Rudd explains the various types of regression models, when to use them, and his work on the book "Regression: A Friendly Guide" to help programmers add regression techniques to their toolbox.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Matthew Rudd about the applications of statistical modeling and regression, and how to start using it for your work

Interview

Introductions

How did you get introduced to Python?

Can you start by describing some use cases for statistical regression?

What was your motivation for writing a book to explain this family of algorithms to programmers?

What are your goals for the book?

Who is the target audience?

What are some of the different categories of regression algorithms?

What are some heuristics for identifying which regression to use?

How have you approached the balance of using software principles for explaining the work of building the models with the mathematical underpinnings that make them work?

What are some of the concepts that are most challenging for people who are first working with regression models?

What are the most interesting, innovative, or unexpected ways that you have seen statistical regression models used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on your book?

What are some of the resources that you recommend for folks who want to learn more about the inner workings and applications of regression models after they finish your book?

Keep In Touch

LinkedIn

@MatthewBRudd on Twitter

Picks

Tobias

The Argument podcast from the NY Times

Matthew

Primus

Claypool Lennon Delirium

South of Reality

Links

Regression: A Friendly Guide

Sewanee University of the South

Sewanee Data Lab

Mark Lutz Python books

Elements of Statistical Learning

Linear Regression

Logistic Regression

Modeling Binary Data

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Fast, Flexible, and Incremental Task Automation With doit** Dec 27, 2021
Summary

Every software project needs a tool for managing the repetitive tasks that are involved in building, running, and deploying the code. Frustrated with the limitations of tools like Make, Scons, and others Eduardo Schettino created doit to handle task automation in his own work and released it as open source. In this episode he shares the story behind the project, how it is implemented under the hood, and how you can start using it in your own projects to save you time and effort.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Eduardo Schettino about Doit, a flexible and low overhead task automation tool

Interview

Introductions

How did you get introduced to Python?

Can you describe what doit is and the story behind it?

What are the main goals and use cases of doit?

Can you describe how you approached the implementation of Doit?

How has the design changed or evolved since you first began working on it?

The realm of task automation tools for developers is an exceedingly crowded one, with each tool prioritizing certain use cases. How would you characterize the position of doit in the current ecosystem?

How does it compare to e.g. Click, Invoke, Typer, etc.?

What is your guiding philosophy for when and how to add new features?

You have been running the project for ~13 years now. How has the evolution of the Python language and ecosystem influenced your approach to the development and maintenance of doit?

What is the workflow for getting started with doit and integrating it into your development process?

For every project there are some tasks that are identical and some that are bespoke for that application. What are the options for maintaining a standard set of tasks across repositories and composing them with per-project activites?

What are some of the useful patterns that you and the community have established for designing tasks and execution graphs?

How do you use doit in your own work?

What are the most interesting, innovative, or unexpected ways that you have seen doit used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on doit?

When is doit the wrong choice?

What do you have planned for the future of doit?

Keep In Touch

LinkedIn

schettino72 on GitHub

Picks

Tobias

The Matrix series

Eduardo

John Pilger

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

doit

Zope

Twisted

Django

Pyflakes

scons

Make

Nikola

Podcast Episode

Nose

Pytest

Podcast Episode

Click

Typer

Invoke

Puppet

Ansible

Chef

Sphinx

Snakemake

Airflow

Luigi

pytest-incremental

import-deps

dbm

MetalK8s

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Technological, Business, and Sales Challenges Of Building The Ethical Ads Network** Dec 20, 2021
Summary

Whether we like it or not, advertising is a common and effective way to make money on the internet. In order to support the work being done at Read The Docs they decided to include advertisements on the documentation sites they were hosting, but they didn’t want to alienate their users or collect unnecessary information. In this episode David Fischer explains how they built the Ethical Ads network to solve their problem, the technical and business challenges that are involved, and the open source application that they built to power their network.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing David Fischer about the Ethical Ads marketplace and the technology that runs

Interview

Introductions

How did you get introduced to Python?

Can you describe what the Ethical Ads project is and the story behind it?

What are the technical and organizational requirements involved in running an ad network?

How have you approached the problem of kickstarting the flywheel for the two-sided marketplace?

What are some of the challenges that you face in building an accurate profile of your audience without using detailed tracking methods?

What are the benefits that you see in focusing exclusively on developers in your publisher relationships?

Can you describe the design and implementation of the ad server?

How has the architecture evolved since you first began working on it?

If you were to start over today what might you do differently?

How have you approached scaling for performance and geographic distribution?

What mechanisms do you use for tracking impressions/measuring ad effectiveness?

How can advertisers experiment with A/B testing of ad copy?

If someone wants to run their own advertisements with the ethical ads server, what is involved in getting it deployed and integrated into their sites?

What are the integration and extension points available for customizing the behavior of the platform?

What are some of the most notable lessons that you have learned about online advertising since you first started working on the Ethical Ads project?

What are the most interesting, innovative, or unexpected ways that you have seen Ethical Ads used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Ethical Ads platform?

What do you have planned for the future of the Ethical Ads platform?

Keep In Touch

davidfischer on GitHub

@djfische on Twitter

LinkedIn

Picks

Tobias

Ship It! Podcast

David

Local Python Meetup

Click CLI framework

useragents library

TLD for parsing internet domains

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Ethical Ads Network

Ethical Ads Server

San Diego Python

Read The Docs

Podcast Episode

CodeFund

CPM == Cost Per Mille

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Accidentally Building A Business With Python At Listen Notes** Dec 12, 2021
Summary

Podcasts are one of the few mediums in the internet era that are still distributed through an open ecosystem. This has a number of benefits, but it also brings the challenge of making it difficult to find the content that you are looking for. Frustrated by the inability to pick and choose single episodes across various shows for his listening Wenbin Fang started the Listen Notes project to fulfill his own needs. He ended up turning that project into his full time business which has grown into the most full featured podcast search engine on the market. In this episode he explains how he build the Listen Notes application using Python and Django, his work to turn it into a sustainable business, and the various ways that you can build other applications and experiences on top of his API.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Wenbin Fang about the technology powering the Listen Notes podcast discovery platform

Interview

Introductions

How did you get introduced to Python?

Can you describe what Listen Notes is and the story behind it?

What are some of the main goals that listeners have when searching for a podcast?

What are the challenges that they commonly encounter when looking for information in a podcast?

What are the different sources of information that you can use to extract useful details about a podcast?

How do you identify and prioritize new features or product enhancements?

Can you describe how the Listen Notes platform is architected?

How has it changed or evolved since you first began working on it?

How did you approach the technology selection for the initial version of Listen Notes?

If you were to start over today, what might you do differently?

What are the technical challenges that are posed by the ecosystem around podcasts?

What are the biggest changes that have happened in the methods of production and consumption for podcasts since you first became involved in the space?

How do you approach the design and contracts of the Listen Notes web API given how core that is to your platform?

What are the most complex or complicated engineering projects that you have done for Listen Notes?

What are the pieces of the infrastructure for podcasts that you would like to see improved, changed, or replaced?

What are some of the kinds of projects that developers can build with the Listen Notes API?

What, if any, impact have the introduction of podcasts to closed platforms such as Spotify, Amazon Music, etc. had on your business?

What are some of the most surprising things that you have learned about podcasts and their consumption while building Listen Notes?

What are the most interesting, innovative, or unexpected ways that you have seen Listen Notes used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Listen Notes?

What do you have planned for the future of Listen Notes?

Keep In Touch

Website

LinkedIn

wenbinf on GitHub

@wenbinf on Twitter

Picks

Tobias

Wheel of Time TV Series

Wenbin

Superhuman email client

Links

Listen Notes

Graphviz

NextDoor

PostgreSQL

Elasticsearch

Redis

RabbitMQ

Celery

ReactJS

Django

Bootstrap CSS

Digital Ocean

Tailwind CSS

Entity Resolution

Clickhouse

Data Engineering Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Making Orbital Mechanics More Accessible With Poliastro** Nov 27, 2021
Summary

Outer space holds a deep fascination for people of all ages, and the key principle in its exploration both near and far is orbital mechanics. Poliastro is a pure Python package for exploring and simulating orbit calculations. In this episode Juan Luis Cano Rodriguez shares the story behind the project, how you can use it to learn more about space travel, and some of the interesting projects that have used it for planning planetary and interplanetary missions.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Juan Luis Cano Rodriguez about Poliastro, an open source library for interactive Astrodynamics and Orbital Mechanics, with a focus on ease of use, speed, and quick visualization.

Interview

Introductions

How did you get introduced to Python?

Can you describe what Poliastro is and the story behind it?

What are some of the simulations that Poliastro is designed to be used for?

How much knowledge of orbital mechanics is necessary to get started with Poliastro?

Can you describe how the project is implemented?

How have the goals and design of the project changed or evolved since you first started it?

What are some of the design philosophies that you focus on to make the package accessible to the range of users that you support?

Can you talk through the workflow of using Poliastro to do something like track the path of the ISS and its traversal of the debris field from the recent satellite destruction?

What are some of the other libraries or frameworks that are commonly used with Poliastro?

How are you using Poliastro in your own work?

What are some overlooked or underused aspects of the project that you would like to highlight?

What are the most interesting, innovative, or unexpected ways that you have seen Poliastro used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Poliastro?

When is Poliastro the wrong choice?

What do you have planned for the future of Poliastro?

Keep In Touch

LinkedIn

GitHub

Email

Twitter

Picks

Tobias

Josh Blue (comedian)

Juan Luis

DJ Cotts

DJ Weaver

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Poliastro

Fortran 90 (if only this community existed back then! https://ondrejcertik.com/blog/2021/03/resurrecting-fortran/)?utm_source=rss&utm_medium=rss

Satellogic

Read the Docs

Wolfram Alpha

Mathematica

SageMath

2-Body Problem

AstroPy

Podcast Episode

Numba

Import Linter

Vallado "Fundamentals of Astrodynamics"

International Space Station

Starlink Satellites

Planetary Ephemeritas Data

Satellite Data

Kerbal Space Program

NumFOCUS

Open Collective

Python SGP4

Libre Space Foundation

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Declarative Deep Learning From Your Laptop To Production With Ludwig and Horovod** Nov 22, 2021
Summary

Deep learning frameworks encourage you to focus on the structure of your model ahead of the data that you are working with. Ludwig is a tool that uses a data oriented approach to building and training deep learning models so that you can experiment faster based on the information that you actually have, rather than spending all of our time manipulating features to make them match your inputs. In this episode Travis Addair explains how Ludwig is designed to improve the adoption of deep learning for more companies and a wider range of users. He also explains how the Horovod framework plugs in easily to allow for scaling your training workflow from your laptop out to a massive cluster of servers and GPUs. The combination of these tools allows for a declarative workflow that starts off easy but gives you full control over the end result.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Travis Adair about building and training machine learning models with Ludwig and Horovod

Interview

Introductions

How did you get introduced to Python?

Can you describe what Horovod and Ludwig are?

How do the projects work together?

What was your path to being involved in those projects and what is your current role?

There are a number of AutoML libraries available for frameworks such as scikit-learn, etc. What are the challenges that are introduced by applying that workflow to deep learning architectures?

What are the use cases that Ludwig is designed to enable?

Who are the target users of Ludwig?

How do the workflows change/progress for the different personas?

How is the underlying framework architected?

What are the available extension points to provide a progressive exposure of complexity?

How have the goals and design of the project changed or evolved as it has gained more widespread adoption beyond Uber?

What was the motivation for migrating the core of Ludwig from Tensorflow to Pytorch?

Can you describe the workflow of building a model definition with Ludwig?

How much knowledge of neural network architectures and their relevant characteristics is necessary to use Ludwig effectively?

What are the motivating factors for adding Horovod to the process?

What is involved in moving from a single machine/single process training loop to a multi-core or multi-machine distributed training process?

The combination of Ludwig and Horovod provide a shallower learning curve for building and scaling model training. What do you see as their potential impact on the availability and adoption of more sophisticated ML capabilities across organizations of varying scale?

What do you see as other significant barriers to widespread use of ML functionality?

What are the most interesting, innovative, or unexpected ways that you have seen Ludwig and/or Horovod used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ludwig and Horovod?

When is Ludwig and/or Horovod the wrong choice?

What do you have planned for the future of both projects?

Keep In Touch

LinkedIn

@TravisAddair on Twitter

tgaddair on GitHub

Picks

Tobias

Zeal and Ardor

Travis

Opeth

Agaloch

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Ludwig

Horovod

Predibase

Uber

Michelangelo

Tensorflow

PyTorch

Podcast Episode

Gradient Boosted Trees

XGBoost

CatBoost

LightGBM

PyCaret

HyperBand

scikit-optimize

Keras

Vision Transformer Architecture

HuggingFace

Jax

DeepSpeed

AllReduce

Nvidia Collective Communications Library (NCCL)

Training Epoch

ElasticDL

Raft Consensus Algorithm

TorchScript

Transfer Learning

Gordon Bell Prize

Anyscale

Ray

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build Better Analytics And Models With A Focus On The Data Experience** Nov 22, 2021
Summary

A lot of time and energy goes into data analysis and machine learning projects to address various goals. Most of the effort is focused on the technical aspects and validating the results, but how much time do you spend on considering the experience of the people who are using the outputs of these projects? In this episode Benn Stancil explores the impact that our technical focus has on the perceived value of our work, and how taking the time to consider what the desired experience will be can lead us to approach our work more holistically and increase the satisfaction of everyone involved.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Benn Stancil about the perennial frustrations of working with data and thoughts on how to improve the experience

Interview

Introductions

How did you get introduced to Python?

Can you start by discussing your perspective on the most frustrating elements of working with data in an organization?

How might that compound when working with machine learning?

What are the sources of the disconnect between our level of technical sophistication and our ability to produce meaningful insights from our data?

There have been a number of formulations about a "hierarchy of needs" pertaining to data. When the goal is to bring ML/AI methods to bear on an organization’s processes or products how can thinking about the intended experience act to improve the end result?

What are some failure modes or suboptimal outcomes that might be expected when building from a tooling/technology/technique first mindset?

What are some of the design elements that we can incorporate into our development environments/data infrastructure/data modeling that can incentivize a more experience driven process for building data products/analyses/ML models?

How does the design and capabilities of the Mode platform allow teams to progress along the journey from data discovery to descriptive analytics, to ML experiments?

What are the most interesting, innovative, or unexpected approaches that you have seen for encouraging the creation of positive data experiences?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Mode and data analysis?

When is a data experience the wrong approach?

What do you have planned for the future of Mode to support this ideal?

Keep In Touch

LinkedIn

@bennstancil on Twitter

Picks

Tobias

Venture Unlocked Podcast

Benn

Wrap Text by Bobby Pinero

Counting Stuff by Randy Au

Ray Data Co by Mr Ben

Modern Data Democracy By JP Monteiro

Bad Blood Podcast

Bad Blood Book

Links

Mode Analytics

Tidyverse

Airflow

Fivetran

Data Engineering Podcast Episode

dbt

Data Engineering Podcast Episode

Conway’s Law

Cinchy

Data Engineering Podcast Episode

Reverse ETL

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building Conversational AI to Augment Sales Teams at Structurely** Nov 06, 2021
Summary

The true power of artificial intelligence is its ability to work collaboratively with humans. Nate Joens co-founded Structurely to create a conversational AI platform that augments human sales teams to help guide potential customers through the initial steps of the funnel. In this episode he discusses the technical and social considerations that need to be combined for a seamless conversational experience and how he and his team are tackling the problem.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Nate Joens about his work at Structurely to build conversational AI utilities that augment human sales interactions

Interview

Introductions

How did you get introduced to Python?

Can you describe what Structurely is and the story behind it?

What are the elements that comprise a "conversational AI"?

How is it distinct from the wave of chatbots that were popular in recent years?

What lessons from that approach can we take forward into AI enabled conversational platforms?

How are you applying AI to the sales process?

How much domain expertise is necessary to make an effective and engaging conversational AI? (e.g. knowledge of sales techniques vs. knowledge of real estate, etc.)

Can you describe how you have designed the Structurely platform?

What are the biggest engineering challenges that you have had to work through?

What challenges or complexities have been most persistent?

What are the design complexities that you have to work through to make the AI accessible for end users?

What are some of the advancements in AI/NLP/transfer learning that have been most beneficial for teams building conversational AI?

What are the signals that you emphasize when monitoring the performance of your models?

What is your approach for feeding real-world customer interactions back into your model development and training loop?

What are the most active areas of research in conversational AI applications and techniques?

What are the most interesting, innovative, or unexpected ways that you have seen Structurely and/or conversational AI used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on conversational AI at Structurely?

When is conversational AI the wrong choice?

What do you have planned for the future of Structurely?

Keep In Touch

@whonatejoens on Twitter

LinkedIn

Picks

Tobias

Vantage AWS Cost Management

Nate

VideoForm

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Stucturely

GIS

Generative AI

GPT-3

Sanky Diagram

PyTorch

Podcast Episode

Allen Institute for AI

F Score

Snorkel

Podcast Episode

Few-Shot Learning

Zero Shot Learning

Voxable

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build Composable And Reusable Feature Engineering Pipelines with Feature-Engine** Oct 31, 2021
Summary

Every machine learning model has to start with feature engineering. This is the process of combining input variables into a more meaningful signal for the problem that you are trying to solve. Many times this process can lead to duplicating code from previous projects, or introducing technical debt in the form of poorly maintained feature pipelines. In order to make the practice more manageable Soledad Galli created the feature-engine library. In this episode she explains how it has helped her and others build reusable transformations that can be applied in a composable manner with your scikit-learn projects. She also discusses the importance of understanding the data that you are working with and the domain in which your model will be used to ensure that you are selecting the right features.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Soledad Galli about feature-engine, a Python library to engineer features for use in machine learning models

Interview

Introductions

How did you get introduced to Python?

Can you describe what feature-engine is and the story behind it?

What are the complexities that are inherent to feature engineering?

What are the problems that are introduced due to incidental complexity and technical debt?

What was missing in the available set of libraries/frameworks/toolkits for feature engineering that you are solving for with feature-engine?

What are some examples of the types of domain knowledge that are needed to effectively build features for an ML model?

Given the fact that features are constructed through methods such as normalizing data distributions, imputing missing values, combining attributes, etc. what are some of the potential risks that are introduced by incorrectly applied transformations or invalid assumptions about the impact of these manipulations?

Can you describe how feature-engine is implemented?

How have the design and goals of the project changed or evolved since you started working on it?

What (if any) difference exists in the feature engineering process for frameworks like scikit-learn as compared to deep learning approaches using PyTorch, Tensorflow, etc.?

Can you describe the workflow of identifying and generating useful features during model development?

What are the tools that are available for testing and debugging of the feature pipelines?

What do you see as the potential benefits or drawbacks of integrating feature-engine with a feature store such as Feast or Tecton?

What are the most interesting, innovative, or unexpected ways that you have seen feature-engine used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on feature-engine?

When is feature-engine the wrong choice?

What do you have planned for the future of feature-engine?

Keep In Touch

LinkedIn

@Soledad_Galli on Twitter

solegalli on GitHub

Picks

Tobias

Dune Movie

Dune Series

Soledad

The Social Dilemma

Don’t Be Evil by Rana Foroohar

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

feature-engine

Feature Engineering

Python Feature Engineering Cookbook

scikit-learn

Feature Stores

Podcast Episode

Pandas

Podcast Episode

PyTorch

Podcast Episode

Tensorflow

Feast

Tecton

Data Engineering Podcast Episode

Kaggle

Dask

Data Engineering Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Speed Up Your Python Data Applications By Parallelizing Them With Bodo** Oct 25, 2021
Summary

The speed of Python is a subject of constant debate, but there is no denying that for compute heavy work it is not the optimal tool. Rather than rewriting your data oriented applications, or having to rearchitect them, the team at Bodo wrote a compiler that will do the optimization for you. In this episode Ehsan Totoni explains how they are able to translate pure Python into massively parallel processes that are optimized for high performance compute systems.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Ehsan Totoni about Bodo, an inferential compiler for Python that automatically parallelizes your data oriented projects

Interview

Introductions

How did you get introduced to Python?

Can you describe what Bodo is and the story behind it?

What are some of the use cases that it is being applied to?

What are the motivating factors for something like Dask or Ray as compared to Bodo?

What are the software patterns that contribute to slowdowns in data processing code?

What are some of the ways that the compiler is able to optimize those operations?

Can you describe how Bodo is implemented?

How does Bodo process the Python code for compiling to the optimized form?

What are the compilation techniques for understanding the semantics of the code being processed?

How do you manage packages that rely on C extensions?

What do you use as an intermediate representation for translating into the optimized output?

What is the workflow for applying Bodo to a Python project?

What debugging utilities does it provide for identifying any errors that occur due to the added parallelism?

What kind of support does Bodo have for optimizing a machine learning project with Bodo? (e.g. using PyTorch/Tensorflow/MxNet/etc.)

When working with a workflow orchestrator such as Dagster for Airflow, what would the integration process look like for being able to take advantage of the optimized Bodo output?

What are the most interesting, innovative, or unexpected ways that you have seen Bodo used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bodo?

When is Bodo the wrong choice?

What do you have planned for the future of Bodo?

Keep In Touch

LinkedIn

@EhsanTn on Twitter

ehsantn on GitHub

Picks

Tobias

Paracord Crafts

Ehsan

[

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

Bodo

Data Engineering Podcast Episode

University of Illinois Urbana-Champaign

HPC

MPI

Elastic Fabric Adapter

All-to-All Communication

Dask

Data Engineering Podcast Episode

Ray

Podcast Episode

Pandas Extension Arrays

Podcast Episode

GeoPandas

Numba

LLVM

scikit-learn

Horovod

Dagster

Podcast.__init__ Episode

Data Engineering Podcast Episode

Airflow

Podcast Episode

IPython Parallel

Parquet

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**An Exploration Of Financial Exchange Risk Management Strategies** Oct 16, 2021
Summary

The world of finance has driven the development of many sophisticated techniques for data analysis. In this episode Paul Stafford shares his experiences working in the realm of risk management for financial exchanges. He discusses the types of risk that are involved, the statistical methods that he has found most useful for identifying strategies to mitigate that risk, and the software libraries that have helped him most in his work.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management

When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Paul Stafford about building risk models to guard against financial exchange rate volatility

Interview

Introductions

How did you get introduced to Python?

What are the principles involved in risk management, and how are statistical methods used?

How did you get involved in financial markets?

In what ways did your background in science and engineering prepare you for work in finance and risk management?

What are the tools that you have found most useful in your career in finance?

How have recent trends such as the widespread adoption of deep learning impacted the capabilities and risks present in foreign exchange strategies?

What are the challenges that you face in obtaining and validating the input data that you are relying on for building financial and statistical models?

How has the volatility of the pandemic impacted the robustness and resilience of your predictive capabilities?

What are the areas where the available tools are typically insufficient?

What are the most interesting, innovative, or unexpected strategies or techniques that you have seen applied to risk management?

What are the most interesting, unexpected, or challenging lessons that you have learned while working in risk management?

What are the economic and industry trends that you are keeping a close eye on for your work at Deaglo and your own personal projects?

Keep In Touch

LinkedIn

Picks

Tobias

The Vault (movie)

Paul

Motorcycle Trip of the Grand Canyon

Links

Deaglo Partners, LLC.

Value At Risk (VaR)

Black-Scholes Equation

Linear Algebra

Principal Component Analysis

Eigenvectors and Eigenvalues

Markov Chain Monte Carlo

Violin Plot

Kurtosis

PyMC3

Podcast Episode

Bayesian Regression

Constrained Optimization

Ethereum

Smart Contracts

Behavioral Finance

Black Swan by Nassim Nicholas Taleb (affiliate link)

SciPy Convention

RealPython

3Blue1Brown

Sentiment Analysis

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build Better Machine Learning Models By Understanding Their Decisions With SHAP** Oct 09, 2021
Summary

Machine learning and deep learning techniques are powerful tools for a large and growing number of applications. Unfortunately, it is difficult or impossible to understand the reasons for the answers that they give to the questions they are asked. In order to help shine some light on what information is being used to provide the outputs to your machine learning models Scott Lundberg created the SHAP project. In this episode he explains how it can be used to provide insight into which features are most impactful when generating an output, and how that insight can be applied to make more useful and informed design choices. This is a fascinating and important subject and this episode is an excellent exploration of how to start addressing the challenge of explainability.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Scott Lundberg about SHAP, a library that implements a game theoretic approach to explain the output of any machine learning model

Interview

Introductions

How did you get introduced to Python?

Can you describe what SHAP is and the story behind it?

What are some of the contexts that create the need to explain the reasoning behind the outputs of an ML model?

How do different types of models (deep learning, CNN/RNN, bayesian vs. frequentist, etc.) and different categories of ML (e.g. NLP, computer vision) influence the challenge of understanding the meaningful signals in their reasoning?

Taking a step back, how do you define "explainability" when discussing inferences produced by ML models?

What are the degrees of specificity/accuracy when seeking to understand the decision processes involved?

Can you describe how SHAP is implemented?

What are the signals that you are tracking to understand what features are being used to determine a given output?

What are the assumptions that you had as you started this project that have been challenged or updated as you explored the problem in greater depth?

Can you describe the workflow for someone using SHAP?

What are the challenges faced by practitioners in interpreting the visualizations generated from SHAP?

How much domain knowledge and context is necessary to use SHAP effectively?

What are the ongoing areas of research around tracking of ML decision processes?

How are you using SHAP in your own work?

What are the most interesting, innovative, or unexpected ways that you have seen SHAP used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on SHAP?

When is SHAP the wrong choice?

What do you have planned for the future of SHAP?

Keep In Touch

slundberg on GitHub

Website

LinkedIn

Picks

Tobias

Reminiscence

Scott

Augustine’s Confessions

Links

SHAP

Microsoft Research

Matlab

Game Theory

Computational Biology

LIME

Shapley Values

Julia Language

ResNet

CNN == Convolutional Neural Network

RNN == Recurrent Neural Network

A* Algorithm

CFPB == Consumer Financial Protection Bureau

NP Hard

Huggingface

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Numba

Log Odds

InterpretML

Polyjuice

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Accelerating Drug Discovery Using Machine Learning With TorchDrug** Sep 30, 2021
Summary

Finding new and effective treatments for disease is a complex and time consuming endeavor, requiring a high degree of domain knowledge and specialized equipment. Combining his expertise in machine learning and graph algorithms with is interest in drug discovery Jian Tang created the TorchDrug project to help reduce the amount of time needed to find new candidate molecules for testing. In this episode he explains how the project is being used by machine learning researchers and biochemists to collaborate on finding effective treatments for real-world diseases.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Jian Tang about TorchDrug

Interview

Introductions

How did you get introduced to Python?

Can you describe what TorchDrug is and the story behind it?

What are the goals of the TorchDrug project?

Who are the target users of the project?

What are the main ways that it is being used?

What are the challenges faced by biologists and chemists working on development and discovery of pharmaceuticals?

What are some of the other tools/techniques that they would use (in isolation or combination with TorchDrug)?

Can you describe how TorchDrug is implemented?

How have you approached the design of the project and its APIs to make it accessible to engineers that don’t possess domain expertise in drug discovery research?

How do graph structures help when modeling and experimenting with chemical structures for drug discovery?

What are the formats and sources of data that you are working with?

What are some of the complexities/challenges that you have had to deal with to integrate with up or downstream systems to fit into the overall research process?

Can you talk through the workflow of using TorchDrug to build and validate a model?

What is involved in determining and codifying a goal state for the model to optimize for?

What are the biggest open questions in the area of drug discovery and research?

How is TorchDrug being used to assist in the exploration of those problems?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on TorchDrug?

When is TorchDrug the wrong choice?

What do you have planned for the future of TorchDrug?

Keep In Touch

tangjianpku on GitHub

@tangjianpku on Twitter

Website

LinkedIn

Picks

Tobias

Rope refactoring library

Jian

Attending conferences once the pandemic is over

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

TorchDrug

Mila

Yoshua Bengio

Alphafold

Few-shot learning

Metalearning

PyTorch Geometric

DeepGraph Library

NetworKit

Podcast Episode

graph-tool

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**An Exploration Of Automated Speech Recognition** Sep 26, 2021
Summary

The overwhelming growth of smartphones, smart speakers, and spoken word content has corresponded with increasingly sophisticated machine learning models for recognizing speech content in audio data. Dylan Fox founded Assembly to provide access to the most advanced automated speech recognition models for developers to incorporate into their own products. In this episode he gives an overview of the current state of the art for automated speech recognition, the varying requirements for accuracy and speed of models depending on the context in which they are used, and what is required to build a special purpose model for your own ASR applications.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Dylan Fox about the challenges of training and deploying large models for automated speech recognition

Interview

Introductions

How did you get introduced to Python?

What is involved in building an ASR model?

How does the complexity/difficulty compare to models for other data formats? (e.g. computer vision, NLP, NER, etc.)

How have ASR models changed over the last 5, 10, 15 years?

What are some other categories of ML applications that work with audio data?

How does the level of complexity compare to ASR applications?

What is the typical size of an ASR model that you are deploying at Assembly?

What are the factors that contribute to the overall size of a given model?

How does accuracy compare with model size?

How does the size of a model contribute to the overall challenge of deploying/monitoring/scaling it in a production environment?

How can startups effectively manage the time/cost that comes with training large models?

What are some techniques that you use/attributes that you focus on for feature definitions in the source audio data?

Can you describe the lifecycle stages of an ASR model at Assembly?

What are the aspects of ASR which are still intractable or impractical to productionize?

What are the most interesting, innovative, or unexpected ways that you have seen ASR technology used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on ASR?

What are the trends in research or industry that you are keeping an eye on?

Keep In Touch

LinkedIn

@YouveGotFox on Twitter

Picks

Tobias

The Hitman’s Wife’s Bodyguard

Dylan

Inspiration 4 Documentary

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Learn Python The Hard Way

DeepSpeech

Wav2Letter

BERT

GPT-3

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

Mycroft

Podcast Episode

CMU Sphinx

Pocket Sphinx

Gaussian Mixture Model (GMM)

Hidden Markov Model (HMM)

DeepSpeech Paper

Transformer Architecture

Audio Analytic Sound Recognition Podcast Episode

Horovod distributed training library

Knowledge Distillation

Libre Speech Data Set

Lambda Labs

Wav2Vec

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Experimenting With Reinforcement Learning Using MushroomRL** Sep 19, 2021
Summary

Reinforcement learning is a branch of machine learning and AI that has a lot of promise for applications that need to evolve with changes to their inputs. To support the research happening in the field, including applications for robotics, Carlo D’Eramo and Davide Tateo created MushroomRL. In this episode they share how they have designed the project to be easy to work with, so that students can use it in their study, as well as extensible so that it can be used by businesses and industry professionals. They also discuss the strengths of reinforcement learning, how to design problems that can leverage its capabilities, and how to get started with MushroomRL for your own work.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Davide Tateo and Carlo D’Eramo about MushroomRL, a library for building reinforcement learning experiments

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what reinforcement learning is and how it differs from other approaches for machine learning?

What are some example use cases where reinforcement learning might be necessary?

Can you describe what MushroomRL is and the story behind it?

Who are the target users of the project?

What are its main goals?

What are your suggestions to other developers for implementing a succesful library?

What are some of the core concepts that researchers and/or engineers need to understand to be able to effectively use reinforcement learning techniques?

Can you describe how MushroomRL is architected?

How have the goals and design of the project changed or evolved since you began working on it?

What is the workflow for building and executing an experiment with MushroomRL?

How do you track the states and outcomes of experiments?

What are some of the considerations involved in designing an environment and reward functions for an agent to interact with?

What are some of the open questions that are being explored in reinforcement learning?

How are you using MushroomRL in your own research?

What are the most interesting, innovative, or unexpected ways that you have seen MushroomRL used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on MushroomRL?

When is MushroomRL the wrong choice?

What do you have planned for the future of MushroomRL?

How can the open-source community contribute to MushroomRL?

What kind of support you are willing to provide to users?

Keep In Touch

Davide

boris-il-forte on GitHub

Website

Carlo

carloderamo on GitHub

Website

Picks

Tobias

Britannia TV Series

Davide

1984 by George Orwell

Carlo

Twin Peaks TV Series

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

MushroomRL

TU Darmstadt

MuJoCo

PyBullet

iGibson

Habitat

OpenAI Gym

PyTorch

Podcast Episode

RLLib

Ray

Podcast Episode

OpenAI Baselines

Stable Baselines

ROS

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Doing Dask Powered Data Science In The Saturn Cloud** Sep 10, 2021
Summary

A perennial problem of doing data science is that it works great on your laptop, until it doesn’t. Another problem is being able to recreate your environment to collaborate on a problem with colleagues. Saturn Cloud aims to help with both of those problems by providing an easy to use platform for creating reproducible environments that you can use to build data science workflows and scale them easily with a managed Dask service. In this episode Julia Signall, head of open source at Saturn Cloud, explains how she is working with the product team and PyData community to reduce the points of friction that data scientists encounter as they are getting their work done.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Julia Signell about building distributed processing workflows in Python through the power of Dask

Interview

Introductions

How did you get introduced to Python?

Can you describe what you are building at Saturn Cloud?

Who are your target users and how does that inform the features and priorities that you build into your platform?

What are the road blocks that data scientists typically encounter when working on their laptop/workstation?

How does open source factor into the Saturn product?

What are some of the projects that you are collaborating with/contributing to as part of your work at Saturn?

How has your experience at Anaconda informed your work at Saturn?

Can you describe how the Saturn Cloud platform is architected?

How has it changed or evolved since it was first launched?

Can you describe the learning curve that data scientists go through when adopting Dask?

What are some examples of projects or workflows that Dask enables which are not possible/practical to do locally?

How would you characterize the overall awareness/adoption of Dask in the Python data science community?

What are the most interesting, innovative, or unexpected ways that you have seen Saturn Cloud used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Saturn Cloud?

When is Saturn Cloud the wrong choice?

What do you have planned for the future of Saturn Cloud?

Keep In Touch

@jsignell on Twitter

jsignell on GitHub

Picks

Tobias

Peter Rabbit 2

Julia

PawPaw Fruit

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Saturn Cloud

Dask

Podcast Episode

Pangeo

XArray

Conda

Mamba

Holoviz

Dash

Anaconda

Podcast Episode

Kubernetes

Tornado

Podcast Episode

Prefect

Podcast Episode

Dagster

Podcast Episode

Airflow

Ray

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Monitor The Health Of Your Machine Learning Products In Production With Evidently** Sep 03, 2021
Summary

You’ve got a machine learning model trained and running in production, but that’s only half of the battle. Are you certain that it is still serving the predictions that you tested? Are the inputs within the range of tolerance that you designed? Monitoring machine learning products is an essential step of the story so that you know when it needs to be retrained against new data, or parameters need to be adjusted. In this episode Emeli Dral shares the work that she and her team at Evidently are doing to build an open source system for tracking and alerting on the health of your ML products in production. She discusses the ways that model drift can occur, the types of metrics that you need to track, and what to do when the health of your system is suffering. This is an important and complex aspect of the machine learning lifecycle, so give it a listen and then try out Evidently for your own projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Emeli Dral about monitoring machine learning models in production with Evidently

Interview

Introductions

How did you get introduced to Python?

Can you describe what Evidently is and the story behind it?

What are the metrics that are useful for determining the performance and health of a machine learning model?

What are the questions that you are trying to answer with those metrics?

How does monitoring of machine learning models compare to monitoring of infrastructure or "traditional" software projects?

What are the failure modes for a model?

Can you describe the design and implementation of Evidently?

How has the architecture changed or evolved since you started working on it?

What categories of model is Evidently designed to work with?

What are some strategies for making models conducive to monitoring?

What is involved in monitoring a model on a continuous basis?

What are some considerations when establishing useful thresholds for metrics to alert on?

Once an alert has been triggered what is the process for resolving it?

If the training process takes a long time, how can you mitigate the impact of a model failure until the new/updated version is deployed?

What are the most interesting, innovative, or unexpected ways that you have seen Evidently used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Evidently?

When is Evidently the wrong choice?

What do you have planned for the future of Evidently?

Keep In Touch

LinkedIn

@EmeliDral on Twitter

emeli-dral on GitHub

Picks

Tobias

The Suicide Squad

Emeli

Airflow

Links

Evidently AI

Open Source

Yandex

Grafana

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Making Automated Machine Learning More Accessible With EvalML** Aug 25, 2021
Summary

Building a machine learning model is a process that requires a lot of iteration and trial and error. For certain classes of problem a large portion of the searching and tuning can be automated. This allows data scientists to focus their time on more complex or valuable projects, as well as opening the door for non-specialists to experiment with machine learning. Frustrated with some of the awkward or difficult to use tools for AutoML, Angela Lin and Jeremy Shih helped to create the EvalML framework. In this episode they share the use cases for automated machine learning, how they have designed the EvalML project to be approachable, and how you can use it for building and training your own models.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Angela Lin, Jeremy Shih about EvalML, an AutoML library which builds, optimizes, and evaluates machine learning pipelines

Interview

Introductions

How did you get introduced to Python?

Can you describe what EvalML is and the story behind it?

What do we mean by the term AutoML?

What are the kinds of problems that are best suited to applications of automated ML?

What does the landscape for AutoML tools look like?

What was missing in the available offerings that motivated you and your team to create EvalML?

Who is the target audience for EvalML?

How is the EvalML project implemented?

How has the project changed or evolved since you first began working on it?

What is the workflow for building a model with EvalML?

Can you describe the preprocessing steps that are necessary and the input formats that it is expecting?

What are the supported algorithms/model architectures?

How does EvalML explore the search space for an optimal model?

What decision functions does it employ to determine an appropriate stopping point?

What is involved in operationalizing an AutoML pipeline?

What are some challenges or edge cases that you see users of EvalML run into?

What are the most interesting, innovative, or unexpected ways that you have seen EvalML used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on EvalML?

When is EvalML the wrong choice?

When is auto ML the wrong approach?

What do you have planned for the future of EvalML?

Keep In Touch

Angela

angela97lin on GitHub

LinkedIn

Jeremy

jeremyliweishih on GitHub

LinkedIn

Picks

Tobias

Gloryhammer

Angela

Sarma mediterranean restaurant

Jeremy

Crucial Conversations by Stephen Covey (affiliate link)

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

EvalML

FeatureLabs

Alteryx

Scheme

NetLogo

Flask

AutoML

Woodwork

FeatureTools

Compose

Random Forest

XGBoost

Prophet

GreyKite

Shap

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Growing And Supporting The Data Science Community At Anaconda** Aug 19, 2021
Summary

Data scientists are tasked with answering challenging questions using data that is often messy and incomplete. Anaconda is on a mission to make the lives of data professionals more manageable through creation and maintenance of high quality libraries and frameworks, the distribution of an easy to use Python distribution and package ecosystem, and high quality training material. In this episode Kevin Goldsmith, CTO of Anaconda, discusses the technical and social challenges faced by data scientists, the ways that the Python ecosystem has evolved to help address those difficulties, and how Anaconda is engaging with the community to provide high quality tools and education for this constantly changing practice.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Kevin Goldsmith about Anaconda’s contributions to the Python ecosystem for data science

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Anaconda focuses on solving for?

What was your path into the CTO position?

From your perspective as the CTO of Anaconda, what are the biggest challenges facing data scientists today?

What is the breakdown between technical and organizational sources for those difficulties?

How is the Anaconda product suite architected to help address some of those problems?

Where are you spending your focus to allow Anaconda to address the current and future needs of data scientists?

Python has been a dominant force in the data and analytics ecosystem for several years now. What do you see as the future of the space? (e.g. monoglot vs. polyglot workflows)

What are the most interesting, innovative, or unexpected ways that you have seen the Anaconda platform used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Anaconda and data science tooling?

Keep In Touch

LinkedIn

@KevinGoldsmith on Twitter

Website

Picks

Tobias

Perdido Street Station

The Scar

Iron Council

Kevin

Lego Typewriter

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Anaconda

Spotify

Lisp

Scheme

C#

Anaconda Nucleus

PyData

AnacondaCon

Grid Computing

PyTorch

Podcast Episode

Tensorflow

Pyston

Podcast Episode

Dask

Podcast Episode

Numba

Panel dashboard framework

Datashader

Jupyter

R

Julia

AstroPy

Podcast Episode

Arrow

Data Teams by Jesse Anderson

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Network Analysis At The Speed Of C With The Power Of Python Using NetworKit** Aug 15, 2021
Summary

Analysing networks is a growing area of research in academia and industry. In order to be able to answer questions about large or complex relationships it is necessary to have fast and efficient algorithms that can process the data quickly. In this episode Eugenio Angriman discusses his contributions to the NetworKit library to provide an accessible interface for these algorithms. He shares how he is using NetworKit for his own research, the challenges of working with large and complex networks, and the kinds of questions that can be answered with data that fits on your laptop.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Eugenio Angriman about NetworKit, an open-source toolkit for large-scale network analysis

Interview

Introductions

How did you get introduced to Python?

Can you describe what NetworKit is and the story behind it?

A core focus of the project is for use with graphs containing millions to billions of nodes. What are some of the situations where you might encounter networks of that scale?

There are a number of network analysis libraries in Python. How would you characterize NetworKit’s position in the ecosystem?

What are the algorithmic challenges that graph structures pose when aiming for scalability and performance?

How do you approach building efficient algorithms for complex network analysis?

Can you describe how NetworKit is architected?

What are the design principles that you focus on for the library?

How have the design and goals of the project changed or evolved since you have been working on it?

NetworKit’s code base has now a discrete size and several developers contributed to it. Are there any minimum quality requirements that new code needs to fulfill before it can be merged into NetworKit? How do you ensure that such requirements are met?

What are some of the active areas of research for networked data analysis?

How are you using NetworKit for your own work?

What are kind of background knowledge in graph analysis is necessary for users of NetworKit?

What are some of the underutilized or overlooked aspects of NetworKit that you think should be highlighted?

What are the most interesting, innovative, or unexpected ways that you have seen NetworKit used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on NetworKit?

When is NetworKit the wrong choice?

What do you have planned for the future of NetworKit?

Keep In Touch

angriman on GitHub

LinkedIn

Picks

Tobias

Edgar Allen Poe

NetworKit

The Spinoza Problem

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

NetworKit

Humboldt University Berlin

graph-tool

Podcast Episode

NetworkX

Adjacency List

Cython

Podcast Episode

Node Embeddings

Centrality Score

NetworKit In The Cloud

Gunrock

Hornet

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Delivering Deep Learning Powered Speech Recognition As A Service For Developers At AssemblyAI** Aug 04, 2021
Summary

Building a software-as-a-service (SaaS) business is a fairly well understood pattern at this point. When the core of the service is a set of machine learning products it introduces a whole new set of challenges. In this episode Dylan Fox shares his experience building Assembly AI as a reliable and affordable option for automatic speech recognition that caters to a developer audience. He discusses the machine learning development and deployment processes that his team relies on, the scalability and performance considerations that deep learning models introduce, and the user experience design that goes into building for a developer audience. This is a fascinating conversation about a unique cross-section of considerations and how Dylan and his team are building an impressive and useful service.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Dylan Fox about AssemblyAI, a powerful and easy to use speech recognition API designed for developers

Interview

Introductions

How did you get introduced to Python?

Can you describe what Assembly AI is and the story behind it?

Speech recognition is a service that is being added to every cloud platform, video service, and podcast product. What do you see as the motivating factors for the current growth in this industry?

How would you characterize your overall position in the market?

What are the core goals that you are focused on with AssemblyAI?

Can you describe the different ways that you are using Python across the company?

How is the AssemblyAI platform architected?

What are the complexities that you have to work around to maintain high uptime for an API powered by a deep learning model?

What are the scaling challenges that crop up, whether on the training or serving?

What are the axes for improvement for a speech recognition model?

How do you balance tradeoffs of speed and accuracy as you iterate on the model?

What is your process for managing the deep learning workflow?

How do you manage CI/CD for your deep learning models?

What are the open areas of research in speech recognition?

What are the most interesting, innovative, or unexpected ways that you have seen AssemblyAI used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on AssemblyAI?

When is AssemblyAI the wrong choice?

What do you have planned for the future of AssemblyAI?

Keep In Touch

LinkedIn

@YouveGotFox on Twitter

Picks

Tobias

H.P. Lovecraft

Dylan

Project Hail Mary by Andy Weir

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

AssemblyAI

Two Scoops of Django

Nuance

Dragon Natural Speaking

PyTorch

Podcast Episode

Tensorflow

FastAPI

Flask

Tornado

Podcast Episode

Neural Magic

Podcast Episode

The Martian

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Taking Aim At The Legacy Of SQL With The Preql Relational Language** Jul 28, 2021
Summary

SQL has gone through many cycles of popularity and disfavor. Despite its longevity it is objectively challenging to work with in a collaborative and composable manner. In order to address these shortcomings and build a new interface for your database oriented workloads Erez Shinan created Preql. It is based on the same relational algebra that inspired SQL, but brings in more robust computer science principles to make it more manageable as you scale in complexity. In this episode he shares his motivation for creating the Preql project, how he has used Python to develop a new language for interacting with database engines, and the challenges of taking on the legacy of SQL as an individual.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Erez Shinan about Preql, an interpreted, relational programming language, that specializes in database queries

Interview

Introductions

How did you get introduced to Python?

Can you describe what Preql is and the story behind it?

What are goals and target use cases for the project?

There have been numerous projects that aim to make SQL more maintainable and composable. What is it about the language and syntax that makes it so challenging?

How does Preql approach this problem that is different from other efforts? (e.g. ORMs, dbt-style Jinja, PyPika)

How did you approach the design of the syntax to make it familiar to people who know SQL?

Can you describe how Preql is implemented?

How has the design and architecture changed or evolved since you began working on it?

What is a typical workflow for someone using Preql to build a library of analytical queries?

Beyond strict compilation to SQL, what are some of the other features that you have incorporated into Preql?

How does a Preql program get executed against a target database, particularly when using capabilities that can’t be directly translated to SQL?

** What are the main difficulties / challenges of compiling to SQL ?

What are some of the features or use cases that are not immediately obvious or prone to be overlooked that you think are worth mentioning?

What are the most interesting, innovative, or unexpected ways that you have seen Preql used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Preql?

When is Preql the wrong choice?

What do you have planned for the future of Preql?

Keep In Touch

erezsh on GitHub

erezsh on Twitter

Picks

Tobias

Counterpart

Erez

Bansko, Bulgaria

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Preql

Lark

Postgres

Data Engineering Podcast Episode

MySQL

Relational Algebra

Pandas

Podcast Episode

ORM == Object Relational Mapper

dbt

Data Engineering Podcast Episode

PyPika

GraphQL

Julia

runtype

Rich terminal UI library

prompt-toolkit

DuckDB

Askgit

BigQuery

Snowflake

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Unleash The Power Of Dataframes At Any Scale With Modin** Jul 22, 2021
Summary

When you start working on a data project there are always a variety of unknown factors that you have to explore. One of those is the volume of total data that you will eventually need to handle, and the speed and scale at which it will need to be processed. If you optimize for scale too early then it adds a high barrier to entry due to the complexities of distributed systems, but if you invest in a lot of engineering up front then it can be challenging to refactor for scale. Modin is a project that aims to remove that decision by letting you seamlessly replace your existing Pandas code and scale across CPU cores or across a cluster of machines. In this episode Devin Petersohn explains why he started working on solving this problem, how Modin is architected to allow for a smooth escalation from small to large volumes of data and compute, and how you can start using it today to accelerate your Pandas workflows.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Devin Petersohn about Modin, a Pandas compatible dataframe library for datasets from 1MB to 1TB+

Interview

Introductions

How did you get introduced to Python?

Can you describe what Modin is and the story behind it?

Why study dataframes?

How do dataframes compare to databases?

What can you do in a dataframe that you couldn’t in a database?

What are your overall goals for the Modin project?

Who are the target users of Modin and how does that influence your prioritization of features?

What are some of the API inconsistencies that you have had to abstract and work around between Pandas, Ray, and Dask to give users a seamless experience?

What are some of the considerations in terms of capabilities or user experience that will influence whether to use Ray or Dask as the execution engine?

Can you describe how Modin is implemented?

How has the constraint of replicating the Pandas API influenced your architectural choices?

What are the most complex or challenging Pandas APIs to replicate in Modin?

In addition to the core Pandas API you have also added experimental features such as SQL support and a spreadsheet interface. How have those capabilities affected the range of potential use cases and end users?

What are some of the complexities that come from acting as a middleware between the Pandas API and the Ray and Dask frameworks?

What are some of the initial ideas or assumptions that you had about the design or utility of Modin that have been challenged as you worked through building and releasing it?

What are the most interesting, innovative, or unexpected ways that you have seen Modin used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Modin?

When is Modin the wrong choice?

What do you have planned for the future of Modin?

Keep In Touch

devin-petersohn on GitHub

LinkedIn

Picks

Tobias

xxh

Devin

Lux

Podcast Episode

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Modin

UC Berkeley

RISELAB

XArray

Pandas

Podcast Episode

Dask

Podcast Episode

Ray

Podcast Episode

Spark

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Exploring The SpeechBrain Toolkit For Speech Processing** Jul 14, 2021
Summary

With the rising availability of computation in everyday devices, there has been a corresponding increase in the appetite for voice as the primary interface. To accomodate this desire it is necessary for us to have high quality libraries for being able to process and generate audio data that can make sense of human speech. To facilitate research and industry applications for speech data Mirco Ravanelli and Peter Plantinga are building SpeechBrain. In this episode they explain how it works under the hood, the projects that they are using it for, and how you can get started with it today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Mirco Ravanelli and Peter Plantinga about SpeechBrain, an open-source and all-in-one speech toolkit powered by PyTorch

Interview

Introductions

How did you get introduced to Python?

Can you describe what SpeechBrain is and the story behind it?

What are the goals and target use cases of the SpeechBrain project?

What are some of the ways that processing audio with a focus on speech differs from more general audio processing?

What are some of the other libraries/frameworks/services that are available to work with speech data and what are the unique capabilities that SpeechBrain offers?

How is SpeechBrain implemented?

What was your decision process for determining which framework to build on top of?

What are some of the original ideas and assumptions that you had for SpeechBrain which have been changed or invalidated as you worked through implementing it?

Can you talk through the workflow of using SpeechBrain?

What would be involved in developing a system to automate transcription with speaker recognition and diarization?

In the documentation it mentions that SpeechBrain is built to be used for research purposes. What are some of the kinds of research that it is being used for?

What are some of the features or capabilities of SpeechBrain which might be non-obvious that you would like to highlight?

What are the most interesting, innovative, or unexpected ways that you have seen SpeechBrain used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on SpeechBrain?

When is SpeechBrain the wrong choice?

What do you have planned for the future of SpeechBrain?

Keep In Touch

Mirco

mravanelli on GitHub

LinkedIn

@mirco_ravanelli on Twitter

Peter

pplantinga on GitHub

@ComPeterScience on Twitter

Website

LinkedIn

Picks

Tobias

x.ai

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

SpeechBrain

Mila

Speech Processing

Speech Enhancement

NumPy

SciPy

Theano

PyTorch

Podcast Episode

Speech Recognition

NeMo

ESPNet

Sequence to Sequence (Seq2Seq)

HyperParameters

TorchAudio

PyTorch Lightning

Keras

HuggingFace

Generative Adversarial Network

Snorkel

Data Engineering Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Fast And Educational Exploration And Analysis Of Graph Data Structures With graph-tool** Jul 07, 2021
Summary

If you are interested in a library for working with graph structures that will also help you learn more about the research and theory behind the algorithms then look no further than graph-tool. In this episode Tiago Peixoto shares his work on graph algorithms and networked data and how he has built graph-tool to help in that research. He explains how it is implemented, how it evolved from a simple command line tool to a full-fledged library, and the benefits that he has found from building a personal project in the open.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Tiago Peixoto about graph-tool, an efficient Python module for manipulation and statistical analysis of graphs

Interview

Introductions

How did you get introduced to Python?

Can you describe what graph-tool is and the story behind it?

What are some scenarious where someone might encounter a graph oriented data set?

In what ways are those graphs typically represented?

In your experience, what is the overlap of people who are working with networked data, and the use of graph-native databases? (e.g. Neo4J, DGraph, etc.)

What kinds of analysis or manipulation might someone need to perform on a graph structure?

There are a few different tools in Python for working with networked data. How would you characterize the current ecosystem and why someone might choose graph-tool?

Can you describe how graph-tool is implemented?

How have the goals and design of the package changed or evolved since you first began working on it?

Who are your target users and what are the guiding principles that you use to inform the API design for the package?

How much knowledge of graph theory or algorithms are required to make effective use of graph-tool?

Can you talk through an example workflow of using graph-tool to load, process, and analyze a graph?

What are some of the overlooked or underutilized aspects of graph-tool that you think more people should know about?

What are some systems/applications that you have seen which would be simplified by adopting a graph model for their data?

What is your impression of the overall awareness of the benefits of graphs for simplifying aspects of data processing and analysis?

What are some cases where a graph structure adds unnecessary complexity?

What are the most interesting, innovative, or unexpected ways that you have seen graph-tool used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on graph-tool?

When is graph-tool the wrong choice?

What do you have planned for the future of graph-tool?

Keep In Touch

Website

graph-tool

Picks

Tobias

97 Things Every Data Engineer Should Know

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Central European University

NetworkX

GML

GraphML

Neo4J

DGraph

Data Engineering Podcast Episode

NetworKit

igraph

Matplotlib

C++ Templates

Boost Graph Library

OpenMP

Maximum Matching

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Lightening The Load For Deep Learning With Sparse Networks Using Neural Magic** Jun 30, 2021
Summary

Deep learning has largely taken over the research and applications of artificial intelligence, with some truly impressive results. The challenge that it presents is that for reasonable speed and performance it requires specialized hardware, generally in the form of a dedicated GPU (Graphics Processing Unit). This raises the cost of the infrastructure, adds deployment complexity, and drastically increases the energy requirements for training and serving of models. To address these challenges Nir Shavit combined his experiences in multi-core computing and brain science to co-found Neural Magic where he is leading the efforts to build a set of tools that prune dense neural networks to allow them to execute on commodity CPU hardware. In this episode he explains how sparsification of deep learning models works, the potential that it unlocks for making machine learning and specialized AI more accessible, and how you can start using it today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Nir Shavit about Neural Magic and the benefits of using sparsification techniques for deep learning models

Interview

Introductions

How did you get introduced to Python?

Can you describe what Neural Magic is and the story behind it?

What are the attributes of deep learning architectures that influence the bias toward GPU hardware for training them?

What are the mathematical aspects of neural networks that have biased the current generation of software tools toward that architectural style?

How does sparsifying a network architecture allow for improved performance on commodity CPU architectures?

What is involved in converting a dense neural network into a sparse network?

Can you describe the components of the Neural Magic architecture and how they are used together to reduce the footprint of deep learning architectures and accelerate their performance on CPUs?

What are some of the goals or design approaches that have changed or evolved since you first began working on the Neural Magic platform?

For someone who has an existing model defined, what is the process to convert it to run with the DeepSparse engine?

What are some of the options for applications of deep learning that are unlocked by enabling the models to train and run without GPU or other specialized hardware?

The current set of components for Neural Magic is either open source or free to use. What is your long-term business model, and how are you approaching governance of the open source projects?

What are the most interesting, innovative, or unexpected ways that you have seen Neural Magic and model sparsification used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Neural Magic?

When is Neural Magic or sparse networks the wrong choice?

What do you have planned for the future of Neural Magic?

Keep In Touch

Research Overview

LinkedIn

Picks

Tobias

The Tick TV show

Nir

Bauhaus documentary

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Neural Magic

MIT

Computational Neurobiology

6.006 MIT Course

FLOPS == FLoating point OPerations per Second

Perceptron

Convolutional Neural Network

Lisp

Quantization of ML

YOLO ML Model

Federated Learning

Podcast Episode

Reinforcement Learning

GPT-3

OpenAI

Transfer Learning

Podcast Episode about Transfer Learning for NLP

Tensor Columns

Neural Magic DeepSparse Engine

ONNX

CUDA

Sparse Zoo

Tab9

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Finding The Core Of Python For A Bright Future With Brett Cannon** Jun 23, 2021
Summary

Brett Cannon has been a long-time contributor to the Python language and community in many ways. In this episode he shares some of his work and thoughts on modernizing the ecosystem around the language. This includes standards for packaging, discovering the true core of the language, and how to make it possible to target mobile and web platforms.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.

Your host as usual is Tobias Macey and today I’m interviewing Brett Cannon about improvements in the packaging ecosystem, the promise of WebAssembly, and his recent explorations of CPython’s interpreter

Interview

Introductions

How did you get introduced to Python?

As a core contributor to CPython, a member of the steering Council, and the team lead for VSCode’s Python extension, what are your current areas of focus for the language?

One of the PEPs that you were involved with recently introduced the pyproject.toml file for simplifying the work of building Python packages. Can you share some of the background behind that work and the goals that you had for it?

Since its introduction a lot of people have co-opted that file for other project configuration. What was your reaction to that, and if you had foreseen that usage what might you have changed or added in the PEP to account for it?

What are the long term impacts on the packaging ecosystem that you anticipate with the standardization efforts that are happening?

Another area where there is a lot of attention right now is being able to target additional deployment environments such as the browser, with web assembly, and mobile devices, with projects like BriefCase and Kivy. You had a recent post where you posed some questions about the true nature of Python and the possibility of removing pieces of it to simplify building for these other runtimes. What is your personal sense of the minimal set of features that we need for something to still be Python?

How have projects such as MicroPython and PyOdide influenced your thinking on the matter?

You have also recently been writing a series of articles about the implementation details of different syntactic elements of Python. What was your inspiration for that?

What are some of the interesting or surprising details that you encountered while unwrapping the way that the interpreter handles those syntactic elements?

How have those explorations helped you in your efforts to identify the core of Python?

Recent releases of Python have brought in some substantial changes to the interpreter and new language features (e.g. PEG parser, pattern matching). What are some of the other large initiatives that you are keeping track of?

What are your personal goals for the near to medium term future of Python?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on the Python language and related tooling?

If you were to redesign Python today, what are some of the things that you would do differently?

Keep In Touch

brettcannon on GitHub

@brettsky on Twitter

Blog

Picks

Tobias

Cold Brew Iced Tea

Loki on Disney+

Brett

Rich

Textual

The physics facts included in all of the Python 3.10 release announcements, e.g. you will never see a green star

Links

Brett’s Blog

Python VSCode Extension

Python Steering Council

Python Package Authority

UC Berkeley

Vancouver, BC

Squamish, Musquiam, Tsleil-waututh First Nations

Pascal

Python

C

O’Reilly

PyCon US 2021 Steering Council Keynote

Python Developer-In-Residence

PSF Visionary Sponsorship

Setuptools

Pip

Python Wheels

PyPI

PEP 518

PEP 517

PEP 621

pyproject.toml

Flit

Enscons

PyPA Build

PyOxidizer

Pex

Shiv

cx_Freeze

cibuildwheel

Thomas Kluyver

Poetry

Vaults of Parnassus

MicroPython

Podcast Episode

CircuitPython

Podcast Episode

Desugaring Python Blog Series

JupyterHub

PyOdide

JupyterLite

ANSI C99

PyPy

Jython

IPython

ncurses

Kivy

Briefcase

Toga

PEP 401

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Traversing The Challenges And Promise Of Graph Machine Learning** Jun 16, 2021
Summary

The foundation of every ML model is the data that it is trained on. In many cases you will be working with tabular or unstructured information, but there is a growing trend toward networked, or graph data sets. Benedek Rozemberczki has focused his research and career around graph machine learning applications. In this episode he discusses the common sources of networked data, the challenges of working with graph data in machine learning projects, and describes the libraries that he has created to help him in his work. If you are dealing with connected data then this interview will provide a wealth of context and resources to improve your projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.

Your host as usual is Tobias Macey and today I’m interviewing Benedek Rozemberczki about his work on machine learning for graph data, including a variety of libraries to support his efforts

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of when you might want to do machine learning on networked/graph data?

How do networked data sets change the way that you approach machine learning tasks?

Can you describe the current state of the ecosystem for machine learning on graphs?

You have created a number of libraries to address different aspects of machine learning on graphs. Can you list them and share some of the stories behind their creation?

How do the different tools relate to each other?

Can you talk through some of the structural and user experience design principles that you lean on when building these libraries?

When you are working with networked data sets, what is your current workflow from idea to completion?

What are the most difficult aspects of working with networked data sets for machine learning applications?

What are the most interesting, innovative, or unexpected ways that you have seen graph ML used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on graph ML problems?

What are some examples of when you would choose not to use some or all of your own libraries?

What do you have planned for the future of your libraries/what new libraries do you anticipate needing to build?

Keep In Touch

benedekrozemberczki on GitHub

@benrozemberczki on Twitter

LinkedIn

Picks

Tobias

Wrath of Man

Benedek

Hunt for the Wilderpeople

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Karate Club

PyTorch Geometric Temporal

AstraZeneca

Budapest

University of Edinburgh

Matlab

R

Bipartite Graph

Node Classification

Graph Classification

PyTorch

Podcast Episode

PyTorch Geometric

DGL (Deep Graph Library)

Parametric Machine Learning

graph-tool

Jax

NetworkX

Little Ball of Fur

GCN == Graph Convolutional Network

NetworKit

Gensim

Podcast Episode

Nvidia cuGraph

Random Walk

scikit-learn

MalNet

Graph Representation Learning by William Hamilton

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Keep Your Analytics Lint Free With SQLFluff** Jun 09, 2021
Summary

The growth of analytics has accelerated the use of SQL as a first class language. It has also grown the amount of collaboration involved in writing and maintaining SQL queries. With collaboration comes the inevitable variation in how queries are written, both structurally and stylistically which can lead to a significant amount of wasted time and energy during code review and employee onboarding. Alan Cruickshank was feeling the pain of this wasted effort first-hand which led him down the path of creating SQLFluff as a linter and formatter to enforce consistency and find bugs in the SQL code that he and his team were working with. In this episode he shares the story of how SQLFluff evolved from a simple hackathon project to an open source linter that is used across a range of companies and fosters a growing community of users and contributors. He explains how it has grown to support multiple dialects of SQL, as well as integrating with projects like DBT to handle templated queries. This is a great conversation about the long detours that are sometimes necessary to reach your original destination and the powerful impact that good tooling can have on team productivity.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Your host as usual is Tobias Macey and today I’m interviewing Alan Cruickshank about SQLFluff, a dialect-flexible and configurable SQL linter

Interview

Introductions

How did you get introduced to Python?

Can you describe what SQLFluff is and the story behind it?

SQL is one of the oldest programming languages that is still in regular use. Why do you think that there are so few linters for it?

Who are the target users of SQLFluff and how do those personas influence the design and user experience of the project?

What are some of the characteristics of SQL and how it is used that contribute to readability/comprehension challenges?

What are some of the additional difficulties that are introduced by templating in the queries?

How is SQLFluff implemented?

How have the goals and design of the project changed since you first began working on it?

How do you handle support of varying SQL dialects without undue maintenance burdens?

What are some of the stylistic elements and strategies for making SQL code more maintainable?

What are some strategies for making queries self-documenting?

What are some signs that you should document it anyway?

What are some of the kinds of bugs that you are able to identify with SQLFluff?

What are some of the resources/references that you relied on for identifying useful linting rules?

What are some methods for measuring code quality in SQL?

What are the most interesting, innovative, or unexpected ways that you have seen SQLFluff used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on SQLFluff?

When is SQLFluff the wrong choice?

What do you have planned for the future of SQLFluff?

Keep In Touch

alanmcruickshank on GitHub

Website

LinkedIn

Picks

Tobias

The Nevers

Alan

Lost Connections: Uncovering the Real Causes of Depression – and the Unexpected Solutions by Johann Hari (affiliate link)

The Wim Hof Method by Wim Hof

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

SQLFluff

Tails.com

Hypothesis

Podcast Episode

Project Euler

Flake8

Podcast Episode

Black

dbt

Data Engineering Podcast Episode

Snowflake

Data Engineering Podcast Episode

BigQuery

SQL Window Functions

ANSI SQL

PostgreSQL

MS SQL Server

Oracle DB

Airflow

SQL Subquery

Common Table Expression (CTE)

The Rise Of The Data Engineer blog post

The Downfall Of The Data Engineer blog post

Object-Relational Mapper (ORM)

Tableau

Fishtown Analytics SQL Styleguide

Mozilla SQL Styleguide

The Zen of Python

dbt Packages

yapf

Set Theory

Flake8 SQL Plugin

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Exploring The Patterns And Practices For Deep Learning With Andrew Ferlitsch** Jun 02, 2021
Summary

Deep learning is gaining an immense amount of popularity due to the incredible results that it is able to offer with comparatively little effort. Because of this there are a number of engineers who are trying their hand at building machine learning models with the wealth of frameworks that are available. Andrew Ferlitsch wrote a book to capture the useful patterns and best practices for building models with deep learning to make it more approachable for newcomers ot the field. In this episode he shares his deep expertise and extensive experience in building and teaching machine learning across many companies and industries. This is an entertaining and educational conversation about how to build maintainable models across a variety of applications.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Scaling your data infrastructure is hard. Maintaining data quality standards as you scale is harder. Databand solves this. Their Unified Data Observability platform gives data engineers visibility over their stack without changing existing pipeline code. Get end-to-end visibility on your pipelines, and identify the root cause of issues before bad data is delivered. Seamlessly integrate with over 20 tools like Apache Airflow, Spark, Snowflake, and more. Use customizable dashboards to see where pipelines are broken and how that impacts delivery downstream. Get alerts on leading indicators of pipeline failure. Open up your pipeline and see exactly which code strings are broken – so you can fix the issue immediately. Create more reliable data products. Go to pythonpodcast.com/databand today to start your free trial!

Your host as usual is Tobias Macey and today I’m interviewing Andrew Ferlitsch about the patterns and practices for deep learning applications

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the major elements of a model architecture?

What is the relationship between the specific learning task being addressed and the architecture of the learning network?

In your experience, what is the level of awareness of a typical ML engineer or data scientist with respect to the most current design patterns in deep learning?

Your currently working on a book about deep learning patterns and practices. What was your motivation for starting that project?

What are your goals for the book?

How have advancements in the operability of machine learning influenced the ways that the models are designed and trained?

How do recent approaches such as transfer learning impact the needs of the supporting tools and infrastructure?

Can you describe the different design patterns that you cover in your book and the selection process for when and how to apply them?

What are the aspects of bringing deep learning to production that continue to be a challenge?

What are some of the emerging practices that you are optimistic about?

What are some of the industry trends or areas of current research that you are most excited about?

What are the most interesting, innovative, or unexpected patterns that you have encountered?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on the book?

What are some of the other resources that you recommend for listeners to learn more about how to build production ready models?

Keep In Touch

LinkedIn

@AndrewFerlitsch on Twitter

andrewferlitsch on GitHub

Picks

Tobias

Designing Data Intensive Applications (affiliate link)

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Google Cloud AI

Sharp Corporation

Deep Learning Patterns and Practices (affiliate link) use the code *podinit21* at checkout for 35% off all books at Manning!

CID Bioscience

Latent Space

AI Winter

Numerical Stability

Surrogate Model

GAN == Generative Adversarial Network

Gradient Descent

The Gang of 4 – Design Patterns: Elements of Reusable Object-Oriented Software (affiliate link)

The Lottery Hypothesis

Manning Publications (affiliate link)

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Automatically Generate Your Unit Tests From Scratch With Pynguin** May 25, 2021
Summary

Unit tests are an important tool to ensure the proper functioning of your application, but writing them can be a chore. Stephan Lukasczyk wants to reduce the monotony of the process for Python developers. As part of his PhD research he created the Pynguin project to automate the creation of unit tests. In this episode he explains the complexity involved in generating useful tests for a dynamic language, how he has designed Pynguin to address the challenges, and how you can start using it today for your own work.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.

Your host as usual is Tobias Macey and today I’m interviewing Stephan Lukasczyk about Pynguin, the PYthoN General UnIt test geNerator

Interview

Introductions

How did you get introduced to Python?

Can you describe what Pynguin is and the story behind it?

What are the problems that Pynguin is designed to solve?

What other projects are you drawing inspiration from?

What are some of the use cases for automatic test generation?

How is Pynguin implemented?

What are the challenges that the dynamic nature of Python introduces?

What are some of the packages and libraries that have been most helpful while building Pynguin?

Can you talk through the workflow of using Pynguin to generate tests for a project?

What are some of the limitations on what kinds of projects Pynguin can be used for?

What are some design or implementation strategies in the code that you are generating tests for that will help make Pynguin’s job easier?

Once a test suite has been created, what are the next steps?

What are some of the initial assumptions or goals of the project that have been revised or challenged once you began implementing it?

What are the most interesting, innovative, or unexpected ways that you have seen Pynguin used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pynguin?

When is Pynguin the wrong choice?

What do you have planned for the future of Pynguin?

Keep In Touch

Related to Pynguin: best via GitHub

Find me on Twitter

Picks

Tobias

Concourse CI

Stephan

Cycling

Take care of your health

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Pynguin

University of Passau

Passau, Germany

Evosuite

Hypothesis

Podcast Episode

Astor

Walrus Operator

MyPy

Podcast Episode

Pytest

Podcast Episode

UnitTest

Bytecode library

Pytype

Monkeytype

Podcast Episode

Atheris from Google – coverage-guided fuzzing

Blog series about “Python behind the scenes”: Ten thousand meters by Victor Skvortsov

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Leveling Up Natural Language Processing with Transfer Learning** May 18, 2021
Summary

Natural language processing is a powerful tool for extracting insights from large volumes of text. With the growth of the internet and social platforms, and the increasing number of people and communities conducting their professional and personal activities online, the opportunities for NLP to create amazing insights and experiences are endless. In order to work with such a large and growing corpus it has become necessary to move beyond purely statistical methods and embrace the capabilities of deep learning, and transfer learning in particular. In this episode Paul Azunre shares his journey into the application and implementation of transfer learning for natural language processing. This is a fascinating look at the possibilities of emerging machine learning techniques for transforming the ways that we interact with technology.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.

Your host as usual is Tobias Macey and today I’m interviewing Paul Azunre about using transfer learning for natural language processing

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what transfer learning is?

How is transfer learning being applied to natural language processing?

What motivated you to write a book about the application of transfer learning to NLP?

What are some of the applications of NLP that are impractical on intractable without transfer learning?

At a high level, what are the steps for building a new language model via transfer learning?

There have been a number of base models created recently, such as BERT and ERNIE, ELMo, GPT-3, etc. What are the factors that need to be considered when selecting which model to build from?

If there are multiple models that contain the seeds for different aspects of the end goal that you are trying to obtain, what is the feasibility of extracting the relevant capabilities from each of them and combining them in the final model?

What are some of the tools or frameworks that you have found most useful while working with NLP and transfer learning?

How would you characterize the current state of the ecosystem for transfer learning and deep learning techniques applied to NLP problems?

What are the most interesting, innovative, or unexpected applications of transfer learning with NLP that you have seen?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on the book?

When is transfer learning the wrong choice for an NLP project?

What are the trends or techniques that you are most excited for?

Keep In Touch

LinkedIn

Website

@pazunre on Twitter

Picks

Tobias

Infected Mushroom

Paul

Tenet

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Transfer Learning for Natural Language Processing by Paul Azunre (affiliate link) Use the code *podinit21* at checkout for 35% off all books at Manning!

Low Resource Languages

Fortran

C++

MatLab

MIT 6.003

Transfer Learning

Computer Vision

Deep Neural Network

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

GLUE == General Lanuage Understanding Evaluation

NLP SuperGLUE

NLP Encoder

Named Entity Recognition

ImageNet

Mathematical Optimization

Gradient Descent

Yonder AI

ELMo language model from Allen NLP

Ghana

ArXiv

BERT language model

TF-IDF == Term Frequency – Inverse Document Frequency

Word2Vec

GPT-3

Ghana NLP

Automatic Speech Recognition

ULM Fit

Keras

Tensorflow

Huggingface Transformers

Multi-Task Learning

Fast.ai

OpenAI

AWS SageMaker

Kaggle Kernels

Colab Notebooks

Azure ML Studio

BLEU Score

Khaya application

Android

iOS

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Federated Learning For All With Flower** May 11, 2021
Summary

Machine learning is a tool that has typically been performed on large volumes of data in one place. As more computing happens at the edge on mobile and low power devices, the learning is being federated which brings a new set of challenges. Daniel Beutel co-created the Flower framework to make federated learning more manageable. In this episode he shares his motivations for starting the project, how you can use it for your own work, and the unique challenges and benefits that this emerging model offers. This is a great exploration of the federated learning space and a framework that makes it more approachable.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.

Your host as usual is Tobias Macey and today I’m interviewing Daniel Beutel about Flower, a framework for building federated learning systems

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what federated learning is?

What is Flower and what’s the story behind it?

What are the trade-offs between federated and centralized models of machine learning?

What are some of the types of use cases or workloads that federated learning is used for?

Federated learning appears to be a growing area of interest. How would you characterize the current state of the ecosystem?

What are the most complex or challenging aspects of federating model training?

How does Flower simplify the process of distributing the model training process?

Can you describe how Flower is implemented?

How have the goals and/or design of Flower changed or evolved since you first began working on it?

One of the design principles that you list is "understandability". What are some of the ways that that manifests in the project?

It also mentions extensibility. What are the interfaces that Flower exposes for integration or extending its capabilities?

For someone who has an existing project that runs in a centralized manner, what are some indicators that a federated approach would be beneficial?

What is involved in translating the existing project to run in a federated fashion using Flower?

What is involved in building a production ready system with Flower?

How does your work at Adap inform the design and product direction for Flower?

What are some of the most interesting, innovative, or unexpected ways that you have seen Flower used?

What are the most interesting, unexpected, or challenging lessons that you have learned from your work on and with Flower?

When is Flower the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

LinkedIn

danieljanes on GitHub

@daniel_janes on Twitter

Picks

Tobias

Rummy Card Game

Daniel

Stand Up Paddling

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Flower

Adap

Hyperparameter Optimization

Federated Learning

University of Oxford

University of Cambridge

Nvidia Jetson

PyTorch

Podcast Episode

Tensorflow Lite

Tensorflow Federated

PySyft

Flower Summit

Jax

CNN == Convolutional Neural Network

Keras

gRPC

MQTT

NumPy NDArray

AWS Device Farm

Ray Framework

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Data Exploration and Visualization Made Effortless with Lux** May 04, 2021
Summary

Data exploration is an important step in any analysis or machine learning project. Visualizing the data that you are working with makes that exploration faster and more effective, but having to remember and write all of the code to build a scatter plot or histogram is tedious and time consuming. In order to eliminate that friction Doris Lee helped create the Lux project, which wraps your Pandas data frame and automatically generates a set of visualizations without you having to lift a finger. In this episode she explains how Lux works under the hood, what inspired her to create it in the first place, and how it can help you create a better end result. The Lux project is a valuable addition to the toolbox of anyone who is doing data wrangling with Pandas.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.

Your host as usual is Tobias Macey and today I’m interviewing Doris Lee about Lux, a Python library that facilitates fast and easy data exploration by automating the visualization and data analysis process

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Lux is and how the project got started?

What is the role of visualization in a data science workflow?

What are the challenges that data scientists face in the exploratory phase of their analysis?

There are a wide variety of data visualization tools in the Python ecosystem with differing areas of focus. What is the role of Lux in that ecosystem?

How does Lux compare to tools such as scikit-yb?

What is the workflow for someone using Lux in their analysis and what problems does it solve for them?

Can you talk through how Lux is architected?

How have the goals and design of Lux changed or evolved since you first began working on it?

Data visualization is a broad field. How do you determine which kinds of charts or plots are best suited to a particular data set or exploration?

What are some of the capabilities of Lux that are often overlooked or underutilized?

How has Lux impacted your own work in data analysis/data science?

What are some of the other gaps that you see in the available tooling for data science?

What are some of the most interesting, innovative, or unexpected ways that you have seen Lux used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Lux?

When is Lux the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

dorisjlee on GitHub

Website

LinkedIn

Picks

Tobias

Pirates of the Carribean movies

Doris

Snake Wrangling for Kids

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Lux

UC Berkeley

RISE Lab

School of Information

Pandas

Podcast Episode

Bokeh

Podcast Episode

Seaborn

Altair

Podcast Episode

Matplotlib

Grammar of Graphics

Plotly

Scikit YellowBrick

Podcast Episode

D3.js

Vega

Numpy

xarray

Tensorflow

Jupyter Widget

Chloropleth Map

G10 Countries

Ray

Podcast Episode

Modin

Dask

Data Engineering Podcast Episode

Podcast Interview About Coiled

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Extensible Open Source Authorization For Your Applications With Oso** Apr 27, 2021
Summary

Any project that is used by more than one person will eventually need to handle permissions for each of those users. It is certainly possible to write that logic yourself, but you’ll almost certainly do it wrong at least once. Rather than waste your time fighting with bugs in your authorization code it makes sense to use a well-maintained library that has already made and fixed all of the mistakes so that you don’t have to. In this episode Sam Scott shares the Oso framework to give you a clean separation between your authorization policies and your application code. He explains how you can call a simple function to ask if something is allowed, and then manage the complex rules that match your particular needs as a separate concern. He describes the motivation for building a domain specific language based on logic programming for policy definitions, how it integrates with the host language (such as Python), and how you can start using it in your own applications today. This is a must listen even if you never use the project because it is a great exploration of all of the incidental complexity that is involved in permissions management.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.

Your host as usual is Tobias Macey and today I’m interviewing Sam Scott about Oso, an open source library for managing authorization in your applications

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Oso is and the story behind it?

What was missing from the ecosystem of authorization libraries/frameworks that motivated you to create a new one?

What are some of the most common mistakes that you see developers make when implementing authorization logic?

At a high level, what is the process of using Oso to add access control policies to a piece of software?

What is the motivation for using a DSL for defining policies as opposed to writing those definitions in pure Python?

How have you approached the design of the policy language, particularly deciding what constraints to impose?

What other policy frameworks or dialects have you drawn inspiration from?

How is the Oso framework implemented?

How have the goals and design of Oso changed or evolved since you first began working on it?

What are some useful design patterns for integrating Oso into an application?

How does the type of application (e.g. web app vs. system daemon, etc.) affect the ways that Oso is used?

Given that Oso supports multiple language runtimes, what is involved in defining and enforcing policies that span multiple processes? (e.g. Python backend and Javascript frontend, Python microservice communicating with Go microservice, etc.)

What are some of the common mistakes or areas of confusion for users who are getting started with Oso and Polar?

What are some of the capabilities of Oso that are often overlooked or misunderstood?

I noticed that you’re backed by some venture firms. What is your current product vision and how does that relate to your current open source goals?

What are some of the most interesting, innovative, or unexpected ways that you have seen Oso used?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with oso?

When is Oso the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

LinkedIn

samscott89 on GitHub

@samososos on Twitter

Picks

Tobias

Chaos Walking

Sam

Hades video game

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Oso

Oso Authorization Academy

Number Theory

Sage Math

RBAC == Role-Based Access Control

ABAC == Attribute-Based Access Control

Polar Policy Language

Prolog

Logic Programming

Open Policy Agent

AWS IAM

XACML

Google Zanzibar

Rust

Web Assembly (Wasm)

OAuth Scopes

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Teaching Geeks The Value And Skills Of Public Speaking** Apr 20, 2021
Summary

Being able to present your ideas is one of the most valuable and powerful skills to have as a professional, regardless of your industry. For software engineers it is especially important to be able to communicate clearly and effectively because of the detail-oriented nature of the work. Unfortunately, many people who work in software are more comfortable in front of the keyboard than a crowd. In this episode Neil Thompson shares his story of being an accidental public speaker and how he is helping other engineers start down the road of being effective presenters. He discusses the benefits for your career, how to build the skills, and how to find opportunities to practice them. Even if you never want to speak at a conference, it’s still worth your while to listen to Neil’s advice and find ways to level up your presentation and speaking skills.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.

Your host as usual is Tobias Macey and today I’m interviewing Neil Thompson about the value of public speaking skills as a developer and how to gain them

Interview

Introductions

How did you get into engineering?

Can you start by discussing the different types of public speaking that we are talking about and some of the different venues where it might take place?

How did you get into public speaking?

What are some of the ways that our speaking abilities can impact the value that we provide and the trajectory of our career as engineers?

What were some of the methods and resources that you used to improve your own public speaking skills?

What are the common mistakes that people make when speaking to a group?

What are some of the non-obvious ways that speaking skills can be useful as an engineer?

What was your approach to learning how to be an effective speaker?

What are some of the mis-steps or dead ends that you encountered?

What are the different skills or capabilities that are necessary for being an effective presenter?

What are some ways that engineers can practice their presentation skills?

How do different audiences/venues influence the approach that you take to how to prepare for a presentation?

How has your experience in public speaking factored into the work you do for your podcast?

What are some of the most interesting, innovative, or unexpected presentations or speaking techniques that you have seen or used/created?

What are the most interesting, unexpected, or challenging lessons that you have learned from speaking and teaching others to speak in a professional context?

What resources do you recommend for engineers who want to improve their speaking and presenting skills?

Keep In Touch

LinkedIn

@neil_i_thompson on Twitter

Picks

Tobias

Falcon and the Winter Soldier

Neil

Teach The Geek To Speak

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Materials Science

Toastmasters

Teach The Geek To Speak

Teach The Geek Podcast

Developer Advocate

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Let The Robots Do The Work Using Robotic Process Automation with Robocorp** Apr 13, 2021
Summary

One of the great promises of computers is that they will make our work faster and easier, so why do we all spend so much time manually copying data from websites, or entering information into web forms, or any of the other tedious tasks that take up our time? As developers our first inclination is to "just write a script" to automate things, but how do you share that with your non-technical co-workers? In this episode Antti Karjalainen, CEO and co-founder of Robocorp, explains how Robotic Process Automation (RPA) can help us all cut down on time-wasting tasks and let the computers do what they’re supposed to. He shares how he got involved in the RPA industry, his work with Robot Framework and RPA framework, how to build and distribute bots, and how to decide if a task is worth automating. If you’re sick of spending your time on mind-numbing copy and paste then give this episode a listen and then let the robots do the work for you.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Software is read more than it is written, so complex and poorly organized logic slows down everyone who has to work with it. Sourcery makes those problems a thing of the past, giving you automatic refactoring recommendations in your IDE or text editor while you write (I even have it working in Emacs). It isn’t just another linting tool that nags you about issues. It’s like pair programming with a senior engineer, finding and applying structural improvements to your functions so that you can write cleaner code faster. Best of all, listeners of Podcast.__init__ get 6 months of their Pro tier for free if you go to pythonpodcast.com/sourcery today and use the promo code INIT when you sign up.

Your host as usual is Tobias Macey and today I’m interviewing Antti Karjalainen about the RPA Framework for automating your daily tasks and his work at Robocorp to manage your robots in production

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what Robotic Process Automation is?

What are some of the ways that RPA might be used?

What are the advantages over writing a custom library or script in Python to automate a given task?

How does the functionality of RPA compare to automation services like Zapier, IFTTT, etc.?

What are you building at Robocorp and what was your motivation for starting the business?

Who is your target customer and how does that inform the products that you are building?

Can you give an overview of the state of the ecosystem for RPA tools and products and how Robocorp and RPA framework fit within it?

How does the RPA Framework relate to Robot Framework?

What are some of the challenges that developers and end users often run into when trying to build, use, or implement an RPA system?

How is the RPA framework itself implemented?

How has the design of the project evolved since you first began working on it?

Can you talk through an example workflow for building a robot?

Once you have built a robot, what are some of the considerations for local execution or deploying it to a production environment?

How can you chain together multiple robots?

What is involved in extending the set of operations available in the framework?

What are the available integration points for plugging a robot written with RPA Framework into another Python project?

What are the dividing lines between RPA Framework and Robocorp?

How are you handling the governance of the open source project?

What are some of the most interesting, innovative, or unexpected ways that you have seen RPA Framework and the Robocorp platform used?

What are the most interesting, unexpected, or challenging lessons that you have learned while building and growing RPA Framework and the Robocorp business?

When is RPA and RPA Framework the wrong choice for automation?

What do you have planned for the future of the framework and business?

Keep In Touch

aikarjal on GitHub

@aikarjal on Twitter

LinkedIn

Picks

Tobias

WandaVision

Antti

Tenet

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Robocorp

RPA Framework

RCC

Robotic Process Automation

Zapier

IFTTT (If This Then That)

Robot Framework

Selenium

Playwright

Conda

Micro Mamba

PyOxidizer

Podcast Episode

XKCD "Is It Worth The Time?"

XKCD Automation Curve

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Keep Your Code Clean And Maintainable Using Static Analysis With Flake8** Apr 06, 2021
Summary

When you are writing code it is all to easy to introduce subtle bugs or leave behind unused code. Unused variables, unused imports, overly complex logic, etc. If you are careful and diligent you can find these problems yourself, but isn’t that what computers are supposed to help you with? Thankfully Python has a wealth of tools that will work with you to keep your code clean and maintainable. In this episode Anthony Sottile explores Flake8, one of the most popular options for identifying those problematic lines of code. He shares how he became involved in the project and took over as maintainer and explains the different categories of code quality tooling and how Flake8 compares to other static analyzers. He also discusses the ecosystem of plugins that have grown up around it, including some detailed examples of how you can write your own (and why you might want to).

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Your host as usual is Tobias Macey and today I’m interviewing Anthony Sottile about Flake8

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what Flake8 is and how you got involved with the project?

There are a variety of tools available for checking or enforcing code quality. How would you characterize Flake8 in comparison to the other options?

What do you see as the motivating factors for individuals or teams to integrate static analysis/linting in their toolchain and workflow?

What are some of the challenges that might prevent someone from adopting something like Flake8?

How can developers add Flake8 to an existing project without spending hours or days fixing all of the violations?

Can you describe the overall design and implementation of Flake8?

How has the design and goals of the project changed or evolved?

There are a wide array of plugins for Flake8. What is involved in adding new functionality or linting rules?

What capabilities does Flake8 provide that make it a viable platform for building plugins?

What are some of the limitations of Flake8 as a platform?

What do you see as the factors that have contributed to the widespread usage of Flake8 and the large number of available plugins?

What challenges does that pose as a maintainer of Flake8?

What are some of the other tools that you see developers use alongside Flake8 to help manage code quality and style enforcement?

What are some of the most interesting, innovative, or unexpected ways that you have seen Flake8 and its plugin ecosystem used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Flake8?

When is Flake8 the wrong choice?

What do you have planned for the future of Flake8?

Keep In Touch

@codewithanthony on Twitter

asottile on GitHub

LinkedIn

Picks

Tobias

SEVENEVES by Neal Stephenson

Anthony

pre-commit CI

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Flake8

PyFlakes

PyCodestyle

McCabe

pre-commit

Podcast Episode

PEP 484

MyPy

Pylance

Pyright

Pylint

Black

yapf

autopep8

pyupgrade

isort

reorder-python-imports

Static Analysis

pydocstyle

autoflake

pyproject.toml

Abstract Syntax Tree

Concrete Syntax Tree

Dagster

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Make Your Code More Readable With The Magic Of Refactoring Using Sourcery** Mar 30, 2021
Summary

Writing code that is easy to read and understand will have a lasting impact on you and your teammates over the life of a project. Sometimes it can be difficult to identify opportunities for simplifying a block of code, especially if you are early in your journey as a developer. If you work with senior engineers they can help by pointing out ways to refactor your code to be more readable, but they aren’t always available. Brendan Maginnis and Nick Thapen created Sourcery to act as a full time pair programmer sitting in your editor of choice, offering suggestions and automatically refactoring your Python code. In this episode they share their journey of building a tool to automatically find opportunities for refactoring in your code, including how it works under the hood, the types of refactoring that it supports currently, and how you can start using it in your own work today. It always pays to keep your tool box organized and your tools sharp and Sourcery is definitely worth adding to your repertoire.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Your host as usual is Tobias Macey and today I’m interviewing Nick Thapen and Brendan Maginnis about Sourcery, an advanced refactoring engine that cleans up your code as you work

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what Sourcery is?

What was your motivation for building a system for performing automated refactoring?

What are your goals and priorities with Sourcery?

There are a number of services that aim to automate portions of the developer workflow, such as code completions, quality checks, refactoring, etc. What was lacking in the existing tooling that made Sourcery a necessary project?

How does Sourcery compare with some of the other services that offer AI or ML powered assistance? (e.g. Kite, Tab9, Codata(?))

What was your reasoning for focusing solely on Python for your refactoring, rather than trying to support multiple language targets?

Can you give some examples of the types of refactoring that you are able to automate?

Can you describe how Sourcery is implemented?

What are some of the ways that the system has changed or evolved in design and/or scope?

What are some examples of the types of refactorings that Sourcery is ill-suited for and which still require manual intervention?

What is involved in adding support for a new editor?

How much variation is there in terms of implementation or available functionality across editors?

How has the introduction of the Language Server Protocol influenced your approach to editor integration?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on Sourcery?

When is Sourcery the wrong choice?

What do you have planned for the future of Sourcery

Keep In Touch

Nick

LinkedIn

@nthapen on Twitter

Brendan

LinkedIn

@brendan_m6s on Twitter

brendanator on GitHub

Picks

Tobias

The Croods: New Age

Nick

The Magicians TV Series

Brendan

David Copperfield by Charles Dickens

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Sourcery

IBM RPG

Delphi

Java

Scala

PyTorch

Podcast Episode

NLP == Natural Language Processing

Tensorflow

Language Server Protocol

Kent Beck

Martin Fowler

MyPy

Clojure

Lisp

Abstract Syntax Tree

ASTroid

Podcast Episode

Rope

Sans I/O

pre-commit framework

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Be Data Driven At Any Scale With Superset** Mar 22, 2021
Summary

Becoming data driven is the stated goal of a large and growing number of organizations. In order to achieve that mission they need a reliable and scalable method of accessing and analyzing the data that they have. While business intelligence solutions have been around for ages, they don’t all work well with the systems that we rely on today and a majority of them are not open source. Superset is a Python powered platform for exploring your data and building rich interactive dashboards that gets the information that your organization needs in front of the people that need it. In this episode Maxime Beauchemin, the creator of Superset, shares how the project got started and why it has become such a widely used and popular option for exploring and sharing data at companies of all sizes. He also explains how it functions, how you can customize it to fit your specific needs, and how to get it up and running in your own environment.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Your host as usual is Tobias Macey and today I’m interviewing Max Beauchemin about Superset, an open source platform for data exploration and visualization

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what Superset is and what it might be used for?

What problem were you trying to solve when you created it?

What tools or platforms did you consider before deciding to build something new?

There are a few different ways that someone might categorize Superset, such as business intelligence, data exploration, dashboarding, data visualization. How would you characterize it and how it fits in the current state of the industry and ecosystem?

What are some of the lessons that you have learned from your work on Airflow that you applied to Superset?

Can you give an overview of how Superset is implemented?

How have the goals, design and architecture evolved since you first began working on it?

Given its origin as a hackathon project the choice of Python seems natural. What are some of the challenges that choice has posed over the life of the project?

If you were to start the whole project over today what might you do differently?

Can you describe what’s involved in getting started with a new setup of Superset?

What are the available interfaces and integration points for someone who wants to extend it or add new functionality?

What are some of the most often overlooked, misunderstood, or underused capabilities of Superset?

One of the perennial challenges with a tool that allows users to build data visualizations is the potential to build dashboards or charts that are visually appealing but ultimately meaningless or wrong. How much guidance does Superset provide in helping to select a useful representation of the data?

In addition to being the original author and a project maintainer you have also started a company to offer Superset as a service. What are your goals with that business and what is the opportunity that it provides?

What are some of the most interesting, innovative, or unexpected ways that you have seen Superset used?

What are the most interesting, unexpected, or challenging lessons that you have learned while building and growing the Superset project and community?

When is Superset the wrong choice?

What do you have planned for the future of Superset and Preset?

Keep In Touch

LinkedIn

@mistercrunch on Twitter

mistercrunch on GitHub

Picks

Tobias

SOPS

Max

Frank Zappa Documentary

Accelerate: The Science of Lean Software and DevOps

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Superset

Preset

Blog

Airflow

Podcast Episode

AirBnB

Lyft

Django

Flask

CRUD == Create, Read, Update, Delete

Business Intelligence

Apache Druid

Presto

Trino (formerly known as Presto SQL)

Redash

Podcast Episode

Looker

Data Engineering Podcast Episode

Metabase

Data Engineering Podcast Episode

Flask App Builder

React Redux

Typescript

GraphQL

Celery

Redis

RabbitMQ

S3

AirBnB Superset Blog Post

D3

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Practical Advice On Using Python To Power A Business** Mar 16, 2021
Summary

Python is a language that is used in almost every imaginable context and by people from an amazing range of backgrounds. A lot of the people who use it wouldn’t even call themselves programmers, because that is not the primary focus of their job. In this episode Chris Moffitt shares his experience writing Python as a business user. In order to share his insights and help others who have run up against the limits of Excel he maintains the site Practical Business Python where he publishes articles that help introduce newcomers to Python and explain how to perform tasks such as building reports, automating Excel files, and doing data analysis. This is a great conversation that illustrates how useful it is to learn Python even if you never intend to write software professionally.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.

Your host as usual is Tobias Macey and today I’m interviewing Chris Moffitt about how Python is used to help manage business needs and processes and his work to share advice on this topic at Practical Business Python

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of your mission at Practical Business Python?

What was your inspiration for starting the site and what keeps you motivated?

What are some of the kinds of problems that a business user is looking to solve for themselves?

Why is Python a viable tool for a business user to become familiar with?

How would you characterize the difference between the ways that a software engineer and a business user approach Python?

What do you see as the tipping point of complexity or time investment past which a business user will pass a given project on to a software engineer?

How much familiarity with adjacent concerns such as version control, software design, etc. do you consider useful for a business user?

What are some of the ways that you use Python in your day-to-day?

What are some of the onramps for integrating Python into a user’s workflow?

What are some common stumbling blocks that business users run into when getting started with Python?

What are some of the most interesting, innovative, or impressive ways that you have seen Python employed by business users?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on the Practical Business Python site?

What are some cases where you would advocate for a tool other than Python for a business use case?

What do you have planned for the future of the site?

Keep In Touch

LinkedIn

chris1610 on GitHub

@chris1610 on Twitter

Picks

Tobias

The Data Science Roundup Newsletter

This Week In Data Newsletter

Chris Moffitt

Line Of Duty BBC Series

Out Of The Dark by David Weber

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Practical Business Python blog

Electrical Engineering

Unix

Perl

Data Science

Django

Raspberry Pi

Pandas

Excel

VBA == Visual Basic for Applications

VSCode

Excel PowerFX

Pathlib

Conda

Python Wheels

PEP 582

SAP

Salesforce

Tableau

Prophet library for timeseries forecasting

Talk Python Course Moving From Excel To Python

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Analyzing The Ecosystem of Python Data Companies With Tony Liu** Mar 09, 2021
Summary

There are a large and growing number of businesses built by and for data science and machine learning teams that rely on Python. Tony Liu is a venture investor who is following that market closely and betting on its continued success. In this episode he shares his own journey into the role of an investor and discusses what he is most excited about in the industry. He also explains what he looks at when investing in a business and gives advice on what potential founders and early employees of startups should be thinking about when starting on that journey.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Tony Liu about his perspectives on the landscape of Python in the data ecosystem from his role as an investor

Interview

Introductions

How did you get introduced to Python?

Can you start by sharing your background in the data ecosystem?

What led you to your current role as a venture investor?

What is your current area of focus in your investments?

What do you see as the major strengths of Python in the current landscape for data and analytics?

What are the areas where the ecosystem is still lacking?

Where are you seeing growth in the space and what do you see as the motivating factors?

As an investor, what are the qualities that you look for in a startup that is trying to compete in the data ecosystem?

What is your process for learning about and identifying companies that demonstrate the potential to succeed?

Do you focus on a particular problem domain and research a grouping of companies that are focused on that problem, or do you start from a given company to determine where to place your bets?

How has COVID changed the competitive landscape?

Can you share some of the companies that you have invested in?

What was noteable about their respective businesses that provided you with the confidence that they were worth investing in?

What are some of the most interesting, unexpected, or challenging lessons that you have learned from your experience as a venture investor?

What are some of the companies that you are keeping a close eye on, whether as potential investments or as competitors to your existing portfolio?

What are some of the problem spaces that you would like to see companies try to tackle?

What advice do you have for engineers who might be considering building a new business?

Do you have any advice for engineers who are working at a startup as to how best to compete in the current market?

Keep In Touch

LinkedIn

Picks

Tobias

The Sleepover movie

What do ya do with a Bernie Sanders? music video

Tony

Uncut Gems

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Costanoa Ventures

Sports Analytics

Turo

Databricks

Koalas

DataRobot

Faust

Podcast Episode

Oozie

Azkaban

Airflow

Podcast Episode

Prefect

Data Engineering Podcast Episode

Dagster

Podcast Episode

Data Engineering Podcast Episode

Kubeflow

MLFlow

Metaflow

Podcast Episode

Pandas

Podcast Episode

Spark

Data Engineering Podcast Episode

DBT

Data Engineering Podcast Episode

SnowflakeDB

Data Engineering Podcast Episode

Coiled

Podcast Episode

Noteable

Dask

Data Engineering Podcast Episode

Data Engineering Podcast Episode About Notebooks at Netflix

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Go From Notebook To Pipeline For Your Data Science Projects With Orchest** Mar 02, 2021
Summary

Jupyter notebooks are a dominant tool for data scientists, but they lack a number of conveniences for building reusable and maintainable systems. For machine learning projects in particular there is a need for being able to pivot from exploring a particular dataset or problem to integrating that solution into a larger workflow. Rick Lamers and Yannick Perrenet were tired of struggling with one-off solutions when they created the Orchest platform. In this episode they explain how Orchest allows you to turn your notebooks into executable components that are integrated into a graph of execution for running end-to-end machine learning workflows.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Rick Lamers and Yannick Perrenet about Orchest, a development environment designed for building data science pipelines from notebooks and scripts.

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what Orchest is and the story behind it?

Who are the users that you are building Orchest for and what are their biggest challenges?

What are some examples of the types of tools or workflows that they are using now?

What are some of the other tools or strategies in the data science ecosystem that Orchest might replace? (e.g. MLFlow, Metaflow, etc.)

What problems does Orchest solve?

Can you describe how Orchest is implemented?

How have the design and goals of the project changed since you first started working on it?

What is the workflow for someone who is using Orchest?

What are some of the sharp edges that they might run into?

What is the deployable unit once a pipeline has been created?

How do you handle verification and promotion of pipelines across staging and production environments?

What are the interfaces available for integrating with or extending Orchest?

How might an organization incorporate a pipeline defined in Orchest with the rest of their data orchestration workflows?

How are you approaching governance and sustainability of the Orchest project?

What are the most interesting, innovative, or unexpected ways that you have seen Orchest used?

What are the most interesting, unexpected, or challenging lessons that you have learned while building Orchest?

When is Orchest the wrong choice?

What do you have planned for the future of the project and company?

Keep In Touch

Rick

ricklamers on GitHub

LinkedIn

@RickLamers on Twitter

Yannick

yannickperrenet on GitHub

LinkedIn

Picks

Tobias

Fresh Bagels

Rick

Vaex

Yannick

Cookiecutter

Pyenv

Links

Orchest

Geoffrey Hinton

Yann LeCun

CoffeeScript

Vim

GAN == Generative Adversarial Network

Git

SQL

BigQuery

Software Carpentry

Podcast Episode

Google Colab

Airflow

Podcast Episode

Kedro

Data Engineering Podcast Episode

nbdev

Podcast Episode

Papermill

Data Engineering Podcast Episode

MLFlow

Metaflow

Podcast Episode

DVC

Podcast Episode

Andrew Ng

Kubeflow

Lua

Caddy

Traefik

DAG == Directed Acyclic Graph

Jupyter Enterprise Gateway

Streamlit

Kubernetes

Dagster

Podcast.__init__ Episode

Data Engineering Podcast Episode

DBT

Data Engineering Podcast Episode

GitLab

Spark

ETL

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Write Your Python Scripts In A Flow Based Visual Editor With Ryven** Feb 23, 2021
Summary

When you are writing a script it can become unwieldy to understand how the logic and data are flowing through the program. To make this easier to follow you can use a flow-based approach to building your programs. Leonn Thomm created the Ryven project as an environment for visually constructing a flow-based program. In this episode he shares his inspiration for creating the Ryven project, how it changes the way you think about program design, how Ryven is implemented, and how to get started with it for your own programs.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Leon Thomm about Ryven, a flow-based visual scripting environment for Python

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what Ryven is and what inspired you to create it?

What is flow-based visual scripting?

What are other popular flow-based visual scripting systems out there and have they been inspiring to the project?

What problem(s) do these try to solve?

What are some of the places where you are drawing inspiration for Ryven?

What are the kinds of projects that someone might build with Ryven?

How are you using Ryven in your personal projects?

How does structuring a project as a set of nodes in a flow graph influence the way that you think about how to design the solution to a problem?

Can you describe how Ryven is implemented?

How has the design or goals of the project changed or evolved since you first began working on it?

For someone who wants to use Ryven to build a project can you describe their workflow?

How do you handle things like code quality and tests for a Ryven project?

How do you manage collaboration for a Ryven project? (e.g. version control)

What are some of the most interesting, innovative, or unexpected ways that you have seen Ryven used?

What are the most interesting, unexpected, or challenging lessons that you have learned while building Ryven?

When is Ryven the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

leon-thomm on GitHub

Picks

Tobias

PyInfra

Leon

A Universe from Nothing! by Lawrence M. Krauss

Links

Ryven

Switzerland

Qt C++ framework

Flow-based Scripting

Unreal Engine

Node-RED

IFTTT == IF This Then That

DAG == Directed Acyclic Graph

Mind Map

Literate Programming

nbdev

Podcast Episode

Org Mode

OpenCV

scikit-learn

Unreal Python

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**CrossHair: Your Automatic Pair Programmer** Feb 16, 2021
Summary

One of the perennial challenges in software engineering is to reduce the opportunity for bugs to creep into the system. Some of the tools in our arsenal that help in this endeavor include rich type systems, static analysis, writing tests, well defined interfaces, and linting. Phillip Schanely created the CrossHair project in order to add another ally in the fight against broken code. It sits somewhere between type systems, automated test generation, and static analysis. In this episode he explains his motivation for creating it, how he uses it for his own projects, and how to start incorporating it into yours. He also discusses the utility of writing contracts for your functions, and the differences between property based testing and SMT solvers. This is an interesting and informative conversation about some of the more nuanced aspects of how to write well-behaved programs.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Phillip Schanely about CrossHair, an analysis tool for Python that blurs the line between testing and type systems.

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what the CrossHair project is and how it got started?

What are some examples of the types of tools that CrossHair might augment or replace? (e.g. Pydantic, Doctest, etc.)

What are the categories of bugs or problems in your code that CrossHair can help to identify or discover?

Can you explain the benefits of implementing contracts in your software?

What are the limitations of contract implementations?

What are the available interfaces for creating and validating contracts?

How does the use of contracts in your software influence the overall design of the system?

How does CrossHair compare to type systems in terms of use cases or capabilities?

Can you describe how CrossHair is implemented?

How has the design or goal of CrossHair changed or evolved since you first began working on it?

What are some of the other projects that you have gained inspiration or ideas from while working on CrossHair? (inside or outside of the Python ecosystem)

For someone who wants to get started with CrossHair, can you talk through the developer workflow?

I noticed that you recently added support for validating the functional equivalency of different method implementations. What was the inspiration for that capability?

What kinds of use cases does that enable?

How much of CrossHair are you able to dogfood while developing CrossHair?

What are some of the most interesting, innovative, or unexpected ways that you have seen CrossHair used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on CrossHair?

When is CrossHair the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

pschanely on GitHub

@pschanely on Twitter

LinkedIn

Picks

Tobias

The War With Grandpa

Phillip

Hammock chairs! (affiliate link)

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

CrossHair

NLTK == Natural Language ToolKit

ACL2

Liquid Haskell

SMT Solver

Doctest

Property Based Testing

Hypothesis

Podcast Episode

Halting Problem

Pydantic

PEP 316

icontract

Eiffel programming language

Design By Contract

Metamorphic Testing

Higher Order Types

Fuzz Testing

The Fuzzing Book

Python Audit Hooks

GitHub Scientist

Laboratory Python implementation of GitHub Scientist

Podcast Episode

Taint Analysis

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Giving Your Data Science Projects And Teams A Home At DagsHub** Feb 09, 2021
Summary

Collaborating on software projects is largely a solved problem, with a variety of hosted or self-managed platforms to choose from. For data science projects, collaboration is still an open question. There are a number of projects that aim to bring collaboration to data science, but they are all solving a different aspect of the problem. Dean Pleban and Guy Smoilovsky created DagsHub to give individuals and teams a place to store and version their code, data, and models. In this episode they explain how DagsHub is designed to make it easier to create and track machine learning experiments, and serve as a way to promote collaboration on open source data science projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Dean Pleban and Guy Smoilovsky about DagsHub, a platform to track experiments, and version data, models & pipelines for your data science and machine learning projects.

Interview

Introduction

How did you first get introduced to Python?

Can you start by describing what the DagsHub platform is and why you built it?

There are a number of projects and platforms that aim to support collaboration among data scientists. What are the distinguishing features of DagsHub and how does it compare to the other options in the ecosystem?

What are the biggest opportunities for improvement that you still see in the space of collaboration on data projects?

What do you see as the biggest points of friction for building experiments and managing source data collaboratively?

Can you describe how the DagsHub platform is implemented?

How have the design and goals of the system changed or evolved since you first began working on it?

How has your own understanding and practices of working on data science/ML projects changed changed?

GitHub has a number of convenience features beyond just storing a git repository. What are the capabilities that you are focusing on to add value to the data science workflow within DagsHub?

How are you approaching the bootstrapping problem of building a critical mass of users to be able to generate a beneficial network effect?

Are there any conventions that make it easier or more familiar for newcomers to a given project? (e.g. code layout, data labeling/tagging formats, etc.)

What are your recommendations for managing onwership/licensing of data assets in public projects?

What are some of the most interesting, innovative, or unexpected ways that you have seen DagsHub used?

What are the most interesting, unexpected, or challenging lessons that you have learned while building DagsHub?

When is DagsHub the wrong choice?

What do you have planned for the future of the platform and business?

Keep In Touch

Follow us on Twitter or LinkedIn, join our Discord, sign up to DAGsHub

@DeanPlbn

@Guy_T_Sky

@TheRealDAGsHub

DagsHub Discord

Picks

Tobias

The Remarkable Journey of Prince Jen by Lloyd Alexander

Dean

Quantum Computing Since Democritus by Scott Aaronson

The Expanse TV Series

Guy

Try to consume only the very best of available content, not the things that are coming out right now.

Applies to textbooks, TV shows, movies

Less Wrong blog

Slate Star Codex \ Astral Codex Ten

Avatar: The Last Airbender

3 Blue 1 Brown YouTube Channel

Haskell

Clojure

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

DagsHub

DVC

Podcast Episode

Data Science Cookiecutter

Jupyter Notebooks

Papers With Code

Connected Papers

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Exploring Literate Programming For Python Projects With nbdev** Feb 02, 2021
Summary

Creating well designed software is largely a problem of context and understanding. The majority of programming environments rely on documentation, tests, and code being logically separated despite being contextually linked. In order to weave all of these concerns together there have been many efforts to create a literate programming environment. In this episode Jeremy Howard of fast.ai fame and Hamel Husain of GitHub share the work they have done on nbdev. The explain how it allows you to weave together documentation, code, and tests in the same context so that it is more natural to explore and build understanding when working on a project. It is built on top of the Jupyter environment, allowing you to take advantage of the other great elements of that ecosystem, and it provides a number of excellent out of the box features to reduce the friction in adopting good project hygiene, including continuous integration and well designed documentation sites. Regardless of whether you have been programming for 5 days, 5 years, or 5 decades you should take a look at nbdev to experience a different way of looking at your code.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Jeremy Howard and Hamel Husain about nbdev, a library for turning Jupyter notebooks into Python libraries.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what nbdev is and the goals of the project?

What is the story behind how and why it got started?

Who is the target audience for the nbdev project?

How does that focus influence the features and design of nbdev?

What do you see as the primary challenges of building and collaborating on projects written in notebooks?

What are some of the other projects that are working to simplify or improve the experience of using notebooks?

How does nbdev compare to or complement those other tools?

Can you describe how nbdev is implemented?

How has the design and goals of the project evolved since it was first started?

What is the workflow of someone who is using nbdev?

At what point in the lifecycle of a notebook oriented project should someone start integrating nbdev?

How does nbdev scale when working on a project that spans multiple notebooks/modules?

How does working in a notebook environment change your approach to software development and project design?

What are the most interesting, innovative, or unexpected ways that you have seen nbdev used?

What are the most interesting, unexpected, or challenging lessons that you have learned from working on nbdev?

When is nbdev the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

Jeremy

LinkedIn

@jeremyphoward on Twitter

jph00 on GitHub

Hamel

hamelsmu on GitHub

Website

@HamelHusain on Twitter

LinkedIn

Picks

Tobias

Rivals! Frenemies Who Changed The World

Jeremy

Chess

Hamel

Moonwalking With Einstein by Joshua Foer (affiliate link)

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

nbdev

fast.ai

GitHub

Perl

Fastmail

R Studio

R Markdown

Literate Programming

fastcore

JupyterLab

nteract

Jupyter Voilà

GitHub Actions

Sphinx

Google Colab

Working In Public by Nadia Eghbal (affiliate link)

Jekyll

Hugo

Cython

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Making The Sans I/O Ideal A Reality For The Websockets Library** Jan 26, 2021
Summary

Working with network protocols is a common need for software projects, particularly in the current age of the internet. As a result, there are a multitude of libraries that provide interfaces to the various protocols. The problem is that implementing a network protocol properly and handling all of the edge cases is hard, and most of the available libraries are bound to a particular I/O paradigm which prevents them from being widely reused. To address this shortcoming there has been a movement towards "sans I/O" implementations that provide the business logic for a given protocol while remaining agnostic to whether you are using async I/O, Twisted, threads, etc. In this episode Aymeric Augustin shares his experience of refactoring his popular websockets library to be I/O agnostic, including the challenges involved in how to design the interfaces, the benefits it provides in simplifying the tests, and the work needed to add back support for async I/O and other runtimes. This is a great conversation about what is involved in making an ideal a reality.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Aymeric Augustin about his work on the websockets library and the work involved in making it sans I/O

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of your work on the websockets library and how the project got started?

What does "sans I/O" mean and what are the goals associated with it?

Can you share the history of your work on the websockets project?

What was your motivation for starting down the path of rearchitecting a project that is already production ready?

Can you talk through how the websockets library is architected currently?

How has the design of the project evolved since you first began working on it?

At a high level, what were the changes required to make it functionally sans i/o?

What do you see as the primary challenges associated with making network related libraries sans i/o?

In your experience of porting websockets to be purely protocol oriented, what are the technical and design challenges that you faced?

One of the goals of the Sans I/O approach is to support reusability and composability of network protocol implementations. What has your experience been as to the viability of those goals in practice?

What is your current perspective on the cost/benefit of the sans i/o conversion?

Who are the primary consumers of the websockets library?

How do you foresee the target audience changing once you have completed extracting the protocol logic?

What are some of the most interesting, innovative, or unexpected ways that you have seen the websockets project used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on the websockets project and sans i/o conversion?

What do you have planned for the future of the project?

Keep In Touch

LinkedIn

@aymericaugustin on Twitter

Website

Picks

Tobias

Jigsaw Puzzles

Aymeric

Inside Qonto interview

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Sans I/O: When The Rubber Meets The Road

Websockets library

Websockets Protocol

Qonto

Tulip

Asyncio

CERN Particle Accelerator

Sans I/O

Cory Benfield

HTTP/2

Twisted

Curio

Trio

Inversion of Control

ohneio helper library for implementing sans I/O network protocols

SOCKS Proxy

Sanic

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Driving Toward A Faster Python Interpreter With Pyston** Jan 19, 2021
Summary

One of the common complaints about Python is that it is slow. There are languages and runtimes that can execute code faster, but they are not as easy to be productive with, so many people are willing to make that tradeoff. There are some use cases, however, that truly need the benefit of faster execution. To address this problem Kevin Modzelewski helped to create the Pyston intepreter that is focused on speeding up unmodified Python code. In this episode he shares the history of the project, discusses his current efforts to optimize a fork of the CPython interpreter, and his goals for building a business to support the ongoing work to make Python faster for everyone. This is an interesting look at the opportunities that exist in the Python ecosystem and the work being done to address some of them.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Kevin Modzelewski about his work on Pyston, an interpreter for Python focused on compatibility and speed.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Pyston is and how it got started?

Can you share some of the history of the project and the recent changes?

What is your motivation for focusing on Pyston and Python optimization?

What are the use cases that you are primarily focused on with Pyston?

Why do you think Python needs another performance project?

Can you describe the technical implementation of Pyston?

How has the project evolved since you first began working on it?

What are the biggest challenges that you face in maintaining compatibility with CPython?

How does the approach to Pyston compare to projects like PyPy and Pyjion?

How are you approaching sustainability and governance of the project?

What are some of the most interesting, innovative, or unexpected uses for Pyston that you have seen?

What have you found to be the most interesting, unexpected, or challenging lessons that you have learned while working on Pyston?

When is Pyston the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

kmod on GitHub

Blog

LinkedIn

Picks

Tobias

Last Week In AWS Newsletter

Kevin

Meditation

Calm App

Headspace

Links

Pyston

Discord Chat

Dropbox

CPython

PyPy

Pyjion

Podcast Episode

Jython

hpy

Podcast Episode

JIT Compiler

Python Software Foundation

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Project Scaffolding That Evolves With Your Software Using Copier** Jan 12, 2021
Summary

Every software project has a certain amount of boilerplate to handle things like linting rules, test configuration, and packaging. Rather than recreate everything manually every time you start a new project you can use a utility to generate all of the necessary scaffolding from a template. This allows you to extract best practices and team standards into a reusable project that will save you time. The Copier project is one such utility that goes above and beyond the bare minimum by supporting project *evolution*, letting you bring in the changes to the source template after you already have a project that you have dedicated significant work on. In this episode Jairo Llopis explains how the Copier project works under the hood and the advanced capabilities that it provides, including managing the full lifecycle of a project, composing together multiple project templates, and how you can start using it for your own work today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Jairo Llopis about Copier, a library for managing project templates

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what the Copier project is?

How did you get involved in the project?

Can you share some of the history of the project?

What do you see as the most common uses for a project templating tool?

There are a variety of different tools for scaffolding projects across a wide range of languages. What are the distinguishing features of Copier that might lead someone to choose it over the alternatives?

Can you describe how the Copier project is implemented?

How has the design and feature set evolved over time?

What is the workflow for someone building a template with Copier?

What are some of the edge cases or complexities that they might run into?

What are the options for extensibility or integration with Copier?

What are some of the capabilities or use cases for Copier that are often overlooked?

What are some of the most interesting, innovative, or unexpected ways that you have seen Copier used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Copier?

When is Copier the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

Yajo on GitHub

__yajo on Twitter

Website

Picks

Tobias

Playing Cards

Jairo

Mozilla Hubs

Links

Copier

Tecnativa

Odoo Open Source ERP

Cookiecutter

Yeoman

Jinja

Cookiecutter, Yeoman, and Copier Blog Post

doodba-copier-template

Copier Templates

A Story of Duplicate Code

Traefik

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**How Python's Evolution Impacts Your Fluency With Luciano Ramalho** Jan 05, 2021
Summary

On its surface Python is a simple language which is what has contributed to its rise in popularity. As you move to intermediate and advanced usage you will find a number of interesting and elegant design elements that will let you build scalable and maintainable systems and design friendly interfaces. Luciano Ramalho is best known as the author of Fluent Python which has quickly become a leading resource for Python developers to increase their facility with the language. In this episode he shares his journey with Python and his perspective on how the recent changes to the interpreter and ecosystem are influencing who is adopting it and how it is being used. Luciano has an interesting perspective on how the feedback loop between the community and the language is driving the curent and future priorities of the features that are added.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Luciano Ramalho about the recent and upcoming changes in the Python language

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of the role that Python has played in your career?

What other languages do you work with on a regular basis?

How has that experience influenced the ways that you use Python?

What do you see as the biggest changes that have been added to Python in recent years?

How have the changes in Python changed the way that you approach program design?

How has your work on Fluent Python influenced your perspective on the language and its utility?

What do you find to be the most confusing aspects of Python, whether for newcomers or experienced developers?

How would you characterize the types of features that have been added to Python in recent years?

What, if any, trends have you observed in the types of features that are proposed and included in Python and what do you see as the motivating factors for them?

What changes to the language are you tracking?

Which are you personally invested in?

What new features or capabilities would you like to see included in Python?

Keep In Touch

@ramalhoorg on Twitter

ramalho on GitHub

LinkedIn

Picks

Tobias

Magic: The Gathering: Arena

Luciano

The Queen’s Gambit

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Fluent Python

Library and Information Sciences

Thoughtworks

São Paulo, Brazil

Perl

PHP

Object Oriented Programming

Dunder Methods

Python Essential Reference

Python In A Nutshell

Python Typing Module

Pytype

Pyre

MyPy

AsyncIO

Typing Protocols

Duck Typing

Static Typing Where Possible, Dynamic Typing Where Needed

TypeScript

Ruby 3 Type Annotations

C#

Go Language

KotlinJS

Matrix Multiplication Operator

Walrus Operator == Assignment Expressions

CPython PEG Parser

Podcast Episode

PEP 3099: Things that will Not Change in Python 3000

Elixir

Pattern Matching

Erlang

Prolog

Python Pattern Matching PEP

SWIG

Symbolic Computation

Python Descriptors

Beeware

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Making Content Management A Smooth Experience With A Headless CMS** Dec 28, 2020
Summary

Building a web application requires integrating a number of separate concerns into a single experience. One of the common requirements is a content management system to allow product owners and marketers to make the changes needed for them to do their jobs. Rather than spend the time and focus of your developers to build the end to end system a growing trend is to use a headless CMS. In this episode Jake Lumetta shares why he decided to spend his time and energy on building a headless CMS as a service, when and why you might want to use one, and how to integrate it into your applications so that you can focus on the rest of your application.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Python has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.

Your host as usual is Tobias Macey and today I’m interviewing Jake Lumetta about Butter CMS and the role of a headless CMS in the modern web ecosystem.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what a headless CMS is?

How does the use case and user experience differ from working with a traditional CMS (e.g. WordPress, etc.)?

How does a headless CMS compare to using a framework such as Django CMS or Wagtail?

Can you describe what you have built at ButterCMS?

What was your motivation for starting a business to provide a CMS as a service?

How would you characterize the current state of the CMS ecosystem?

How does ButterCMS compare to the available open source and commercial options?

What are the trends in the web ecosystem that have made a headless CMS necessary or useful?

What types of information are people managing in a CMS?

How are people integrating headless CMS systems into their Python applications?

Can you describe the architecture for Butter?

How has the system changed or evolved since you first began working on it?

What was your decision process for determining what language(s) and technology stack to use for building the platform?

What are the aspects of building and maintaining a CMS that are most complex?

What are some of the most interesting, innovative, or unexpected ways that you have seen ButterCMS used?

What have you found to be the most interesting, unexpected, or challenging lessons that you have learned while building ButterCMS?

When is ButterCMS the wrong choice?

What do you have planned for the future of ButterCMS?

Keep In Touch

LinkedIn

@jakelumetta on Twitter

Picks

Tobias

The Arrow TV Show

Jake

Ghost In The Wires by Kevin Mitnick

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

ButterCMS

Hiring: Dir of Engineering

PHP

Django

MVC == Model, View, Controller

Headless CMS

WordPress

Django CMS

Wagtail

Podcast Episode

SEO == Search Engine Optimization

JAM (Javascript, APIs, and Markup) Stack

Netlify

Vercel

Cloudflare Pages

Vue.js

React.js

Django Rest Framework

Fastly

CDN == Content Delivery Network

AWS Cloudfront

Ionic

React Native

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Turning Notebooks Into Collaborative And Dynamic Data Applications With Hex** Dec 21, 2020
Summary

Notebooks have been a useful tool for analytics, exploratory programming, and shareable data science for years, and their popularity is continuing to grow. Despite their widespread use, there are still a number of challenges that inhibit collaboration and use by non-technical stakeholders. Barry McCardel and his team at Hex have built a platform to make collaboration on Jupyter notebooks a first class experience, as well as allowing notebooks to be parameterized and exposing the logic through interactive web applications. In this episode Barry shares his perspective on the state of the notebook ecosystem, why it is such as powerful tool for computing and analytics, and how he has built a successful business around improving the end to end experience of working with notebooks. This was a great conversation about an important piece of the toolkit for every analyst and data scientist.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.

Python has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.

Your host as usual is Tobias Macey and today I’m interviewing Barry McCardel about Hex, a managed platform to turn your notebooks into collaborative, interactive data apps and stories

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what you have built at Hex and your motivation for starting the business?

Who are the primary users of the Hex platform?

How has that focus influenced your product direction and the features that you prioritize?

What are the biggest roadblocks that you see data analysts and data consumers running into?

How have those roadblocks shifted in recent years?

What is it about the concept of a notebook that has caused them to see such a massive rise in usage and popularity?

What are the barriers to productivity and accessibility that still exist in the notebook ecosystem?

What are the pieces for working in and with notebooks that are still missing?

What does Hex add to the experience of working with notebooks?

Can you describe how the Hex platform implemented?

How has the design of the platform changed or evolved since you first began working on it?

Where does Hex sit in the lifecycle of notebook creation and usage?

How does it compare to other services built to support users of notebooks such as Zepl, Saturn Cloud, Noteable, etc.?

You focus on the Jupyter platform, but there are a number of other notebook frameworks that have sprung up in recent years. What do you see as being the relative strengths of the available options?

What are the trends in the tooling, capabilities, and use cases for notebooks that you are keeping an eye on?

What are the most interesting, innovative, or unexpected ways that you have seen the Hex platform used?

What are the most interesting, unexpected, or challenging lessons that you have learned while building Hex?

When is Hex the wrong choice?

What do you have planned for the future of the Hex business and product?

Keep In Touch

LinkedIn

@TheRealBarryM on Twitter

Picks

Tobias

Flakehell

DC Extended Universe Movies

Barry

Wingspan

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Hex

Palantir

IPython

Podcast Episode

Jupyter

Mathematica

IDE == Integrated Development Environment

nbconvert

Observable Javascript Notebooks

React

BlueprintJS

Papermill

Streamlit

Podcast Episode

Shiny

Redshift

Snowflake

Data Engineering Podcast Episode

BigQuery

PostgreSQL

Data Engineering Podcast Episode

Noteable

Saturn Cloud

Zepl

Zeplin Notebooks

JupyterHub

Binder

Kubeflow

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Add Anomaly Detection To Your Time Series Data With Luminaire** Dec 15, 2020
Summary

When working with data it’s important to understand when it is correct. If there is a time dimension, then it can be difficult to know when variation is normal. Anomaly detection is a useful tool to address these challenges, but a difficult one to do well. In this episode Smit Shah and Sayan Chakraborty share the work they have done on Luminaire to make anomaly detection easier to work with. They explain the complexities inherent to working with time series data, the strategies that they have incorporated into Luminaire, and how they are using it in their data pipelines to identify errors early. If you are working with any kind of time series then it’s worth giving Luminaure a look.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Python has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.

Your host as usual is Tobias Macey and today I’m interviewing Smit Shah and Sayan Chakraborty about Luminaire, a machine learning based package for anomaly detection on timeseries data

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Luminaire is and how the project got started?

Where does the name come from?

How does Luminaire compare to other frameworks for working with timeseries data such as Prophet?

What are the main use cases that Luminaire is powering at Zillow?

What are some of the complexities inherent to anomaly detection that are non-obvious at first glance?

How are you addressing those challenges in Luminaire?

Can you describe how Luminaire is implemented?

How has the design of the project evolved since it was first started?

What was the motivation for releasing Luminaire as open source?

For someone who is using Luminaire, what is the process for training and deploying a model with it?

What are some common ways that it is used within a larger system?

How do sustained anomalies such as the current pandemic affect the work of identifying other sources of meaningful outliers?

What are some of the most interesting, innovative, or unexpected ways that you have seen Luminaire being used?

What are some of the most interesting, unexpected, or challening lessons that you have learned while building and using Luminaire?

When is Luminaire the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

Smit

LinkedIn

shahsmit14 on GitHub

Sayan

LinkedIn

Website

@tweettosayan on Twitter

Picks

Tobias

Flakehell

Smit

Apache Ranger

Sayan

Prediction Machines: The Simple Economics Of Artificial Intelligence

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Luminaire

Zillow

Anomaly Detection

Facebook Prophet

IEEE Big Data Conference

Unsupervised Learning

ARIMA (Autoregressive Integrated Moving Average) Model

Airflow

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building Big Data Pipelines For Audio With Klio** Dec 07, 2020
Summary

Technologies for building data pipelines have been around for decades, with many mature options for a variety of workloads. However, most of those tools are focused on processing of text based data, both structured and unstructured. For projects that need to manage large numbers of binary and audio files the list of options is much shorter. In this episode Lynn Root shares the work that she and her team at Spotify have done on the Klio project to make that list a bit longer. She discusses the problems that are specific to working with binary data, how the Klio project is architected to allow for scalable and efficient processing of massive numbers of audio files, why it was released as open source, and how you can start using it today for your own projects. If you are struggling with ad-hoc infrastructure and a medley of tools that have been cobbled together for analyzing large or numerous binary assets then this is definitely a tool worth testing out.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.

Python has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.

Your host as usual is Tobias Macey and today I’m interviewing Lynn Root about Klio, an open source pipeline for processing audio and binary data

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Klio is and how it got started?

What are some of the challenges that are unique to processing audio data as compared to text?

What use cases does Klio enable?

What are some of the alternative options available for working with binary data?

What capabilities were lacking in other solutions that made it worthwhile to build a new system from scratch?

Can you describe the design and architecture of Klio?

What was the motivation for implementing Klio as a Python framework, rather than building on top of the Scio project?

How much of a challenge has it been to interface to the Beam framework from Python? (Java <-> Python impedance mismatch)

One of the interesting optimizations in Klio is the option for bottom up execution of a job to avoid processing a given file unless absolutely necessary. What are some of the other useful or interesting capabilities that are built into Klio?

What was the motivation and process for releasing Klio as open source?

For someone who is building a pipeline with Klio, can you talk through the workflow?

What are the extension and integration points that are exposed?

How does Klio handle third party dependencies for a given job?

What are some of the challenges, misunderstandings, or edge cases that users of Klio should be aware of?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while building and growing the Klio project?

What are some of the most interesting, innovative, or unexpected ways that you have seen Klio used?

What do you have planned for the future of the project?

Keep In Touch

GitHub

Twitter

LinkedIn

Picks

Tobias

PSF Fundraiser

Lynn

Roam note-taking tool

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Klio

Announcement Blog Post

Docs

GitHub

Spotify

PyLadies SF

Luigi

RAML

ramlfications

Interrogate

Apache Beam

Librosa

PyAudio

Pillow

Podcast Episode

FFMPeg

ImageMagick

Music Information Retrieval

Machine Hearing

Data Engineering Podcast Episode

Scio

Microsoft Azure

Google Cloud Platform

Google Cloud Dataflow

Protocol Buffers

Apache Spark

PySpark

DAG == Directed Acyclic Graph

ISMIR Conference

Digital Signal Processing (DSP)

Python Pickle

Research paper on separating vocals from instrumentals of a song

New York Times: Why songs of the summer sound the same

Microsoft’s Rocket Platform for video analytics

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Open Sourcing The Anvil Full Stack Python Web App Platform** Dec 01, 2020
Summary

Building a complete web application requires expertise in a wide range of disciplines. As a result it is often the work of a whole team of engineers to get a new project from idea to production. Meredydd Luff and his co-founder built the Anvil platform to make it possible to build full stack applications entirely in Python. In this episode he explains why they released the application server as open source, how you can use it to run your own projects for free, and why developer tooling is the sweet spot for an open source business model. He also shares his vision for how the end-to-end experience of building for the web should look, and some of the innovative projects and companies that were made possible by the reduced friction that the Anvil platform provides. Give it a listen today to gain some perspective on what it could be like to build a web app.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.

Python has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.

Your host as usual is Tobias Macey and today I’m interviewing Meredydd Luff about the process and motivations for releasing the Anvil platform as open source

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what Anvil is and some of the story behind it?

What is new or different in Anvil since we last spoke in June of 2019?

What are the most common or most impressive use cases for Anvil that you have seen?

On your website you mention Anvil being used for deploying models and productionizing notebooks. How does Anvil help in those use cases?

How much of the adoption of Anvil do you attribute to the use of Skulpt and providing a way to write Python for the browser?

What are some of the complications that users might run into when trying to integrate with the broader Javascript ecosystem?

How does the release of the Anvil App Server affect your business model?

How does the workflow for users of the Anvil platform change if they decide to run their own instance?

What is involved in getting it deployed to production?

What other tools or companies did you look to for positive and negative examples of how to run a successful business based on open source?

What was your motivation for open sourcing the core runtime of Anvil?

What was involved in getting the code cleaned up and ready for a public release?

What are the other ways that your business relies on or contributes to the open source ecosystem?

What do you see as the primary threats to open source business models?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while building and growing Anvil?

What do you have planned for the future of the platform and business?

Keep In Touch

LinkedIn

@meredydd on Twitter

meredydd on GitHub

Picks

Tobias

Magic: The Gathering

Meredydd

Anvil Advent Calendar

Anvil Podcast

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Anvil

Podcast Episode

Visual Basic

Skulpt

Streamlit

Podcast Episode

Plot.ly Dash

Anvil Uplink

DOM == Document Object Model

SQLAlchemy

Brython

Transcrypt

Podcast Episode

Comparison of Python in the browser implementations

Blog post about Anvil object serializer

Create React App

Webpack

Jetbrains

Traefik

Let’s Encrypt

Corey Quinn

WebAssembly

PyOdide

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Pants Has Got Your Python Monorepo Covered** Nov 23, 2020
Summary

In a software project writing code is just one step of the overall lifecycle. There are many repetitive steps such as linting, running tests, and packaging that need to be run for each project that you maintain. In order to reduce the overhead of these repeat tasks, and to simplify the process of integrating code across multiple systems the use of monorepos has been growing in popularity. The Pants build tool is purpose built for addressing all of the drudgery and for working with monorepos of all sizes. In this episode core maintainers Eric Arellano and Stu Hood explain how the Pants project works, the benefits of automatic dependency inference, and how you can start using it in your own projects today. They also share useful tips for how to organize your projects, and how the plugin oriented architecture adds flexibility for you to customize Pants to your specific needs.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Python has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.

Feature flagging is a simple concept that enables you to ship faster, test in production, and do easy rollbacks without redeploying code. Teams using feature flags release new software with less risk, and release more often. ConfigCat is a feature flag service that lets you easily add flags to your Python code, and 9 other platforms. By adopting ConfigCat you and your manager can track and toggle your feature flags from their visual dashboard without redeploying any code or configuration, including granular targeting rules. You can roll out new features to a subset or your users for beta testing or canary deployments. With their simple API, clear documentation, and pricing that is independent of your team size you can get your first feature flags added in minutes without breaking the bank. Go to pythonpodcast.com/configcat today to get 35% off any paid plan with code *PYTHONPODCAST* or try out their free forever plan.

Your host as usual is Tobias Macey and today I’m interviewing Eric Arellano and Stu Hood about Pants, a flexible build system that works well with monorepos.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Pants is and how it got started?

What’s the story behind the name?

What is a monorepo and why might I want one?

What are the challenges caused by working with a monorepo?

Why are monorepos so uncommon in Python projects?

What is the workflow for a developer or team who is managing a project with Pants?

How does Pants integrate with the broader ecosystem of Python tools for dependency management and packaging (e.g. Poetry, Pip, pip-tools, Flit, Twine, Pex, Shiv, etc.)?

What is involved in setting up Pants for working with a new Python project?

What complications might developers encounter when trying to implement Pants in an existing project?

How is Pants itself implemented?

How have the design, goals, or architecture evolved since Pants was first created?

What are the major changes in the v2 release?

What was the motivation for the major overhaul of the project?

How do you recommend developers lay out their projects to work well with Python?

How can I handle code shared between different modules or packages, and reducing the third party dependencies that are built into the respective packages?

What are some of the most interesting, unexpected, or innovative ways that you have seen Pants used?

What have you found to be the most interesting, unexpected, or challenging aspects of working on Pants?

What are the cases where Pants is the wrong choice?

What do you have planned for the future of the pants project?

Keep In Touch

Eric

LinkedIn

Eric-Arellano on GitHub

@EArellanoAZ on Twitter

Stu

stuhood on GitHub

@stuhood on Twitter

LinkedIn

Picks

Tobias

Cursed TV show

Eric

Turtle Graphics

Stu

Faster Than Lime blog

Links

Pants

Foursquare

Twitter

Toolchain

Bazel build tool

Ant build tool

Monorepo

isort

Tox

Poetry

distutils

setuptools

mypy

Bandit

Flake8

Sample Python Pants Project

gRPC

Protocol Buffers

Rust

GIL == Global Interpreter Lock

PEP 420

Blog post about using Pants to migrate from Python 2 to 3

Pex

Shiv

PyOxidizer

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Scale Your Data Science Teams With Machine Learning Operations Principles** Nov 17, 2020
Summary

Building a machine learning model is a process that requires well curated and cleaned data and a lot of experimentation. Doing it repeatably and at scale with a team requires a way to share your discoveries with your teammates. This has led to a new set of operational ML platforms. In this episode Michael Del Balso shares the lessons that he learned from building the platform at Uber for putting machine learning into production. He also explains how the feature store is becoming the core abstraction for data teams to collaborate on building machine learning models. If you are struggling to get your models into production, or scale your data science throughput, then this interview is worth a listen.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.

Python has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.

Your host as usual is Tobias Macey and today I’m interviewing Mike Del Balso about what is involved in operationalizing machine learning, and his work at Tecton to provide that platform as a service

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what is encompassed by the term "Operational ML"?

What other approaches are there to building and managing machine learning projects?

How do these approaches differ from operational ML in terms of the use cases that they enable or the scenarios where they can be employed?

How would you characterize the current level of maturity for the average organization or enterprise in terms of their capacity for delivering ML projects?

What are the necessary components for an operational ML platform?

You helped to build the Michelangelo platform at Uber. How did you determine what capabilities were necessary to provide a unified approach for building and deploying models?

How did your work on Michelangelo inform your work on Tecton?

How does the use of a feature store influence the structure and workflow of a data team?

In addition to the feature store, what are the other necessary components of a full pipeline for identifying, training, and deploying machine learning models?

Once a model is in production, what signals or metrics do you track to feed into the next iteration of model development?

One of the common challenges in data science and machine learning is managing collaboration. How do tools such as feature stores or the Michelangelo platform address that problem?

What are the most interesting, unexpected, or challenging lessons that you have learned while building operational ML platforms?

What advice or recommendations do you have for teams who are trying to work with machine learning?

What do you have planned for the future of Tecton?

Keep In Touch

LinkedIn

Picks

Tobias

Sandman graphic novel series by Neil Gaiman

Mike

At Home: A Short History of Private Life by Bill Bryson

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Tecton

Michelangelo

sklearn

Pandas

Data Engineering Podcast Episode About StreamSQL

Feature Store

Master Data Management

Amundsen

Data Engineering Podcast Episode

Jupyter

Algorithmia

Unix philosophy

Feast feature store

Kubeflow

Andreesen Horowitz Post On Emerging Data Architectures

What is a feature store? post on the Tecton blog

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Making The Case For A (Semi) Formal Specification Of CPython** Nov 10, 2020
Summary

The CPython implementation has grown and evolved significantly over the past ~25 years. In that time there have been many other projects to create compatible runtimes for your Python code. One of the challenges for these other projects is the lack of a fully documented specification of how and why everything works the way that it does. In the most recent Python language summit Mark Shannon proposed implementing a formal specification for CPython, and in this episode he shares his reasoning for why that would be helpful and what is involved in making it a reality.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.

Your host as usual is Tobias Macey and today I’m interviewing Mark Shannon about his efforts to create a formal specification for the CPython interpreter

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the current state of how the Python language and the CPython runtime are defined?

What is your motivation in advocating for a specification?

After ~25 years of the language, why is now the time to pursue this effort?

How does the history of the language and the scope of the ecosystem and community impact the effort required to make this a reality?

What is involved in creating the specification and where would it be located once complete?

What are some examples of languages that are formally specified?

What are the possible benefits of creating a specification for the CPython virtual machine?

What is the distinction between a specification for the VM as opposed to a specification for the language?

What are some potential downsides to having a (semi-)formal specification become part of the definition of the interpreter?

Can you describe the process of doing the work to create the specification?

How are you approaching the actual definition of the specification (e.g. prose vs programmatic)?

What are the tradeoffs of prose vs. an executable specification (e.g. TLA+, Alloy)?

How does this work tie into your goals of improving the speed of the CPython interpreter?

What are some of the most interesting, unexpected, or challenging aspects of your efforts to bring this specification to CPython?

How can the community contribute to this effort?

Keep In Touch

markshannon on GitHub

Website

Picks

Tobias

American Gods book and TV series

Mark

Roadside Picnic

In Death (VR game) –On Steam

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

CPython

PyPy

PEP 380 yield from

Language Summit

RustPython

Jython

C++

ML programming language

Java

Python Formal Semantics git repository

CPython PEG Parser Episode with Pablo Galindo and Lysandros Nikolaou

IETF RFCs

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Bringing Artificial Intelligence Projects From Idea To Production** Nov 03, 2020
Summary

Artificial intelligence applications can provide dramatic benefits to a business, but only if you can bring them from idea to production. Henrik Landgren was behind the original efforts at Spotify to leverage data for new product features, and in his current role he works on an AI system to evaluate new businesses to invest in. In this episode he shares advice on how to identify opportunities for leveraging AI to improve your business, the capabilities necessary to enable aa successful project, and some of the pitfalls to watch out for. If you are curious about how to get started with AI, or what to consider as you build a project, then this is definitely worth a listen.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.

Equalum’s end to end data ingestion platform is relied upon by enterprises across industries to seamlessly stream data to operational, real-time analytics and machine learning environments. Equalum combines streaming Change Data Capture, replication, complex transformations, batch processing and full data management using a no-code UI. Equalum also leverages open source data frameworks by orchestrating Apache Spark, Kafka and others under the hood. Tool consolidation and linear scalability without the legacy platform price tag. Go to pythonpodcast.com/equalum today to start a free 2 week test run of their platform, and don’t forget to tell them that we sent you.

Your host as usual is Tobias Macey and today I’m interviewing Henrik Landgren about his experiences building AI platforms to transform business capabilities.

Interview

Introductions

How did you get introduced to Python?

Can you start by sharing your thoughts on when, where, and how AI/ML are useful tools for a business?

What has been your experience in building AI platforms?

For organizations who are considering investing in AI capabilities, what are some alternative strategies that they might consider first?

What are the cases where AI is likely to be a wasted effort, or will fail to create a return on investment?

In order to be succesful in bringing AI products to production, what are the foundational capabilities that are necessary?

What have you found to be a useful composition of roles and skills for building AI products?

There are various statistics that all point to a remarkably low success rate for bringing AI into production. What are some of the pitfalls that organizations and engineers should be aware of when undertaking such a project?

What is your strategy for identifying opportunities for a successful AI product?

Once you have determined the possible utility for such a project, how do you approach the work of making it a reality?

What are the common factors in what you built at Spotify and EQT ventures?

Where do the two efforts diverge?

Your work on Motherbrain is interesting because of the fact that it is dealing in what seems to be intangible or unpredictable forces. What kinds of input are you relying on to generate useful predictions?

What are some of the most interesting, innovative, or unexpected uses of AI that you have seen?

What are some of the biggest failures of AI that you are aware of?

In your work at Spotify and EQT ventures, what are the most interesting, unexpected, or challenging lessons that you have learned?

What advice or recommendations do you have for anyone who wants to learn more about the potential for AI and the work involved in bringing it to production?

Keep In Touch

LinkedIn

@hlandgren on Twitter

Picks

Tobias

Whale

bat

Henrik

Observable

Dataform

Data Engineering Podcast Episode

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

EQT Ventures

Stockholm Sweden

Motherbrain

Accenture

Spotify

Basic

C#

ASP.NET

Javascript

Hadoop

McKinsey

Deep Learning

Data Engineer

Data Scientist

Machine Learning Engineer

Discover Weekly Spotify Playlist

GPT-3

Deep Fakes

DBT

Data Engineering Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Power Up Your Java Using Python With JPype** Oct 26, 2020
Summary

Python and Java are two of the most popular programming languages in the world, and have both been around for over 20 years. In that time there have been numerous attempts to provide interoperability between them, with varying methods and levels of success. One such project is JPype, which allows you to use Java classes in your Python code. In this episode the current lead developer, Karl Nelson, explains why he chose it as his preferred tool for combining these ecosystems, how he and his team are using it, and when and how you might want to use it for your own projects. He also discusses the work he has done to enable use of JPype on Android, and what is in store for the future of the project. If you have ever wanted to use a library or module from Java, but the rest of your project is already in Python, then this episode is definitely worth a listen.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Karl Nelson about JPype, a language bridge that lets you use Java classes in your Python programs

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what JPype is?

What was your motivation for becoming such a regular contributor to the project?

Why might someone want to be able to call into the Java ecosystem from a Python program?

There have been a number of other projects aiming to combine the capabilities of Java and Python, such as Jython and PyJNIus. What are the relative tradeoffs between the different options?

Many of those other projects have stalled or stopped altogether. What about JPype has allowed it to survive for so long?

Can you explain how JPype is implemented?

How has the design and implementation of the project evolved since it was first implemented?

How do the relative language versions influence the compatibility of programs on either side of the bridge?

What is involved in creating a project that uses JPype?

How are dependencies, packaging, distribution, etc. handled across the Java and Python portions of the code?

What are some of the ways that JPype can be used for Android applications?

What are some of the sharp edges or pitfalls that users of JPype should be aware of?

What are some of the most interesting, innovative, or unexpected ways that you have seen JPype used?

What have you found to be the most interesting or challenging aspects of building JPype?

When is JPype the wrong choice?

What is in store for the future of the project?

Keep In Touch

Thrameos on GitHub

LinkedIn

Picks

Tobias

Hiking

All Trails

The Hiking Project

Karl

Summoner’s Rift

Links

JPype

Java

Overview of Python to Java bridges

Lawrence Livermore National Lab

GTK–

Gnome

Perl

C++

Matlab

Java Native Interface (JNI)

SciPy

NumPy

Matplotlib

Jython

PyJNIus

Py4J

Jep

Ruby

Reflection

Ivy

Maven

JDBC

Kivy

Android

Python Slots

PyPy

Java ASM

Arrow Columnar Memory Format

Protocol Buffers

GraalVM

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Journey To Replace Python's Parser And What It Means For The Future** Oct 19, 2020
Summary

The release of Python 3.9 introduced a new parser that paves the way for brand new features. Every programming language has its own specific syntax for representing the logic that you are trying to express. The way that the rules of the language are defined and validated is with a grammar definition, which in turn is processed by a parser. The parser that the Python language has relied on for the past 25 years has begun to show its age through mounting technical debt and a lack of flexibility in defining new syntax. In this episode Pablo Galindo and Lysandros Nikolaou explain how, together with Python’s creator Guido van Rossum, they replaced the original parser implementation with one that is more flexible and maintainable, why now was the time to make the change, and how it will influence the future evolution of the language.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Pablo Galindo and Lysandros Nikolaou about their work on replacing the parser in CPython and what that means for the language

Interview

Introductions

How did you get introduced to Python?

Can you start by discussing the role of the parser in the lifecycle of a Python program?

What were the limitations of the previous parser, and how did that contribute to complexity and technical debt in the CPython runtime?

What are the options for styles of parsers, and what are the benefits of using a PEG style grammar?

How does the new parser impact the approachability of the CPython code for new contributors?

What was the process for reimplementing the parser and guarding against regressions in the syntax?

As developers switch to the 3.9 release, what potential edge cases/bugs might they see from introducing the new parser?

What new syntax options does this parser provide for the Python language?

Are there any specific features that are planned for implementation in the 3.10 release that are enabled by the new parser grammar?

As the language evolves due to new capabilities offered by the updated parser, how will that impact other implementations such as PyPy?

What were the most interesting, unexpected, or challenging aspects of this project?

What other aspects of the CPython code do you think should be reconsidered or reimplemented in light of the changes in computing and the usage of the language?

Keep In Touch

Pablo

pablogsal on GitHub

@pyblogsal on Twitter

LinkedIn

Lysandros

LinkedIn

lysnikolaou on GitHub

@lysnikolaou on Twitter

Picks

Tobias

Annual Python Developer Survey

Jessica Jones TV show

Pablo

Raised By Wolves TV Series

Lysandros

Afterlife TV show

Links

PEP 617 – New PEG Parser for CPython

Podcast Episode About Parsers

CPython

Bloomberg

PEG Parsers

Seafair

LL(1) Parsers

Łukasz Langa

Parser Generator

Concrete Syntax Tree

Abstract Syntax Tree

PyPy

RustPython

Podcast Episode

IronPython

Structural Pattern Matching – PEP 622

Pylint

ASTroid

Podcast Episode

Hy

Podcast Episode

Walrus Operator/Assignment Expressions

C99

Reference Counting

Cycle Hunting/Generational Garbage Collection

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Cloud Native Application Delivery Using GitOps** Oct 12, 2020
Summary

The way that applications are being built and delivered has changed dramatically in recent years with the growing trend toward cloud native software. As part of this movement toward the infrastructure and orchestration that powers your project being defined in software, a new approach to operations is gaining prominence. Commonly called GitOps, the main principle is that all of your automation code lives in version control and is executed automatically as changes are merged. In this episode Victor Farcic shares details on how that workflow brings together developers and operations engineers, the challenges that it poses, and how it influences the architecture of your software. This was an interesting look at an emerging pattern in the development and release cycle of modern applications.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Tree Schema is a data catalog that is making metadata management accessible to everyone. With Tree Schema you can create your data catalog and have it fully populated in under five minutes when using one of the many automated adapters that can connect directly to your data stores. Tree Schema includes essential cataloging features such as first class support for both tabular and unstructured data, data lineage, rich text documentation, asset tagging and more. Built from the ground up with a focus on the intersection of people and data, your entire team will find it easier to foster collaboration around your data. With the most transparent pricing in the industry – $99/mo for your entire company – and a money-back guarantee for excellent service, you’ll love Tree Schema as much as you love your data. Go to pythonpodcast.com/treeschema today to get your first month free, and mention this podcast to get %50 off your first three months after the trial.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Victor Farcic about using GitOps practices to manage your application and your infrastructure in the same workflow

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what GitOps is?

What are the architectural or design elements that developers need to incorporate to make their applications work well in a GitOps workflow?

What are some of the tools that facilitate a GitOps approach to managing applications and their target environments?

What are some useful strategies for managing local developer environments to maintain parity with how production deployments are architected?

As developers acquire more resonsibility for building the automation to provision the production environment for their applications, what are some of the operations principles that they need to understand?

What are some of the development principles that operators and systems administrators need to acquire to be effective in contributing to an environment that is managed by GitOps?

What are the areas for collaboration and dividing lines of responsibility between developers and platform engineers in a GitOps environment?

Beyond the application development and deployment, what are some of the additional concerns that need to be built into an application in order for it to be manageable and maintainable once it is in production?

What are some of the organizational principles that contribute to a successful implementation of GitOps?

What are some of the most interesting, innovative, or unexpected ways that you have seen GitOps employed?

What have you found to be the most challenging aspects of creating a scalable and maintainable GitOps practice?

When is GitOps the wrong choice, and what are the alternatives?

What resources do you recommend for anyone who wants to dig deeper into this subject?

Keep In Touch

LinkedIn

Blog

@vfarcic on Twitter

Picks

Tobias

Pulumi

Podcast Episode

Victor

Loki

Links

GitOps

CodeFresh

Kubernetes

DevOps Paradox Podcast

Perl

Cloud Native

ArgoCD

Flux

Observability

Prometheus

Helm

KNative

MiniKube

Viktor’s Udemy Books and Courses

Viktor’s YouTube channel

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Threading The Needle Of Interesting And Informative While You Learn To Code** Oct 06, 2020
Summary

Learning to code is a neverending journey, which is why it’s important to find a way to stay motivated. A common refrain is to just find a project that you’re interested in building and use that goal to keep you on track. The problem with that advice is that as a new programmer, you don’t have the knowledge required to know which projects are reasonable, which are difficult, and which are effectively impossible. Steven Lott has been sharing his programming expertise as a consultant, author, and trainer for years. In this episode he shares his insights on how to help readers, students, and colleagues interested enough to learn the fundamentals without losing sight of the long term gains. He also uses his own difficulties in learning to maintain, repair, and captain his sailboat as relatable examples of the learning process and how the lessons he has learned can be translated to the process of learning a new technology or skill. This was a great conversation about the various aspects of how to learn, how to stay motivated, and how to help newcomers bridge the gap between what they want to create and what is within their grasp.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Steven F. Lott about finding a project that you care about to aid in learning to program

Interview

Introductions

How did you get introduced to Python?

Can you start by outlining your experiences working with and teaching Python?

Does your day-to-day experience at work suggest ways to help newcomers learn about Python?

How have your experiences as an author influenced your perspective on how to help newcomers become motivated to learn programming?

One of the common pieces of advice that I and others have given to people learning Python or other languages is to find a project that they want to build, but that’s not necessarily a practical approach. What are some of the difficulties that might come of that approach?

What are some strategies that you have tried for helping learners identify what kinds of project are possible and practical?

Beyond the difficulty of understanding what is possible and what is going to require a dedicated team of engineers to even attempt, there is the question of remaining motivated for long enough to follow through on a project in the face of syntax errors and design challenges. What can language developers and ecosystems do to improve the newcomer experience in exploring possibilities?

How can we make syntax errors educational and recoverable, rather than needing accrued knowledge, or hours of web searches?

As an author, there are complementary goals that may lead to conflict in the form of wanting to provide structured guidance and progression while allowing for creativity and experimentation. How have you approached those objectives in your books?

What are some of the projects that have motivated you to learn new skills?

What advice do you have for anyone who is working on or considering writing a book to teach a technical skill?

What advice do you have for anyone who is trying to learn programming or acquire a skill in a new language, platform, or framework?

Why are both of you movie picks black and white? Are you a film noir fan?

Keep In Touch

Website

Blog

LinkedIn

slott56 on GitHub

@s_lott on Twitter

Picks

Tobias

The Hobbit Trilogy: Extended Edition (affiliate link)

The Lord Of The Rings Trilogy: Extended Edition (affiliate link)

Steven

Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb

The General

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Modern Python Cookbook

Packt Publishing

Eiffel

Modula 3

COBOL

Stack Overflow

Capital One

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Solving Python Package Creation For End User Applications With PyOxidizer** Sep 29, 2020
Summary

Python is a powerful and expressive programming language with a vast ecosystem of incredible applications. Unfortunately, it has always been challenging to share those applications with non-technical end users. Gregory Szorc set out to solve the problem of how to put your code on someone else’s computer and have it run without having to rely on extra systems such as virtualenvs or Docker. In this episode he shares his work on PyOxidizer and how it allows you to build a self-contained Python runtime along with statically linked dependencies and the software that you want to run. He also digs into some of the edge cases in the Python language and its ecosystem that make this a challenging problem to solve, and some of the lessons that he has learned in the process. PyOxidizer is an exciting step forward in the evolution of packaging and distribution for the Python language and community.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Gregory Szorc about his work on PyOxidizer, a revolutionary new approach to building and distributing self-contained Python applications

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview on the shortcomings of the current state of the art for distributing Python projects, both for deployment and end-user consumption?

What is PyOxidizer and what motivated you to create it?

How does PyOxidizer differ from projects such as CxFreeze, Py2Exe, or Shiv?

What are the characteristics of CPython and the packaging ecosystem that make it so challenging to easily distribute self-contained applications?

For someone using PyOxidizer, what is their workflow for building an executable that they can share with end users?

What are some of the edge cases or special considerations that they need to be aware of?

How is PyOxidizer implemented?

How has the design or direction evolved since you first began working on it?

From your experience in working on PyOxidizer, what changes would you like to see in the Python language or the CPython reference implementation?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on PyOxidizer?

What do you have planned for the future of PyOxidizer?

What are the ways that listeners can contribute to PyOxidizer?

Keep In Touch

Website

indygreg on GitHub

Picks

Tobias

Carlos Santana

Gregory

Home Air Quality Monitor

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

PyOxidizer

Mercurial

Podcast Episode

Mozilla

Virtualenv

Pip

Docker

Py2Exe

CXFreeze

Beeware

Shiv

FPM

Python Build Standalone

Importlib

Rust

Russell Keith-Magee Black Swans Keynote

Followup Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Flexible Network Security Detection And Response With Grapl** Sep 22, 2020
Summary

Servers and services that have any exposure to the public internet are under a constant barrage of attacks. Network security engineers are tasked with discovering and addressing any potential breaches to their systems, which is a never-ending task as attackers continually evolve their tactics. In order to gain better visibility into complex exploits Colin O’Brien built the Grapl platform, using graph database technology to more easily discover relationships between activities within and across servers. In this episode he shares his motivations for creating a new system to discover potential security breaches, how its design simplifies the work of identifying complex attacks without relying on brittle rules, and how you can start using it to monitor your own systems today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Colin O’Brien about Grapl, an open source platform for detection and response of system security incidents

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Grapl is and the problem that you are trying to solve with it?

What was your original motivation to create it?

What were the existing options for security detection and response, and how is Grapl differentiated from them?

Who is the target audience for the Grapl project?

How is the Grapl system architected?

How has the design of the system evolved since you first began working on it?

How much effort would it be to separate the Grapl architecture from AWS to migrate it to other environments?

What have you found to be the benefits of splitting the implementation of the system between Rust for the system and Python for the exploration?

What challenges have you faced as a result of working across those languages?

What data sources does Grapl use to build its graph of events within a system?

Can you talk through the overall workflow for someone using Grapl?

What are some examples of the types of exploits that you can identify with Grapl?

What are some of the most interesting, unexpected, or innovative ways that you have seen Grapl used?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while building it?

When is Grapl the wrong choice?

What do you have planned for the future of Grapl?

Keep In Touch

insanitybit on GitHub

LinkedIn

@InsanityBit on Twitter

Picks

Tobias

Artemis Fowl book series by Eoin Colfer

Artemis Fowl Movie

Colin

PyO3

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Grapl

Grapl Security

SIEM == Security Information and Event Management

Rapid7

Metasploit

Insight IDR

Erlang

DGraph

Splunk

Elasticsearch

AWS Lambda

Sysdig

Sysmon

AWS CloudTrail

Guard Duty

OpenFaaS

AWS SQS

DynamoDB

PyO3

Dropper Malware

SSH Session Hijacking

Vagrant

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Simplified Data Extraction And Analysis For Current Events With Newspaper** Sep 15, 2020
Summary

News media is an important source of information for understanding the context of the world. To make it easier to access and process the contents of news sites Lucas Ou-Yang built the Newspaper library that aids in automatic retrieval of articles and prepare it for analysis. In this episode he shares how the project got started, how it is implemented, and how you can get started with it today. He also discusses how recent improvements in the utility and ease of use of deep learning libraries open new possibilities for future iterations of the project.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Lucas Ou-Yang about Newspaper, a framework for easily extracting and processing online articles.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what the Newspaper project is and your motivations for creating it?

What are the main use cases that Newspaper is built for?

What are some libraries or tools that Newspaper might replace?

What are the common structures in news sites that allow you to abstract across them for content extraction?

What are some ways of determining whether a site will be a good candidate for using with Newspaper?

Can you talk through the developer workflow of someone using Newspaper?

What are some of the other libraries or tools that are commonly used alongside Newspaper?

How is Newspaper implemented?

How has the design of he project evolved since you first began working on it?

What are some of the most complex or challenging aspects of building an automated article extraction tool?

What are some of the most interesting, unexpected, or innovative projects that you have seen built with Newspaper?

What keeps you interested in the ongoing support and maintenance of the project?

What do you have planned for the future of Newspaper?

Keep In Touch

LinkedIn

@LucasOuYang on Twitter

Website

codelucas on GitHub

Picks

Tobias

Million Bazillion Podcast

Lucas

Hackers and Painters: Big Ideas from the Computer Age by Paul Graham

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Newspaper

Los Angeles

Reddit

Django

NLP == Natural Language Processing

Web Scraping

Podcast Episode

Requests

Wintria

Python Goose

Diffbot

Heuristics

Stop Words

RSS

SpaCy

Podcast Episode

Gensim

Podcast Episode

PyTorch

Podcast Episode

NLTK

LXML

Beautiful Soup

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Digging Into Dagster: An Opinionated Open Source Framework For Data Orchestration** Sep 07, 2020
Summary

Data applications are complex and continually evolving, often requiring collaboration across multiple teams. In order to keep everyone on the same page a high level abstraction is needed to facilitate a cross-cutting view of the data orchestration across integration, transformation, analytics, and machine learning. Dagster is an innovative new framework that leans on the power and flexibility of Python to provide an extensible interface to the complete lifecycle of data projects. In this episode Nick Schrock explains how he designed the Dagster project to allow for integration with the entire data ecosystem while providing an opinionated structure for connecting the different stages of computation. He also discusses how he is working to grow an open ecosystem around the Dagster project, and his thoughts on building a sustainable business on top of it without compromising the integrity of the community. This was a great conversation about playing the long game when building a business while providing a valuable utility to a complex problem domain.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Nick Schrock about Dagster, an open source data orchestrator for powering data engineering, analytics, and machine learning

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Dagster is and how it got started?

What are the most common difficulties that organizations face when working with data projects?

How does Dagster help in addressing those challenges?

There are a number of workflow orchestration platforms, spanning a few generations of tooling. What do you see as the defining characteristics of the various options, and how does Dagster fit in that ecosystem?

What are the assumptions that you made at the start of building Dagster and how have they been challenged, updated, or invalidated over the past year of working with end users?

How are the internals of Dagster implemented?

How has the design changed or evolved since you first began working on it?

For someone who is building on top of Dagster, what is their workflow from first steps through to production?

What are your guiding principles for desigining the user facing API?

What are the available extension points for Dagster?

What was your reason for implementing Dagster as a Python framework?

With the benefit of hindsight, would you make the same decision today?

What are some of the most interesting, innovative, or unexpected ways that you have seen Dagster used?

What are the most interesting, unexpected, or challenging lessons that you have learned while building Dagster and working to grow its ecosystem?

When is Dagster the wrong choice?

As you continue to build Dagster, what is your vision for it and its ecosystem?

What are the next steps that you are taking to achieve that vision?

Keep In Touch

@schrockn on Twitter

schrockn on GitHub

LinkedIn

Picks

Tobias

Caddy web server

Nick

Black code formatter

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Dagster

Elementl

IronPython

Fluent Python

GraphQL

Maslow’s Hierarchy of Needs

Hierarchy of Data Needs

DAG == Directed Acyclic Graph

Informatica

Airflow

Luigi

Dagster Config Schema

Dask

Data Engineering Podcast Episode

Coiled Episode

gRPC

MyPy

Podcast Episode

Data Lineage

Pandas

Podcast Episode

Amundsen

Podcast Episode

DataHub

Podcast Episode

Gatsby.js

Panama Papers

Mode Analytics

Podcast Episode

Papermill

Podcast Episode

DBT

Podcast Episode

Databricks

Tobias’ Dagster Repository

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**When, Why, and How To Use Web Scraping In A Nutshell** Sep 01, 2020
Summary

The internet is a rich source of information, but a majority of it isn’t accessible programmatically through APIs or databases. To address that shortcoming there are a variety of web scraping frameworks that aid in extracting structured data from web pages. In this episode Attila Tóth shares the challenges of web data extraction, the ways that you can use it, and how Scrapy and ScrapingHub can help you with your projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Attila Tóth about doing data extraction with web scraping.

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what web scraping is and when you might want to use it?

How did you first get started with web scraping?

There are a number of options for web scraping tools in Python, as well as other languages. What are the characteristics of the Scrapy project and community that have made it stand out and retain such widespread popularity?

One of the perpetual questions with web scraping is that of copyright and content ownership. What should we all be aware of when scraping a given website?

What are some of the most challenging aspects of crawling and scraping the web?

What are some of the features of Scrapy that aid in those challenges?

Once you have retrieved the content from a site, what are some of the considerations for storing and processing the data that we should be thinking about?

How can we guard against a scraper breaking due to changes in the layout of a site, or simple updates that weren’t accounted for in the initial implementation?

What are some of the most complicated aspects of scaling web scrapers?

For someone who is interested in using Scrapy, what are some of the common pitfalls that they should be aware of?

What are some of the most interesting, innovative, or unexpected projects that are built with Scrapy and ScrapingHub?

What are the most interesting, unexpected, or challenging lessons that you have learned while working with web scrapers and ScrapingHub?

What resources would you recommend to anyone who is looking to learn more about web scraping?

Keep In Touch

LinkedIn

Picks

Tobias

Gov’t Mule

Attila

Awesome Web Scraping

Awesome Scrapy

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Web Scraping

ScrapingHub

Java

Android

Scrapy

JSoup

HTMLUnit

Selenium

Pandas

robots.txt

Puppeteer

Splash

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Working In The Code Mines: Mining Software Repositories With PyDriller** Aug 25, 2020
Summary

A large portion of the software industry has standardized on Git as the version control sytem of choice. But have you thought about all of the information that you are generating with your branches, commits, and code changes? Davide Spadini created the PyDriller framework to simplify the work of mining software repositories to perform research on the technical and social aspects of software engineering. In this episode he shares some of the insights that you can gain by exploring the history of your code, the complexities of building a framework to interact with Git, and some of the interesting ways that PyDriller can be used to inform your own development practices.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Davide Spadini about PyDriller, a framework for mining software repositories

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what PyDriller is and how the project got started?

How is Pydriller different from other Git frameworks?

What kinds of information can you discover by mining a software repository?

Where and how might the collected information be used?

What are the limitations of the capabilities offered by Git for investigating the repository?

What are the additional metrics that you are able to extract using PyDriller?

Can you describe how PyDriller itself is implemented?

How has the project evolved since you first began working on it?

I noticed that for testing PyDriller you crafted a set of repositories to serve as test cases. What has been the most complex or challenging aspect of writing meaningful tests to ensure a reasonable coverage of this problem domain?

What would be required to add support for other version control systems?

How have you used PyDriller in your own research?

What are some of the most interesting, unexpected, or innovative ways that you have seen PyDriller used?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with PyDriller?

What do you have planned for the future of PyDriller?

Keep In Touch

Website

ishepard on GitHub

@DavideSpadini on Twitter

Picks

Tobias

pre-commit

Davide

Fall guys

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

PyDriller

Delft

Git

GitPython

PyGit2

RepoDriller

Mining Software Repositories Conference

Lizard

Hadoop

Mercurial

Podcast Episode

Subversion

CVS

Neo4J

GraphRepo

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building The Open Data Ecosystem For Music And More At Metabrainz** Aug 17, 2020
Summary

The Musicbrainz project was an early entry in the movement to build an open data ecosystem. In recent years, the Metabrainz Foundation has fostered a growing ecosystem of projects to support the contribution of, and access to, metadata, listening habits, and review of music. The majority of those projects are written in Python, and in this episode Param Singh explains how they are built, how they fit together, and how they support the goals of the Metabrains Foundation. This was an interesting exporation of the work involved in building an ecosystem of open data, the challenges of making it sustainable, and the benefits of building for the long term rather than trying to achieve a quick win.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Before you put your code into production you need to make sure that it passes all of the tests, that it has been packaged with all of the dependencies, and that you haven’t introduced any security issues. Instead of running all of that on your laptop, let Codefresh handle it automatically with their continuous integration and continuous delivery platform. Built for the modern era of cloud-native computing, they make publishing to Kubernetes, serverless platforms, and virtual machines fast and seamless. With a growing library of pre-made steps, a flexible pipeline definition, and unlimited scale Codefresh lets you ship faster and safer than ever. Go to pythonpodcast.com/codefresh today to get unlimited builds on your free account.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Param Singh about the ways that Python is being used across the various Metabrainz projects

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of what the Metabrainz organization is and the various projects that it encompasses?

What are the motivations for creating those projects and some of the origin story for Metabrainz?

The Musicbrainz server is the longest running project and is written in Perl. What was the reason for switching to Python for all of the other *brainz projects?

How does the MetaBrainz Foundation sustain itself? Where do the funds come from?

How do you determine where and how to allocate the funding that you receive?

Which of the *brainz projects is the most complex or challenging to build, whether due to technical or sociological reasons?

How do you source and manage the information that powers all of the Metabrainz projects?

How is development of the various projects organized?

How does that influence the amount of code sharing that is possible between them?

Of the projects that you have been involved in, how are they architected?

What are the main ways that the projects differ in how they are implemented?

What are some of the ways that you are using Python in support of the various projects that you work on?

What are some of the most interesting, innovative, or unexpected ways that you have seen the projects or data built by Metabrainz being used?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working as a contributor and maintainer of the Metabrainz projects?

What is in store for the future of the existing Metabrainz projects?

What are the next domains that are being considered for building a Metabrainz platform for?

Keep In Touch

LinkedIn

paramsingh on GitHub

Website

Picks

Tobias

Beets music library organizer

Podcast Episode

Param

Prateek Kuhad

Links

Metabrainz

Musicbrainz

Listenbrainz

Acousticbrainz

Bookbrainz

Critiquebrainz

Picard

Stripe

The Himalayas

Dublin Ireland

XKCD Import Antigravity

Antigravity Python Module

Last.fm

Google Summer of Code

CDDB

Perl

Flask

SQLAlchemy

3rd anniversary cake

Redis

PostgreSQL

RabbitMQ

Spark

Music Technology Group

Splunk

Artist Origins Map on ListenBrainz

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Growing Dask To Make Scaling Python Data Science Easier At Coiled** Aug 10, 2020
Summary

Python is a leading choice for data science due to the immense number of libraries and frameworks readily available to support it, but it is still difficult to scale. Dask is a framework designed to transparently run your data analysis across multiple CPU cores and multiple servers. Using Dask lifts a limitation for scaling your analytical workloads, but brings with it the complexity of server administration, deployment, and security. In this episode Matthew Rocklin and Hugo Bowne-Anderson discuss their recently formed company Coiled and how they are working to make use and maintenance of Dask in production. The share the goals for the business, their approach to building a profitable company based on open source, and the difficulties they face while growing a new team during a global pandemic.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Matthew Rocklin and Hugo Bowne-Anderson about their work building a business around the Dask ecosystem at Coiled

Interview

Introductions

How did you get introduced to Python?

Can you give a quick overview of what Dask is and your motivations for creating it?

How has Dask changed or evolved in the past 3 1/2 years since we last talked about it?

How has the rest of the ecosystem changed in that time?

After working on Dask for the past few years, what led you to the decision to build a business around it?

What are the sharp edges of programming for Dask that users are looking for help on solving?

What are the difficulties that users face in deploying and maintaining a production installation of Dask?

What are the limitations of Dask when scaling both up and down?

What are you building at Coiled to improve the user experience for users of Python and Dask?

What are your thoughts on the pros and cons of orienting your messaging around the scalability of Python, as opposed to focusing on a specific industry or problem domain?

What are the challenges that you are facing in managing the tensions between the open source and proprietary work that you are doing?

How are you handling the ongoing governance of the Dask project?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while building and launching a company based on an open source project?

What do you have planned for the future of both Coiled and Dask?

Keep In Touch

Matt

Website

@mrocklin on Twitter

mrocklin on GitHub

Hugo

LinkedIn

@hugobowne on Twitter

Website

Picks

Tobias

The Hobbit

Audiobook

Audible Free Trial (affiliate link)

Matt

Prefect

Hugo

Race After Technology by Ruha Benjamin

Ruha Benjamin on deep learning: Computational depth without sociological depth is ‘superficial learning’

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Sign up for the Coiled Beta!

Coiled

Dask

Data Engineering Podcast Interview About Dask

PyData

NumPy

SciPy

Cell Biology

Datacamp

Dataframed

Matthew Rocklin on Podcast.__init__ about functional programming with Toolz

IPython Notebook

PyTorch

Podcast Episode

Airflow

Prefect

XGBoost

Tornado

Coiled Blog Post About The Goals of Dask

Spark

AsyncIO

Concurrent.futures

Pangeo

Xarray

RAPIDS

Nvidia

Cuda

Prefect

Data Engineering Podcast Episode

Celery

Life Sciences

Tensorflow

Snorkel

Data Engineering Podcast Episode

Dagster

Data Engineering Podcast Episode

DevOps

Docker

Kubernetes

Metaflow

Podcast Episode

Ray

Podcast Episode

Anyscale

Yarn

Gartner Hype Cycle

Travis Oliphant

Postgres

Amazon ECS

Django

Django Allauth

Quansight

Wes McKinney

Podcast Interview

Ursa Labs

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Supporting The Full Lifecycle Of Machine Learning Projects With Metaflow** Aug 04, 2020
Summary

Netflix uses machine learning to power every aspect of their business. To do this effectively they have had to build extensive expertise and tooling to support their engineers. In this episode Savin Goyal discusses the work that he and his team are doing on the open source machine learning operations platform Metaflow. He shares the inspiration for building an opinionated framework for the full lifecycle of machine learning projects, how it is implemented, and how they have designed it to be extensible to allow for easy adoption by users inside and outside of Netflix. This was a great conversation about the challenges of building machine learning projects and the work being done to make it more achievable.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Savin Goyal about Netflix’s infrastructure for machine learning

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the work you are doing at Netflix to support their machine learning workloads?

How are you addressing the impedance mismatch of machine learning/data science work between local experimentation and production deployment?

What was the motivation for building Metaflow?

How does Metaflow compare to other tools in the ecosystem such as MLFlow?

What was missing in the other available tools that made Metaflow necessary?

workflow for someone using Metaflow

How do you approach the design of the developer interface to make it approachable to machine learning engineers?

level of coupling with overall Netflix data stack

How is Metaflow implemented?

How has the architecture and design of the system evolved since you first began working on it?

supporting infrastructure/integration points

motivation/benefits of releasing it as open source

What are some of the most interesting, unexpected, or challenging lessons that you have learned while building infrastructure and tooling for machine learning?

When is Metaflow the wrong choice?

What do you have planned for the future of Metaflow and

Keep In Touch

LinkedIn

@savingoyal on Twitter

savingoyal on GitHub

Picks

Tobias

vdist

Savin

Reparing Vintage Watches

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Metaflow

OCaml

EC2

S3

Data Lake

PyTorch

Tensorflow

Netflix Data Stack

Spinnaker

Chaos Engineering

Chaos Toolkit Podcast Episode

Chaos Monkey

Netflix Simian Army

Netflix Titus

AWS Batch

Netflix Meson

Dataflow Programming

DAG == Directed Acyclic Graph

MLFlow

DVC (Data Version Control)

Podcast Episode

CML (Continuous Machine Learning)

AWS Step Functions

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Learning To Program By Building Tiny Python Projects** Jul 28, 2020
Summary

One of the best methods for learning programming is to just build a project and see how things work first-hand. With that in mind, Ken Youens-Clark wrote a whole book of Tiny Python Projects that you can use to get started on your journey. In this episode he shares his inspiration for the book, his thoughts on the benefits of teaching testing principles and the use of linting and formatting tools, as well as the benefits of trying variations on a working program to see how it behaves. This was a great conversation about useful strategies for supporting new programmers in their efforts to learn a valuable skill.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Ken Youens-Clark about his book Tiny Python Projects

Interview

Introductions

How did you get introduced to Python?

What is your goal with your book of Tiny Python Projects?

What motivated you to start writing it?

Who is the target audience that you wrote the book for?

One of the notable aspects of the book is the fact that you introduce linting and testing in the first chapter. Why is that a useful subject for the first steps of someone getting started in Python?

What are some of the problems that users experience if they are introduced to these tools after they have already established a set of habits?

How did you approach the structure of the book to be approachable by newcomers to Python?

What was your process for deciding on the scope of the information to include in the book?

What are some of the challenges that you faced in identifying self-contained projects that could fit into a single chapter?

As a book that is intended to serve as a learning resource, what was your process for soliciting feedback to determine if your tone and structure is effective in teaching the reader?

What elements of the Python language and ecosystem did you consciously leave out to avoid overwhelming the readers?

What are some of the most interesting, unexpected, or challenging lessons that you learned while working on the book?

What are your thoughts on useful resources and next steps for readers who are interested in progressing in their use of Python?

Keep In Touch

kyclark on GitHub

Website

@kycl4rk on Twitter

Picks

Tobias

Marvel Cinematic Universe

Ken

Parks & Recreation TV Show

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Tiny Python Projects

University of Arizona

BioInformatics

Perl

BioPython

Podcast Episode

Seq

Podcast Episode

Pytest

Podcast Episode

Windows Subsystem for Linux

Pylint

Podcast Episode

YAPF

Black Python Formatter

Mad Libs

Boolean Algebra

Object Oriented Programming

Delphi

OmniGraffle

Kent Beck

Test Driven Development

Clojure

Regular Expression

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Idiomatic Functional Programming With DRY Python** Jul 21, 2020
Summary

Python is an intuitive and flexible language, but that versatility can also lead to problematic designs if you’re not careful. Nikita Sobolev is the CTO of Wemake Services where he works on open source projects that encourage clean coding practices and maintainable architectures. In this episode he discusses his work on the DRY Python set of libraries and how they provide an accessible interface to functional programming patterns while maintaining an idiomatic Python interface. He also shares the story behind the wemake Python styleguide plugin for Flake8 and the benefits of strict linting rules to engender good development habits. This was a great conversation about useful practices to build software that will be easy and fun to work on.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about his work with DRY Python and Wemake Services

Interview

Introductions

How did you get introduced to Python?

Can you start by sharing your overarching philosophies or design aesthetics for writing maintainable software?

What is your process for starting a new project, beginning at the design phase?

What are some of the challenges or shortcomings that you see in the "default" way that most developers write Python?

What is DRY Python is and how does it help in addressing those concerns?

What was your motivation for creating these projects?

There are a number of different projects that are being built under the DRY Python umbrella. Can you list the ones that are currently active and outline how they fit together?

What are some of the initial challenges that newcomers to the DRY Python libraries encounter?

How do you approach the design of the API and developer experience to make these development approaches more accessible?

What have you seen in terms of real world impact on the maintainability and extensibility of projects that you have built on top of the DRY Python components?

In addition to DRY Python you are also involved with development of the wemake-python-styleguide. Can you describe that projects goal and how it got started?

If you make the linting too restrictive then developers are likely to just ignore or disable it. What have you found to be the right balance to which rules will fail a build and which are just informational?

Why do you push the responsibility for things like formatting onto the developer, rather than an autoformatter such as YAPF or Black?

What are some of the other supporting technologies that you rely on during your development workflow?

What are some of the elements that you think are missing in the common toolbox for Python developers?

What tools are we lacking entirely?

What are the cases where DRY Python is the wrong choice?

What are your goals and plans for the future of DRY Python and the various Wemake libraries?

Keep In Touch

Blog

sobolevn on GitHub

Picks

Tobias

The Map To Everywhere

Nikita

Russian Python Week

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

DRY Python

Wemake Services

wemake-python-styleguide

Turbogears 2

Dotenv Linter

Returns

Wemake Python Package Cookiecutter Template

Test Driven Development

Requirements Analysis

RESTs

Django Rest Framework

Classes

Monads

Functors

Scala

Kotlin

Haskell

Punq dependency injection library

Flake8

Wemake Django Template

Flake8 Baseline

isort

Nitpick

Mypy

Darglint

Poetry

Pip Dependency Resolver

Podcast Episode

Hypothesis

Podcast Episode

Schemathesis

Pytest Auto Hypothesis

Typescript

Rust

Elixir

Zio Scala

GitHub Sponsors

Do Not Log blog post

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Past, Present, And Future Of The FLUFL: Barry Warsaw Shares His History With Python** Jul 13, 2020
Summary

Barry Warsaw has been a member of the Python community since the very beginning. His contributions to the growth of the language and its ecosystem are innumerable and diverse, earning him the title of Friendly Language Uncle For Life. In this episode he reminisces on his experiences as a core developer, a member of the Python Steering Committee, and his roles at Canonical and LinkedIn supporting the use of Python at those companies. In order to know where you are going it is always important to understand where you have been and this was a great conversation to get a sense of the history of how Python has gotten to where it is today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This episode of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Barry Warsaw about his role in the Python community, past, present, and future.

Interview

Introductions

How did you get introduced to Python?

For anyone who isn’t familiar with you, how would you characterize your role in the Python language and community?

What have been your main areas of focus in your role as a core developer?

What are some of the other forms that your contributions to the language and community have taken?

What are the contributions to Python that you are most proud of?

Looking back at the past 25 years of Python, what do you find most interesting/surprising/exciting?

How has the focus of the community changed or evolved since you first began using it?

What are you currently focused on in your role in the steering council?

What are the aspects of the language and community that you think need greater attention?

What are the core strengths of the language and community that you believe will carry it through the next 25 years?

In your current and previous roles you acted as a guiding force for Python. What are the main use cases for Python at LinkedIn?

What kinds of projects are you involved with to support the other engineers in their use of Python?

How much of an impact has the invisible hand of the PSU had on the overall trajectory of Python?

Outside of Python, what are the programming languages or communities that you look to for inspiration?

What are your personal goals for the future of Python?

Keep In Touch

Website

warsaw on GitHub

warsaw on GitLab

Blog

@pumpichank on Twitter

Picks

Tobias

Hanna TV Series

Barry

Midnight Gospel

The Expanse

TV Series

Audio Books

Free 30 Day Audible Trial (Affiliate Link)

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

FLUFL PEP 401

Python Steering Council

The PEP Talk episode

Usenet

BBS == Bulletin Board System

comp.lang.python

NIST == National Institute of Standards and Technology

CNRI == Corporation for National Research Initiatives

BayPIGgies

Tcl/Tk

PEP 572 := The Walrus Operator

"The Grand Renaming"

IETF == Internet Engineering Task Force

RFC

WebAssembly

Python Software Foundation

Podcast Episode

Python Black Swans keynote by Russell Keith-Magee

Followup Podcast Episode

Ewa Jodlowska

Canonical Launchpad

Mypy

Podcast Episode

Python Type Annotations

Iris Event Paging System

OnCall Pager Rotation System

Shiv

PyOxidizer

Rust

Flake8

isort

Black

Sphinx

Read The Docs

Podcast Episode

Sybil

Manuel

Doctest

Pytest

Coverage.py

Cargo package system

Tai Chi

Python Core Mentorship

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Pure Python Configuration Management With PyInfra** Jul 06, 2020
Summary

Building and managing servers is a challenging task. Configuration management tools provide a framework for handling the various tasks involved, but many of them require learning a specific syntax and toolchain. PyInfra is a configuration management framework that embraces the familiarity of Pure Python, allowing you to build your own integrations easily and package it all up using the same tools that you rely on for your applications. In this episode Nick Barrett explains why he built it, how it is implemented, and the ways that you can start using it today. He also shares his vision for the future of the project and you can get involved. If you are tired of writing mountains of YAML to set up your servers then give PyInfra a try today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

This portion of Podcast.__init__ is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Nick Barrett about PyInfra, a pure Python framework for agentless configuration management

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what PyInfra is and its origin story?

There are a number of options for configuration management of various levels of complexity and language options. What are the features of PyInfra that might lead someone to choose it over other systems?

What do you see as the major pain points in dealing with infrastructure today?

For someone who is using PyInfra to manage their servers, what is the workflow for building and testing deployments?

How do you handle enforcement of idempotency in the operations being performed?

Can you describe how PyInfra is implemented?

How has its design or focus evolved since you first began working on it?

What are some of the initial assumptions that you had at the outset which have been challenged or updated as it has grown?

The library of available operations seems to have a good baseline for deploying and managing services. What is involved in extending or adding operations to PyInfra?

With the focus of the project being on its use of pure Python and the easy integration of external libraries, how do you handle execution of python functions on remote hosts that requires external dependencies?

What are some of the other options for interfacing with or extending PyInfra?

What are some of the edge cases or points of confusion that users of PyInfra should be aware of?

What has been the community response from developers who first encounter and trial PyInfra?

What have you found to be the most interesting, unexpected, or challenging aspects of building and maintaining PyInfra?

When is PyInfra the wrong choice for managing infrastructure?

What do you have planned for the future of the project?

Keep In Touch

Fizzadar on GitHub

Website

@Fizzadar on Twitter

LinkedIn

Picks

Tobias

My Spy

Nick

Das Keyboard Ultimate

Korean Short Ribs

Kimchi Fried Rice

Links

PyInfra

Oxygem

WordPress

Lua

Gary’s Mod

Java

Ansible

SaltStack

Chef

Puppet

EC2

Boto 3

Hashicorp Vault

Vagrant

Docker

Testinfra

SaltStack Testinfra Plugin

Dockerfile

Idempotence

Nginx

POSIX

gevent

Jinja2

Click

Zero Tier

BSD

AST Module

RedBaron

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build Your Own Domain Specific Language in Python With textX** Jun 30, 2020
Summary

Programming languages are a powerful tool and can be used to create all manner of applications, however sometimes their syntax is more cumbersome than necessary. For some industries or subject areas there is already an agreed upon set of concepts that can be used to express your logic. For those cases you can create a Domain Specific Language, or DSL to make it easier to write programs that can express the necessary logic with a custom syntax. In this episode Igor Dejanović shares his work on textX and how you can use it to build your own DSLs with Python. He explains his motivations for creating it, how it compares to other tools in the Python ecosystem for building parsers, and how you can use it to build your own custom languages.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Your host as usual is Tobias Macey and today I’m interviewing Igor Dejanović about textX, a meta-language for building domain specific languges in Python

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what a domain specific language is and some examples of when you might need one?

What is textX and what was your motivation for creating it?

There are a number of other libraries in the Python ecosystem for building parsers, and for creating DSLs. What are the features of textX that might lead someone to choose it over the other options?

What are some of the challenges that face language designers when constructing the syntax of their DSL?

Beyond being able to parse and process an arbitrary syntax, there are other concerns for consumers of the definition in terms of tooling. How does textX provide support to those end users?

How is textX implemented?

How has the design or goals of textX changed since you first began working on it?

What is the workflow for someone using textX to build their own DSL?

Once they have defined the grammar, how do they distribute the generated interpreter for others to use?

What are some of the common challenges that users of textX face when trying to define their DSL?

What are some of the cases where a PEG parser is unable to unambiguously process a defined grammar?

What are some of the most interesting/innovative/unexpected ways that you have seen textX used?

What have you found to be the most interesting, unexpected, or challenging lessons that you have learned while building and maintaining textX and its associated projects?

While preparing for this interview I noticed that you have another parser library in the form of Parglare. How has your experience working with textX informed your designs of that project?

What lessons have you taken back from Parglare into textX?

When is textX the wrong choice, and someone might be better served by another DSL library, different style of parser, or just hand-crafting a simple parser with a regex?

What do you have planned for the future of textX?

Keep In Touch

Website

igordejanovic on GitHub

@dejanovicigor on Twitter

Picks

Tobias

wemake-python-styleguide

Igor

Interactive Fiction genre

Awesome Interactive Fiction

The Interactive Fiction Database

TADS

Inform 7

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

textX

U of Novi Sad

Serbia

DSL course

Secondary Notation

Django

Xtext

Eclipse

PLY

SLY

PyParsing

Lark

PEG Grammar

Language Workbench

Language Server Protocol

Visual Studio Code

textX-LS

Arpeggio Parser

Context-Free Grammar

pyTabs

Guitar Tablatures

Parglare

GLR parsing

TEP 1

Evennia

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Adding Observability To Your Python Applications With OpenTelemetry** Jun 23, 2020
Summary

Once you release an application into production it can be difficult to understand all of the ways that it is interacting with the systems that it integrates with. The OpenTracing project and its accompanying ecosystem of technologies aims to make observability of your systems more accessible. In this episode Austin Parker and Alex Boten explain how the correlation of tracing and metrics collection improves visibility of how your software is behaving, how you can use the Python SDK to automatically instrument your applications, and their vision for the future of observability as the OpenTelemetry standard gains broader adoption.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Austin Parker and Alex Boten about the OpenTelemetry project and its efforts to standardize the collection and analysis of observability data for your applications

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what OpenTelemetry is and some of the story behind it?

How do you define observability and in what ways is it separate from the "traditional" approach to monitoring?

What are the goals of the OpenTelemetry project?

For someone who wants to begin using OpenTelemetry clients in their Python application, what is the process of integrating it into their application?

How does the definition and adoption of a cross-language standard for telemetry data benefit the broader software community?

How do you avoid the trap of limiting the whole ecosystem to the lowest common denominator?

What types of information are you focused on collecting and analyzing to gain insights into the behavior of applications and systems?

What are some of the challenges that are commonly faced in interpreting the collected data?

With so many implementations of the specification, how are you addressing issues of feature parity?

For the Python SDK, how is it implemented?

What are some of the initial designs or assumptions that have had to be revised or reconsidered as it gains adoption?

What is your approach to integration with the broader ecosystem of tools and frameworks in the Python community?

What are some of the interesting or unexpected challenges that you have faced or lessons that you have learned while working on instrumentation of Python projects?

Once an application is instrumented, what are the options for delivering and storing the collected data?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with the OpenTelemetry ecosystem?

What are some of the most interesting, innovative, or unexpected ways that you have seen components in the OpenTelemetry ecosystem used?

When is OpenTelemetry the wrong choice?

What is in store for the future of the OpenTelemetry project?

Keep In Touch

Austin

@austinlparker on Twitter

austinlparker on GitHub

Alex

LinkedIn

@codeboten on Twitter

codeboten on GitHub

Picks

Tobias

Pulumi

Podcast Episode

Austin

Helm 3

Alex

Algorithms To Live By: The Computer Science Of Everyday Decisions by Brian Christian and Tom Griffiths

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

OpenTelemetry

Lightstep

OpenTracing

OpenCensus

Distributed Tracing

Jaeger

Zipkin

Observability

Kubernetes

Spring

Flask

gRPC

Structlog

Filebeat

W3C Trace Context

OpenTelemetry Python SDK

OpenTelemetry Django

OpenTelemetry Flask

OpenTelemetry Collector

OTLP == Open Telemetry Protocol

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build A Personal Knowledge Store With Topic Modeling In Contextualize** Jun 15, 2020
Summary

Our thought patterns are rarely linear or hierarchical, instead following threads of related topics in unpredictable directions. Topic modeling is an approach to knowledge management which allows for forming a graph of associations to make capturing and organizing your thoughts more natural. In this episode Brett Kromkamp shares his work on the Contextualize project and how you can use it for building your own topic models. He explains why he wrote a new topic modeling engine, how it is architected, and how it compares to other systems for organizing information. Once you are done listening you can take Contextualize for a test run for free with his hosted instance.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Brett Kromkamp about Contextualise, a topic modeling application that helps you build a mind map for information-heavy projects

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Contextualize is and some of the types of projects that it can be used for?

What was your motivation for creating it?

How do you use topic maps in your own work and creative endeavors?

The space of personal note-taking and knowledge management is vast and varied. What does Contextualize do well that you have been unable to find or implement in other tools?

For someone using Contextualize, what does that workflow look like?

How are you approaching integration with different creative contexts (e.g. text editors, graphics editors, word processing, etc.)?

Can you describe how Contextualize is implemented?

How has the design evolved since you first began working on it?

In the documentation for Contextualize it mentions that this is the latest in a string of topic mapping platforms that you have built. What are some of the lessons that you have learned from previous efforts that have influenced the design of this one?

One of the challenges with many knowledge management tools is that they are proscriptive in how to work with them. In what ways has your own preference for how to interact with information influenced the direction of Contextualize?

Being an open source application, how has its exposure to the public directed your software and user design?

How do you approach the challenge of reducing friction in adding content and relations while allowing for flexibility and context management?

What are some of the projects that you are using Contextualize for?

What are your thoughts on the utility of something like Contextualize for capturing and organizing the collective knowledge of a team of collaborators, whether in a work or casual context?

What have you found to be the most interesting, complex, or complicated aspects of building a topic mapping platform?

When is Contextualize the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

Website

@brettkromkamp on Twitter

brettkromkamp on GitHub

Picks

Tobias

Pydantic

Podcast Episode

MyPy

Podcast Episode

Brett

Black Lives Matter

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Contextualise

GitHub Repository

Norway

IBM Rexx

Java

Semantic Web

Topic Map

ISO standard for topic maps

RDF

Spain

Knowledge Management

Graph Database

Worldbuilding

Roam Research

TopicDB

Twitter Bootstrap

Hypergraph

Digital Gardening

Notion

TiddlyWiki

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Open Source Product Analytics With PostHog** Jun 08, 2020
Summary

You spend a lot of time and energy on building a great application, but do you know how it’s actually being used? Using a product analytics tool lets you gain visibility into what your users find helpful so that you can prioritize feature development and optimize customer experience. In this episode PostHog CTO Tim Glaser shares his experience building an open source product analytics platform to make it easier and more accessible to understand your product. He shares the story of how and why PostHog was created, how to incorporate it into your projects, the benefits of providing it as open source, and how it is implemented. If you are tired of fighting with your user analytics tools, or unwilling to entrust your data to a third party, then have a listen and then test out PostHog for yourself.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

You listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code *AISPRINGBOARD* when you enroll.

Your host as usual is Tobias Macey and today I’m interviewing Tim Glaser about PostHog, an open source platform for product analytics

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what PostHog is and what motivated you to build it?

What are the goals of PostHog and who are the target audience?

In the description of PostHog it mentions being a product focused analytics platform, as opposed to session based. What are the meaningful differences between the two?

Customer analytics is a rather crowded market, with a large number of both commercial and open source offerings (e.g. Google Analytics, Heap, Matomo, Snowplow, etc.). How does PostHog fit in that landscape and what are the differentiating factors that would lead someone to select it over the alternativs?

For anyone interested in using PostHog, do you offer a migration path from other platforms?

necessary features for a customer analytics tool

privacy and security issues around analytics

How is PostHog implemented and how has its design evolved since you first began building it?

reason for choosing Python

benefits of Django

thoughts on introducing Channels

option to include it as a pluggable Django app

integration points

data lake integration

challenges of providing understandable statistics and exposing options for detailed analysis

Having data about how users are interacting with your site or application is interesting, but how does it help in determining the useful actions to drive success?

business model and project governance

What are the most complex, complicated, or misunderstood aspects of building a product analytics platform?

What have you found to be the most interesting, unexpected, or challenging lessons that you have learned in the process of building PostHog?

When is PostHog the wrong choice?

What do you have planned for the future of PostHog?

Keep In Touch

timgl on GitHub

LinkedIn

@timgl on Twitter

Picks

Tobias

Hitchhiker’s Guide To The Galaxy

Tim

Triumph Of The City by Edward Glaeser

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

PostHog

MixPanel

Amplitude

Heap

Data Engineering Podcast Episode

Snowplow

Data Engineering Podcast Episode

Looker

Data Engineering Podcast Episode

SnowflakeDB

Data Engineering Podcast Episode

Tableau

DOM == Document Object Model for web pages

Django

Django Rest Framework

React.js

Kea state management for React.js

Redux

TypeScript

Django Stubs

Django Channels

Sentry

Podcast Episode

Pluggable Django App

PostgreSQL

ELT

Data Lake

Optimizely

Feature Flags

Podcast Episode

PostHog Roadmap

PostHog Employee Handbook

Matomo (formerly Piwik)

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Extending The Life Of Python 2 Projects With Tauthon** Jun 02, 2020
Summary

The divide between Python 2 and 3 lasted a long time, and in recent years all of the new features were added to version 3. To help bridge the gap and extend the viability of version 2 Naftali Harris created Tauthon, a fork of Python 2 that backports features from Python 3. In this episode he explains his motivation for creating it, the process of maintaining it and backporting features, and the ways that it is being used by developers who are unable to make the leap. This was an interesting look at how things might have been if the elusive Python 2.8 had been created as a more gentle transition.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

You listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.

Your host as usual is Tobias Macey and today I’m interviewing Naftali Harris about his work on Tauthon, a fork of Python 2 that backports features from Python 3

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Tauthon is and your motivations for creating it?

What’s the story behind the name?

What types of applications and environments are you using Tauthon in?

How much adoption of Tauthon have you seen?

What are some of the different ways that your users are employing it?

Is this the missing "2.8" release? In other words, is this intended to be a bridge for simplifying the migration of existing Python 2 code to Python 3, or as an extended support window for Python 2?

What features have you backported from Python 3?

What is your process for identifying and prioritizing features to bring into Tauthon?

What is your workflow for implementing the backported functionality in Tauthon?

What are some of the cases where you have had to compromise on the functionality or syntax of a feature that you have backported in order to fit into Python 2?

What is your governing philosophy for how to manage syntax or behavior differences between Python 2 and 3?

What have been the most challenging features to backport and maintain?

What are some of the ways that Tauthon might break existing Python 2 code?

What is the story for compatibility with libraries that are Python 3 only?

What have you seen in terms of adoption of Tauthon?

Do you have any sense of the commonalities among those users?

What are some of the ecosystem challenges that faces users of Tauthon? (e.g. Pip support, package compatibility, etc.)

What are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of creating and maintaining Tauthon?

What are your long-term plans for Tauthon, and how have they changed since you first started working on it?

Keep In Touch

Website

@naftaliharris on Twitter

naftaliharris on GitHub

Picks

Tobias

Dagster

PyCon 2020 Online

Naftali

Sentilink

Timsort

Tim Peters

Links

Tauthon

Function Annotations

Tau

Nick Coghlan

MyPy

Podcast Episode

Matrix Multiplier Operator

Python 3.9 PEG Parser

lazysorted

nonlocal keyword

Valgrind

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Dependency Management Improvements In Pip's Resolver** May 25, 2020
Summary

Dependency management in Python has taken a long and winding path, which has led to the current dominance of Pip. One of the remaining shortcomings is the lack of a robust mechanism for resolving the package and version constraints that are necessary to produce a working system. Thankfully, the Python Software Foundation has funded an effort to upgrade the dependency resolution algorithm and user experience of Pip. In this episode the engineers working on these improvements, Pradyun Gedam, Tzu-Ping Chung, and Paul Moore, discuss the history of Pip, the challenges of dependency management in Python, and the benefits that surrounding projects will gain from a more robust resolution algorithm. This is an exciting development for the Python ecosystem, so listen now and then provide feedback on how the new resolver is working for you.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.

Your host as usual is Tobias Macey and today I’m interviewing Tzu-ping Chung, Pradyun Gedam, and Paul Moore about their work to improve the dependency resolution capabilities of Pip and its user experience

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the focus of the work that you are doing?

What is the scope of the work, and what is the established criteria for when it is considered complete?

What is your history with working on the Pip source code and what interests you most about this project?

What are the main sources or manifestations of technical debt that exist in Pip as of today?

How does it currently handle dependency resolution?

What are some of the workarounds that developers have had to resort to in the absence of a robust dependency resolver in Pip?

How is the new dependency resolver implemented?

How has your initial design evolved or shifted as you have gotten further along in its implementation?

What are the pieces of information that the resolver will rely on for determining which packages and versions to install? (e.g. will it install setuptools > 45.x in a Python 2 virtualenv?)

What are the new capabilities in Pip that will be enabled by this upgrade to the dependency resolver?

What projects or features in the encompassing ecosystem will be unblocked with the introduction of this upgrade?

What are some of the changes that users will need to make to adopt the updated Pip?

How do you anticipate the changes in Pip impacting the viability or adoption of Python and its ecosystem within different communities or industries?

What are some of the additional changes or improvements that you would like to see in Pip or other core elements of the Python landscape?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on these updates to Pip?

Keep In Touch

Pradyun

Website

pradyunsg on GitHub

@pradyunsg on Twitter

Paul

pfmoore on GitHub

Tzu-Ping

uranusjr on GitHub

Website

@uranusjr on Twitter

Picks

Tzu-ping

Python Launcher

Joe Abercrombie author

The Shattered Sea Trilogy

Anime

PipX Standalone

Paul

pipx

Black

nox

tox

scoop

Neil Gaiman

Good Omens

Book

TV Series

Pradyun

because my picks can be anything — things that have kept me sane in this lockdown world

Music: Chris Daughtry

Video Game: Parkitect

Tobias

Language Server Protocol

Emacs lsp-mode

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Pip

Podcast interview with Donald Stufft

Macdown

Taiwan

Pipenv

PyPI

Podcast Episode

TOML

Python Package Metadata Standards

iBook G4

Acorn Computer

distutils

easy_install

Python Eggs

setuptools

Python Wheels

CPAN

Conda

Inside The Cheeseshop

Google Summer of Code

Zazo

PEP517

pip-tools

Poetry

resolvelib

SAT Solver

Trove Classifiers

PyPA

pyproject.toml

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Easy Data Validation For Your Python Projects With Pydantic** May 18, 2020
Summary

One of the most common causes of bugs is incorrect data being passed throughout your program. Pydantic is a library that provides runtime checking and validation of the information that you rely on in your code. In this episode Samuel Colvin explains why he created it, the interesting and useful ways that it can be used, and how to integrate it into your own projects. If you are tired of unhelpful errors due to bad data then listen now and try it out today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show because you love Python and want to keep your skills up to date. Machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.

Your host as usual is Tobias Macey and today I’m interviewing Samuel Colvin about Pydantic, a library for enforcing type hints at runtime

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Pydantic is and what motivated you to create it?

What are the main use cases that benefit from Pydantic?

There are a number of libraries in the Python ecosystem to handle various conventions or "best practices" for settings management. How does pydantic fit in that category and why might someone choose to use it over the other options?

There are also a number of libraries for defining data schemas or validation such as Marshmallow and Cerberus. How does Pydantic compare to the available options for those cases?

What are some of the challenges, whether technical or conceptual, that you face in building a library to address both of these areas?

The 3.7 release of Python added built in support for dataclasses as a means of building containers for data with type validation. What are the tradeoffs of pydantic vs the built in dataclass functionality?

How much overhead does pydantic add for doing runtime validation of the modelled data?

In the documentation there is a nuanced point that you make about parsing vs validation and your choices as to what to support in pydantic. Why is that a necessary distinction to make?

What are the limitations in terms of usage that you are accepting by choosing to allow for implicit conversion or potentially silent loss of precision in the parsed data?

What are the benefits of punting on the strict validation of data out of the box?

What has been your design philosophy for constructing the user facing API?

How is Pydantic implemented and how has the overall architecture evolved since you first began working on it?

What have you found to be the most challenging aspects of building a library for managing the consistency of data structures in a dynamic language?

What are some of the strengths and weaknesses of Python’s type system?

What is the workflow for a developer who is using Pydantic in their code?

What are some of the pitfalls or edge cases that they might run into?

What is involved in integrating with other libraries/frameworks such as Django for web development or Dagster for building data pipelines?

What are some of the more advanced capabilities or use cases of Pydantic that are less obvious?

What are some of the features or capabilities of Pydantic that are often overlooked which you think should be used more frequently?

What are some of the most interesting, innovative, or unexpected ways that you have seen Pydantic used?

What are some of the most interesting, challenging, or unexpected lessons that you have learned through your work on or with Pydantic?

When is Pydantic the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

samuelcolvin on GitHub

Website

LinkedIn

@samuel_colvin on Twitter

Picks

Tobias

Devil Sticks

Samuel

Flash Boys by Michael Lewis

Algorithms To Live By by Brian Christian and Tom Griffiths

NGrok.com

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Pydantic

Matlab

C#

FastAPI

Podcast Episode

Marshmallow

Podcast Episode

Cerberus

12 Factor App

Django

Python Type Hints

Cython

Podcast Episode

MyPy

Podcast Episode

Duck Typing

Haskell

Higher Order Types

PyCharm Pydantic Plugin

Django Rest Framework

Avro

Parquet

Dagster

Data Engineering Podcast Episode

Starlette

Flask

Ludwig

Deep Pavlov

Fast MRI

Reagent

Pynt

Open Source Has Failed article

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Managing Distributed Teams In The Age Of Remote Work** May 11, 2020
Summary

More of us are working remotely than ever before, many with no prior experience with a remote work environment. In this episode Quinn Slack discusses his thoughts and experience of running Sourcegraph as a fully distributed company. He covers the lessons that he has learned in moving from partially to fully remote, the practices that have worked well in managing a distributed workforce, and the challenges that he has faced in the process. If you are struggling with your remote work situation then this conversation has some useful tips and references for further reading to help you be successful in the current environment.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.

Your host as usual is Tobias Macey and today I’m interviewing Quinn Slack about his experience managing a fully remote company and useful tips for remote work

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of the team structure at Sourcegraph?

You recently moved to being fully remote. What was the motivating factor and how has it changed your personal workflow?

What is your prior history with working remote?

team practices for visibility of progress

impact of remote teams on how code is written and organized

reducing review burden by writing clearer code

structuring meetings when remote

points of friction for remote developer teams

benefits of being fully remote

incentivizing documentation

compensation structure

Keep In Touch

LinkedIn

@sqs on Twitter

sqs on GitHub

Picks

Tobias

Joplin App

Quinn

Skunkworks by Ben Rich

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Sourcegraph

Quinn’s Python Search Engine

Sourcegraph Employee Handbook

Gitlab

Gitlab Handbook

Zapier

Zapier Guide To Remote Work

Automattic

Automattic Blog On Distributed Work

Comments Showing Intent

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Maintainable Infrastructure As Code In Pure Python With Pulumi** May 04, 2020
Summary

After you write your application, you need a way to make it available to your users. These days, that usually means deploying it to a cloud provider, whether that’s a virtual server, a serverless platform, or a Kubernetes cluster. To manage the increasingly dynamic and flexible options for running software in production, we have turned to building infrastructure as code. Pulumi is an open source framework that lets you use your favorite language to build scalable and maintainable systems out of cloud infrastructure. In this episode Luke Hoban, CTO of Pulumi, explains how it differs from other frameworks for interacting with infrastructure platforms, the benefits of using a full programming language for treating infrastructure as code, and how you can get started with it today. If you are getting frustrated with switching contexts when working between the application you are building and the systems that it runs on, then listen now and then give Pulumi a try.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.

Your host as usual is Tobias Macey and today I’m interviewing Luke Hoban about building and maintaining infrastructure as code with Pulumi

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the concept of "infrastructure as code"?

What is Pulumi and what is the story behind it?

Where does the name come from?

How does Pulumi compare to other infrastructure as code frameworks, such as Terraform?

What are some of the common challenges in managing infrastructure as code?

How does use of a full programming language help in addressing those challenges?

What are some of the dangers of using a full language to manage infrastructure?

How does Pulumi work to avoid those dangers?

Why is maintaining a record of the provisioned state of your infrastructure necessary, as opposed to relying on the state contained by the infrastructure provider?

What are some of the design principles and constraints that developers should be considering as they architect their infrastructure with Pulumi?

Can you describe how Pulumi is implemented?

How does Pulumi manage support for multiple languages while maintaining feature parity across them?

How do you manage testing and validation of the different providers?

The strength of any tool is largely measured in the ecosystem that exists around it, which is one of the reasons that Terraform has been so successful. How are you approaching the problem of bootstrapping the community and prioritizing platform support?

Can you talk through the workflow of working with Pulumi to build and maintain a proper infrastructure?

What are some of the ways to approach testing of infrastructure code?

What does the CI/CD lifecycle for infrastructure look like?

What are the limitations of infrastructure as code?

How do configuration management tools fit with frameworks such as Pulumi?

The core framework of Pulumi is open source, and your business model is focused around a managed platform for tracking state. How are you approaching governance of the project to ensure its continued viability and growth?

What are some of the most interesting, innovative, or unexpected design patterns that you have seen your users include in their infrastructure projects?

When is Pulumi the wrong choice?

What do you have planned for the future of Pulumi?

Keep In Touch

LinkedIn

lukehoban on GitHub

@lukehoban on Twitter

Picks

Tobias

Bookshelf App

Luke

GoBinaries.com

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Pulumi

Terraform

IronPython

HCL == Hashicorp Config Language

Kubernetes

TypeScript

DevOps

CloudFormation

ARM == Azure Resource Manager

AWSx

GCP == Google Cloud Platform

Pulumi SaaS

SaltStack

Podcast Episode

Ansible

Elastic Beanstalk

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Teaching Python Machine Learning** Apr 28, 2020
Summary

Python has become a major player in the machine learning industry, with a variety of widely used frameworks. In addition to the technical resources that make it easy to build powerful models, there is also a sizable library of educational resources to help you get up to speed. Sebastian Raschka’s contribution of the Python Machine Learning book has come to be widely regarded as one of the best references for newcomers to the field. In this episode he shares his experiences as an author, his views on why Python is the right language for building machine learning applications, and the insights that he has gained from teaching and contributing to the field.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Sebastian Raschka about his experiences writing the popular Python Machine Learning book

Interview

Introductions

How did you get introduced to Python?

How did you get started in machine learning?

What were the concepts that you found most difficult in your career with statistics and machine learning?

One of your notable contributions to the field is your book "Python Machine Learning". What inspired you to write the initial version?

How did you approach the challenge of striking the right balance of depth, breadth, and accessibility for the content?

What was your process for determining which aspects of machine learning to include?

You have made 3 editions of the book from 2015 through December of 2019. In what ways has the book changed?

What are the biggest changes to the ecosystem and approaches to ML in that timeframe?

What are the fundamental challenges of developing machine learning projects that continue to present themselves?

What new difficulties have arisen with the introduction of new technologies and the rise of deep learning?

What are some of the ways that the Python language lends itself to analytical work?

What are its shortcomings and how has the community worked around them?

What do you see as the biggest risks to the popularity of Python in the data and analytics space?

What are some of the common pitfalls that your readers and students face while learning about different aspects of machine learning?

What are some of the industries that can benefit most from applications of machine learning?

What are you most excited about in the applications or capabilities of machine learning?

What are you most worried about?

Keep In Touch

Website

@rasbt on Twitter

rasbt on GitHub

LinkedIn

Picks

Tobias

Trolls World Tour

Sebastian

FFMPeg Normalize

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Python Machine Learning (Packt)

Buy On Amazon (affiliate link)

UW Madison

Pascal

Delphi

R

Perl

Bioinformatics

Seq

Podcast Episode

BioPython

Podcast Episode

CodeCademy

Udacity CS101

Andrew Ng

Coursera

Support-Vector Machine

Bayesian Statistics

Matlab

scikit-learn

NumPy

Pandas

Podcast Episode

Sebastian’s Blog

Perceptron

Heatmaps In R

The Hundred Page Machine Learning Book by Andriy Burkov

ImageNet

Random Forest

Logistic Regression

XGBoost

Theano

Generative Adversarial Networks

Is This Person Real / This Person Does Not Exist

Reinforcement Learning

AlphaGo

AlphaStar

Ray

RLlib

Open AI

Google DeepMind

Google Colab

CUDA

Julia

Sebastian Raschka, Joshua Patterson, and Corey Nolet (2020). *Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence*. Information 2020, 11, 193

Swift Language

Swift for TensorFlow

Matplotlib

Differential Privacy

PrivacyNet

YouTube recordings of Stat453: Introduction to Deep Learning and Generative Models (Spring 2020)

ffmpeg-normalize

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build The Next Generation Of Python Web Applications With FastAPI** Apr 20, 2020
Summary

Python has an embarrasment of riches when it comes to web frameworks, each with their own particular strengths. FastAPI is a new entrant that has been quickly gaining popularity as a performant and easy to use toolchain for building RESTful web services. In this episode Sebastián Ramirez shares the story of the frustrations that led him to create a new framework, how he put in the extra effort to make the developer experience as smooth and painless as possible, and how he embraces extensability with lightweight dependency injection and a straightforward plugin interface. If you are starting a new web application today then FastAPI should be at the top of your list.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Sebastián Ramirez about FastAPI, a framework for building production ready APIs in Python 3

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what FastAPI is?

What are the main frustrations that you ran into with other frameworks that motivated you to create an entirely new one?

What are some of the main use cases that FastAPI is designed for?

Many web frameworks focus on managing the end-to-end functionality of a website, including the UI. Why did you focus on just API capabilities?

What are the benefits of building an API only framework?

If you wanted to integrate a presentation layer, what would be involved in that effort?

What API formats does FastAPI support?

What would be involved in adding support for additional specifications such as GraphQL or JSON-LD?

There are a huge number of web frameworks available just in the Python ecosystem. How does FastAPI fit into that landscape and why might someone choose it over the other options?

Can you share your design philosophy for the project?

What are your main sources of inspiration for the framework?

You have also built the Typer CLI library which you refer to as the little sibling of FastAPI. How have your experiences building these two projects influenced their counterpart’s evolution?

What are the benefits of incorporating type annotations into a web framework and in what ways do they manifest in its functionality?

What is the workflow for a developer building a complex application in FastAPI?

Can you describe how FastAPI itself is architected and how its design has evolved since you first began working on it?

What are the extension points that are available for someone to build plugins for FastAPI?

What are some of the challenges that you have faced in building an async framework that is leveraging the new ASGI specification?

What are some sharp edges that users should keep an eye out for?

What are some unique or underutilized features of FastAPI that users might not be aware of?

What are some of the most interesting, unexpected, or innovative ways that you have seen FastAPI used?

When is FastAPI the wrong choice?

What are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of building and maintaining FastAPI?

What do you have planned for the future of the project?

Keep In Touch

@tiangolo on Twitter. @tiangolo on GitHub.

Picks

Tobias

Once Upon A Time TV Show

Sebastián

Cloud Atlas Movie

Isaac Asimov’s robot short stories

Python devtools debug function

async compatible requests with HTTPX

RescueTime for automatic time tracking

Joplin for Notes

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

FastAPI

Typer

Typer CLI

FastAPI Alternatives, Inspiration and Comparisons

Explosion’s spaCy

Explosion’s Prodigy

Starlette

Pydantic

Uvicorn

Hypercorn

fastapi-utils

Class Based Views

GrahQL Ariadne

Coronavirus Tracker API

Terminals from browser: termpair

XPublish

Uber’s Ludwig

Netflix Dispatch

Colombia

Berlin Germany

Explosion AI

Python Type Annotations

Django Rest Framework

Flask

Swagger/OpenAPI

Sanic

NodeJS

JSON Schema

OAuth2

Swagger UI

ReDoc

React

VueJS

Angular

REST == REpresentational State Transfer

JSON-LD

Go Language

Hug API framework

Click CLI Framework

Flask Blueprints

Tom Christie

Podcast Interview

Dependency Injection

ASGI

Podcast Episode

WSGI

Thread Local Variables

Context Vars

OAUTH2 Scopes

PipX

XArray

JAM Stack

NextJS

Hugo

GatsbyJS

FastAPI Project Templates

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Distributed Computing In Python Made Easy With Ray** Apr 14, 2020
Summary

Distributed computing is a powerful tool for increasing the speed and performance of your applications, but it is also a complex and difficult undertaking. While performing research for his PhD, Robert Nishihara ran up against this reality. Rather than cobbling together another single purpose system, he built what ultimately became Ray to make scaling Python projects to multiple cores and across machines easy. In this episode he explains how Ray allows you to scale your code easily, how to use it in your own projects, and his ambitions to power the next wave of distributed systems at Anyscale. If you are running into scaling limitations in your Python projects for machine learning, scientific computing, or anything else, then give this a listen and then try it out!

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Your host as usual is Tobias Macey and today I’m interviewing Robert Nishihara about Ray, a framework for building and running distributed applications and machine learning

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Ray is and how the project got started?

How did the environment of the RISE lab factor into the early design and development of Ray?

What are some of the main use cases that you were initially targeting with Ray?

Now that it has been publicly available for some time, what are some of the ways that it is being used which you didn’t originally anticipate?

What are the limitations for the types of workloads that can be run with Ray, or any edge cases that developers should be aware of?

For someone who is building on top of ray, what is involved in either converting an existing application to take advantage of Ray’s parallelism, or creating a greenfield project with it?

Can you describe how Ray itself is implemented and how it has evolved since you first began working on it?

How does the clustering and task distriubtion mechanism in Ray work?

How does the increased parallelism that Ray offers help with machine learning workloads?

Are there any types of ML/AI that are easier to do in this context?

What are some of the additional layers or libraries that have been built on top of the functionality of Ray?

What are some of the most interesting, challenging, or complex aspects of building and maintaining Ray?

You and your co-founders recently announced the formation of Anyscale to support the future development of Ray. What is your business model and how are you approaching the governance of Ray and its ecosystem?

What are some of the most interesting or unexpected projects that you have seen built with Ray?

What are some cases where Ray is the wrong choice?

What do you have planned for the future of Ray and Anyscale?

Keep In Touch

Website

@robertnishihara on Twitter

robertnishihara on GitHub

Picks

Tobias

D&D Castle Ravenloft board game

One Deck Dungeon

Robert

The Everything Store

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Ray

Anyscale

UC Berkeley

RISELab

MATLAB

Deep Learning

Theano

Tensorflow

PyTorch

Podcast Episode

Philip Moritz

Reinforcement Learning

Hyperparameter Tuning

IPython Parallel

AMPLab

Apache Spark

Data Engineering Podcast Episode

Actor Model

Horovod(?)

Flink

Data Engineering Podcast Episode

Spark Streaming

Dask

Data Engineering Podcast Episode

gRPC

Tune

Rust

C++

C

Apache Arrow

Wes McKinney

Podcast Interview

DataBricks

MongoDB

Elastic

Data Engineering Podcast Episode

Confluent

Embarassingly Parallel

Ant Financial

Flame Graph

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building The Seq Language For Bioinformatics** Apr 07, 2020
Summary

Bioinformatics is a complex and computationally demanding domain. The intuitive syntax of Python and extensive set of libraries make it a great language for bioinformatics projects, but it is hampered by the need for computational efficiency. Ariya Shajii created the Seq language to bridge the divide between the performance of languages like C and C++ and the ecosystem of Python with built-in support for commonly used genomics algorithms. In this episode he describes his motivation for creating a new language, how it is implemented, and how it is being used in the life sciences. If you are interested in experimenting with sequencing data then give this a listen and then give Seq a try!

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. And now, the events are coming to you, with no travel necessary! We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference on April 6th and ODSC East which has also gone virtual starting April 16th. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Ariya Shajii about Seq, a programming language built for bioinformatics and inspired by Python

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Seq is and your motivation for creating it?

What was lacking in other languages or libraries for your use case that is made easier by creating a custom language?

If someone is already working in Python, possibly using BioPython, what might motivate them to consider migrating their work to Seq?

Can you give an impression of the scope and nature of the tasks or projects that a biologist or geneticist might build with Seq?

What was your process for identifying and prioritizing features and algorithms that would be beneficial to the target audience?

For someone using Seq can you describe their workflow and how it might differ from performing the same task in Python?

How is Seq implemented?

What are some of the features that are included to simplify the work of bioinformatics?

What was your process of designing the language and runtime?

How has the scope or direction of the project evolved since it was first conceived?

What impact do you anticipate Seq having on the domain of bioinformatics and genomics?

What have you found to be the most interesting, unexpected, and/or challenging aspects of building a language for this problem domain?

What is in store for the future of Seq?

Keep In Touch

arshajii on GitHub

Website

Picks

Tobias

Board Games

Labyrinth Boardgame

Board Game Geek

Ariya

Breakthrough documentary

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Seq

MIT CSAIL

Bioinformatics

LLVM

Intermediate Representation

MatLab

Moore’s Law

BioPython

Smith Waterman Algorithm

Hamming Distance

Pattern Matching in Functional Programming

SIMD == Single Instruction Multiple Data

Computational Genomics

Phylogenetics

Sequence Read Archive public data set

Google Cloud Life Sciences

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**An Open Source Toolchain For Natural Language Processing From Explosion AI** Mar 30, 2020
Summary

The state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of SpaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. And now, the events are coming to you, with no travel necessary! We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference on April 6th and ODSC East which has also gone virtual starting April 16th. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Matthew Honnibal about the Thinc and Prodigy tools and an update on SpaCy

Interview

Introductions

How did you get introduced to Python?

Can you start by giving an overview of your mission at Explosion?

We spoke previously about your work on SpaCy. What has changed in the past 3 1/2 years?

How have recent innovations in language models such as BERT and GPT-2 influenced the direction or implementation of the project?

When I last looked SpaCy only supported English and German, but you have added several new languages. What are the most challenging aspects of building the additional models?

What would be required for supporting symbolic or right-to-left languages?

How has the ecosystem for language processing in Python shifted or evolved since you first introduced SpaCy?

Another project that you have released is Prodigy to support labelling of datasets. Can you talk through the motivation for creating it and describe the workflow for someone using it?

What was lacking in the other annotation tools that you have worked with that you are trying to solve for in Prodigy?

What are some of the most challenging or problematic aspects of labelling data sets for use in machine learning projects?

What is a typical scale of data that can be reasonably handled by an individual or small team working with Prodigy?

At what point do you find that it makes sense to use a labeling service rather than generating the labels yourself?

Your most recent project is Thinc for building and using deep learning models. What was the motivation for creating it and what problem does it solve in the ecosystem?

How does its design and usage compare to other deep learning frameworks such as PyTorch and Tensorflow?

How does it compare to projects such as Keras that abstract across those frameworks?

How do the SpaCy, Prodigy, and Thinc libraries work together?

What are some of the biggest challenges that you are facing in building open source tools to meet the needs of data scientists and machine learning engineers?

What are some of the most interesting or impressive projects that you have seen built with the tools your team is creating?

What do you have planned for the future of Explosion, SpaCy, Prodigy, and Thinc?

Keep In Touch

LinkedIn

@honnibal on Twitter

honnibal on GitHub

Picks

Tobias

Onward movie

Matthew

Coronavirus Preparedness

Ray

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Explosion AI

SpaCy

Podcast Episode

Thinc

Prodigy

Natural Language Processing

Perl

NLTK

GPU == Graphics Processing Unit

TPU == Tensor Processing Unit

Transfer Learning

Airflow

Luigi

Perceptron

PyTorch

Tensorflow

Functional Programming

MxNet

Keras

Cuda

C Language

Continuous Integration

Blackstone

Allen AI Institute

SciSpaCy

Holmes

Sense2Vec

FastAPI

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**A Flexible Open Source ERP Framework To Run Your Business** Mar 23, 2020
Summary

Running a successful business requires some method of organizing the information about all of the processes and activity that take place. Tryton is an open source, modular ERP framework that is built for the flexibility needed to fit your organization, rather than requiring you to model your workflows to match the software. In this episode core developers Nicolas Évrard and Cédric Krier are joined by avid user Jonathan Levy to discuss the history of the project, how it is being used, and the myriad ways that you can adapt it to suit your needs. If you are struggling to keep a consistent view of your business and ensure that all of the necessary workflows are being observed then listen now and give Tryton a try.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Nicolas Évrard, Cédric Krier, and Jonathan Levy about Tryton

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Tryton is and how it got started?

What kinds of businesses is Tryton most suited to?

What kinds of businesses is Tryton not a good fit for?

Within a business, who are the primary users of Tryton?

Can you talk through a typical workflow for interacting with Tryton?

What are some of the most complex or challenging aspects of modeling a business while maintaining a high degree of customizability?

Can you describe how Tryton is architected and how its design has evolved since it was first started?

If you were to start over today, what would you do differently?

There are a number of plugins for Tryton. What kinds of functionality can be customized using the available interfaces?

What is the process for building a custom module for Tryton?

How do you manage sustainability of the Tryton project?

Given the criticality of the Tryton platform, how do you approach ongoing stability and security of the project?

What is involved in deploying and maintaining an installation of Tryton?

What are some of the most interesting, innovative, or unexpected ways that you have seen Tryton used?

What is in store for the future of Tryton?

Keep In Touch

Nicolas

nicoe on GitHub

@nicoe on Twitter

Cédric

@cedrickrier on Twitter

cedk on GitHub

Jonathan

LinkedIn

Picks

Tobias

Audio Books

Audible free trial (Affiliate Link)

Overdrive – ebooks and audiobooks from your local library

Public Domain Audiobooks

Nicolas

Civilization VI

FreeCiv

The 3 Body Problem

Cédric

Valérian and Laureline

Jonathan

Roil.com

Links

Tryton

B2CK

Tryton Foundation

Advocate Consulting Legal Group

Scheme

Lisp

Belgium

EuroPython Conference

Plone

Zope

VBA (Visual Basic for Applications)

Django

Odoo

ERP == Enterprise Resource Planning

Small/Medium Enterprise (SME)

GTK (Gnome ToolKit)

3-Tier Application

Cookiecutter

Tryton Module Cookiecutter

Tryton Repository

Docker

GNU Health

Nereid

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Getting A Handle On Portable C Extensions With hpy** Mar 16, 2020
Summary

One of the driving factors of Python’s success is the ability for developers to integrate with performant languages such as C and C++. The challenge is that the interface for those extensions is specific to the main implementation of the language. This contributes to difficulties in building alternative runtimes that can support important packages such as NumPy. To address this situation a team of developers are working to create the hpy project, a new interface for extension developers that is standardized and provides a uniform target for multiple runtimes. In this episode Antonio Cuni discusses the motivations for creating hpy, how it benefits the whole ecosystem, and ways to contribute to the effort. This is an exciting development that has the potential to unlock a new wave of innovation in the ways that you can run your Python code.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

As a developer, maintaining a state of flow is key to your productivity. Don’t let something as simple as the wrong function ruin your day. Kite is the smartest completions engine available for Python, featuring a machine learning model trained by the brightest stars of GitHub. Featuring ranked suggestions sorted by relevance, offering up to full lines of code, and a programming copilot that offers up the documentation you need right when you need it. Get Kite for free today at getkite.com with integrations for top editors, including Atom, VS Code, PyCharm, Spyder, Vim, and Sublime.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Antonio Cuni about hpy, a project aiming to reimagine the C API for Python

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what the hpy project is and how it got started?

What are the goals for the project?

Who else is involved?

How much engagement have you had with CPython core contributors or the steering council?

Who are the consumers of the current C API for the CPython implementation?

What are some of the pain points or shortcomings for those consumers?

What impact does that have for users of a given library that leverages C extensions?

Can you talk through the structure of the hpy project?

What are some of the design challenges that you are facing for determining the external API?

What is involved in integrating the hpy interface into alternate runtimes such as PyPy or RustPython?

What is the potential or observed performance impact for libraries that currently rely on the existing C API?

How has the vision and scope of this project been updated as you have gotten further along in the implementation?

What are the downstream impacts that you anticipate in projects such as PyPy and Cython?

What have you found to be the most challenging or contentious aspects of implementing hpy so far?

What are some of the most interesting/unexpected/useful lessons that you have learned while working on hpy?

What do you have planned for the near to medium term for hpy?

Keep In Touch

antocuni on GitHub

Website

@antocuni on Twitter

Picks

Tobias

Poetry

Antonio

Collapse: How Societies Choose To Fail Or Succeed by Jared Diamond

Links

hpy

PyPy

Alex Martelli

Podcast Interview

Python C Extensions

EuroPython

Victor Stinner

Cython

Podcast Episode

Armin Rigo

NumPy

ultrajson

GIL == Global Interpreter Lock

RustPython

Podcast Episode

GraalPython

hpy-rust

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Open Source Machine Learning On Quantum Computers With Xanadu AI** Mar 10, 2020
Summary

Quantum computers promise the ability to execute calculations at speeds several orders of magnitude faster than what we are used to. Machine learning and artificial intelligence algorithms require fast computation to churn through complex data sets. At Xanadu AI they are building libraries to bring these two worlds together. In this episode Josh Izaac shares his work on the Strawberry Fields and Penny Lane projects that provide both high and low level interfaces to quantum hardware for machine learning and deep neural networks. If you are itching to get your hands on the coolest combination of technologies, then listen now and then try it out for yourself.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

As a developer, maintaining a state of flow is key to your productivity. Don’t let something as simple as the wrong function ruin your day. Kite is the smartest completions engine available for Python, featuring a machine learning model trained by the brightest stars of GitHub. Featuring ranked suggestions sorted by relevance, offering up to full lines of code, and a programming copilot that offers up the documentation you need right when you need it. Get Kite for free today at getkite.com with integrations for top editors, including Atom, VS Code, PyCharm, Spyder, Vim, and Sublime.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Josh Izaac about how the work that he is doing at Xanadu AI to make it easier to build applications for quantum processors

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what you are working on at Xanadu AI?

How do the specifics of your quantum hardware influence the way in which developers need to build their algorithms? (e.g. as compared to DWave)

What are some of the underlying principles that developers need to understand in order to take full advantage of the capabilities provided by quantum processors?

Can you outline the different components and libraries that you are building to simplify the work of building machine learning/AI projects for quantum processors?

What’s the story behind all of the Beatles references?

How do the different libraries fit together?

What are some of the workloads and use cases that you and your customers are focused on?

What are some of the most challenging aspects of designing a library that is accessible to developers while being able to take advantage of the underlying hardware?

How does the workflow for machine learning on quantum computers differ from what is being done in classical environments?

Given the magnitude of computational power and data processing that can be achieved in a quantum processor it seems that there is a potential for small bugs to have disproportionately large impacts. How can developers identify and mitigate potential sources of error in their algorithms?

For someone who is building an application or algorithm to be executed on a Xanadu processor, what does their workflow look like?

What are some of the common errors or misconceptions that you have seen in customer code?

Can you describe the design and implementation of the Penny Lane and Strawberry Fields libraries and how they have evolved since you first began working on them?

What are some of the most ambitious or exciting use cases for quantum systems that you have seen?

How are you using the computational capabilities of your platform to feed back into the research and design of successive generations of hardware?

What are some useful heuristics for determining whether it is worthwhile to build for a quantum processor rather than leveraging classical hardware?

What are some of the most interesting/unexpected/useful lessons that you have learned while working on quantum algorithms and the libraries to support them?

What is in store for the future of the Xanadu software ecosystem?

What are your predictions for the near to medium term of quantum computing?

Keep In Touch

josh146 on GitHub

Website

LinkedIn

Picks

Tobias

Knives Out movie

Josh

Baking Sourdough Bread

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Xanadu AI

Strawberry Fields

PennyLane

Quantum Physics

ASIC == Application Specific Integrated Circuit

FPGA == Field Programmable Gate Array

GPU == Graphics Processing Unit

Quantum Photonics

Qubit

Trapped Ions

Quantum Optics

Coherent Light

Heisenberg’s Uncertainty Principle

Wave/Particle Duality

Continuous Variable Quantum Computation

NetworkX

Tensorflow

The Walrus

Rigetti Computing

PyTorch

Podcast Episode

The Walrus Operator (Assignment Expressions)

Fortran

NumPy

SciPy

IPython

Podcast Episode

Jax

Quantum Machine Learning

Xanadu User Discussion Forum

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Advanced Python Task Scheduler** Mar 02, 2020
Summary

Most long-running programs have a need for executing periodic tasks. APScheduler is a mature and open source library that provides all of the features that you need in a task scheduler. In this episode the author, Alex Grönholm, explains how it works, why he created it, and how you can use it in your own applications. He also digs into his plans for the next major release and the forces that are shaping the improved feature set. Spare yourself the pain of triggering events at just the right time and let APScheduler do it for you.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Alex Grönholm about APScheduler, a library for scheduling tasks in your Python projects

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what APScheduler is and the main use cases that APScheduler is designed for?

What was your movitvation for creating it?

What is the workflow for integrating APScheduler into an application?

In the documentation it says not to run more than one instance of the scheduler, what are some strategies for scaling schedulers?

What are some common architectures for applications that take advantage of APScheduler?

What are some potential pitfalls that developers should be aware of?

Can you describe how APScheduler is implemented and how its design has evolved since you first began working on it?

What have you found to be the most complex or challenging aspects of building or using a scheduling framework?

What are some of the most interesting/innovative/unexpected ways that you have seen APScheduler used?

What are some of the features or capabilities that you have consciously left out?

What design strategies or features of APScheduler are often overlooked or underappreciated?

What are some of the most useful or interesting lessons that you have learned while building and maintaining APScheduler?

When is APScheduler the wrong choice for managing task execution?

What do you have planned for the future of the project?

Keep In Touch

agronholm on GitHub

Picks

Tobias

The Data Exchange Podcast

Alex

Tenacity

Links

APScheduler

PHP

Java

ECMAScript

Celery

ERP == Enterprise Resource Planning

Cron Daemon

RPyC

Zookeeper

Data Engineering Podcast Episode

RethinkDB

Daylight Saving Time

Falsehoods Programmers Believe About Time

PyTZ

Celery Beats

Asphalt Framework

Podcast Episode

AnyIO

Twisted

Podcast Episode

Py2EXE

PyInstaller

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Reducing The Friction Of Embedded Software Development With PlatformIO** Feb 25, 2020
Summary

Embedded software development is a challenging endeavor due to a fragmented ecosystem of tools. Ivan Kravets experienced the pain of programming for different hardware platforms when embroiled in a home automation project. As a result he built the PlatformIO ecosystem to reduce the friction encountered by engineers working with multiple microcontroller architectures. In this episode he describes the complexities associated with targeting multiple platforms, the tools that PlatformIO offers to simplify the workflow, and how it fits into the development process. If you are feeling the pain of working with different editing environments and build toolchains for various microcontroller vendors then give this interview a listen and then try it out for yourself.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Ivan Kravets about PlatformIO, an open source ecosystem for IoT development including a cross-platform IDE, unified debugger, remote unit testing, and firmware updates.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what PlatformIO is?

What was your motivation for creating it?

What are the aspects of embedded development that keep you interested and engaged in this space?

What are some of the types of projects that someone might use PlatformIO to build?

What are some of the common challenges that a developer might encounter when working on embedded systems?

What are the additional complexities that get introduced as more hardware targets get added to a project?

What is the workflow for someone using PlatformIO for embedded systems development?

What are the different elements of PlatformIO and how do they simplify the work of building embedded systems projects?

How is PlatformIO implemented and how has the system design evolved since you first began working on it?

What was your reason for selecting Python as the implementation language?

If you were to start over today what would you do differently?

How has the embedded hardware and software landscape changed since you first started work on PlatformIO?

How has that impacted your product direction?

How do developers handle testing and validation of their applications?

How does PlatformIO help with updating deployed devices with new firmware?

What have been some of the most interesting/unexpected/innovative projects that you have seen built with PlatformIO?

What have been some of the most interesting/unexpected/challenging aspects of building and maintaining PlatformIO?

How are you approaching sustainability of the project and business?

What do you have planned for the future of PlatformIO?

Keep In Touch

LinkedIn

Website

ivankravets on GitHub

@ikravets on Twitter

Picks

Tobias

UMass Amherst Making Electricity From Thin Air

Ivan

Don’t focus on the money side of your project, just focus on building a great product.

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

PlatformIO

Ukraine

Home Automation

Home Assistant

Podcast Episode

Twisted

Podcast Episode

Zigbee Radio

Serial I/O

RS-232

ARM CPU Architecture

RISC-V

AVR Microcontrollers

Arduino

Texas Instruments Launchpad

Eclipse IDE

MCU == MicroController Unit

VSCode

PlatformIO Extension

SCons

Make

Raspberry Pi

ESP8266

Marlin 3D Printer Firmware

ESP Home

Zephyr Realtime Operating System

Western Digital

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**APIs, Sustainable Open Source and The Async Web With Tom Christie** Feb 18, 2020
Summary

Tom Christie is probably best known as the creator of Django REST Framework, but his contributions to the state the web in Python extend well beyond that. In this episode he shares his story of getting involved in web development, his work on various projects to power the asynchronous web in Python, and his efforts to make his open source contributions sustainable. This was an excellent conversation about the state of asynchronous frameworks for Python and the challenges of making a career out of open source.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Tom Christie about the Encode organization and the work he is doing to drive the state of the art in async for Python

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what the Encode organization is and how it came to be?

What are some of the other approaches to funding and sustainability that you have tried in the past?

What are the benefits to the developers provided by an organization which you were unable to achieve through those other means?

What benefits are realized by your sponsors as compared to other funding arrangements?

What projects are part of the Encode organization?

How do you determine fund allocation for projects and participants in the organization?

What is the process for becoming a member of the Encode organization and what benefits and responsibilities does that entail?

A large number of the projects that are part of the organization are focused on various aspects of asynchronous programming in Python. Is that intentional, or just an accident of your own focus and network?

For those who are familiar with Python web programming in the context of WSGI, what are some of the practices that they need to unlearn in an async world, and what are some new capabilities that they should be aware of?

Beyond Encode and your recent work on projects such as Starlette you are also well known as the creator of Django Rest Framework. How has your experience building and growing that project influenced your current focus on a technical, community, and professional level?

Now that Python 2 is officially unsupported and asynchronous capabilities are part of the core language, what future directions do you foresee for the community and ecosystem?

What are some areas of potential focus that you think are worth more attention and energy?

What do you have planned for the future of Encode, your own projects, and your overall engagement with the Python ecosystem?

Keep In Touch

Website

tomchristie on Github

@_tomchristie on Twitter

Picks

Tobias

Maleficent: Mistress of Evil

Abominable

Tom

The Lobster

The Master And His Emissary by Ian McGilchrist

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Encode

Django Rest Framework

Starlette

Zope

Django

Django Piston

Django Tastypie

Andrew Godwin

ASGI

Django Channels

Podcast Episode

Flask

Pyramid

Sentry

Podcast Episode

Tidelift

Uvicorn

HTTPX

Tidelift

Open Collective

Stripe

Github Sponsors

Python Software Foundation

Podcast Episode

Firebase

Databases

ORM

HTTP3

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Learning To Program Python By Building Video Games With Arcade** Feb 11, 2020
Summary

Video games have been a vehicle for learning to program since the early days of computing. Continuing in that tradition, Paul Craven created the Arcade library as a modern alternative to PyGame for use in his classroom. In this episode he explains his motivations for starting a new framework for video game development, his view on the benefits of games in computer education, and how his students and the broader community are using it to build interesting and creative projects. If you are looking for a way to get new programmers engaged, or just want to experiment with building your own games, then this is the conversation for you. Give it a listen and then give Arcade a try for yourself.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Paul Craven about Arcade, an easy-to-learn Python library for creating 2D video games

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Arcade is?

What inspired you to begin working on it?

Who is your primary audience?

As an educator, what have you found to be most effective about using games as a vehicle for teaching programming?

What elements of programming or computer science do you have difficulty in addressing within the context of a video game?

For someone who wants to move on from working on games to something like web development or data analytics, what elements of software design and structure are easily translated to other domains?

Can you describe how Arcade is implemented and how the architecture has evolved since you first began working on it?

If you were to start over today, what would you do differently?

What have you found to be the most interesting/unexpected/challenging aspects of building and maintaining Arcade?

What are some of the most interesting/innovative/unexpected ways that you have seen Arcade used?

When is Arcade the wrong platform, or at what point does someone need to move on from Arcade?

What do you have planned for the future of Arcade?

Keep In Touch

@professorcraven on Twitter

pvcraven on GitHub

Faculty Page

Picks

Tobias

Ori And The Blind Forest

Paul

Fahrenheit 451 by Ray Bradbury

“Mistakes can be profited by Man, when i was young I showed my ignorance in people’s faces. They beat me with sticks. By the time I was forty my blunt instrument had been honed to a fine cutting point for me. If you hide your ignorance, no one will hit you and you’ll never learn.”

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Arcade

Simpson College

PyGame

SDL

OpenGL

Unity

Unreal Engine

GoDot

Automate The Boring Stuff With Python

Minesweeper

Pyglet

Spatial Hashing

Tiled Map Editor

Python Type Hints

F Strings

Data Classes

PyMunk

FFMPEG

PyWeek

Podcast Episode

Python Discord

Arcade Enhancement Requests

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build Your Own Personal Data Repository With Nostalgia** Feb 04, 2020
Summary

The companies that we entrust our personal data to are using that information to gain extensive insights into our lives and habits while not always making those findings accessible to us. Pascal van Kooten decided that he wanted to have the same capabilities to mine his personal data, so he created the Nostalgia project to integrate his various data sources and query across them. In this episode he shares his motivation for creating the project, how he is using it in his day-to-day, and how he is planning to evolve it in the future. If you’re interested in learning more about yourself and your habits using the personal data that you share with the various services you use then listen now to learn more.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Pascal van Kooten about his nostalgia project, a nascent framework for taking control of your personal data

Interview

Introductions

How did you get introduced to Python?

Can you start by describing your mission with the nostalgia project?

How did the topic of personal data management come to be a focus for you?

What other options exist for users to be able to collect and manage their own data?

What capabilities were lacking in those options that made you feel the need to build Nostalgia?

What is your target audience for this set of projects?

How are you using Nostalgia in your own life?

What are some of the insights that you have been able to gain as a result of integrating your data with Nostalgia?

Can you describe the current architecture of the Nostalgia platform and how it has evolved since you began work on it?

What are some of the assumptions that you are using to direct the focus of your development and interaction design?

What are the minimum number of data sources needed to make this useful?

What are some of the challenges that you are facing in collating and integrating different data sources?

What are some of the drawbacks of using something like Nostalgia for managing your personal data?

What are some of the most interesting/challenging/unexpected aspects of your work on Nostalgia so far?

What do you have planned for the future of the project?

Keep In Touch

Website

LinkedIn

@kootenpv on Twitter

kootenpv on GitHub

Picks

Tobias

Jumanji: The Next Level

Jumanji

Pascal

Bup

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

timeliner

qs_ledger

Nostalgia

Shrynk

Whereami

R Language

Duck Duck Go

Caddy

Perkeep

Dark Programming Language

Pandas

Podcast Episode

Neo4J

Pandas Extension Arrays

Podcast Episode

Parquet

Data Engineering Podcast Episode

ElectronJS

Zincbase

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Simplifying Social Login For Your Web Applications** Jan 27, 2020
Summary

A standard feature in most modern web applications is the ability to log in or register using accounts that you already own on other sites such as Google, Facebook, or Twitter. Building your own integrations for each service can be complex and time consuming, distracting you from the features that you and your users actually care about. Fortunately the Python social auth library makes it easy to support third party authentication with a large and growing number of services with minimal effort. In this episode Matías Aguirre discusses his motivation for creating the library, how he has designed it to allow for flexibility and ease of use, and the benefits of delegating identity and authentication to third parties rather than managing passwords yourself.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Matías Aguirre about Python social auth and the complexities of third-party authentication

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what the Python social auth project is and your motivation for starting it?

Why might someone want to integrate with or rely on a third-party identity provider in their projects?

What are some of the tradeoffs or drawbacks of implementing

Can you describe the current architecture of the library and how it has evolved since you first began working on it?

There are a number of pre-built integrations with different web frameworks in the social auth github organization, but Django is the only one that has seen any commits recently. What are the contributing factors for that state of affairs?

There are a number of authentication protocols that you support. What are the common capabilities that they each support and what are some of the more challenging differences between them?

How have you implemented the interface for plugging different authentication mechanisms to allow for the variation between them while keeping the library code maintainable?

What is involved in adding support for a new authentication provider or protocol?

Many times authorization and authentication are conflated or used interchangeably. How does Python social auth address those concerns and what are the limitations of different mechanisms for defining permissions?

For someone who is using Python social auth, what is the workflow for integrating it with their application as a consumer?

What are some of the most interesting/unexpected/innovative ways that you have seen Python social auth used?

What are some of the most interesting/useful/unexpected lessons that you have learned in the process of building and maintaining Python social auth?

When is Python social auth more effort than it’s worth?

What do you have planned for the future of the project?

Keep In Touch

omab on GitHub

Website

@linuxaddict on Twitter

LinkedIn

Picks

Tobias

Joker movie

Matías

Sanic asynchronous web framework

Star Trek Picard TV series

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Python Social Auth

Uruguay

Django

Ruby on Rails

MonkeyLearn

Social Authentication

Django Social Auth

Salted and hashed passwords

Magic Link Authentication

OAuth

OpenID

SAML

FastAPI

Sanic

ASGI

WSGI

AsyncIO

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building A Business On Building Data Driven Businesses** Jan 20, 2020
Summary

In order for an organization to be data driven they need easy access to their data and a simple way of sharing it. Arik Fraimovich built Redash as a way to address that need by connecting to any data source and building attractive dashboards on top of them. In this episode he shares the origin story of the project, his experiences running a business based on open source, and the challenges of working with data effectively.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Arik Fraimovich about Redash, an open source business intelligence platform that helps you make sense of your data.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Redash is and its origin story?

What are the primary ways that it is used?

The business intelligence market is quite mature and has many commercial and open source projects to choose from. What are the aspects of Redash that have allowed you to be successful?

What would you consider to be your closest competitors?

What was your background with data before starting on Redash?

What are some of the most notable lessons that you have learned about business intelligence since starting the project?

How has the landscape for business intelligence and data analysis changed since you began the project?

Beyond just accessing data, Redash focuses on enabling visualization of the results. What types of visualizations do you support and how do you support users in choosing the most effective ways to represent the information?

What are some of the common challenges that your users and customers encounter when communicating with data?

One of the critical aspects of enabling data access in an organization is the ability to collaborate on asking and answering questions. How do you approach that challenge in Redash?

How is Redash implemented and how has the overall design and architecture evolved since you first started working on it?

How do you manage the complexity of supporting so many different data sources?

If you were to start over today, what would you do differently?

Beyond the code of Redash, you also have a business around providing it as a hosted service. What are some of the most interesting, challenging, or unexpected lessons that you have learned in the process of building and growing that service?

How do you approach the direction and governance of the open source project and balance that against the wants and needs of the community?

What are some of the most interesting, innovative, or unexpected ways that you have seen Redash used?

When is Redash the wrong platform to use?

What do you have planned for the future of the Redash business and project?

Keep In Touch

arikfr on GitHub

Website

@arikfr on Twitter

Picks

Tobias

Data Engineering Podcast

Arik

Peewee ORM

Amazon ECS

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Redash

Google App Engine

EverythingMe

RedShift

Metabase

Data Engineering Podcast Interview

Apache Superset

Elasticsearch

Data Engineering Podcast Interview

Tableau

Looker

Data Engineering Podcast Interview

PowerBI

Data Warehouse

Data Lake

Athena

Spark

Data Engineering Podcast Interview

Redash Funnel Visualization

Stephen Few

Flask

SQLAlchemy

Redis

PostgreSQL

Data Engineering Podcast Interview

Celery

RQ

Tornado

Django ORM

AngularJS

ReactJS

NodeJS

Redash Query Results Data Source

IBM DB2

Retool

Forest Admin

Grafana

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Using Deliberate Practice To Level Up Your Python** Jan 13, 2020
Summary

An effective strategy for teaching and learning is to rely on well structured exercises and collaboration for practicing the material. In this episode long time Python trainer Reuven Lerner reflects on the lessons that he has learned in the 5 years since his first appearance on the show, how his teaching has evolved, and the ways that he has incorporated more hands-on experiences into his lessons. This was a great conversation about the benefits of being deliberate in your approach to ongoing education in the field of technology, as well as having some helpful references for ways to keep your own skills sharp.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m pleased to welcome back Reuven Lerner to talk about the benefits of deliberate practice for learning and improving programming skills

Interview

Introductions

How did you get introduced to Python?

In your first appearance on the show back in episode 2 we talked about your experience as a Python trainer. How has your teaching style evolved in the past 5 years?

How has the focus and scope of your training changed in that time period?

What have you found to be some of the most helpful and effective tactics in your training?

From the learner perspective, what are some strategies that you recommend for retaining information, particularly in the context of gaining technical knowledge?

In-person training vs. real-time online training vs. recorded videos, advantages and disadvantages of each.

Blended learning, in which we combine aspects of the above

Beyond in-person training, what are your preferred methods for learning and maintaining new skills?

What is deliberate practice and how does it differ from the habits that many of us might default to?

What are some of the resources that you provide for students of your trainings for practicing?

What are some of the outside resources which you have found most useful or effective?

Keep In Touch

Website

Blog

@reuvenmlerner on Twitter

Picks

Tobias

The Manager’s Path by Camille Fournier

Reuven

Lab Rats: How Silicon Valley Made Work Miserable For The Rest Of Us by Dan Lyons

Links

Deliberate Practice

Reuven On Episode 2

CGI == Common Gateway Interface

Language Phrasebook

Jupyter Notebook

Walrus Operator

PyCon 2019 Presentation

Python Bytes

List Comprehension

Weekly Python Exercise

Python Morsels

PyBites

Practice Your Python

Python Workout book by Reuven Lerner

PyTest

Brian Okken

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Checking Up On Python's Role in DevOps** Jan 06, 2020
Summary

Python has been part of the standard toolkit for systems administrators since it was created. In recent years there has been a shift in how servers are deployed and managed, and how code gets released due to the rise of cloud computing and the accompanying DevOps movement. The increased need for automation and speed of iteration has been a perfect use case for Python, cementing its position as a powerful tool for operations. In this episode Moshe Zadka reflects on his experiences using Python in a DevOps context and the book that he wrote on the subject. He also discusses the difference in what aspects of the language are useful as an introduction for system operators and where they can continue their learning.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Moshe Zadke about his recent book DevOps In Python

Interview

Introductions

How did you get introduced to Python?

How did you gain experience in managing systems with Python?

What is DevOps?

What makes Python a good fit for managing systems?

What is unique to the devops/sysadmin domain in terms of what software is used and what aspects of the language are useful?

What are the main ways that Python is used for managing servers and infrastructure?

What are some of the most notable changes in the ways that Python is used for server administration over the past several years?

How has Python3 impacted the lives of operators?

What was your motivation for writing a book about Python focused specifically on DevOps and server automation?

What are some of the tools that have been replaced in your own workflow over the years?

Keep In Touch

Website

LinkedIn

@moshezadka on Twitter

Picks

Tobias

SaltStack

Podcast Episode

Moshe

Automat

Podcast Episode

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

DevOps In Python

SurveyMonkey

Twisted Episode

DevOps

B=hive

CI/CD

Amoeba OS

Python OS module

Requests

Canary Deployments

Post Mortem

Bash Shell

Z Shell

Linux

Unix

AWS

Boto3

GitHub

GitLab

Debian

Ubuntu

CentOS

Pip

Poetry

Pipenv

pip-tools

dh-virtualenv

Docker

Hyneck Schlaweck Presentation On Building Docker Images

Ansible

SaltStack

Chef

Puppet

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Python's Built In IDE Isn't Just Sitting IDLE** Dec 23, 2019
Summary

One of the first challenges that new programmers are faced with is figuring out what editing environment to use. For the past 20 years, Python has had an easy answer to that question in the form of IDLE. In this episode Tal Einat helps us explore its history, the ways it is used, how it was built, and what is in store for its future. Even if you have never used the IDLE editor yourself, it is still an important piece of Python’s strength and history, and this conversation helps to highlight why that is.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Tal Einat about the IDLE editor for Python, it’s history, and what is in store for its future

Interview

Introductions

How did you get introduced to Python?

For anyone who hasn’t used it, can you start by explaining what IDLE is?

IDLE has been part of the standard library for Python for quite some time now. What was the motivation for adding it to the core of Python?

How has the evolution of our computing environment changed the motivation for maintaining IDLE and the use cases that it is most beneficial for?

What are the benefits of including a basic editor in the default distribution of Python?

What are some of the ways in which it is often used?

What are the limiting factors that lead users to other IDEs or text editors?

What role do you think IDLE has played in the growth of Python?

What was your motivation for getting involved as a Python contributor and working on the implementation of IDLE?

How is IDLE implemented and what are some of the ways that it has evolved since its initial introduction?

How well has the code for IDLE aged as new features and capabilities are added to the language?

What are some of the integration points available for extending IDLE?

What are some of the most interesting or innovative ways that you have seen IDLE used and extended?

What is planned for the future of the IDLE module?

Keep In Touch

LinkedIn

@TalEinat on Twitter

taleinat on GitHub

Picks

Tobias

Mr. Robot

Tal

Captain Fantastic

The Lesson To Unlearn article by Paul Graham

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

IDLE

FullProof

Israel

Mandatory Military Service

Eric Idle

Monty Python

Visual Studio

IDLE-fork

Vi

Emacs

Sublime Text

Visual Studio Code

REPL == Read Eval Print Loop

Tcl/Tk

Tkinter

RPC == Remote Procedure Call

IDLEx

VPython

Podcast Episode

Python Turtle

SVN (Subversion)

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Riding The Rising Tides Of Python** Dec 16, 2019
Summary

The past two decades have seen massive growth in the language, community, and ecosystem of Python. The career of Pete Fein has occurred during that same period and his use of the language has paralleled some of the major shifts in focus that have occurred. In this episode he shares his experiences moving from a trader writing scripts, through the rise of the web, to the current renaissance in data. He also discusses how his engagement with the community has evolved, why he hasn’t needed to use any other languages in his career, and what he is keeping an eye on for the future.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Pete Fein about his voyage on the rising tide of Python

Interview

Introductions

How did you get introduced to Python?

I understand that you have used Python exclusively in your professional life. What other languages have you been exposed to and taken inspiration from?

What are some of the projects that you have been involved with which you are most proud of?

How has the community and your involvement with it changed over the years?

In your experience, how has the growth in the size and breadth of the community impacted its accessibility to newcomers?

You have been using Python and participating in the community for quite some time now, and there have been significant changes in both within that period. What are some of the most significant technological shifts that you have noticed and been a part of?

How have those shifts influenced the direction of your career?

As you have moved through the different phases of your career with different areas of focus, what are some of the aspects of the work which have remained constant?

What have been the biggest differences across the different problem domains?

What are some of the aspects of the language or its ecosystem which you feel are lacking or don’t get enough attention?

What are some of the industry trends which you are keeping a close eye on and how do you anticipate them influencing the direction of the community and your career in the upcoming years?

Keep In Touch

Consulting Website

Personal Website

@wearpants on Twitter

LinkedIn

wearpants on GitHub

Picks

Tobias

Matomo Analytics

Pete

FastAPI

PyDantic

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Chicago

Scheme

Structure and Interpretation of Computer Programs

David Beazley

Podcast Episode

Twiggy logging library

Jesse Noller

Log4J

Debian

RedHat

StructLog

Elliot

Podcast Episode

Logbook

Armin Ronacher

Podcast Episode

Pittsburgh Python Meetup

Boltons library

Elixir

ChiPy Chicago Python user group

Subversion

Ruby On Rails

Django

Data Engineering

Data Engineering Podcast

Internet of Things

Pittsburgh

Artificial Pancreas Project

Eric Holscher

Read The Docs

Podcast Episode

Circuit Playground Express

CircuitPython

Podcast Episode

Rust Language

PyOhio

PyGotham

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Debugging Python Projects With PySnooper** Dec 09, 2019
Summary

Debugging is a painful but necessary practice in software development. The tools that are available in Python range from the built-in debugger, to tools integrated with your coding environment, to the trusty print function. In this episode Ram Rachum describes his work on PySnooper and how it can be used to speed up your problem solving in complex or legacy applications.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, or running your build servers, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Ram Rachum about PySnooper, an alternative approach to debugging your python projects

Interview

Introductions

How did you get introduced to Python?

How do developers normally debug their code, and what need does PySnooper address that isn’t addressed by the established methods?

What is the workflow for using PySnooper for investigating or debugging a project? (This will probably be answered in the answer to the question above)

What are some of the pieces of information that it surfaces and how do they aid the developer in directing their investigation?

What were some of the projects that you were testing it with and how did they influence the direction that you took PySnooper?

Can you describe how PySnooper is implemented and some of the ways that it has evolved since you first began working on it?

What are some of the initial goals that you had for the project which you have since abandoned as either not useful or too challenging to implement?

What are some of the edge cases or technical challenges that you have encountered while working on PySnooper, either in Python itself or in the tool?

There is another project called Snoop which builds on top of your work on PySnooper to add some extra functionality and developer ergonomics. What, if anything, was your reaction to it and how has it influenced your work on PySnooper?

One of the notable aspects of your work on PySnooper is the amount of attention that it garnered shortly after you published it. How has that visibility affected the long-term popularity and use of PySnooper?

What have been some of the most interesting, unexpected, or difficult aspects of creating, maintaining, and promoting PySnooper?

What do you have planned for the future of the project?

Keep In Touch

cool-RR on GitHub

Personal Website

Consulting Website

Picks

Tobias

PyCon US

Call for proposals

Registration

Ram

Nonviolent communication

Links

PySnooper

Ram’s Python workshops

The PyWeb-IL meetup

BlueVine’s career page Submit your CV to Ram’s email mailto:ram@rachum.com

Tel Aviv Israel

Paul Graham

Y Combinator startup accelerator

Wing IDE

PyCharm

sys.settrace

Python f_trace

coverage.py

Podcast.init Interview

PEP == Python Enhancement Proposal

Podcast Episode

snoop project

Alex Hall

pdb

pudb

pdb++

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Making Complex Software Fun And Flexible With Plugin Oriented Programming** Dec 03, 2019
Summary

Starting a new project is always exciting because the scope is easy to understand and adding new features is fun and easy. As it grows, the rate of change slows down and the amount of communication necessary to introduce new engineers to the code increases along with the complexity. Thomas Hatch, CTO and creator of SaltStack, didn’t want to accept that as an inevitable fact of software, so he created a new paradigm and a proof-of-concept framework to experiment with it. In this episode he shares his thoughts and findings on the topic of plugin oriented programming as a way to build and scale complex projects while keeping them fun and flexible.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Thomas Hatch about his work on the POP library and how he is using plugin oriented programming in his work at SaltStack

Interview

Introductions

How did you get introduced to Python?

Can you start by giving your definition of Plugin Oriented Programming and your thoughts on what benefits it provides?

You created the POP library as a framework for enabling developers to incorporate this pattern into their own projects. What capabilities does that framework provide and what was your motivation for creating it?

How has your work on Salt influenced your thinking on how to implement plugins for software projects?

How does POP fit into the future of the SaltStack project?

What are some of the advanced patterns or paradigms that the POP model allows for?

Can you describe how the POP library itself is implemented and some of the ways that its design has evolved since you first began experimenting with it?

What are some of the languages or libraries that you have looked at for inspiration in your design and philosophy around this development pattern?

For someone who is building a project on top of POP what does their workflow look like and what are some of the up-front design considerations they should be thinking of?

How do you define and validate the contract exposed by or expected from a plugin subsystem?

One of the interesting capabilities that you highlight in the documentation is the concept of merging applications. What are your thoughts on the challenges that an engineer might face when merging library or microservice applications built with POP into a single deployable artifact?

What would be involved in going the other direction to split a single application into independently runnable microservices?

When extracting common functionality from a group of existing applications, what are the relative merits of creating a plugin sybsystem vs writing a library?

How does the system design of a POP application impact the available range of communication patterns for software and the teams building it?

What are some antipatterns that you anticipate for teams building their projects on top of POP?

In the documentation you mention that POP is just an example implementation of the broader pattern and that you hope to see other languages and developer communities adopt it. What are some of the barriers to adoption that you foresee?

What are some of the limitations of POP or cases where you would recommend against following this paradigm?

What are some of the most interesting, innovative, or unexpected ways that you have seen POP used?

What have been some of the most interesting, unexpected, or challenging aspects of building POP?

What do you have planned for the future of the POP library, or any applications where you plan to employ this pattern?

Keep In Touch

thatch45 on GitHub

@thatch45 on Twitter

Picks

Tobias

The Man In The High Castle TV series

Thomas

Jack Ryan TV Series

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Episode 1

POP

SaltStack

Ruby

Microservices

Linus Torvalds

SaltConf

SaltStack Thorium

Salt Beacons

Salt Reactors

Salt Grains

Idem

AsyncIO

Nim

OCaml

Julia

LLVM

Object Oriented Programming

Go Language

Rust

RBAC == Role Based Access Control

The Mythical Man Month

Linux Kernel

Heist

Umbra

Flow Programming

Magic The Gathering

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Faster And Safer Software Development With Feature Flags** Nov 26, 2019
Summary

Any software project that is worked on or used by multiple people will inevitably reach a point where certain capabilities need to be turned on or off. In this episode Pete Hodgson shares his experience and insight into when, how, and why to use feature flags in your projects as a way to enable that practice. In addition to the simple on and off controls for certain logic paths, feature toggles also allow for more advanced patterns such as canary releases and A/B testing. This episode has something useful for anyone who works on software in any language.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Pete Hodgson about the concept of feature flags and how they can benefit your development workflow

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what a feature flag is?

What was your first experience with feature flags and how did it affect your approach to software development?

What are some of the ways that feature flags are used?

What are some antipatterns that you have seen for teams using feature flags?

What are some of the alternative development practices that teams will employ to achieve the same or similar outcomes to what is possible with feature flags?

Can you describe some of the different approaches to implementing feature flags in an application?

What are some of the common pitfalls or edge cases that teams run into when building an in-house solution?

What are some useful considerations when making a build vs. buy decision for a feature toggling service?

What are some of the complexities that get introduced by feature flags for mantaining application code over the long run?

What have you found to be useful or effective strategies for cataloging and documenting feature toggles in an application, particularly if they are long lived or for open source applications where there is no institutional context?

Can you describe some of the lifecycle considerations for feature flags, and how the design, implementation, or use of them changes for short-lived vs long-lived use cases?

What are some cases where the overhead of implementing and maintaining a feature flag infrastructure outweighs the potential benefit?

What advice or references do you recommend for anyone who is interested in using feature flags for their own work?

Keep In Touch

Website

@ph1 on Twitter

moredip on GitHub

Picks

Tobias

Circuit Playground Express

CircuitPython Episode

Pete

Accelerate by Nicole Forsgren, Jez Humble, and Gene Kim

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Perl

Ruby

Django

Feature Flag

Pete’s Blog Post On Feature Flags

Thoughtworks

Continuous Delivery

Continuous Delivery Book

Trunk Based Development

Branch By Abstraction

Technical Debt

Strategy Pattern

Polymorphism

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**From Simple Script To Beautiful Web Application With Streamlit** Nov 18, 2019
Summary

Building well designed and easy to use web applications requires a significant amount of knowledge and experience across a range of domains. This can act as an impediment to engineers who primarily work in so-called back-end technologies such as machine learning and systems administration. In this episode Adrien Treuille describes how the Streamlit framework empowers anyone who is comfortable writing Python scripts to create beautiful applications to share their work and make it accessible to their colleagues and customers. If you have ever struggled with hacking together a simple web application to make a useful script self-service then give this episode a listen and then go experiment with how Streamlit can level up your work.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Adrien Treuille about Streamlit, an open source app framework built for machine learning and data science teams

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Streamlit is and its origin story?

What are some of the types of applications that are commonly built by data teams and who are the typical consumers of those projects?

What are some of the challenges or complications that are unique to this problem space?

What are some of the complications or challenges that you have faced to integrate Streamlit with so many different machine learning frameworks?

Can you describe the technical implementation of Streamlit and how it has evolved since you began working on it?

How did you approach the design of the API and development workflow to tailor it for the needs and capabilities of machine learning engineers?

If you were to start the project from scratch today what would you do differently?

What is a typical workflow for someone working on a machine learning application and how does Streamlit fit in?

What are some of the types of tools or processes that it replaces?

What are some of the most interesting or unexpected ways that you have seen Streamlit used?

What have you found to be some of the most challenging or unexpected aspects of building and evolving Streamlit?

How do you see Python evolving in light of Streamlit and other work in the machine learning space?

What do you have in store for the future of Streamlit or any adjacent products and services?

How are you approaching the governance and sustainability of the Streamlit open source project?

Keep In Touch

Website

LinkedIn

@myelbows on Twitter

treuille on GitHub

Picks

Tobias

The Book Of Why by Judea Pearl

Adrien

No Self, No Problem by Anam Thubten

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Streamlit

Forum

GitHub

Twitter

Carnegie Mellon University

Google X

Zoox

IBM

Cornell University

NumPy

SciPy

Machine Learning Engineer

Jupyter

DeckGL

Matplotlib

Plotly

Seaborn

Altair

PyTorch

Tensorflow

Protocol Buffers

Streamlit for teams

Heroku

EC2

React JS

Awesome Streamlit

Flask

Plotly Dash

Voila

NeurIPS

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Automate Your Server Security With GrapheneX** Nov 11, 2019
Summary

The internet is rife with bots and bad actors trying to compromise your servers. To counteract these threats it is necessary to diligently harden your systems to improve server security. Unfortunately, the hardening process can be complex or confusing. In this week’s episode 18 year old Orhun Parmaksiz shares the story of how he and his friends created the GrapheneX framework to simplify the process of securing and maintaining your servers using the power and flexibility of Python. If you run your own software then this is definitely worth a listen.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Orhun Parmaksiz about GrapheneX, a framework for simplifying the process of hardening your servers

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what we mean when we talk about hardening of servers?

What are the common ways of hardening a system, which techniques can we use for this purpose?

What are some of the high level categories of threats that operators should be considering?

What is GrapheneX and what was your motivation for creating it?

How does GrapheneX aid users in the process of increasing the security of their infrastructure?

Is any extra operating system knowledge required for using GrapheneX?

Can you talk through the workflow for someone using GrapheneX to harden their systems?

What options does it support for managing deployment across a fleet of servers?

Some security controls can actually prevent proper operation of the applications and services that are deployed on a server. How do you approach preventing those scenarios or educating the users in determining which controls are appropriate?

Why did you choose Python for a project like GrapheneX?

How is GrapheneX implemented?

How has the design evolved since you first began working on it?

If you were to start the project over today, what would you do differently?

Do you accept contributions to the framework? If so, what kind of contributions are needed for improving GrapheneX?

For someone who is interested in adding a new module to the framework, what is involved?

What have you found to be the most interesting or challenging aspects of your work on GrapheneX?

What, if any, aspects of server security have you consciously avoided implementing in GrapheneX?

What are your future plans about the GrapheneX?

Keep In Touch

Orhun

GitHub

Twitter

LinkedIn

Picks

Tobias

Chess

Orhun

Creeping in My Soul by Cryoshell

Gravity Hurts by Cryoshell

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

GrapheneX

GitHub

Website

PyPI

Twitter

Trello

Graphene

New Modules for GNU/Linux & Windows (Issue)

Flask

Flask-SocketIO

React

trimstray/linux-hardening-checklist

The Windows Server Hardening Checklist

Firewall

Windows Firewall

Linux iptables

PCI-DSS 2.2 requirement- server hardening standards

CIS Benchmarks

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Accelerating The Adoption Of Python At Wayfair** Nov 03, 2019
Summary

Large companies often have a variety of programming languages and technologies being used across departments to keep the business running. Python has been gaining ground in these environments because of its flexibility, ease of use, and developer productivity. In order to accelerate the rate of adoption at Wayfair this week’s guest Jonathan Biddle started a team to work with other engineering groups on their projects and show them how best to take advantage of the benefits of Python. In this episode he explains their operating model, shares their success stories, and provides advice on the pitfalls to avoid if you want to follow in his footsteps. This is definitely worth a listen if you are using Python in your work or would like to aid in its adoption.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Having all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Jonathan Biddle about his work to encourage and empower Wayfair engineers in their use of Python

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the mission statement for you and your team at Wayfair?

What is the origin story for how your group got started?

How and where was Python being used within Wayfair at the time?

What are the primary languages that are used throughout Wayfair?

What is involved in the selection process for a language and technology stack for new projects within Wayfair?

Can you describe how and why you work with different groups throughout Wayfair?

What are some of the common misconceptions or barriers that you encounter when working with other engineering and product teams about how and where Python will be useful?

How large is your team currently and what is the length of a typical engagement?

How has the scale and scope of your work changed since your group was first formed?

How many different product teams have you worked with at this point and what are some of the notable outcomes?

What are some of the most challenging aspects, both technical and organizational, of educating other engineers on when and how to use Python?

Can you share some examples of engagements that you would classify as a failure?

What lessons have you learned from those situations?

What advice do you have for other groups or organizations who may be considering or actively launching similar initiatives?

Keep In Touch

Website

LinkedIn

@jonbiddle on Twitter

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Picks

Tobias

Learning Bayesian Statistics Podcast

Jonathan

PyDantic

FastAPI

MKDocs

Links

Wayfair

Zope

Django

PHP

Java

Javascript

.NET

Kafka

Jack Diederich – Stop Writing Classes

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building Quantum Computing Algorithms In Python** Oct 29, 2019
Summary

Quantum computers are the biggest jump forward in processing power that the industry has seen in decades. As part of this revolution it is necessary to change our approach to algorithm design. D-Wave is one of the companies who are pushing the boundaries in quantum processing and they have created a Python SDK for experimenting with quantum algorithms. In this episode Alexander Condello explains what is involved in designing and implementing these algorithms, how the Ocean SDK helps you in that endeavor, and what types of problems are well suited to this approach.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Alex Condello about the Ocean SDK from D-Wave for building quantum algorithms in Python

Interview

Introductions

How did you get introduced to Python?

Can you start by giving a high-level overview of quantum computing?

What is the Ocean SDK and how does it fit into the business model for D-Wave?

What are some of the problem types that a quantum processor is uniquely well suited for?

How does the overall system design for a quantum computer compare to that of the Von Neumann architecture that is common for the machines that we are all familiar with?

What are some of the differences in algorithm design when programming for a quantum processor?

Is there any specialized background knowledge that is necessary for making effective use of the QPU’s capabilities?

What are some of the common difficulties that you have seen users struggle with?

How does the Ocean SDK assist the developer in implementing and understanding the patterns necessary for Quantum algorithms?

What was the motivation for choosing Python as the target language for an SDK to attract developers to experiment with quantum algorithms?

Can you describe how the SDK is implemented and some of the integrations that are necessary for being able to operate on a quantum processor?

What have you found to be some of the most interesting, challenging, or unexpected aspects of your work on the Ocean software stack?

How do you handle the abstraction of the execution context to allow for replicating the program behavior on CPU/GPU vs QPU

Is there any potential for quantum computing to impact research in previously intractable computer science research, such as the P vs NP problem?

What are your current scaling limits in terms of providing compute to customers for their problems?

What are some of the most interesting, innovative, or unexpected ways that you have seen developers use the Ocean SDK and quantum processors?

What are you most excited for as you look to the future capabilities of quantum systems?

What are some of the upcoming challenges that you anticipate for the quantum computing industry?

Keep In Touch

arcondello on GitHub

Picks

Tobias

QuTip Podcast Interview

Alex

Cython

Podcast Interview

Links

Ocean SDK

D-Wave

Quantum Computing

Quantum Annealing

Quantum Superposition

Qubit

D-Wave Leap

Von Neumann Architecture

Cuda

Linear Programming

D-Wave ML Papers

D-Wave NetworkX

Maximum Cut Problem

Ising Problem

Los Alamos National Laboratory

Vertex Cover Problem

D-Wave Hybrid

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Illustrating The Landscape And Applications Of Deep Learning** Oct 22, 2019
Summary

Deep learning is a phrase that is used more often as it continues to transform the standard approach to artificial intelligence and machine learning projects. Despite its ubiquity, it is often difficult to get a firm understanding of how it works and how it can be applied to a particular problem. In this episode Jon Krohn, author of Deep Learning Illustrated, shares the general concepts and useful applications of this technique, as well as sharing some of his practical experience in using it for his work. This is definitely a helpful episode for getting a better comprehension of the field of deep learning and when to reach for it in your own projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Jon Krohn about his recent book, deep learning illustrated

Interview

Introductions

How did you get introduced to Python?

Can you start by giving a brief description of what we’re talking about when we say deep learning and how you got involved with the field?

How does your background in neuroscience factor into your work on designing and building deep learning models?

What are some of the ways that you leverage deep learning techniques in your work?

What was your motivation for writing a book on the subject?

How did the idea of including illustrations come about and what benefit do they provide as compared to other books on this topic?

While planning the contents of the book what was your thought process for determining the appropriate level of depth to cover?

How would you characterize the target audience and what level of familiarity and proficiency in employing deep learning do you wish them to have at the end of the book?

How did you determine what to include and what to leave out of the book?

The sequencing of the book follows a useful progression from general background to specific uses and problem domains. What were some of the biggest challenges in determining which domains to highlight and how deep in each subtopic to go?

Because of the continually evolving nature of the field of deep learning and the associated tools, how have you guarded against obsolescence in the content and structure of the book?

Which libraries did you focus on for your examples and what was your selection process?

Now that it is published, is there anything that you would have done differently?

One of the critiques of deep learning is that the models are generally single purpose. How much flexibility and code reuse is possible when trying to repurpose one model pipeline for a slightly different dataset or use case?

I understand that deployment and maintenance of models in production environments is also difficult. What has been your experience in that regard, and what recommendations do you have for practitioners to reduce their complexity?

What is involved in actually creating and using a deep learning model?

Can you go over the different types of neurons and the decision making that is required when selecting the network topology?

In terms of the actual development process, what are some useful practices for organizing the code and data that goes into a model, given the need for iterative experimentation to achieve desired levels of accuracy?

What is your personal workflow when building and testing a new model for a new use case?

What are some of the limitations of deep learning and cases where you would recommend against using it?

What are you most excited for in the field of deep learning and its applications?

What are you most concerned by?

Do you have any parting words or closing advice for listeners and potential readers?

Keep In Touch

Website

@jonkrohnlearns on Twitter

jonkrohn on GitHub

Picks

Tobias

Spurious Correlations

Jon

Data Elixir Newsletter

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Untapt

Deep Learning Illustrated

Pearson

Columbia University

New York City Data Science Academy

NIH (National Institutes of Health)

Oxford Uniersity

Matlab

R Language

Neuroscience

Artificial Neural Network

Deep Learning

Natural Language Processing

Computer Vision

Generative Adversarial Networks

Deep Learning by Ian Goodfellow, et al.

Hands On Machine Learning by Aurélien Géron

O’Reilly Online Learning

Transfer Learning

Keras

Tensorflow

PyTorch

Gary Marcus

Judea Pearl

Artificial General Intelligence

Explainable AI

Yuval Noah Harrari

Sapiens

Home Deus

Wait But Why?

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Andrew's Adventures In Coderland** Oct 14, 2019
Summary

Software development is a unique profession in many ways, and it has given rise to its own subculture due to the unique sets of challenges that face developers. Andrew Smith is an author who is working on a book to share his experiences learning to program, and understand the impact that software is having on our world. In this episode he shares his thoughts on programmer culture, his experiences with Python and other language communities, and how learning to code has changed his views on the world. It was interesting getting an anthropological perspective from a relative newcomer to the world of software.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, Data Council in Barcelona, and the Data Orchestration Summit. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Andrew Smith about his anthropological study of software engineering culture in his upcoming book Adventures In Coderland.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the scope and intent of your work on Adventures In Coderland?

What was your motivation for embarking on this particular project?

Prior to the start of your research for this book, what was your level of familiarity with software development as a discipline and a cultural phenomenon?

How are you approaching the research for this book and to what level of detail are you trying to address the problem space?

What are some of the most striking contrasts that you have identified between software engineers and coding culture as it compares to that of a layperson?

We met at the most recent PyCon US, which I understand you attended as a means of conducting research for your book. What are some of the notable aspects of the Python community that you discovered while you were attending?

What are some of the other programming communities that you have engaged with?

What are some of the differentiating factors that you have noticed between the communities that you have interacted with?

What are some of the most surprising discoveries that you have made in the process of writing this book?

What is your metric for determining when you have gathered enough raw material to complete the book?

Now that you have delved into the peculiarities of "coderland", how has it changed your own outlook on both the software industry, and society at large?

What advice do you have for the engineers who are listening as it pertains to your experiences in writing your book?

Keep In Touch

Website

@wiresmith on Twitter

Picks

Tobias

Throughline Podcast

Andrew

20 Thousand Hertz Podcast

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Linksj

Adventures In Coderland

https://us.pycon.org?utm_source=rss&utm_medium=rss

Nicholas Tollervey

1843 Magazine

The Economist

Free Code Camp

Code Golf

Moon Dust book about the astronauts who first landed on the moon

The Face magazine

The Observer

The Guardian

Charlie Duke

Totally Wired

Code For America

Supercollider programming environment

SonicPi

George Boole

FMRI (Functional Magnetic Resonance Imaging)

Ruby Language

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Network Automation At Enterprise Scale With Python** Oct 08, 2019
Summary

Designing and maintaining enterprise networks and the associated hardware is a complex and time consuming task. Network automation tools allow network engineers to codify their workflows and make them repeatable. In this episode Antoine Fourmy describes his work on eNMS and how it can be used to automate enterprise grade networks. He explains how his background in telecom networking led him to build an open source platform for network engineers, how it is architected, and how you can use it for creating your own workflows. This is definitely worth listening to as a way to gain some appreciation for all of the work that goes on behind the scenes to make the internet possible.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Antoine Fourmy about eNMS, an enterprise-grade vendor-agnostic network automation platform.

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what eNMS is

What was your motivation for creating it?

Who are the target users of eNMS and how much background knowledge of network management is required to be effective with it?

What are some of the alternative tools that exist in this space and why might a network operator choose to use eNMS in their place?

What are some of the most challenging aspects of network creation and maintenance and how does eNMS assist with them?

What are some of the mundane and/or error-prone tasks that can be replaced or automated with eNMS?

What are some of the additional features that come into play for more complex networking tasks?

Can you describe the system architecture of eNMS and how it has evolved since you first began working on it?

eNMS is an impressive project that looks to have a substantial amount of polish. How large is the overall community of users and contributors?

For someone who wants to get involved in contributing to eNMS what are some of the types of skills and background that would be helpful?

What are some of the most innovative/unexpected ways that you have seen eNMS used?

When is eNMS the wrong choice?

What do you have planned for the future of the project?

Keep In Touch

Website

LinkedIn

afourmy on GitHub

Picks

Tobias

Tedeschi Trucks Band

Antoine

CheckIO

Podcast Episode

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

eNMS

Orange

Netmiko

NAPALM

Podcast Episode

Paramiko

Ansible

Requests

OpenNMS

LibreNMS

Ansible Tower

Rundeck

SaltStack

Podcast Episode

StackStorm

Podcast Episode

SaltStack Proxy Minions

Hashicorp Vault

VirtualBox

Flask

Django

SQLAlchemy

APScheduler

Docker

Podcast Episode

Redis

Celery

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building A Modern Discussion Forum In Python To Support Healthy Communities** Oct 01, 2019
Summary

Building and sustaining a healthy community requires a substantial amount of effort, especially online. The design and user experience of the digital space can impact the overall interactions of the participants and guide them toward respectful conversation. In this episode Rafał Pitoń shares his experience building the Misago platform for creating community forums. He explains his motivation for creating the project, the lessons he has learned in the process, and how it is being used by himself and others. This was a great conversation about how technology is just a means, and not the end in itself.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, Data Council in Barcelona, and the Data Orchestration Summit. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Rafał Pitoń about Misago, a fully featured modern forum application that is fast, scalable, and responsive

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Misago is and your motivation for creating it?

How does it compare to other modern forum options such as Discourse and Flarum?

How did you generate and prioritize the set of features that you have implemented and what are the main capabilities that are still on your roadmap?

Is Misago intended to be run in isolation, or does it allow for integrating into a larger Django project?

Is there any support for multi-tenancy?

How is Misago itself implemented and how has the architecture evolved since you first began working on it?

If you were to start it today, what are some of the choices that you would make differently?

What are the extension points that developers can hook into for adding custom functionality?

In addition to the technical challenges, managing a forum involves a fair amount of social challenges. How does Misago help with management of a healthy community?

How do different design elements factor into promoting healthy conversation and sustainable engagement?

What are some of the aspects of community management and the accompanying platform features that enable them which aren’t initially obvious?

For someone who wants to use Misago, what is involved in deploying and configuring it?

What are some of the routine maintenance tasks that they should be aware of?

What are some of the most interesting or unexpected ways that you have seen Misago used?

What have you found to be the most interesting, unexpected, and challenging aspects of building and maintaining a forum platform?

What do you have planned for the future of Misago?

Keep In Touch

rafalp on GitHub

@RafalPiton on Twitter

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Picks

Tobias

Fear Innoculum by Tool

Rafał

github.com/encode

Ariadne GraphQL Library

Links

Misago

Poland

Mirumee

Saleor Episode

PHP

Discourse

Flarum

MySQL

PostgreSQL

Data Engineering Podcast Interview

jQuery

DJango Rest Framework

EmberJS

MithrilJS

AngularJS

ReactJS

PHPBB

Celery

GDPR == General Data Privacy Regulation

Docker

misago_docker

VPS == Virtual Private Server

Nginx

Starlette Async API framework

Ariadne GraphQL Library

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Exploratory Data Analysis Made Easy At The Command Line** Sep 23, 2019
Summary

There are countless tools and libraries in Python for data scientists to perform powerful analyses, but they often have a setup cost that acts as a barrier to ad-hoc exploration of data. Visidata is a command line application that eliminates the friction involved with starting the discovery process. In this episode Saul Pwanson explains his motivation for creating it, why a terminal environment is a useful place for this work, and how you can use Visidata for your own work. If you have ever avoided looking at a data set because you couldn’t be bothered with the boilerplate for a Jupyter notebook, then Visidata is the perfect addition to your toolbox.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Saul Pwanson about Visidata, a terminal oriented interactive multitool for tabular data

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Visidata is and how the project got started?

What are the main use cases for Visidata?

What are some tools that it has replaced in your workflow?

Can you talk through a typical workflow for data exploration and analysis with Visidata?

One of the capabilities that you mention on the website is quickly opening large files. What are some strategies that you have used to enable performant access for files that might crash a typical editor (e.g. Vim, Emacs)?

Can you describe how Visidata is implemented and how it has evolved since you started working on it (including the upcoming 2.0 release)?

What libraries or language features have proven most useful?

Why did you choose to implement Visidata as a terminal only tool and what constraints does that bring with it?

What are some of the most challenging aspects of building a terminal UI for data exploration and analysis?

Because of its manifestation as a terminal/CLI application it relies heavily on keyboard bindings. How do you approach key assignments to ensure a consistent and intuitive user experience?

What are some of the types of analysis that Visidata can be used for out of the box?

What are some of the most interesting/unexpected/innovative ways that you have seen Visidata used?

How much community adoption have you seen and how do you approach project governance as a solo developer?

What do you have planned for the future of Visidata?

Keep In Touch

Website

saulpw on GitHub

@saulfp on Twitter

LinkedIn

Picks

Tobias

Data Is Plural newsletter

Saul

TMate

Mosh – The Mobile Shell

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Visidata

F5 Networks

HDF5

PyTables

Podcast Interview

vgit

vping

Jeremy Singer-Vine

data.boston.gov

Recurse Center

Curses

dateutil

decorators

Electron

OpenRefine

Tmux

Visicalc

Windows Subsystem For Linux

Saul’s Lightning Talk

The Book of Visidata

Where In The World Is Carmen San Diego

Oh My Zsh

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Cultivating The Python Community In Argentina** Sep 18, 2019
Summary

The Python community in Argentina is large and active, thanks largely to the motivated individuals who manage and organize it. In this episode Facundo Batista explains how he helped to found the Python user group for Argentina and the work that he does to make it accessible and welcoming. He discusses the challenges of encompassing such a large and distributed group, the types of events, resources, and projects that they build, and his own efforts to make information free and available. He is an impressive individual with a substantial list of accomplishments, as well as exhibiting the best of what the global Python community has to offer.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Facundo Batista about his experiences founding and fostering the Argentinian Python community, working as a core developer, and his career in Python

Interview

Introductions

How did you get introduced to Python?

What was your motivation for organizing a Python user group in Argentina?

How does the geography and culture of Argentina influence the focus of the community?

Argentina is a fairly large country. What is the reasoning for having the user group encompass the whole nation and how is it organized to provide access to everyone?

What are some notable projects that have been built by or for members of PyAr?

What are some of the challenges that you faced while building CDPedia and what aspects of it are you most proud of?

How did you get started as a core developer?

What areas of the language and runtime have you been most involved with?

As a core developer, what are some of the most interesting/unexpected/challenging lessons that you have learned?

What other languages do you currently use and what is it about Python that has motivated you to spend so much of your attention on it?

What are some of the shortcomings in Python that you would like to see addressed in the future?

Outside of CPython, what are some of the projects that you are most proud of?

How has your involvement with core development and PyAr influenced your life and career?

Keep In Touch

@facundobatista on Twitter

Blog

Picks

Tobias

Dictionary of Difficult Words

Facundo

Fades

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

PyAr

Argentina

PyAr Mailing List

PyAr Telegram

PyCon Argentina

Buenos Aires

Cordoba

Rosario

Mendoza

CDPedia

PyCamp

PSF == Python Software Foundation

Wikipedia

Internet Archive

Decimal Module

PEP 327

Tim Peters

Canonical

Tennis

Fades

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Python Powered Journalistic Freedom With SecureDrop** Sep 10, 2019
Summary

The internet has made it easier than ever to share information, but at the same time it has increased our ability to track that information. In order to ensure that news agencies are able to accept truly anonymous material submissions from whistelblowers, the Freedom of the Press foundation has supported the ongoing development and maintenance of the SecureDrop platform. In this episode core developers of the project explain what it is, how it protects the privacy and identity of journalistic sources, and some of the challenges associated with ensuring its security. This was an interesting look at the amount of effort that is required to avoid tracking in the modern era.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Jen Helsby and Kushal Das about SecureDrop, a secure platform for submitting and receiving documents anonymously

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what SecureDrop is and how it got started?

How did you get involved in the project?

Can you give some background on where and why it is useful?

For someone using a running instance, what does their workflow look like?

What are some of the ways that you minimize user experience hurdles to prevent them from circumventing the security through laziness or apathy?

I was a bit surprised to see the references to the messaging system that is included. Why is that an important feature?

What form do the submissions generally take and what are the limits on formats that you can accept?

How is the system itself architected and how has the design evolved since the first implementation?

In terms of the security protocols and technologies that are implemented, what factors are you considering as you develop the project?

What are the weak points or edge cases that could lead to compromise and how do you guard against them?

In terms of the deployment and maintenance of a SecureDrop instance, how much technological sophistication is necessary for the organization running it, and how much effort do you put into simplifying it?

What are some of the notable uses of a SecureDrop deployment and what motivates you to continue working on it?

What are the most interesting/innovative/unexpected uses of SecureDrop that you have seen?

How do you approach the sustainability of the platform?

What have you found most challenging/interested/unexpected in your work on SecureDrop?

What is in store for the future of the project?

Keep In Touch

Jen

@redshiftzero on Twitter

redshiftzero on GitHub

Blog

Kushal

Website

@kushaldas on Twitter

kushaldas on GitHub

Picks

Tobias

Laser Tag

Kushal

Permanent Record by Edward Snowden

Jen

Permanent Record by Edward Snowden

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

SecureDrop

Aaron Swartz

Freedom Of The Press Foundation

SecureDrop Directory

TOR Browser

TOR == The Onion Router

Tails OS

Ubuntu

IDS == Intrusion Detection System

Ansible

DEF CON

Mozilla Open Source Support (MOSS)

Testinfra

Flask

Molecule unit test library for Ansible

Bandit

Safety

Qubes OS

Qt

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Combining Python And SQL To Build A PyData Warehouse** Sep 02, 2019
Summary

The ecosystem of tools and libraries in Python for data manipulation and analytics is truly impressive, and continues to grow. There are, however, gaps in their utility that can be filled by the capabilities of a data warehouse. In this episode Robert Hodges discusses how the PyData suite of tools can be paired with a data warehouse for an analytics pipeline that is more robust than either can provide on their own. This is a great introduction to what differentiates a data warehouse from a relational database and ways that you can think differently about running your analytical workloads for larger volumes of data.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Taking a look at recent trends in the data science and analytics landscape, it’s becoming increasingly advantageous to have a deep understanding of both SQL and Python. A hybrid model of analytics can achieve a more harmonious relationship between the two languages. Read more about the Python and SQL Intersection in Analytics at mode.com/init. Specifically, we’re going to be focusing on their similarities, rather than their differences.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Robert Hodges about how the PyData ecosystem can play nicely with data warehouses

Interview

Introductions

How did you get introduced to Python?

To start with, can you give a quick overview of what a data warehouse is and how it differs from a "regular" database for anyone who isn’t familiar with them?

What are the cases where a data warehouse would be preferable and when are they the wrong choice?

What capabilities does a data warehouse add to the PyData ecosystem?

For someone who doesn’t yet have a warehouse, what are some of the differentiating factors among the systems that are available?

Once you have a data warehouse deployed, how does it get populated and how does Python fit into that workflow?

For an analyst or data scientist, how might they interact with the data warehouse and what tools would they use to do so?

What are some potential bottlenecks when dealing with the volumes of data that can be contained in a warehouse within Python?

What are some ways that you have found to scale beyond those bottlenecks?

How does the data warehouse fit into the workflow for a machine learning or artificial intelligence project?

What are some of the limitations of data warehouses in the context of the Python ecosystem?

What are some of the trends that you see going forward for the integration of the PyData stack with data warehouses?

What are some challenges that you anticipate the industry running into in the process?

What are some useful references that you would recommend for anyone who wants to dig deeper into this topic?

Keep In Touch

LinkedIn

hodgesrm on GitHub

Picks

Tobias

Foundations Of Architecting Data Solutions: Managing Successful Data Projects by Ted Malaska & Jonathan Seidman

Robert

Reading old academic papers such as CStore

Python Machine Learning by Sebastian Raschka

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

Altinity

Clickhouse

Data Engineering Podcast Interview

MySQL

Data Warehouse

Column Oriented Database

SIMD == Single Instruction Multiple Data

PostgreSQL

Data Engineering Podcast Episode

Microsoft SQL Server

Pandas

NumPy

Tensorflow

Jupyter

Data Sampling

Dask

Data Engineering Podcast

Ray

Map/Reduce

Vertica

Sharding

Hadoop

SnowflakeDB

Delta Lake

Data Engineering Podcast Episode

BigQuery

RedShift

Snowflake Data Sharing

OracleDB

Kubernetes

DBT

Data Engineering Podcast Episode

CSV

Parquet

Data Engineering Podcast Episode

Kafka

UC Davis

Web Scraping

Clickhouse Python Driver

SQLAlchemy

Altinity Blog Post

Materialized View

PyTorch

Podcast Interview

scikit-learn

Spark

Data Engineering Podcast Interview

BigQuery ML

Apache Arrow

Wes McKinney

Podcast Interview

User Defined Function

KDB

CStore Paper by Dr. Michael Stonebraker, et al

Kinetica

MapD/OmniSci

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**AI Driven Automated Code Review With DeepCode** Aug 26, 2019
Summary

Software engineers are frequently faced with problems that have been fixed by other developers in different projects. The challenge is how and when to surface that information in a way that increases their efficiency and avoids wasted effort. DeepCode is an automated code review platform that was built to solve this problem by training a model on a massive array of open sourced code and the history of their bug and security fixes. In this episode their CEO Boris Paskalev explains how the company got started, how they build and maintain the models that provide suggestions for improving your code changes, and how it integrates into your workflow.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Your host as usual is Tobias Macey and today I’m interviewing Boris Paskalev about DeepCode, an automated code review platform for detecting security vulnerabilities in your projects

Interview

Introductions

Can you start by explaining what DeepCode is and the story of how it got started?

How is the DeepCode platform implemented?

What are the current languages that you support and what was your guiding principle in selecting them?

What languages are you targeting next?

What is involved in maintaining support for languages as they release new versions with new features?

How do you ensure that the recommendations that you are making are not using languages features that are not available in the runtimes that a given project is using?

For someone who is using DeepCode, how does it fit into their workflow?

Can you explain the process that you use for training your models?

How do you curate and prepare the project sources that you use to power your models?

How much domain expertise is necessary to identify the faults that you are trying to detect?

What types of labelling do you perform to ensure that the resulting models are focusing on the proper aspects of the source repositories?

How do you guard against false positives and false negatives in your analysis and recommendations?

Does the code that you are analyzing and the resulting fixes act as a feedback mechanism for a reinforcement learning system to update your models?

How do you guard against leaking intellectual property of your scanned code when surfacing recommendations?

What have been some of the most interesting/unexpected/challenging aspects of building the DeepCode product?

What do you have planned for the future of the platform and business?

Keep In Touch

LinkedIn

Picks

Tobias

Redwall Series by Brian Jacques

Boris

Artifical Intelligence

Get outside

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Links

DeepCode

Zurich, Switzerland

BigCode

ETH Zurich

Datalog

F Strings

Data Classes

DeepCode Research

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Security, UX, and Sustainability For The Python Package Index** Aug 19, 2019
Summary

PyPI is a core component of the Python ecosystem that most developer’s have interacted with as either a producer or a consumer. But have you ever thought deeply about how it is implemented, who designs those interactions, and how it is secured? In this episode Nicole Harris and William Woodruff discuss their recent work to add new security capabilities and improve the overall accessibility and user experience. It is a worthwhile exercise to consider how much effort goes into making sure that we don’t have to think much about this piece of infrastructure that we all rely on.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Counsil. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Nicole Harris and William Woodruff about the work they are doing on the PyPI service to improve the security and utility of the package repository that we all rely on

Interview

Introductions

How did you get introduced to Python?

Can you start by sharing how you each got involved in working on PyPI?

What was the state of the system at the time that you first began working on it?

Once you committed to working on PyPI how did you each approach the process of identifying and prioritizing the work that needed to be done?

What were the most significant issues that you were faced with at the outset?

How often have the issues that you each focused on overlapped at the cross section of UX and security?

How do you balance the tradeoffs that exist at that boundary?

What is the surface area of the domains that you are each working in? (e.g. web UI, system API, data integrity, platform support, etc.)

What are some of the pain points or areas of confusion from a user perspective that you have dealt with in the process of improving the platform?

What have been the most notable features or improvements that you have each introduced to PyPI?

What were the biggest challenges with implementing or integrating those changes?

How do you approach introducing changes to PyPI given the volume of traffic that it needs to support and the level of importance that it serves in the community?

What are some examples of attack vectors that exist as a result of the nature of the PyPI platform and what are you most concerned by?

How does poor accessibility or user experience impact the utility of PyPI and the community members who interact with it?

What have you found to be the most interesting/challenging/unexpected aspects of working on Warehouse?

What are some of the most useful lessons that you have learned in the process?

What do you have planned for future improvements to the platform?

How can the listeners get involved and help out?

How was this work funded?

Keep In Touch

Nicole

@nlhkabu on Twitter

Website

If you’re using CI to upload to PyPI and would like to speak with Nicole please book a time here

If you’re using assistive technology and would like to speak with Nicole please book a time here

William

@8x5clPW2

Website

Email

Please get in touch if you’d like to work with Trail of Bits on your next security project!

Picks

Tobias

The Expanse TV Series

Nicole

The Great Hack documentary

William

Abraham Lincoln Autobiography by Carl Sandburg

Links

PyPI

Warehouse

Issue Tracker

Good First Issues

PeopleDoc

Trail of Bits

OSQuery

Django

Ruby

Python Software Foundation

Python Packaging Working Group

Podcast Episode

Donald Stufft

Podcast Episode

UX (User Experience) Design

OTF == Open Technology Fund

Bootstrap

TOTP

WebauthN

Yubikey

Changeset Consulting

Sumana Harihareswara

WCAG (Web Content Accessibility Guidelines) 2.0

Macaroon Security Tokens

Docker Compose

MOSS = Mozilla Open Source Support

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Learning To Program In Python With CodeGrades** Aug 12, 2019
Summary

With the increasing role of software in our world there has been an accompanying focus on teaching people to program. There are numerous approaches that have been attempted to achieve this goal with varying levels of success. Nicholas Tollervey has begun a new effort that blends the approach adopted by musicians and martial artists that uses a series of grades to provide recognition for the achievements of students. In this episode he explains how he has structured the study groups, syllabus, and evaluations to help learners build projects based on their interests and guide their own education while incorporating useful skills that are necessary for a career in software. If you are interested in learning to program, teach others, or act as a mentor then give this a listen and then get in touch with Nicholas to help make this endeavor a success.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today Nicholas Tollervey is back to talk about his work on CodeGrades, a new effort that he is building to blend his backgrounds in music, education, and software to help teach kids of all ages how to program.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what CodeGrades is and what motivated you to start this project?

How does it differ from other approaches to teaching software development that you have encountered?

Is there a particular age or level of background knowledge that you are targeting with the curriculum that you are developing?

What are the criteria that you are measuring against and how does that criteria change as you progress in grade levels?

For someone who completes the full set of levels, what level of capability would you expect them to have as a developer?

Given your affiliation with the Python community it is understandable that you would target that language initially. What would be involved in adapting the curriculum, mentorship, and assessments to other languages?

In what other ways can this idea and platform be adapted to accomodate other engineering skills? (e.g. system administration, statistics, graphic design, etc.)

What interesting/exciting/unexpected outcomes and lessons have you found while iterating on this idea?

For engineers who would like to be involved in the CodeGrades platform, how can they contribute?

What challenges do you anticipate as you continue to develop the curriculum and mentor networks?

How do you envision the future of CodeGrades taking ship in the medium to long term?

Keep In Touch

ntoll on GitHub

Website

@ntoll on Twitter

Picks

Tobias

Parsy

Nevermoor: The Trials of Morrigan Crow

Nicholas

Kivy

Wittgenstein: The Duty Of Genious

The Hitchhiker’s Guide To The Galaxy by Douglas Adams

Links

CodeGrades

Blog Post

C#

.NET

London

IronPython

Musical Grades

Autodidact

Lambda School

How To Draw An Owl

Dunder (double underscore) methods

Duck Typing

Impostor Syndrome

Django Girls

Mu Editor

Baroque Music

Chamber Music

PyData

Adafruit

CircuitPython

Podcast Interview

PyPortal

Hypercard

Pypercard

Kivy

Podcast Interview

Alan Turing

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Build Your Own Knowledge Graph With Zincbase** Aug 05, 2019
Summary

Computers are excellent at following detailed instructions, but they have no capacity for understanding the information that they work with. Knowledge graphs are a way to approximate that capability by building connections between elements of data that allow us to discover new connections among disparate information sources that were previously uknown. In our day-to-day work we encounter many instances of knowledge graphs, but building them has long been a difficult endeavor. In order to make this technology more accessible Tom Grek built Zincbase. In this episode he explains his motivations for starting the project, how he uses it in his daily work, and how you can use it to create your own knowledge engine and begin discovering new insights of your own.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Tom Grek about knowledge graphs, when they’re useful, and his project Zincbase that makes them easier to build

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what a knowledge graph is and some of the ways that they are used?

How did you first get involved in the space of knowledge graphs?

You have built the Zincbase project for building and querying knowledge graphs. What was your motivation for creating this project and what are some of the other tools that are available to perform similar tasks?

Can you describe how Zincbase is implemented and some of the ways that it has evolved since you first began working on it?

What are some of the assumptions that you had at the outset of the project which have been challenged or updated in the process of working on and with it?

What are some of the common challenges when building or using knowledge graphs?

How has the domain of knowledge graphs changed in recent years as new approaches to entity resolution and data processing have been introduced?

Can you talk through a use case and workflow for using Zincbase to design and populate a knowledge graph?

What are some of the ways that you are using Zincbase in your own projects?

What have you found to be the most challenging/interesting/unexpected lessons that you have learned in the process of building and maintaining Zincbase?

What do you have planned for the future of the project?

Keep In Touch

tomgrek on GitHub

Website

@tomgrek on Twitter

LinkedIn

Picks

Tobias

Banana Blueberry Oat Bars

Tom

Pickled Habañero

Links

Zincbase

Commodore 64

Electronic Engineering

Artificial Intelligence

Primer.ai

Artificial General Intelligence

Matlab

IPython

NumPy

Excel

Jupyter

Pandas

Knowledge Graph

Data Engineering Podcast Episode About Enigma Knowledge Graph

The Matrix

Keanu Reeves

Ontology

Semantic Web

Word2Vec

SparQL

Neo4J

Graph Database

Data Engineering Podcast Episode About DGraph

AWS Neptune

PostgreSQL

Data Engineering Podcast Episode

Dask

Data Engineering Podcast Episode

BBC Micro

BASIC

Prolog

NLP

ELMO

BERT

GPT-2

Winograd Schema Challenge

PyTorch BigGraph

Ampligraph

SpaCy

Podcast.__init__ Episode

AI Winter

PyTorch

Podcast Episode

scikit-learn

NetworkX

SciPy

CircleCI

Read The Docs

Podcast Episode

Project Gutenberg

Allen NLP

Doctest

Reinforcement Learning

Metacognition

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Docker Best Practices For Python In Production** Jul 29, 2019
Summary

Docker is a useful technology for packaging and deploying software to production environments, but it also introduces a different set of complexities that need to be understood. In this episode Itamar Turner-Trauring shares best practices for running Python workloads in production using Docker. He also explains some of the security implications to be aware of and digs into ways that you can optimize your build process to cut down on wasted developer time. If you are using Docker, thinking about using it, or just heard of it recently then it is worth your time to listen and learn about some of the cases you might not have considered.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

To connect with the startups that are shaping the future and take advantage of the opportunities that they provide, check out Angel List where you can invest in innovative business, find a job, or post a position of your own. Sign up today at pythonpodcast.com/angel and help support this show.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Itamar Turner-Trauring about what you need to know about running Python workloads in Docker

Interview

Introductions

How did you get introduced to Python?

For anyone who is unfamiliar with it, can you describe what Docker is and the benefits that it can provide?

What was your motivation for dedicating so much time and energy to the specific area of using Docker for Python production usage?

What are some of the common issues that developers and operations engineers run into when dealing with Docker and its build system?

What are some of the issues that are specific to Python that you have run into when using Docker?

How does the ecosystem for Python in containers compare to other languages that you are familiar with?

What are some of the security issues that engineers are likely to run into when using some of the advice and pre-existing containers that are publicly available?

One of the issues that you call out is the speed of container builds. What are some of the contributing factors that lead to such slow packaging times?

Can you talk through some of the aspects of multi-layer packages and useful ways to take proper advantage of them?

There have been some recent projects that attempt to work around the shortcomings of the Dockerfile itself. What are your thoughts on that overall effort and any specific tools that you have experimented with?

When is Docker the wrong choice for a production environment?

What are some useful alternatives to Docker, for Python specifically and for software distribution in general that you have had good luck with?

Keep In Touch

Website

@itamarst on Twitter

itamarst on GitHub

Picks

Tobias

Shazam Movie

Itamar

Veronica Mars

Links

Itamar’s Best Practices Guide

Docker

Zope

GitLab CI

Heresy In The Church Of Docker

Poetry

Pipenv

Dockerfile

40 Years of DSL Disasters (Slides)

Ubuntu

Debian

Docker Layers

Bitnami

Alpine Linuxhttps://alpinelinux.org?utm_source=rss&utm_medium=rss

PodMan

Nix

Heroku Buildpacks

Itamar’s Docker Template

Hashicorp Packer

Rkt

Solaris Zones

BSD Jails

PyInstaller

Snap

FlatPak

Conda

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Protecting The Future Of Python By Hunting Black Swans** Jul 22, 2019
Summary

The Python language has seen exponential growth in popularity and usage over the past decade. This has been driven by industry trends such as the rise of data science and the continued growth of complex web applications. It is easy to think that there is no threat to the continued health of Python, its ecosystem, and its community, but there are always outside factors that may pose a threat in the long term. In this episode Russell Keith-Magee reprises his keynote from PyCon US in 2019 and shares his thoughts on potential black swan events and what we can do as engineers and as a community to guard against them.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to grow your professional network and find opportunities with the startups that are changing the world then Angel List is the place to go. Go to pythonpodcast.com/angel to sign up today.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Upcoming events include the O’Reilly AI Conference, the Strata Data Conference, and the combined events of the Data Architecture Summit and Graphorum. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Russell Keith-Magee about potential black swans for the Python language, ecosystem, and community and what we can do about them

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what a Black Swan is in the context of our conversation?

You were the opening keynote for PyCon this year, where you talked about some of the potential challenges facing Python. What motivated you to choose this topic for your presentation?

What effect did your talk have on the overall tone and focus of the conversations that you experienced during the rest of the conference?

What were some of the most notable or memorable reactions or pieces of feedback that you heard?

What are the biggest potential risks for the Python ecosystem that you have identified or discussed with others?

What is your overall sentiment about the potential for the future of Python?

As developers and technologists, does it really matter if Python continues to be a viable language?

What is your personal wish list of new capabilities or new directions for the future of the Python language and ecosystem?

For listeners to this podcast and members of the Python community, what are some of the ways that we can contribute to the long-term success of the language?

Keep In Touch

BeeWare

freakboy3742 on GitHub

@freakboy3742 on Twitter

Website

Picks

Tobias

Jethro Tull

Russell

pytest-tldr

pdbpp

Links

PyCon 2019 Keynote Presentation

Perth

Western Australia

Django

BeeWare

RedHat

Emacs

Vim

Lisp

Glyph

Twisted

Cal Henderson

Flickr

Slack

Black Swan

Animal

Book

Metaphor

Nassim Nicholas Taleb

PyCon US

Ewa Jodlowska

Python Software Foundation

Podcast Interview

Swift

JavaScript

Django Girls

Briefcase packaging tool

PyPy

Web Assembly (WASM)

COBOL

Tidelift

Cricket unit test runner

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**A Modern Open Source Project Management Platform** Jul 15, 2019
Summary

Project management is a discipline that has been through many incarnations, spawning an entire industry of businesses and tools. The challenge is to build a platform that is sufficiently powerful and adaptable to fit the workflow of your teams, while remaining opinionated enough to be useful. It also helps to have an open and extensible platform that can be customized as needed. In this episode Pablo Ruiz Múzquiz explains the motivation for creating the open source tool Taiga, how it compares to the other options in the market, and how you can use it for your own projects. He also discusses the challenges inherent to project management tools, his philosophies on what makes a project successful, and how to manage your team workflows to be most effective. It was helpful learning from Pablo’s long experience in the software industry and managing teams of various sizes.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Pablo Ruiz Múzquiz about Taiga, a project management platform for agile developers & designers and project managers who want a beautiful tool that makes work truly enjoyable

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Taiga is and the reason for building it?

Project management platforms have been available for a long time. Can you describe how Taiga fits into that market and what makes it stand out?

Can you describe how you view project management and some of the unique challenges that it poses when building a tool for it?

How do the requirements differ between project management for software teams vs other disciplines?

How is Taiga implemented and how has the system design evolved since it was first started?

For someone who is using Taiga can you talk through the features of the platform and how it fits into a typical workflow?

How do you maintain a balance between usability and structure in managing project workflows against flexibility and customization?

Within an engineering team how do you view the responsibility for driving and maintaining the lifecycle of a project?

What are the most common points of friction within a project management workflow and how are you working to address them in Taiga?

Onboarding and discovery for a new contributor in a given project is often painful. What are some steps that a project manager or product team can take to make that process more palatable?

How has the landscape of project management practices and tools changed since you first began working on Taiga and how has that influenced your roadmap?

What have been the most challenging or difficult aspects of building and growing the Taiga project and community?

What lessons have you learned in the process that have been particularly valuable or unexpected?

What are some of the most interesting/unexpected/innovative ways that you have seen Taiga used?

When is Taiga the wrong choice for a given project or team?

What do you have planned for the future of Taiga?

Added by Pablo

Why did you choose AGPLv3 for a license?

How can Taiga integrate itself with other platforms that are typically used by teams?

Keep In Touch

@diacritica on Twitter

LinkedIn

Website

Picks

Tobias

Marchway Hydration Pack

Pablo

Archery

3D Archery

Links

Taiga

Madrid, Spain

Traditional Archery

Kaleidos

Perl

Monty Python

Blender

Agile

Project Management

Redmine

Trac

Agile Manifesto

REST

Django

AngularJS

Django REST Framework

Scrum

Kanban

Taiga Mobile App

Webhooks

AGPLv3

FOSDEM

Iocaine

The Princess Bride

Taiga Tribe

Fedora

Atlassian

Jira

Trello

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Domain Driven Design For Python** Jul 08, 2019
Summary

When your software projects start to scale it becomes a greater challenge to understand and maintain all of the pieces. In this episode Henry Percival shares his experiences working with domain driven design in large Python projects. He explains how it is helpful, and how you can start using it for your own applications. This was an informative conversation about software architecture patterns for large organizations and how they can be used by Python developers.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Harry Percival about domain driven design and enterprise application architecture in Python

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what "application architecture" is and how it compares to the types of application designs that Python developers and teams typically rely on? how does it contrast with "enterprise architecture"?

What are the influences that tend to lead engineers into sub-optimal architectures and how can they guard against them?

One of the core concepts in this problem space is that of "domain driven design". Can you unpack that term and explain the benefits that it provides to software architecture?

What are some of the other concepts that are common among application architecture patterns?

What are some of the common points of confusion among engineers who are first working with DDD?

Is there any particular size or scope of project and organization that merits the approach of domain driven design or is it applicable even at small scales of complexity and team size?

Now that we’ve convinced everyone that they should be using DDD can you talk through the steps involved in identifying and encapsulating the various implementation details that they will need to work through?

How does that process change when dealing with an existing application as opposed to a "greenfield" project?

How do Python language constructs and libraries impact the approach to implementation of application architecture patterns as compared to more traditional "enterprise" languages such as Java and C#?

What are some of the architectural anti-patterns to watch out for when implementing DDD?

On any given team, who is responsible for identifying and ensuring adherence to proper architectural principles?

Are there any publicly visible projects that implement DDD which listeners can look at and learn from?

To help Python developers in their efforts to learn and implement DDD and other aspects of enterprise architecture you have been working on a book. Can you talk about your motivation for that undertaking, what listeners can expect to learn when the read it, and any challenges that you have encountered in the process?

What are some trends in terms of system design and architecture, or technology influences, that you are keeping an eye on?

Keep In Touch

@hjwp on Twitter

hjwp on GitHub

Website

LinkedIn

Picks

Tobias

Dragon Pearl by Yoon Ha Lee

Harry

Tremé

Why We Sleep: Unlocking The Power Of Sleep and Dreams by Matthew Walker PhD

Links

MADE

Obey The Testing Goat

Python Anywhere

XP (eXtreme Programming)

Django

Dive Into Python

Domain Driven Design

Design Patterns

Gang Of Four Book

MVC (Model View Controller)

Microservices

µCon

"Uncle" Bob Martin

Clean Architecture book

Python LEAP Book

Dependency Injection

Inversion Of Control

Test Pyramid

Gary Bernhardt

Podcast Interview

Functional Core, Imperative Shell

Harry’s Blog

The "Blue" Book by Eric Evans

Gartner Hype Cycle

The Clean Architecture In Python by Leonardo Giordani

DRY Python

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Open Source Automated Machine Learning With MindsDB** Jul 01, 2019
Summary

Machine learning is growing in popularity and capability, but for a majority of people it is still a black box that we don’t fully understand. The team at MindsDB is working to change this state of affairs by creating an open source tool that is easy to use without a background in data science. By simplifying the training and use of neural networks, and making their logic explainable, they hope to bring AI capabilities to more people and organizations. In this interview George Hosu and Jorge Torres explain how MindsDB is built, how to use it for your own purposes, and how they view the current landscape of AI technologies. This is a great episode for anyone who is interested in experimenting with machine learning and artificial intelligence. Give it a listen and then try MindsDB for yourself.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing George Hosu and Jorge Torres about MindsDB, a framework for streamlining the use of neural networks

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what MindsDB is and the problem that it is trying to solve?

What was the motivation for creating the project?

Who is the target audience for MindsDB?

Before we go deep into MindsDB can you explain what a neural network is for anyone who isn’t familiar with the term?

For someone who is using MindsDB can you talk through their workflow?

What are the types of data that are supported for building predictions using MindsDB?

How much cleaning and preparation of the data is necessary before using it to generate a model?

What are the lower and upper bounds for volume and variety of data that can be used to build an effective model in MindsDB?

One of the interesting and useful features of MindsDB is the built in support for explaining the decisions reached by a model. How do you approach that challenge and what are the most difficult aspects?

Once a model is generated, what is the output format and can it be used separately from MindsDB for embedding the prediction capabilities into other scripts or services?

How is MindsDB implemented and how has the design changed since you first began working on it?

What are some of the assumptions that you made going into this project which have had to be modified or updated as it gained users and features?

What are the limitations of MindsDB and what are the cases where it is necessary to pass a task on to a data scientist?

In your experience, what are the common barriers for individuals and organizations adopting machine learning as a tool for addressing their needs?

What have been the most challenging, complex, or unexpected aspects of designing and building MindsDB?

What do you have planned for the future of MindsDB?

Keep In Touch

George

Blog

George3d6 on GitHub

@Cerebralab2 on Twitter

LinkedIn

Jorge

LinkedIn

MindsDB

Website

@mindsdb on Twitter

mindsdb on GitHub

Picks

Tobias

Bose QuietComfort 25 noise cancelling headphones

George

Open CourseWare – Brain And Cognitive Sciences

Cerebralab Blog

Jorge

Lightwood

MKDocs with Google Material Templates

Links

MindsDB

GitHub

3Blue1Brown – Neural Networks

Think Bayes

Backpropagation

Reverse Automatic Differentiation

Ludwig deep learning toolbox

Lightwood

Tensorflow

PyTorch

Podcast Interview

Aerospike

scikit-learn

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Behind The Scenes At The Python Software Foundation** Jun 24, 2019
Summary

One of the secrets of the success of Python the language is the tireless efforts of the people who work with and for the Python Software Foundation. They have made it their mission to ensure the continued growth and success of the language and its community. In this episode Ewa Jodlowska, the executive director of the PSF, discusses the history of the foundation, the services and support that they provide to the community and language, and how you can help them succeed in their mission.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Ewa Jodlowska about the Python Software Foundation and the role that it serves in the language and community

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what the PSF is for anyone who isn’t familiar with it?

How did you get involved with the PSF and what is your current role?

What was the motivation for creating the PSF?

What are the primary responsibilities of the PSF?

How has the scope and scale of the responsibilities for the PSF shifted in the years since its foundation?

What is the relationship between the PSF and the language core developers?

What are some reasons that someone would want to become a member of the PSF and what is involved in gaining membership?

What are the challenges confronted by you and the PSF, currently and in the recent past?

What are you most worried about and most proud of in the PSF, the core language, or the community?

What challenges or changes do you foresee for the PSF in the near to medium future?

What are some of the most interesting/unexpected/challenging lessons that you have learned while working with the PSF?

How are the PSF and the PSU (Python Secret Underground) related?

Outside of the PSF, how can the community contribute to the health and longevity of the language, its ecosystem, and its community?

Keep In Touch

Ewa

@ewa_jodlowska on Twitter

Email

The Python Software Foundation

Website

@thepsf on Twitter

Blog

Picks

Tobias

Russell Keith-Magee PyCon 2019 Keynote

Ewa

Donate To The PSF

Links

The PSF

Informix

PHP

PyCon

PyLadies

PyPI

Denmark

PSF Mission Statement

ChiPy

Brett Cannon PyCon 2018 Keynote

Mozilla Open Source Support Fund

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Algorithmic Trading In Python Using Open Tools And Open Data** Jun 17, 2019
Summary

Algorithmic trading is a field that has grown in recent years due to the availability of cheap computing and platforms that grant access to historical financial data. QuantConnect is a business that has focused on community engagement and open data access to grant opportunities for learning and growth to their users. In this episode CEO Jared Broad and senior engineer Alex Catarino explain how they have built an open source engine for testing and running algorithmic trading strategies in multiple languages, the challenges of collecting and serving currrent and historical financial data, and how they provide training and opportunity to their community members. If you are curious about the financial industry and want to try it out for yourself then be sure to listen to this episode and experiment with the QuantConnect platform for free.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

The Python Software Foundation is the lifeblood of the community, supporting all of us who want to run workshops and conferences, run development sprints or meetups, and ensuring that PyCon is a success every year. They have extended the deadline for their 2019 fundraiser until June 30th and they need help to make sure they reach their goal. Go to pythonpodcast.com/psf today to make a donation. If you’re listening to this after June 30th of 2019 then consider making a donation anyway!

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Jared Broad and Alex Catarino about QuantConnect, a platform for building and testing algorithmic trading strategies on open data and cloud resources

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what QuantConnect is and how the business got started?

What is your mission for the company?

I know that there are a few other entrants in this market. Can you briefly outline how you compare to the other platforms and maybe characterize the state of the industry?

What are the main ways that you and your customers use Python?

For someone who is new to the space can you talk through what is involved in writing and testing a trading algorithm?

Can you talk through how QuantConnect itself is architected and some of the products and components that comprise your overall platform?

I noticed that your trading engine is open source. What was your motivation for making that freely available and how has it influenced your design and development of the project?

I know that the core product is built in C# and offers a bridge to Python. Can you talk through how that is implemented?

How do you address latency and performance when bridging those two runtimes given the time sensitivity of the problem domain?

What are the benefits of using Python for algorithmic trading and what are its shortcomings?

How useful and practical are machine learning techniques in this domain?

Can you also talk through what Alpha Streams is, including what makes it unique and how it benefits the users of your platform?

I appreciate the work that you are doing to foster a community around your platform. What are your strategies for building and supporting that interaction and how does it play into your product design?

What are the categories of users who tend to join and engage with your community?

What are some of the most interesting, innovative, or unexpected tactics that you have seen your users employ?

For someone who is interested in getting started on QuantConnect what is the onboarding process like?

What are some resources that you would recommend for someone who is interested in digging deeper into this domain?

What are the trends in quantitative finance and algorithmic trading that you find most exciting and most concerning?

What do you have planned for the future of QuantConnect?

Keep In Touch

Jared

LinkedIn

@jaredbroad on Twitter

Alex

AlexCatarino on GitHub

LinkedIn

@AlexCatx on Twitter

QuantConnect

@QuantConnect on Twitter

Website

Picks

Tobias

Good Omens book and miniseries

Jared

Chernobyl HBO Series

Alex

The 100

Links

QuantConnect

LEAN algorithm engine

Alpha Streams

Google Spanner

PyCharm

Visual Studio Code

IronPython

NumPy

SymPy

Pandas

PythonNet

Tensorflow

Keras

Udemy

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Web Application Development Entirely In Python With Anvil** Jun 10, 2019
Summary

The knowledge and effort required for building a fully functional web application has grown at an accelerated rate over the past several years. This introduces a barrier to entry that excludes large numbers of people who could otherwise be producing valuable and interesting services. To make the onramp easier Meredydd Luff and Ian Davies created Anvil, a platform for full stack web development in pure Python. In this episode Meredydd explains how the Anvil platform is built and how you can use it to build and deploy your own projects. He also shares some examples of people who were able to create profitable businesses themselves because of the reduced complexity. It was interesting to get Meredydd’s perspective on the state of the industry for web development and hear his vision of how Anvil is working to make it available for everyone.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

The Python Software Foundation is the lifeblood of the community, supporting all of us who want to run workshops and conferences, run development sprints or meetups, and ensuring that PyCon is a success every year. They have extended the deadline for their 2019 fundraiser until June 30th and they need help to make sure they reach their goal. Go to pythonpodcast.com/psf today to make a donation. If you’re listening to this after June 30th of 2019 then consider making a donation anyway!

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Meredydd Luff about Anvil, platform for building full stack web applications entirely in Python

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Anvil is and the story of how and why you created it?

Web applications come in a vast array of styles. What are the primary formats of web applications that Anvil supports building and what are its limitations?

Are there certain categories of users that tend to gravitate toward Anvil?

How do you approach user experience design and overall usability given the varied backgrounds of your customers?

For someone who wants to use Anvil can you talk through a typical workflow and highlight the different components of the platform?

Can you describe how Anvil itself is implemented and how it has evolved since you first began working on it?

For the javascript transpilation, are you using an existing project such as Transcrypt or PyJS, or did you develop your own?

Given that the Python dependencies on your servers are managed by how, how do you approach version upgrades to avoid breaking your customer’s applications?

What are the main assumptions that you had going into the project and how have those assumptions been challenged or updated in the process of growing the business?

What have been some of the biggest challenges that you have faced in the process of building and growing Anvil?

What are some of the edge cases that you have run into while developing Anvil? (e.g. browser APIs, javascript <-> Python impedance mismatch, etc.)

Can you talk through how you manage deployments of your customer’s applications?

What are some of the features of Anvil that are often overlooked, under-utilized, or misunderstood which you think users would benefit from knowing about?

What are some of the most interesting/innovative/unexpected ways that you have seen Anvil used?

What are the limitations of Anvil and when is it the wrong choice?

What do you have planned for the future of Anvil?

Keep In Touch

@meredydd on Twitter

LinkedIn

Website

meredydd on GitHub

Picks

Tobias

Pipx

Meredydd

Skulpt

Python in the Browser implementations generally

Links

Anvil

Delphi

Visual Basic

Human-Computer Interaction

Amazon RDS (Relational Database Service)

Bokeh

Podcast Interview

Plotly

Raspberry Jam by the Raspberry Pi Foundation

PyCharm

Websockets

Skulpt

Comparing implementations of Python in the Browser on Python Tips

Brython

The Matrix

Pyodide

How Skulpt works (PyCon 2017 Lightning Talk)

How Anvil’s autocompleter works (PyCon UK 2017 Lightning Talk)

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building A Business On Serverless Technology** Jun 04, 2019
Summary

Serverless computing is a recent category of cloud service that provides new options for how we build and deploy applications. In this episode Raghu Murthy, founder of DataCoral, explains how he has built his entire business on these platforms. He explains how he approaches system architecture in a serverless world, the challenges that it introduces for local development and continuous integration, and how the landscape has grown and matured in recent years. If you are wondering how to incorporate serverless platforms in your projects then this is definitely worth your time to listen to.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

The Python Software Foundation is the lifeblood of the community, supporting all of us who want to run workshops and conferences, run development sprints or meetups, and ensuring that PyCon is a success every year. They have extended the deadline for their 2019 fundraiser until June 30th and they need help to make sure they reach their goal. Go to pythonpodcast.com/psf2019 today to make a donation. If you’re listening to this after June 30th of 2019 then consider making a donation anyway!

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Raghu Murthy from DataCoral about his experience building and deploying a personalized SaaS platform on top of serverless technologies

Interview

Introductions

How did you get introduced to Python?

Can you start by giving a brief overview of DataCoral?

Before we get too deep can you share your definition of what types of technologies fall under the umbrella of "serverless"?

How are you using serverless technologies at DataCoral?

How has your usage evolved as your business and the underlying technologies have evolved?

How do serverless technologies impact your approach to application architecture?

What are some of the main benefits for someone to target services such as Lambda?

What is your litmus test for determining whether a given project would be a good fit for a Function as a Service platform?

What are the most challenging aspects of running code on Lambda?

What are some of the major design differences between running on Lambda vs the more familiar server-oriented paradigms?

What are some of the other services that are most commonly used alongside Function as as Service (e.g. Lambda) to build full featured applications?

With serverless function platforms there is the cold start problem, can you explain what that means and some application design patterns that can help mitigate it?

When building on cloud-based technologies, especially proprietary ones, local development can be a challenge. How are you handling that issue at DataCoral?

In addition to development this new deployment paradigm upends some of the traditional approaches to CI/CD. How are you approaching testing and deployment of your services?

How do you identify and maintain dependency graphs between your various microservices?

In addition to deployment, it is also necessary to track performance characteristics and error events across service boundaries. How are you managing observability and alerting in your product?

What are you most excited for in the serverless space that listeners should know about?

Keep In Touch

LinkedIn

Medium

Picks

Tobias

Avengers Endgame

Raghu

Golden State Warriors

Links

DataCoral

Data Engineering Podcast Interview

Perl

Airflow

Podcast Interview

Serverless Computing

DynamoDB

Aurora

SNS

SQS

Lambda

S3

API Gateway

EMR

Apache Hive

AWS Glue

RedShift

SnowflakeDB

Hadoop

Function As A Service

Distributed Systems

Conway’s Law

SRE == Site Reliability Engineer

Rollbar

AWS Batch

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**A Data Catalog For Your PyData Projects** May 27, 2019
Summary

One of the biggest pain points when working with data is getting is dealing with the boilerplate code to load it into a usable format. Intake encapsulates all of that and puts it behind a single API. In this episode Martin Durant explains how to use the Intake data catalogs for encapsulating source information, how it simplifies data science workflows, and how to incorporate it into your projects. It is a lightweight way to enable collaboration between data engineers and data scientists in the PyData ecosystem.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Martin Durant about Intake, a lightweight package for finding, investigating, loading and disseminating data

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Intake is and the story behind its creation?

Can you outline some of the other projects and products that intersect with the functionality of Intake and describe where it fits in terms of use case and capabilities? (e.g. Quilt Data, Arrow, Data Retriever)

Can you describe the workflows for using Intake, both from the data scientist and the data engineer perspective?

One of the persistent challenges in working with data is that of cataloging and discovery of what already exists. In what ways does Intake address that problem?

Does it have any facilities for capturing and exposing data lineage?

For someone who needs to customize their usage of Intake, what are the extension points and what is involved in building a plugin?

Can you describe how Intake is implemented and how it has evolved since it first started?

What are some of the most challenging, complex, or novel aspects of the Intake implementation?

Intake focuses primarily on integrating with the PyData ecosystem (e.g. NumPy, Pandas, SciPy, etc.). What are some other communities that are, or could be, benefiting from the work being done on Intake?

What are some of the assumptions that are baked into Intake that would need to be modified to make it more broadly applicable?

What are some of the assumptions that were made going into this project that have needed to be reconsidered after digging deeper into the problem space?

What are some of the most interesting/unexpected/innovative ways that you have seen Intake leveraged?

What are your plans for the future of Intake?

Keep In Touch

martindurant on GitHub

Website

@martin_durant_ on Twitter

Picks

Tobias

Ubersuggest SEO tool

Links

Intake

Anaconda

Dask

Data Engineering Podcast Interview

Fast Parquet

IDL

Space Telescope Institute

Blaze

Quilt Data

Podcast Interview

Arrow

Data Retriever

Podcast Interview

Parquet

Data Engineering Podcast Interview

DataFrame

Apache Spark

Dremio

Data Engineering Podcast Interview

Dat Project – distributed peer-to-peer data sharing

Data Engineering Podcast Interview

GeoPandas

XArray

Solr

Streamz

PyViz

S3FS

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Hardware Hacking Made Easy With CircuitPython** May 20, 2019
Summary

Learning to program can be a frustrating process, because even the simplest code relies on a complex stack of other moving pieces to function. When working with a microcontroller you are in full control of everything so there are fewer concepts that need to be understood in order to build a functioning project. CircuitPython is a platform for beginner developers that provides easy to use abstractions for working with hardware devices. In this episode Scott Shawcroft explains how the project got started, how it relates to MicroPython, some of the cool ways that it is being used, and how you can get started with it today. If you are interested in playing with low cost devices without having to learn and use C then give this a listen and start tinkering!

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Scott Shawcroft about CircuitPython, the easiest way to program microcontrollers

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what CircuitPython is and how the project got started?

I understand that you work at Adafruit and I know that a number of their products support CircuitPython. What other runtimes do you support?

Microcontrollers have typically been the domain of C because of the resource and performance constraints. What are the benefits of using Python to program hardware devices?

With the wide availability of powerful computing platforms, what are the benefits of experimenting with microcontrollers and their peripherals?

I understand that CircuitPython is a friendly fork of MicroPython. What have you changed in your version?

How do you structure your development to avoid conflicts with the upstream project?

What are some changes that you have contributed back to MicroPython?

What are some of the features of CircuitPython that make it easier for users to interact with sensors, motors, etc.?

CircuitPython provides an easy on-ramp for experimenting with hardware projects. Is there a point where a user will outgrow it and need to move to a different language or framework?

What are some of the most interesting/innovative/unexpected projects that you have seen people build using CircuitPython?

Are there any cases of someone building and shipping a production grade project in CircuitPython?

What have been some of the most interesting/challenging/unexpected aspects of building and maintaining CircuitPython?

What is in store for the future of the project?

Keep In Touch

@tannewt on Twitter

Website

tannewt on GitHub

Picks

Tobias

Wings Of Fire book series

Scott

Brandon Sanderson

The Wheel Of Time Series

Mist Born

Links

Adafruit

CircuitPython

MicroPython

Podcast Interview

Microcontroller

Arduino

Microsoft MakeCode

NodeBots

Espruino

I2C

Hackspace Magazine

Adafruit Blinka

learn.adafruit.com

Scott Hanselman Blog Post

Reflow Oven

Adafruit Crickit – Creative Robotics Platform

Adabox

Moore’s Law

SparkFun

DigiKey

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building A Privacy Preserving Voice Assistant** May 13, 2019
Summary

Being able to control a computer with your voice has rapidly moved from science fiction to science fact. Unfortunately, the majority of platforms that have been made available to consumers are controlled by large organizations with little incentive to respect users’ privacy. The team at Snips are building a platform that runs entirely off-line and on-device so that your information is always in your control. In this episode Adrien Ball explains how the Snips architecture works, the challenges of building a speech recognition and natural language understanding toolchain that works on limited resources, and how they are tackling issues around usability for casual consumers. If you have been interested in taking advantage of personal voice assistants, but wary of using commercially available options, this is definitely worth a listen.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Adrien Ball about SNIPS, a set of technologies to make voice controlled systems that respect user’s privacy

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what the Snips is and how it got started?

For someone who wants to use Snips can you talk through the onboarding proces?

One of the interesting features of your platform is the option for automated training data generation. Can you explain how that works?

Can you describe the overall architecture of the Snips platform and how it has evolved since you first began working on it?

Two of the main components that can be used independently are the ASR (Automated Speech Recognition) and NLU (Natural Language Understanding) engines. Each of those have a number of competitors in the market, both open source and commercial. How would you describe your overall position in the market for each of those projects?

I know that one of the biggest challenges in conversational interfaces is maintaining context for multi-step interactions. How is that handled in Snips?

For the NLU engine, you recently ported it from Python to Rust. What was your motivation for doing so and how would you characterize your experience between the two languages?

Are you continuing to maintain both implementations and if so how are you maintaining feature parity?

How do you approach the overall usability and user experience, particularly for non-technical end users?

How is discoverability handled (e.g. finding out what capabilities/skills are available)

One of the compelling aspects of Snips is the ability to deploy to a wide variety of devices, including offline support. Can you talk through that deployment process, both from a user perspective and how it is implemented under the covers?

What is involved in updating deployed models and keeping track of which versions are deployed to which devices?

What is involved in adding new capabilities or integrations to the Snips platform?

What are the limitations of running everything offline and on-device?

When is Snips the wrong choice?

In the process of building and maintaining the various components of Snips, what have been some of the most useful/interesting/unexpected lessons that you have learned?

What have been the most challenging aspects?

What are some of the most interesting/innovative/unexpected ways that you have seen the Snips technologies used?

What is in store for the future of Snips?

Keep In Touch

LinkedIn

adrienball on GitHub

@adrien_ball on Medium

@adrien_ball on Twitter

Picks

Tobias

Chrome OS

Adrien

Google I/O

Facebook F8

User Privacy

Links

Snips

2048 Game

Smart Cities

Raspberry Pi

WikiData

MQTT

Google Assistant

Amazon Alexa

Microsoft Cortana

Mozilla Common Voice

Rust Language

Snips Hermes messaging library

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Hacking The Government With The USDS** May 07, 2019
Summary

The U.S. government has a vast quantity of software projects across the various agencies, and many of them would benefit from a modern approach to development and deployment. The U.S. Digital Services Agency has been tasked with making that happen. In this episode the current director of engineering for the USDS, David Holmes, explains how the agency operates, how they are using Python in their efforts to provide the greatest good to the largest number of people, and why you might want to get involved. Even if you don’t live in the U.S.A. this conversation is worth listening to so you can see an interesting model of how to improve government services for everyone.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing David Holmes about his work at the US Digital Services organization

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what the USDS is and how you got involved with it?

The terminology that is used around "Tours of Service" is interesting. Can you explain what that entails?

relocation

what if you have a house and career?

Can you explain the model of how the USDS works?

What is involved in staffing a new project?

What is your typical toolkit, and how does that vary with the specific departments that you are working with?

What are some of the most interesting projects that you and the team at USDS have worked on?

What are some of the most challenging projects that you have been involved with?

What are some projects that you hope to be asked to work on?

Keep In Touch

davideholmes on GitHub

Picks

Tobias

Captain Marvel movie

David

Avengers: Endgame

Game Of Thrones television series

Links

US Digital Services

US Digital Services Job Application

US Digital Services Projects

18F

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Probabilistic Modeling In Python (And What That Even Means)** Apr 29, 2019
Summary

Most programming is deterministic, relying on concrete logic to determine the way that it operates. However, there are problems that require a way to work with uncertainty. PyMC3 is a library designed for building models to predict the likelihood of certain outcomes. In this episode Thomas Wiecki explains the use cases where Bayesian statistics are necessary, how PyMC3 is designed and implemented, and some great examples of how it is being used in real projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Thomas Wiecki about PyMC3, a project for probabilistic programming in Python

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what probabilistic programming is?

What is the PyMC3 project and how did you get involved with it?

The opening line for the project README is packed with a slew of terms that are rather opaque to the lay-person. Can you unpack that a bit and discuss some of the ways that PyMC3 is used in real-world projects?

How much knowledge of statistical modeling and Bayesian statistics is necessary to make effective use of PyMC3?

Can you talk through an example use case for PyMC3 to illustrate how you would use it in a project?

How does it compare to the way that you would approach the same problem in a deterministic or frequentist modeling framework?

Can you describe how PyMC3 is implemented?

There are a number of other projects that build on top of PyMC3, what are some that you find particularly interesting or noteworthy?

What do you find to be the most useful features of PyMC3 and what are some areas that you would like to see it improved?

What have been the most interesting/unexpected/challenging lessons that you have learned in the process of building and maintaining PyMC3?

What is in store for the future of PyMC3?

Keep In Touch

PyMC

GitHub

Discourse Forum

Thomas

twiecki on GitHub

@twiecki on Twitter

Website

Picks

Tobias

Fantastic Beasts And Where To Find Them

Fantastic Beasts: The Crimes Of Grindelwald

Thomas

Hyperion by Dan Simmons

The Mind Illuminated

Links

PyMC3

Quantopian

University of Tubingen

MatLab

Probabilistic Modeling

Probability Distribution

A/B Testing

Bayesian Statistics

Beta Distribution

Bernoulli Distribution

P-Value

Hamiltonian Monte Carlo sampling algorithm

Metropolis Hastings Inference Algorithm

Theano

Bayesian Methods For Hackers by Cameron Davidson-Pilon

Bayesian Analysis With Python by Osvaldo Martin

Tensorflow

MXNet deep learning framework

PyTorch

Tensorflow Probability

BAMBI package to build generalized linear models

PMProphet PyMC3 implementation of Facebook’s Prophet for timeseries prediction

Exoplanet

BEAT (Bayesian Earthquake Analysis Tool)

PyMC3 in Google Summer of Code

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Exploring Indico: A Full Featured Event Management Platform** Apr 22, 2019
Summary

Managing an event is rife with inherent complexity that scales as you move from scheduling a meeting to organizing a conference. Indico is a platform built at CERN to handle their efforts to organize events such as the Computing in High Energy Physics (CHEP) conference, and now it has grown to manage booking of meeting rooms. In this episode Adrian Mönnich, core developer on the Indico project, explains how it is architected to facilitate this use case, how it has evolved since its first incarnation two decades ago, and what he has learned while working on it. The Indico platform is definitely a feature rich and mature platform that is worth considering if you are responsible for organizing a conference or need a room booking system for your office.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Adrian Mönnich about Indico, the effortless open-source tool for event organisation, archival and collaboration

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Indico is and how the project got started?

What are some other projects which target a similar use case and what were they lacking that led to Indico being necessary?

Can you talk through an example workflow for setting up and managing an event in Indico?

How does the lifecycle change when working with larger events, such as PyCon?

Can you describe how Indico is architected and how its design has evolved since it was first built?

What are some of the most complex or challenging portions of Indico to implement and maintain?

There are a lot of areas for exercising constraint resolution algorithms. Can you talk through some of the business logic of how that operates?

Most of Indico is highly configurable and flexible. How do you approach managing sane defaults to prevent users getting overwhelmed when onboarding?

What is your approach to testing given how complex the project is?

What are some of the most interesting or unexpected ways that you have seen Indico used?

What are some of the most interesting/unexpected lessons that you have learned in the process of building Indico?

What do you have planned for the future of the project?

Keep In Touch

Indico

Website

GitHub

IRC

Adrian

ThiefMaster on GitHub

Picks

Tobias

Mortal Engines movie

Adrian

Virtual Reality

Portal VR

Links

Indico

Tornado

Podcast Interview

CERN

High Energy Physics

CHEP (Computing in High Energy Physics) conference

ZODB

PostgreSQL

Data Engineering Podcast Interview

SQLAlchemy

Flask

WSGI == Web Server Gateway Interface

Mako Templates

Jinja

ReactJS

Stripe

Paypal

Indico Introduction Video

Reveal.js

Mod_Python

Zope

Doodle

LDAP == Lightweight Directory Access Protocol

Daylight Saving Time

Indico User Guide

Py.Test

Podcast Episode

Selenium

Flask Plugin Engine

CERN Indico Plugins

Linux Plumber’s Conference

Open SUSE

F-Strings

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Exploring Python's Internals By Rewriting Them In Rust** Apr 15, 2019
Summary

The CPython interpreter has been the primary implementation of the Python runtime for over 20 years. In that time other options have been made available for different use cases. The most recent entry to that list is RustPython, written in the memory safe language Rust. One of the added benefits is the option to compile to WebAssembly, offering a browser-native Python runtime. In this episode core maintainers Windel Bouwman and Adam Kelly explain how the project got started, their experience working on it, and the plans for the future. Definitely worth a listen if you are curious about the inner workings of Python and how you can get involved in a relatively new project that is contributing to new options for running your code.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host is Tobias Macey and today I’m interviewing Adam Kelly and Windel Bouwman about RusPython, a project to implement a new Python interpreter in Rust

Interview

Introduction

How did you get introduced to Python?

Can you start by explaining what Rust is for anyone who isn’t familiar with it?

How did RustPython got started and what are your goals for the project?

Can you discuss what is involved in implementing a fully compliant Python interpreter?

What are some of the challenges that you face in replicating the capabilities of the CPython interpreter?

Are you attempting to maintain bug parity?

How much of the stdlib needs to be reimplemented?

Can you compare and contrast the benefits of Rust vs C?

Will the end result be compatible with libraries that rely on C extensions such as NumPy?

What is the current state of the project?

What are some of the notable missing features?

Can you talk through your vision of how the WebAssembly support will manifest and the types of applications that it will enable?

How much effort have you put into size optimization for the webassembly target to reduce client-side load time?

Are there any existing options for minification of Python code so that it can be delivered to users with less bandwidth?

What have been some of the most interesting/challenging/unexpected aspects of implementing a Python runtime?

What do you have planned for the future of the project?

What are the risks that you anticipate which could derail the project before it becomes production ready?

Contact Info

Windel

windelbouwman on GitHub

Website

@windelbouwman on Twitter

@windel@todon.nl on Mastodon

Adam

cthulahoops on GitHub

@cthulahoops on Twitter

Picks

Tobias

Oysterhead

Adam

FZF fuzzy finder

Windel

TQDM Python progress bar

Links

RustPython

Windel Presentation EuroPython

Rust

C++

Rust Memory Safety

MicroPython

Podcast Episode

PyPy

Ouroboros – Pure Python standard library

WebAssembly

lalrpop – Rust parser generator

Rust Crates

PickItUp in-browser Python game engine

QuickSilver Game Engine

PEP 441

JIT (Just-In-Time) Compilation

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

**Version Control For Your Machine Learning Projects** Apr 08, 2019
Summary

Version control has become table stakes for any software team, but for machine learning projects there has been no good answer for tracking all of the data that goes into building and training models, and the output of the models themselves. To address that need Dmitry Petrov built the Data Version Control project known as DVC. In this episode he explains how it simplifies communication between data scientists, reduces duplicated effort, and simplifies concerns around reproducing and rebuilding models at different stages of the projects lifecycle. If you work as part of a team that is building machine learning models or other data intensive analysis then make sure to give this a listen and then start using DVC today.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.

You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Dmitry Petrov about DVC, an open source version control system for machine learning projects

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what DVC is and how it got started?

How do the needs of machine learning projects differ from other software applications in terms of version control?

Can you walk through the workflow of a project that uses DVC?

What are some of the main ways that it differs from your experience building machine learning projects without DVC?

In addition to the data that is used for training, the code that generates the model, and the end result there are other aspects such as the feature definitions and hyperparameters that are used. Can you discuss how those factor into the final model and any facilities in DVC to track the values used?

In addition to version control for software applications, there are a number of other pieces of tooling that are useful for building and maintaining healthy projects such as linting and unit tests. What are some of the adjacent concerns that should be considered when building machine learning projects?

What types of metrics do you track in DVC and how are they collected?

Are there specific problem domains or model types that require tracking different metric formats?

In the documentation it mentions that the data files live outside of git and can be managed in external storage systems. I’m wondering if there are any plans to integrate with systems such as Quilt or Pachyderm that provide versioning of data natively and what would be involved in adding that support?

What was your motivation for implementing this system in Python?

If you were to start over today what would you do differently?

Being a venture backed startup that is producing open source products, what is the value equation that makes it worthwile for your investors?

What have been some of the most interesting, challenging, or unexpected aspects of building DVC?

What do you have planned for the future of DVC?

Keep In Touch

dmpetrov on GitHub

Blog

@fullstackml on Twitter

LinkedIn

Picks

Tobias

Otter.ai

Dmitry

Go outside and get some fresh air

Links

DVC

Iterative.ai

Linear Regression

Logistic Regression

C++

Perl

Git

Version Control System

Uber Michaelangelo

Domino Data Lab

Git LFS

AUC == Area Under Curve metric for evaluating machine learning model performance

Wes McKinney Interview

PyTorch

Podcast Interview

Tensorflow

TensorBoard

MLFlow

Quilt Data

Data Engineering Podcast Episode

Pachyderm

Data Engineering Podcast Episode

Apache Airflow

Podcast Interview

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building Scalable Ecommerce Sites On Saleor** Apr 01, 2019
Summary

Ecommerce is an industry that has largely faded into the background due to its ubiquity in recent years. Despite that, there are new trends emerging and room for innovation, which is what the team at Mirumee focuses on. To support their efforts, they build and maintain the open source Saleor framework for Django as a way to make the core concerns of online sales easy and painless. In this episode Mirek Mencel and Patryk Zawadzki discuss the projects that they work on, the current state of the ecommerce industry, how Saleor fits with their technical and business strategy, and their predictions for the near future of digital sales.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Check out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI

You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Your host as usual is Tobias Macey and today I’m interviewing Mirek Mencel and Patryk Zawadzki about their work at Mirumee, building ecommerce applications in Python, based on their open source framework Saleor

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the types of projects that you work on at Mirumee and how the company got started?

There are a number of libraries and frameworks that you build and maintain. What is your motivation for providing these components freely and how does that play into your overall business strategy?

The most substantial project that you maintain is Saleor. Can you describe what it is and the story behind its creation?

How does it compare to other ecommerce implementations in the Python space?

If someone is agnostic to language and web framework, what would make them choose Saleor over other options that would be available to them?

What are some of the most challenging aspects of building a successful ecommerce platform?

How do the technical needs of an ecommerce site differ as it grows from small to medium and large scale?

Which components of an online store are often overlooked?

One of the common features of ecommerce sites that can drive substantial revenue is a well-built recommender system. What are some best practice strategies that you have discovered during your client work?

What are some projects that you have seen built with Saleor that were particular interesting, innovative, or unexpected?

What are your predictions for the future of the ecommerce industry?

What do you have planned for the future of the Saleor framework and the Mirumee business?

Keep In Touch

Mirumee

Website

Github

@mirumeelabs on Twitter

Mirek

@mirekmencel on Twitter

mirekm on GitHub

Patryk

patrys on GitHub

@patrys on Twitter

Website

Picks

Tobias

Wreck It Ralph: Ralph Breaks The Internet

Mirek

A Guide To The Good Life: The Ancient Art Of Stoic Joy by William B. Irvine

Patryk

Release It: Design And Deploy Production Ready Software by Michael Nygard

Links

Mirumee

Saleor

Django

PHP

Pyramid web framework

Pylons

Magento eCommerce platform

Ecommerce

Satchmo

Satchless

Prices library for handling price data

French National Assembly

Django Oscar

Podcast Interview

David Winterbottom

Ebay

Amazon

Etsy

Shopify

Ariadne GraphQL framework for Python

Graphene GraphQL framework for Python

Podcast Interview

Apollo JavaScript GraphQL framework

PWA == Progressive Web Apps

SKU == Stock Keeping Unit

Collective Intelligence

Elasticsearch

Data Engineering Podcast Interview

A/B Testing

Room Lab store built on Saleor

Augmented Reality

WebGL

Saleor Cloud

ASGI

Podcast Interview

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**A Quick Python Check-in With Naomi Ceder** Mar 25, 2019
Summary

Naomi Ceder was fortunate enough to learn Python from Guido himself. Since then she has contributed books, code, and mentorship to the community. Currently she serves as the chair of the board to the Python Software Foundation, leads an engineering team, and has recently completed a new draft of the Quick Python Book. In this episode she shares her story, including a discussion of her experience as a technical author and a detailed account of the role that the PSF plays in supporting and growing the community.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Check out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI

You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Your host as usual is Tobias Macey and today I’m interviewing Naomi Ceder about her career and contributions in the Python community

Interview

Introductions

How did you get introduced to Python?

How are you using Python in your current day-to-day?

You have been working with Python for a long time at this point, and you have become very involved in supporting and growing the community. What is your motivation for dedicating so much of your time and energy into work that isn’t directly related to paying the bills?

You have been the chair of the PSF for a few years now. What are your responsibilities in that position?

What do you find to be the most under-rated, misunderstood, or overlooked activities of the PSF?

How much of the success of the Python language and its community can be attributed to the presence and support of the PSF?

In addition to the work you do with the PSF, other community activities, and your day job, you have also written the 2nd and 3rd editions of the Quick Python Book. Can you give a synopsis of what the book covers and the audience that it is intended for?

In the process of writing the book and updating it between revisions, what are some of the features of the language or standard library that you discovered or learned more about which you have been able to use in your work?

What are some of the other language communities that you have been involved with and what lessons have you learned from them that you would like to see reflected in Python?

What are some of the other projects that you have been involved with that you are most proud of, whether technical or otherwise?

What are you most excited about in the near to medium future?

Keep In Touch

@NaomiCeder on Twitter

Web

Quick Python Book

Get 40% off everything at Manning with code *podinit19* at checkout

Enter to win a free copy

Picks

Tobias

Inkscape vector graphics editor

Naomi

La Casa de las Flores (House of Flowers) (Netflix)

Links

The Quick Python Book

Dick Blick Art Materials

The PSF

@ThePSF on Twitter

Manning Publishers

PEP8

ETL

Collections Module

Turtle Library

PyCon Hatchery

PyCon Charlas

Podcast Episode

The GIL

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Wes McKinney's Career In Python For Data Analysis** Mar 18, 2019
Summary

Python has become one of the dominant languages for data science and data analysis. Wes McKinney has been working for a decade to make tools that are easy and powerful, starting with the creation of Pandas, and eventually leading to his current work on Apache Arrow. In this episode he discusses his motivation for this work, what he sees as the current challenges to be overcome, and his hopes for the future of the industry.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Check out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Your host as usual is Tobias Macey and today I’m interviewing Wes McKinney about his contributions to the Python community and his current projects to make data analytics easier for everyone

Interview

Introductions

How did you get introduced to Python?

You have spent a large portion of your career on building tools for data science and analytics in the Python ecosystem. What is your motivation for focusing on this problem domain?

Having been an open source author and contributor for many years now, what are your current thoughts on paths to sustainability?

What are some of the common challenges pertaining to data analysis that you have experienced in the various work environments and software projects that you have been involved in?

What area(s) of data science and analytics do you find are not receiving the attention that they deserve?

Recently there has been a lot of focus and excitement around the capabilities of neural networks and deep learning. In your experience, what are some of the shortcomings or blind spots to that class of approach that would be better served by other classes of solution?

Your most recent work is focused on the Arrow project for improving interoperability across languages. What are some of the cases where a Python developer would want to incorporate capabilities from other runtimes?

Do you think that we should be working to replicate some of those capabilities into the Python language and ecosystem, or is that wasted effort that would be better spent elsewhere?

Now that Pandas has been in active use for over a decade and you have had the opportunity to get some space from it, what are your thoughts on its success?

With the perspective that you have gained in that time, what would you do differently if you were starting over today?

You are best known for being the creator of Pandas, but can you list some of the other achievements that you are most proud of?

What projects are you most excited to be working on in the near to medium future?

What are your grand ambitions for the future of the data science community, both in and outside of the Python ecosystem?

Do you have any parting advice for active or aspiring data scientists, or resources that you would like to recommend?

Keep In Touch

wesm on GitHub

Website

@wesmckinn on Twitter

Picks

Tobias

Roald Dahl

Wes

The Soul Of A New Machine by Tracy Kidder

Links

Ursa Labs

Pandas

Podcast Interview with Jeff Reback

Pandas Extension Arrays Interview with Tom Augsburger

AQR Capital Management

Distributed Computing

SQL

Excel

Duke University

AppNexus

Chang She

Ibis

Open Source Governance

Apache Software Foundation

Paul Graham

Schlep Blindness

Big Data File Formats

Avro

Parquet

ORC

Data Engineering Podcast Episode

Apache Arrow

Hadoop

Spark

Data Engineering Podcast Episode

Apache Impala

R Language

Ruby

Rust

Pandas 2.0 Design Docs

Apache Arrow and the 10 Things I Hate About Pandas

GeoPandas

Statsmodels

Python For Data Analysis by Wes McKinney

2 Sigma

R Studio

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Past, Present, and Future of Deep Learning In PyTorch** Mar 10, 2019
Summary

The current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. One of the most popular frameworks for building deep learning applications is PyTorch, in large part because of their focus on ease of use. In this episode Adam Paszke explains how he started the project, how it compares to other frameworks in the space such as Tensorflow and CNTK, and how it has evolved to support deploying models into production and on mobile devices.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Check out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Your host as usual is Tobias Macey and today I’m interviewing Adam Paszke about PyTorch, an open source deep learning platform that provides a seamless path from research prototyping to production deployment

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what deep learning is and how it relates to machine learning and artificial intelligence?

Can you explain what PyTorch is and your motivation for creating it?

Why was it important for PyTorch to be open source?

There is currently a large and growing ecosystem of deep learning tools built for Python. Can you describe the current landscape and how PyTorch fits in relation to projects such as Tensorflow and CNTK?

What are some of the ways that PyTorch is different from Tensorflow and CNTK, and what are the areas where these frameworks are converging?

How much knowledge of machine learning, artificial intelligence, or neural network topologies are necessary to make use of PyTorch?

What are some of the foundational topics that are most useful to know when getting started with PyTorch?

Can you describe how PyTorch is architected/implemented and how it has evolved since you first began working on it?

You recently reached the 1.0 milestone. Can you talk about the journey to that point and the goals that you set for the release?

What are some of the other components of the Python ecosystem that are most commonly incorporated into projects based on PyTorch?

What are some of the most novel, interesting, or unexpected uses of PyTorch that you have seen?

What are some cases where PyTorch is the wrong choice for a problem?

What is the process for incorporating these new techniques and discoveries into the PyTorch framework?

What are the areas of active research that you are most excited about?

What are some of the most interesting/useful/unexpected/challenging lessons that you have learned in the process of building and maintaining PyTorch?

What do you have planned for the future of PyTorch?

Keep In Touch

apaszke on GitHub

@apaszke on Twitter

LinkedIn

Picks

Tobias

Un Lun Dun by China Miéville

Adam

In Praise Of Copying by Marcus Boon

Links

PyTorch

University of Warsaw

Poland

Polish Olympiad In Informatics

Deep Learning

Automatic Differentiation

Torch 7

Lua

Tensorflow

CNTK

Tensorflow 2

Caffe2

EPFL (Ecole polytechnique fédérale de Lausanne)

Fast.ai

TorchScript

ONNX

Transfer Learning

C++

Reinforcement Learning

NumPy

SciPy

MatPlotLib

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**How To Include Redis In Your Application Architecture** Mar 04, 2019
Summary

The Redis database recently celebrated its 10th birthday. In that time it has earned a well-earned reputation for speed, reliability, and ease of use. Python developers are fortunate to have a well-built client in the form of redis-py to leverage it in their projects. In this episode Andy McCurdy and Dr. Christoph Zimmerman explain the ways that Redis can be used in your application architecture, how the Python client is built and maintained, and how to use it in your projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.

Your host as usual is Tobias Macey and today I’m interviewing Andy McCurdy and Christoph Zimmerman about the Redis database, and some of the various ways that it is used by Python developers

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Redis is and how you got involved in the project?

How does the redis-py project relate to the Redis database and what motivated you to create the Python client?

What are some of the main use cases that Redis enables?

Can you describe how Redis-py is implemented and some of the primitives that it provides for building applications on top of?

How do the release cycles of redis-py and the Redis database relate to each other?

How closely does redis-py match the features of the Redis database?

What are some of the convenience methods or features that you have added to make the client more Pythonic?

Redis is often used as a key/value cache for web applications, in some cases replacing Memcached. What are the characteristics of Redis that lend themselves well to this purpose?

What are some edge cases or gotchas that users should be aware of?

What are some of the common points of confusion or difficulties when storing and retrieving values in Redis?

What have been some of the most challenging aspects of building and maintaining the Redis Python client?

What are some of the anti-patterns that you have seen around how developers build on top of Redis?

What are some of the most interesting or unexpected ways that you have seen Redis used?

What are some of the least used or most misunderstood features of Redis that you think developers should know about?

What are some of the recent and near-future improvements or features in Redis that you are most excited by?

Keep In Touch

Andy

@andymccurdy on Twitter

andymccurdy on GitHub

Christoph

chrisAtRedis on GitHub

LinkedIn

Picks

Tobias

Rowan Atkinson

Andy

The Food Lab: Better Home Cooking Through Science by J. Kenji Lopez-Alt

Dota 2 Auto Chess (Community Mod)

Christoph

IPA Infused With Grapefruit Juice

redis-py

Selenium Python client

Daniel Suarez

Influx

Links

redis-py

Redis DB

Redis Labs

PHP

Django

Reflective Operating System Architectures

TCL

Perl

Linux

Memcached

NextCloud

C programming language

uWSGI

Flask

Gevent

PyPy

re-json

redis-graph

Redis-search

MongoDB

Bloom Filter

hiredis

Redis Sentinel HA plugin

Lua programming language

OpenWRT

LuCI

MicroPython

Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Marshmallow Data Validation Library** Feb 25, 2019
Summary

Any time that your program needs to interact with other systems it will have to deal with serializing and deserializing data. To prevent duplicate code and provide validation of the data structures that your application is consuming Steven Loria created the Marshmallow library. In this episode he explains how it is built, how to use it for rendering data objects to various serialization formats, and some of the interesting and unique ways that it is incorporated into other projects.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th, both run by our friends at O’Reilly Media. Go to pythonpodcast.com/stratacon and pythonpodcast.com/aicon to register today and get 20% off

Your host as usual is Tobias Macey and today I’m interviewing Steven Loria about Marshmallow, a Python serialization library that is agnostic to your framework and object mapper of choice

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Marshmallow is and the history of the project?

What are some of the capabilities that make it unique from other similar projects in the Python ecosystem?

What are some of the main use cases for schematized serialization and deserialization?

Can you walk through how a user would get started with Marshmallow, particularly for complex or nested schemas?

Can you describe how Marshmallow is implemented?

How has that design evolved since you first began working on it?

How have the changes in the Python language and ecosystem impacted the requirements and use cases for Marshmallow?

What are some of the most interesting or unexpected ways that you have seen Marshmallow used?

What have been some of the most interesting, complex, or challenging aspects of building the Marshmallow project and community?

What are lessons you’ve learned from maintaining marshmallow?

What have been some of the benefits and drawbacks of keeping Marshmallow agnostic to any frameworks or object mappers?

What are some of the edge cases that users of Marshmallow should be aware of?

What are some of the little-known features of Marshmallow that you find most useful?

What do you have planned for the future of Marshmallow?

Keep In Touch

Email

Website

@sloria1 on Twitter

Picks

Tobias

Sherlock BBC tv series

Steven

Greater Than Code podcast

Links

Marshmallow

Butterfly Network

Biology

ORM (Object Relational Mapper)

ODM (Object Document Mapper)

Webargs

Avro

Swagger/OpenAPI

REST (REpresentational State Transfer)

JSON-Schema

Environs

Django Rest Framework

WTForms

DynamoDB

MongoDB

Etsy’s boundary-layer for building Airflow DAGs from config files

Airflow Podcast Episode

Toasted Marshmallow

Lyft Blog Post

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Unpacking The Python Toolkit For Chaos Engineering** Feb 18, 2019
Summary

Chaos engineering is the practice of injecting failures into your production systems in a controlled manner to identify weaknesses in your applications. In order to build, run, and report on chaos experiments Sylvain Hellegouarch created the Chaos Toolkit. In this episode he explains his motivation for creating the toolkit, how to use it for improving the resiliency of your systems, and his plans for the future. He also discusses best practices for building, running, and learning from your own experiments.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Sylvain Hellegouarch about Chaos Toolkit, a framework for building and automating chaos engineering experiments

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Chaos Engineering is?

What is the Chaos Toolkit and what motivated you to create it?

How does it compare to the Gremlin platform?

What is the workflow for using Chos Toolkit to build and run an experiment?

What are the best practices for building a useful experiment?

Once you have an experiment created, how often should it be executed?

When running an experiment, what are some strategies for identifying points of failure, particularly if they are unexpected?

What kinds of reporting and statistics are captured during a test run?

Can you describe how Chaos Toolkit is implemented and how it has evolved since you began working on it?

What are some of the most challenging aspects of ensuring that the experiments run via the Chaos Toolkit are safe and have a reliable rollback available?

What have been some of the most interesting/useful/unexpected lessons that you have learned in the process of building and maintaining the Chaos Toolkit project and community?

What do you have planned for the future of the project?

Keep In Touch

lawouach on GitHub

Blog

@lawouach on Twitter

LinkedIn

Picks

Tobias

Time Trap

Sylvain

Playing Guitar

Step away from the computer

Links

Chaos Toolkit

Chaos IQ

Gremlin chaos engineering service

Russ Miles Chaos IQ co-founder

Zope

CherryPy minimalist Python web framework

Cherrypy Essentials book

Chaos Engineering

Chaos Engineering Book

DevOps

SRE (Site Reliability Engineering)

Dark Debt

Netflix Simian Army

Chaos Monkey

Terraform

Kubecon

Istio service mesh

Chaos Platform

PyInstaller

Composition vs Inheritance

Open Chaos Initiative

CNCF

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Computational Musicology For Python Programmers** Feb 11, 2019
Summary

Music is a part of every culture around the world and throughout history. Musicology is the study of that music from a structural and sociological perspective. Traditionally this research has been done in a manual and painstaking manner, but the advent of the computer age has enabled an increase of many orders of magnitude in the scope and scale of analysis that we can perform. The music21 project is a Python library for computer aided musicology that is written and used by MIT professor Michael Scott Cuthbert. In this episode he explains how the project was started, how he is using it personally, professionally, and in his lectures, as well as how you can use it for your own exploration of musical analysis.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Michael Cuthbert about music21, a toolkit for computer aided musicology

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what computational musicology is?

What is music21 and what motivated you to create it?

What are some of the use cases that music21 supports, and what are some common requests that you purposefully don’t support?

How much knowledge of musical notation, structure, and theory is necessary to be able to work with music21?

Can you talk through a typical workflow for doing analysis of one or more pieces of existing music?

What are some of the common challenges that users encounter when working with it (either on the side of Python or musicology/musical theory)?

What about for doing exploration of new musical works?

As a professor at MIT, what are some of the ways that music21 has been incorporated into your classroom?

What have they enjoyed most about it?

How is music21 implemented, and how has its structure evolved since you first started it?

What have been the most challenging aspects of building and maintaining the music21 project and community?

What are some of the most interesting, unusual, or unexpected ways that you have seen music21 used?

What are some analyses that you have performed which yielded unexpected results?

What do you have planned for the future of music21?

Beyond computational analysis of musical theory, what are some of the other ways that you are using Python in your academic and professional pursuits?

Keep In Touch

mscuthbert on GitHub

@mscuthbert on Twitter

Picks

Tobias

Mozart’s Requiem performed by Berlin Philharmonik and conducted by Claudio Abbado

Michael

von Karajan Institute – Karajan was a major conductor of the 60s — his Institute now sponsors research into new projects in music technology and are big advocates of using Python for their data analysis.

Ruth Crawford Seeger, String Quartet (1931) performed by The Playground Ensemble

Links

music21

Studies in Western Music History: Quantitative and Computational Approaches to Music History on MIT Open Courseware

MIT

Perl

National Bureau of Economic Research

Zen of Python

Musicology

Matplotlib

Orange

Podcast Episode

scikit-learn

Abjad Python Package

SciPy

numpy

Pandas

Podcast Episode

PyLevenshtein

Levenshtein Distance

PyGame

AVL Tree

Subversion (SVN)

Bach Chorales

Artusi.xyz Interactive Music Theory

VexFlow

MIT Digital Humanities

NLTK

Flask

Fortran

Django

Humdrum

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Classic Computer Science For Pythonistas** Feb 04, 2019
Summary

Software development is a career that attracts people from all backgrounds, and Python in particular helps to make it an approachable occupation. Because of the variety of paths that can be taken it is becoming increasingly common for practitioners to bypass the traditional computer science education. In this episode David Kopec discusses some of the classic problems that he has found most useful to understand in his work as a professor and practitioner of software engineering. He shares his motivation for writing the book "Classic Computer Science Problems In Python", the practical approach that he took, and an overview of how the contents can be used in your day-to-day work.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing David Kopec about his recent book "Classic Computer Science Problems In Python"

Interview

Introductions

How did you get introduced to Python?

Can you start by discussing your motivation for creating this book and the subject matter that it covers?

How do you define a "classic" computer science problem and what was your criteria for selecting the specific cases that you included in the book?

What are your favorite features of the Python language, and which of them did you learn as part of the process of writing the examples for this book?

Which classes of problems have you found to be most difficult for your readers and students to master?

Which do you consider to be most relevant/useful to professional software engineers?

I was pleasantly surprised to see introductory aspects of artificial intelligence included in the subject matter that you covered. How did you approach the challenge of making the underlying principles accessible to readers who don’t necessarily have a background in the related fields of mathematics?

What are some of the most interesting or unexpected changes that you had to make in the process of adapting your examples from Swift to Python in order to make them appropriately idiomatic?

By aiming for an intermediate audience you free yourself of the need to incorporate fundamental aspects of programming, but there can be a wide variety of experiences at that level of experience. How did you approach the challenge of making the text accessible while still being accurate and engaging?

What are some of the resources that you would recommend to readers who would like to continue learning about computer science after completing your book?

Keep In Touch

@davekopec on Twitter

Website

Book Discount And Giveaway

Use code *podinit19* to get 40% off all Manning products

Picks

Tobias

Elementor

David

nesdev

The Curse of Oak Island

Links

Classic Computer Science Problems in Python

Classic Computer Science Problems in Swift

Dart For Absolute Beginners

Dart

Swift

Manning Publications

Apress

Python Data Classes

Python Type Hints

Recursion

A* Search Algorithm

Neural Network

Champlain College

Burlington, VT, USA

HyperLoop

Data Structures And Algorithms In Python by Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser

MyPy

Podcast Interview

PyTorch

Minimax

Dartmouth College

Big O Notation

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**What You Need To Know About Open Source Licenses And Intellectual Property** Jan 28, 2019
Summary

As a developer and user of open source code, you interact with software and digital media every day. What is often overlooked are the rights and responsibilities conveyed by the intellectual property that is implicit in all creative works. Software licenses are a complicated legal domain in their own right, and they can often conflict with each other when you factor in the web of dependencies that your project relies on. In this episode Luis Villa, Co-Founder of Tidelift, explains the catagories of software licenses, how to select the right one for your project, and what to be aware of when you contribute to someone else’s code.

Announcements

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Luis Villa about software licensing and intellectual property rules that developers need to know

Interview

Introductions

How did you get started as a programmer?

Intellectual property law and licensing of software, data, and media are complicated topics that are often poorly understood by developers. Can you start off by giving an overview of categories of intellectual property that we should be thinking of?

Most of us who have created or used software, whether it is open or closed source, have at some point come across various licenses. What may not be immediately obvious is that there are degrees of compatibility between these licenses. What are some guiding principles for determining which licenses are in conflict?

In an organization, who is responsible for ensuring compliance with software and content licensing within a given project?

When introducing new dependencies into a project or system what steps should be taken to evaluate license compatibility and compliance?

When creating a new project, one of the steps in the process is to select a license. What are some useful guidelines or questions to determine which license to use?

Another aspect of software licensing that developers might run into is when contributing to an open source project where a contributor license agreement might be necessary. What should we be thinking about when deciding whether to sign such an agreement?

In addition to software libraries, developers might need to use content such as images, audio, or video in their projects which have their own copyright and licensing considerations. What are some of the things that we should be looking for in those situations?

Another component of our systems that has grown in its importance with the rise of advanced analytics is data. We may need to use open data sources, pay for access to data repositories, or provide access to data that is under our control. What are some common approaches to licensing or terms of use for these contexts?

What should we be wary of when using or providing data in our applications?

How much of the work that you do at Tidelift is spent on educating developers and customers on the finer points of intellectual property management?

What are some of the most common difficulties or points of confusion that you encounter?

What are some useful resources that you would recommend to anyone who is interested in learning more about intellectual property and software licensing?

Keep In Touch

Website

@luis_in_140 on Twitter

LinkedIn

Picks

Tobias

Spider Man: Into The Spiderverse

Luis

The Good Place

Twitter and Teargas by Zeynep Tufecki

Links

Intellectual Property and Open Source: A Practical Guide To Protecting Code by Van Lindberg

Tidelift

BASIC

Apple //e

Copyright

Trademark

Patent

Copyleft

OSI Approved Licenses

Permissive Licenses

Strong and Weak Copyleft

SSPL (Server Side Public License)

OSI (Open Source Initiative)

Contributor License Agreement

FSF (Free Software Foundation)

DCO (Developer Certificate of Origin)

Creative Commons

Noun Project

Free Music Archive

Wikimedia Commons

TL;DR Legal

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Counteracting Code Complexity With Wily** Jan 21, 2019
Summary

As we build software projects, complexity and technical debt are bound to creep into our code. To counteract these tendencies it is necessary to calculate and track metrics that highlight areas of improvement so that they can be acted on. To aid in identifying areas of your application that are breeding grounds for incidental complexity Anthony Shaw created Wily. In this episode he explains how Wily traverses the history of your repository and computes code complexity metrics over time and how you can use that information to guide your refactoring efforts.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Anthony Shaw about Wily, a command-line application for tracking and reporting on complexity of Python tests and applications

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Wily is and what motivated you to create it?

What is software complexity and why should developers care about it?

What are some methods for measuring complexity?

I know that Python has the McCabe tool, but what other methods are there for determining complexity, both in Python and for other languages?

What kinds of useful signals can you derive from evaluating historical trends of complexity in a codebase?

What are some other useful metrics for tracking and maintaining the health of a software project?

Once you have established the points of complexity in your software, what are some strategies for remediating it?

What are your favorite tools for refactoring?

What are some of the aspects of developer-oriented tools that you have found to be most important in your own projects?

What are your plans for the future of Wily, or any other tools that you have in mind to aid in producing healthy software?

Keep In Touch

anthonywritescode on GitHub

@anthonypjshaw on Twitter

Website

Medium

Picks

Tobias

Baobab

Impractical Jokers

Anthony

Line Of Duty

Fierce Girls

Links

Wily

Dimension Data

Pluralsight

Real Python

Seattle

C#

Cyclomatic Complexity

McCabe

Git

C

Assembly

Halstead

Radon

The Zen Of Python

Vocabulary Metric

Java

Anti Patterns

God Object

Pre-Commit

Codeclimate

Glom

ASQ

PyCharm

PyDocStyle

PyLint

Black

Sunburst Chart

Visual Studio Code

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Teaching Digital Archaeology With Jupyter Notebooks** Jan 14, 2019
Summary

Computers have found their way into virtually every area of human endeavor, and archaeology is no exception. To aid his students in their exploration of digital archaeology Shawn Graham helped to create an online, digital textbook with accompanying interactive notebooks. In this episode he explains how computational practices are being applied to archaeological research, how the Online Digital Archaeology Textbook was created, and how you can use it to get involved in this fascinating area of research.

Introduction

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Shawn Graham about his work on the Online Digital Archaeology Textbook

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what digital archaeology is?

To facilitate your teaching you have collaborated on the O-DATE textbook and associated Jupyter notebooks. Can you describe what that resource covers and how the project got started?

What have you found to be the most critical lessons for your students to help them be effective archaeologists?

What are the most useful aspects of leveraging computational techniques in an archaeological context?

Can you describe some of the sources and formats of data that would commonly be encountered by digital archaeologists?

The notebooks that accompany the text have a mixture of R and Python code. What are your personal guidelines for when to use each language?

How have the skills and tools of software engineering influenced your views and approach to research and education in the realm of archaeology?

What are some of the most novel or engaging ways that you have seen computers applied to the field of archaeology?

What are your goals and aspirations for the O-DATE project?

Keep In Touch

Blog

@electricarchaeo on Twitter

Picks

Tobias

TaoTronics Noise Cancelling Earbuds

Shawn

Ian Rankin

In A House Of Lies

Links

O-DATE Textbook

Carleton University

Ottawa Canada

Simulation Modeling

Agent Based Modeling

NetLogo

Complexity Theory

Archaeology

Digital Archaeology

The Programming Historian

University of Western Ontario

Historical GIS

ArcGIS

QGIS

Digital Humanities

Project Jupyter

Podcast Episode

Binder – Service for hosting Jupyter notebooks

E-Campus Ontario

Graph Databases

SparQL

OpenContext.org

TDAR (The Digital Archaeology Record)

R Language

R OpenSci

Arrow

Pandas

Podcast Episode

Neural Networks

Generative Adversarial Networks

Computer Vision

Archaeogaming

Alamagordo Atari Excavation

Leiden University

Interactive Pasts Conference

Photogrammetry

LIDAR

Palmyran Arch

Ben Marwick

Matt Harris

Jolene Smith

Sara Perry

Rachel Opitz

Colleen Morgan

Patrick Burns

Ethan Watrall

Andrew Reinhard

Neha Gupta

Katherine Cook

Value Foundation

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Analyzing Satellite Image Data Using PyTroll** Jan 07, 2019
Summary

Every day there are satellites collecting sensor readings and imagery of our Earth. To help make sense of that information, developers at the meteorological institutes of Sweden and Denmark worked together to build a collection of Python packages that simplify the work of downloading and processing satellite image data. In this episode one of the core developers of PyTroll explains how the project got started, how that data is being used by the scientific community, and how citizen scientists like you are getting involved.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Martin Raspaud about PyTroll, a suite of projects for processing earth observing satellite data

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what PyTroll is and how the overall project got started?

What is the story behind the name?

What are the main use cases for PyTroll? (e.g. types of analysis, research domains, etc.)

What are the primary types of data that would be processed and analayzed with PyTroll? (e.g. images, sensor readings, etc.)

When retrieving the data, are you communicating directly with the satellites, or are there facilities that fetch the information periodically which you can then interface with?

How do you locate and select which satellites you wish to retrieve data from?

What are the main components of PyTroll and how do they fit together?

For someone processing satellite data with PyTroll, can you describe the workflow?

What are some of the main data formats that are used by satellites?

What tradeoffs are made between data density/expressiveness and bandwidth optimization?

What are some of the common issues with data cleanliness or data integration challenges?

Once the data has been retrieved, what are some of the types of analysis that would be performed with PyTroll?

Are there other tools that would commonly be used in conjunction with PyTroll?

What are some of the unique challenges posed by working with satellite observation data?

How has the design and capability of the various PyTroll packages evolved since you first began working on it?

What are some of the most interesting or unusual ways that you have seen PyTroll used?

What are some of the lessons that you have learned while building PyTroll that you have found to be most useful or unexpected?

What do you have planned for the future of PyTroll?

Keep In Touch

Martin

mraspaud on GitHub

@MartinRaspaud on Twitter

Pytroll

Website

Slack

Mailing List

@PyTroll on Twitter

Picks

Tobias

Tool

A Perfect Circle

Martin

Vulfpeck

Links

PyTroll

Swedish Meteorological and Hydrological Institute

Common Lisp

Danish Meteorological Institute

Trolls in Scandinavian Lore

NumPy

KISS (Keep It Simple Stupid)

Spectroscopy

Radiance

Polar Orbiting Satellite

Geostationary Satellite

EUMETSAT

SatPy

PyResample

Cartographic Projection

Proj4

GOES16

[GOES17](https://en.wikipedia.org/wiki/GOES-17?utm_source=rss&utm_medium=rss

Dask

Data Engineering Podcast Episode

NetCDF

HDF5

PySpectral

PyCoast

SupervisorD

TrollCast

European Space Agency

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building GraphQL APIs in Python Using Graphene with Syrus Akbary** Dec 31, 2018
Summary

The web has spawned numerous methods for communicating between applications, including protocols such as SOAP, XML-RPC, and REST. One of the newest entrants is GraphQL which promises a simplified approach to client development and reduced network requests. To make implementing these APIs in Python easier, Syrus Akbary created the Graphene project. In this episode he explains the origin story of Graphene, how GraphQL compares to REST, how you can start using it in your applications, and how he is working to make his efforts sustainable.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Syrus Akbary about Graphene, a python library for building your APIs with GraphQL

Interview

Introductions

How did you get introduced to Python?

What is GraphQL and what is the benefit vs a REST-based API?

How does it compare to specifications such as OpenAPI (formerly Swagger) or RAML?

Can you explain what Graphene is and your motivation for building it?

In addition to the Python implementation there is also a JavaScript library. Is that primarily for use as a client or can it also be used in Node for serving APIs?

What is involved in building a GraphQL API?

What does Graphene do to simplify this process?

How is Graphene implemented and how has that evolved since you first started working on it?

Is there a set of tests for verifying the compliance of Graphene or a specific API with the GraphQL specification?

What are some of the most complex or confusing aspects of building a GraphQL API?

What are some of the unique capabilities that are offered by building an application with GraphQL as the communication interface?

While reading through documentation in preparation for our conversation I noticed the Quiver project. Can you explain what that is and how it fits with the other Graphene projects?

What is it doing under the hood to optimize serving of the API?

For someone who is interested in adding a GraphQL interface to an existing application, what would be involved?

The documentation mentions creation of a schema, as well as defining queries. Is it possible for a client to craft queries that don’t match directly with those defined in the server layer?

What are some of the most interesting or surprising uses of Graphene and GraphQL that you have seeen?

What are some cases where it would be more practical to implement an API using REST instead of GraphQL?

What are some references that you would recommend for anyone who wants to learn more about GraphQL and its ecosystem?

What are your plans for the future of Graphene?

Keep In Touch

syrusakbary on GitHub

Website

@syrusakbary on Twitter

Picks

Tobias

Audible

Syrus

Web Assembly

Links

Graphene

GraphQL

REST (REpresentational State Transfer

OpenAPI

RAML

PHP

Facebook Engineering

Graphene-SQLAlchemy

Graphene-Django

GraphiQL

PyJade

Django Rest Framework

How To GraphQL

Python 3.7 Dataclasses

Graphene GitHub Issue

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**AIORTC: An Asynchronous WebRTC Framework with Jeremy Lainé** Dec 24, 2018
Summary

Real-time communication over the internet is an amazing feat of modern engineering. The protocol that powers a majority of video calling platforms is WebRTC. In this episode Jeremy Lainé explains why he wrote a Python implementation of this protocol in the form of AIORTC. He also discusses how it works, how you can use it in your own projects, and what he has planned for the future.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Jeremy Lainé about AIORTC, an asynchronous implementation of the WebRTC and ObjectRTC protocols in Python

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what the WebRTC and ObjectRTC protocols are?

What are some of the main use cases for these protocols?

What is AIORTC and what was your motivation for creating it?

How does it compare to other implementations of the RTC protocols?

Why do you think there haven’t been any other Python implementations?

What are some of the benefits of having a Python implementation of the RTC protocol?

How is AIORTC implemented?

What have been some of the most difficult or challenging aspects of implementing a WebRTC compliant library?

What are some of the most interesting or useful lessons that you have learned in the process?

What is involved in building an application on top of AIORTC?

What would be required to integrate AIORTC into an existing application built with something such as Flask or Django?

What are some of the most interesting uses of AIORTC that you have seen?

What are some of the projects that you would like to build with AIORTC?

What are some cases where it would make more sense to use a different library or framework for your WebRTC projects?

What are your plans for the future of AIORTC?

Keep In Touch

jlaine on GitHub

Website

@JeremyLaine on Twitter

Picks

Tobias

Tengger Cavalry

Jeremy

PyAV

Mike Boers

Links

AIORTC

WebRTC

Electrical Engineering

[C](https://en.wikipedia.org/wiki/C_(programming_language)?utm_source=rss&utm_medium=rss

C++

PHP

Ruby

STUN (Session Traversal Utilities for NAT)

TURN (Traversal Using Relays around NAT)

ICE (Internet Connectivity Establishment)

TLS (Transport Layer Security)

RTP (Real-time Transport Protocol)

Zencastr

Jitsi

RawRTC

AsyncIO

AIOICE

Cryptography

Podcast.init Episode

OpenCV

PyAV

FFMPEG

Edge Detection

Asterisk

Raspberry Pi

Datagram Transport Security

Mozilla

Augmented Reality

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Polyglot: Multi-Lingual Natural Language Processing with Rami Al-Rfou** Dec 17, 2018
Summary

Using computers to analyze text can produce useful and inspirational insights. However, when working with multiple languages the capabilities of existing models are severely limited. In order to help overcome this limitation Rami Al-Rfou built Polyglot. In this episode he explains his motivation for creating a natural language processing library with support for a vast array of languages, how it works, and how you can start using it for your own projects. He also discusses current research on multi-lingual text analytics, how he plans to improve Polyglot in the future, and how it fits in the Python ecosystem.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Rami Al-Rfou about Polyglot, a natural language pipeline with support for an impressive amount of languages

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Polyglot is and your reasons for starting the project?

What are the types of use cases that Polyglot enables which would be impractical with something such as NLTK or SpaCy?

A majority of NLP libraries have a limited set of languages that they support. What is involved in adding support for a given language to a natural language tool?

What is involved in adding a new language to Polyglot?

Which families of languages are the most challenging to support?

What types of operations are supported and how consistently are they supported across languages?

How is Polyglot implemented?

Is there any capacity for integrating Polyglot with other tools such as SpaCy or Gensim?

How much domain knowledge is required to be able to effectively use Polyglot within an application?

What are some of the most interesting or unique uses of Polyglot that you have seen?

What have been some of the most complex or challenging aspects of building Polyglot?

What do you have planned for the future of Polyglot?

What are some areas of NLP research that you are excited for?

Keep In Touch

Picks

Tobias

Duolingo

Rami

The Wizard and the Prophet: Two Remarkable Scientists and Their Dueling Visions to Shape Tomorrow’s World by Charles C. Mann

Links

Polyglot

Polyglot-NER

Jordan

NLP (Natural Language Processing)

Stony Brook University

Arabic

Sentiment Analysis

Assembly Language

C

.NET

Stack Overflow

Deep Learning

Word Embedding

Wikipedia

Word2Vec

NLTK (Python Natural Language Toolkit)

SpaCy

Podcast Episode

Gensim

Podcast Episode

Morphology

Morpheme

Transfer Learning

Read The Docs

BERT (Bidirectional Encoder Representations from Transformers)

FastText

data.world

Data Engineering Podcast Episode

Quilt package management for data

Data Engineering Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Gnocchi: A Scalable Time Series Database For Your Metrics with Julien Danjou** Dec 10, 2018
Summary

Do you know what your servers are doing? If you have a metrics system in place then the answer should be “yes”. One critical aspect of that platform is the timeseries database that allows you to store, aggregate, analyze, and query the various signals generated by your software and hardware. As the size and complexity of your systems scale, so does the volume of data that you need to manage which can put a strain on your metrics stack. Julien Danjou built Gnocchi during his time on the OpenStack project to provide a time oriented data store that would scale horizontally and still provide fast queries. In this episode he explains how the project got started, how it works, how it compares to the other options on the market, and how you can start using it today to get better visibility into your operations.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.

And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Julien Danjou about Gnocchi, an open source time series database built to handle large volumes of system metrics

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Gnocchi is and how the project got started?

What was the motivation for moving Gnocchi out of the Openstack organization and into its own top level project?

The space of time series databases and metrics as a service platforms are both fairly crowded. What are the unique features of Gnocchi that would lead someone to deploy it in place of other options?

What are some of the tools and platforms that are popular today which hadn’t yet gained visibility when you first began working on Gnocchi?

How is Gnocchi architected?

How has the design changed since you first started working on it?

What was the motivation for implementing it in Python and would you make the same choice today?

One of the interesting features of Gnocchi is its support of resource history. Can you describe how that operates and the types of use cases that it enables?

Does that factor into the multi-tenant architecture?

What are some of the drawbacks of pre-aggregating metrics as they are being written into the storage layer (e.g. loss of fidelity)?

Is it possible to maintain the raw measures after they are processed into aggregates?

One of the challenging aspects of building a scalable metrics platform is support for high-cardinality data. What sort of labelling and tagging of metrics and measures is available in Gnocchi?

For someone who wants to implement Gnocchi for their system metrics, what is involved in deploying, maintaining, and upgrading it?

What are the available integration points for extending and customizing Gnocchi?

Once metrics have been stored, aggregated, and indexed, what are the options for querying and analyzing the collected data?

When is Gnocchi the wrong choice?

What do you have planned for the future of Gnocchi?

Keep In Touch

jd on GitHub

Website

@juldanjou on Twitter

Picks

Tobias

Marketplace Podcast

Julien

Mergify

Links

Gnocchi

RedHat

OpenStack

Object Oriented Programming

O’Reilly

Debian

Ceilometer

Prometheus

Time Series

MySQL

Gerrit

Zuul

Podcast Episode

GitHub

GitLab

Graphite

Podcast Episode

DataDog

RabbitMQ

InfluxDB

Ceph

Podcast Episode

S3

OpenStack Swift

Cassandra

Honeycomb Observability Service

Podcast Episode

AMQP

Redis

DSL (Domain Specific Language)

Golang

RBAC (Role-Based Access Control)

CollectD

StatsD

Gnocchi Client

Telegraf

Grafana

TimescaleDB

Podcast Episode

OpenStack Heat

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Keeping Up With The Python Community For Fun And Profit with Dan Bader** Dec 03, 2018
Summary

Keeping up with the work being done in the Python community can be a full time job, which is why Dan Bader has made it his! In this episode he discusses how he went from working as a software engineer, to offering training, to now managing both the Real Python and PyCoders properties. He also explains his strategies for tracking and curating the content that he produces and discovers, how he thinks about building products, and what he has learned in the process of running his businesses.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Dan Bader about finding, filtering, and creating resources for Python developers at Real Python, PyCoders, and his own trainings

Interview

Introductions

How did you get introduced to Python?

Let’s start by discussing your primary job these days and how you got to where you are.

In the past year you have also taken over management of the Real Python site. How did that come about and what are your responsibilities?

You just recently took over management of the PyCoders newsletter and website. Can you describe the events that led to that outcome and the responsibilities that came along with it?

What are the synergies that exist between your various roles and projects?

What are the areas of conflict? (e.g. time constraints, conflicts of interest, etc.)

Between PyCoders, Real Python, your training materials, your Python tips newsletter, and your coaching you have a lot of incentive to keep up to date with everything happening in the Python ecosystem. What are your strategies for content discovery?

With the diversity in use cases, geography, and contributors to the landscape of Python how do you work to counteract any bias or blindspots in your work?

There is a constant stream of information about any number of topics and subtopics that involve the Python language and community. What is your process for filtering and curating the resources that are ultimately included in the various media properties that you oversee?

In my experience with the podcast one of the most difficult aspects of maintaining relevance as a content creator is obtaining feedback from your audience. What do you do to foster engagement and facilitate conversations around the work that you do?

You have also built a few different product offerings. Can you discuss the process involved in identifying the relevant opportunities and the creation and marketing of them?

Creating, collecting, and curating content takes a significant investment of time and energy. What are your avenues for ensuring the sustainability of your various projects?

What are your plans for the future growth and development of your media empire?

As someone who is so deeply involved in the conversations flowing through and around Python, what do you see as being the greatest threats and opportunities for the language and its community?

Keep In Touch

@dbader_org on Twitter

Website

dbader on GitHub

Picks

Tobias

Data Engineering Podcast

Dan

Black code formatter

Łukasz Langa

Links

Dan Bader

Nerd Lettering

Real Python

PyCoders

Computer Science

Vancouver, BC

Django

Raymond Hettinger

Data Science

Flask

Pythonista Cafe

Python Tricks

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Using Calibre To Keep Your Digital Library In Order with Kovid Goyal** Nov 26, 2018
Summary

Digital books are convenient and useful ways to have easy access to large volumes of information. Unfortunately, keeping track of them all can be difficult as you gain more books from different sources. Keeping your reading device synchronized with the material that you want to read is also challenging. In this episode Kovid Goyal explains how he created the Calibre digital library manager to solve these problems for himself, how it grew to be the most popular application for organizing ebooks, and how it works under the covers. Calibre is an incredibly useful piece of software with a lot of hidden complexity and a great story behind it.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Kovid Goyal about Calibre, the powerful and free ebook management tool

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Calibre is and how the project got started?

How are you able to keep up to date with device support in Calibre, given the continual release of new devices and platforms that a user can read ebooks on?

What are the main features of Calibre?

What are some of the most interesting and most popular plugins that have been creatd for Calibre?

Can you describe the software architecture for the project and how it has evolved since you first started working on it?

You have been maintaining and improving Calibre for a long time now. What is your motivation to keep working on it?

How has the focus of the project and the primary use cases changed over the years that you have been working on it?

In addition to its longevity, Calibre has also become a de-facto standard for ebook management. What is your opinion as to why it has gained and kept its popularity?

What are some of the competing options and how does Calibre differentiate from them?

In addition to the myriad devices and platforms, there is a significant amount of complexity involved in supporting the different ebook formats. What have been the most challenging or complex aspects of managing and converting between the formats?

One of the challenges around maintaining a private library of electronic resources is the prevalence of DRM restricted content available through major publishers and retailers. What are your thoughts on the current state of digital book marketplaces?

What was your motivation for implementing Calibre in Python?

If you were to start the project over today would you make the same choice?

Are there any aspects of the project that you would implement differently if you were starting over?

What are your plans for the future of Calibre?

Keep In Touch

kovidgoyal on GitHub

Website

Patreon

Picks

Tobias

American Gods by Neil Gaiman

Kovid

Into Thin Air by John Krakauer
About how an expedition to climb Everest went wrong. Wonderful account of the difficulties of high altitude mountaineering and the determination it needs.

The Steerswoman’s Road by Rosemary Kirstein
About the spirit of scientific enquiry in a fallen civilization on an alien planet with partial terraforming that is slowly failing.

Links

Calibre

KDE

Caltech

Sony PRS500

Linux

Kindle

Kobo

ePUB

Calibre Recipes

Rapydscrypt NG

Goodreads

Qt

PyQt

build-calibre

Kitty

DRM (Digital Rights Management)

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Entity Extraction, Document Processing, And Knowledge Graphs For Investigative Journalists with Friedrich Lindenberg** Nov 19, 2018
Summary

Investigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode today to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Registration for PyCon US, the largest annual gathering across the community, is open now. Don’t forget to get your ticket and I’ll see you there!

Your host as usual is Tobias Macey and today I’m interviewing Friedrich Lindenberg about Aleph, a tool to perform entity extraction across documents and structured data

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Aleph is and how the project got started?

What is investigative journalism?

How does Aleph fit into their workflow?

What are some other tools that would be used alongside Aleph?

What are some ways that Aleph could be useful outside of investigative journalism?

How is Aleph architected and how has it evolved since you first started working on it?

What are the major components of Aleph?

What are the types of documents and data formats that Aleph supports?

Can you describe the steps involved in entity extraction?

What are the most challenging aspects of identifying and resolving entities in the documents stored in Aleph?

Can you describe the flow of data through the system from a document being uploaded through to it being displayed as part of a search query?

What is involved in deploying and managing an installation of Aleph?

What have been some of the most interesting or unexpected aspects of building Aleph?

Are there any particularly noteworthy uses of Aleph that you are aware of?

What are your plans for the future of Aleph?

Keep In Touch

Website

@pudo on Twitter

pudo on GitHub

Picks

Tobias

Mechanical Soup

Friedrich

phonenumbers – because it’s useful

pyicu – super nerdy but amazing

sqlalchemy – my all-time favorite python package

Links

Aleph

Organized Crime and Corruption Reporting Project

OCR (Optical Character Recognition)

Jorge Luis Borges

Buenos Aires

Investigative Journalism

Azerbaijan

Signal

Open Corporates

Open Refine

Money Laundering

E-Discovery

CSV

SQL

Entity Extraction (Named Entity Recognition)

Apache Tika

Polyglot

SpaCy

Podcast.__init__ Episode

LibreOffice

Tesseract

followthemoney

Elasticsearch

Knowledge Graph

Neo4J

Gephi

Edward Snowden

Document Cloud

Overview Project

Veracrypt

Qubes OS

I2 Analyst Notebook

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Bringing Python To The Spanish Language Community with Maricela Sanchez** Oct 29, 2018
Summary

The Python Community is large and growing, however a majority of articles, books, and presentations are still in English. To increase the accessibility for Spanish language speakers, Maricela Sanchez helped to create the Charlas track at PyCon US, and is an organizer for Python Day Mexico. In this episode she shares her motivations for getting involved in community building, her experiences working on Python Day Mexico and PyCon Charlas, and the lessons that she has learned in the process.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Maricela Sanchez Miranda about her work in organizing PyCon Charlas, the spanish language track at PyCon US, as well as Python Day Mexico

Interview

Introductions

How did you get introduced to Python?

Can you briefly describe PyCon Charlas and Python Day Mexico?

What has been your motivation for getting involved with organizing these community events?

What do you find to be the unique characteristics of the Python community in Mexico?

What kind of feedback have you gotton from the Charlas track at PyCon?

What are your goals for fostering these Spanish language events?

What are some of the lessons that you have learned from PyCon Charlas that were useful in organizing Python Day Mexico?

What have been the most challenging or complicated aspects of organizing Python Day Mexico?

How many attendees do you anticipate? How has that affected your planning and preparation?

Are there any aspects of the geography, infrastructure, or culture of Mexico that you have found to be either beneficial or challenging for organizing a conference?

Do you anticipate PyCon Charlas and Python Day Mexico becoming annual events?

What is your advice for anyone who is interested in organizing a conference in their own region or language?

Keep In Touch

mayela on GitHub

@mayela0x14 on Twitter

Picks

Tobias

CardLine Dinosaurs

Maricela

Links

Python Day Mexico

PyCon Charlas

PyCon Hatchery

PyCon Latin America

Mexico City

Guadalajara

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Of Checklists, Ethics, and Data with Emily Miller and Peter Bull** Oct 22, 2018
Summary

As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how you can start using it today.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Emily Miller and Peter Bull about Deon, an ethics checklist for data projects

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Deon is and your motivation for creating it?

Why a checklist, specifically? What’s the advantage of this over an oath, for example?

What is unique to data science in terms of the ethical concerns, as compared to traditional software engineering?

What is the typical workflow for a team that is using Deon in their projects?

Deon ships with a default checklist but allows for customization. What are some common addendums that you have seen?

Have you received pushback on any of the default items?

How does Deon simplify communication around ethics across team boundaries?

What are some of the most often overlooked items?

What are some of the most difficult ethical concerns to comply with for a typical data science project?

How has Deon helped you at Driven Data?

What are the customer facing impacts of embedding a discussion of ethics in the product development process?

Some of the items on the default checklist coincide with regulatory requirements. Are there any cases where regulation is in conflict with an ethical concern that you would like to see practiced?

What are your hopes for the future of the Deon project?

Keep In Touch

Emily

LinkedIn

ejm714 on GitHub

Peter

LinkedIn

@pjbull on Twitter

pjbull on GitHub

Driven Data

@drivendataorg on Twitter

drivendataorg on GitHub

Website

Picks

Tobias

Richard Bond Glass Art

Emily

Tandem Coffee in Portland, Maine

Peter

The Model Bakery in Saint Helena and Napa, California

Links

Deon

Driven Data

International Development

Brookings Institution

Stata

Econometrics

Metis Bootcamp

Pandas

Podcast Episode

C#

.NET

Podcast.__init__ Episode On Software Ethics

Jupyter Notebook

Podcast Episode

Word2Vec

cookiecutter data science

Logistic Regression

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**How Python Is Used To Build A Startup At Wanderu with Chris Kirkos and Matt Warren** Oct 15, 2018
Summary

The breadth of use cases that Python supports, coupled with the level of productivity that it provides through its ease of use have contributed to the incredible popularity of the language. To explore the ways that it can contribute to the success of a young and growing startup two of the lead engineers at Wanderu discuss their experiences in this episode. Matt Warren, the technical operations lead, explains the ways that he is using Python to build and scale the infrastructure that Wanderu relies on, as well as the ways that he deploys and runs the various Python applications that power the business. Chris Kirkos, the lead software architect, describes how the original Django application has grown into a suite of microservices, where they have opted to use a different language and why, and how Python is still being used for critical business needs. This is a great conversation for understanding the business impact of the Python language and ecosystem.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Matt Warren and Chris Kirkos and about the ways that they are using Python at Wanderu

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Wanderu does?

How is the platform architected?

What are the broad categories of problems that you are addressing with Python?

What are the areas where you chose to use a different language or service?

What ratio of new projects and features are implemented using Python?

How much of that decision process is influenced by the fact that you already have so much pre-existing Python code?

For the projects where you don’t choose Python, what are the reasons for going elsewhere?

What are some of the limitations of Python that you have encountered while working at Wanderu?

What are some of the places that you were surprised to find Python in use at Wanderu?

What have you enjoyed most about working with Python?

What are some of the sharp edges that you would like to see smoothed over in future versions of the language?

What is the most challenging bug that you have dealt with at Wanderu that was attributable in some sense to the fact that the code was written in Python?

If you were to start over today on any of the pieces of the Wanderu platform, are there any that you would write in a different language?

Which libraries have been the most useful for your work at Wanderu?

Which ones have caused you the most pain?

Keep In Touch

Matt

@matthewwwarren on Twitter

LinkedIn

Chris

LinkedIn

Picks

Tobias

DataGrip

Matt

Chacarero

Chris

PDB

IPDB

PUDB

VSCode

Links

Wanderu

Northeastern University

C++

Perl

Microservices

PostgreSQL

Data Engineering Podcast Episode

MongoDB

Django

Node.js

Go-lang

AWS

ETL (Extract, Transform, and Load)

Data Warehouse

Graph Database

Twisted

Podcast Episode

Gevent

Scrapy

Virtualenv

Ruby

Rbenv

Boto3

PyMongo

Ansible

Pip

TLS

Cryptography

Podcast Episode

Setuptools

Openstack

Requests

PyCountry

SOAP (Simple Object Access Protocol)

XML

Jinja

OpenSSL

pytest

Bandit

Podcast Episode

Gang of Four

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building A Game In Python At PyWeek with Daniel Pope** Oct 09, 2018
Summary

Many people learn to program because of their interest in building their own video games. Once the necessary skills have been acquired, it is often the case that the original idea of creating a game is forgotten in favor of solving the problems we confront at work. Game jams are a great way to get inspired and motivated to finally write a game from scratch. This week Daniel Pope discusses the origin and format for PyWeek, his experience as a participant, and the landscape of options for building a game in Python. He also explains how you can register and compete in the next competition.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Daniel Pope about PyWeek, a one week challenge to build a game in Python

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what PyWeek is and how the competition got started?

What is your current role in relation to PyWeek and how did you get involved?

What are the strengths of the Python lanaguage and ecosystem for developing a game?

What are some of the common difficulties encountered by participants in the challenge?

What are some of the most commonly used libraries and tools for creating and packaging the games?

What are some shortcomings in the available tools or libraries for Python when it comes to game development?

What are some examples of libraries or tools that were created and released as a result of a team’s efforts during PyWeek?

How often do games that get started during PyWeek continue to be developed and improved?

Have there ever been games that went on to be commercially viable?

What are some of the most interesting or unusual games that you have seen submitted to PyWeek?

Can you describe your experience as a competitor in PyWeek?

How do you structure your time during the competition week to ensure that you can complete your game?

What are the benefits and difficulties of the one week constraint for development?

How has PyWeek changed over the years that you have been involved with it?

What are your hopes for the competition as it continues into the future?

Keep In Touch

@lordmauve on Twitter

Blog

lordmauve on GitHub

Picks

Tobias

The Architecht Show

Dan

Red Blob Games

Designing Virtual Worlds by Richard Bartle

Links

PyWeek

Two Sigma

Game Jam

Richard Jones

PyGame

Pyglet

SDL

PyGame Zero

Cocos 2D

Doctor Corovich’s Flying Atomic Squid

Mortimer The Lepidopterist

Ludum Dare

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Managing Application Secrets with Brian Kelly** Oct 02, 2018
Summary

Any application that communicates with other systems or services will at some point require a credential or sensitive piece of information to operate properly. The question then becomes how best to securely store, transmit, and use that information. The world of software secrets management is vast and complicated, so in this episode Brian Kelly, engineering manager at Conjur, aims to help you make sense of it. He explains the main factors for protecting sensitive information in your software development and deployment, ways that information might be leaked, and how to get the whole team on the same page.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Brian Kelly about how to store, deploy, and use sensitive information in your applications

Interview

Introductions

How did you get introduced to Python?

To begin with, how do you define a secret in the context of an application?

What are the broad categories for solutions to secrets management?

What are the different aspects of secrets management in the lifecycle of developing, deploying, and maintaining an application?

How does the scale of a project or organization impact the strategies that are reasonable for secrets management?

What are some of the most challenging aspects of secrets management at the different stages of usage?

What are some of the common reasons that secrets management strategies fail?

What are some of the vulnerabilities or attack vectors that development teams should be thinking about when working with credentials?

What are your thoughts on versioning of secrets?

Beyond storing and deploying sensitive information, what are some of the secondary concerns around secrets management that development teams should be thinking about?

How does the use of multiple environments (e.g. dev, QA, production, etc.) affect the strategies used for secrets management?

What are some of the most useful resources that you have found for anyone looking to learn more about this subject?

Keep In Touch

@brikelly on Twitter

Blog

brikelly on GitHub

Picks

Tobias

The Inheritance Cycle

Brian

Donegal Ireland

Links

Conjur

CyberArk

Datawire

Transpiler

IDL

CSRF (Cross-Site Request Forgery)

Hashicorp Vault

Continuous Integration

Continuous Delivery

TLS (Transport Layer Security)

RBAC (Role Based Access Control)

Terraform

SQL Injection

Secretless

MFA

Duo Security

Kubernetes

Summon

OWASP Top 10

Configuration Management

Puppet

Chef

Ansible

SaltStack

Immutable Infrastructure

Conjur Blog

Krebs On Security

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Django, Channels, And The Asynchronous Web with Andrew Godwin** Sep 24, 2018
Summary

Once upon a time the web was a simple place with one main protocol and a predictable sequence of request/response interactions with backend applications. This is the era when Django began, but in the intervening years there has been an explosion of complexity with new asynchronous protocols and single page Javascript applications. To help bridge the gap and bring the most popular Python web framework into the modern age Andrew Godwin created Channels. In this episode he explains how the first version of the asynchronous layer for Django applications was created, how it has changed in the jump to version 2, and where it will go in the future. Along the way he also discusses the challenges of async development, his work on designing ASGI as the spiritual successor to WSGI, and how you can start using all of this in your own projects today.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Andrew Godwin about Django Channels 2.x and the ASGI specification for modern, asynchronous web protocols

Interview

Introductions

How did you get introduced to Python?

Can you start with an overview of the problem that Channels is aiming to solve?

Asynchronous frameworks have existed in Python for a long time. What are the tradeoffs in those frameworks that would lead someone to prefer the combination of Django and Channels?

For someone who is familiar with traditional Django or working on an existing application, what are the steps involved in integrating Channels?

Channels is a project that you have been working on for a significant amount of time and which you recently re-architected. What were the shortcomings in the 1.x release that necessitated such a major rewrite?

How is the current system architected?

What have you found to be the most challenging or confusing aspects of managing asynchronous web protocols both as an author of Channels/ASGI and someone building on top of them?

While reading through the documentation there were mentions of the synchronous nature of the Django ORM. What are your thoughts on asynchronous database access and how important that is for future versions of Django and Channels?

As part of your implementation of Channels 2.x you introduced a new protocol for asynchronous web applications in Python in the form of ASGI. How does this differ from the WSGI standard and what was your process for developing this specification?

What are your hopes for what the Python community will do with ASGI?

What are your plans for the future of Channels?

What are some of the most interesting or unexpected uses of Channels and/or ASGI?

Keep In Touch

@andrewgodwin on Twitter

Website

andrewgodwin on GitHub

Picks

Tobias

Nobody Listens To Paula Poundstone

Andrew

Literary Appreciation Of The Olson Timezones Database

Links

Channels

ASGI

Django

South

Django Migrations

PHP

Turbogears

WSGI

Websockets

Eventlet

HTTP

WebRTC

IPFS

Twisted

Tornado

Podcast Episode

Daphne

Redis

Uvicorn

Heisenbugs

Deadlock

CherryPy

Flask

WSGI 2

Podcast Episode

Starlette

Django Rest Framework

Thom Christie

PEP Process Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Business Of Technical Authoring With William Vincent** Sep 17, 2018
Summary

There are many aspects of learning how to program and at least as many ways to go about it. This is multiplicative with the different problem domains and subject areas where software development is applied. In this episode William Vincent discusses his experiences learning how web development mid-career and then writing a series of books to make the learning curve for Django newcomers shallower. This includes his thoughts on the business aspects of technical writing and teaching, the challenges of keeping content up to date with the current state of software, and the ever-present lack of sufficient information for new programmers.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing William Vincent about his experience learning to code mid-career and then writing a series of books to bring you along on his journey from beginner to advanced Django developer

Interview

Introductions

How did you get introduced to Python?

How has your experience as someone who began working as a developer mid-career influenced your approach to software?

How do you compare Python options for web development (Django/Flask) to others such as Ruby on Rails or Node/Express in the JavaScript world?

What was your motivation for writing a beginner guide to Django?

What was the most difficult aspect of determining the appropriate level of depth for the content?

At what point did you decide to publish the tutorial you were compiling as a book?

In the posts that you wrote about your experience authoring the books you give a detailed description of the economics of being an author. Can you discuss your thoughts on that?

Focusing on a library or framework, such as Django, increases the maintenance burden of a book, versus one that is written about fundamental principles of computing. What are your thoughts on the tradeoffs involved in selecting a topic for a technical book?

Challenges of creating useful intermediate content (lots of beginner tutorials and deep dives, not much in the middle)

After your initial foray into technical authoring you decided to follow it with two more books. What other topics are you covering with those?

Once you are finished with the third do you plan to continue writing, or will you shift your focus to something else?

Translating content to reach a larger audience

What advice would you give to someone who is considering writing a book of their own?

What alternative avenues do you think would be more valuable for themselves and their audience?

Alternative avenues for providing useful training to developers

Keep In Touch

Email

wsvincent on GitHub

Website

Picks

Tobias

Practical AI

William

awesome-django

The Digital Doctor by Robert Wachter

Links

Quizlet

Django

Learn Python The Hard Way

Invent Your Own Computer Games with Python

Ruby on Rails

Node.js

Express

LearnBoost

David Heinemeier Hanson

Meteor.js

Class-Based Views

Rails Tutorial

Leanpub

Gumroad

Stack Overflow

Egghead.io

Frontend Masters

Gatsby.js

Jekyll

Pachyderm

Data Engineering Podcast

Pachyderm Interview

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Keep Your Code Clean Using pre-commit with Anthony Sottile** Sep 10, 2018
Summary

Maintaining the health and well-being of your software is a never-ending responsibility. Automating away as much of it as possible makes that challenge more achievable. In this episode Anthony Sottile describes his work on the pre-commit framework to simplify the process of writing and distributing functions to make sure that you only commit code that meets your definition of clean. He explains how it supports tools and repositories written in multiple languages, enforces team standards, and how you can start using it today to ship better software.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Anthony Sottile about pre-commit, a framework for managing and maintaining hooks for multiple languages

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what a pre-commit hook is and some of the ways that they are useful for developers?

What was you motivation for creating a framework to manage your pre-commit hooks?

How does it differ from other projects built to manage these hooks?

What are the steps for getting someone started with pre-commit in a new project?

Which other event hooks would be most useful to implement for maintaining the health of a repository?

What types of operations are most useful for ensuring the health of a project?

What types of routines should be avoided as a pre-commit step?

Installing the hooks into a user’s local environment is a manual step, so how do you ensure that all of your developers are using the configured hooks?

What factors have you found that lead to developers skipping or disabling hooks?

How is pre-commit implemented and how has that design evolved from when you first started?

What have been the most difficult aspects of supporting multiple languages and package managers?

What would you do differently if you started over today?

Would you still use Python?

For someone who wants to write a plugin for pre-commit, what are the steps involved?

What are some of the strangest or most unusual uses of pre-commit hooks that you have seen?

What are your plans for the future of pre-commit?

Keep In Touch

asottile on GitHub

@codewithanthony on Twitter

anthonywritescode on twitch

anthonywritescode on YouTube

Picks

Tobias

Tag

Anthony

Yes Theory

Links

pre-commit

List of hooks

Lyft

Careers

Git

Git hooks

https://githooks.com/?utm_source=rss&utm_medium=rss

Flake8

Make

Tox

Type Annotations

xargs

Bash

shlex

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Infection Monkey Vulnerability Scanner with Daniel Goldberg** Sep 03, 2018
Summary

How secure are your servers? The best way to be sure that your systems aren’t being compromised is to do it yourself. In this episode Daniel Goldberg explains how you can use his project Infection Monkey to run a scan of your infrastructure to find and fix the vulnerabilities that can be taken advantage of. He also discusses his reasons for building it in Python, how it compares to other security scanners, and how you can get involved to keep making it better.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Daniel Goldberg about Infection Monkey, an open source system breach simulation tool for evaluating the security of your network

Interview

Introductions

How did you get introduced to Python?

What is infection monkey and what was the reason for building it?

What was the reasoning for building it in Python?

If you were to start over today what would you do differently?

Penetration testing is typically an endeavor that requires a significant amount of knowledge and experience of security practices. What have been some of the most difficult aspects of building an automated vulnerability testing system?

How does a deployed instance keep up to date with recent exploits and attack vectors?

How does Infection Monkey compare to other tools such as Nessus and Nexpose?

What are some examples of the types of vulnerabilities that can be discovered by Infection Monkey?

What kinds of information can Infection Monkey discover during a scan?

How does that information get reported to the user?

How much security experience is necessary to understand and address the findings in a given report generated from a scan?

What techniques do you use to ensure that the simulated compromises can be safely reverted?

What are some aspects of network security and system vulnerabilities that Infection Monkey is unable to detect and/or analyze?

For someone who is interested in using Infection Monkey what are the steps involved in getting it set up?

What is the workflow for running a scan?

Is Infection Monkey intended to be run continuously, or only with the interaction of an operator?

What are your plans for the future of Infection Monkey?

Keep In Touch

danielguardicore on GitHub

Guardicore Blog

Picks

Tobias

Darkest Hour

Daniel

How Complex Systems Fail

Links

Infection Monkey

Guardicore

Stack Overflow

Metasploit

AsyncIO

React

Nessus

Nexpose

Shellshock

Wannacry

Simian Army

Chaos Engineering

Capuchin Monkey

Google Summer of Code

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Fast Stream Processing In Python Using Faust with Ask Solem** Aug 27, 2018
Summary

The need to process unbounded and continually streaming sources of data has become increasingly common. One of the popular platforms for implementing this is Kafka along with its streams API. Unfortunately, this requires all of your processing or microservice logic to be implemented in Java, so what’s a poor Python developer to do? If that developer is Ask Solem of Celery fame then the answer is, help to re-implement the streams API in Python. In this episode Ask describes how Faust got started, how it works under the covers, and how you can start using it today to process your fast moving data in easy to understand Python code. He also discusses ways in which Faust might be able to replace your Celery workers, and all of the pieces that you can replace with your own plugins.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Ask Solem about Faust, a library for building high performance, high throughput streaming systems in Python

Interview

Introductions

How did you get introduced to Python?

What is Faust and what was your motivation for building it?

What were the initial project requirements that led you to use Kafka as the primary infrastructure component for Faust?

Can you describe the architecture for Faust and how it has changed from when you first started writing it?

What mechanism does Faust use for managing consensus and failover among instances that are working on the same stream partition?

What are some of the lessons that you learned while building Celery that were most useful to you when designing Faust?

What have you found to be the most common areas of confusion for people who are just starting to build an application on top of Faust?

What has been the most interesting/unexpected/difficult aspects of building and maintaining Faust?

What have you found to be the most challenging aspects of building streaming applications?

What was the reason for releasing Faust as an open source project rather than keeping it internal to Robinhood?

What would be involved in adding support for alternate queue or stream implementations?

What do you have planned for the future of Faust?

Keep In Touch

@asksol on Twitter

ask on GitHub

Picks

Tobias

Super Troopers 2

Ask

Microsound by Curtis Roads

Links

Faust

RobinHood

Kafka Streams

RabbitMQ

AsyncIO

Celery

Kafka

Confluent

Write-Ahead Log

RocksDB

Redis

Pulsar

KSQL

Exactly Once Semantics

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Don't Just Stand There, Get Programming! with Ana Bell** Aug 20, 2018
Summary

Writing a book is hard work, especially when you are trying to teach such a broad concept as programming. In this episode Ana Bell discusses her recent work in writing Get Programming: Learn To Code With Python, including her views on how to separate the principles from the implementation, making the book evergreen in its appeal, and how her experience as a lecturer at MIT has helped her maintain the perspectives of beginners. She also shares her views on the values of learning about programming, even when you have no intention of doing it as a career and ways to take the next steps if that is your goal.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

As you know, Python has become one of the most popular programming languages in the world, due to the size, scope, and friendliness of the language and community. But, it can be tough learning it when you’re just starting out. Luckily, there’s an easy way to get involved. Written by MIT lecturer Ana Bell and published by Manning Publications, Get Programming: Learn to code with Python is the perfect way to get started working with Python. Ana’s experience as a teacher of Python really shines through, as you get hands-on with the language without being drowned in confusing jargon or theory. Filled with practical examples and step-by-step lessons to take on, Get Programming is perfect for people who just want to get stuck in with Python. Get your copy of the book with a special 40% discount for Podcast.__init__ listeners at podcastinit.com/get-programming using code: Bell40!

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Ana Bell about her book, Get Programming: Learn to code with Python, and her approach to teaching how to code

Interview

Introductions

How did you get introduced to Python?

Can you start by describing your motivation for writing a book about learning to program?

Who is the target audience for this book?

What level of competence do you want the reader to have when they have completed it?

What were the most challenging aspects of writing a book for beginning programmers?

What did you do to recapture the “beginner mind” while writing?

There are a large variety of books on learning to program and at least as many approaches. Can you describe the techniques that you use in your book to help readers grasp the concepts that you cover?

One of the problems of writing a book about technology is that there is no stationary target to aim for due to the constant advancement of the industry. How do you reconcile that reality with the need for a book to remain relevant for an extended period of time?

How do you decide what to include and what to leave out when writing about learning how to program?

What advice do you have for people who have read your book and want to continue on to a career in development?

Keep In Touch

MIT Bio

@anabellphd on Twitter

Picks

Tobias

Netdata

Ana

Star Realms

Between Two Cities

Links

Get Programming by Ana Bell

edX

MIT

Machine Learning

Github

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Masonite Web Framework With Joe Mancuso** Aug 13, 2018
Summary

Masonite is an ambitious new web framework that draws inspiration from many other successful projects in other languages. In this episode Joe Mancuso, the primary author and maintainer, explains his goal of unseating Django from its position of prominence in the Python community. He also discusses his motivation for building it, how it is architected, and how you can start using it for your own projects.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Joe Mancuso about Masonite, the modern and developer centric python web framework.

Interview

Introductions

How did you get introduced to Python?

What is Masonite and what was the motivation for creating it?

How does it fit in the current landscape of Python web frameworks?

Why might someone choose to use Masonite over Python frameworks?

If someone isn’t already decided on using Python, what are some reasons that they might choose Masonite over frameworks in other languages?

Can you describe the framework architecture and how it has evolved over the lifetime of the project?

What are some examples of projects that have been built with Masonite and what aspects of the framework are they leveraging?

For someone who is starting a new project with Masonite what are some of the concepts that they should be familiar with?

What is their workflow for starting their project?

How does that workflow change when working with an existing application?

What are some of the plans that you have for the future of Masonite?

Keep In Touch

Joe

Blog

@masoniteproject on Twitter

josephmancuso on GitHub

Masonite

MasoniteFramework on GitHub

Docs

Slack

Picks

Tobias

Yeti Mugs

Joe

Gitbook.io

Dev.to

Links

Masonite on GitHub

Codecademy

PHP

Django

Laravel

Dependency Injection

Inversion of Control

WSGI

Gunicorn

Waitress

Nexmo

Masonite Slack

Mathias Johansson

Trello

@masoniteproject

Masonite Repo

Masonite Documentation

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Helping Teacher's Bring Python Into The Classroom With Nicholas Tollervey** Aug 06, 2018
Summary

There are a number of resources available for teaching beginners to code in Python and many other languages, and numerous endeavors to introduce programming to educational environments. Sometimes those efforts yield success and others can simply lead to frustration on the part of the teacher and the student. In this episode Nicholas Tollervey discusses his work as a teacher and a programmer, his work on the micro:bit project and the PyCon UK education summit, as well as his thoughts on the place that Python holds in educational programs for teaching the next generation.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Nicholas Tollervey about his efforts to improve the accessibility of Python for educators

Interview

Introductions

How did you get introduced to Python?

How has your experience as a teacher influenced your work as a software engineer?

What are some of the ways that practicing software engineers can be most effective in supporting the efforts teachers and students to become computationally literate?

What are your views on the reasons that computational literacy is important for students?

What are some of the most difficult barriers that need to be overcome for students to engage with Python?

How important is it, in your opinion, to expose students to text-based programming, as opposed to the block-based environment of tools such as Scratch?

At what age range do you think we should be trying to engage students with programming?

When the teacher’s day was introduced as part of the education summit for PyCon UK what was the initial reception from the educators who attended?

How has the format for the teacher’s portion of the conference changed in the subsequent years?

What have been some of the most useful or beneficial aspects for the teacher’s and how much engagement occurs between the conferences?

What was your involvement in the initiative that brought the BBC micro:bit to UK classrooms?

What kinds of feedback have you gotten from students who have had an opportunity to use them?

What are some of the most interesting or unexpected uses of the micro:bit that you have seen?

Keep In Touch

@ntoll on Twitter

ntoll on GitHub

Website

Picks

Tobias

The Dark Materials Trilogy Audiobooks by Phillip Pullman

Nicholas

Moon Dust by Andrew Smith

Totally Wired by Andrew Smith

Links

ntoll.org

Tuba

Royal College of Music

Fry IT

MicroPython

Podcast Interview With Damien George

MicroPython Book

Mu

Scratch

Jupyter

John Pinner

London Python Code Dojo

Alan Turing

Tim Berners-Lee

Charles Babbage

REPL (Read-Eval-Print Loop

Daniel Pope

PyGame

Raspberry Pi Foundation

PyGame Zero

Network Zero

GPIO Zero

Computing At School

BBC

PSF

TouchDevelop

TypeScript

Damien George

ARM

Code Kingdoms

micro:bit

Barclay’s

PyCon US Education Summit

Raspberry Pi Foundation Code Club

Qumisha Goss Keynote

Adafruit

CircuitPython

NeoPixel

PyBoard

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Continuous Delivery For Complex Systems Using Zuul with Monty Taylor** Jul 30, 2018
Summary

Continuous integration systems are important for ensuring that you don’t release broken software. Some projects can benefit from simple, standardized platforms, but as you grow or factor in additional projects the complexity of checking your deployments grows. Zuul is a deployment automation and gating system that was built to power the complexities of OpenStack so it will grow and scale with you. In this episode Monty Taylor explains how he helped start Zuul, how it is designed for scale, and how you can start using it for your continuous delivery systems. He also discusses how Zuul has evolved and the directions it will take in the future.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Monty Taylor about Zuul, a platform that drives continuous integration, delivery, and deployment systems with a focus on project gating and interrelated projects.

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Zuul is and how the project got started?

How do you view Zuul in the broader landscape of CI/CD systems (e.g. GoCD, Jenkins, Travis, etc.)?

What is the workflow for someone who is defining a pipeline in Zuul?

How are the pipelines tested and promoted?

One of the problems that are often encountered in CI/CD systems is the difficulty of testing changes locally. What kind of support is available in Zuul for that?

Can you describe the project architecture?

What aspects of the architecture enable it to scale to large projects and teams?

How difficult would it be to swap the Ansible integration for another orchestration tool?

What would be involved in adding support for additional version control systems?

What are your plans for the future of the project?

Keep In Touch

emonty on GitHub

Website

@e_monty on Twitter

Picks

Tobias

Hitchhiker’s Guide To The Galaxy

Monty

Bojack Horseman

Links

Red Hat

Zuul

OpenStack

Jim Blair

Perl

SNPP

Rackspace

NASA

Drizzle

Sun Microsystems

MySQL

Continuous Integration

Continuous Delivery

Launchpad

Bzr

Jenkins

Jess Frazelle

Graphite

StatsD

graphite.openstack.org

grafana.openstack.org

subunit

Ansible

Helm

Software Factory

Gerrit

Git

Perforce

Subversion

Zookeeper

Gearman

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Michael Foord On Testing, Mock, TDD, And The Python Community** Jul 23, 2018
Summary

Michael Foord has been working on building and testing software in Python for over a decade. One of his most notable and widely used contributions to the community is the Mock library, which has been incorporated into the standard library. In this episode he explains how he got involved in the community, why testing has been such a strong focus throughout his career, the uses and hazards of mocked objects, and how he is transitioning to freelancing full time.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Michael Foord mockingly, about his career in Python

Interview

Introductions

How did you get introduced to Python?

One of the main threads in your career appears to be software testing. What aspects of testing do you find so interesting and how did you first get exposed to that aspect of building software?

How has the language and ecosystem support for testing evolved over the course of your career?

What are some of the areas that you find it to still be lacking?

Mock is one of your projects that has been widely adopted and ultimately incorporated into the standard library. What was your reason for starting it in the first place?

Mocking can be a controversial topic. What are your current thoughts on how and when to use mocks, stubs, and fixtures?

How do you view the state of the art for testing in Python as it compares to other languages that you have worked in?

You were fairly early in the move to supporting Python 2 and 3 in a single project with Mock. How has that overall experience changed in the intervening years since Python 2.4 and 3.2?

What are some of the notable evolutions in Python and the software industry that you have experienced over your career?

You recently transitioned to acting as a software trainer and consultant full time. Where are you focusing your energy currently and what are your grand plans for the future?

Keep In Touch

Email

Website

Twitter

Picks

Tobias

-Ology Books

Michael

Imaginary Authors

Falling Into The Sea

City On Fire

Links

IronPython

London

IronPython in Action

Mock

UnitTest

Play By Email

Smalltalk

Regular Expression

Dijkstra’s Algorithm

urllib2

Resolver Systems

TDD (Test-Driven Development)

PyCon

Trent Nelson

Fractals

Unicode

Joel Spolsky (Unicode)

OOP (Object-Oriented Programming)

End-to-end Testing

Unit Testing

Canonical

Selenium

Ansible

Ansible Tower

AWX (Open Source Tower Codebase)

Continuous Integration

Continuous Delivery

Agile Software Development

GitHub

GitLab

Jenkins

Nightwatch.js

py.test

Martin Fowler

Monkey Patching

Decorator

Context Manager

autospec

Golang

2to3

Six

Instagram Keynote

Trans-code

Django Girls

PyLadies

Agile Abstractions

David Beazley

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Past, Present, and Future of Twisted with Moshe Zadka** Jul 16, 2018
Summary

Twisted is one of the earliest frameworks for developing asynchronous applications in Python and it has yet to fulfill its original purpose. It can be used to build network servers that integrate a multitude of protocols, increase the performance of your I/O bound applications, serve as the full web stack for your WSGI projects, and anything else that needs a battle tested and performant foundation. In this episode long time maintainer Moshe Zadka discusses the history of Twisted, how it has evolved over the years, the transition to Python 3, some of its myriad use cases, and where it is headed in the future. Try it out today and then send some thanks to all of the people who have dedicated their time to building it.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Join the community in the new Zulip chat workspace at podcastinit.com/chat

Your host as usual is Tobias Macey and today I’m interviewing Moshe Zadka about Twisted, the original multi-function tool for asynchronous operations and network protocols in Python

Interview

Introductions

How did you get introduced to Python?

For anyone who isn’t familiar with Twisted can you share a brief overview of what it is?

What was the original motivation for creating it?

How did you get involved with the project and what is your current role in the team?

How can people learn to use Twisted?

What are some of the common difficulties that new users encounter?

What did you learn working on Twisted?

Who uses Twisted?

When is Twisted the wrong choice?

What are some examples of systems that aren’t using Twisted but should be?

What are some of the ways that Twisted has evolved and changed over the years?

What are some of the ways people can support Twisted?

What are some of the plans for the future of Twisted?

Keep In Touch

Moshe Zadka

Twisted

Mailing List

IRC

Picks

Tobias

Leatherman Wave+

Moshe

Unsong Book

Links

Twisted

Glyph Lefkowitz

IRC

async/await

Pyvideo

PyCon 2017 Tutorial

asyncio

GTK

SNMP

Gunicorn

uWSGI

WSGI

Nginx

Supervisor

asynchat

asyncore

Ncolony

The Ultimate Quality Development System

Moshe’s article on UQDS

Unicode prefix

2to3

Six

Unit Tests

Automat

TLA+

PyCon CA Presentation

Sans IO

Cory Benfield’s talk

Tubes

Hyper

H2

H11

Apple Calendar Server Github

Duo Security using Cyclone

Matrix — Used by French government

AIOHTTP

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Mike Driscoll And His Career In Python** Jul 08, 2018
Summary

Mike Driscoll has been writing blogs and books for the Python community for years, including his popular series on the Python Module Of The Week. In his daily work he uses Python to test graphical interfaces written in C++ and QT for embedded platforms. In this episode he explains his work, how he got involved in writing as a regular exercise, and an overview of his recent books.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Mike Driscoll about using Python to test QT UIs for embedded platforms, his experience running a popular Python blog, and being a self-published author

Technically, I am testing a C++ Qt app that is deployed to an embedded system

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the way in which you are using Python for your work?

What benefits does Python provide for writing and running tests for projects written in other languages?

What are the drawbacks or limitations?

What are some of the tools or techniques that you have found most useful for your work?

How much of that was hard-earned knowledge vs finding it in reference material or prior art?

What are some of the most interesting and/or difficult aspects of testing graphical interfaces?

What are some of the most surprising or unexpected aspects of the problem space that you have discovered through your work?

What are some of the other ways in which you have worked with the Python language and community?

What are you most interested in working toward in the future?

Keep In Touch

Blog

@driscollis on Twitter

driscollis on GitHub

Books

Picks

Tobias

Draw.io

Mike

Qt for Python

Jupyter Notebook

Links

Mouse vs. Python

C++

Qt

Ag Leader

Squish

CFFI

Ctypes

Tcl

Javascript

Ruby

Froglogic

Selenium

Pillow

OpenCV

WxPython

PSF

PyCon

Brett Cannon

Carol Willing

ReportLab

PDFRW

Brett Cannon PyCon 2018 Keynote

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**The Pulp Artifact Repository with Bihan Zhang and Austin Macdonald** Jul 02, 2018
Summary

Hosting your own artifact repositories can have a huge impact on the reliability of your production systems. It reduces your reliance on the availability of external services during deployments and ensures that you have access to a consistent set of dependencies with known versions. Many repositories only support one type of package, thereby requiring multiple systems to be maintained, but Pulp is a platform that handles multiple content types and is easily extendable to manage everything you need for running your applications. In this episode maintainers Bihan Zhang and Austin Macdonald explain how the Pulp project works, the exciting new changes coming in version 3, and how you can get it set up to use for your deployments today.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Austin Macdonald and Bihan Zhang about Pulp, a platform for hosting and managing software package repositories

Interview

Introductions

How did you get introduced to Python?

What is Pulp and how did the project get started?

What are the use cases/benefits for hosting your own artifact repository?

What is the high level architecture of the platform?

Pulp 3 appears to be a fairly substantial change in architecture and design. What will be involved in migrating an existing installation to the new version when it is released?

What is involved in adding support for a new type of artifact/package?

How does Pulp compare to other artifact repositories?

What are the major pieces of work that are required before releasing Pulp 3?

What have been some of the most interesting/unexpected/challenging aspects of building and maintaining Pulp?

What are your plans for the future of Pulp?

Keep In Touch

Austin

asmacdo on GitHub

@asmacdo on Twitter

Bihan

LinkedIn

Pulp Project

Email

GitHub

Website

#pulp on freenode

Picks

Tobias

Soonish

Austin

Shostakovitch String Quartet #8

Bihan

AOPA: Air Safety Institute YouTube Channel

Links

Pulp

RedHat

French Horn

XKCD

RPM

Debian

PyPI

Center For Open Science

SciPy

Ansible

Django Project

Django Storages

Artifactory

Warehouse

OCI (Open Container Initiative)

Crane

Docker

Twinehttps://github.com/pypa/twine?utm_source=rss&utm_medium=rss

Maven

Read-through Cache

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Bringing Africa Online At Ascoderu with Clemens Wolff** Jun 25, 2018
Summary

The future is here, it’s just not evenly distributed. One of the places where this is especially true is in sub-Saharan Africa which is a vast region with little to no reliable internet connectivity. To help communities in this region leapfrog infrastructure challenges and gain access to opportunities for education and market information the Ascoderu non-profit has built Lokole. In this episode one of the lead engineers on the project, Clemens Wolff, explains what it is, how it is built, and how the venerable e-mail protocols can continue to provide access cheaply and reliably.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Clemens Wolff about how Ascoderu is using Python to help communities in sub-Saharan Africa gain access to the digital age

Interview

Introductions

How did you get introduced to Python?

What is the mission of Ascoderu and how did the organization get started?

How did you get involved?

The primary project that you build and maintain is Lokole. What is it and how does it help you in achieving the goals of the organization?

What are the limitations of using e-mail as the only interface to the broader internet?

What are some of the most interesting or unexpected uses of email in isolation have you seen?

From the user perspective, can you describe the overall experience of interacting with Lokole?

What is happening in the background?

Did you consider using a binary message format such as Avro, protocol buffers, or msgpack in place of JSON?

What kind of fault tolerance techniques are built into the overall information flow?

What are the most challenging or unexpected aspects of building Lokole and interacting with the user communities?

What projects do you have planned for the future?

Keep In Touch

Email

GitHub

LinkedIn

Picks

Tobias

Hubspot CRM

Clemens

Ali Farka Toure

Links

Ascoderu

Lokole

NLTK

Haskell

DRC

Lokole client

Lokole server

Ali Express

Raspberry Pi

Orange Pi

Uganda

Tanzania

JSON

Avro

msgpack

gzip

Gmail

Lingala

wvdial

USB Modeswitch

Gnome SIM database

Benin

Agricultural Engineer

Outernet

Internet In A Box

mkvvconf

Azure for non-profits

Kubernetes

Connexion

Zalando

Open API

Sendgrid

Azure Service Bus

Ambassador Container

Pillow

United Nations Sustainable Development Goals

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Understanding Machine Learning Through Visualizations with Benjamin Bengfort and Rebecca Bilbro** Jun 17, 2018
Summary

Machine learning models are often inscrutable and it can be difficult to know whether you are making progress. To improve feedback and speed up iteration cycles Benjamin Bengfort and Rebecca Bilbro built Yellowbrick to easily generate visualizations of model performance. In this episode they explain how to use Yellowbrick in the process of building a machine learning project, how it aids in understanding how different parameters impact the outcome, and the improved understanding among teammates that it creates. They also explain how it integrates with the scikit-learn API, the difficulty of producing effective visualizations, and future plans for improvement and new features.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Rebecca Bilbro and Benjamin Bengfort about Yellowbrick, a scikit extension to use visualizations for assisting with model selection in your data science projects.

Interview

Introductions

How did you get introduced to Python?

Can you describe the use case for Yellowbrick and how the project got started?

What is involved in visualizing scikit-learn models?

What kinds of information do the visualizations convey?

How do they aid in understanding what is happening in the models?

How much direction does yellowbrick provide in terms of knowing which visualizations will be helpful in various circumstances?

What does the workflow look like for someone using Yellowbrick while iterating on a data science project?

What are some of the common points of confusion that your students encounter when learning data science and how has yellowbrick assisted in achieving understanding?

How is Yellowbrick iplemented and how has the design changed over the lifetime of the project?

What would be required to integrate with other visualization libraries and what benefits (if any) might that provide?

What about other ML frameworks?

What are some of the most challenging or unexpected aspects of building and maintaining Yellowbrick?

What are the limitations or edge cases for yellowbrick?

What do you have planned for the future of yellowbrick?

Beyond visualization, what are some of the other areas that you would like to see innovation in how data science is taught and/or conducted to make it more accessible?

Keep In Touch

Rebecca Bilbro

Github

Twitter

Benjamin Bengfort

Github

Twitter

Picks

Tobias

Poutine

Rebecca

The color yellow

Benjamin

ALL CAPS

Links

Hadoop

Natural Language Processing

Machine Learning

scikit-learn

Model Selection Triple

the machine learning workflow

scikit-yb

Yellowbrick

Visualizer API

Visual Tests

Jupyter

Matplotlib

Tensorflow

Hyperparameter

Parallel Coordinates

Radviz

Rank2D

Prediction Error Plot

Residuals Plot

Validation Curves

Alpha Selection

Frequency Distribution Plot

Bayes Theorem

Seaborn

Stop Words

N-gram

Craig – Bias and Fairness of Algorithms

Shiny

Bokeh

Keras

StatsModels

Tensorboard

PyTorch

NumPy

Voxel

Wizard of Oz

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Modern Database Clients On The Command Line with Amjith Ramanujam** Jun 11, 2018
Summary

The command line is a powerful and resilient interface for getting work done, but the user experience is often lacking. This can be especially pronounced in database clients because of the amount of information being transferred and examined. To help improve the utility of these interfaces Amjith Ramanujam built PGCLI, quickly followed by MyCLI with the Prompt Toolkit library. In this episode he describes his motivation for building these projects, how their popularity led him to create even more clients, and how these tools can help you in your command line adventures.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Amjith Ramanujam about DBCLI, an umbrella project for command line database clients with autocompletion and syntax highlighting.

Interview

Introductions

How did you get introduced to Python?

What is the DBCLI project?

Which of the clients was the first to be created and what was your motivation for starting it?

At what point did you decide to create the DBCLI umbrella for the different projects and what benefits does it provide?

How much functionality is shared between the different clients?

What additional functionality do the different clients provide over those that are distributed with their respective engines?

How do you optimize for cases where large volumes of data are returned from a query?

What are some of the most interesting or surprising things that you have learned about database engines in the process of building client interfaces for them?

What are the most challenging aspects of building the different database clients?

What are some unexpected hardships that you encountered through this open source project?

What are some unexpected pleasant surprises that you encountered through this project?

Why did you hand over the project leadership for pgcli and mycli to other devs? Was it a hard decision?

Why do you optimize on being nice over being right?

How did Microsoft get involved with dbcli? mssql-cli

What’s been the reception for the projects?

What are your plans for upcoming releases of the various clients?

Which database engines are you planning to target next?

Keep In Touch

amjith on GitHub

@amjithr on Twitter

Blog

Picks

Tobias

Downsizing

Amjith

Dosas

Sarasate

Links

DBCLI

Haskell

Learn you as haskell

List Comprehension

PGCLI

MyCLI

MSSQL-CLI

Prompt Toolkit

Podcast.__init__ Interview

BPython

DjangoCon EU

CLI Helpers

Python Generators

PGSpecial

Longboarding

Irina Truong

Thomas Roten(sp)

PostGreSQL

MySQL

Microsoft SQL Server

SQLite

Oracle DB

Cassandra DB

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Pandas Extension Arrays with Tom Augspurger** Jun 04, 2018
Summary

Pandas is a swiss army knife for data processing in Python but it has long been difficult to customize. In the latest release there is now an extension interface for adding custom data types with namespaced APIs. This allows for building and combining domain specific use cases and alternative storage mechanisms. In this episode Tom Augspurger describes how the new ExtensionArray works, how it came to be, and how you can start building your own extensions today.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Tom Augspurger about the extension interface for Pandas data frames and the use cases that it enables

Interview

Introductions

How did you get introduced to Python?

Most people are familiar with Pandas, but can you describe at a high level the new extension interface?

What is the story behind the implementation of this functionality?

Prior to this interface what was the option for anyone who wanted to extend Pandas?

What are some of the new data types that are available as external packages?

What are some of the unique use cases that they enable?

How is the new interface implemented within Pandas?

What were the most challenging or difficult aspects of building this new functionality?

What are some of the more interesting possibilities that you are aware of for new extension types?

What are the limitations of the interface for libraries that add new array functionality?

What is the next major change or improvement that you would like to add in Pandas?

Keep In Touch

tomaugspurger on GitHub

@TomAugspurger on Twitter

Picks

Tobias

Black Panther

Tom

Dask-ML

Links

Pandas

ExtensionArray

Original IP Address proposal

Mid-implementation blog post

Dataframe

Numpy

Cyberpandas

Geopandas

GIS

Arrow

CuPy

JQ

Wes McKinney

Array ufunc

Matplotlib

Altair

Seaborn

Bokeh

Podcast.__init__ Interview

Dask

Data Engineering Interview

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Making A Difference Through Software With Eric Schles** May 27, 2018
Summary

Software development is a skill that can create value and reduce drudgery in a wide variety of contexts. Sometimes the causes that are most in need of software expertise are also the least able to pay for it. By volunteering our time and abilities to causes that we believe in, we can help make a tangible difference in the world. In this episode Eric Schles describes his experiences working on social justice initiatives and the types of work that proved to be the most helpful to the groups that he was working with.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Eric Schles about how to get involved with social justice causes as an engineer

Interview

Introductions

How did you get introduced to Python?

What are some ways that engineers can create real-world impact with their skills?

What are some of the common roadblocks to contribution that people should be aware of?

What are some of the types of projects or tools that can provide the most value compared to the amount of effort?

Do you have any advice for picking an organization or cause that will benefit the most from technical expertise?

Many of the tools and systems that get built for public or non-profit organizations require some amount of data for them to be useful. Do you have any advice on methods for identifying, locating, or collecting the necessary information for feeding into these projects?

What are some of the design factors that should be considered when building tools for these organizations to allow them to be maintainable and sustainable in the absense of an experienced engineer?

Keep In Touch

EricSchles on GitHub

@EricSchles on Twitter

Picks

Tobias

Shoes without laces

Eric

Catboost

Pomegranate

Links

USDS

18F

OCW

Python Course

SAS

R

Machine Learning

Version Control

GitHub

Agile

OCR (Optical Character Recognition)

Eric Schles Interview On Podcast.__init__

Excel

ETL (Extract Transform Load)

Automate The Boring Stuff

Web Scraping

Thomas Levine

Elasticsearch

Trello

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Asking Questions From Data Using Active Learning with Tivadar Danka** May 21, 2018
Summary

One of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Tivadar Danka about modAL, a modular active learning framework for Python3

Interview

Introductions

How did you get introduced to Python?

What is active learning?

How does it differ from other approaches to machine learning?

What is modAL and what was your motivation for starting the project?

For someone who is using modAL, what does a typical workflow look like to train their models?

How do you avoid oversampling and causing the human in the loop to become overwhelmed with labeling requirements?

What are the most challenging aspects of building and using modAL?

What do you have planned for the future of modAL?

Keep In Touch

@TivadarDanka on Twitter

cosmic-cortex on GitHub

https://www.tivadardanka.com?utm_source=rss&utm_medium=rss for anything else

Picks

Tobias

Peter Rabbit Movie

Tivadar

Uri Alon: An Introduction to Systems Biology – Design Principles of Biological Circuits, book and online lectures

Links

modAL homepage

modAL on GitHub

modAL paper

Bioinformatics

Hungary

Phenotypes

Active Learning

Supervised Learning

Unsupervised Learning

Snorkel

Active Feature-Value Acquisition

scikit-learn

Entropy

PyTorch

Tensorflow

Keras

Jupyter Notebooks

Bayesian Optimization

Hyperparameters

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Great Expectations For Your Data Pipelines with Abe Gong and James Campbell** May 13, 2018
Summary

Testing is a critical activity in all software projects, but one that is often neglected in data pipelines. The complexities introduced by the inherent statefulness of the problem domain and the interdependencies between systems contribute to make pipeline testing difficult to manage. To make this endeavor more manageable Abe Gong and James Campbell have created Great Expectations. In this episode they discuss how you can use the project to create tests in the exploratory phase of building a pipeline and leverage those to monitor your systems in production. They also discussed how Great Expectations works, the difficulties associated with pipeline testing and managing associated technical debt, and their future plans for the project.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com

Your host as usual is Tobias Macey and today I’m interviewing James Campbell and Abe Gong about Great Expectations, a tool for testing the data in your analytics pipelines

Interview

Introduction

How did you first get introduced to Python?

What is Great Expectations and what was your motivation for starting it?

What are some of the complexities associated with testing analytics pipelines?

What types of tests can be executed to ensure data integrity and accuracy?

What are some examples of the potential impact of pipeline debt?

What is Great Expectations and how does it simplify the process of building and executing pipeline tests?

What are some examples of the types of tests that can be built with Great Expectations?

For someone getting started with Great Expectations what does the workflow look like?

What was your reason for using Python for building it?

How does the choice of language benefit or hinder the contexts in which Great Expectations can be used?

What are some cases where Great Expectations would not be usable or useful?

What have been some of the most challenging aspects of building and using Great Expectations?

What are your hopes for Great Expectations going forward?

Contact Info

James

jpcampb2 on GitHub

Abe

abegong on GitHub

Website

@AbeGong on Twitter

Picks

Tobias

Fitbit Versa

James

Unplug and spend some time away from the computer

Abe

Superconductive Health

Slack: Getting Past Burnout, Busy Work, and the Myth of Total Efficiency

Links

Superconductive Health

Laboratory for Analytical Sciences

Great Expectations

Medium Post

DAG (Directed Acyclic Graph)

SLA (Service Level Agreement)

Integration Testing

Data Engineering

Histogram

Pandas

SQLAlchemy

Tutorial Videos

Jupyter Notebooks

Dataframe

Airflow

Luigi

Spark

Oozie

Azkaban

JSON

XML

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

**Exploring Color Theory In Python With Thomas Mansencal** May 06, 2018
Summary

We take it for granted every day, but creating and displaying vivid colors in our digital media is a complicated and often difficult process. There are different ways to represent color, the ways in which they are displayed can cause them to look different, and translating between systems can cause losses of information. To simplify the process of working with color information in code Thomas Mansencal wrote the Colour project. In this episode we discuss his motiviation for creating and sharing his library, how it works to translate and manage color representations, and how it can be used in your projects.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

Your host as usual is Tobias Macey and today I’m interviewing Thomas Mansencal about Colour, a python library for working with algorithms and transformations to explore color theory

Interview

Introductions

How did you get introduced to Python?

What is color theory?

How does Colour assist in the process of working with some of the practical applications of colour science?

What was your motivation for creating Colour?

What are some example use cases for colour?

One of the aspects of color in digital environments that is often confusing is the number of different ways that it can be represented. What are the relative benefits of things like RGB, HSV, CMYK, etc.?

How is the Colour library architected and how has that evolved over time?

Are there new developments in the area of color theory that need to be periodically incorporated into the library?

What have you found to be some of the most often misunderstood aspects of color?

What have been some of the most difficult or frustrating aspects of building, maintaining, and promoting Colour?

What are some of the most interesting or unexpected uses of Colour that you have seen?

What are your plans for the future of Colour?

Keep In Touch

Website

Picks

Tobias

Beasts of Olympus by Lucy Coates

Thomas

Coursera Mathematics Machine Learning Course

Links

Colour

Color Theory

Color Science

Weta Digital

Wingnut AR

Visual Effects Artist

Allegro

AutoDesk Maya

PyQT

Isaac Newton

Color Wheel

Colorimetry

CIE

VY Canis Majoris (Red Hypergiant)

Rigel (Blue-White Supergiant)

Kelvin Temperature Scale

Black Body Radiation

HDRI (High Dynamic Range Imaging)

Adobe DNG SDK

ICC

OpenColorIO

MERCK Group

Color Space

RGB

HSV

CMYK

CIE XYZ

CIE RGB

CIE Lab

CIE Luv

sRGB

Gamma Correction

Additive Color Space

Subtractive Color Space

Color Blindness

Gustavo Machado

Rods and Cones

Dichromacy

Color Appearance Model

Uniform Color Spaces

JOSS

ArXiv

CIECAM02 Color Appearance Model

Cinematic Color

Jeremy Selan (Author of OpenColorIO)

Academy Color Encoding System

Color Appearance Models by Mark D. Fairchild

The Reproduction of Colour by Dr. R.W.G. Hunt

Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition by Günther Wyszecki and W. S. Stiles

Katherine Crowson

Google Colab

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Destroy All Software With Gary Bernhardt** Apr 30, 2018
Summary

Many developers enter the market from backgrounds that don’t involve a computer science degree, which can lead to blind spots of how to approach certain types of problems. Gary Bernhardt produces screen casts and articles that aim to teach these principles with code to make them approachable and easy to understand. In this episode Gary discusses his views on the state of software education, both in academia and bootcamps, the theoretical concepts that he finds most useful in his work, and some thoughts on how to build better software.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

Your host as usual is Tobias Macey and today I’m interviewing Gary Bernhardt about teaching and learning Python in the current software landscape

Interview

Introductions

How did you get introduced to Python?

As someone who makes a living from teaching aspects of programming what is your view on the state of software education?

What are some of the ways that we as an industry can improve the experience of new developers?

What are we doing right?

You spend a lot of time exploring some of the fundamental aspects of programming and computation. What are some of the lessons that you have learned which transcend software languages?

Utility of graphs in understanding software

Mechanical sympathy

What are the benefits of ‘from scratch’ tutorials that explore the steps involved in building simple versions of complex topics such as compilers or web frameworks?

Keep In Touch

@garybernhardt on Twitter

garybernhardt on GitHub

Picks

Tobias

Terry Pratchett

Gary

Destroy All Software

Deconstruct Conference

Out Of The Tarpit

Algorithms + Data Structures = Programs by Niklaus Wirth

Dan Grossman Programming Languages Course (click the “Videos” links under “course materials”)

U of W

John Carmack post reconsidering some earlier positions

Links

Wat

Birth and Death of Javascript

Destroy All Software

Deconstruct

Data Structures

Computer Science

Compilers

Programming Bootcamps

Graph Theory

Julia Evans

@b0rk on Twitter

Allen Downey

Jupyter Notebook

Halting Problem

Idris

Visual Basic 3.0

Set Theory

ML Family of Languages

SML, a simple dialect of ML

SML/NJ, a compiler for SML

OCamL, a more modern dialect of ML

F#, an even newer dialect of ML

Clojure, a modern Lisp-like language

Lua Grammar (scroll to the very bottom for the full grammar)

John Carmack

Twitter Thread Explaining Episode Context

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Scaling Deep Learning Using Polyaxon with Mourad Mourafiq** Apr 23, 2018
Summary

With libraries such as Tensorflow, PyTorch, scikit-learn, and MXNet being released it is easier than ever to start a deep learning project. Unfortunately, it is still difficult to manage scaling and reproduction of training for these projects. Mourad Mourafiq built Polyaxon on top of Kubernetes to address this shortcoming. In this episode he shares his reasons for starting the project, how it works, and how you can start using it today.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

Your host as usual is Tobias Macey and today I’m interviewing Mourad Mourafiq about Polyaxon, a platform for building, training and monitoring large scale deep learning applications.

Interview

Introductions

How did you get introduced to Python?

Can you give a quick overview of what Polyaxon is and your motivation for creating it?

What is a typical workflow for building and testing a deep learning application?

How is Polyaxon implemented?

How has the internal architecture evolved since you first started working on it?

What is unique to deep learning workloads that makes it necessary to have a dedicated tool for deploying them?

What does Polyaxon add on top of the existing functionality in Kubernetes?

It can be difficult to build a docker container that holds all of the necessary components for a complex application. What are some tips or best practices for creating containers to be used with Polyaxon?

What are the relative tradeoffs of the various deep learning frameworks that you support?

For someone who is getting started with Polyaxon what does the workflow look like?

What is involved in migrating existing projects to run on Polyaxon?

What have been the most challenging aspects of building Polyaxon?

What are your plans for the future of Polyaxon?

Keep In Touch

Website

@mmourafiq on Twitter

mouradmourafiq on GitHub

Picks

Tobias

Kubernetes

Kubernetes Up And Running

Kelsey Hightower

Food Fight Show With Kelsey Hightower

Mourad

Schopenhauer

Links

Polyaxon

Investment Banking

Luxembourg

Matlab

Text Mining

Tensorflow

Docker

Kubernetes

Deep Learning

Free Deep Learning Textbook

Machine Learning Engineer

Hyperparameters

Continuous Integration

PyTorch

MXNet

Scikit-Learn

Helm

Mesos

Spark

SparkML

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Electricity Map: Real Time Visibility of Power Generation with Olivier Corradi** Apr 15, 2018
Summary

One of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

Your host as usual is Tobias Macey and today I’m interviewing Olivier Corradi about Electricity Map and using Python to analyze data of global power generation

Interview

Introductions

How did you get introduced to Python?

What was your motivation for creating Electricity Map?

How can an average person use or benefit from the information that is available in the map?

What sources are you using to gather the information about how electricity is generated and distributed in various geographic regions?

Is there any standard format in which this data is produced?

What are the biggest difficulties associated with collecting and consuming this data?

How much confidence do you have in the accuracy of the data sources?

Is there any penalty for misrepresenting the fuel consumption or waste generation for a given plant?

Can you describe the architecture of the system and how it has evolved?

What are some of the most interesting uses of the data in your database and API that you are aware of?

How do you measure the impact or effectiveness of the information that you provide through the different interfaces to the data that you have aggregated?

How have you built a community around the project?

How has the community helped in building and growing Electricity Map?

What are some of the most unexpected things that you have learned in the process of building Electricity Map?

What are your plans for the future of Electricity Map?

Keep In Touch

@corradio on Twitter

LinkedIn

corradio on GitHub

Picks

Tobias

Rollerblading

Olivier

Deep Mind AlphaGo Documentary

Consumer’s Guide To Climate Change Impact

Links

Electricity Map

Machine Learning

Youtube

Climate Change

Fossil Fuels

Carbon Intensity

Greenhouse Gas Equivalencies Calculations

Open Data

Electricity Map Project Source

Lignite

Marginal Carbon Intensity

Electricity Map Forecast API

IPCC (Intergovernmental Panel on Climate Change

Redis

D3.js

Spark

Tensorflow

Spatiotemporal Data

MongoDB

Matrix Inversion

PyGRIB

Tomorrow Co.

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Building And Growing Nylas with Christine Spang** Apr 08, 2018
Summary

Email is one of the oldest methods of communication that is still in use on the internet today. Despite many attempts at building a replacement and predictions of its demise we are sending more email now than ever. Recognizing that the venerable inbox is still an important repository of information, Christine Spang co-founded Nylas to integrate your mail with the rest of your tools, rather than just replacing it. In this episode Christine discusses how Nylas is built, how it is being used, and how she has helped to grow a successful business with a strong focus on diversity and inclusion.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Christine Spang about Nylas and the modern era of email

Interview

Introductions

How did you get introduced to Python?

Can you explain what Nylas is and some of its history?

What do you think it is about email as a protocol and a means of communication that has made it so resilient in the face of technological evolution?

What lessons did you learn from your initial offering of the N1 mail client and how has that informed your current focus?

Nylas as a company appears to have a strong focus on diversity and inclusion. Can you speak to how you encourage that type of environment and how it manifests at work?

What are some of the ways that Python is used at Nylas?

Can you share some examples of services that you have written in other languages and why you felt that Python was not the right choice?

What are some of the use cases that Nylas enables?

What are some of the most interesting or innovative uses of the Nylas platform that you have seen?

How do you manage privacy and security in your sync service given the sensitivity of the data that you are handling?

What are some of the biggest challenges that you are currently facing at Nylas?

What do you think will be the future of email?

Keep In Touch

LinkedIn

@spang on Twitter

Website

GitHub

Picks

Tobias

Trello

Christine

Founders For Change

Links

Nylas

MIT

KSplice

Debian

Lisp

REST

Email

N1 Mail Client

Mailspring

Nylas Employee Handbook

Hackbright Academy

Code2040

TextIO

Key Values

IMAP

OAuth

MySQL

Gevent

React

CRM (Customer Relationship Management)

SendGrid

MailGun

MailChimp

GDPR (General Data Protection Regulation)

SOC2

OWASP Top 10

Principle of Least Privilege

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Synthetic Data Generation Using Mimesis with Nikita Sobolev** Apr 01, 2018
Summary

Most applications require data to operate on in order to function, but sometimes that data is hard to come by, so why not just make it up? Mimesis is a library for randomly generating data of different types, such as names, addresses, and credit card numbers, so that you can use it for testing, anonymizing real data, or for placeholders. This week Nikita Sobolev discusses how the project got started, the challenges that it has posed, and how you can use it in your applications.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

Your host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about Mimesis, a library for quickly generating synthetic data

Interview

Introductions

How did you get introduced to Python?

What is mimesis and how does it compare to other projects such as faker and factory_boy?

What was the motivation for creating it?

One of the features that is advertised is the speed of Mimesis. What techniques are used to ensure that the data is generated quickly?

What are the built in mechanisms for generating data?

What options do users have for customizing the types of data that can get generated?

What are some of the most complicated providers to write and maintain?

What are some of the use cases outside of unit or integration tests where Mimesis could be beneficial?

How would you use Mimesis to anonymize data from a production environment to be used for testing?

What are the most challenging aspects of maintaining the Mimesis project?

What are some of the plans that you have for the future of Mimesis?

Keep In Touch

sobolevn on GitHub

@sobolevn on Twitter

Email

Picks

Tobias

Coco

Nikita

I Am A Mediocre Developer

Links

Mimesis

Django

Faker

Factory Boy

Internationalization (I18N)

Unicode

Enum

Pipfile

GeoJSON

Mimesis Cloud

Sanic

GraphQL

Impostor Syndrome

Imposter Syndrome Disclaimer: Add this to all of your projects!

Jacob Kaplan-Moss PyCon Keynote

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Luminoth: AI Powered Computer Vision for Python with Joaquin Alori** Mar 25, 2018
Summary

Making computers identify and understand what they are looking at in digital images is an ongoing challenge. Recent years have seen notable increases in the accuracy and speed of object detection due to deep learning and new applications of neural networks. In order to make it easier for developers to take advantage of these techniques Tryo Labs built Luminoth. In this interview Joaquín Alori explains how how Luminoth works, how it can be used in your projects, and how it compares to API oriented services for computer vision.

Introduction

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

For complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

Your host as usual is Tobias Macey and today I’m interviewing Joaquín Alori about Luminoth, a deep learning toolkit for computer vision in Python

Interview

Introductions

How did you get introduced to Python?

What is Luminoth and what was your motivation for creating it?

Computer vision has been a focus of AI research for decades. How do current approaches with deep learning compare to previous generations of tooling?

What are some of the most difficult problems in visual processing that still need to be solved?

What are the limitations of Luminoth for building a computer vision application and how do they differ from the capabilities of something built with a prior generation of tooling such as OpenCV?

For someone who is interested in using Luminoth in their project what is the current workflow?

How do the capabilities of Luminoth compare with some of the various service based options such as Rekognition for Amazon or the Cloud Vision API from Google?

What are some of the motivations for using Luminoth in place of these services?

What are some of the highest priority features that you are focusing on implementing in Luminoth?

When is Luminoth the wrong choice for a computer vision application and what are some of the strongest alternatives at the moment?

Keep In Touch

@JoaquinAlori on Twitter

LinkedIn

Picks

Tobias

PyCon US

Joaquin

3Blue1Brown

Links

Luminoth

Luminoth Release Announcement

Tryo Labs

Uruguay

Industrial Engineering

Manufacturing Engineering

Elon Musk

Artificial Intelligence

Deep Learning

Neural Networks

Object Detection

Image Segmentation

Convolutional Neural Network

Recurrent Neural Network

Back Propagation

Geoff Hinton

Capsule Networks

Generative Adversarial Networks

SVM (Support Vector Machine)

Haar Classifiers

OpenCV

Drones

GPU (Graphics Processing Unit)

Rekognition

Cloud Vision API

TensorFlow Object Detection API

Sonnet

DeepMind

Caffe

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Thonny: The IDE For Beginning Programmers with Aivar Annamaa** Mar 18, 2018
Summary

Learning to program is a rewarding pursuit, but is often challenging. One of the roadblocks on the way to proficiency is getting a development environment installed and configured. In order to simplify that process Aivar Annamaa built Thonny, a Python IDE designed for beginning programmers. In this episode he discusses his initial motivations for starting Thonny and how it helps newcomers to Python learn and understand how to write software.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.

For complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.

To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.

Visit podcastinit.com to subscribe to the show, sign up for the newsletter, and read the show notes.

Your host as usual is Tobias Macey and today I’m interviewing Aivar Annamaa about Thonny, a Python IDE for beginning programmers

Interview

Introductions

How did you get introduced to Python?

What was your motivation for building an IDE focused on beginning programmers?

What are the features of Thonny that make it easier for users to understand what is happening in their programs?

What have you found to be the types of issues that users most frequently struggle with and how does Thonny help overcome those gaps in understanding?

What kinds of tutorials or supporting material have you found to be the most useful for teaching students the principles that they need to be able to take advantage of the environment that Thonny provides?

How is Thonny built and what have been the most challenging aspects of writing an IDE in Python?

What are some of the interface design choices that you have made to avoid confusing or overwhelming beginning users?

Once a user becomes more proficient in Python is there a point where it no longer makes sense to continue using Thonny for development?

I noticed that Thonny has an plugin architecture and there is an extension for interacting with the BBC micro:bit. What are some of the other types of extensions that you would like to see built for Thonny?

Keep In Touch

Aivar

@aivarannamaa on Twitter

aivarannamaa on GitHub

Google Scholar Page

Thonny

Website

Forum

@thonnyide on Twitter

Source repository and wiki

Picks

Tobias

Data Engineering Podcast

Kubo and the Two Strings

Aivar

MicroPython

Podcast.__init__ Interview

How to Talk So Kids Will Listen & Listen So Kids Will Talk

Links

Thonny

University of Tartu

Estonia

Recursion

TKinter

Aivar Estonian Textbook

Pascal

MyPy

Podcast.__init__ Interview

BBC Micro:bit

Version Control

GitHub

GitLab

Elm Compiler Messages

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Keeping The Beets with Adrian Sampson** Mar 12, 2018
Summary

Maintaining a consistent taxonomy for your music library is a challenging and time consuming endeavor. Eventually you end up with a mess of folders and files with inconsistent names and missing metadata. Beets is built to solve this problem by programmatically managing the tags and directory structure for all of your music files and providing a fast lookup when you are trying to find that perfect song to play. Adrian Sampson began the project because he was trying to clean up his own music collection and in this episode he discusses how the project was built, how streaming media is affecting our relationship to digital music, and how he envisions Beets position in the ecosystem in the future.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Adrian Sampson about Beets, the swiss army knife for managing your music library.

Interview

Introductions

How did you get introduced to Python?

What is Beets and what was your reason for creating it?

What was your reason for using Python and if you were to start over today would you make the same choice?

If I have a directory with inconsistent naming conventions, poor organization, and some random folders full of mixed MP3 files how can Beets help me and what does the workflow look like?

How is Beets architected to allow for interactively processing a large volume of media files and how has the design evolved over the time that you have been working on it?

What are your thoughts on the current trend toward streaming music services replacing local media files?

What have been some of the most challenging aspects of building Beets?

What are some of the most interesting uses for Beets that you have seen?

What are some of the other projects for managing a music library and how does Beets compare to them?

Are there any features that you have planned for the future of Beets, or any new functionality that you would like to see contributed?

Keep In Touch

sampsyo on GitHub

Website

@samps on Twitter

Picks

Tobias

Mozart’s Requiem

Wikipedia

YouTube

Gov’t Mule

Darkest Hour

Adrian

Spiralizer

Spiralized Beats With Pesto

Links

Beets

SQLite

Mutagen

ID3 Tags

Musicbrainz

Bandcamp

Free Music Archive

Cornell

AcoustID

Chromaprint

Musicbrainz Picard

iTunes

Spotify

Amazon Music

DLNA

UPnP

AURA

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Salabim: Logistics Simulation with Ruud van der Ham** Mar 04, 2018
Summary

Determining the best way to manage the capacity and flow of goods through a system is a complicated issue and can be exceedingly expensive to get wrong. Rather than experimenting with the physical objects to determine the optimal algorithm for managing the logistics of everything from global shipping lanes to your local bank, it is better to do that analysis in a simulation. Ruud van der Ham has been working in this area for the majority of his professional life at the Dutch port of Rotterdam. Using his acquired domain knowledge he wrote Salabim as a library to assist others in writing detailed simulations of their own and make logistical analysis of real world systems accessible to anyone with a Python interpreter.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Ruud van der Ham about Salabim, a Python library for conducting discrete event simulations

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Discrete Event Simulation is and how Salabim helps with that?

Can you explain how you chose the name?

What was your motivation for creating Salabim and how does it compare to other tools for discrete event simulation?

How does discrete event simulation compare with state machines?

How is Salabim implemented and how has the design evolved over the time that you have been working on it?

I understand that you have done a majority of Salabim was written on an iPad. Can you speak about why you have chosen that as your development environment and your experience working in that manner?

What are some examples of the types of models that you can model with Salabim?

What would an implementation of one of these models look like for someone using Salabim?

What options does a user have to verify the accuracy of a simulation created with Salabim?

One of the nice aspects of Salabim is the fact that it provides a visual output as a simulation runs. Can you describe the workflow for someone who wants to use Salabim for modeling and visualizing a system?

At what point does a system become too complex to encapsulate in a simulation and what techniques can you use to modularize it to make a simulation useful?

When is Salabim not the right tool to use and what would you suggest for people who find themselves in that situation?

What have been some of the most complicated or difficult aspects of building and maintaining Salabim?

What are some of the new features or improvements that you have planned for the future of Salabim?

Keep In Touch

Email

Picks

Tobias

Cuisinart Burr Mill Coffee Grinder

Ruud

Pythonista (Python for iOS)

Python Notes for Professionals

Fluent Python by Luciano Ramalho

Links

Salabim

GitHub

Dining Philosophers Animation

Elevator Animation

Rotterdam

Discrete Event Simulation

Container Terminal Automation

Basic

Algol

Pascal

Operations Research

Continuous Simulation

Simula

Coroutines

SymPy

Another DES in Python: SimPy

DES in Julia: SimJulia

DES in R: Simmer

DES in Delphi/Pascal: Tomas

Pillow

PyPy

Delphi

PyGame

PyQT

TkInter

Inspect Module

OpenCV

Blender

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Laboratory: Safer Refactoring with Joe Alcorn** Feb 26, 2018
Summary

Every piece of software that has been around long enough ends up with some piece of it that needs to be redesigned and refactored. Often the code that needs to be updated is part of the critical path through the system, increasing the risks associated with any change. One way around this problem is to compare the results of the new code against the existing logic to ensure that you aren’t introducing regressions. This week Joe Alcorn shares his work on Laboratory, how the engineers at GitHub inspired him to create it as an analog to the Scientist gem, and how he is using it for his day job.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

A brief announcement before we start the show:

If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.

Your host as usual is Tobias Macey and today I’m interviewing Joe Alcorn about using Laboratory as a safety net for your refactoring.

Interview

Introductions

How did you get introduced to Python?

Can you start be explaining what Laboratory is and what motivated you to start the project?

How much of the design and implementation were directly inspired by the Scientist project from GitHub and how much of it did you have to figure out from scratch due to differences in the target languages?

What have been some of the most challenging aspects of building and maintaining Laboratory, and have you had any opportunities to use it on itself?

For someone who would like to use Laboratory in their project, what does the workflow look like and what potential pitfalls should they watch out for?

In the documentation you mention that portions of code that perform I/O and create side effects should be avoided. Have you found any strategies to allow for stubbing out the external interactions while still executing the rest of the logic?

How do you keep track of the results for active experiments and what sort of reporting is available?

What are some examples of the types of routines that would be good candidates for conducting an experiment?

What are some of the most complicated or difficult pieces of code that you have refactored with the help of Laboratory?

Given the fact that Laboratory is intended to be run in production and provide a certain measure of safety, what methods do you use to ensure that users of the library will not suffer from a drastic increase in overhead or unintended aberrations in the functionality of their software?

Are there any new features or improvements that you have planned for future releases of Laboratory?

Keep In Touch

joealcorn on GitHub

Website

Picks

Tobias

Chronicles of Narnia

Joe

Why We Sleep: Unlocking The Power of Sleep and Dreams by Matthew Walker, PhD

Links

Marvel App

GitHub: Move Fast and Fix Things

GitHub Scientist: Measure Twice, Cut Over Once

Scientist

Laboratory

Sure Footed Refactoring

Graphite

StatsD

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Software Architecture For Developers with Neal Ford** Feb 18, 2018
Summary

Whether it is intentional or accidental, every piece of software has an existing architecture. In this episode Neal Ford discusses the role of a software architect, methods for improving the design of your projects, pitfalls to avoid, and provides some resources for continuing to learn about how to design and build successful systems.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

A few announcements before we start the show:

There is still time to register for the O’Reilly Software Architecture Conference in New York. Use the link podcastinit.com/sacon-new-york to register and save 20%

If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.

With many thanks to O’Reilly Media, I have two items to give away. To sign up you just need to subscribe to the mailing list at podcastinit.com and you will have the chance to win either a copy of Neal’s book, Building Evolutionary Architectures, or a Bronze ticket to the O’Reilly Software Architecture Conference in New York. I will be picking the winners on February 21st.

Your host as usual is Tobias Macey and today I’m interviewing Neal Ford about principles of software architecture for developers

Interview

Introductions

How did you get introduced to Python?

A majority of your work has been focused on software architectures and how that can be used to facilitate delivery of working systems. Can you start by giving a high level description of what software architecture is and how it fits into the overall development process?

One of the difficulties that arise in long-lived projects is that technical debt accrues to the point that forward progress stagnates due to fear that any changes will cause the system to stop functioning. What are some methods that developers can use to either guard against that eventuality, or address it when it happens?

What are some of the broad categories of architectural patterns that developers should be aware of?

Are there aspects of the language that a system or application is being implemented in which influence the style of architecture that is commonly used?

What are some architectural anti-patterns that you have found to be the most commonly occurring?

Software is useless if there is no way to deliver it to the end user. What are some of the challenges that are most often overlooked by engineering teams and how do you solve for them?

Beyond the purely technological aspects, what other elements of software production and delivery are necessary for a successful architecture?

What resources can you recommend for someone who is interested in learning more about software architecture, whether as an individual contributor or in a full time architect role?

Keep In Touch

Website

@neal4d on Twitter

Picks

Tobias

Jumanji: Welcome to the Jungle

Lost City of Z

Neal

DeveloperToArchitect.com

EvolutionaryArchitecture.com

Broken Earth Series

Links

Thoughtworks

Neal’s Blog

Lisp

Thoughtworks Technology Radar

Martin Fowler: Who Needs an Architect?

O’Reilly Software Architecture Conference

Soft Skills

Microservices

Building Evolutionary Architectures

Github: Move Fast and Fix Things

Continuous Delivery

Github Scientist

Laboratory (Scientist in Python)

Agile Development

The Accidental Architect

System Quality Attributes

Pipes and Filters

MapReduce

Hadoop

Service Oriented Architecture

Linux

DevOps

Configuration Management

React

Alibaba Open Source

Baidu Open Source

Pragmatic Programmer

Trunk Based Development

PlantUML

Visio

Mermaid Diagrams

Graphviz

Evernote

Software Architecture Fundamentals

Enterprise Integration Patterns

Architectural Katas

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**ZimboPy** Feb 11, 2018
Summary

Learning to code is one of the most effective ways to be successful in the modern economy. To that end, Marlene Mhangami and Ronald Maravanyika created the ZimboPy organization to teach women and girls in Zimbabwe how to program in Python. In this episode they are joined by Mike Place to discuss how ZimboPy got started, the projects that their students have worked on, and how the community can get involved.

Preface

mu- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
– I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
– When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
– If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
– Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
– To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
– Your host as usual is Tobias Macey and today I’m interviewing Marlene Mhangami, Mike Place, and Ronald Maravanyika about ZimboPy, an organization that teaches women and girls in Zimbabwe how to program using Python

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what the mission of ZimboPy is and how it got started?

Which languages did you consider using for your lessons and what was your reason for choosing Python?

What subject matter do you cover in addition to pure programming concepts?

What are some of the types of projects that the students have completed as part of their work with ZimboPy?

What have been the most challenging aspects of running ZimboPy?

How is ZimboPy supported and what are your plans to ensure future sustainability?

Can you share some success stories for the women and girls that you have worked with?

For anyone who is interested in replicating your work for other communities what advice do you have?

Keep In Touch

Mike

cachedout on GitHub

@cachedout on Twitter

cachedout on Keybase

Ronald

Rmaravanyika on GitHub

@Rmaravanyika on Twitter

Marlene

@marlene_zw on Twitter

LinkedIn

Picks

Tobias

Click

Ronald

Odoo formerly OpenERP

Links

ZimboPy

Unilever

Django Girls

Thomas Hatch

SaltStack

Zimbabwe

Mechatronics

Raspberry Pi

OpenCV

ZimboPy Curriculum

ZimboPy Storefront

Oxfam

Open Collective

ZimboPy Mentorship Registration

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**PyRay: Pure Python 3D Rendering with Rohit Pandey** Feb 05, 2018
Summary

Using a rendering library can be a difficult task due to dependency issues and complicated APIs. Rohit Pandey wrote PyRay to address these issues in a pure Python library. In this episode he explains how he uses it to gain a more thorough understanding of mathematical models, how it compares to other options, and how you can use it for creating your own videos and GIFs.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

A few announcements before we start the show:

There’s still time to get your tickets for PyCon Colombia, happening February 9th and 10th. Go to pycon.co to learn more and register.

There is also still time to register for the O’Reilly Software Architecture Conference in New York. Use the link podcastinit.com/sacon-new-york to register and save 20%

If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.

Your host as usual is Tobias Macey and today I’m interviewing Rohit Pandey about PyRay, a 3d rendering library written completely in python

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what PyRay is and what motivated you to create it?
[rohit] PyRay is an open source library written completely in Python that let’s you render three and higher dimensional objects and scenes. Development on it has been ongoing and new features have so far come about from videos for my Youtube channel.

What does the internal architecture of PyRay look like and how has that design evolved since you first started working on it?

What capabilities are unlocked by having a pure Python rendering library which would otherwise be impractical or impossible for Python developers to do with existing options?
[rohit] Having a pure Python library makes it accessible with minimal fixed cost to Python users. The tradeoff is you lose on speed, but for many applications that isn’t an issue. I haven’t seen a library coded completely in Python that let’s you manipulate 3d and higher dimensional objects. The core usecase right now is Mathematical artwork. Google geometric gifs and you’ll see some fascinating, mesmerizing results. But those are created for the most part using tools that are not Python. Which is a pity since Python has a very extensive library of Mathematical functions.

What have been some of the most challenging aspects of building and maintaining PyRay?
[rohit] 3d objects – getting mesh plots. I have to develop routines from scratch for almost everything – shading objects, etc. Animated routines for characters.

What are some of the most interesting or unexpected uses of PyRay that you are aware of?
[rohit] Physical simulations. Ex: Testing if a solid is a fair die, getting lower bounds for space packing efficiencies of solids. Creating interactive demos where a user can draw to provide input.

For someone who wanted to contribute to PyRay are there any particular skills or experience that would be most helpful?
Basic linear algebra and python

What are some of the features or improvements that you have planned for the future of PyRay?

Keep In Touch

pyray repo – https://github.com/ryu577/pyray?utm_source=rss&utm_medium=rss
– Email
– GitHub
– LinkedIn

Picks

Tobias

Berserker Series by Fred Saberhagen

Rohit

Samurai Math Youtube Channel

3 Blue 1 Brown Youtube Channel

Isaac Arthur Youtube Channel

Links

PyRay

PyRay Youtube Videos

Microsoft Azure

Data Science

Columbia University

R

Nielsen

3Blue1Brown – Music and Measure Theory

Manim

Python Subreddit

Maya

Blender

Panda3D

POVRay

Pillow

NumPy

SciPy

Support Vector Machine

Logistic Regression

Geometric GIFs

Vapory

RGB vs HSL Color Scales

FFMPEG

Quaternions

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**MonkeyType with Carl Meyer and Matt Page** Jan 28, 2018
Summary

One of the draws of Python is how dynamic and flexible the language can be. Sometimes, that flexibility can be problematic if the format of variables at various parts of your program is unclear or the descriptions are inaccurate. The growing middle ground is to use type annotations as a way of providing some verification of the format of data as it flows through your application and enforcing gradual typing. To make it simpler to get started with type hinting, Carl Meyer and Matt Page, along with other engineers at Instagram, created MonkeyType to analyze your code as it runs and generate the type annotations. In this episode they explain how that process works, how it has helped them reduce bugs in their code, and how you can start using it today.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

A few announcements before we start the show:

There’s still time to get your tickets for PyCon Colombia, happening February 9th and 10th. Go to pycon.co to learn more and register.

There is also still time to register for the O’Reilly Software Architecture Conference in New York Feb 25-28. Use the link podcastinit.com/sacon-new-york to register and save 20%

If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.

Your host as usual is Tobias Macey and today I’m interviewing Carl Meyer and Matt Page about MonkeyType, a system to collect type information at runtime for your Python 3 code

Interview

Introductions

How did you get introduced to Python?

What is MonkeyType and how did the project get started?

How much overhead does the MonkeyType tracing add to the running system, and what techniques have you used to minimize the impact on production systems?

Given that the type information is collected from call traces at runtime, and some functions may accept multiple different types for the same arguments (e.g. `add`), do you have any logic that will allow for combining that information into a higher-order type that gets set as the annotation?

How does MonkeyType function internally and how has the implementation evolved over the time that you have been working on it?

Once the type annotations are present in your code base, what other tooling are you using to take advantage of that information?

It seems as though using MonkeyType to trace your running production systems could be a way to inadvertantly identify dead sections of code that aren’t being executed. Have you investigated ways to use the collected type information perform that analysis?

What have been some of the most challenging aspects of building, using, and maintaining MonkeyType?

What have been some of the most interesting or noteworthy things that you have learned in the process of working on and with MonkeyType?

What have you found to be the most useful and most problematic aspects of the typing capabilities provided in recent versions of Python?

For someone who wants to start using MonkeyType today, what is involved in getting it set up and using it in a new or existing codebase?

What features or improvements do you have planned for future releases of MonkeyType?

Keep In Touch

Carl

Email

@carljm on Twitter

Matt

Email

@void_star on Twitter

Picks

Tobias

LyxPro HAS-30 Headphones

Carl

Broadchurch

Happy Valley

Matt

Anova Sous Vide

Links

MonkeyType

Instagram

Dive Into Python

Python 3 Typing Module

MyPy

Project Page

Podcast.init Interview

Mike Krieger

PyAnnotate

Type Annotations

Type Stubs

PEP 523 frame evaluation api

Scuba

Haskell

Rust

PEP 563 Postponed Evaluation of Annotations

Gary Bernhardt – Ideology

coverage.py

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Learn Leap Fly: Using Python To Promote Global Literacy with Kjell Wooding** Jan 21, 2018
Summary

Learning how to read is one of the most important steps in empowering someone to build a successful future. In developing nations, access to teachers and classrooms is not universally available so the Global Learning XPRIZE serves to incentivize the creation of technology that provides children with the tools necessary to teach themselves literacy. Kjell Wooding helped create Learn Leap Fly in order to participate in the competition and used Python and Kivy to build a platform for children to develop their reading skills in a fun and engaging environment. In this episode he discusses his experience participating in the XPRIZE competition, how he and his team built what is now Kasuku Stories, and how Python and its ecosystem helped make it possible.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Kjell Wooding about Learn Leap Fly, a startup using Python on mobile devices to facilitate global learning

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what Learn Leap Fly does and how the company got started?

What was your motivation for using Kivy as the primary technology for your mobile applications as opposed to the platform native toolkits or other multi-platform frameworks?

What are some of the pedagogical techniques that you have incorporated into the technological aspects of your mobile application and are there any that you were unable to translate to a purely technical implementation.

How do you measure the effectiveness of the work that you are doing?

How has the framework of the XPRIZE influenced the way in which you have approached the design and development of your work?

What have been some of the biggest challenges that you faced in the process of developing and deploying your submission for the XPRIZE?

What are some of the features that you have planned for future releases of your platform?

Keep In Touch

Learn Leap Fly

Website

@learnleapfly on Twitter

Kjell

llfkj on GitHub

@pdokj on Twitter

Picks

Tobias

Yamaha YHT-4930UBL Home Theater System

Kjell

Instant Pot

Anova Sous Vide

Modernist Cooking at Home

Links

Programming Python (O’Reilly)

Learn Leap Fly

Tim Ferriss

Peter Diamandis

Global Learning XPRIZE

Kasuku Beta Program

XPRIZE Foundation

Kivy

Kivy Flappy Bird

Podcast.init Kivy Interview

Deliberate Practice

Google Pixel C

Bayesian Learning

SciPy

NumPy

Keras

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Healthchecks.io: Open Source Alerting For Your Cron Jobs with Pēteris Caune** Jan 14, 2018
Summary

Your backups are running every day, right? Are you sure? What about that daily report job? We all have scripts that need to be run on a periodic basis and it is easy to forget about them, assuming that they are working properly. Sometimes they fail and in order to know when that happens you need a tool that will let you know so that you can find and fix the problem. Pēteris Caune wrote Healthchecks to be that tool and made it available both as an open source project and a hosted version. In this episode he discusses his motivation for starting the project, the lessons he has learned while managing the hosting for it, and how you can start using it today.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Pēteris Caune about Healthchecks, a Django app which serves as a watchdog for your cron tasks

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what Healthchecks is and what motivated you to build it?

How does Healthchecks compare with other cron monitoring projects such as Cronitor or Dead Man’s Snitch?

Your pricing on the hosted service for Healthchecks.io is quite generous so I’m curious how you arrived at that cost structure and whether it has proven to be profitable for you?

How is Healthchecks functionality implemented and how has the design evolved since you began working on and using it?

What have been some of the most challenging aspects of working on Healthchecks and managing the hosted version?

For someone who wants to run their own instance of the service what are the steps and services involved?

What are some of the most interesting or unusual uses of Healtchecks that you are aware of?

Given that Healthchecks is intended to be used as part of an operations management and alerting system, what are the considerations that users should be aware of when deploying it in a highly available configuration?

What improvements or features do you have planned for the future of Healthchecks?

Keep In Touch

cuu508 on GitHub

Blog

@cuu508 on Twitter

Picks

Tobias

LG 55UJ6300

Pēteris

Zwift

TrainerRoad

Links

Healthchecks.io

GitHub

Riga

Latvia

Cross Country Cycling

Semantic Web

Django

Flask

Cron

Cronitor.io

Dead Man’s Snitch

IPv6

Load Balancing

PostGreSQL

MySQL

Fabric

Ansible

Dokku

Kubernetes

Hetzner

CloudFlare

PGPool II

Streaming Replication

Citus Data

Website

Data Engineering Podcast Interview

Heroku Fork

the Evolution of healthchecks.io Hosting Setup

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Bonobo: Lightweight ETL Toolkit for Python 3 with Romain Dorgueil** Jan 07, 2018
Summary

A majority of the work that we do as programmers involves data manipulation in some manner. This can range from large scale collection, aggregation, and statistical analysis across distrbuted systems, or it can be as simple as making a graph in a spreadsheet. In the middle of that range is the general task of ETL (Extract, Transform, and Load) which has its own range of scale. In this episode Romain Dorgueil discusses his experiences building ETL systems and the problems that he routinely encountered that led him to creating Bonobo, a lightweight, easy to use toolkit for data processing in Python 3. He also explains how the system works under the hood, how you can use it for your projects, and what he has planned for the future.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Romain Dorgueil about Bonobo, a data processing toolkit for modern Python

Interview

Introductions

How did you get introduced to Python?

What is Bonobo and what was your motivation for creating it?

What is the story behind the name?

How does Bonobo differ from projects such as Luigi or Airflow?
[RD] After I explain why that’s totally different things, maybe a good follow up would be to ask about differences from other data streaming solutions, like Apache Beam or Spark.

How is Bonobo implemented and how has its architecture evolved since you began working on it?

What have been some of the most challenging aspects of building and maintaining Bonobo?

What are some extensions that you would like to have but don’t have the time to implement?

What are some of the most interesting or creative uses of Bonobo that you are aware of?

What do you have planned for the future of Bonobo?

Keep In Touch

Bonobo Project

Bonobo ETL

Slack

GitHub

Romain

Website

@rdorgueil on Twitter

hartym on GitHub

Picks

Tobias

Data Skeptic: Quantum Computing

Romain

Medikit, or how to manage hundreds of projects at the same time, still being able to sleep at night.

Rocker, a better builder for docker images.

Links

Bonobo

RedHat

Anaconda Installer

ETL

Pentaho

RDC.ETL

DAG (Directed Acyclic Graph)

Luigi

Airflow

NamedTuple

Jupyter

OAuth

Graphviz

Dask

Data Engineering Podcast

Dask Interview

Selenium

Zapier

IFTTT (If This Then That)

FPGA

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Orange: Visual Data Mining Toolkit with Janez Demšar and Blaž Zupan** Dec 31, 2017
Summary

Data mining and visualization are important skills to have in the modern era, regardless of your job responsibilities. In order to make it easier to learn and use these techniques and technologies Blaž Zupan and Janez Demšar, along with many others, have created Orange. In this episode they explain how they built a visual programming interface for creating data analysis and machine learning workflows to simplify the work of gaining insights from the myriad data sources that are available. They discuss the history of the project, how it is built, the challenges that they have faced, and how they plan on growing and improving it in the future.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Blaž Zupan and Janez Demsar about Orange, a toolbox for interactive machine learning and data visualization in Python

Interview

Introductions

How did you get introduced to Python?

What is Orange and what was your motivation for building it?

Who is the target audience for this project?

How is the graphical interface implemented and what kinds of workflows can be implemented with the visual components?

What are some of the most notable or interesting widgets that are available in the catalog?

What are the limitations of the graphical interface and what options do user have when they reach those limits?

What have been some of the most challenging aspects of building and maintaining Orange?

What are some of the most common difficulties that you have seen when users are just getting started with data analysis and machine learning, and how does Orange help overcome those gaps in understanding?

What are some of the most interesting or innovative uses of Orange that you are aware of?

What are some of the projects or technologies that you consider to be your competition?

Under what circumstances would you advise against using Orange?

What are some widgets that you would like to see in future versions?

What do you have planned for future releases of Orange?

Keep In Touch

Blaž

University Bio

@bzupan on Twitter

BlazZupan on GitHub

Google Scholar

Janez

University Bio

@jademsar on Twitter

janezd on GitHub

Google Scholar

Picks

Tobias

Data Stories: What’s Going On In This Graph?

Blaž

How I Built This

Janez

Advent of Code

Links

University of Ljubljani

Data Explorer

Silicon Graphics

Visual Programming

PyQT

Linear Regression

t-SNE

K-Means

TCL/TK

Numpy

Scikit-Learn

SciPy

Textable.io

RapidMiner

Single Cell Genomics

Transfer Learning

Orange Video Tutorials

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Dramatiq: Distributed Task Queue For Python 3 with Bogdan Popa** Dec 24, 2017
Summary

A majority of projects will eventually need some way of managing periodic or long-running tasks outside of the context of the main application. This is where a distributed task queue becomes useful. For many in the Python community the standard option is Celery, though there are other projects to choose from. This week Bogdan Popa explains why he was dissatisfied with the current landscape of task queues and the features that he decided to focus on while building Dramatiq, a new, opinionated distributed task queue for Python 3. He also describes how it is designed, how you can start using it, and what he has planned for the future.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Bogdan Popa about Dramatiq, a distributed task processing library for Python with a focus on simplicity, reliability and performance

Interview

Introductions

How did you get introduced to Python?

What is Dramatiq and what was your motivation for creating it?

How does Dramatiq compare to other task queues in Python such as Celery or RQ?

How is Dramatiq implemented and how has the internal architecture evolved?

What have been some of the most difficult aspects of building Dramatiq?

What are some of the features that you are most proud of?

For someone who is interested in integrating Dramatiq into an application, can you describe the steps involved and the API?

Do you provide any form of migration path or compatibility layer for people who are currently using Celery or RQ?

Can you describe the licensing structure for the project and your reasoning?

How did you determine the price point for commercial licenses?

Have you been successful in selling licenses for commercial use?

What are some of the features that you have planned for future releases?

Keep In Touch

Project Website

Personal Website

Bogdanp on GitHub

@Bogdanp on Twitter

Picks

Tobias

The Anybodies by N.E. Bode

Bogdan

Pipenv

Links

Dramatiq

LeadPages

Lisp

Celery

RQ

Billiard

Kombu

Google App Engine

GAE Task Queue

RabbitMQ

APScheduler

Redis

Memcached

LRU (Least Recently Used)

Middleware

Gevent

Pika

SQS (Amazon Simple Queue Service)

Google Cloud PubSub

Django

API*

Bundler

Cargo

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Jake Vanderplas: Data Science For Academic Research** Dec 17, 2017
Summary

Jake Vanderplas is an astronomer by training and a prolific contributor to the Python data science ecosystem. His current role is using Python to teach principles of data analysis and data visualization to students and researchers at the University of Washington. In this episode he discusses how he got started with Python, the challenges of teaching best practices for software engineering and reproducible analysis, and how easy to use tools for data visualization can help democratize access to, and understanding of, data.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Jake Vanderplas about data science best practices, and applying them to academic sciences

Interview

Introductions

How did you get introduced to Python?

How has your astronomy background informed and influenced your current work?

In your work at the University of Washington, what are some of the most common difficulties that students face when learning data science?

How does that list differ for professional scientists who are learning how to apply data science to their work?

Where is the tooling still lacking in terms of enabling consistent and repeatable workflows?

One of the projects that you are spending time on now is Altair, which is a library for generating visualizations from Pandas dataframes. How does that work factor into your teaching?

What are some of the most novel applications of data science that you have been involved with?

What are some of the trends in data analysis that you are most excited for?

Keep In Touch

Website

@jakevdp

jakevdp on GitHub

Picks

Tobias

The Redwall Cookbook

Jake

Kevin M. Kruse

White Flight by Kevin Kruse

Links

UW eScience Institute

NumPy

SciPy

SciPy Conference

PyCon

Pandas

Sloan Digital Sky Survey

Spectroscopy

Software Carpentry

Data Carpentry

Git

Mercurial

Matplotlib

Altair

Conda

Xonsh

Jupyter

Jupyter Lab

Vega

Vega-lite

Interactive Data Lab

D3

Mike Bostock

Brian Granger

Bokeh

Grammar of Graphics

ggplot2

Holoviews

Wikimedia

AstroPy

Podcast.__init__ Interview About AstroPy

LIGO

Wes McKinney

Feather

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Kenneth Reitz** Dec 10, 2017
Summary

Kenneth Reitz has contributed many things to the Python community, including projects such as Requests, Pipenv, and Maya. He also started the community written Hitchhiker’s Guide to Python, and serves on the board of the Python Software Foundation. This week he talks about his career in the Python community and digs into some of his current work.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Kenneth Reitz about his career in Python

Interview

Introductions

How did you get introduced to Python?

An overarching theme of your open source projects is the idea of making them “For Humans”. Can you elaborate on how that came to be a focus for you and how that informs the way that you design and write your code?

What are the projects that you are most proud of and which do you think have had the biggest impact on the Python community?
A: Requests, Hitchhiker’s Guide to Python, and Pipenv (yet to come to full fruition).

Which projects have you authored which are relatively unknown but you think people would benefit from using more often?
A: Maya: Datetime for Humans, and Records: SQL for Humans.

Outside of the code that you write, what are some of your personal missions for the software industry in general and the Python community in particular?
A: I consider myself a “spiritual alchemist”, which means “transformation of dark into light”. I seek to do “the great work”, in however in manifests, outside of the programming world, as well as within it.

What do you think is the biggest gap in the tool chest for Python developers?
A: I seek to fill all the voids that I see, and I’ve done my best to do that to the best of my ability. I think we have a lot of work to do in the area of single-file executable builds (a-la Go).

What are your ambitions for future projects?
A: At the moment, I have no current plans for future projects, but I’m sure something will come along at some point

If you weren’t working with Python what would you be doing instead?
A: I’d have a lot less money and I’d be a lot less fufilled.

Keep In Touch

Website

@kennethreitz on Twitter

kennethreitz on GitHub

Picks

Tobias

Algorithms to Live By

Kenneth

The Linux Programming Interface

Links

Heroku

Salesforce

PSF Board of Directors

Caldera Linux

C

Pascal

Basic

Groovy

Java

PHP

Ruby

The Design of Everyday Things

Requests

Hitchhiker’s Guide

Pipenv

Pipfile

The Update Framework

Falsehoods Programmer’s Believe About Time

PEP20

Py2EXE

Cxfreeze

Briefcase

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Asphalt: A Framework For Asynchronous Network Applications with Alex Grönholm** Dec 03, 2017
Summary

As we rely more on small, distributed processes for building our applications, being able to take advantage of asynchronous I/O is increasingly important for performance. This week Alex Grönholm explains how the Asphalt Framework was created to make it easier to build these network oriented software stacks and the technical challenges that he faced in the process.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Alex Grönholm about the Asphalt Framework, a Python microframework for network oriented applications

Interview

Introductions

How did you get introduced to Python?

What is Asphalt and what was your reason for building it?

How does Asphalt compare to Twisted?

What are the most challenging parts of writing asynchronous and event-based applications and how does Asphalt help simplify that process?

When building an Asphalt application it can be easy to accidentally block an async loop by pulling in third party libraries that don’t support asynchronous execution. What are some of the techniques for identifying and resolving blocking portions of your application?

What does the internal architecture of Asphalt look like and how has that evolved from when you first started working on it?

What have been some of the most difficult aspects of building and evolving Asphalt?

What are some of the most interesting or unexpected uses of Asphalt that you have seen?

What are some of the new features or improvements that you have planned for the future of Asphalt?

Keep In Touch

Gitter

IRC

GitHub

agronholm on GitHub

@agronholm on Twitter

Picks

Tobias

Thor: Ragnarok

Alex

Two Steps From Hell

Links

Asphalt

ERP

Asyncio

Tornado

Twisted

SQLAlchemy

PEP 550

Sanic

WAMP

Podcast.init Interview About Crossbar

Tee

FlexGet

APScheduler

BitTorrent

uvloop

Tokio

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Golem: End-To-End Test Automation Framework with Luciano Renzi** Nov 25, 2017
Summary

The importance of testing your software is widely talked about and well understood. What is not as often discussed is the different types of testing, and how end-to-end tests can benefit your team to ensure proper functioning of your application when it gets released to production. This week Luciano Renzi shares the work that he has done on Golem, a framework for building and executing an automation suite to exercise the entire system from the perspective of the user. He discusses his reasons for creating the project, how he things about testing, and where he plans on taking Golem in the future. Give it a listen and then take it for a test drive.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Luciano Renzi about Golem, a framework and automation tool for end-to-end testing in Python

Interview

Introductions

How did you get introduced to Python?

What is golem and what motivated you to create it?

What was your inspiration for the name?

Why did you choose to use Python for Golem and if you were to start over today would you make the same choice?

For someone who is unfamiliar with the concept, can you describe what end-to-end testing is and the reasons for making it part of their development process?

What is the main goal of Golem

What does the internal architecture and implementation of Golem look like and how has that evolved from when you first started the project?

How does Golem compare to other Python libraries for automated browser testing and what was lacking in the existing solutions when you created it?

What are the differences between golem and robot framework?

What about projects written in other languages such as protractor?

One of the intriguing features of Golem is the web interface for constructing tests. What are the benefits of codeless automation & record-playback functionality?

What are some of the most challenging aspects of building and maintaining Golem?

It seems that every browser automation library is ultimately a wrapper around Selenium. Why is a wrapper necessary and why haven’t any strong alternatives been created?

What are the advantages of making Golem a framework for test automation, rather than a library?

What are some of the most interesting or unexpected uses for Golem that you have seen?

What do you have planned for the future of Golem?

What is the current state of end to end automation and how do you see it evolving in the future?

How do you think machine learning and AI will be used in test automation?

Keep In Touch

luciano-renzi on GitHub

@lucianorenzi_ on Twitter

Picks

Tobias

Weapons of Math Destruction

Links

Golem

Elementum

Pascal

Watir

JUnit

Selenium

Page Object Pattern

Selenium Grid

Sauce Labs

py.test

Podcast.init Interview About Py.Test

Robot Framework

Mechanize

Acceptance Tests

Protractor

Webdriver.io

Appium

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Graphite Metrics Stack with Jason Dixon and Dan Cech** Nov 19, 2017
Summary

Do you know what is happening in your production systems right now? If you have a comprehensive metrics platform then the answer is yes. If your answer is no, then this episode is for you. Jason Dixon and Dan Cech, core maintainers of the Graphite project, talk about how graphite is architected to capture your time series data and give you the ability to use it for answering questions. They cover the challenges that have been faced in evolving the project, the strengths that have let it stand the tests of time, and the features that will be coming in future releases.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Now is a good time to start planning your conference schedule for 2018. To help you out with that, guest Jason Dixon is offering a $100 discount for Monitorama in Portland, OR on June 4th – 6th and guest Dan Cech is offering a €50 discount to Grafanacon in Amsterdam, Netherlands March 1st and 2nd. There is also still time to get your tickets to PyCascades in Vancouver, BC Canada January 22nd and 23rd. All of the details are in the show notes

Your host as usual is Tobias Macey and today I’m interviewing Jason Dixon and Dan Cech about Graphite

Interview

Introductions

How did you get introduced to Python?

What is Graphite and how did you each get involved in the project?

Why should developers be thinking about collecting and reporting on metrics from their software and systems?

How do you think the Graphite project has contributed to or influenced the overall state of the art in systems monitoring?

There are a number of different projects that comprise a fully working Graphite deployment. Can you list each of them and describe how they fit together?

What are some of the early design choices that have proven to be problematic while trying to evolve the project?

What are some of the challenges that you have been faced with while maintaining and improving the various Graphite projects?

What will be involved in porting Graphite to run on Python 3?

If you were to start the project over would you still use Python?

What are the options for scaling Graphite and making it highly available?

Given the level of importance to a companies visibility into their systems, what development practices do you use to ensure that Graphite can operate reliably and fail gracefully?

What are some of the biggest competitors to Graphite?

When is Graphite not the right choice for tracking your system metrics?

What are some of the most interesting or unusual uses of Graphite that you are aware of?

What are some of the new features and enhancements that are planned for the future of Graphite?

Keep In Touch

Jason

@obfuscurity on Twitter

Website

obfuscurity on GitHub

Dan

@dancech on Twitter

Website

DanCech on GitHub

Picks

Tobias

Archery

Jason

Rocket League

Monitorama $100 Discount (Limited Quantity)

Dan

Home Assistant

Podcast.__init__ Interview

GrafanaCon €50 discount with PODCASTINIT2018

Links

Graphite

Sensu

Monitorama

RainTank

Grafana Labs

Librato

GitHub

Dyn

Telemetry

Perl

PHP

React

O’Reilly Graphite Book

Time Series

RRDTool

InfluxDB

Adrian Cockcroft

NVMe

Prometheus

CNCF

ASAP Smoothing

PyCascades

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Surprise! Recommendation Algorithms with Nicolas Hug** Nov 11, 2017
Summary

A relevant and timely recommendation can be a pleasant surprise that will delight your users. Unfortunately it can be difficult to build a system that will produce useful suggestions, which is why this week’s guest, Nicolas Hug, built a library to help with developing and testing collaborative recommendation algorithms. He explains how he took the code he wrote for his PhD thesis and cleaned it up to release as an open source library and his plans for future development on it.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Nicolas Hug about Surprise, a scikit library for building recommender systems

Interview

Introductions

How did you get introduced to Python?

What is Surprise and what was your motivation for creating it?

What are the most challenging aspects of building a recommender system and how does Surprise help simplify that process?

What are some of the ways that a user or company can bootstrap a recommender system while they accrue data to use a collaborative algorithm?

What are some of the ways that a recommender system can be used, outside of the typical ecommerce example?

Once an algorithm has been deployed how can a user test the accuracy of the suggestions?

How is Surprise implemented and how has it evolved since you first started working on it?

What have been the most difficult aspects of building and maintaining Surprise?

competitors?

What are the attributes of the system that can be modified to improve the relevance of the recommendations that are provided?

For someone who wants to use Surprise in their application, what are the steps involved?

What are some of the new features or improvements that you have planned for the future of Surprise?

Keep In Touch

Website

@hug_nicolas on Twitter

nicolashug on GitHub

Picks

Tobias

Silk profiler for Django

Links

Surprise

Gridsearch

Cold Start Problem

Content-Based Recommendation

Ensemble Learning

Spotlight

Lightfm

Pandas

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Rasa: Build Your Own AI Chatbot with Joey Faulkner** Nov 04, 2017
Summary

With the proliferation of messaging applications, there has been a growing demand for bots that can understand our wishes and perform our bidding. The rise of artificial intelligence has brought the capacity for understanding human language. Combining these two trends gives us chatbots that can be used as a new interface to the software and services that we depend on. This week Joey Faulkner shares his work with Rasa Technologies and their open sourced libraries for understanding natural language and how to conduct a conversation. We talked about how the Rasa Core and Rasa NLU libraries work and how you can use them to replace your dependence on API services and own your data.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Joey Faulkner about Rasa Core and Rasa NLU for adding conversational AI to your projects.

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining the goals of Rasa as a company and highlighting the projects that you have open sourced?

What are the differences between the Rasa Core and Rasa NLU libraries and how do they relate to each other?

How does the interaction model change when going from state machine driven bots to those which use Rasa Core and what capabilities does it unlock?

How is Rasa NLU implemented and how has the design evolved?

What are the motivations for someone to use Rasa core or NLU as a library instead of available API services such as wit.ai, LUIS, or Dialogflow?

What are some of the biggest challenges in gathering and curating useful training data?

What is involved in supporting multiple languages for an application using Rasa?

What are the biggest challenges that you face, past, present, and future, building and growing the tools and platform for Rasa?

What would be involved for projects such as OpsDroid, Kalliope, or Mycroft to take advantage of Rasa and what benefit would that provide?

On the comparison page for the hosted Rasa platform it mentions a feature of collaborative model training, can you describe how that works and why someone might want to take advantage of it?

What are some of the most interesting or unexpected uses of the Rasa tools that you have seen?

What do you have planned for the future of Rasa?

Keep In Touch

Gitter

Twitter

@joeymfaulkner

@Rasa_HQ

Email

GitHub

Picks

Tobias

Information Architecture

Joey

Dog Spotting

Rasa NLU Trainer

Links

Rasa Technologies

Rasa NLU

Rasa Core

SpaCy

Podcast.__init__ Interview with SpaCy Creator

yt-project

Podcast.__init__ Interview with yt-project

Chatbot

Word2Vec

State Machine

Podcast.__init__ Episode About Automat with Glyph

Recursive Neural Network

MITIE

Support Vector Machine

Scikit Learn

wit.ai

LUIS

Dialogflow

Keras

Reinforcement Learning

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Eliot: Effective Logging with Itamar Turner-Trauring** Oct 29, 2017
Summary

Understanding what is happening in a software system can be difficult, especially when you have inconsistent log messages. Itamar Turner-Trauring created Eliot to make it possible for your project to tell you a story about how transactions flow through your program. In this week’s episode we go deep on proper logging practices, anti patterns, and how to improve your ability to debug your software with log messages.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Itamar Turner-Trauring about Eliot, a library for managing complex logs across multiple processes.

Interview

Introductions

How did you get introduced to Python?

What is Eliot and what problem were you trying to solve by creating it?

How is Eliot implemented and how has the design evolved since you first started working on it?

Why is it so important to have a standardized format for your application logs?

What are some of the anti-patterns that you consider to be the most harmful when developers are setting up logging in their projects?

What have been the most challenging aspects of building and maintaining Eliot?

How does Eliot compare to some of the other third party logging libraries available such as structlog or logbook?

What are some of the improvements or additional features that you have planned for the future of Eliot?

Keep In Touch

Website

@itamarst on Twitter

Picks

Tobias

Moonshot Podcast

Itamar

Middlemarch by George Eliot

Links

Eliot

Zope

PHP

OpenTracing

Zipkin

Carl De Marcken

Sentry

Elasticsearch

Logstash

Kibana

Eliot-Tree

Daniel Lebrero

Flocker

Context Local Variables PEP (PEP 550)

Flamegraph

Brendan Gregg

DAG

Structlog

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Donkey: Building Self Driving Cars with Will Roscoe** Oct 22, 2017
Summary

Do you wish that you had a self-driving car of your own? With Donkey you can make that dream a reality. This week Will Roscoe shares the story of how he got involved in the arena of self-driving car hobbyists and ended up building a Python library to act as his pilot. We talked about the hardware involved, how he has evolved the code to meet unexpected challenges, and how he plans to improve it in the future. So go build your own self driving car and take it for a spin!

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Will Roscoe about Donkey, a python library for building DIY self driving cars.

Interview

Introductions

How did you get introduced to Python?

What is Donkey and what was your reason for creating it?

What is the story behind the name?

What was your reason for choosing Python as the language for implementing Donkey and if you were to start over today would you make the same choice?

How is Donkey implemented and how has its software architecture evolved?

Is the library built in a way that you can process inputs from additional sensor types, such as proximity detectors or LIDAR?

For training the autopilot what are the input features that the model is testing against for the input data, and is it possible to change the features that it will try to detect?

Do you have plans to incorporate any negative reinforcement techniques for training the pilot models so that errors in data collection can be identified as undesirable outcomes?

What have been some of the most interesting or humorous successes and failures while testing your cars?

What are some of the challenges involved with getting such a sophisticated stack of software running on a Raspberry Pi?

What are some of the improvements or new features that you have planned for the future of Donkey?

Media

Donkey Car Photos

Keep In Touch

Donkey Slack Channel

Wills Twitter – @dataduce

#donkeycar on social

Picks

Tobias

Orgzly

Org Mode for Sublime

Org Mode for VSCode

Org Mode for Vim

Will

Algorithms to Live By

The Structure of Scientific Revolutions

A song I can’t stop nodding my head to

Links

Donkey Car

DIY Robocars

Tornado

[Tornado on Podcast.init](https://www.pythonpodcast.com/episode-40-ben-darnell-on-tornado/?utm_source=rss&utm_medium=rss

Raspberry Pi

TensorFlow

Convolutional Neural Network

Adafruit

LIDAR

ROS (Robot Operating System)

Unity

Udacity self driving car nano-degree

SparkFun

Beagleboard

Adam Conway

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Event Sourcing with John Bywater** Oct 15, 2017
Summary

The way that your application handles data and the way that it is represented in your database don’t always match, leading to a lot of brittle abstractions to reconcile the two. In order to reduce that friction, instead of overwriting the state of your application on every change you can log all of the events that take place and then render the current state from that sequence of events. John Bywater joins me this week to discuss his work on the Event Sourcing library, why you might want to use it in your applications, and how it can change the way that you think about your data.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports the show on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.

If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.

Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing John Bywater about event sourcing, an architectural approach to make your data layer easier to scale and maintain.

Interview

Introductions

How did you get introduced to Python?

Can you start by describing the concept of event sourcing and the benefits that it provides?

What is the event sourcing library and what was your reason for starting it?

What are some of the reasons that someone might not want to implement an event sourcing approach in their persistence layer?

Given that you are storing a record for each event that occurs on a domain object, how does that affect the amount of storage necessary to support an event sourced application?

What is the impact on performance and latency from an end user perspective when the application is using event sourcing to render the current state of the system?

What does the internal architecture and design of your library look like and how has that evolved over time?

In the case where events are delivered out of order, how can you ensure that the present view of an object is reflected accurately?

For someone who wants to incorporate an event sourcing design into an existing application, how would they do that?

How do you manage schema changes in your domain model when you need to reconstruct present state from the beginning of an objects event sequence?

What are some of the most interesting uses of event sourcing that you have seen?

What are some of the features or improvements that you have planned for the future of you event sourcing library?

Keep In Touch

John

johnbywater on GitHub

@johnbywater on Twitter

Picks

Tobias

Heresy In The Church Of Docker

John

QuantDSL

Links

CKAN

Data.gov

Patterns of Enterprise Application Architecture

Object Relational Impedance Mismatch

Event Sourcing (Pattern)

Event Sourcing (Library)

N-Tiered Architecture

Domain Driven Design

Event Storming

ORM, The Vietnam of Computer Science

Vaughn Vernon, Implementing Domain Driven Design

Active Record Pattern

Optimistic Concurrency Control

Paxos

DynamoDB

Martin Fowler

Eric Evans

The Dark Side of Event Sourcing

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Kalliope with Nicolas Marcq and Thibaud Buffet** Oct 08, 2017
Summary

Wouldn’t it be nice to have a personal assistant to answer your questions, help you remember important tasks, and control your environment? Meet Kalliope, a Python powered, modular, voice controlled automation platform. This week Nicolas Marcq and Thibaud Buffet explain how they started the project, what makes it stand out from other open source and commercial options, and how you can start using it today.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.

Your host as usual is Tobias Macey and today I’m interviewing Nicolas Marcq and Thibaud Buffet about Kalliope, a modular always-on voice controlled personal assistant designed for home automation.

Interview

Introductions

How did you get introduced to Python?

What is the Kalliope project and how did it get started?

How does Kalliope compare to commercial options such as Amazon Alexa and Google Home, as well as other open source projects such as Mycroft or Jasper?

The majority of voice assistant projects that I have seen default to interacting in English, whereas Kalliope is multi-lingual. What led you to that design choice and how is that implemented?

One of the perennial questions around voice assistants is privacy, so how does Kalliope work to mitigate the issues associated with having an always on device listening in people’s homes?

How is Kalliope architected internally and how has the design evolved over time?

What are some of the most difficult or challenging aspects of building Kalliope and its associated projects?

What are some of the most interesting uses of Kalliope that you are aware of?

What are some of the most notable features or improvements that you have planned for the future of Kalliope?

How has the choice of Python as the implementation worked for you, and if you were to start over today do you think you would make the same decision?

Keep In Touch

Nicolas

@Sispheor on Twitter

Sispheor on GitHub

Website

Thibaud

@Tib_Tac on Twitter

LaMonF on GitHub

Picks

Tobias

Kiwi Crate

Nicolas

Raspberry Pi Speaker

Thibaud

ReactiveX in Python

Links

Snowboy

Mycroft

Mycroft Interview

Amazon Alexa

Google Home

Jasper

Kalliope

TTS

STT

CMU Sphinx

Abstract Base Class

MQTT

RxPy Interview

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Modoboa with Antoine Nguyen** Oct 01, 2017
Summary

Email has long been the most commonly used means of communication on the internet. This week Antoine Nguyen talks about his work on the Modoboa project to make hosting your own mail server easier to manage. He discusses how the project got started, the tools that it ties together, and how he used Django to build a webmail and admin interface to make it more approachable.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.

Your host as usual is Tobias Macey and today I’m interviewing Antoine Nguyen about Modoboa, a project to make mail hosting simple.

Interview

Introductions

How did you get introduced to Python?

What is Modoboa and what is the problem that you were trying to solve when you started it?

Where does the name come from?

Self-hosting an email server was a common activity during the early stages of the internet, what are some of the reasons that someone should consider running their own mail server now that there are so many options for third-party hosting such as Gmail and Outlook?

Email hosting has become more complicated in recent years with the need to jump through a lot of hoops to maintain a sufficient reputation to keep your messages from being flagged as spam. Are there any utilities in Modoboa to assist with that process?

There are a lot of components that you have brought together for running an email server. Can you describe how the different pieces fit together and what layers you have built on top to help make the overall system more manageable?

What does the scaling strategy look like for Modoboa?

What is the most challenging aspect of building and maintaining Modoboa?

What are some of the features that you have planned for the future of Modoboa?

Keep In Touch

Email

@antngu on Twitter

Picks

Tobias

Dropbox Paper

Antoine

Capoeira

Links

PyTk

Postfix

Dovecot

Nextcloud

Owncloud

SPF Records

DKIM

DMARC

SMTP

IMAP

Apache Libcloud

Amavis

Mail Transfer Agent

Radicale

Ansible

Docker

Gentoo

Packer

Synology

Drobo

Prosody

Lua

XMPP

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**QuTiP with Paul Nation** Sep 24, 2017
Summary

The future of computation and our understanding of the world around us is driven by the quantum world. This week Paul Nation explains how the Quantum Toolbox in Python (QuTiP) is being used in research projects that are expanding our knowledge of the physical universe.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.

Your host as usual is Tobias Macey and today I’m interviewing Paul Nation about QuTIP, the quantum toolbox in Python.

Interview

Introductions

How did you get introduced to Python?

Before we start talking about QuTiP, can you provide us with a baseline definition of what quantum mechanics is?

What is QuTIP and how did the project get started?

Is QuTiP used purely in academics, or are there other users?

What are some of the practical innovations that have been created as a result of research into different areas of quantum optics?

How do you foresee the advent of practical quantum computers impacting the state of quantum mechanical research?

Given the inherent complexity of the subject matter that you are dealing with, how do you approach the challenge of trying to present a usable API to users of QuTiP while not inhibiting their ability to operate at a low level when necessary?

What is the process for incorporating new understandings of quantum mechanical theory into the QuTiP package?

What are some of the most difficult aspects of simulating quantum systems in a standard computational environment?

What is the most enjoyable aspect of working on QuTiP, what is the least enjoyable?

What are some of the most notable research results that you are aware of which used QuTiP as part of their studies?

What are some resources that you can recommend for anyone who wants to learn more about quantum mechanics?

Keep In Touch

QuTiP

QuSTaR

Picks

Tobias

edx.org

Paul

Cython

Matplotlib

Cheyenne Mountain Zoo

Links

Quantum Optics

2 Level System

Complex Numbers

Qubit

Quantum Computing

Harmonic Oscillator

Nature Scientific Journal

IBM Quantum Experience

D-Wave

Rigetti Quantum Computing

Quantum Supremacy

Hamiltonian

Sparse Matrix

Richard Feynman

Dask

Project Q

Quantum State Transfer via Noisy Photonic and Phononic Waveguides paper by Peter Zoller

Extending the lifetime of a quantum bit with error correction in superconducting circuits paper by Rob Shoelkopf (Yale)

QuTiP Documentation

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Lego Robotics with David Lechner and Denis Demidov** Sep 17, 2017
Summary

Do you like Legos, robots, and Python? This week I am joined by David Lechner and Denis Demidov to talk about the ev3dev project and how you can program your Lego Mindstorms with Python!

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.

Your host as usual is Tobias Macey and today I’m interviewing David Lechner and Denis Demidov about using Python with the ev3dev platform for programming LEGO robots

Interview

Introductions

How did you get introduced to Python?

Can you explain what the ev3dev project is and some of the story about how and why it got started?

What is LEGO’s opinion of the ev3dev project?

For anyone who isn’t familiar with the MINDSTORMS EV3 product from LEGO, can you give a brief overview of the hardware that they come with?

Other than allowing users to program in environments other than the block-based editor that LEGO provides, what capabilities does the ev3dev project add to the MINDSTORMS EV3 platform?

How are the language bindings generated and how do the different implementations compare to each other?

What are the most challenging aspects of building and maintaining the ev3dev distribution and various language bindings?

One of the things that my son is curious about is the possibility for integrating his MINDSTORMS with projects such as Kalliope or Mycroft to allow for voice controlled robots. Are you aware of anyone having done so or how you would approach something like that?

What are some of the most interesting or innovative projects that you have seen people make with the MINDSTORMS platform running ev3dev?

Why would someone want to use MINDSTORMS instead of any of the other robotics platforms that are available?

For someone who is interested in learning more about intermediate and advanced robotics, what are some resources that you would recommend?

Keep In Touch

Denis

@denis_demidov on Twitter

ddemidov on Github

David

dlech on Github

Website

Picks

Tobias

Raspberry Pi

Kalliope

Denis

pybind11

David

Local food

LocalHarvest

Links

ev3dev

Lego MINDSTORMS

BeagleBone

Lego Mindstorms Community

C++

Jupyter Notebooks

Ralph Hempel

Forth

RCX

NXT

EV3

ARMv5

Debian

PiStorms

BrickPi

EVB

UART

EV3 Schematics Look for “EV3 Hardware Developer Kit” in “Advanced Users” section.

I2C

RPyC

Laurens Valk

Liquid Templates

Delta Robot

Quest For Space

Lego Technic

Mindsensors.com

Cool robots built with ev3dev

Micropython

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Cloud-Init with Scott Moser** Sep 10, 2017
Summary

Server administration is a complex endeavor, but there are some tools that can make life easier. If you are running your workload in a cloud environment then cloud-init is here to help. This week Scott Moser explains what cloud-init is, how it works, and how it became the de-facto tool for configuring your Linux servers at boot.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.

Your host as usual is Tobias Macey and today I’m interviewing Scott Moser about cloud-init, a set of python scripts and utilities to make your cloud images be all they can be!

Interview

Introductions

How did you get introduced to Python?

What is cloud-init and how did the project get started?

Why was Python chosen as the language for implementing cloud-init?

How has cloud-init come to be the de-facto utility for managing cloud instances across vendors and distributions?

Are there any viable competitors to cloud-init? coreos-cloudinit, some others.

How much overlap is there between cloud-init and configuration management tools such as SaltStack, Ansible, Chef, etc.

How have you architected cloud-init to allow for compatibility across operating system distributions?

What is the most difficult or complex aspect of building and maintaining cloud-init? [os integration, networking, goal of “do stuff without reboot”]

Given that it is used as a critical component of the production deployment mechanics for a large number of people, how do you ensure an appropriate level of stability and security while developing cloud-init?

How do you think the status of cloud-init as a Canonical project has affected the level of contributions that you receive?

How much of the support and roadmap is contributed by individual vs corporate users such as AWS and Azure?

What are some of the most unexpected or creative uses of cloud-init that you have seen? [https://wiki.ubuntu.com/OpenCompute?utm_source=rss&utm_medium=rss “disposable use os”]

In your experience, what has been the biggest stumbling block for new users of cloud-init?

Do you have any notable features or improvements planned for the future of cloud-init, or do you feel that it has reached a state of feature-completeness?

Keep In Touch

smoser on GitHub

Picks

Tobias

mu4e

isync

Scott

LXD

Links

IBM – Linux Technology Center

Cloud-Init

Ubuntu

Canonical

CoreOS

EC2

OpenStack

CentOS

RHEL

coreos-cloudinit

JuJu

Puppet

SystemV

Upstart

SystemD

Joyent SmartOS

Digital Ocean

IPv4

IPv6

Canonical MaaS+

JSON-Schema

LXD

Launchpad

Bzr

Git

SUSE

FreeBSD

KVM

Go-lang

Pretty Table

RAID

ZFS

LVM

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Biopython with Peter Cock, Wibowo Arindrarto, and Tiago Antão** Sep 03, 2017
Summary

Advances in the techniques used for genome sequencing are providing us with more information to unlock the secrets of biology. But how does that data get processed and analyzed? With Python of course! This week I am joined by some of the core maintainers of Biopython to discuss what bioinformatics is, how Python is used to help power the research in the field, and how Biopython helps to tie everything together.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.

Your host as usual is Tobias Macey and today I’m interviewing Peter Cock, Wibowo Arindrarto, and Tiago Antão about biopython, a suite of python tools for computational molecular biology.

Interview

Introductions

How did you get introduced to Python?

Can you start by explaining what bioinformatics is and highlight some of the different areas of research?

What is biopython and how did it get started?

Biopython has a long history behind it. How has the project evolved over that time to meet the changing needs in terms of both research amd computation?

How does Biopython compare to the sibling Bio* projects in other programming languages?

What does a common workflow look like for someone who is working with biological data?

What are some of the most interesting or innovative uses of Biopython that you are aware of?

What are some of the most challenging aspects of developing and supporting Biopython?

What are some of the most exciting developments in bioinformatics, either recently or coming up?

How much domain knowledge is necessary for someone who wants to contribute to the project?

What are some of the most problematic limitations of Biopython and how do you work around them?

Keep In Touch

Peter

Website

Wibowo

Website

@_bow_ on Twitter

Tiago

Website

@tiagoantao on Twitter

Biopython

GitHub

Picks

Tobias

Keep it Low Conf

Peter

Jupyter Notebooks (formerly IPython) for producing notebooks combining code, graphical output and descriptive code. Can be seen as a modern take on Donald Knuth’s Literate programming?

Wibowo

Conda for installing software, including BioConda for community packaged software in bioinformatics.

Tiago

Brython project for writing Python 3 in your browser using JavaScript

Glacier National Park in North West Montana

Links

BioJava

BioRuby

BioPerl

BioJS

Open Bioinformatics Foundation

Software In The Public Interest

Oxford Nanopore Technology (for sequencing in the field etc)

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**opsdroid with Jacob Tomlinson** Aug 26, 2017
Summary

Server administration is an activity that often happens in an isolated context in a terminal. ChatOps is a way of bringing that work into a shared environment and unlocking more collaboration. This week Jacob Tomlinson talks about the work he has done on opsdroid, a new bot framework targeted at tying together the various services and environments that modern production systems rely on.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.

Your host as usual is Tobias Macey and today I’m interviewing Jacob Tomlinson about opsdroid

Interview

Introductions

How did you get introduced to Python?

What is opsdroid and what was the problem that you were trying to solve when you started the project?

What led you to choose Python as the language for implementing opsdroid?

What did you find lacking in the multitude of other chat bots that necessitated starting a new project? (e.g. Hubot, Errbot, Lita)

One of the main features that you list in the documentation is the ease of installation. Why is that such an important aspect of the project and how is that implemented?

What has been the most interesting and the most challenging aspect of implementing opsdroid?

On the opsdroid organisation on GitHub there are many repositories for plugin modules. Do you see this being a management issue in the long term?

How is opsdroid architected and what were the system requirements that led to the current system design?

How do you manage authorization and authentication for performing commands against your production infrastructure in a group chat environment?

What are some of the other security implications that users should be aware of when deploying a bot for interfacing with their deployment environment?

How does a chat-oriented bot framework differ from those that are being created for voice-oriented interaction?

What do you have planned for the future of opsdroid?

Keep In Touch

Website

@_JacobTomlinson on Twitter

jacobtomlinson on GitHub

Picks

Tobias

Rough Translation Podcast

Jacob

Home Assistant Podcast

Links

Iron Man Movie

Puppet

Hubot

ChatOps

asyncio

Home Assistant

Podcast.init Interview

api.ai

Luis

Lex

Slack

Mycroft

Kalliope

Amazon Alexa

opsdroid audio

Snowboy

Google Home

Wit.ai

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Ergonomica with Liam Schumm** Aug 20, 2017
Summary

As developers we spend a lot of our work day in a terminal window, using shells that were designed 30 years ago. This week Liam Schumm joins me to explain why he decided to write a new, more ergonomic shell environment to simplify his workflow.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.

Your host as usual is Tobias Macey and today I’m interviewing Liam Schumm about Ergonomica

Interview

Introductions

How did you get introduced to Python?

What is Ergonomica and what was your reason for creating it?

What are some of the most difficult aspects of the project that you have experienced?

How is Ergonomica implemented?

What was your reason for using a dialect of Lisp as the interface for a terminal environment as opposed to iterating on the idioms in shells such as Bash?

How does Ergonomica’s implementation differ from traditional shells such as Bash, Csh, and Powershell?

How does Ergonomica’s implementation differ from other alternative shells such as Xonsh, ZSH, and Fish?

Why did you choose to implement Ergonomica in Python?

What’s your target group for Ergonomica?

What do you have planned for the future of Ergonomica?

Reading through your website you are fairly well accomplished. How does your age factor into the kinds of projects that you are engaged in?

Keep In Touch

Liam’s GitHub

Email

@liamschumm on Twitter

Picks

Tobias

Magic The Gathering: Arena of the Planeswalkers

Liam

GitLab CE

Python-prompt-toolkit

Thriftbooks – 15% off your first order

Links

PyGame

Minecraft

Beeware

ChiPy

Chi Hack Night

XKCD Tar Comic

POSIX

Colorama

PLY

Peter Norvig

How to write a lisp interpreter in python

ZSH

Fish

Xonsh

PyVim

sh

Homebrew

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Data Retriever with Henry Senyondo** Aug 12, 2017
Summary

Analyzing and interpreting data is a large portion of the work involved in scientific research. Getting to that point can be a lot of work on its own because of all of the steps required to download, clean, and organize the data prior to analysis. This week Henry Senyondo talks about the work he is doing with Data Retriever to make data preparation as easy as `retriever install`.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Henry Senyondo about Data Retriever, the package manager for public data sets.

Interview

Introductions

How did you get introduced to Python?

Can you explain what data retriever is and the problem that it was built to solve?

Are there limitations as to the types of data that can be managed by data retriever?

What kinds of data sets are currently available and who are the target users?

What is involved in preparing a new dataset to be available for installation?

How much of the logic for installing the data is shared between the R and Python implementations of Data Retriever and how do you ensure that the two packages evolve in parallel?

How is the project designed and what are some of the most difficult technical aspects of building it?

What is in store for the future of data retriever?

Keep In Touch

Github

@henrykironde on Twitter

Picks

Tobias

Otium Bluetooth Receiver

Panasonic Ergofit Headphones

Nitize Adhesive Pocket Clip

Henry

The Three Idiots

Links

Weecology Lab

University of Florida

Data Retriever

LG

R

Julia

Open Knowledge Foundation

Frictionless Data Format

Data Weaver

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Coverage.py with Ned Batchelder** Aug 06, 2017
Summary

We write tests to make sure that our code is correct, but how do you make sure the tests are correct? This week Ned Batchelder explains how coverage.py fills that need, how he became the maintainer, and how it works under the hood.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Ned Batchelder about coverage.py, the ubiquitous tool for measuring your test coverage.

Interview

Introductions

How did you get introduced to Python?

What is coverage.py and how did you get involved with the project?

The coverage project has become the de facto standard for measuring test coverage in Python. Why do you think that is?

What is the utility of measuring test coverage?

What are the downsides to measuring test coverage?

One of the notable capabilities that was introduced recently was the plugin for measuring coverage of Django templates. Why is that an important capability and how did you manage to make that work?

How does coverage conduct its measurements and how has that algorithm evolved since you first started work on it?

What are the most challenging aspects of building and maintaining coverage.py?

While I was looking at the bug tracker I was struck by the vast array of contexts in which coverage is used. Do you find it overwhelming trying to support so many operating systems and Python implementations?

What might be added to coverage in the future?

Keep In Touch

@nedbat on Twitter

Website

Picks

Tobias

Org-Journal

Ned

Hypothesis

The Infinite Monkey Cage

Links

edX

Lotus Notes

Zope

Coverage.py

Gareth Rees

Trace in stdlib

Fig Leaf

State Machines

CodeCov

Coveralls

Cobertura

Turing Completeness

Django Templates

Jinja2

Mako

Hy-lang

GCov

Jython

Code Triage Service

Who Tests What

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Yosai with Darin Gordon** Jul 30, 2017
Summary

For any program that is used by more than one person you need a way to control identity and permissions. There are myriad solutions to that problem, but most of them are tied to a specific framework. Yosai is a flexible, general purpose framework for managing role-based access to your applications that has been decoupled from the underlying platform. This week the author of Yosai, Darin Gordon, joins us to talk about why he started it, his experience porting it from Java, and where he hopes to take it in the future.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing
Darin Gordon about Yosai, a security framework for Python applications

Interview

Introductions

How did you get introduced to Python?

What is Yosai and what is the problem that you were trying to solve when you started it?

How does Yosai compare to existing libraries for web frameworks such as Flask-Security or Django Guardian and why might someone choose Yosai instead?

In the documentation it mentions that Yosai is a port of the Apache Shiro framework from Java to Python. What was most difficult about exposing a Pythonic interface while maintaining the core principles of the original?

Authentication and authorization are difficult problem domains and can cause significant issues if they are not implemented in a secure fashion. How do you ensure an appropriate level of quality in Yosai to be confident having people use it?

To start can you describe how the framework is architected and what is involved in integrating it with a project?

Outside of the context of web applications, what are some situations where someone should consider integrating authentication and authorization into their project?

What have been some of the most challenging aspects of building the Yosai project?

Tell us about the Rust extension you wrote earlier this year

What do you have planned for the future of Yosai?

Keep In Touch

Website

GitHub

@darin_gordon on Twitter

Picks

Tobias

Brains On! podcast

Darin

The Asphalt Framework. Asphalt is an asyncio-based microframework for network oriented applications.

Links

Yosai Project Web Page

Github Repo

RBAC

Apache Shiro

TOTP

Pyramid

SOLID

Builder Pattern

POJO

Corey Benfield

Hyper HTTP/2 Library

Passlib

Hugo

MKDocs

YAML

Middleware

IoT

Authz in Rust

PyO3

Snaek

PyCon Canada

PyCascades

JSON Web Tokens

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Moving to MongoDB with Michael Kennedy** Jul 22, 2017
Summary

There are dozens of decisions that need to be made when building an application. Sometimes this can lead to analysis paralysis and prevent you from making progress, so don’t let the perfect be the enemy of the good. This week Michael Kennedy shares his experience with evolving his application architecture when his business needs outgrew his initial designs.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

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Your host as usual is Tobias Macey and today I’m interviewing Mike Kennedy about his work scaling his apps and his business

Interview

Introductions

How did you get introduced to Python?

In some of your recent episodes you have mentioned the work that you did to migrate your applications to run on MongoDB. Can you start by describing the business case for these applications and how you arrived at the initial design?

What was the limiting factor that led you to consider such a drastic shift in how you store and manage your data and what benefits did you gain when the work was complete?

If the issue was with scaling, how did you identify the choke points?

Why go from relational (SQLite) to document (Mongo) instead of what would seem a more obvious choice of a production grade relational engine such as PostGreSQL or MySQL?

Are there any particular synergies that arise from using a document as opposed to a relational store when working with Python and what are some of the main considerations when deciding between them?

What was happening in your business that precipitated the need for this work?

How are you talking to MongoDB from Python? Directly (via pymongo) or ORM-style?

Why did you make that choice?

How well is that working out? Advantages / drawbacks?

In addition to podcasting you have also been working to create a number of successful courses to teach people how to use Python. Is there anything specific to the language that translates into how you design the material?

For anyone who wants to learn more about the benefits and tradeoffs of using a document store with their Python applications, what are some resources that you recommend?

Keep In Touch

Michael

@mkennedy on Twitter

Websites

Talk Python

Python Bytes

Picks

Tobias

Org Mode

Levar Burton Reads

Mike

Newspaper

Robomongo (now Robo 3T)

The Dark Secret at the Heart of AI

Haibike SDURO Cross 4.0

Links

SQLAlchemy

SQLIte

MySQL

PostGreSQL

NoSQL

MongoDB

Database Normalization

Foreign Keys

Document Database

Rollbar

MongoEngine

Mongo Security Checkup

MLab

MongoDB Atlas

MongoDB World

O’Reilly Python Mongo Book

MongoDB For Python Developers

Momentum Dash

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

**Zulip Chat with Tim Abbott** Jul 16, 2017
Summary

In modern work environments the email is being edged out by group chat as the preferred method of communication. The majority of the platforms used are commercial and closed source, but there is one project that is working to change that. Zulip is a project that aims to redefine how effective teams communicate and it is already gaining ground. This week Tim Abbott shares the story of how Zulip got started, how it is built, and why you might want to start using it.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.

I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.

When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.

Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.

Your host as usual is Tobias Macey and today I’m interviewing Tim Abbott about Zulip, a powerful open source group chat platform

Interview

Introductions

How did you get introduced to Python?

What is Zulip and what was the initial inspiration for creating it?

Where does the name come from?

My understanding is that the project was initally intended to be a commercial product. Can you share some of the history of the acquisition by Dropbox and the journey to open sourcing it?

How has your experience at Dropbox influenced the evolution and implementation of the Zulip project?

There are a large number of group chat platforms available, both commercial and open source. How does Zulip differentiate itself from other options such as Slack or Mattermost?

Typically real-time communication is difficult to achieve in a WSGI application. How is Zulip architected to allow for interactive communication?

What have been the most challenging aspects of building and maintaining the Zulip project?

What is involved in installing and running a Zulip server?

For a large installation, what are the options for scaling it out to handle greater load?

There is a large and healthy community that has built up around the Zulip project. What are some of the methods that you and others have used to foster that growth and engagement?

What has been the most unexpected aspect of working on Zulip, whether technically or in terms of the community around it?

What do you have planned for the future of Zulip?

Keep In Touch

Zulip

Chat

@zuliposs on Twitter

Tim

@tabbott3 on Twitter

Website

Picks

Tobias

Lego Mindstorms EV3

Tim

Checklist Manifesto

Tim’s Recipe Wiki

Links

Zulip

Ksplice

Electron

React Native

IFTTT

Zapier

Zephyr

Barn Owl

MyPy

Tornado

Django

Zulip Tornado Documentation

MySQL

PostGreSQL

ElasticSearch

Code Triage

Emoji

Podcast.__init__ Zulip Chat

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Automat State Machines with Glyph Lefkowitz Jul 02, 2017

Summary

The venerable ‘if’ statement is a cornerstone of program flow and busines logic, but sometimes it can grow unwieldy and lead to unmaintainable software. One alternative that can result in cleaner and easier to understand code is a state machine. This week Glyph explains how Automat was created and how it has been used to upgrade portions of the Twisted project.

Preface

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Your host as usual is Tobias Macey and today I’m interviewing Glyph about Automat, a library that provides self-service finite-state machines for the programmer on the go.

Interview

Introductions
How did you get introduced to Python?
What is a state machine and when might you want to use one?
There are a number of libraries available on PyPI that facilitate the creation of state machines. Why did you feel the need to build a new option and how does it differ from what was already available?
Why do you think developers fall into the trap of complicated conditional structures rather than reaching for a state machine?
For someone who wants to integrate Automat into their project how would they go about that and what are some of the gotchas that they should keep in mind?
What do the internals of Automat look like and how did you approach the overall design of the project?
What are some of the more difficult aspects of designing and implementing state machines properly?
What are some of the technical hurdles that you have been faced with in the process of building a library for implementing state machines?
What do you have planned for the future of Automat?
What are some of the most interesting use cases of Automat that you have seen?