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    The Python Podcast.__init__

    The podcast about Python and the people who make it great

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    Copyright: © 2023 Boundless Notions, LLC.

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    Latest Episodes:
    From Simple Script To Beautiful Web Application With Streamlit Nov 18, 2019
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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

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