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    News

    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:
    When, Why, and How To Use Web Scraping In A Nutshell Sep 01, 2020
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Supporting The Full Lifecycle Of Machine Learning Projects With Metaflow Aug 04, 2020
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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
    Show notes

    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

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