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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:
    Build Your Own Personal Data Repository With Nostalgia Feb 04, 2020
    Show notes

    Summary

    The companies that we entrust our personal data to are using that information to gain extensive insights into our lives and habits while not always making those findings accessible to us. Pascal van Kooten decided that he wanted to have the same capabilities to mine his personal data, so he created the Nostalgia project to integrate his various data sources and query across them. In this episode he shares his motivation for creating the project, how he is using it in his day-to-day, and how he is planning to evolve it in the future. If you’re interested in learning more about yourself and your habits using the personal data that you share with the various services you use then listen now to learn more.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Pascal van Kooten about his nostalgia project, a nascent framework for taking control of your personal data

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing your mission with the nostalgia project?
      • How did the topic of personal data management come to be a focus for you?
    • What other options exist for users to be able to collect and manage their own data?
      • What capabilities were lacking in those options that made you feel the need to build Nostalgia?
    • What is your target audience for this set of projects?
    • How are you using Nostalgia in your own life?
      • What are some of the insights that you have been able to gain as a result of integrating your data with Nostalgia?
    • Can you describe the current architecture of the Nostalgia platform and how it has evolved since you began work on it?
      • What are some of the assumptions that you are using to direct the focus of your development and interaction design?
    • What are the minimum number of data sources needed to make this useful?
    • What are some of the challenges that you are facing in collating and integrating different data sources?
    • What are some of the drawbacks of using something like Nostalgia for managing your personal data?
    • What are some of the most interesting/challenging/unexpected aspects of your work on Nostalgia so far?
    • What do you have planned for the future of the project?

    Keep In Touch

    • Website
    • LinkedIn
    • @kootenpv on Twitter
    • kootenpv on GitHub

    Picks

    • Tobias
      • Jumanji: The Next Level
      • Jumanji
    • Pascal
      • Bup

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • timeliner
    • qs_ledger
    • Nostalgia
    • Shrynk
    • Whereami
    • R Language
    • Duck Duck Go
    • Caddy
    • Perkeep
    • Dark Programming Language
    • Pandas
      • Podcast Episode
    • Neo4J
    • Pandas Extension Arrays
      • Podcast Episode
    • Parquet
      • Data Engineering Podcast Episode
    • ElectronJS
    • Zincbase

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

    Simplifying Social Login For Your Web Applications Jan 27, 2020
    Show notes

    Summary

    A standard feature in most modern web applications is the ability to log in or register using accounts that you already own on other sites such as Google, Facebook, or Twitter. Building your own integrations for each service can be complex and time consuming, distracting you from the features that you and your users actually care about. Fortunately the Python social auth library makes it easy to support third party authentication with a large and growing number of services with minimal effort. In this episode Matías Aguirre discusses his motivation for creating the library, how he has designed it to allow for flexibility and ease of use, and the benefits of delegating identity and authentication to third parties rather than managing passwords yourself.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Matías Aguirre about Python social auth and the complexities of third-party authentication

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what the Python social auth project is and your motivation for starting it?
    • Why might someone want to integrate with or rely on a third-party identity provider in their projects?
      • What are some of the tradeoffs or drawbacks of implementing
    • Can you describe the current architecture of the library and how it has evolved since you first began working on it?
    • There are a number of pre-built integrations with different web frameworks in the social auth github organization, but Django is the only one that has seen any commits recently. What are the contributing factors for that state of affairs?
    • There are a number of authentication protocols that you support. What are the common capabilities that they each support and what are some of the more challenging differences between them?
      • How have you implemented the interface for plugging different authentication mechanisms to allow for the variation between them while keeping the library code maintainable?
      • What is involved in adding support for a new authentication provider or protocol?
    • Many times authorization and authentication are conflated or used interchangeably. How does Python social auth address those concerns and what are the limitations of different mechanisms for defining permissions?
    • For someone who is using Python social auth, what is the workflow for integrating it with their application as a consumer?
    • What are some of the most interesting/unexpected/innovative ways that you have seen Python social auth used?
    • What are some of the most interesting/useful/unexpected lessons that you have learned in the process of building and maintaining Python social auth?
    • When is Python social auth more effort than it’s worth?
    • What do you have planned for the future of the project?

    Keep In Touch

    • omab on GitHub
    • Website
    • @linuxaddict on Twitter
    • LinkedIn

    Picks

    • Tobias
      • Joker movie
    • Matías
      • Sanic asynchronous web framework
      • Star Trek Picard TV series

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Python Social Auth
    • Uruguay
    • Django
    • Ruby on Rails
    • MonkeyLearn
    • Social Authentication
    • Django Social Auth
    • Salted and hashed passwords
    • Magic Link Authentication
    • OAuth
    • OpenID
    • SAML
    • FastAPI
    • Sanic
    • ASGI
    • WSGI
    • AsyncIO

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

    Building A Business On Building Data Driven Businesses Jan 20, 2020
    Show notes

    Summary

    In order for an organization to be data driven they need easy access to their data and a simple way of sharing it. Arik Fraimovich built Redash as a way to address that need by connecting to any data source and building attractive dashboards on top of them. In this episode he shares the origin story of the project, his experiences running a business based on open source, and the challenges of working with data effectively.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Arik Fraimovich about Redash, an open source business intelligence platform that helps you make sense of your data.

    Interview

    • Introductions

    • How did you get introduced to Python?

    • Can you start by describing what Redash is and its origin story?

      • What are the primary ways that it is used?

      • The business intelligence market is quite mature and has many commercial and open source projects to choose from. What are the aspects of Redash that have allowed you to be successful?

      • What would you consider to be your closest competitors?

    • What was your background with data before starting on Redash?

      • What are some of the most notable lessons that you have learned about business intelligence since starting the project?
      • How has the landscape for business intelligence and data analysis changed since you began the project?

    • Beyond just accessing data, Redash focuses on enabling visualization of the results. What types of visualizations do you support and how do you support users in choosing the most effective ways to represent the information?

    • What are some of the common challenges that your users and customers encounter when communicating with data?

    • One of the critical aspects of enabling data access in an organization is the ability to collaborate on asking and answering questions. How do you approach that challenge in Redash?

    • How is Redash implemented and how has the overall design and architecture evolved since you first started working on it?

      • How do you manage the complexity of supporting so many different data sources?
      • If you were to start over today, what would you do differently?

    • Beyond the code of Redash, you also have a business around providing it as a hosted service. What are some of the most interesting, challenging, or unexpected lessons that you have learned in the process of building and growing that service?

    • How do you approach the direction and governance of the open source project and balance that against the wants and needs of the community?

    • What are some of the most interesting, innovative, or unexpected ways that you have seen Redash used?

    • When is Redash the wrong platform to use?

    • What do you have planned for the future of the Redash business and project?

    Keep In Touch

    • arikfr on GitHub
    • Website
    • @arikfr on Twitter

    Picks

    • Tobias
      • Data Engineering Podcast
    • Arik
      • Peewee ORM
      • Amazon ECS

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Redash
    • Google App Engine
    • EverythingMe
    • RedShift
    • Metabase
      • Data Engineering Podcast Interview
    • Apache Superset
    • Elasticsearch
      • Data Engineering Podcast Interview
    • Tableau
    • Looker
      • Data Engineering Podcast Interview
    • PowerBI
    • Data Warehouse
    • Data Lake
    • Athena
    • Spark
      • Data Engineering Podcast Interview
    • Redash Funnel Visualization
    • Stephen Few
    • Flask
    • SQLAlchemy
    • Redis
    • PostgreSQL
      • Data Engineering Podcast Interview
    • Celery
    • RQ
    • Tornado
    • Django ORM
    • AngularJS
    • ReactJS
    • NodeJS
    • Redash Query Results Data Source
    • IBM DB2
    • Retool
    • Forest Admin
    • Grafana

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

    Using Deliberate Practice To Level Up Your Python Jan 13, 2020
    Show notes

    Summary

    An effective strategy for teaching and learning is to rely on well structured exercises and collaboration for practicing the material. In this episode long time Python trainer Reuven Lerner reflects on the lessons that he has learned in the 5 years since his first appearance on the show, how his teaching has evolved, and the ways that he has incorporated more hands-on experiences into his lessons. This was a great conversation about the benefits of being deliberate in your approach to ongoing education in the field of technology, as well as having some helpful references for ways to keep your own skills sharp.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m pleased to welcome back Reuven Lerner to talk about the benefits of deliberate practice for learning and improving programming skills

    Interview

    • Introductions

    • How did you get introduced to Python?

    • In your first appearance on the show back in episode 2 we talked about your experience as a Python trainer. How has your teaching style evolved in the past 5 years?

      • How has the focus and scope of your training changed in that time period?

    • What have you found to be some of the most helpful and effective tactics in your training?

    • From the learner perspective, what are some strategies that you recommend for retaining information, particularly in the context of gaining technical knowledge?

    • In-person training vs. real-time online training vs. recorded videos, advantages and disadvantages of each.

    • Blended learning, in which we combine aspects of the above

      • Beyond in-person training, what are your preferred methods for learning and maintaining new skills?

    • What is deliberate practice and how does it differ from the habits that many of us might default to?

      • What are some of the resources that you provide for students of your trainings for practicing?
      • What are some of the outside resources which you have found most useful or effective?

    Keep In Touch

    • Website
    • Blog
    • @reuvenmlerner on Twitter

    Picks

    • Tobias
      • The Manager’s Path by Camille Fournier
    • Reuven
      • Lab Rats: How Silicon Valley Made Work Miserable For The Rest Of Us by Dan Lyons

    Links

    • Deliberate Practice
    • Reuven On Episode 2
    • CGI == Common Gateway Interface
    • Language Phrasebook
    • Jupyter Notebook
    • Walrus Operator
      • PyCon 2019 Presentation
    • Python Bytes
    • List Comprehension
    • Weekly Python Exercise
    • Python Morsels
    • PyBites
    • Practice Your Python
    • Python Workout book by Reuven Lerner
    • PyTest
    • Brian Okken

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

    Checking Up On Python's Role in DevOps Jan 06, 2020
    Show notes

    Summary

    Python has been part of the standard toolkit for systems administrators since it was created. In recent years there has been a shift in how servers are deployed and managed, and how code gets released due to the rise of cloud computing and the accompanying DevOps movement. The increased need for automation and speed of iteration has been a perfect use case for Python, cementing its position as a powerful tool for operations. In this episode Moshe Zadka reflects on his experiences using Python in a DevOps context and the book that he wrote on the subject. He also discusses the difference in what aspects of the language are useful as an introduction for system operators and where they can continue their learning.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Moshe Zadke about his recent book DevOps In Python

    Interview

    • Introductions
    • How did you get introduced to Python?
    • How did you gain experience in managing systems with Python?
    • What is DevOps?
    • What makes Python a good fit for managing systems?
    • What is unique to the devops/sysadmin domain in terms of what software is used and what aspects of the language are useful?
    • What are the main ways that Python is used for managing servers and infrastructure?
    • What are some of the most notable changes in the ways that Python is used for server administration over the past several years?
    • How has Python3 impacted the lives of operators?
    • What was your motivation for writing a book about Python focused specifically on DevOps and server automation?
    • What are some of the tools that have been replaced in your own workflow over the years?

    Keep In Touch

    • Website
    • LinkedIn
    • @moshezadka on Twitter

    Picks

    • Tobias
      • SaltStack
        • Podcast Episode
    • Moshe
      • Automat
        • Podcast Episode

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • DevOps In Python
    • SurveyMonkey
    • Twisted Episode
    • DevOps
    • B=hive
    • CI/CD
    • Amoeba OS
    • Python OS module
    • Requests
    • Canary Deployments
    • Post Mortem
    • Bash Shell
    • Z Shell
    • Linux
    • Unix
    • AWS
    • Boto3
    • GitHub
    • GitLab
    • Debian
    • Ubuntu
    • CentOS
    • Pip
    • Poetry
    • Pipenv
    • pip-tools
    • dh-virtualenv
    • Docker
    • Hyneck Schlaweck Presentation On Building Docker Images
    • Ansible
    • SaltStack
    • Chef
    • Puppet

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

    Python's Built In IDE Isn't Just Sitting IDLE Dec 23, 2019
    Show notes

    Summary

    One of the first challenges that new programmers are faced with is figuring out what editing environment to use. For the past 20 years, Python has had an easy answer to that question in the form of IDLE. In this episode Tal Einat helps us explore its history, the ways it is used, how it was built, and what is in store for its future. Even if you have never used the IDLE editor yourself, it is still an important piece of Python’s strength and history, and this conversation helps to highlight why that is.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Tal Einat about the IDLE editor for Python, it’s history, and what is in store for its future

    Interview

    • Introductions
    • How did you get introduced to Python?
    • For anyone who hasn’t used it, can you start by explaining what IDLE is?
    • IDLE has been part of the standard library for Python for quite some time now. What was the motivation for adding it to the core of Python?
      • How has the evolution of our computing environment changed the motivation for maintaining IDLE and the use cases that it is most beneficial for?
    • What are the benefits of including a basic editor in the default distribution of Python?
      • What are some of the ways in which it is often used?
      • What are the limiting factors that lead users to other IDEs or text editors?
    • What role do you think IDLE has played in the growth of Python?
    • What was your motivation for getting involved as a Python contributor and working on the implementation of IDLE?
    • How is IDLE implemented and what are some of the ways that it has evolved since its initial introduction?
      • How well has the code for IDLE aged as new features and capabilities are added to the language?
    • What are some of the integration points available for extending IDLE?
    • What are some of the most interesting or innovative ways that you have seen IDLE used and extended?
    • What is planned for the future of the IDLE module?

    Keep In Touch

    • LinkedIn
    • @TalEinat on Twitter
    • taleinat on GitHub

    Picks

    • Tobias
      • Mr. Robot
    • Tal
      • Captain Fantastic
      • The Lesson To Unlearn article by Paul Graham

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • IDLE
    • FullProof
    • Israel
      • Mandatory Military Service
    • Eric Idle
    • Monty Python
    • Visual Studio
    • IDLE-fork
    • Vi
    • Emacs
    • Sublime Text
    • Visual Studio Code
    • REPL == Read Eval Print Loop
    • Tcl/Tk
    • Tkinter
    • RPC == Remote Procedure Call
    • IDLEx
    • VPython
      • Podcast Episode
    • Python Turtle
    • SVN (Subversion)

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

    Riding The Rising Tides Of Python Dec 16, 2019
    Show notes

    Summary

    The past two decades have seen massive growth in the language, community, and ecosystem of Python. The career of Pete Fein has occurred during that same period and his use of the language has paralleled some of the major shifts in focus that have occurred. In this episode he shares his experiences moving from a trader writing scripts, through the rise of the web, to the current renaissance in data. He also discusses how his engagement with the community has evolved, why he hasn’t needed to use any other languages in his career, and what he is keeping an eye on for the future.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Pete Fein about his voyage on the rising tide of Python

    Interview

    • Introductions
    • How did you get introduced to Python?
    • I understand that you have used Python exclusively in your professional life. What other languages have you been exposed to and taken inspiration from?
    • What are some of the projects that you have been involved with which you are most proud of?
    • How has the community and your involvement with it changed over the years?
      • In your experience, how has the growth in the size and breadth of the community impacted its accessibility to newcomers?
    • You have been using Python and participating in the community for quite some time now, and there have been significant changes in both within that period. What are some of the most significant technological shifts that you have noticed and been a part of?
      • How have those shifts influenced the direction of your career?
    • As you have moved through the different phases of your career with different areas of focus, what are some of the aspects of the work which have remained constant?
      • What have been the biggest differences across the different problem domains?
    • What are some of the aspects of the language or its ecosystem which you feel are lacking or don’t get enough attention?
    • What are some of the industry trends which you are keeping a close eye on and how do you anticipate them influencing the direction of the community and your career in the upcoming years?

    Keep In Touch

    • Consulting Website
    • Personal Website
    • @wearpants on Twitter
    • LinkedIn
    • wearpants on GitHub

    Picks

    • Tobias
      • Matomo Analytics
    • Pete
      • FastAPI
      • PyDantic

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Chicago
    • Scheme
    • Structure and Interpretation of Computer Programs
    • David Beazley
      • Podcast Episode
    • Twiggy logging library
    • Jesse Noller
    • Log4J
    • Debian
    • RedHat
    • StructLog
    • Elliot
      • Podcast Episode
    • Logbook
    • Armin Ronacher
      • Podcast Episode
    • Pittsburgh Python Meetup
    • Boltons library
    • Elixir
    • ChiPy Chicago Python user group
    • Subversion
    • Ruby On Rails
    • Django
    • Data Engineering
    • Data Engineering Podcast
    • Internet of Things
    • Pittsburgh
    • Artificial Pancreas Project
    • Eric Holscher
    • Read The Docs
      • Podcast Episode
    • Circuit Playground Express
    • CircuitPython
      • Podcast Episode
    • Rust Language
    • PyOhio
    • PyGotham

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

    Debugging Python Projects With PySnooper Dec 09, 2019
    Show notes

    Summary

    Debugging is a painful but necessary practice in software development. The tools that are available in Python range from the built-in debugger, to tools integrated with your coding environment, to the trusty print function. In this episode Ram Rachum describes his work on PySnooper and how it can be used to speed up your problem solving in complex or legacy applications.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, or running your build servers, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Ram Rachum about PySnooper, an alternative approach to debugging your python projects

    Interview

    • Introductions
    • How did you get introduced to Python?
    • How do developers normally debug their code, and what need does PySnooper address that isn’t addressed by the established methods?
    • What is the workflow for using PySnooper for investigating or debugging a project? (This will probably be answered in the answer to the question above)
    • What are some of the pieces of information that it surfaces and how do they aid the developer in directing their investigation?
    • What were some of the projects that you were testing it with and how did they influence the direction that you took PySnooper?
    • Can you describe how PySnooper is implemented and some of the ways that it has evolved since you first began working on it?
    • What are some of the initial goals that you had for the project which you have since abandoned as either not useful or too challenging to implement?
    • What are some of the edge cases or technical challenges that you have encountered while working on PySnooper, either in Python itself or in the tool?
    • There is another project called Snoop which builds on top of your work on PySnooper to add some extra functionality and developer ergonomics. What, if anything, was your reaction to it and how has it influenced your work on PySnooper?
    • One of the notable aspects of your work on PySnooper is the amount of attention that it garnered shortly after you published it. How has that visibility affected the long-term popularity and use of PySnooper?
    • What have been some of the most interesting, unexpected, or difficult aspects of creating, maintaining, and promoting PySnooper?
    • What do you have planned for the future of the project?

    Keep In Touch

    • cool-RR on GitHub
    • Personal Website
    • Consulting Website

    Picks

    • Tobias
      • PyCon US
        • Call for proposals
        • Registration
    • Ram
    • Nonviolent communication

    Links

    • PySnooper

    • Ram’s Python workshops

    • The PyWeb-IL meetup

    • BlueVine’s career page Submit your CV to Ram’s email mailto:ram@rachum.com

    • Tel Aviv Israel

    • Paul Graham

    • Y Combinator startup accelerator

    • Wing IDE

    • PyCharm

    • sys.settrace

    • Python f_trace

    • coverage.py

      • Podcast.init Interview

    • PEP == Python Enhancement Proposal

      • Podcast Episode

    • snoop project

    • Alex Hall

    • pdb

    • pudb

    • pdb++

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

    Making Complex Software Fun And Flexible With Plugin Oriented Programming Dec 03, 2019
    Show notes

    Summary

    Starting a new project is always exciting because the scope is easy to understand and adding new features is fun and easy. As it grows, the rate of change slows down and the amount of communication necessary to introduce new engineers to the code increases along with the complexity. Thomas Hatch, CTO and creator of SaltStack, didn’t want to accept that as an inevitable fact of software, so he created a new paradigm and a proof-of-concept framework to experiment with it. In this episode he shares his thoughts and findings on the topic of plugin oriented programming as a way to build and scale complex projects while keeping them fun and flexible.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Thomas Hatch about his work on the POP library and how he is using plugin oriented programming in his work at SaltStack

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by giving your definition of Plugin Oriented Programming and your thoughts on what benefits it provides?
    • You created the POP library as a framework for enabling developers to incorporate this pattern into their own projects. What capabilities does that framework provide and what was your motivation for creating it?
      • How has your work on Salt influenced your thinking on how to implement plugins for software projects?
      • How does POP fit into the future of the SaltStack project?
    • What are some of the advanced patterns or paradigms that the POP model allows for?
    • Can you describe how the POP library itself is implemented and some of the ways that its design has evolved since you first began experimenting with it?
      • What are some of the languages or libraries that you have looked at for inspiration in your design and philosophy around this development pattern?
    • For someone who is building a project on top of POP what does their workflow look like and what are some of the up-front design considerations they should be thinking of?
    • How do you define and validate the contract exposed by or expected from a plugin subsystem?
    • One of the interesting capabilities that you highlight in the documentation is the concept of merging applications. What are your thoughts on the challenges that an engineer might face when merging library or microservice applications built with POP into a single deployable artifact?
      • What would be involved in going the other direction to split a single application into independently runnable microservices?
    • When extracting common functionality from a group of existing applications, what are the relative merits of creating a plugin sybsystem vs writing a library?
    • How does the system design of a POP application impact the available range of communication patterns for software and the teams building it?
    • What are some antipatterns that you anticipate for teams building their projects on top of POP?
    • In the documentation you mention that POP is just an example implementation of the broader pattern and that you hope to see other languages and developer communities adopt it. What are some of the barriers to adoption that you foresee?
    • What are some of the limitations of POP or cases where you would recommend against following this paradigm?
    • What are some of the most interesting, innovative, or unexpected ways that you have seen POP used?
    • What have been some of the most interesting, unexpected, or challenging aspects of building POP?
    • What do you have planned for the future of the POP library, or any applications where you plan to employ this pattern?

    Keep In Touch

    • thatch45 on GitHub
    • @thatch45 on Twitter

    Picks

    • Tobias
      • The Man In The High Castle TV series
    • Thomas
      • Jack Ryan TV Series

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Episode 1
    • POP
    • SaltStack
    • Ruby
    • Microservices
    • Linus Torvalds
    • SaltConf
    • SaltStack Thorium
    • Salt Beacons
    • Salt Reactors
    • Salt Grains
    • Idem
    • AsyncIO
    • Nim
    • OCaml
    • Julia
    • LLVM
    • Object Oriented Programming
    • Go Language
    • Rust
    • RBAC == Role Based Access Control
    • The Mythical Man Month
    • Linux Kernel
    • Heist
    • Umbra
    • Flow Programming
    • Magic The Gathering

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

    Faster And Safer Software Development With Feature Flags Nov 26, 2019
    Show notes

    Summary

    Any software project that is worked on or used by multiple people will inevitably reach a point where certain capabilities need to be turned on or off. In this episode Pete Hodgson shares his experience and insight into when, how, and why to use feature flags in your projects as a way to enable that practice. In addition to the simple on and off controls for certain logic paths, feature toggles also allow for more advanced patterns such as canary releases and A/B testing. This episode has something useful for anyone who works on software in any language.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Pete Hodgson about the concept of feature flags and how they can benefit your development workflow

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what a feature flag is?
      • What was your first experience with feature flags and how did it affect your approach to software development?
    • What are some of the ways that feature flags are used?
      • What are some antipatterns that you have seen for teams using feature flags?
    • What are some of the alternative development practices that teams will employ to achieve the same or similar outcomes to what is possible with feature flags?
    • Can you describe some of the different approaches to implementing feature flags in an application?
      • What are some of the common pitfalls or edge cases that teams run into when building an in-house solution?
      • What are some useful considerations when making a build vs. buy decision for a feature toggling service?
    • What are some of the complexities that get introduced by feature flags for mantaining application code over the long run?
    • What have you found to be useful or effective strategies for cataloging and documenting feature toggles in an application, particularly if they are long lived or for open source applications where there is no institutional context?
    • Can you describe some of the lifecycle considerations for feature flags, and how the design, implementation, or use of them changes for short-lived vs long-lived use cases?
    • What are some cases where the overhead of implementing and maintaining a feature flag infrastructure outweighs the potential benefit?
    • What advice or references do you recommend for anyone who is interested in using feature flags for their own work?

    Keep In Touch

    • Website
    • @ph1 on Twitter
    • moredip on GitHub

    Picks

    • Tobias
      • Circuit Playground Express
        • CircuitPython Episode
    • Pete
      • Accelerate by Nicole Forsgren, Jez Humble, and Gene Kim

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Perl
    • Ruby
    • Django
    • Feature Flag
    • Pete’s Blog Post On Feature Flags
    • Thoughtworks
    • Continuous Delivery
    • Continuous Delivery Book
    • Trunk Based Development
    • Branch By Abstraction
    • Technical Debt
    • Strategy Pattern
    • Polymorphism

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

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