Cover image of The Python Podcast.__init__
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Rank #149 in Technology category

Technology

The Python Podcast.__init__

Updated 5 days ago

Rank #149 in Technology category

Technology
Read more

The podcast about Python and the people who make it great

Read more

The podcast about Python and the people who make it great

iTunes Ratings

50 Ratings
Average Ratings
34
7
8
0
1

Really interesting podcast

By Alex123456789098765431 - Apr 11 2019
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I started listening to this podcast a few weeks ago, and it has been great!

very good

By dingneigorfai - Mar 12 2017
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another awesome python podcast, very informative

iTunes Ratings

50 Ratings
Average Ratings
34
7
8
0
1

Really interesting podcast

By Alex123456789098765431 - Apr 11 2019
Read more
I started listening to this podcast a few weeks ago, and it has been great!

very good

By dingneigorfai - Mar 12 2017
Read more
another awesome python podcast, very informative
Cover image of The Python Podcast.__init__

The Python Podcast.__init__

Updated 5 days ago

Rank #149 in Technology category

Read more

The podcast about Python and the people who make it great

Rank #1: Docker Best Practices For Python In Production

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Summary

Docker is a useful technology for packaging and deploying software to production environments, but it also introduces a different set of complexities that need to be understood. In this episode Itamar Turner-Trauring shares best practices for running Python workloads in production using Docker. He also explains some of the security implications to be aware of and digs into ways that you can optimize your build process to cut down on wasted developer time. If you are using Docker, thinking about using it, or just heard of it recently then it is worth your time to listen and learn about some of the cases you might not have considered.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • To connect with the startups that are shaping the future and take advantage of the opportunities that they provide, check out Angel List where you can invest in innovative business, find a job, or post a position of your own. Sign up today at pythonpodcast.com/angel and help support this show.
  • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Itamar Turner-Trauring about what you need to know about running Python workloads in Docker

Interview

  • Introductions
  • How did you get introduced to Python?
  • For anyone who is unfamiliar with it, can you describe what Docker is and the benefits that it can provide?
  • What was your motivation for dedicating so much time and energy to the specific area of using Docker for Python production usage?
  • What are some of the common issues that developers and operations engineers run into when dealing with Docker and its build system?
  • What are some of the issues that are specific to Python that you have run into when using Docker?
  • How does the ecosystem for Python in containers compare to other languages that you are familiar with?
  • What are some of the security issues that engineers are likely to run into when using some of the advice and pre-existing containers that are publicly available?
  • One of the issues that you call out is the speed of container builds. What are some of the contributing factors that lead to such slow packaging times?
    • Can you talk through some of the aspects of multi-layer packages and useful ways to take proper advantage of them?
  • There have been some recent projects that attempt to work around the shortcomings of the Dockerfile itself. What are your thoughts on that overall effort and any specific tools that you have experimented with?
  • When is Docker the wrong choice for a production environment?
    • What are some useful alternatives to Docker, for Python specifically and for software distribution in general that you have had good luck with?

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

Jul 29 2019
44 mins
Play

Rank #2: Classic Computer Science For Pythonistas

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Summary

Software development is a career that attracts people from all backgrounds, and Python in particular helps to make it an approachable occupation. Because of the variety of paths that can be taken it is becoming increasingly common for practitioners to bypass the traditional computer science education. In this episode David Kopec discusses some of the classic problems that he has found most useful to understand in his work as a professor and practitioner of software engineering. He shares his motivation for writing the book "Classic Computer Science Problems In Python", the practical approach that he took, and an overview of how the contents can be used in your day-to-day work.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing David Kopec about his recent book "Classic Computer Science Problems In Python"

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by discussing your motivation for creating this book and the subject matter that it covers?
    • How do you define a "classic" computer science problem and what was your criteria for selecting the specific cases that you included in the book?
  • What are your favorite features of the Python language, and which of them did you learn as part of the process of writing the examples for this book?
  • Which classes of problems have you found to be most difficult for your readers and students to master?
    • Which do you consider to be most relevant/useful to professional software engineers?
  • I was pleasantly surprised to see introductory aspects of artificial intelligence included in the subject matter that you covered. How did you approach the challenge of making the underlying principles accessible to readers who don’t necessarily have a background in the related fields of mathematics?
  • What are some of the most interesting or unexpected changes that you had to make in the process of adapting your examples from Swift to Python in order to make them appropriately idiomatic?
  • By aiming for an intermediate audience you free yourself of the need to incorporate fundamental aspects of programming, but there can be a wide variety of experiences at that level of experience. How did you approach the challenge of making the text accessible while still being accurate and engaging?
  • What are some of the resources that you would recommend to readers who would like to continue learning about computer science after completing your book?

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Book Discount And Giveaway

  • Use code podinit19 to get 40% off all Manning products

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

Feb 04 2019
47 mins
Play

Rank #3: Django, Channels, And The Asynchronous Web with Andrew Godwin

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Summary

Once upon a time the web was a simple place with one main protocol and a predictable sequence of request/response interactions with backend applications. This is the era when Django began, but in the intervening years there has been an explosion of complexity with new asynchronous protocols and single page Javascript applications. To help bridge the gap and bring the most popular Python web framework into the modern age Andrew Godwin created Channels. In this episode he explains how the first version of the asynchronous layer for Django applications was created, how it has changed in the jump to version 2, and where it will go in the future. Along the way he also discusses the challenges of async development, his work on designing ASGI as the spiritual successor to WSGI, and how you can start using all of this in your own projects today.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at podcastinit.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Andrew Godwin about Django Channels 2.x and the ASGI specification for modern, asynchronous web protocols

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start with an overview of the problem that Channels is aiming to solve?
  • Asynchronous frameworks have existed in Python for a long time. What are the tradeoffs in those frameworks that would lead someone to prefer the combination of Django and Channels?
  • For someone who is familiar with traditional Django or working on an existing application, what are the steps involved in integrating Channels?
  • Channels is a project that you have been working on for a significant amount of time and which you recently re-architected. What were the shortcomings in the 1.x release that necessitated such a major rewrite?
    • How is the current system architected?
  • What have you found to be the most challenging or confusing aspects of managing asynchronous web protocols both as an author of Channels/ASGI and someone building on top of them?
    • While reading through the documentation there were mentions of the synchronous nature of the Django ORM. What are your thoughts on asynchronous database access and how important that is for future versions of Django and Channels?
  • As part of your implementation of Channels 2.x you introduced a new protocol for asynchronous web applications in Python in the form of ASGI. How does this differ from the WSGI standard and what was your process for developing this specification?
    • What are your hopes for what the Python community will do with ASGI?
  • What are your plans for the future of Channels?
  • What are some of the most interesting or unexpected uses of Channels and/or ASGI?

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

Sep 24 2018
41 mins
Play

Rank #4: Understanding Machine Learning Through Visualizations with Benjamin Bengfort and Rebecca Bilbro

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Summary

Machine learning models are often inscrutable and it can be difficult to know whether you are making progress. To improve feedback and speed up iteration cycles Benjamin Bengfort and Rebecca Bilbro built Yellowbrick to easily generate visualizations of model performance. In this episode they explain how to use Yellowbrick in the process of building a machine learning project, how it aids in understanding how different parameters impact the outcome, and the improved understanding among teammates that it creates. They also explain how it integrates with the scikit-learn API, the difficulty of producing effective visualizations, and future plans for improvement and new features.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Rebecca Bilbro and Benjamin Bengfort about Yellowbrick, a scikit extension to use visualizations for assisting with model selection in your data science projects.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you describe the use case for Yellowbrick and how the project got started?
  • What is involved in visualizing scikit-learn models?
    • What kinds of information do the visualizations convey?
    • How do they aid in understanding what is happening in the models?
  • How much direction does yellowbrick provide in terms of knowing which visualizations will be helpful in various circumstances?
  • What does the workflow look like for someone using Yellowbrick while iterating on a data science project?
  • What are some of the common points of confusion that your students encounter when learning data science and how has yellowbrick assisted in achieving understanding?
  • How is Yellowbrick iplemented and how has the design changed over the lifetime of the project?
  • What would be required to integrate with other visualization libraries and what benefits (if any) might that provide?
    • What about other ML frameworks?
  • What are some of the most challenging or unexpected aspects of building and maintaining Yellowbrick?
  • What are the limitations or edge cases for yellowbrick?
  • What do you have planned for the future of yellowbrick?
  • Beyond visualization, what are some of the other areas that you would like to see innovation in how data science is taught and/or conducted to make it more accessible?

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

Jun 17 2018
55 mins
Play

Rank #5: Destroy All Software With Gary Bernhardt

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Summary

Many developers enter the market from backgrounds that don’t involve a computer science degree, which can lead to blind spots of how to approach certain types of problems. Gary Bernhardt produces screen casts and articles that aim to teach these principles with code to make them approachable and easy to understand. In this episode Gary discusses his views on the state of software education, both in academia and bootcamps, the theoretical concepts that he finds most useful in his work, and some thoughts on how to build better software.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • Your host as usual is Tobias Macey and today I’m interviewing Gary Bernhardt about teaching and learning Python in the current software landscape

Interview

  • Introductions
  • How did you get introduced to Python?
  • As someone who makes a living from teaching aspects of programming what is your view on the state of software education?
    • What are some of the ways that we as an industry can improve the experience of new developers?
    • What are we doing right?
  • You spend a lot of time exploring some of the fundamental aspects of programming and computation. What are some of the lessons that you have learned which transcend software languages?
    • Utility of graphs in understanding software
    • Mechanical sympathy
  • What are the benefits of ‘from scratch’ tutorials that explore the steps involved in building simple versions of complex topics such as compilers or web frameworks?

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

Apr 30 2018
52 mins
Play

Rank #6: Software Architecture For Developers with Neal Ford

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Summary

Whether it is intentional or accidental, every piece of software has an existing architecture. In this episode Neal Ford discusses the role of a software architect, methods for improving the design of your projects, pitfalls to avoid, and provides some resources for continuing to learn about how to design and build successful systems.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • A few announcements before we start the show:
    • There is still time to register for the O’Reilly Software Architecture Conference in New York. Use the link podcastinit.com/sacon-new-york to register and save 20%
    • If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.
  • With many thanks to O’Reilly Media, I have two items to give away. To sign up you just need to subscribe to the mailing list at podcastinit.com and you will have the chance to win either a copy of Neal’s book, Building Evolutionary Architectures, or a Bronze ticket to the O’Reilly Software Architecture Conference in New York. I will be picking the winners on February 21st.
  • Your host as usual is Tobias Macey and today I’m interviewing Neal Ford about principles of software architecture for developers

Interview

  • Introductions
  • How did you get introduced to Python?
  • A majority of your work has been focused on software architectures and how that can be used to facilitate delivery of working systems. Can you start by giving a high level description of what software architecture is and how it fits into the overall development process?
  • One of the difficulties that arise in long-lived projects is that technical debt accrues to the point that forward progress stagnates due to fear that any changes will cause the system to stop functioning. What are some methods that developers can use to either guard against that eventuality, or address it when it happens?
  • What are some of the broad categories of architectural patterns that developers should be aware of?
  • Are there aspects of the language that a system or application is being implemented in which influence the style of architecture that is commonly used?
  • What are some architectural anti-patterns that you have found to be the most commonly occurring?
  • Software is useless if there is no way to deliver it to the end user. What are some of the challenges that are most often overlooked by engineering teams and how do you solve for them?
  • Beyond the purely technological aspects, what other elements of software production and delivery are necessary for a successful architecture?
  • What resources can you recommend for someone who is interested in learning more about software architecture, whether as an individual contributor or in a full time architect role?

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

Feb 18 2018
50 mins
Play

Rank #7: Infection Monkey Vulnerability Scanner with Daniel Goldberg

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Summary

How secure are your servers? The best way to be sure that your systems aren’t being compromised is to do it yourself. In this episode Daniel Goldberg explains how you can use his project Infection Monkey to run a scan of your infrastructure to find and fix the vulnerabilities that can be taken advantage of. He also discusses his reasons for building it in Python, how it compares to other security scanners, and how you can get involved to keep making it better.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at podcastinit.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Daniel Goldberg about Infection Monkey, an open source system breach simulation tool for evaluating the security of your network

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is infection monkey and what was the reason for building it?
    • What was the reasoning for building it in Python?
    • If you were to start over today what would you do differently?
  • Penetration testing is typically an endeavor that requires a significant amount of knowledge and experience of security practices. What have been some of the most difficult aspects of building an automated vulnerability testing system?
    • How does a deployed instance keep up to date with recent exploits and attack vectors?
  • How does Infection Monkey compare to other tools such as Nessus and Nexpose?
  • What are some examples of the types of vulnerabilities that can be discovered by Infection Monkey?
  • What kinds of information can Infection Monkey discover during a scan?
    • How does that information get reported to the user?
    • How much security experience is necessary to understand and address the findings in a given report generated from a scan?
  • What techniques do you use to ensure that the simulated compromises can be safely reverted?
  • What are some aspects of network security and system vulnerabilities that Infection Monkey is unable to detect and/or analyze?
  • For someone who is interested in using Infection Monkey what are the steps involved in getting it set up?
    • What is the workflow for running a scan?
    • Is Infection Monkey intended to be run continuously, or only with the interaction of an operator?
  • What are your plans for the future of Infection Monkey?

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

Sep 03 2018
34 mins
Play

Rank #8: Wes McKinney's Career In Python For Data Analysis

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Summary

Python has become one of the dominant languages for data science and data analysis. Wes McKinney has been working for a decade to make tools that are easy and powerful, starting with the creation of Pandas, and eventually leading to his current work on Apache Arrow. In this episode he discusses his motivation for this work, what he sees as the current challenges to be overcome, and his hopes for the future of the industry.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Check out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI
  • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Your host as usual is Tobias Macey and today I’m interviewing Wes McKinney about his contributions to the Python community and his current projects to make data analytics easier for everyone

Interview

  • Introductions
  • How did you get introduced to Python?
  • You have spent a large portion of your career on building tools for data science and analytics in the Python ecosystem. What is your motivation for focusing on this problem domain?
  • Having been an open source author and contributor for many years now, what are your current thoughts on paths to sustainability?
  • What are some of the common challenges pertaining to data analysis that you have experienced in the various work environments and software projects that you have been involved in?
    • What area(s) of data science and analytics do you find are not receiving the attention that they deserve?
  • Recently there has been a lot of focus and excitement around the capabilities of neural networks and deep learning. In your experience, what are some of the shortcomings or blind spots to that class of approach that would be better served by other classes of solution?
  • Your most recent work is focused on the Arrow project for improving interoperability across languages. What are some of the cases where a Python developer would want to incorporate capabilities from other runtimes?
    • Do you think that we should be working to replicate some of those capabilities into the Python language and ecosystem, or is that wasted effort that would be better spent elsewhere?
  • Now that Pandas has been in active use for over a decade and you have had the opportunity to get some space from it, what are your thoughts on its success?
    • With the perspective that you have gained in that time, what would you do differently if you were starting over today?
  • You are best known for being the creator of Pandas, but can you list some of the other achievements that you are most proud of?
  • What projects are you most excited to be working on in the near to medium future?
  • What are your grand ambitions for the future of the data science community, both in and outside of the Python ecosystem?
  • Do you have any parting advice for active or aspiring data scientists, or resources that you would like to recommend?

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

Mar 18 2019
51 mins
Play

Rank #9: Entity Extraction, Document Processing, And Knowledge Graphs For Investigative Journalists with Friedrich Lindenberg

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Summary

Investigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode today to get a $20 credit and launch a new server in under a minute.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at podcastinit.com/chat
  • Registration for PyCon US, the largest annual gathering across the community, is open now. Don’t forget to get your ticket and I’ll see you there!
  • Your host as usual is Tobias Macey and today I’m interviewing Friedrich Lindenberg about Aleph, a tool to perform entity extraction across documents and structured data

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what Aleph is and how the project got started?
  • What is investigative journalism?
    • How does Aleph fit into their workflow?
    • What are some other tools that would be used alongside Aleph?
    • What are some ways that Aleph could be useful outside of investigative journalism?
  • How is Aleph architected and how has it evolved since you first started working on it?
  • What are the major components of Aleph?
    • What are the types of documents and data formats that Aleph supports?
  • Can you describe the steps involved in entity extraction?
    • What are the most challenging aspects of identifying and resolving entities in the documents stored in Aleph?
  • Can you describe the flow of data through the system from a document being uploaded through to it being displayed as part of a search query?
  • What is involved in deploying and managing an installation of Aleph?
  • What have been some of the most interesting or unexpected aspects of building Aleph?
  • Are there any particularly noteworthy uses of Aleph that you are aware of?
  • What are your plans for the future of Aleph?

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

Nov 19 2018
39 mins
Play

Rank #10: Pandas Extension Arrays with Tom Augspurger

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Summary

Pandas is a swiss army knife for data processing in Python but it has long been difficult to customize. In the latest release there is now an extension interface for adding custom data types with namespaced APIs. This allows for building and combining domain specific use cases and alternative storage mechanisms. In this episode Tom Augspurger describes how the new ExtensionArray works, how it came to be, and how you can start building your own extensions today.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Tom Augspurger about the extension interface for Pandas data frames and the use cases that it enables

Interview

  • Introductions
  • How did you get introduced to Python?
  • Most people are familiar with Pandas, but can you describe at a high level the new extension interface?
    • What is the story behind the implementation of this functionality?
    • Prior to this interface what was the option for anyone who wanted to extend Pandas?
  • What are some of the new data types that are available as external packages?
    • What are some of the unique use cases that they enable?
  • How is the new interface implemented within Pandas?
  • What were the most challenging or difficult aspects of building this new functionality?
  • What are some of the more interesting possibilities that you are aware of for new extension types?
  • What are the limitations of the interface for libraries that add new array functionality?
  • What is the next major change or improvement that you would like to add in Pandas?

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

Jun 04 2018
33 mins
Play

Rank #11: The Masonite Web Framework With Joe Mancuso

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Summary

Masonite is an ambitious new web framework that draws inspiration from many other successful projects in other languages. In this episode Joe Mancuso, the primary author and maintainer, explains his goal of unseating Django from its position of prominence in the Python community. He also discusses his motivation for building it, how it is architected, and how you can start using it for your own projects.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at podcastinit.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Joe Mancuso about Masonite, the modern and developer centric python web framework.

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Masonite and what was the motivation for creating it?
    • How does it fit in the current landscape of Python web frameworks?
  • Why might someone choose to use Masonite over Python frameworks?
    • If someone isn’t already decided on using Python, what are some reasons that they might choose Masonite over frameworks in other languages?
  • Can you describe the framework architecture and how it has evolved over the lifetime of the project?
  • What are some examples of projects that have been built with Masonite and what aspects of the framework are they leveraging?
  • For someone who is starting a new project with Masonite what are some of the concepts that they should be familiar with?
    • What is their workflow for starting their project?
    • How does that workflow change when working with an existing application?
  • What are some of the plans that you have for the future of Masonite?

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

Aug 13 2018
43 mins
Play

Rank #12: Algorithmic Trading In Python Using Open Tools And Open Data

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Summary

Algorithmic trading is a field that has grown in recent years due to the availability of cheap computing and platforms that grant access to historical financial data. QuantConnect is a business that has focused on community engagement and open data access to grant opportunities for learning and growth to their users. In this episode CEO Jared Broad and senior engineer Alex Catarino explain how they have built an open source engine for testing and running algorithmic trading strategies in multiple languages, the challenges of collecting and serving currrent and historical financial data, and how they provide training and opportunity to their community members. If you are curious about the financial industry and want to try it out for yourself then be sure to listen to this episode and experiment with the QuantConnect platform for free.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
  • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • The Python Software Foundation is the lifeblood of the community, supporting all of us who want to run workshops and conferences, run development sprints or meetups, and ensuring that PyCon is a success every year. They have extended the deadline for their 2019 fundraiser until June 30th and they need help to make sure they reach their goal. Go to pythonpodcast.com/psf today to make a donation. If you’re listening to this after June 30th of 2019 then consider making a donation anyway!
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Jared Broad and Alex Catarino about QuantConnect, a platform for building and testing algorithmic trading strategies on open data and cloud resources

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what QuantConnect is and how the business got started?
  • What is your mission for the company?
  • I know that there are a few other entrants in this market. Can you briefly outline how you compare to the other platforms and maybe characterize the state of the industry?
  • What are the main ways that you and your customers use Python?
  • For someone who is new to the space can you talk through what is involved in writing and testing a trading algorithm?
  • Can you talk through how QuantConnect itself is architected and some of the products and components that comprise your overall platform?
  • I noticed that your trading engine is open source. What was your motivation for making that freely available and how has it influenced your design and development of the project?
  • I know that the core product is built in C# and offers a bridge to Python. Can you talk through how that is implemented?
    • How do you address latency and performance when bridging those two runtimes given the time sensitivity of the problem domain?
  • What are the benefits of using Python for algorithmic trading and what are its shortcomings?
    • How useful and practical are machine learning techniques in this domain?
  • Can you also talk through what Alpha Streams is, including what makes it unique and how it benefits the users of your platform?
  • I appreciate the work that you are doing to foster a community around your platform. What are your strategies for building and supporting that interaction and how does it play into your product design?
  • What are the categories of users who tend to join and engage with your community?
  • What are some of the most interesting, innovative, or unexpected tactics that you have seen your users employ?
  • For someone who is interested in getting started on QuantConnect what is the onboarding process like?
    • What are some resources that you would recommend for someone who is interested in digging deeper into this domain?
  • What are the trends in quantitative finance and algorithmic trading that you find most exciting and most concerning?
  • What do you have planned for the future of QuantConnect?

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

Jun 17 2019
50 mins
Play

Rank #13: Thonny: The IDE For Beginning Programmers with Aivar Annamaa

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Summary

Learning to program is a rewarding pursuit, but is often challenging. One of the roadblocks on the way to proficiency is getting a development environment installed and configured. In order to simplify that process Aivar Annamaa built Thonny, a Python IDE designed for beginning programmers. In this episode he discusses his initial motivations for starting Thonny and how it helps newcomers to Python learn and understand how to write software.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • For complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit podcastinit.com to subscribe to the show, sign up for the newsletter, and read the show notes.
  • Your host as usual is Tobias Macey and today I’m interviewing Aivar Annamaa about Thonny, a Python IDE for beginning programmers

Interview

  • Introductions
  • How did you get introduced to Python?
  • What was your motivation for building an IDE focused on beginning programmers?
  • What are the features of Thonny that make it easier for users to understand what is happening in their programs?
  • What have you found to be the types of issues that users most frequently struggle with and how does Thonny help overcome those gaps in understanding?
  • What kinds of tutorials or supporting material have you found to be the most useful for teaching students the principles that they need to be able to take advantage of the environment that Thonny provides?
  • How is Thonny built and what have been the most challenging aspects of writing an IDE in Python?
  • What are some of the interface design choices that you have made to avoid confusing or overwhelming beginning users?
  • Once a user becomes more proficient in Python is there a point where it no longer makes sense to continue using Thonny for development?
  • I noticed that Thonny has an plugin architecture and there is an extension for interacting with the BBC micro:bit. What are some of the other types of extensions that you would like to see built for Thonny?

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

Mar 18 2018
29 mins
Play

Rank #14: The Past, Present, and Future of Deep Learning In PyTorch

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Summary

The current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. One of the most popular frameworks for building deep learning applications is PyTorch, in large part because of their focus on ease of use. In this episode Adam Paszke explains how he started the project, how it compares to other frameworks in the space such as Tensorflow and CNTK, and how it has evolved to support deploying models into production and on mobile devices.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Check out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI
  • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Your host as usual is Tobias Macey and today I’m interviewing Adam Paszke about PyTorch, an open source deep learning platform that provides a seamless path from research prototyping to production deployment

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what deep learning is and how it relates to machine learning and artificial intelligence?
  • Can you explain what PyTorch is and your motivation for creating it?
    • Why was it important for PyTorch to be open source?
  • There is currently a large and growing ecosystem of deep learning tools built for Python. Can you describe the current landscape and how PyTorch fits in relation to projects such as Tensorflow and CNTK?
    • What are some of the ways that PyTorch is different from Tensorflow and CNTK, and what are the areas where these frameworks are converging?
  • How much knowledge of machine learning, artificial intelligence, or neural network topologies are necessary to make use of PyTorch?
    • What are some of the foundational topics that are most useful to know when getting started with PyTorch?
  • Can you describe how PyTorch is architected/implemented and how it has evolved since you first began working on it?
    • You recently reached the 1.0 milestone. Can you talk about the journey to that point and the goals that you set for the release?
  • What are some of the other components of the Python ecosystem that are most commonly incorporated into projects based on PyTorch?
  • What are some of the most novel, interesting, or unexpected uses of PyTorch that you have seen?
  • What are some cases where PyTorch is the wrong choice for a problem?
  • What is the process for incorporating these new techniques and discoveries into the PyTorch framework?
    • What are the areas of active research that you are most excited about?
  • What are some of the most interesting/useful/unexpected/challenging lessons that you have learned in the process of building and maintaining PyTorch?
  • What do you have planned for the future of PyTorch?

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

Mar 10 2019
42 mins
Play

Rank #15: Kenneth Reitz

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Summary

Kenneth Reitz has contributed many things to the Python community, including projects such as Requests, Pipenv, and Maya. He also started the community written Hitchhiker’s Guide to Python, and serves on the board of the Python Software Foundation. This week he talks about his career in the Python community and digs into some of his current work.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Kenneth Reitz about his career in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • An overarching theme of your open source projects is the idea of making them “For Humans”. Can you elaborate on how that came to be a focus for you and how that informs the way that you design and write your code?

  • What are the projects that you are most proud of and which do you think have had the biggest impact on the Python community?
    A: Requests, Hitchhiker’s Guide to Python, and Pipenv (yet to come to full fruition).

  • Which projects have you authored which are relatively unknown but you think people would benefit from using more often?
    A: Maya: Datetime for Humans, and Records: SQL for Humans.

  • Outside of the code that you write, what are some of your personal missions for the software industry in general and the Python community in particular?
    A: I consider myself a “spiritual alchemist”, which means “transformation of dark into light”. I seek to do “the great work”, in however in manifests, outside of the programming world, as well as within it.

  • What do you think is the biggest gap in the tool chest for Python developers?
    A: I seek to fill all the voids that I see, and I’ve done my best to do that to the best of my ability. I think we have a lot of work to do in the area of single-file executable builds (a-la Go).

  • What are your ambitions for future projects?
    A: At the moment, I have no current plans for future projects, but I’m sure something will come along at some point

  • If you weren’t working with Python what would you be doing instead?
    A: I’d have a lot less money and I’d be a lot less fufilled.

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

Dec 10 2017
42 mins
Play

Rank #16: How To Include Redis In Your Application Architecture

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Summary

The Redis database recently celebrated its 10th birthday. In that time it has earned a well-earned reputation for speed, reliability, and ease of use. Python developers are fortunate to have a well-built client in the form of redis-py to leverage it in their projects. In this episode Andy McCurdy and Dr. Christoph Zimmerman explain the ways that Redis can be used in your application architecture, how the Python client is built and maintained, and how to use it in your projects.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
  • Your host as usual is Tobias Macey and today I’m interviewing Andy McCurdy and Christoph Zimmerman about the Redis database, and some of the various ways that it is used by Python developers

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what Redis is and how you got involved in the project?
  • How does the redis-py project relate to the Redis database and what motivated you to create the Python client?
  • What are some of the main use cases that Redis enables?
  • Can you describe how Redis-py is implemented and some of the primitives that it provides for building applications on top of?
    • How do the release cycles of redis-py and the Redis database relate to each other?
    • How closely does redis-py match the features of the Redis database?
    • What are some of the convenience methods or features that you have added to make the client more Pythonic?
  • Redis is often used as a key/value cache for web applications, in some cases replacing Memcached. What are the characteristics of Redis that lend themselves well to this purpose?
    • What are some edge cases or gotchas that users should be aware of?
  • What are some of the common points of confusion or difficulties when storing and retrieving values in Redis?
  • What have been some of the most challenging aspects of building and maintaining the Redis Python client?
  • What are some of the anti-patterns that you have seen around how developers build on top of Redis?
  • What are some of the most interesting or unexpected ways that you have seen Redis used?
  • What are some of the least used or most misunderstood features of Redis that you think developers should know about?
  • What are some of the recent and near-future improvements or features in Redis that you are most excited by?

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

Mar 04 2019
1 hour 1 min
Play

Rank #17: Orange: Visual Data Mining Toolkit with Janez Demšar and Blaž Zupan

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Summary

Data mining and visualization are important skills to have in the modern era, regardless of your job responsibilities. In order to make it easier to learn and use these techniques and technologies Blaž Zupan and Janez Demšar, along with many others, have created Orange. In this episode they explain how they built a visual programming interface for creating data analysis and machine learning workflows to simplify the work of gaining insights from the myriad data sources that are available. They discuss the history of the project, how it is built, the challenges that they have faced, and how they plan on growing and improving it in the future.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Blaž Zupan and Janez Demsar about Orange, a toolbox for interactive machine learning and data visualization in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Orange and what was your motivation for building it?
  • Who is the target audience for this project?
  • How is the graphical interface implemented and what kinds of workflows can be implemented with the visual components?
  • What are some of the most notable or interesting widgets that are available in the catalog?
  • What are the limitations of the graphical interface and what options do user have when they reach those limits?
  • What have been some of the most challenging aspects of building and maintaining Orange?
  • What are some of the most common difficulties that you have seen when users are just getting started with data analysis and machine learning, and how does Orange help overcome those gaps in understanding?
  • What are some of the most interesting or innovative uses of Orange that you are aware of?
  • What are some of the projects or technologies that you consider to be your competition?
  • Under what circumstances would you advise against using Orange?
  • What are some widgets that you would like to see in future versions?
  • What do you have planned for future releases of Orange?

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

Dec 31 2017
49 mins
Play

Rank #18: Jake Vanderplas: Data Science For Academic Research

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Summary

Jake Vanderplas is an astronomer by training and a prolific contributor to the Python data science ecosystem. His current role is using Python to teach principles of data analysis and data visualization to students and researchers at the University of Washington. In this episode he discusses how he got started with Python, the challenges of teaching best practices for software engineering and reproducible analysis, and how easy to use tools for data visualization can help democratize access to, and understanding of, data.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Jake Vanderplas about data science best practices, and applying them to academic sciences

Interview

  • Introductions
  • How did you get introduced to Python?
  • How has your astronomy background informed and influenced your current work?
  • In your work at the University of Washington, what are some of the most common difficulties that students face when learning data science?
    • How does that list differ for professional scientists who are learning how to apply data science to their work?
  • Where is the tooling still lacking in terms of enabling consistent and repeatable workflows?
  • One of the projects that you are spending time on now is Altair, which is a library for generating visualizations from Pandas dataframes. How does that work factor into your teaching?
  • What are some of the most novel applications of data science that you have been involved with?
  • What are some of the trends in data analysis that you are most excited for?

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

Dec 17 2017
49 mins
Play

Rank #19: Rasa: Build Your Own AI Chatbot with Joey Faulkner

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Summary

With the proliferation of messaging applications, there has been a growing demand for bots that can understand our wishes and perform our bidding. The rise of artificial intelligence has brought the capacity for understanding human language. Combining these two trends gives us chatbots that can be used as a new interface to the software and services that we depend on. This week Joey Faulkner shares his work with Rasa Technologies and their open sourced libraries for understanding natural language and how to conduct a conversation. We talked about how the Rasa Core and Rasa NLU libraries work and how you can use them to replace your dependence on API services and own your data.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Joey Faulkner about Rasa Core and Rasa NLU for adding conversational AI to your projects.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining the goals of Rasa as a company and highlighting the projects that you have open sourced?
  • What are the differences between the Rasa Core and Rasa NLU libraries and how do they relate to each other?
  • How does the interaction model change when going from state machine driven bots to those which use Rasa Core and what capabilities does it unlock?
  • How is Rasa NLU implemented and how has the design evolved?
  • What are the motivations for someone to use Rasa core or NLU as a library instead of available API services such as wit.ai, LUIS, or Dialogflow?
  • What are some of the biggest challenges in gathering and curating useful training data?
  • What is involved in supporting multiple languages for an application using Rasa?
  • What are the biggest challenges that you face, past, present, and future, building and growing the tools and platform for Rasa?
  • What would be involved for projects such as OpsDroid, Kalliope, or Mycroft to take advantage of Rasa and what benefit would that provide?
  • On the comparison page for the hosted Rasa platform it mentions a feature of collaborative model training, can you describe how that works and why someone might want to take advantage of it?
  • What are some of the most interesting or unexpected uses of the Rasa tools that you have seen?
  • What do you have planned for the future of Rasa?

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

Nov 04 2017
49 mins
Play

Rank #20: Event Sourcing with John Bywater

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Summary

The way that your application handles data and the way that it is represented in your database don’t always match, leading to a lot of brittle abstractions to reconcile the two. In order to reduce that friction, instead of overwriting the state of your application on every change you can log all of the events that take place and then render the current state from that sequence of events. John Bywater joins me this week to discuss his work on the Event Sourcing library, why you might want to use it in your applications, and how it can change the way that you think about your data.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports the show on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing John Bywater about event sourcing, an architectural approach to make your data layer easier to scale and maintain.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing the concept of event sourcing and the benefits that it provides?
  • What is the event sourcing library and what was your reason for starting it?
  • What are some of the reasons that someone might not want to implement an event sourcing approach in their persistence layer?
  • Given that you are storing a record for each event that occurs on a domain object, how does that affect the amount of storage necessary to support an event sourced application?
  • What is the impact on performance and latency from an end user perspective when the application is using event sourcing to render the current state of the system?
  • What does the internal architecture and design of your library look like and how has that evolved over time?
  • In the case where events are delivered out of order, how can you ensure that the present view of an object is reflected accurately?
  • For someone who wants to incorporate an event sourcing design into an existing application, how would they do that?
  • How do you manage schema changes in your domain model when you need to reconstruct present state from the beginning of an objects event sequence?
  • What are some of the most interesting uses of event sourcing that you have seen?
  • What are some of the features or improvements that you have planned for the future of you event sourcing library?

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

Oct 15 2017
1 hour 8 mins
Play

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