Data Science

Jake Vanderplas: Data Science For Academic Research - Episode 140

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 [email protected])
  • 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

Surprise! Recommendation Algorithms with Nicolas Hug - Episode 135

Summary

A relevant and timely recommendation can be a pleasant surprise that will delight your users. Unfortunately it can be difficult to build a system that will produce useful suggestions, which is why this week’s guest, Nicolas Hug, built a library to help with developing and testing collaborative recommendation algorithms. He explains how he took the code he wrote for his PhD thesis and cleaned it up to release as an open source library and his plans for future development on it.

Preface

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

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Surprise and what was your motivation for creating it?
  • What are the most challenging aspects of building a recommender system and how does Surprise help simplify that process?
  • What are some of the ways that a user or company can bootstrap a recommender system while they accrue data to use a collaborative algorithm?
  • What are some of the ways that a recommender system can be used, outside of the typical ecommerce example?
  • Once an algorithm has been deployed how can a user test the accuracy of the suggestions?
  • How is Surprise implemented and how has it evolved since you first started working on it?
  • What have been the most difficult aspects of building and maintaining Surprise?
  • competitors?
  • What are the attributes of the system that can be modified to improve the relevance of the recommendations that are provided?
  • For someone who wants to use Surprise in their application, what are the steps involved?
  • What are some of the new features or improvements that you have planned for the future of Surprise?

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  • Tobias
    • Silk profiler for Django

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

Cauldron with Scott Ernst - Episode 111

Summary

The notebook format that has been exemplified by the IPython/Jupyter project has gained in popularity among data scientists. While the existing formats have proven their value, they are still susceptible with difficulties in collaboration and maintainability. Scott Ernst created the Cauldron notebook to be testable, production ready, and friendly to version control. This week we explore the capabilities, use cases, and architecture of Cauldron and how you can start using it today!

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Scott Ernst about Cauldron, a new notebook format built with software engineering best practices in mind.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what Cauldron is and what problem you were trying to solve when you created it?
  • In the documentation it mentions that you can use any editor for creating the content of the notebook. Can you describe a typical workflow of authoring the various files and cells and viewing the output?
  • How does Cauldron compare to the Jupyter notebook format and what factors would lead someone to choose one over the other?
  • Does Cauldron support running languages other than Python? If not then what would be involved in adding that capability?
  • Cauldron notebooks support unit tests of individual cells. How does that process work and what are the limitations?
  • The option for running the notebook in the context of a task workflow tool appears to be a powerful capability. What are some of the considerations that are necessary when writing a notebook to be run in that manner?
  • What are some of the most interesting or unexpected projects that you have seen people using Cauldron for?
  • What do you have planned for the future of Cauldron?

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

MetPy: Taming The Weather With Python - Episode 100

Summary

What’s the weather tomorrow? That’s the question that meteorologists are always trying to get better at answering. This week the developers of MetPy discuss how their project is used in that quest and the challenges that are inherent in atmospheric and weather research. It is a fascinating look at dealing with uncertainty and using messy, multidimensional data to model a massively complex system.

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 has donated to the show. Your contributions help us make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Your host as usual is Tobias Macey and today I’m interviewing Ryan May, Sean Arms, and John Leeman about MetPy, a collection of tools and notebooks for analyzing meteorological data in Python.

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is MetPy and what is the problem that prompted you to create it?
  • Can you explain the problem domain for Meteorology and how it compares to other domains such as the physical sciences?
  • How do you deal with the inherent uncertainty of atmospheric and weather data?
  • What are some of the data sources and data formats that a meteorologist works with?
  • To what degree is machine learning or artificial intelligence employed when modelling climate and local weather patterns?
  • The MetPy documentation has a number of examples of how to use the library and a number of them produce some fairly complex plots and graphs. How prevalent is the need to interact with meteorological data visually to properly understand what it is trying to tell you?
  • I read through your developer guide and watched your SciPy talk about development automation in MetPy. My understanding is that individuals with a pure science background tend to eschew formal code styles and software engineering practices so I’m curious what your experience has been when interacting with your user community.
  • What are some of the interesting innovations in weather science that you are looking forward to?

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

Pandas with Jeff Reback - Episode 98

Summary

Pandas is one of the most versatile and widely used tools for data manipulation and analysis in the Python ecosystem. This week Jeff Reback explains why that is, how you can use it to make your life easier, and what you can look forward to in the months to come.

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 has donated to the show. Your contributions help us make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • When you’re writing Python you need a powerful editor to automate routine tasks, maintain effective development practices, and simplify challenging things like refactoring. Our sponsor JetBrains delivers the perfect solution for you in the form of PyCharm, providing a complete set of tools for productive Python, Web, Data Analysis and Scientific development, available in 2 editions. The free and open-source PyCharm Community Edition is perfect for pure Python coding. PyCharm Professional Edition is a full-fledged tool, designed for professional Python, Web and Data Analysis developers. Today JetBrains is offering a 3-month free PyCharm Professional Edition individual subscription. Don’t miss this chance to use the best-in-class tool with intelligent code completion, automated testing, and integration with modern tools like Docker – go to <www.pythonpodcast.com/pycharm> and use the promo code podcastinit during checkout.
  • Visit the site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Your host as usual is Tobias Macey and today I’m interviewing Jeff Reback about Pandas, the swiss army knife of data analysis in Python.

Interview

  • Introductions
  • How did you get introduced to Python?
  • To start off, what is Pandas and what is its origin story?
    • How did you get involved in the project’s development?
  • For someone who is just getting started with Pandas what are the fundamental ideas and abstractions in the library that are necessary to understand how to use it for working with data?
  • Pandas has quite an extensive API and I noticed that the most recent release includes a nice cheat sheet. How do you balance the power and flexibility of such an expressive API with the usability issues that can be introduced by having so many options of how to manipulate the data?
  • There is a strong focus for use in science and data analytics, but there are a number of other areas where Pandas is useful as well. What are some of the most interesting or unexpected uses that you have seen or heard of?
  • What are some of the biggest challenges that you have encountered while working on Pandas?
  • Do you find the constraint of only supporting two dimensional arrays to be limiting, or has it proven to be beneficial for the success of pandas?
  • What’s coming for pandas? Pandas 2.0!

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

SpaCy with Matthew Honnibal - Episode 87

Summary

As the amount of text available on the internet and in businesses continues to increase, the need for fast and accurate language analysis becomes more prominent. This week Matthew Honnibal, the creator of SpaCy, talks about his experiences researching natural language processing and creating a library to make his findings accessible to industry.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • You’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • Your host as usual is Tobias Macey and today I’m interviewing Matthew Honnibal about SpaCy and Explosion.AI

Interview with Matthew Honnibal

  • Introductions
  • How did you get introduced to Python?
  • Can you start by sharing what SpaCy is and what problem you were trying to solve when you created it?
  • Another project for natural language processing that has been part of the Python ecosystem for a number of years is the Natural Language Tool Kit (NLTK). How does SpaCy differ from the NLTK and are there any cases where that would be the better choice?
  • How much knowledge of NLP and computational linguistics is necessary to be able to use SpaCy?
  • What does the internal design and architecture of SpaCy look like and what are the biggest challenges associated with its development to date and into the future?
  • One of the projects that you have built around SpaCy which I think is really cool and caught my attention when I first found your project is the displaCy visualization tool. Can you explain what that is and why you think it is important?
  • What are some kinds of applications where SpaCy would be useful which might not be obvious candidates for it?
  • Why is speed such an important focus for an NLP library?
  • One of the ways that you have been able to gain a speed boost is through releasing the GIL and allowing for true parallelism via Cython. How have you managed to ensure that this doesn’t lead to data races and program failures?
  • Building on the success of SpaCy you founded a company called Explosion AI. Can you explain what your goals are for this endeavor and the kinds of services that you are offering?
  • What are some of the most interesting uses of SpaCy that you have seen?
  • What do you have planned for the future of SpaCy?

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

HouseCanary with Travis Jungroth - Episode 83

Summary

Housing is something that we all have experience with, but many don’t understand the complexities of the market. This week Travis Jungroth talks about how HouseCanary uses data to make the business of real estate more transparent.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • You’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • Your host as usual is Tobias Macey and today I’m interviewing Travis Jungrot about HouseCanary, a company that is using Python and machine learning to help you make real estate decisions.

Interview with Travis Jungroth

  • Introductions
  • How did you get introduced to Python?
  • What is HouseCanary and what problem is it trying to solve?
  • Who are your customers?
  • Is it possible to get data and predictions at the neighborhood level for individual homebuyers to use in their purchasing decisions?
  • What do you use for your data sources and how do you validate their accuracy?
    • What are some of the sources of bias that are present in your data and what strategies are you using to account for them?
  • Can you describe where Python is leveraged in your environment?
  • What are some of the biggest software design and architecture challenges that you are facing while you continue to grow?
  • What are the areas where Python isn’t the right choice and which languages are used in its place?
  • What are the biggest predictors of future value for residential real estate?
  • Can your system be used to identify risks associated with the housing market, similar to those seen in the bubble that triggered the 2008 economic failure?
  • What are some of the most interesting details that you have discovered about real estate and housing markets while working with HouseCanary?

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

PsychoPy with Jonathan Peirce - Episode 76

Summary

We’re delving into the complex workings of your mind this week on Podcast.init with Jonathan Peirce. He tells us about how he started the PsychoPy project and how it has grown in utility and popularity over the years. We discussed the ways that it has been put to use in myriad psychological experiments, the inner workings of how to design and execute those experiments, and what is in store for its future.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • Hired is sponsoring us this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.
  • Once you land a job you can check out our other sponsor Linode for running your awesome new Python apps. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • You want to make sure your apps are error-free so give our last sponsor, Rollbar, a look. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • By leaving a review on iTunes, or Google Play Music it becomes easier for other people to find us.
  • Join our community! Visit discourse.pythonpodcast.com to help us grow and connect our wonderful audience.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Jonathan Peirce about PsychoPy, an open source application for the presentation and collection of stimuli for psychological experimentation

Interview with Jonathan Peirce

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you start by telling us what PsychoPy is and how the project got started? – Tobias
  • How does PsychoPy compare feature wise against some of the proprietary alternatives? – Chris
  • In the documentation you mention that this project is useful for the fields of psychophysics, cognitive neuroscience and experimental psychology. Can you provide some insight into how those disciplines differ and what constitutes an experiment? – Tobias
  • Do you find that your users who have no previous formal programming training come up to speed with PsychoPy quickly? What are some of the challenges there? -Chris
  • Can you describe the internal architecture of PsychoPy and how you approached the design? – Tobias
  • How easy is it to extend PsychoPy with new types of stimulus? – Chris
  • What are some interesting challenges you faced when implementing PsychoPy? – Chris
  • I noticed that you support a number of output data formats, including pickle. What are some of the most popular analysis tools for users of PsychoPy? – Tobias
    • Have you investigated the use of the new Feather library? – Tobias
  • How is data input typically managed? Does PsychoPy support automated readings from test equipment or is that the responsibility of those conducting the experiment? – Tobias
  • What are some of the most interesting experiments that you are aware of having been conducted using PsychoPy? – Chris
  • While reading the docs I found the page describing the integration with the OSF (Open Science Framework) for sharing and validating an experiment and the collected data with other members of the field. Can you explain why that is beneficial to the researchers and compare it with other options such as GitHub for use within the sciences? – Tobias
  • Do you have a roadmap of features that you would like to add to PsychoPy or is it largely driven by contributions from practitioners who are extending it to suit their needs? – Tobias

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

Python at Zalando - Episode 74

Summary

Open source has proven its value in many ways over the years. In many companies that value is purely in terms of consuming available projects and platforms. In this episode Zalando describes their recent move to creating and releasing a number of their internal projects as open source and how that has benefited their business. We also discussed how they are leveraging Python and a couple of the libraries that they have published.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • Rollbar is also sponsoring us this week. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.
  • Hired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Jie Bao and João Santos about their use of Python at Zalando

Interview with Zalando

  • Introductions
  • How did you get introduced to Python? – Tobias
  • Can you start by telling us a bit about what Zalando does and some of the technologies that you use? – Tobias
  • What role does Python play in your environment? – Tobias
  • Is the use of Python for a particular project governed by any particular operational guidelines or is it largely a matter of developer choice? – Tobias
  • Given that you have such a variety of platforms to support, how do you architect your systems to keep them easy to maintain and reason about? – Tobias
  • One of the projects that you have open sourced is Connexion. Can you explain a bit about what that is and what it is used for at Zalando? – Tobias
  • What made you choose to standardize on Swagger/OpenAPI vs RAML or some of the other API standards? – Tobias
  • Did Connexion start its life as open source or was it extracted from another project? – Tobias
  • ExpAn is another one of your projects that is written in Python. What do you use that for? – Tobias
  • Can you describe the internal implementation of ExpAn and what it takes to get it set up? – Tobias
  • Given the potential complexity of and the need for statistical significance in the data for proper A/B testing, how did you design ExpAn to satisfy those requirements? – Tobias
  • Given the laws in Germany around digital privacy, were there any special considerations that needed to be made in the collection strategy for the data that gets used in ExpAn? – Tobias

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

Dave Beazley - Episode 72

Summary

Dave Beazley has been using and teaching Python since the early days of the language. He has also been instrumental in spreading the gospel of asynchronous programming and the many ways that it can improve the performance of your programs. This week I had the pleasure of speaking with him about his history with the language and some of his favorite presentations and projects.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit!
  • Hired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Dave Beazley about his career with Python

Interview with Dave Beazley

  • Introductions
  • How did you get introduced to Python? – Tobias
  • How has Python and its community helped to shape your career? – Tobias
  • What are some of the major themes that you have focused on in your work? – Tobias
  • One of the things that you are known for is doing live-coding presentations, many of which are fairly advanced. What is it about that format that appeals to you? – Tobias
    • What are some of your favorite stories about a presentation that didn’t quite go as planned? – Tobias
  • You have given a large number of talks at various conferences. What are some of your favorites? – Tobias
  • What impact do you think that asynchronous programming will have on the future of the Python language and ecosystem? – Tobias
  • Are there any features that you see in other languages that you would like to have incorporated in Python? – Tobias
  • On the about page for your website you talk about some of the low-level code and hardware knowledge that you picked up by working with computers as a kid. Do you think that people who are getting started with programming now are missing out by not getting exposed to the kinds of hardware and software that was present before computing became mainstream?
  • You have had the opportunity to work on a large variety of projects, both on a hobby and professional level. What are some of your favorites? – Tobias
  • What is it about Python that has managed to hold your interest for so many years? – Tobias

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