Linode

Building Scalable Ecommerce Sites On Saleor - Episode 205

Summary

Ecommerce is an industry that has largely faded into the background due to its ubiquity in recent years. Despite that, there are new trends emerging and room for innovation, which is what the team at Mirumee focuses on. To support their efforts, they build and maintain the open source Saleor framework for Django as a way to make the core concerns of online sales easy and painless. In this episode Mirek Mencel and Patryk Zawadzki discuss the projects that they work on, the current state of the ecommerce industry, how Saleor fits with their technical and business strategy, and their predictions for the near future of digital sales.

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 [email protected])
  • 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 what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. 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. 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 Mirek Mencel and Patryk Zawadzki about their work at Mirumee, building ecommerce applications in Python, based on their open source framework Saleor

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing the types of projects that you work on at Mirumee and how the company got started?
  • There are a number of libraries and frameworks that you build and maintain. What is your motivation for providing these components freely and how does that play into your overall business strategy?
  • The most substantial project that you maintain is Saleor. Can you describe what it is and the story behind its creation?
    • How does it compare to other ecommerce implementations in the Python space?
    • If someone is agnostic to language and web framework, what would make them choose Saleor over other options that would be available to them?
  • What are some of the most challenging aspects of building a successful ecommerce platform?
    • How do the technical needs of an ecommerce site differ as it grows from small to medium and large scale?
  • Which components of an online store are often overlooked?
  • One of the common features of ecommerce sites that can drive substantial revenue is a well-built recommender system. What are some best practice strategies that you have discovered during your client work?
  • What are some projects that you have seen built with Saleor that were particular interesting, innovative, or unexpected?
  • What are your predictions for the future of the ecommerce industry?
  • What do you have planned for the future of the Saleor framework and the Mirumee business?

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

A Quick Python Check-in With Naomi Ceder - Episode 204

Summary

Naomi Ceder was fortunate enough to learn Python from Guido himself. Since then she has contributed books, code, and mentorship to the community. Currently she serves as the chair of the board to the Python Software Foundation, leads an engineering team, and has recently completed a new draft of the Quick Python Book. In this episode she shares her story, including a discussion of her experience as a technical author and a detailed account of the role that the PSF plays in supporting and growing the community.

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 [email protected])
  • 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 what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. 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. 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 Naomi Ceder about her career and contributions in the Python community

Interview

  • Introductions
  • How did you get introduced to Python?
  • How are you using Python in your current day-to-day?
  • You have been working with Python for a long time at this point, and you have become very involved in supporting and growing the community. What is your motivation for dedicating so much of your time and energy into work that isn’t directly related to paying the bills?
  • You have been the chair of the PSF for a few years now. What are your responsibilities in that position?
  • What do you find to be the most under-rated, misunderstood, or overlooked activities of the PSF?
    • How much of the success of the Python language and its community can be attributed to the presence and support of the PSF?
  • In addition to the work you do with the PSF, other community activities, and your day job, you have also written the 2nd and 3rd editions of the Quick Python Book. Can you give a synopsis of what the book covers and the audience that it is intended for?
  • In the process of writing the book and updating it between revisions, what are some of the features of the language or standard library that you discovered or learned more about which you have been able to use in your work?
  • What are some of the other language communities that you have been involved with and what lessons have you learned from them that you would like to see reflected in Python?
  • What are some of the other projects that you have been involved with that you are most proud of, whether technical or otherwise?
  • What are you most excited about in the near to medium future?

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

Wes McKinney's Career In Python For Data Analysis - Episode 203

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 [email protected])
  • 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

The Past, Present, and Future of Deep Learning In PyTorch - Episode 202

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 [email protected])
  • 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

How To Include Redis In Your Application Architecture - Episode 201

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 [email protected])
  • 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

Marshmallow Data Validation Library - Episode 200

Summary

Any time that your program needs to interact with other systems it will have to deal with serializing and deserializing data. To prevent duplicate code and provide validation of the data structures that your application is consuming Steven Loria created the Marshmallow library. In this episode he explains how it is built, how to use it for rendering data objects to various serialization formats, and some of the interesting and unique ways that it is incorporated into other 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 [email protected])
  • 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 the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th, both run by our friends at O’Reilly Media. Go to pythonpodcast.com/stratacon and pythonpodcast.com/aicon to register today and get 20% off
  • Your host as usual is Tobias Macey and today I’m interviewing Steven Loria about Marshmallow, a Python serialization library that is agnostic to your framework and object mapper of choice

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what Marshmallow is and the history of the project?
    • What are some of the capabilities that make it unique from other similar projects in the Python ecosystem?
  • What are some of the main use cases for schematized serialization and deserialization?
  • Can you walk through how a user would get started with Marshmallow, particularly for complex or nested schemas?
  • Can you describe how Marshmallow is implemented?
    • How has that design evolved since you first began working on it?
    • How have the changes in the Python language and ecosystem impacted the requirements and use cases for Marshmallow?
  • What are some of the most interesting or unexpected ways that you have seen Marshmallow used?
  • What have been some of the most interesting, complex, or challenging aspects of building the Marshmallow project and community?
    • What are lessons you’ve learned from maintaining marshmallow?
  • What have been some of the benefits and drawbacks of keeping Marshmallow agnostic to any frameworks or object mappers?
  • What are some of the edge cases that users of Marshmallow should be aware of?
  • What are some of the little-known features of Marshmallow that you find most useful?
  • What do you have planned for the future of Marshmallow?

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

Unpacking The Python Toolkit For Chaos Engineering - Episode 199

Summary

Chaos engineering is the practice of injecting failures into your production systems in a controlled manner to identify weaknesses in your applications. In order to build, run, and report on chaos experiments Sylvain Hellegouarch created the Chaos Toolkit. In this episode he explains his motivation for creating the toolkit, how to use it for improving the resiliency of your systems, and his plans for the future. He also discusses best practices for building, running, and learning from your own experiments.

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 [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.
  • 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 Sylvain Hellegouarch about Chaos Toolkit, a framework for building and automating chaos engineering experiments

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what Chaos Engineering is?
  • What is the Chaos Toolkit and what motivated you to create it?
    • How does it compare to the Gremlin platform?
  • What is the workflow for using Chos Toolkit to build and run an experiment?
    • What are the best practices for building a useful experiment?
    • Once you have an experiment created, how often should it be executed?
  • When running an experiment, what are some strategies for identifying points of failure, particularly if they are unexpected?
    • What kinds of reporting and statistics are captured during a test run?
  • Can you describe how Chaos Toolkit is implemented and how it has evolved since you began working on it?
  • What are some of the most challenging aspects of ensuring that the experiments run via the Chaos Toolkit are safe and have a reliable rollback available?
  • What have been some of the most interesting/useful/unexpected lessons that you have learned in the process of building and maintaining the Chaos Toolkit project and community?
  • What do you have planned for the future of the project?

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

Computational Musicology For Python Programmers - Episode 198

Summary

Music is a part of every culture around the world and throughout history. Musicology is the study of that music from a structural and sociological perspective. Traditionally this research has been done in a manual and painstaking manner, but the advent of the computer age has enabled an increase of many orders of magnitude in the scope and scale of analysis that we can perform. The music21 project is a Python library for computer aided musicology that is written and used by MIT professor Michael Scott Cuthbert. In this episode he explains how the project was started, how he is using it personally, professionally, and in his lectures, as well as how you can use it for your own exploration of musical analysis.

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 [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.
  • 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 Michael Cuthbert about music21, a toolkit for computer aided musicology

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what computational musicology is?
  • What is music21 and what motivated you to create it?
    • What are some of the use cases that music21 supports, and what are some common requests that you purposefully don’t support?
  • How much knowledge of musical notation, structure, and theory is necessary to be able to work with music21?
  • Can you talk through a typical workflow for doing analysis of one or more pieces of existing music?
    • What are some of the common challenges that users encounter when working with it (either on the side of Python or musicology/musical theory)?
    • What about for doing exploration of new musical works?
  • As a professor at MIT, what are some of the ways that music21 has been incorporated into your classroom?
    • What have they enjoyed most about it?
  • How is music21 implemented, and how has its structure evolved since you first started it?
    • What have been the most challenging aspects of building and maintaining the music21 project and community?
  • What are some of the most interesting, unusual, or unexpected ways that you have seen music21 used?
    • What are some analyses that you have performed which yielded unexpected results?
  • What do you have planned for the future of music21?
  • Beyond computational analysis of musical theory, what are some of the other ways that you are using Python in your academic and professional pursuits?

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

Classic Computer Science For Pythonistas - Episode 197

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 [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.
  • 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

What You Need To Know About Open Source Licenses And Intellectual Property - Episode 196

Summary

As a developer and user of open source code, you interact with software and digital media every day. What is often overlooked are the rights and responsibilities conveyed by the intellectual property that is implicit in all creative works. Software licenses are a complicated legal domain in their own right, and they can often conflict with each other when you factor in the web of dependencies that your project relies on. In this episode Luis Villa, Co-Founder of Tidelift, explains the catagories of software licenses, how to select the right one for your project, and what to be aware of when you contribute to someone else’s code.

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 [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.
  • 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 Luis Villa about software licensing and intellectual property rules that developers need to know

Interview

  • Introductions
  • How did you get started as a programmer?
  • Intellectual property law and licensing of software, data, and media are complicated topics that are often poorly understood by developers. Can you start off by giving an overview of categories of intellectual property that we should be thinking of?
  • Most of us who have created or used software, whether it is open or closed source, have at some point come across various licenses. What may not be immediately obvious is that there are degrees of compatibility between these licenses. What are some guiding principles for determining which licenses are in conflict?
    • In an organization, who is responsible for ensuring compliance with software and content licensing within a given project?
    • When introducing new dependencies into a project or system what steps should be taken to evaluate license compatibility and compliance?
  • When creating a new project, one of the steps in the process is to select a license. What are some useful guidelines or questions to determine which license to use?
  • Another aspect of software licensing that developers might run into is when contributing to an open source project where a contributor license agreement might be necessary. What should we be thinking about when deciding whether to sign such an agreement?
  • In addition to software libraries, developers might need to use content such as images, audio, or video in their projects which have their own copyright and licensing considerations. What are some of the things that we should be looking for in those situations?
  • Another component of our systems that has grown in its importance with the rise of advanced analytics is data. We may need to use open data sources, pay for access to data repositories, or provide access to data that is under our control. What are some common approaches to licensing or terms of use for these contexts?
    • What should we be wary of when using or providing data in our applications?
  • How much of the work that you do at Tidelift is spent on educating developers and customers on the finer points of intellectual property management?
    • What are some of the most common difficulties or points of confusion that you encounter?
  • What are some useful resources that you would recommend to anyone who is interested in learning more about intellectual property and software licensing?

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