The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

March 25, 2019A Quick Python Check-in With Naomi Ceder
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?
Keep In Touch
@NaomiCeder on Twitter
Web
Quick Python Book
Get 40% off everything at Manning with code podinit19 at checkout
Enter to win a free copy
Picks
Tobias
Inkscape vector graphics editor
Naomi
La Casa de las Flores (House of Flowers) (Netflix)
Links
The Quick Python Book
Dick Blick Art Materials
The PSF
@ThePSF on Twitter
Manning Publishers
PEP8
ETL
Collections Module
Turtle Library
PyCon Hatchery
PyCon Charlas
Podcast Episode
The GIL
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
39 min
March 18, 2019 Wes McKinney's Career In Python For Data Analysis
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.
52 min
March 18, 2019Wes McKinney's Career In Python For Data Analysis
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?
Keep In Touch
wesm on GitHub
Website
@wesmckinn on Twitter
Picks
Tobias
Roald Dahl
Wes
The Soul Of A New Machine by Tracy Kidder
Links
Ursa Labs
Pandas
Podcast Interview with Jeff Reback
Pandas Extension Arrays Interview with Tom Augsburger
AQR Capital Management
Distributed Computing
SQL
Excel
Duke University
AppNexus
Chang She
Ibis
Open Source Governance
Apache Software Foundation
Paul Graham
Schlep Blindness
Big Data File Formats
Avro
Parquet
ORC
Data Engineering Podcast Episode
Apache Arrow
Hadoop
Spark
Data Engineering Podcast Episode
Apache Impala
R Language
Ruby
Rust
Pandas 2.0 Design Docs
Apache Arrow and the 10 Things I Hate About Pandas
GeoPandas
Statsmodels
Python For Data Analysis by Wes McKinney
2 Sigma
R Studio
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
52 min
March 10, 2019 The Past, Present, and Future of Deep Learning In PyTorch
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.
43 min
March 10, 2019The Past, Present, and Future of Deep Learning In PyTorch
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?
Keep In Touch
apaszke on GitHub
@apaszke on Twitter
LinkedIn
Picks
Tobias
Un Lun Dun by China Miéville
Adam
In Praise Of Copying by Marcus Boon
Links
PyTorch
University of Warsaw
Poland
Polish Olympiad In Informatics
Deep Learning
Automatic Differentiation
Torch 7
Lua
Tensorflow
CNTK
Tensorflow 2
Caffe2
EPFL (Ecole polytechnique fédérale de Lausanne)
Fast.ai
TorchScript
ONNX
Transfer Learning
C++
Reinforcement Learning
NumPy
SciPy
MatPlotLib
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
43 min
March 04, 2019 How To Include Redis In Your Application Architecture
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.
1 hr 2 min
March 04, 2019How To Include Redis In Your Application Architecture
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?
Keep In Touch
Andy
@andymccurdy on Twitter
andymccurdy on GitHub
Christoph
chrisAtRedis on GitHub
LinkedIn
Picks
Tobias
Rowan Atkinson
Andy
The Food Lab: Better Home Cooking Through Science by J. Kenji Lopez-Alt
Dota 2 Auto Chess (Community Mod)
Christoph
IPA Infused With Grapefruit Juice
redis-py
Selenium Python client
Daniel Suarez
Influx
Links
redis-py
Redis DB
Redis Labs
PHP
Django
Reflective Operating System Architectures
TCL
Perl
Linux
Memcached
NextCloud
C programming language
uWSGI
Flask
Gevent
PyPy
re-json
redis-graph
Redis-search
MongoDB
Bloom Filter
hiredis
Redis Sentinel HA plugin
Lua programming language
OpenWRT
LuCI
MicroPython
Podcast Episode
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 2 min
February 25, 2019 Marshmallow Data Validation Library
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.
35 min
February 25, 2019Marshmallow Data Validation Library
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?
Keep In Touch
Email
Website
@sloria1 on Twitter
Picks
Tobias
Sherlock BBC tv series
Steven
Greater Than Code podcast
Links
Marshmallow
Butterfly Network
Biology
ORM (Object Relational Mapper)
ODM (Object Document Mapper)
Webargs
Avro
Swagger/OpenAPI
REST (REpresentational State Transfer)
JSON-Schema
Environs
Django Rest Framework
WTForms
DynamoDB
MongoDB
Etsy’s boundary-layer for building Airflow DAGs from config files
Airflow Podcast Episode
Toasted Marshmallow
Lyft Blog Post
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
35 min
February 18, 2019 Unpacking The Python Toolkit For Chaos Engineering
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.
1 hr