Why do people list to this podcast? Sure, they're looking for technical explorations of new libraries and ideas. But often it's to hear the story behind them. If that speaks to you, then I have the perfect episode lined up. I have Barry Warsaw, Paul Everitt, Carol Willing, and Brett Cannon all back on the show to share stories from the history of Python. You'll hear about how import this came to be and how the first PyCon had around 30 attendees (two of whom are guests on this episode!). Sit back and enjoy the humorous stories from Python's past.
Episode sponsors
Posit
Agntcy
Talk Python Courses
Links from the show
Barry's Zen of Python song: youtube.com
Jake Vanderplas - Keynote - PyCon 2017: youtube.com
Why it’s called “Python” (Monty Python fan-reference): geeksforgeeks.org
import antigravity: python-history.blogspot.com
NIST Python Workshop Attendees: legacy.python.org
Paul Everitt open-sources Zope: old.zope.dev
Carol Willing wins ACM Software System Award: awards.acm.org
Watch this episode on YouTube: youtube.com
Episode #513 deep-dive: talkpython.fm/513
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
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Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
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#512: Building a JIT Compiler for CPython
Do you like to dive into the details and intricacies of how Python executes and how we can optimize it? Well, do I have an episode for you. We welcome back Brandt Bucher to give us an update on the upcoming JIT compiler for Python and why it differs from JITs for languages such as C# and Java.
Episode sponsors
Posit
Talk Python Courses
Links from the show
Brandt Bucher: github.com/brandtbucher
PyCon Talk: What they don't tell you about building a JIT compiler for CPython: youtube.com
Specializing, Adaptive Interpreter Episode: talkpython.fm
Watch this episode on YouTube: youtube.com
Episode #512 deep-dive: talkpython.fm/512
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
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#511: From Notebooks to Production Data Science Systems
If you're doing data science and have mostly spent your time doing exploratory or just local development, this could be the episode for you. We are joined by Catherine Nelson to discuss techniques and tools to move your data science game from local notebooks to full-on production workflows.
Episode sponsors
Agntcy
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses
Links from the show
New Course: LLM Building Blocks for Python: training.talkpython.fm
Catherine Nelson LinkedIn Profile: linkedin.com
Catherine Nelson Bluesky Profile: bsky.app
Enter to win the book: forms.google.com
Going From Notebooks to Scalable Systems - PyCon US 2025: us.pycon.org
Going From Notebooks to Scalable Systems - Catherine Nelson – YouTube: youtube.com
From Notebooks to Scalable Systems Code Repository: github.com
Building Machine Learning Pipelines Book: oreilly.com
Software Engineering for Data Scientists Book: oreilly.com
Jupytext - Jupyter Notebooks as Markdown Documents: github.com
Jupyter nbconvert - Notebook Conversion Tool: github.com
Awesome MLOps - Curated List: github.com
Watch this episode on YouTube: youtube.com
Episode #511 deep-dive: talkpython.fm/511
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
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#510: 10 Polars Tools and Techniques To Level Up Your Data Science
Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course.
Episode sponsors
Agntcy
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses
Links from the show
New Theme Song (Full-Length Download and backstory): talkpython.fm/blog
Polars for Power Users Course: training.talkpython.fm
Awesome Polars: github.com
Polars Visualization with Plotly: docs.pola.rs
Dataframely: github.com
Patito: github.com
polars_iptools: github.com
polars-fuzzy-match: github.com
Nucleo Fuzzy Matcher: github.com
polars-strsim: github.com
polars_encryption: github.com
polars-xdt: github.com
polars_ols: github.com
Least Mean Squares Filter in Signal Processing: www.geeksforgeeks.org
polars-pairing: github.com
Pairing Function: en.wikipedia.org
polars_list_utils: github.com
Harley Schema Helpers: tomburdge.github.io
Marimo Reactive Notebooks Episode: talkpython.fm
Marimo: marimo.io
Ahoy Narwhals Podcast Episode Links: talkpython.fm
Watch this episode on YouTube: youtube.com
Episode #510 deep-dive: talkpython.fm/510
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
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#509: GPU Programming in Pure Python
If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python.
Episode sponsors
Posit
Agntcy
Talk Python Courses
Links from the show
Bryce Adelstein Lelbach on Twitter: @blelbach
Episode Deep Dive write up: talkpython.fm/blog
NVIDIA CUDA Python API: github.com
Numba (JIT Compiler for Python): numba.pydata.org
Applied Data Science Podcast: adspthepodcast.com
NVIDIA Accelerated Computing Hub: github.com
NVIDIA CUDA Python Math API Documentation: docs.nvidia.com
CUDA Cooperative Groups (CCCL): nvidia.github.io
Numba CUDA User Guide: nvidia.github.io
CUDA Python Core API: nvidia.github.io
Numba (JIT Compiler for Python): numba.pydata.org
NVIDIA’s First Desktop AI PC ($3,000): arstechnica.com
Google Colab: colab.research.google.com
Compiler Explorer (“Godbolt”): godbolt.org
CuPy: github.com
RAPIDS User Guide: docs.rapids.ai
Watch this episode on YouTube: youtube.com
Episode #509 deep-dive: talkpython.fm/509
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive
deep into the popular packages and software developers, data scientists, and incredible hobbyists doing
amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community
by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite
packages and the hot new ones coming out of open source.