Talk Python To Me

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 [https://talkpython.fm/connect-cloud] Agntcy [https://talkpython.fm/agntcy] Talk Python Courses [https://talkpython.fm/training] LINKS FROM THE SHOW Barry's Zen of Python song: youtube.com [https://www.youtube.com/watch?v=i6G6dmVJy74] Jake Vanderplas - Keynote - PyCon 2017: youtube.com [https://www.youtube.com/watch?v=ZyjCqQEUa8o&ab_channel=PyCon2017] Why it’s called “Python” (Monty Python fan-reference): geeksforgeeks.org [https://www.geeksforgeeks.org/history-of-python/?featured_on=talkpython] import antigravity: python-history.blogspot.com [https://python-history.blogspot.com/2010/06/import-antigravity.html?featured_on=talkpython] NIST Python Workshop Attendees: legacy.python.org [https://legacy.python.org/workshops/1994-11/attendees.html?featured_on=talkpython] Paul Everitt open-sources Zope: old.zope.dev [https://old.zope.dev/Members/paul/BusinessDecision/?featured_on=talkpython] Carol Willing wins ACM Software System Award: awards.acm.org [https://awards.acm.org/award_winners/willing_1304832?featured_on=talkpython] Watch this episode on YouTube: youtube.com [https://www.youtube.com/watch?v=et9WtazSpZQ] Episode #513 deep-dive: talkpython.fm/513 [https://talkpython.fm/episodes/show/513/stories-from-python-history#takeaways-anchor] Episode transcripts: talkpython.fm [https://talkpython.fm/episodes/transcript/513/stories-from-python-history] --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com [https://talkpython.fm/youtube] Talk Python on Bluesky: @talkpython.fm at bsky.app [https://bsky.app/profile/talkpython.fm] Talk Python on Mastodon: talkpython [https://fosstodon.org/web/@talkpython] Michael on Bluesky: @mkennedy.codes at bsky.app [https://bsky.app/profile/mkennedy.codes?featured_on=talkpython] Michael on Mastodon: mkennedy [https://fosstodon.org/web/@mkennedy]

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 [https://talkpython.fm/ppm] Talk Python Courses [https://talkpython.fm/training] LINKS FROM THE SHOW Brandt Bucher: github.com/brandtbucher [https://github.com/brandtbucher?featured_on=talkpython] PyCon Talk: What they don't tell you about building a JIT compiler for CPython: youtube.com [https://www.youtube.com/watch?v=NE-Oq8I3X_w&ab_channel=PyConUS] Specializing, Adaptive Interpreter Episode: talkpython.fm [https://talkpython.fm/episodes/show/381/python-perf-specializing-adaptive-interpreter] Watch this episode on YouTube: youtube.com [https://www.youtube.com/watch?v=abNY_RcO-BU] Episode #512 deep-dive: talkpython.fm/512 [https://talkpython.fm/episodes/show/512/building-a-jit-compiler-for-cpython#takeaways-anchor] Episode transcripts: talkpython.fm [https://talkpython.fm/episodes/transcript/512/building-a-jit-compiler-for-cpython] --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com [https://talkpython.fm/youtube] Talk Python on Bluesky: @talkpython.fm at bsky.app [https://bsky.app/profile/talkpython.fm] Talk Python on Mastodon: talkpython [https://fosstodon.org/web/@talkpython] Michael on Bluesky: @mkennedy.codes at bsky.app [https://bsky.app/profile/mkennedy.codes?featured_on=talkpython] Michael on Mastodon: mkennedy [https://fosstodon.org/web/@mkennedy]

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 [https://talkpython.fm/agntcy] Sentry Error Monitoring, Code TALKPYTHON [https://talkpython.fm/sentry] Talk Python Courses [https://talkpython.fm/training] LINKS FROM THE SHOW New Course: LLM Building Blocks for Python: training.talkpython.fm [https://training.talkpython.fm/courses/llm-building-blocks-for-python] Catherine Nelson LinkedIn Profile: linkedin.com [https://www.linkedin.com/in/catherinenelson1/?featured_on=talkpython] Catherine Nelson Bluesky Profile: bsky.app [https://bsky.app/profile/catnelson.bsky.social?featured_on=talkpython] Enter to win the book: forms.google.com [https://forms.gle/1okKtSdSNTtAd4SRA?featured_on=talkpython] Going From Notebooks to Scalable Systems - PyCon US 2025: us.pycon.org [https://us.pycon.org/2025/schedule/presentation/51/?featured_on=talkpython] Going From Notebooks to Scalable Systems - Catherine Nelson – YouTube: youtube.com [https://www.youtube.com/watch?v=o4hyA4hotxw&ab_channel=PyConUS] From Notebooks to Scalable Systems Code Repository: github.com [https://github.com/catherinenelson1/from_notebooks_to_scalable?featured_on=talkpython] Building Machine Learning Pipelines Book: oreilly.com [https://www.oreilly.com/library/view/building-machine-learning/9781492053187/?featured_on=talkpython] Software Engineering for Data Scientists Book: oreilly.com [https://www.oreilly.com/library/view/software-engineering-for/9781098136192/?featured_on=talkpython] Jupytext - Jupyter Notebooks as Markdown Documents: github.com [https://github.com/mwouts/jupytext?featured_on=talkpython] Jupyter nbconvert - Notebook Conversion Tool: github.com [https://github.com/jupyter/nbconvert?featured_on=talkpython] Awesome MLOps - Curated List: github.com [https://github.com/visenger/awesome-mlops?featured_on=talkpython] Watch this episode on YouTube: youtube.com [https://www.youtube.com/watch?v=n2WFfVIqlDw] Episode #511 deep-dive: talkpython.fm/511 [https://talkpython.fm/episodes/show/511/from-notebooks-to-production-data-science-systems#takeaways-anchor] Episode transcripts: talkpython.fm [https://talkpython.fm/episodes/transcript/511/from-notebooks-to-production-data-science-systems] --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com [https://talkpython.fm/youtube] Talk Python on Bluesky: @talkpython.fm at bsky.app [https://bsky.app/profile/talkpython.fm] Talk Python on Mastodon: talkpython [https://fosstodon.org/web/@talkpython] Michael on Bluesky: @mkennedy.codes at bsky.app [https://bsky.app/profile/mkennedy.codes?featured_on=talkpython] Michael on Mastodon: mkennedy [https://fosstodon.org/web/@mkennedy]

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 [https://talkpython.fm/agntcy] Sentry Error Monitoring, Code TALKPYTHON [https://talkpython.fm/sentry] Talk Python Courses [https://talkpython.fm/training] LINKS FROM THE SHOW New Theme Song (Full-Length Download and backstory): talkpython.fm/blog [https://talkpython.fm/flasksong/] Polars for Power Users Course: training.talkpython.fm [https://training.talkpython.fm/courses/polars-for-power-users] Awesome Polars: github.com [https://github.com/ddotta/awesome-polars?featured_on=talkpython] Polars Visualization with Plotly: docs.pola.rs [https://docs.pola.rs/user-guide/misc/visualization/#plotly] Dataframely: github.com [https://github.com/Quantco/dataframely?featured_on=talkpython] Patito: github.com [https://github.com/JakobGM/patito?featured_on=talkpython] polars_iptools: github.com [https://github.com/erichutchins/polars_iptools?featured_on=talkpython] polars-fuzzy-match: github.com [https://github.com/bnmoch3/polars-fuzzy-match?featured_on=talkpython] Nucleo Fuzzy Matcher: github.com [https://github.com/helix-editor/nucleo?featured_on=talkpython] polars-strsim: github.com [https://github.com/foxcroftjn/polars-strsim?featured_on=talkpython] polars_encryption: github.com [https://github.com/zlobendog/polars_encryption?featured_on=talkpython] polars-xdt: github.com [https://github.com/pola-rs/polars-xdt?featured_on=talkpython] polars_ols: github.com [https://github.com/azmyrajab/polars_ols?featured_on=talkpython] Least Mean Squares Filter in Signal Processing: www.geeksforgeeks.org [https://www.geeksforgeeks.org/least-mean-squares-filter-in-signal-processing/?featured_on=talkpython] polars-pairing: github.com [https://github.com/apcamargo/polars-pairing?featured_on=talkpython] Pairing Function: en.wikipedia.org [https://en.wikipedia.org/wiki/Pairing_function?featured_on=talkpython] polars_list_utils: github.com [https://github.com/dashdeckers/polars_list_utils?featured_on=talkpython] Harley Schema Helpers: tomburdge.github.io [https://tomburdge.github.io/harley/reference/harley/schema_helpers/?featured_on=talkpython] Marimo Reactive Notebooks Episode: talkpython.fm [https://talkpython.fm/episodes/show/501/marimo-reactive-notebooks-for-python#links-section] Marimo: marimo.io [https://marimo.io/?featured_on=talkpython] Ahoy Narwhals Podcast Episode Links: talkpython.fm [https://talkpython.fm/episodes/show/480/ahoy-narwhals-are-bridging-the-data-science-apis] Watch this episode on YouTube: youtube.com [https://www.youtube.com/watch?v=aIdvlJN1bNQ] Episode #510 deep-dive: talkpython.fm/510 [https://talkpython.fm/episodes/show/510/10-polars-tools-and-techniques-to-level-up-your-data-science#takeaways-anchor] Episode transcripts: talkpython.fm [https://talkpython.fm/episodes/transcript/510/10-polars-tools-and-techniques-to-level-up-your-data-science] --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com [https://talkpython.fm/youtube] Talk Python on Bluesky: @talkpython.fm at bsky.app [https://bsky.app/profile/talkpython.fm] Talk Python on Mastodon: talkpython [https://fosstodon.org/web/@talkpython] Michael on Bluesky: @mkennedy.codes at bsky.app [https://bsky.app/profile/mkennedy.codes?featured_on=talkpython] Michael on Mastodon: mkennedy [https://fosstodon.org/web/@mkennedy]

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 [https://talkpython.fm/ppm] Agntcy [https://talkpython.fm/agntcy] Talk Python Courses [https://talkpython.fm/training] LINKS FROM THE SHOW Bryce Adelstein Lelbach on Twitter: @blelbach [https://x.com/blelbach?lang=en&featured_on=talkpython] Episode Deep Dive write up: talkpython.fm/blog [https://talkpython.fm/blog/posts/deep-dive-retrospective-at-talk-python/] NVIDIA CUDA Python API: github.com [https://github.com/NVIDIA/cuda-python?featured_on=talkpython] Numba (JIT Compiler for Python): numba.pydata.org [https://numba.pydata.org/?featured_on=talkpython] Applied Data Science Podcast: adspthepodcast.com [https://adspthepodcast.com?featured_on=talkpython] NVIDIA Accelerated Computing Hub: github.com [https://github.com/NVIDIA/accelerated-computing-hub?featured_on=talkpython] NVIDIA CUDA Python Math API Documentation: docs.nvidia.com [https://docs.nvidia.com/cuda/nvmath-python?featured_on=talkpython] CUDA Cooperative Groups (CCCL): nvidia.github.io [https://nvidia.github.io/cccl/cuda_cooperative/?featured_on=talkpython] Numba CUDA User Guide: nvidia.github.io [https://nvidia.github.io/numba-cuda/user/kernels.html#introduction] CUDA Python Core API: nvidia.github.io [https://nvidia.github.io/cuda-python/cuda-core?featured_on=talkpython] Numba (JIT Compiler for Python): numba.pydata.org [https://numba.pydata.org/?featured_on=talkpython] NVIDIA’s First Desktop AI PC ($3,000): arstechnica.com [https://arstechnica.com/ai/2025/01/nvidias-first-desktop-pc-can-run-local-ai-models-for-3000/?featured_on=talkpython] Google Colab: colab.research.google.com [https://colab.research.google.com/?featured_on=talkpython] Compiler Explorer (“Godbolt”): godbolt.org [https://godbolt.org/?featured_on=talkpython] CuPy: github.com [https://github.com/cupy/cupy?featured_on=talkpython] RAPIDS User Guide: docs.rapids.ai [https://docs.rapids.ai/user-guide/?featured_on=talkpython] Watch this episode on YouTube: youtube.com [https://www.youtube.com/watch?v=57POBhLVbcc] Episode #509 deep-dive: talkpython.fm/509 [https://talkpython.fm/episodes/show/509/gpu-programming-in-pure-python#takeaways-anchor] Episode transcripts: talkpython.fm [https://talkpython.fm/episodes/transcript/509/gpu-programming-in-pure-python] --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com [https://talkpython.fm/youtube] Talk Python on Bluesky: @talkpython.fm at bsky.app [https://bsky.app/profile/talkpython.fm] Talk Python on Mastodon: talkpython [https://fosstodon.org/web/@talkpython] Michael on Bluesky: @mkennedy.codes at bsky.app [https://bsky.app/profile/mkennedy.codes?featured_on=talkpython] Michael on Mastodon: mkennedy [https://fosstodon.org/web/@mkennedy]