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.
Similar Podcasts
The Infinite Monkey Cage
Brian Cox and Robin Ince host a witty, irreverent look at the world through scientists' eyes.
The Top Shelf
ThePrimeagen and teej_dv are on a quest to find the best possible technical speakers and ask the best possible questions we can find. You all know ThePrimeagen can't read, so this is a great format for him to really shine. Teej is here to make sure that Prime knows who the guest is and also to interrupt Prime wherever possible
24H24L
Evento en línea, de 24 horas de duración que consiste en la emisión de 24 audios de diversas temáticas sobre GNU/Linux. Estos son los audios del evento en formato podcast.
#367: Say Hello to PyScript (WebAssembly Python)
Despite Python being overwhelmingly popular and positive, there are major areas of computing where Python is not present. Most notably on mobile and on the frontend side of the web. PyScript, a new project launched by Fabio Pliger from Anaconda, just might change that. It was made public and announced at PyCon just two weeks ago by Peter Wang and now has over 10,000 GitHub stars. But what is hype vs. reality vs. projected hopes and dreams? We're going to find out on this episode. Fabio is here to tell us all about his new project. Links from the show Fabio on Twitter: @b_smoke PyScript: pyscript.net Birth and Death of Javascript: destroyallsoftware.com Power On: The Story of Xbox: xbox.com PyScript source: github.com JupyterLite: jupyterlite.readthedocs.io Compiling CPython for WebAssembly: python.org Space WebGL Demo: pyscript.net/examples Antigravity Demo: pyscript.net/examples D3 Demo: pyscript.net/examples Most examples: pyscript.net/examples Michael's pyscript PWA YouTube video: youtube.com Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft Talk Python Training AssemblyAI
#366: Optimizing PostgreSQL DB Queries with pgMustard
Does your app have a database? Does that database play an important role in how the app operations and users perceive its quality? Most of you probably said yes to the first, and definitely to the second. But what if your DB isn't doing as well as it should? How would you know? And once you know, what do you do about it? On this episode, we're joined by Michael Christofides, co-creator of pgMustard, to discuss and explore the EXPLAIN command for Postgres and other databases as well as all the recommendations you might dig into as a result of understanding exactly what's happening with you queries. Links from the show Michael Christofides: @michristofides Datagrip: jetbrains.com pgMustard: pgmustard.com pgMustard example 1: app.pgmustard.com pgMustard example 2: app.pgmustard.com pgMustard example 3: app.pgmustard.com Arctype: arctype.com Postico: eggerapps.at/postico Laetitia Avrot Secrets of 'psql'— Video: youtube.com Beekeeper Studio: beekeeperstudio.io DBeaver: dbeaver.io SQLite Browser: sqlitebrowser.org Michael's new Up and Running with Git course: talkpython.fm/git Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training AssemblyAI
#365: Solving Negative Engineering Problems with Prefect
How much time do you spend solving negative engineering problems? And can a framework solve them for you? Think of negative engineering as things you do to avoid bad outcomes in software. At the lowest level, this can be writing good error handling with try / except. But it's broader than that: logging, observability (like Sentry tools), retries, failover (as in what you might get from Kubernetes), and so on. We have a great chat with Chris White about Prefect, a tool for data engineers and data scientists meaning to solve many of these problems automatically. But it's a conversation applicable to a broader software development community as well. Links from the show Chris White: @markov_gainz Prefect: prefect.io Fermat's Enigma Book (mentioned by Michael): amazon.com Prefect Docs (2.0): orion-docs.prefect.io Prefect source code: github.com A Brief History of Dataflow Automation: prefect.io/blog Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft Talk Python Training AssemblyAI
#364: Symbolic Math with Python using SymPy
We're all familiar with the data science tools like numpy, pandas, and others. These are numerical tools working with floating point numbers, often to represent real-world systems. But what if you exactly specify the equations, symbolically like many of us did back in Calculus and Differential Equations courses? With SymPy, you can do exactly that. Create equations, integrate, differentiate, and solve them. Then you can convert those solutions into Python (or even C++ and Fortran code). We're here with two of the core maintainer: Ondřej Čertík and Aaron Meurer to learn all about SymPy. Links from the show Ondrej Certik: @OndrejCertik Aaron Meurer: @asmeurer SymPy: sympy.org SymPy Docs: docs.sympy.org/dev Tutorials: docs.sympy.org The SymPy/HackerRank DMCA Incident: asmeurer.com SymEngine: github.com SymPy Gamma: gamma.sympy.org Sovled derivative problem - wait for derivative steps to appear: gamma.sympy.org Github Takedown Repo: github.com e: The Story of a Number book: amazon.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft Sentry Error Monitoring, Code TALKPYTHON AssemblyAI Talk Python Training
#363: Python for .NET and C# developers
Are you coming to Python from another language and ecosystem? It can seem a bit daunting at first. But Python is very welcoming and has a massive array of tools and libraries. In this episode, I speak to my friend Cecil Philip who does both Python and .NET development. We discuss what it's like coming to Python from .NET as well as a whole bunch of compare and contrasts across the two ecosystems. Links from the show Cecil on Twitter: @cecilphillip Los Alamos Space Division Job: talkpython.fm/losalamos Stripe: stripe.com Python: python.org .NET/C#: dotnet.microsoft.com C#'s async/await: docs.microsoft.com Entity Framework: docs.microsoft.com Python's Packaging Ecosystem: pypi.org .NET's Packaging Ecosystem: nuget.org VS Code: code.visualstudio.com C# Lang Repo: github.com Blazor web framework: dotnet.microsoft.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors CAST AI AssemblyAI Talk Python Training
#362: Hypermodern Python Projects
What would a modern Python project look like? Maybe it would use Poetry rather than pip directly for its package management. Perhaps its test automation would be controlled with Nox. You might automate its release notes with Release Drafter. The list goes on and on. And that list is the topic of this episode. Join me and Claudio Jolowicz as we discuss his Hypermodern Python project and template. Links from the show Claudio on Twitter: @cjolowicz Hypermodern Python Article: cjolowicz.github.io Hypermodern Python Project: github.com Features: github.com Nox: github.com PEP 594: peps.python.org Music by Claudio: claudiojolowicz.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft RedHat Talk Python Training
#361: Pangeo Data Ecosystem
Python's place is climate research is an important one. In this episode, you'll meet Joe Hamman and Ryan Abernathy, two researchers using powerful cloud computing systems and Python to understand how the world around us is changing. They are both involved in the Pangeo project which brings a great set of tools for scaling complex compute with Python. Links from the show Ryan Abernathey: @rabernat Joe Hamman: @HammanHydro Pangeo.: pangeo.io xarray: xarray.dev Pangeo Forge: pangeo-forge.org fsspec: filesystem-spec.readthedocs.io Step-by-Step Guide to Building a Big Data Portal: medium.com Coiled: coiled.io Pangeo Gallery: gallery.pangeo.io Pangeo Quickstart: pangeo.io JupyterLite: jupyterlite.readthedocs.io Jupyter: jupyter.org Pangeo Packages: pangeo.io Pangeo Discourse: discourse.pangeo.io Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors SignalWire Sentry Error Monitoring, Code TALKPYTHON Talk Python Training
#360: Removing Python's Dead Batteries (in just 5 years)
Python has come a long way since it was released in 1991. It originally released when the Standard Library was primary the totality of functionality you could leverage when building your applications. With the addition of pip and the 368,000 packages on PyPI, it's a different world where what we need and expect from the Standard Library. Brett Cannon and Christian Heimes have introduced PEP 594 which is the first step in trimming outdated and unmaintained older modules from the Standard Library. Join us to dive into the history and future of Python's Standard Library. Links from the show Brett Cannon: @brettsky Christian Heimes: @ChristianHeimes PEP 594: peps.python.org PEP 594 deprecated modules: peps.python.org Python WebAssembly REPL: repl.ethanhs.me Pyodide: github.com JupyterLite: jupyterlite.readthedocs.io "How to run Python in the browser" - Katie Bell: youtube.com .NET's Blazor: dotnet.microsoft.com wasmtime: pypi.org Python 3.10.4 Release Notes: docs.python.org Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft FusionAuth Talk Python Training
#359: Lifecycle of a machine learning project
Are you working on or considering a machine learning project? On this episode, we'll meet three people from the MLOps community: Demetrios Brinkmann, Kate Kuznecova, and Vishnu Rachakonda. They are here to tell us about the lifecycle of a machine learning project. We'll talk about getting started with prototypes and choosing frameworks, the development process, and finally moving into deployment and production. Links from the show Demetrios Brinkmann: @DPBrinkm Kate Kuznecova: linkedin.com Vishnu Rachakonda: linkedin.com MLOps Community: mlops.community Feature stores: mlops.community Great Expectations: github.com source control: DVC: dvc.org StreamLit: streamlit.io MLOps Jobs: mlops.pallet.com Made With ML Apps: madewithml.com Banana.dev: banana.dev FastAPI: fastapi.tiangolo.com MLOps without too much Ops: towardsdatascience.com NBDev: nbdev.fast.ai The "Works on My Machine" Certification Program: codinghorror.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Stack Overflow Talk Python Training
#358: Understanding Pandas visually with PandasTutor
Pandas is a great library that allows you to accomplish a ton of filtering and processing in condensed syntax. But how well do you understand what's happening? Sam Lau and Philip Guo built a great site to help use visually explore how Pandas is processing your dataset with your specific syntax. It's called PandasTutor, and Sam is here to tell us about it. Links from the show Sam Lau: samlau.me PandasTutor: pandastutor.com PythonTutor: pythontutor.com Principles and Techniques of Data Science book: textbook.ds100.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors SignalWire Stack Overflow Talk Python Training
#357: Python and the James Webb Space Telescope
Telescopes have been fundamental in our understanding of our place in the universe. And when you think about images that have shaped our modern view of space, you probably think about Hubble. But just this year, the JWST or James Web Space Telescope, was launch. JWST will go far beyond what Hubble has discovered. And did you know Python is used extensively in the whole data pipeline of JWST? We have two great guests here to tell us about it: Megan Sosey and Mike Swam. Links from the show James Web Space Telescope: webbtelescope.org JWST at NASA: jwst.nasa.gov JWST's YouTube channel: youtube.com JWST Repo on GitHub: github.com/spacetelescope/jwst STSci's AstroConda: ssb.stsci.edu/astroconda Telescope pointing: github.com/spacetelescope/gwcs Simulator: github.com/spacetelescope/webbpsf STSci's Archive and Tools: archive.stsci.edu htcondor: datasci.danforthcenter.org/htcondor Silly faker: github.com/cube-drone/silly Nancy Grace Roman Space Telescope: roman.gsfc.nasa.gov Myst Parser: myst-parser.readthedocs.io Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Datadog Stack Overflow Talk Python Training
#356: Tips for ML / AI startups
Have you been considering launching a product or even a business based on Python's AI / ML stack? We have a great guest on the episode this week, Dylan Fox, who is the cofounder of AssemblyAI and has been building his startup successfully over the past few years. He has interesting stories of 100s of GPUs in the cloud, evolving ML models, and much more that I know you'll enjoy hearing. Links from the show Dylan Twitter: @YouveGotFox AssemblyAI: assemblyai.com TensorFlow: tensorflow.org PyTorch: pytorch.org hugging face: huggingface.co SciKit-Learn: scikit-learn.org GeForce Card: nvidia.com pLS: twitter.com This journalist’s Otter.ai scare is a reminder that cloud transcription isn’t completely private: theverge.com Programming language trends: insights.stackoverflow.com Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?: the-diy-life.com PyTorch vs TensorFlow in 2022: assemblyai.com/blog/pytorch-vs-tensorflow Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Stack Overflow Sentry Error Monitoring, Code TALKPYTHON Talk Python Training
#355: EdgeDB - Building a database in Python
What database are you using in your apps these days? If you like most Python people, it's probably PostgreSQL. If you roll with NoSQL like me, you're probably using MongoDB. Maybe you're even using a graph database focused more on relationships. But there's a new Python database in town, and as you learn in during this episode, many critical Python libraries have come into existence because of it. This database is called EdgeDB. EdgeDB is built upon Postgres, implemented mostly in python, and is something of a marriage of a traditional relational database and an ORM. Python's async and await keywords, uvloop - the high performance asyncio event loop, and asyncpg all have ties back to the creation of EdgeDB. Yury Selivanov, the co-founder & CEO of EdgeDB, PSF fellow, and Python core developer is here to tell use about EdgeDB along with the history of many of these impactful language features and packages. Links from the show Yury Selivanov: @1st1 MagicPython: github.com/MagicStack/MagicPython uvloop: github.com/MagicStack/uvloop asyncpg: github.com/MagicStack/asyncpg TaskGroups and ExceptionGroups: twitter.com EdgeDB: edgedb.com Schema modeling: edgedb.com/showcase/data-modeling Easy EdgeDB book: edgedb.com/easy-edgedb Roadmap: edgedb.com/roadmap pgMustard: pgmustard.com PyBay: Building a Database with Python Talk: youtube.com Michael's course on async and await + Cython + uvloop: talkpython.fm/async Michael's PyBay talk: Flask + HTMX: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON SignalWire Talk Python Training
#354: Sphinx, MyST, and Python Docs in 2022
When you think about the power of Python, the clean language or powerful standard library may come to mind. You might certainly point to the external packages too. But what about the relative ease of picking up new libraries or even parts of the standard library? Documentation plays an important role there. And the tools in the Python space for building solid documentation and even publishing articles and books involving live code are huge assets. In this episode, we have Paul Everitt, Pradyun Gedam, Chris Holdgraf, and Chris Sewell to update us on Sphinx, MyST-Parser, ExecutableBooks, JupyerBook, Sphinx Themes, and much more. Links from the show Pradyun’s personal website: pradyunsg.me Chris’s personal website: predictablynoisy.com Paul Everitt: @paulweveritt Paul's free Sphinx and Markdown course: training.talkpython.fm Sphinx: sphinx-doc.org Python documentation: docs.python.org ExecutableBooks: executablebooks.org Jupyter Book: jupyterbook.org MyST parser: myst-parser.readthedocs.io Sphinx Book Theme: sphinx-book-theme.readthedocs.io PyData Sphinx Theme: pydata-sphinx-theme.readthedocs.io Sphinx Themes Gallery: sphinx-themes.org Furo Theme: pradyunsg.me sphinx-theme-builder: github.com Python Documentation WG issue tracker: github.com ReadTheDocs CZI: blog.readthedocs.com Pandoc: pandoc.org Google circa 1996: web.archive.org Python doc example: docs.python.org Tailwind doc example: tailwindcss.com/docs Typora app: typora.io CommonMark: commonmark.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors SignalWire Tonic Talk Python Training
#352: Running Python in Production
Do we talk about running Python in production enough? I can tell you that the Talk Python infrastructure (courses, podcasts, APIs, etc.) get a fair amount of traffic, but they look nothing like what Google, or Instagram, or insert [BIG TECH NAME] here's deployments do. Yet, mostly, we hear about interesting feats of engineering at massive scale that is impressive but often is also outside of the world most Python devs need for their companies and services. I have three great guests who do think we should talk more about small to medium-sized Python deployments: Emily Moorehouse, Hynek, and Glyph. I think you'll enjoy the conversation. They each bring their own interesting perspectives. Links from the show Emily on Twitter: @emilyemorehouse Hynek on Twitter: @hynek Glyph on Twitter: @glyph Main article by Hynek Python in Production Article: hynek.me Supporting articles Solid Snakes or: How to Take 5 Weeks of Vacation: hynek.me How to Write Deployment-friendly Applications: hynek.me Common Infrastructure Errors I've Made: matduggan.com Thoughts on Monoliths Give me back my monolith: craigkerstiens.com Goodbye Microservices: From 100s of problem children to 1 superstar: segment.com Configuring uWSGI for Production Deployment: techatbloomberg.com https://martinfowler.com/bliki/MicroservicePremium.html https://martinfowler.com/bliki/MonolithFirst.html More tools CuttleSoft: cuttlesoft.com pgMustard: Helps you review Postgres query plans quickly: pgmustard.com JSON:API: jsonapi.org Tenacity package: tenacity.readthedocs.io glom package: glom.readthedocs.io boltons package: boltons.readthedocs.io Joke: The Torture Never Stops: devops.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors SignalWire Tonic Talk Python Training