Talk Python To Me - Python Conversations For Passionate Developers

It's an exciting time for the capabilities of Python. We have the Faster CPython initiative going strong, the recent async work, the adoption of typing and on this episode we discuss a new isolation and parallelization capability coming to Python through sub-interpreters. We have Eric Snow who spearheaded the work to get them added to Python 3.12 and is working on the Python API for 3.13 along with Anthony Shaw who has been pushing the boundaries of what you can already do with subinterpreters. Episode sponsors Pybites PDM Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Guests Anthony Shaw: @tonybaloney@fosstodon.org Eric Snow: @ericsnowcurrently@mastodon.social PEP 684 – A Per-Interpreter GIL: peps.python.org PEP 734 – Multiple Interpreters in the Stdlib: peps.python.org Running Python Parallel Applications with Sub Interpreters: fosstodon.org pytest subinterpreters: fosstodon.org Long-Term Vision for a Parallel Python Programming Model?: fosstodon.org Hypercorn Server

Why is Python so popular? There is plenty of room for debate on this but one solid reason is it's easy to adopt, easy to use, and caters to people who are not quite developers/data scientists but need to do some computing. Do you know where there largest untapped set of that group hang out? Excel. That's why it's super exciting that Python is now going to be built directly into Excel. Just go into a cell and type =PY and you're off writing full Python 3 code that is backed by a lite Anaconda distribution of Python. And we have Dr. Sarah Kaiser here to give us the rundown on Python in Excel. Episode sponsors Posit Pybites PDM Talk Python Courses Links from the show Sarah's website: sckaiser.com Sarah on Mastodon: @crazy4pi314@mathstodon.xyz Get started with Python in Excel: microsoft.com Python in SQL Server: microsoft.com 8 of the Biggest Excel Mistakes of All Time: blog.hurree.co Security and Python in Excel: microsoft.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us

When you run your code in the cloud, how much do you know about where it runs? I mean, the hardware it runs on and the data center it runs in? There are just a couple of hyper-scale cloud providers in the world. This episode is a very unique chance to get a deep look inside one of them: Microsoft Azure. Azure is comprised of over 200 physical data centers, each with 100,000s of servers. A look into how code runs on them is fascinating. Our guide for this journey will be Mark Russinovich. Mark is the CTO of Microsoft Azure and a Technical Fellow, Microsoft's senior-most technical position. He's also a bit of a programming hero of mine. Even if you don't host your code in the cloud, I think you'll enjoy this conversation. Let's dive in. Episode sponsors Posit Pybites PDM Talk Python Courses Links from the show Mark Russinovich: @markrussinovich Mark Russinovich on LinkedIn: linkedin.com SysInternals: learn.microsoft.com Zero Day: A Jeff Aiken Novel: amazon.com Inside Azure Datacenters: youtube.com What run

Are you early in your software dev or data science career? Maybe it hasn't even really started yet and you're still in school. On this episode we have Sydney Runkle who has had a ton of success in the Python space and she hasn't even graduated yet. We sit down to talk about what she's done and might do differently again to achieve that success. It's "The Young Coder's Blueprint to Success" on episode 444 of Talk Python To Me. Links from the show Sydney Runkle: linkedin.com Pydantic: pydantic.dev Code Combat: codecombat.com Humanitarian Toolbox: www.htbox.org PyCon 2024: pycon.org Good first issue example: github.com 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 Mastodon: talkpython Follow Michael on Mastodon: mkennedy --- Episode sponsors --- Talk Python Training

Special crossover episode of Python Bytes to wrap up 2023. Topics include: Michael #1: Hatch v1.8 Brian #2: svcs : A Flexible Service Locator for Python Michael #3: Steering Council 2024 Term Election Results Brian #4: Python protocols. When to use them in your projects to abstract and decoupling ExtrasJoke: Joke: The dream is dead? --- Episode sponsors --- Posit Talk Python Training

If you're a fan of Pydantic or dataclasses, you'll definitely be interested in this episode. We are talking about a super fast data modeling and validation framework called msgspec. Some of the types in here might even be better for general purpose use than Python's native classes. Join me and Jim Crist-Harif to talk about his data exchange framework, mspspec. Links from the show Jim Crist-Harif: jcristharif.com Jim @ GitHub: github.com Jim @ Mastdon: @jcristharif@hachyderm.io msgspec: github.com Projects using msgspec: github.com msgspec on Conda Forge: anaconda.org msgspec on PyPI: pypi.org Litestar web framework: litestar.dev Litestar episode: talkpython.fm Pydantic V2 episode: talkpython.fm JSON parsing with msgspec article: pythonspeed.com msgspec bencharmks: jcristharif.com msgspec vs. pydantic v1 and pydantic v2: github.com 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 Mastod

You've probably heard the term "syntactic sugar", that is, syntax within a programming language that is designed to make things easier to read or to express. It makes the language "sweeter" for human use. It turns out Brett Cannon has spent 2 years diving into and writing about Python's sweet language features and how they really work down inside CPython. He joins me on the show today to dive into a few of the more relevant posts he's written about it. Links from the show Brett Cannon: @brettcannon@fosstodon.org Syntactic sugar series: snarky.ca Syntactic sugar: wikipedia.org Unravelling attribute access in Python: snarky.ca Unravelling binary arithmetic operations: snarky.ca Unravelling the import statement: snarky.ca record-type: pypi.org 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 Mastodon: talkpython Follow Michael on Mastodon: mkennedy --- Episode sponsors --- Talk Python Trai

We all know that LLMs and generative AI has been working its way into many products. It's Jupyter's turn to get a really awesome integration. We have David Qiu here to tell us about Jupyter AI. Jupyter AI provides a user-friendly and powerful way to apply generative AI to your notebooks. It lets you choose from many different LLM providers and models to get just the help you're looking for. And it does way more than just a chat pane in the UI. Listen to find out. Links from the show David Qiu: linkedin.com Jupyter AI: jupyter-ai.readthedocs.io Asking about something in your notebook: jupyter-ai.readthedocs.io Generating a new notebook: jupyter-ai.readthedocs.io Learning about local data: jupyter-ai.readthedocs.io Formatting the output: jupyter-ai.readthedocs.io Interpolating in prompts: jupyter-ai.readthedocs.io JupyterCon 2023 Talk: youtube.com PyData Seattle 2023 Talk: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us

On this episode we have Wolf Vollprecht and Ruben Arts from the pixi project here to talk about pixi, a high performance package manager for Python and other languages that actually manages Python itself too. They have a lot of interesting ideas on where Python packaging should go and are putting their time and effort behind them. Will pixi become your next package manager? Listen in to find out. Links from the show Black Friday at Talk Python: talkpython.fm/blackfriday Guests Wolf Vollprecht: github.com/wolfv Ruben Arts: github.com/ruben-arts pixi: prefix.dev Prefix: prefix.dev Launching pixi: prefix.dev Conda: docs.conda.io Conda Forge: conda-forge.org NixOS: nixos.org Packaging Con 2023: packaging-con.org 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 Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Posit Python Tutor Talk Python Training

Jupyter Notebooks and Jupyter Lab have to be one of the most important parts of Python when it comes to bring new users to the Python ecosystem and certainly for the day to day work of data scientists and general scientists who have made some of the biggest discoveries of recent times. And that platform has recently gotten a major upgrade with JupyterLab 4 released and Jupyter Notebook being significantly reworked to be based on the changes from JupyterLab as well. We have an excellent panel of guests, Sylvain Corlay, Frederic Collonval, Jeremy Tuloup, and Afshin Darian here to tell us what's new in these and other parts of the Jupyter ecosystem. Links from the show Guests Sylvain Corlay Frederic Collonval Jeremy Tuloup Afshin Darian JupyterLab 4.0 is Here: blog.jupyter.org Announcing Jupyter Notebook 7: blog.jupyter.org JupyterCon 2023 Videos: youtube.com Jupyterlite: github.com Download JupyterLab Desktop: github.com Mythical Man Month Book: wikipedia.org Blender in Jupyter: twitter.com Watch this episo

Are you considering or struggling with replacing much of the interactivity of your Django app with frontend JavaScript frameworks? After all, your users do expect an interactive and modern app, right? Before you make a rash decision, you owe it to yourself to check out HTMX. It goes well with Django. We have Christopher Trudeau to run through a whole awesome list of HTMX and Python and tell us about his new HTMX + Django course. Links from the show Chris on ExTwitter: @cltrudeau Django in Action book: manning.com Django: djangoproject.com HTMX + Django course: talkpython.fm HTMX: htmx.org awesome-htmx: github.com awesome-python-htmx: github.com django-js-lib-htmx: github.com htmxflask: github.com fastapi-sse-htmx: github.com django-htmx-patterns: github.com jinja2-fragments: github.com jinja_partials: github.com chameleon_partials: github.com django-render-block: github.com flask-htmx: github.com htmx-flask: github.com asgi-htmx: github.com hx-requests: github.com django-dashboards: github.com A Real World

How well do you know your Python packaging tools? These are things like pip which install your project's dependencies and their dependencies and so on. In this mix, we have more modern tools such as Poetry, Flit, Hatch and others. And even tools outside of Python itself which may attempt to manage Python itself in addition to the libraries. To make sense of all of this, we welcome back Anna-Lena Popkes for an unbiased evaluation of environment and packaging tools. Links from the show Anna-Lena's website: alpopkes.com Anna-Lena on GitHub: github.com Accompanying Blog Post: alpopkes.com Talk from PyCon DE: youtube.com Talk from EuroPython: youtube.com Talk Python's Data Science Jumpstart with 10 Projects course: talkpython.fm Rye: github.com Poetry: python-poetry.org Material for MkDocs: squidfunk.github.io 100 Days of Python in a Magical Universe Episode: talkpython.fm pip-tools: pip-tools.readthedocs.io Hatch: hatch.pypa.io PDM: pdm.fming.dev Flit: flit.pypa.io Conda: docs.conda.io Pipenv: pipenv.pypa.io

Do you worry about your developer / data science supply chain safety? All the packages for the Python ecosystem are much of what makes Python awesome. But the are also a bit of an open door to your code and machine. Luckily the PSF is taking this seriously and hired Mike Fiedler as the full time PyPI Safety & Security Engineer (not to be confused with the Security Developer in Residence staffed by Seth Michael Larson). Mike is here to give us the state of the PyPI security and plans for the future. Links from the show Mike on Twitter: @mikefiedler Mike on Mastodon: @miketheman@hachyderm.io Supply Chain examples SolarWinds: csoonline.com XcodeGhost: wikipedia.org Google Ad Malware: medium.com PyPI: pypi.org OWASP Top 10: owasp.org Trusted Publishers: docs.pypi.org libraries.io: libraries.io GitHub Full 2FA: github.blog Mike's Latest Blog Post: blog.pypi.org pprintpp package: github.com ICDiff: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with u

Are you building a mobile app and wondering where Python fits in the mix? Are you support others building these apps with backend APIs written in Python? Can you write your entire app, end to end, in Python? I have a great panel put together to discuss exactly this. And they all have a different and unique take on the options. Welcome to Loren Aguey, Harout Boujakjian, Andréas Kühne, Jeyfrin and, Joshua. Links from the show Guests Loren Aguey: linkedin.com Harout Boujakjian: linkedin.com Andréas Kühne: linkedin.com Jeyfrin, Joshua Talk Python Mobile App: training.talkpython.fm/apps Epic Skies App: play.google.com PinPlanet App: pinplanetapp.com My Club App: apps.apple.com vid3d App: play.google.com Flutter: flutter.dev Flutter Showcase: flutter.dev pub.dev, Flutter's PyPI: pub.dev FastAPI: fastapi.tiangolo.com Litestar: litestar.dev Pyramid Web Framework: trypyramid.com Flask: flask.palletsprojects.com Django: djangoproject.com Django REST Framework: django-rest-framework.org Kivy: kivy.org Swift: devel

We all know about Flask and Django. And of course FastAPI made a huge splash when it came on the scene a few years ago. But new web frameworks are being creating all the time. And they have these earlier frameworks to borrow from as well. On this episode we dive into a new framework gaining a lot of traction called Litestar. Will it be the foundation of your next project? Join me as I get to know Litestar with its maintainers: Jacob Coffee, Janek Nouvertné, and Cody Fincher. Links from the show Guests Jacob Coffee Jacob on Github: github.com Jacob on Twitter: @_scriptr Jacob on Mastodon: @Monorepo Cody Fincher Cody on LinkedIn: linkedin.com Cody on GitHub: github.com Email: cody.fincher@gmail.com Janek Nouvertné Janek on GitHub: github.com Email: j.a.nouvertne@posteo.de Litestar: litestar.dev Litestar Documentation: litestar.dev Litestar on Twitter: @LitestarAPI Litestar on Mastodon: @litestar Litestar Blog: blog.litestar.dev Discord: discord.gg Reddit r/Litestar: eddit.com Litestar on PyPI: pypi.org Ben

By now, surely you've heard how awesome Pydantic version 2 is. The team led by Samual Colvin spent almost a year refactoring and reworking the core into a high-performance Rust version while keeping the public API in Python and largely unchanged. The main benefit of this has been massive speed ups for frameworks and devs using Pydantic. But just how much work is it to take a framework deeply built on Pydantic and make that migration? What are some of the pitfalls? On this episode, we welcome back Roman Right to talk about his experience converting Beanie, the popular MongoDB async framework based on Pydantic, from Pydantic v1 to v2. And we'll have some fun talking MongoDB as well while we are at it. Links from the show Beanie: beanie-odm.dev Beanie on GitHub: github.com Roman on Twitter: @roman_the_right Beanie Release 1.21.0: github.com Talk Python's MongoDB with Async Python Course: talkpython.fm Pydantic Migration Guide: docs.pydantic.dev Customizing validation with __get_pydantic_core_schema__: doc

Every year Python has a new major release. This year it's Python 3.12 and it'll come out on October 2, 2023. That's 4 days from when this episode was published. There is quite process involved to test, build, and ship Python across many platforms and channels. We have Seth Michael Larson here to give us a detailed rundown on what exactly is involved in releasing CPython. Links from the show Seth on Mastodon: fosstodon.org/@sethmlarson Seth on Twitter: @sethmlarson Seth on Github: github.com Announcing Security Developer-in-Residence: sethmlarson.dev Visualizing the CPython Release Process: sethmlarson.dev PEP 101: peps.python.org CPython on Github: github.com Best Open SSF: best.openssf.org pip-audit: github.com PyPA Advisory Database: github.com Omnivore App: omnivore.app What's New in 3.12: docs.python.org release-tools package: github.com Talk Python's HTMX + Django course: talkpython.fm/htmx-django Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us

So, you've got this amazing machine learning model you created. And you want to share it and let your colleagues and users experiment with it on the web. How do you get started? Learning Flask or Django? Great frameworks, but you might consider Gradio which is a rapid development UI framework for ML models. On this episode, we have Freddy Boulton, to introduce us all to Gradio. Links from the show Freddy on Twitter: @freddy_alfonso_ Gradio: gradio.app Use as API Example: huggingface.co Components: gradio.app Svelte: svelte.dev Flutter UI/Code structure: docs.flutter.dev XKCD Matplotlib Theme: matplotlib.org Gradio XKCD Full Theme: huggingface.co PrivateGPT: ai.meta.com Langchain: docs.langchain.com pipdeptree: pypi.org 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 Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm Sentry Error Monitoring, Code TALKPYTHON Talk

We write tests to show us when there are problems with our code. But what if there are intermittent problems with the tests themselves? That can be big hassle. In this episode, we have Gregory Kapfhammer and Owain Parry on the show to share their research and advice for taming flaky tests. Links from the show Gregory Kapfhammer: gregorykapfhammer.com Owain Parry on Twitter: @oparry9 Radon: pypi.org pytest-xdist: github.com awesome-pytest: github.com Tenacity: readthedocs.io Stamina: github.com Flaky Test Management: docs.cypress.io Flaky Test Management (Datadog): datadoghq.com Flaky Test Management (Spotify): engineering.atspotify.com Flaky Test Management (Google): testing.googleblog.com Detecting Test Pollution: github.com Surveying the developer experience of flaky tests paper: www.gregorykapfhammer.com Build Kite CI/CD: buildkite.com Flake It: Finding and Fixing Flaky Test Cases: github.com Unflakable: unflakable.com CircleCI Test Detection: circleci.com Watch this episode on YouTube: youtube.com ---

Have you heard of Django? It's this little web framework that, well, kicked off much of Python's significance in the web space back in 2005. And that makes Django officially an adult. That's right, Django is now 18. And Django continues to lead the way on how community should be done for individual projects such as web frameworks. We have Carlton Gibson and Will Vincent back on the show this episode to discuss a bit of the Django history, Django trends in 2023, a little HTMX + Django, and lots more. Links from the show Guests Will Vincent: wsvincent.com Carlton Gibson: @carlton@fosstodon.org Button.dev: btn.dev Learn Django: learndjango.com Django News: django-news.com Yak-Shaving to Where the Puck is Going to Be Talk: youtube.com Open Source for the Long Haul: fosstodon.org Django 4.2: docs.djangoproject.com Django 5: docs.djangoproject.com Environs: github.com Neapolitan: github.com Django Template Paritals: github.com Jinja Partials: github.com Django Chat Podcast: djangochat.com Locality of Behavior Es