Talk Python To Me - Python Conversations For Passionate Developers

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

Getting started in Python is pretty easy. There's even a t-shirt that jokes about it: I learned Python, it was a good weekend. But to go from know how to create variables and writing loops, to building amazing things like FastAPI or Instagram, well there is this little gap between those two things. On this episode we welcome Eric Matthes to the show. He has thought a lot about teaching Python and comes to share his 10 tips for going from Python beginner to expert. Links from the show Eric on LinkedIn: linkedin.com Mostly Python Newsletter: mostlypython.substack.com Python Crash Course Book: nostarch.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 Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

One of the most exciting initiatives in the Python space these days is pyscript which enables Python running natively in your browser. With consistent support from the folks at Anaconda, this project has been making solid strides since its initial release. On this episode we catch up with Fabio Pliger and Nicholas Tollervey to see where they are with the pyscript project. Links from the show Guests and Host Links Nicholas Tollervey: @ntoll@mastodon.social Fabio Pliger: @b_smoke Michael Kennedy: @mkennedy@fosstodon.org pyscript: pyscript.net pyscript on Github: github.com Tic Tac Toe Example App: pyscriptapps.com PyperCard: github.com MicroPython: micropython.org pyscript core: pyscript.net Nich's PyScript gets Python anywhere there's a browser video: youtube.com HTMX: htmx.org Birth and Death of JavaScript: destroyallsoftware.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

Understanding how your Python application is using memory can be tough. First, Python has it's own layer of reused memory (arenas, pools, and blocks) to help it be more efficient. And many important Python packages are built in natively compiled languages like C and Rust often times making that section of your memory opaque. But with Memray, you can way deeper insight into your memory usage. We have Pablo Galindo Salgado and Matt Wozniski back on the show to dive into Memray, the sister project to their pystack one we recently covered. Links from the show Pablo Galindo Salgado: @pyblogsal Matt Wozniski: github.com pytest-memray: github.com PEP 669 – Low Impact Monitoring for CPython: peps.python.org Memray discussions: github.com Mandlebrot Flamegraph example: bloomberg.github.io Python allocators: bloomberg.github.io Profiling in Python: docs.python.org PEP 693 – Python 3.12 Release Schedule: peps.python.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch wi

If you want to share your data science results as interactive web apps, you could learn Flask or Django and a bunch of other web technologies. Or, you could pick up one of the powerful frameworks for deploying data science specifically. And if you're searching through that space, you've likely hear of Shiny -- but that's just for the R side of data science, right? Not any longer. Joe Cheng is here to introduce us to the recently released Shiny for Python. And it looks like a very solid new framework on the block. Links from the show Joe on Twitter: @jcheng Shiny: shiny.posit.co Shiny for Python code: github.com Discord community for Shiny: discord.gg Reactive programming inside Shiny: shiny.posit.co Shiny Gallery: shiny.posit.co Examples: shiny.posit.co Orbital mechanics in Shiny: shiny.posit.co Wordle in Shiny: shiny.posit.co Keynote introducing Shiny for Python: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTu

Python is used for a wide variety of software projects. One area it's really gained a huge amount of momentum is in the computational space (including data science). On this episode we welcome back Allen Downey to dive into a particular slice of this space: simulation problems and Python in Physics and Engineering in general. Links from the show Allen’s web page: allendowney.com Allen’s blog (Probably Overthinking It): allendowney.com/blog Allen on Twitter: @allendowney Allen on Mastodon: @allendowney@fosstodon.org Modeling and Simulation in Python book: allendowney.github.io Programming as a Way of Thinking: blogs.scientificamerican.com Think Python book: greenteapress.com Think OS book: greenteapress.com Pint package: pint.readthedocs.io Free version of the book and Jupyter notebooks: allendowney.github.io Published version: nostarch.com Elm programming language: elm-lang.org SymPy examples: docs.sympy.org Guinness World Record won for bungee 'dunk' into cup of tea: youtube.com Watch this episode on YouT

Regardless of which side of Python, software developer or data scientist, you sit on, you surely know that data scientists and software devs seem to have different styles and priorities. But why? And what are the benefits as well as the pitfalls of this separation. That's the topic of conversation with our guest, Dr. Jodie Burchell, data science developer advocate at JetBrains. Links from the show Jodie on Twitter: @t_redactyl Jodie's PyCon Talk: youtube.com Deep Learning with Python book: manning.com Keras: keras.io scikit-learn: scikit-learn.org Matplotlib: matplotlib.org XKCD Matplotlib: matplotlib.org Pandas: pandas.pydata.org Polars: pola.rs Polars on Talk Python: talkpython.fm Jupyter: jupyter.org Ponder: ponder.io Dask: dask.org Explosion AI's Prodigy discount code: get a personal license for 25% off using the discount code TALKPYTHON. 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 Pytho

When you think of Netflix (as a technology company), you probably imagine them as cloud innovators. They were one of the first companies to go all-in on a massive scale for cloud computing as well as throwing that pesky chaos monkey into the servers. But they have become a hive of amazing Python activity. From their CDN, demand predictions and failover, security, machine learning, executable notebooks and lots more, the Python at play is super interesting. On this episode, we have Zoran Simic and Amjith Ramanujam on the show to give us this rare inside look. Links from the show Zoran on Twitter: @zsimic Amjith on Mastodon: @amjith@fosstodon.org Python at Netflix blog post: netflixtechblog.com pdb++: github.com Pickley: github.com Pickley vs. pipx: github.com DB CLI: dbcli.com Learn you a Haskell: learnyouahaskell.com How Much of the Internet's Bandwidth Does Netflix Use?: makeuseof.com PtPython: github.com BPython: bpython-interpreter.org Flask REST-Plus: readthedocs.io RustUp: rustup.rs Rye: github.com PE

When you use a SQL database like Postgres, you have to understand the subtleties of isolation levels from "read committed" to "serializable." And distributed databases like MongoDB offer a range of consistency levels, from "eventually consistent" to "linearizable" and many options in between. Plus, it's easy enough to confuse "isolation" with "consistency!" We have A. Jesse Jiryu Davis from MongoDB back on the podcast to break it all down for us. Links from the show Jesse on Twitter: @jessejiryudavis Jesse on Mastodon: @jessejiryudavis@mas.to Files related to PyCon Talk: github.com Consistency and Isolation for Python Programmers blog post: emptysqua.re Consistency Models and Visuals: jepsen.io MongoDB Replication: mongodb.com MongoDB Transactions: mongodb.com Jesse's PyCon Talk: youtube.com Database Types: mongodb.com MongoDB Labs: 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 Pyth

Here's the situation. You have a Python app that is locked or even has completely crashed and all you're left with is a core dump on the server. Now what? It's time for PyStack! You can capture a view of your app as if you've set a breakpoint and even view the callstack and locals across language calls (for example from Python to C++ and back). We have the maintainers, Pablo Galindo Salgado and Matt Wozniski, here to dive into PyStack. You'll definitely want to have this tool in your toolbox. Links from the show Pablo Galindo Salgado: @pyblogsal Matt Wozniski: github.com pystack: 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 Sponsors Sentry Error Monitoring, Code TALKPYTHON RedHat Talk Python Training

Think about the different APIs and databases your application works with. Every one of them requires either an API key or a database connection string that itself contains a password. How do you let your application access this sensitive information without storing it in source code or putting in other compromising locations? We have Glyph Lefkowitz on the show to share his security fable as well as just good advice for keeping secrets out of Python code. Links from the show Glyph on Mastodon: @glyph@mastodon.social ShhGit: github.com Encrust: github.com GitHub Security Alerts: github.com CIA Triad: fortinet.com pinpal: github.com XKCD Authorization: xkcd.com Tokenring: github.com AWS Vault: github.com Gimme-AWS-creds: github.com Secrets in GitHub Actions: github.com Python Client for HashiCorp Vault: python-hvac.org Pomodouroboros app: github.com DateType: pypi.org Haveibeenpwned: haveibeenpwned.com PEP 541: peps.python.org Glyph's security talk at PyCon: us.pycon.org Watch this episode on YouTube: youtube

Large language models and chat-based AIs are kind of mind blowing at the moment. Many of us are playing with them for working on code or just as a fun alternative to search. But others of us are building applications with AI at the core. And when doing that, the slightly unpredictable nature and probabilistic nature of LLMs make writing and testing Python code very tricky. Enter promptimize from Maxime Beauchemin and Preset. It's a framework for non-deterministic testing of LLMs inside our applications. Let's dive inside the AIs with Max. Links from the show Max on Twitter: @mistercrunch Promptimize: github.com Introducing Promptimize ("the blog post"): preset.io Preset: preset.io Apache Superset: Modern Data Exploration Platform episode: talkpython.fm ChatGPT: chat.openai.com LeMUR: assemblyai.com Microsoft Security Copilot: blogs.microsoft.com AutoGPT: github.com Midjourney: midjourney.com Midjourney generated pytest tips thumbnail: talkpython.fm Midjourney generated radio astronomy thumbnail: talkpython.

If you're looking for fun data sets for learning, for teaching, maybe a conference talk, or even if you're just really into them, sports offers up a continuous stream of rich data that many people can relate to. Yet, accessing that data can be tricky. Sometimes it's locked away in obscure file formats. Other times, the data exists but without a clear API to access it. On this episode, we talk about PySport - something of an awesome list of a wide range of libraries (mostly but not all Python) for accessing a wide variety of sports data from the NFL, NBA, F1, and more. We have Koen Vossen, maintainer of PySport to talk through some of the more popular projects. Links from the show Koen on Twitter: @mr_le_fox PySport on Twitter: @PySportOrg Calling R from Python: medium.com DuckDB: duckdb.org PySport Playground: playground.pysport.org NFLVerse: github.com NBA Stats: nba.com Sports Databases: opensource.pysport.org Data sets: opensource.pysport.org Visualizations: opensource.pysport.org I/O: opensource.pysport

The release of Pydantic 2.0, its partial rewrite in Rust, and its refactoring into Pydantic core and top-level Pydantic in Python is big news. In fact, the alpha of Pydantic 2 was just released. Of course, these changes will have potentially wide ranging (and positive!) effects on libraries that are built upon Pydantic such as FastAPI, Beanie, and others. That's why this chance I had to catch up with Samuel Colvin from Pydantic and Sebastián Ramírez from FastAPI together, live from PyCon 2023. It's a super fun and wide ranging interview I'm sure you'll enjoy. Plus, there is a bit of an easter egg in the middle. Links from the show Sebastián Ramírez: @tiangolo Samuel Colvin: @samuel_colvin FastAPI: fastapi.tiangolo.com Pydantic: pydantic.dev Pydantic V2 Pre Release: pydantic.dev 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 Spo

At PyCon 2023, there was a section of the expo floor dedicated to new Python-based companies called Startup Row. I wanted to bring their stories and the experience of talking with these new startups to you. So in this episode, we'll talk with founders from these companies for 5 to 10 minutes each. Links from the show Ponder: ponder.io generally intelligent: generallyintelligent.com Wherobots: wherobots.ai Neptyne: neptyne.com Nixtla: nixtla.io Predibase: predibase.com Pynecone: pynecone.io 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 Sentry Error Monitoring, Code TALKPYTHON Talk Python Training