Talk Python To Me - Python Conversations For Passionate Developers

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

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