Talk Python To Me - Python Conversations For Passionate 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 Mich

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

Python's place in climate research is an important one. In this episode, you'll meet Joe Hamman and Ryan Abernathey, 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

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: youtu

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

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 Sam on Twitter: @samlau95 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

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

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 t

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 uv

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 Th

Two frameworks that have taken the Python world by storm are FastAPI and Pydantic. Once you already have your data exchange modeled in Pydantic, you might want to use that code for storing it in the database. And, if you have DB models you might want to somehow use them to power and document the APIs built with FastAPI. But the popular ORMs, such as SQLAlchemy and others, far predate Pydantic. But could they be put together? Sebastián Ramírez is here to tell us the answer is yes. We're covering his project SQLModel which is the marriage between Pydantic and SQLAlchemy. Links from the show Sebastián Ramírez: @tiangolo SQLModel: sqlmodel.tiangolo.com Create a SQLModel Model: sqlmodel.tiangolo.com Multiple Models: sqlmodel.tiangolo.com FastAPI Talk Python episode: talkpython.fm/284 FastAPI Dependency Injection: fastapi.tiangolo.com ODMantic for MongoDB: github.com Beanie for MongoDB: github.com Michael's Short video on Pydantic: youtube.com FastAPI courses by Michael: training.talkpython.fm/fastapi

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 Infrastru

The world of AI is changing fast. And the AI / ML space is a bit out of the ordinary for software developers. Typically in software, we can prove that given a certain situations, the code will always behave the same. We can point to where and why a decision is made. ML isn't like that. We set it up and then it takes on a life of its own. Regulators and governments are starting to step in and make rules over AI. The EU is one of the first to do so. That's why it's great to have Ines Montani and Katharine Jarmul, both awesome data scientists and EU residents, here to give us an overview of the coming regulations and other benefits and pitfalls of the AI / ML space. Links from the show Katharine Jarmul on Twitter: @kjam Katharine's site: kjamistan.com Ines Montani on Twitter: @_inesmontani Explosion AI: explosion.ai EU proposes new Artificial Intelligence Regulation: nortonrosefulbright.com The EU’s leaked AI regulation is ambitious but disappointingly vague: techmonitor.ai EU ARTIFICIAL INTELLIGENCE A

For 30 years, Python was overseen by Guido van Rossum since he created and released it around in 1990. When he retired in 2018 he left the creation of the new governing body up to the core developers. After a few stressful months, they concept of the steering council became the way forward. On this episode, I welcome the outgoing steering council to give us a look back and how this past year has gone. We welcome Barry Warsaw, Carol Willing, Brett Cannon, Pablo Galindo Salgado, and Thomas Wouters to the show. They are going to give us a rundown on of the important decisions for 2021. Links from the show Guests / Steering Council Members: Barry Warsaw: @pumpichank Carol Willing: @WillingCarol Brett Cannon: @brettsky Pablo Galindo Salgado: @pyblogsal Thomas Wouters: @Yhg1s Python Steering Council: python.org Meet the Python Developer in Residence: Lukasz Langa episode: talkpython.fm/331 @PSF joke thread: twitter.com Do you even need loops #PythonShort video: youtube.com Episode transcripts: talkpytho

This podcast episode you're listening to right now was delivered to you, in part, by MongoDB and Python powering our web apps and production processes. But if you're using pymongo, the native driver from MongoDB to talk to the server, you're doing it wrong. Basing your app on a foundation of exchanging raw dictionaries is a castle of sand. BTW, see the joke at the end of the show about this. You should be using an ODM. This time we're talking about Beanie which is one of the exciting, new MongoDB Object Document Mappers which is based on Pydantic and is async-native. Join me as I discuss this project with its creator: Roman Right. Links from the show Roman on Twitter: @roman_the_right Beanie ODM: github.com Tutorial: roman-right.github.io Beanie Relations, Cache, Actions and more!

I'm always on the look out for a good Python UI framework. This episode focuses on Dear PyGui. Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies, created by Jonathan Hoffstadt and Preston Cothren. They are here to tell us all about it. Links from the show Jonathan Hoffstadt: @jhoffs1 Preston Cothren: @toulaboy3 Dear PyGUI source: github.com Video tutorials: dearpygui.readthedocs.io Getting started tutorial: dearpygui.readthedocs.io OpenFOAM: openfoam.org Vulkan: vulkan.org Michael's Python Shorts video series The playlist: talkpython.fm/python-shorts Michael's YouTube Channel: 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 TopTal AssemblyAI Talk Python Training

The team at Instagram dropped a performance bomb on the Python world when they open-sourced Cider, their performance oriented fork of CPython. It contains a number of performance optimizations, including bytecode inline caching, eager evaluation of coroutines, a method-at-a-time JIT, and an experimental bytecode compiler that uses type annotations to emit type-specialized bytecode that performs better in the JIT. While it's not a general purpose runtime we can all pick up and use, it contains many powerful features and optimizations that may make their way back to mainline Python. We welcome Dino Viehland to dive into Cinder. Links from the show Dino on Twitter: @DinoViehland Cinder Python Runtime: github.com/facebookincubator Dino's PyCon talk: youtube.com IronPython: ironpython.net Sam Gross's NoGil work: github.com/colesbury/nogil Pyjion: trypyjion.com uWSGI: uwsgi-docs.readthedocs.io Configuring uWSGI at Bloomberg: techatbloomberg.com Locust perf testing: locust.io Watch this episode on YouTube:

Do you enjoy the "final 2 questions" I always ask at the end of the show? I think it's a great way to track the currents of the Python community. This episode focuses in on one of those questions: "What notable PyPI package have you come across recently? Not necessarily the most popular one but something that delighted you and people should know about?" Our guest, Antonio Andrade put together a GitHub repository cataloging guests' response to this question over the past couple of years. So I invited him to come share the packages covered there. We touch on over 40 packages during this episode so I'm sure you'll learn a few new gems to incorporate into your workflow. Links from the show Antonio on Twitter: @AntonioAndrade Notable PyPI Package Repo: github.com/xandrade/talkpython.fm-notable-packages Antonio's recommended packages from this episode: Sumy: Extract summary from HTML pages or plain texts: github.com gTTS (Google Text-to-Speech): github.com Packages discussed during the episode 1. FastAPI

You know that feeling when one of your developer friends or colleague tells you about some amazing tool, library, or shell environment that you never heard of that you just have to run out and try right away? This episode is jam-packed full of those moments. We welcome back Jay Miller to discuss tools and tips for developer productivity. The title says 10 tips, but we actually veer into many more along the way. I think you'll really enjoy this useful and light-hearted episode. Links from the show Jay on Twitter: @kjaymiller More Oh my ZSH plugins: github.com exa: the.exa.website bat: github.com ripgrep/amber: github.com Neovim: neovim.io RUMPS macOS Framework: github.com Black: github.com pypi-changes package: readthedocs.io asdf-python: github.com WAVE Web Accessibility Evaluation Tool: wave.webaim.org Google PageSpeed: pagespeed.web.dev XKCD Commit messages: xkcd.com secure package: github.com OWASP Top 10: owasp.org ngrok: ngrok.com starship: starship.rs Homebrew: brew.sh Chocolatey: chocolatey.org pip-t

SQLAlchemy is the most widely used ORM (Object Relational Mapper) for Python developers. It's been around since February 2006. But we might be in for the most significant release since the first one: SQLAlchemy 2.0. This version adds async and await support, new context-manager friendly features everywhere, and even a unified query syntax. Mike Bayer is back to give us a glimpse of what's coming and why Python's database story is getting stronger. Links from the show SQLAlchemy: sqlalchemy.org Mike on Twitter: @zzzeek Migrating to SQLAlchemy 2.0: sqlalchemy.org awesome-sqlalchemy: github.com sqlalchemy-continuum versioning: readthedocs.io enum support: github.com alembic: sqlalchemy.org GeoAlchemy: geoalchemy.org sqltap profiling: github.com nplusone: github.com Unit of work: duckduckgo.com ORM + Dataclasses: sqlalchemy.org SQLModel: sqlmodel.tiangolo.com Cython example: cython.org Async SQLAlchemy example: sqlalchemy.org ORM Usages Stats (see ORM section): jetbrains.com Watch this episode on YouTube: youtu