Talk Python To Me - Python Conversations For Passionate Developers

PyPI has been in the news for a bunch of reasons lately. Many of them good. But also, some with a bit of drama or mixed reactions. On this episode, we have Dustin Ingram, one of the PyPI maintainers and one of the directors of the PSF, here to discuss the whole 2FA story, securing the supply chain, and plenty more related topics. This is another important episode that people deeply committed to the Python space will want to hear. Links from the show Dustin on Twitter: @di_codes Hardware key giveaway: pypi.org OpenSSF funds PyPI: openssf.org James Bennet's take: b-list.org Atomicwrites (left-pad on PyPI): reddit.com 2FA PyPI Dashboard: datadoghq.com github 2FA - all users that contribute code by end of 2023: github.blog GPG - not the holy grail: caremad.io Sigstore for Python: pypi.org pip-audit: pypi.org PEP 691: peps.python.org PEP 694: peps.python.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

Pydantic has become a core building block for many Python projects. After 5 years, it's time for a remake. With version 2, the plan is to rebuild the internals (with benchmarks already showing a 17x performance improvement) and clean up the API. Sounds great, but what does that mean for us? Samuel Colvin, the creator of Pydantic, is here to share his plan for Pydantic v2. Links from the show Samuel on Twitter: @samuel_colvin Pydantic v2 plan: pydantic-docs.helpmanual.io Py03: pyo3.rs FastAPI: fastapi.tiangolo.com Beanie: github.com SQLModel: sqlmodel.tiangolo.com Speedate: docs.rs Pytests running on Pydantic in browser: githubproxy.samuelcolvin.workers.dev JSON to Pydantic tool: jsontopydantic.com Pyscript: pyscript.net Michael's Pyscript + WebAssembly: Python Web Apps video: youtube.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 Twitter: @talkpython Follow Michael on Twitter: @mke

Every year, the Python core developers and a few other key players in the Python ecosystem meet to discuss the pressing issues and important advancements at an event called the Python Language Summit. While Python is a community known for openness, this meeting is typically held behind closed doors mostly for efficiency's sake. On this episode, we'll give you a look behind that door. We have Alex Waygood here on this episode to break it down for us and give a look inside the summit. Links from the show Alex on Twitter: @alexwaygood 2022 Python Language Summit: pyfound.blogspot.com Individual Talks Python without the GIL: pyfound.blogspot.com Reaching a per-interpreter GIL: pyfound.blogspot.com The "Faster CPython" project: 3.12 and beyond: pyfound.blogspot.com WebAssembly: Python in the browser and beyond: pyfound.blogspot.com F-strings in the grammar: pyfound.blogspot.com Cinder Async Optimizations: pyfound.blogspot.com The issue and PR backlog: pyfound.blogspot.com The path forward for immortal objects:

Every year, the PSF and JetBrains team up to do a Python community survey. The most recent one was Fall of 2021. For this episode, I've gathered a great group of Python enthusiasts to discuss the results. I think you'll really enjoy the group discussion on this episode. Links from the show Guests Gina Häußge: @foosel Emily Morehouse-Valcarcel: @emilyemorehouse Tonya Sims: @TonyaSims Brett Cannon: @brettsky Jay Miller: @kjaymiller Paul Everitt: @paulweveritt 2021 Survey Results: jetbrains.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 Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON RedHat AssemblyAI Talk Python Training

Deploying and managing your application after you create it can be a big challenge. Cloud platforms such as Azure have literally hundreds of services. Which ones should you choose? How do you link them together? In this episode, Anthony Shaw and Shayne Boyer share a new CLI tool and template they've created for jump starting you use of modern Python apps and deploying them to Azure. We're talking FastAPI, Beanie and MongoDB, async and await, Bicep DevOps, automated CI/CD pipelines and more. Plus we catch up on other Python work happening that Anthony is involved with. If you're interested in deploying or structuring modern Python apps, you'll find some interesting take aways from our conversation. Links from the show Anthony on Twitter: @anthonypjshaw Shayne Boyer: @spboyer Azure azd CLI tools: aka.ms Beanie ODM: github.io Pydantic: helpmanual.io Give me back my monolith article: craigkerstiens.com Python creator Guido van Rossum joins Microsoft: techcrunch.com Making Python Faster with Guido and Mark epis

Often when we learn about or work with Math, it's done so in a very detached style. You might learn the rules and techniques for differentiation, for example. But how often do you get to apply them to meaningful and interesting problems? In this episode, we have Vince Knight and Geraint Palmer on to discuss solving a wide variety of applied and approachable math problems using Python. Whether you're deeply into math or not so much, I think there is a lot to enjoy from this episode. Links from the show Applied Mathematics with Open-Source Software: taylorfrancis.com Book source files: ithub.com Vince on Twitter: @drvinceknight Geraint on Twitter: @geraintpalmer Traces Package: traces.readthedocs.io A Beautiful Mind: wikipedia.org Nashpy: github.com e: The Story of a Number: amazon.com SymPy episode: talkpython.fm 8451: 8451.com Stack Overflow Trends: stackoverflow.com PYCON UK 2017: Python for conducting operational research in healthcare: youtube.com Ciw package: github.com Python ternary: github.com Micha

I'm sure you're familiar with package managers for your OS even if you don't use them. On macOS we have Homebrew, Chocolatey on Windows, and apt, yum, and others on Linux. But if you want to install Python applications, you typically have to fallback to managing them with pip. Maybe you install them for your account with the --user flag. But with pipx you get a clean, isolated install for every Python application that you use. And if you distribute Python apps, pipx is a definitely worth considering as a channel. Links from the show Chad Smith: @cs01_software Pipx: github.com Entry Points: dev.to Python Packaging Dashboard: chadsmith.dev MKDocStrings: mkdocstrings.github.io gdbgui: github.com termpair: github.com httpie: httpie.io pls (ls-replacement): dhruvkb.github.io Glances: nicolargo.github.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 Twitter: @talkpython Follow Michael on T

You may have heard of the Bloomberg terminal. It's expensive software that can monitor and analyze real-time financial market data and place trades on the electronic trading platform. But have you heard of OpenBB? It's similar software for real-time and long term analysis for finance and investing. The difference is it's open source and built entirely with Python and gives you access to analyze a massive amount of real-time and historical data using the full Python data science stack. On this episode, we have one of the cofounders, James Maslek here to give us a look inside this cool piece of Python-based software. Links from the show James Maslek: linkedin.com OpenBB: openbb.co OpenBB Feature Gallery: openbb.co $8.5M seed funding announcement: openbb.co/blog How to get rich talk by Naval (less money-focused than the title implies): youtube.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

Python is undergoing a performance renaissance. We already have Python 3.11 20-40% faster than even Python 3.10. On this episode, we'll dive into a new proposal to make Python even more efficient using lazy imports laid out in PEP 690. We have all three folks involved on the episode: Carl Meyer, Germán Méndez Bravo, and Barry Warsaw. Are you ready to get into making Python faster still? Let's dive in. Links from the show Guests Barry Warsaw: @pumpichank Germán Méndez Bravo: @germbravo Carl Meyer: @carljm PEP 690: peps.python.org PEP 690 Discussion: discuss.python.org Cinder project: github.com Python Lazy Imports With Cinder on the Meta blog: developers.facebook.com Python performance renaissance: #339: Making Python Faster: talkpython.fm Performance benchmarks for Python 3.11 are amazing: phoronix.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 Twitter: @talkpython Follow Michael

How do you test whether your web sites are working well? Unit tests are great. But for web apps, the number of pieces that have to click together "just so" are many. You have databases, server code (such as a Flask app), server templates (Jinja for example), CSS, Javascript, and even deployment topologies (think nginx + uvicorn). Unit tests won't cover all of that integration. But Playwright does. Playwright is a modern, Pythonic take on testing webs apps using code driving a browser core to interact with web apps the way real users and API clients do. I think you'll find a lot to like there. And we have Pandy Knight from Automation Panda here to break it down for us. Links from the show Pandy's Twitter: @AutomationPanda Pandy's blog: automationpanda.com Playwright: playwright.dev Pandy's Playwright tutorial: github.com pytest: pytest.org applitools: applitools.com Screenplay package: pypi.org/project/screenplay Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch

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 thi

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

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 --- Subsc

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 YouT

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