Talk Python To Me - Python Conversations For Passionate Developers

Python 3.11 is here! Keeping with the annual release cycle, the Python core devs have released the latest version of Python. And this one is a big one. It has more friendly error messages and is massively faster than 3.10 (between 10 to 60% faster) which is a big deal for a year over year release of a 30 year old platform. On this episode, we have Irit Katriel, Pablo Galindo Salgado, Mark Shannon, and Brandt Bucher all of whom participated in releasing Python this week on the show to tell us about that process and some of the highlight features. Links from the show Guests Irit Katriel: @iritkatriel Mark Shannon: linkedin.com Pablo Galindo Salgado: @pyblogsal Brandt Bucher: github.com Python 3.11.0 is now available: blog.python.org PEP 101 - Releasing Python: peps.python.org PEP 678 – Enriching Exceptions with Notes: peps.python.org PEP 654 – Exception Groups and except*: peps.python.org PEP 657 – Include Fine Grained Error Locations in Tracebacks: peps.python.org Python Buildbot: python.org Making Pytho

Do you have a large or growing Python code base? If you struggle to run builds, tests, linting, and other quality checks regularly or quickly, you'll want to hear what Benjy Weinberger has to say. He's here to introduce Pants Build to us. Pants is a fast, scalable, user-friendly build system for codebases of all sizes. It's currently focused on Python, Go, Java, Scala, Kotlin, Shell, and Docker. Links from the show Benjy on Twitter: @benjy Pants Build: pantsbuild.org Pants Source: github.com Getting help in the Pants community: pantsbuild.org/docs/getting-help An example repo to demonstrate Python support in Pants: github.com Toolchain: toolchain.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 Local Maximum Podcast Microsoft AssemblyAI Talk Python Training

Python's data science and data visualization capabilities are certainly one of the reasons for Python's meteoric rise over the past 10 years. But often thens visuals have been corralled into notebooks used by data scientists themselves or into static web pages. Recently, a host of excellent dashboard build and hosting frameworks have come along to turn these visuals into interactive apps for everyone. On this episode, we'll talk about H20 Wave. One of these excellent dashboard frameworks. We have Martin Turoci from H2O.ai here to tell us about Wave. Links from the show Martin Turóci on Twitter: @unusual_code H20 Wave: wave.h2o.ai H20 Wave Source: github.com H20 Widgets: wave.h2o.ai Hydrogen Torch: docs.h2o.ai PyCharm H20 Wave Plugin: plugins.jetbrains.com Testing with Playright episode: talkpython.fm/368 Hacktoberfest: hacktoberfest.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 T

Do you love Python's async and await but feel that you could use more flexibility and higher-order constructs like running a group of tasks and child tasks as a single operation, or streaming data between tasks, combining async tasks with multiprocessing or threads, or even async file support? You should check out AnyIO. On this episode we have Alex Grönholm the creator of AnyIO here to give us the whole story. Links from the show Alex: github.com/agronholm AnyIO: anyio.readthedocs.io sqlacodegen: github.com apscheduler: github.com typeguard: github.com timescale: timescale.com asphalt framework: github.com Talk Python Trio episode: talkpython.fm/167 Trio: github.com Poetry Package manager: python-poetry.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 Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors RedHat Talk Python Training AssemblyAI

Do you struggle to know where to start in the wide range of Python's visualization frameworks? Not sure when to use Plotly vs. Matplotlib vs. Altair? Then this episode is for you. We have Chris Moffitt, a Talk Python course author and founder of Practical Business Python, back on the show to discuss getting started with Python's data visualization frameworks. Links from the show Chris on Twitter: @chris1610 Python Data Visualization course: talkpython.fm 10 tips to move from Excel to Python episode: talkpython.fm Escaping Excel Hell with Python and Pandas episode: talkpython.fm PB Python: pbpython.com matplotlib: matplotlib.org Seaborn example: seaborn.pydata.org Altair: altair-viz.github.io Plotly sunburst: plotly.com Plotly treemap: plotly.com streamlit: streamlit.io Dash: dash.gallery Streamlit Talk Python episode: talkpython.fm splink package: github.com redframes package: github.com Edward Tufte book: edwardtufte.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Sta

For all the amazing powers of Python, deploying packaged apps that leverage native OS-level capabilities isn't one of them. But it can be done and we have a great guest, Rhet Turnbull, here to tell us how he built his distributable macOS app Textinator that uses macOS's native vision recognition framework through Python. Links from the show Rhet Turnbull on Twitter: @RhetTurnbull Textinator app: github.com Homebrew Python Is Not For You: justinmayer.com asdf: asdf-vm.com Space Force: spaceforce.com TextSniper app: textsniper.app Apple's Natural Language ML: developer.apple.com Apple's Vision ML: developer.apple.com py2app: py2app.readthedocs.io py2exe: pypi.org pyinstaller: pyinstaller.org RUMPS: github.com Michael's RUMPS app - URLify: github.com Home Brew: brew.sh pipx: github.com Gooey: github.com PLS CLI: github.com textX: pypi.org Join Space Force: spaceforce.com Beeware: beeware.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe t

When you think data exploration using Python, Jupyter notebooks likely come to mind. They are excellent for those of us who gravitate towards Python. But what about your everyday power user? Think of that person who is really good at Excel but has never written a line of code? They can still harness the power of modern Python using a cool application called Superset. This open source Python-based web app is all about connecting to live data and creating charts and dashboards based on it using only UI tools. It's super popular too with almost 50,000 GitHub stars. Its creator, Max Beauchemin is here to introduce it to us all. Links from the show Max on Twitter: @mistercrunch Superset: superset.apache.org 60 notebook environments: talkpython.fm SQL Fluff linter: sqlfluff.com DB API PEP: peps.python.org Preset Company: preset.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:

We are on the edge of a major jump in Python performance. With the work done by the Faster CPython team and Python 3.11 due out in around a month, your existing Python code might see an increase of well over 25% in speed with no changes. One of the main reasons is its new specializing, adaptive interpreter. This episode is about that new feature and a great tool called Specialist which lets you visualize how Python is speeding up your code and where it can't unless you make minor changes. Its creator, Brandt Bucher is here to tell us all about. Links from the show Brandt Bucher: github.com Specialist package: github.com Faster CPython: github.com Faster CPython Ideas: github.com pymtl package: pypi.org PeachPy: 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 Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Microsoft RedHat Talk Python Training

Terminals seem like the very lowest common denominator for software platforms. They have to work over SSH. They only show text. You can't do much with them. Or can you? Will McGugan and team have been building Textual (based on Rich) which looks more like an animated web app than a terminal app. And he has learned a bunch of lessons trying to maximize terminal based apps. He's here to share his 7 lessons he's learned while building a modern TUI (text user interface) framework. Links from the show Will McGugan: @willmcgugan 7 things I've learned building a modern TUI framework post: textualize.io Prior Talk Python Episode: talkpython.fm Textualize: textualize.io Kitty terminal: sw.kovidgoyal.net Pydantic Immutability: pydantic-docs.helpmanual.io Monodraw: monodraw.helftone.com Async's lru cache: github.com Rich CLI: github.com Nerd Fonts: nerdfonts.com Oh My Posh: ohmyposh.dev Python Object Allocator ASCII Art: github.com Balsamiq wireframes: balsamiq.com Watch this episode on YouTube: youtube.com Episode tr

Do you write web apps in Django? The framework has come a long way lately with versions 3 and 4 adopting many of the modern Python capabilities (async, for example). But there are so many other libraries and apps that you can use to do more with less code in plugin new functionality. I'm happy to have Christopher Trudeau here on talk Python to take us through his 17 favorite libraries you should be using in Django. Links from the show Chris on Twitter: @cltrudeau Django: Getting Started Course (by Chris): training.talkpython.fm Foundational libraries: coverage: coverage.readthedocs.io Sphinx: sphinx-doc.org Sphinx-rtd-theme: github.com pudb: documen.tician.de tox: tox.wiki Pillow (sort of Django, needed by ImageField): github.com Django libraries: Django Ninja: django-ninja.rest-framework.com DRF: django-rest-framework.org Grappelli: github.com django-import-export: github.com Django Debug Toolbar: github.com Django local flavor: github.com Django admin-extra-buttons: github.com django-awl: github.com dj

Have you heard of Flutter? It's a modern and polished UI framework to write mobile apps, desktop apps, and even web apps. While interesting, you may have kept your distance because Flutter is a Dart language-based framework. But with the project we're covering today, Flet, many Flutter UIs can now be written in pure Python. Flet is a very exciting development in the GUI space for Python devs. And we have the creator, Feodor Fitsner, here to take us through it. Links from the show Feodor on GitHub: github.com Flet: flet.dev Flutter: flutter.dev Dart: dart.dev Flet Tutorials: flet.dev It's All Widgets Showcase: itsallwidgets.com Roadmap: flet.dev pglet: pglet.io Flutter Flow Designer: flutterflow.io Fluent UI for Flutter Showcase App: bdlukaa.github.io macOS UI: pub.dev Flet Mobile Strategy: flet.dev Michael's flutter doctor output: flutter-doctor.png Pyscript: pyscript.net Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube:

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