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Do you have kids? Maybe nieces and nephews? Or maybe you work in a school environment? Maybe it's just friend's who know you're a programmer and ask about how they should go about introducing programming concepts with them. Anna-Lena Popkes is back on the show to share her research on when and how to teach kids programming. We spend the second half of the episode talking about concrete apps and toys you might consider for each age group. Plus, some of these things are fun for adults too. ;)

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Do you have text that you want to process automatically? Maybe you want to pull out key products or topics of conversation? Maybe you want to get the sentiment? The possibilities are many with this week's topic: NLP with spaCy and Python. Our guest, Vincent D. Warmerdam, has worked on spaCy and other tools at Explosion AI and he's here to give us his tips and tricks for working with text from Python.

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A couple of weeks ago, Charlie Marsh and the folks at Astral made another big splash with a major release of uv called "uv: Unified Python packaging" which has many far reaching features. We had to have Charlie on the show to give us the inside look into this development. Let's get to it.

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Every year the core developers meet to discuss and propose the major changes and trends in Python itself. This invite-only conference of about 50 people happens inside PyCon in the US. Because it's private, we rarely get detailed looks inside this event. On this episode, we have Seth Michael Larson here to give us his account of the sessions and proposals. It's a unique look into the zeitgeist of CPython.

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Python performance has come a long way in recent times. And it's often the data scientists, with their computational algorithms and large quantities of data, who care the most about this form of performance. It's great to have Stan Seibert back on the show to talk about Python's performance for data scientists. We cover a wide range of tools and techniques that will be valuable for many Python developers and data scientists.

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Do you feel like ADHD is holding you back? Maybe you don't personally have ADHD but you work with folks who do and you'd like to support them better. Either way, how ADHD interplays with programming and programmers is pretty fascinating. On this episode we have Chris Ferdinandi who himself has ADHD and has written a lot about it to share his journey and his advice for thriving with ADHD as a programmer or data scientist.

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Flask is one of the most important Python web frameworks and powers a bunch of the internet. David Lord, Flask's lead maintainer is here to give us an update on the state of Flask and Pallets in 2024. If you care about where Flask is and where it's going, you'll definitely want to listen in.

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If you want to get better at something, often times the path is pretty clear. If you get better at swimming, you go to the pool and practice your strokes and put in time doing the laps. If you want to get better at mountain biking, hit the trails and work on drills focusing on different aspects of riding. You can do the same for programming. Reuven Lerner is back on the podcast to talk about his book Pandas Workout. We dive into strategies for learning Pandas and Python as well as some of his workout exercises.

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Python is special. It's used by the big tech companies but also by those you would rarely classify as developers. On this episode, we get a look inside how Python is being used at a Children's Hospital to speed and improve patient care. We have Dr. Somak Roy here to share how he's using Python in his day to day job to help kids get well a little bit faster.

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Python is one of the most popular languages of the current era. It dominates data science, it an incredible choice for web development, and its many people's first language. But it's not super great on front-end programing, is it? Frameworks like React, Vue and other JavaScript frameworks rule the browser and few other languages even get a chance to play there. But with pyscript, which I've covered several times on this show, we have the possibility of Python on the front end. Yet it's not really a front end framework, just a runtime in the browser. That's why I'm excited to have Ken Kinder on the podcast to talk about his project PuePy, a reactive frontend framework in Python.

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I've gathered a group of Python experts who have been thinking deeply about where Python is going and who have lived through where it has been. This episode is all about near-term Python trends and things we each believe will be important to focus on as Python continues to grow. Our panelists are Jodie Burchell, Carol Willing, and Paul Everett.

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I have a special episode for you this time around. We're coming to you live from PyCon 2024. I had the chance to sit down with some amazing people from the data science side of things: Jodie Burchell, Maria Jose Molina-Contreras, and Jessica Greene. We cover a whole set of recent topics from a data science perspective. Though we did have to cut the conversation a bit short as they were coming from and go to talks they were all giving but it was still a pretty deep conversation.

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You're using Pydantic and it seems pretty straightforward, right? But could you adopt some simple changes to your code that would make it a lot faster and more efficient? Chances are, you'll find a couple of the tips from Sydney Runkle that will do just that. Join us to talk about Pydantic performance tips here on Talk Python.

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There hasn't been a boom like the AI boom since the .com days. And it may look like a space destined to be controlled by a couple of tech giants. But Ines Montani thinks open source will play an important role in the future of AI. I hope you join us for this excellent conversation about the future of AI and open source.

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Do you want to look inside your Django request? How about all of your requests in development and see where they overlap? If that sounds useful, you should check out Kolo. It's a pretty incredible extension for your editor (VS Code at the moment, more editors to come most likely). We have Wilhelm Klopp on to tell us all about it.

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So you've created a web app with Python using Flask, Django, FastAPI, or even Emmett. It works great on your machine. How do you get it out to the world? You'll need a production-ready web server. On this episode, we have Giovanni Barillari to tell us about his relatively-new server named Granian. It promises better performance and much better consistency than many of the more well known ones today.

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This episode dives into some of the most important data science libraries from the Python space with one of its pioneers: Wes McKinney. He's the creator or co-creator of pandas, Apache Arrow, and Ibis projects and an entrepreneur in this space.

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Do you use Python in an academic setting? Maybe you run a research lab or teach courses using Python. Maybe you're even a student using Python. Whichever it is, you'll find a ton of great advice in this episode. I talk with Keiland Cooper about how he is using Python at his neuroscience lab at the University of California, Irvine.

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Do you find yourself or your team building internal apps frequently for your company? Are you familiar with the term "forms over data"? They are super empowering for your org but they can be pretty repetitive and you might find yourself spending more time than you'd like working on them rather than core products and services. I invited Jimmy Chan from Dropbase to tell us about their service who's tagline is "Build internal web apps with just Python." It's a cool service and a fun conversation.

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We all know that tools like ChatGPT have really empowered developers to tackle bigger problems. Are you using TailwindCSS and need a login page? Try asking Chat "What is the HTML for a login page with the login username, password, and button in its own section in the center of the page?" It will literally give you a first pass version of it. But how far can you push this? Fred Tubiermont may have taken it farther than most. He built a functioning SaaS product with paying customers by only using ChatGPT and Python. It's fascinating to hear his story.

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What is the state of serverless computing and Python in 2024? What are some of the new tools and best practices? We are lucky to have Tony Sherman who has a lot of practical experience with serverless programming on the show.

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We've spoken previously about security and software supply chains and we are back at it this episode. We're diving in again with Charles Coggins. Charles works at a software supply chain company and is on to give us the insiders and defender's perspective on how to keep our Python apps and infrastructure safe.

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Do you know what custom GPTs are? They're configurable and shareable chat experiences with a name, logo, custom instructions, conversation starters, access to OpenAI tools, and custom API actions. And, you can build them with Python! Ian Maurer has been doing just that and is here to share his experience building them.

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Interested in data science but you're not quite working in it yet? In software, getting that very first job can truly be the hardest one to land. On this episode, we have Avery Smith from Data Career Jumpstart here to share his advice for getting your first data job.

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Do you have data that you pull from external sources or is generated and appears at your digital doorstep? I bet that data needs processed, filtered, transformed, distributed, and much more. One of the biggest tools to create these data pipelines with Python is Dagster. And we are fortunate to have Pedram Navid on the show this episode. Pedram is the Head of Data Engineering and DevRel at Dagster Labs. And we're talking data pipelines this week at Talk Python.

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Have you ever been wait around for pip to do its thing while installing packages or syncing a virtual environment or through some higher level tool such as pip-tools? Then you'll be very excited to hear about the tool just announced from Astral called uv. It's like pip, but 100x faster. Charlie Marsh from Ruff fame and founder of Astral is here to dive in. Let's go.

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Have you heard of Quart? It's the fully-async version of Flask created by Philip Jones who is working closely with the Flask team on these parallel projects. The TL;DR; version is that if you want to take advantage of async and await and you're using Flask, you want to give Quart a solid look. We've spoken to Philip previously about Quart. This time around here's here to share his top Quart extensions and libraries you can adopt today.

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Are you interested in contributing to Django? Then there is an amazing mentorship program that helps Python and Django enthusiasts, because contributes and potentially core developers of Django. It's called Djangonauts and their slogan is "where contributors launch." On this episode, we have Sarah Boyce from the Django team and former Djangonaut and now Djangonaut mentor, Tushar Gupta. Not only is this excellent for the Django community, many of other open source communities would do well to keep an eye on how this creative project is working.

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You've built an awesome set of APIs and you have a wide array of devices and clients using them. Then you need to upgrade an end point or change them in a meaningful way. Now what? That's the conversation I dive into over the next hour with Stanislav Zmiev. We're talking about Versioning APIs.

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Building web UIs in Python has always been in interesting proposition. On one end, we have a the full web design story with artisanal HTML and CSS. On another end there are several Python platforms that aim to the bring RAD, rapid app development, style of building with Python. Those can be great, and I've covered a couple of them, but they usually reach a limit on what they can do or how they integrate with the larger web ecosystem. On this episode, we have Samuel Colvin to share his latest exciting project FastUI. With FastUI, you build responsive web applications using React without writing a single line of JavaScript, or touching npm. Yet designers and other tools can focus on React front-ends for a professional SPA like app experience.

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So you've created a Python-based open source project and it's started to take off. You're getting contributors, lots of buzz in the podcast space, and more. But you have that day job working on Java. How do you make the transition from popular hobby project to full time job? After all, you are giving away your open source project for free, right? Well, on this episode, I have put together an amazing panel of guests who all have done exactly this: Turned their project into full time work and even companies in some cases. We have Samuel Colvin, Gina Häußge, Sebastián Ramírez, Charlie Marsh, Will McGugan and Eric Holscher on to share their stories.

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It's an exciting time for the capabilities of Python. We have the Faster CPython initiative going strong, the recent async work, the adoption of typing and on this episode we discuss a new isolation and parallelization capability coming to Python through sub-interpreters. We have Eric Snow who spearheaded the work to get them added to Python 3.12 and is working on the Python API for 3.13 along with Anthony Shaw who has been pushing the boundaries of what you can already do with subinterpreters.

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Why is Python so popular? There is plenty of room for debate on this but one solid reason is it's easy to adopt, easy to use, and caters to people who are not quite developers/data scientists but need to do some computing. Do you know where there largest untapped set of that group hang out? Excel. That's why it's super exciting that Python is now going to be built directly into Excel. Just go into a cell and type =PY and you're off writing full Python 3 code that is backed by a lite Anaconda distribution of Python. And we have Dr. Sarah Kaiser here to give us the rundown on Python in Excel.

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When you run your code in the cloud, how much do you know about where it runs? I mean, the hardware it runs on and the data center it runs in? There are just a couple of hyper-scale cloud providers in the world. This episode is a very unique chance to get a deep look inside one of them: Microsoft Azure. Azure is comprised of over 200 physical data centers, each with 100,000s of servers. A look into how code runs on them is fascinating. Our guide for this journey will be Mark Russinovich. Mark is the CTO of Microsoft Azure and a Technical Fellow, Microsoft's senior-most technical position. He's also a bit of a programming hero of mine. Even if you don't host your code in the cloud, I think you'll enjoy this conversation. Let's dive in.

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Are you early in your software dev or data science career? Maybe it hasn't even really started yet and you're still in school. On this episode we have Sydney Runkle who has had a ton of success in the Python space and she hasn't even graduated yet. We sit down to talk about what she's done and might do differently again to achieve that success. It's "The Young Coder's Blueprint to Success" on episode 444 of Talk Python To Me.

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Special crossover episode of Python Bytes to wrap up 2023. Topics include:

Michael #1: Hatch v1.8
Brian #2: svcs : A Flexible Service Locator for Python
Michael #3: Steering Council 2024 Term Election Results
Brian #4: Python protocols. When to use them in your projects to abstract and decoupling
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Joke: Joke: The dream is dead?

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If you're a fan of Pydantic or dataclasses, you'll definitely be interested in this episode. We are talking about a super fast data modeling and validation framework called msgspec. Some of the types in here might even be better for general purpose use than Python's native classes. Join me and Jim Crist-Harif to talk about his data exchange framework, mspspec.

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You've probably heard the term "syntactic sugar", that is, syntax within a programming language that is designed to make things easier to read or to express. It makes the language "sweeter" for human use. It turns out Brett Cannon has spent 2 years diving into and writing about Python's sweet language features and how they really work down inside CPython. He joins me on the show today to dive into a few of the more relevant posts he's written about it.

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We all know that LLMs and generative AI has been working its way into many products. It's Jupyter's turn to get a really awesome integration. We have David Qiu here to tell us about Jupyter AI. Jupyter AI provides a user-friendly and powerful way to apply generative AI to your notebooks. It lets you choose from many different LLM providers and models to get just the help you're looking for. And it does way more than just a chat pane in the UI. Listen to find out.

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On this episode we have Wolf Vollprecht and Ruben Arts from the pixi project here to talk about pixi, a high performance package manager for Python and other languages that actually manages Python itself too. They have a lot of interesting ideas on where Python packaging should go and are putting their time and effort behind them. Will pixi become your next package manager? Listen in to find out.

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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

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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 Essay: htmx.org

HTMX: htmx.org

You're Fullstack Now Meme: twitter.com

Deployment Checklist: docs.djangoproject.com

Django-HTMX: github.com

Django @Instagram DjangoChat: djangochat.com

Talk Python HTMX Course: talkpython.fm

Watch this episode on YouTube: youtube.com

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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

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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

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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

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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

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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 YouTube: youtube.com

Episode transcripts: talkpython.fm

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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.

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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

PEP 711 - Distributing Python Binaries episode: talkpython.fm

Portable Python: github.com

Python Build Standalone: github.com

How Netflix does failovers in 7 minutes flat: opensource.com

Security Monkey: github.com

Watch this episode on YouTube: youtube.com

Episode transcripts: talkpython.fm

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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

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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 langage 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

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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.

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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.

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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.

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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.

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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.


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Did you make this year's PyCon event in the US? There was a lot of excitement this time around in Salt Lake City. In this episode I'll bring you a bunch of experiences we had this year. It starts where frequent guest Jay Miller turns the tables and interviews me at the Microsoft booth on the expo hall floor in front of a live audience. Then you'll hear from Mario Munoz, Nick Muoh, Chris Williams, Ray McLendon, and Sean Tibor about their time at the conference.

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What if we distributed CPython, the runtime, in the same way we distributed Python packages - as prebuilt binary wheels that only need to be downloaded and unzipped to run? For starters, that would mean we could ship and deploy Python apps without worrying whether Python itself is available or up-to-date on the platform. Nathaniel Smith has just proposed a PEP to do just that, PEP 711. And we'll dive into that with him next.

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What advice would you give someone just getting into Python? What did you learn over time through hard work and a few tears that would have really helped you? It's a fun game to play and we have Jason McDonald on the podcast to give us his take. Enjoy!

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AI has taken the world by storm. It's gone from near zero to amazing in just a few years. We have ChatGPT, we have Stable Diffusion. But what about Jupyter Notebooks and pandas? In this episode, we meet Justin Waugh, the creator of Sketch. Sketch adds the ability to have conversational AI interactions about your pandas data frames (code and data). It's pretty powerful and I know you'll enjoy the conversation.

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We all know that privacy regulations are getting more strict. And that many of our users no longer believe that "privacy is dead". But for even medium-sized organizations, actually tracking how we are using personal info in our myriad of applications and services is very tricky and error prone. On this episode, we have Thomas La Piana from the Fides project to discuss privacy in our applications and how Fides can enforce and track privacy requirements in your Python apps.

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In recent years, there has been a lot of experimenting how we work with dependencies and external libraries for our Python code. There is pip, pip-tools, Poetry, pdm, pyenv, pipenv, Hatch and others workflows. We dove into this deeply back on episode 406: Reimagining Python's Packaging Workflows. We're back with Ofek Lev to take a deeper look at Hatch.


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If you're like most people, the simplicity and easy of getting started is a big part of pytest's appeal. But beneath that simplicity, there is a lot of power and depth. We have Brian Okken on this episode to dive into his latest pytest tips and tricks for beginners and power users.

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The great power of Python is its over 400,000 packages on PyPI to serve as building blocks for your app. How do you get those needed packages on to your dev machine and managed within your project? What about production and QA servers? I don't even know where to start if you're shipping built software to non-dev end users. There are many variations on how this works today. And where we should go from here has become a hot topic of discussion. So today, that's the topic for Talk Python. I have a great panel of guests: Steve Dower, Pradyun Gedam, Ofek Lev, and Paul Moore.

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So you know about dependencies and testing, right? If you're talking to a DB in your app, you have to decide how to approach that with your tests. There are lots of solid options you might pick and they vary by goals. Do you mock out the DB layer for isolation or do you use a test DB to make it as real as possible? Do you just punt and use the real DB for expediency? What if your dependency was a huge array of radio telescopes and a rack of hundreds of bespoke servers? That's the challenge on deck today were we discuss testing radio astronomy with pytest with our guest James Smith. He's a Digital Signal Processing engineer at the South African Radio Astronomy Observatory and has some great stories and tips to share.


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Clean code is one of those aspects of your programming career that's easy to put on the back burner (sometimes by management more than yourself). But it's important in the short term for writing more debuggable and readable code. And important in the long run for avoiding having your program take on the dreaded "legacy code" moniker. We're fortunate to have Bob Belderbos back on the show. He's been thinking and writing about clean code and Python a lot lately and we'll dive into a bunch of tips you can use right away to make your code cleaner.


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Imagine a world with free and unlimited clean energy. That's the musings of a great science fiction story. But nuclear fusion (the kind that powers the sun) has always been close at hand, we see the sun every day, and yet impossibly far away as a technology. We took a major step towards this becoming a reality with the folks at the Lawrence Livermore National Labratory in the US achieved "ignition" where they got significantly more energy out than they put in. And Python played a major role in this research and experiment. We have Jay Salmonson here to give us a look at the science and the Python code of this discovery.

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When you think about processing tabular data in Python, what library comes to mind? Pandas, I'd guess. But there are other libraries out there and Polars is one of the more exciting new ones. It's built in Rust, embraces parallelism, and can be 10-20x faster than Pandas out of the box.

We have Polars' creator, Ritchie Vink here to give us a look at this exciting new data frame library.

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At some point, you've probably migrated an app from one framework or major runtime version to another. For example, Django to Flask, Python 2 to Python 3, or even Angular to Vue.js. This can be a big challenge. If you had 100s of active devs and millions of lines of code, it's a huge challenge. We have Ben Bariteau from Yelp here to recount their story moving 3.8M lines of code from Python 2 to Python 3. But this is not just a 2-to-3 story. It has many lessons on how to migrate code in many situations. There are plenty of gems to take from his experience.


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Our code quality tools (linters, test frameworks, and others) play an important role in keeping our code error free and conforming to the rules our teams have chosen. But when these tools become sluggish and slow down development, we often avoid running them or even turn them off. On this episode, we have Charlie Marsh here to introduce Ruff, a fast Python linter, written in Rust. To give you a sense of what he means with fast, common Python linters can take 30-60 seconds to lint the CPython codebase. Ruff takes 300 milliseconds. I ran it on the 20,000 lines of Python code for our courses web app at Talk Python Training, and it was instantaneous. It's the kind of tool that can change how you work. I hope you're excited to learn more about it.

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Monorepos are contrary to how many of us have been taught to use source control. To start a project or app, the first thing we do is create a git repo for it. This leads to many focused and small repositories. A quick check of my GitHub account shows there are 179 non-fork repositories. That's a lot but I think many of us work that way.

But it's not like this with monorepos. There you create one (or a couple) repositories for your entire company. This might have 100s or 1,000s of employees working on multiple projects within the single repo. Famously, Google, Meta, Microsoft, and Airbnb all employ very large monorepos with varying strategies of coordination.

On this episode, we have David Vujic here to give us his perspective on monorepos as well as highlight an architectural pattern and set of tools for accomplishing this in Python.

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The iconic and first ever image of a black hole was recently released. It took over a decade of work and is a major achievement for astronomy and broadens our understanding of the universe for all of us. Would it surprise you to know that Python played a major part in this discovery? Of course it did, and Dr. Sara Issaoun is here to give us the full story.

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The beauty of open source software and libraries is that you're not stuck with a single option some vendor is offering. This is especially true when that support is poor and antiquated. Almost any capability you think of has multiple options even for a single language such as Python. Just think about how many web frameworks you can pick today.

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For links and very detailed show notes, please view the original episode page over on Python Bytes. Thanks for listening!
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If you maintain projects on places like GitHub, you know that having a classy readme is important and that maintaining a change log can be helpful for you and consumers of the project. It can also be a pain. That's why I'm excited to welcome back Ned Batchelder to the show. He has a lot of tools to help here as well as some opinions we're looking forward to hearing. We cover his tools and a bunch of others he and I found along the way.

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Jupyter is an amazing environment for exploring data and generating executable reports with Python. But there are many external tools, extensions, and libraries to make it so much better and make you more productive. On this episode, we are going to cover a ton of them. We have Markus Schanta, the maintainer of the awesome-jupyter list on the show and we'll highlight a bunch of Jupyter gems.

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Space science is one of the few sciences that can spark wonder and imagining in almost anyone. It also happens to be the domain of Python with many missions, telescopes, and analysis happening with Python playing a major role. On this episode we have Thomas Albin who has worked on several recent space missions. He has created a delightful YouTube channel called Space Science with Python. It's approachable and yet concrete and realistic. We are going to dive into some of his videos and see how Python can model things like astroid fly-bys and render comets in 3D.

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When you think data science, Jupyter notebooks and associated tools probably come to mind. But I want to broaden your toolset a bit and encourage you to look around at other tools that are literally at your fingertips. The terminal and shell command line tools.

On this episode, you'll meed Jeroen Janssens. He wrote the book Data Science on The Command Line Book and there are a bunch of fun and useful small utilities that will make your life simpler that you can run immediately in the terminal. For example, you can query a CSV file with SQL right from the command line.

Links from the show

Jeroen's Website: jeroenjanssens.com
Jeroen on LinkedIn: linkedin.com
Jeroen cohort-based course, Embrace the Command Line. Listeners can use coupon code TALKPYTHON20 for a 20% discount: maven.com

Data Science on The Command Line Book: datascienceatthecommandline.com
McFly Shell History Tool: github.com
Explain Shell: explainshell.com
CSVKit: csvkit.readthedocs.io
sql2csv: csvkit.readthedocs.io
pipx: github.com
PyProject.toml to add entry points: github.com
rich-cli: github.com
Typer: typer.tiangolo.com
FasD: github.com
Nerd Fonts: nerdfonts.com
Xonsh: xon.sh
iTerm: iterm2.com
Windows Terminal: microsoft.com
ohmyposh: ohmyposh.dev
ohmyz: ohmyz.sh
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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No Python announcement of 2022 was met with more fanfare than pyscript. This project, announced at PyCon 2022, allows you to write Python files and run them in your browser in place of JavaScript or even with interactions between Python and JavaScript. There was just one catch: The runtime download was a 9MB WebAssembly file. That made its uses quite limited.

On this episode, we dive into some news that might change that calculus. The MicroPython and PyScript folks have been teaming up to get PyScript running in the browser on MicroPython. Yes, that's the embedded chip Python. Here's the good news: MicroPython's WebAssembly is just 300k to download and loads in under 100ms. Now that could unlock some possibilities.

We have Brett Cannon, Nicholas Tollervey, and Fabio Pliger on the show to discuss.

YOUTUBE: id=ABVn6uMG1OI

Links from the show

Guests and Host Links

Brett Cannon: @brettcannon@fosstodon.org

Nicholas Tollervey: @ntoll@mastodon.social

Fabio Pliger: @b_smoke

Michael Kennedy: @mkennedy@fosstodon.org

Web Assembly: developer.mozilla.org

pyodide: pyodide.org

micropython: micropython.org

Picture of TFT ESP32 Board: pythonbytes.fm

pyscript: pyscript.net

Simon Willison's Post About micropython + pyscript: fedi.simonwillison.net

WASI: github.com

Watch this episode on YouTube: youtube.com

Episode transcripts: talkpython.fm

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Wondering what Mastodon is all about? More importantly, what does it offer Python developers and other open source folks compared to Twitter? There is a huge amount of interest in the tech community about what's happening at Twitter and whether they should expand to or even move to a new location. So I decided to put together a set of experienced Python developers who have been Mastodon inhabitants for a long time to discuss what this unexpected shift means for one of our important online watering holes.

On this episode you'll meet Gina Häußge, Simon Willison, and Juan Luis Cano Rodríguez. We're going to have a great time talking about the technology and the culture of Mastodon from this Python perspective.

Links from the show

Gina on Mastodon: chaos.social/@foosel

Juan on Mastodon: social.juanlu.space/@astrojuanlu

Simon on Mastodon: simonwillison.net/@simon

Carol on Mastodon: @willingc@hachyderm.io

Michael on Mastodon: fosstodon.org/@mkennedy

Talk Python on Mastodon: fosstodon.org/@talkpython

Mastodon: joinmastodon.org

Fosstodon: fosstodon.org

Mastodon is just blogs article: simonwillison.net

C-Suite Folks Resign at Twitter: twitter.com

The Importance of Choosing the Correct Mastodon Instance: carlchenet.com

Wizard to choose your instance: instances.social

Here’s how a Twitter engineer says it will break in the coming weeks: technologyreview.com

Dealing with unwanted content: docs.joinmastodon.org

Twitter poll on moving to Mastodon: twitter.com

Toot package: pypi.org

Takahe server: github.com

Validating your Github account on your Mastodon profile: mastodon.social

Twitter’s potential collapse could wipe out vast records of recent human history: technologyreview.com

Simon's Streaming API: gist.github.com

Mastodon 4.0 is out (day after recording): mastodon.social

Watch this episode on YouTube: youtube.com

Episode transcripts: talkpython.fm

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If you're a fan of Python's async and await keywords and the powers they unlock, then this episode is for you. We have Timo Furrer here to share a whole bunch of asyncio related Python packages. Timo runs the awesome-asyncio list and he and I picked out some of our favorites to share with you.

Links from the show

Timo on Twitter: @tuxtimo

awesome-asyncio list: github.com

Some of the highlighted packages

FastAPI: github.com

starlette: github.com

sanic: github.com

uvicorn - The lightning-fast ASGI server: github.com

Tech Empower Python Framework benchmarks: techempower.com

aioamqp - AMQP implementation using asyncio: github.com

pyzmq - Python bindings for ZeroMQ: github.com

Scaling Python and Jupyter with ZeroMQ Talk Python episode: talkpython.fm/306

asyncpg - Fast PostgreSQL Database Client: github.com

Piccolo - An ORM / query builder: github.com

aiosqlite: github.com

motor - The async Python driver for MongoDB: github.com

AsyncSSH: github.com

HTTPX: github.com

pytest-asyncio - Pytest support for asyncio: github.com

uvloop - Ultra fast implementation of asyncio event loop: github.com

aiocache - Cache manager for different backends: github.com

aiofiles - File support for asyncio: github.com

aiopath - Asynchronous pathlib for asyncio: github.com

Video: Demystifying Python's Async and Await Keywords - JetBrains TV 2020 (Michael Kennedy): youtube.com

tenacity: readthedocs.io

Michael's full 5 hour async course: talkpython.fm/async

Watch this episode on YouTube: youtube.com

Episode transcripts: talkpython.fm

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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 Python Faster Talk Python Episode: talkpython.fm

Specializing, Adaptive Interpreter on Talk Python: talkpython.fm

Specialist Visualizer: github.com

"Zero cost" exception handling: github.com

Pyodide: pyodide.org

pyscript: pyscript.net

Watch this episode on YouTube: youtube.com

Episode transcripts: talkpython.fm

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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

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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

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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

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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

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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

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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

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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

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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 transcripts: talkpython.fm

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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

django-airplane: github.com

Django Extensions: github.com

django-allauth: github.com

awesome-django: github.com

‘Unstoppable’ Python: infoworld.com

asciimatics: github.com

Watch this episode on YouTube: youtube.com

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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

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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

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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

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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: pyfound.blogspot.com

Sponsor: Reflect.run demo video: youtube.com

Sponsor: Reflect.run sign up: app.reflect.run

Sponsor: Microsoft for Startups Founder's Hub: startups.microsoft.com

Watch this episode on YouTube: youtube.com

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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

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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 episode: talkpython.fm

Watch this episode on YouTube: youtube.com

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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

Michael's in-person FastAPI course: maven.com

Reimbursement templates for our courses

Expense a Course at Talk Python: zoho.com

Expense Course Bundle at Talk Python: zoho.com

Expense Cohort Course at Talk Python: zoho.com

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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

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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

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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

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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

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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 this episode on YouTube: youtube.com

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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.org

Michael's new Up and Running with Git course: talkpython.fm/git

Watch this episode on YouTube: youtube.com

Episode transcripts: talkpython.fm

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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

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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 YouTube: youtube.com

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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

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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

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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

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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: youtube.com

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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 touch with us ---

Subscribe to us on YouTube: youtube.com

Follow Talk Python on Twitter: @talkpython

Follow Michael on Twitter: @mkennedy

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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 to us on YouTube: youtube.com

Follow Talk Python on Twitter: @talkpython

Follow Michael on Twitter: @mkennedy

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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

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

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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 touch with us ---

Subscribe to us on YouTube: youtube.com

Follow Talk Python on Twitter: @talkpython

Follow Michael on Twitter: @mkennedy

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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

uvloop: github.com/MagicStack/uvloop

asyncpg: github.com/MagicStack/asyncpg

TaskGroups and ExceptionGroups: twitter.com

EdgeDB: edgedb.com

Schema modeling: edgedb.com/showcase/data-modeling

Easy EdgeDB book: edgedb.com/easy-edgedb

Roadmap: edgedb.com/roadmap

pgMustard: pgmustard.com

PyBay: Building a Database with Python Talk: youtube.com

Michael's course on async and await + Cython + uvloop: talkpython.fm/async

Michael's PyBay talk: Flask + HTMX: 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: @mkennedy

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