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Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive deep into the popular packages and software developers, data scientists, and incredible hobbyists doing amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite packages and the hot new ones coming out of open source.
430 Episodes
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Every year Python has a new major release. This year it's Python 3.12 and it'll come out on October 2, 2023. That's 4 days from when this episode was published. There is quite process involved to test, build, and ship Python across many platforms and channels. We have Seth Michael Larson here to give us a detailed rundown on what exactly is involved in releasing CPython.
So, you've got this amazing machine learning model you created. And you want to share it and let your colleagues and users experiment with it on the web. How do you get started? Learning Flask or Django? Great frameworks, but you might consider Gradio which is a rapid development UI framework for ML models. On this episode, we have Freddy Boulton, to introduce us all to Gradio.
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.
4Have 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.
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.
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.
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.
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.
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.
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.
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.
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.
Here's the situation. You have a Python app that is locked or even has completely crashed and all you're left with is a core dump on the server. Now what? It's time for PyStack! You can capture a view of your app as if you've set a breakpoint and even view the callstack and locals across language calls (for example from Python to C++ and back). We have the maintainers, Pablo Galindo Salgado and Matt Wozniski, here to dive into PyStack. You'll definitely want to have this tool in your toolbox.
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.
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.
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.
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.
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.
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.
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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Great podcast! The best part was about deployment tools py2app and PyInstaller. That is exactly what I was looking for. After listening about it, I just used PyInstaller at the company and it worked like a charm. Thank you for doing it and keep up a good work!
voice quality is terrible
this episode is gold, the article submitted with it is gold too
yo so I'm barely starting to get into this or I really want to learn how to code what do you recommend for me to start I have very little knowledge just being honest
nix the intro music
It was fun, thanks for having me over
awesome!
Carlton's talk is on YouTube as "DjangoCon 2019 - Using Django as a Micro-Framework: Hacking on the HTTP handlers.. by Carlton Gibson" https://2019.djangocon.us/talks/using-django-as-a-micro-framework-on-the/ Couldn't find it in the show notes.
notes
Michael, At the end of each episode you could ask "Is it Gif or Jif?" Just for the fun of it.
great podcast - testing your tests all night (without even being there) - some good coding discipline there for us noobs
great episode! I've been using Python on Windows for the past two years and I love it. I've never had any problems specific to Windows.
at the 53:12 what is the package name? pip install eo? eil?
Pyodide is undeniably cool. There's also a micropython port to wasm that might make sense for basic webapps.
ummm. But the mic sounds terrible hahah
you remind me of me! excellent Googler, master of science, business-related experience, passionate about teaching... the only thing I've not done like you is actually create my own course... you wanna take on a mentee? I'm game please ^--^
I listen for a year, I fell like Michael Kennedy is my best friend, im so grateful for showing me that excitement and possibilities with this language, this is my new road in life. thanks so much 10/10
Python勉強中の方は必聴！
Stackoverflow users are really mean but I still love it, there is no better alternative to it and the meanness keeps bad contents at bay. So it's good to be mean I guess.