Talk Python To Me

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.

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!

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.

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.