#520: pyx - the other side of the uv coin (announcing pyx)

The team at Instagram dropped a performance bomb on the Python world when they open-sourced Cider, their performance oriented fork of CPython. It contains a number of performance optimizations, including bytecode inline caching, eager evaluation of coroutines, a method-at-a-time JIT, and an experimental bytecode compiler that uses type annotations to emit type-specialized bytecode that performs better in the JIT.

8 Tammi 20221h 11min

Do you enjoy the "final 2 questions" I always ask at the end of the show? I think it's a great way to track the currents of the Python community. This episode focuses in on one of those questions: "What notable PyPI package have you come across recently? Not necessarily the most popular one but something that delighted you and people should know about?"

21 Joulu 20211h 13min

You know that feeling when one of your developer friends or colleague tells you about some amazing tool, library, or shell environment that you never heard of that you just have to run out and try right away? This episode is jam-packed full of those moments. We welcome back Jay Miller to discuss tools and tips for developer productivity. The title says 10 tips, but we actually veer into many more along the way. I think you'll really enjoy this useful and light-hearted episode.

15 Joulu 20211h 16min

SQLAlchemy is the most widely used ORM (Object Relational Mapper) for Python developers. It's been around since February 2006. But we might be in for the most significant release since the first one: SQLAlchemy 2.0. This version adds async and await support, new context-manager friendly features everywhere, and even a unified query syntax. Mike Bayer is back to give us a glimpse of what's coming and why Python's database story is getting stronger.

9 Joulu 20211h 6min

Here's a question: What's the most common way to explore data? Would you say pandas and matplotlib? Maybe you went more general and said Jupyter notebooks. How about Excel, or Google Sheets, or Numbers, or some other spreadsheet app? Yeah, my bet is on Excel. And while it has many drawbacks, it makes exploring tabular data very accessible to many people, most of whom aren't even developers or data scientists. On this episode, we're talking about a tool called Mito. This is an add-in for Jupyter notebooks that injects an Excel-like interface into the notebook. You pass it data via a pandas dataframe (or some other source) and then you can explore it as if you're using Excel. The cool thing is though, just below that, it's writing the pandas code you'd need to do to actually accomplish that outcome in code.

30 Marras 20211h 6min

At PyCon 2017, Jake Vanderplas gave a great keynote where he said, "Python is a mosaic." He described how Python is stronger and growing because it's being adopted and used by people with diverse technical backgrounds. In this episode, we're adding to that mosaic by diving into how Python is being used in the architecture, engineering, and construction industry. Our guest, Gui Talarico, has worked as an architect who help automate that world by bringing Python to solve problems others were just doing by point-and-click tooling. I think you'll enjoy this look into that world. We also touch on his project pyairtable near the end as well.

23 Marras 20211h 1min

Do you do anything with Jupyter notebooks? If you do, there is a very good chance you're working with the pandas library. This is one of THE primary tools of anyone doing computational work or data exploration with Python. Yet, this library is massive and knowing the idiomatic way to use it can be hard to discover.

17 Marras 202159min

Is Python slow? We touched on that question with Guido and Mark last episode. This time we welcome back friend of the show, Anthony Shaw. Here's there to share the massive amount of work he's been doing to answer that question and speed things up where they answer is yes. He's just released version 1.0 of the Pyjion project.

10 Marras 20211h 13min