Talk Python To Me

I'm always on the look out for a good Python UI framework. This episode focuses on Dear PyGui. Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies, created by Jonathan Hoffstadt and Preston Cothren. They are here to tell us all about it.

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.

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?"

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.

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.

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.

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.

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.

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.

There has a been a bunch of renewed interested in making Python faster. While for some of us, Python is already plenty fast. For others, such as those in data science, scientific computing, and even the large tech companies, making Python even a little faster would be a big deal. This episode is the first of several that dive into some of the active efforts to increase the speed of Python while maintaining compatibility with existing code and packages.

How do you build and maintain a complex suite of Python packages? Of course, you want to put them on PyPI. The best format there is as a wheel. This means that when developers use your code, it comes straight down and requires no local tooling to install and use.

Have you heard of Kedro? It's a Python framework for creating reproducible, maintainable and modular data science code. We all know that reproducibility and related topics are important ones in the data science space. The freedom to pop open a notebook and just start exploring is much of the magic. Yet, that free-form style can lead to difficulties in versioning, reproducibility, collaboration, and moving to production. Solving these problems is the goal of Kedro. And we have 3 great guests from the Kedro community here to give us the rundown: Yetunde Dada, Waylon Walker, and Ivan Danov.

Have you heard of the package Rich? This library allows you to create very, well, rich terminal-based UIs in Python. When you think of what you can typically build with basic print statements, that may seem quite limited. But with Rich, imagine justified tables, progress bars, rendering of markdown, and way more. This is one of the fastest growing projects in the Python space these days. And the creator, Will McGugan is here to give is the whole history and even a peak at the future of Rich and a follow on library called Textual.

Gene therapy holds the promise to permanently cure diseases that have been considered life-long challenges. But the complexity of rewriting DNA is truly huge and lives in its own special kind of big-data world. On this episode, you'll meet David Born, a computational biologist who uses Python to help automate genetics research and helps move that work to production.

On this episode, Rob Emanuele and Tom Augspurger join us to talk about building and running Microsoft's Planetary Computer project. This project is dedicated to providing the data around climate records and the compute necessary to process it with the mission of help use all understand climate change better. It combines multiple petabytes of data with a powerful hosted Jupyterlab notebook environment to process it.