Talk Python To Me

What's it's like building a startup with Python and going through a tech accelerator? You're about to find out. On this episode, you'll meet Elissa Shevinsky from Faster Than Light. They are building a static code analysis as a service business for Python and other code bases. We touch on a bunch of fun topics including static code analysis, entrepreneurship, and tech accelerators.

Did you come to software development outside of traditional computer science? This is common, and even how I got into programming myself. I think it's especially true for data science and scientific computing. That's why I'm thrilled to bring you an episode with Daniel Chen about maintainable data science tips and techniques.

If you're a data scientist, how do you deliver your analysis and your models to the people who need them? A really good option is to serve them over Flask as an API. But there are some special considerations you might keep in mind. How should you structure this API? What type of project structures work best for data science and Flask web apps? That and much more on this episode of Talk Python To Me with guest AJ Pryor.

Have you heard that Python is not good for writing concurrent asynchronous code? This is generally a misconception. But there is one class of parallel computing that Python is not good at: CPU bound work running the Python layer.

Back in May of 2018, Bob Belderbos, Julian Sequeira, and I started on what would be a 9-month project. We wanted to create a dedicated, 100 days of code course specifically for Python web developers. Much of what we created for that course, we had prior experience with. But much of it was also new to us.

Have you tried to teach programming to beginners? Python is becoming a top choice for the language, but you still have to have them work with the language and understand core concepts like loops, variables, classes, and more. It turns out, video game programming, when kept simple, can be great for this. Need to repeat items in a scene? There's a natural situation to introduce loops. Move an item around? Maybe make a function to redraw it at a location.

Do you have data you want to visualize and share? It's easy enough to make a static graph of it. But what if you want to zoom in and highlight different sections? What if you need to rerun your ML model on selected data? Then you might want to consider working with Bokeh. It does this and much more. Join me on this episode where you'll meet Bryan Van de Ven who heads up the Bokeh project.

How do we get kids excited about programming? Make programming tangible with embedded devices. Did you know that after kids learned to code with the BBC micro:bit, 90% of kids "thought coding was for everyone" and 86% said it made CS topics more interesting?

On this episode, you'll meet Francesca Lazzeri and hear story how she went from Research Fellow in Economics at Harvard Business School to working on the AI and data science stack on the Azure team.

In the US, we have a very interesting civil option that is quite new: The United States Digital Service. This service was created by President Obama to fix broken government software systems such as the rocky start of the healthcare system.

Do you have stateless code that needs to run in the cloud? The clear answer years ago was to create and HTTP, or even, gasp! A SOAP service before then. While HTTP services are still very important, some of this code can move entirely away from the framework that runs it with serverless programming and hosted functions.

On this episode, I meet up with Rong Lu and Katherine Kampf from Microsoft while I was at BUILD this year. We cover a bunch of topics around data science and talk about two opposing styles of data science development and related tooling: Notebooks vs Python code files and editors.

One of the questions I often ask at the end of the show is "When you write some Python code, what editor do you use?" Increasingly the most common answer is Visual Studio Code. Despite it's Windows only namesake, Visual Studio Code is cross-platform and has been gaining a lot of traction.

Have you ever wondered about the software stack powering Talk Python, the training website, mobile apps, video and audio delivery, and more? While at first glance it might seem pretty simple, there's quite a bit going on. We have our own custom search engines. We deliver 15-20 TB of data per month. Our course video streams from 8 locations throughout the world. Our database server is sending about 12 MBit of traffic / sec with no media in the mix. And it's all powered with Python.

Python 3.8 is coming soon. It's scheduled for release at the end of October 2019 and you can already download test versions today. Given that Python ships on an 18-month cycle, it's time to talk about what's coming for us Python developers in the fall.