PyPalettes is a new Python library designed to simplify the use of color palettes in Python charts. Read more...

The 2024 Scientific Python Developer summit was held 3–5 June in Seattle. Here’s a summary of what we did. Read more...

TL;DR: If you have GPU code in your project, setup a GitHub hosted GPU runner today. It is fairly quick to do and will free you from having to run tests manually.

Read more...

We are happy to announce that we have organized the necessary infrastructure and processes to allow volunteers to start translating multiple project websites. In this blog post, we will discuss how we set up manage translations for Scientific Python projects, and how you can participate in the translation and localization effort. Read more...

Eighteen years since the release of NumPy 1.0, we are thrilled to announce the launch of NumPy 2.0! This major release marks a significant milestone in the evolution of NumPy, bringing a wealth of enhancements and improvements to users, and setting the stage for future feature development.

Read more...

Given the practical challenges of achieving true randomness, deterministic algorithms, known as Pseudo Random Number Generators (RNGs), are employed in science to create sequences that mimic randomness. These generators are used for simulations, experiments, and analysis where it is essential to have numbers that appear unpredictable. I want to share here what I have learned about best practices with pseudo RNGs and especially the ones available in NumPy.

Read more...

One outcome of the 2023 Scientific Python Developer Summit was the Scientific Python Development Guide, a comprehensive guide to modern Python package development, complete with a new project template supporting 10+ build backends and a WebAssembly-powered checker with checks linked to the guide. The guide covers topics like modern, compiled, and classic packaging, style checks, type checking, docs, task runners, CI, tests, and much more! There also are sections of tutorials, principles, and some common patterns.

Read more...

In mid-2018 I started learning Python by reading textbooks and watching online tutorials. I had absolutely zero background in computer science, but it seemed interesting so I continued to try. At some point, I decided I wanted to do a master’s degree in statistics, so I began to work on more statistics-based programming. That’s when I found SciPy. I became (and still am) fascinated by the idea of open-source software that is completely free to use and supported by a community of diligent programmers.

Read more...