Preliminaries
Professional Python Environment Setup: Training Preparation & Reference Guide
About this guide¶
This guide serves two purposes:
Pre-training preparation: If you’re attending our Python training, please work through these installation steps beforehand to ensure your computer is ready.
Reference for setting up a professional Python environment: This guide provides step-by-step instructions to set up a complete, production-ready Python environment suitable for scientific computing, data analysis, and software development. You can share it with colleagues, students, or use it to set up new machines.
Why this particular setup?¶
You might wonder why we recommend these specific tools instead of just installing Python from python.org. Here’s why:
For reproducibility and collaboration: The tools we’ll install (UV, Pixi) help you manage different Python versions and package dependencies reliably. This means your code will work the same way on your colleague’s computer, in production, or when you revisit a project months later.
For scientific computing: Some scientific Python packages have complex dependencies. The package managers we recommend handle these automatically, saving you from common installation headaches.
For professional development: Version control (Git, Mercurial), quality editors (VSCode, Spyder, JupyterLab), and proper environment management aren’t just nice to have. They’re essential for serious Python work, whether in research, industry, or education.
This setup may seem extensive at first, but each tool solves a real problem you’ll encounter as you work with Python professionally.
What you’ll install¶
We will guide you through installing and configuring:
Utilities such as curl or Meld;
Package managers (tools for installing software reliably):
Editors and IDEs: VSCode, Spyder, and JupyterLab (choose based on your workflow);
Version control tools: Git and Mercurial (to track changes and collaborate);
Installation instructions by operating system¶
The installation steps vary depending on your operating system (Windows, macOS, or Linux). We’ve created dedicated pages for each system to make the process as smooth as possible.
Need help?¶
If you encounter problems during installation:
Check that you followed each step carefully
Try searching for the specific error message online
Explain what you did and what you get (copy and paste the commands and the errors) to a LLM like Le Chat (Mistral) or Claude (Antropic).
If you’re still stuck, please open an issue in the py-edu-fr issue tracker. Describe what you did and copy-paste any error messages you received. This is also excellent practice for collaborating on technical projects. Learning to report issues clearly is an important skill!