Preliminaries

Preliminaries#

Professional Python Environment Setup: Training Preparation & Reference Guide

About this guide#

This guide serves two purposes:

  1. Pre-training preparation: If you’re attending our Python training, please work through these installation steps beforehand to ensure your computer is ready.

  2. 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.

Note

One of the goals of this training is to help you understand that the Python ecosystem’s strength lies in its diversity. Different tools (editors, package managers, version control systems) are designed for different workflows and needs. By experiencing multiple options during the training, you’ll learn to choose the right tool for each situation.

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):

    • for macOS only: Homebrew,

    • for Python packages from PyPI: UV,

    • for conda packages from conda-forge: Pixi and Miniforge;

  • Editors and IDEs: VSCode, Spyder, and JupyterLab (choose based on your workflow);

  • Version control tools: Git and Mercurial (to track changes and collaborate);

Note

Don’t worry if this list looks long! We provide step-by-step instructions for each tool in the following pages. The process is straightforward and shouldn’t take more than an hour or two with a good internet connection.

Some tools are marked as optional. They’re useful for certain workflows but not required for the training itself.

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.

Warning

Some installations require administrator permissions. If you don’t have admin rights on your computer, please ask your IT administrator to help with this setup. You can share this link with them directly.

Need help?#

If you encounter problems during installation:

  1. Check that you followed each step carefully

  2. Try searching for the specific error message online

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!

Table of contents#

Contents