Virtual Environments

Isolated execution spaces that prevent dependency conflicts by locking library versions (like Django 5.0 or NumPy 1.26) to specific project directories.

Virtual environments eliminate the 'it works on my machine' excuse by creating standalone directory trees for every software project. Instead of risking a global Python 3.12 installation, developers use tools like venv or Conda to manage discrete site-packages. This setup allows Project A to run on Flask 2.0 while Project B remains stable on Flask 3.0. It is a standard requirement for reproducible builds: activate the environment (source bin/activate), install dependencies via pip, and maintain a clean system. This approach ensures that local development mirrors production exactly.

https://docs.python.org/3/library/venv.html