Introduction

Before diving into Data or ML frameworks, it’s important to have a clean and reproducible development setup. A good environment makes you:

• Faster: less time fighting dependencies.
• Consistent: same results across laptops, servers, and teammates.
• Confident: tools catch errors before they become bugs.

A consistent developer experience saves hours of debugging. You spend more time solving problems, less time fixing environments.

Python Virtual Environment

• A virtual environment is like a sandbox for Python.
• It isolates your project’s dependencies from the global Python installation.
• Easy to manage different versions of library.
• Must depend on requirements.txt, it has to be managed manually.

Without it, installing one package for one project may break another project.