[Avg. reading time: 5 minutes]
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.
