Python Virtual Environment Guide 2025

For most new projects in 2025: use uv. It's 10-100x faster than pip, manages Python versions (no pyenv needed), creates virtual environments, and supports lock files. For library publishing to PyPI, Poetry's dependency groups and publish workflow remain excellent. For ML/GPU work that needs non-Python binaries (CUDA, GDAL), conda is still valuable. For simple scripts, the built-in venv is perfectly adequate.

venv is built into Python 3.3+ as a standard library module — no installation needed. virtualenv is a third-party tool that predates venv, offering more features like cross-Python-version environments and faster creation. For most use cases, the built-in venv is sufficient. virtualenv is commonly used under the hood by other tools like tox and pipenv.

uv is written in Rust by Astral (creators of ruff). It uses a parallel dependency resolver that downloads and installs packages concurrently, implements aggressive caching, and avoids Python startup overhead. A fresh install of a large package set that takes 30+ seconds with pip often completes in under 2 seconds with uv.

No — never commit the venv/ or .venv/ folder to git. Virtual environments are local and platform-specific; they contain binary files and absolute paths that won't work on another machine. Add venv/, .venv/, env/ to .gitignore. Instead, commit requirements.txt (pip), pyproject.toml + poetry.lock (Poetry), or uv.lock (uv) so others can reproduce the exact environment.

requirements.txt is a flat list of pinned dependencies — simple and universal but lacks metadata about your project. pyproject.toml is the modern PEP 517/518 standard that combines project metadata, dependencies, build configuration, and tool settings (ruff, pytest, mypy) in one file. pip can read both; uv, Poetry, Hatch, and PDM all use pyproject.toml. New projects should prefer pyproject.toml.