Python virtual environments: venv vs conda vs uv — which one?
posée a month ago275 vuesen
I'm doing data science and web dev in Python. Tutorials use venv, conda, or uv. Which should I standardize on?
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- uv: extremely fast, manages Python versions *and* dependencies with a lockfile (uv.lock), and works with standard pyproject.toml. Great default for web and most data work in 2026. - venv + pip: built into Python and always available. Fine for simple projects, but you need extra tools for locking. - conda / mamba: its strength is non-Python binary dependencies (CUDA toolkits, GDAL, some scientific C libraries). Choose it when pip wheels for your stack are painful.
Pragmatic setup: uv everywhere, and conda only for the rare environment that needs complex native packages. Since PyTorch and most ML libraries ship pip wheels with CUDA, uv covers most data science too.
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