pip vs uv: A Quick Comparative Study to Get You Started

Python package management is at the heart of every developer’s workflow. For years, pip has been the default package manager, handling everything from installing dependencies to managing virtual environments.

But recently, uv, a Rust-powered package manager, has entered the scene—promising faster installs and modern workflows. Naturally, I wanted to see what all the hype was about.

This short blog isn’t an in-depth technical breakdown. It’s a quick, practical look at how pip and uv perform when installing 20 popular Python packages. If you’re wondering whether it’s worth giving uv a try, these results will help you decide.

Packages Tested

Here’s the list of packages I used for this comparison (a mix of data science, web dev, and utilities):

1. numpy

2. pandas

3. scipy

4. matplotlib

5. seaborn

6. scikit-learn

7. streamlit

8. fastapi

9. requests

10. beautifulsoup4

11. sqlalchemy

12. pydantic

13. uvicorn

14. pillow

15. plotly

16. nltk

17. opencv-python

18. jupyterlab

19. black

20. flake8

Results: Installation Time (in seconds)

Observations

• uv was about 3x faster than pip for this set of packages.

• Dependency resolution and wheel downloads were significantly quicker with uv, thanks to its Rust backend.

• The end result (installed packages) was identical—no compatibility issues.

Should You Switch?

If speed is a priority for you (especially in large projects or CI pipelines), uv is definitely worth trying out. It integrates well with virtual environments and feels like a drop-in replacement for most use cases.

That said, pip remains rock-solid, mature, and deeply integrated into the Python ecosystem. If you’re happy with it, there’s no urgent need to switch—but knowing you have faster alternatives is always good.

Final Thoughts

This was a quick-start comparison—no deep dives, just results to help you get a feel for the difference. Want more details like dependency resolution strategies, security, or ecosystem support? Maybe for a future post 😉

Give uv a try and see how it fits your workflow!

References

• uv Documentation & Blog: https://astral.sh/blog/uv

• uv GitHub Repository: https://github.com/astral-sh/uv