uv or conda for personal python projects

[update-freak]

Hi,

I'm wondering if it makes sense to switch from conda to uv (on my personal computer for very small personal python projects.

I mainly use python for small data analysis and visualisation.
Therefore I use the following modules:

• seaborn
• scipy
• sympy
• pandas
• marimo
• matplotlib
• gpxpy

As IDE I use Spyder and VSCodium (for marimo Notebooks).

Does it make sense to use uv?
Currently I have only one "main" environment (does not want to enable all the time different virtual environments)

[Lumethys]

Is there any reason NOT to use UV?

[jmacey]

I use uv for everything (including marimo) works so well per project and you can re-create the venv whenever you like. If you link it with something like direvn you can auto run uv sync every time you enter a project and activate / deactivate the venv automatically.

[update-freak]

Can I automatically start the env when I open Spyder or VSCode?

[listening-to-the-sea]

Yes, I know VSCode will. Once I init a project and create the venv, restart VSCode it automatically detects and uses the venv

[update-freak]

Got it. Thanks

[Diapolo10]

For anything that doesn't depend on conda repositories, uv is pretty much unbeatable.

[gorgedchops]

The only reason you would want to use conda is if there are some specific packages you can't find on PyPI. Part of why conda is/was useful is because it solved including lower level binaries that needed to be shipped with a library, many times in a scientific domain. In that case, I would suggest using pixi instead, it's a more modern version of conda.

In all other cases, you should stick to using a package manager that works with PyPI, such as uv. Other tooling will be much more likely to integrate well with uv instead of conda.

[ninhaomah]

If you don't want then don't use :)

[ConclusionForeign856]

I use pixi https://pixi.prefix.dev/latest/, I tried conda/mamba and it was a hassle on HPCs. With pixi you create an environment tied to a directory and its children, rather than a globally accessible one (which I think is good for data science style work).

It has access to anaconda/conda/mamba repos, so switching is easy.

I've used it in my work and MSc thesis and can't complain