[mushufasa]

> Wetlands not only simplifies the creation of isolated Conda environments with specific dependencies, but also allows you to run

I've been using Conda for 10 years as my default package manager on my devices (not pipenv or poetry etc). I started because it was "the way" for data science but I kept with it because the syntax is really intuitive to me (conda create, conda activate).

I'm not sure what problem you are solving here -- the issues with conda IMO are that it is overkill for the rest of the python community, so conda-forge has gradually declined and I typically create a conda environment then use pip for the latest libraries. Managing the conda environments though is not my issue -- that part works so well that I keep with it.

If you could explain why you created this and what problems you are solving with an example, that would be helpful. All package managers are aimed at "avoiding dependency conflicts" so that doesn't really communicate to me what this is and what real problem it solves.

[N1H1L]

I used to use conda, but have switched entirely to uv now

[oezi]

As someone new to Python: what was ever the appeal of conda that uv doesn't satisfy?

[aeroevan]

conda doesn't just package python libraries, but also the C/Fortran/other bits that the scipy stack often depended on. With the rise of binary wheels that is less needed though

[kjkjadksj]

Fundamentally it is a fresh usr/bin per environment with all that can go into that. Not just python tooling. R packages. Binaries. All of that. Env can be exported as a yaml file and trivially shared without appending some header to all scripts you write.

[jaimergp]

I think it's more about tool X vs Y, but about ecosystems and packaging approaches; in other words Python packaging (which has tools like pip, uv or poetry) vs conda packaging (which has tools like conda itself, mamba or pixi). https://pypackaging-native.github.io/ is an excellent starting point to learn about the limitations on Python packaging for native dependencies and compiled extensions.

[aldanor]

Distributing non Python packages via the same channel that Python packages may depend on. E.g, h5py depending on libhdf5.

[N1H1L]

As someone using Python from 2017, there was no uv then, and conda worked fine but was glacially slow.