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.

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

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

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.

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