For as much improvement as there has been with what can be distributed via PyPI, there are still some domains that have gnarlier dependencies than wheels happily handle alone, and you either need to reach for the system package manager (and loose the ability to really control the dependency environment from that mismatch), or take advantage of the Conda ecosystem.

My org does a lot of work combining machine learning with oceanographic and climate modeling, which are both domains that have deep dependency chains that don't always mesh well, especially as our researchers mix in R and other languages as the same time, and the Conda ecosystem helps us a ton with that, but there are issues that `conda` and `mamba` don't help us out with.

Pixi takes a swing at some of what the Conda ecosystem hasn't been great at (at least without a lot of manual custom ceremony) that Cargo, Poetry, Pipenv, PDM, and other dependency and workflow management tools have demonstrated can be done such as lock files, cross platform dependency management, task running, and defining multiple related environments.

What's really cool when you have a mix of projects, Pixi can work almost entirely PyPI native out of a `pyproject.toml`, other than installing Python from Conda-Forge, so you can mix and match environments but stay with the same tool. https://prefix.dev/blog/using_python_projects_with_pixi docs: https://pixi.sh/latest/advanced/pyproject_toml/