[pwang]

This is one of the reasons people use Anaconda/miniconda for non-data science work: conda environments are self-contained Python installs, so if you conda/pip install packages into those environments, they will not break each other. This design requirement arose from the specific needs of numerical computing (which always drags in a ton of system-level C/C++/FORTRAN dependencies), but is a generically useful design construct.

Anaconda is a distro, and conda is a package manager, that works across OS platforms and hardware architectures, and installs cleanly into userland without requiring admin privileges. The only way we achieve this difficult goal is by creating a distro and build system that creates "portable" packages that can be relocated/relinked at install-time.

Ultimately, Python's challenges in this department come from the fact that it has such great integration with low-level C/C++ libraries. This gives it super powers as duct tape/glue language, but it also drags it down into the packaging tech debt of C/C++. Hmm... maybe I should write that blog post: "Python Packaging Isn't The Problem; C/C++ Is." :-)