I wish you luck with tracking down versions of software used when you're writing papers... especially if you're using multiple conda environments. This is pretty much the example used in the article -- version mismatches.

But, I think this illustrates the problem very well.

Conda isn't just used for Python. It's used for general tools and libraries that Python scripts depend on. They could be C/C++ that needs to be compiled. It could be a Cython library. It could be...

When you're trying to be a package manager that operates on-top of the operating system's package manager, you're always going to have issues. And that is why Conda is such a mess, it's trying to do too much. Installation issues are one of the reason why I stopped writing so many projects in Python. For now, I'm only doing smaller scripts in Python. Anything larger than a module gets written in something else.

People here have mentioned Rust as an example of a language with a solid dependency toolchain. I've used more Go, which similarly has had dependency management tooling from the begining. By and large, these languages aren't trying to bring in C libraries that need to be compiled and linked into Python accessible code (it's probably possible, but not the main use-case).

For Python code though, when I do need to import a package, I always start with a fresh venv virtual environment, install whatever libraries are needed in that venv, and then always run the python from that absolute path (ex: `venv/bin/python3 script.py`). This has solved 99% of my dependency issues. If you can separate yourself from the system python as much as possible, you're 90% of the way there.

Side rant: Which, is why I think there is a problem with Python to begin with -- *nix OSes all include a system level Python install. Dependencies only become a problem when you're installing libraries in a global path. If you can have separate dependency trees for individual projects, you're largely safe. It's not very storage efficient, but that's a different issue.