I mean, you're not entirely wrong but Python really really doesn't make it easy.

Consider R, which is filled with the same kind of people. There's one package repository and if your package doesn't build cleanly under the latest version of R, it's removed from the repo.

Don't get me wrong, this has other problems but at least it means that all packages will work with a single language version.

Python is a much older and generalist language than R, so yes, while it would be great to impose this kind of order on things, it’s not practical for its current extent of use.

That being said, after two decades of using Python professionally, the only really problems I’ve ever encountered are “package doesn’t support this version for {reasons}” and “ML library is doing something undocumented and/or dumb with a specific Python version.” The former is normally because the package author is no longer their package and the latter is because, again, the ML community is among the absolute worst at creating solid tooling.

I don't disagree that Python's place in the ecosystem ("generalist" - i.e. load-bearing distro fossilization in everything from old binary linux distros, container layers, SIEM/SOAR products, serverless runtimes...) leads to much packaging complexity that R just doesn't have

However, Python (1991) is only 2 yrs older than R (1993)

Oh, my bad. I though R was quite a bit younger.

And R is a clone of S which is from 1973.

When people could design systems that work for long periods of time.

> I mean, you're not entirely wrong but Python really really doesn't make it easy.

That's a vast exaggeration. It is not "really really" hard to spin up a venv and specify your requirements. People just don't do it, and blame the tools for what are bad engineering practices agnostic to any language.

"Really really" not easy would be handling C, C++, etc. dependencies.

I work in data science in python (and the parent was about ML) and basically everything in that space has C and Fortran level dependencies and this is where Python is really really bad, so no it is not as simple as you're making out.

I really really wish it was, as then I wouldn't have had to learn Docker.

Generally that is a straight forward process of compiling, reading the error message, googling “$dist install $dirname of missing dep” running the apt-get / emerge / yum “ command and then repeating the compile command. Sometimes people will depend on a rare and not bundled dep, but not that often. Worst case you need to upgrade auto make tool chain or rebuild boost or something.

Maybe more time than getting python deps to work but more deterministic and takes less cleverness.