I think it's something unique to python's ML ecosystem, to be honest. There is a lot of up-in-the-air about how to handle models, binaries and all of that in a contained package, and that results in quite a few hand-rolled solutions, some of which encroach on the package manager's territory, plus of course drivers and windows.

I've worked on/with some fairly large/complex python projects, and they almost never have any packaging issues that aren't just obvious errors by users. Yes, every once in a while we have to be explicit about a dependency because some dependent project isn't very strict with their versioning policy and their API layers.

I've not used python professionally for years - but I have had to do this maybe once in many years of usage. Seen it like once more in my team(s). A rounding error.

I've seen someone having to do this in node like once every month, no matter which year, no matter which project or company.