You are right and your FreeBSD vs Linux analogy really hits the target.
Thanks to the advancement of hardare and the development of libraries like Numpy, Python has moved from it's scripting niche and became a global, general purpose language.
Was that an error? If we look at the "batteries included" philosophy and the poor state of Python's package management after all these years...I'd say yes.
Since quality of life tools and other soft things are hard to prove, I'll tell my anecdata story point. I sought out any package manager experience in python. I landed on Pipenv which uses pip. It failed to solve the tree. This led me to find poetry and the reason for existing which is exactly my experience. That was 2-3 years ago.
https://github.com/python-poetry/poetry#why
Combine this with asdf and it aligns with most other languages.
asdf + yarn/npm = javascript
asdf + poetry = python
asdf + bundler = ruby
asdf + cargo = rust
asdf + mix = elixir
asdf + shards = crystal
asdf + go.mod (others) = go
asdf + composer = php
In legacy (don't break anything) mode, there's still no reason to not switch. I export `requirements.txt` with poetry just for pip legacy reasons and it works great. If I just update some scripts, I could avoid it. It's running all the time in CI, it's exercised quite a bit.What's wrong with just using pip and requirements.txt? There's no dev section. In addition, bumping deps is not the same. I have a blog post explaining semver updates to a python dev:
https://squarism.com/2021/09/10/sciencing-out-updates/
my strong assertion: Python and Go missed it from the start. That's why it is so confusing. There's no other choice in Rust but Cargo. Rust devs are never confused on how to add a package, semver it. The answer is always Cargo. It's in the tutorial. It's in the book. It's in the culture.
I think I've heard that pip might support the pyproject spec, poetry already does. If you want scripts like npm, you can have that too with "taskipy". You don't have to.
Syntax integration would be great. It's such a hassle to learn two syntaxes and two data/object models to work with numpy -- as inevitably your code converts from Python objects <-> numpy objects at some point (singletons!), and it makes learners' lives much more difficult than needed.
I wonder if there was no adoption into the language at that time as a result of fractured communities and lack of consensus.
It was really only during the break-away adoption by the data science community (and other mass audiences) that started adding pressure to bloat the language. The adoption of 3rd party libraries like numpy, which are so critical to modern Python, also destroyed the community's "batteries included" philosophy.
Older Python felt a lot like using FreeBSD, where the whole system is cohesive and designed to work together, whereas modern Python feels a lot more like Linux – a bunch of disparate systems that try to work well together (in the best of times).
Both approaches have their strengths, and the language has certainly improved in some areas, but I prefer the older style.
In any case, it is fascinating to see how mainstream Python is now. Maybe this change in mindset was required for it to take over the world. We've come so far since Paul Graham cited the language as esoteric and its usage a high signal of competence (http://www.paulgraham.com/pypar.html).