The design and success of e.g. Golang is pretty strong support for the idea that you can't and shouldn't separate a language from its broader ecosystem of tooling and packages.
> The success of python is due to not needing a broader ecosystem for A LOT of things.
I honestly think that was a coincidence. Perl and Ruby had other disadvantages, Python won despite having bad package management and a bloated standard library, not because of it.
The bloated standard library is the only reason I kept using python in spite of the packaging nightmare. I can do most things with no dependencies, or with one dependency I need over and over like matplotlib
If python had been lean and needed packages to do anything useful, while still having a packaging nightmare, it would have been unusable
Ruby was competing on the web market and lost to many others, including Python. In part, because python had a much broader ecosystem, and php had wide adoption through wordpress and others, and javascript was expanding from browsers.
IDK it's become too verbose IMHO, looks almost like COBOL now. (I think it was Fortran 66 that was the last Fortran true to its nature as a "Formula Translator"...)
I hear this so much from Python people -- almost like they are paid by the word to say it. Is it different from Perl, Ruby, Java, or C# (DotNet)? Not in my experience, except people from those communities don't repeat that phrase so much.
The irony here: We are talking about data science. 98% of "data science" Python projects start by creating a virtual env and adding Pandas and NumPy which have numerous (really: squillions of) dependencies outside the foundation library.
Someone correct me if I'm completely wrong, but by default (i.e. precompiled wheels) numpy has 0 dependencies and pandas has 5, one of which is numpy. So not really "squillions" of dependencies.
NumPy will fall back to internal and very slow BLAS and LAPACK implementations if your system does not have a better one, but assuming you're using NumPy for its performance and not just the convenience of adding array programming features to Python, you're really gonna want better ones, and what that is heavily depends on the computer you're using.
This isn't really a Python thing, though. It's a hard problem to solve with any kind of scientific computing. If you insist on using a dynamic interpreted language, which you probably have to do for exploratory interactive analysis, and you still need speed over large datasets, you're gonna need to have a native FFI and link against native libraries. Thanks to standardization, you'll have many choices and which is fastest depends heavily on your hardware setup.
The wheels will most likely come with openblas, so while you can get the original blas (which is really only slow by comparison, for small tasks it's likely users won't notice), this is generally not an issue.
They're not represented, because those are build-time dependencies. Most users when they do pip install numpy or equivalent, just get the precompiled binaries and none of those get installed. And even if you compile it yourself, you still don't need those for running numpy.