Other than the fact those libraries aren't written in Python, they are only bindings to native code libraries that can be used from any language with FFI.
This is a common trope that does not really match reality.
Pytorch readme quote
pytorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python.
could be applied to many key libraries of this ecosystem.
For sure, a lot of those libraries rely on tons of compiled code in C/C++/Fortran, but the python code is not mere wrapping on top of those libs. And most of the compiled code inside those repos has a lot of internal logic as well.
Some examples (using cloc on git main branch, as tokei does not understand cython):
* sklearn: 150 kloc python, ~10kloc cython, not much of other languages
* NumPy: 200kloc python, 350kloc for C/C++
* scipy: 800 kloc C, 200 kloc python (the 800kloc surprised me and it looks like a lot of it is C generated code from cython)
It's massive work to reimplement say, Numpy and Pandas in javascript, in part because the language is different, and in part because the API is huge. It's technically possible, but not something that's available right now (nor there seems to be any real interest/serious effort going on).
It's more likely that something like Julia ends up replacing Python, even though I still believe it's a low probability thing.
hmm, there's 1.6M lines of C++ in V8. And that's not a collection of simple mathematical functions - it's an entire compiler with many interdependent components. I think there's this weird but popular idea that people think it's easier to build a fast language than port a couple hundred independent functions.
