[smallnamespace]

> But if I'm doing something that I don't know will fit into an existing Python mold (that I know about) then I'll strongly consider using it.

The thing is, for anything performance intensive and scientific, you're almost guaranteed to find a Python binding.

It has all these bindings because scientists are almost always writing either Python, C++, or Fortran (with a smattering of R or Octave on the side).

Want to do linear algebra? NumPy is always there, otherwise you can go lower level: https://docs.scipy.org/doc/scipy/reference/linalg.blas.html

Old-school optimization? https://www.roguewave.com/sites/rw/files/attachments/PyIMSLS...

Computer vision? https://pypi.org/project/opencv-python/

In fact, I'd basically argue against using most language-native implementations of algorithms where performance is at stake, because most implementations don't have all the algorithmic optimizations.

[danielscrubs]

And they will cover a small part of what is found in research papers.

Python has the best ecosystem, but Rust was made by a competent team so we will root for it.