> 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, fo

> 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 https://docs.scipy.org/doc/scipy/reference/linalg.blas.html Old-school optimization? https://www.roguewave.com/sites/rw/files/attachments/PyIMSLStudioFeatures.pdf https://www.roguewave.com/sites/rw/files/attachments/PyIMSLS... Computer vision? https://pypi.org/project/opencv-python/ 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.