Agree, but to add, there are many transcendental functions (e.g. I needed to use the Mittag-Leffler function many times in my work) which are near-impossible to

Agree, but to add, there are many transcendental functions (e.g. I needed to use the Mittag-Leffler function many times in my work) which are near-impossible to implement in Numpy in a way that gives anywhere near usable performance. It's not a common enough function to be pre-implemented in Scipy special functions. In my case, I wrote the algorithm in Numpy/python myself which was almost unusably slow. I then outsourced it to a precompiled fortran program. Finally I just switched to Julia. There was already a library, MittagLeffler.jl which did everything I needed and was written in pure Julia. In the end everything was much faster than the Python/Fortran frankenstein code I had. Edit: For more info on Julia and difficult (computationally) mathematical functions see: https://youtu.be/mSgXWpvQEHE?t=1262 https://youtu.be/mSgXWpvQEHE?t=1262