[enriquto]

No, nothing to do with bootstraping, this is completely different. My point is that you cannot develop the very algorithms that you are using. Numerical math is not only about using ready-made algorithms, it is mostly about implementing new algorithms. For example, if you invent a new matrix factorization algorithm, it is very likely that you cannot implement it in Python (or if you can, it will be either very slow or very cumbersome). Python+numpy is not a natural way to write many numerical algorithms, based on explicit loops and new conditions inside them. Whereas in Fortran or in C, the implementation is likely to be much simpler, natural and fast.

[7thaccount]

Nobody is arguing that, but they're saying it doesn't matter to the majority of scientists who just want to invert a matrix for some study and don't need to implement a new matrix inversion algorithm. I would use C or C++ for that most likely. That is a valid use case for some scientists, but I would expect it to be a very small number compared to those that just need to use the existing tools in the ecosystem.

I think we may be speaking past each other a bit.