> Probably explains Fortans continued hold on numerical computing

indeed! a sizeable part of numpy is written in fortran

Only insofar as numpy relies on BLAS and LAPACK for certain functionality, and most (all?) implementations are written in Fortran. The actual numpy source code contains no Fortran; you don't need a Fortran compiler to build numpy. Scipy does contain Fortran, though.

"certain functionality" is a bit of an understatement, here. Those are precisely the core algorithms, that allow you to do things with matrices, solve systems, etc. As you put it, numpy is simply a wrapper for this.

I think that's an unfair characterization of numpy's functionality. Numpy introduces data structures not present in the core python library (another one of your complaints about python, though it's not clear to me why it matters that numpy is a separate library), and certain manipulations do not require BLAS/LAPACK acceleration, eg. multiplying an array by a scalar (yes, I know dscal exists in BLAS) or array slicing. Further, it is possible to use numpy without any BLAS/LAPACK libraries at all, and it continues to work (albeit more slowly).

Note that most accelerated implementations of BLAS are not written in Fortran, but use e.g. SIMD instructions directly.

See also: https://stackoverflow.com/questions/1303182/how-does-blas-ge...