The trick to high-performance scientific calculations in Python is to use libraries like NumPy (possibly via Pandas), and their large set of vectorized operations. Then majority of the number crunching happens in optimized C/C++, with Python primarily 'orchestrating' these.

For the cases where the data-manipulation functionality desired is missing and pure Python is problematic performance wise, one can use Cython to bridge the gap.

Or if that is not enough, write those functions in C against the NumPy C API, and expose that as Python APIs.