with numpy arrays, no need to define matrix_c at the beginning of the function. Also, since I can't access the beginning of this module would need to add "import numpy as np".

Below is a suggestion to reduce the number of code lines. Maybe I'm missing the point of this module, but using numpy would be a lot more efficient. I know the rest of the doc has no docstring, but I figured I would add one in the suggestion.

You can the entire function with just 8 lines of code.

This definitely works. In fact, a lot of stuff in this project can be done using in-built/library functions, but I think the idea of this project is to show how the core algorithms work.

Using numpy.dot would definitely make it work in lesser lines of code, and possibly even be faster, but that level of abstraction would hide what exactly is happening.

I may be wrong though, and if what we're going for is smaller code and speed, your suggestion would work (Although I think numpy.matmul would be a better choice)

That makes sense. You're right on both points ☺. Best leave your initial commit than.

I never heard of numpy.matmul, now I know.
https://www.numpy.org/devdocs/reference/generated/numpy.dot.html

TLDR: If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred.

Appreciate the feedback!