[Python] Numpyic way to take the first N rows and columns out of every M rows and columns from...

I have a 20 x 20 square matrix. I want to take the first 2 rows and columns out of every 5 rows and columns, which means the output should be a 8 x 8 square matrix. This can be done in 2 consecutive steps as follows:

import numpy as np

m = 5
n = 2
A = np.arange(400).reshape(20,-1)
B = np.asarray([row for i, row in enumerate(A) if i % m < n])
C = np.asarray([col for j, col in enumerate(B.T) if j % m < n]).T


However, I am looking for efficiency. Is there a more Numpyic way to do this? I would prefer to do this in one step.

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