Computing Average For Numpy Array

I have a 2d numpy array (6 x 6) elements. I want to create another 2D array out of it, where each block is the average of all elements within a blocksize window. Currently, I have

Solution 1:

To compute some operation slice by slice in numpy, it is very often useful to reshape your array and use extra axes.

To explain the process we'll use here: you can reshape your array, take the mean, reshape it again and take the mean again. Here I assume blocksize is 2

t = np.array([[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5],[0, 1, 2, 3, 4, 5],[0, 1, 2, 3, 4, 5],[0, 1, 2, 3, 4, 5],[0, 1, 2, 3, 4, 5],])
t = t.reshape([6, 3, 2])
t = np.mean(t, axis=2)
t = t.reshape([3, 2, 3])
np.mean(t, axis=1)

outputs

array([[ 0.5,  2.5,  4.5],
       [ 0.5,  2.5,  4.5],
       [ 0.5,  2.5,  4.5]])

Now that it's clear how this works, you can do it in one pass only:

t = t.reshape([3, 2, 3, 2])
np.mean(t, axis=(1, 3))

works too (and should be quicker since means are computed only once - I guess). I'll let you substitute height/blocksize, width/blocksize and blocksize accordingly.

See @askewcan nice remark on how to generalize this to any dimension.