Fast way to take average of every N rows in a .npy array

Question

I have a very large masked NumPy array (originalArray) with many rows and two columns. I want take the average of every two rows in originalArray

Answer 1

Your problem (average of every two rows with two columns):

>>> a = np.reshape(np.arange(12),(6,2))
>>> a
array([[ 0,  1],
       [ 2,  3],
       [ 4,  5],
       [ 6,  7],
       [ 8,  9],
       [10, 11]])
>>> a.transpose().reshape(-1,2).mean(1).reshape(2,-1).transpose()
array([[  1.,   2.],
       [  5.,   6.],
       [  9.,  10.]])

Other dimensions (average of every four rows with three columns):

>>> a = np.reshape(np.arange(24),(8,3))
>>> a
array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11],
       [12, 13, 14],
       [15, 16, 17],
       [18, 19, 20],
       [21, 22, 23]])
>>> a.transpose().reshape(-1,4).mean(1).reshape(3,-1).transpose()
array([[  4.5,   5.5,   6.5],
       [ 16.5,  17.5,  18.5]])

General formula for taking the average of r rows for a 2D array a with c columns:

a.transpose().reshape(-1,r).mean(1).reshape(c,-1).transpose()