NumPy sum along disjoint indices

You can use np.add.reduceat as a general approach to this problem. This works even if the ranges are not all the same length.

To sum the slices 0:25, 25:50 and 50:75 along axis 0, pass in indices [0, 25, 50]:

np.add.reduceat(a, [0, 25, 50], axis=0)

This method can also be used to sum non-contiguous ranges. For instance, to sum the slices 0:25, 37:47 and 51:75, write:

np.add.reduceat(a, [0,25, 37,47, 51], axis=0)[::2]

An alternative approach to summing ranges of the same length is to reshape the array and then sum along an axis. The equivalent to the first example above would be:

a.reshape(3, a.shape[0]//3, a.shape[1], a.shape[2]).sum(axis=1)

Just sum each portion and use the results to create a new array.

import numpy as npi1, i2 = (2,7)a = np.ones((10,5,3))b = np.sum(a[0:i1,...], 0)c = np.sum(a[i1:i2,...], 0)d = np.sum(a[i2:,...], 0)g = np.array([b,c,d])>>> g.shape(3, 5, 3)>>> garray([[[ 2.,  2.,  2.],        [ 2.,  2.,  2.],        [ 2.,  2.,  2.],        [ 2.,  2.,  2.],        [ 2.,  2.,  2.]],       [[ 5.,  5.,  5.],        [ 5.,  5.,  5.],        [ 5.,  5.,  5.],        [ 5.,  5.,  5.],        [ 5.,  5.,  5.]],       [[ 3.,  3.,  3.],        [ 3.,  3.,  3.],        [ 3.,  3.,  3.],        [ 3.,  3.,  3.],        [ 3.,  3.,  3.]]])>>>