CODEWITHSUNDEEP
pythonnumpy
cristian hantig
cristian hantig

Reputation: 1310

Is there a method in numpy that computes the mean on every 2d array from a 3d array?

For example:

import numpy as np

a = np.array([[[1,2,3],[1,2,3]],[[4,5,6],[7,8,7]]])

print(a.shape)
# (2, 2, 3)

So, on every 2d grid (3 grids on the example above) i want ot compute the mean:

mean = [np.mean(a[:, :, i]) for i in range(3)]
print(mean)
# [3.25, 4.25, 4.75]

Is there a method in numpy that achieves this?

I tried using mean on the axis but the result is not as expected.

Upvotes: 0

Views: 170

Answers (1)

sshashank124
sshashank124

Reputation: 32189

You can accomplish this using np.mean(axis = ...) and specifying a tuple of dimensions to average over

a.mean(axis=tuple(range(len(a.shape) - 1)))

This will compute the mean on every dimension/axis except the last one (note how the range of axis indices goes from 0 up to len - 1 (exclusive) thus ignoring the last axis.

This method is extensible to deeper arrays. For example if you have an array of shape (2, 6, 5, 4, 3), it will compute mean as a.mean(axis=(0, 1, 2, 3)) thus giving you an array of 3 means (corresponding to 3 elements in the last dimension)

Upvotes: 1

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