I have a 3D numpy array data and another array pos of indexes (an index is a numpy array on its own, which makes the latter array a 2D array):

import numpy as np
data = np.arange(8).reshape(2, 2, -1)
#array([[[0, 1],
#    [2, 3]],
#
#  [[4, 5],
#    [6, 7]]])pos = np.array([[1, 1, 0], [0, 1, 0], [1, 0, 0]])
#array([[1, 1, 0],
#       [0, 1, 0],
#       [1, 0, 0]])

I want to select and/or mutate the elements from data using the indexes from pos. I can do the selection using a for loop or a list comprehension:

[data[tuple(i)] for i in pos]
#[6, 2, 4]
data[[i for i in pos.T]]
#array([6, 2, 4])

But this does not seem to be a numpy way. Is there a vectorized numpy solution to this problem?

You can split pos into 3 separate arrays and index, like so—

>>> i, j, k = pos.T
>>> data[i, j, k]
array([6, 2, 4])

Here, the number of columns in pos correspond to the depth of data. As long as you're dealing with 3D matrices, getting i, j, and k well never get more complicated than this.

On python-3.6+, you can shorten this to—

>>> data[[*pos.T]]
array([6, 2, 4])