当在numpy中删除维度时产生的数组形状

Question

Trying to understand how numpy selects elements when indexing in more than 2 dimensions.

import numpy as np
x = np.arange(24).reshape(2,3,4)
x[:,:,0].shape #(2, 3)

In writing x[:,:,0] I am selecting all the elements along the "depth", all the "rows" and the first column. When thinking about this visually I would have thought numpy would return something with a shape of (2,3,1) but instead the last dimension is dropped. This is reasonable but how does numpy populate the result? Ie in this example, why does x[:,:,0] result in the elements [0,12] forming the first column. Just trying to figure out the general logic which for some reason I am not comprehending at the moment.

Answer 1

General NumPy indexing is complicated, but this is still the easy case. I've always felt that it helps to think in terms of how indexing the result corresponds to indexing the original array.

The result of x[:, :, 0] is an array such that for any indices i and j ,

result[i, j] == x[i, j, 0]

Similarly, if you index a 5D array a as a[:, 1, :, 2, :] , the result is such that

result[i, j, k] == a[i, 1, j, 2, k]