Hi,
I have discovered what I believe is a bug with array slicing involving 3D (and higher) dimension arrays. When slicing a 3D array by a single value for axis 0, all values for axis 1, and a list to slice axis 2, the dimensionality of the resulting 2D array is flipped. However, slicing more than a single index for axis 0 or performing the slicing in two steps results in the correct dimensionality. Below is a quick example to demonstrate this behavior. import numpy as np arr = np.arange(54).reshape(2, 3, 9) list = [0, 2, 4, 5, 8] print(arr.shape) # (2, 3, 9) print(arr[0, :, list].shape) # (5, 3) -- but it should be (3, 5)? print(arr[0][:, list].shape) # (3, 5), as expected print(arr[0:1, :, list].shape) # (1, 3, 5), as expected This behavior carries over to 4D arrays as well, where the axis sliced with a list becomes the 0th axis regardless of order. Below demonstrates that. arr2 = np.arange(324).reshape(2, 3, 6, 9) print(arr2[0, :, :, list].shape) # (5, 3, 6), but I expect (3, 6, 5) arr3 = np.arange(324).reshape(2, 3, 9, 6) print(arr3[0, :, list].shape) # (5, 3, 6), expected (3, 5, 6) print(arr3[0, :, list, :].shape) # same as above Can anyone explain this behavior, or is this a bug? Best, Michael
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