1. 1D and 2D data
.T is equivalent to .transopse
2. 3D and more dimensional data
For the transformation of the z-axis and the x-axis
In [40]: arr = np.arange(16).reshape((2, 2, 4)) In [41]: arr Out[41]: array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7]], [[ 8, 9, 10, 11], [12, 13, 14, 15]]]) In [42]: arr.transpose((1, 0, 2)) Out[42]: array([[[ 0, 1, 2, 3], [ 8, 9, 10, 11]], [[ 4, 5, 6, 7], [12, 13, 14, 15]]])The transformation of transpose is based on shape
The shape before conversion is (0, 1, 2)
[[(0,0,0), (0,0,1), (0,0,2), (0,0,3)] ／／ [[[ 0, 1, 2, 3],
[(0,1,0), (0,1,1), (0,1,2), (0,1,3)], ／／ [ 4, 5, 6, 7]],
[(1,0,0), (1,0,1), (1,0,2), (1,0,3)] ／／ [[ 8, 9, 10, 11],
[(1,1,0), (1,1,1), (1,1,2), (1,1,3)]]. ／／[12, 13, 14, 15]]]
The converted shape is (1, 0, 2), that is, the shape on the z-axis and the x-axis is swapped
[[(0,0,0), (0,0,1), (0,0,2), (0,0,3)]
(1,0,0), (1,0,1), (1,0,2), (1,0,3)],
[(0,1,0), (0,1,1), (0,1,2), (0,1,3)]
[(1,1,0), (1,1,1), (1,1,2), (1,1,3)]]
Fill in the value corresponding to the shape before conversion to get
[1,2,3,4]
[8,9,10,11]
[4,5,6,7]
[12,13,14,15]so perfect just corresponds to the output