Implicit Transposing In Numpy Array Indexing

I came across a weird problem: from numpy import zeros, arange aa = zeros([1, 3, 10]) aa[0, :, arange(5)].shape Running this gives me (5,3), but I'm expecting (3,5). However, runn

Solution 1:

This is a case of mixed basic and advanced indexing. The 1st and last indexes are numeric, and the middle a slice. It selects values based the 0 and arange(5), and appends the : dimension at the end.

aa[0, :, :5].shape

should produce the (3,5) you expect.

http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing

Numpy 3D array transposed when indexed in single step vs two steps contrasts the behavior of

y = x[0, :, mask]
z = x[0, :, :][:, mask]

Be sure to check the comments to my answer, for the argument that this is a bug and will be fixed.