numpy - Python multi-dimensional notation transpose automatically

Question

I have the following minimal example:

a = np.zeros((5,5,5))
a[1,1,:] = [1,1,1,1,1]
print(a[1,:,range(4)])

I would expect as output an array with 5 rows and 4 columns, where we have ones on the second row. Instead it is an array with 4 rows and 5 columns with ones on the second column. What is happening here, and what can I do to get the output I expected?

Answer 1

This is an example of mixed basic and advanced indexing, as discussed in https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#combining-advanced-and-basic-indexing

The slice dimension has been appended to the end.

With one scalar index this is a marginal case for the ambiguity described there. It's been discussed in previous SO questions and one or more bug/issues.

Numpy sub-array assignment with advanced, mixed indexing

In this case you can replace the range with a slice, and get the expected order:

In [215]: a[1,:,range(4)].shape
Out[215]: (4, 5)               # slice dimension last
In [216]: a[1,:,:4].shape
Out[216]: (5, 4)
In [219]: a[1][:,[0,1,3]].shape
Out[219]: (5, 3)