Question

Given the following code, I expect the last two lines to behave the same, however the don’t.

import numpy as np

C = np.matrix(np.zeros((4,4)))
C[0, 0:2] = np.matrix([[1, 2]]) # Works as expected.
C[0, [0,1]] = np.matrix([[1, 2]]) # Throws an "array is not broadcastable to correct shape" error.

When using an ndarray instead, things work as expected (adjusting the right-hand-side of the assignment to a one-dimensional ndarray):

D = np.zeros((4,4))
D[0, 0:2] = np.array([1, 2]) # Works as expected.
D[0, [0,1]] = np.array([1, 2]) # Works too.

And to make things even weirder, if one is only indexing the matrix C (as opposed to assigning to it), it seems using slice indices or a list just return the same:

C[0, 0:2] # => matrix([[ 1.,  2.]])
C[0, [0, 1]] # => matrix([[ 1.,  2.]])

The question is, why is the behavior of the two approaches in assignment different? What am I missing?

(Edit: typo)

Answer 1

It appears to be a bug in numpy: http://projects.scipy.org/numpy/ticket/803 . The solution is to assign an ordinary list or numpy array instead of assigning a matrix to the selected elements.