Numpy ndarray shape with 3 parameters

Question

I am confused about what is the shape of ndarray when 3 parameters are provided:

For example, what is the difference between:

np.zeros((2, 1, 3))
array([[[ 0.,  0.,  0.]],

       [[ 0.,  0.,  0.]]])

and:

np.zeros((1, 2, 3))
array([[[ 0.,  0.,  0.],
        [ 0.,  0.,  0.]]])

It seems to me that both of them represent 2*3 matrices.

Answer 1

No, the shapes are different, you have to pay attention to the square brackets:

>>> np.zeros((2, 1, 3))
array([[[ 0.,  0.,  0.]],

       [[ 0.,  0.,  0.]]])

versus:

>>> np.zeros((1, 2, 3))
array([[[ 0.,  0.,  0.],
        [ 0.,  0.,  0.]]])

as you can see, in the first call, we have two times a pair of square brackets for the second dimension, whereas in the latter, we only have one such pair.

The shape is also different:

>>> np.zeros((2, 1, 3)).shape
(2, 1, 3)
>>> np.zeros((1, 2, 3)).shape
(1, 2, 3)

So in the former we have a list that contains two sublists. Each of these sublists contains one element: a list of three elements. In the latter we have a list with one element: a sublist with two elements, and these two elements are lists with three elements.

So a vanilla Python list equivalent would be:

[ [ [0, 0, 0] ], [ [0, 0, 0] ] ]

versus:

[ [ [0, 0, 0], [0, 0, 0] ] ]