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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.
805
Importing the NumPy package enables us to use the array function in python. To create a three-dimensional array, we pass the object representing x by y by z in python, where x is the nested lists in the object, y is the nested lists inside the x nested lists, and z is the values inside each y nested list.
If you have an array of shape (2,4) then reshaping it with (-1, 1), then the array will get reshaped in such a way that the resulting array has only 1 column and this is only possible by having 8 rows, hence, (8,1).
The shape of the array can also be changed using the resize() method.
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] ] ]
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