Array 1:  [1 2 3]
Shape of Array 1:  (3,)
Array 2:  [[1 2 3]
 [4 5 6]]
Shape of Array 2:  (2, 3)
Array 3:  [[1 2 3]]
Shape of Array 3:  (1, 3)

The output of numpy.ndarray.shape shows the lengths of the corresponding array dimensions. For example,

array1:  [1 2 3]

So, the shape of array1 is (3,). It means array1 is a one-dimensional array and the length of the corresponding array’s dimension is 3. In other words, array1 has three elements along its dimension.

Similarly,

Array 2:  
[[1 2 3]
 [4 5 6]]
Shape of Array 2:  (2, 3)

It means array2 is 2-dimensional. And it has 2 rows and 3 columns.

And,

Array 3:  [[1 2 3]]
Shape of Array 3:  (1, 3)

It means that array3 is 2-dimensional. And it has one row and three columns.

So, if the array is [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], the shape of the array will be (2, 2, 2). It means the array is 3-dimensional. And it has 2 elements along every dimension.