Numpy stack function

The function prototype

numpy.stack(arrays, axis=0, out=None)

Function interpretation

Stack arrays with the same shape on the specified dimension

The usage function

A simple example of a two-dimensional array

>>> a = np.arange(9).reshape((3, 3))
>>> a
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> b = np.arange(9, 18).reshape((3, 3))
>>> b
array([[ 9, 10, 11],
       [12, 13, 14],
       [15, 16, 17]])
>>> c = np.arange(18, 27).reshape((3, 3))
>>> c
array([[18, 19, 20],
       [21, 22, 23],
       [24, 25, 26]])
#  take a、b、c Three arrays on the axis axis=0 Stack on , Get a three-dimensional array 
>>> d = np.stack((a, b, c), axis=0)
>>> d
array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8]],

       [[ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17]],

       [[18, 19, 20],
        [21, 22, 23],
        [24, 25, 26]]])
#  take a、b、c Three arrays on the axis axis=1 Stack on , Also get a three-dimensional array 
>>> e = np.stack((a, b, c), axis=1)
>>> e
array([[[ 0,  1,  2],
        [ 9, 10, 11],
        [18, 19, 20]],

       [[ 3,  4,  5],
        [12, 13, 14],
        [21, 22, 23]],

       [[ 6,  7,  8],
        [15, 16, 17],
        [24, 25, 26]]])
>>> f = np.stack((a, b, c), axis=2)
>>> f
array([[[ 0,  9, 18],
        [ 1, 10, 19],
        [ 2, 11, 20]],

       [[ 3, 12, 21],
        [ 4, 13, 22],
        [ 5, 14, 23]],

       [[ 6, 15, 24],
        [ 7, 16, 25],
        [ 8, 17, 26]]])

Let's take another example of a three-dimensional array

>>> a = np.zeros((2, 2, 2), dtype=int)
>>> a
array([[[0, 0],
        [0, 0]],

       [[0, 0],
        [0, 0]]])
>>> b = np.ones((2, 2, 2), dtype=int)
>>> b
array([[[1, 1],
        [1, 1]],

       [[1, 1],
        [1, 1]]])
>>> c = np.full((2, 2, 2), 2, dtype=int)
>>> c
array([[[2, 2],
        [2, 2]],

       [[2, 2],
        [2, 2]]])
#  In the shaft axis=0 Stack on 
>>> d = np.stack((a, b, c), axis=0)
>>> d
array([[[[0, 0],
         [0, 0]],

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


       [[[1, 1],
         [1, 1]],

        [[1, 1],
         [1, 1]]],


       [[[2, 2],
         [2, 2]],

        [[2, 2],
         [2, 2]]]])
>>> d.shape
(3, 2, 2, 2)
>>> e = np.stack((a, b, c), axis=1)
>>> e
array([[[[0, 0],
         [0, 0]],

        [[1, 1],
         [1, 1]],

        [[2, 2],
         [2, 2]]],


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

        [[1, 1],
         [1, 1]],

        [[2, 2],
         [2, 2]]]])
>>> e.shape
(2, 3, 2, 2)
#  In the shaft axis=2 Stack on 
>>> f = np.stack((a, b, c), axis=2)
>>> f
array([[[[0, 0],
         [1, 1],
         [2, 2]],

        [[0, 0],
         [1, 1],
         [2, 2]]],


       [[[0, 0],
         [1, 1],
         [2, 2]],

        [[0, 0],
         [1, 1],
         [2, 2]]]])
>>> f.shape
(2, 2, 3, 2)
#  amount to g = np.stack((a, b, c), axis=-1)
>>> g = np.stack((a, b, c), axis=3)
>>> g
array([[[[0, 1, 2],
         [0, 1, 2]],

        [[0, 1, 2],
         [0, 1, 2]]],


       [[[0, 1, 2],
         [0, 1, 2]],

        [[0, 1, 2],
         [0, 1, 2]]]])
>>> g.shape
(2, 2, 2, 3)