除了其他答案之外，您还可以对numpy.newaxis使用切片：

>>> from numpy import zeros, newaxis
>>> a = zeros((6, 8))
>>> a.shape
(6, 8)
>>> b = a[:, :, newaxis]
>>> b.shape
(6, 8, 1)

或者甚至是这样的（它适用于任意维数）：

>>> b = a[..., newaxis]
>>> b.shape
(6, 8, 1)

numpy.reshape(array, array.shape + (1,))

import numpy as np

# create a 2D array
a = np.array([[1,2,3], [4,5,6], [1,2,3], [4,5,6],[1,2,3], [4,5,6],[1,2,3], [4,5,6]])

print(a.shape) 
# shape of a = (8,3)

b = np.reshape(a, (8, 3, -1)) 
# changing the shape, -1 means any number which is suitable

print(b.shape) 
# size of b = (8,3,1)

import numpy as np

a= np.eye(3)
print a.shape
b = a.reshape(3,3,1)
print b.shape

希望这个函数能帮助你把二维数组转换成三维数组。

Args:
  x: 2darray, (n_time, n_in)
  agg_num: int, number of frames to concatenate. 
  hop: int, number of hop frames. 

Returns:
  3darray, (n_blocks, agg_num, n_in)

def d_2d_to_3d(x, agg_num, hop):

    # Pad to at least one block. 
    len_x, n_in = x.shape
    if (len_x < agg_num): #not in get_matrix_data
        x = np.concatenate((x, np.zeros((agg_num - len_x, n_in))))

    # main 2d to 3d. 
    len_x = len(x)
    i1 = 0
    x3d = []
    while (i1 + agg_num <= len_x):
        x3d.append(x[i1 : i1 + agg_num])
        i1 += hop

    return np.array(x3d)

如果您只想向（x，y，1）添加第三个轴（x，y），Numpy允许您使用dstack命令轻松完成此操作。

import numpy as np
a = np.eye(3) # your matrix here
b = np.dstack(a).T

你需要转置（.T），使它变成你想要的（x，y，1）格式。

您可以使用整形来完成此操作
例如，您有一个形状为35 x 750（二维）的数组A，您可以使用A.reshape（35，25，30）将形状更改为35 x 25 x 30（三维）
更多信息请参见此处的文档

简单的方法，加上一些数学

一开始你知道数组元素的数量，比如说100，然后分3步划分100，比如：
25 * 2 * 2 =一百
或：4 * 5 * 5 = 100

import numpy as np
D = np.arange(100)
# change to 3d by division of 100 for 3 steps 100 = 25 * 2 * 2
D3 = D.reshape(2,2,25) # 25*2*2 = 100

另一种方式：

another_3D = D.reshape(4,5,5)
print(another_3D.ndim)

至4D：

D4 = D.reshape(2,2,5,5)
print(D4.ndim)

import numpy as np
# create a 2-D ndarray
a = np.array([[2,3,4], [5,6,7]])
print(a.ndim)
>> 2
print(a.shape)
>> (2, 3)

# add 3rd dimension

第一个选项：整形

b = np.reshape(a, a.shape + (1,))
print(b.ndim)
>> 3
print(b.shape)
>> (2, 3, 1)

第二个选项：扩展尺寸

c = np.expand_dims(a, axis=2)
print(c.ndim)
>> 3
print(c.shape)
>> (2, 3, 1)