如何使 2d numpy 數組成為 3d 數組？

Question

我有一個形狀為 (x, y) 的二維數組，我想將其轉換為形狀為 (x, y, 1) 的三維數組。 有沒有一個很好的 Pythonic 方法來做到這一點？

Answer 1

除了其他答案之外，您還可以將切片與numpy.newaxis一起numpy.newaxis ：

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

甚至這個（它將適用於任意數量的維度）：

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

Answer 2

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

Answer 3

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)

Answer 4

import numpy as np

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

Answer 5

希望這個功能可以幫助您將 2D 數組轉換為 3D 數組。

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)

Answer 6

如果您只想將第三個軸 (x,y) 添加到 (x,y,1)，Numpy 允許您使用dstack命令輕松完成此操作。

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

您需要轉置 ( .T ) 以將其轉換為所需的 (x,y,1) 格式。

Answer 7

你可以通過重塑來做到這一點

例如，您有一個形狀為 35 x 750（二維）的數組 A，您可以使用 A.reshape(35, 25, 30) 將形狀更改為 35 x 25 x 30（三維）

此處的文檔中的更多信息

Answer 8

簡單的方法，用一些數學

首先你知道數組元素的數量，假設 100，然后在 3 個步驟中划分 100，例如：

25 * 2 * 2 = 100

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

其他方式：

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

到 4D：

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

Answer 9

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

第一種選擇：重塑

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

第二個選項：expand_dims

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