Question

我有以下格式的numpy数组

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如何在第二维中复制第一个尺寸并将其转换为以下3D数组？

(1440, 40)

Answer 1

x是np.array：

print(x.shape == (1440, 40)) #True

expected_output = np.repeat(x[:, :, np.newaxis], 40, axis=2)

print(expected_output.shape == (1440, 40, 40)) #True

Answer 2

您可以创建具有所需尺寸的新数组，然后根据需要复制数据。

类似这样的东西：

import numpy as np

a = np.array([[1, 2, 3], [1, 2, 3]])
b = np.zeros((a.shape[0], a.shape[0], a.shape[1]))

for i in range(a.shape[0]):
    b[i] = a[i]

print(a.shape) # (2,3)
print(b.shape) # (2,2,3)

######Sample Output########
[[1 2 3]
 [1 2 3]] #a

[[[1. 2. 3.]
  [1. 2. 3.]]
 [[1. 2. 3.]
  [1. 2. 3.]]] #b

我不确定复制数据的真正含义。我希望这可以解决您的疑问。

Answer 3

如果只想将2d数组平铺到3d数组中，则可以使用numpy.tile命令：

>>> import numpy as np
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> print(x.shape)
(2, 3)
>>> print(x)
[[1 2 3]
 [4 5 6]]
>>> x_3d = np.tile(x, (2, 1, 1))
>>> print(x_3d.shape)
(2, 2, 3)
>>> print(x_3d)
[[[1 2 3]
  [4 5 6]]

 [[1 2 3]
  [4 5 6]]]

Python-将数组的维复制到同一数组的另一个维中（从2D数组到3D数组）

3 个答案: