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NumPy教程-NumPy的复制和视图

整理:python架构师

输入数组的副本在某个其他位置物理存储,并返回存储在该特定位置的内容,这是输入数组的副本,而在视图的情况下,返回同一内存位置的不同视图。

在本教程的本节中,我们将考虑从某个内存位置生成不同副本和视图的方法。

数组赋值

将numpy数组赋值给另一个数组不会直接复制原始数组,而是创建另一个具有相同内容和相同id的数组。它表示对原始数组的引用。对这个引用所做的更改也会反映在原始数组中。

id()函数返回数组的通用标识符,类似于C中的指针。

考虑以下示例。

示例

import numpy as np  a = np.array([[1,2,3,4],[9,0,2,3],[1,2,3,19]])  print("Original Array:\n",a)  print("\nID of array a:",id(a))  b = a   print("\nmaking copy of the array a")  print("\nID of b:",id(b))  b.shape = 4,3;  print("\nChanges on b also reflect to a:")  print(a)

输出:

Original Array: [[ 1  2  3  4] [ 9  0  2  3] [ 1  2  3 19]]
ID of array a: 139663602288640
making copy of the array a
ID of b: 139663602288640
Changes on b also reflect to a:[[ 1 2 3] [ 4 9 0] [ 2 3 1] [ 2 3 19]]

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ndarray.view()方法

ndarray.view()方法返回一个包含与原始数组相同内容的新数组对象。由于它是一个新的数组对象,因此对此对象进行的更改不会反映在原始数组中。

考虑以下示例。

示例

import numpy as np  a = np.array([[1,2,3,4],[9,0,2,3],[1,2,3,19]])  print("Original Array:\n",a)  print("\nID of array a:",id(a))  b = a.view()  print("\nID of b:",id(b))  print("\nprinting the view b")  print(b)  b.shape = 4,3;  print("\nChanges made to the view b do not reflect a")  print("\nOriginal array \n",a)  print("\nview\n",b)

输出:

Original Array: [[ 1  2  3  4] [ 9  0  2  3] [ 1  2  3 19]]
ID of array a: 140280414447456
ID of b: 140280287000656
printing the view b[[ 1 2 3 4] [ 9 0 2 3] [ 1 2 3 19]]
Changes made to the view b do not reflect a
Original array [[ 1 2 3 4] [ 9 0 2 3] [ 1 2 3 19]]
view [[ 1 2 3] [ 4 9 0] [ 2 3 1] [ 2 3 19]]

ndarray.copy()方法

它返回原始数组的深度副本,与原始数组不共享任何内存。对深度副本进行的修改不会反映在原始数组中。

考虑以下示例。

示例

import numpy as np  a = np.array([[1,2,3,4],[9,0,2,3],[1,2,3,19]])  print("Original Array:\n",a)  print("\nID of array a:",id(a))  b = a.copy()  print("\nID of b:",id(b))  print("\nprinting the deep copy b")  print(b)  b.shape = 4,3;  print("\nChanges made to the copy b do not reflect a")  print("\nOriginal array \n",a)  print("\nCopy\n",b)

输出:

Original Array: [[ 1  2  3  4] [ 9  0  2  3] [ 1  2  3 19]]
ID of array a: 139895697586176
ID of b: 139895570139296
printing the deep copy b[[ 1 2 3 4] [ 9 0 2 3] [ 1 2 3 19]]
Changes made to the copy b do not reflect a
Original array [[ 1 2 3 4] [ 9 0 2 3] [ 1 2 3 19]]
Copy [[ 1 2 3] [ 4 9 0] [ 2 3 1] [ 2  3 19]
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