Hướng dẫn dùng array copy python

Let us see how to copy arrays in Python. There are 3 ways to copy arrays : 

• Simply using the assignment operator.
• Shallow Copy
• Deep Copy

Assigning the Array

We can create a copy of an array by using the assignment operator (=). 

Syntax : 

new_arr = old_ arr

In Python, Assignment statements do not copy objects, they create bindings between a target and an object. When we use = operator user thinks that this creates a new object; well, it doesn’t. It only creates a new variable that shares the reference of the original object.

Example:

Python3

from numpy import *                  

arr1 = array([2, 6, 9, 4])            

print(id(arr1))

arr2 = arr1                         

print(id(arr2))

arr1[1] = 7                        

print(arr1)

print(arr2)

Output : 

117854800
117854800
[2 7 9 4]
[2 7 9 4]

We can see that both the arrays reference the same object. 

Shallow Copy

A shallow copy means constructing a new collection object and then populating it with references to the child objects found in the original. The copying process does not recurse and therefore won’t create copies of the child objects themselves. In the case of shallow copy, a reference of the object is copied in another object. It means that any changes made to a copy of the object do reflect in the original object. We will be implementing shallow copy using the view() function.

Example : 

Python3

from numpy import *                 

arr1 = array([2, 6, 9, 4])

print(id(arr1))

arr2 = arr1.view() 

print(id(arr2))

arr1[1] = 7                       

print(arr1)

print(arr2)

This time although the 2 arrays reference different objects, still on changing the value of one, the value of another also changes.

Deep Copy

Deep copy is a process in which the copying process occurs recursively. It means first constructing a new collection object and then recursively populating it with copies of the child objects found in the original. In the case of deep copy, a copy of the object is copied into another object. It means that any changes made to a copy of the object do not reflect in the original object. We will be implementing deep copy using the copy() function.

Python3

from numpy import *                 

arr1 = array([2, 6, 9, 4])

print(id(arr1))

arr2 = arr1.copy()

print(id(arr2))

arr1[1] = 7                       

print(arr1)

print(arr2)

Output : 

121258976
125714048
[2 7 9 4]
[2 6 9 4]

This time the changes made in one array are not reflected in the other array.

Deep Copy Continued

If you are dealing with NumPy matrices, then numpy.copy() will give you a deep copy. However, if your matrix is simply a list of lists then consider the below two approaches in the task of rotating an image (represented as a list of a list) 90 degrees:

Python3

import copy

def rotate_matrix(image):

    copy_image_one = copy.deepcopy(image)

    print("Original", matrix)

    print("Copy of original", copy_image_one)

    N = len(matrix)

    for row in range(N):

        for column in range(N):

            copy_image_one[row][column] = image[row][N-column-1]

    print("After modification")

    print("Original", matrix)

    print("Copy", copy_image_one)

    copy_image_two = [list(row) for row in copy_image_one]

    for row in range(N):

        for column in range(N):

            copy_image_two[column][row] = copy_image_one[row][column]

    return copy_image_two

if __name__ == "__main__":

    matrix = [[1, 2, 3],

              [4, 5, 6],

              [7, 8, 9]]

    print("Rotated image", rotate_matrix(matrix))

Output:

Original [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Copy of original [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
After modification
Original [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Copy [[3, 2, 1], [6, 5, 4], [9, 8, 7]]
Rotated image [[3, 6, 9], [2, 5, 8], [1, 4, 7]]