NumPy consists of different methods to duplicate an original array. The two main functions for this duplication are copy and view. The duplication of the array means an array assignment. When we duplicate the original array, the changes made in the new array may or may not reflect. The duplicate array may use the same location or may be at a new memory location.

Copy or Deep copy in NumPy

It returns a copy of the original array stored at a new location. The copy doesn’t share data or memory with the original array. The modifications are not reflected. The copy function is also known as deep copy.

import numpy as np
arr = np.array([20,30,50,70])
a= arr.copy()
#changing a value in original array
arr[0] = 100

print(arr)
print(a)

Output

[100 30 50 70]

[20 30 50 70]

Changes made in the original array are not reflected in the copy.

import numpy as np
arr = np.array([20,30,50,70])
a= arr.copy()
#changing a value in copy array
a[0] = 5

print(arr)
print(a)

Output

[20 30 50 70]

[ 5 30 50 70]

Changes made in copy are not reflected in the original array

#numpy tutorials #numpy copy #numpy views #numpy