The ndarray.copy() method returns a copy of the array.

It is used to create a new array that is a copy of an existing array but does not share memory with it. This means that making any changes to the original array won't affect the existing array.

Example

# Python program explaining  
# numpy.ndarray.copy() function

import numpy as geek


x = geek.array([[0, 1, 2, 3], [4, 5, 6, 7]],
                                 order ='F')
print("x is: \n", x)

# copying x to y
y = x.copy()
print("y is :\n", y)
print("\nx is copied to y")

Output

x is: 
 [[0 1 2 3]
 [4 5 6 7]]
y is :
 [[0 1 2 3]
 [4 5 6 7]]
x is copied to y

Syntax

 Syntax: numpy.ndarray.copy(order='C') 

Parameters:

• Order : Controls the memory layout of the copy. 'C' means C-order, 'F' means F-order, 'A' means 'F' if a is Fortran contiguous, 'C' otherwise'K' means match the layout of a as closely as possible

Returns: Copy of an Array

How to make a copy of a NumPy array

To make a copy of a NumPy array in Python, we use ndarray.copy method of the NumPy library

Let us understand it better with an example:

Example: Make a Copy of ndarray

import numpy as geek
x = geek.array([[0, 1, ], [2, 3]])
print("x is:\n", x)

# copying x to y
y = x.copy()

# filling x with 1's
x.fill(1)
print("\n Now x is : \n", x)

print("\n y is: \n", y)

x is:
 [[0 1]
 [2 3]]

 Now x is : 
 [[1 1]
 [1 1]]

 y is: 
 [[0 1]
 [2 3]]