NumPy copy()

import numpy as np

# create an array 
array1 = np.arange(5)

# create a copy of the original array
array2 = np.copy(array1)

print(array2)

# Output: [0 1 2 3 4]

copy() Syntax

The syntax of copy() is:

numpy.copy(array, order = 'K', subok = False)

copy() Arguments

The copy() method takes three arguments:

• array - input data
• subok(optional) - determines whether to subclass the output array if the data type is changed or to return a base-class array
• order (optional) - specifies the order in which the elements are filled in copied class.

copy() Return Value

The copy() method returns the array interpretation of given input.

Example 1: Create Array With copy()

import numpy as np

# copy an array from another array
array0 = np.arange(5)
array1 = np.copy(array0)
print('Array copied from Array: ',array1)

# copy an array from a list
list1 = [1, 2, 3, 4, 5]
array2 = np.copy(list1)
print('Array copied from List: ',array2)

# copy an array from another array
tuple1 = (1, 2, 3, 4, 5)
array1 = np.copy(tuple1)
print('Array copied from Tuple: ',array1)

Output

Array copied from Array:  [0 1 2 3 4]
Array copied from List:  [1 2 3 4 5]
Array copied from Tuple:  [1 2 3 4 5]

Example 2: Create Array With '=' and np.copy()

We can also create an array by copying it using the assignment operator.

import numpy as np

# create an array
array0 = np.arange(5) # [0 1 2 3 4]

# copy an array using assignment  operator
array1 = array0

# change 1st element of new array 
array1[0] = -1

# print both arrays
print(array0)
print(array1)

Output

[-1  1  2  3  4]
[-1  1  2  3  4]

As shown in the example, modifying array1 also modifies array0 because they refer to the same underlying data.

If you want to create an independent copy of array0, you can use the np.copy() function to create a deep copy explicitly

Lets look at an example using np.copy().

import numpy as np

# create an array
array0 = np.arange(5) # [0 1 2 3 4]

# copy an array using np.copy()
array1 = np.copy(array0)

# change 1st element of new array 
array1[0] = -1

# print both arrays
print(array0)
print(array1)

Output

[0 1 2 3 4]
[-1  1  2  3  4]

Using subok Argument in copy()

The subok parameter in copy() function determines whether the copy should be a subclass of the original array's class (True) or a basic ndarray (False).

Let's look at an example.

import numpy as np

# create a subclass of ndarray
class CustomArray(np.ndarray):
    pass

# create an array using the custom subclass
arr = np.array([1, 2, 3]).view(CustomArray)

# create a copy with subclass preservation
copySubclass = np.copy(arr, subok=True)

# create a basic ndarray copy
copyNdarray = np.copy(arr, subok=False)

print("Copy with Subclass:",copySubclass)
print("Type of Subclass:",type(copySubclass))

print("Copy as ndArray:",copyNdarray)
print("Type of ndArray Copy:",type(copyNdarray))

Output

Copy with Subclass: [1 2 3]
Type of Subclass: <class '__main__.CustomArray'>
Copy as ndArray: [1 2 3]
Type of ndArray Copy: <class 'numpy.ndarray'>

Using Optional Order Argument in copy()

The order argument specifies the order in which the copies are filled.

The order can be:

• 'C' - elements are stored row-wise
• 'F' - elements are stored column-wise
• 'A' - tries to preserve the original array's order, otherwise defaults to C-order.
• 'K' - copies the elements in the order they occur in memory and uses C-order by default.

Note: The order argument only affects the memory layout of the copied array and does not change the values or shape of the array.

See Also:

• Python copy