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 :
# importing the module from numpy import * # creating the first array arr1 = array([2, 6, 9, 4]) # displaying the identity of arr1 print(id(arr1)) # assigning arr1 to arr2 arr2 = arr1 # displaying the identity of arr2 print(id(arr2)) # making a change in arr1 arr1[1] = 7 # displayin the arrays print(arr1) print(arr2) |
Output :
117854800 117854800 [2 7 9 4] [2 7 9 4]
We can see that both the arrays reference to 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 other 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 :
# importing the module from numpy import * # creating the first array arr1 = array([2, 6, 9, 4]) # displaying the identity of arr1 print(id(arr1)) # shallow copy arr1 in arr2 using view() arr2 = arr1.view() # displaying the identity of arr2 print(id(arr2)) # making a change in arr1 arr1[1] = 7 # displayin the arrays print(arr1) print(arr2) |
This time although the 2 arrays reference different objects, but 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 case of deep copy, a copy of object is copied in other object. It means that any changes made to a copy of object do not reflect in the original object. We will be implementing deep copy using the copy() function.
# importing the module from numpy import * # creating the first array arr1 = array([2, 6, 9, 4]) # displaying the identity of arr1 print(id(arr1)) # shallow copy arr1 in arr2 using view() arr2 = arr1.copy() # displaying the identity of arr2 print(id(arr2)) # making a change in arr1 arr1[1] = 7 # displayin the arrays print(arr1) print(arr2) |
Output :
121258976 125714048 [2 7 9 4] [2 6 9 4]
This time the changes made in one array is not reflected in the another array.
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.
