Unlock the Power of Object Copying in Python
When working with objects in Python, it’s essential to understand the difference between creating a new reference and creating a true copy. Using the = operator may seem like it creates a new object, but in reality, it only creates a new variable that shares the reference of the original object.
The Pitfall of Shared References
Let’s take a closer look at what happens when we use the = operator to create a new variable. In the example below, we create a list named old_list and pass an object reference to new_list using the = operator.
old_list = [[1, 2], [3, 4]]
new_list = old_list
print(id(old_list)) # 140673303268168
print(id(new_list)) # 140673303268168
As you can see, both old_list and new_list share the same ID, which means they are referencing the same object. This can lead to unexpected behavior when modifying the values in either list.
The Solution: Shallow and Deep Copies
Fortunately, Python provides two ways to create copies of objects: shallow copies and deep copies. To create these copies, we use the copy module.
Shallow Copies: A New Object with Shared References
A shallow copy creates a new object that stores the references of the original elements. This means that a shallow copy doesn’t create a copy of nested objects; instead, it just copies the reference of nested objects.
import copy
old_list = [[1, 2], [3, 4]]
new_list = copy.copy(old_list)
print(id(old_list)) # 140673303268168
print(id(new_list)) # 140673303268192
As you can see, new_list has a different ID than old_list, indicating that it’s a new object. However, when we modify the nested objects in old_list, the changes appear in new_list.
Deep Copies: Independent Objects with Recursive Copies
A deep copy creates a new object and recursively adds the copies of nested objects present in the original elements. This means that a deep copy creates independent copies of all objects, including nested ones.
import copy
old_list = [[1, 2], [3, 4]]
new_list = copy.deepcopy(old_list)
print(id(old_list)) # 140673303268168
print(id(new_list)) # 140673303268224
When we create a deep copy, we can modify the nested objects in old_list without affecting new_list. This is because new_list has its own independent copies of the nested objects.
Conclusion
In Python, understanding the difference between shallow and deep copies is crucial when working with objects. By using the copy module, we can create independent copies of objects and avoid unexpected behavior due to shared references. Whether you need a shallow copy or a deep copy depends on your specific use case, but with this knowledge, you’ll be able to make informed decisions when working with objects in Python.