Understanding Shallow Copy and Deep Copy in Python

Managing objects in Python, especially copying objects, is an essential skill for every Python developer. Python offers two main methods for copying objects: shallow copy and deep copy. These two techniques have different uses and implications, especially when working with complex objects or collections of objects. In this article, we will explore the differences between these two copying methods, their uses, and when to favor one over the other, with concrete examples.

Understanding Shallow Copy (Shallow Copy)

A shallow copy creates a new top-level object but does not create copies of the nested objects. Instead, it copies references to the nested objects. Thus, the shallow copy and the original object share the nested objects. This can lead to unintuitive behaviors because modifying a nested object in the copy will also affect the original object.

Shallow Copy Example

1
2
3
4
5
6
7

import copy

original_list = [1, 2, [3, 4]]
copied_list = copy.copy(original_list)
copied_list[2][0] = 99

print(original_list)  # Displays: [1, 2, [99, 4]]

In this example, modifying the nested list in copied_list also affects original_list, because both lists share the same reference to the nested list.

Using Shallow Copy

Shallow copy is useful when you want to create a new collection containing the same objects as the original, without duplicating the objects themselves. It is often used to duplicate data structures containing immutable objects or when the nested objects should not be copied for performance reasons.

Understanding Deep Copy (Deep Copy)

Conversely, a deep copy creates a new top-level object and recursively copies all the objects it contains. This means the copy is completely independent of the original, with its own copies of the nested objects. Changes made to the copy do not affect the original object, and vice versa.

Deep Copy Example

1
2
3
4
5
6
7

import copy

original_list = [1, 2, [3, 4]]
copied_list = copy.deepcopy(original_list)
copied_list[2][0] = 99

print(original_list)  # Displays: [1, 2, [3, 4]]

In this example, modifying the nested list in copied_list does not affect original_list, because the deep copy created an independent copy of all the nested objects.

Using Deep Copy

Deep copy is essential when working with mutable objects and you need a completely independent copy of the original. It is particularly useful for complex data structures, where changes in the copy should not affect the original.

When to Use Shallow Copy vs Deep Copy?

The decision to use a shallow copy or a deep copy depends on your specific use case. If you are working with immutable objects or collections of objects where the nested objects should not be duplicated, a shallow copy may suffice. However, if you need to ensure that changes made to the copy do not affect the original object, a deep copy is necessary.

Conclusion

Understanding the differences between shallow copy and deep copy is crucial for proper object management in Python. Choosing the appropriate copying method allows you to control the independence of objects and avoid unintended side effects. By mastering these concepts, you can write safer, more efficient, and more predictable programs.