Understanding the Difference Between Shallow Copy and Deep Copy in Python

Copying data structures is a fundamental aspect of programming in Python, as it allows developers to duplicate objects. However, the way you copy an object—using either a shallow copy or a deep copy—can lead to different outcomes. In this post, we will explore what these copies are, their differences, and when to use them.

Table of Contents

• Introduction to Copying in Python

• What is a Shallow Copy?

• How to Create a Shallow Copy

• Examples of Shallow Copy

• What is a Deep Copy?

• How to Create a Deep Copy

• Examples of Deep Copy

• Key Differences Between Shallow Copy and Deep Copy

• When to Use Shallow Copy vs. Deep Copy

• Conclusion

Introduction to Copying in Python

Copying objects is sometimes necessary for creating duplicates without affecting the original. The method used for copying has significant implications, particularly with mutable objects containing nested objects.

What is a Shallow Copy?

Shallow copying creates a new object without duplicating the objects that the original references. It merely copies the top-level structure of the original.

How to Create a Shallow Copy

Shallow copies can be made using:

• The copy module: shallow_copy = copy.copy(original_object)

• The list's copy method: shallow_copy = original_list.copy()

• List slicing: shallow_copy = original_list[:]

Examples of Shallow Copy

Consider an example where:

import copy
original_list = [1, 2, [3, 4]]
shallow_copy = copy.copy(original_list)
shallow_copy[0] = 100
shallow_copy[2][0] = 300

The change to shallow_copy[2][0] affects original_list[2][0] because the nested list is not duplicated.

What is a Deep Copy?

In contrast, deep copying creates a new object and duplicates all objects within the original, ensuring that the new object is entirely independent.

How to Create a Deep Copy

Deep copies are created using the copy module: deep_copy = copy.deepcopy(original_object)

Examples of Deep Copy

For a deep copy scenario:

import copy
original_list = [1, 2, [3, 4]]
deep_copy = copy.deepcopy(original_list)
deep_copy[0] = 100
deep_copy[2][0] = 300

Here, changes to deep_copy do not affect original_list, even in the nested objects.

Key Differences Between Shallow Copy and Deep Copy

• Top-Level vs. Nested Copying: Shallow copies share references to nested objects, while deep copies do not.

• Memory Independence: Changes to nested objects in a shallow copy can affect the original, whereas deep copies are independent.

• Performance: Shallow copies are faster and less memory-intensive, while deep copies are slower and require more memory.

When to Use Shallow Copy vs. Deep Copy

Choose shallow copy for speed and when changes to nested objects should propagate. Use deep copy when you need full independence between the original and the copy.

Conclusion

Grasping the distinction between shallow and deep copying is vital for managing memory and preventing side effects in Python programs. Knowing when to use each type of copying will help maintain reliable code and optimize Python development efficiency.