Deep Copy vs Shallow Copy Understanding copy Module

Understand the difference between shallow and deep copies in Python using the copy module. Learn how to avoid unexpected behavior when copying mutable objects.

Deep Copy vs Shallow Copy in Python: Mastering the copy Module

Copying objects in Python is not as straightforward as it might seem, especially when dealing with mutable objects like lists or dictionaries. There are two primary ways to copy objects: shallow copy and deep copy. Understanding the difference is critical to avoid unintended side effects in your programs.

What is a Shallow Copy?

A shallow copy creates a new object but does not create copies of nested objects. Instead, it copies references to the nested objects. This means changes to the nested objects in the copy will also affect the original object.

In Python, you can create a shallow copy using the copy.copy() function from the copy module.

import copy

original_list = [[1, 2], [3, 4]]
shallow_copied_list = copy.copy(original_list)

Here, shallow_copied_list is a new list, but its nested lists are shared with original_list.

What is a Deep Copy?

A deep copy creates a new object and recursively copies all nested objects, resulting in a completely independent clone. Modifying nested objects in the deep copy does not affect the original object.

In Python, use copy.deepcopy() to perform a deep copy:

deep_copied_list = copy.deepcopy(original_list)

Now, changes to deep_copied_list are fully isolated from original_list.

Example: Shallow vs Deep Copy

Let’s demonstrate how they behave differently:

import copy

original = [[1, 2], [3, 4]]

shallow = copy.copy(original)
deep = copy.deepcopy(original)

# Modify nested list in shallow copy
shallow[0][0] = 99

print(\\"Original:\\", original)  # [[99, 2], [3, 4]]
print(\\"Shallow:\\", shallow)    # [[99, 2], [3, 4]]

# Modify nested list in deep copy
deep[1][1] = 88

print(\\"Original:\\", original)  # [[99, 2], [3, 4]]
print(\\"Deep:\\", deep)          # [[1, 2], [3, 88]]

Notice how modifying shallow also affects original, while deep remains independent.

When to Use Each

• Use shallow copies when working with flat, non-nested data structures or when sharing nested objects is intentional.
• Use deep copies when you need fully independent copies of complex, nested structures.

Conclusion

Knowing the distinction between shallow and deep copies can save you from subtle bugs, especially when handling mutable objects in Python. Always analyze your data structure before deciding which copy strategy to use.

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