What's the Difference Between Normal Assignment, Shallow Copy, and Deep Copy in Python?

Understanding Different Copying Operations: Normal Assignment, Shallow Copy, and Deep Copy

In Python, there are various ways to create copies of variables and objects. Normal assignment operations, shallow copies, and deep copies differ in how they handle the relationship between the original and the copied variables.

Normal Assignment

Normal assignment operations (e.g., a = b) simply point the new variable to the existing object. Both variables now reference the same object in memory. Changes made to one variable will affect the other.

Shallow Copy

A shallow copy creates a new object that references the same underlying data as the original object. For immutable objects (e.g., strings, numbers), this is equivalent to normal assignment. However, for mutable objects (e.g., lists, dictionaries), the new object contains references to the original objects.

In the following code snippet:

import copy

a = [1, 2, 3]
b = copy.copy(a)

a and b are both lists. When copying a using copy.copy, a new list is created, but it contains references to the same list elements as a. Therefore, modifying b will also modify a.

Deep Copy

A deep copy creates a new object and recursively copies all the data from the original object. This means that even if the original object contains references to other mutable objects, the new object will have independent copies of those objects.

In the following code snippet:

import copy

a = [1, 2, 3]
c = copy.deepcopy(a)

c is a new list that contains its own copies of the list elements. Any changes made to c will not affect a.

Conclusion

The choice of copying operation depends on the specific use case. For immutable objects, normal assignment or shallow copy is sufficient. For mutable objects, deep copying is recommended to ensure that changes to the copy do not affect the original.

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