What is the difference between shallow and deep copying in Python?

Table of Contants

Introduction:

When working with data structures like lists and dictionaries in Python, understanding how to copy data is crucial for managing and manipulating it effectively. Python offers two types of copying mechanisms: shallow copying and deep copying. These methods differ in how they handle the copying of nested objects and their references.

Shallow Copy

A shallow copy creates a new object, but it inserts references into it to the objects found in the original. In other words, the new object is a copy of the outermost structure, but it contains references to the same elements as the original. Changes to the elements within the copied object will be reflected in the original and vice versa.

Creating a Shallow Copy

You can create a shallow copy using:

1. The copy module’s copy() function.
2. Object-specific methods, like the copy() method for lists.
3. List slicing.

Example : Using copy.copy()

• The nested list inside original_list is affected by changes made to shallow_copy_list because both lists share the same nested list.

Example : Using List Slicing

• Similar to the copy.copy() method, changes to the nested list in shallow_copy_list reflect in original_list.

Deep Copy

A deep copy creates a new object and recursively copies all objects found within the original. This means that not only the outermost object but also all nested objects are duplicated. As a result, changes made to the deep copy will not affect the original object, and vice versa.

Creating a Deep Copy

You can create a deep copy using:

1. The copy module’s deepcopy() function.

Example: Using copy.deepcopy()

• Changes to the nested list in deep_copy_list do not affect original_list because all nested objects are copied independently.

Key Differences Between Shallow and Deep Copy

1. References vs. Independent Copies:
   • Shallow Copy: Copies only the outermost object. Nested objects are still referenced, not duplicated.
   • Deep Copy: Copies the outermost object and all nested objects, resulting in fully independent copies.
2. Impact of Modifications:
   • Shallow Copy: Modifications to nested objects in the copy affect the original object.
   • Deep Copy: Modifications to nested objects in the copy do not affect the original object.
3. Performance:
   • Shallow Copy: Generally faster since it only copies references.
   • Deep Copy: Slower because it involves copying all nested objects.

Practical Use Cases

1. When to Use Shallow Copy:
   • When you need a new collection but don’t need to modify nested elements.
   • When working with large datasets where performance is a concern, and modifications to nested objects are not required.
2. When to Use Deep Copy:
   • When you need a complete duplicate of the original object, including all nested objects.
   • When changes to the copied object should not affect the original.

Conclusion:

Understanding the difference between shallow and deep copying in Python is essential for managing data structures effectively. Shallow copying duplicates the outermost object and keeps references to nested objects, while deep copying creates entirely new instances of all nested objects. By choosing the appropriate copying method, you can control how changes to copied objects affect the original data and optimize performance based on your needs