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Oct 20, 2023 3 mins

Understanding Shallow Copy and Deep Copy in Python Lists

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Python as a versatile programming language, provides multiple ways to duplicate lists. Two commonly used methods are shallow copy and deep copy, each with distinct characteristics and use cases. In this article, we'll explore the differences between these two approaches and when to choose one over the other.

Python Shallow Copy

A shallow copy of a list is a replication that creates a new list object but does not generate new objects for the elements within the list. Instead, the new list contains references to the same objects as the original list. This means that changes made to the elements inside the original list may affect the corresponding elements in the shallow copy, and vice versa.

Implementation

import copy

original_digit_list = [1, [2, 3], [4, 5]]
shallow_copied_digit_list = copy.copy(original_digit_list)

Characteristics

  • References to the Same Objects: The shallow copy retains references to the original objects within the list.
  • Changes Propagation: Modifications to elements in the original list reflect in the shallow copy and vice versa.
  • Resource Efficiency: Shallow copies are more memory-efficient since they don’t duplicate the inner objects.

Use Cases

Shallow copying is suitable for simple, one-dimensional lists where maintaining references to the same objects is not an issue. It provides a lightweight solution when the goal is to have a new list structure without creating entirely new objects.

Shallow Copy Example

original_digit_list[1][0] = 99
print(shallow_copied_digit_list)  # Output: [1, [99, 3], [4, 5]]

Python Deep Copy

A deep copy, on the other hand, creates a completely new list object and recursively generates new objects for all elements within the original list, including nested structures. This ensures complete independence between the new list and the original list, as changes made to the original list or its elements do not affect the deep copy, and vice versa.

Implementation

import copy

original_digit_list = [1, [2, 3], [4, 5]]
deep_copied_list = copy.deepcopy(original_digit_list)

Characteristics

  • Independent Objects: The deep copy creates entirely new objects for all elements, ensuring independence from the original list.
  • Changes Isolation: Modifications to the original list or its elements do not impact the deep copy and vice versa.
  • Resource Intensiveness: Deep copying may be more resource-intensive, especially for nested structures, due to the creation of entirely new objects.

Use Cases

Deep copying is essential when dealing with nested structures or lists containing mutable objects. It guarantees that modifications in one list do not inadvertently affect the other, providing a clear separation of data.

Deep Copy Example

original_digit_list[1][0] = 99
print(deep_copied_list)  # Output: [1, [2, 3], [4, 5]]

Choosing Between Shallow Copy and Deep Copy

The choice between shallow copy and deep copy depends on the specific requirements of your program and the characteristics of the data structures involved.

  • Shallow Copy for Efficiency: If memory efficiency is a concern, and maintaining references to the same objects is acceptable, then a shallow copy is a lightweight solution. It is suitable for simple, flat lists where object interdependence is not an issue.

  • Deep Copy for Independence: When dealing with nested structures or lists containing mutable objects, where maintaining independence is crucial, a deep copy is the preferred choice. It ensures that modifications to one list do not inadvertently affect the other, providing a clear and isolated duplication of data.

Conclusion

Understanding the distinction between shallow copy and deep copy is essential for effective list manipulation in Python. Whether you opt for the memory-efficient shallow copy or the independent deep copy depends on the nature of your data and the desired behavior. By carefully choosing between these approaches, you can ensure that your program behaves as intended and that modifications to one list do not unintentionally impact another.


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