Python Copying Mechanisms: Shallow Copy vs. Deep Copy Explained

As a lifelong lover of mathematics, I recently delved into Python to solve the permutations of a string. During this exploration, I encountered the crucial concepts of shallow and deep copying. Fascinated by their impact on data handling, I realized the importance of understanding these mechanisms. Inspired by this discovery, I decided to write an article to demystify shallow and deep copies in Python.

In the realm of Python programming, particularly when handling complex data structures, the concepts of shallow and deep copying play a pivotal role. These mechanisms determine how objects are duplicated and how changes to one copy affect the other.

When you create a copy of data structures in python, one can choose between a shallow and deep copy. Let’s explore them.

Shallow Copy: A Quick Duplication

A shallow copy creates a new object, but inserts references into it to the objects found in the original. This means that while the outer structure is new, the elements inside it are not — they are still references to the same objects.

Deep Copy: Complete Independence

A deep copy, on the other hand, creates a new object and recursively copies all objects found in the original. This means that the new copy is entirely independent of the original, including all nested objects.

Knowing the difference between shallow and deep copy give an edge while dealing with mutable objects. Utilizing wrong type of copy gives unintended side effects where changes in one objects directly affects another. Knowing the distinction between shallow copy and deep copy helps in applications involving complex data manipulation, such as ML, Data Analysis and such as while solving DSA problem(as i discovered during the generating the permutations).

I hope this article sheds light on these essential mechanisms and helps you navigate your coding endeavors with greater ease and confidence. Happy Coding.

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