Concept of Deep Copy in Python

In Python, similar to other object-oriented languages, the need to create an exact duplicate of an object arises, such that changing one doesn't affect the other. This operation is known as a 'deep copy' and is particularly relevant when working with objects that contain other objects.

In Python, everything is an object, and variables are just references to these objects. Therefore, when you assign an object to another variable, they both point to the same memory location. This behavior leads to a situation where modifying one affects the other. To ensure the two objects are independent, a 'deep copy' becomes necessary.

The `copy` module in Python provides the `deepcopy` method to achieve this purpose.

Let's delve into an illustrative example. Imagine a class `Student` which contains a list of `subjects`:

class Student:
    def __init__(self, name, age, subjects):
        self.name = name
        self.age = age
        self.subjects = subjects

You can instantiate a `Student` object as follows:

student1 = Student("Alice", 20, ["Math", "Science"])

If you try to create a copy of `student1` using assignment:

student2 = student1

Both `student1` and `student2` would refer to the same object. If you modify the `subjects` list of `student2`, it will affect `student1` as well:

student2.subjects.append("History")
print(student1.subjects)  # Outputs: ['Math', 'Science', 'History']

To avoid this, Python provides the `deepcopy` function:

import copy

student3 = copy.deepcopy(student1)

Now, `student3` is an independent object. Modifying `student3` will not impact `student1`:

student3.subjects.append("Geography")
print(student1.subjects)  # Outputs: ['Math', 'Science', 'History']
print(student3.subjects)  # Outputs: ['Math', 'Science', 'History', 'Geography']

This approach ensures that even nested objects within the main object are copied properly, creating a truly independent object in memory.