Mutable vs Immutable datatypes in Python

Mutable vs Immutable datatypes in Python

In Python, objects can be categorized as mutable or immutable based on whether their values can be changed after creation. This distinction is important when understanding how variables and references work in Python, especially regarding memory management.

Mutable Objects: Changing Values in Place

Mutable objects, such as lists, dictionaries, and sets, allow modifications to their content after creation. When you create a mutable object, a reference to its memory location is stored in a variable. Subsequent modifications happen in-place, meaning the memory address remains constant.

The consequence of mutability is that if multiple variables reference the same object, changes made through one variable affect the object accessed through other variables.

Immutable Objects: Safeguarding Values

Immutable objects, such as integers, floats, strings, and tuples, cannot be modified after creation. When you modify an immutable object, a new object is created, and the variable is updated to reference the new memory location.

Immutable Objects and Garbage Collection:

Immutable objects don't pose the same concerns about inadvertent changes from multiple references. When an immutable object is no longer referenced, it becomes eligible for garbage collection.

Reference counting, a primary mechanism for garbage collection in Python, tracks the number of references to an object. When the reference count drops to zero, the object is considered unreachable and can be deallocated.

In conclusion, while immutable objects contribute to memory efficiency and simplify certain aspects of memory management, the garbage collector remains a critical component in reclaiming memory when these objects are no longer in use.

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