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Memory management in Python is handled by the Python Memory Manager. Python uses a private heap space to store objects that are created during runtime. The Memory Manager handles all allocation and deallocation of heap memory for Python objects.

The Memory Manager in Python uses two techniques to manage memory: reference counting and garbage collection.

Reference Counting: Python uses reference counting to keep track of how many times an object is referenced in the code. Every time a new reference to an object is created, Python increments a reference counter for that object. Similarly, every time a reference is deleted, the counter is decremented. When the counter reaches zero, it means that no references to the object exist and the object can be safely deleted from memory.

Garbage Collection: Garbage collection is the process of freeing up memory that is no longer needed. Even with reference counting, there are situations where an object may have circular references, making it impossible to determine when the object is no longer needed. In such cases, Python uses a garbage collector to identify and remove objects that are no longer in use.

The garbage collector runs periodically, checking all objects in memory and identifying those that are no longer reachable. These unreachable objects are then marked for deletion and their memory is freed up.

In addition to reference counting and garbage collection, Python also uses other memory optimization techniques such as object re-use, object sharing, and memory pooling.

Overall, Python's memory management system is designed to be efficient and automatic, allowing developers to focus on their code rather than managing memory manually.

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