How does Python handle memory management?

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Python handles memory management through a combination of automatic memory allocation, reference counting, and garbage collection.

At its core, Python uses a private heap space to store all objects and data structures. The memory for these objects is managed internally by the Python memory manager, which interacts with the object allocator for handling raw memory blocks.

A primary mechanism Python uses for memory management is reference counting. Every object in Python maintains a count of references to it. When an object’s reference count drops to zero, meaning no part of the program is using it, the memory is deallocated.

However, reference counting alone cannot handle circular references (where two or more objects reference each other). To deal with this, Python includes a cyclic garbage collector as part of the gc module, which can detect and collect objects involved in reference cycles.

Additionally, Python uses memory pools through a system called PyMalloc to improve efficiency in allocating small objects (common in Python programs). This reduces overhead from frequent calls to the system memory allocator.

Memory management in Python is mostly automatic, but developers can influence it by:

• Using del to reduce reference counts.

• Importing and using the gc module to inspect and collect garbage.

• Writing memory-efficient code (e.g., using generators, avoiding global variables).

Overall, Python’s memory management aims to be efficient, automatic, and largely invisible to the developer.

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