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, making it easier for developers to focus on coding without manual memory handling.

At the core is reference counting. Every object in Python has a reference count, which tracks how many references point to it. When this count drops to zero (i.e., no references exist), Python automatically deallocates the memory.

To deal with circular references—where objects reference each other but are no longer used—Python uses a garbage collector (via the gc module). This collector periodically scans for groups of objects involved in reference cycles and frees them if they’re unreachable.

Python also uses private heaps to store objects and data structures. The memory manager handles allocation within this space, and the object-specific allocators (like those for integers, lists, and dictionaries) optimize performance.

Memory in Python is further managed through pools and arenas via a mechanism called PyMalloc, which reduces fragmentation and speeds up allocations for small objects.

Developers can influence memory usage by minimizing unnecessary references, using generators instead of large lists, and explicitly deleting unused objects with del.

Overall, Python’s memory management is largely automatic, combining efficiency and safety, though understanding its internals can help developers write more memory-efficient code.

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