How does Python handle memory management?

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Python handles memory management using a combination of reference counting, garbage collection, and private heap space.

At its core, Python uses reference counting to keep track of how many references point to an object. When an object's reference count drops to zero, it is automatically deallocated. However, reference counting alone can’t handle circular references (e.g., when two objects reference each other), which would keep their reference counts above zero even if they are no longer needed.

To address this, Python includes a cyclic garbage collector as part of the gc module. It periodically scans for groups of objects involved in circular references and frees them if they are no longer accessible.

All Python objects and data structures are stored in a private heap, managed internally by the Python memory manager. This manager interacts with the operating system's memory allocation mechanisms and provides efficient memory handling for Python programs.

Python also has built-in memory pools (through the pymalloc allocator) that reduce fragmentation and speed up allocation for small objects.

Lastly, developers can influence memory usage by explicitly deleting objects using del, using weak references (weakref module), or manually managing the garbage collector.

Together, these mechanisms ensure that Python handles memory automatically and efficiently, though it may not always be as predictable as in languages with manual memory management.

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