How is Memory managed in Python?

Python uses a mechanism known as memory pools to efficiently manage memory allocation and deallocation. Memory pools are segregated into different size classes to allocate memory blocks of appropriate sizes for various objects.Python employs a cyclic garbage collector that identifies and reclaims memory occupied by objects with circular references.

Python effectively manages memory using a variety of methods, such as automated memory management, reference counting, and garbage collection.

Below is some point to be consider in memory management :-

Memory in Python is managed by Python private heap space.
All Python objects and data structures are located in a private heap. This private heap is taken care of by Python Interpreter itself, and a programmer doesn’t have access to this private heap.
Python memory manager takes care of the allocation of Python private heap space.
Memory for Python private heap space is made available by Python’s in-built garbage collector, which recycles and frees up all the unused memory.
The allocation of heap space for Python objects and other internal buffers is performed on demand by the Python memory manager through the Python/C API functions listed in this document.
To avoid memory corruption, extension writers should never try to operate on Python objects with the functions exported by the C library: malloc(), calloc(), realloc() and free().

For more details click below link

Memory Management — Python 3.9.7 documentation

Conclusion:

Python's memory management system is intended to be user-friendly, which frees developers from having to manually allocate and deallocate memory so they can concentrate on developing code.

This solution is provided by Shubham mishra,

This article was contributed by the Developer Indian team. Please leave comments if you find anything incorrect or if you want to share more information about the topic discussed above.

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