How is memory managed python

Automatic Memory Management:
Python uses an automatic memory management system, freeing developers from the burden of manual memory allocation and deallocation. This mechanism helps prevent common memory-related errors, such as memory leaks and dangling pointers.
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Memory Allocation:
When a Python program runs, the Python memory manager allocates memory to objects dynamically. Objects can include variables, data structures, and other elements used in the program. Memory is allocated as needed during the execution of the program.
Reference Counting:
Python employs a simple and efficient technique called reference counting to keep track of the number of references to an object. Each object has a reference count, and when this count drops to zero, indicating that no references point to the object, the memory occupied by the object can be freed.
Garbage Collection:
While reference counting is effective, it may not handle all cases, such as circular references. To address this, Python includes a garbage collector that identifies and collects objects that are no longer in use, even if they have circular references. The garbage collector helps reclaim memory occupied by unreferenced objects.
Cyclic Garbage Collector:
Python's garbage collector includes a cyclic garbage collector that specifically targets circular references. This collector identifies cycles of objects that reference each other, ensuring their proper cleanup and preventing memory leaks.
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Memory Pools:
Python uses a system of memory pools for small objects to optimize memory allocation. Memory pools reduce the overhead of frequent memory requests by allocating memory in chunks and then distributing smaller portions from these chunks as needed.