How does Python handle memory management?

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Khushi Singh · Answer

The automatic memory cleanup and efficient memory utilization in Python occur through strategic techniques within the system. The following details explain the operating mechanism:
1. Automatic Memory Management
Python employs automatic memory management because programmers do not have to manually handle memory allocation or deallocation. The Python memory manager operates within its internal system to manage memory.
2. Private Heap Space
Python heap functions as the private memory field that contains all data structures and objects of the programming language. The Python memory manager allocates memory from the heap space to allocate memory through interactions with the operating system when needed.
3. Reference Counting
The main method that Python employs for resource management is reference counting. A Python object maintains a reference count as an integral part that shows how many variables point to it. The object becomes eligible for deletion once its reference count reaches zero because it indicates no longer being used by the program.
Example:
a = [1, 2, 3]  # Reference count is 1
b = a          # Reference count becomes 2
del a          # Reference count becomes 1
del b          # Reference count becomes 0, object is deleted
4. Garbage Collection
Python features a memory retrieval function that helps reclaim inaccessible objects, especially when objects form reference cycles (two objects linking to each other). The
gc module contains the garbage collector that operates in combination with reference counting.
5. Memory Pools
Python depends on pymalloc to control and allocate small memory blocks in an optimized manner. Memory pools shorten memory fragmentation and boost performance during allocation procedures.
6. Dynamic Typing
Memory allocation in Python handles objects dynamically since the programming language determines allocation decisions based on actual object types and sizes during runtime.
Python provides an effective memory management solution that unites reference counting with garbage collection and internal memory pooling mechanisms to give developers automatic memory management while they focus on coding.
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How does Python handle memory management?

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