How is memory managed in Python?

Memory management in Python is handled by the Python interpreter, which uses a combination of techniques to manage memory and ensure that it is used efficiently. Here are some of the main techniques used by Python to manage memory:

Reference counting

Python uses reference counting to keep track of the number of references to an object. Every object has a reference count, which is the number of references to it. When an object's reference count drops to zero, it is considered garbage and is automatically deleted. This technique is fast and efficient, but it can't handle circular references.

Garbage collection

To handle circular references, Python uses a garbage collector that periodically checks for objects that are no longer being used and frees the memory they occupy. The garbage collector is not always active, but it runs automatically when the interpreter detects that memory is running low. The garbage collector uses a combination of reference counting and cycle detection to identify and free unreachable objects.

Memory pooling

Python also uses a memory pooling technique to reduce the overhead of memory allocation and deallocation. The memory manager in Python pre-allocates chunks of memory for small objects of the same size. When an object is created, Python tries to allocate it from one of these pre-allocated chunks, rather than requesting a new block of memory from the operating system. This technique reduces the number of system calls and the overhead of memory allocation.