Python Memory Management Methods

Python has several ways of managing memory based on values.

1. Reference counting: Python uses reference counting to track the number of references to an object. When an object is referenced, its reference count increments by 1; when a reference to an object is deleted, its reference count decrements by 1. When an object’s reference count reaches 0, it means that there are no references pointing to that object, and Python immediately reclaims the memory space of that object.
2. Garbage Collection: In addition to reference counting, Python also utilizes a garbage collection mechanism to handle memory leaks in cases like circular references. Python’s garbage collection mechanism periodically checks the reference relationships of objects, identifies those no longer referenced, and releases them to free up memory.
3. Memory Pool: Python utilizes a memory pool to manage the allocation and deallocation of memory for small objects. The memory pool pre-allocates a certain size of memory block and stores them in a global memory pool. When there is a need to allocate memory for small objects, Python will allocate a memory block from the memory pool for the object to use, thus avoiding frequent system calls and memory fragmentation issues.

In general, Python’s memory management is value-based, effectively managing memory space for objects through mechanisms such as reference counting, garbage collection, and memory pools to ensure the efficiency and stability of memory usage in programs.