How does Python manage memory and garbage collection for objects that are no longer referenced in the code?

Python manages memory and garbage collection for objects that are no longer referenced in the code through an automatic process called reference counting. Here are the steps involved:

1. Reference counting: Python keeps track of the number of references to an object. Every time a reference to an object is created, the reference count for that object is incremented. Similarly, when a reference is deleted or goes out of scope, the reference count is decremented.

2. Garbage collection: In addition to reference counting, Python also employs a garbage collector to handle cyclic references. Cyclic references occur when two or more objects reference each other, creating a loop that cannot be resolved by reference counting alone.

3. Mark and sweep algorithm: The garbage collector uses a mark and sweep algorithm to identify and collect objects that are no longer reachable. It starts by marking all objects that are still reachable from the root objects (e.g., global variables, local variables, etc.). Then, it sweeps through the memory, deallocating the memory occupied by objects that were not marked during the marking phase.

4. Finalization: Before an object is deallocated, Python calls the object's __del__ method (if defined) to perform any necessary cleanup operations. However, it's important to note that the __del__ method is not guaranteed to be called immediately when an object becomes unreachable, as it depends on the garbage collector's scheduling.

Overall, Python's memory management and garbage collection mechanisms ensure that objects that are no longer referenced in the code are automatically cleaned up and their memory is reclaimed. This helps in efficient memory utilization and prevents memory leaks.