Python utilizes automatic memory management through a process known as garbage collection. Garbage collection is responsible for reclaiming memory that is no longer in use by the program, freeing up resources and preventing memory leaks.

Python garbage collection works by using a technique called reference counting. Every object in Python has a reference count, which keeps track of the number of references pointing to that object. When the reference count of an object reaches zero, it means that the object is no longer reachable and can be safely reclaimed.

However, reference counting alone cannot handle all cases of memory management. For example, if there are cyclic references where objects reference each other in a loop, the reference counts would never reach zero, resulting in memory leaks. To address this, Python employs a secondary garbage collection mechanism called “cycle detection” or “tracing garbage collection.”

The cycle detection mechanism periodically runs a cycle detection algorithm that identifies and collects cyclically referenced objects. It traverses the object graph, starting from the root objects (e.g., global variables, objects referenced by the stack frames), and marks objects as “reachable” during the traversal. Any objects not marked as reachable are considered garbage and are eligible for collection.

Python’s garbage collection module provides additional control and customization options for managing the garbage collection process. Some of the notable functions and methods in this module include:

• gc.enable(): Enables automatic garbage collection.
• gc.disable(): Disables automatic garbage collection.
• gc.collect(): Triggers an immediate garbage collection cycle.
• gc.get_count(): Returns the current collection counts.
• gc.get_threshold(): Returns the current collection thresholds.
• gc.set_threshold(): Sets the collection thresholds.
• gc.get_objects(): Returns a list of all objects tracked by the garbage collector.

It’s important to note that in most cases, you don’t need to explicitly interact with the garbage collector. Python’s automatic garbage collection mechanism handles memory management transparently for you. However, understanding the basics of garbage collection can be helpful when dealing with certain situations that require fine-tuning or debugging memory-related issues.