Python handles memory management automatically through a system called garbage collection, which frees up memory by deleting objects that are no longer in use, allowing developers to focus on coding without worrying about memory allocation.

Memory management is a critical aspect of any programming language, and Python approaches this challenge with an automated system that helps developers manage memory efficiently without delving into low-level details. At the heart of Python's memory management is a mechanism known as garbage collection. This system monitors the allocation of memory for objects in Python and automatically reclaims memory that is no longer needed. When an object is created, Python allocates a block of memory to store it. Once the object is no longer referenced in the program, the garbage collector identifies it as eligible for deletion. This automatic memory reclamation process reduces the likelihood of memory leaks, which can occur when developers forget to release memory, leading to inefficient memory usage over time. Python also employs a technique called reference counting, where each object maintains a count of the number of references pointing to it. When the reference count drops to zero, the object is immediately considered garbage and can be safely deleted. Additionally, Python uses cyclic garbage collection to detect and clean up reference cycles, which can occur when objects reference each other in a loop, preventing their reference counts from dropping to zero. While Python's automatic memory management simplifies development and allows programmers to focus on writing code rather than managing memory manually, developers should still be mindful of memory consumption, especially when dealing with large datasets or resource-intensive applications. Understanding how Python manages memory can lead to more efficient code and better resource management in applications.