Python manages memory internally through a system known as automatic memory management. This process involves private heap space where all Python objects and data structures are stored. The management of this private heap is handled by Python’s built-in memory manager, which ensures that memory is allocated efficiently and safely to different parts of a running program.

At a basic level, Python uses reference counting as its primary memory management technique. Each object keeps track of the number of references pointing to it. When the reference count drops to zero (meaning no part of the program needs it anymore), the memory occupied by the object is automatically deallocated.

However, reference counting alone is not sufficient because it cannot handle cyclic references—situations where two or more objects reference each other, creating a loop. To address this, Python uses a garbage collector, specifically designed to identify and clean up these cycles. The garbage collector periodically searches for groups of objects that are no longer accessible and safely reclaims their memory.

In addition to memory cleanup, Python optimizes memory use with memory pools using a system called pymalloc. This allocates small blocks of memory efficiently, reducing the time and overhead needed to allocate and free memory repeatedly.

Python also allows programmers to manage memory manually to some extent, by deleting objects with the del keyword or by using context managers (with statements) to manage resources like file streams and database connections. However, for most applications, Python’s internal systems handle memory management well enough without developer intervention.

Understanding how Python manages memory is essential for writing efficient, scalable programs. This topic is usually introduced in any good python course for beginners, helping students develop a strong foundation early in their programming journey.