Python's memory management in embedded systems differs from traditional languages like C due to its dynamic memory allocation. In embedded systems, where resources are limited, efficient memory management is crucial. Python relies on a built-in memory management mechanism called garbage collection to automatically manage memory by reclaiming unused objects. Python uses a reference counting system to keep track of how many variables refer to a particular object. When the reference count of an object drops to zero, the memory occupied by that object is freed.

Additionally, Python includes a cyclic garbage collector to handle memory leaks caused by reference cycles, where objects reference each other, preventing their automatic deletion. However, the dynamic nature of Python introduces memory overhead, which can be challenging in embedded systems with limited RAM.

Due to this overhead, MicroPython and CircuitPython were developed as lightweight versions of Python specifically optimized for resource-constrained devices. These variants remove features that consume significant memory, such as complex libraries, and focus on minimal resource usage while maintaining Python's simplicity and ease of use.

To enhance your skills and better understand Python's memory management in embedded systems, consider enrolling in a Python certification course to gain hands-on experience.