Python handles garbage collection automatically using a mechanism called reference counting, along with a cyclic garbage collector to deal with complex memory management scenarios. When you create an object in Python, the interpreter keeps track of how many references point to that object. This count increases when new references are made and decreases when references are deleted. Once an object’s reference count drops to zero, it means that object is no longer needed and Python will automatically deallocate its memory.

However, reference counting alone cannot handle circular references — situations where two or more objects reference each other, creating a loop. To address this, Python includes a cyclic garbage collector within its gc module, which periodically checks for and cleans up such reference cycles. This ensures that memory is reclaimed even when objects are self-referencing or interconnected in a circular fashion.

Python’s garbage collection is mostly invisible to the developer, but it can be controlled and monitored. Developers can manually trigger garbage collection using gc.collect() or disable it in performance-critical sections if they know it’s safe. Although Python does a great job managing memory for you, understanding how garbage collection works can help you write more efficient and memory-safe code.

For example, if you’re handling large datasets or building long-running applications, unintentional memory retention through references or closures can lead to memory bloat. In such cases, using tools like gc, objgraph, or memory profilers can help diagnose and resolve issues.

In summary, Python’s garbage collection relies on reference counting with a backup cyclic collector, ensuring efficient memory management. This makes Python a robust language for developers of all skill levels.

To explore this topic and others in more detail, consider enrolling in a Python course for beginners.