Alright, Python garbage collection! This magical feature automatically manages memory for us like a fairy godmother in coding land. Here are some key details:

– In the olden days, developers had to manually manage memory using malloc() or new(). But this was tedious and error-prone forgetting to free up some memory could lead to memory leaks and crashes due to running out of memory.

– Python’s garbage collector handles all that for us! When you create an object, it keeps track of it until the object is no longer referenced by your code (i.e., there are no more variables pointing to it). Then, when its reference count drops to zero, it marks the object as “dead” and adds it to a list waiting for collection.

– Reference counting: Every time you create a new variable that points to an object, its reference count increases by one. When that variable goes out of scope (i.e., it’s assigned to another variable or deleted), the garbage collector decrements the reference count for that object.

If the reference count drops to zero, the object is marked as “dead” and added to the list waiting for collection.

– Generational garbage collection: The Python garbage collector uses a two-generation system young generation and old generation. Newly created objects go into the young generation, which is collected more frequently (usually every few hundred milliseconds). Objects that survive multiple collections are promoted to the old generation, where they’re collected less often (typically once per second or so).

– The gc module offers a number of ways to interact with the garbage collector.

You can enable or disable it using the gc. enable() and gc. disable() functions, respectively. But be careful disabling the garbage collector is not recommended unless you have a very specific use case!