Today we’re going to talk about something that’s near and dear to our hearts (or at least our memory leaks): garbage collection.

In case you didn’t know, garbage collection is the process by which a programming language automatically manages memory allocation for us.In Python, this means that we don’t have to worry about manually freeing up memory when we’re done with it instead, the interpreter takes care of that for us!

But how does it work? Well, let me tell you a little story…

Once upon a time, in a land far, far away (aka your computer), there was a magical creature called “the garbage collector”. This creature had the power to roam around and collect any objects that were no longer being used by our program. ️

Now, you might be wondering: how does this creature know which objects are still in use? Well, bro friend, let me explain…

In Python, every object has a reference count basically, it’s like a little counter that keeps track of how many times an object is being used. When we create a new variable or assign a value to an existing one, the garbage collector increments the reference count for that object by 1. And when we delete a variable (or set it to `None`), the reference count decreases by 1.

So, if an object’s reference count goes down to zero, then the garbage collector knows that nobody is using it anymore and can safely collect it!

But wait what happens when we have a cyclic reference? Let me explain with another story…

Imagine you have two objects: `a` and `b`. You assign `a` to variable `x`, which increments the reference count for `a` by 1. Then, you assign `b` to `a.y`, which creates a new object (let’s call it `c`) that has references to both `a` and `b`.

Now, if we delete `x`, then the reference count for `a` goes down to zero but wait! The garbage collector can’t collect `a` because there’s still a reference to it from object `c` (which is referenced by both `a` and `b`)!

To handle this situation, Python uses a technique called “mark-and-sweep” garbage collection. Essentially, the garbage collector marks all of the objects that are still in use (by following their reference chains), then sweeps through the memory to collect any unmarked objects.

That’s how Python’s garbage collection mechanism works a magical creature called “the garbage collector” roams around collecting unused objects, and we don’t have to worry about manually freeing up memory anymore!

So next time you see an error message that says “MemoryError”, just remember: it’s not your fault the garbage collector is probably just having a bad day.