Stop. Before you scroll past this, let me ask you something.
When you write x = 10 in Python, what do you think happens?
If your answer is "Pytho...
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That shallow vs deep copy distinction is super relevant for us at The Printing World. We deal with complex nestings of custom box dimensions, print layers, and dielines, so accidentally copying references in our order script could totally wreck production!
Absolutely! That’s a great real-world example. With nested box dimensions, layers, and dielines, a shared reference could easily lead to unexpected changes in production. Glad the shallow vs deep copy distinction was useful!
The "variables are labels, not boxes" framing is the thing I wish I'd read two weeks ago.
I'm a beginner — I started learning Python about two weeks ago and I've been writing beginner tutorials about it. And the exact mental model you're describing is what tripped me up.
When I first learned a = [1, 2, 3] and b = a, I assumed b was a copy. Then I did b.append(4) and a changed too. I spent a solid hour convinced Python was broken.
It wasn't broken. I was.
The id() experiment is what fixed it for me. Seeing the same memory address print twice made the "label" idea click in a way that no amount of explanation could.
I haven't touched the advanced stuff yet — slots, garbage collection, deep copy vs shallow copy. I'm still at try/except and file handling. But the mental model you just gave me makes me feel like those things will actually make sense when I get there, instead of feeling like magic.
Saving this. Running the experiments tonight
Haha, “Python was broken” is honestly a very relatable beginner moment 😄. Glad the id() experiment made the label idea click for you. Once that mental model is clear, a lot of Python’s “weird” behavior starts feeling much less magical. Keep going, you’re building the right foundation!
Since you're at file handling right now — the "with open(...) as f" pattern you're using is actually the same object model paying off early. f is a label on a file object, and the with block guarantees Python calls the file's cleanup the moment the block ends, whether it exits normally or via an exception, because the object's reference count doesn't have to hit zero for that to happen — the context manager forces it deterministically. That's the practical reason "always use with, don't call open() and close() by hand" isn't just a style rule: forgetting the close() leaves the file object alive and the handle open for however long reference counting takes to catch up, which in a long-running program can be a while. Same labels-not-boxes idea, just showing up as "why does this specific syntax exist" instead of a list-mutation surprise.
Nice visualization of Python internals! I use Python extensively for building API backends and this kind of understanding really helps with optimization. Curious — have you looked at how Python's GIL affects API server throughput under concurrent requests?
a@kyisaiah47 It doesn't try to find cycles at all, which is the clever part. The collector walks every container object it tracks and, for each one, subtracts from a temporary copy of its refcount every reference that comes from another tracked container. Whatever is left over is the number of references coming from outside the tracked set: a local variable, a module global, the stack. Objects with a leftover count above zero are reachable roots, and anything reachable from them is marked alive. Everything still sitting at zero is only held up by other garbage, cycle or not, and gets collected. So a reachable graph with a cycle survives because at least one of its nodes still has an external reference after the subtraction.
Exactly! That’s the part I find really interesting too. It’s less about “detecting a cycle” and more about figuring out whether anything outside the tracked graph can still reach those objects. Once you see it that way, cyclic GC becomes much easier to reason about.
The
id(x) == id(y)example is the right way to teach this — I've debugged code from three different devs who got burned by mutable defaults in function arguments for the exact same reason. They thoughtdef f(items=[])created a fresh list each call. It doesn't. It's one list object, one address, every call shares it. Runid(items)inside that function across two calls and you'll see the same address both times. That single experiment would've saved me about 4 hours one Saturday.This is the kind of Python explanation I wish more beginners came across early. The “variables are labels, not boxes” mental model makes so many confusing things—mutability, assignment, function arguments, and shallow copies—suddenly feel much less mysterious.
I especially liked that you didn’t stop at explaining the concepts and actually encouraged people to run the experiments themselves. Seeing id() values change or watching two names affect the same list is one of those things that sticks much longer than reading another definition.
The mutable default argument example also brought back a few debugging memories 😅. It’s amazing how a tiny piece of Python syntax can teach you so much about how the language actually works.
Really enjoyed the visual/experimental approach here. “Break things and see what happens” is honestly one of the best ways to build a solid mental model of programming.
How does the cycle collector distinguish an unreachable cycle from a reachable object graph that happens to contain a cycle?
Good question! It doesn’t really look for the cycle itself. It checks whether the objects in that cycle still have references coming from outside the tracked group. If an external reference remains, the graph is reachable and survives. If not, the objects are only keeping each other alive, so they can be collected.