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Python’s memory model can trip you up. Learn how reference counting, garbage collection, and mutability impact performance — and how to optimize it.
Have you ever spent hours pursuing a memory leak only to discover you were just hanging onto a reference? Yes, me too.
A time ago, I built a Python script that began as a basic data processor. However, when I added additional features, it began to consume far more RAM than planned. It turned out that I wasn’t copying objects; instead, I was passing references. A rookie error.
Python’s memory model may appear to be a black box, but once understood, you may avoid these costly traps. Let us break it down.
1. Python’s Memory Model
Python automatically manages memory for you — but that doesn’t mean it’s perfect. Under the hood:
- Everything in Python is an object. Yes, even None, integers, and functions.
- Memory is managed with reference counting. When an object’s references hit zero, Python frees it.
- Mutable vs. immutable objects behave differently. And that’s where things can get tricky.
2. The Mutability Trap
Take a guess — what’s the output of this?
a = [1, 2, 3]
b = a
b.append(4)
print(a) # ???If you said [1, 2, 3, 4], you’re right — but why?
When you do b = a, you’re not making a copy. You’re just creating a new reference to the same object in memory. So when b.append(4) runs, it modifies a too.
How to Avoid This?
If you actually want a copy, do this:
b = a.copy() # Or list(a) or a[:]This minor error has resulted in memory bloat in several Python programs. Look out for it
3. Python Garbage Collection
Python does not simply delete objects when you delete them. It depends on two mechanisms:
- Reference Counting: Objects with zero references are removed immediately.
- Garbage Collector: Handles objects stuck in reference cycles (like…
