Mutable vs Immutable in Python: Explained with Examples and Memory Diagrams

Ever changed a list and wondered why your original variable changed too? Or assigned a string and somehow it didn’t? That’s Python being clever with mutable and immutable types.

Don’t worry if it sounds confusing — let’s break it down like we’re chatting over chai ☕.

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🔁 So, what’s "mutable" anyway?

In Python:

• Mutable = can change after it's created

• Immutable = can’t change once made

Think of it like writing on a whiteboard (mutable) vs carving something in stone (immutable).

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🧱 Immutable Types (Stone Mode)

Examples:

• int, float, str, tuple, bool

These stay the same no matter what. If you think you're changing them, Python is secretly making a new object in the background.

Example:

pythonCopyEdita = 10
b = a
b = b + 5

print(a)  # Still 10
print(b)  # 15

Memory behind the scenes:

Notice how b points to a new place when we do b + 5.

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🔄 Mutable Types (Whiteboard Mode)

Examples:

• list, dict, set

You can update them right there in memory. No copying or creating a new object.

Example:

pythonCopyEditx = [1, 2, 3]
y = x
y.append(4)

print(x)  # [1, 2, 3, 4]
print(y)  # [1, 2, 3, 4]

What’s going on?

Both x and y point to the same list — so changing one changes both!

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🧠 Quick Hack: Use id() to Peek at Memory

pythonCopyEdita = "hi"
b = a
print(id(a), id(b))  # Same memory address

b = b + " there"
print(id(a), id(b))  # Now different!

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💡 TL;DR

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🎯 Final Thoughts

Understanding mutable and immutable objects helps you write smarter, cleaner code — and avoid bugs that’ll have you scratching your head.

So next time your variables start acting funny, remember:
🪨 Stone or 🧼 Whiteboard?