🧊 Immutability

Immutability means data does not change after it's created. Instead of mutating existing values, you create new ones. This reduces bugs caused by shared state and makes code easier to reason about.

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✅ Why Immutability?

• Predictable behavior — values don't change unexpectedly.
• Safer concurrency — no race conditions on shared data.
• Easier debugging — state changes are explicit.

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✅ Immutable vs Mutable in Python

Type	Mutable?	Examples
int, float, str, tuple	❌ No	numbers, strings, tuples
list, dict, set	✅ Yes	collections

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✅ Non-Mutating Patterns

Strings (Immutable)

name = "alice"
upper = name.upper()  # creates a new string

Tuples (Immutable)

point = (1, 2)
new_point = (point[0] + 1, point[1])

Lists (Use Copies)

nums = [1, 2, 3]
new_nums = nums + [4]   # new list

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✅ Copy-on-Write

Instead of mutating an object, create a modified copy.

original = {"name": "Alice", "age": 30}
updated = {**original, "age": 31}

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✅ Immutability Tips

• Prefer tuple over list when you don’t need mutation.
• Return new objects instead of mutating inputs.
• Use dataclasses with frozen=True for immutable models.

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🔍 Key Takeaways

• Immutability reduces side effects.
• You can emulate immutability with copies.
• Immutable data works great with pure functions.

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Next: Higher-Order Functions