Day 43: Mutating Arguments Passed to Functions

 

🐍 Python Mistakes Everyone Makes ❌

Day 43: Mutating Arguments Passed to Functions

This is one of those bugs that looks harmless, works fine in small tests —
and then causes mysterious behavior later in production.

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❌ The Mistake

Modifying a mutable argument (like a list or dictionary) inside a function.

def add_item(items):
    items.append("apple") # ❌ mutates the caller's list

my_list = []
add_item(my_list)

print(my_list) # ['apple']

At first glance, this seems fine.
But the function silently changes data it does not own.

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❌ Why This Is Dangerous

• ❌ Side effects are hidden

• ❌ Makes debugging extremely hard

• ❌ Breaks assumptions about data immutability

• ❌ Functions stop being predictable

• ❌ Reusing the function becomes risky

The caller didn’t explicitly ask for the list to be modified — but it happened anyway.

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⚠️ A More Subtle Example

def process(data):

    data["count"] += 1 # ❌ mutates shared state

If data is shared across multiple parts of your app, this change ripples everywhere.

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✅ The Correct Way: Avoid Mutation

✔️ Option 1: Work on a copy

def add_item(items):
    new_items = items.copy()
    new_items.append("apple")
    return new_items

my_list = [] 

my_list = add_item(my_list)

Now the function is pure and predictable.

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✔️ Option 2: Be explicit about mutation

If mutation is intentional, make it obvious:

def add_item_in_place(items): 

items.append("apple")

Clear naming prevents surprises.

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🧠 Why This Matters

Functions should:

• Do one thing

• Have clear contracts

• Avoid unexpected side effects

Predictable code is maintainable code.

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🧠 Simple Rule to Remember

🧠 If a function mutates its arguments, make it explicit or avoid it.

When in doubt:

Return new data instead of modifying input.

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🚀 Final Takeaway

Hidden mutation is one of Python’s most common foot-guns.

Write functions that:

• Are safe to reuse

• Don’t surprise callers

• Make data flow obvious

Your future self (and teammates) will thank you.