The for Loop & The Iterator Protocol

The for loop is the workhorse of Python data processing. Unlike the while loop, which runs until a condition changes, the for loop is designed for Definite Iteration—it executes once for every item in a collection (an Iterable).

To master the for loop, you must understand the “contract” between the loop and the data it traverses.

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1. Basic Iteration

In Python, the for loop doesn’t use a counter (like i++ in C). Instead, it “asks” the collection for its next item.

simple_for.py

fruits = ["Apple", "Banana", "Cherry"]

for fruit in fruits:
    print(f"Current fruit: {fruit}")

The “For-In” Logic

1. Collection: fruits is the iterable.
2. Variable: fruit is a temporary label that points to the current object.
3. Flow: Python automatically stops when there are no items left. No IndexError is possible!

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2. The Power of range()

When you need to iterate a specific number of times, or need an index, Python provides the range() generator.

Anatomy: range(start, stop, step)

• range(5) -> 0, 1, 2, 3, 4 (Starts at 0, ends before 5)
• range(2, 6) -> 2, 3, 4, 5
• range(0, 10, 2) -> 0, 2, 4, 6, 8

counting.py

# Calculate the sum of numbers 1 to 100
total = 0
for n in range(1, 101):
    total += n
print(total) # 5050

Memory Efficiency

The range function does not create a list of a million numbers in memory. It is a Lazy Object that calculates the next number only when the loop asks for it. This allows you to use range(1_000_000_000) without crashing your computer.

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3. Under the Hood: The Iterator Protocol

This is the “secret sauce” of Python. When you write for item in collection:, Python performs a specific dance behind the scenes.

1. Get Iterator: Python calls iter(collection). This returns an Iterator object.
2. Request Next: The loop calls next(iterator).
3. Assign: The returned value is assigned to your loop variable.
4. Repeat: This continues until the iterator raises a special error called StopIteration.
5. Clean Exit: The for loop catches StopIteration and terminates gracefully.

Why this matters

This protocol allows for loops to work on anything: lists, strings, files, database cursors, and even infinite streams of data. As long as an object implements __iter__ and __next__, Python can loop over it.

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4. Useful Patterns: enumerate and zip

enumerate(): Getting the Index

If you need both the item and its position (index), don’t use range(len(list)). Use enumerate.

names = ["Alice", "Bob", "Charlie"]
for index, name in enumerate(names):
    print(f"{index}: {name}")

zip(): Iterating in Parallel

If you have two related lists, zip allows you to traverse them together.

users = ["Alice", "Bob"]
ids = [101, 102]

for name, user_id in zip(users, ids):
    print(f"User {name} has ID {user_id}")

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5. Summary Table

Feature	for Loop	while Loop
Iteration Type	Definite (fixed number of items)	Indefinite (depends on condition)
Mechanism	Iterator Protocol (__next__)	Boolean Evaluation
Safety	No risk of infinite loops (usually)	High risk of infinite loops
Performance	Faster (optimized in C)	Slower (manual bytecode evaluation)

Loop Performance

Avoid performing expensive operations (like opening a file or database query) inside a loop if they could be done once before the loop starts.