How to Implement the Iterator Protocol in Python

A manual iterator class using __iter__ and __next__, compared with an equivalent generator using yield.

Easy Python 3.9+ Aug 9, 2026 Comprehensions & generators 13 views 0 copies

iterator generator protocol yield

Python code

27 lines

Python 3.9+

class ManualCounter:
    def __init__(self, limit):
        self.limit = limit
        self.current = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.current >= self.limit:
            raise StopIteration
        value = self.current
        self.current += 1
        return value


def generator_counter(limit):
    for i in range(limit):
        yield i


if __name__ == "__main__":
    manual = ManualCounter(5)
    print("Manual iterator:", list(manual))

    gen = generator_counter(5)
    print("Generator:", list(gen))

Output

stdout

Manual iterator: [0, 1, 2, 3, 4]
Generator: [0, 1, 2, 3, 4]

How it works

The ManualCounter class follows the iterator protocol by defining __iter__ (returning itself) and __next__ (returning the next value or raising StopIteration when done). list(manual) repeatedly calls __next__ until the exception is raised. The generator_counter function uses yield instead — each call to next() resumes execution right after the last yield. Both produce identical sequences, but the generator version is shorter and handles state automatically. range() is already an iterable, so iterating over it in the generator keeps memory constant regardless of limit.

Common mistakes

Forgetting to implement __iter__ when defining a custom iterator class
Mutating the iterable while iterating over it
Raising StopIteration from inside a generator instead of using return
Assuming an iterator is reusable — list(manual) can only be consumed once

Variations

Use iter() and next() builtins explicitly to step through a manual iterator
Convert a manual iterator to an iterator of tuples with zip(*[iter(obj)]*n) for batching

Real-world use cases

Building a custom streaming reader that yields database rows one at a time.
Implementing an infinite or stateful sequence, such as a pseudo-random number generator.
Creating a lazy wrapper around an expensive paginated API response.

How to Implement the Iterator Protocol in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Real-world use cases

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