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Iterators

In Python, an Iterator is an object that allows us to traverse through all elements of a collection, regardless of its implementation. It is the engine behind for loops, map(), filter(), and list comprehensions.

Iterable vs. Iterator​

It's easy to confuse these two, but they serve different roles in the Iterator Protocol.

TermDefinitionExamples
IterableAny object we can loop over. It can produce an iterator.list, str, tuple, dict
IteratorThe stateful object that actually performs the traversal.map object, zip object, generator
Vending Machine Analogy

The Iterable is the Vending Machine. It holds all the items. The Iterator is the Mechanical Arm. It knows which item is next and hands it to us one by one.

The Iterator Protocol​

For an object to be considered an iterator in Python, it must implement two methods:

  1. __iter__(): Returns the iterator object itself. This allows an iterator to be used where an iterable is expected.
  2. __next__(): Returns the next item in the sequence. If there are no items left, it must raise StopIteration.

Manual Interaction​

We can manually interact with this protocol using the built-in iter() and next() functions:

data = [10, 20]
it = iter(data)  # Calls data.__iter__()

print(next(it))  # Calls it.__next__() -> 10
print(next(it))  # Calls it.__next__() -> 20

Output:

10
20

Key Characteristics​

Lazy Evaluation​

Iterators use Lazy Evaluation. They don't calculate or store all their values upfront. They only compute the "next" value when we explicitly ask for it. This allows for massive memory savings—we can iterate over a 10GB file or even an infinite sequence without crashing our RAM.

Exhaustion (One-Way Street)​

Iterators are consumable. Once we have traversed to the end, the iterator is "exhausted."

Exhaustion Trap

Calling list(my_iterator) or sum(my_iterator) consumes the entire stream. Any subsequent loops over that specific iterator object will be empty.

nums = [1, 2, 3]
mp = map(lambda x: x*x, nums)

print(list(mp)) # [1, 4, 9] -> This "consumed" the iterator.

# This loop will NOT run because mp is empty now:
for i in mp:
    print(i)

Output:

[1, 4, 9]

Custom Iterator Example​

Here is how we build an iterator from scratch by following the protocol.

class PowerOfTwo:
    """Iterator that yields powers of 2 up to a limit."""
    def __init__(self, max_exponent):
        self.max = max_exponent
        self.current = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.current <= self.max:
            result = 2 ** self.current
            self.current += 1
            return result
        raise StopIteration

# Usage
powers = PowerOfTwo(3)
for p in powers:
    print(p) # 1, 2, 4, 8

Output:

1
2
4
8