Python — Iterators

Every for loop uses this protocol

for x in [10, 20, 30]:
    print(x)

Under the hood:

it = iter([10, 20, 30])     # 1. get an iterator from the iterable
next(it)                    # 2. pull values one at a time → 10
next(it)                    # 20
next(it)                    # 30
next(it)                    # 💥 StopIteration → for catches it & ends

Term	Meaning	Examples
Iterable	can produce an iterator (iter() works)	list, str, dict, file, range
Iterator	the thing being pulled (next() works); remembers position	what iter() returns

Iterators are consumed — once exhausted they stay empty:

it = iter([1, 2])
list(it)      # [1, 2]
list(it)      # [] — already spent!

Building your own class-based iterator

class Countdown:
    def __init__(self, start):
        self.n = start

    def __iter__(self):
        return self              # the object is its own iterator

    def __next__(self):
        if self.n <= 0:
            raise StopIteration
        self.n -= 1
        return self.n + 1

for n in Countdown(3):
    print(n)          # 3, 2, 1

The duet: __iter__ returns the iterator; __next__ produces the next value or raises StopIteration.

Generators — iterators without ceremony

yield turns any function into a generator (an iterator factory):

def countdown(start):
    while start > 0:
        yield start         # pause here, hand out a value
        start -= 1

for n in countdown(3):
    print(n)

Same behavior as the class above with a tenth of the code. State persists between yields automatically.

Why generators matter: laziness

Values are produced ON DEMAND — nothing exists until asked:

def squares():
    n = 1
    while True:             # infinite!
        yield n * n
        n += 1

sq = squares()
next(sq)   # 1
next(sq)   # 4
next(sq)   # 9    — an infinite series in finite memory

Reading huge files line-by-line, streaming API data, infinite sequences — all generator territory.

Handy iteration tools

nums = [5, 3, 8]

sum(nums); max(nums); min(nums)       # consume any iterable
sorted(nums, reverse=True)
reversed(nums)                        # lazy reverse iterator

# itertools — iteration superpowers
from itertools import count, chain
chain([1], [2, 3])                    # 1,2,3 across containers
count(10, step=2)                     # infinite: 10,12,14…

Gotchas: reusing a spent iterator silently yields nothing · next(it) on empty raises unless you pass a default: next(it, None).

Mini Practice

1. Manually iter/next a string; catch StopIteration.
2. Prove exhaustion by consuming twice.
3. Class-based Fibonacci iterator.
4. Rewrite it as a generator; compare lines of code.
5. Infinite even-numbers generator + islice to take five.

Next: polymorphism →