Iterators & Generators

Everything you loop over is an 'iterable'. Behind the scenes, Python creates an 'iterator' that gives you one item at a time. You can do this manually with `iter()` and `next()`.

Python
fruits = ['apple', 'banana']
it = iter(fruits)

print(next(it))  # 'apple'
print(next(it))  # 'banana'
# next(it)  # StopIteration Exception

Normal functions return once. Generators yield multiple times, pausing execution between each value. This state preservation is powerful for custom traversal logic.

Python
def countdown(n):
    while n > 0:
        yield n
        n -= 1
    yield 'Blastoff!'

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

Generators can go on forever! Since they generate values on the fly, you can represent infinite sequences like prime numbers or sensor data streams without crashing memory.

Python
def infinite_id():
    num = 0
    while True:
        yield f"ID-{num}"
        num += 1

gen = infinite_id()
print(next(gen))  # ID-0
print(next(gen))  # ID-1

Concise and memory-efficient. Use `()` instead of `[]`. Great for pipelines where you process data stage-by-stage without storing intermediate lists.

Python
# Sum of squares of even numbers
# Computes one by one, zero memory overhead!
total = sum(x*x for x in range(1000000) if x % 2 == 0)
print(total)