Iterators and Generators
Iterators are objects that allow you to traverse through a sequence. Generators are a simple way to create iterators using the `yield` keyword. They are memory‑efficient and useful for processing large datasets.
Iterators
An object is an iterator if it implements `__iter__()` and `__next__()`. The `iter()` function gets an iterator from a sequence, and `next()` gets the next value. Many built‑ins are iterable, but you can also create custom iterators.
numbers = [1, 2, 3]
it = iter(numbers)
print(next(it)) # 1
print(next(it)) # 2
class CountDown:
def __init__(self, start):
self.current = start
def __iter__(self):
return self
def __next__(self):
if self.current <= 0:
raise StopIteration
value = self.current
self.current -= 1
return value
for i in CountDown(3):
print(i) # 3,2,1
Generators
A generator is a function that uses `yield` to return values lazily. When called, it returns a generator iterator. It suspends state between yields.
def count_down(start):
while start > 0:
yield start
start -= 1
for i in count_down(3):
print(i) # 3,2,1
# Generator expression (like list comprehension but lazy)
squares = (x*x for x in range(10))
print(next(squares)) # 0
Two Minute Drill
- Iterators support `__iter__` and `__next__`; they produce values one at a time.
- Generators use `yield` and are easier to write; they suspend state.
- Generator expressions are like list comprehensions but lazy.
- Both are memory‑efficient for large sequences.
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