Generators

Python · Reference cheat sheet

📋 Overview

Generators produce values lazily with yield. They pause and resume, saving memory for large or infinite streams. Generator functions return generator iterators; generator expressions use (...).

🔧 Core concepts

Generators implement the iterator protocol (__iter__, __next__).

💡 Examples

Basic generator:

from collections.abc import Iterator

def countdown(n: int) -> Iterator[int]:
    while n > 0:
        yield n
        n -= 1

print(list(countdown(3)))  # [3, 2, 1]

yield from and piping:

def chain_iters(*iters: Iterator[int]) -> Iterator[int]:
    for it in iters:
        yield from it

print(list(chain_iters(iter([1, 2]), iter([3]))))

Infinite stream (take n):

from collections.abc import Iterator
from itertools import islice

def naturals() -> Iterator[int]:
    n = 0
    while True:
        yield n
        n += 1

print(list(islice(naturals(), 5)))  # [0, 1, 2, 3, 4]

Generator expression:

paths = ("a.txt", "b.txt", "c.txt")
sizes = (len(p) for p in paths)
print(sum(sizes))
# sizes is exhausted — sum(sizes) again is 0

⚠️ Pitfalls

• Generators are single-use; convert to list if you need multiple passes.
• Mixing heavy side effects with yield makes control flow hard to follow.
• Forgetting to iterate means the body never runs (lazy).
• return inside a generator does not return to the caller as a normal value.
• Prefer async generators (async def + yield) for async streams — see async.

🔗 Related

• Iterators
• Comprehensions
• Itertools
• Loops
• Async / await
• Context managers