generators.md

Python by Example: Generators

Generators produce values one at a time using yield. Unlike a list, a generator doesn't compute all values upfront—it yields each one on demand, making them memory-efficient for large or infinite sequences. A generator function looks like a normal function but uses yield instead of return.

What you'll learn:

• Defining generator functions with yield
• Advancing with next()
• Generator expressions (like list comprehensions, but lazy)
• Infinite generators

def count_up(n):
    for i in range(n):
        yield i


gen = count_up(3)
print(next(gen))
print(next(gen))
print(next(gen))

for value in count_up(5):
    print(value)

# Generator expression—like a list comprehension but lazy
squares = (x**2 for x in range(5))
print(list(squares))


def integers():
    n = 0
    while True:
        yield n
        n += 1


gen = integers()
print([next(gen) for _ in range(5)])

Each call to next() resumes the function from where it last yielded. When the function returns, the generator raises StopIteration—a for loop handles this automatically. The integers() generator is infinite but safe because we only pull five values.

To run this program:

$ python source/generators.py
0
1
2
0
1
2
3
4
[0, 1, 4, 9, 16]
[0, 1, 2, 3, 4]

Tip: Prefer a generator expression (x for x in ...) over list(...) when you only iterate once—you skip building the entire list in memory.

Try it: Write a generator that yields Fibonacci numbers indefinitely.

Source: generators.py

Next: Classes