Python - Iterators & Generators

Overview

Estimated time: 35–45 minutes

Understand the iteration protocol (iter/next), build generator functions with yield, and use generator expressions for efficient pipelines.

Learning Objectives

• Explain iterable vs iterator and how for loops work under the hood.
• Write generator functions and generator expressions.
• Know when generators improve memory and readability.

Prerequisites

• Python - Comprehensions

Iteration protocol

xs = [1, 2, 3]
it = iter(xs)
print(next(it))
print(next(it))
print(next(it))
# next(it)  # StopIteration

Expected Output: 1\n2\n3

Generator function

def count_up(n):
    i = 1
    while i <= n:
        yield i
        i += 1

for v in count_up(3):
    print(v)

Expected Output: 1\n2\n3

Generator expression

total = sum(x*x for x in range(5))
print(total)

Expected Output: 30

Common Pitfalls

• Exhausting an iterator/generator and then expecting it to produce values again.
• Using list when a generator would avoid building large intermediate results.

Checks for Understanding

1. What exception ends an iterator?
2. When might a generator help with memory usage?

Exercises

1. Write a generator that yields even numbers up to N.
2. Use a generator expression to compute the sum of cubes from 1..N.