Iterators and generators

Iteration is everywhere in Python. Every for loop, every in check, every list comprehension, every unpacking — all of them go through the iterator protocol. Generators are the ergonomic way to build your own iterators, and itertools is a standard-library toolkit for composing them.

Understanding these well unlocks a style of programming that's both concise and memory-efficient — you can process files that don't fit in memory, pipelines that transform millions of records, and data flows where computation happens only when a value is actually needed.

Sections

• Learn — four notebooks: how iteration works under the hood, writing generator functions, generator expressions and itertools, and building custom iterator classes for the cases where generators don't fit.
• Recipes — processing large files lazily, chaining and grouping iterables, combining generators, and the mistakes that lose you a debugging evening.
• Reference — the iterator protocol, generator syntax, and a compact itertools cheatsheet.
• Concepts — essays on laziness and memory, and on iteration as an interface (the cultural thing that makes Python's ecosystem compose so well).

New to the topic? Start with Learn → Iteration protocol. Here for a specific task? Recipes is task-focused and Reference is for lookups.