Iterators & Generators

Why for, with, and @decorator all reduce to the same idea - objects that implement a protocol, and closures that carry state between calls - the mental model behind every page in this section.

Understand Python's iterator protocol, implement __iter__ and __next__, and build custom iterables for efficient data processing.

Learn how Python generators and the yield keyword create memory-efficient, lazy sequences for large files, infinite data, and ETL pipelines.

Leverage Python's itertools for efficient iteration. Learn to chain, slice, group, and create Cartesian products without intermediate lists.

Learn to use Python's functools module for caching, partial application, and single-dispatch generics. Optimize code with lru_cache, partial, and singledispatch.

Learn how closures capture variables from their enclosing scope. Understand late binding, factory functions, and decorators with practical Python examples.

Learn best practices for Python iterators, generators, and decorators to build memory-safe and debuggable services. Improve ETL pipelines and frameworks.

A single-page roundup of every highlight bullet from the 11 pages in the Iterators & Generators section, grouped by source page so you can scan all 44 takeaways without opening each article individually.