Chapter 14. Iterables, Iterators, and Generators

When I see patterns in my programs, I consider it a sign of trouble. The shape of a program should reflect only the problem it needs to solve. Any other regularity in the code is a sign, to me at least, that I’m using abstractions that aren’t powerful enough—often that I’m generating by hand the expansions of some macro that I need to write.1

Paul Graham, Lisp hacker and venture capitalist

Iteration is fundamental to data processing. And when scanning datasets that don’t fit in memory, we need a way to fetch the items lazily, that is, one at a time and on demand. This is what the Iterator pattern is about. This chapter shows how the Iterator pattern is built into the Python language so you never need to implement it by hand.

Python does not have macros like Lisp (Paul Graham’s favorite language), so abstracting away the Iterator pattern required changing the language: the yield keyword was added in Python 2.2 (2001).2 The yield keyword allows the construction of generators, which work as iterators.


Every generator is an iterator: generators fully implement the iterator interface. But an iterator—as defined in the GoF book—retrieves items from a collection, while a generator can produce items “out of thin air.” That’s why the Fibonacci sequence generator is a common example: an infinite series of numbers cannot be stored in a collection. However, be aware that the Python community treats iterator and generator as synonyms ...

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