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Iterators Revisited: Generators

Python today supports procrastination much more than it did in the past—it provides tools that produce results only when needed, instead of all at once. In particular, two language constructs delay result creation whenever possible:

 

Generator functions are coded as normal def statements but use yield statements to return results one at a time, suspending and resuming their state between each. Generator expressions are similar to the list comprehensions of the prior section, but they return an object that produces results on demand instead of building a result list.

Because neither constructs a result list all at once, they save memory space and allow computation time to be split across result requests. As we’ll see, both of these ultimately perform their delayed-results magic by implementing the iteration protocol we studied in Chapter 14.