Python Generators and Iterators: Memory-Efficient Data Processing

Generators are functions that yield values one at a time instead of returning a complete list. They use the yield keyword and maintain their state between calls, making them perfect for processing large or infinite sequences.

Generator expressions use () instead of [] for list comprehensions: (x**2 for x in range(1000000)). They're lazy — values are computed only when needed, saving memory.

Iterators are objects that implement __iter__() and __next__() methods. For loops work with any iterable. You can create custom iterators by implementing these methods in a class.

The itertools module provides powerful iterator building blocks: chain(), cycle(), repeat(), islice(), groupby(), and more. These compose beautifully for complex data processing pipelines.

Experiment with generators in CompileZ's Python editor. Try creating an infinite Fibonacci sequence generator and consuming only the values you need.