Why Python Generators Outshine Iterators: A Beginner’s Guide

Generators are one of the most challenging concepts for junior Python developers, yet they appear in many projects and are essential to master.

What is an Iterator

An iterator is an object used for iteration (e.g., in a for loop). Any object that implements the __next__ method (or next in Python 2) qualifies as an iterator.

Unlike a list, an iterator does not load all elements into memory at once; it yields elements lazily, which saves memory. For example, a list of ten million integers may require over 400 MB, while an iterator for the same data occupies only a few dozen bytes.

Example: implementing a Fibonacci iterator (see image).

What is a Generator

After understanding iterators, we can discuss generators. A normal function returns a value with return, but a generator function uses the yield keyword. Calling a generator function returns a generator object, which is essentially an iterator with a more concise implementation.

Simple generator function example (see image).

When the function is called, the yielded value is not returned immediately; it is produced only when next() (or the for loop) requests it.

Why use generators? They are more elegant than iterators, require less boilerplate code, and offer the same high performance, especially when processing large data sets. The Fibonacci sequence implemented with a generator is shown below.

Generator Expressions

Generator expressions look similar to list comprehensions but return a generator object instead of a list. They are ideal for iterating over massive data because they evaluate items lazily.