Mastering Python Generators and Coroutines: Improving Performance and Efficiency

Boost your Python skills by understanding generators and coroutines, plus hands-on code examples

Photo by Kalen Emsley on Unsplash

Introduction

Have you heard of Python generators and coroutines, but unsure what they mean or why they matter? Don’t worry! By the end of this guide, you’ll understand the basics and know how to apply them to improve code performance and efficiency.

What Are Generators?

A generator is a special kind of iterator returning one value at a time. Compared to regular functions, which execute entirely and store return values in memory, generators maintain state across multiple calls, enabling sequential execution. Benefits include lower memory footprints and enhanced concurrency support.

Basic Generator Example

Let’s look at a simple generator example:

def my_generator(stop):
    counter = 0
    while counter < stop:
        yield counter
        counter += 1

my_gen = my_generator(5)
print([next(my_gen) for _ in range(5)])
# Output: [0, 1, 2, 3, 4]

The yield keyword makes the function behave like a generator. Calling next retrieves the…