Testing and Optimizing Python Code for Performance

Learn how to test and optimize to improve the execution time and memory usage of your Python code

Photo by Jake Givens on Unsplash

Testing and optimizing code for performance is an essential part of software development. Poorly optimized code can lead to slow execution times and increased memory usage, which can negatively impact user experience and reduce the overall performance of an application.

I will divide this tutorial into two parts, measurements and optimizations. To optimize, you will first need to measure and identify the bottlenecks to optimize.

Let’s begin.

Measuring Performance

Before we can optimize our code, we need to measure its performance to identify the bottlenecks. There are several tools and libraries that can be used to measure performance in Python, including timeit, cProfile, and line_profiler.

timeit

timeit is a built-in module that can be used to measure the execution time of a single statement or block of code. Here's a code snippet to use timeit:

import timeit

def test_func():
    # code to be tested
    return True

print(timeit.timeit(test_func, number=10000))