Book Image

Mastering Python

By : Rick van Hattem
Book Image

Mastering Python

By: Rick van Hattem

Overview of this book

Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges.
Table of Contents (22 chapters)
Mastering Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
6
Generators and Coroutines – Infinity, One Step at a Time
Index

cProfile – finding the slowest components


The profile module makes it easily possible to analyze the relative CPU cycles used in a script/application. Be very careful not to compare these with the results from the timeit module. While the timeit module tries as best as possible to give an accurate benchmark of the absolute amount of time it takes to execute a code snippet, the profile module is only useful for relative results. The reason is that the profiling code itself incurs such a slowdown that the results are not comparable with non-profiled code. There is a way to make it a bit more accurate however, but more about that later.

Note

Within this section we will be talking about the profile module but in the examples we will actually use the cProfile module. The cProfile module is a high-performance emulation of the pure Python profile module.

First profiling run

Let's profile our Fibonacci function from Chapter 5, Decorators– Enabling Code Reuse by Decorating, both with and without the...