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

Hyper-threading versus physical CPU cores


In most cases, hyper-threading is very useful and improves performance, but when you truly maximize CPU usage it is generally better to only use the physical processor count. To demonstrate how this affects the performance, we will run the tests from the previous section again. This time with 1, 2, 4, 8, and 16 processes to demonstrate how it affects the performance. Luckily, the multiprocessing library has a nice Pool class to manage the processes for us:

import sys
import datetime
import multiprocessing


def busy_wait(n):
    while n > 0:
        n -= 1


if __name__ == '__main__':
    n = 10000000
    start = datetime.datetime.now()
    if sys.argv[-1].isdigit():
        processes = int(sys.argv[-1])
    else:
        print('Please specify the number of processes')
        print('Example: %s 4' % ' '.join(sys.argv))
        sys.exit(1)

    with multiprocessing.Pool(processes=processes) as pool:
        # Execute the busy_wait function 8 times...