Book Image

Modern Python Cookbook. - Second Edition

By : Steven F. Lott
Book Image

Modern Python Cookbook. - Second Edition

By: Steven F. Lott

Overview of this book

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand. This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications. The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it. By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.
Table of Contents (18 chapters)
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Testing things that involve randomness

Many applications rely on the random module to create random values or put values into a random order. In many statistical tests, repeated random shuffling or random selection is done. When we want to test one of these algorithms, any intermediate results or details of the processing are essentially impossible to predict.

We have two choices for trying to make the random module predictable enough to write meaningful unit tests:

  • Set a known seed value; this is common, and we've made heavy use of this in many other recipes
  • Use unittest.mock to replace the random module with something predictable

In this recipe, we'll look at ways to unit test algorithms that involve randomness.

Getting ready

Given a sample dataset, we can compute a statistical measure such as a mean or median. A common next step is to determine the likely values of these statistical measures for some overall population....