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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Computing the coefficient of a correlation

In the Using the built-in statistics library and Average of values in a counter recipes in this chapter, we looked at ways to summarize data. These recipes showed how to compute a central value, as well as variance and extrema.

Another common statistical summary involves the degree of correlation between two sets of data. One commonly used metric for correlation is called Pearson's r. The r-value is the number between -1 and +1 that expresses the degree to which the data values correlate with one another.

A value of zero says the data is random. A value of 0.95 suggests that 95% of the values correlate, and 5% don't correlate well. A value of -0.95 says that 95% of the values have an inverse correlation: when one variable increases, the other decreases.

This is not directly supported by Python's standard library. We can follow a design pattern similar to those shown in the Average of values in a counter recipe...