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 an autocorrelation

In many cases, events occur in a repeating cycle. If the data correlates with itself, this is called an autocorrelation. With some data, the interval may be obvious because there's some visible external influence, such as seasons or tides. With some data, the interval may be difficult to discern.

If we suspect we have cyclic data, we can leverage the correlation() function from the Computing the coefficient of correlation recipe, earlier in this chapter, to compute an autocorrelation.

Getting ready

The core concept behind autocorrelation is the idea of a correlation through a shift in time, T. The measurement for this is sometimes expressed as : the correlation between x and x with a time shift of T.

Assume we have a handy correlation function, . It compares two sequences of length n, and , and returns the coefficient of correlation between the two sequences:

We can apply this to autocorrelation by using it as a...