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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Locating outliers

A set of measurements may include sample values that can be described as outliers. An outlier deviates from other samples, and may indicate bad data or a new discovery. Outliers are, by definition, rare events.

Outliers may be simple mistakes in data gathering. They might represent a software bug, or perhaps a measuring device that isn't calibrated properly. Perhaps a log entry is unreadable because a server crashed, or a timestamp is wrong because a user entered data improperly. We can blame high-energy cosmic ray discharges near extremely small electronic components, too.

Outliers may also be of interest because there is some other signal that is difficult to detect. It might be novel, or rare, or outside the accurate calibration of our devices. In a web log, this might suggest a new use case for an application or signal the start of a new kind of hacking attempt.

In this recipe, we'll look at one algorithm for identifying potential outliers...