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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Analyzing many variables in one pass

In many cases, we'll have data with multiple variables that we'd like to analyze. The data can be visualized as filling in a grid, with each row containing a specific outcome. Each outcome row has multiple variables in columns. Many recipes in this chapter have a very narrow grid with only two variables, x and y. Two recipes earlier in this chapter, Computing an autocorrelation, and Confirming that the data is random – the null hypothesis, have relied on data with more than two variables.

For many of these recipes, we have followed a pattern of treating the data as if it is provided in column-major order: the recipe processed each variable (from a column of data) independently. This leads to visiting each row of data multiple times. For a large number of rows, this can become inefficient.

The alternative is to follow a pattern of row-major order. This means processing all the variables at once for each row of data. This...