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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Using the built-in statistics library

A great deal of exploratory data analysis (EDA) involves getting a summary of the data. There are several kinds of summary that might be interesting:

  • Central Tendency: Values such as the mean, mode, and median can characterize the center of a set of data.
  • Extrema: The minimum and maximum are as important as the central measures of a set of data.
  • Variance: The variance and standard deviation are used to describe the dispersal of the data. A large variance means the data is widely distributed; a small variance means the data clusters tightly around the central value.

This recipe will show how to create basic descriptive statistics in Python.

Getting ready

We'll look at some simple data that can be used for statistical analysis. We've been given a file of raw data, called anscombe.json. It's a JSON document that has four series of (x,y) pairs.

We can read this data with the...