Book Image

Practical Data Analysis Cookbook

By : Tomasz Drabas
Book Image

Practical Data Analysis Cookbook

By: Tomasz Drabas

Overview of this book

Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors. This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews. By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.
Table of Contents (19 chapters)
Practical Data Analysis Cookbook
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Determining if a population of sheep is in danger of extinction due to a wolf pack


One of the most famous agent-based simulations is the sheep-wolf predation example.

The model simulates two populations of animals: sheep and wolves coexisting together on a plane. Sheep move around and eat grass that grows on the plane; eating grass gives the sheep energy. Wolves predate sheep and that is how they get their energy. To move around the plane costs the animal some energy. If any animal's energy falls below 0, it dies.

In this recipe, we will build a 300-by-300 plane (grid) and populate it with 6,000 sheep and 200 wolves (initially). We will also introduce the concept of inheritance: we will create a generic Animal class and then derive Sheep and Wolf classes from it. The idea behind it is simple: as the animals share some common characteristic (that is, both of them need to move around the plane), we do not have to implement the same code in two places of the script.

Getting ready

To execute this...