Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda
Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Cleaning and exploring the data


Now that we've acquired the data and learned a little about what the fields mean, the next step is to clean up the data and conduct some exploratory analysis.

Getting ready

Make sure you have the packages mentioned at the beginning of this chapter under the Requirements section installed and you have successfully imported the FINVIZ data into R using the steps in the previous sections.

How to do it...

To clean and explore the data, closely follow the ensuing instructions:

  1. Imported numeric data often contains special characters such as percentage signs, dollar signs, commas, and so on. This causes R to think that the field is a character field instead of a numeric field. For example, our FINVIZ dataset contains numerous values with percentage signs that must be removed. To do this, we will create a clean_numeric function that will strip away any unwanted characters using the gsub command. We will create this function once and then use it multiple times throughout...