Book Image

Practical Data Wrangling

By : Allan Visochek
Book Image

Practical Data Wrangling

By: Allan Visochek

Overview of this book

Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You’ll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You’ll work with different data structures and acquire and parse data from various locations. You’ll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases. The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you’ll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.
Table of Contents (16 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Handling NA values


Sometimes, it is acceptable to have NA values in the dataset. However, for many types of analysis, NA values need to be either removed or replaced. In the case of road length, a better estimate of total road length could be generated if the NA values were replaced with best guesses. In the following subsections, I will walk through these three approaches to handling NA values:

  • Deletion
  • Insertion
  • Imputation

Deleting missing values

The simplest way to handle NA values is to delete any entry that contains an NA value, or a certain number of NA values. When removing entries with NA values, there is a trade-off between the correctness of the data and the completeness of the data. Data entries that contain NA values may also contain several useful non-NA values, and and removing too many data entries could reduce the dataset to a point where it is no longer useful.

For this dataset, it is not that important to have all of the years present; even one year is enough to give us a rough...