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

Understanding the need for pattern recognition


The simplest way to process the values of text fields to treat them as categorical variables. In a categorical variable, the data entries take on a fixed number of values. To illustrate working with categorical variables, consider a categorical field, such as the US states. If the state of Connecticut, for instance, were to appear in a large enough number of data entries, you might expect to see certain characteristic misspellings, such as the following:

  • Conecticut
  • Conneticut
  • Connetict

An easy way to fix all of the misspellings might be to iterate through each of the data entries and check against a list of common misspellings as is done in the following demonstration. Note that the following code sample is just for demonstration purposes and doesn't belong to a particular file:

misspellings = ["Conecticut", "Conneticut", "Connectict"]
for ind in range(len(data)): 
    if data[ind]["state"] in misspellings:
        data[ind]["state"] = "Connecticut...