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

Quantifying the existence of patterns


If you look through the addresses output from the previous step, you may notice that not all of them have a street address as outlined earlier.

A common deviation from the street address pattern is the addition of an N or S to a street name. Another deviation is initial street names that contain more than one word:

3649 N Southport Ave 4022 N Mozart St Irving Park 260-300 Osceola Ave S St Paul, MN 55102, USA

103 & 105 Misty Morning Way Savannah, Georgia 1656 Mount Eagle Place Alexandria, Virginia

Yet another deviation is the omission of street numbers:

West Outer Drive Dearborn, Michigan Crown St New Haven, CT, USA

Depending on the project, you will usually need to decide how far to go to capture all of the variations in the data. The more complex the pattern, the more work it will take to capture.

Due to this trade-off, it is helpful to quantify how much of the data is captured by a particular pattern. In the next few subsections, I will walk through the...