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

Logistical overview


Three Python scripts will be used for the demonstrations in this chapter. The first of these scripts, regex_intro.py, will be a program to introduce and demonstrate the use of regular expressions in Python. The second, explore_addresses.py, will be a simple program to explore the dataset and look for patterns. The third, extract_street_names.py, will be a program to extract the street names from the original dataset and output a revised dataset with a column for street names. The finished product for each of these files is available in the code folder of the reference material. All of the reference material can be found at the following link: https://goo.gl/8S58ra.

Data

For the exercise in this chapter, you will be working with another dataset containing Seeclickfix issue reports. This time, I've put the dataset in the CSV format and extracted just a few fields from the data. I've also limited the entries to the continental US in order to make the address formats a bit...