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

Extracting patterns


There are a few approaches that can be used to extract the street name from the street address. The one I will use here is to make a regular expression to recognize just the street number. The street number regular expression can be used to split the street address string. In the resulting array, the second entry should contain the street name. 

In the following continuation of extract_street_addresses.py, an additional regular expression is created to match just the street number and the following white space. Within the for loop that iterates over the data, the street_number_regex regular expression is used to split the street_address string into two components, the second of which contains the street name:

....
### JUST THE STREET NUMBER 
## match street number at the beginning of string
street_number_pattern_string = "^[0-9]+"
## match space characters
street_number_pattern_string += "\s+"

## compile the pattern
street_address_regex = re.compile(street_address_pattern_string...