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Practical Data Wrangling

You're reading from   Practical Data Wrangling Expert techniques for transforming your raw data into a valuable source for analytics

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286139
Length 204 pages
Edition 1st Edition
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Author (1):
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 Visochek Visochek
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Visochek
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Toc

Table of Contents (16) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Programming with Data FREE CHAPTER 2. Introduction to Programming in Python 3. Reading, Exploring, and Modifying Data - Part I 4. Reading, Exploring, and Modifying Data - Part II 5. Manipulating Text Data - An Introduction to Regular Expressions 6. Cleaning Numerical Data - An Introduction to R and RStudio 7. Simplifying Data Manipulation with dplyr 8. Getting Data from the Web 9. Working with Large Datasets

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...
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