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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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Allan Visochek Allan Visochek
Author Profile Icon Allan Visochek
Allan Visochek
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Table of Contents (10) Chapters Close

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

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...

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