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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

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...

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