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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

Modifying a dataset


Looking over the data variables available in your notes, you should be able to get a sense of what information is available in each data entry and what information might be useful to you. Once you have observed the contents of a dataset, modification of the data is naturally what comes next. Here are some examples of changes that you might make:

  • Extracting particular data variables
  • Merging data sources
  • Converting between formats
  • Restructuring the data
  • Removing outliers
  • Correcting errors

In this exercise, I'm just going to extract some data variables from the original dataset, specifically the following:

  • address
  • created_at
  • description
  • lng
  • lat
  • rating

Extracting data variables from the original dataset

In the following steps, you will iterate through the data entries of the original dataset using a for loop. For each of the individual entries, you will collect the previously mentioned variables into a new data entry and place the new data entries in to an array.

As is done in the following...

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