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
About the Author
About the Reviewer
Customer Feedback

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...