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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Using the XML module to parse XML data


In this next section, I will walk through some of the steps for using Python to parse and process XML data in a basic project to convert a dataset from XML to JSON.

In Python, XML is represented using a tree-like structure and parsed using the xml.etree.ElementTree module. Navigating this tree is a bit more sophisticated than navigating the structure of JSON data because the structure of XML does not fit as neatly into python data structures. 

The first step to processing XML data is to read the XML data into Python's tree-like XML representation with the xml.etree.ElementTree module, using the following steps:

  1. Import the xml.etree.ElementTree module.
  2. Open the file containing the XML data.
  3. Use the ElementTree.parse() function to create an ElementTree object.
  4. Use the .getroot function of the ElementTree object to return an element object representing the root of the element tree.

The result is a representation of the XML data in python that you can navigate...