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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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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

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...

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