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Data Wrangling with Python

You're reading from   Data Wrangling with Python Creating actionable data from raw sources

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789800111
Length 452 pages
Edition 1st Edition
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Authors (2):
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Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
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Table of Contents (12) Chapters Close

Data Wrangling with Python
Preface
1. Introduction to Data Wrangling with Python FREE CHAPTER 2. Advanced Data Structures and File Handling 3. Introduction to NumPy, Pandas, and Matplotlib 4. A Deep Dive into Data Wrangling with Python 5. Getting Comfortable with Different Kinds of Data Sources 6. Learning the Hidden Secrets of Data Wrangling 7. Advanced Web Scraping and Data Gathering 8. RDBMS and SQL 9. Application of Data Wrangling in Real Life Appendix

Reading Data from XML


XML, or Extensible Markup Language, is a web markup language that's similar to HTML but with significant flexibility (on the part of the user) built in, such as the ability to define your own tags. It was one of the most hyped technologies in the 1990s and early 2000s. It is a meta-language, that is, a language that allows us to define other languages using its mechanics, such as RSS, MathML (a mathematical markup language widely used for web publication and the display of math-heavy technical information), and so on. XML is also heavily used in regular data exchanges over the web, and as a data wrangling professional, you should have enough familiarity with its basic features to tap into the data flow pipeline whenever you need to extract data for your project.

Exercise 87: Creating an XML File and Reading XML Element Objects

Let's create some random data to understand the XML data format better. Type in the following code snippets:

  1. Create an XML file using the following...

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