Book Image

The Data Wrangling Workshop - Second Edition

By : Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar
Book Image

The Data Wrangling Workshop - Second Edition

By: Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar

Overview of this book

While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you’re a beginner, then The Data Wrangling Workshop will help to break down the process for you. You’ll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you’ll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you’ll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources.
Table of Contents (11 chapters)

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 and MathML (a mathematical markup language widely used for web publication and the display of math-heavy technical information). 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 7.07: Creating an XML File and Reading XML Element Objects

In this exercise, we'll create some random data and store it in XML format. We'll then read from the XML file and examine...