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)


In this chapter, we have looked into several different types of data formats and how to work with them. These formats include CSV, PDF, Excel, Plain Text, and HTML. HTML documents are the cornerstone of the World Wide Web and, given the amount of data that's contained in it, we can easily infer the importance of HTML as a data source.

We learned about bs4 (BeautifulSoup 4), a Python library that gives us Pythonic ways to read and query HTML documents. We used bs4 to load an HTML document and explored several different ways to navigate the loaded document.

We also looked at how we can create a pandas DataFrame from an HTML document (which contains a table). Although there are some built-in ways to do this job in pandas, they fail as soon as the target table is encoded inside a complex hierarchy of elements. So, the knowledge we gathered in this topic to transform an HTML table into a pandas DataFrame in a step-by-step manner is invaluable.

Finally, we looked at...