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 went through several important concepts and learning modules related to advanced data gathering and web scraping. We started by reading data from web pages using two of the most popular Python libraries – requests and BeautifulSoup. In this task, we utilized the knowledge we gained in the previous chapter about the general structure of HTML pages and their interaction with Python code. We extracted meaningful data from the Wikipedia home page during this process.

Then, we learned how to read data from XML and JSON files – two of the most widely used data streaming/exchange formats on the web. For XML, we showed you how to traverse the tree-structure data string efficiently to extract key information. For JSON, we mixed it with reading data from the web using an API. The API we consumed was RESTful, which is one of the major standards in web APIs.

At the end of this chapter, we went through a detailed exercise using regex techniques in...