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 learned about interesting ways to deal with list data by using a generator expression. They are easy and elegant and, once mastered, they give us a powerful trick that we can use repeatedly to simplify several common data wrangling tasks. We also examined different ways to format data. Formatting data is not only useful for preparing beautiful reports – it is often very important to guarantee data integrity for the downstream system.

We ended this chapter by checking out some methods to identify and remove outliers. This is important for us because we want our data to be properly prepared and ready for all our fancy downstream analysis jobs. We also observed how important it is to take the time to and use domain expertise to set up rules for identifying outliers, as doing this incorrectly can do more harm than good.

In the next chapter, we will cover how to read web pages, XML files, and APIs.