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)

Fundamentals of Regular Expressions (RegEx)

Regular expressions or regex are used to identify whether a pattern exists in a given sequence of characters (a string) or not. They help with manipulating textual data, which is often a prerequisite for data science projects that involve text mining.

RegEx in the Context of Web Scraping

Web pages are often full of text, and while there are some methods in BeautifulSoup or XML parsers to extract raw text, there is no method for the intelligent analysis of that text. If, as a data wrangler, you are looking for a particular piece of data (for example, email IDs or phone numbers in a special format), you have to do a lot of string manipulation on a large corpus to extract email IDs or phone numbers. RegEx is very powerful and can save a data wrangling professional a lot of time and effort with string manipulation because they can search for complex textual patterns with wildcards of an arbitrary length.

RegEx is like a mini-programming...