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)


So far in this book, we have focused on studying pandas DataFrame objects as the main data structure for the application of wrangling techniques. In this chapter, we will learn about various techniques by which we can read data into a DataFrame from external sources. Some of these sources could be text-based (such as CSV, HTML, and JSON), whereas others could be binary (that is, not in ASCII format; for example, from Excel or PDFs). We will also learn how to deal with data that is present in web pages or HTML documents.

Being able to deal with and extract meaningful data from various sources is of paramount interest to a data practitioner. Data can, and often does, come in various forms and flavors. It is essential to be able to get the data into a form that is useful for performing predictive or other kinds of downstream tasks.

As we have gone through detailed examples of basic operations with NumPy and pandas, in this chapter, we will often skip trivial code snippets...