Practical Python Data Wrangling and Data Quality

Chapter 1. Introduction to Data Wrangling and Data Quality

These days it seems like data is the answer to everything: we use the data in product and restaurant reviews to decide what to buy and where to eat; companies use the data about what we read, click, and watch to decide what content to produce and which advertisements to show; recruiters use data to decide which applicants get job interviews; the government uses data to decide everything from how to allocate highway funding to where your child goes to school. Data—whether it’s a basic table of numbers or the foundation of an “artificial intelligence” system—permeates our lives. The pervasive impact that data has on our experiences and opportunities every day is precisely why data wrangling is—and will continue to be—an essential skill for anyone interested in understanding and influencing how data-driven systems operate. Likewise, the ability to assess—and even improve—data quality is indispensable for anyone interested in making these sometimes (deeply) flawed systems work better.

Yet because both the terms data wrangling and data quality will mean different things to different people, we’ll begin this chapter with a brief overview of the three main topics addressed in this book: data wrangling, data quality, and the Python programming language. The goal of this overview is to give you a sense of my approach to these topics, partly so you can determine if this book is right for you. After that, we’ll spend some time on ...