Introduction

Abstract

We live in a world full of data. In fact, there is so much data that it’s nearly impossible to comprehend it all. We rely more heavily than ever on computers to assist us in making sense of this massive amount of information. Whether it’s data discovery via search engines, presentation via graphical user interfaces, or aggregation via algorithms, we use software to process, extract, and present the data in ways that make sense to us. pandas has become an increasingly popular package for working with big data sets. Whether it’s analyzing large amounts of data, presenting it, or normalizing it and re-storing it, pandas has a wide range of features that support big data needs. While pandas is not the most performant option available, it’s written in Python, so it’s easy for beginners to learn, quick to write, and has a rich API.