Welcome! In this section, we’ll familiarize ourselves with the core mechanics of pandas and its two primary data structures: the one-dimensional Series and the two-dimensional DataFrame. Chapter 1 begins with an analysis of a data set with pandas so you can immediately get a sense of what is possible with the library. From there, we proceed to an in-depth exploration of the Series in chapters 2 and 3. We learn how to create a Series from scratch; import it from an external data set; and apply a slew of mathematical, statistical, and logical operations to it. In chapter 4, we introduce the tabular DataFrame and various ways to extract rows, columns, and values from its data. Finally, chapter 5 focuses on extracting subsets of DataFrame rows by applying logical criteria. Along the way, we’ll work through eight datasets that cover everything from box-office grosses to NBA players to Pokémon.
This part covers the essentials of pandas, the fundamentals you need to know to work effectively with the library. I’ve made every effort to start from square one, from the smallest building blocks possible, and proceed to the larger and more complex elements. The following five chapters build the foundation for your mastery of pandas. Good luck!