Part I
Introduction
Chapter 1 Pandas DataFrame Basics
Chapter 2 Pandas Data Structures Basics
This book begins with an introduction to the Pandas Python library for data analytics. It first covers the very basics of using the pandas library, loading your first data set and doing basic filtering and subsetting commands with your data (Chapter 1). It then goes into more detail about the DataFrame and Series objects, where we cover more of the attributes and methods these objects can do, including how to save data sets for storage (Chapter 2). It then pivots into data visualization with matplotlib and seaborn plotting libraries as well as the built-in pandas plotting methods (Chapter 3). Next, this part covers one of the fundamental concepts in data literacy, tidy data principles. Where it discusses what a “clean” and “tidy” data set looks like so you can process data with a goal and target in mind (Chapter 4). Finally, this part covers writing functions and applying them to your data, and lays down the foundation for any custom data processing steps in the future (Chapter 5).
Think of this part of the book as the core data literacy knowledge on how to work and think about your data. It also aims to teach you the relevant bits of the Python programming language by using the Pandas library as the motivational use case.