In part 1, we laid the groundwork for our mastery of pandas. Now that we’re comfortable working with Series and DataFrames, we can expand our horizons and learn how to tackle common problems in data analysis. Chapter 6 dives right into working with messy text data, including dealing with whitespace and inconsistent character casing. In chapter 7, we learn how to use the powerful MultiIndex to store and extract hierarchical data. Chapters 8 and 9 focus on aggregation: pivoting our DataFrames, grouping data into buckets, summarizing data, and more. In chapter 10, we explore how to merge datasets by using a variety of joins. Immediately afterward, we learn the ins and outs of working with another common data type, datetimes, in chapter 11. In chapter 12, we look at importing and exporting data sets to and from pandas. Chapter 13 covers how to adjust the library’s configuration settings. Finally, chapter 14 provides a tutorial on creating visualizations from our DataFrames.
Along the way, we’ll practice pandas concepts on more than 30 datasets that cover everything from baby names to breakfast cereals, from Fortune 1000 companies to Nobel Prize winners. You are welcome to proceed through the chapters linearly or explore whichever topic piques your interest most. Consider each chapter here to be a new specialization to add to your pandas toolbox. Good luck!