This book contains nearly 100 recipes, ranging from very simple to advanced. All recipes strive to be written in clear, concise, and modern idiomatic pandas code. The How it works... sections contain extremely detailed descriptions of the intricacies of each step of the recipe. Often, in the There's more... section, you will get what may seem like an entirely new recipe. This book is densely packed with an extraordinary amount of pandas code.

As a generalization, the recipes in the first six chapters tend to be simpler and more focused on the fundamental and essential operations of pandas than the last five chapters, which focus on more advanced operations and are more project-driven. Due to the wide range of complexity, this book can be useful to both the novice and everyday user alike. It has been my experience that even those who use pandas regularly will not master it without being exposed to idiomatic pandas code. This is somewhat fostered by the breadth that pandas offers. There are almost always multiple ways of completing the same operation, which can have users get the result they want but in a very inefficient manner. It is not uncommon to see an order of magnitude or more performance difference between two sets of pandas solutions to the same problem.

The only real prerequisite for this book is fundamental knowledge of Python. It is assumed that the reader is familiar with all the common built-in data containers in Python, such as lists, sets, dictionaries, and tuples.

There are a couple of things you can do to get the most out of this book. First, and most importantly, you should download all the code, which will be stored in Jupyter Notebooks. While reading through each recipe, run each step of code in the notebook. Make sure you explore on your own as you run through the code. Second, have the pandas official documentation open (http://pandas.pydata.org/pandas-docs/stable/) in one of your browser tabs. The pandas documentation is an excellent resource containing over 1,000 pages of material. There are examples for most of the pandas operations in the documentation, and they will often be directly linked from the See also section. While it covers the basics of most operations, it does so with trivial examples and fake data that don't reflect situations that you are likely to encounter when analyzing datasets from the real world.