Pandas Cookbook: Practical recipes for scientific computing, time series, and exploratory data analysis using Python, Third Edition
William Ayd and Matthew Harrison
Who this book is for
This book contains a huge number of 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.
While not strictly required, users are advised to read the book chronologically. The recipes are structured in such a way that they first introduce concepts and features using very small, directed examples, but continuously build from there into more complex applications.
Due to the wide range of complexity, this book can be useful to both novice and everyday users 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 in performance difference between two sets of pandas solutions to the same problem.
The only real prerequisite for this book is a 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.
What this book covers
Chapter 1 , pandas Foundations , introduces the main pandas objects, namely, Series , DataFrames, and Index .
Chapter 2 , Selection and Assignment , shows you how to sift through the data that you have loaded into any of the pandas data structures.
Chapter 3 , Data Types , explores the type system underlying pandas. This is an area that has evolved rapidly and will continue to do so, so knowing the types and what distinguishes them is invaluable information.
Chapter 4 , The pandas I/O System , shows why pandas has long been a popular tool to read from and write to a variety of storage formats.
Chapter 5 , Algorithms and How to Apply Them , introduces you to the foundation of performing calculations with the pandas data structures.
Chapter 6 , Visualization , shows you how pandas can be used directly for plotting, alongside the seaborn library which integrates well with pandas.
Chapter 7 , Reshaping DataFrames , discusses the many ways in which data can be transformed and summarized robustly via the pandas.
Chapter 8 , Group By , showcases how to segment and summarize subsets of your data contained within a.
Chapter 9 , Temporal Data Types and Algorithms , introduces users to the date/time types which underlie time-series-based analyses that pandas is famous for and highlights usage against real data.
Chapter 10 , General Usage/Performance Tips , goes over common pitfalls users run into when using pandas, and showcases the idiomatic solutions.
Chapter 11 , The pandas Ecosystem , discusses other open source libraries that integrate, extend, and/or complement pandas.