#### Overview of this book

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
Preface
Pandas Foundations
Free Chapter
Essential DataFrame Operations
Creating and Persisting DataFrames
Exploratory Data Analysis
Selecting Subsets of Data
Combining Pandas Objects
Visualization with Matplotlib, Pandas, and Seaborn
Debugging and Testing Pandas
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Index

# Producing Cartesian products

Whenever a Series or DataFrame operates with another Series or DataFrame, the indexes (both the row index and column index) of each object align first before any operation begins. This index alignment happens behind the scenes and can be very surprising for those new to pandas. This alignment always creates a Cartesian product between the indexes unless the indexes are identical.

A Cartesian product is a mathematical term that usually appears in set theory. A Cartesian product between two sets is all the combinations of pairs of both sets. For example, the 52 cards in a standard playing card deck represent a Cartesian product between the 13 ranks (A, 2, 3,…, Q, K) and the four suits.

Producing a Cartesian product isn't always the intended outcome, but it's essential to be aware of how and when it occurs so as to avoid unintended consequences. In this recipe, two Series with overlapping but non-identical indexes are added together...