The pandas library provides a flexible and high-performance "groupby" facility that enables you to slice, dice, and summarize data sets. This process follows a pattern known as split-apply-combine. This pattern data is first categorized into groups based on a criteria such as the indexes or values within the columns. Each group is then processed with an aggregation or transformation function, returning a set of data with transformed values or a single aggregate summary for each group. pandas then combines all of these results and presents it in a single data structure.

We will start by seeing how pandas is used to split data. This will start with a demonstration of how to group data both using categorical values in the columns of a DataFrame object or using the levels in the index of a pandas object. Using the result from a grouping operation, we will examine how to access the data in each group, as well as retrieve various basic statistical values...