One of the most fundamental tasks during a data analysis involves splitting data into independent groups before performing a calculation on each group. This methodology has been around for quite some time but has more recently been referred to as split-apply-combine. This chapter covers the powerful groupby method, which allows you to group your data in any way imaginable and apply any type of function independently to each group before returning a single dataset.

Before we get started with the recipes, we will need to know just a little terminology. All basic groupby operations have grouping columns, and each unique combination of values in these columns represents an independent grouping of the data. The syntax looks as follows:

>>> df.groupby(['list', 'of', 'grouping', 'columns'])
>>> df.groupby('single_column')  # when grouping by a single column

The result of this operation returns a groupby object. It is this groupby object that will be the engine that drives all the calculations for this entire chapter. Pandas actually does very little when creating this groupby object, merely validating that grouping is possible. You will have to chain methods on this groupby object in order to unleash its powers.