pandas.DataFrame.groupby

DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False)

Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns

Parameters:

by : mapping function / list of functions, dict, Series, or tuple /

list of column names. Called on each element of the object index to determine the groups. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups

axis : int, default 0

level : int, level name, or sequence of such, default None

If the axis is a MultiIndex (hierarchical), group by a particular level or levels

as_index : boolean, default True

For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output

sort : boolean, default True

Sort group keys. Get better performance by turning this off

group_keys : boolean, default True

When calling apply, add group keys to index to identify pieces

squeeze : boolean, default False

reduce the dimensionaility of the return type if possible, otherwise return a consistent type

Returns:

GroupBy object

Examples

# DataFrame result >>> data.groupby(func, axis=0).mean()

# DataFrame result >>> data.groupby([‘col1’, ‘col2’])[‘col3’].mean()

# DataFrame with hierarchical index >>> data.groupby([‘col1’, ‘col2’]).mean()