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()