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# pandas.Series.groupby

`Series.``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()

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