def __init__(self, id_column, groupby_aggregations):
        """Group and aggregate features by specified configuration


        Args:
            id_column: Column with id's which will be preprocessed
            groupby_aggregations: list of tuples

        Example
            groupby_aggregations = [(['f0'], [('f2', 'min'),
                                              ('f2', 'median')]),
                                    (['f0', 'f1'], [('f2', 'mean'),
                                                    ('f2', 'max'),
                                                    ('f2', 'kurt')])]
            X = np.array([[0, 0, 0],
                          [0, 0, 1],
                          [0, 1, 0]])
            X = pd.DataFrame(X)
            X.columns = ['f0', 'f1', 'f2']

            tr = GroupbyAggregate(list(range(3)), groupby_aggregations)
            aggregations = tr.fit_transform(X)
        """
        super().__init__()
        self.id_column = id_column
        self.groupby_aggregations = groupby_aggregations
def __init__ ( self , id_column , groupby_aggregations ) : super ( ) . __init__ ( ) self . id_column = id_column self . groupby_aggregations = groupby_aggregations
Group and aggregate features by specified configuration


        Args:
            id_column: Column with id's which will be preprocessed
            groupby_aggregations: list of tuples

        Example
            groupby_aggregations = [(['f0'], [('f2', 'min'),
                                              ('f2', 'median')]),
                                    (['f0', 'f1'], [('f2', 'mean'),
                                                    ('f2', 'max'),
                                                    ('f2', 'kurt')])]
            X = np.array([[0, 0, 0],
                          [0, 0, 1],
                          [0, 1, 0]])
            X = pd.DataFrame(X)
            X.columns = ['f0', 'f1', 'f2']

            tr = GroupbyAggregate(list(range(3)), groupby_aggregations)
            aggregations = tr.fit_transform(X)