h2o-py/h2o/frame.py
def pivot(self, index, column, value):
        """
        Pivot the frame designated by the three columns: index, column, and value. Index and column should be
        of type enum, int, or time.
        For cases of multiple indexes for a column label, the aggregation method is to pick the first occurrence in the data frame

        :param index: Index is a column that will be the row label
        :param column: The labels for the columns in the pivoted Frame
        :param value: The column of values for the given index and column label
        :returns:
        """
        assert_is_type(index, str)
        assert_is_type(column, str)
        assert_is_type(value, str)
        col_names = self.names
        if index not in col_names:
            raise H2OValueError("Index not in H2OFrame")
        if column not in col_names:
            raise H2OValueError("Column not in H2OFrame")
        if value not in col_names:
            raise H2OValueError("Value column not in H2OFrame")
        if self.type(column) not in ["enum","time","int"]:
            raise H2OValueError("'column' argument is not type enum, time or int")
        if self.type(index) not in ["enum","time","int"]:
            raise H2OValueError("'index' argument is not type enum, time or int")
        return H2OFrame._expr(expr=ExprNode("pivot",self,index,column,value))
def pivot(self, index, column, value):
        """
        Pivot the frame designated by the three columns: index, column, and value. Index and column should be
        of type enum, int, or time.
        For cases of multiple indexes for a column label, the aggregation method is to pick the first occurrence in the data frame

        :param index: Index is a column that will be the row label
        :param column: The labels for the columns in the pivoted Frame
        :param value: The column of values for the given index and column label
        :returns:
        """
        assert_is_type(index, str)
        assert_is_type(column, str)
        assert_is_type(value, str)
        col_names = self.names
        if index not in col_names:
            raise H2OValueError("Index not in H2OFrame")
        if column not in col_names:
            raise H2OValueError("Column not in H2OFrame")
        if value not in col_names:
            raise H2OValueError("Value column not in H2OFrame")
        if self.type(column) not in ["enum","time","int"]:
            raise H2OValueError("'column' argument is not type enum, time or int")
        if self.type(index) not in ["enum","time","int"]:
            raise H2OValueError("'index' argument is not type enum, time or int")
        return H2OFrame._expr(expr=ExprNode("pivot",self,index,column,value))
def pivot ( self , index , column , value ) : assert_is_type ( index , str ) assert_is_type ( column , str ) assert_is_type ( value , str ) col_names = self . names if index not in col_names : raise H2OValueError ( "Index not in H2OFrame" ) if column not in col_names : raise H2OValueError ( "Column not in H2OFrame" ) if value not in col_names : raise H2OValueError ( "Value column not in H2OFrame" ) if self . type ( column ) not in [ "enum" , "time" , "int" ] : raise H2OValueError ( "'column' argument is not type enum, time or int" ) if self . type ( index ) not in [ "enum" , "time" , "int" ] : raise H2OValueError ( "'index' argument is not type enum, time or int" ) return H2OFrame . _expr ( expr = ExprNode ( "pivot" , self , index , column , value ) )
Pivot the frame designated by the three columns: index, column, and value. Index and column should be
        of type enum, int, or time.
        For cases of multiple indexes for a column label, the aggregation method is to pick the first occurrence in the data frame

        :param index: Index is a column that will be the row label
        :param column: The labels for the columns in the pivoted Frame
        :param value: The column of values for the given index and column label
        :returns:
Pivot the frame designated by the three columns : index column and value . Index and column should be of type enum int or time . For cases of multiple indexes for a column label the aggregation method is to pick the first occurrence in the data frame