data_algebra/data_ops.py
def eval(self, data_map, *, data_model=None, narrow=True):
        """
         Evaluate operators with respect to Pandas data frames.

         :param data_map: map from table names to data frames
         :param data_model: adaptor to data dialect (Pandas for now)
         :param narrow logical, if True don't copy unexpected columns
         :return: table result
         """

        if data_map is not None:
            assert isinstance(data_map, dict)
        if data_model is None:
            data_model = data_algebra.default_data_model
        assert isinstance(data_model, data_algebra.data_model.DataModel)
        if (data_map is not None) and (len(data_map) > 0):
            self.columns_used()  # for table consistency check/raise
            tables = self.get_tables()
            self.check_constraints(
                {k: x.columns for (k, x) in data_map.items()}, strict=not narrow
            )
            for k in tables.keys():
                if k not in data_map.keys():
                    raise ValueError("Required table " + k + " not in data_map")
                else:
                    if not data_model.is_appropriate_data_instance(data_map[k]):
                        raise ValueError("data_map[" + k + "] was not a usable type")
        return self.eval_implementation(
            data_map=data_map, data_model=data_model, narrow=narrow
        )

         Evaluate operators with respect to Pandas data frames.

         :param data_map: map from table names to data frames
         :param data_model: adaptor to data dialect (Pandas for now)
         :param narrow logical, if True don't copy unexpected columns
         :return: table result
         
Evaluate operators with respect to Pandas data frames.
Evaluate operators with respect to Pandas data frames .
def eval(self, data_map, *, data_model=None, narrow=True):
        if data_map is not None:
            assert isinstance(data_map, dict)
        if data_model is None:
            data_model = data_algebra.default_data_model
        assert isinstance(data_model, data_algebra.data_model.DataModel)
        if (data_map is not None) and (len(data_map) > 0):
            self.columns_used()  
            tables = self.get_tables()
            self.check_constraints(
                {k: x.columns for (k, x) in data_map.items()}, strict=not narrow
            )
            for k in tables.keys():
                if k not in data_map.keys():
                    raise ValueError("Required table " + k + " not in data_map")
                else:
                    if not data_model.is_appropriate_data_instance(data_map[k]):
                        raise ValueError("data_map[" + k + "] was not a usable type")
        return self.eval_implementation(
            data_map=data_map, data_model=data_model, narrow=narrow
        )
def eval ( self , data_map , * , data_model = None , narrow = True ) : if data_map is not None : assert isinstance ( data_map , dict ) if data_model is None : data_model = data_algebra . default_data_model assert isinstance ( data_model , data_algebra . data_model . DataModel ) if ( data_map is not None ) and ( len ( data_map ) > 0 ) : self . columns_used ( ) tables = self . get_tables ( ) self . check_constraints ( { k : x . columns for ( k , x ) in data_map . items ( ) } , strict = not narrow ) for k in tables . keys ( ) : if k not in data_map . keys ( ) : raise ValueError ( "Required table " + k + " not in data_map" ) else : if not data_model . is_appropriate_data_instance ( data_map [ k ] ) : raise ValueError ( "data_map[" + k + "] was not a usable type" ) return self . eval_implementation ( data_map = data_map , data_model = data_model , narrow = narrow )
Evaluate operators with respect to Pandas data frames.
Evaluate operators with respect to Pandas data frames .
"""
         Evaluate operators with respect to Pandas data frames.

         :param data_map: map from table names to data frames
         :param data_model: adaptor to data dialect (Pandas for now)
         :param narrow logical, if True don't copy unexpected columns
         :return: table result
         """ # for table consistency check/raise
self
data_map
data_model
narrow