data_algebra/data_ops.py
def ex(self, *, data_model=None, narrow=True, allow_limited_tables=False):
        """
        Evaluate operators with respect to Pandas data frames already stored in the operator chain.

        :param data_model: adaptor to data dialect (Pandas for now)
        :param narrow: logical, if True don't copy unexpected columns
        :param allow_limited_tables: logical, if True allow execution on non-complete tables
        :return: table result
        """
        tables = self.get_tables()
        for tv in tables.values():
            assert isinstance(tv, TableDescription)
            assert tv.head is not None
            if len(tables) > 1:
                assert tv.table_name_was_set_by_user
            if not allow_limited_tables:
                assert tv.nrows == tv.head.shape[0]
        return self.eval(data_map=None, data_model=data_model, narrow=narrow)

        Evaluate operators with respect to Pandas data frames already stored in the operator chain.

        :param data_model: adaptor to data dialect (Pandas for now)
        :param narrow: logical, if True don't copy unexpected columns
        :param allow_limited_tables: logical, if True allow execution on non-complete tables
        :return: table result
        
Evaluate operators with respect to Pandas data frames already stored in the operator chain.
Evaluate operators with respect to Pandas data frames already stored in the operator chain .
def ex(self, *, data_model=None, narrow=True, allow_limited_tables=False):
        tables = self.get_tables()
        for tv in tables.values():
            assert isinstance(tv, TableDescription)
            assert tv.head is not None
            if len(tables) > 1:
                assert tv.table_name_was_set_by_user
            if not allow_limited_tables:
                assert tv.nrows == tv.head.shape[0]
        return self.eval(data_map=None, data_model=data_model, narrow=narrow)
def ex ( self , * , data_model = None , narrow = True , allow_limited_tables = False ) : tables = self . get_tables ( ) for tv in tables . values ( ) : assert isinstance ( tv , TableDescription ) assert tv . head is not None if len ( tables ) > 1 : assert tv . table_name_was_set_by_user if not allow_limited_tables : assert tv . nrows == tv . head . shape [ 0 ] return self . eval ( data_map = None , data_model = data_model , narrow = narrow )
Evaluate operators with respect to Pandas data frames already stored in the operator chain.
Evaluate operators with respect to Pandas data frames already stored in the operator chain .
"""
        Evaluate operators with respect to Pandas data frames already stored in the operator chain.

        :param data_model: adaptor to data dialect (Pandas for now)
        :param narrow: logical, if True don't copy unexpected columns
        :param allow_limited_tables: logical, if True allow execution on non-complete tables
        :return: table result
        """
None
self
data_model
adaptor to data dialect (Pandas for now)
adaptor to data dialect ( Pandas for now )
narrow
logical, if True don't copy unexpected columns
logical if True don ' t copy unexpected columns
allow_limited_tables
logical, if True allow execution on non-complete tables
logical if True allow execution on non - complete tables