How to use the pandasticsearch.types.Column function in pandasticsearch

def test_column(self):
        col = Column('b')
        self._assert_equal_filter(col > 2, Greater('b', 2))
        self._assert_equal_filter(col >= 2, GreaterEqual('b', 2))
        self._assert_equal_filter(col < 2, Less('b', 2))
        self._assert_equal_filter(col <= 2, LessEqual('b', 2))
        self._assert_equal_filter(col == 2, Equal('b', 2))
        self._assert_equal_filter(col != 2, ~Equal('b', 2))
        self._assert_equal_filter(col.isin([1, 2, 3]), IsIn('b', [1, 2, 3]))
        self._assert_equal_filter(col.like('a*b'), Like('b', 'a*b'))
        self._assert_equal_filter(col.rlike('a*b'), Rlike('b', 'a*b'))
        self._assert_equal_filter(col.startswith('jj'), Startswith('b', 'jj'))
        self._assert_equal_filter(col.isnull, IsNull('b'))
        self._assert_equal_filter(col.notnull, NotNull('b'))

def __getattr__(self, name):
        """
        Returns a :class:`types.Column ` object denoted by ``name``.
        """
        if name not in self.columns:
            raise AttributeError(
                "'%s' object has no attribute '%s'" % (self.__class__.__name__, name))
        return Column(name)

def __getitem__(self, item):
        if isinstance(item, six.string_types):
            if item not in self._columns:
                raise ColumnExprException('Column does not exist: [{0}]'.format(item))
            return Column(item)
        elif isinstance(item, Filter):
            self._filter = item
            return self
        else:
            raise ColumnExprException('Unsupported expr: [{0}]'.format(item))

def __getitem__(self, item):
        if isinstance(item, six.string_types):
            if item not in self.columns:
                raise TypeError('Column does not exist: [{0}]'.format(item))
            return Column(item)
        elif isinstance(item, BooleanFilter):
            self._filter = item
            return self
        else:
            raise TypeError('Unsupported expr: [{0}]'.format(item))

def groupby(self, *cols):
        """
        Returns a new :class:`DataFrame ` object grouped by the specified column(s).

        :param cols: A list of column names, :class:`Column ` or :class:`Grouper ` objects
        """
        columns = []
        if len(cols) == 1 and isinstance(cols[0], Grouper):
            groupby = cols[0].build()
        else:
            for col in cols:
                if isinstance(col, six.string_types):
                    columns.append(getattr(self, col))
                elif isinstance(col, Column):
                    columns.append(col)
                else:
                    raise TypeError('{0} is supposed to be str or Column'.format(col))
            names = [col.field_name() for col in columns]
            groupby = Grouper.from_list(names).build()

        return DataFrame(client=self._client,
                         index=self._index,
                         doc_type=self._doc_type,
                         mapping=self._mapping,
                         filter=self._filter,
                         groupby=groupby,
                         aggregation=self._aggregation,
                         projection=self._projection,
                         sort=self._sort,
                         limit=self.limit,