packages/vaex-core/vaex/dataframe.py
def to_astropy_table(self, column_names=None, selection=None, strings=True, virtual=False, index=None):
        """Returns a astropy table object containing the ndarrays corresponding to the evaluated data

        :param column_names: list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used
        :param selection: {selection}
        :param strings: argument passed to DataFrame.get_column_names when column_names is None
        :param virtual: argument passed to DataFrame.get_column_names when column_names is None
        :param index: if this column is given it is used for the index of the DataFrame
        :return: astropy.table.Table object
        """
        from astropy.table import Table, Column, MaskedColumn
        meta = dict()
        meta["name"] = self.name
        meta["description"] = self.description

        table = Table(meta=meta)
        for name, data in self.to_items(column_names=column_names, selection=selection, strings=strings, virtual=virtual):
            if self.dtype(name) == str_type:  # for astropy we convert it to unicode, it seems to ignore object type
                data = np.array(data).astype('U')
            meta = dict()
            if name in self.ucds:
                meta["ucd"] = self.ucds[name]
            if np.ma.isMaskedArray(data):
                cls = MaskedColumn
            else:
                cls = Column
            table[name] = cls(data, unit=self.unit(name), description=self.descriptions.get(name), meta=meta)
        return table
def to_astropy_table(self, column_names=None, selection=None, strings=True, virtual=False, index=None):
        from astropy.table import Table, Column, MaskedColumn
        meta = dict()
        meta["name"] = self.name
        meta["description"] = self.description

        table = Table(meta=meta)
        for name, data in self.to_items(column_names=column_names, selection=selection, strings=strings, virtual=virtual):
            if self.dtype(name) == str_type:  # for astropy we convert it to unicode, it seems to ignore object type
                data = np.array(data).astype('U')
            meta = dict()
            if name in self.ucds:
                meta["ucd"] = self.ucds[name]
            if np.ma.isMaskedArray(data):
                cls = MaskedColumn
            else:
                cls = Column
            table[name] = cls(data, unit=self.unit(name), description=self.descriptions.get(name), meta=meta)
        return table
def to_astropy_table ( self , column_names = None , selection = None , strings = True , virtual = False , index = None ) : from astropy . table import Table , Column , MaskedColumn meta = dict ( ) meta [ "name" ] = self . name meta [ "description" ] = self . description table = Table ( meta = meta ) for name , data in self . to_items ( column_names = column_names , selection = selection , strings = strings , virtual = virtual ) : if self . dtype ( name ) == str_type : # for astropy we convert it to unicode, it seems to ignore object type data = np . array ( data ) . astype ( 'U' ) meta = dict ( ) if name in self . ucds : meta [ "ucd" ] = self . ucds [ name ] if np . ma . isMaskedArray ( data ) : cls = MaskedColumn else : cls = Column table [ name ] = cls ( data , unit = self . unit ( name ) , description = self . descriptions . get ( name ) , meta = meta ) return table
Returns a astropy table object containing the ndarrays corresponding to the evaluated data

        :param column_names: list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used
        :param selection: {selection}
        :param strings: argument passed to DataFrame.get_column_names when column_names is None
        :param virtual: argument passed to DataFrame.get_column_names when column_names is None
        :param index: if this column is given it is used for the index of the DataFrame
        :return: astropy.table.Table object
Returns a astropy table object containing the ndarrays corresponding to the evaluated data