def rank(self,
             method='ordinal',
             ascending=True,
             mask=NotSpecified,
             groupby=NotSpecified):
        """
        Construct a new Factor representing the sorted rank of each column
        within each row.

        Parameters
        ----------
        method : str, {'ordinal', 'min', 'max', 'dense', 'average'}
            The method used to assign ranks to tied elements. See
            `scipy.stats.rankdata` for a full description of the semantics for
            each ranking method. Default is 'ordinal'.
        ascending : bool, optional
            Whether to return sorted rank in ascending or descending order.
            Default is True.
        mask : zipline.pipeline.Filter, optional
            A Filter representing assets to consider when computing ranks.
            If mask is supplied, ranks are computed ignoring any asset/date
            pairs for which `mask` produces a value of False.
        groupby : zipline.pipeline.Classifier, optional
            A classifier defining partitions over which to perform ranking.

        Returns
        -------
        ranks : zipline.pipeline.factors.Rank
            A new factor that will compute the ranking of the data produced by
            `self`.

        Notes
        -----
        The default value for `method` is different from the default for
        `scipy.stats.rankdata`.  See that function's documentation for a full
        description of the valid inputs to `method`.

        Missing or non-existent data on a given day will cause an asset to be
        given a rank of NaN for that day.

        See Also
        --------
        :func:`scipy.stats.rankdata`
        :class:`zipline.pipeline.factors.factor.Rank`
        """

        if groupby is NotSpecified:
            return Rank(self, method=method, ascending=ascending, mask=mask)

        return GroupedRowTransform(
            transform=rankdata if ascending else rankdata_1d_descending,
            transform_args=(method,),
            factor=self,
            groupby=groupby,
            dtype=float64_dtype,
            missing_value=nan,
            mask=mask,
            window_safe=True,
        )