Returns Manager containing quantiles along an axis for numeric columns.

        Returns:
            DataManager containing quantiles of original DataManager along an axis.

def quantile_for_list_of_values(self, **kwargs):
        """Returns Manager containing quantiles along an axis for numeric columns.

        Returns:
            DataManager containing quantiles of original DataManager along an axis.
        """
        if self._is_transposed:
            kwargs["axis"] = kwargs.get("axis", 0) ^ 1
            return self.transpose().quantile_for_list_of_values(**kwargs)
        axis = kwargs.get("axis", 0)
        q = kwargs.get("q")
        numeric_only = kwargs.get("numeric_only", True)
        assert isinstance(q, (pandas.Series, np.ndarray, pandas.Index, list))

        if numeric_only:
            new_columns = self.numeric_columns()
        else:
            new_columns = [
                col
                for col, dtype in zip(self.columns, self.dtypes)
                if (is_numeric_dtype(dtype) or is_datetime_or_timedelta_dtype(dtype))
            ]
        if axis:
            # If along rows, then drop the nonnumeric columns, record the index, and
            # take transpose. We have to do this because if we don't, the result is all
            # in one column for some reason.
            nonnumeric = [
                col
                for col, dtype in zip(self.columns, self.dtypes)
                if not is_numeric_dtype(dtype)
            ]
            query_compiler = self.drop(columns=nonnumeric)
            new_columns = query_compiler.index
        else:
            query_compiler = self

        def quantile_builder(df, **kwargs):
            result = df.quantile(**kwargs)
            return result.T if axis == 1 else result

        func = query_compiler._prepare_method(quantile_builder, **kwargs)
        q_index = pandas.Float64Index(q)
        new_data = query_compiler._map_across_full_axis(axis, func)

        # This took a long time to debug, so here is the rundown of why this is needed.
        # Previously, we were operating on select indices, but that was broken. We were
        # not correctly setting the columns/index. Because of how we compute `to_pandas`
        # and because of the static nature of the index for `axis=1` it is easier to
        # just handle this as the transpose (see `quantile_builder` above for the
        # transpose within the partition) than it is to completely rework other
        # internal methods. Basically we are returning the transpose of the object for
        # correctness and cleanliness of the code.
        if axis == 1:
            q_index = new_columns
            new_columns = pandas.Float64Index(q)
        result = self.__constructor__(new_data, q_index, new_columns)
        return result.transpose() if axis == 1 else result