.

        Groups with missing keys (where `pd.isna()` is True) will be labeled with `NaN`
        and will be skipped from the count.

        Parameters
        ----------
        ascending : bool, default True
            If False, number in reverse, from number of group - 1 to 0.

        Returns
        -------
        Series
            Unique numbers for each group.

        See Also
        --------
        .cumcount : Number the rows in each group.

        Examples
        --------
        >>> df = pd.DataFrame({"color": ["red", None, "red", "blue", "blue", "red"]})
        >>> df
           color
        0    red
        1   None
        2    red
        3   blue
        4   blue
        5    red
        >>> df.groupby("color").ngroup()
        0    1.0
        1    NaN
        2    1.0
        3    0.0
        4    0.0
        5    1.0
        dtype: float64
        >>> df.groupby("color", dropna=False).ngroup()
        0    1
        1    2
        2    1
        3    0
        4    0
        5    1
        dtype: int64
        >>> df.groupby("color", dropna=False).ngroup(ascending=False)
        0    1
        1    0
        2    1
        3    2
        4    2
        5    1
        dtype: int64
        r