Note that the
        numbers given to the groups match the order in which the groups
        would be seen when iterating over the groupby object, not the
        order they are first observed.

        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