st', 'dense'}, default 'average'
            How to rank the group of records that have the same value (i.e. ties):

            * average: average rank of the group
            * min: lowest rank in the group
            * max: highest rank in the group
            * first: ranks assigned in order they appear in the array
            * dense: like 'min', but rank always increases by 1 between groups.

        numeric_only : bool, default False
            For DataFrame objects, rank only numeric columns if set to True.

            .. versionchanged:: 2.0.0
                The default value of ``numeric_only`` is now ``False``.

        na_option : {'keep', 'top', 'bottom'}, default 'keep'
            How to rank NaN values:

            * keep: assign NaN rank to NaN values
            * top: assign lowest rank to NaN values
            * bottom: assign highest rank to NaN values

        ascending : bool, default True
            Whether or not the elements should be ranked in ascending order.
        pct : bool, default False
            Whether or not to display the returned rankings in percentile
            form.

        Returns
        -------
        same type as caller
            Return a Series or DataFrame with data ranks as values.

        See Also
        --------
        core.groupby.DataFrameGroupBy.rank : Rank of values within each group.
        core.groupby.SeriesGroupBy.rank : Rank of values within each group.

        Examples
        --------
        >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
        ...                                    'spider', 'snake'],
        ...                         'Number_legs': [4, 2, 4, 8, np.nan]})
        >>> df
            Animal  Number_legs
        0      cat          4.0
        1  penguin          2.0
        2      dog          4.0
        3   spider          8.0
        4    snake          NaN

        Ties are assigned the mean of the ranks (by default) for the group.

        >>> s = pd.Series(range(5), index=list("abcde"))
        >>> s["d"] = s["b"]
        >>> s.rank()
        a    1.0
        b    2.5
        c    4.0
        d    2.5
        e    5.0
        dtype: float64

        The following example shows how the method behaves with the above
        parameters:

        * default_rank: this is the default behaviour obtained without using
          any parameter.
        * max_rank: setting ``method = 'max'`` the records that have the
          same values are ranked using the highest rank (e.g.: since 'cat'
          and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
        * NA_bottom: choosing ``na_option = 'bottom'``, if there are records
          with NaN values they are placed at the bottom of the ranking.
        * pct_rank: when setting ``pct = True``, the ranking is expressed as
          percentile rank.

        >>> df['default_rank'] = df['Number_legs'].rank()
        >>> df['max_rank'] = df['Number_legs'].rank(method='max')
        >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
        >>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
        >>> df
            Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
        0      cat          4.0           2.5       3.0        2.5     0.625
        1  penguin          2.0           1.0       1.0        1.0     0.250
        2      dog          4.0           2.5       3.0        2.5     0.625
        3   spider          8.0           4.0       4.0        4.0     1.000
        4    snake          NaN           NaN       NaN        5.0       NaN
        >