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# pandas.api.typing.SeriesGroupBy.rank

SeriesGroupBy.rank(method='average', ascending=True, na_option='keep', pct=False)

  Provide the rank of values within each group.

  This method assigns ranks to values within each group, with options for handling duplicate values, NaN values, and computing percentage ranks.

  Parameters:
    method{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’

      - average: average rank of group.

      - min: lowest rank in group.

      - max: highest rank in group.

      - first: ranks assigned in order they appear in the array.

      - dense: like ‘min’, but rank always increases by 1 between groups.

    ascending bool, default True

      False for ranks by high (1) to low (N).

    na_option{‘keep’, ‘top’, ‘bottom’}, default ‘keep’

      - keep: leave NA values where they are.

      - top: smallest rank if ascending.

      - bottom: smallest rank if descending.

    pct bool, default False

      Compute percentage rank of data within each group.

  Returns:
    DataFrame

      The ranking of values within each group.

  See also

  `Series.rank`

    Apply function rank to a Series.

  `DataFrame.rank`

    Apply function rank to each row or column of a DataFrame.

  Examples

      >>> df = pd.DataFrame(
      ...     {
      ...         "group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"],
      ...         "value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5],
      ...     }
      ... )
      >>> df
        group  value
      0     a      2
      1     a      4
      2     a      2
      3     a      3
      4     a      5
      5     b      1
      6     b      2
      7     b      4
      8     b      1
      9     b      5
      >>> for method in ["average", "min", "max", "dense", "first"]:
      ...     df[f"{method}_rank"] = df.groupby("group")["value"].rank(method)
      >>> df
        group  value  average_rank  min_rank  max_rank  dense_rank  first_rank
      0     a      2           1.5       1.0       2.0         1.0         1.0
      1     a      4           4.0       4.0       4.0         3.0         4.0
      2     a      2           1.5       1.0       2.0         1.0         2.0
      3     a      3           3.0       3.0       3.0         2.0         3.0
      4     a      5           5.0       5.0       5.0         4.0         5.0
      5     b      1           1.5       1.0       2.0         1.0         1.0
      6     b      2           3.0       3.0       3.0         2.0         3.0
      7     b      4           4.0       4.0       4.0         3.0         4.0
      8     b      1           1.5       1.0       2.0         1.0         2.0
      9     b      5           5.0       5.0       5.0         4.0         5.0