# pandas.core.groupby.DataFrameGroupBy.rank

`DataFrameGroupBy.``rank`(axis=0, numeric_only=None, method='average', na_option='keep', ascending=True, pct=False)

  Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values

  Parameters:

  axis : {0 or ‘index’, 1 or ‘columns’}, default 0

  > Ranks over columns (0) or rows (1)

  numeric_only : boolean, default None

  > Include only float, int, boolean data

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

  > - 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

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

  > - keep: leave NA values where they are
  > - top: smallest rank if ascending
  > - bottom: smallest rank if descending

  ascending : boolean, default True

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

  pct : boolean, default False

  > Computes percentage rank of data

  Returns: | ranks : DataFrame