pandas.core.groupby.DataFrameGroupBy.rank — pandas 2.2.3 documentation (original) (raw)

DataFrameGroupBy.rank(method='average', ascending=True, na_option='keep', pct=False, axis=<no_default>)[source]#

Provide the rank of values within each group.

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.

ascendingbool, 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.

pctbool, default False

Compute percentage rank of data within each group.

axisint, default 0

The axis of the object over which to compute the rank.

Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary.

Returns:

DataFrame with ranking of values within each group

See also

Series.groupby

Apply a function groupby to a Series.

DataFrame.groupby

Apply a function groupby 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