pandas.core.groupby.SeriesGroupBy.cummin — pandas 3.0.0.dev0+2098.g9c5b9ee823 documentation (original) (raw)

SeriesGroupBy.cummin(numeric_only=False, **kwargs)[source]#

Cumulative min for each group.

Parameters:

numeric_onlybool, default False

Include only float, int or boolean data.

**kwargsdict, optional

Additional keyword arguments to be passed to the function, such as skipna, to control whether NA/null values are ignored.

Returns:

Series or DataFrame

Cumulative min for each group. Same object type as the caller.

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

For SeriesGroupBy:

lst = ["a", "a", "a", "b", "b", "b"] ser = pd.Series([1, 6, 2, 3, 0, 4], index=lst) ser a 1 a 6 a 2 b 3 b 0 b 4 dtype: int64 ser.groupby(level=0).cummin() a 1 a 1 a 1 b 3 b 0 b 0 dtype: int64

For DataFrameGroupBy:

data = [[1, 0, 2], [1, 1, 5], [6, 6, 9]] df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["snake", "rabbit", "turtle"] ... ) df a b c snake 1 0 2 rabbit 1 1 5 turtle 6 6 9 df.groupby("a").groups {1: ['snake', 'rabbit'], 6: ['turtle']} df.groupby("a").cummin() b c snake 0 2 rabbit 0 2 turtle 6 9