pandas.api.typing.SeriesGroupBy.cummax#

SeriesGroupBy.cummax(numeric_only=False, skipna=True, **kwargs)[source]#

Cumulative max for each group.

Returns the cumulative maximum of values within each group. The result has the same size as the input, with each element representing the maximum of all preceding elements (including itself) within its group.

Parameters:

numeric_onlybool, default False: Include only float, int or boolean data.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
**kwargsdict, optional: Additional keyword arguments to be passed to the function.

Deprecated since version 3.1.0: Passing **kwargs to GroupBy.cummax is deprecated and will be removed in a future version of pandas.

Returns:

Series or DataFrame: Cumulative max for each group. Same object type as the caller.

See also

Series.cummax: Apply function cummax to a Series.
DataFrame.cummax: Apply function cummax 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, 1, 4], index=lst)
>>> ser
a    1
a    6
a    2
b    3
b    1
b    4
dtype: int64
>>> ser.groupby(level=0).cummax()
a    1
a    6
a    6
b    3
b    3
b    4
dtype: int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 1, 0], [2, 6, 9]]
>>> df = pd.DataFrame(
...     data, columns=["a", "b", "c"], index=["cow", "horse", "bull"]
... )
>>> df
        a   b   c
cow     1   8   2
horse   1   1   0
bull    2   6   9
>>> df.groupby("a").groups
{1: ['cow', 'horse'], 2: ['bull']}
>>> df.groupby("a").cummax()
        b   c
cow     8   2
horse   8   2
bull    6   9