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modin.pandas.SeriesGroupBy.cummax¶

SeriesGroupBy.cummax(axis: Union[int, Literal['index', 'columns', 'rows']] = 0, numeric_only: bool = False, *args, **kwargs)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.17.0/src/snowflake/snowpark/modin/pandas/groupby.py#L371-L380)¶

Cumulative max for each group.

Return type:: Series or DataFrame

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, 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