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snowflake.snowpark.modin.plugin.extensions.groupby_overrides.SeriesGroupBy.cummin¶

SeriesGroupBy.cummin(axis: Union[int, Literal['index', 'columns', 'rows']] = 0, numeric_only: bool = False, *args, **kwargs)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/groupby_overrides.py#L504-L512)¶

Cumulative min 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, 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