pandas.api.typing.DataFrameGroupBy.cummin#

DataFrameGroupBy.cummin(numeric_only=False, skipna=True, **kwargs)[source]#

Cumulative min for each group.

Returns a same-sized object where each value is replaced by the minimum of all preceding values in 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.cummin is deprecated and will be removed in a future version of pandas.

Returns:

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

See also

Series.cummin: Apply function cummin to a Series.
DataFrame.cummin: Apply function cummin 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