pandas.api.typing.DataFrameGroupBy.cumprod

DataFrameGroupBy.cumprod(numeric_only=False, skipna=True, *args, **kwargs)

Cumulative product for each group.

Returns a Series or DataFrame of the same size with the cumulative product computed within each group.

Parameters:

numeric_onlybool, default False

Include only float, int, boolean columns.

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

*argstuple

Positional arguments to be passed to func.

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

**kwargsdict

Additional keyword arguments to be passed to the function.

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

Returns:

Series or DataFrame

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

See also

Series.cumprod

Apply function cumprod to a Series.

DataFrame.cumprod

Apply function cumprod to each row or column of a DataFrame.

Examples

For SeriesGroupBy:

>>> lst = ["a", "a", "b"]
>>> ser = pd.Series([6, 2, 0], index=lst)
>>> ser
a    6
a    2
b    0
dtype: int64
>>> ser.groupby(level=0).cumprod()
a    6
a   12
b    0
dtype: int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [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   2   5
bull    2   6   9
>>> df.groupby("a").groups
{1: ['cow', 'horse'], 2: ['bull']}
>>> df.groupby("a").cumprod()
        b   c
cow     8   2
horse  16  10
bull    6   9