pandas.core.groupby.DataFrameGroupBy.prod#

DataFrameGroupBy.prod(numeric_only=False, min_count=0)[source]#

Compute prod of group values.

Parameters:

numeric_onlybool, default False: Include only float, int, boolean columns.

Changed in version 2.0.0: numeric_only no longer accepts None.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns:

Series or DataFrame: Computed prod of values within each group.

See also

Series.prod: Return the product of the values over the requested axis.
DataFrame.prod: Return the product of the values over the requested axis.

Examples

For SeriesGroupBy:

>>> lst = ["a", "a", "b", "b"]
>>> ser = pd.Series([1, 2, 3, 4], index=lst)
>>> ser
a    1
a    2
b    3
b    4
dtype: int64
>>> ser.groupby(level=0).prod()
a    2
b   12
dtype: int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
>>> df = pd.DataFrame(
...     data,
...     columns=["a", "b", "c"],
...     index=["tiger", "leopard", "cheetah", "lion"],
... )
>>> df
          a  b  c
  tiger   1  8  2
leopard   1  2  5
cheetah   2  5  8
   lion   2  6  9
>>> df.groupby("a").prod()
     b    c
a
1   16   10
2   30   72