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# pandas.api.typing.SeriesGroupBy.prod

SeriesGroupBy.prod(numeric_only=False, min_count=0, skipna=True)

  Compute prod of group values.

  Parameters:
    numeric_only bool, default False

      Include only float, int, boolean columns.

      Changed in version 2.0.0: numeric_only no longer accepts `None`.

    min_count int, 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.

    skipna bool, default True

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

      Added in version 3.0.0.

  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