oupby.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
    * [ pandas.core.groupby.SeriesGroupBy.skew ](pandas.core.groupby.SeriesGroupBy.skew.html)
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.DataFrameGroupBy.prod  #

DataFrameGroupBy.  prod  (  _ numeric_only  =  False  _ , _ min_count  =  0  _
)  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L3155-L3199) #

    

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.

Returns  :

    

Series or DataFrame

    

Computed prod of values within each group.

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
    

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  * ` DataFrameGroupBy.prod()  `

[ __ Show Source
](../../_sources/reference/api/pandas.core.groupby.DataFrameGroupBy.prod.rst.txt)

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