Series.
product
Return the product of the values.
Note
unlike pandas’, pandas-on-Spark’s emulates product by exp(sum(log(...))) trick. Therefore, it only works for positive numbers.
exp(sum(log(...)))
Axis for the function to be applied on.
Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.
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.
min_count
Examples
On a DataFrame:
Non-numeric type column is not included to the result.
>>> psdf = ps.DataFrame({'A': [1, 2, 3, 4, 5], ... 'B': [10, 20, 30, 40, 50], ... 'C': ['a', 'b', 'c', 'd', 'e']}) >>> psdf A B C 0 1 10 a 1 2 20 b 2 3 30 c 3 4 40 d 4 5 50 e
>>> psdf.prod() A 120 B 12000000 dtype: int64
If there is no numeric type columns, returns empty Series.
>>> ps.DataFrame({"key": ['a', 'b', 'c'], "val": ['x', 'y', 'z']}).prod() Series([], dtype: float64)
On a Series:
>>> ps.Series([1, 2, 3, 4, 5]).prod() 120
By default, the product of an empty or all-NA Series is 1
1
>>> ps.Series([]).prod() 1.0
This can be controlled with the min_count parameter
>>> ps.Series([]).prod(min_count=1) nan