pyspark.pandas.Series.product

Series.product(axis: Union[int, str, None] = None, skipna: bool = True, numeric_only: bool = None, min_count: int = 0) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, Series]

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.

Parameters

axis: {index (0), columns (1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values when computing the result.

Changed in version 3.4.0: Supported including NA/null values.

numeric_only: bool, default None

Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.

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.

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

>>> ps.Series([]).prod()
1.0

This can be controlled with the min_count parameter

>>> ps.Series([]).prod(min_count=1)
nan

pyspark.pandas.Series.eq pyspark.pandas.Series.dot