mean

Computes the mean (average) of values.

null values are ignored.

All primitive numeric types are supported: Byte, Short, Int, Long, Float, and Double.

mean also supports the "mixed" Number type, as long as the column consists only of the aforementioned primitive numbers. The numbers are automatically converted to a common type for the operation.

The return type is always Double; Double.NaN for empty columns.

All operations on Double/Float/Number have the skipNaN option, which is set to false by default. This means that if a NaN is present in the input, it will be propagated to the result. When it's set to true, NaN values are ignored.

df.mean() // mean of values per every numeric column
df.mean { age and weight } // mean of all values in `age` and `weight`
df.meanFor(skipNaN = true) { age and weight } // mean of values per `age` and `weight` separately, skips NaN
df.meanOf { (weight ?: 0) / age } // median of expression evaluated for every row

df.mean()
df.age.mean()
df.groupBy { city }.mean()
df.pivot { city }.mean()
df.pivot { city }.groupBy { name.lastName }.mean()

See statistics for details on complex data aggregations.

Type Conversion

The following automatic type conversions are performed for the mean operation:

Conversion	Result for Empty Input
Int -> Double	Double.NaN
Byte -> Double	Double.NaN
Short -> Double	Double.NaN
Long -> Double	Double.NaN
Double -> Double	Double.NaN
Float -> Double	Double.NaN
Number -> Conversion(Common number type) -> Double	Double.NaN
Nothing -> Double	Double.NaN

20 May 2025