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.

See column selectors for how to select the columns for this operation.

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

22 August 2025