min / max

Computes the minimum / maximum of values.

null values in the input are ignored. The operations either throw an exception when the input is empty (after filtering null or NaN values), or they return null when using the -orNull overloads.

They are available for self-comparable columns (so columns of type T : Comparable<T>, like DateTime, String, Int, etc.) which includes all primitive number columns, but no mix of different number types.

All operations on Double/Float 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.min() // min of values per every comparable column
df.min { age and weight } // min of all values in `age` and `weight`
df.minFor(skipNaN = true) { age and weight } // min of values per `age` and `weight` separately
df.minOf { (weight ?: 0) / age } // min of expression evaluated for every row
df.minBy { age } // DataRow with minimal `age`

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

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 min and max operations. (Note that null only appears in the return type when using -orNull overloads).

Conversion	Result for Empty Input
T -> T where T : Comparable\<T\>	null
Int -> Int	null
Byte -> Byte	null
Short -> Short	null
Long -> Long	null
Double -> Double	null
Float -> Float	null
Nothing -> Nothing	null

22 August 2025