median

Computes the median of values.

This is also called the "middle" of a sorted list, the "50th percentile ", or the 2-quantile.

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.

All primitive numeric types are supported: Byte, Short, Int, Long, Float, and Double, but no mix of different number types. In these cases, the return type is always Double?. When the number of values is even, the median is the average of the two middle values.

The operation is also available for self-comparable columns (so columns of type T : Comparable<T>, like DateTime, String, etc.) In this case, the return type remains T?. When the number of values is even, the median is the low of the two middle values.

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.median() // median of values per every comparable column
df.median { age and weight } // median of all values in `age` and `weight`
df.medianFor(skipNaN = true) { age and weight } // median of values per `age` and `weight` separately
df.medianOf { (weight ?: 0) / age } // median of expression evaluated for every row
df.medianBy { age } // DataRow where the median age lies (lower-median for an even number of values)

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

See statistics for details on complex data aggregations.

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