Summary statistics

• count

• sum

• min/max

• mean

• median

• std

• valueCounts

Every summary statistics can be used in aggregations of:

• DataFrame

• DataColumn

• GroupBy DataFrame

• Pivot

• PivotGroupBy

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

sum, mean, std are available for numeric columns of types Int, Double, Float, BigDecimal, Long, Byte.

min/max, median are available for Comparable columns.

When statistics x is applied to several columns, it can be computed in several modes:

• x(): DataRow computes separate value per every suitable column

• x { columns }: Value computes single value across all given columns

• xFor { columns }: DataRow computes separate value per every given column

• xOf { rowExpression }: Value computes single value across results of row expression evaluated for every row

min and max statistics have additional mode by:

• minBy { rowExpression }: DataRow finds a row with minimal result of expression

df.sum() // sum of values per every numeric column
df.sum { age and weight } // sum of all values in `age` and `weight`
df.sumFor { age and weight } // sum of values per `age` and `weight` separately
df.sumOf { (weight ?: 0) / age } // sum of expression evaluated for every row

df.groupBy { city }.mean { age } // [`city`, `mean`]
df.groupBy { city }.meanOf { age / 2 } // [`city`, `mean`]

df.groupBy { city }.mean("mean age") { age } // [`city`, `mean age`]
df.groupBy { city }.meanOf("custom") { age / 2 } // [`city`, `custom`]

df.groupBy { city }.meanFor { age and weight } // [`city`, `age`, `weight`]
df.groupBy { city }.mean() // [`city`, `age`, `weight`, ...]

df.groupBy { city }.pivot { name.lastName }.mean { age }
df.groupBy { city }.pivot { name.lastName }.meanOf { age / 2.0 }

val city by column<String?>()
val age by column<Int>()
val name by columnGroup()
val lastName by name.column<String>()

df.groupBy { city }.pivot { lastName }.mean { age }
df.groupBy { city }.pivot { lastName }.meanOf { age() / 2.0 }

df.groupBy("city").pivot { "name"["lastName"] }.mean("age")
df.groupBy("city").pivot { "name"["lastName"] }.meanOf { "age"<Int>() / 2.0 }

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

df.groupBy { city }.pivot { name.lastName }.meanFor(separate = true) { age and weight }
df.groupBy { city }.pivot { name.lastName }.mean(separate = true)