added stddev_pop and stddev_samp

dataset/src/main/scala/frameless/functions/AggregateFunctions.scala

@@ -137,6 +137,44 @@ trait AggregateFunctions {
  new TypedAggregate[T, Double](untyped.stddev(column.untyped))
  }
 
+ /**
+ * Aggregate function: returns the standard deviation of a column by population.
+ *
+ * @note In Spark stddev always returns Double
+ * [[https://github.com/apache/spark/blob/4a3c09601ba69f7d49d1946bb6f20f5cfe453031/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/CentralMomentAgg.scala#L137]]
+ *
+ * apache/spark
+ */
+ def stddev_pop[A, T](column: TypedColumn[T, A])(
+ implicit
+ evCanBeDoubleA: CatalystCast[A, Double]
+ ): TypedAggregate[T, Option[Double]] = {
+ implicit val c1 = column.uencoder
+
+ new TypedAggregate[T, Option[Double]](
+ untyped.stddev_pop(column.cast[Double].untyped)
+ )
+ }
+
+ /**
+ * Aggregate function: returns the standard deviation of a column by sample.
+ *
+ * @note In Spark stddev always returns Double
+ * [[https://github.com/apache/spark/blob/4a3c09601ba69f7d49d1946bb6f20f5cfe453031/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/CentralMomentAgg.scala#L154]]
+ *
+ * apache/spark
+ */
+ def stddev_samp[A, T](column: TypedColumn[T, A])(
+ implicit
+ evCanBeDoubleA: CatalystCast[A, Double]
+ ): TypedAggregate[T, Option[Double]] = {
+ implicit val c1 = column.uencoder
+
+ new TypedAggregate[T, Option[Double]](
+ untyped.stddev_samp(column.cast[Double].untyped)
+ )
+ }
+
  /** Aggregate function: returns the maximum value of the column in a group.
  *
  * apache/spark

dataset/src/test/scala/frameless/functions/AggregateFunctionsTests.scala

@@ -529,6 +529,42 @@ class AggregateFunctionsTests extends TypedDatasetSuite {
  ): Prop = univariatePropTemplate(xs)(skewness[A,X2[Int, A]],org.apache.spark.sql.functions.skewness)
 
 
+ check(forAll(prop[Double] _))
+ check(forAll(prop[Int] _))
+ check(forAll(prop[Short] _))
+ check(forAll(prop[BigDecimal] _))
+ check(forAll(prop[Byte] _))
+ }
+
+ test("stddev_pop") {
+ val spark = session
+ import spark.implicits._
+
+ def prop[A: TypedEncoder](xs: List[X2[Int, A]])(
+ implicit
+ encEv: Encoder[(Int, A)],
+ evCanBeDoubleA: CatalystCast[A, Double]
+ ): Prop = univariatePropTemplate(xs)(stddev_pop[A,X2[Int, A]],org.apache.spark.sql.functions.stddev_pop)
+
+
+ check(forAll(prop[Double] _))
+ check(forAll(prop[Int] _))
+ check(forAll(prop[Short] _))
+ check(forAll(prop[BigDecimal] _))
+ check(forAll(prop[Byte] _))
+ }
+
+ test("stddev_samp") {
+ val spark = session
+ import spark.implicits._
+
+ def prop[A: TypedEncoder](xs: List[X2[Int, A]])(
+ implicit
+ encEv: Encoder[(Int, A)],
+ evCanBeDoubleA: CatalystCast[A, Double]
+ ): Prop = univariatePropTemplate(xs)(stddev_samp[A,X2[Int, A]],org.apache.spark.sql.functions.stddev_samp)
+
+
  check(forAll(prop[Double] _))
  check(forAll(prop[Int] _))
  check(forAll(prop[Short] _))