Struct method
You can define the udf
function as
def myFunc: (String => (String, String)) = { s => (s.toLowerCase, s.toUpperCase)}
import org.apache.spark.sql.functions.udf
val myUDF = udf(myFunc)
and use .*
as
val newDF = df.withColumn("newCol", myUDF(df("Feature2"))).select("Feature1", "Feature2", "Feature 3", "newCol.*")
I have returned Tuple2
for testing purpose (higher order tuples can be used according to how many multiple columns are required) from udf
function and it would be treated as struct
column. Then you can use .*
to select all the elements in separate columns and finally rename them.
You should have output as
+--------+--------+---------+---+---+
|Feature1|Feature2|Feature 3|_1 |_2 |
+--------+--------+---------+---+---+
|1.3 |3.4 |4.5 |3.4|3.4|
+--------+--------+---------+---+---+
You can rename _1
and _2
Array method
udf
function should return an array
def myFunc: (String => Array[String]) = { s => Array("s".toLowerCase, s.toUpperCase)}
import org.apache.spark.sql.functions.udf
val myUDF = udf(myFunc)
And the you can select elements of the array
and use alias
to rename them
val newDF = df.withColumn("newCol", myUDF(df("Feature2"))).select($"Feature1", $"Feature2", $"Feature 3", $"newCol"(0).as("Slope"), $"newCol"(1).as("Offset"))
You should have
+--------+--------+---------+-----+------+
|Feature1|Feature2|Feature 3|Slope|Offset|
+--------+--------+---------+-----+------+
|1.3 |3.4 |4.5 |s |3.4 |
+--------+--------+---------+-----+------+
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