How to freeze and transpose the column in scala spark

Question

I have this dataframe with a single row, with values that are string, int, bool, array:

col1	col2	col3	col4	col5
val1	val2	val3	val4	val5

And I want to transpose it like this:

col1	col2	Columns	values
val1	val2	col3	val3
val1	val2	col4	val4
val1	val2	col5	val5

The first 2 columns are to be AS IS, and the remaining columns need to be unpivoted.

Answer 1

You can use the melt function to unpivot the dataframe:

def melt(df: DataFrame,
        idVars: Array[String],
        valueVars: Array[String],
        varName: String = "Columns",
        valueName: String = "values"): DataFrame = {

val columns = valueVars.map(c => Array(lit(c), col(c))).flatten
val varsAndVals = map(columns: _*)
df.select(idVars.map(col(_)).:+(explode(varsAndVals)): _*)
  .withColumnRenamed("key", varName)
  .withColumnRenamed("value", valueName)
}

then call ii:

melt(df, Array("col1", "col2"), Array("col3", "col4", "col5")).show()

gives:

+----+----+-------+------+
|col1|col2|Columns|values|
+----+----+-------+------+
|val1|val2|   col3|  val3|
|val1|val2|   col4|  val4|
|val1|val2|   col5|  val5|
+----+----+-------+------+