Multiple-columns operations in Spark - scala

Question

Using Python's Pandas, one can do bulk operations on multiple columns in one pass like this:
# assuming we have a DataFrame with, among others, the following columns
cols = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8']
df[cols] = df[cols] / df['another_column']
Is there a similar functionality using Spark in Scala?
Currently I end up doing:
val df2 = df.withColumn("col1", $"col1" / $"another_column")
.withColumn("col2", $"col2" / $"another_column")
.withColumn("col3", $"col3" / $"another_column")
.withColumn("col4", $"col4" / $"another_column")
.withColumn("col5", $"col5" / $"another_column")
.withColumn("col6", $"col6" / $"another_column")
.withColumn("col7", $"col7" / $"another_column")
.withColumn("col8", $"col8" / $"another_column")

Answer 1

You can use foldLeft to process the column list as below:
val df = Seq(
(1, 20, 30, 4),
(2, 30, 40, 5),
(3, 10, 30, 2)
).toDF("id", "col1", "col2", "another_column")
val cols = Array("col1", "col2")
val df2 = cols.foldLeft( df )( (acc, c) =>
acc.withColumn( c, df(c) / df("another_column") )
)
df2.show
+---+----+----+--------------+
| id|col1|col2|another_column|
+---+----+----+--------------+
| 1| 5.0| 7.5| 4|
| 2| 6.0| 8.0| 5|
| 3| 5.0|15.0| 2|
+---+----+----+--------------+

Answer 2

For completeness: a slightly different version from #Leo C's, not using foldLeft but a single select expression instead:
import org.apache.spark.sql.functions._
import spark.implicits._
val toDivide = List("col1", "col2")
val newColumns = toDivide.map(name => col(name) / col("another_column") as name)
val df2 = df.select(($"id" :: newColumns) :+ $"another_column": _*)
Produces the same output.

Answer 3

You can use plain select on operated columns. The solution is very similar to the Python Panda solution.
//Define the dataframe df1
case class ARow(col1: Int, col2: Int, anotherCol: Int)
val df1 = spark.createDataset(Seq(
ARow(1, 2, 3),
ARow(4, 5, 6),
ARow(7, 8, 9))).toDF
// Perform the operation using a map
val cols = Array("col1", "col2")
val opCols = cols.map(c => df1(c)/df1("anotherCol"))
// Select the columns operated
val df2 = df1.select(opCols: _*)
The .show on df2
df2.show()
+-------------------+-------------------+
|(col1 / anotherCol)|(col2 / anotherCol)|
+-------------------+-------------------+
| 0.3333333333333333| 0.6666666666666666|
| 0.6666666666666666| 0.8333333333333334|
| 0.7777777777777778| 0.8888888888888888|
+-------------------+-------------------+