How to “negative select” columns in spark's dataframe

Question

I can't figure it out, but guess it's simple. I have a spark dataframe df. This df has columns "A","B" and "C". Now let's say I have an Array containing the name of the columns of this df:

column_names = Array("A","B","C")

I'd like to do a df.select() in such a way, that I can specify which columns not to select. Example: let's say I do not want to select columns "B". I tried

df.select(column_names.filter(_!="B"))

but this does not work, as it gives

org.apache.spark.sql.DataFrame cannot be applied to (Array[String])

Answer 1

Since Spark 1.4 you can use drop method:

For Scala:

case class Point(x: Int, y: Int)

val df = sqlContext.createDataFrame(Point(0, 0) :: Point(1, 2) :: Nil)

df.drop("y")

For PySpark:

df = sc.parallelize([(0, 0), (1, 2)]).toDF(["x", "y"])

df.drop("y")

## DataFrame[x: bigint]