How to "negative select" columns in spark's dataframe

Since Spark 1.4 you can use drop method:

Scala:

case class Point(x: Int, y: Int)
val df = sqlContext.createDataFrame(Point(0, 0) :: Point(1, 2) :: Nil)
df.drop("y")

Python:

df = sc.parallelize([(0, 0), (1, 2)]).toDF(["x", "y"])
df.drop("y")
## DataFrame[x: bigint]

I had the same problem and solved it this way (oaffdf is a dataframe):

val dropColNames = Seq("col7","col121")
val featColNames = oaffdf.columns.diff(dropColNames)
val featCols = featColNames.map(cn => org.apache.spark.sql.functions.col(cn))
val featsdf = oaffdf.select(featCols: _*)

https://forums.databricks.com/questions/2808/select-dataframe-columns-from-a-sequence-of-string.html

OK, it's ugly, but this quick spark shell session shows something that works:

scala> val myRDD = sc.parallelize(List.range(1,10))
myRDD: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[17] at parallelize at <console>:21

scala> val myDF = myRDD.toDF("a")
myDF: org.apache.spark.sql.DataFrame = [a: int]

scala> val myOtherRDD = sc.parallelize(List.range(1,10))
myOtherRDD: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[20] at parallelize at <console>:21

scala> val myotherDF = myRDD.toDF("b")
myotherDF: org.apache.spark.sql.DataFrame = [b: int]

scala> myDF.unionAll(myotherDF)
res2: org.apache.spark.sql.DataFrame = [a: int]

scala> myDF.join(myotherDF)
res3: org.apache.spark.sql.DataFrame = [a: int, b: int]

scala> val twocol = myDF.join(myotherDF)
twocol: org.apache.spark.sql.DataFrame = [a: int, b: int]

scala> val cols = Array("a", "b")
cols: Array[String] = Array(a, b)

scala> val selectedCols = cols.filter(_!="b")
selectedCols: Array[String] = Array(a)

scala> twocol.select(selectedCols.head, selectedCols.tail: _*)
res4: org.apache.spark.sql.DataFrame = [a: int]

Providings varargs to a function that requires one is treated in other SO questions. The signature of select is there to ensure your list of selected columns is not empty – which makes the conversion from the list of selected columns to varargs a bit more complex.