Scala/Spark dataframes: find the column name corresponding to the max

Question

In Scala/Spark, having a dataframe:

val dfIn = sqlContext.createDataFrame(Seq(
  ("r0", 0, 2, 3),
  ("r1", 1, 0, 0),
  ("r2", 0, 2, 2))).toDF("id", "c0", "c1", "c2")

I would like to compute a new column maxCol holding the name of the column corresponding to the max value (for each row). With this example, the output should be:

+---+---+---+---+------+
| id| c0| c1| c2|maxCol|
+---+---+---+---+------+
| r0|  0|  2|  3|    c2|
| r1|  1|  0|  0|    c0|
| r2|  0|  2|  2|    c1|
+---+---+---+---+------+

Actually the dataframe have more than 60 columns. Thus a generic solution is required.

The equivalent in Python Pandas (yes, I know, I should compare with pyspark...) could be:

dfOut = pd.concat([dfIn, dfIn.idxmax(axis=1).rename('maxCol')], axis=1) 

Answer 1

With a small trick you can use greatest function. Required imports:

import org.apache.spark.sql.functions.{col, greatest, lit, struct}

First let's create a list of structs, where the first element is value, and the second one column name:

val structs = dfIn.columns.tail.map(
  c => struct(col(c).as("v"), lit(c).as("k"))
)

Structure like this can be passed to greatest as follows:

dfIn.withColumn("maxCol", greatest(structs: _*).getItem("k"))

+---+---+---+---+------+
| id| c0| c1| c2|maxCol|
+---+---+---+---+------+
| r0|  0|  2|  3|    c2|
| r1|  1|  0|  0|    c0|
| r2|  0|  2|  2|    c2|
+---+---+---+---+------+

Please note that in case of ties it will take the element which occurs later in the sequence (lexicographically (x, "c2") > (x, "c1")). If for some reason this is not acceptable you can explicitly reduce with when:

import org.apache.spark.sql.functions.when

val max_col = structs.reduce(
  (c1, c2) => when(c1.getItem("v") >= c2.getItem("v"), c1).otherwise(c2)
).getItem("k")

dfIn.withColumn("maxCol", max_col)

+---+---+---+---+------+
| id| c0| c1| c2|maxCol|
+---+---+---+---+------+
| r0|  0|  2|  3|    c2|
| r1|  1|  0|  0|    c0|
| r2|  0|  2|  2|    c1|
+---+---+---+---+------+

In case of nullable columns you have to adjust this, for example by coalescing to values to -Inf.