Fill Nan with mean of the row in Scala-Spark - scala

Question

I have an RDD with 6 columns, where the last 5 columns might contain NaNs. My intention is to replace the NaNs with the average value of the rest of the last 5 values of the row which are not Nan. For instance, having this input:
1, 2, 3, 4, 5, 6
2, 2, 2, NaN, 4, 0
3, NaN, NaN, NaN, 6, 0
4, NaN, NaN, 4, 4, 0
The output should be:
1, 2, 3, 4, 5, 6
2, 2, 2, 2, 4, 0
3, 3, 3, 3, 6, 0
4, 3, 3, 4, 4, 0
I know how to fill those NaNs with the average value of the column transforming the RDD to DataFrame:
var aux1 = df.select(df.columns.map(c => mean(col(c))) :_*)
var aux2 = df.na.fill(/*get values of aux1*/)
My question is, how can you do this operation but instead of filling the NaN with the column average, fill it with an average of the values of a subgroup of the row?

Answer 1

You can do this by defining a function to get the mean, and another function to fill nulls in a row.
Given the DF you presented:
val df = sc.parallelize(List((Some(1),Some(2),Some(3),Some(4),Some(5),Some(6)),(Some(2),Some(2),Some(2),None,Some(4),Some(0)),(Some(3),None,None,None,Some(6),Some(0)),(Some(4),None,None,Some(4),Some(4),Some(0)))).toDF("a","b","c","d","e","f")
We need a function to get the mean of a Row:
import org.apache.spark.sql.Row
def rowMean(row: Row): Int = {
val nonNulls = (0 until row.length).map(i => (!row.isNullAt(i), row.getAs[Int](i))).filter(_._1).map(_._2).toList
nonNulls.sum / nonNulls.length
}
And another to fill nulls in a Row:
def rowFillNulls(row: Row, fill: Int): Row = {
Row((0 until row.length).map(i => if (row.isNullAt(i)) fill else row.getAs[Int](i)) : _*)
}
Now we can first compute each row mean:
val rowWithMean = df.map(row => (row,rowMean(row)))
And then fill it:
val result = sqlContext.createDataFrame(rowWithMean.map{case (row,mean) => rowFillNulls(row,mean)}, df.schema)
Finally view before and after...
df.show
+---+----+----+----+---+---+
| a| b| c| d| e| f|
+---+----+----+----+---+---+
| 1| 2| 3| 4| 5| 6|
| 2| 2| 2|null| 4| 0|
| 3|null|null|null| 6| 0|
| 4|null|null| 4| 4| 0|
+---+----+----+----+---+---+
result.show
+---+---+---+---+---+---+
| a| b| c| d| e| f|
+---+---+---+---+---+---+
| 1| 2| 3| 4| 5| 6|
| 2| 2| 2| 2| 4| 0|
| 3| 3| 3| 3| 6| 0|
| 4| 3| 3| 4| 4| 0|
+---+---+---+---+---+---+
This will work for any width DF with Int columns. You can easily update this to other datatypes, even non-numeric (hint, inspect the df schema!)

Answer 2

A bunch of imports:
import org.apache.spark.sql.functions.{col, isnan, isnull, round, when}
import org.apache.spark.sql.Column
A few helper functions:
def nullOrNan(c: Column) = isnan(c) || isnull(c)
def rowMean(cols: Column*): Column = {
val sum = cols
.map(c => when(nullOrNan(c), lit(0.0)).otherwise(c))
.fold(lit(0.0))(_ + _)
val count = cols
.map(c => when(nullOrNan(c), lit(0.0)).otherwise(lit(1.0)))
.fold(lit(0.0))(_ + _)
sum / count
}
A solution:
val mean = round(
rowMean(df.columns.tail.map(col): _*)
).cast("int").alias("mean")
val exprs = df.columns.tail.map(
c => when(nullOrNan(col(c)), mean).otherwise(col(c)).alias(c)
)
val filled = df.select(col(df.columns(0)) +: exprs: _*)

Answer 3

Well, this is a fun little problem - I will post my solution, but I will definitely watch and see if someone comes up with a better way of doing it :)
First I would introduce a couple of udfs:
val avg = udf((values: Seq[Integer]) => {
val notNullValues = values.filter(_ != null).map(_.toInt)
notNullValues.sum/notNullValues.length
})
val replaceNullWithAvg = udf((x: Integer, avg: Integer) => if(x == null) avg else x)
which I would then apply to the DataFrame like this:
dataframe
.withColumn("avg", avg(array(df.columns.tail.map(s => df.col(s)):_*)))
.select('col1, replaceNullWithAvg('col2, 'avg) as "col2", replaceNullWithAvg('col3, 'avg) as "col3", replaceNullWithAvg('col4, 'avg) as "col4", replaceNullWithAvg('col5, 'avg) as "col5", replaceNullWithAvg('col6, 'avg) as "col6")
This will get you what you are looking for, but arguably not the most sophisticated code I have ever put together...