Spark Scala replace Dataframe blank records to "0"

Empty string can't be converted to Integer, you need to make the Score nullable so that if the field is missing, it is represented as null, you can try the following:

import scala.util.{Try, Success, Failure}

1) Define a customized parse function which returns None, if the string can't be converted to an Int, in your case empty string;

def parseScore(s: String): Option[Int] = {
  Try(s.toInt) match {
    case Success(x) => Some(x)
    case Failure(x) => None
  }
}

2) Define the score field in your case class to be an Option[Int] type;

case class CInspections (business_id:Int, score: Option[Int], date:String, type1:String)

val raw_inspections = sc.textFile("test.csv")
val raw_inspectionsmap = raw_inspections.map(line => line.split("\t"))

3) Use the customized parseScore function to parse the score field;

val raw_inspectionsRDD = raw_inspectionsmap.map(raw_inspections => 
    CInspections(raw_inspections(0).toInt, parseScore(raw_inspections(1)), 
                 raw_inspections(2),raw_inspections(3)))

val raw_inspectionsDF = raw_inspectionsRDD.toDF
raw_inspectionsDF.createOrReplaceTempView ("Inspections")

raw_inspectionsDF.printSchema
//root
// |-- business_id: integer (nullable = false)
// |-- score: integer (nullable = true)
// |-- date: string (nullable = true)
// |-- type1: string (nullable = true)

raw_inspectionsDF.show()

+-----------+-----+----+-----+
|business_id|score|date|type1|
+-----------+-----+----+-----+
|          1| null|   a|    b|
|          2|    3|   s|    k|
+-----------+-----+----+-----+

4) After parsing the file correctly, you can easily replace null value with 0 using na functions fill:

raw_inspectionsDF.na.fill(0).show
+-----------+-----+----+-----+
|business_id|score|date|type1|
+-----------+-----+----+-----+
|          1|    0|   a|    b|
|          2|    3|   s|    k|
+-----------+-----+----+-----+