spark data frame operation row and column level useing scala

Question

Original Data frame
0.2 0.3

+------+------------- -+
|  name| country |
+------+---------------+
|Raju  |UAS         |
|Ram  |Pak.         |
|nul         


        

Answer 1

Using StringUtils of apache:

val transcodificationName: UserDefinedFunction =
    udf { (name: String) => {
        if (StringUtils.isBlank(name)) 0.0
        else 0.2
        }
    }
val transcodificationCountry: UserDefinedFunction =
    udf { (country: String) => {
        if (StringUtils.isBlank(country)) 0.0
        else 0.3
        }
    }

dataframe
    .withColumn("Nwet", transcodificationName(col("name"))).cast(DoubleType)
    .withColumn("wetCon", transcodificationCountry(col("country"))).cast(DoubleType)
    .select("Nwet", "wetcon")

edit:

val transcodificationColumns: UserDefinedFunction =
        udf { (input: String, columnName:String) => {
                if (StringUtils.isBlank(country)) 0.0
                else if(columnName.equals("name")) 0.2
                else if(columnName.equals("country") 0.3
                else 0.0
            }
        }


    dataframe
        .withColumn("Nwet", transcodificationColumns(col("name"), "name")).cast(DoubleType)
        .withColumn("wetCon", transcodificationColumns(col("country")), "country").cast(DoubleType)
        .select("Nwet", "wetcon")