val spark = SparkSession.builder.master("local").appName("my-spark-app").getOrCreate()
val df = spark.read.option("header",true).option("inferSchema",true).csv("C:\gg.csv").cache()

12,13,14
11,10,5
3,2,45

define,col1,col2,col3
c1,12,13,14
c2,11,10,5
c3,3,2,45

df.withColumn("columnName", column) //here "columnName" should be "define" for you

import org.apache.spark.sql.SparkSession
    import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
    import org.apache.spark.sql.Row


    //First off the dataframe needs to be loaded with the expected schema

    val spark = SparkSession.builder().appName().getOrCreate()


    val schema = new StructType()
      .add("col1",IntegerType,true)
      .add("col2",IntegerType,true)
      .add("col3",IntegerType,true)

    val df = spark.read.format("csv").schema(schema).load("C:\gg.csv").cache()

    val rddWithId = df.rdd.zipWithIndex

    // Prepend "define" column of type Long
    val newSchema = StructType(Array(StructField("define", StringType, false))         ++ df.schema.fields)

    val dfZippedWithId =  spark.createDataFrame(rddWithId.map{
      case (row, index) =>
        Row.fromSeq(Array("c" + index) ++ row.toSeq)}, newSchema)
    // Show results
    dfZippedWithId.show

+------+----+----+----+
|define|col1|col2|col3|
+------+----+----+----+
|    c0|  12|  13|  14|
|    c1|  11|  10|   5|
|    c2|   3|   2|  45|
+------+----+----+----+