Converting Dataframe String type to Integer

acbc32a57245:bin z002qhl$ ./spark-shell
19/10/06 12:05:28 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
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Spark context available as 'sc' (master = local[*], app id = local-1570343764090).
Spark session available as 'spark'.
Welcome to
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     / __/__  ___ _____/ /__
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   /___/ .__/\_,_/_/ /_/\_\   version 2.4.1
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Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_191)
Type in expressions to have them evaluated.
Type :help for more information.

scala> import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.IntegerType

scala> import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StringType

scala> import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StructType

scala> import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.StructField

scala> import org.apache.spark.sql.Row
import org.apache.spark.sql.Row

scala> import scala.collection.JavaConversions._
import scala.collection.JavaConversions._

scala>     val data = Seq(("Java", "20000"), ("Python", "100000"), ("Scala", "3000"))
data: Seq[(String, String)] = List((Java,20000), (Python,100000), (Scala,3000))

scala>     val tableColumns = List(("order_number", "string"), ("external_order_number", "string"))
tableColumns: List[(String, String)] = List((order_number,string), (external_order_number,string))

scala>     var schema = new StructType
schema: org.apache.spark.sql.types.StructType = StructType()

scala>     for (i <- p="" tablecolumns="">     |       schema = schema.add(i._1, i._2)

scala>       val rowData = data.map(attributes => Row(attributes._1, attributes._2))
rowData: Seq[org.apache.spark.sql.Row] = List([Java,20000], [Python,100000], [Scala,3000])

scala>       var dfFromData4 = spark.createDataFrame(rowData,schema)
dfFromData4: org.apache.spark.sql.DataFrame = [order_number: string, external_order_number: string]

scala>       dfFromData4.show(2)
+------------+---------------------+
|order_number|external_order_number|
+------------+---------------------+
|        Java|                20000|
|      Python|               100000|
+------------+---------------------+
only showing top 2 rows


scala>

scala> val dfFromData5 = dfFromData4.withColumn("external_order_number", 'external_order_number cast "int")
dfFromData5: org.apache.spark.sql.DataFrame = [order_number: string, external_order_number: int]

scala> dfFromData5.printSchema
root
 |-- order_number: string (nullable = true)
 |-- external_order_number: integer (nullable = true)
<- p="" tablecolumns="">
<- p="" tablecolumns="">
<- p="" tablecolumns="">
<- p="" tablecolumns="">
<- p="" tablecolumns="">---------
<- p="" tablecolumns="">
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.Row
import scala.collection.JavaConversions._
    val data = Seq(("Java", "20000"), ("Python", "100000"), ("Scala", "3000"))
    val tableColumns = List(("order_number", "string"), ("external_order_number", "string"))
    var schema = new StructType
    for (i <- p="" tablecolumns="">      schema = schema.add(i._1, i._2)
      val rowData = data.map(attributes => Row(attributes._1, attributes._2))
      var dfFromData4 = spark.createDataFrame(rowData,schema)
      dfFromData4.show(2)


val dfFromData6 =dfFromData4.withColumn("external_order_number", dfFromData4("external_order_number").cast("int"))
<- p="" tablecolumns="">
dfFromData6.printSchema