The equivalent of Scala mapping in Pyspark

[Mich Talebzadeh]

Hi,

I generate an array of random data and create a DF in Spark scala as follows

val end = start + numRows - 1
println (" starting at ID = " + start + " , ending on = " +  end )
val usedFunctions = new UsedFunctions

*val text = ( start to end ).map(i =>*
*             (*
*                 i.toString*
*               , usedFunctions.clustered(i,numRows).toString*
*               , usedFunctions.scattered(i,numRows).toString*
*               , usedFunctions.randomised(i,numRows).toString*
*               , usedFunctions.randomString(chars.mkString(""),50)*
*               , usedFunctions.padString(i, " ", 50)*
*               , usedFunctions.padSingleChar("x ", 4000)*
*             )*
*           ).*
*    toArray*

then I create a DF
val df = sc.parallelize(text).
                              map(p => columns(
                                                  p._1.toString.toInt
                                                , p._2.toString.toDouble
                                                , p._3.toString.toDouble
                                                , p._4.toString.toDouble
                                                , p._5.toString
                                                , p._6.toString
                                                , p._7.toString
                                              )
                                 ).
    toDF


What is the equivalent of this in Pyspark, especially the first part val
text = ..


Thanks


Mich


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