scala save to array with format

Question

I have two vals:

// 0~250::250~500::500~750::750~1000
val step_x_ranges = stepsAsString.mkString("::")

// 1::22::7::16
val step_y = histogram_final.map{case (x , y) => y}.mkString("::")

And I want to save it to an Array like:

0~250::1
250~500::22
500~750::7
750::1000::16

But still not try out: Please help me

Here is my wrong code:

var toArray : Array[String] = Array()
   for (i <- 0 to 3)
   {
     val ArrayRow = step_x_ranges(i) + "::" + step_y(i)
     toArray = toArray :+ ArrayRow
   }
   println(toArray(0)) 
   println(toArray(1))

Result:

0::4
~::8

Do you want to zip these Seqs? safaribooksonline.com/library/view/scala-cookbook/9781449340292/… : stepsAsString.zip(histogramFinal).map(... — Dmitry Ginzburg
– Dmitry Ginzburg, Commented Apr 24, 2015 at 9:26

Nikita · Accepted Answer · 2015-04-24 09:42:34Z

3

Use zip:

scala> val step_x_ranges = Array("0~250", "250~500", "500~750","750~1000")
step_x_ranges: Array[String] = Array(0~250, 250~500, 500~750, 750~1000)

scala> val step_y = Array(1, 22, 7, 16)
step_y: Array[Int] = Array(1, 22, 7, 16)

scala> val result = step_x_ranges.zip(step_y).map{case (x, y) => s"$x::$y"}
res1: Array[String] = Array(0~250::1, 250~500::22, 500~750::7, 750~1000::16)
scala> scala.tools.nsc.io.File("test.txt").writeAll(result.mkString("\n"))

And test.txt:

0~250::1
250~500::22
500~750::7
750~1000::16

edited Apr 24, 2015 at 9:42

answered Apr 24, 2015 at 9:27

Nikita

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5 Comments

user2492364 Over a year ago

You are right! I get Vector(0~250::1, 250~500::22, 500~750::7, 750~1000::16) But how can I save this to a txt file ? ```` 0~250::1 250~500::22 500~750::7 750::1000::16````

Nikita Over a year ago

I added saving to file to my answer. Use` mkString(" ")` if you need saving in one row.

user2492364 Over a year ago

I met an error :Exception in thread "main" java.lang.NoClassDefFoundError: scala/reflect/io/AbstractFile$ Do you know this?

The Archetypal Paul Over a year ago

@user2492364, scala or Spark version mismatch, maybe?

Nikita Over a year ago

@Paul has a good idea. IIn addition if you use spark and your data is rdd, then you should use result.saveAsTextFile("pathToFile"). Anyway there is not enough information about your problem, you would better create another example with your configs.

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