I have the following issue I have the following list as input
val input:List[Item]=List(FRDVE,12
SDED,13
prog-d,11
PROG-D,15
a-prog-d,17)
with
case class Item(Name:String,Number:Int)
The aim is to find only first line where name contains either prog-d or PROG-D
so for this case the expected output is:
val output="prog-d"
I wrote the following code :
def getName(input:List[Item]):Option[String]={
val matchLine=input.filter(Name.contains("prog-d"))
matchLine match {
case head::tail => Some(matchLine.head.split(",")(0))
case isEmpty => None
}
}
so here I am getting an error saying that the Name doesn't exist and I don't know how to put different possibilities in the contains : here it should basically be : Name.contains("prog-d" ||"PROG-D")
Any recommendations please
Thanks a lot
You can use collectFirst:
input.collectFirst { case Item(s, _) if s.equalsIgnoreCase("prog-d") => s }
This avoids both map and filter, so that only the minimal necessary amount of entries is inspected.
Full code:
case class Item(name: String, number: Int)
val input: List[Item] = List(
Item("FRDVE", 12),
Item("SDED", 13),
Item("prog-d", 11),
Item("PROG-D", 15),
Item("a-prog-d", 17),
)
val output = input.collectFirst {
case Item(s, _) if s.equalsIgnoreCase("prog-d") => s
}
println(output)
You can also use find where this function returns the option of first elements whichever matches your condition (stops iterating on remaining elements).
case class Item(Name:String,Number:Int)
val input = List(Item("FRDVE",12), Item("SDED",13), Item("prog-d",11), Item("PROG-D",15), Item("a-prog-d",17))
input.find(_.Name.equalsIgnoreCase("prog-d")) match {
case Some(item) => item.Name
case None => "" //your default string
}
you can use item.Name == "prog-d" || item.Name == "PROG-D" or item.Name.equalsIgnoreCase("prog-d")
scala> val input = List(Item("FRDVE",12), Item("SDED",13), Item("prog-d",11), Item("PROG-D",15), Item("a-prog-d",17))
input: List[Item] = List(Item(FRDVE,12), Item(SDED,13), Item(prog-d,11), Item(PROG-D,15), Item(a-prog-d,17))
scala> input.filter(item => item.Name.equalsIgnoreCase("prog-d")).map(_.Name)
res1: List[String] = List(prog-d, PROG-D)
If you want the first match, do headOption and play with it based on what data you want.
scala> val output = input.filter(item => item.Name.equalsIgnoreCase("prog-d")).headOption
output: Option[Item] = Some(Item(prog-d,11))
scala> val outputName = input.filter(item => item.Name.equalsIgnoreCase("prog-d")).headOption.map(_.Name)
outputName: Option[String] = Some(prog-d)
NOTE: (.head is not safe to use because List().head will explode when list is empty)
scala> List.empty[Item].head
java.util.NoSuchElementException: head of empty list
at scala.collection.immutable.Nil$.head(List.scala:428)
at scala.collection.immutable.Nil$.head(List.scala:425)
... 28 elided
Related
I have a few vals that match for matching values
Here is an example:
val job_ = Try(jobId.toInt) match {
case Success(value) => jobs.findById(value).map(_.id)
.getOrElse( Left(WrongValue("jobId", s"$value is not a valid job id")))
case Failure(_) => jobs.findByName(jobId.toString).map(_.id)
.getOrElse( Left(WrongValue("jobId", s"'$jobId' is not a known job title.")))
}
// Here the value arrives as a string e.i "yes || no || true || or false" then converted to a boolean
val bool_ = bool.toLowerCase() match {
case "yes" => true
case "no" => false
case "true" => true
case "false" => false
case other => Left(Invalid("bool", s"wrong value received"))
}
Note: invalid case is case class Invalid(x: String, xx: String)
above i'm looking for a given job value and checking whether it exist in the db or not,
No I have a few of these and want to add to a list, here is my list val and flatten it:
val errors = List(..all my vals errors...).flatten // <--- my_list_val (how do I include val bool_ and val job_)
if (errors.isEmpty) { do stuff }
My result should contain errors from val bool_ and val job_
THANK!
You need to fix the types first. The type of bool_ is Any. Which does not give you something you can work with.
If you want to use Either, you need to use it everwhere.
Then, the easiest approach would be to use a for comprehension (I am assuming you're dealing with Either[F, T] here, where WrongValue and Invalid are both sub-classes of F and you're not really interested in the errors).
for {
foundJob <- job_
_ <- bool_
} yield {
// do stuff
}
Note, that in Scala >= 2.13 you can use toIntOption when converting the String to Int:
vaj job_: Either[F, T] = jobId.toIntOption match {
case Some(value) => ...
case _ => ...
}
Also, in case expressions, you can use alternatives when you have the same statement for several cases:
val bool_: Either[F, Boolean] = bool.toLowerCase() match {
case "yes" | "true" => Right(true)
case "no" | "false" => Right(false)
case other => Left(Invalid("bool", "wrong value received"))
}
So, according to your question, and your comments, these are the types you're dealing with.
type ID = Long //whatever id is
def WrongValue(x: String, xx: String) :String = "?-?-?"
case class Invalid(x: String, xx: String)
Now let's create a couple of error values.
val job_ :Either[String,ID] = Left(WrongValue("x","xx"))
val bool_ :Either[Invalid,Boolean] = Left(Invalid("x","xx"))
To combine and report them you might do something like this.
val errors :List[String] =
List(job_, bool_).flatMap(_.swap.toOption.map(_.toString))
println(errors.mkString(" & "))
//?-?-? & Invalid(x,xx)
After checking types as #cbley explained. You can just do a filter operation with pattern matching on your list:
val error = List(// your variables ).filter(_ match{
case Left(_) => true
case _ => false
})
I have some Seq[Strings] that I need to convert to Option[Seq[Long]]. I'm using the following:
val idsAsLongsUDF: Seq[String] => Option[Seq[Long]] = {
idsSeqStr: Seq[String] =>
import scala.util.Try
Try(idsSeqStr.flatMap(id => Try(id.toLong).toOption)).toOption match {
case Some(List()) => None: Option[Seq[Long]]
case x => x
}
}
The catch is that an empty list returns as Some(List()) rather than None - which is correct but not desired. So the last few lines fix the empty list issue, by returning None when empty list is found.
I would like to get Option[List[Long]] returns suggested by this trial data:
val s = Seq("abc", null) // None
val n : Seq[String] = null // None
val l = Seq("a", "1", "2") // Some(List(1,2))
Is there a more elegant or efficient means than what I have written?
There's probably more than one way to skin this cat, and elegant is in the eye of the beholder, but here's how I would write it:
val idsAsLongsUDF: Seq[String] => Option[Seq[Long]] = { idsSeqStr =>
import scala.util.Try
Option(idsSeqStr)
.map(_.flatMap(id => Try(id.toLong).toOption))
.filterNot(_.isEmpty)
}
There are many nice libraries for writing/reading Scala case classes to/from CSV files. I'm looking for something that goes beyond that, which can handle nested cases classes. For example, here a Match has two Players:
case class Player(name: String, ranking: Int)
case class Match(place: String, winner: Player, loser: Player)
val matches = List(
Match("London", Player("Jane",7), Player("Fred",23)),
Match("Rome", Player("Marco",19), Player("Giulia",3)),
Match("Paris", Player("Isabelle",2), Player("Julien",5))
)
I'd like to effortlessly (no boilerplate!) write/read matches to/from this CSV:
place,winner.name,winner.ranking,loser.name,loser.ranking
London,Jane,7,Fred,23
Rome,Marco,19,Giulia,3
Paris,Isabelle,2,Julien,5
Note the automated header line using the dot "." to form the column name for a nested field, e.g. winner.ranking. I'd be delighted if someone could demonstrate a simple way to do this (say, using reflection or Shapeless).
[Motivation. During data analysis it's convenient to have a flat CSV to play around with, for sorting, filtering, etc., even when case classes are nested. And it would be nice if you could load nested case classes back from such files.]
Since a case-class is a Product, getting the values of the various fields is relatively easy. Getting the names of the fields/columns does require using Java reflection.
The following function takes a list of case-class instances and returns a list of rows, each is a list of strings. It is using a recursion to get the values and headers of child case-class instances.
def toCsv(p: List[Product]): List[List[String]] = {
def header(c: Class[_], prefix: String = ""): List[String] = {
c.getDeclaredFields.toList.flatMap { field =>
val name = prefix + field.getName
if (classOf[Product].isAssignableFrom(field.getType)) header(field.getType, name + ".")
else List(name)
}
}
def flatten(p: Product): List[String] =
p.productIterator.flatMap {
case p: Product => flatten(p)
case v: Any => List(v.toString)
}.toList
header(classOf[Match]) :: p.map(flatten)
}
However, constructing case-classes from CSV is far more involved, requiring to use reflection for getting the types of the various fields, for creating the values from the CSV strings and for constructing the case-class instances.
For simplicity (not saying the code is simple, just so it won't be further complicated), I assume that the order of columns in the CSV is the same as if the file was produced by the toCsv(...) function above.
The following function starts by creating a list of "instructions how to process a single CSV row" (the instructions are also used to verify that the column headers in the CSV matches the the case-class properties). The instructions are then used to recursively produce one CSV row at a time.
def fromCsv[T <: Product](csv: List[List[String]])(implicit tag: ClassTag[T]): List[T] = {
trait Instruction {
val name: String
val header = true
}
case class BeginCaseClassField(name: String, clazz: Class[_]) extends Instruction {
override val header = false
}
case class EndCaseClassField(name: String) extends Instruction {
override val header = false
}
case class IntField(name: String) extends Instruction
case class StringField(name: String) extends Instruction
case class DoubleField(name: String) extends Instruction
def scan(c: Class[_], prefix: String = ""): List[Instruction] = {
c.getDeclaredFields.toList.flatMap { field =>
val name = prefix + field.getName
val fType = field.getType
if (fType == classOf[Int]) List(IntField(name))
else if (fType == classOf[Double]) List(DoubleField(name))
else if (fType == classOf[String]) List(StringField(name))
else if (classOf[Product].isAssignableFrom(fType)) BeginCaseClassField(name, fType) :: scan(fType, name + ".")
else throw new IllegalArgumentException(s"Unsupported field type: $fType")
} :+ EndCaseClassField(prefix)
}
def produce(instructions: List[Instruction], row: List[String], argAccumulator: List[Any]): (List[Instruction], List[String], List[Any]) = instructions match {
case IntField(_) :: tail => produce(tail, row.drop(1), argAccumulator :+ row.head.toString.toInt)
case StringField(_) :: tail => produce(tail, row.drop(1), argAccumulator :+ row.head.toString)
case DoubleField(_) :: tail => produce(tail, row.drop(1), argAccumulator :+ row.head.toString.toDouble)
case BeginCaseClassField(_, clazz) :: tail =>
val (instructionRemaining, rowRemaining, constructorArgs) = produce(tail, row, List.empty)
val newCaseClass = clazz.getConstructors.head.newInstance(constructorArgs.map(_.asInstanceOf[AnyRef]): _*)
produce(instructionRemaining, rowRemaining, argAccumulator :+ newCaseClass)
case EndCaseClassField(_) :: tail => (tail, row, argAccumulator)
case Nil if row.isEmpty => (Nil, Nil, argAccumulator)
case Nil => throw new IllegalArgumentException("Not all values from CSV row were used")
}
val instructions = BeginCaseClassField(".", tag.runtimeClass) :: scan(tag.runtimeClass)
assert(csv.head == instructions.filter(_.header).map(_.name), "CSV header doesn't match target case-class fields")
csv.drop(1).map(row => produce(instructions, row, List.empty)._3.head.asInstanceOf[T])
}
I've tested this using:
case class Player(name: String, ranking: Int, price: Double)
case class Match(place: String, winner: Player, loser: Player)
val matches = List(
Match("London", Player("Jane", 7, 12.5), Player("Fred", 23, 11.1)),
Match("Rome", Player("Marco", 19, 13.54), Player("Giulia", 3, 41.8)),
Match("Paris", Player("Isabelle", 2, 31.7), Player("Julien", 5, 16.8))
)
val csv = toCsv(matches)
val matchesFromCsv = fromCsv[Match](csv)
assert(matches == matchesFromCsv)
Obviously this should be optimized and hardened if you ever want to use this for production...
I have a Seq[String] in Scala, and if the Seq contains certain Strings, I append a relevant message to another list.
Is there a more 'scalaesque' way to do this, rather than a series of if statements appending to a list like I have below?
val result = new ListBuffer[Err]()
val malformedParamNames = // A Seq[String]
if (malformedParamNames.contains("$top")) result += IntegerMustBePositive("$top")
if (malformedParamNames.contains("$skip")) result += IntegerMustBePositive("$skip")
if (malformedParamNames.contains("modifiedDate")) result += FormatInvalid("modifiedDate", "yyyy-MM-dd")
...
result.toList
If you want to use some scala iterables sugar I would use
sealed trait Err
case class IntegerMustBePositive(msg: String) extends Err
case class FormatInvalid(msg: String, format: String) extends Err
val malformedParamNames = Seq[String]("$top", "aa", "$skip", "ccc", "ddd", "modifiedDate")
val result = malformedParamNames.map { v =>
v match {
case "$top" => Some(IntegerMustBePositive("$top"))
case "$skip" => Some(IntegerMustBePositive("$skip"))
case "modifiedDate" => Some(FormatInvalid("modifiedDate", "yyyy-MM-dd"))
case _ => None
}
}.flatten
result.toList
Be warn if you ask for scala-esque way of doing things there are many possibilities.
The map function combined with flatten can be simplified by using flatmap
sealed trait Err
case class IntegerMustBePositive(msg: String) extends Err
case class FormatInvalid(msg: String, format: String) extends Err
val malformedParamNames = Seq[String]("$top", "aa", "$skip", "ccc", "ddd", "modifiedDate")
val result = malformedParamNames.flatMap {
case "$top" => Some(IntegerMustBePositive("$top"))
case "$skip" => Some(IntegerMustBePositive("$skip"))
case "modifiedDate" => Some(FormatInvalid("modifiedDate", "yyyy-MM-dd"))
case _ => None
}
result
Most 'scalesque' version I can think of while keeping it readable would be:
val map = scala.collection.immutable.ListMap(
"$top" -> IntegerMustBePositive("$top"),
"$skip" -> IntegerMustBePositive("$skip"),
"modifiedDate" -> FormatInvalid("modifiedDate", "yyyy-MM-dd"))
val result = for {
(k,v) <- map
if malformedParamNames contains k
} yield v
//or
val result2 = map.filterKeys(malformedParamNames.contains).values.toList
Benoit's is probably the most scala-esque way of doing it, but depending on who's going to be reading the code later, you might want a different approach.
// Some type definitions omitted
val malformations = Seq[(String, Err)](
("$top", IntegerMustBePositive("$top")),
("$skip", IntegerMustBePositive("$skip")),
("modifiedDate", FormatInvalid("modifiedDate", "yyyy-MM-dd")
)
If you need a list and the order is siginificant:
val result = (malformations.foldLeft(List.empty[Err]) { (acc, pair) =>
if (malformedParamNames.contains(pair._1)) {
pair._2 ++: acc // prepend to list for faster performance
} else acc
}).reverse // and reverse since we were prepending
If the order isn't significant (although if the order's not significant, you might consider wanting a Set instead of a List):
val result = (malformations.foldLeft(Set.empty[Err]) { (acc, pair) =>
if (malformedParamNames.contains(pair._1)) {
acc ++ pair._2
} else acc
}).toList // omit the .toList if you're OK with just a Set
If the predicates in the repeated ifs are more complex/less uniform, then the type for malformations might need to change, as they would if the responses changed, but the basic pattern is very flexible.
In this solution we define a list of mappings that take your IF condition and THEN statement in pairs and we iterate over the inputted list and apply the changes where they match.
// IF THEN
case class Operation(matcher :String, action :String)
def processInput(input :List[String]) :List[String] = {
val operations = List(
Operation("$top", "integer must be positive"),
Operation("$skip", "skip value"),
Operation("$modify", "modify the date")
)
input.flatMap { in =>
operations.find(_.matcher == in).map { _.action }
}
}
println(processInput(List("$skip","$modify", "$skip")));
A breakdown
operations.find(_.matcher == in) // find an operation in our
// list matching the input we are
// checking. Returns Some or None
.map { _.action } // if some, replace input with action
// if none, do nothing
input.flatMap { in => // inputs are processed, converted
// to some(action) or none and the
// flatten removes the some/none
// returning just the strings.
My list looks like the following: List(Person,Invite,Invite,Person,Invite,Person...). I am trying to match based on a inviteCountRequired, meaning that the Invite objects following the Person object in the list is variable. What is the best way of doing this? My match code so far looks like this:
aList match {
case List(Person(_,_,_),Invitee(_,_,_),_*) => ...
case _ => ...
}
First stack question, please go easy on me.
Let
val aList = List(Person(1), Invite(2), Invite(3), Person(2), Invite(4), Person(3), Invite(6), Invite(7))
Then index each location in the list and select Person instances,
val persons = (aList zip Stream.from(0)).filter {_._1.isInstanceOf[Person]}
namely, List((Person(1),0), (Person(2),3), (Person(3),5)) . Define then sublists where the lower bound corresponds to a Person instance,
val intervals = persons.map{_._2}.sliding(2,1).toArray
res31: Array[List[Int]] = Array(List(0, 3), List(3, 5))
Construct sublists,
val latest = aList.drop(intervals.last.last) // last Person and Invitees not paired
val associations = intervals.map { case List(pa,pb,_*) => b.slice(pa,pb) } ++ latest
Hence the result looks like
Array(List(Person(1), Invite(2), Invite(3)), List(Person(2), Invite(4)), List(Person(3), Invite(6), Invite(7)))
Now,
associations.map { a =>
val person = a.take(1)
val invitees = a.drop(1)
// ...
}
This approach may be seen as a variable size sliding.
Thanks for your tips. I ended up creating another case class:
case class BallotInvites(person:Person,invites:List[Any])
Then, I populated it from the original list:
def constructBallotList(ballots:List[Any]):List[BallotInvites] ={
ballots.zipWithIndex.collect {
case (iv:Ballot,i) =>{
BallotInvites(iv,
ballots.distinct.takeRight(ballots.distinct.length-(i+1)).takeWhile({
case y:Invitee => true
case y:Person =>true
case y:Ballot => false})
)}
}}
val l = Ballot.constructBallotList(ballots)
Then to count based on inviteCountRequired, I did the following:
val count = l.count(b=>if ((b.invites.count(x => x.isInstanceOf[Person]) / contest.inviteCountRequired)>0) true else false )
I am not sure I understand the domain but you should only need to iterate once to construct a list of person + invites tuple.
sealed trait PorI
case class P(i: Int) extends PorI
case class I(i: Int) extends PorI
val l: List[PorI] = List(P(1), I(1), I(1), P(2), I(2), P(3), P(4), I(4))
val res = l.foldLeft(List.empty[(P, List[I])])({ case (res, t) =>
t match {
case p # P(_) => (p, List.empty[I]) :: res
case i # I(_) => {
val head :: tail = res
(head._1, i :: head._2) :: tail
}
}
})
res // List((P(4),List(I(4))), (P(3),List()), (P(2),List(I(2))), (P(1),List(I(1), I(1))))