scala 如何对元组列表求和
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How to sum a list of tuples
提问by j3d
Given the following list of tuples...
鉴于以下元组列表...
val list = List((1, 2), (1, 2), (1, 2))
... how do I sum all the values and obtain a single tuple like this?
...我如何对所有值求和并获得这样的单个元组?
(3, 6)
回答by Lomig Mégard
Using the foldLeftmethod. Please look at the scaladocfor more information.
使用foldLeft方法。请查看scaladoc以获取更多信息。
scala> val list = List((1, 2), (1, 2), (1, 2))
list: List[(Int, Int)] = List((1,2), (1,2), (1,2))
scala> list.foldLeft((0, 0)) { case ((accA, accB), (a, b)) => (accA + a, accB + b) }
res0: (Int, Int) = (3,6)
Using unzip. Not as efficient as the above solution. Perhaps more readable.
使用unzip. 不如上述解决方案有效。也许更具可读性。
scala> list.unzip match { case (l1, l2) => (l1.sum, l2.sum) }
res1: (Int, Int) = (3,6)
回答by rightfold
Very easy: (list.map(_._1).sum, list.map(_._2).sum).
很简单:(list.map(_._1).sum, list.map(_._2).sum)。
回答by Valy Dia
You can solve this using Monoid.combineAllfrom the catslibrary:
您可以使用解决这个Monoid.combineAll从cats库:
import cats.instances.int._ // For monoid instances for `Int`
import cats.instances.tuple._ // for Monoid instance for `Tuple2`
import cats.Monoid.combineAll
def main(args: Array[String]): Unit = {
val list = List((1, 2), (1, 2), (1, 2))
val res = combineAll(list)
println(res)
// Displays
// (3, 6)
}
You can see more about this in the cats documentationor Scala with Cats.
您可以在cat 文档或Scala with Cats 中了解更多相关信息。
回答by Mario Galic
Scalaz solution (suggestied by Travis and for some reason a deleted answer):
Scalaz 解决方案(由 Travis 建议,出于某种原因删除了答案):
import scalaz._
import Scalaz._
val list = List((1, 2), (1, 2), (1, 2))
list.suml
which outputs
哪个输出
res0: (Int, Int) = (3,6)
回答by Magesh Somasundaram
answering to this question while trying to understand aggregate function in spark
在尝试理解 spark 中的聚合函数时回答这个问题
scala> val list = List((1, 2), (1, 2), (1, 2))
list: List[(Int, Int)] = List((1,2), (1,2), (1,2))
scala> list.aggregate((0,0))((x,y)=>((y._1+x._1),(x._2+y._2)),(x,y)=>(x._1+y._2,y._2+x._2))
res89: (Int, Int) = (3,6)
Here is the link to the SO QA that helped to understand and answer this [Explain the aggregate functionality in Spark
这是 SO QA 的链接,有助于理解和回答这个问题 [解释 Spark 中的聚合功能

