Spark withColumn-使用非列类型变量添加列 [英] Spark withColumn - add column using non-Column type variable

问题描述

如何从变量值向数据框中添加一列?

How can I add a column to a data frame from a variable value?

我知道我可以使用.toDF(colName)创建数据框，并且.withColumn是添加列的方法.但是，当我尝试以下操作时，出现类型不匹配错误:

I know that I can create a data frame using .toDF(colName) and that .withColumn is the method to add the column. But, when I try the following, I get a type mismatch error:

val myList = List(1,2,3)
val myArray = Array(1,2,3)

myList.toDF("myList")
  .withColumn("myArray", myArray)

类型不匹配，预期:实际列:Array [Int]

Type mismatch, expected: Column, actual: Array[Int]

此编译错误在.withColumn调用内的myArray上.如何将其从Array [Int]转换为Column类型?

This compile error is on myArray within the .withColumn call. How can I convert it from an Array[Int] to a Column type?

推荐答案

错误消息正好出现了，您需要输入一列(或lit())作为第二个参数，作为withColumn()

The error message has exactly what is up, you need to input a column (or a lit()) as the second argument as withColumn()

尝试

import org.apache.spark.sql.functions.typedLit

val myList = List(1,2,3)
val myArray = Array(1,2,3)

myList.toDF("myList")
  .withColumn("myArray", typedLit(myArray))

:)

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