将数据框中的向量列转换回数组列 [英] Converting a vector column in a dataframe back into an array column

问题描述

我有一个包含两列的数据框，其中一列(称为 dist)是一个密集向量.如何将其转换回整数数组列.

I have a dataframe with two columns one of which (called dist) is a dense vector. How can I convert it back into an array column of integers.

+---+-----+
| id| dist| 
+---+-----+
|1.0|[2.0]|
|2.0|[4.0]|
|3.0|[6.0]|
|4.0|[8.0]|
+---+-----+

我尝试使用以下 udf 的几种变体，但它返回类型不匹配错误

I tried using several variants of the following udf but it returns a type mismatch error

val toInt4 = udf[Int, Vector]({ (a) => (a)})  

val result = df.withColumn("dist", toDf4(df("dist"))).select("dist")

推荐答案

我认为最简单的方法是转到 RDD API，然后返回.

I think it's easiest to do it by going to the RDD API and then back.

import org.apache.spark.mllib.linalg.DenseVector
import org.apache.spark.sql.DataFrame
import org.apache.spark.rdd.RDD
import sqlContext._

// The original data.
val input: DataFrame =
  sc.parallelize(1 to 4)
    .map(i => i.toDouble -> new DenseVector(Array(i.toDouble * 2)))
    .toDF("id", "dist")

// Turn it into an RDD for manipulation.
val inputRDD: RDD[(Double, DenseVector)] =
  input.map(row => row.getAs[Double]("id") -> row.getAs[DenseVector]("dist"))

// Change the DenseVector into an integer array.
val outputRDD: RDD[(Double, Array[Int])] =
  inputRDD.mapValues(_.toArray.map(_.toInt))

// Go back to a DataFrame.
val output = outputRDD.toDF("id", "dist")
output.show

你得到:

+---+----+
| id|dist|
+---+----+
|1.0| [2]|
|2.0| [4]|
|3.0| [6]|
|4.0| [8]|
+---+----+

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