如何在Spark Scala中从多个数组创建DataFrame？

Question

val tvalues: Array[Double] = Array(1.866393526974307, 2.864048126935307, 4.032486069215076, 7.876169953355888, 4.875333799256043, 14.316322626848278)
val pvalues: Array[Double] = Array(0.064020056478447, 0.004808399479386827, 8.914865448939047E-5, 7.489564524121306E-13, 2.8363794106756046E-6, 0.0)

我有两个如上所述的数组,我需要从这个数组构建一个DataFrame,如下所示,

Tvalues                Pvalues
1.866393526974307      0.064020056478447
2.864048126935307      0.004808399479386827
......                 .....

截至目前我StringBuilder在Scala 尝试.没有按预期进行.请帮帮我.

Answer 1

试试吧

val df = sc.parallelize(tpvalues zip pvalues).toDF("Tvalues","Pvalues")

因此

scala> df.show
+------------------+--------------------+
|          Tvalues|             Pvalues|
+------------------+--------------------+
| 1.866393526974307|   0.064020056478447|
| 2.864048126935307|0.004808399479386827|
| 4.032486069215076|8.914865448939047E-5|
| 7.876169953355888|7.489564524121306...|
| 4.875333799256043|2.836379410675604...|
|14.316322626848278|                 0.0|
+------------------+--------------------+

使用parallelize我们获得一个RDD元组 - 第一个数组中的第一个元素,另一个数组中的第二个元素 - ,它被转换为行的数据帧,每个元组一行.

更新

对于数据帧的多个阵列(都具有相同的大小),例如4个阵列,请考虑

case class Row(i: Double, j: Double, k: Double, m: Double)

val xs = Array(arr1, arr2, arr3, arr4).transpose
val rdd = sc.parallelize(xs).map(ys => Row(ys(0), ys(1), ys(2), ys(3))
val df = rdd.toDF("i","j","k","m")