在Spark Dataframe中的列列表中添加一列rowums

Question

我有一个包含多个列的Spark数据帧.我想在数据帧上添加一列,它是一定数量的列的总和.

例如,我的数据如下所示:

ID var1 var2 var3 var4 var5
a   5     7    9    12   13
b   6     4    3    20   17
c   4     9    4    6    9
d   1     2    6    8    1

我想要添加一列来汇总特定列的行:

ID var1 var2 var3 var4 var5   sums
a   5     7    9    12   13    46
b   6     4    3    20   17    50
c   4     9    4    6    9     32
d   1     2    6    8    10    27

我知道如果您知道要添加的特定列,可以将列添加到一起:

val newdf = df.withColumn("sumofcolumns", df("var1") + df("var2"))

但是可以传递列名列表并将它们一起添加吗？基于这个答案基本上是我想要的,但它使用的是python API而不是scala(在PySpark数据框中添加列和作为新列)我觉得这样的事情会起作用:

//Select columns to sum
val columnstosum = ("var1", "var2","var3","var4","var5")

// Create new column called sumofcolumns which is sum of all columns listed in columnstosum
val newdf = df.withColumn("sumofcolumns", df.select(columstosum.head, columnstosum.tail: _*).sum)

这会抛出错误值sum并不是org.apache.spark.sql.DataFrame的成员.有没有办法对列进行求和？

在此先感谢您的帮助.

Answer 1

您应该尝试以下方法:

import org.apache.spark.sql.functions._

val sc: SparkContext = ...
val sqlContext = new SQLContext(sc)

import sqlContext.implicits._

val input = sc.parallelize(Seq(
  ("a", 5, 7, 9, 12, 13),
  ("b", 6, 4, 3, 20, 17),
  ("c", 4, 9, 4, 6 , 9),
  ("d", 1, 2, 6, 8 , 1)
)).toDF("ID", "var1", "var2", "var3", "var4", "var5")

val columnsToSum = List(col("var1"), col("var2"), col("var3"), col("var4"), col("var5"))

val output = input.withColumn("sums", columnsToSum.reduce(_ + _))

output.show()

然后结果是:

+---+----+----+----+----+----+----+
| ID|var1|var2|var3|var4|var5|sums|
+---+----+----+----+----+----+----+
|  a|   5|   7|   9|  12|  13|  46|
|  b|   6|   4|   3|  20|  17|  50|
|  c|   4|   9|   4|   6|   9|  32|
|  d|   1|   2|   6|   8|   1|  18|
+---+----+----+----+----+----+----+

Answer 2

干净利落:

import org.apache.spark.sql.Column
import org.apache.spark.sql.functions.{lit, col}

def sum_(cols: Column*) = cols.foldLeft(lit(0))(_ + _)

val columnstosum = Seq("var1", "var2", "var3", "var4", "var5").map(col _)
df.select(sum_(columnstosum: _*))

与Python等价:

from functools import reduce
from operator import add
from pyspark.sql.functions import lit, col

def sum_(*cols):
    return reduce(add, cols, lit(0))

columnstosum = [col(x) for x in ["var1", "var2", "var3", "var4", "var5"]]
select("*", sum_(*columnstosum))

如果行中缺少值,则两者都将默认为NA.您可以使用DataFrameNaFunctions.fill或coalesce功能来避免这种情况.