Spark中的多列操作 [英] Multiple-columns operations in Spark

问题描述

使用Python的Pandas，可以在一遍中对多列进行批量操作，如下所示:

Using Python's Pandas, one can do bulk operations on multiple columns in one pass like this:

# assuming we have a DataFrame with, among others, the following columns
cols = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8']
df[cols] = df[cols] / df['another_column']

在Scala中使用Spark是否有类似的功能?

Is there a similar functionality using Spark in Scala?

目前，我最终会这样做:

Currently I end up doing:

val df2 = df.withColumn("col1", $"col1" / $"another_column")
            .withColumn("col2", $"col2" / $"another_column")
            .withColumn("col3", $"col3" / $"another_column")
            .withColumn("col4", $"col4" / $"another_column")
            .withColumn("col5", $"col5" / $"another_column")
            .withColumn("col6", $"col6" / $"another_column")
            .withColumn("col7", $"col7" / $"another_column")
            .withColumn("col8", $"col8" / $"another_column")

推荐答案

您可以使用foldLeft处理列列表，如下所示:

You can use foldLeft to process the column list as below:

val df = Seq(
  (1, 20, 30, 4),
  (2, 30, 40, 5),
  (3, 10, 30, 2)
).toDF("id", "col1", "col2", "another_column")

val cols = Array("col1", "col2")

val df2 = cols.foldLeft( df )( (acc, c) =>
  acc.withColumn( c, df(c) / df("another_column") )
)

df2.show
+---+----+----+--------------+
| id|col1|col2|another_column|
+---+----+----+--------------+
|  1| 5.0| 7.5|             4|
|  2| 6.0| 8.0|             5|
|  3| 5.0|15.0|             2|
+---+----+----+--------------+

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