如何“否定选择"spark数据框中的列 [英] How to "negative select" columns in spark's dataframe

问题描述

我无法弄清楚，但我想这很简单.我有一个火花数据框 df.这个 df 有列A"、B"和C".现在假设我有一个包含此 df 列名称的数组:

I can't figure it out, but guess it's simple. I have a spark dataframe df. This df has columns "A","B" and "C". Now let's say I have an Array containing the name of the columns of this df:

column_names = Array("A","B","C")

我想以这种方式执行 df.select()，以便我可以指定不选择哪些列.示例:假设我不想选择列B".我试过了

I'd like to do a df.select() in such a way, that I can specify which columns not to select. Example: let's say I do not want to select columns "B". I tried

df.select(column_names.filter(_!="B"))

但这不起作用，因为

org.apache.spark.sql.DataFrame不能应用于 (Array[String])

org.apache.spark.sql.DataFrame cannot be applied to (Array[String])

所以，此处 它说它应该与 Seq 一起使用.但是，尝试

So, here it says it should work with a Seq instead. However, trying

df.select(column_names.filter(_!="B").toSeq)

结果

org.apache.spark.sql.DataFrame不能应用于 (Seq[String]).

org.apache.spark.sql.DataFrame cannot be applied to (Seq[String]).

我做错了什么?

推荐答案

自 Spark 1.4 你可以使用 drop 方法:

Since Spark 1.4 you can use drop method:

Scala:

case class Point(x: Int, y: Int)
val df = sqlContext.createDataFrame(Point(0, 0) :: Point(1, 2) :: Nil)
df.drop("y")

Python:

df = sc.parallelize([(0, 0), (1, 2)]).toDF(["x", "y"])
df.drop("y")
## DataFrame[x: bigint]

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