PySpark - 使用 withColumnRenamed 重命名多列 [英] PySpark - rename more than one column using withColumnRenamed

问题描述

我想使用 spark withColumnRenamed 函数更改两列的名称.当然，我可以写:

I want to change names of two columns using spark withColumnRenamed function. Of course, I can write:

data = sqlContext.createDataFrame([(1,2), (3,4)], ['x1', 'x2'])
data = (data
       .withColumnRenamed('x1','x3')
       .withColumnRenamed('x2', 'x4'))

但我想一步完成(有新名称的列表/元组).不幸的是，这两者都不是:

but I want to do this in one step (having list/tuple of new names). Unfortunately, neither this:

data = data.withColumnRenamed(['x1', 'x2'], ['x3', 'x4'])

也不是这个:

data = data.withColumnRenamed(('x1', 'x2'), ('x3', 'x4'))

正在工作.可以这样做吗?

is working. Is it possible to do this that way?

推荐答案

不可能使用单个 withColumnRenamed 调用.

It is not possible to use a single withColumnRenamed call.

• 您可以使用 DataFrame.toDF 方法*

data.toDF('x3', 'x4')

或

new_names = ['x3', 'x4']
data.toDF(*new_names)

也可以使用简单的select重命名:

from pyspark.sql.functions import col

mapping = dict(zip(['x1', 'x2'], ['x3', 'x4']))
data.select([col(c).alias(mapping.get(c, c)) for c in data.columns])

同样在 Scala 中，您可以:

Similarly in Scala you can:

• 重命名所有列:

• Rename all columns:

val newNames = Seq("x3", "x4")

data.toDF(newNames: _*)

使用 select 从映射中重命名:

val  mapping = Map("x1" -> "x3", "x2" -> "x4")

df.select(
  df.columns.map(c => df(c).alias(mapping.get(c).getOrElse(c))): _*
)

或 foldLeft + withColumnRenamed

mapping.foldLeft(data){
  case (data, (oldName, newName)) => data.withColumnRenamed(oldName, newName) 
}

* 不要与 RDD.toDF 混淆，后者不是可变参数函数，并将列名作为列表，

* Not to be confused with RDD.toDF which is not a variadic functions, and takes column names as a list,

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