PySpark - 使用 withColumnRenamed 重命名多个列 [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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