scala - 在 Spark Scala 中重命名 DataFrame 的列名称

Question

我正在尝试转换 Spark-Scala 中 DataFrame 的所有标题/列名称。截至目前，我想出了以下代码，它仅替换单个列名称。

for( i <- 0 to origCols.length - 1) {
  df.withColumnRenamed(
    df.columns(i), 
    df.columns(i).toLowerCase
  );
}

Answer 1

如果结构是扁平的:

val df = Seq((1L, "a", "foo", 3.0)).toDF
df.printSchema
// root
//  |-- _1: long (nullable = false)
//  |-- _2: string (nullable = true)
//  |-- _3: string (nullable = true)
//  |-- _4: double (nullable = false)

您可以做的最简单的事情就是使用 toDF 方法:

val newNames = Seq("id", "x1", "x2", "x3")
val dfRenamed = df.toDF(newNames: _*)

dfRenamed.printSchema
// root
// |-- id: long (nullable = false)
// |-- x1: string (nullable = true)
// |-- x2: string (nullable = true)
// |-- x3: double (nullable = false)

如果您想重命名各个列，可以使用 select 和 alias:

df.select($"_1".alias("x1"))

可以很容易地推广到多列:

val lookup = Map("_1" -> "foo", "_3" -> "bar")

df.select(df.columns.map(c => col(c).as(lookup.getOrElse(c, c))): _*)

或withColumnRenamed:

df.withColumnRenamed("_1", "x1")

与 foldLeft 一起使用来重命名多个列:

lookup.foldLeft(df)((acc, ca) => acc.withColumnRenamed(ca._1, ca._2))

对于嵌套结构(structs)，一种可能的选择是通过选择整个结构来重命名:

val nested = spark.read.json(sc.parallelize(Seq(
    """{"foobar": {"foo": {"bar": {"first": 1.0, "second": 2.0}}}, "id": 1}"""
)))

nested.printSchema
// root
//  |-- foobar: struct (nullable = true)
//  |    |-- foo: struct (nullable = true)
//  |    |    |-- bar: struct (nullable = true)
//  |    |    |    |-- first: double (nullable = true)
//  |    |    |    |-- second: double (nullable = true)
//  |-- id: long (nullable = true)

@transient val foobarRenamed = struct(
  struct(
    struct(
      $"foobar.foo.bar.first".as("x"), $"foobar.foo.bar.first".as("y")
    ).alias("point")
  ).alias("location")
).alias("record")

nested.select(foobarRenamed, $"id").printSchema
// root
//  |-- record: struct (nullable = false)
//  |    |-- location: struct (nullable = false)
//  |    |    |-- point: struct (nullable = false)
//  |    |    |    |-- x: double (nullable = true)
//  |    |    |    |-- y: double (nullable = true)
//  |-- id: long (nullable = true)

请注意，它可能会影响可空性元数据。另一种可能性是通过转换重命名:

nested.select($"foobar".cast(
  "struct<location:struct<point:struct<x:double,y:double>>>"
).alias("record")).printSchema

// root
//  |-- record: struct (nullable = true)
//  |    |-- location: struct (nullable = true)
//  |    |    |-- point: struct (nullable = true)
//  |    |    |    |-- x: double (nullable = true)
//  |    |    |    |-- y: double (nullable = true)

或者:

import org.apache.spark.sql.types._

nested.select($"foobar".cast(
  StructType(Seq(
    StructField("location", StructType(Seq(
      StructField("point", StructType(Seq(
        StructField("x", DoubleType), StructField("y", DoubleType)))))))))
).alias("record")).printSchema

// root
//  |-- record: struct (nullable = true)
//  |    |-- location: struct (nullable = true)
//  |    |    |-- point: struct (nullable = true)
//  |    |    |    |-- x: double (nullable = true)
//  |    |    |    |-- y: double (nullable = true)