I am trying to convert all the headers / column names of a DataFrame
in Spark-Scala. as of now I come up with following code which only replaces a single column name.
for( i <- 0 to origCols.length - 1) {
df.withColumnRenamed(
df.columns(i),
df.columns(i).toLowerCase
);
}
If structure is flat:
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)
the simplest thing you can do is to use toDF
method:
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)
If you want to rename individual columns you can use either select
with alias
:
df.select($"_1".alias("x1"))
which can be easily generalized to multiple columns:
val lookup = Map("_1" -> "foo", "_3" -> "bar")
df.select(df.columns.map(c => col(c).as(lookup.getOrElse(c, c))): _*)
or withColumnRenamed
:
df.withColumnRenamed("_1", "x1")
which use with foldLeft
to rename multiple columns:
lookup.foldLeft(df)((acc, ca) => acc.withColumnRenamed(ca._1, ca._2))
With nested structures (structs
) one possible option is renaming by selecting a whole structure:
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, $"id").printSchema
// root
// |-- record: struct (nullable = false)
// | |-- location: struct (nullable = false)
// | | |-- point: struct (nullable = false)
// | | | |-- x: double (nullable = true)
// | | | |-- y: double (nullable = true)
// |-- id: long (nullable = true)
Note that it may affect nullability
metadata. Another possibility is to rename by casting:
nested.select($"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)
or:
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)