Apache Spark how to add new column from list / array to Spark dataframe
I am using Apache Spark 2.0 Dataframe / Dataset API I want to add a new column to my dataframe from a list of values. My list has the same number of values as this frame.
val list = List(4,5,10,7,2)
val df = List("a","b","c","d","e").toDF("row1")
I would like to do something like:
val appendedDF = df.withColumn("row2",somefunc(list))
df.show()
// +----+------+
// |row1 |row2 |
// +----+------+
// |a |4 |
// |b |5 |
// |c |10 |
// |d |7 |
// |e |2 |
// +----+------+
For any ideas I would really like, my dataframe actually contains more columns.
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You can do it like this:
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
// create rdd from the list
val rdd = sc.parallelize(List(4,5,10,7,2))
// rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[31] at parallelize at <console>:28
// zip the data frame with rdd
val rdd_new = df.rdd.zip(rdd).map(r => Row.fromSeq(r._1.toSeq ++ Seq(r._2)))
// rdd_new: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[33] at map at <console>:32
// create a new data frame from the rdd_new with modified schema
spark.createDataFrame(rdd_new, df.schema.add("new_col", IntegerType)).show
+----+-------+
|row1|new_col|
+----+-------+
| a| 4|
| b| 5|
| c| 10|
| d| 7|
| e| 2|
+----+-------+
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Adding for completeness: the fact that the input list
(which exists in driver memory) is the same size as it DataFrame
suggests that this is a small DataFrame to start with, so you might want to consider collect()
- with list
and convert back to DataFrame
:
df.collect()
.map(_.getAs[String]("row1"))
.zip(list).toList
.toDF("row1", "row2")
It won't be any faster, but if the data is really small it might be minor and the code is (possibly) clearer.
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