I have seen this question earlier here and I have took lessons from that. However I am not sure why I am getting an error when I feel it should work.
I want to crea
Conditional statement In Spark
import org.apache.spark.sql.functions.{when, _}
import spark.sqlContext.implicits._
val spark: SparkSession = SparkSession.builder().master("local[1]").appName("SparkByExamples.com").getOrCreate()
val data = List(("James ","","Smith","36636","M",60000),
("Michael ","Rose","","40288","M",70000),
("Robert ","","Williams","42114","",400000),
("Maria ","Anne","Jones","39192","F",500000),
("Jen","Mary","Brown","","F",0))
val cols = Seq("first_name","middle_name","last_name","dob","gender","salary")
val df = spark.createDataFrame(data).toDF(cols:_*)
1. Using “when otherwise” on DataFrame
Replace the value of gender with new value
val df1 = df.withColumn("new_gender", when(col("gender") === "M","Male")
.when(col("gender") === "F","Female")
.otherwise("Unknown"))
val df2 = df.select(col("*"), when(col("gender") === "M","Male")
.when(col("gender") === "F","Female")
.otherwise("Unknown").alias("new_gender"))
2. Using “case when” on DataFrame
val df3 = df.withColumn("new_gender",
expr("case when gender = 'M' then 'Male' " +
"when gender = 'F' then 'Female' " +
"else 'Unknown' end"))
Alternatively,
val df4 = df.select(col("*"),
expr("case when gender = 'M' then 'Male' " +
"when gender = 'F' then 'Female' " +
"else 'Unknown' end").alias("new_gender"))
3. Using && and || operator
val dataDF = Seq(
(66, "a", "4"), (67, "a", "0"), (70, "b", "4"), (71, "d", "4"
)).toDF("id", "code", "amt")
dataDF.withColumn("new_column",
when(col("code") === "a" || col("code") === "d", "A")
.when(col("code") === "b" && col("amt") === "4", "B")
.otherwise("A1"))
.show()
Output:
+---+----+---+----------+
| id|code|amt|new_column|
+---+----+---+----------+
| 66| a| 4| A|
| 67| a| 0| A|
| 70| b| 4| B|
| 71| d| 4| A|
+---+----+---+----------+