PySpark: Convert T-SQL Case When Then statement to PySpark [duplicate] - sql-server

Question

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 create a new column in existing Spark DataFrame by some rules. Here is what I wrote. iris_spark is the data frame with a categorical variable iris_spark with three distinct categories.
from pyspark.sql import functions as F
iris_spark_df = iris_spark.withColumn(
"Class",
F.when(iris_spark.iris_class == 'Iris-setosa', 0, F.when(iris_spark.iris_class == 'Iris-versicolor',1)).otherwise(2))
Throws the following error.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-157-21818c7dc060> in <module>()
----> 1 iris_spark_df=iris_spark.withColumn("Class",F.when(iris_spark.iris_class=='Iris-setosa',0,F.when(iris_spark.iris_class=='Iris-versicolor',1)))
TypeError: when() takes exactly 2 arguments (3 given)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-157-21818c7dc060> in <module>()
----> 1 iris_spark_df=iris_spark.withColumn("Class",F.when(iris_spark.iris_class=='Iris-setosa',0,F.when(iris_spark.iris_class=='Iris-versicolor',1)))
TypeError: when() takes exactly 2 arguments (3 given)
Any idea why?

Answer 1

Correct structure is either:
(when(col("iris_class") == 'Iris-setosa', 0)
.when(col("iris_class") == 'Iris-versicolor', 1)
.otherwise(2))
which is equivalent to
CASE
WHEN (iris_class = 'Iris-setosa') THEN 0
WHEN (iris_class = 'Iris-versicolor') THEN 1
ELSE 2
END
or:
(when(col("iris_class") == 'Iris-setosa', 0)
.otherwise(when(col("iris_class") == 'Iris-versicolor', 1)
.otherwise(2)))
which is equivalent to:
CASE WHEN (iris_class = 'Iris-setosa') THEN 0
ELSE CASE WHEN (iris_class = 'Iris-versicolor') THEN 1
ELSE 2
END
END
with general syntax:
when(condition, value).when(...)
or
when(condition, value).otherwise(...)
You probably mixed up things with Hive IF conditional:
IF(condition, if-true, if-false)
which can be used only in raw SQL with Hive support.

Answer 2

Conditional statement In Spark
Using “when otherwise” on DataFrame
Using “case when” on DataFrame
Using && and || operator
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|
+---+----+---+----------+

Answer 3

There are different ways you can achieve if-then-else.
Using when function in DataFrame API.
You can specify the list of conditions in when and also can specify otherwise what value you need. You can use this expression in nested form as well.
expr function.
Using "expr" function you can pass SQL expression in expr. PFB example. Here we are creating new column "quarter" based on month column.
cond = """case when month > 9 then 'Q4'
else case when month > 6 then 'Q3'
else case when month > 3 then 'Q2'
else case when month > 0 then 'Q1'
end
end
end
end as quarter"""
newdf = df.withColumn("quarter", expr(cond))
selectExpr function.
We can also use the variant of select function which can take SQL expression. PFB example.
cond = """case when month > 9 then 'Q4'
else case when month > 6 then 'Q3'
else case when month > 3 then 'Q2'
else case when month > 0 then 'Q1'
end
end
end
end as quarter"""
newdf = df.selectExpr("*", cond)

Answer 4

you can use this:
if(exp1, exp2, exp3) inside spark.sql()
where exp1 is condition and if true give me exp2, else give me exp3.
now the funny thing with nested if-else is. you need to pass every exp inside
brackets {"()"}
else it will raise error.
example:
if((1>2), (if (2>3), True, False), (False))