Unpivot in spark-sql/pyspark

Question

I have a problem statement at hand wherein I want to unpivot table in spark-sql/pyspark. I have gone through the documentation and I could see there is support only for pivot but no support for un-pivot so far. Is there a way I can achieve this?

Let my initial table look like this:

when I pivot this in pyspark using below mentioned command:

df.groupBy("A").pivot("B").sum("C")

I get this as the output:

Now I want to unpivot the pivoted table. In general this operation may/may not yield the original table based on how I've pivoted the original table.

Spark-sql as of now doesn't provide out of the box support for unpivot. Is there a way I can achieve this?

Answer 1

You can use the built in stack function, for example in Scala:

scala> val df = Seq(("G",Some(4),2,None),("H",None,4,Some(5))).toDF("A","X","Y", "Z")
df: org.apache.spark.sql.DataFrame = [A: string, X: int ... 2 more fields]

scala> df.show
+---+----+---+----+
|  A|   X|  Y|   Z|
+---+----+---+----+
|  G|   4|  2|null|
|  H|null|  4|   5|
+---+----+---+----+


scala> df.select($"A", expr("stack(3, 'X', X, 'Y', Y, 'Z', Z) as (B, C)")).where("C is not null").show
+---+---+---+
|  A|  B|  C|
+---+---+---+
|  G|  X|  4|
|  G|  Y|  2|
|  H|  Y|  4|
|  H|  Z|  5|
+---+---+---+

Or in pyspark:

In [1]: df = spark.createDataFrame([("G",4,2,None),("H",None,4,5)],list("AXYZ"))

In [2]: df.show()
+---+----+---+----+
|  A|   X|  Y|   Z|
+---+----+---+----+
|  G|   4|  2|null|
|  H|null|  4|   5|
+---+----+---+----+

In [3]: df.selectExpr("A", "stack(3, 'X', X, 'Y', Y, 'Z', Z) as (B, C)").where("C is not null").show()
+---+---+---+
|  A|  B|  C|
+---+---+---+
|  G|  X|  4|
|  G|  Y|  2|
|  H|  Y|  4|
|  H|  Z|  5|
+---+---+---+

Answer 2

How to pass in the variables as below please suggest very urgent.

def unpivot(*groupby): df.selectExpr(*groupby, "stack(3, 'X', X, 'Y', Y, 'Z', Z) as (B, C)").show()

unpivot('col1','col2','col3')

getting error when using the *args but running fine we calling as below

def unpivot(*groupby): df.selectExpr('col1','col2','col3', "stack(3, 'X', X, 'Y', Y, 'Z', Z) as (B, C)").show()