Unpivot in Spark SQL / PySpark - apache-spark

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:
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

Spark 3.4+
df = df.melt(['A'], ['X', 'Y', 'Z'], 'B', 'C')
# OR
df = df.unpivot(['A'], ['X', 'Y', 'Z'], 'B', 'C')
+---+---+----+
| A| B| C|
+---+---+----+
| G| Y| 2|
| G| Z|null|
| G| X| 4|
| H| Y| 4|
| H| Z| 5|
| H| X|null|
+---+---+----+
To filter out nulls: df = df.filter("C is not null")
Spark 3.3 and below
to_melt = {'X', 'Y', 'Z'}
new_names = ['B', 'C']
melt_str = ','.join([f"'{c}', `{c}`" for c in to_melt])
df = df.select(
*(set(df.columns) - to_melt),
F.expr(f"stack({len(to_melt)}, {melt_str}) ({','.join(new_names)})")
).filter(f"!{new_names[1]} is null")
Full test:
from pyspark.sql import functions as F
df = spark.createDataFrame([("G", 4, 2, None), ("H", None, 4, 5)], list("AXYZ"))
to_melt = {'X', 'Y', 'Z'}
new_names = ['B', 'C']
melt_str = ','.join([f"'{c}', `{c}`" for c in to_melt])
df = df.select(
*(set(df.columns) - to_melt),
F.expr(f"stack({len(to_melt)}, {melt_str}) ({','.join(new_names)})")
).filter(f"!{new_names[1]} is null")
df.show()
# +---+---+---+
# | A| B| C|
# +---+---+---+
# | G| Y| 2|
# | G| X| 4|
# | H| Y| 4|
# | H| Z| 5|
# +---+---+---+