CODEWITHSUNDEEP
apache-sparkpysparkapache-spark-sqlpivotunpivot
Manish Mehra
Manish Mehra

Reputation: 1501

Unpivot in Spark SQL / PySpark

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:

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:

After pivot table looks like this

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?

Upvotes: 34

Views: 63073

Answers (2)

ZygD
ZygD

Reputation: 24386

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|
# +---+---+---+

Upvotes: 7

Andrew Ray
Andrew Ray

Reputation: 766

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|
+---+---+---+

Upvotes: 63

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