取消在spark-sql/pyspark中的透视 [英] Unpivot in spark-sql/pyspark

问题描述

我手头有一个问题说明，其中我想取消显示spark-sql/pyspark中的表.我已经阅读了文档，并且可以看到到目前为止仅支持数据透视，但不支持取消数据透视. 有什么办法可以做到这一点?

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?

让我的初始表格如下:

当我使用下面提到的命令在pyspark中旋转它时:

when I pivot this in pyspark using below mentioned command:

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

我得到这个作为输出:

现在，我想取消透视表.通常，根据我对原始表格的处理方式，此操作可能会/可能不会产生原始表.

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尚未提供对unpivot的开箱即用支持.有什么办法可以做到这一点?

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

推荐答案

您可以使用内置的堆栈函数，例如在Scala中:

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

或在pyspark中:

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

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