scala – Spark数据帧中两行之间的差异

发布时间：2020-12-16 09:08:23 所属栏目：安全来源：网络整理

导读：我通过groupby column1和date在Spark中创建了一个数据框,并计算了数量. val table = df1.groupBy($"column1",$"date").sum("amount") Column1 |Date |AmountA |1-jul |1000A |1-june |2000A |1-May |2000A |1-dec |3000A |1-Nov |2000B |1-jul |100B |1-june

我通过groupby column1和date在Spark中创建了一个数据框,并计算了数量.

val table = df1.groupBy($"column1",$"date").sum("amount")

Column1 |Date   |Amount
A   |1-jul  |1000
A   |1-june |2000
A   |1-May  |2000
A   |1-dec  |3000
A   |1-Nov  |2000
B   |1-jul  |100
B   |1-june |300    
B   |1-May  |400
B   |1-dec  |300

现在,我想添加新列,表中任意两个日期的数量之间存在差异.

解决方法

如果计算固定为计算前几个月之间的差异,或者计算前两个月之间的差异,则可以使用窗口函数…等等.为此,您可以使用窗口的滞后和导联功能.

但为此您需要更改日期列,如下所示,以便可以订购.

+-------+------+--------------+------+
|Column1|Date  |Date_Converted|Amount|
+-------+------+--------------+------+
|A      |1-jul |2017-07-01    |1000  |
|A      |1-june|2017-06-01    |2000  |
|A      |1-May |2017-05-01    |2000  |
|A      |1-dec |2017-12-01    |3000  |
|A      |1-Nov |2017-11-01    |2000  |
|B      |1-jul |2017-07-01    |100   |
|B      |1-june|2017-06-01    |300   |
|B      |1-May |2017-05-01    |400   |
|B      |1-dec |2017-12-01    |300   |
+-------+------+--------------+------+

您可以通过执行操作找到上个月和当前月份之间的差异

import org.apache.spark.sql.expressions._
val windowSpec = Window.partitionBy("Column1").orderBy("Date_Converted")
import org.apache.spark.sql.functions._
df.withColumn("diff_Amt_With_Prev_Month",$"Amount" - when((lag("Amount",1).over(windowSpec)).isNull,0).otherwise(lag("Amount",1).over(windowSpec)))
   .show(false)

你应该有

+-------+------+--------------+------+------------------------+
|Column1|Date  |Date_Converted|Amount|diff_Amt_With_Prev_Month|
+-------+------+--------------+------+------------------------+
|B      |1-May |2017-05-01    |400   |400.0                   |
|B      |1-june|2017-06-01    |300   |-100.0                  |
|B      |1-jul |2017-07-01    |100   |-200.0                  |
|B      |1-dec |2017-12-01    |300   |200.0                   |
|A      |1-May |2017-05-01    |2000  |2000.0                  |
|A      |1-june|2017-06-01    |2000  |0.0                     |
|A      |1-jul |2017-07-01    |1000  |-1000.0                 |
|A      |1-Nov |2017-11-01    |2000  |1000.0                  |
|A      |1-dec |2017-12-01    |3000  |1000.0                  |
+-------+------+--------------+------+------------------------+

您可以增加前两个月的滞后位置

df.withColumn("diff_Amt_With_Prev_two_Month",2).over(windowSpec)).isNull,2).over(windowSpec)))
  .show(false)

哪个会给你

+-------+------+--------------+------+----------------------------+
|Column1|Date  |Date_Converted|Amount|diff_Amt_With_Prev_two_Month|
+-------+------+--------------+------+----------------------------+
|B      |1-May |2017-05-01    |400   |400.0                       |
|B      |1-june|2017-06-01    |300   |300.0                       |
|B      |1-jul |2017-07-01    |100   |-300.0                      |
|B      |1-dec |2017-12-01    |300   |0.0                         |
|A      |1-May |2017-05-01    |2000  |2000.0                      |
|A      |1-june|2017-06-01    |2000  |2000.0                      |
|A      |1-jul |2017-07-01    |1000  |-1000.0                     |
|A      |1-Nov |2017-11-01    |2000  |0.0                         |
|A      |1-dec |2017-12-01    |3000  |2000.0                      |
+-------+------+--------------+------+----------------------------+

我希望答案是有帮助的

（编辑：李大同）

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