Question

我想创建一个表，其中每一行都是唯一的ID，“地点和城市”列包含一个人访问过的所有地点和城市（按访问日期排序），可以使用Pyspark或Hive。

   df.groupby("ID").agg(F.concat_ws("|",F.collect_list("Place")))

进行并置，但无法按日期订购。另外，对于每一列，我都需要继续单独执行此步骤。

我也尝试使用本文中提到的Windows函数（collect_list by preserving order based on another variable）但是抛出错误：java.lang.UnsupportedOperationException：窗口操作不支持'collect_list（'）。我要：

1-按旅行日期的顺序对连接的列进行排序

2-对多列执行此步骤

数据

| ID | Date | Place | City |

| 1  | 2017 | UK    | Birm |
| 2  | 2014 | US    | LA   |
| 1  | 2018 | SIN   | Sin  |
| 1  | 2019 | MAL   | KL   |
| 2  | 2015 | US    | SF   |
| 3  | 2019 | UK    | Lon  |

预期

| ID | Place       | City          | 

| 1  |  UK,SIN,MAL |  Birm,Sin,KL  |
| 2  |  US,US      |  LA,SF        |
| 3  |  UK         |  Lon          |

Answer 1

>>> from pyspark.sql import functions as F
>>> from pyspark.sql import Window
>>> w = Window.partitionBy('ID').orderBy('Date')

//Input data frame
>>> df.show()
+---+----+-----+----+
| ID|Date|Place|City|
+---+----+-----+----+
|  1|2017|   UK|Birm|
|  2|2014|   US|  LA|
|  1|2018|  SIN| Sin|
|  1|2019|  MAL|  KL|
|  2|2015|   US|  SF|
|  3|2019|   UK| Lon|
+---+----+-----+----+

>>> df2 = df.withColumn("Place",F.collect_list("Place").over(w)).withColumn("City",F.collect_list("City").over(w)).groupBy("ID").agg(F.max("Place").alias("Place"), F.max("City").alias("City"))

 //Data value in List
>>> df2.show()
+---+--------------+---------------+
| ID|         Place|           City|
+---+--------------+---------------+
|  3|          [UK]|          [Lon]|
|  1|[UK, SIN, MAL]|[Birm, Sin, KL]|
|  2|      [US, US]|       [LA, SF]|
+---+--------------+---------------+


//If you want value in String 
>>> df2.withColumn("Place", F.concat_ws(" ", "Place")).withColumn("City", F.concat_ws(" ", "City")).show()
+---+----------+-----------+
| ID|     Place|       City|
+---+----------+-----------+
|  3|        UK|        Lon|
|  1|UK SIN MAL|Birm Sin KL|
|  2|     US US|      LA SF|
+---+----------+-----------+

按特定顺序为每个唯一ID连接多个字符串行

1 个答案: