我在PySpark中有一个像下面这样的数据框。我想选择serial_num，devicetype，device_model并distinct of timestamp for each serial_num从下面的数据帧：
+-------------+-----------------+---------------+------------------------+
| serial_num | devicetype | device_model | timestamp |
+-------------+-----------------+---------------+------------------------+
| 58172A0396 | | | 2003-01-02 17:37:15.0 |
| 58172A0396 | | | 2003-01-02 17:37:15.0 |
| 46C5Y00693 | Mac Pro | Mac PC | 2018-01-03 17:17:23.0 |
| 1737K7008F | Windows PC | Windows PC | 2018-01-05 11:12:31.0 |
| 1737K7008F | Network Device | Unknown | 2018-01-05 11:12:31.0 |
| 1737K7008F | Network Device | Unknown | 2018-01-05 11:12:31.0 |
| 1737K7008F | Network Device | | 2018-01-06 03:12:52.0 |
| 1737K7008F | Windows PC | Windows PC | 2018-01-06 03:12:52.0 |
| 1737K7008F | Network Device | Unknown | 2018-01-06 03:12:52.0 |
| 1665NF01F3 | Network Device | Unknown | 2018-01-07 03:42:34.0 |
+----------------+-----------------+---------------+---------------------+
我已经尝试过如下
df1 = df.select('serial_num', 'devicetype', 'device_model', f.count('distinct timestamp').over(Window.partitionBy('serial_num')).alias('val')
我想要的结果是：
+-------------+-----------------+---------------+-----+
| serial_num | devicetype | device_model |count|
+-------------+-----------------+---------------+-----+
| 58172A0396 | | | 1 |
| 58172A0396 | | | 1 |
| 46C5Y00693 | Mac Pro | Mac PC | 1 |
| 1737K7008F | Windows PC | Windows PC | 2 |
| 1737K7008F | Network Device | Unknown | 2 |
| 1737K7008F | Network Device | Unknown | 2 |
| 1737K7008F | Network Device | | 2 |
| 1737K7008F | Windows PC | Windows PC | 2 |
| 1737K7008F | Network Device | Unknown | 2 |
| 1665NF01F3 | Network Device | Unknown | 1 |
+-------------+-----------------+---------------+-----+
我该如何实现？
不幸的countDistinct是，Windows不支持。然而，组合collect_set和size可用于acheive相同的最终结果。仅Spark 2.0+版本支持此功能，请按以下方式使用：
import pyspark.sql.funcions as F
w = Window.partitionBy('serial_num')
df1 = df.select(..., F.size(F.collect_set('timestamp').over(w)).alias('count'))
对于较旧的Spark版本，您可以使用groupby并countDistinct创建一个包含所有计数的新数据框。然后join将此数据帧与原始数据帧一起。
df2 = df.groupby('serial_num').agg(F.countDistinct('timestamp').alias('count'))
df1 = df.join(df2, 'serial_num')
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我来说两句