Pyspark keep only most recent timestamps that meet condition答案

【问题标题】：Pyspark keep only most recent timestamps that meet conditionPyspark keep only most recent timestamps that meet condition
【发布时间】：2022-12-02 06:06:31
【问题描述】：

I have the following dataset:

id  col1  timestamp
1    a    01.01.2022 9:00:00
1    b    01.01.2022 9:01:00
1    c    01.01.2022 9:02:00
1    a    01.01.2022 10:00:00
1    b    01.01.2022 10:01:00
1    d    01.01.2022 10:02:00
2    a    01.01.2022 12:00:00 
2    b    01.01.2022 12:01:00
2    a    01.01.2022 13:00:00
2    c    01.01.2022 13:01:00

What i would like to do is to keep all the timestamps after the last occurrence of 'a' for each id. This is what the dataset will look like:

id  col1  timestamp
1    a    01.01.2022 10:00:00
1    b    01.01.2022 10:01:00
1    d    01.01.2022 10:02:00
2    a    01.01.2022 13:00:00
2    c    01.01.2022 13:01:00

It is important to identify 'a' as the starting point. Any idea on how can I do it? I was thinking of using groupby and take the max timestamp but it only seems to work in some specific situations

【问题讨论】：

标签： python pyspark group-by

【解决方案1】：

spark.sql("set spark.sql.legacy.timeParserPolicy=LEGACY")

  w = Window.partitionBy('id')
( #column cum_a =1 when col1=a else cum_a=0. Once populated, calculate the cumulative sum of cum_a for every id ordered by timestamp
  df.withColumn('cum_a', sum(when(col('col1')=='a',1).otherwise(0)).over(w.orderBy(to_timestamp('timestamp','dd.MM.yyyy HH:mm:ss'))))
  #Find the maximum cum_a value per id 
 .withColumn('max', max('cum_a').over(w))
  #Filter out where cum_a equals to max value of cum_a per id 
  .where(((col('cum_a')==col('max'))))
  #Drop unwamted intermediatary columns
  .drop('cum_a','max')
).show()


+---+----+-------------------+
| id|col1|          timestamp|
+---+----+-------------------+
|  1|   a|01.01.2022 10:00:00|
|  1|   b|01.01.2022 10:01:00|
|  1|   d|01.01.2022 10:02:00|
|  2|   a|01.01.2022 13:00:00|
|  2|   c|01.01.2022 13:01:00|
+---+----+-------------------+

【讨论】：