时间间隔内的 Pyspark 组数据帧

Question

我有一个已排序的 PYSPARK 数据框（“时间戳”和“船”升序）：

+----------------------+------+
|        timestamp     | ship |
+----------------------+------+
| 2018-08-01 06:01:00  |    1 |
| 2018-08-01 06:01:30  |    1 |
| 2018-08-01 09:00:00  |    1 |
| 2018-08-01 09:00:00  |    2 |
| 2018-08-01 10:15:43  |    2 |
| 2018-08-01 11:00:01  |    3 |
| 2018-08-01 06:00:13  |    4 |
| 2018-08-01 13:00:00  |    4 |
| 2018-08-13 14:00:00  |    5 |
| 2018-08-13 14:15:03  |    5 |
| 2018-08-13 14:45:08  |    5 |
| 2018-08-13 14:50:00  |    5 |
+-----------------------------+

我想在名为“trip”的数据框中添加一个新列。行程定义为从数据框中的船舶记录开始后 2 小时内航行的船号。如果在两小时内船号发生变化，则应将新的行程号添加到数据框列“行程”中。

所需的输出如下所示：

+----------------------+------+-------+
|        timestamp     | ship | trip  |
+----------------------+------+-------+
| 2018-08-01 06:01:00  |    1 |    1  | # start new ship number
| 2018-08-01 06:01:30  |    1 |    1  | # still within 2 hours of same ship number
| 2018-08-01 09:00:00  |    1 |    2  | # more than 2 hours of same ship number = new trip
| 2018-08-01 09:00:00  |    2 |    3  | # new ship number = new trip
| 2018-08-01 10:15:43  |    2 |    3  | # still within 2 hours of same ship number
| 2018-08-01 11:00:01  |    3 |    4  | # new ship number = new trip
| 2018-08-01 06:00:13  |    4 |    5  | # new ship number = new trip
| 2018-08-01 13:00:00  |    4 |    6  | # more than 2 hours of same ship number = new trip
| 2018-08-13 14:00:00  |    5 |    7  | # new ship number = new trip
| 2018-08-13 14:15:03  |    5 |    7  | # still within 2 hours of same ship number
| 2018-08-13 14:45:08  |    5 |    7  | # still within 2 hours of same ship number
| 2018-08-13 14:50:00  |    5 |    7  | # still within 2 hours of same ship number
+-----------------------------+-------+

在 Pandas 中会这样做：

dt_trip = 2 # time duration trip per ship (in hours)
total_time = df['timestamp'] - df.groupby('name')['timestamp'].transform('min')
trips = total_time.dt.total_seconds().fillna(0)//(dt_trip*3600)
df['trip'] = df.groupby(['name', trips]).ngroup()+1

在 PYSPARK 中如何做到这一点？

Answer 1

尝试使用 window functions、row_number()、collect_list() 和 incremental sum > 创造的条件。

from pyspark.sql import functions as F
from pyspark.sql.window import Window

w1=Window().partitionBy("ship").orderBy(F.unix_timestamp("timestamp")).rangeBetween(-7199, Window.currentRow)
w2=Window().partitionBy("ship").orderBy("timestamp")
w3=Window().orderBy("ship","timestamp")

df.withColumn("trip", F.sum(F.when(F.row_number().over(w2)==1, F.lit(1))\
                       .when(F.size(F.collect_list("ship").over(w1))==1, F.lit(1))\
                       .otherwise(F.lit(0))).over(w3)).orderBy("ship","timestamp").show()

#+-------------------+----+----+
#|          timestamp|ship|trip|
#+-------------------+----+----+
#|2018-08-01 06:01:00|   1|   1|
#|2018-08-01 06:01:30|   1|   1|
#|2018-08-01 09:00:00|   1|   2|
#|2018-08-01 09:00:00|   2|   3|
#|2018-08-01 10:15:43|   2|   3|
#|2018-08-01 11:00:01|   3|   4|
#|2018-08-01 06:00:13|   4|   5|
#|2018-08-01 13:00:00|   4|   6|
#|2018-08-13 14:00:00|   5|   7|
#|2018-08-13 14:15:03|   5|   7|
#|2018-08-13 14:45:08|   5|   7|
#|2018-08-13 14:50:00|   5|   7|
#+-------------------+----+----+