一种方法是将 $dollars 列收集为每个窗口的列表，然后使用udf:

One way is to collect the $dollars column as a list per window, and then calculate the median of the resulting lists using an udf:

from pyspark.sql.window import Window
from pyspark.sql.functions import *
import numpy as np 
from pyspark.sql.types import FloatType

w = (Window.orderBy(col("timestampGMT").cast('long')).rangeBetween(-2, 0))
median_udf = udf(lambda x: float(np.median(x)), FloatType())

df.withColumn("list", collect_list("dollars").over(w)) \
  .withColumn("rolling_median", median_udf("list")).show(truncate = False)

+-------+---------------------+------------+--------------+
|dollars|timestampGMT         |list        |rolling_median|
+-------+---------------------+------------+--------------+
|25     |2017-03-18 11:27:18.0|[25]        |25.0          |
|17     |2017-03-18 11:27:19.0|[25, 17]    |21.0          |
|13     |2017-03-18 11:27:20.0|[25, 17, 13]|17.0          |
|27     |2017-03-18 11:27:21.0|[17, 13, 27]|17.0          |
|13     |2017-03-18 11:27:22.0|[13, 27, 13]|13.0          |
|43     |2017-03-18 11:27:23.0|[27, 13, 43]|27.0          |
|12     |2017-03-18 11:27:24.0|[13, 43, 12]|13.0          |
+-------+---------------------+------------+--------------+