这可以使用窗口函数和窗口范围内的window.unboundpreceding值的组合来完成，如下所示：

from pyspark.sql import Window
from pyspark.sql import functions as F
windowval = (Window.partitionBy('class').orderBy('time')
             .rangeBetween(Window.unboundedPreceding, 0))
df_w_cumsum = df.withColumn('cum_sum', F.sum('value').over(windowval))
df_w_cumsum.show()

+----+-----+-----+-------+
|time|value|class|cum_sum|
+----+-----+-----+-------+
|   1|    3|    b|      3|
|   2|    3|    b|      6|
|   1|    2|    a|      2|
|   2|    2|    a|      4|
|   3|    2|    a|      6|
+----+-----+-----+-------+

我试过这种方法，它对我有效。

from pyspark.sql import Window
from pyspark.sql import functions as f
import sys
cum_sum = DF.withColumn('cumsum', f.sum('value').over(Window.partitionBy('class').orderBy('time').rowsBetween(-sys.maxsize, 0)))
cum_sum.show()