这可以使用窗口函数和窗口范围中的Window.unboundedPreceding值的组合来完成，如下所示：
第一个

更新之前的答案。正确和精确的方法是：

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

windowval = (Window.partitionBy('class').orderBy('time')
             .rowsBetween(Window.unboundedPreceding, 0))
df_w_cumsum = df.withColumn('cum_sum', F.sum('value').over(windowval))
df_w_cumsum.show()

我试过这种方法，它对我很有效。

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()

我在此链接中创建此函数以供用途：kolang/列函数/累计和

def cumulative_sum(col: Union[Column, str],
                   on_col: Union[Column, str],
                   ascending: bool = True,
                   partition_by: Union[Column, str, List[Union[Column, str]]] = None) -> Column:
    on_col = on_col if ascending else F.desc(on_col)
    if partition_by is None:
        w = Window.orderBy(on_col).rangeBetween(Window.unboundedPreceding, 0)
    else:
        w = Window.partitionBy(partition_by).orderBy(on_col).rangeBetween(Window.unboundedPreceding, 0)
    return F.sum(col).over(w)