Pyspark - Cumulative sum with reset condition

Question

I have this dataframe

+---+----+---+
|  A|   B|  C|
+---+----+---+
|  0|null|  1|
|  1| 3.0|  0|
|  2| 7.0|  0|
|  3|null|  1|
|  4| 4.0|  0|
|  5| 3.0|  0|
|  6|null|  1|
|  7|null|  1|
|  8|null|  1|
|  9| 5.0|  0|
| 10| 2.0|  0|
| 11|null|  1|
+---+----+---+

What I need do is a cumulative sum of values from column C until the next value is zero.

Expected output:

+---+----+---+----+
|  A|   B|  C|   D|
+---+----+---+----+
|  0|null|  1|   1|
|  1| 3.0|  0|   0|
|  2| 7.0|  0|   0|
|  3|null|  1|   1|
|  4| 4.0|  0|   0|
|  5| 3.0|  0|   0|
|  6|null|  1|   1|
|  7|null|  1|   2|
|  8|null|  1|   3|
|  9| 5.0|  0|   0|
| 10| 2.0|  0|   0|
| 11|null|  1|   1|
+---+----+---+----+

To reproduce dataframe:

from pyspark.shell import sc
from pyspark.sql import Window
from pyspark.sql.functions import lag, when, sum

x = sc.parallelize([
    [0, None], [1, 3.], [2, 7.], [3, None], [4, 4.],
    [5, 3.], [6, None], [7, None], [8, None], [9, 5.], [10, 2.], [11, None]])
x = x.toDF(['A', 'B'])

# Transform null values into "1"
x = x.withColumn('C', when(x.B.isNull(), 1).otherwise(0))

Answer 1

Create a temporary column (grp) that increments a counter each time column C is equal to 0 (the reset condition) and use this as a partitioning column for your cumulative sum.

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

x.withColumn(
    "grp", 
    f.sum((f.col("C") == 0).cast("int")).over(Window.orderBy("A"))
).withColumn(
    "D",
    f.sum(f.col("C")).over(Window.partitionBy("grp").orderBy("A"))
).drop("grp").show()
#+---+----+---+---+
#|  A|   B|  C|  D|
#+---+----+---+---+
#|  0|null|  1|  1|
#|  1| 3.0|  0|  0|
#|  2| 7.0|  0|  0|
#|  3|null|  1|  1|
#|  4| 4.0|  0|  0|
#|  5| 3.0|  0|  0|
#|  6|null|  1|  1|
#|  7|null|  1|  2|
#|  8|null|  1|  3|
#|  9| 5.0|  0|  0|
#| 10| 2.0|  0|  0|
#| 11|null|  1|  1|
#+---+----+---+---+