Pyspark : how to code complicated dataframe calculation lead sum - apache-spark-sql

Question

I have given dataframe that looks like this.
THIS dataframe is sorted by date, and col1 is just some random value.
TEST_schema = StructType([StructField("date", StringType(), True),\
StructField("col1", IntegerType(), True),\
])
TEST_data = [('2020-08-01',3),('2020-08-02',1),('2020-08-03',-1),('2020-08-04',-1),('2020-08-05',3),\
('2020-08-06',-1),('2020-08-07',6),('2020-08-08',4),('2020-08-09',5)]
rdd3 = sc.parallelize(TEST_data)
TEST_df = sqlContext.createDataFrame(TEST_data, TEST_schema)
TEST_df.show()
+----------+----+
| date|col1|
+----------+----+
|2020-08-01| 3|
|2020-08-02| 1|
|2020-08-03| -1|
|2020-08-04| -1|
|2020-08-05| 3|
|2020-08-06| -1|
|2020-08-07| 6|
|2020-08-08| 4|
|2020-08-09| 5|
+----------+----+
LOGIC : lead(col1) +1, if col1 ==-1, then from the previous value lead(col1) +2...
the resulted dataframe will look like this (want column is what i want as output)
+----------+----+----+
| date|col1|WANT|
+----------+----+----+
|2020-08-01| 3| 2|
|2020-08-02| 1| 6|
|2020-08-03| -1| 5|
|2020-08-04| -1| 4|
|2020-08-05| 3| 8|
|2020-08-06| -1| 7|
|2020-08-07| 6| 5|
|2020-08-08| 4| 6|
|2020-08-09| 5| -1|
+----------+----+----+
Let's look at last row, where col1==5, that 5 is leaded +1 which is in want==6 (2020-08-08)
If we have col==-1, then we add +1 more ,, if we have col==-1 repeated twice, then we add +2 more..
this is hard to explain in words,lastly since it created last column instead of null, replace with -1. I have a diagram

Answer 1

You can check if the following code and logic works for you:
create a sub-group label g which take running sum of int(col1!=-1), and we only concern about Rows with col1 == -1, and nullify all other Rows.
the residual is 1 and if col1 == -1, plus the running count on Window w2
take the prev_col1 over w1 which is not -1 (using nullif), (the naming of prev_col1 might be confusion since it takes only if col1 = -1 using typical pyspark's way to do ffill, otherwise keep the original).
set val = prev_col1 + residual, take the lag and set null to -1
Code below:
from pyspark.sql.functions import when, col, expr, count, desc, lag, coalesce
from pyspark.sql import Window
w1 = Window.orderBy(desc('date'))
w2 = Window.partitionBy('g').orderBy(desc('date'))
TEST_df.withColumn('g', when(col('col1') == -1, expr("sum(int(col1!=-1))").over(w1))) \
.withColumn('residual', when(col('col1') == -1, count('*').over(w2) + 1).otherwise(1)) \
.withColumn('prev_col1',expr("last(nullif(col1,-1),True)").over(w1)) \
.withColumn('want', coalesce(lag(expr("prev_col1 + residual")).over(w1),lit(-1))) \
.orderBy('date').show()
+----------+----+----+--------+---------+----+
| date|col1| g|residual|prev_col1|want|
+----------+----+----+--------+---------+----+
|2020-08-01| 3|null| 1| 3| 2|
|2020-08-02| 1|null| 1| 1| 6|
|2020-08-03| -1| 4| 3| 3| 5|
|2020-08-04| -1| 4| 2| 3| 4|
|2020-08-05| 3|null| 1| 3| 8|
|2020-08-06| -1| 3| 2| 6| 7|
|2020-08-07| 6|null| 1| 6| 5|
|2020-08-08| 4|null| 1| 4| 6|
|2020-08-09| 5|null| 1| 5| -1|
+----------+----+----+--------+---------+----+