How to iterate over a group and create an array column with Pyspark?

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I have a dataframe that contains groups and percentages

| Group | A % | B % | Target % |
| ----- | --- | --- | -------- |
| A     | .05 | .85 | 1.0      |
| A     | .07 | .75 | 1.0      |
| A     | .08 | .95 | 1.0      |
| B     | .03 | .80 | 1.0      |
| B     | .05 | .83 | 1.0      |
| B     | .04 | .85 | 1.0      |

I want to be able to iterate column A % by column Group and find an array of values from column B % that when summed with each value in column A% is less than or equal to column Target %.

| Group | A % | B % | Target % | SumArray     |
| ----- | --- | --- | -------- | ------------ |
| A     | .05 | .85 | 1.0      | [.85,.75,.95]|
| A     | .07 | .75 | 1.0      | [.85,.75]    |
| A     | .08 | .95 | 1.0      | [.85,.75]   |
| B     | .03 | .80 | 1.0      | [.80,.83,.85]|
| B     | .05 | .83 | 1.0      | [.80,.83,.85]|
| B     | .04 | .85 | 1.0      | [.80,.83,.85]|

I'd like to be able to use PySpark for this problem. Any ideas how to approach this?

pyspark apache-spark-sql apache-spark group-by
1 Answers

You can use collect_list function to get an array of B % column values grouped by Group column then filter the resulting array using your condition A + B <= Target :

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

df2 = df.withColumn(
    "SumArray",
    F.collect_list(F.col("B")).over(Window.partitionBy("Group"))
).withColumn(
    "SumArray",
    F.expr("filter(SumArray, x -> x + A <= Target)")
)
df2.show()

# +-----+----+----+------+------------------+
# |Group|   A|   B|Target|          SumArray|
# +-----+----+----+------+------------------+
# |    B|0.03| 0.8|   1.0| [0.8, 0.83, 0.85]|
# |    B|0.05|0.83|   1.0| [0.8, 0.83, 0.85]|
# |    B|0.04|0.85|   1.0| [0.8, 0.83, 0.85]|
# |    A|0.05|0.85|   1.0|[0.85, 0.75, 0.95]|
# |    A|0.07|0.75|   1.0|      [0.85, 0.75]|
# |    A|0.08|0.95|   1.0|      [0.85, 0.75]|
# +-----+----+----+------+------------------+
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