Here is an example to illustrate my point.
In this toy example, we are collecting a list of the other products that
each user has bought, and appending it as a new column. (Also note, that we
are filtering on some arbitrary column 'good_bad'.)
I would like to know if we support NOT including the CURRENT ROW in the
PARTITION BY.
(E.g. transaction 1 would have `other_purchases = [prod2, prod3]` rather
than `other_purchases = [prod1, prod2, prod3]`)
------------------- Code Below -------------------
df = spark.createDataFrame([
(1, "user1", "prod1", "good"),
(2, "user1", "prod2", "good"),
(3, "user1", "prod3", "good"),
(4, "user2", "prod3", "bad"),
(5, "user2", "prod4", "good"),
(5, "user2", "prod5", "good")],
("trans_id", "user_id", "prod_id", "good_bad")
)
df.show()
df = df.selectExpr(
"trans_id",
"user_id",
"COLLECT_LIST(CASE WHEN good_bad == 'good' THEN prod_id END)
OVER(PARTITION BY user_id) AS other_purchases"
)
df.show()
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