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PySpark: Get best of each row (in a group)

I have a dataframe like this
df = sc.parallelize([
(1, 1, 1),
(2, 1, 1),
(3, 1, 2),
(1, 2, 1),
(4, 4, 1),
(1, 1, 1),
(2, 2, 1),
(4, 4, 1),
(1, 4, 1),
(1, 4, 1),
(1, 1, 1),
(2, 2, 2),
])

The rows are items that exist in group 1, group 2 and group 3 (or 4, there are 4 groups). I want to compare the (lowest common denominator) row in group 1 with the (highest common denominator) row in group 2 with the (next lowest) row in group 3 and so on.
I have solved the problem by comparing row by row and keeping track of the lowest and highest common denominator between each comparison, but it’s very slow since I’m comparing 11 rows of 4 groups.
Is there a more efficient way to do this?
P.S.
Python3, Spark2.2

A:

You can use itertools.groupby:
from pyspark.sql.functions import col
from itertools import groupby

df.withColumn(“grp”, col(“id”)).withColumn(“gcd”, col(“id”)).withColumn(“max
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