How to iterate Groupby in Python/PySpark

[DrData82]

I have to create several summary tables and include 2 below for simplicity. I'm trying to think of a way to minimize the code...rather than type out 10 of these blocks. Is there a straightforward way to iterate by the groupby variables? The first run uses col1/col2, the second run uses col3/col4, and on and on.

I'm somewhat new to Python, so appreciate any advice!

NEED1= HAVE.groupBy('col1',"col2")\
                      .agg(F.sum('col5').alias('col5'), \
                             F.sum('col6').alias('col6'), \
                             F.sum('col7').alias('col7'), \
                             F.sum('col8').alias('col8')) \
                      .sort('col1','col2')
NEED2= HAVE.groupBy('col3',"col4")\
                      .agg(F.sum('col5').alias('col5'), \
                             F.sum('col6').alias('col6'), \
                             F.sum('col7').alias('col7'), \
                             F.sum('col8').alias('col8')) \
                      .sort('col3','col4')

NEED1.show()
NEED2.show()

[Larz60+]

something like:

def create_group(col1x, coly):
    return HAVE.groupBy(colx, coly), \
        .agg(F.sum('col5').alias('col5'), \
        F.sum('col6').alias('col6'), \
        F.sum('col7').alias('col7'), \
        F.sum('col8').alias('col8')) \
        .sort(colx, coly)

def groups():
    NEED1 = create_group(col1, col2)
    NEED2 = create_group(col3, col4)

[DrData82]

This worked perfectly, thank you!