PySpark: conditionally fill NA based on aggregation of corresponding column values

Question

I have a dataframe with 3 columns- brand, color, and rating:

values = [(\'Lacoste\', \'Red\', 6), (\'Gap\', \'Orange\', 8), (\'Lacoste\', \'Green\', 5),