pyspark dataframe groupby with aggregate unique values

I looked up for any reference for pyspark equivalent of pandas df.groupby(upc)['store'].unique() where df is any dataframe in pandas.

Please use this piece of code for data frame creation in Pyspark

from pyspark.sql.types import StructType,StructField, StringType, IntegerType
from pyspark.sql import *
from datetime import date
import pyspark.sql.functions as F

spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()

data2 = [("36636","M",3000),
    ("40288","M",4000),
    ("42114","M",3000),
    ("39192","F",4000),
    ("39192","F",2000)
  ]

schema = StructType([ \
    StructField("upc", StringType(), True), \
    StructField("store", StringType(), True), \
    StructField("sale", IntegerType(), True) \
  ])
 
df = spark.createDataFrame(data=data2,schema=schema)

I know pyspark groupby unique_count, but need help with unique_values

>Solution :

You can use collect_set to get unique values

from pyspark.sql import functions as F
from pyspark.sql.functions import col
df_group = df.groupBy('upc').agg(F.collect_set(col('store')))