PySpark drop leading zero values by group in dataframe

Question

I have this dataframe -

data = [(0,1,5,5,0,4),
        (1,1,5,6,0,7),
        (2,1,5,7,1,1), 
        (3,1,4,8,1,8), 
        (4,1,5,9,1,1), 
        (5,1,5,10,1,0),
        (6,2,3,4,0,2),
        (7,2,3,5,0,6),
        (8,2,3,6,3,8),
        (9,2,3,7,0,2),
        (10,2,3,8,0,6),
        (11,2,3,9,6,1)
      ]
data_cols = ["id","item","store","week","sales","inventory"]
data_df = spark.createDataFrame(data=data, schema = data_)
display(deptDF)

What I want is to groupby on item, store and week and then delete all rows with leading 0 in sales per group, like so

data_new = [(2,1,5,7,1,1), 
        (3,1,4,8,1,8), 
        (4,1,5,9,1,1), 
        (5,1,5,10,1,0),
        (8,2,3,6,3,8),
        (9,2,3,7,0,2),
        (10,2,3,8,0,6),
        (11,2,3,9,6,1)
      ]
dep_cols = ["id","item","store","week","sales","inventory"]
data_df_new = spark.createDataFrame(data=data_new, schema = dep_cols)
display(data_df_new)

I need to do this in PySpark and I am new to it. Please help!