Spark Dataframe vs Pandas Dataframe memory usage comparison

I'm new to spark, and had written some sample code, to check whether using spark is feasible (reducing memory usage), so I had created a sample dataframe, converted to spark DF, and was comparing the memory usage of both. The sample code is:

emp = [(1, "Smith", -1, "2018", "10", "M", 3000),
       (2, "Rose", 1, "2010", "20", "M", 4000),
       (3, "Williams", 1, "2010", "10", "M", 1000),
       (4, "Jones", 2, "2005", "10", "F", 2000),
       (5, "Brown", 2, "2010", "40", "", -1),
       (6, "Brown", 2, "2010", "50", "", -1)]*800000
empColumns = ["emp_id", "name", "superior_emp_id", "year_joined",
              "emp_dept_id", "gender", "salary"]

df = pd.DataFrame(emp, columns=empColumns)
print(sys.getsizeof(df))

empDF = spark.createDataFrame(data=emp, schema=empColumns)
print(sys.getsizeof(empDF))
print(sys.getsizeof(empDF.collect()))
print(sys.getsizeof(empDF.toPandas()))

The values I'm getting is:

1267200144
48
42915440
1267200144

So now, I have 2 questions:

1. What is the meaning of sys.getsizeof(empDF)and which part is of 48 bytes here.
2. Why is there such a difference between spark and pandas df?