python - grouping dataframes in pandas efficiently? -

i have following dataframe in pandas there's unique index (employee) each row , group label type:

df = pandas.dataframe({"employee": ["a", "b", "c", "d"], "type": ["x", "y", "y", "y"], "value": [10,20,30,40]}) df = df.set_index("employee") 

i want group employees type , calculate statistic each type. how can , final dataframe type x statistic, example type x (mean of types)? tried using groupby:

g = df.groupby(lambda x: df.ix[x]["type"]) result = g.mean() 

this inefficient since references index ix of df each row - there better way?

like @sza says, can use:

in [11]: g = df.groupby("type")  in [12]: g.mean() out[12]:       value type x        10 y        30 

see groupby docs more...