How to group by multiple columns on a pandas series - series

Question

The pandas.Series groupby method makes it possible to group by another series, for example:
data = {'gender': ['Male', 'Male', 'Female', 'Male'], 'age': [20, 21, 20, 20]}
df = pd.DataFrame(data)
grade = pd.Series([5, 6, 7, 4])
grade.groupby(df['age']).mean()
However, this approach does not work for a groupby using two columns:
grade.groupby(df[['age','gender']])
ValueError: Grouper for class pandas.core.frame.DataFrame not 1-dimensional.
In the example, it is easy to add the column to the dataframe and get the desired result as follows:
df['grade'] = grade
y = df.groupby(['gender','age']).mean()
y.to_dict()
{'grade': {('Female', 20): 7.0, ('Male', 20): 4.5, ('Male', 21): 6.0}}
But that can get quite ugly in real life situations. Is there any way to do this groupby on multiple columns directly on the series?

Answer 1

Since I don't know of any direct way to solve the problem, I've made a function that creates a temporary table and performs the groupby on it.
def pd_groupby(series,group_obj):
df = pd.DataFrame(group_obj).copy()
groupby_columns = list(df.columns)
df[series.name] = series
return df.groupby(groupby_columns)[series.name]
Here, group_obj can be a pandas Series or a Pandas DataFrame. Starting from the sample code, the desired result can be achieved by:
y = pd_groupby(grade,df[['gender','age']]).mean()
y.to_dict()
{('Female', 20): 7.0, ('Male', 20): 4.5, ('Male', 21): 6.0}