Different aggfunc based on different logics for pandas pivot table

Question

I would like to apply different "aggfunc" logics to a pandas pivot table. Lets suppose that I have the below df.

df1 = pd.DataFrame({'Country':['Italy', 'Italy', 'Italy', 'Germany','Germany', 'Germany', 'France', 'France'],
                   'City':['Rome','Rome',"Florence",'Berlin', 'Munich', 'Koln', "Paris", "Paris"],
                    'Numbers':[100,200,300,400,500,600,700,800]})

I would like to calculate the sum of "Numbers" per City and the mean of "Numbers" based on the Country. I should get the below output.

I must use the pd.pivot. But if you have better solutions, you can ALSO suggest that.

Would you be able to help me out?

Country	City	SUM	MEAN
France	Paris	1500	750
Germany	Berlin	400	500
Germany	Köln	600	500
Germany	Munich	500	500
Italy	Florence	300	200
Italy	Rome	300	200

I have tried using the following but it obviously does not work.

pd.pivot_table(df1, values = 'Numbers', index=['Country', 'City'], aggfunc=[np.sum, np.mean])

Answer 1

use GroupBy.transform

new_df = \
df1.assign(
    SUM = df1.groupby('City', sort=False)['Numbers'].transform('sum'),
    MEAN = df1.groupby('Country', sort=False)['Numbers'].transform('mean')
).drop_duplicates(['Country', 'City']).drop('Numbers', axis=1)

   Country      City   SUM  MEAN
0    Italy      Rome   300   200
1    Italy      Rome   300   200
2    Italy  Florence   300   200
3  Germany    Berlin   400   500
4  Germany    Munich   500   500
5  Germany      Koln   600   500
6   France     Paris  1500   750
7   France     Paris  1500   750