有可能，但真的很复杂：
np.random.seed(234)
df= pd.DataFrame(np.random.randint(5,8,(1000,4)), columns=['a','b','c','d'])
wm = lambda x: (x * df.loc[x.index, "c"]).sum() / x.sum()
wm.__name__ = 'wa'
f = lambda x: x.sum() / df['b'] .sum()
f.__name__ = '%'
g = df.groupby('a').agg(
{'b':['sum', f, 'mean', wm],
'c':['sum','mean'],
'd':['sum']})
g.columns = g.columns.map('_'.join)
print (g)
d_sum c_sum c_mean b_sum b_% b_mean b_wa
a
5 2104 2062 5.976812 2067 0.344672 5.991304 5.969521
6 1859 1857 5.951923 1875 0.312656 6.009615 5.954667
7 2058 2084 6.075802 2055 0.342671 5.991254 6.085645
适用的解决方案：
def func(x):
# print (x)
b1 = x['b'].sum()
b2 = x['b'].sum() / df['b'].sum()
b3 = (x['b'] * x['c']).sum() / x['b'].sum()
b4 = x['b'].mean()
c1 = x['c'].sum()
c2 = x['c'].mean()
d1 = x['d'].sum()
cols = ['b sum','b %','wa', 'b mean', 'c sum', 'c mean', 'd sum']
return pd.Series([b1,b2,b3,b4,c1,c2,d1], index=cols)
g = df.groupby('a').apply(func)
print (g)
b sum b % wa b mean c sum c mean d sum
a
5 2067.0 0.344672 5.969521 5.991304 2062.0 5.976812 2104.0
6 1875.0 0.312656 5.954667 6.009615 1857.0 5.951923 1859.0
7 2055.0 0.342671 6.085645 5.991254 2084.0 6.075802 2058.0
g.loc['total']=g.sum()
print (g)
b sum b % wa b mean c sum c mean d sum
a
5 2067.0 0.344672 5.969521 5.991304 2062.0 5.976812 2104.0
6 1875.0 0.312656 5.954667 6.009615 1857.0 5.951923 1859.0
7 2055.0 0.342671 6.085645 5.991254 2084.0 6.075802 2058.0
total 5997.0 1.000000 18.009832 17.992173 6003.0 18.004536 6021.0
