如何在 pandas 中的分组数据上按列应用用户定义的 function

Question

How to apply a user defined function column wise on grouped data in pandas. The user defined function returns a series of fixed shape.

def getStats(col):
names = ['mean', 'std']
return pd.Series([np.mean(col), np.std(col)], index = names, name = col.name)

df = pd.DataFrame({'city':['c1','c2','c1','c2'],
               'age':[10,20,30,40],
               'sal':[1000,2000,3000,4000]})

grp_data = df.groupby('city')
grp_data.apply(getStats)

I have tried above snippet. But I am not getting the result in expected format.

city| level| age | sal

c1 | mean | x | y

c2 | std | x1 | y1

Could you pls help on this.

Answer 1

I think custom function here is not necessary, rather aggregate by GroupBy.agg with list of aggregate functions and reshape by DataFrame.stack , last DataFrame.rename_axis is for city and level labels:

df = df.groupby('city').agg([np.mean,np.std]).stack().rename_axis(['city','level'])
print (df)
                  age          sal
city level                        
c1   mean   20.000000  2000.000000
     std    14.142136  1414.213562
c2   mean   30.000000  3000.000000
     std    14.142136  1414.213562

---

def q(c):
    def f1(x):
        return x.quantile(c)
    f1.__name__ = f'q{c}'
    return f1

df = (df.groupby('city')
        .agg([np.mean,np.std, q(0.25), q(0.75)])
        .stack()
        .rename_axis(['city','level']))

print (df)
                  age          sal
city level                        
c1   mean   20.000000  2000.000000
     std    14.142136  1414.213562
     q0.25  15.000000  1500.000000
     q0.75  25.000000  2500.000000
c2   mean   30.000000  3000.000000
     std    14.142136  1414.213562
     q0.25  25.000000  2500.000000
     q0.75  35.000000  3500.000000