5. 分组聚合后unstacking

#  读取employee数据集，求出每个种族的平均工资
 In[34]: employee = pd.read_csv('data/employee.csv')
 In[35]: employee.groupby('RACE')['BASE_SALARY'].mean().astype(int)
out[35]: RACE
         American Indian or Alaskan Native    60272
         Asian/Pacific Islander               61660
         Black or African American            50137
         Hispanic/Latino                      52345
         Others                               51278
         White                                64419
         Name: BASE_SALARY, dtype: int64

#  对种族和性别分组，求平均工资
 In[36]: agg = employee.groupby(['RACE', 'GENDER'])['BASE_SALARY'].mean().astype(int)
         agg
out[36]: RACE                               GENDER
         American Indian or Alaskan Native  Female    60238
                                            Male      60305
         Asian/Pacific Islander             Female    63226
                                            Male      61033
         Black or African American          Female    48915
                                            Male      51082
         Hispanic/Latino                    Female    46503
                                            Male      54782
         Others                             Female    63785
                                            Male      38771
         White                              Female    66793
                                            Male      63940
         Name: BASE_SALARY, dtype: int64

#  对索引层GENDER做unstack
 In[37]: agg.unstack('GENDER')
out[37]:

#  对索引层RACE做unstack
 In[38]: agg.unstack('RACE')
out[38]:

更多

#  按RACE和GENDER分组，求工资的平均值、最大值和最小值
 In[39]: agg2 = employee.groupby(['RACE', 'GENDER'])['BASE_SALARY'].agg(['mean', 'max', 'min']).astype(int)
         agg2
out[39]:

#  此时unstack('GENDER')会生成多级列索引，可以用stack和unstack调整结构
agg2.unstack('GENDER')