python - 如何在单行 python pandas 代码中应用 2 个不同的条件？

Question

根据下表:

FamilyVsWWTotal = pd.DataFrame({'date':['2011-4-3','2011-3-5','2011-4-3','2011-5-7'], 'Country':['USA','CAN','USA','MEX'],'Revenues':[1200,1800,1000,800],'Customer':['Exxon','Google','Google','Microsoft']})

FamilyVsWWTotal = FamilyVsWWTotal.loc[FamilyVsWWTotal['Customer'] == 'Google']
FamilyVsWWTotal['total1'] = FamilyVsWWTotal.groupby(['date','Country'])['Revenues']\
                                           .transform('sum')

FamilyVsWWTotal['total1'] 是创建的新列。那么，如何将上面的代码组合成一行代码呢？

Answer 1

我认为您需要先过滤，然后再将输出添加到过滤列:

m = FamilyVsWWTotal['Customer'] == 'Google'

FamilyVsWWTotal.loc[m, 'total1']=FamilyVsWWTotal[m].groupby(['date','Country'])['Revenues']\
                                                   .transform('sum')

print (FamilyVsWWTotal)
  Country   Customer  Revenues      date  total1
0     USA      Exxon      1200  2011-4-3     NaN
1     CAN     Google      1800  2011-3-5  1800.0
2     USA     Google      1000  2011-4-3  1000.0
3     MEX  Microsoft       800  2011-5-7     NaN

对于单行解决方案:

FamilyVsWWTotal.loc[FamilyVsWWTotal['Customer'] == 'Google', 'total1'] = \
FamilyVsWWTotal[FamilyVsWWTotal['Customer']=='Google'].groupby(['date','Country'])['Revenues']\
                                                      .transform('sum')

print (FamilyVsWWTotal)
  Country   Customer  Revenues      date  total1
0     USA      Exxon      1200  2011-4-3     NaN
1     CAN     Google      1800  2011-3-5  1800.0
2     USA     Google      1000  2011-4-3  1000.0
3     MEX  Microsoft       800  2011-5-7     NaN