如何在python pandas中参考其他数据框创建新列

Question

输入：

df1=pd.DataFrame({
    "BusId":['abc1','abc2','abc3'],
    "Fair Increase":[2,3,5]
})
df2=pd.DataFrame({
    'BusId':['abc1','abc2','abc3','abc4','abc5'],
    "Fair":[5,6,7,8,9]
})

仅需要针对df2上df1中存在的 BusId 进行计算。

计算增加的​​公平 df2公平+ df1公平增长

预期输出：

BusId   Fair    Increased Fair
abc1    5           7
abc2    6           9
abc3    7           12

Answer 1

您可以将map用于字典查找

m = dict(df2.values)
df1.assign(**{'Increased Fair': df1.BusId.map(m) + df1['Fair Increase']})

  BusId  Fair Increase  Increased Fair
0  abc1              2               7
1  abc2              3               9
2  abc3              5              12

Answer 2

您可以使用df.merge合并df2和df2，创建新列Increased Fair并删除旧列Fair Increase

>>> df3 = df2.merge(df1).set_index('BusId')
>>> df3['Increased Fair'] = df3['Fair'] + df3['Fair Increase']
>>> del df3['Fair Increase']
>>> df3
       Fair  Increased Fair
BusId                      
abc1      5               7
abc2      6               9
abc3      7              12