python – PANDAS vlookup对使用地图的常见索引系列

import pandas as pd
import numpy as np

pb = {"mark_up_id":{"0":"123","1":"456","2":"789","3":"111","4":"222"},"mark_up":{"0":1.2987,"1":1.5625,"2":1.3698,"3":1.3333,"4":1.4589}}

data = {"id":{"0":"K69","1":"K70","2":"K71","3":"K72","4":"K73","5":"K74","6":"K75","7":"K79","8":"K86","9":"K100"},"cost":{"0":29.74,"1":9.42,"2":9.42,"3":9.42,"4":9.48,"5":9.48,"6":24.36,"7":5.16,"8":9.8,"9":3.28},"mark_up_id":{"0":"123","1":"456","2":"789","3":"111","4":"222","5":"333","6":"444","7":"555","8":"666","9":"777"}}

pb = pd.DataFrame(data=pb).set_index('mark_up_id')
df = pd.DataFrame(data=data)

我知道我可以使用类似的东西：

df['mark_up_id'].map(pb['mark_up'])

执行V查找.我想把这个回报加标,然后用每个成本乘以一个公共索引来产生一个名为price的新列.

我知道我可以将两者合并然后运行计算.这就是我产生所需输出的方式.我希望能够做到这一点类似于你如何遍历字典并使用键在另一个字典中查找值并在循环内执行某种计算.考虑到PANDAS数据帧位于字典之上,必须有一种方法可以使用join / map / apply的组合来实现这一点,而无需实际连接内存中的两个数据集.

期望的输出：

desired_output = {"cost":{"0":29.74,"1":9.42,"2":9.42,"3":9.42,"4":9.48},"id":{"0":"K69","1":"K70","2":"K71","3":"K72","4":"K73"},"mark_up_id":{"0":"123","1":"456","2":"111","3":"123","4":"789"},"price":{"0":38.623338,"1":14.71875,"2":12.559686,"3":12.233754,"4":12.985704}}
do = pd.DataFrame(data=desired_output)

奖励积分：

解释接受的答案和……之间的区别

pb.loc[df['mark_up_id']]['mark_up'] * df.set_index('mark_up_id')['cost']

以及为什么我从上面得到的以下lambda函数遇到错误…

df.apply(lambda x : x['cost']*pb.loc[x['mark_up_id']],axis=1 )

返回错误说：

KeyError: ('the label [333] is not in the [index]', u'occurred at index 5')

解决方法:

尝试

df['price'] = df['mark_up_id'].map(pb['mark_up']) * df['cost']

你得到

    cost    id  mark_up_id  price
0   29.74   K69 123         38.623338
1   9.42    K70 456         14.718750
2   9.42    K71 111         12.559686
3   9.42    K72 123         12.233754
4   9.48    K73 789         12.985704