基于多列的 Python 合并

Question

我希望合并到两个数据帧 Df1 和 Df2

import pandas as pd

    Df1 = pd.DataFrame({
        'name' : ['jack', None, None],
        'Surname' : ['Peterson', 'Macleans', None],
        'city' : ['Sydney', 'Delhi', 'New york']})

,

Df2 = pd.DataFrame({
        'name' : ['jack', 'Riti', 'Aadi','Jeff'],
        'Surname' : ['Peterson', 'Macleans', 'McDonald','Cooper'],
        'city' : ['Sydney', 'Delhi', 'New york','Tokyo'],
        'Rating' : ['AAA', 'AA', 'A','BBB']})

我希望 Pandas 先根据第一列合并，如果匹配失败，则根据第二列合并，如果失败则根据第三列合并。

我用过

new_df = pd.DataFrame([])
new_df = pd.merge(Df1, Df2,  how='left', left_on=['name','Surname','city'], right_on = ['name','Surname','city'])

但这并没有生成我想要的数据帧

Final_Df = pd.DataFrame({
        'name' : ['jack', None, None],
        'Surname' : ['Peterson', 'Macleans', None],
        'city' : ['Sydney', 'Delhi', 'New york'],
        'Rating' : ['AAA', 'AA', 'A']})

编辑 1： 感谢“Quang Hoang”提供答案！

让我们尝试一个for 循环：

Df1['Rating']=np.nan

for col in Df1.columns[:-1]:
    Df1['Rating'] = Df1['Rating'].fillna(Df1[col].map(Df2.set_index(col)['Rating']))

输出：

   name   Surname      city Rating
0  jack  Peterson    Sydney    AAA
1  None  Macleans     Delhi     AA
2  None      None  New york      A

编辑 2： 如果 Df1 中有额外的列，而 Df2 中没有，则正确的代码如下所示：

import numpy as np

Df1['Rating']=np.nan

for col in ['name', 'Surname','city']:
    Df1['Rating'] = Df1['Rating'].fillna(Df1[col].map(Df2.set_index(col)['Rating']))

编辑 3： 如果 Df1 列中存在重复项，则以下代码确实有效。

import numpy as np

Df1['Rating']=np.nan

for col in ['name', 'Surname','city']:
    Df1['Rating'] = Df1['Rating'].fillna(Df1[col].map(Df2.drop_duplicates(col).set_index(col)['Rating']))

Answer 1

让我们尝试一个for 循环：

Df1['Rating']=np.nan

for col in Df1.columns[:-1]:
    Df1['Rating'] = Df1['Rating'].fillna(Df1[col].map(Df2.set_index(col)['Rating']))

输出：

   name   Surname      city Rating
0  jack  Peterson    Sydney    AAA
1  None  Macleans     Delhi     AA
2  None      None  New york      A