Python Pandas 中的 Concat (Merge) 非对称数据框表创建关系表

Question

所以我有两个表，Table1 和 Table2

表1：

Key1	Key2
A1	B1

表2：

Key3	Key4	Key5
C1	D1	E1
C2	D2	E2
C3	D3	E3
...	...	...

我想结合表 3 中的两种材料

Key2	Key4
B1	D1
B1	D2
B1	D3
B1	...

pandas 中是否有一个函数可以实现这一点

谢谢。

更新： 下面使用 Andrej Kesely 的解决方案。

代码：

#create relationship tables from dataframes above
userinforel = pd.merge(computerlist,userinfo , how="cross")[["Name","UserInfo.UserName"]] #works
monitorlistrel = pd.merge(computerlist,monitorlist , how="cross")[["Name","SerialNumber"]] #does not work

输出：

KeyError: "None of [Index(['Name', 'SerialNumber'], dtype='object')] are in the [columns]"
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
p:\Tech Support\LoginReport\LoginReport-To-SQL\LoginReport-to-SQL.py in <module>
    127 
    128 if __name__ == "__main__":
--> 129     main()

p:\Tech Support\LoginReport\LoginReport-To-SQL\LoginReport-to-SQL.py in main()
    117             json_to_sql(file.path) """
    118 
--> 119     json_to_sql('SK82-081AL101-20210903.0853.json') #loginreport v2
    120     #json_to_sql('SK82-081AL026-20210803.0849.json') #loginreport v1
    121 

p:\Tech Support\LoginReport\LoginReport-To-SQL\LoginReport-to-SQL.py in json_to_sql(JSONFILE)
     80         #create relationship tables from dataframes above
     81         userinforel = pd.merge(computerlist,userinfo , how="cross")[["Name","UserInfo.UserName"]]
---> 82         monitorlistrel = pd.merge(computerlist,monitorlist , how="cross")[["Name","SerialNumber"]]
     83         #printerlistrel = pd.merge(computerlist,printerlist , how="cross")[["Name","ID"]]
     84         #programlistrel = pd.merge(computerlist,programlist , how="cross")[["Name","IDName"]]

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   3459             if is_iterator(key):
   3460                 key = list(key)
-> 3461             indexer = self.loc._get_listlike_indexer(key, axis=1)[1]
   3462 
   3463         # take() does not accept boolean indexers

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis)
   1312             keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
   1313 
-> 1314         self._validate_read_indexer(keyarr, indexer, axis)
   1315 
   1316         if needs_i8_conversion(ax.dtype) or isinstance(

~\AppData\Local\Programs\Python\Python39\lib\site-packages\pandas\core\indexing.py in _validate_read_indexer(self, key, indexer, axis)
   1372                 if use_interval_msg:
   1373                     key = list(key)
-> 1374                 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
   1375 
   1376             not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())

KeyError: "None of [Index(['Name', 'SerialNumber'], dtype='object')] are in the [columns]"

更新 2： 原来有与下面提到的 Andrej Kesely 类似的列名，使用后缀来解决问题。

#create relationship tables from dataframes above
userinforel = pd.merge(computerlist,userinfo, how="cross")[["Name","UserInfo.UserName"]] #works
monitorlistrel = pd.merge(computerlist,monitorlist, how="cross")[["Name_x","SerialNumber_y"]] #works

Answer 1

您可以尝试交叉合并：

df_out = pd.merge(df1, df2, how="cross")[["Key2", "Key4"]]
print(df_out)

打印：

  Key2 Key4
0   B1   D1
1   B1   D2
2   B1   D3