外连接 Pandas 数据框

我正在尝试外部连接（在 df1 上）两个 pandas 数据框。以下是示例数据框：

df1:
Index   Team 1   Team 2   Team1_Score    Team2_Score
 0       A        B        25              56
 1       B        C        30              55
 2       D        E        35              75

df2:
Index   Team 1   Team 2   Team1_Avg     Team2_Avg
 0       A        B        5              15
 1       G        F        10             25
 2       C        B        15             35

dfcombined
Index   Team 1   Team 2   Team1_Score    Team2_Score    Team2_Avg     Team1_Avg
 0       A        B        25              56           5             15
 1       B        C        30              55           35            15
 2       D        E        35              75        

我试图使用 pandasql 模块，但我不确定如何处理在 df1 中加入索引 1 和在 df2 中加入索引 2 的情况，因为团队的顺序是相反的。通过 pandasql 模块，我不确定如果团队顺序相反，如何切换组合数据框中的团队平均值。

我将不胜感激任何帮助。

Setup -

df1

      Team 1 Team 2  Team1_Score  Team2_Score
Index                                        
0          A      B           25           56
1          B      C           30           55
2          D      E           35           75

df2

      Team 1 Team 2  Team1_Avg  Team2_Avg
Index                                    
0          A      B          5         15
1          F      G         25         10
2          B      C         35         15

首先，我们需要对Team *列，并相应地排序Team*_Score列以同样的方式。我们将使用argsort这样做。

i = np.arange(len(df1))[:, None]
j = np.argsort(df1[['Team 1', 'Team 2']], axis=1).values

df1[['Team 1', 'Team 2']] = df1[['Team 1', 'Team 2']].values[i, j]
df1[['Team1_Score', 'Team2_Score']] = df1[['Team1_Score', 'Team2_Score']].values[i, j]

现在，重复相同的过程df2, with Team * and Team*_Avg.

j = np.argsort(df2[['Team 1', 'Team 2']], axis=1).values

df2[['Team 1', 'Team 2']] = df2[['Team 1', 'Team 2']].values[i, j]
df2[['Team1_Avg', 'Team2_Avg']] = df2[['Team1_Avg', 'Team2_Avg']].values[i, j]

现在，执行左外merge -

df1.merge(df2, on=['Team 1', 'Team 2'], how='left')

  Team 1 Team 2  Team1_Score  Team2_Score Team1_Avg Team2_Avg
0      A      B           25           56         5        15
1      B      C           30           55        35        15
2      D      E           35           75