python - 在 Pandas 中合并 "one-to-one"数据帧

Question

我有两个数据框:

df1 = pd.DataFrame(data = 
{'Invoice' : [1, 2, 3, 4, 5], 'Value' : [10, 25, 40, 10, 15]}) 
df2 = pd.DataFrame(data =
{'Invoice' : [2, 3, 5, 2], 'Value' : [25, 11, 15,25], 'TestData':["A",'B','C','D']})  

我已经合并它们并得到 df3:

df3=pd.merge(df1,df2, left_on=["Invoice","Value"], right_on=["Invoice","Value"])

Df3 输出:

   Invoice    Value   TestData
0      2    25        A
1      2    25        D
2      5    15        C

我的问题是如何使用“一对一”合并数据框(我的意思是 - 当发票号 2 在两个数据框之一中仅出现一次(或通常更少)时，则不要使用发票创建另一行合并数据框中的第 2 个)。我想得到这样的东西:

   Invoice    Value   TestData
0      2    25        A
1      5    15        C

或者这个:

   Invoice    Value   TestData
0      2    25        D
1      5    15        C

我只尝试了左右合并，但这行不通 - 总是有两行发票编号为 2。

谢谢你，
亚里克

Answer 1

使用drop_duplicates指定列名，参数 keep='last' 用于最后复制的行:

df2 = df2.drop_duplicates(["Invoice","Value"])
#same as
#df2 = df2.drop_duplicates(["Invoice","Value"], keep='first')
df3=pd.merge(df1,df2, on=["Invoice","Value"])
print (df3)
   Invoice  Value TestData
0        2     25        A
1        5     15        C

---

df2 = df2.drop_duplicates(["Invoice","Value"], keep='last')
df3=pd.merge(df1,df2, on=["Invoice","Value"])
print (df3)
   Invoice  Value TestData
0        2     25        D
1        5     15        C

编辑:

如果需要按所有行分组，有必要添加新列以确保唯一性:

df1['g'] = df1.groupby(['Invoice','Value']).cumcount()
df2['g'] = df2.groupby(['Invoice','Value']).cumcount()

print (df1)
   Invoice  Value  g
0        1     10  0
1        2     25  0
2        3     40  0
3        4     10  0
4        5     15  0

print (df2)
   Invoice TestData  Value  g
0        2        A     25  0
1        3        B     11  0
2        5        C     15  0
3        2        D     25  1

df3=pd.merge(df1,df2, on=["Invoice","Value", "g"]).drop('g', axis=1)
print (df3)
   Invoice  Value TestData
0        2     25        A
1        5     15        C