Question

我是使用DataFrame的新手，我想知道如何在一系列表的多个列上执行左外连接的SQL等价

示例：

df1: 
Year    Week    Colour    Val1 
2014       A       Red      50
2014       B       Red      60
2014       B     Black      70
2014       C       Red      10
2014       D     Green      20

df2:
Year    Week    Colour    Val2
2014       A     Black      30
2014       B     Black     100
2014       C     Green      50
2014       C       Red      20
2014       D       Red      40

df3:
Year    Week    Colour    Val3
2013       B       Red      60
2013       C     Black      80
2013       B     Black      10
2013       D     Green      20
2013       D       Red      50

基本上我想做类似这样的SQL代码（请注意df3未在Year上加入）：

SELECT df1.*, df2.Val2, df3.Val3
FROM df1
  LEFT OUTER JOIN df2
    ON df1.Year = df2.Year
    AND df1.Week = df2.Week
    AND df1.Colour = df2.Colour
  LEFT OUTER JOIN df3
    ON df1.Week = df3.Week
    AND df1.Colour = df3.Colour

结果如下：

Year    Week    Colour    Val1    Val2    Val3
2014       A       Red      50    Null    Null
2014       B       Red      60    Null      60
2014       B     Black      70     100    Null
2014       C       Red      10      20    Null
2014       D     Green      20    Null    Null

我尝试过使用合并和连接，但无法弄清楚如何在多个表上进行操作以及何时涉及多个关节。有人可以帮我吗？

由于

Answer 1

首先将它们合并为df1和df2两个步骤，然后将结果合并到df3。

In [33]: s1 = pd.merge(df1, df2, how='left', on=['Year', 'Week', 'Colour'])

我从df3开始，因为你最后一次加入时不需要它。

In [39]: df = pd.merge(s1, df3[['Week', 'Colour', 'Val3']],
                       how='left', on=['Week', 'Colour'])

In [40]: df
Out[40]: 
   Year Week Colour  Val1  Val2 Val3
0  2014    A    Red    50   NaN  NaN
1  2014    B    Red    60   NaN   60
2  2014    B  Black    70   100   10
3  2014    C    Red    10    20  NaN
4  2014    D  Green    20   NaN   20

[5 rows x 6 columns]

Answer 2

也可以使用@ TomAugspurger的答案的紧凑版本来做到这一点，如下所示：

df = df1.merge(df2, how='left', on=['Year', 'Week', 'Colour']).merge(df3[['Week', 'Colour', 'Val3']], how='left', on=['Week', 'Colour'])

Pandas左外连接多个列上的多个数据帧

2 个答案: