pandas 左外连接多个列上的多个数据框 [英] Pandas left outer join multiple dataframes on multiple columns

问题描述

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SQL等效的左外部联接

I am new to using DataFrame and I would like to know how to perform a SQL equivalent of left outer join on multiple columns on a series of tables

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

本质上，我想执行类似以下SQL代码的操作(注意df3在Year上未加入):

Essentially I want to do something like this SQL code (Notice that df3 is not joined on Year):

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

结果应如下所示:

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联接，但无法弄清楚如何在多个表上以及涉及多个联接时执行此操作.有人可以帮我吗?

I have tried using merge and join but can't figure out how to do it on multiple tables and when there are multiple joints involved. Could someone help me on this please?

谢谢

推荐答案

将它们合并为两个步骤，首先df1和df2，然后将其结果合并到df3.

Merge them in two steps, df1 and df2 first, and then the result of that to df3.

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

我从df3删除了一年，因为您不需要上次加入.

I dropped year from df3 since you don't need it for the last join.

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]

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