熊猫数据框合并重复值以对齐

Question

这里是原始数据集源的链接： dataset for capacity 和 dataset for type

或修改版dataset modified1和dataset modified2

我有 2 个要合并的数据框：

  first_df=pd.DataFrame([['2001','Abu Dhabi','100-','462'],['2001','Abu Dhabi','100','44'],['2001','Abu Dhabi','200','462'],['2001','Dubai','100-','40'],['2001','Dubai','100','30'],['2001','Dubai','200','51'],['2002','Abu Dhabi','100-','300'],['2002','Abu Dhabi','100','220'],['2002','Abu Dhabi','200','56'],['2002','Dubai','100-','55'],['2002','Dubai','100','67'],['2002','Dubai','200','89']],columns=['Year','Emirate','Capacity','Number'])

  second_df=pd.DataFrame([['2001','Abu Dhabi','Performed','45'],['2001','Abu Dhabi','Not Performed','76'],['2001','Dubai','Performed','90'],['2001','Dubai','Not Performed','50'],['2002','Abu Dhabi','Performed','78'],['2002','Abu Dhabi','Not Performed','45'],['2002','Dubai','Performed','76'],['2002','Dubai','Not Performed','58']],columns=['Year','Emirate','Type','Value'])

所以我为两个数据框设置了 multiIndex：

first=first_df.set_index(['Year','Emirate'])
second=second_df.set_index(['Year','Emirate'])

并合并：

merged=first.merge(second,how='outer',right_index=True,left_index=True)

结果如下：

合并

| Year , Emirate | Capacity | count | friday | count | |:----------------------|:-----------|--------:|:--------------|--------:| | ('2001', 'Abu Dhabi') | 100- | 462 | Performed | 45 | | ('2001', 'Abu Dhabi') | 100- | 462 | Not Performed | 76 | | ('2001', 'Abu Dhabi') | 100 | 44 | Performed | 45 | | ('2001', 'Abu Dhabi') | 100 | 44 | Not Performed | 76 | | ('2001', 'Abu Dhabi') | 200 | 657 | Performed | 45 | | ('2001', 'Abu Dhabi') | 200 | 657 | Not Performed | 76 | | ('2001', 'Dubai') | 100- | 40 | Performed | 90 | | ('2001', 'Dubai') | 100- | 40 | Not Performed | 50 | | ('2001', 'Dubai') | 100 | 30 | Performed | 90 | | ('2001', 'Dubai') | 100 | 30 | Not Performed | 50 | | ('2001', 'Dubai') | 200 | 51 | Performed | 90 | | ('2001', 'Dubai') | 200 | 51 | Not Performed | 50 | | ('2002', 'Abu Dhabi') | 100- | 300 | Performed | 78 | | ('2002', 'Abu Dhabi') | 100- | 300 | Not Performed | 45 | | ('2002', 'Abu Dhabi') | 100 | 220 | Performed | 78 | | ('2002', 'Abu Dhabi') | 100 | 220 | Not Performed | 45 | | ('2002', 'Abu Dhabi') | 200 | 56 | Performed | 78 | | ('2002', 'Abu Dhabi') | 200 | 56 | Not Performed | 45 | | ('2002', 'Dubai') | 100- | 55 | Performed | 76 | | ('2002', 'Dubai') | 100- | 55 | Not Performed | 58 | | ('2002', 'Dubai') | 100 | 67 | Performed | 76 | | ('2002', 'Dubai') | 100 | 67 | Not Performed | 58 | | ('2002', 'Dubai') | 200 | 89 | Performed | 76 | | ('2002', 'Dubai') | 200 | 89 | Not Performed | 58 |

并尝试与以下结果连接：

joined=pd.concat([first,second])

已加入

| Year , Emirate | Capacity | Number | Type | Value | |:----------------------|:-----------|---------:|:--------------|--------:| | ('2001', 'Abu Dhabi') | 100- | 462 | nan | nan | | ('2001', 'Abu Dhabi') | 100 | 44 | nan | nan | | ('2001', 'Abu Dhabi') | 200 | 657 | nan | nan | | ('2001', 'Dubai') | 100- | 40 | nan | nan | | ('2001', 'Dubai') | 100 | 30 | nan | nan | | ('2001', 'Dubai') | 200 | 51 | nan | nan | | ('2002', 'Abu Dhabi') | 100- | 300 | nan | nan | | ('2002', 'Abu Dhabi') | 100 | 220 | nan | nan | | ('2002', 'Abu Dhabi') | 200 | 56 | nan | nan | | ('2002', 'Dubai') | 100- | 55 | nan | nan | | ('2002', 'Dubai') | 100 | 67 | nan | nan | | ('2002', 'Dubai') | 200 | 89 | nan | nan | | ('2001', 'Abu Dhabi') | nan | nan | Performed | 45 | | ('2001', 'Abu Dhabi') | nan | nan | Not Performed | 76 | | ('2001', 'Dubai') | nan | nan | Performed | 90 | | ('2001', 'Dubai') | nan | nan | Not Performed | 50 | | ('2002', 'Abu Dhabi') | nan | nan | Performed | 78 | | ('2002', 'Abu Dhabi') | nan | nan | Not Performed | 45 | | ('2002', 'Dubai') | nan | nan | Performed | 76 | | ('2002', 'Dubai') | nan | nan | Not Performed | 58 |

所以连接在一起的两个数据框不应该有重复（如第一次合并）或向下移动（如 concat 变体）。 有什么解决方案可以使 2 个数据框很好地对齐？

以下是所需输出的样子：

| | Year | Emirate | Capacity | Number | Type | Value | |---:|-------:|:----------|:-----------|---------:|:--------------|--------:| | 0 | | | 100- | 462 | Performed | 45 | | 1 | | Abu Dhabi | 100 | 44 | Not Performed | 76 | | 2 | | | 200 | 657 | NaN | nan | | 3 | 2001 | | 100- | 40 | Performed | 90 | | 4 | | Dubai | 100 | 30 | Not Performed | 50 | | 5 | | | 200 | 51 | NaN | nan | | 6 | | | 100- | 300 | Performed | 78 | | 7 | | Abu Dhabi | 100 | 220 | Not Performed | 45 | | 8 | 2002 | | 200 | 56 | NaN | nan | | 9 | | | 100- | 55 | Performed | 76 | | 10 | | Dubai | 100 | 67 | Not Performed | 58 | | 11 | | | 200 | 89 | NaN | nan |

enter code here

Answer 1

我在这里看到了问题，当您加入 ['year','Emirate'] 时，您的数据会导致交叉连接。例如，2001 Abu Dhabi 与 2001 Abu Dhabi 在“执行和未执行”的两个数据框中加入。基本上这是 m x n 关系连接数据集。除非您指定一个可以唯一标识每一行的主键，否则您最终会得到相同的结果。

Answer 2

我假设您的数据还不正确，因为您的预期输出是可能的，但现在不符合您的逻辑。

您在second_df 中缺少第三个key column，即capacity。如果我们添加此列并执行left merge，我们可以实现您的预​​期输出。

顺便说一句，我们不需要将列设置为索引，所以解决方案如下所示。

# Clean up and create correct dataframes
first_df=pd.DataFrame([['2001','Abu Dhabi','100-','462'],
                       ['2001','Abu Dhabi','100','44'],
                       ['2001','Abu Dhabi','200','657'],
                       ['2001','Dubai','100-','40'],
                       ['2001','Dubai','100','30'],
                       ['2001','Dubai','200','51'],
                       ['2002','Abu Dhabi','100-','300'],
                       ['2002','Abu Dhabi','100','220'],
                       ['2002','Abu Dhabi','200','56'],
                       ['2002','Dubai','100-','55'],
                       ['2002','Dubai','100','67'],
                       ['2002','Dubai','200','89']],columns=['Year','Emirate','Capacity','Number'])
second_df=pd.DataFrame([['2001','Abu Dhabi','100-','Performed','45'],
                        ['2001','Abu Dhabi','100','Not Performed','76'],
                        ['2001','Abu Dhabi','','',''],
                        ['2001','Dubai','100-','Performed','90'],
                        ['2001','Dubai','100','Not Performed','50'],
                        ['2001','Dubai','','',''],
                        ['2002','Abu Dhabi','100-','Performed','78'],
                        ['2002','Abu Dhabi','100','Not Performed','45'],
                        ['2002','Abu Dhabi','', '', ''],
                        ['2002','Dubai','100-','Performed','76'],
                        ['2002','Dubai','100','Not Performed','58'],
                        ['2002','Dubai', '', '', '']],columns=['Year','Emirate','Capacity','Type','Value'])

# Perform a left merge to get correct output
merged=first_df.merge(second_df,how='left',on=['Year', 'Emirate', 'Capacity'])

输出

    Year    Emirate     Capacity    Number  Type            Value
0   2001    Abu Dhabi   100-        462     Performed       45
1   2001    Abu Dhabi   100         44      Not Performed   76
2   2001    Abu Dhabi   200         657     NaN             NaN
3   2001    Dubai       100-        40      Performed       90
4   2001    Dubai       100         30      Not Performed   50
5   2001    Dubai       200         51      NaN             NaN
6   2002    Abu Dhabi   100-        300     Performed       78
7   2002    Abu Dhabi   100         220     Not Performed   45
8   2002    Abu Dhabi   200         56      NaN             NaN
9   2002    Dubai       100-        55      Performed       76
10  2002    Dubai       100         67      Not Performed   58
11  2002    Dubai       200         89      NaN             NaN