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python - 在 Python 中合并两个不规则数据框

问题描述

我有两个数据框 df1 和 df2

    ID      Range(US)            Count(US)          Mean(US)
0   690      1-3                 266                4.0
1            4-7                 277                NaN
2   354      1-3                 233                2.0
3            4-7                 85                 NaN
4   947      1-3                 156                4.0

和

    ID   Range(UK)           Count(UK)          Mean(UK)
0   690      1-3                 186                4.0
1            4-7                 25                 NaN
2   354      1-3                 44                 1.0
3   947      1-3                 213                3.0
4            4-7                 33                 NaN

我使用代码合并：
In:df=df1.merge(df2, left_on='deviceid',right_on='deviceid', how='left') df 

 ID  Range(US)   Count(US)    Mean(US)   Range(UK)  Count(UK)    Mean(UK)       
 0  690    1-3      266         4.0        1-3        186         4.0
 1         4-7      277         NaN        4-7        25          NaN
 2         4-7      277         NaN        4-7        33          NaN
 3  354    1-3      233         2.0        1-3        44          1.0
 4         4-7      85          NaN        4-7        25          NaN
 5         4-7      85          NaN        4-7        33          NaN
 6  947    1-3      156         4.0        1-3        213         3.0

从上面我们看到，如果某些值不存在，这些值会再次重复

但预期的输出是

   ID  Range(US)   Count(US)  Mean(US)   Range(UK)  Count(UK)    Mean(UK)       
 0  690    1-3      266         4.0        1-3        186         4.0
 1         4-7      277         NaN        4-7        25          NaN
 2  354    1-3      233         2.0        1-3        44          1.0
 3         4-7      85          NaN        Nan        NaN         NaN
 4  947    1-3      156         4.0        1-3        213         3.0
 5         4-7      Nan         Nan        4-7        33          Nan

标签： pythonpython-3.xpandasdataframemerge

解决方案

首先删除duplicated ID两者中的替换DataFrames：

#df1['ID'] = df1['ID'].mask(df['ID'].duplicated(), '') 
#df2['ID'] = df2['ID'].mask(df['ID'].duplicated(), '') 

print (df1)
    ID Range(US)  Count(US)  Mean(US)
0  690       1-3        266       4.0
1  690       4-7        277       NaN
2  354       1-3        233       2.0
3  354       4-7         85       NaN
4  947       1-3        156       4.0

print (df2)
    ID Range(UK)  Count(UK)  Mean(UK)
0  690       1-3        186       4.0
1  690       4-7         25       NaN
2  354       1-3         44       1.0
3  947       1-3        213       3.0
4  947       4-7         33       NaN

然后通过外连接将两列合并：

df = df1.merge(df2, left_on=['ID', 'Range(US)'], right_on=['ID', 'Range(UK)'], how='outer')
print (df)
    ID Range(US)  Count(US)  Mean(US) Range(UK)  Count(UK)  Mean(UK)
0  690       1-3      266.0       4.0       1-3      186.0       4.0
1  690       4-7      277.0       NaN       4-7       25.0       NaN
2  354       1-3      233.0       2.0       1-3       44.0       1.0
3  354       4-7       85.0       NaN       NaN        NaN       NaN
4  947       1-3      156.0       4.0       1-3      213.0       3.0
5  947       NaN        NaN       NaN       4-7       33.0       NaN

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