使用完全外部联接在 pandas 中联接两个数据框 [英] Joining two dataframes in pandas using full outer join

问题描述

我在熊猫中有两个数据框，如下所示. EmpID是两个数据帧中的主键.

I've two dataframes in pandas as shown below. EmpID is a primary key in both dataframes.

df_first = pd.DataFrame([[1, 'A',1000], [2, 'B',np.NaN],[3,np.NaN,3000],[4, 'D',8000],[5, 'E',6000]], columns=['EmpID', 'Name','Salary'])
df_second = pd.DataFrame([[1, 'A','HR','Delhi'], [8, 'B','Admin','Mumbai'],[3,'C','Finance',np.NaN],[9, 'D','Ops','Banglore'],[5, 'E','Programming',np.NaN],[10, 'K','Analytics','Mumbai']], columns=['EmpID', 'Name','Department','Location'])

我想将这两个数据框与EmpID结合在一起，以便

I want to join these two dataframes with EmpID so that

1. 如果一个数据帧中的缺失数据存在且键匹配，则可以用另一张表中的值填充
2. 如果有带有新键的观测值，则应将其附加到结果数据框中

我已经使用以下代码实现了这一目标.

I've used below code for achieving this.

merged_df = pd.merge(df_first,df_second,how='outer',on=['EmpID'])

但是此代码为我提供了我不想要的重复列，因此我仅使用两个表中的唯一列进行合并.

But this code gives me duplicate columns which I don't want so I only used unique columns from both tables for merging.

ColNames = list(df_second.columns.difference(df_first.columns))
ColNames.append('EmpID')
merged_df = pd.merge(df_first,df_second,how='outer',on=['EmpID'])

现在我不会得到重复的列，但是在键匹配的观察结果中也不会得到值.

Now I don't get duplicate columns but don't get value either in observations where key matches.

如果有人可以帮助我，我将非常感激.

I'll really appreciate if someone can help me with this.

关于， 凯拉什·奈吉

推荐答案

似乎您需要 set_index ，用于匹配由EmpID列创建的索引:

It seems you need combine_first with set_index for match by indices created by columns EmpID:

df = df_first.set_index('EmpID').combine_first(df_second.set_index('EmpID')).reset_index()
print (df)
   EmpID   Department  Location Name  Salary
0      1           HR     Delhi    A  1000.0
1      2          NaN       NaN    B     NaN
2      3      Finance       NaN    C  3000.0
3      4          NaN       NaN    D  8000.0
4      5  Programming       NaN    E  6000.0
5      8        Admin    Mumbai    B     NaN
6      9          Ops  Banglore    D     NaN
7     10    Analytics    Mumbai    K     NaN

对于某些列顺序，需要 reindex :

For some order of columns need reindex:

#concatenate all columns names togetehr and remove dupes
ColNames = pd.Index(np.concatenate([df_second.columns, df_first.columns])).drop_duplicates()
print (ColNames)
Index(['EmpID', 'Name', 'Department', 'Location', 'Salary'], dtype='object')

df = (df_first.set_index('EmpID')
      .combine_first(df_second.set_index('EmpID'))
      .reset_index()
      .reindex(columns=ColNames))
print (df)
   EmpID Name   Department  Location  Salary
0      1    A           HR     Delhi  1000.0
1      2    B          NaN       NaN     NaN
2      3    C      Finance       NaN  3000.0
3      4    D          NaN       NaN  8000.0
4      5    E  Programming       NaN  6000.0
5      8    B        Admin    Mumbai     NaN
6      9    D          Ops  Banglore     NaN
7     10    K    Analytics    Mumbai     NaN

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