Python 熊猫：连接和重新索引数据帧

我想使用一个新的索引将两个熊猫数据帧合并成一个新的第三个数据帧。假设我从以下内容开始：

df = pd.DataFrame(np.ones(25).reshape((5,5)),index = ['A','B','C','D','E'])
df1 = pd.DataFrame(np.ones(25).reshape((5,5))*2,index = ['A','B','C','D','E'])
df[2] = np.nan
df1[3] = np.nan
df[4] = np.nan
df1[4] = np.nan

我希望以最简单的方式实现以下结果：

NewIndex    OldIndex    df  df1
1   A   1   2
2   B   1   2
3   C   1   2
4   D   1   2
5   E   1   2
6   A   1   2
7   B   1   2
8   C   1   2
9   D   1   2
10  E   1   2
11  A   NaN 2
12  B   NaN 2
13  C   NaN 2
14  D   NaN 2
15  E   NaN 2
16  A   1   NaN
17  B   1   NaN
18  C   1   NaN
19  D   1   NaN
20  E   1   NaN

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最好的方法是什么？

您必须取消数据帧的堆栈，然后重新索引连接的数据帧

import numpy as np
import pandas as pd
# test data
df = pd.DataFrame(np.ones(25).reshape((5,5)),index = ['A','B','C','D','E'])
df1 = pd.DataFrame(np.ones(25).reshape((5,5))*2,index = ['A','B','C','D','E'])
df[2] = np.nan
df1[3] = np.nan
df[4] = np.nan
df1[4] = np.nan

# unstack tables and concat
newdf = pd.concat([df.unstack(),df1.unstack()], axis=1)
# reset multiindex for level 1
newdf.reset_index(1, inplace=True)
# rename columns
newdf.columns = ['OldIndex','df','df1']
# drop old index
newdf = newdf.reset_index().drop('index',1)
# set index from 1
newdf.index = np.arange(1, len(newdf) + 1)
# rename new index
newdf.index.name='NewIndex'
print(newdf)

输出：

         OldIndex   df  df1
NewIndex                   
1               A  1.0  2.0
2               B  1.0  2.0
3               C  1.0  2.0
4               D  1.0  2.0
5               E  1.0  2.0
6               A  1.0  2.0
7               B  1.0  2.0
8               C  1.0  2.0
9               D  1.0  2.0
10              E  1.0  2.0
11              A  NaN  2.0
12              B  NaN  2.0
13              C  NaN  2.0
14              D  NaN  2.0
15              E  NaN  2.0
16              A  1.0  NaN
17              B  1.0  NaN
18              C  1.0  NaN
19              D  1.0  NaN
20              E  1.0  NaN
21              A  NaN  NaN
22              B  NaN  NaN
23              C  NaN  NaN
24              D  NaN  NaN
25              E  NaN  NaN

---

是的，这个答案好多了！谢谢你的评论。