我想使用新索引将两个pandas数据帧合并到一个新的第三个数据帧中。假设我从以下开始:
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
我希望用最简单的方法来实现以下结果:
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
最好的方法是什么?
答案 0 :(得分:1)
您必须取消堆叠数据帧,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