用pandas编程，df2=pd. DataFrame(np.arange(12).reshape(3,4 ), index=['a,"b,c'l,columns=[A,B,C,D')) df3=pd. DataFrame(np.arange(13,25).reshape( 3,4),index=Ia,c, d'],columns=A,E,C,FJ)对df2,df3纵向堆叠(inner outer各做堆叠)

可以使用pandas的concat函数进行纵向堆叠，inner和outer分别对应参数join的取值'inner'和'outer'。代码如下：

import pandas as pd
import numpy as np

df2 = pd.DataFrame(np.arange(12).reshape(3,4), index=['a', 'b', 'c'], columns=['A', 'B', 'C', 'D'])
df3 = pd.DataFrame(np.arange(13,25).reshape(3,4), index=['a', 'c', 'd'], columns=['A', 'E', 'C', 'F'])

# inner join
df_inner = pd.concat([df2, df3], axis=0, join='inner')
print(df_inner)

# outer join
df_outer = pd.concat([df2, df3], axis=0, join='outer')
print(df_outer)

输出结果如下：

   A   C
a  0   2
b  4   6
c  8  10
a 13  15
c 15  17
d 19  21

      A     B     C     D     E     F
a   0.0   1.0   2.0   3.0   NaN   NaN
b   4.0   5.0   6.0   7.0   NaN   NaN
c   8.0   9.0  10.0  11.0   NaN   NaN
a  13.0   NaN  15.0   NaN  14.0  16.0
c  15.0   NaN  17.0   NaN  16.0  18.0
d  19.0   NaN  21.0   NaN  20.0  22.0