首页用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)

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)

时间: 2023-05-11 12:06:15 浏览: 170

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 ```

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