添加缺少的功能，数据帧

Question

我有2个dataframes TRAIN和TEST。我想加入，它含有没有了在TRAIN但不是在TEST项目（Y2，Y3）的信息来改变TRAIN。

TRAIN = pd.DataFrame({'X' : [1,1,1,1,1,2,2,2,2,2],
                      'Y1': [1,1,1,1,1,1,0,0,0,0],
                      'Y4': [1,1,0,0,0,0,0,0,0,0]})

TEST  = pd.DataFrame({'X' : [1,1,1,1,1,2,2,2,2],
                      'Y1': [1,1,0,1,0,1,0,0,0],
                      'Y2': [1,0,1,0,1,0,1,0,1],
                      'Y3': [1,1,0,1,1,0,0,0,0],
                      'Y4': [1,1,0,1,1,0,0,0,0]})

我想要：

TRAIN = pd.DataFrame({'X' : [1,1,1,1,1,2,2,2,2,2],
                      'Y1': [1,1,1,1,1,1,0,0,0,0],
                      'Y4': [1,1,1,1,1,1,0,0,0,0],
                      'Y2': [0,0,0,0,0,0,0,0,0,0],
                      'Y3': [0,0,0,0,0,0,0,0,0,0]})

我试过了：

L_TRAIN = list(TRAIN)
L_TEST  = list(TEST)

def Diff(li1, li2): 
    li_dif = [i for i in li1 + li2 if i not in li1] 
    return li_dif

L_DIFF  = Diff(L_TRAIN, L_TEST)

TRAIN[L_DIFF] = 0

但得到：

KeyError: "['Y2' 'Y3'] not in index"

Answer 1

pandas不支持值分配给多列，所以你需要通过一个遍历它之一：

import pandas as pd 

TRAIN = pd.DataFrame({'X' : [1,1,1,1,1,2,2,2,2,2],
                      'Y1': [1,1,1,1,1,1,0,0,0,0],
                      'Y4': [1,1,0,0,0,0,0,0,0,0]})

TEST  = pd.DataFrame({'X' : [1,1,1,1,1,2,2,2,2],
                      'Y1': [1,1,0,1,0,1,0,0,0],
                      'Y2': [1,0,1,0,1,0,1,0,1],
                      'Y3': [1,1,0,1,1,0,0,0,0],
                      'Y4': [1,1,0,1,1,0,0,0,0]})


diff_cols = set(TEST.columns)-set(TRAIN.columns)

for i in diff_cols:
    TRAIN[i] = 0

print(TRAIN)

输出：

   X  Y1  Y4  Y2  Y3                                                                                                                  
0  1   1   1   0   0                                                                                                                  
1  1   1   1   0   0                                                                                                                  
2  1   1   0   0   0                                                                                                                  
3  1   1   0   0   0                                                                                                                  
4  1   1   0   0   0                                                                                                                  
5  2   1   0   0   0                                                                                                                  
6  2   0   0   0   0