问Pandas连接不能正常工作
EN

Stack Overflow用户

提问于 2019-06-20 07:55:50

回答 1查看 124关注 0票数 0

所以我已经在我的深度学习分类器上建立了一个标签归档，我想将已经存在的2D归档的标签连接到我刚刚创建的一个。

存在的是'y_trainvalid‘( 39209，43)，它代表43个类别的39209个图像。我尝试添加的新标签档案是'new_file_label‘(23，43)。在这些归档文件中，如果与类匹配，则将数字设置为1，如果不匹配，则设置为0。下面是这两者的示例：

print(y_trainvalid)
print(new_file_label)

       0    1    2    3    4    5    6   ...   36   37   38   39   40   41   42
0     0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
1     0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
2     0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
3     0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4     0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  1.0  0.0  0.0  0.0  0.0
5     0.0  0.0  0.0  0.0  0.0  1.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
6     0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
7     0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  1.0  0.0  0.0  0.0
8     0.0  1.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
9     0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
10    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
11    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
12    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
13    0.0  0.0  1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
14    0.0  0.0  0.0  1.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
15    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
16    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
17    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
18    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
19    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
20    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
21    0.0  1.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
22    0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
23    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
24    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
25    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
26    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
27    0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
28    0.0  0.0  1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
29    0.0  0.0  0.0  0.0  0.0  1.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...
4380  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4381  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4382  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4383  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4384  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4385  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4386  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4387  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4388  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4389  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4390  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4391  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4392  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4393  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4394  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4395  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4396  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4397  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4398  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4399  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4400  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4401  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4402  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4403  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4404  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4405  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4406  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4407  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4408  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4409  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0

[39209 rows x 43 columns]
      0    1    2    3    4    5    6  ...   36   37   38   39   40   41   42
0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
1   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
2   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
3   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
4   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
5   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
6   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
7   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
8   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
9   0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
10  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
11  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
12  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
13  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
14  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
15  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
16  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
17  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
18  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
19  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
20  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
21  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
22  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0

[23 rows x 43 columns]

当我尝试使用以下命令进行连接时：

y_trainvalid2 = pd.concat([y_trainvalid, new_file_label], ignore_index=True)

像这样的东西出现了：

 0    1    2    3    4    5    6  ...   41   42    5    6    7    8    9
39204  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN
39205  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN
39206  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN
39207  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN
39208  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN
39209  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39210  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39211  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39212  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39213  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39214  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39215  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39216  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39217  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39218  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39219  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39220  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39221  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39222  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39223  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39224  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39225  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39226  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39227  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39228  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39229  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39230  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0
39231  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0

就好像它将列数增加了一倍以适应数据，而不是将新数据放在它的下面。我不确定为什么会发生这种情况，因为我非常确定两个标签存档的列数是相同的。

当我使用'y_trainvalid2.head().to_dict()‘命令打印时，显示如下：

{0: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '0': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 1: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '1': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 10: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '10': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 11: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '11': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 12: {0: 1.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '12': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 13: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '13': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 14: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '14': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 15: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '15': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 16: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '16': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 17: {0: 0.0, 1: 1.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '17': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 18: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '18': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 19: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '19': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 2: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '2': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 20: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '20': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 21: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '21': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 22: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '22': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 23: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '23': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 24: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '24': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 25: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '25': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 26: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '26': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 27: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '27': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 28: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '28': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 29: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '29': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 3: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '3': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 30: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '30': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 31: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '31': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 32: {0: 0.0, 1: 0.0, 2: 0.0, 3: 1.0, 4: 0.0},
 '32': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 33: {0: 0.0, 1: 0.0, 2: 1.0, 3: 0.0, 4: 0.0},
 '33': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 34: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '34': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 35: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '35': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 36: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '36': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 37: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '37': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 38: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 1.0},
 '38': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 39: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '39': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 4: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '4': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 40: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '40': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 41: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '41': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 42: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '42': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 5: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '5': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 6: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '6': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 7: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '7': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 8: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '8': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan},
 9: {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
 '9': {0: nan, 1: nan, 2: nan, 3: nan, 4: nan}}

我该如何解决这个问题？

python

arrays

pandas

dataframe

concatenation

回答 1

Stack Overflow用户

回答已采纳

发布于 2019-06-20 08:27:17

y_trainvalid.columns = [str(x) for x in y_trainvalid.columns]
new_file_label.columns = [str(x) for x in new_file_label.columns]
y_trainvalid2 = pd.concat([y_trainvalid, new_file_label])

票数 1

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原文链接：

https://stackoverflow.com/questions/56677321

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