熊猫串联无法正常工作答案

【问题标题】：Pandas Concatenation not working properly熊猫串联无法正常工作
【发布时间】：2019-06-19 23:55:50
【问题描述】：

所以我一直在我的深度学习分类器上设置一个标签存档，我想将已经存在的 2D 存档的标签连接到我刚刚制作的一个中。

存在的一个是 'y_trainvalid' (39209, 43)，它代表 43 个类别中的 39209 个图像。我要添加的新标签存档是“new_file_label”（23、43）。在这些档案中，如果它与类匹配，则数字设置为 1，如果不匹配，则设置为 0。以下是两者的示例：

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]

当我尝试使用此命令进行连接时：

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

出现了这样的东西：

 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

好像它增加了一倍的列数来适应数据，而不是把新数据放在它下面。我不确定为什么会发生这种情况，因为我很确定两个标签存档的列数相同。

当我使用 'y_trainvalid2.head().to_dict()' 命令打印时，会出现：

{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？

【问题讨论】：

你试试y_trainvalid2 = pd.concat([y_trainvalid, new_file_label]吗？请给我们您的初始数据框样本；)
当我尝试 y_trainvalid2 = pd.concat([y_trainvalid, new_file_label] 时问题保持不变，但最后一个索引号更改为 0 到 22。我将编辑原始数据帧
这是不可能的，你在两个数据框中没有相同的列名？
您能否对两个小型（例如 5x5）数据框进行硬编码来演示此问题并在您的示例中使用这些数据框？目前的例子，呃，至少可以说是压倒性的。
您的标题不同……您有 int 和 string 类型。

标签： python arrays pandas dataframe concatenation

【解决方案1】：

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])

【讨论】：