因此，我一直在我的深度学习分类器上设置标签档案，我想将一个已经存在的2D档案的标签连接到我刚刚制作的标签中。
存在的是'y_trainvalid'（39209，43），代表43类的39209张图像。我要添加的新标签档案是“ new_file_label”（23、43）。在这些档案中，如果数字与类匹配，则将其设置为1，否则将其设置为0。 这是它们两者的示例：
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)
这样的事情出现了：
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
好像它使列数加倍以适合数据，而不是将新数据放在其正下方。我不确定为什么会这样，因为我很确定两个标签档案的列数都相同。
当我使用'y_trainvalid2.head（）。to_dict（）'命令进行打印时，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？
答案 0 :(得分：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])