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?
https://stackoverflow.com/questions/56677321
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