Итак, я настраивал архив меток в своем классификаторе с углубленным изучением и хотел объединить метки уже существующего 2D-архива в один, который я только что создал.
То, что существует, это y_trainvalid (39209, 43), что означает 39209 изображений в 43 классах. Архив новой метки, который я пытаюсь добавить, это 'new_file_label' (23, 43). В этих архивах число устанавливается равным 1, если оно соответствует классу, и 0, если оно не соответствует.
Вот образец их обоих:
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]
Когда я пытался объединить с помощью этой команды:
y_trainvalid2 = pd.concat([y_trainvalid, new_file_label], ignore_index=True)
Нечто подобное появилось:
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
Как будто он удваивает количество столбцов, чтобы соответствовать данным, вместо того, чтобы помещать новые данные чуть ниже него. Я не уверен, почему это происходит, потому что я почти уверен, что оба архива меток имеют одинаковое количество столбцов.
Когда я печатаю с помощью команды 'y_trainvalid2.head (). To_dict ()', это выглядит так:
{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}}
Как мне решить эту проблему?