Kaio Giovanni
Reputation: 3
Pandas Concatenation not working properly
So I've been setting up a label archive on my deep learning classifier and I wanted to concatenate the labels of an already existing 2D archive into one I just made.
The one that exists is 'y_trainvalid' (39209, 43), which stands for 39209 images in 43 classes. The new label archive I'm trying to add is 'new_file_label' (23, 43). On these archives, the number set to 1 if it matches the class and 0 if it doesn't. Here's a sample of both of them:
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]
When I tried to concatenate using this command:
y_trainvalid2 = pd.concat([y_trainvalid, new_file_label], ignore_index=True)
Something like this appeared:
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
As if it doubled the amount of columns to fit the data instead of putting the new data just below it. I'm not sure why this is happening cause I'm pretty sure both label archives have the same number of columns.
When I print use the 'y_trainvalid2.head().to_dict()' command, this appears:
{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}}
How do I solve this problem?
Upvotes: 0
Views: 279
Answers (1)
B.Gees
Reputation: 1155
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])
Upvotes: 1
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