Converting classes to nearest group with maximum vote - pandas

Question

I have a time series data with a column of a multiclass object, I would like to convert the object happening in less than two instances to the nearest bigger group. Here is an example of the data frame. I wish to convert 'No' appears at the 4th row to 'Yes' and similarly, 'Yes' in the 16th to 'No'.
df = pd.DataFrame(data = {'A': ['Yes', 'Yes', 'Yes', 'No', 'Yes', 'Yes', 'Probable',
'Probable', 'Probable', 'Yes', 'Yes', 'Yes',
'No', 'No','No', 'Yes', 'No','No'],
'Counter': [1, 2, 3, 1, 1, 2, 1, 2, 3, 1, 2, 3, 1, 2,
3, 1, 1, 2]})
Does anyone could help me to define a loop or function? Thank you in advance.

Answer 1

The idea is to identify groups of repeated categories and calculate total count per group; then for groups <2 count change their categories to the nearest (which we take to be its preceding group with >=2 total count)
This identifies nearby groups and calculates the sum per group, putting the result in gc (for 'group counter'):
group_id = df['A'].ne(df['A'].shift()).cumsum()
df['gc'] = df.groupby(group_id, group_keys = False)['Counter'].transform(sum)
df
output:
A Counter gc
-- -------- --------- ----
0 Yes 1 6
1 Yes 2 6
2 Yes 3 6
3 No 1 1
4 Yes 1 3
5 Yes 2 3
6 Probable 1 6
7 Probable 2 6
8 Probable 3 6
9 Yes 1 6
10 Yes 2 6
11 Yes 3 6
12 No 1 6
13 No 2 6
14 No 3 6
15 Yes 1 1
16 No 1 3
17 No 2 3
Now we replace categories with NaNs for those where gc is < 2, and fill forward:
import numpy as np
df.loc[df['gc'] <2,'A'] = np.NaN
df.fillna(method = 'ffill').drop(columns = 'gc')
output:
A Counter
-- -------- ---------
0 Yes 1
1 Yes 2
2 Yes 3
3 Yes 1
4 Yes 1
5 Yes 2
6 Probable 1
7 Probable 2
8 Probable 3
9 Yes 1
10 Yes 2
11 Yes 3
12 No 1
13 No 2
14 No 3
15 No 1
16 No 1
17 No 2