How to change the 'True' boolean to 'False' boolean incase we have only one 'True' boolean between two 'False' boalean in a column in dataframe

Question

I have unknown number of dataframes. The number and location of 'True' boolean are unknown in a column "label_number_hours" in dataframes. There is a possibility to be unlimited numbers of 'True' booleans between two 'False' booleans in a column "label_number_hours" in dataframes. I am looking to change the 'True' boolean to 'False' boolean in this column if the number of 'True' boolean is only one, for example, False - True - False, I want to be False - False - False.

This is an example of one of dataframe I have:

df =

label_number_hours some_other_column

0 True 0.174998

1 False 0.235088

2 True 0.076127

3 True 0.817929

4 True 0.781144

5 False 0.904597

6 True 0.703006

7 False 0.923654

8 True 0.261100

9 True 0.803631

10 False 0.149026

This is the dataframe which I am looking for:

df =

label_number_hours some_other_column

0 True 0.174998

1 False 0.235088

2 True 0.076127

3 True 0.817929

4 True 0.781144

5 False 0.904597

6 False 0.703006

7 False 0.923654

8 True 0.261100

9 True 0.803631

10 False 0.149026

This is the code:

falses_idx, = np.where(~df["label_number_hours"]) if falses_idx.size > 0: df.iloc[falses_idx[0]:falses_idx[-1], df.columns.get_loc("label_number_hours")] = False

This is the result:

label_number_hours some_other_column

0 True 0.174998

1 False 0.235088

2 False 0.076127

3 False 0.817929

4 False 0.781144

5 False 0.904597

6 False 0.703006

7 False 0.923654

8 False 0.261100

9 False 0.803631

10 False 0.149026

I need really to your help