How to distribute pandas dataframe rows evenly across timestamps based on value of the column

E.g. DF which contains number of executions across timestamps.

             DateTime           Execution
0 2023-04-03 07:00:00                   4
1 2023-04-03 10:00:00                   1
2 2023-04-03 12:00:00                   1
3 2023-04-03 14:00:00                   1
4 2023-04-03 18:00:00                   1

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5080 entries, 0 to 5079

Below is the output I’m trying to achieve

             DateTime           Execution
0 2023-04-03 07:00:00                   1
1 2023-04-03 08:00:00                   1
2 2023-04-03 09:00:00                   1
3 2023-04-03 10:00:00                   1
4 2023-04-03 10:00:00                   1
5 2023-04-03 12:00:00                   1
6 2023-04-03 14:00:00                   1
7 2023-04-03 18:00:00                   1

So want to distribute the each executions more than 1 to evenly hourly timestamp.

I tried: How to divide up 'supply' evenly among rows in a dataframe by rank in Python? but this doesn’t give the desired output.

tried this but it is only for arranging evenly arrange dataframe rows based on the values in a given column

>Solution :

Use Index.repeat with DataFrame.loc for repeat rows, set 1 and add hours by to_timedelta with GroupBy.cumcount:

#if string repr of datetimes
df['DateTime'] = pd.to_datetime(df['DateTime'])

out = (df.loc[df.index.repeat(df['Execution'])]
         .assign(Execution=1, 
                 DateTime = lambda x:  x['DateTime'] + 
                            pd.to_timedelta(x.groupby(level=0).cumcount(), unit='H'))
         .reset_index(drop=True))
print (out)
             DateTime  Execution
0 2023-04-03 07:00:00          1
1 2023-04-03 08:00:00          1
2 2023-04-03 09:00:00          1
3 2023-04-03 10:00:00          1
4 2023-04-03 10:00:00          1
5 2023-04-03 12:00:00          1
6 2023-04-03 14:00:00          1
7 2023-04-03 18:00:00          1