How to add calculated rows below each row in a pandas DataFrame

I have a dataframe_1 as such:

Index   Time          Label
0       0.000 ns      Segment 1
1       2.749 sec     baseline
2       3.459 min     begin test
3       7.009 min     end of test

And I would like to add multiple new rows in between each of dataframe_1‘s rows, where the Time column for each new row would add an additional minute until reaching dataframe_1‘s next row’s time (and corresponding Label). For example, the above table should ultimately look like this:

Index     Time               Label
0         0.000 ns           Segment 1
1         2.749 sec          baseline
2         00:01:02.749000    baseline + 1min
3         00:02:02.749000    baseline + 2min
4         00:03:02.749000    baseline + 3min
5         3.459 min          begin test
6         00:04:27.540000    begin test + 1min
7         00:05:27.540000    begin test + 2min
8         00:06:27.540000    begin test + 3min
9         7.009 min          end of test

Using Timedelta type via pd.to_timedelta() is perfectly fine.

I thought the best way to do this would be to break up each row of dataframe_1 into its own dataframe, and then adding rows for each added minute, and then concating the dataframes back together. However, I am unsure of how to accomplish this.

Should I use a nested for-loop to [first] iterate over the rows of dataframe_1 and then [second] iterate over a counter so I can create new rows with added minutes?

I was previously not splitting up the individual rows into new dataframes, and I was doing the second iteration like this:

    baseline_row = df_legend[df_legend['Label'] == 'baseline']
    [baseline_index] = baseline_row.index
    baseline_time = baseline_row['Time']

    interval_mins = 1
    new_time = baseline_time + pd.Timedelta(minutes=interval_mins)

    cutoff_time_np = df_legend.iloc[baseline_row.index + 1]['Time']
    cutoff_time = pd.to_timedelta(cutoff_time_np)
    
    while new_time.reset_index(drop=True).get(0) < cutoff_time.reset_index(drop=True).get(0):

        new_row = baseline_row.copy()
        new_row['Label'] = f'minute {interval_mins}'
        new_row['Time'] = baseline_time + pd.Timedelta(minutes=interval_mins)
        new_row.index = [baseline_index + interval_mins - 0.5]

        df_legend = df_legend.append(new_row, ignore_index=False)
        df_legend = df_legend.sort_index().reset_index(drop=True)
        pdb.set_trace()

        interval_mins += 1
        new_time = baseline_time + pd.Timedelta(minutes=interval_mins)

But since I want to do this for each row in the original dataframe_1, then I was thinking to split it up into separate dataframes and put it back together. I’m just not sure what the best way is to do that, especially since pandas is apparently very slow if iterating over the rows.

I would really appreciate some guidance.