I have a data frame with every second column filled with time data configured the way: .
`
year:month:day hour:minute:second.fractions_of_second

`

My goal is to change all the records in time columns to total seconds number and then subtract the minimum value from any time column (it is a test from 16 ((1 signal + 1 time line)*2 = 32 columns) channels with unsychronized timelines). It works but the only way I found to convert datetime to total seconds is to use value.timestamp() record by record which is incredibly slow.

Do you know any way to speed up the process?
Code to reproduce it:

   def pzt_time_convert(df):

        min_array = []
        new_col = np.zeros(df_pzt.shape[0])
        for i in range(0, 16):
            
            #df_pzt[f'time{i}'] = df_pzt[f'time{i}'].dt.seconds() #DOESNT WORK

            col_num = df_pzt.columns.get_loc(f'time{i}')

            for j in range(0, int(df_pzt.shape[0])):
                new_col[j] = df_pzt.iloc[j, col_num].timestamp() #WORKS VERY SLOW


            min_array.append(new_col[0])
            df_pzt[f'time{i}'] = new_col


        min_time_df = min(min_array)

        for i in range(0, 16):
            df_pzt[f'time{i}'] = df_pzt[f'time{i}'] - min_time_df

Exemplary data frame:

values = [[Timestamp('2024-04-12 11:14:24.358056') -0.0006875504968152438
     Timestamp('2024-04-12 11:14:24.358056') -0.00041178275313580315
     Timestamp('2024-04-12 11:14:24.358056') 7.364900769430663e-05
     Timestamp('2024-04-12 11:14:24.358056') -0.0004661741754045164
     Timestamp('2024-04-12 11:14:24.358056') -0.0015942548166320398
     Timestamp('2024-04-12 11:14:24.358056') -0.0021384553975074957
     Timestamp('2024-04-12 11:14:24.358056') -0.00039135882617280515
     Timestamp('2024-04-12 11:14:24.358056') 0.0007415602000625972
     Timestamp('2024-04-12 11:14:24.357797') 0.0009018262284073795
     Timestamp('2024-04-12 11:14:24.357797') -0.0010126974839954682
     Timestamp('2024-04-12 11:14:24.357797') -0.0005472086561974499
     Timestamp('2024-04-12 11:14:24.357797') -0.0038311580789279387
     Timestamp('2024-04-12 11:14:24.357797') -0.00020165785427062911
     Timestamp('2024-04-12 11:14:24.357797') 0.00101211173219427
     Timestamp('2024-04-12 11:14:24.357797') -0.00036739739321287653
     Timestamp('2024-04-12 11:14:24.357797') -0.0014596976004591774]
    [Timestamp('2024-04-12 11:14:24.358061') -0.001393217874484563
     Timestamp('2024-04-12 11:14:24.358061') 0.000632806043004822
     Timestamp('2024-04-12 11:14:24.358061') -0.0009482395579276977
     Timestamp('2024-04-12 11:14:24.358061') 0.0009302471556326194
     Timestamp('2024-04-12 11:14:24.358061') -0.000901158768599161
     Timestamp('2024-04-12 11:14:24.358061') -0.0031716724511709534
     Timestamp('2024-04-12 11:14:24.358061') -0.0014356856355145864
     Timestamp('2024-04-12 11:14:24.358061') 0.00039756264859636733
     Timestamp('2024-04-12 11:14:24.357802') -0.00016250123298466672
     Timestamp('2024-04-12 11:14:24.357802') -0.0017167618679096996
     Timestamp('2024-04-12 11:14:24.357802') 0.0005116228811787183
     Timestamp('2024-04-12 11:14:24.357802') -0.004182242412716671
     Timestamp('2024-04-12 11:14:24.357802') -0.0012577073144788766
     Timestamp('2024-04-12 11:14:24.357802') 0.00030706346666221993
     Timestamp('2024-04-12 11:14:24.357802') -0.00036739739321287653
     Timestamp('2024-04-12 11:14:24.357802') -0.0011094467495909785]
    [Timestamp('2024-04-12 11:14:24.358066') -0.0017460515633192228
     Timestamp('2024-04-12 11:14:24.358066') 0.000632806043004822
     Timestamp('2024-04-12 11:14:24.358066') -0.001288869079801699
     Timestamp('2024-04-12 11:14:24.358066') -0.0008152795081638005
     Timestamp('2024-04-12 11:14:24.358066') -0.002633898888681358
     Timestamp('2024-04-12 11:14:24.358066') -0.0028272667666164675
     Timestamp('2024-04-12 11:14:24.358066') -0.00039135882617280515
     Timestamp('2024-04-12 11:14:24.358066') 0.0007415602000625972
     Timestamp('2024-04-12 11:14:24.357807') -0.0005172770534486821
     Timestamp('2024-04-12 11:14:24.357807') -0.0006606652920383522
     Timestamp('2024-04-12 11:14:24.357807') 0.00015867903538666246
     Timestamp('2024-04-12 11:14:24.357807') -0.001724652076195543
     Timestamp('2024-04-12 11:14:24.357807') -0.0012577073144788766
     Timestamp('2024-04-12 11:14:24.357807') 0.00101211173219427
     Timestamp('2024-04-12 11:14:24.357807') -0.00036739739321287653
     Timestamp('2024-04-12 11:14:24.357807') -0.0021601993021955757]]

    headers = ['time0', 'channel0', 'time1', 'channel1', 'time2', 'channel2', 'time3',
          'channel3', 'time4', 'channel4', 'time5', 'channel5', 'time6',
          'channel6', 'time7', 'channel7', 'time8', 'channel8', 'time9',
          'channel9', 'time10', 'channel10', 'time11', 'channel11', 'time12',
          'channel12', 'time13', 'channel13', 'time14', 'channel14', 'time15',
          'channel15']

    df = pd.DataFrame(values, columns = headers)