How do I drop rows from a pandas dataframe in a certain time interval

# requires 
from datetime import datetime
from datetime import date
from datetime import time
from datetime import timedelta

# sample data taken from above question
data = ('135 2021-10-29 10:16:00 167 167.0 -3.0 15.45 15.45 17.95 17.45\n'
        '155 2021-10-29 12:56:00 162 162.0 -15.0 15.35 15.35 17.95 16.00\n'
        '243 2021-10-29 20:16:00 133 133.0 0.0 15.25 15.25 19.85 15.75\n'
        '245 2021-10-29 20:26:00 134 134.0 0.0 15.50 15.50 15.75 15.60\n'
        '113 2021-10-29 09:26:00 130 130.0 1.0 16.75 16.75 0.00 21.70')
rawtable = pd.read_csv(StringIO(data), header=None, delimiter=' ')

# sort the table on the time values. NB modifiy this to include the dates
table = rawtable.sort_values(2, 0, key=lambda col: col.str.lower(), ignore_index=True)

tmin = timedelta(0, 0, 0, 0, 60) # the differences must be at least 60 minutes
tprev = datetime.combine(datetime.min, time(0, 0))

# iterate the rows of data to find any deltas less than the minimum
for index, row in table.iterrows():
    s = row.iloc[1:3]
    dt = date.fromisoformat(s[1])    # capture the date field
    td = time.fromisoformat(s[2])    # and the time
    dttd = datetime.combine(dt, td)  # create the DateTime value
    # print(index, dttd)
    delta = dttd - tprev             # calculate the difference between this and the previous row
    if delta < tmin:  # if the interval is too small
        # print('index:', index, 'delta:', delta)
        table.drop(index, inplace=True) # drop this row from the table
    tprev = dttd  # save the current time as the new previous value
print("\n\n")
print(table)  # display the modified table