Time difference in pandas columns

Question

I have the following pandas dataset containing information about flights

YEAR                int64
MONTH               int64
DAY                 int64
DAY_OF_WEEK         int64
AIRLINE            object
FLIGHT_NUMBER       int64
TAIL_NUMBER        object
DEPARTURE_TIME     object
ARRIVAL_TIME       object
ELAPSED_TIME      float64
CANCELLED           int64
dtype: object

and I want to calculate the time difference in minutes between DEPARTURE_TIME and ARRIVAL_TIME columns. The two columns have 'datetime.time' dtypes and when I try to subtract them using

(f.DEPARTURE_TIME-f.ARRIVAL_TIME)

I get the following error

TypeError: unsupported operand type(s) for -: 'datetime.time' and 'datetime.time'

How can I get the time difference in minutes between the two columns? I want to use this difference to subtract from EPASED_TIME column in a later step so I want to be a float.

Edit:

The original data can be found here

The steps I followed to create the dataframe are:

import pandas as pd

# read dataset
flights = pd.read_csv('flights.csv',
                      dtype = {'DEPARTURE_TIME': object, 'ARRIVAL_TIME': object},
                      low_memory=False)
# keep only columns of interest
F = flights[['YEAR', 'MONTH', 'DAY', 
                           'DAY_OF_WEEK', 'AIRLINE',
                           'FLIGHT_NUMBER', 'TAIL_NUMBER',
                           'DEPARTURE_TIME', 'ARRIVAL_TIME', 
                           'ELAPSED_TIME', 'CANCELLED']]
# drop rows contain na 
f = F.dropna()
# format columns to datetime
f['DEPARTURE_TIME'] = pd.to_datetime(f['DEPARTURE_TIME'],format='%H%M', errors='coerce').dt.time

f['ARRIVAL_TIME'] = pd.to_datetime(f['ARRIVAL_TIME'], format='%H%M', errors = 'coerce').dt.time
# calculate difference
(f.DEPARTURE_TIME-f.ARRIVAL_TIME)