I have some test data which looks like this:

test_data = {'equipment_barcode': ['0001', '0001', '0001', '0001', '0002', '0002', '0002', '0002'], 
         'date': ['2022-04-16', '2022-04-17', '2022-04-18', '2022-04-19', '2022-05-13', '2022-05-14', '2022-05-15', '2022-05-16'],
         'downtime_minutes':[1200, 240, 1440, 600, 360, 1440, 1440, 180]}

I have converted this into a pandas dataframe:

df = pd.DataFrame(test_data)

print(df)

    equipment_barcode   date          downtime_minutes
0   0001                2022-04-16    1200
1   0001                2022-04-17    240
2   0001                2022-04-18    1440
3   0001                2022-04-19    600
4   0002                2022-05-13    360
5   0002                2022-05-14    1440
6   0002                2022-05-15    1440
7   0002                2022-05-16    180

There are 1,440 minutes in one day. This information is required to calculate the NUMBER of faults. A fault may occur over consecutive number of days and thus will still be counted as ONE fault only.

The task is to find the number of faults for each equipment_barcode. For example equipment 0001 had 2 faults [one fault on 16th of April lasting for 1200 minutes and one fault on 17th-19th of April lasting for 2280 minutes(240 + 1440 + 600)].

For added clarity; equipment 0002 had 1 fault [fault on 13th-16th of May lasting for 3420 minutes (360 + 1440 + 1440 + 180)].

In the end I would like a resulting dataframe that shows something like this:

    equipment_barcode   fault_number    total_downtime_minutes
0   0001                1               1200
1   0001                2               2280
2   0002                1               3420