Parse a column in data frame separated by known delimiter the reshape it

I have a data frame for the cars with these columns.

Car_code, car_model, update_Date, sensor_codes each car has multiple sensors listed in sensor_codes and separated by / as below:

df = pd.DataFrame([['x','iii-2019-10-16','18/04/2019','115/556/879/115'],
                   ['x','iii-2019-10-16','21/07/2019','87/998/115'],
                   ['x','iii-2019-10-16','','115/556/879'],
                   ['x','zzz-2020-10-25','12/04/2022',''],
                   ['y','qqq-2018-05-28','10/12/2017','789/554/745'], 
                   ['y','qqq-2018-05-28','15/02/2018','789/554/75'],
                   ['y','ooo-2019-11-22','30/05/2019','55'],
                   ['y','rrr-16-12-2020','16/12/2020',''],
                   ['z','ppt-2019-12-03','07/02/2018','889/654/750'],
                   ['z','ttt-2019-12-03','28/05/2019','119/55/75'],
                   ['z','ttt-2019-12-03','09/09/2019'],
                   ['z','ttt-2019-12-03','30/09/2019']
                  
                  ],
                  columns=['Car_code','car_model','update_Date','sensor_codes'])
df

I need to create a new data frame that has just two columns: Car_code, and sensor code (which will contain the unique sensor) so for each car_code there will be multiple rows each one has one sensor like below:

You could try:

df_result = (
    df[['Car_code']]
    .assign(sensor_codes=df['sensor_codes'].str.split('/'))
    .explode('sensor_codes')
    .loc[lambda df:
         df['sensor_codes'].notna() & df['sensor_codes'].str.strip().ne('')
    ].drop_duplicates(keep='first')
    .assign(sensor_codes=lambda df: df['sensor_codes'].astype('int'))
    .reset_index(drop=True)
)

• Pick the column Car_code as a dataframe (therefore the double brackets) and add sensor_codes as new column, but use .str.split('/') to split the items along '/' into lists.
• Then .explode the sensor_codes column to flatten it, which keeps the respective items in Car_code.
• Afterwards use .loc to filter out the rows that have either a NaN (here None) or an empty string in sensor_codes (the .str.strip is just in case there's only whitespace in an item).
• Then drop duplicate rows except for the first ones.
• Finally cast sensor_codes into integers and reset the index (both optional, might well be that you don't need it).

Result for the sample:

   Car_code sensor_codes
0         x          115
1         x          556
2         x          879
3         x           87
4         x          998
5         y          789
6         y          554
7         y          745
8         y           75
9         y           55
10        z          889
11        z          654
12        z          750
13        z          119
14        z           55
15        z           75