Extract time from string in dataframe's rows into new columns

I have a a dataframe where each lines contains text, and the start of this text is a time that is in the format of 00:00:00-00:00:01.
I would like to extract the starting time, the ending time but also the difference between those time into 3 columns of my df.

Here is a sample of the dataframe :

Here is the expected result

I think using str.extract like this would do the trick but can’t get the matching as I would like, where the pattern match a 00:00:00 format

df['start time'] = df['text'].str.extract(r'( ^Pattern)')  # ^ for the begining of the string where the start time is.
df['end time'] = df['text'].str.extract(r'( -Patern)')     
#the - because the end time is between a "-" so the pattern for the end time would have to start with "-" to get it right 

And for the delta, I was basically thinking about converting the start and end time strings to datetime and doing :

df['delta'] = df['end time']- df['start time']

>Solution :

For start_time and end_time, you can do:

df[['start_time','end_time']] = df['text'].str.extract('(\d{2}:\d{2}:\d{2})-(\d{2}:\d{2}:\d{2})')

Then for delta, you can convert the strings into Timedelta, get the difference, and convert to string:

df['delta'] = (pd.to_timedelta(df['end_time'])
    .sub(pd.to_timedelta(df['start_time']))
)