How can I convert transform column in Pandas using regex

I have got a time column like this:

df = pd.DataFrame({'time': {0: '1 h 50 min', 1: '50 min', 2: '2 h 3 min', 3: '3 min'}})

        time
0  1 h 50 min
1      50 min
2   2 h 3 min
3       3 min

I want the information in seconds like:

I was trying to use regex and eval to evaluate the time in seconds but was not successful what's the best possible way to achieve this?

Solution

First use the regex to get hr and minute, then convert both to minutes and calculate the sum, finally multiply the result by 60 to get the seconds.

df['time'].str.findall('(\d+)\s*(h|min)').apply(lambda x: sum(int(t)*60 if u=='h' else int(t) for t,u in x))*60

OUTPUT:

0    6600
1    3000
2    7380
3     180
Name: time, dtype: int64

If you want, you can just have separate columns of hour and minute:

out = df.assign(hours=df['time'].str.extract('(\d+)\s*h'),
          minutes=df['time'].str.extract('(\d+)\s*min')).fillna(0)

         time hours minutes
0  1 h 50 min     1      50
1      50 min     0      50
2   2 h 3 min     2       3
3       3 min     0       3

Now , you can just convert them to integer and get the number of seconds:

out['hours'].astype(int)*3600+out['minutes'].astype(int)*60

0    6600
1    3000
2    7380
3     180
dtype: int32