Calculate time difference in seconds in pandas

I have a pandas DataFrame with a datetime column (‘time’) in m/d/yyyy h:mm:ss format. I need to calculate the difference between each row and the previous row and display the result in a new column (‘SEC’) in seconds.

For example:

time                    SEC
4/18/2023 2:43:00       
4/18/2023 3:13:00       1800
4/18/2023 3:35:53       1373
4/18/2023 3:36:03       10

>Solution :

Use diff + .dt.total_seconds (ty @QuangHoang)

df['time'].diff().dt.total_seconds

If your time column is not yet a datetime64 dtype, you can do

df['time'] = pd.to_datetime(df['time'], format='%m/%d/%Y %H:%M:%S')
df['time'].diff().dt.total_seconds