Multiple elif statements and logic to populate dataframe

I am having difficulty figuring out the logic and flow of a problem. I have a dataframe with start times and end times of certain events over a period of many minutes (the real dataframe is huge). I want to find out how many of those events (i.e. offsets) occurred in each minute.

   onset = pd.Dataframe([32.1,45.3,78.3,121.1,150.3,190.1],index= index)
   offset = pd.DataFrame([30.1,41.3,71.3,119.1,148.3,185.1],index= index)
   timestamps = pd.concat(onset, offset], axis=1)


n=1
seconds = 60
offset_df = []
for offset in timestamps['offset']:
    if offset < seconds: 
        offset_df.append({'clip_offset': offset, 'seconds': seconds}) 
    elif seconds <= offset <= seconds+60:  # 180 < 150ms < 120
        seconds = seconds + 60 
        offset_df.append({'clip_offset': offset, 'seconds': seconds})
    elif offset > seconds+60: 
        new_n = offset / seconds
        n+=int(new_n)
        seconds = 60*n
        offset_df.append({'clip_offset': offset, 'seconds': seconds})  

I know my logic about updating 'seconds' is incorrect and I know I need a contingency in the event that there aren't any offset events in a given 60 second chunk.