Use map or applymap on pandas groupby

Question

I have a Pandas dataframe which I am grouping by one of the columns in the dataframe. Then I have a function which needs to be applied once to each group. The function modifies the group. So what I want to do is something like this:

df = pd.DataFrame([[1, 'coupe', 2004], [2, 'sedan', 2004], [1, 'sedan', 2015], [3, 'coupe', 2010]], columns=['group_id', 'model', 'year_manufactured'])

	group_id	model	year_manufactured
0	1	coupe	2004
1	2	sedan	2004
2	1	sedan	2015
3	3	coupe	2010

def my_function(group):
    if 'coupe' in group['model']:
        group['model'].replace('coupe', 'hatchback', inplace=True)
        group['year_manufactured'].replace(2004, 2005, inplace=True)
    else:
        newest_car = group['year_manufactured'].idxmax()
        newest_car_type = group.loc[newest_car, 'model']
        group['model'] = group['model'].map(lambda x : x + newest_car_type)

df.groupby('group_id').map(my_function)

However, map can't be applied to a groupby. Is there any way I can work around this while not compromising speed. Also the function I have can't just be mapped to each row of the database since the way it acts on one row is dependent on previous rows.