How to create a function and apply for each row in pandas?

I have a somewhat-complex function that I am having difficulty writing. Essentially, I have a df that stores medical records and I need to identify the first site that a person goes to after their discharge date (I wish it was simple as choosing the first location after the initial stay, but it's not). The df is grouped by ID.

There are 3 options: (1) within a group, if any of the rows have a begin_date that matches the first rows end_date, return that location as the first site (if there are two rows that meet this condition, either are correct). (2) if the first option does not exist, then if there is an instance that the patient had location 'Health', then return 'Health'. (3) else, if conditions 1 and 2 do not exist, then return 'Home'

df

ID    color  begin_date    end_date     location
1     red    2017-01-01    2017-01-07   initial
1     green  2017-01-05    2017-01-07   nursing
1     blue   2017-01-07    2017-01-15   rehab
1     red    2017-01-11    2017-01-22   Health
2     red    2017-02-22    2017-02-26   initial
2     green  2017-02-26    2017-02-28   nursing
2     blue   2017-02-26    2017-02-28   rehab
3     red    2017-03-11    2017-03-22   initial
4     red    2017-04-01    2017-04-07   initial
4     green  2017-04-05    2017-04-07   nursing
4     blue   2017-04-10    2017-04-15   Health

finial result I am appending to a different df:

ID    first_site
1     rehab
2     nursing
3     home
4     Health

My approach is to write a function with these conditions, then use apply() to iterate over each row.

def conditions(x):
    if x['begin_date'].isin(x['end_date'].iloc[[0]]).any():
        return x['location'] 
    elif df[df['Health']] == True:
        return 'Health'
    else:
        return 'Home'

final = pd.DateFrame()
final['first'] = df.groupby('ID').apply(lambda x: conditions(x))

I am getting an error:

TypeError: incompatible index of inserted column with frame index