drop duplicated and concat pandas - python-3.x

Question

I have a dataframe that looks like this:
'id': ["1", "2", "1", "3", "3", "4"],
'date': ["2017", "2011", "2019", "2013", "2017", "2018"],
'code': ["CB25", "CD15", "CZ10", None, None, "AZ51"],
'col_example': ["22", None, "22", "55", "55", "121"],
'comments': ["bonjour", "bonjour", "bonjour", "hola", "Hello", None]
Result:
id date code col_example .... comments
0 1 2019 CB25/CZ10 22 .... bonjour (and not bonjour // bonjour)
1 2 2011 CD15 None .... bonjour
2 3 2017 None 55 .... hola // Hello
3 4 2018 AZ51 121 .... None
I want to keep a single id
If two ids are the same, I would like:
If comments = None and = str: Keep only the comments which are not None (example: id = 1, keep the comments "hello")
If two comments = str: Concaten the two comments with a "//" (example id = 3, comments = "hola // hello")
For the moment I tried with sort_value, and drop_duplicate without success
thank you

Answer 1

I believe you need DataFrame.dropna by column comments and then GroupBy.agg with join and GroupBy.last, last add DataFrame.mask for replace empty strings to None rows:
df1 = (df.groupby('id')
.agg({'date': 'last',
'comments': lambda x: ' // '.join(x.dropna())})
.replace({'comments': {'': None}})
.reset_index())
print (df1)
id date comments
0 1 2019 bonjour
1 2 2011 bonjour
2 3 2017 hola // Hello
3 4 2018 None
EDIT: For avoid removed all columns is necessary aggregate all of them, you can create dictionary for aggregation dynamic like:
df = pd.DataFrame({'id': ["1", "2", "1", "3", "3", "4"],
'date': ["2017", "2011", "2019", "2013", "2017", "2018"],
'code': ["CB25", "CD15", "CB25", None, None, "AZ51"],
'col_example': ["22", None, "22", "55", "55", "121"],
'comments': [None, "bonjour", "bonjour", "hola", "Hello", None]})
print (df)
id date code col_example comments
0 1 2017 CB25 22 None
1 2 2011 CD15 None bonjour
2 1 2019 CB25 22 bonjour
3 3 2013 None 55 hola
4 3 2017 None 55 Hello
5 4 2018 AZ51 121 None
d = dict.fromkeys(df.columns.difference(['id','comments']), 'last')
d['comments'] = lambda x: ' // '.join(x.dropna())
print (d)
{'code': 'last', 'col_example': 'last', 'date': 'last',
'comments': <function <lambda> at 0x000000000ECA99D8>}
df1 = (df.groupby('id')
.agg(d)
.replace({'comments': {'': None}})
.reset_index())
print (df1)
id code col_example date comments
0 1 CB25 22 2019 bonjour
1 2 CD15 None 2011 bonjour
2 3 None 55 2017 hola // Hello
3 4 AZ51 121 2018 None