Pandas add column based_domain from existing column-CodePudding

I new to pandas. I have a dataset like this one:

df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'],
                'Event':['music.example.com', 'poetry.example.com', 'theatre.domain.com', 'comedy.domain.com'],
                'Cost':[10000, 5000, 15000, 2000]})

And would like to add a column for "base domain" so I can do aggregate functions on base domain instead of subdomain. In this example there new column would have values

'baseDomain':['example.com', 'example.com', 'domain.com', 'domain.com'],

It should not just blindly split on the "." so should probably use something like tld although the domains are not as URLs

========== Update

Used adhg and Henry Ecker solution and did it like this:

def get_base_domain(event):
    ext = tldextract.extract(event)
    return ext.domain   '.'   ext.suffix

df['baseDomain']  = df.apply(lambda x: get_base_domain(x['Event']), axis=1)

CodePudding user response：

you can do this:

def get_base_domain(event):
    return event[event.index('.') 1:]

df['baseDomain']  = df.apply(lambda x: get_base_domain(x['Event']), axis=1)

desired result:

         Date   Event               Cost     baseDomain
0   10/2/2011   music.example.com   10000   example.com
1   11/2/2011   poetry.example.com  5000    example.com
2   12/2/2011   theatre.domain.com  15000   domain.com
3   13/2/2011   comedy.domain.com   2000    domain.com

adjust get_base_domain if you have unclean Event domain data