Pandas and apply function to match a string

Question

I have a df column containing various links, some of them containing the string "search".

I want to create a function that - being applied to the column - returns a column containing "search" or "other".

I write a function like:

search = 'search'
def page_type(x):
if x.str.contains(search):
    return 'Search'
else:
    return 'Other'   

df['link'].apply(page_type)

but it gives me an error like:

AttributeError: 'unicode' object has no attribute 'str'

I guess I'm missing something when calling the str.contains().

Answer 1

I think you need numpy.where:

df = pd.DataFrame({'link':['search','homepage d','login dd', 'profile t', 'ff']})

print (df)
         link
0      search
1  homepage d
2    login dd
3   profile t
4          ff

search = 'search'
profile = 'profile'
homepage = 'homepage'
login = "login"

def page_type(x):
    if search in x:
        return 'Search'
    elif profile in x:
        return 'Profile'
    elif homepage in x:
        return 'Homepage'
    elif login in x:
        return 'Login'
    else:
        return 'Other'  

df['link_new'] = df['link'].apply(page_type)

df['link_type'] = np.where(df.link.str.contains(search),'Search', 
                  np.where(df.link.str.contains(profile),'Profile', 
                  np.where(df.link.str.contains(homepage), 'Homepage', 
                  np.where(df.link.str.contains(login),'Login','Other')))) 


print (df)
         link  link_new link_type
0      search    Search    Search
1  homepage d  Homepage  Homepage
2    login dd     Login     Login
3   profile t   Profile   Profile
4          ff     Other     Other

Timings:

#[5000 rows x 1 columns]
df = pd.DataFrame({'link':['search','homepage d','login dd', 'profile t', 'ff']})
df = pd.concat([df]*1000).reset_index(drop=True)

In [346]: %timeit df['link'].apply(page_type)
1000 loops, best of 3: 1.72 ms per loop

In [347]: %timeit np.where(df.link.str.contains(search),'Search', np.where(df.link.str.contains(profile),'Profile', np.where(df.link.str.contains(homepage), 'Homepage', np.where(df.link.str.contains(login),'Login','Other'))))
100 loops, best of 3: 11.7 ms per loop

Answer 2

You can use also a list comprehesion if you want to find the word search within a link:

Fo example:

df['Search'] = [('search' if 'search' in item else 'other') for item in df['link']]

The output:

  ColumnA                       link  Search
0       a        http://word/12/word   other
1       b     https://search-125.php  search
2       c      http://news-8282.html   other
3       d http://search-hello-1.html  search

Create function:

def page_type(x, y):
    df[x] = [('search' if 'search' in item else 'other') for item in df[y]]

page_type('Search', 'link')

In [6]: df
Out[6]:
  ColumnA                        link  Search
0       a         http://word/12/word   other
1       b      https://search-125.php  search
2       c       http://news-8282.html   other
3       d  http://search-hello-1.html  search 

Answer 3

.str applies to the whole Series but here you are dealing with the value inside the Series.

You can either do : df['link'].str.contains(search)
Or like you want : df['link'].apply(lambda x: 'Search' if search in x else 'Other')

Edit

More generic way:

def my_filter(x, val, c_1, c_2):
    return c_1 if val in x else  c_2 

df['link'].apply(lambda x: my_filter(x, 'homepage', 'homepage', 'other'))