How to extract specific content in a pandas dataframe with a regex?

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Consider the following pandas dataframe:

In [114]:

df['movie_title'].head()

​
Out[114]:

0     Toy Story (1995)
1     GoldenEye (1995)
2    Four Rooms (1995)
3    Get Shorty (1995)
4       Copycat (1995)
...
Name: movie_title, dtype: object

Update: I would like to extract with a regular expression just the titles of the movies. So, let's use the following regex: \b([^\d\W]+)\b. So I tried the following:

df_3['movie_title'] = df_3['movie_title'].str.extract('\b([^\d\W]+)\b')
df_3['movie_title']

However, I get the following:

0       NaN
1       NaN
2       NaN
3       NaN
4       NaN
5       NaN
6       NaN
7       NaN
8       NaN

Any idea of how to extract specific features from text in a pandas dataframe?. More specifically, how can I extract just the titles of the movies in a completely new dataframe?. For instance, the desired output should be:

Out[114]:

0     Toy Story
1     GoldenEye
2    Four Rooms
3    Get Shorty
4       Copycat
...
Name: movie_title, dtype: object
pandas python-2.7 python regex string
4 Answers

I wanted to extract the text after the symbol "@" and before the symbol "." (period) I tried this, it worked more or less because I have the symbol "@" but I don not want this symbol, anyway:

df['col'].astype(str).str.extract('(@.+.+)
user_114678770

Using regular expressions to find a year stored between parentheses. We specify the parantheses so we don't conflict with movies that have years in their titles

movies_df['year'] = movies_df.title.str.extract('(\(\d\d\d\d\))',expand=False)

Removing the parentheses:

movies_df['year'] = movies_df.year.str.extract('(\d\d\d\d)',expand=False)

Removing the years from the 'title' column:

movies_df['title'] = movies_df.title.str.replace('(\(\d\d\d\d\))', '')

Applying the strip function to get rid of any ending whitespace characters that may have appeared:

movies_df['title'] = movies_df['title'].apply(lambda x: x.strip())
user_140048350
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