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

You can try str.extract and strip, but better is use str.split, because in names of movies can be numbers too. Next solution is replace content of parentheses by regex and strip leading and trailing whitespaces:

#convert column to string
df['movie_title'] = df['movie_title'].astype(str)

#but it remove numbers in names of movies too
df['titles'] = df['movie_title'].str.extract('([a-zA-Z ]+)', expand=False).str.strip()
df['titles1'] = df['movie_title'].str.split('(', 1).str[0].str.strip()
df['titles2'] = df['movie_title'].str.replace(r'\([^)]*\)', '').str.strip()
print df
          movie_title      titles      titles1      titles2
0  Toy Story 2 (1995)   Toy Story  Toy Story 2  Toy Story 2
1    GoldenEye (1995)   GoldenEye    GoldenEye    GoldenEye
2   Four Rooms (1995)  Four Rooms   Four Rooms   Four Rooms
3   Get Shorty (1995)  Get Shorty   Get Shorty   Get Shorty
4      Copycat (1995)     Copycat      Copycat      Copycat

You should assign text group(s) with () like below to capture specific part of it.

new_df['just_movie_titles'] = df['movie_title'].str.extract('(.+?) \(')
new_df['just_movie_titles']

pandas.core.strings.StringMethods.extract

StringMethods.extract(pat, flags=0, **kwargs)

Find groups in each string using passed regular expression

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('(@.+.+)