JSON validate
function IsJsonString(str) {
try {
JSON.parse(str);
} catch (e) {
return false;
}
return true;
}
4
2
import numpy as np
df['first_five_Letter']=df['Country (region)'].str.extract(r'(^w{5})')
df.head()
Thank you!
2
4
(2 Votes)
0
3.5
2
# Get countries starting with letter P
S=pd.Series(['Finland','Colombia','Florida','Japan','Puerto Rico','Russia','france'])
S[S.str.match(r'(^P.*)')==True]
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2
3.5
(2 Votes)
0
0
0
S=pd.Series(['Finland','Colombia','Florida','Japan','Puerto Rico','Russia','france'])
[itm[0] for itm in S.str.findall('^[Ff].*') if len(itm)>0]
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0
0
0
3.89
9
#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
Thank you!
9
3.89
(9 Votes)
0
3.89
9
**Output:** ['Finland', 'Florida', 'france']
Thank you!
9
3.89
(9 Votes)
0
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