json validate

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showing results for - "json validate"
Neele
15 Jan 2020
1function IsJsonString(str) {
2    try {
3        JSON.parse(str);
4    } catch (e) {
5        return false;
6    }
7    return true;
8}
Eduardo
24 Jan 2018
1import numpy as np
2df['first_five_Letter']=df['Country (region)'].str.extract(r'(^w{5})')
3df.head()
4
Sara Sofía
27 Feb 2019
1S=pd.Series(['Finland','Colombia','Florida','Japan','Puerto Rico','Russia','france'])
2[itm[0] for itm in S.str.findall('^[Ff].*') if len(itm)>0]
3
Giacomo
30 May 2019
1**Output:** ['Finland', 'Florida', 'france']
2
Anthony
18 Mar 2016
1#convert column to string
2df['movie_title'] = df['movie_title'].astype(str)
3
4#but it remove numbers in names of movies too
5df['titles'] = df['movie_title'].str.extract('([a-zA-Z ]+)', expand=False).str.strip()
6df['titles1'] = df['movie_title'].str.split('(', 1).str[0].str.strip()
7df['titles2'] = df['movie_title'].str.replace(r'\([^)]*\)', '').str.strip()
8print df
9          movie_title      titles      titles1      titles2
100  Toy Story 2 (1995)   Toy Story  Toy Story 2  Toy Story 2
111    GoldenEye (1995)   GoldenEye    GoldenEye    GoldenEye
122   Four Rooms (1995)  Four Rooms   Four Rooms   Four Rooms
133   Get Shorty (1995)  Get Shorty   Get Shorty   Get Shorty
144      Copycat (1995)     Copycat      Copycat      Copycat
15
Nicola
25 Sep 2019
1# Get countries starting with letter P
2S=pd.Series(['Finland','Colombia','Florida','Japan','Puerto Rico','Russia','france'])
3S[S.str.match(r'(^P.*)')==True]
4
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