Pandas: Get the details of the columns title and genres of the DataFrame

Pandas: IMDb Movies Exercise-5 with Solution

Write a Pandas program to get the details of the columns title and genres of the DataFrame.

Sample Solution:

Python Code :

import pandas as pd
df = pd.read_csv('movies_metadata.csv')
result = df[['title', 'genres']]
print("Details of title and genres:")
print(result)

Sample Output:

Details of title and genres:
                             title  \
0                        Toy Story   
1                          Jumanji   
2                 Grumpier Old Men   
3                Waiting to Exhale   
4      Father of the Bride Part II   
5                             Heat   
6                          Sabrina   
7                     Tom and Huck   
8                     Sudden Death   
9                        GoldenEye   
10          The American President   
11     Dracula: Dead and Loving It   
12                           Balto   
13                           Nixon   
14                Cutthroat Island   
15                          Casino   
16           Sense and Sensibility   
17                      Four Rooms   
18  Ace Ventura: When Nature Calls   
19                     Money Train   
20                      Get Shorty   
21                         Copycat   
22                       Assassins   
23                          Powder   
24               Leaving Las Vegas   
25                         Othello   
26                    Now and Then   
27                      Persuasion   
28       The City of Lost Children   
29                  Shanghai Triad   
30                 Dangerous Minds   
31                  Twelve Monkeys   
32                Wings of Courage   
33                            Babe   
34                      Carrington   
35                Dead Man Walking   
36          Across the Sea of Time   
37                    It Takes Two   
38                        Clueless   
39        Cry, the Beloved Country   
40                     Richard III   
41                 Dead Presidents   
42                     Restoration   
43                   Mortal Kombat   
44                      To Die For   
45   How To Make An American Quilt   
46                           Se7en   
47                      Pocahontas   
48           When Night Is Falling   
49              The Usual Suspects   

                                               genres  
0   [{'id': 16, 'name': 'Animation'}, {'id': 35, '...  
1   [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...  
2   [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...  
3   [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  
4                      [{'id': 35, 'name': 'Comedy'}]  
5   [{'id': 28, 'name': 'Action'}, {'id': 80, 'nam...  
6   [{'id': 35, 'name': 'Comedy'}, {'id': 10749, '...  
7   [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...  
8   [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...  
9   [{'id': 12, 'name': 'Adventure'}, {'id': 28, '...  
10  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  
11  [{'id': 35, 'name': 'Comedy'}, {'id': 27, 'nam...  
12  [{'id': 10751, 'name': 'Family'}, {'id': 16, '...  
13  [{'id': 36, 'name': 'History'}, {'id': 18, 'na...  
14  [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...  
15  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...  
16  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  
17  [{'id': 80, 'name': 'Crime'}, {'id': 35, 'name...  
18  [{'id': 80, 'name': 'Crime'}, {'id': 35, 'name...  
19  [{'id': 28, 'name': 'Action'}, {'id': 35, 'nam...  
20  [{'id': 35, 'name': 'Comedy'}, {'id': 53, 'nam...  
21  [{'id': 18, 'name': 'Drama'}, {'id': 53, 'name...  
22  [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...  
23  [{'id': 18, 'name': 'Drama'}, {'id': 14, 'name...  
24  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  
25                      [{'id': 18, 'name': 'Drama'}]  
26  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  
27  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  
28  [{'id': 14, 'name': 'Fantasy'}, {'id': 878, 'n...  
29  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...  
30  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...  
31  [{'id': 878, 'name': 'Science Fiction'}, {'id'...  
32  [{'id': 10749, 'name': 'Romance'}, {'id': 12, ...  
33  [{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'na...  
34  [{'id': 36, 'name': 'History'}, {'id': 18, 'na...  
35                      [{'id': 18, 'name': 'Drama'}]  
36  [{'id': 12, 'name': 'Adventure'}, {'id': 36, '...  
37  [{'id': 35, 'name': 'Comedy'}, {'id': 10751, '...  
38  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  
39                      [{'id': 18, 'name': 'Drama'}]  
40  [{'id': 18, 'name': 'Drama'}, {'id': 10752, 'n...  
41  [{'id': 28, 'name': 'Action'}, {'id': 80, 'nam...  
42  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  
43  [{'id': 28, 'name': 'Action'}, {'id': 14, 'nam...  
44  [{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'na...  
45  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  
46  [{'id': 80, 'name': 'Crime'}, {'id': 9648, 'na...  
47  [{'id': 12, 'name': 'Adventure'}, {'id': 16, '...  
48  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  
49  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...    
	                                       

Python-Pandas Code Editor:

Sample Table:

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