Pandas: Get the details of the columns title and genres of the DataFrame
5. Details of Columns Title and Genres
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...
For more Practice: Solve these Related Problems:
- Write a Pandas program to load movies_metadata.csv and display only the title and genres columns for the first 20 records.
- Write a Pandas program to extract the title and genres columns and count how many unique genres exist across all movies.
- Write a Pandas program to display the title and genres columns and then filter out movies with empty genre entries.
- Write a Pandas program to load movies_metadata.csv, select the title and genres columns, and export this subset to a new CSV file.
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