w3resource

Pandas: Access those movies,released after 1995-01-01

Pandas: IMDb Movies Exercise-11 with Solution

Write a Pandas program to access those movies,released after 1995-01-01.

Sample Solution:

Python Code :

import pandas as pd
df = pd.read_csv('movies_metadata.csv')
# Create a smaller dataframe
small_df = df[['title', 'release_date', 'budget', 'revenue', 'runtime']]
result = small_df[small_df['release_date'] > '1995-01-01']
print("DataFrame based on release date>'1995-01-01'.")
print(result)

Sample Output:

DataFrame based on release date>'1995-01-01'.
                             title release_date    budget    revenue  runtime
0                        Toy Story   1995-10-30  30000000  373554033     81.0
1                          Jumanji   1995-12-15  65000000  262797249    104.0
2                 Grumpier Old Men   1995-12-22         0          0    101.0
3                Waiting to Exhale   1995-12-22  16000000   81452156    127.0
4      Father of the Bride Part II   1995-02-10         0   76578911    106.0
5                             Heat   1995-12-15  60000000  187436818    170.0
6                          Sabrina   1995-12-15  58000000          0    127.0
7                     Tom and Huck   1995-12-22         0          0     97.0
8                     Sudden Death   1995-12-22  35000000   64350171    106.0
9                        GoldenEye   1995-11-16  58000000  352194034    130.0
10          The American President   1995-11-17  62000000  107879496    106.0
11     Dracula: Dead and Loving It   1995-12-22         0          0     88.0
12                           Balto   1995-12-22         0   11348324     78.0
13                           Nixon   1995-12-22  44000000   13681765    192.0
14                Cutthroat Island   1995-12-22  98000000   10017322    119.0
15                          Casino   1995-11-22  52000000  116112375    178.0
16           Sense and Sensibility   1995-12-13  16500000  135000000    136.0
17                      Four Rooms   1995-12-09   4000000    4300000     98.0
18  Ace Ventura: When Nature Calls   1995-11-10  30000000  212385533     90.0
19                     Money Train   1995-11-21  60000000   35431113    103.0
20                      Get Shorty   1995-10-20  30250000  115101622    105.0
21                         Copycat   1995-10-27         0          0    124.0
22                       Assassins   1995-10-06  50000000   30303072    132.0
23                          Powder   1995-10-27         0          0    111.0
24               Leaving Las Vegas   1995-10-27   3600000   49800000    112.0
25                         Othello   1995-12-15         0          0    123.0
26                    Now and Then   1995-10-20  12000000   27400000    100.0
27                      Persuasion   1995-09-27         0          0    104.0
28       The City of Lost Children   1995-05-16  18000000    1738611    108.0
29                  Shanghai Triad   1995-04-30         0          0    108.0
30                 Dangerous Minds   1995-08-11         0  180000000     99.0
31                  Twelve Monkeys   1995-12-29  29500000  168840000    129.0
32                Wings of Courage   1996-09-18         0          0     50.0
33                            Babe   1995-07-18  30000000  254134910     89.0
34                      Carrington   1995-11-08         0          0    121.0
35                Dead Man Walking   1995-12-29  11000000   39363635    122.0
36          Across the Sea of Time   1995-10-20         0          0     51.0
37                    It Takes Two   1995-11-17         0          0    101.0
38                        Clueless   1995-07-19  12000000          0     97.0
39        Cry, the Beloved Country   1995-12-15         0     676525    106.0
40                     Richard III   1995-12-29         0          0    104.0
41                 Dead Presidents   1995-10-06  10000000          0    119.0
42                     Restoration   1995-12-29  19000000          0    117.0
43                   Mortal Kombat   1995-08-18  18000000  122195920    101.0
44                      To Die For   1995-05-20  20000000   21284514    106.0
45   How To Make An American Quilt   1995-10-06  10000000   23574130    116.0
46                           Se7en   1995-09-22  33000000  327311859    127.0
47                      Pocahontas   1995-06-14  55000000  346079773     81.0
48           When Night Is Falling   1995-05-05         0          0     96.0
49              The Usual Suspects   1995-07-19   6000000   23341568    106.0	                                       

Python-Pandas Code Editor:

Sample Table:


Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: Write a Pandas program to sort the DataFrame based on release_date.
Next: Write a Pandas program to sort movies on runtime in descending order.

What is the difficulty level of this exercise?

Test your Programming skills with w3resource's quiz.



Become a Patron!

Follow us on Facebook and Twitter for latest update.

It will be nice if you may share this link in any developer community or anywhere else, from where other developers may find this content. Thanks.

https://www.w3resource.com/python-exercises/pandas/movies/python-pandas-movies-exercise-11.php