Pandas: Sort the DataFrame based on release_date

Python Pandas: IMDb Movies Exercise-10 with Solution

Write a Pandas program to sort the DataFrame based on release_date.

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.sort_values('release_date')
print("DataFrame based on release date.")
print(result)

Sample Output:

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

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 create a smaller DataFrame with a subset of all features.

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