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:
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