

In [3]:

    
import pandas as pd

movies = pd.read_csv("fandango_score_comparison.csv")

def preview(df):
    print("Dimensions: {0} rows x {1} columns".format(df.shape[0], df.shape[1]))
    return df.head()

preview(movies)









    



Dimensions: 146 rows x 22 columns






    Out[3]:






  
    
      
      FILM
      RottenTomatoes
      RottenTomatoes_User
      Metacritic
      Metacritic_User
      IMDB
      Fandango_Stars
      Fandango_Ratingvalue
      RT_norm
      RT_user_norm
      ...
      IMDB_norm
      RT_norm_round
      RT_user_norm_round
      Metacritic_norm_round
      Metacritic_user_norm_round
      IMDB_norm_round
      Metacritic_user_vote_count
      IMDB_user_vote_count
      Fandango_votes
      Fandango_Difference
    
  
  
    
      0
      Avengers: Age of Ultron (2015)
      74
      86
      66
      7.1
      7.8
      5.0
      4.5
      3.70
      4.3
      ...
      3.90
      3.5
      4.5
      3.5
      3.5
      4.0
      1330
      271107
      14846
      0.5
    
    
      1
      Cinderella (2015)
      85
      80
      67
      7.5
      7.1
      5.0
      4.5
      4.25
      4.0
      ...
      3.55
      4.5
      4.0
      3.5
      4.0
      3.5
      249
      65709
      12640
      0.5
    
    
      2
      Ant-Man (2015)
      80
      90
      64
      8.1
      7.8
      5.0
      4.5
      4.00
      4.5
      ...
      3.90
      4.0
      4.5
      3.0
      4.0
      4.0
      627
      103660
      12055
      0.5
    
    
      3
      Do You Believe? (2015)
      18
      84
      22
      4.7
      5.4
      5.0
      4.5
      0.90
      4.2
      ...
      2.70
      1.0
      4.0
      1.0
      2.5
      2.5
      31
      3136
      1793
      0.5
    
    
      4
      Hot Tub Time Machine 2 (2015)
      14
      28
      29
      3.4
      5.1
      3.5
      3.0
      0.70
      1.4
      ...
      2.55
      0.5
      1.5
      1.5
      1.5
      2.5
      88
      19560
      1021
      0.5
    
  

5 rows × 22 columns

Histograms



In [7]:

    
import matplotlib.pyplot as plt
%matplotlib inline

plt.hist(movies["Metacritic_norm_round"])
plt.show()

plt.hist(movies["Fandango_Stars"], bins=5)
plt.show()

Metacritic ranges from 0.5-4.5 while Fandango ranges from 3.0-5.0, which is pretty biased.

Mean, median, and standard deviation



In [14]:

    
import numpy as np

print("Metacritic mean: {0}".format(movies["Metacritic_norm_round"].mean()))
print("Metacritic median: {0}".format(movies["Metacritic_norm_round"].median()))
print("Metacritic standard deviation: {0}".format(np.std(movies["Metacritic_norm_round"])))

print("Fandango mean: {0}".format(movies["Fandango_Stars"].mean()))
print("Fandango median: {0}".format(movies["Fandango_Stars"].median()))
print("Fandango standard deviation: {0}".format(np.std(movies["Fandango_Stars"])))









    



Metacritic mean: 2.97260273973
Metacritic median: 3.0
Metacritic standard deviation: 0.987561029704
Fandango mean: 4.08904109589
Fandango median: 4.0
Fandango standard deviation: 0.53853216127

Scatter plots



In [16]:

    
plt.scatter(movies["Metacritic_norm_round"], movies["Fandango_Stars"])
plt.show()



In [21]:

    
movies["fm_diff"] = np.absolute(movies["Metacritic_norm_round"] - movies["Fandango_Stars"])
movies["fm_diff"].head()









    Out[21]:





0    1.5
1    1.5
2    2.0
3    4.0
4    2.0
Name: fm_diff, dtype: float64



In [27]:

    
movies.sort_values(by="fm_diff", ascending=False).head()









    Out[27]:






  
    
      
      FILM
      RottenTomatoes
      RottenTomatoes_User
      Metacritic
      Metacritic_User
      IMDB
      Fandango_Stars
      Fandango_Ratingvalue
      RT_norm
      RT_user_norm
      ...
      RT_norm_round
      RT_user_norm_round
      Metacritic_norm_round
      Metacritic_user_norm_round
      IMDB_norm_round
      Metacritic_user_vote_count
      IMDB_user_vote_count
      Fandango_votes
      Fandango_Difference
      fm_diff
    
  
  
    
      3
      Do You Believe? (2015)
      18
      84
      22
      4.7
      5.4
      5.0
      4.5
      0.90
      4.20
      ...
      1.0
      4.0
      1.0
      2.5
      2.5
      31
      3136
      1793
      0.5
      4.0
    
    
      85
      Little Boy (2015)
      20
      81
      30
      5.9
      7.4
      4.5
      4.3
      1.00
      4.05
      ...
      1.0
      4.0
      1.5
      3.0
      3.5
      38
      5927
      811
      0.2
      3.0
    
    
      47
      Annie (2014)
      27
      61
      33
      4.8
      5.2
      4.5
      4.2
      1.35
      3.05
      ...
      1.5
      3.0
      1.5
      2.5
      2.5
      108
      19222
      6835
      0.3
      3.0
    
    
      19
      Pixels (2015)
      17
      54
      27
      5.3
      5.6
      4.5
      4.1
      0.85
      2.70
      ...
      1.0
      2.5
      1.5
      2.5
      3.0
      246
      19521
      3886
      0.4
      3.0
    
    
      134
      The Longest Ride (2015)
      31
      73
      33
      4.8
      7.2
      4.5
      4.5
      1.55
      3.65
      ...
      1.5
      3.5
      1.5
      2.5
      3.5
      49
      25214
      2603
      0.0
      3.0
    
  

5 rows × 23 columns

Correlations



In [33]:

    
from scipy import stats

corr, p = stats.pearsonr(movies["Fandango_Stars"], movies["Metacritic_norm_round"])
print("Correlation: {0}".format(corr))









    



Correlation: 0.178449190739



In [38]:

    
slope, intercept, r_value, p_value, std_err = stats.linregress(movies["Metacritic_norm_round"], movies["Fandango_Stars"])

print("Metacritic: 3.0, predicted Fandango: {0}".format(slope * 3.0 + intercept))
print("Metacritic: 4.0, predicted Fandango: {0}".format(slope * 4.0 + intercept))









    



Metacritic: 3.0, predicted Fandango: 4.09170715282
Metacritic: 4.0, predicted Fandango: 4.1890182308

Finding residuals



In [42]:

    
plt.scatter(movies["Metacritic_norm_round"], movies["Fandango_Stars"])
x = [3.0, 4.0]
y = [4.09, 4.19]
plt.plot(x, y)
plt.show()



In [ ]:

	FILM	RottenTomatoes	RottenTomatoes_User	Metacritic	Metacritic_User	IMDB	Fandango_Stars	Fandango_Ratingvalue	RT_norm	RT_user_norm	...	IMDB_norm	RT_norm_round	RT_user_norm_round	Metacritic_norm_round	Metacritic_user_norm_round	IMDB_norm_round	Metacritic_user_vote_count	IMDB_user_vote_count	Fandango_votes	Fandango_Difference
0	Avengers: Age of Ultron (2015)	74	86	66	7.1	7.8	5.0	4.5	3.70	4.3	...	3.90	3.5	4.5	3.5	3.5	4.0	1330	271107	14846	0.5
1	Cinderella (2015)	85	80	67	7.5	7.1	5.0	4.5	4.25	4.0	...	3.55	4.5	4.0	3.5	4.0	3.5	249	65709	12640	0.5
2	Ant-Man (2015)	80	90	64	8.1	7.8	5.0	4.5	4.00	4.5	...	3.90	4.0	4.5	3.0	4.0	4.0	627	103660	12055	0.5
3	Do You Believe? (2015)	18	84	22	4.7	5.4	5.0	4.5	0.90	4.2	...	2.70	1.0	4.0	1.0	2.5	2.5	31	3136	1793	0.5
4	Hot Tub Time Machine 2 (2015)	14	28	29	3.4	5.1	3.5	3.0	0.70	1.4	...	2.55	0.5	1.5	1.5	1.5	2.5	88	19560	1021	0.5

	FILM	RottenTomatoes	RottenTomatoes_User	Metacritic	Metacritic_User	IMDB	Fandango_Stars	Fandango_Ratingvalue	RT_norm	RT_user_norm	...	RT_norm_round	RT_user_norm_round	Metacritic_norm_round	Metacritic_user_norm_round	IMDB_norm_round	Metacritic_user_vote_count	IMDB_user_vote_count	Fandango_votes	Fandango_Difference	fm_diff
3	Do You Believe? (2015)	18	84	22	4.7	5.4	5.0	4.5	0.90	4.20	...	1.0	4.0	1.0	2.5	2.5	31	3136	1793	0.5	4.0
85	Little Boy (2015)	20	81	30	5.9	7.4	4.5	4.3	1.00	4.05	...	1.0	4.0	1.5	3.0	3.5	38	5927	811	0.2	3.0
47	Annie (2014)	27	61	33	4.8	5.2	4.5	4.2	1.35	3.05	...	1.5	3.0	1.5	2.5	2.5	108	19222	6835	0.3	3.0
19	Pixels (2015)	17	54	27	5.3	5.6	4.5	4.1	0.85	2.70	...	1.0	2.5	1.5	2.5	3.0	246	19521	3886	0.4	3.0
134	The Longest Ride (2015)	31	73	33	4.8	7.2	4.5	4.5	1.55	3.65	...	1.5	3.5	1.5	2.5	3.5	49	25214	2603	0.0	3.0