In 2015 Journalist Walt Hickey found that Fandago was rounding up its movie ratings to the highest whole number. I will be looking at more recent data to see if this is still the case.
Lets start by reading in the 2015 data, and the 2016-2017 data:
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
fand = pd.read_csv('fandango_score_comparison.csv')
movr = pd.read_csv('movie_ratings_16_17.csv')
Lets see what the data look like:
fand.shape
(146, 22)
fand.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 146 entries, 0 to 145 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 FILM 146 non-null object 1 RottenTomatoes 146 non-null int64 2 RottenTomatoes_User 146 non-null int64 3 Metacritic 146 non-null int64 4 Metacritic_User 146 non-null float64 5 IMDB 146 non-null float64 6 Fandango_Stars 146 non-null float64 7 Fandango_Ratingvalue 146 non-null float64 8 RT_norm 146 non-null float64 9 RT_user_norm 146 non-null float64 10 Metacritic_norm 146 non-null float64 11 Metacritic_user_nom 146 non-null float64 12 IMDB_norm 146 non-null float64 13 RT_norm_round 146 non-null float64 14 RT_user_norm_round 146 non-null float64 15 Metacritic_norm_round 146 non-null float64 16 Metacritic_user_norm_round 146 non-null float64 17 IMDB_norm_round 146 non-null float64 18 Metacritic_user_vote_count 146 non-null int64 19 IMDB_user_vote_count 146 non-null int64 20 Fandango_votes 146 non-null int64 21 Fandango_Difference 146 non-null float64 22 year 146 non-null object dtypes: float64(15), int64(6), object(2) memory usage: 26.4+ KB
movr.shape
(214, 15)
movr.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 214 entries, 0 to 213 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 movie 214 non-null object 1 year 214 non-null int64 2 metascore 214 non-null int64 3 imdb 214 non-null float64 4 tmeter 214 non-null int64 5 audience 214 non-null int64 6 fandango 214 non-null float64 7 n_metascore 214 non-null float64 8 n_imdb 214 non-null float64 9 n_tmeter 214 non-null float64 10 n_audience 214 non-null float64 11 nr_metascore 214 non-null float64 12 nr_imdb 214 non-null float64 13 nr_tmeter 214 non-null float64 14 nr_audience 214 non-null float64 dtypes: float64(10), int64(4), object(1) memory usage: 25.2+ KB
We will seperate the columns we are interested in into seperate groups:
fsc = fand[['FILM', 'Fandango_Stars', 'Fandango_Ratingvalue', 'Fandango_votes', 'Fandango_Difference']]
mr = movr[['movie', 'year', 'fandango']]
What does this look like ?
fsc.head()
FILM | Fandango_Stars | Fandango_Ratingvalue | Fandango_votes | Fandango_Difference | |
---|---|---|---|---|---|
0 | Avengers: Age of Ultron (2015) | 5.0 | 4.5 | 14846 | 0.5 |
1 | Cinderella (2015) | 5.0 | 4.5 | 12640 | 0.5 |
2 | Ant-Man (2015) | 5.0 | 4.5 | 12055 | 0.5 |
3 | Do You Believe? (2015) | 5.0 | 4.5 | 1793 | 0.5 |
4 | Hot Tub Time Machine 2 (2015) | 3.5 | 3.0 | 1021 | 0.5 |
mr.head()
movie | year | fandango | |
---|---|---|---|
0 | 10 Cloverfield Lane | 2016 | 3.5 |
1 | 13 Hours | 2016 | 4.5 |
2 | A Cure for Wellness | 2016 | 3.0 |
3 | A Dog's Purpose | 2017 | 4.5 |
4 | A Hologram for the King | 2016 | 3.0 |
The population of interest is Post 2015. To see if their are any changes. The 2016-2017 data does say the movie has to have significant number of votes but doesn't state how many votes. I would prefer to access the complete population dataset.
Due to the limitations of the samples, the goal has to change to identify whether there are changes in 2016 compared to the 2015 measurement of popular films with min of 30 user votes.
I will seperate out the 2015 and 2016 data.
mr[mr['year'] == 2016]
movie | year | fandango | |
---|---|---|---|
0 | 10 Cloverfield Lane | 2016 | 3.5 |
1 | 13 Hours | 2016 | 4.5 |
2 | A Cure for Wellness | 2016 | 3.0 |
4 | A Hologram for the King | 2016 | 3.0 |
5 | A Monster Calls | 2016 | 4.0 |
6 | A Street Cat Named Bob | 2016 | 4.5 |
7 | Alice Through the Looking Glass | 2016 | 4.0 |
8 | Allied | 2016 | 4.0 |
9 | Amateur Night | 2016 | 3.5 |
10 | Anthropoid | 2016 | 4.0 |
11 | Approaching the Unknown | 2016 | 3.5 |
12 | Arrival | 2016 | 4.0 |
14 | Assassin's Creed | 2016 | 4.0 |
15 | Bad Moms | 2016 | 4.5 |
16 | Bad Santa 2 | 2016 | 3.5 |
17 | Barbershop: The Next Cut | 2016 | 4.5 |
18 | Batman V Superman: Dawn of Justice | 2016 | 4.0 |
21 | Before the Flood | 2016 | 3.5 |
22 | Ben-Hur | 2016 | 4.0 |
24 | Blair Witch | 2016 | 3.0 |
25 | Bleed for This | 2016 | 4.0 |
26 | Blood Father | 2016 | 4.0 |
27 | Bridget Jones's Baby | 2016 | 4.0 |
28 | Busanhaeng | 2016 | 4.5 |
29 | Cabin Fever | 2016 | 4.0 |
30 | Cafe Society | 2016 | 3.5 |
31 | Captain America: Civil War | 2016 | 4.5 |
32 | Captain Fantastic | 2016 | 4.0 |
33 | Cell | 2016 | 3.0 |
34 | Central Intelligence | 2016 | 4.5 |
35 | Collateral Beauty | 2016 | 4.0 |
36 | Collide | 2016 | 3.5 |
37 | Come and Find Me | 2016 | 4.0 |
38 | Criminal | 2016 | 4.0 |
39 | Crouching Tiger, Hidden Dragon: Sword of Destiny | 2016 | 4.0 |
40 | Deadpool | 2016 | 4.5 |
41 | Deepwater Horizon | 2016 | 4.5 |
42 | Dirty Grandpa | 2016 | 3.5 |
43 | Doctor Strange | 2016 | 4.5 |
44 | Don't Breathe | 2016 | 4.0 |
45 | Eddie the Eagle | 2016 | 4.5 |
46 | Elle | 2016 | 3.5 |
47 | Elvis & Nixon | 2016 | 3.5 |
49 | Everybody Wants Some!! | 2016 | 3.5 |
50 | Exposed | 2016 | 2.5 |
51 | Fantastic Beasts and Where to Find Them | 2016 | 4.5 |
52 | Fences | 2016 | 4.0 |
54 | Fifty Shades of Black | 2016 | 2.5 |
55 | Finding Dory | 2016 | 4.5 |
57 | Florence Foster Jenkins | 2016 | 4.0 |
58 | Free State of Jones | 2016 | 4.0 |
59 | Genius | 2016 | 3.5 |
60 | Get a Job | 2016 | 3.0 |
62 | Ghostbusters | 2016 | 4.0 |
63 | Gods of Egypt | 2016 | 3.5 |
64 | Gold | 2016 | 3.5 |
65 | Hacksaw Ridge | 2016 | 4.5 |
66 | Hail, Caesar! | 2016 | 2.5 |
67 | Hands of Stone | 2016 | 4.0 |
68 | Hell or High Water | 2016 | 4.5 |
69 | Hidden Figures | 2016 | 5.0 |
70 | How to Be Single | 2016 | 4.0 |
71 | Hunt for the Wilderpeople | 2016 | 4.5 |
72 | Hush | 2016 | 4.0 |
73 | I, Daniel Blake | 2016 | 4.5 |
74 | I.T. | 2016 | 3.5 |
75 | Ice Age: Collision Course | 2016 | 4.0 |
76 | Imperium | 2016 | 4.5 |
77 | In a Valley of Violence | 2016 | 4.0 |
78 | Incarnate | 2016 | 3.0 |
79 | Independence Day: Resurgence | 2016 | 3.0 |
80 | Inferno | 2016 | 3.5 |
81 | Jack Reacher: Never Go Back | 2016 | 4.0 |
82 | Jackie | 2016 | 3.5 |
83 | Jane Got a Gun | 2016 | 3.5 |
84 | Jason Bourne | 2016 | 4.0 |
86 | Julieta | 2016 | 3.5 |
87 | Keanu | 2016 | 4.0 |
88 | Keeping Up with the Joneses | 2016 | 3.5 |
89 | Kickboxer | 2016 | 4.0 |
90 | Kingsglaive: Final Fantasy XV | 2016 | 4.5 |
92 | Kubo and the Two Strings | 2016 | 4.5 |
93 | Kung Fu Panda 3 | 2016 | 4.5 |
94 | La La Land | 2016 | 4.0 |
95 | Lights Out | 2016 | 4.0 |
96 | Lion | 2016 | 4.0 |
97 | Live by Night | 2016 | 3.5 |
99 | London Has Fallen | 2016 | 4.5 |
100 | Love & Friendship | 2016 | 3.5 |
101 | Loving | 2016 | 4.0 |
102 | Manchester by the Sea | 2016 | 3.5 |
103 | Manhattan Night | 2016 | 3.5 |
104 | Marauders | 2016 | 4.5 |
105 | Masterminds | 2016 | 3.5 |
106 | Max Steel | 2016 | 3.5 |
107 | Me Before You | 2016 | 4.5 |
108 | Mechanic: Resurrection | 2016 | 4.0 |
109 | Midnight Special | 2016 | 3.5 |
110 | Mike and Dave Need Wedding Dates | 2016 | 4.0 |
111 | Miracles from Heaven | 2016 | 4.5 |
112 | Misconduct | 2016 | 3.0 |
113 | Miss Peregrine's Home for Peculiar Children | 2016 | 4.0 |
114 | Moana | 2016 | 4.5 |
115 | Money Monster | 2016 | 4.0 |
116 | Moonlight | 2016 | 4.0 |
117 | Morgan | 2016 | 3.5 |
118 | Mr. Church | 2016 | 4.5 |
119 | My Big Fat Greek Wedding 2 | 2016 | 4.0 |
120 | Neighbors 2: Sorority Rising | 2016 | 3.5 |
121 | Nerve | 2016 | 4.0 |
122 | Nine Lives | 2016 | 4.0 |
123 | Nocturnal Animals | 2016 | 3.5 |
124 | Norm of the North | 2016 | 3.0 |
125 | Now You See Me 2 | 2016 | 4.0 |
126 | Office Christmas Party | 2016 | 3.5 |
127 | Ouija: Origin of Evil | 2016 | 3.5 |
128 | Our Kind of Traitor | 2016 | 3.5 |
129 | Passengers | 2016 | 4.0 |
130 | Patriots Day | 2016 | 4.5 |
131 | Pele: Birth of a Legened | 2016 | 4.5 |
132 | Pete's Dragon | 2016 | 4.5 |
133 | Precious Cargo | 2016 | 3.0 |
134 | Pride and Prejudice and Zombies | 2016 | 4.0 |
135 | Race | 2016 | 4.5 |
136 | Resident Evil: The Final Chapter | 2016 | 4.0 |
137 | Ride Along 2 | 2016 | 4.0 |
139 | Risen | 2016 | 4.5 |
140 | Rogue One: A Star Wars Story | 2016 | 4.5 |
141 | Sausage Party | 2016 | 3.5 |
142 | Shut In | 2016 | 3.0 |
143 | Sing | 2016 | 4.5 |
144 | Sing Street | 2016 | 4.5 |
145 | Skiptrace | 2016 | 3.5 |
147 | Snowden | 2016 | 4.0 |
148 | Split | 2016 | 4.0 |
149 | Star Trek Beyond | 2016 | 4.5 |
150 | Storks | 2016 | 4.5 |
151 | Suicide Squad | 2016 | 4.0 |
152 | Sully | 2016 | 4.5 |
153 | Swiss Army Man | 2016 | 4.0 |
156 | Teenage Mutant Ninja Turtles: Out of the Shadows | 2016 | 4.0 |
157 | The 5th Wave | 2016 | 3.5 |
158 | The 9th Life of Louis Drax | 2016 | 3.5 |
159 | The Accountant | 2016 | 4.5 |
160 | The Angry Birds Movie | 2016 | 4.0 |
161 | The Autopsy of Jane Doe | 2016 | 4.5 |
162 | The BFG | 2016 | 4.0 |
163 | The Boss | 2016 | 3.5 |
164 | The Boy | 2016 | 3.5 |
165 | The Brothers Grimsby | 2016 | 3.5 |
167 | The Choice | 2016 | 4.0 |
168 | The Confirmation | 2016 | 4.5 |
169 | The Conjuring 2 | 2016 | 4.5 |
170 | The Darkness | 2016 | 2.5 |
171 | The Disappointments Room | 2016 | 2.5 |
172 | The Duel | 2016 | 3.5 |
173 | The Edge of Seventeen | 2016 | 4.0 |
174 | The Finest Hours | 2016 | 4.0 |
175 | The Forest | 2016 | 3.0 |
176 | The Founder | 2016 | 4.0 |
177 | The Girl on the Train | 2016 | 4.0 |
178 | The Girl with All the Gifts | 2016 | 4.0 |
179 | The Great Wall | 2016 | 4.0 |
180 | The Huntsman: Winter's War | 2016 | 4.0 |
181 | The Infiltrator | 2016 | 4.0 |
182 | The Jungle Book | 2016 | 4.5 |
184 | The Legend of Tarzan | 2016 | 4.5 |
186 | The Light Between Oceans | 2016 | 4.0 |
187 | The Magnificent Seven | 2016 | 4.5 |
188 | The Neon Demon | 2016 | 3.5 |
189 | The Nice Guys | 2016 | 3.5 |
190 | The Other Side of the Door | 2016 | 3.5 |
191 | The Perfect Match | 2016 | 4.0 |
192 | The Purge: Election Year | 2016 | 4.0 |
193 | The Secret Life of Pets | 2016 | 4.0 |
195 | The Shallows | 2016 | 4.0 |
197 | The Take (Bastille Day) | 2016 | 4.0 |
198 | The Whole Truth | 2016 | 3.0 |
199 | The Wild Life | 2016 | 3.0 |
200 | Triple 9 | 2016 | 3.5 |
201 | Trolls | 2016 | 4.5 |
202 | Under the Shadow | 2016 | 4.0 |
203 | Underworld: Blood Wars | 2016 | 4.0 |
204 | War Dogs | 2016 | 4.0 |
205 | War on Everyone | 2016 | 4.0 |
206 | Warcraft | 2016 | 4.0 |
207 | Whiskey Tango Foxtrot | 2016 | 3.5 |
208 | Why Him? | 2016 | 4.0 |
209 | X-Men: Apocalypse | 2016 | 4.0 |
212 | Zoolander 2 | 2016 | 2.5 |
213 | Zootopia | 2016 | 4.5 |
I need a quick way to check that the 2016 films have over 30 user ratings. To do this will take a sample of ten and crossreference the website.
mr.sample(10, random_state = 1)
movie | year | fandango | |
---|---|---|---|
108 | Mechanic: Resurrection | 2016 | 4.0 |
206 | Warcraft | 2016 | 4.0 |
106 | Max Steel | 2016 | 3.5 |
107 | Me Before You | 2016 | 4.5 |
51 | Fantastic Beasts and Where to Find Them | 2016 | 4.5 |
33 | Cell | 2016 | 3.0 |
59 | Genius | 2016 | 3.5 |
152 | Sully | 2016 | 4.5 |
4 | A Hologram for the King | 2016 | 3.0 |
31 | Captain America: Civil War | 2016 | 4.5 |
As of 8/10/2020, the fandango website reviews are now powered by rotten tomatoes.
For the 2015 data the year is in the film name column, so will have to extract this to a seperate column.
fand['year']= fand['FILM'].str[-5:-1]
fsc = fand[fand['year']=='2015']
fsc
FILM | RottenTomatoes | RottenTomatoes_User | Metacritic | Metacritic_User | IMDB | Fandango_Stars | Fandango_Ratingvalue | RT_norm | RT_user_norm | Metacritic_norm | Metacritic_user_nom | 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 | year | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Avengers: Age of Ultron (2015) | 74 | 86 | 66 | 7.1 | 7.8 | 5.0 | 4.5 | 3.70 | 4.30 | 3.30 | 3.55 | 3.90 | 3.5 | 4.5 | 3.5 | 3.5 | 4.0 | 1330 | 271107 | 14846 | 0.5 | 2015 |
1 | Cinderella (2015) | 85 | 80 | 67 | 7.5 | 7.1 | 5.0 | 4.5 | 4.25 | 4.00 | 3.35 | 3.75 | 3.55 | 4.5 | 4.0 | 3.5 | 4.0 | 3.5 | 249 | 65709 | 12640 | 0.5 | 2015 |
2 | Ant-Man (2015) | 80 | 90 | 64 | 8.1 | 7.8 | 5.0 | 4.5 | 4.00 | 4.50 | 3.20 | 4.05 | 3.90 | 4.0 | 4.5 | 3.0 | 4.0 | 4.0 | 627 | 103660 | 12055 | 0.5 | 2015 |
3 | Do You Believe? (2015) | 18 | 84 | 22 | 4.7 | 5.4 | 5.0 | 4.5 | 0.90 | 4.20 | 1.10 | 2.35 | 2.70 | 1.0 | 4.0 | 1.0 | 2.5 | 2.5 | 31 | 3136 | 1793 | 0.5 | 2015 |
4 | Hot Tub Time Machine 2 (2015) | 14 | 28 | 29 | 3.4 | 5.1 | 3.5 | 3.0 | 0.70 | 1.40 | 1.45 | 1.70 | 2.55 | 0.5 | 1.5 | 1.5 | 1.5 | 2.5 | 88 | 19560 | 1021 | 0.5 | 2015 |
5 | The Water Diviner (2015) | 63 | 62 | 50 | 6.8 | 7.2 | 4.5 | 4.0 | 3.15 | 3.10 | 2.50 | 3.40 | 3.60 | 3.0 | 3.0 | 2.5 | 3.5 | 3.5 | 34 | 39373 | 397 | 0.5 | 2015 |
6 | Irrational Man (2015) | 42 | 53 | 53 | 7.6 | 6.9 | 4.0 | 3.5 | 2.10 | 2.65 | 2.65 | 3.80 | 3.45 | 2.0 | 2.5 | 2.5 | 4.0 | 3.5 | 17 | 2680 | 252 | 0.5 | 2015 |
8 | Shaun the Sheep Movie (2015) | 99 | 82 | 81 | 8.8 | 7.4 | 4.5 | 4.0 | 4.95 | 4.10 | 4.05 | 4.40 | 3.70 | 5.0 | 4.0 | 4.0 | 4.5 | 3.5 | 62 | 12227 | 896 | 0.5 | 2015 |
9 | Love & Mercy (2015) | 89 | 87 | 80 | 8.5 | 7.8 | 4.5 | 4.0 | 4.45 | 4.35 | 4.00 | 4.25 | 3.90 | 4.5 | 4.5 | 4.0 | 4.5 | 4.0 | 54 | 5367 | 864 | 0.5 | 2015 |
10 | Far From The Madding Crowd (2015) | 84 | 77 | 71 | 7.5 | 7.2 | 4.5 | 4.0 | 4.20 | 3.85 | 3.55 | 3.75 | 3.60 | 4.0 | 4.0 | 3.5 | 4.0 | 3.5 | 35 | 12129 | 804 | 0.5 | 2015 |
11 | Black Sea (2015) | 82 | 60 | 62 | 6.6 | 6.4 | 4.0 | 3.5 | 4.10 | 3.00 | 3.10 | 3.30 | 3.20 | 4.0 | 3.0 | 3.0 | 3.5 | 3.0 | 37 | 16547 | 218 | 0.5 | 2015 |
15 | Taken 3 (2015) | 9 | 46 | 26 | 4.6 | 6.1 | 4.5 | 4.1 | 0.45 | 2.30 | 1.30 | 2.30 | 3.05 | 0.5 | 2.5 | 1.5 | 2.5 | 3.0 | 240 | 104235 | 6757 | 0.4 | 2015 |
16 | Ted 2 (2015) | 46 | 58 | 48 | 6.5 | 6.6 | 4.5 | 4.1 | 2.30 | 2.90 | 2.40 | 3.25 | 3.30 | 2.5 | 3.0 | 2.5 | 3.5 | 3.5 | 197 | 49102 | 6437 | 0.4 | 2015 |
17 | Southpaw (2015) | 59 | 80 | 57 | 8.2 | 7.8 | 5.0 | 4.6 | 2.95 | 4.00 | 2.85 | 4.10 | 3.90 | 3.0 | 4.0 | 3.0 | 4.0 | 4.0 | 128 | 23561 | 5597 | 0.4 | 2015 |
19 | Pixels (2015) | 17 | 54 | 27 | 5.3 | 5.6 | 4.5 | 4.1 | 0.85 | 2.70 | 1.35 | 2.65 | 2.80 | 1.0 | 2.5 | 1.5 | 2.5 | 3.0 | 246 | 19521 | 3886 | 0.4 | 2015 |
20 | McFarland, USA (2015) | 79 | 89 | 60 | 7.2 | 7.5 | 5.0 | 4.6 | 3.95 | 4.45 | 3.00 | 3.60 | 3.75 | 4.0 | 4.5 | 3.0 | 3.5 | 4.0 | 59 | 13769 | 3364 | 0.4 | 2015 |
21 | Insidious: Chapter 3 (2015) | 59 | 56 | 52 | 6.9 | 6.3 | 4.5 | 4.1 | 2.95 | 2.80 | 2.60 | 3.45 | 3.15 | 3.0 | 3.0 | 2.5 | 3.5 | 3.0 | 115 | 25134 | 3276 | 0.4 | 2015 |
22 | The Man From U.N.C.L.E. (2015) | 68 | 80 | 55 | 7.9 | 7.6 | 4.5 | 4.1 | 3.40 | 4.00 | 2.75 | 3.95 | 3.80 | 3.5 | 4.0 | 3.0 | 4.0 | 4.0 | 144 | 22104 | 2686 | 0.4 | 2015 |
23 | Run All Night (2015) | 60 | 59 | 59 | 7.3 | 6.6 | 4.5 | 4.1 | 3.00 | 2.95 | 2.95 | 3.65 | 3.30 | 3.0 | 3.0 | 3.0 | 3.5 | 3.5 | 141 | 50438 | 2066 | 0.4 | 2015 |
24 | Trainwreck (2015) | 85 | 74 | 75 | 6.0 | 6.7 | 4.5 | 4.1 | 4.25 | 3.70 | 3.75 | 3.00 | 3.35 | 4.5 | 3.5 | 4.0 | 3.0 | 3.5 | 169 | 27380 | 8381 | 0.4 | 2015 |
26 | Ex Machina (2015) | 92 | 86 | 78 | 7.9 | 7.7 | 4.5 | 4.1 | 4.60 | 4.30 | 3.90 | 3.95 | 3.85 | 4.5 | 4.5 | 4.0 | 4.0 | 4.0 | 672 | 154499 | 3458 | 0.4 | 2015 |
27 | Still Alice (2015) | 88 | 85 | 72 | 7.8 | 7.5 | 4.5 | 4.1 | 4.40 | 4.25 | 3.60 | 3.90 | 3.75 | 4.5 | 4.5 | 3.5 | 4.0 | 4.0 | 153 | 57123 | 1258 | 0.4 | 2015 |
29 | The End of the Tour (2015) | 92 | 89 | 84 | 7.5 | 7.9 | 4.5 | 4.1 | 4.60 | 4.45 | 4.20 | 3.75 | 3.95 | 4.5 | 4.5 | 4.0 | 4.0 | 4.0 | 19 | 1320 | 121 | 0.4 | 2015 |
30 | Red Army (2015) | 96 | 86 | 82 | 7.4 | 7.7 | 4.5 | 4.1 | 4.80 | 4.30 | 4.10 | 3.70 | 3.85 | 5.0 | 4.5 | 4.0 | 3.5 | 4.0 | 11 | 2275 | 54 | 0.4 | 2015 |
31 | When Marnie Was There (2015) | 89 | 89 | 71 | 6.4 | 7.8 | 4.5 | 4.1 | 4.45 | 4.45 | 3.55 | 3.20 | 3.90 | 4.5 | 4.5 | 3.5 | 3.0 | 4.0 | 29 | 4160 | 46 | 0.4 | 2015 |
32 | The Hunting Ground (2015) | 92 | 72 | 77 | 7.8 | 7.5 | 4.5 | 4.1 | 4.60 | 3.60 | 3.85 | 3.90 | 3.75 | 4.5 | 3.5 | 4.0 | 4.0 | 4.0 | 6 | 1196 | 42 | 0.4 | 2015 |
33 | The Boy Next Door (2015) | 10 | 35 | 30 | 5.5 | 4.6 | 4.0 | 3.6 | 0.50 | 1.75 | 1.50 | 2.75 | 2.30 | 0.5 | 2.0 | 1.5 | 3.0 | 2.5 | 75 | 19658 | 2800 | 0.4 | 2015 |
34 | Aloha (2015) | 19 | 31 | 40 | 4.0 | 5.5 | 3.5 | 3.1 | 0.95 | 1.55 | 2.00 | 2.00 | 2.75 | 1.0 | 1.5 | 2.0 | 2.0 | 3.0 | 67 | 12255 | 2284 | 0.4 | 2015 |
35 | The Loft (2015) | 11 | 40 | 24 | 2.4 | 6.3 | 4.0 | 3.6 | 0.55 | 2.00 | 1.20 | 1.20 | 3.15 | 0.5 | 2.0 | 1.0 | 1.0 | 3.0 | 80 | 21319 | 811 | 0.4 | 2015 |
36 | 5 Flights Up (2015) | 52 | 47 | 55 | 6.8 | 6.1 | 4.0 | 3.6 | 2.60 | 2.35 | 2.75 | 3.40 | 3.05 | 2.5 | 2.5 | 3.0 | 3.5 | 3.0 | 6 | 2174 | 79 | 0.4 | 2015 |
37 | Welcome to Me (2015) | 71 | 47 | 67 | 6.9 | 5.9 | 4.0 | 3.6 | 3.55 | 2.35 | 3.35 | 3.45 | 2.95 | 3.5 | 2.5 | 3.5 | 3.5 | 3.0 | 33 | 8301 | 56 | 0.4 | 2015 |
38 | Saint Laurent (2015) | 51 | 45 | 52 | 6.8 | 6.3 | 3.5 | 3.1 | 2.55 | 2.25 | 2.60 | 3.40 | 3.15 | 2.5 | 2.5 | 2.5 | 3.5 | 3.0 | 8 | 2196 | 43 | 0.4 | 2015 |
39 | Maps to the Stars (2015) | 60 | 46 | 67 | 5.8 | 6.3 | 3.5 | 3.1 | 3.00 | 2.30 | 3.35 | 2.90 | 3.15 | 3.0 | 2.5 | 3.5 | 3.0 | 3.0 | 46 | 22440 | 35 | 0.4 | 2015 |
40 | I'll See You In My Dreams (2015) | 94 | 70 | 75 | 6.9 | 6.9 | 4.0 | 3.6 | 4.70 | 3.50 | 3.75 | 3.45 | 3.45 | 4.5 | 3.5 | 4.0 | 3.5 | 3.5 | 14 | 1151 | 281 | 0.4 | 2015 |
41 | Timbuktu (2015) | 99 | 78 | 91 | 6.9 | 7.2 | 4.0 | 3.6 | 4.95 | 3.90 | 4.55 | 3.45 | 3.60 | 5.0 | 4.0 | 4.5 | 3.5 | 3.5 | 37 | 6246 | 74 | 0.4 | 2015 |
42 | About Elly (2015) | 97 | 86 | 87 | 9.6 | 8.2 | 4.0 | 3.6 | 4.85 | 4.30 | 4.35 | 4.80 | 4.10 | 5.0 | 4.5 | 4.5 | 5.0 | 4.0 | 23 | 20659 | 43 | 0.4 | 2015 |
43 | The Diary of a Teenage Girl (2015) | 95 | 81 | 87 | 6.3 | 7.0 | 4.0 | 3.6 | 4.75 | 4.05 | 4.35 | 3.15 | 3.50 | 5.0 | 4.0 | 4.5 | 3.0 | 3.5 | 18 | 1107 | 38 | 0.4 | 2015 |
44 | Kingsman: The Secret Service (2015) | 75 | 84 | 58 | 7.9 | 7.8 | 4.5 | 4.2 | 3.75 | 4.20 | 2.90 | 3.95 | 3.90 | 4.0 | 4.0 | 3.0 | 4.0 | 4.0 | 1054 | 272204 | 15205 | 0.3 | 2015 |
45 | Tomorrowland (2015) | 50 | 53 | 60 | 6.4 | 6.6 | 4.0 | 3.7 | 2.50 | 2.65 | 3.00 | 3.20 | 3.30 | 2.5 | 2.5 | 3.0 | 3.0 | 3.5 | 262 | 42937 | 8077 | 0.3 | 2015 |
46 | The Divergent Series: Insurgent (2015) | 30 | 61 | 42 | 5.4 | 6.4 | 4.5 | 4.2 | 1.50 | 3.05 | 2.10 | 2.70 | 3.20 | 1.5 | 3.0 | 2.0 | 2.5 | 3.0 | 206 | 89618 | 7123 | 0.3 | 2015 |
48 | Fantastic Four (2015) | 9 | 20 | 27 | 2.5 | 4.0 | 3.0 | 2.7 | 0.45 | 1.00 | 1.35 | 1.25 | 2.00 | 0.5 | 1.0 | 1.5 | 1.5 | 2.0 | 421 | 39838 | 6288 | 0.3 | 2015 |
49 | Terminator Genisys (2015) | 26 | 60 | 38 | 6.4 | 6.9 | 4.5 | 4.2 | 1.30 | 3.00 | 1.90 | 3.20 | 3.45 | 1.5 | 3.0 | 2.0 | 3.0 | 3.5 | 779 | 85585 | 6272 | 0.3 | 2015 |
50 | Pitch Perfect 2 (2015) | 67 | 68 | 63 | 5.7 | 6.7 | 4.5 | 4.2 | 3.35 | 3.40 | 3.15 | 2.85 | 3.35 | 3.5 | 3.5 | 3.0 | 3.0 | 3.5 | 192 | 56333 | 4577 | 0.3 | 2015 |
51 | Entourage (2015) | 32 | 68 | 38 | 5.2 | 7.1 | 4.5 | 4.2 | 1.60 | 3.40 | 1.90 | 2.60 | 3.55 | 1.5 | 3.5 | 2.0 | 2.5 | 3.5 | 96 | 21914 | 4279 | 0.3 | 2015 |
52 | The Age of Adaline (2015) | 54 | 68 | 51 | 7.4 | 7.3 | 4.5 | 4.2 | 2.70 | 3.40 | 2.55 | 3.70 | 3.65 | 2.5 | 3.5 | 2.5 | 3.5 | 3.5 | 100 | 45510 | 3325 | 0.3 | 2015 |
53 | Hot Pursuit (2015) | 8 | 37 | 31 | 3.7 | 4.9 | 4.0 | 3.7 | 0.40 | 1.85 | 1.55 | 1.85 | 2.45 | 0.5 | 2.0 | 1.5 | 2.0 | 2.5 | 78 | 17061 | 2618 | 0.3 | 2015 |
54 | The DUFF (2015) | 71 | 68 | 56 | 6.4 | 6.6 | 4.5 | 4.2 | 3.55 | 3.40 | 2.80 | 3.20 | 3.30 | 3.5 | 3.5 | 3.0 | 3.0 | 3.5 | 69 | 33594 | 2273 | 0.3 | 2015 |
55 | Black or White (2015) | 39 | 68 | 45 | 7.9 | 6.6 | 4.5 | 4.2 | 1.95 | 3.40 | 2.25 | 3.95 | 3.30 | 2.0 | 3.5 | 2.5 | 4.0 | 3.5 | 24 | 4857 | 1862 | 0.3 | 2015 |
56 | Project Almanac (2015) | 34 | 46 | 47 | 5.4 | 6.4 | 4.0 | 3.7 | 1.70 | 2.30 | 2.35 | 2.70 | 3.20 | 1.5 | 2.5 | 2.5 | 2.5 | 3.0 | 95 | 40057 | 1834 | 0.3 | 2015 |
57 | Ricki and the Flash (2015) | 64 | 53 | 54 | 7.0 | 6.2 | 4.0 | 3.7 | 3.20 | 2.65 | 2.70 | 3.50 | 3.10 | 3.0 | 2.5 | 2.5 | 3.5 | 3.0 | 37 | 1769 | 1462 | 0.3 | 2015 |
58 | Seventh Son (2015) | 12 | 35 | 30 | 3.9 | 5.5 | 3.5 | 3.2 | 0.60 | 1.75 | 1.50 | 1.95 | 2.75 | 0.5 | 2.0 | 1.5 | 2.0 | 3.0 | 126 | 41177 | 1213 | 0.3 | 2015 |
59 | Mortdecai (2015) | 12 | 30 | 27 | 3.2 | 5.5 | 3.5 | 3.2 | 0.60 | 1.50 | 1.35 | 1.60 | 2.75 | 0.5 | 1.5 | 1.5 | 1.5 | 3.0 | 144 | 31878 | 1196 | 0.3 | 2015 |
60 | Unfinished Business (2015) | 11 | 27 | 32 | 3.8 | 5.4 | 3.5 | 3.2 | 0.55 | 1.35 | 1.60 | 1.90 | 2.70 | 0.5 | 1.5 | 1.5 | 2.0 | 2.5 | 39 | 14346 | 821 | 0.3 | 2015 |
61 | American Ultra (2015) | 46 | 52 | 50 | 6.8 | 6.5 | 4.0 | 3.7 | 2.30 | 2.60 | 2.50 | 3.40 | 3.25 | 2.5 | 2.5 | 2.5 | 3.5 | 3.5 | 42 | 3017 | 638 | 0.3 | 2015 |
62 | True Story (2015) | 45 | 41 | 50 | 5.7 | 6.3 | 3.5 | 3.2 | 2.25 | 2.05 | 2.50 | 2.85 | 3.15 | 2.5 | 2.0 | 2.5 | 3.0 | 3.0 | 37 | 16069 | 540 | 0.3 | 2015 |
63 | Child 44 (2015) | 26 | 44 | 41 | 5.3 | 6.4 | 4.0 | 3.7 | 1.30 | 2.20 | 2.05 | 2.65 | 3.20 | 1.5 | 2.0 | 2.0 | 2.5 | 3.0 | 73 | 19220 | 308 | 0.3 | 2015 |
64 | Dark Places (2015) | 26 | 33 | 39 | 7.9 | 6.3 | 4.0 | 3.7 | 1.30 | 1.65 | 1.95 | 3.95 | 3.15 | 1.5 | 1.5 | 2.0 | 4.0 | 3.0 | 18 | 9856 | 55 | 0.3 | 2015 |
66 | The Gift (2015) | 93 | 79 | 77 | 8.3 | 7.6 | 4.0 | 3.7 | 4.65 | 3.95 | 3.85 | 4.15 | 3.80 | 4.5 | 4.0 | 4.0 | 4.0 | 4.0 | 121 | 10891 | 2680 | 0.3 | 2015 |
67 | Unfriended (2015) | 60 | 39 | 59 | 5.8 | 5.9 | 3.0 | 2.7 | 3.00 | 1.95 | 2.95 | 2.90 | 2.95 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 130 | 22348 | 2507 | 0.3 | 2015 |
68 | Monkey Kingdom (2015) | 94 | 77 | 72 | 7.5 | 7.3 | 4.5 | 4.2 | 4.70 | 3.85 | 3.60 | 3.75 | 3.65 | 4.5 | 4.0 | 3.5 | 4.0 | 3.5 | 15 | 883 | 701 | 0.3 | 2015 |
70 | Seymour: An Introduction (2015) | 100 | 87 | 83 | 6.0 | 7.7 | 4.5 | 4.2 | 5.00 | 4.35 | 4.15 | 3.00 | 3.85 | 5.0 | 4.5 | 4.0 | 3.0 | 4.0 | 4 | 243 | 41 | 0.3 | 2015 |
71 | The Wrecking Crew (2015) | 93 | 84 | 67 | 7.0 | 7.8 | 4.5 | 4.2 | 4.65 | 4.20 | 3.35 | 3.50 | 3.90 | 4.5 | 4.0 | 3.5 | 3.5 | 4.0 | 4 | 732 | 38 | 0.3 | 2015 |
72 | American Sniper (2015) | 72 | 85 | 72 | 6.6 | 7.4 | 5.0 | 4.8 | 3.60 | 4.25 | 3.60 | 3.30 | 3.70 | 3.5 | 4.5 | 3.5 | 3.5 | 3.5 | 850 | 251856 | 34085 | 0.2 | 2015 |
73 | Furious 7 (2015) | 81 | 84 | 67 | 6.8 | 7.4 | 5.0 | 4.8 | 4.05 | 4.20 | 3.35 | 3.40 | 3.70 | 4.0 | 4.0 | 3.5 | 3.5 | 3.5 | 764 | 207211 | 33538 | 0.2 | 2015 |
75 | San Andreas (2015) | 50 | 58 | 43 | 5.5 | 6.5 | 4.5 | 4.3 | 2.50 | 2.90 | 2.15 | 2.75 | 3.25 | 2.5 | 3.0 | 2.0 | 3.0 | 3.5 | 199 | 45723 | 9749 | 0.2 | 2015 |
76 | Straight Outta Compton (2015) | 90 | 94 | 72 | 7.3 | 8.4 | 5.0 | 4.8 | 4.50 | 4.70 | 3.60 | 3.65 | 4.20 | 4.5 | 4.5 | 3.5 | 3.5 | 4.0 | 90 | 15982 | 8096 | 0.2 | 2015 |
77 | Vacation (2015) | 27 | 55 | 34 | 6.2 | 6.3 | 4.0 | 3.8 | 1.35 | 2.75 | 1.70 | 3.10 | 3.15 | 1.5 | 3.0 | 1.5 | 3.0 | 3.0 | 72 | 8179 | 3815 | 0.2 | 2015 |
78 | Chappie (2015) | 30 | 57 | 41 | 7.4 | 7.0 | 4.0 | 3.8 | 1.50 | 2.85 | 2.05 | 3.70 | 3.50 | 1.5 | 3.0 | 2.0 | 3.5 | 3.5 | 637 | 125088 | 3642 | 0.2 | 2015 |
79 | Poltergeist (2015) | 31 | 24 | 47 | 3.7 | 5.0 | 3.0 | 2.8 | 1.55 | 1.20 | 2.35 | 1.85 | 2.50 | 1.5 | 1.0 | 2.5 | 2.0 | 2.5 | 142 | 21372 | 2704 | 0.2 | 2015 |
80 | Paper Towns (2015) | 55 | 57 | 56 | 6.2 | 6.9 | 4.0 | 3.8 | 2.75 | 2.85 | 2.80 | 3.10 | 3.45 | 3.0 | 3.0 | 3.0 | 3.0 | 3.5 | 51 | 14156 | 1750 | 0.2 | 2015 |
82 | Blackhat (2015) | 34 | 25 | 51 | 5.4 | 5.4 | 3.0 | 2.8 | 1.70 | 1.25 | 2.55 | 2.70 | 2.70 | 1.5 | 1.5 | 2.5 | 2.5 | 2.5 | 80 | 27328 | 1430 | 0.2 | 2015 |
83 | Self/less (2015) | 20 | 51 | 34 | 8.4 | 6.6 | 4.0 | 3.8 | 1.00 | 2.55 | 1.70 | 4.20 | 3.30 | 1.0 | 2.5 | 1.5 | 4.0 | 3.5 | 77 | 5626 | 1415 | 0.2 | 2015 |
84 | Sinister 2 (2015) | 13 | 34 | 31 | 5.0 | 5.5 | 3.5 | 3.3 | 0.65 | 1.70 | 1.55 | 2.50 | 2.75 | 0.5 | 1.5 | 1.5 | 2.5 | 3.0 | 37 | 3200 | 973 | 0.2 | 2015 |
85 | Little Boy (2015) | 20 | 81 | 30 | 5.9 | 7.4 | 4.5 | 4.3 | 1.00 | 4.05 | 1.50 | 2.95 | 3.70 | 1.0 | 4.0 | 1.5 | 3.0 | 3.5 | 38 | 5927 | 811 | 0.2 | 2015 |
86 | Me and Earl and The Dying Girl (2015) | 81 | 89 | 74 | 8.4 | 8.2 | 4.5 | 4.3 | 4.05 | 4.45 | 3.70 | 4.20 | 4.10 | 4.0 | 4.5 | 3.5 | 4.0 | 4.0 | 41 | 5269 | 624 | 0.2 | 2015 |
87 | Maggie (2015) | 54 | 32 | 52 | 6.5 | 5.6 | 3.5 | 3.3 | 2.70 | 1.60 | 2.60 | 3.25 | 2.80 | 2.5 | 1.5 | 2.5 | 3.5 | 3.0 | 90 | 18986 | 95 | 0.2 | 2015 |
88 | Mad Max: Fury Road (2015) | 97 | 88 | 89 | 8.7 | 8.3 | 4.5 | 4.3 | 4.85 | 4.40 | 4.45 | 4.35 | 4.15 | 5.0 | 4.5 | 4.5 | 4.5 | 4.0 | 2375 | 292023 | 10509 | 0.2 | 2015 |
89 | Spy (2015) | 93 | 82 | 75 | 6.3 | 7.3 | 4.5 | 4.3 | 4.65 | 4.10 | 3.75 | 3.15 | 3.65 | 4.5 | 4.0 | 4.0 | 3.0 | 3.5 | 318 | 66636 | 9418 | 0.2 | 2015 |
90 | The SpongeBob Movie: Sponge Out of Water (2015) | 78 | 55 | 62 | 6.5 | 6.1 | 3.5 | 3.3 | 3.90 | 2.75 | 3.10 | 3.25 | 3.05 | 4.0 | 3.0 | 3.0 | 3.5 | 3.0 | 196 | 26046 | 4493 | 0.2 | 2015 |
91 | Paddington (2015) | 98 | 81 | 77 | 8.2 | 7.2 | 4.5 | 4.3 | 4.90 | 4.05 | 3.85 | 4.10 | 3.60 | 5.0 | 4.0 | 4.0 | 4.0 | 3.5 | 149 | 38593 | 4045 | 0.2 | 2015 |
92 | Dope (2015) | 87 | 86 | 72 | 7.2 | 7.5 | 4.5 | 4.3 | 4.35 | 4.30 | 3.60 | 3.60 | 3.75 | 4.5 | 4.5 | 3.5 | 3.5 | 4.0 | 43 | 4911 | 2195 | 0.2 | 2015 |
93 | What We Do in the Shadows (2015) | 96 | 86 | 75 | 8.3 | 7.6 | 4.5 | 4.3 | 4.80 | 4.30 | 3.75 | 4.15 | 3.80 | 5.0 | 4.5 | 4.0 | 4.0 | 4.0 | 69 | 39561 | 259 | 0.2 | 2015 |
94 | The Overnight (2015) | 82 | 65 | 65 | 8.6 | 6.9 | 3.5 | 3.3 | 4.10 | 3.25 | 3.25 | 4.30 | 3.45 | 4.0 | 3.5 | 3.5 | 4.5 | 3.5 | 13 | 867 | 110 | 0.2 | 2015 |
95 | The Salt of the Earth (2015) | 96 | 90 | 83 | 7.8 | 8.4 | 4.5 | 4.3 | 4.80 | 4.50 | 4.15 | 3.90 | 4.20 | 5.0 | 4.5 | 4.0 | 4.0 | 4.0 | 13 | 6605 | 83 | 0.2 | 2015 |
97 | Fifty Shades of Grey (2015) | 25 | 42 | 46 | 3.2 | 4.2 | 4.0 | 3.9 | 1.25 | 2.10 | 2.30 | 1.60 | 2.10 | 1.5 | 2.0 | 2.5 | 1.5 | 2.0 | 778 | 179506 | 34846 | 0.1 | 2015 |
98 | Get Hard (2015) | 29 | 48 | 34 | 3.8 | 6.1 | 4.0 | 3.9 | 1.45 | 2.40 | 1.70 | 1.90 | 3.05 | 1.5 | 2.5 | 1.5 | 2.0 | 3.0 | 145 | 50022 | 5933 | 0.1 | 2015 |
99 | Focus (2015) | 57 | 54 | 56 | 6.2 | 6.6 | 4.0 | 3.9 | 2.85 | 2.70 | 2.80 | 3.10 | 3.30 | 3.0 | 2.5 | 3.0 | 3.0 | 3.5 | 167 | 101264 | 4933 | 0.1 | 2015 |
100 | Jupiter Ascending (2015) | 26 | 40 | 40 | 4.5 | 5.5 | 3.5 | 3.4 | 1.30 | 2.00 | 2.00 | 2.25 | 2.75 | 1.5 | 2.0 | 2.0 | 2.5 | 3.0 | 503 | 105412 | 4122 | 0.1 | 2015 |
101 | The Gallows (2015) | 16 | 27 | 30 | 7.0 | 4.4 | 3.0 | 2.9 | 0.80 | 1.35 | 1.50 | 3.50 | 2.20 | 1.0 | 1.5 | 1.5 | 3.5 | 2.0 | 80 | 5511 | 1896 | 0.1 | 2015 |
102 | The Second Best Exotic Marigold Hotel (2015) | 62 | 63 | 51 | 6.1 | 6.6 | 4.0 | 3.9 | 3.10 | 3.15 | 2.55 | 3.05 | 3.30 | 3.0 | 3.0 | 2.5 | 3.0 | 3.5 | 41 | 12940 | 1870 | 0.1 | 2015 |
103 | Strange Magic (2015) | 17 | 50 | 25 | 5.3 | 5.7 | 3.5 | 3.4 | 0.85 | 2.50 | 1.25 | 2.65 | 2.85 | 1.0 | 2.5 | 1.5 | 2.5 | 3.0 | 41 | 3658 | 1117 | 0.1 | 2015 |
104 | The Gunman (2015) | 17 | 34 | 39 | 4.3 | 5.8 | 3.5 | 3.4 | 0.85 | 1.70 | 1.95 | 2.15 | 2.90 | 1.0 | 1.5 | 2.0 | 2.0 | 3.0 | 49 | 16663 | 996 | 0.1 | 2015 |
105 | Hitman: Agent 47 (2015) | 7 | 49 | 28 | 3.3 | 5.9 | 4.0 | 3.9 | 0.35 | 2.45 | 1.40 | 1.65 | 2.95 | 0.5 | 2.5 | 1.5 | 1.5 | 3.0 | 67 | 4260 | 917 | 0.1 | 2015 |
106 | Cake (2015) | 49 | 47 | 49 | 6.4 | 6.5 | 3.5 | 3.4 | 2.45 | 2.35 | 2.45 | 3.20 | 3.25 | 2.5 | 2.5 | 2.5 | 3.0 | 3.5 | 44 | 19627 | 482 | 0.1 | 2015 |
107 | The Vatican Tapes (2015) | 13 | 21 | 37 | 5.4 | 4.6 | 3.0 | 2.9 | 0.65 | 1.05 | 1.85 | 2.70 | 2.30 | 0.5 | 1.0 | 2.0 | 2.5 | 2.5 | 5 | 952 | 210 | 0.1 | 2015 |
108 | A Little Chaos (2015) | 40 | 47 | 51 | 7.0 | 6.4 | 4.0 | 3.9 | 2.00 | 2.35 | 2.55 | 3.50 | 3.20 | 2.0 | 2.5 | 2.5 | 3.5 | 3.0 | 7 | 4778 | 83 | 0.1 | 2015 |
109 | The 100-Year-Old Man Who Climbed Out the Windo... | 67 | 69 | 58 | 4.6 | 7.1 | 4.0 | 3.9 | 3.35 | 3.45 | 2.90 | 2.30 | 3.55 | 3.5 | 3.5 | 3.0 | 2.5 | 3.5 | 5 | 17237 | 63 | 0.1 | 2015 |
110 | Escobar: Paradise Lost (2015) | 52 | 52 | 56 | 6.9 | 6.6 | 4.0 | 3.9 | 2.60 | 2.60 | 2.80 | 3.45 | 3.30 | 2.5 | 2.5 | 3.0 | 3.5 | 3.5 | 7 | 7819 | 48 | 0.1 | 2015 |
112 | It Follows (2015) | 96 | 65 | 83 | 7.5 | 6.9 | 3.0 | 2.9 | 4.80 | 3.25 | 4.15 | 3.75 | 3.45 | 5.0 | 3.5 | 4.0 | 4.0 | 3.5 | 551 | 64656 | 2097 | 0.1 | 2015 |
115 | While We're Young (2015) | 83 | 52 | 76 | 6.7 | 6.4 | 3.0 | 2.9 | 4.15 | 2.60 | 3.80 | 3.35 | 3.20 | 4.0 | 2.5 | 4.0 | 3.5 | 3.0 | 65 | 17647 | 449 | 0.1 | 2015 |
116 | Clouds of Sils Maria (2015) | 89 | 67 | 78 | 7.1 | 6.8 | 3.5 | 3.4 | 4.45 | 3.35 | 3.90 | 3.55 | 3.40 | 4.5 | 3.5 | 4.0 | 3.5 | 3.5 | 36 | 11392 | 162 | 0.1 | 2015 |
117 | Testament of Youth (2015) | 81 | 79 | 77 | 7.9 | 7.3 | 4.0 | 3.9 | 4.05 | 3.95 | 3.85 | 3.95 | 3.65 | 4.0 | 4.0 | 4.0 | 4.0 | 3.5 | 15 | 5495 | 127 | 0.1 | 2015 |
118 | Infinitely Polar Bear (2015) | 80 | 76 | 64 | 7.9 | 7.2 | 4.0 | 3.9 | 4.00 | 3.80 | 3.20 | 3.95 | 3.60 | 4.0 | 4.0 | 3.0 | 4.0 | 3.5 | 8 | 1062 | 124 | 0.1 | 2015 |
119 | Phoenix (2015) | 99 | 81 | 91 | 8.0 | 7.2 | 3.5 | 3.4 | 4.95 | 4.05 | 4.55 | 4.00 | 3.60 | 5.0 | 4.0 | 4.5 | 4.0 | 3.5 | 21 | 3687 | 70 | 0.1 | 2015 |
120 | The Wolfpack (2015) | 84 | 73 | 75 | 7.0 | 7.1 | 3.5 | 3.4 | 4.20 | 3.65 | 3.75 | 3.50 | 3.55 | 4.0 | 3.5 | 4.0 | 3.5 | 3.5 | 8 | 1488 | 66 | 0.1 | 2015 |
121 | The Stanford Prison Experiment (2015) | 84 | 87 | 68 | 8.5 | 7.1 | 4.0 | 3.9 | 4.20 | 4.35 | 3.40 | 4.25 | 3.55 | 4.0 | 4.5 | 3.5 | 4.5 | 3.5 | 6 | 950 | 51 | 0.1 | 2015 |
122 | Tangerine (2015) | 95 | 86 | 86 | 7.3 | 7.4 | 4.0 | 3.9 | 4.75 | 4.30 | 4.30 | 3.65 | 3.70 | 5.0 | 4.5 | 4.5 | 3.5 | 3.5 | 14 | 696 | 36 | 0.1 | 2015 |
123 | Magic Mike XXL (2015) | 62 | 64 | 60 | 5.4 | 6.3 | 4.5 | 4.4 | 3.10 | 3.20 | 3.00 | 2.70 | 3.15 | 3.0 | 3.0 | 3.0 | 2.5 | 3.0 | 52 | 11937 | 9363 | 0.1 | 2015 |
124 | Home (2015) | 45 | 65 | 55 | 7.3 | 6.7 | 4.5 | 4.4 | 2.25 | 3.25 | 2.75 | 3.65 | 3.35 | 2.5 | 3.5 | 3.0 | 3.5 | 3.5 | 177 | 41158 | 7705 | 0.1 | 2015 |
125 | The Wedding Ringer (2015) | 27 | 66 | 35 | 3.3 | 6.7 | 4.5 | 4.4 | 1.35 | 3.30 | 1.75 | 1.65 | 3.35 | 1.5 | 3.5 | 2.0 | 1.5 | 3.5 | 126 | 37292 | 6506 | 0.1 | 2015 |
126 | Woman in Gold (2015) | 52 | 81 | 51 | 7.2 | 7.4 | 4.5 | 4.4 | 2.60 | 4.05 | 2.55 | 3.60 | 3.70 | 2.5 | 4.0 | 2.5 | 3.5 | 3.5 | 72 | 17957 | 2435 | 0.1 | 2015 |
127 | The Last Five Years (2015) | 60 | 60 | 60 | 6.9 | 6.0 | 4.5 | 4.4 | 3.00 | 3.00 | 3.00 | 3.45 | 3.00 | 3.0 | 3.0 | 3.0 | 3.5 | 3.0 | 20 | 4110 | 99 | 0.1 | 2015 |
128 | Mission: Impossible – Rogue Nation (2015) | 92 | 90 | 75 | 8.0 | 7.8 | 4.5 | 4.4 | 4.60 | 4.50 | 3.75 | 4.00 | 3.90 | 4.5 | 4.5 | 4.0 | 4.0 | 4.0 | 362 | 82579 | 8357 | 0.1 | 2015 |
129 | Amy (2015) | 97 | 91 | 85 | 8.8 | 8.0 | 4.5 | 4.4 | 4.85 | 4.55 | 4.25 | 4.40 | 4.00 | 5.0 | 4.5 | 4.5 | 4.5 | 4.0 | 60 | 5630 | 729 | 0.1 | 2015 |
130 | Jurassic World (2015) | 71 | 81 | 59 | 7.0 | 7.3 | 4.5 | 4.5 | 3.55 | 4.05 | 2.95 | 3.50 | 3.65 | 3.5 | 4.0 | 3.0 | 3.5 | 3.5 | 1281 | 241807 | 34390 | 0.0 | 2015 |
131 | Minions (2015) | 54 | 52 | 56 | 5.7 | 6.7 | 4.0 | 4.0 | 2.70 | 2.60 | 2.80 | 2.85 | 3.35 | 2.5 | 2.5 | 3.0 | 3.0 | 3.5 | 204 | 55895 | 14998 | 0.0 | 2015 |
132 | Max (2015) | 35 | 73 | 47 | 5.9 | 7.0 | 4.5 | 4.5 | 1.75 | 3.65 | 2.35 | 2.95 | 3.50 | 2.0 | 3.5 | 2.5 | 3.0 | 3.5 | 15 | 5444 | 3412 | 0.0 | 2015 |
133 | Paul Blart: Mall Cop 2 (2015) | 5 | 36 | 13 | 2.4 | 4.3 | 3.5 | 3.5 | 0.25 | 1.80 | 0.65 | 1.20 | 2.15 | 0.5 | 2.0 | 0.5 | 1.0 | 2.0 | 211 | 15004 | 3054 | 0.0 | 2015 |
134 | The Longest Ride (2015) | 31 | 73 | 33 | 4.8 | 7.2 | 4.5 | 4.5 | 1.55 | 3.65 | 1.65 | 2.40 | 3.60 | 1.5 | 3.5 | 1.5 | 2.5 | 3.5 | 49 | 25214 | 2603 | 0.0 | 2015 |
135 | The Lazarus Effect (2015) | 14 | 23 | 31 | 4.9 | 5.2 | 3.0 | 3.0 | 0.70 | 1.15 | 1.55 | 2.45 | 2.60 | 0.5 | 1.0 | 1.5 | 2.5 | 2.5 | 62 | 17691 | 1651 | 0.0 | 2015 |
136 | The Woman In Black 2 Angel of Death (2015) | 22 | 25 | 42 | 4.4 | 4.9 | 3.0 | 3.0 | 1.10 | 1.25 | 2.10 | 2.20 | 2.45 | 1.0 | 1.5 | 2.0 | 2.0 | 2.5 | 55 | 14873 | 1333 | 0.0 | 2015 |
137 | Danny Collins (2015) | 77 | 75 | 58 | 7.1 | 7.1 | 4.0 | 4.0 | 3.85 | 3.75 | 2.90 | 3.55 | 3.55 | 4.0 | 4.0 | 3.0 | 3.5 | 3.5 | 33 | 11206 | 531 | 0.0 | 2015 |
138 | Spare Parts (2015) | 52 | 83 | 50 | 7.1 | 7.2 | 4.5 | 4.5 | 2.60 | 4.15 | 2.50 | 3.55 | 3.60 | 2.5 | 4.0 | 2.5 | 3.5 | 3.5 | 7 | 47377 | 450 | 0.0 | 2015 |
139 | Serena (2015) | 18 | 25 | 36 | 5.3 | 5.4 | 3.0 | 3.0 | 0.90 | 1.25 | 1.80 | 2.65 | 2.70 | 1.0 | 1.5 | 2.0 | 2.5 | 2.5 | 19 | 12165 | 50 | 0.0 | 2015 |
140 | Inside Out (2015) | 98 | 90 | 94 | 8.9 | 8.6 | 4.5 | 4.5 | 4.90 | 4.50 | 4.70 | 4.45 | 4.30 | 5.0 | 4.5 | 4.5 | 4.5 | 4.5 | 807 | 96252 | 15749 | 0.0 | 2015 |
141 | Mr. Holmes (2015) | 87 | 78 | 67 | 7.9 | 7.4 | 4.0 | 4.0 | 4.35 | 3.90 | 3.35 | 3.95 | 3.70 | 4.5 | 4.0 | 3.5 | 4.0 | 3.5 | 33 | 7367 | 1348 | 0.0 | 2015 |
142 | '71 (2015) | 97 | 82 | 83 | 7.5 | 7.2 | 3.5 | 3.5 | 4.85 | 4.10 | 4.15 | 3.75 | 3.60 | 5.0 | 4.0 | 4.0 | 4.0 | 3.5 | 60 | 24116 | 192 | 0.0 | 2015 |
144 | Gett: The Trial of Viviane Amsalem (2015) | 100 | 81 | 90 | 7.3 | 7.8 | 3.5 | 3.5 | 5.00 | 4.05 | 4.50 | 3.65 | 3.90 | 5.0 | 4.0 | 4.5 | 3.5 | 4.0 | 19 | 1955 | 59 | 0.0 | 2015 |
145 | Kumiko, The Treasure Hunter (2015) | 87 | 63 | 68 | 6.4 | 6.7 | 3.5 | 3.5 | 4.35 | 3.15 | 3.40 | 3.20 | 3.35 | 4.5 | 3.0 | 3.5 | 3.0 | 3.5 | 19 | 5289 | 41 | 0.0 | 2015 |
fsc = fsc[['FILM', 'Fandango_Stars', 'Fandango_Ratingvalue', 'Fandango_votes', 'Fandango_Difference']]
fsc.head()
FILM | Fandango_Stars | Fandango_Ratingvalue | Fandango_votes | Fandango_Difference | |
---|---|---|---|---|---|
0 | Avengers: Age of Ultron (2015) | 5.0 | 4.5 | 14846 | 0.5 |
1 | Cinderella (2015) | 5.0 | 4.5 | 12640 | 0.5 |
2 | Ant-Man (2015) | 5.0 | 4.5 | 12055 | 0.5 |
3 | Do You Believe? (2015) | 5.0 | 4.5 | 1793 | 0.5 |
4 | Hot Tub Time Machine 2 (2015) | 3.5 | 3.0 | 1021 | 0.5 |
mr = mr[['movie', 'year', 'fandango']]
mr.head()
movie | year | fandango | |
---|---|---|---|
0 | 10 Cloverfield Lane | 2016 | 3.5 |
1 | 13 Hours | 2016 | 4.5 |
2 | A Cure for Wellness | 2016 | 3.0 |
3 | A Dog's Purpose | 2017 | 4.5 |
4 | A Hologram for the King | 2016 | 3.0 |
I will now use a kernel density plot to compare the two datasets.
import matplotlib.pyplot as plt
from numpy import arange
%matplotlib inline
plt.style.use('fivethirtyeight')
fsc['Fandango_Stars'].plot.kde(label='2015',legend=True, figsize=(8,5.5))
mr['fandango'].plot.kde(label='2016',legend=True)
plt.title('Plot of 2016 and 2015 fandango ratings distribution', y=1.07)
plt.xlabel('Stars')
plt.xlim(0,5)
plt.xticks(arange(0,5.1,0.5))
plt.show()
From the graph above we can see that both datasets are negatively skewed. The 2016 data appears assymetrical between 3.2 - 5 stars. The 2015 data had a greater number 4+ stars.
I will now examine the absolute and relative values of the datasets.
fsc['Fandango_Stars'].value_counts().sort_index()
3.0 11 3.5 23 4.0 37 4.5 49 5.0 9 Name: Fandango_Stars, dtype: int64
mr['fandango'].value_counts().sort_index()
2.5 6 3.0 18 3.5 50 4.0 82 4.5 57 5.0 1 Name: fandango, dtype: int64
Just from looking at absolute values both sets of data move in the same direction. 2016 has more items, so lets have a look at the relative values as well.
fsc['Fandango_Stars'].value_counts(normalize=True)*100
4.5 37.984496 4.0 28.682171 3.5 17.829457 3.0 8.527132 5.0 6.976744 Name: Fandango_Stars, dtype: float64
mr['fandango'].value_counts(normalize=True)*100
4.0 38.317757 4.5 26.635514 3.5 23.364486 3.0 8.411215 2.5 2.803738 5.0 0.467290 Name: fandango, dtype: float64
The relative values do show that 2015 has higher ratings.
I will compare the 3 averages mean,median,mode.
mean2015=fsc['Fandango_Stars'].mean()
mean2016=mr['fandango'].mean()
median2015=fsc['Fandango_Stars'].median()
median2016=mr['fandango'].median()
mode2015=fsc['Fandango_Stars'].mode()[0]
mode2016=mr['fandango'].mode()[0]
summary = pd.DataFrame()
summary['2015']=[mean2015,median2015,mode2015]
summary['2016']=[mean2016,median2016,mode2016]
summary.index=('mean','median','mode')
summary
2015 | 2016 | |
---|---|---|
mean | 4.085271 | 3.89486 |
median | 4.000000 | 4.00000 |
mode | 4.500000 | 4.00000 |
I can see that the 2015 data has a slightly higher average star rating than 2016 (4.08 vs 4). The most frequent star rating in 2015 is 4.5 and 4 for 2016. The median is 4 for both. The results confirm that 2015 has higher ratings. I would not say the difference is noticeable as the numbers of items in the datasets are not the same.
plt.style.use('fivethirtyeight')
summary['2015'].plot.bar(label='2015',legend=True, color='blue',align='center',figsize=(8,5.5), width=.25)
summary['2016'].plot.bar(label='2016',legend=True, color='red',align='edge', width=.25, rot=0)
plt.yticks(arange(0,5.5,0.5))
plt.title('Comparing summary statistics: 2015 vs 2016')
plt.legend(loc='upper center')
plt.ylabel('Stars')
Text(0, 0.5, 'Stars')
In conclusion I can state that the 2015 sample does have higher ratings than the 2016 sample. To get a more definite answer a larger sample of the population is required, with equal items in the sample, also a concrete method of film selection is required . ie min 1000 user votes.
It is interesting to see that fandango now uses rotten tomatoes for its film ratings.