{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas\n", "\n", "movies = pandas.read_csv(\"fandango_score_comparison.csv\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FILMRottenTomatoesRottenTomatoes_UserMetacriticMetacritic_UserIMDBFandango_StarsFandango_RatingvalueRT_normRT_user_norm...IMDB_normRT_norm_roundRT_user_norm_roundMetacritic_norm_roundMetacritic_user_norm_roundIMDB_norm_roundMetacritic_user_vote_countIMDB_user_vote_countFandango_votesFandango_Difference
0Avengers: Age of Ultron (2015)7486667.17.85.04.53.704.30...3.903.54.53.53.54.01330271107148460.5
1Cinderella (2015)8580677.57.15.04.54.254.00...3.554.54.03.54.03.524965709126400.5
2Ant-Man (2015)8090648.17.85.04.54.004.50...3.904.04.53.04.04.0627103660120550.5
3Do You Believe? (2015)1884224.75.45.04.50.904.20...2.701.04.01.02.52.531313617930.5
4Hot Tub Time Machine 2 (2015)1428293.45.13.53.00.701.40...2.550.51.51.51.52.5881956010210.5
5The Water Diviner (2015)6362506.87.24.54.03.153.10...3.603.03.02.53.53.534393733970.5
6Irrational Man (2015)4253537.66.94.03.52.102.65...3.452.02.52.54.03.51726802520.5
7Top Five (2014)8664816.86.54.03.54.303.20...3.254.53.04.03.53.51241687632230.5
8Shaun the Sheep Movie (2015)9982818.87.44.54.04.954.10...3.705.04.04.04.53.562122278960.5
9Love & Mercy (2015)8987808.57.84.54.04.454.35...3.904.54.54.04.54.05453678640.5
10Far From The Madding Crowd (2015)8477717.57.24.54.04.203.85...3.604.04.03.54.03.535121298040.5
11Black Sea (2015)8260626.66.44.03.54.103.00...3.204.03.03.03.53.037165472180.5
12Leviathan (2014)9979927.27.74.03.54.953.95...3.855.04.04.53.54.014522521640.5
13Unbroken (2014)5170596.57.24.54.12.553.50...3.602.53.53.03.53.52187751894430.4
14The Imitation Game (2014)9092738.28.15.04.64.504.60...4.054.54.53.54.04.056633416480550.4
15Taken 3 (2015)946264.66.14.54.10.452.30...3.050.52.51.52.53.024010423567570.4
16Ted 2 (2015)4658486.56.64.54.12.302.90...3.302.53.02.53.53.51974910264370.4
17Southpaw (2015)5980578.27.85.04.62.954.00...3.903.04.03.04.04.01282356155970.4
18Night at the Museum: Secret of the Tomb (2014)5058475.86.34.54.12.502.90...3.152.53.02.53.03.01035029154450.4
19Pixels (2015)1754275.35.64.54.10.852.70...2.801.02.51.52.53.02461952138860.4
20McFarland, USA (2015)7989607.27.55.04.63.954.45...3.754.04.53.03.54.0591376933640.4
21Insidious: Chapter 3 (2015)5956526.96.34.54.12.952.80...3.153.03.02.53.53.01152513432760.4
22The Man From U.N.C.L.E. (2015)6880557.97.64.54.13.404.00...3.803.54.03.04.04.01442210426860.4
23Run All Night (2015)6059597.36.64.54.13.002.95...3.303.03.03.03.53.51415043820660.4
24Trainwreck (2015)8574756.06.74.54.14.253.70...3.354.53.54.03.03.51692738083810.4
25Selma (2014)9986897.17.55.04.64.954.30...3.755.04.54.53.54.03164534470250.4
26Ex Machina (2015)9286787.97.74.54.14.604.30...3.854.54.54.04.04.067215449934580.4
27Still Alice (2015)8885727.87.54.54.14.404.25...3.754.54.53.54.04.01535712312580.4
28Wild Tales (2014)9692778.88.24.54.14.804.60...4.105.04.54.04.54.0107502852350.4
29The End of the Tour (2015)9289847.57.94.54.14.604.45...3.954.54.54.04.04.01913201210.4
..................................................................
116Clouds of Sils Maria (2015)8967787.16.83.53.44.453.35...3.404.53.54.03.53.536113921620.1
117Testament of Youth (2015)8179777.97.34.03.94.053.95...3.654.04.04.04.03.51554951270.1
118Infinitely Polar Bear (2015)8076647.97.24.03.94.003.80...3.604.04.03.04.03.5810621240.1
119Phoenix (2015)9981918.07.23.53.44.954.05...3.605.04.04.54.03.5213687700.1
120The Wolfpack (2015)8473757.07.13.53.44.203.65...3.554.03.54.03.53.581488660.1
121The Stanford Prison Experiment (2015)8487688.57.14.03.94.204.35...3.554.04.53.54.53.56950510.1
122Tangerine (2015)9586867.37.44.03.94.754.30...3.705.04.54.53.53.514696360.1
123Magic Mike XXL (2015)6264605.46.34.54.43.103.20...3.153.03.03.02.53.0521193793630.1
124Home (2015)4565557.36.74.54.42.253.25...3.352.53.53.03.53.51774115877050.1
125The Wedding Ringer (2015)2766353.36.74.54.41.353.30...3.351.53.52.01.53.51263729265060.1
126Woman in Gold (2015)5281517.27.44.54.42.604.05...3.702.54.02.53.53.5721795724350.1
127The Last Five Years (2015)6060606.96.04.54.43.003.00...3.003.03.03.03.53.0204110990.1
128Mission: Impossible – Rogue Nation (2015)9290758.07.84.54.44.604.50...3.904.54.54.04.04.03628257983570.1
129Amy (2015)9791858.88.04.54.44.854.55...4.005.04.54.54.54.06056307290.1
130Jurassic World (2015)7181597.07.34.54.53.554.05...3.653.54.03.03.53.51281241807343900.0
131Minions (2015)5452565.76.74.04.02.702.60...3.352.52.53.03.03.520455895149980.0
132Max (2015)3573475.97.04.54.51.753.65...3.502.03.52.53.03.515544434120.0
133Paul Blart: Mall Cop 2 (2015)536132.44.33.53.50.251.80...2.150.52.00.51.02.02111500430540.0
134The Longest Ride (2015)3173334.87.24.54.51.553.65...3.601.53.51.52.53.5492521426030.0
135The Lazarus Effect (2015)1423314.95.23.03.00.701.15...2.600.51.01.52.52.5621769116510.0
136The Woman In Black 2 Angel of Death (2015)2225424.44.93.03.01.101.25...2.451.01.52.02.02.5551487313330.0
137Danny Collins (2015)7775587.17.14.04.03.853.75...3.554.04.03.03.53.533112065310.0
138Spare Parts (2015)5283507.17.24.54.52.604.15...3.602.54.02.53.53.57473774500.0
139Serena (2015)1825365.35.43.03.00.901.25...2.701.01.52.02.52.51912165500.0
140Inside Out (2015)9890948.98.64.54.54.904.50...4.305.04.54.54.54.580796252157490.0
141Mr. Holmes (2015)8778677.97.44.04.04.353.90...3.704.54.03.54.03.533736713480.0
142'71 (2015)9782837.57.23.53.54.854.10...3.605.04.04.04.03.560241161920.0
143Two Days, One Night (2014)9778898.87.43.53.54.853.90...3.705.04.04.54.53.5123243451180.0
144Gett: The Trial of Viviane Amsalem (2015)10081907.37.83.53.55.004.05...3.905.04.04.53.54.0191955590.0
145Kumiko, The Treasure Hunter (2015)8763686.46.73.53.54.353.15...3.354.53.03.53.03.5195289410.0
\n", "

146 rows × 22 columns

\n", "
" ], "text/plain": [ " FILM RottenTomatoes \\\n", "0 Avengers: Age of Ultron (2015) 74 \n", "1 Cinderella (2015) 85 \n", "2 Ant-Man (2015) 80 \n", "3 Do You Believe? (2015) 18 \n", "4 Hot Tub Time Machine 2 (2015) 14 \n", "5 The Water Diviner (2015) 63 \n", "6 Irrational Man (2015) 42 \n", "7 Top Five (2014) 86 \n", "8 Shaun the Sheep Movie (2015) 99 \n", "9 Love & Mercy (2015) 89 \n", "10 Far From The Madding Crowd (2015) 84 \n", "11 Black Sea (2015) 82 \n", "12 Leviathan (2014) 99 \n", "13 Unbroken (2014) 51 \n", "14 The Imitation Game (2014) 90 \n", "15 Taken 3 (2015) 9 \n", "16 Ted 2 (2015) 46 \n", "17 Southpaw (2015) 59 \n", "18 Night at the Museum: Secret of the Tomb (2014) 50 \n", "19 Pixels (2015) 17 \n", "20 McFarland, USA (2015) 79 \n", "21 Insidious: Chapter 3 (2015) 59 \n", "22 The Man From U.N.C.L.E. (2015) 68 \n", "23 Run All Night (2015) 60 \n", "24 Trainwreck (2015) 85 \n", "25 Selma (2014) 99 \n", "26 Ex Machina (2015) 92 \n", "27 Still Alice (2015) 88 \n", "28 Wild Tales (2014) 96 \n", "29 The End of the Tour (2015) 92 \n", ".. ... ... \n", "116 Clouds of Sils Maria (2015) 89 \n", "117 Testament of Youth (2015) 81 \n", "118 Infinitely Polar Bear (2015) 80 \n", "119 Phoenix (2015) 99 \n", "120 The Wolfpack (2015) 84 \n", "121 The Stanford Prison Experiment (2015) 84 \n", "122 Tangerine (2015) 95 \n", "123 Magic Mike XXL (2015) 62 \n", "124 Home (2015) 45 \n", "125 The Wedding Ringer (2015) 27 \n", "126 Woman in Gold (2015) 52 \n", "127 The Last Five Years (2015) 60 \n", "128 Mission: Impossible – Rogue Nation (2015) 92 \n", "129 Amy (2015) 97 \n", "130 Jurassic World (2015) 71 \n", "131 Minions (2015) 54 \n", "132 Max (2015) 35 \n", "133 Paul Blart: Mall Cop 2 (2015) 5 \n", "134 The Longest Ride (2015) 31 \n", "135 The Lazarus Effect (2015) 14 \n", "136 The Woman In Black 2 Angel of Death (2015) 22 \n", "137 Danny Collins (2015) 77 \n", "138 Spare Parts (2015) 52 \n", "139 Serena (2015) 18 \n", "140 Inside Out (2015) 98 \n", "141 Mr. Holmes (2015) 87 \n", "142 '71 (2015) 97 \n", "143 Two Days, One Night (2014) 97 \n", "144 Gett: The Trial of Viviane Amsalem (2015) 100 \n", "145 Kumiko, The Treasure Hunter (2015) 87 \n", "\n", " RottenTomatoes_User Metacritic Metacritic_User IMDB Fandango_Stars \\\n", "0 86 66 7.1 7.8 5.0 \n", "1 80 67 7.5 7.1 5.0 \n", "2 90 64 8.1 7.8 5.0 \n", "3 84 22 4.7 5.4 5.0 \n", "4 28 29 3.4 5.1 3.5 \n", "5 62 50 6.8 7.2 4.5 \n", "6 53 53 7.6 6.9 4.0 \n", "7 64 81 6.8 6.5 4.0 \n", "8 82 81 8.8 7.4 4.5 \n", "9 87 80 8.5 7.8 4.5 \n", "10 77 71 7.5 7.2 4.5 \n", "11 60 62 6.6 6.4 4.0 \n", "12 79 92 7.2 7.7 4.0 \n", "13 70 59 6.5 7.2 4.5 \n", "14 92 73 8.2 8.1 5.0 \n", "15 46 26 4.6 6.1 4.5 \n", "16 58 48 6.5 6.6 4.5 \n", "17 80 57 8.2 7.8 5.0 \n", "18 58 47 5.8 6.3 4.5 \n", "19 54 27 5.3 5.6 4.5 \n", "20 89 60 7.2 7.5 5.0 \n", "21 56 52 6.9 6.3 4.5 \n", "22 80 55 7.9 7.6 4.5 \n", "23 59 59 7.3 6.6 4.5 \n", "24 74 75 6.0 6.7 4.5 \n", "25 86 89 7.1 7.5 5.0 \n", "26 86 78 7.9 7.7 4.5 \n", "27 85 72 7.8 7.5 4.5 \n", "28 92 77 8.8 8.2 4.5 \n", "29 89 84 7.5 7.9 4.5 \n", ".. ... ... ... ... ... \n", "116 67 78 7.1 6.8 3.5 \n", "117 79 77 7.9 7.3 4.0 \n", "118 76 64 7.9 7.2 4.0 \n", "119 81 91 8.0 7.2 3.5 \n", "120 73 75 7.0 7.1 3.5 \n", "121 87 68 8.5 7.1 4.0 \n", "122 86 86 7.3 7.4 4.0 \n", "123 64 60 5.4 6.3 4.5 \n", "124 65 55 7.3 6.7 4.5 \n", "125 66 35 3.3 6.7 4.5 \n", "126 81 51 7.2 7.4 4.5 \n", "127 60 60 6.9 6.0 4.5 \n", "128 90 75 8.0 7.8 4.5 \n", "129 91 85 8.8 8.0 4.5 \n", "130 81 59 7.0 7.3 4.5 \n", "131 52 56 5.7 6.7 4.0 \n", "132 73 47 5.9 7.0 4.5 \n", "133 36 13 2.4 4.3 3.5 \n", "134 73 33 4.8 7.2 4.5 \n", "135 23 31 4.9 5.2 3.0 \n", "136 25 42 4.4 4.9 3.0 \n", "137 75 58 7.1 7.1 4.0 \n", "138 83 50 7.1 7.2 4.5 \n", "139 25 36 5.3 5.4 3.0 \n", "140 90 94 8.9 8.6 4.5 \n", "141 78 67 7.9 7.4 4.0 \n", "142 82 83 7.5 7.2 3.5 \n", "143 78 89 8.8 7.4 3.5 \n", "144 81 90 7.3 7.8 3.5 \n", "145 63 68 6.4 6.7 3.5 \n", "\n", " Fandango_Ratingvalue RT_norm RT_user_norm ... \\\n", "0 4.5 3.70 4.30 ... \n", "1 4.5 4.25 4.00 ... \n", "2 4.5 4.00 4.50 ... \n", "3 4.5 0.90 4.20 ... \n", "4 3.0 0.70 1.40 ... \n", "5 4.0 3.15 3.10 ... \n", "6 3.5 2.10 2.65 ... \n", "7 3.5 4.30 3.20 ... \n", "8 4.0 4.95 4.10 ... \n", "9 4.0 4.45 4.35 ... \n", "10 4.0 4.20 3.85 ... \n", "11 3.5 4.10 3.00 ... \n", "12 3.5 4.95 3.95 ... \n", "13 4.1 2.55 3.50 ... \n", "14 4.6 4.50 4.60 ... \n", "15 4.1 0.45 2.30 ... \n", "16 4.1 2.30 2.90 ... \n", "17 4.6 2.95 4.00 ... \n", "18 4.1 2.50 2.90 ... \n", "19 4.1 0.85 2.70 ... \n", "20 4.6 3.95 4.45 ... \n", "21 4.1 2.95 2.80 ... \n", "22 4.1 3.40 4.00 ... \n", "23 4.1 3.00 2.95 ... \n", "24 4.1 4.25 3.70 ... \n", "25 4.6 4.95 4.30 ... \n", "26 4.1 4.60 4.30 ... \n", "27 4.1 4.40 4.25 ... \n", "28 4.1 4.80 4.60 ... \n", "29 4.1 4.60 4.45 ... \n", ".. ... ... ... ... \n", "116 3.4 4.45 3.35 ... \n", "117 3.9 4.05 3.95 ... \n", "118 3.9 4.00 3.80 ... \n", "119 3.4 4.95 4.05 ... \n", "120 3.4 4.20 3.65 ... \n", "121 3.9 4.20 4.35 ... \n", "122 3.9 4.75 4.30 ... \n", "123 4.4 3.10 3.20 ... \n", "124 4.4 2.25 3.25 ... \n", "125 4.4 1.35 3.30 ... \n", "126 4.4 2.60 4.05 ... \n", "127 4.4 3.00 3.00 ... \n", "128 4.4 4.60 4.50 ... \n", "129 4.4 4.85 4.55 ... \n", "130 4.5 3.55 4.05 ... \n", "131 4.0 2.70 2.60 ... \n", "132 4.5 1.75 3.65 ... \n", "133 3.5 0.25 1.80 ... \n", "134 4.5 1.55 3.65 ... \n", "135 3.0 0.70 1.15 ... \n", "136 3.0 1.10 1.25 ... \n", "137 4.0 3.85 3.75 ... \n", "138 4.5 2.60 4.15 ... \n", "139 3.0 0.90 1.25 ... \n", "140 4.5 4.90 4.50 ... \n", "141 4.0 4.35 3.90 ... \n", "142 3.5 4.85 4.10 ... \n", "143 3.5 4.85 3.90 ... \n", "144 3.5 5.00 4.05 ... \n", "145 3.5 4.35 3.15 ... \n", "\n", " IMDB_norm RT_norm_round RT_user_norm_round Metacritic_norm_round \\\n", "0 3.90 3.5 4.5 3.5 \n", "1 3.55 4.5 4.0 3.5 \n", "2 3.90 4.0 4.5 3.0 \n", "3 2.70 1.0 4.0 1.0 \n", "4 2.55 0.5 1.5 1.5 \n", "5 3.60 3.0 3.0 2.5 \n", "6 3.45 2.0 2.5 2.5 \n", "7 3.25 4.5 3.0 4.0 \n", "8 3.70 5.0 4.0 4.0 \n", "9 3.90 4.5 4.5 4.0 \n", "10 3.60 4.0 4.0 3.5 \n", "11 3.20 4.0 3.0 3.0 \n", "12 3.85 5.0 4.0 4.5 \n", "13 3.60 2.5 3.5 3.0 \n", "14 4.05 4.5 4.5 3.5 \n", "15 3.05 0.5 2.5 1.5 \n", "16 3.30 2.5 3.0 2.5 \n", "17 3.90 3.0 4.0 3.0 \n", "18 3.15 2.5 3.0 2.5 \n", "19 2.80 1.0 2.5 1.5 \n", "20 3.75 4.0 4.5 3.0 \n", "21 3.15 3.0 3.0 2.5 \n", "22 3.80 3.5 4.0 3.0 \n", "23 3.30 3.0 3.0 3.0 \n", "24 3.35 4.5 3.5 4.0 \n", "25 3.75 5.0 4.5 4.5 \n", "26 3.85 4.5 4.5 4.0 \n", "27 3.75 4.5 4.5 3.5 \n", "28 4.10 5.0 4.5 4.0 \n", "29 3.95 4.5 4.5 4.0 \n", ".. ... ... ... ... \n", "116 3.40 4.5 3.5 4.0 \n", "117 3.65 4.0 4.0 4.0 \n", "118 3.60 4.0 4.0 3.0 \n", "119 3.60 5.0 4.0 4.5 \n", "120 3.55 4.0 3.5 4.0 \n", "121 3.55 4.0 4.5 3.5 \n", "122 3.70 5.0 4.5 4.5 \n", "123 3.15 3.0 3.0 3.0 \n", "124 3.35 2.5 3.5 3.0 \n", "125 3.35 1.5 3.5 2.0 \n", "126 3.70 2.5 4.0 2.5 \n", "127 3.00 3.0 3.0 3.0 \n", "128 3.90 4.5 4.5 4.0 \n", "129 4.00 5.0 4.5 4.5 \n", "130 3.65 3.5 4.0 3.0 \n", "131 3.35 2.5 2.5 3.0 \n", "132 3.50 2.0 3.5 2.5 \n", "133 2.15 0.5 2.0 0.5 \n", "134 3.60 1.5 3.5 1.5 \n", "135 2.60 0.5 1.0 1.5 \n", "136 2.45 1.0 1.5 2.0 \n", "137 3.55 4.0 4.0 3.0 \n", "138 3.60 2.5 4.0 2.5 \n", "139 2.70 1.0 1.5 2.0 \n", "140 4.30 5.0 4.5 4.5 \n", "141 3.70 4.5 4.0 3.5 \n", "142 3.60 5.0 4.0 4.0 \n", "143 3.70 5.0 4.0 4.5 \n", "144 3.90 5.0 4.0 4.5 \n", "145 3.35 4.5 3.0 3.5 \n", "\n", " Metacritic_user_norm_round IMDB_norm_round Metacritic_user_vote_count \\\n", "0 3.5 4.0 1330 \n", "1 4.0 3.5 249 \n", "2 4.0 4.0 627 \n", "3 2.5 2.5 31 \n", "4 1.5 2.5 88 \n", "5 3.5 3.5 34 \n", "6 4.0 3.5 17 \n", "7 3.5 3.5 124 \n", "8 4.5 3.5 62 \n", "9 4.5 4.0 54 \n", "10 4.0 3.5 35 \n", "11 3.5 3.0 37 \n", "12 3.5 4.0 145 \n", "13 3.5 3.5 218 \n", "14 4.0 4.0 566 \n", "15 2.5 3.0 240 \n", "16 3.5 3.5 197 \n", "17 4.0 4.0 128 \n", "18 3.0 3.0 103 \n", "19 2.5 3.0 246 \n", "20 3.5 4.0 59 \n", "21 3.5 3.0 115 \n", "22 4.0 4.0 144 \n", "23 3.5 3.5 141 \n", "24 3.0 3.5 169 \n", "25 3.5 4.0 316 \n", "26 4.0 4.0 672 \n", "27 4.0 4.0 153 \n", "28 4.5 4.0 107 \n", "29 4.0 4.0 19 \n", ".. ... ... ... \n", "116 3.5 3.5 36 \n", "117 4.0 3.5 15 \n", "118 4.0 3.5 8 \n", "119 4.0 3.5 21 \n", "120 3.5 3.5 8 \n", "121 4.5 3.5 6 \n", "122 3.5 3.5 14 \n", "123 2.5 3.0 52 \n", "124 3.5 3.5 177 \n", "125 1.5 3.5 126 \n", "126 3.5 3.5 72 \n", "127 3.5 3.0 20 \n", "128 4.0 4.0 362 \n", "129 4.5 4.0 60 \n", "130 3.5 3.5 1281 \n", "131 3.0 3.5 204 \n", "132 3.0 3.5 15 \n", "133 1.0 2.0 211 \n", "134 2.5 3.5 49 \n", "135 2.5 2.5 62 \n", "136 2.0 2.5 55 \n", "137 3.5 3.5 33 \n", "138 3.5 3.5 7 \n", "139 2.5 2.5 19 \n", "140 4.5 4.5 807 \n", "141 4.0 3.5 33 \n", "142 4.0 3.5 60 \n", "143 4.5 3.5 123 \n", "144 3.5 4.0 19 \n", "145 3.0 3.5 19 \n", "\n", " IMDB_user_vote_count Fandango_votes Fandango_Difference \n", "0 271107 14846 0.5 \n", "1 65709 12640 0.5 \n", "2 103660 12055 0.5 \n", "3 3136 1793 0.5 \n", "4 19560 1021 0.5 \n", "5 39373 397 0.5 \n", "6 2680 252 0.5 \n", "7 16876 3223 0.5 \n", "8 12227 896 0.5 \n", "9 5367 864 0.5 \n", "10 12129 804 0.5 \n", "11 16547 218 0.5 \n", "12 22521 64 0.5 \n", "13 77518 9443 0.4 \n", "14 334164 8055 0.4 \n", "15 104235 6757 0.4 \n", "16 49102 6437 0.4 \n", "17 23561 5597 0.4 \n", "18 50291 5445 0.4 \n", "19 19521 3886 0.4 \n", "20 13769 3364 0.4 \n", "21 25134 3276 0.4 \n", "22 22104 2686 0.4 \n", "23 50438 2066 0.4 \n", "24 27380 8381 0.4 \n", "25 45344 7025 0.4 \n", "26 154499 3458 0.4 \n", "27 57123 1258 0.4 \n", "28 50285 235 0.4 \n", "29 1320 121 0.4 \n", ".. ... ... ... \n", "116 11392 162 0.1 \n", "117 5495 127 0.1 \n", "118 1062 124 0.1 \n", "119 3687 70 0.1 \n", "120 1488 66 0.1 \n", "121 950 51 0.1 \n", "122 696 36 0.1 \n", "123 11937 9363 0.1 \n", "124 41158 7705 0.1 \n", "125 37292 6506 0.1 \n", "126 17957 2435 0.1 \n", "127 4110 99 0.1 \n", "128 82579 8357 0.1 \n", "129 5630 729 0.1 \n", "130 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.hist(movies[\"Fandango_Stars\"])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 1., 2., 20., 14., 0., 22., 27., 20., 25., 15.]),\n", " array([ 0.5, 0.9, 1.3, 1.7, 2.1, 2.5, 2.9, 3.3, 3.7, 4.1, 4.5]),\n", " )" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(movies[\"Metacritic_norm_round\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fandango vs. Metacritic Scores\n", "\n", "There are no scores below a `3.0` in the Fandango reviews. The Fandango reviews also cluster around `4.5` and `4.0`, whereas the Metacritic reviews seem to cluster around `3.0` and `3.5`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.08904109589\n", "2.97260273973\n", "0.540385977979\n", "0.990960561374\n", "4.0\n", "3.0\n" ] } ], "source": [ "import numpy\n", "\n", "f_mean = movies[\"Fandango_Stars\"].mean()\n", "m_mean = movies[\"Metacritic_norm_round\"].mean()\n", "f_std = movies[\"Fandango_Stars\"].std()\n", "m_std = movies[\"Metacritic_norm_round\"].std()\n", "f_median = movies[\"Fandango_Stars\"].median()\n", "m_median = movies[\"Metacritic_norm_round\"].median()\n", "\n", "print(f_mean)\n", "print(m_mean)\n", "print(f_std)\n", "print(m_std)\n", "print(f_median)\n", "print(m_median)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fandango vs. Metacritic Methodology\n", "\n", "Fandango appears to inflate ratings and isn't transparent about how it calculates and aggregates ratings. Metacritic publishes each individual critic rating and is transparent about how they aggregate them to get a final rating." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fandango vs. Metacritic Number Differences\n", "\n", "The median Metacritic score appears higher than the mean metacritic score because a few very low reviews \"drag down\" the median. The median Fandango score is lower than the mean Fandango score because a few very high ratings \"drag up\" the mean.\n", "\n", "Fandango ratings appear clustered between `3` and `5`, and they have a much narrower random range than Metacritic reviews, which go from `0` to `5`.\n", "\n", "Fandango ratings in general appear to be higher than Metacritic ratings.\n", "\n", "These may be due to movie studio influence on Fandango ratings, and the fact that Fandango calculates its ratings in a hidden manner." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(movies[\"Metacritic_norm_round\"], movies[\"Fandango_Stars\"])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "movies[\"fm_diff\"] = numpy.abs(movies[\"Metacritic_norm_round\"] - movies[\"Fandango_Stars\"])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FILMRottenTomatoesRottenTomatoes_UserMetacriticMetacritic_UserIMDBFandango_StarsFandango_RatingvalueRT_normRT_user_norm...RT_norm_roundRT_user_norm_roundMetacritic_norm_roundMetacritic_user_norm_roundIMDB_norm_roundMetacritic_user_vote_countIMDB_user_vote_countFandango_votesFandango_Differencefm_diff
3Do You Believe? (2015)1884224.75.45.04.50.904.20...1.04.01.02.52.531313617930.54.0
85Little Boy (2015)2081305.97.44.54.31.004.05...1.04.01.53.03.53859278110.23.0
47Annie (2014)2761334.85.24.54.21.353.05...1.53.01.52.52.51081922268350.33.0
19Pixels (2015)1754275.35.64.54.10.852.70...1.02.51.52.53.02461952138860.43.0
134The Longest Ride (2015)3173334.87.24.54.51.553.65...1.53.51.52.53.5492521426030.03.0
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5 rows × 23 columns

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" ], "text/plain": [ " FILM RottenTomatoes RottenTomatoes_User Metacritic \\\n", "3 Do You Believe? (2015) 18 84 22 \n", "85 Little Boy (2015) 20 81 30 \n", "47 Annie (2014) 27 61 33 \n", "19 Pixels (2015) 17 54 27 \n", "134 The Longest Ride (2015) 31 73 33 \n", "\n", " Metacritic_User IMDB Fandango_Stars Fandango_Ratingvalue RT_norm \\\n", "3 4.7 5.4 5.0 4.5 0.90 \n", "85 5.9 7.4 4.5 4.3 1.00 \n", "47 4.8 5.2 4.5 4.2 1.35 \n", "19 5.3 5.6 4.5 4.1 0.85 \n", "134 4.8 7.2 4.5 4.5 1.55 \n", "\n", " RT_user_norm ... RT_norm_round RT_user_norm_round \\\n", "3 4.20 ... 1.0 4.0 \n", "85 4.05 ... 1.0 4.0 \n", "47 3.05 ... 1.5 3.0 \n", "19 2.70 ... 1.0 2.5 \n", "134 3.65 ... 1.5 3.5 \n", "\n", " Metacritic_norm_round Metacritic_user_norm_round IMDB_norm_round \\\n", "3 1.0 2.5 2.5 \n", "85 1.5 3.0 3.5 \n", "47 1.5 2.5 2.5 \n", "19 1.5 2.5 3.0 \n", "134 1.5 2.5 3.5 \n", "\n", " Metacritic_user_vote_count IMDB_user_vote_count Fandango_votes \\\n", "3 31 3136 1793 \n", "85 38 5927 811 \n", "47 108 19222 6835 \n", "19 246 19521 3886 \n", "134 49 25214 2603 \n", "\n", " Fandango_Difference fm_diff \n", "3 0.5 4.0 \n", "85 0.2 3.0 \n", "47 0.3 3.0 \n", "19 0.4 3.0 \n", "134 0.0 3.0 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.sort_values(by=\"fm_diff\", ascending=False).head(5)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.17844919073895918" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.stats import pearsonr\n", "\n", "r_value, p_value = pearsonr(movies[\"Fandango_Stars\"], movies[\"Metacritic_norm_round\"])\n", "\n", "r_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fandango and Metacritic Correlation\n", "\n", "The low correlation between Fandango and Metacritic scores indicates that Fandango scores aren't just inflated, they are also fundamentally different. For whatever reason, it appears that Fandango both inflates scores overall and inflates scores differently depending on the movie." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import linregress\n", "\n", "slope, intercept, r_value, p_value, stderr_slope = linregress(movies[\"Metacritic_norm_round\"], movies[\"Fandango_Stars\"])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.0917071528212032" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pred = 3 * slope + intercept\n", "\n", "pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finding Residuals" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "(1, 5)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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++eXcreJ97tq1a0TrqZZmGU/7bIw+Ozo6+q0nly9fXpXXKjssxBgPxxhfiTF2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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pred_1 = 1 * slope + intercept\n", "pred_5 = 5 * slope + intercept\n", "plt.scatter(movies[\"Metacritic_norm_round\"], movies[\"Fandango_Stars\"])\n", "plt.plot([1,5],[pred_1,pred_5])\n", "plt.xlim(1,5)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }