jupyter-files/GP11.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# GP11: Analyzing Movie Reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Read in Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "movies = pandas.read_csv(\"../data/GP11/fandango_score_comparison.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>74</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1330</td>\n",
       "      <td>271107</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>249</td>\n",
       "      <td>65709</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>627</td>\n",
       "      <td>103660</td>\n",
       "      <td>12055</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
       "      <td>22</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>31</td>\n",
       "      <td>3136</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>88</td>\n",
       "      <td>19560</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  RottenTomatoes  RottenTomatoes_User  \\\n",
       "0  Avengers: Age of Ultron (2015)              74                   86   \n",
       "1               Cinderella (2015)              85                   80   \n",
       "2                  Ant-Man (2015)              80                   90   \n",
       "3          Do You Believe? (2015)              18                   84   \n",
       "4   Hot Tub Time Machine 2 (2015)              14                   28   \n",
       "\n",
       "   Metacritic  Metacritic_User  IMDB  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0          66              7.1   7.8             5.0                   4.5   \n",
       "1          67              7.5   7.1             5.0                   4.5   \n",
       "2          64              8.1   7.8             5.0                   4.5   \n",
       "3          22              4.7   5.4             5.0                   4.5   \n",
       "4          29              3.4   5.1             3.5                   3.0   \n",
       "\n",
       "   RT_norm  RT_user_norm         ...           IMDB_norm  RT_norm_round  \\\n",
       "0     3.70           4.3         ...                3.90            3.5   \n",
       "1     4.25           4.0         ...                3.55            4.5   \n",
       "2     4.00           4.5         ...                3.90            4.0   \n",
       "3     0.90           4.2         ...                2.70            1.0   \n",
       "4     0.70           1.4         ...                2.55            0.5   \n",
       "\n",
       "   RT_user_norm_round  Metacritic_norm_round  Metacritic_user_norm_round  \\\n",
       "0                 4.5                    3.5                         3.5   \n",
       "1                 4.0                    3.5                         4.0   \n",
       "2                 4.5                    3.0                         4.0   \n",
       "3                 4.0                    1.0                         2.5   \n",
       "4                 1.5                    1.5                         1.5   \n",
       "\n",
       "   IMDB_norm_round  Metacritic_user_vote_count  IMDB_user_vote_count  \\\n",
       "0              4.0                        1330                271107   \n",
       "1              3.5                         249                 65709   \n",
       "2              4.0                         627                103660   \n",
       "3              2.5                          31                  3136   \n",
       "4              2.5                          88                 19560   \n",
       "\n",
       "   Fandango_votes  Fandango_Difference  \n",
       "0           14846                  0.5  \n",
       "1           12640                  0.5  \n",
       "2           12055                  0.5  \n",
       "3            1793                  0.5  \n",
       "4            1021                  0.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 12.,   0.,  27.,   0.,   0.,  41.,   0.,  55.,   0.,  11.]),\n",
       " array([ 3. ,  3.2,  3.4,  3.6,  3.8,  4. ,  4.2,  4.4,  4.6,  4.8,  5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+D/vVCtCENHvLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66a20e42b0>"
      ]
     },
     "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",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f669fce2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(movies[\"Metacritic_norm_round\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments on Fandango vs Metacritic Scores\n",
    "- Fandango does not score below 3, and is centred around 4.5.\n",
    "- Metacritic is centred around 3 to 3.5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Mean, Median, And Standard Deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.089041095890411\n",
      "2.9726027397260273\n",
      "0.5403859779787333\n",
      "0.9909605613743364\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": [
    "### Comments on Fandango vs Metacritic Methodology\n",
    "- Fandango appears to inflate ratings and isn't transparent about how it calculates and aggregates ratings. \n",
    "- Metacritic publishes each individual critic rating, and is transparent about how they aggregate them to get a final rating.\n",
    "\n",
    "### Comments on Fandango vs Metacritic number differences\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",
    "- Fandango ratings appear clustered between 3 and 5, and have a much narrower random than Metacritic reviews, which go from 0 to 5.\n",
    "- Fandango ratings in general appear to be higher than metacritic ratings.\n",
    "- These may be due to movie studio influence on Fandango ratings, and the fact that Fandango calculates its ratings in a hidden way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f669d7534e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f669d7709e8>"
      ]
     },
     "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": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "      <th>fm_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
       "      <td>22</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.20</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>31</td>\n",
       "      <td>3136</td>\n",
       "      <td>1793</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Little Boy (2015)</td>\n",
       "      <td>20</td>\n",
       "      <td>81</td>\n",
       "      <td>30</td>\n",
       "      <td>5.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>38</td>\n",
       "      <td>5927</td>\n",
       "      <td>811</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Annie (2014)</td>\n",
       "      <td>27</td>\n",
       "      <td>61</td>\n",
       "      <td>33</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>108</td>\n",
       "      <td>19222</td>\n",
       "      <td>6835</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pixels (2015)</td>\n",
       "      <td>17</td>\n",
       "      <td>54</td>\n",
       "      <td>27</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.70</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>246</td>\n",
       "      <td>19521</td>\n",
       "      <td>3886</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>The Longest Ride (2015)</td>\n",
       "      <td>31</td>\n",
       "      <td>73</td>\n",
       "      <td>33</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.55</td>\n",
       "      <td>3.65</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>49</td>\n",
       "      <td>25214</td>\n",
       "      <td>2603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "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": "markdown",
   "metadata": {},
   "source": [
    "## 5. Correlations"
   ]
  },
  {
   "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": [
    "### Comments on Fandango and Metacritic correlation\n",
    "- The low correlation between Fandango and Metacritic scores indicates that Fandango scores aren't just inflated, they are fundamentally different. \n",
    "- For whatever reason, it appears like Fandango both inflates scores overall, and inflates scores differently depending on the movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import linregress\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",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Finding Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9IyKVB3A+cDbw2hDrLwGeAAScA7xYpXEuAx5Lqx2TGFqBs5PnU4HfAKdXW3uW\nGGc1tKeA45Pn9cCLwDlV2J6lxJl6eyZxXA/cXyyWamjLEuOslrZ8F5gxzPqy2zO1PYKI+Bkw3GzV\nlwE/jpwXgGZJreMT3RElxJm6iNgZEb9Knu8lNwlQ4awpqbdniXGmLmmjfcliffIoPJlWDe1ZSpyp\nkzQXuBS4Z4gqqbcllBRnrSi7Pav5HMEc4L285fepwk4j8cVkF+wJSZ9PMxBJ84GzyP13mK+q2nOY\nOKEK2jM5RPAKsIvcrHxV2Z4lxAnpt+edwA3A4SHWV0VbMnKckH5bQi7ZPyVpi6TVRdaX3Z7VnAhq\nxa+AeRFxBvBdYGNagUg6HvgJ8N8jYk9acYxkhDiroj0jojcizgTmAoslfSGNOEZSQpyptqekVcCu\niNgynu9brhLjrIrvJnBe8ju/GPiGpPOP9gWrORF0APmzmM9NyqpKROzp2z2PiMeBekkzxjsOSfXk\nOtf7IuLhIlWqoj1HirNa2jMvni7gGWBlwaqqaM8+Q8VZBe25FPiKpHeBB4ELJd1bUKca2nLEOKug\nLfvi6Eh+7gIeARYXVCm7Pas5ETwKfC05A34OsDsidqYdVCFJJ0m52dclLSbXph+NcwwC/i/wRkTc\nMUS11NuzlDirpD1bJDUnzxuB5cCbBdWqoT1HjDPt9oyIdRExNyLmA1cCT0fENQXVUm/LUuJMuy2T\n950iaWrfc+AioHBEY9ntmdpN5yQ9QO4s/AxJ7wM3kzvZRUTcBTxO7uz3W8CnwF9UaZx/Dnxd0iGg\nG7gyklP342gp8FVgW3K8GOB/APPy4qyG9iwlzmpoz1bgR5LqyP2xPxQRj0lakxdnNbRnKXFWQ3sO\nUoVtWVQVtuUs4JEkH00E7o+IJ4+2PX1lsZlZxlXzoSEzMxsHTgRmZhnnRGBmlnFOBGZmGedEYGaW\ncU4EZmYZ50RgZpZxTgRmZhn3/wH2LIwWJxjj4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66959c6860>"
      ]
     },
     "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 [default]",
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}