testing123

Basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fandango Movie Rating Inflation Investigation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "prior = pd.read_csv(\"fandango_score_comparison.csv\")\n",
    "after = pd.read_csv(\"movie_ratings_16_17.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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  RT_user_norm_round  \\\n",
       "0     3.70           4.3  ...       3.90            3.5                 4.5   \n",
       "1     4.25           4.0  ...       3.55            4.5                 4.0   \n",
       "2     4.00           4.5  ...       3.90            4.0                 4.5   \n",
       "3     0.90           4.2  ...       2.70            1.0                 4.0   \n",
       "4     0.70           1.4  ...       2.55            0.5                 1.5   \n",
       "\n",
       "   Metacritic_norm_round  Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "0                    3.5                         3.5              4.0   \n",
       "1                    3.5                         4.0              3.5   \n",
       "2                    3.0                         4.0              4.0   \n",
       "3                    1.0                         2.5              2.5   \n",
       "4                    1.5                         1.5              2.5   \n",
       "\n",
       "   Metacritic_user_vote_count  IMDB_user_vote_count  Fandango_votes  \\\n",
       "0                        1330                271107           14846   \n",
       "1                         249                 65709           12640   \n",
       "2                         627                103660           12055   \n",
       "3                          31                  3136            1793   \n",
       "4                          88                 19560            1021   \n",
       "\n",
       "   Fandango_Difference  \n",
       "0                  0.5  \n",
       "1                  0.5  \n",
       "2                  0.5  \n",
       "3                  0.5  \n",
       "4                  0.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>metascore</th>\n",
       "      <th>imdb</th>\n",
       "      <th>tmeter</th>\n",
       "      <th>audience</th>\n",
       "      <th>fandango</th>\n",
       "      <th>n_metascore</th>\n",
       "      <th>n_imdb</th>\n",
       "      <th>n_tmeter</th>\n",
       "      <th>n_audience</th>\n",
       "      <th>nr_metascore</th>\n",
       "      <th>nr_imdb</th>\n",
       "      <th>nr_tmeter</th>\n",
       "      <th>nr_audience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 Cloverfield Lane</td>\n",
       "      <td>2016</td>\n",
       "      <td>76</td>\n",
       "      <td>7.2</td>\n",
       "      <td>90</td>\n",
       "      <td>79</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.60</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.95</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Hours</td>\n",
       "      <td>2016</td>\n",
       "      <td>48</td>\n",
       "      <td>7.3</td>\n",
       "      <td>50</td>\n",
       "      <td>83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Cure for Wellness</td>\n",
       "      <td>2016</td>\n",
       "      <td>47</td>\n",
       "      <td>6.6</td>\n",
       "      <td>40</td>\n",
       "      <td>47</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Dog's Purpose</td>\n",
       "      <td>2017</td>\n",
       "      <td>43</td>\n",
       "      <td>5.2</td>\n",
       "      <td>33</td>\n",
       "      <td>76</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Hologram for the King</td>\n",
       "      <td>2016</td>\n",
       "      <td>58</td>\n",
       "      <td>6.1</td>\n",
       "      <td>70</td>\n",
       "      <td>57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     movie  year  metascore  imdb  tmeter  audience  fandango  \\\n",
       "0      10 Cloverfield Lane  2016         76   7.2      90        79       3.5   \n",
       "1                 13 Hours  2016         48   7.3      50        83       4.5   \n",
       "2      A Cure for Wellness  2016         47   6.6      40        47       3.0   \n",
       "3          A Dog's Purpose  2017         43   5.2      33        76       4.5   \n",
       "4  A Hologram for the King  2016         58   6.1      70        57       3.0   \n",
       "\n",
       "   n_metascore  n_imdb  n_tmeter  n_audience  nr_metascore  nr_imdb  \\\n",
       "0         3.80    3.60      4.50        3.95           4.0      3.5   \n",
       "1         2.40    3.65      2.50        4.15           2.5      3.5   \n",
       "2         2.35    3.30      2.00        2.35           2.5      3.5   \n",
       "3         2.15    2.60      1.65        3.80           2.0      2.5   \n",
       "4         2.90    3.05      3.50        2.85           3.0      3.0   \n",
       "\n",
       "   nr_tmeter  nr_audience  \n",
       "0        4.5          4.0  \n",
       "1        2.5          4.0  \n",
       "2        2.0          2.5  \n",
       "3        1.5          4.0  \n",
       "4        3.5          3.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214 entries, 0 to 213\n",
      "Data columns (total 15 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   movie         214 non-null    object \n",
      " 1   year          214 non-null    int64  \n",
      " 2   metascore     214 non-null    int64  \n",
      " 3   imdb          214 non-null    float64\n",
      " 4   tmeter        214 non-null    int64  \n",
      " 5   audience      214 non-null    int64  \n",
      " 6   fandango      214 non-null    float64\n",
      " 7   n_metascore   214 non-null    float64\n",
      " 8   n_imdb        214 non-null    float64\n",
      " 9   n_tmeter      214 non-null    float64\n",
      " 10  n_audience    214 non-null    float64\n",
      " 11  nr_metascore  214 non-null    float64\n",
      " 12  nr_imdb       214 non-null    float64\n",
      " 13  nr_tmeter     214 non-null    float64\n",
      " 14  nr_audience   214 non-null    float64\n",
      "dtypes: float64(10), int64(4), object(1)\n",
      "memory usage: 25.2+ KB\n"
     ]
    }
   ],
   "source": [
    "after.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior = prior[['FILM', 'Fandango_Stars', 'Fandango_Ratingvalue', 'Fandango_votes', 'Fandango_Difference']].copy()\n",
    "after = after[['movie', 'year', 'fandango']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</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>5.0</td>\n",
       "      <td>4.5</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>5.0</td>\n",
       "      <td>4.5</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>5.0</td>\n",
       "      <td>4.5</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>5.0</td>\n",
       "      <td>4.5</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>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0  Avengers: Age of Ultron (2015)             5.0                   4.5   \n",
       "1               Cinderella (2015)             5.0                   4.5   \n",
       "2                  Ant-Man (2015)             5.0                   4.5   \n",
       "3          Do You Believe? (2015)             5.0                   4.5   \n",
       "4   Hot Tub Time Machine 2 (2015)             3.5                   3.0   \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  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>fandango</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 Cloverfield Lane</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 Hours</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Cure for Wellness</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Dog's Purpose</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Hologram for the King</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     movie  year  fandango\n",
       "0      10 Cloverfield Lane  2016       3.5\n",
       "1                 13 Hours  2016       4.5\n",
       "2      A Cure for Wellness  2016       3.0\n",
       "3          A Dog's Purpose  2017       4.5\n",
       "4  A Hologram for the King  2016       3.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change slightly the current goal of our analysis such that:\n",
    "\n",
    "- The population of interest changes and the samples we currently work with become representative.\n",
    "- The new goal is still a fairly good proxy for our initial goal, which was to determine whether there has been any change in Fandango's rating system after Hickey's analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are the datasets reliable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data we're working with was sampled at the moments we want: one sample was taken **prior** to the analysis, and the other was taken **after** the analysis. We want to describe the population, so we need to make sure that the samples are representative.\n",
    "\n",
    "From Hickey's article we can see that he used the following sampling criteria:\n",
    "\n",
    "- The movie must have had at least 30 fan ratings on Fandango's website at the time of sampling (Aug. 24, 2015).\n",
    "- The movie must have had tickets on sale in 2015.\n",
    "\n",
    "The sampling was clearly not random because not every movie had the same chance to be included in the sample — some movies didn't have a chance at all (like those having under 30 fan ratings or those without tickets on sale in 2015)\n",
    "\n",
    "The sampling conditions for our other sample were the following (as it can be read in the README.md of the data set's repository):\n",
    "\n",
    "- The movie must have been released in 2016 or later.\n",
    "- The movie must have had a considerable number of votes and reviews (it's unclear how many from the README.md or from the data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Instead of trying to determine whether there has been any change in Fandango's rating system following Hickey's analysis, our new goal is to determine whether there's any difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. This new goal should also be a fairly good proxy for our initial goal.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Year from `prior` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Year</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>3.5</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",
       "      <td>2015</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>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",
       "      <td>2015</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>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",
       "      <td>2015</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>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>2015</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>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",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 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  ...  RT_norm_round  RT_user_norm_round  \\\n",
       "0     3.70           4.3  ...            3.5                 4.5   \n",
       "1     4.25           4.0  ...            4.5                 4.0   \n",
       "2     4.00           4.5  ...            4.0                 4.5   \n",
       "3     0.90           4.2  ...            1.0                 4.0   \n",
       "4     0.70           1.4  ...            0.5                 1.5   \n",
       "\n",
       "   Metacritic_norm_round  Metacritic_user_norm_round  IMDB_norm_round  \\\n",
       "0                    3.5                         3.5              4.0   \n",
       "1                    3.5                         4.0              3.5   \n",
       "2                    3.0                         4.0              4.0   \n",
       "3                    1.0                         2.5              2.5   \n",
       "4                    1.5                         1.5              2.5   \n",
       "\n",
       "   Metacritic_user_vote_count  IMDB_user_vote_count  Fandango_votes  \\\n",
       "0                        1330                271107           14846   \n",
       "1                         249                 65709           12640   \n",
       "2                         627                103660           12055   \n",
       "3                          31                  3136            1793   \n",
       "4                          88                 19560            1021   \n",
       "\n",
       "   Fandango_Difference  Year  \n",
       "0                  0.5  2015  \n",
       "1                  0.5  2015  \n",
       "2                  0.5  2015  \n",
       "3                  0.5  2015  \n",
       "4                  0.5  2015  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding 'year' column\n",
    "prior['Year'] = prior['FILM'].str[-5:-1]\n",
    "prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2015    129\n",
       "2014     17\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining year distribution\n",
    "prior['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FILM                           object\n",
       "RottenTomatoes                  int64\n",
       "RottenTomatoes_User             int64\n",
       "Metacritic                      int64\n",
       "Metacritic_User               float64\n",
       "IMDB                          float64\n",
       "Fandango_Stars                float64\n",
       "Fandango_Ratingvalue          float64\n",
       "RT_norm                       float64\n",
       "RT_user_norm                  float64\n",
       "Metacritic_norm               float64\n",
       "Metacritic_user_nom           float64\n",
       "IMDB_norm                     float64\n",
       "RT_norm_round                 float64\n",
       "RT_user_norm_round            float64\n",
       "Metacritic_norm_round         float64\n",
       "Metacritic_user_norm_round    float64\n",
       "IMDB_norm_round               float64\n",
       "Metacritic_user_vote_count      int64\n",
       "IMDB_user_vote_count            int64\n",
       "Fandango_votes                  int64\n",
       "Fandango_Difference           float64\n",
       "Year                           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2015    129\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing all 2014 movies\n",
    "fandango_2015 = prior[prior['Year'] == '2015'].copy()\n",
    "\n",
    "fandango_2015['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2016    191\n",
       "2017     23\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie            object\n",
       "year              int64\n",
       "metascore         int64\n",
       "imdb            float64\n",
       "tmeter            int64\n",
       "audience          int64\n",
       "fandango        float64\n",
       "n_metascore     float64\n",
       "n_imdb          float64\n",
       "n_tmeter        float64\n",
       "n_audience      float64\n",
       "nr_metascore    float64\n",
       "nr_imdb         float64\n",
       "nr_tmeter       float64\n",
       "nr_audience     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2016    191\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2016 = after[after['year'] == 2016].copy()\n",
    "fandango_2016['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy import arange\n",
    "%matplotlib inline\n",
    "plt.style.use('tableau-colorblind10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " '_mpl-gallery',\n",
       " '_mpl-gallery-nogrid',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-v0_8',\n",
       " 'seaborn-v0_8-bright',\n",
       " 'seaborn-v0_8-colorblind',\n",
       " 'seaborn-v0_8-dark',\n",
       " 'seaborn-v0_8-dark-palette',\n",
       " 'seaborn-v0_8-darkgrid',\n",
       " 'seaborn-v0_8-deep',\n",
       " 'seaborn-v0_8-muted',\n",
       " 'seaborn-v0_8-notebook',\n",
       " 'seaborn-v0_8-paper',\n",
       " 'seaborn-v0_8-pastel',\n",
       " 'seaborn-v0_8-poster',\n",
       " 'seaborn-v0_8-talk',\n",
       " 'seaborn-v0_8-ticks',\n",
       " 'seaborn-v0_8-white',\n",
       " 'seaborn-v0_8-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fandango_2015['Fandango_Stars'].plot.kde(label='2015', legend=True, figsize=(8,5.5))\n",
    "fandango_2016['fandango'].plot.kde(label='2016', legend=True)\n",
    "\n",
    "plt.title(\"Comparing distribution shapes for Fandango's ratings\\n(2015 vs 2016)\",\n",
    "          y=1.07) # the `y` parameter pads the title upward\n",
    "plt.xlabel('Stars')\n",
    "plt.xlim(0,5) # because ratings start at 0 and end at 5\n",
    "plt.xticks(arange(0,5.1,.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n",
      "----------------\n",
      "Fandango_Stars\n",
      "3.0     8.527132\n",
      "3.5    17.829457\n",
      "4.0    28.682171\n",
      "4.5    37.984496\n",
      "5.0     6.976744\n",
      "Name: proportion, dtype: float64\n",
      "2016\n",
      "----------------\n",
      "fandango\n",
      "2.5     3.141361\n",
      "3.0     7.329843\n",
      "3.5    24.083770\n",
      "4.0    40.314136\n",
      "4.5    24.607330\n",
      "5.0     0.523560\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('2015' + '\\n' + '-' * 16)\n",
    "counts_2015 = fandango_2015['Fandango_Stars'].value_counts(normalize=True).sort_index() * 100\n",
    "print(counts_2015)\n",
    "\n",
    "print('2016' + '\\n' + '-' * 16)\n",
    "counts_2016 = fandango_2016['fandango'].value_counts(normalize=True).sort_index() * 100\n",
    "print(counts_2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a DataFrame for alignment\n",
    "data = pd.DataFrame({'2015': counts_2015, '2016': counts_2016})\n",
    "\n",
    "# Plot side-by-side bars\n",
    "data.plot(kind='bar', width=0.8)  # Automatically aligns and separates bars\n",
    "\n",
    "# Add labels, title, and legend\n",
    "plt.title(\"Comparing Fandango Ratings (2015 vs 2016)\")\n",
    "plt.xlabel('Fandango Stars')\n",
    "plt.ylabel('Proportion (%)')\n",
    "plt.xticks(rotation=0)  # Keep x-ticks horizontal\n",
    "plt.legend(title='Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.085271</td>\n",
       "      <td>3.887435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2015      2016\n",
       "mean    4.085271  3.887435\n",
       "median  4.000000  4.000000\n",
       "mode    4.500000  4.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_2015 = fandango_2015['Fandango_Stars'].mean()\n",
    "mean_2016 = fandango_2016['fandango'].mean()\n",
    "\n",
    "median_2015 = fandango_2015['Fandango_Stars'].median()\n",
    "median_2016 = fandango_2016['fandango'].median()\n",
    "\n",
    "mode_2015 = fandango_2015['Fandango_Stars'].mode()[0] # the output of Series.mode() is a bit uncommon\n",
    "mode_2016 = fandango_2016['fandango'].mode()[0]\n",
    "\n",
    "summary = pd.DataFrame()\n",
    "summary['2015'] = [mean_2015, median_2015, mode_2015]\n",
    "summary['2016'] = [mean_2016, median_2016, mode_2016]\n",
    "summary.index = ['mean', 'median', 'mode']\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = fandango_2015['Fandango_Stars'].mode()\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "summary.plot(kind='bar')\n",
    "\n",
    "plt.title('Comparing summary statistics: 2015 vs 2016', y=1.07)\n",
    "plt.ylim(0,5.5)\n",
    "plt.yticks(arange(0,5.1,.5))\n",
    "plt.ylabel('Stars')\n",
    "plt.legend(framealpha=0, loc='upper center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}