Fandango_Movie_Ratings.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Fandango Displayed vs Actual Movie Ratings\n",
    "\n",
    "Data journalists have noticed discrepancies between the movie ratings that Fandango displays and the actual user movie ratings.  In the project, I will compare Fandango ratings and actual user ratings to see if I notice a difference.\n",
    "\n",
    "I will also compare Fandango before and after these analyses in 2016 to see if Fandngo made changes to their data after the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
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       "  <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",
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       "</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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "fandango_score_comp= pd.read_csv(\"fandango_score_comparison.csv\")\n",
    "fandango_score_comp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
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    {
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       "  <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",
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       "      <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",
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       "      <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",
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       "      <td>4.0</td>\n",
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       "    <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",
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       "</table>\n",
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      ],
      "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_16_17= pd.read_csv(\"movie_ratings_16_17.csv\")\n",
    "ratings_16_17.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
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       "      <th>FILM</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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      "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#isolating score columns into seperate variables\n",
    "previous_scores= fandango_score_comp[['FILM', 'Fandango_Stars', 'Fandango_Ratingvalue', 'Fandango_votes',\n",
    "                             'Fandango_Difference']].copy()\n",
    "\n",
    "previous_scores.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>year</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>10 Cloverfield Lane</td>\n",
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       "      <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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after_scores= ratings_16_17[['movie', 'year', 'fandango']]\n",
    "after_scores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Population \n",
    "\n",
    "The goal of this project is to compare the populations before and after \"fandango-gate\".  The population will be comprised of all movies with at least 30 ratings.  From Hickey's article and from the README.md of the data set's repository, 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 samples of movies were not random because of the above criteria. Because comparing non-random data to random samples from after 2016 will not yield comparable results, I will modify the goal of the research to compare ratings for popular movies from Fandango in 2015 compared to popular Fandngo ratings from 2016.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popular movies\n",
    "\n",
    "I need to redefine popular movies.  These are movies that have 30 fan ratings or more on Fandango's website.  Both of these datasets only containg popular movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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",
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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>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>fandango</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Mechanic: Resurrection</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Warcraft</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Max Steel</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Me Before You</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Fantastic Beasts and Where to Find Them</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Cell</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Genius</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Sully</td>\n",
       "      <td>2016</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",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       movie  year  fandango\n",
       "108                   Mechanic: Resurrection  2016       4.0\n",
       "206                                 Warcraft  2016       4.0\n",
       "106                                Max Steel  2016       3.5\n",
       "107                            Me Before You  2016       4.5\n",
       "51   Fantastic Beasts and Where to Find Them  2016       4.5\n",
       "33                                      Cell  2016       3.0\n",
       "59                                    Genius  2016       3.5\n",
       "152                                    Sully  2016       4.5\n",
       "4                    A Hologram for the King  2016       3.0\n",
       "31                Captain America: Civil War  2016       4.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking number of popular movies:\n",
    "after_scores.sample(10, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>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",
       "  </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",
       "\n",
       "   Fandango_votes  Fandango_Difference  \n",
       "0           14846                  0.5  \n",
       "1           12640                  0.5  \n",
       "2           12055                  0.5  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous_scores.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</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>5.0</td>\n",
       "      <td>4.5</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>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2015</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",
       "\n",
       "   Fandango_votes  Fandango_Difference  Year  \n",
       "0           14846                  0.5  2015  \n",
       "1           12640                  0.5  2015  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous_scores['Year'] = previous_scores['FILM'].str[-5:-1]\n",
    "previous_scores.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    129\n",
       "2014     17\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous_scores['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    129\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2015 = previous_scores[previous_scores['Year'] == '2015'].copy()\n",
    "fandango_2015['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    191\n",
       "2017     23\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the later dataset\n",
    "after_scores['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    191\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Isolating 2016 data\n",
    "fandango_2016 = after_scores[after_scores['year'] == 2016].copy()\n",
    "fandango_2016['year'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# High Level Data Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy import arange\n",
    "%matplotlib inline\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y1tDQkH369ClnG8H9/ueff670HKmoqLAGBgZsampqjb9zmzdvFpbR19dX7Dri/jZlZWWx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\nRkkIIYQQwoT/AtCjzvAf6pvxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f37cb3e2630>"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "The 2015 data appears to have a left-tail.  The average movie rating on Fandango also appears to be higher in 2015 than in 2016.  The 2016 data is normally distributed.  It appears that popular movies on Fandango received more stars in 2016 than in 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Granular examination of the Data\n",
    "\n",
    "Examining the frequency distribution tables of the distributions doesn't make sense because there are different numbers of movies.  It makes more sense to compare normalized distributions for each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: Fandango_Stars, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2015['Fandango_Stars'].value_counts(normalize = True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: fandango, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2016['fandango'].value_counts(normalize = True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of stars appears to be more in the center for 2016 data, and more concentrated towards the top for 2015 data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Mean, Median, Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "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": 51,
     "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]\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": "markdown",
   "metadata": {},
   "source": [
    "The mean and mode are lower in 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uePLkCS5cuIBjx47hxYsXMDY2hqOjI6ZOnSpzz6G5c+dCV1cXAQEB+PXXX2Fk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\nwPQINAAAwPQINAAAwPT+Hy22/1WJc7thAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f37c2fcb1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "summary['2015'].plot.bar(color = '#0066FF', align = 'center', label = '2015', width = .25)\n",
    "summary['2016'].plot.bar(color = '#CC0000', align = 'edge', label = '2016', width = .25,\n",
    "                         rot = 0, figsize = (8,5))\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()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "My analysis showed that movie ratings in 2016 were lower than in 2015.  Although, I am not sure of the reason, this could be because Fandango fixed it's biases based on data jounalist stories."
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}