Mission288Solutions.ipynb

{
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   "source": [
    "# Did Fandango Inflate Ratings?\n",
    "\n",
    "In October 2015, Walt Hickey from FiveThirtyEight published [a popular article](https://fivethirtyeight.com/features/fandango-movies-ratings/) where he presented strong evidence that suggests that Fandango's movie rating system was biased and dishonest. In this project, we'll analyze more movie ratings data to determine whether there was any change in Fandango's rating system following Hickey's analysis.\n",
    "\n",
    "# Understanding the Data\n",
    "\n",
    "We'll work with two samples of movie ratings: the data in one sample was collected _prior_ to Hickey's analysis, while the other sample was collected _after_. Let's start by reading in the two samples (which are stored as CSV files) and exploring their structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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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>Metacritic_norm</th>\n",
       "      <th>Metacritic_user_nom</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>3.30</td>\n",
       "      <td>3.55</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>3.35</td>\n",
       "      <td>3.75</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>3.20</td>\n",
       "      <td>4.05</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",
       "  </tbody>\n",
       "</table>\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",
       "\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",
       "\n",
       "   RT_norm  RT_user_norm  Metacritic_norm  Metacritic_user_nom  IMDB_norm  \\\n",
       "0     3.70           4.3             3.30                 3.55       3.90   \n",
       "1     4.25           4.0             3.35                 3.75       3.55   \n",
       "2     4.00           4.5             3.20                 4.05       3.90   \n",
       "\n",
       "   RT_norm_round  RT_user_norm_round  Metacritic_norm_round  \\\n",
       "0            3.5                 4.5                    3.5   \n",
       "1            4.5                 4.0                    3.5   \n",
       "2            4.0                 4.5                    3.0   \n",
       "\n",
       "   Metacritic_user_norm_round  IMDB_norm_round  Metacritic_user_vote_count  \\\n",
       "0                         3.5              4.0                        1330   \n",
       "1                         4.0              3.5                         249   \n",
       "2                         4.0              4.0                         627   \n",
       "\n",
       "   IMDB_user_vote_count  Fandango_votes  Fandango_Difference  \n",
       "0                271107           14846                  0.5  \n",
       "1                 65709           12640                  0.5  \n",
       "2                103660           12055                  0.5  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100  # Avoid having displayed truncated output\n",
    "\n",
    "previous = pd.read_csv('fandango_score_comparison.csv')\n",
    "after = pd.read_csv('movie_ratings_16_17.csv')\n",
    "\n",
    "previous.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <th>n_imdb</th>\n",
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       "      <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>4.5</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>2.5</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>2.0</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",
       "  </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",
       "\n",
       "   n_metascore  n_imdb  n_tmeter  n_audience  nr_metascore  nr_imdb  \\\n",
       "0         3.80    3.60       4.5        3.95           4.0      3.5   \n",
       "1         2.40    3.65       2.5        4.15           2.5      3.5   \n",
       "2         2.35    3.30       2.0        2.35           2.5      3.5   \n",
       "\n",
       "   nr_tmeter  nr_audience  \n",
       "0        4.5          4.0  \n",
       "1        2.5          4.0  \n",
       "2        2.0          2.5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we isolate only the columns that provide information about Fandango to make the relevant data more readily available for later use. We'll make copies [to avoid any `SettingWithCopyWarning`](https://www.dataquest.io/blog/settingwithcopywarning/) later on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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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",
       "\n",
       "   Fandango_votes  Fandango_Difference  \n",
       "0           14846                  0.5  \n",
       "1           12640                  0.5  \n",
       "2           12055                  0.5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_previous = previous[['FILM', 'Fandango_Stars', 'Fandango_Ratingvalue', 'Fandango_votes',\n",
    "                             'Fandango_Difference']].copy()\n",
    "fandango_after = after[['movie', 'year', 'fandango']].copy()\n",
    "\n",
    "fandango_previous.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>fandango</th>\n",
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       "  </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",
       "  </tbody>\n",
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      ],
      "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"
      ]
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     "execution_count": 4,
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   "source": [
    "fandango_after.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to determine whether there was any change in Fandango's rating system following Hickey's analysis. The population of interest for our analysis comprises all the movie ratings stored on Fandango's website, regardless of the releasing year.\n",
    "\n",
    "Because we want to determine if the parameters of this population changed after Hickey's analysis, we're interested in sampling the population at two different periods in time — before and after Hickey's analysis — so we can compare the two states.\n",
    "\n",
    "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; otherwise, we should expect a large sampling error and, ultimately, inaccurate conclusions.\n",
    "\n",
    "From Hickey's article and from the `README.md` of [the data set's repository](https://github.com/fivethirtyeight/data/tree/master/fandango), 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). It's questionable whether this sample is representative of the entire population we're interested in describing. It seems more likely that it isn't, mostly because this sample is subject to *temporal trends* (e.g., movies in 2015 might have been outstandingly good or bad compared to other years).\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](https://github.com/mircealex/Movie_ratings_2016_17)):\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).\n",
    "\n",
    "This second sample is also subject to temporal trends, and it's unlikely to be representative of our population of interest.\n",
    "\n",
    "Both these authors had certain research questions in mind when they sampled the data, and they used a set of criteria to get a sample that would fit their questions. Their sampling method is called [**purposive sampling**](https://youtu.be/CdK7N_kTzHI) (or judgmental/selective/subjective sampling). While these samples were good enough for their research, they don't seem too useful for us.\n",
    "\n",
    "# Changing the Goal of our Analysis\n",
    "\n",
    "At this point, we can either collect new data or change our the goal of our analysis. We choose the latter and place some limitations on our initial goal.\n",
    "\n",
    "Instead of trying to determine whether there was 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.\n",
    "\n",
    "# Isolating the Samples We Need\n",
    "\n",
    "With this new research goal, we have two populations of interest:\n",
    "\n",
    "1. All Fandango's ratings for popular movies released in 2015.\n",
    "2. All Fandango's ratings for popular movies released in 2016.\n",
    "\n",
    "We need to be clear about what counts as popular movies. We'll use Hickey's benchmark of 30 fan ratings and count a movie as popular only if it has 30 fan ratings or more on Fandango's website."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you explore the two datasets, you'll notice that there are movies with a release year different than 2015 or 2016. For our purposes, we'll need to isolate only the movies released in 2015 and 2016.\n",
    "\n",
    "Let's start with Hickey's dataset and isolate only the movies released in 2015. There's no special column for the releasing year, but we should be able to extract it from the strings in the `FILM` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "<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",
       "  </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  \n",
       "0           14846                  0.5  \n",
       "1           12640                  0.5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_previous.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_previous['Year'] = fandango_previous['FILM'].str[-5:-1]\n",
    "fandango_previous.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the frequency distribution for the `Year` column and then isolate the movies released in 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    129\n",
       "2014     17\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_previous['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    129\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2015 = fandango_previous[fandango_previous['Year'] == '2015'].copy()\n",
    "fandango_2015['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now, let's isolate the movies in the other dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "  </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"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_after.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    191\n",
       "2017     23\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_after['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    191\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2016 = fandango_after[fandango_after['year'] == 2016].copy()\n",
    "fandango_2016['year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Distribution Shapes for 2015 and 2016\n",
    "\n",
    "Our goal is to determine whether or not there was any difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. One way to do this is to analyze and compare the distributions of movie ratings for the two samples.\n",
    "\n",
    "We'll start with comparing the shape of the two distributions using kernel density plots. We'll use [the FiveThirtyEight style](https://www.dataquest.io/blog/making-538-plots/) for the plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy import arange\n",
    "%matplotlib inline\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "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": [
    "Two aspects are interesting in the figure above:\n",
    "* Both distributions are strongly left-skewed.\n",
    "* The 2016 distribution is slightly shifted to the left relative to the 2015 distribution.\n",
    "\n",
    "The left skew suggests that movies on Fandango are given mostly high and very high fan ratings. Coupled with the fact that Fandango sells tickets, the high ratings are a bit dubious. It'd be really interesting to investigate this further — ideally in a separate project, since this is irrelevant for the current goal of our analysis.\n",
    "\n",
    "The slight left shift of the 2016 distribution is very interesting for our analysis. It shows that ratings were slightly lower in 2016 compared to 2015. This suggests that there was a difference indeed between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. We can also see the direction of the difference: the ratings in 2016 were slightly lower compared to 2015.\n",
    "\n",
    "\n",
    "# Comparing Relative Frequencies\n",
    "\n",
    "It seems we're following a good thread so far, but we need to analyze more granular information. Let's examine the frequency tables of the two distributions to analyze some numbers. Because the datasets have different numbers of movies, we normalize the tables and show percentages instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n",
      "----------------\n"
     ]
    },
    {
     "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('2015' + '\\n' + '-' * 16) # To help us distinguish between the two tables immediately and\n",
    "                                # avoid silly mistakes as we read to and fro\n",
    "fandango_2015['Fandango_Stars'].value_counts(normalize = True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n",
      "----------------\n"
     ]
    },
    {
     "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('2016' + '\\n' + '-' * 16)\n",
    "fandango_2016['fandango'].value_counts(normalize = True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2016, very high ratings (4.5 and 5 stars) had significantly lower percentages compared to 2015. In 2016, under 1% of the movies had a perfect rating of 5 stars, compared to 2015 when the percentage was close to 7%. Ratings of 4.5 were also more popular in 2015 — there were approximately 13% more movies rated with a 4.5 in 2015 compared to 2016.\n",
    "\n",
    "The minimum rating is also lower in 2016 — 2.5 instead of 3 stars, the minimum of 2015. There clearly is a difference between the two frequency distributions.\n",
    "\n",
    "For some other ratings, the percentage went up in 2016. There was a greater percentage of movies in 2016 that received 3.5 and 4 stars, compared to 2015. 3.5 and 4.0 are high ratings, and this challenges the direction of the change we saw on the kernel density plots.\n",
    "\n",
    "# Determining the Direction of the Change\n",
    "\n",
    "Let's take a couple of summary metrics for more precise information about the direction of the change. In what follows, we'll compute the mean, the median, and the mode for both distributions, and then we'll use a bar graph to plot the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean rating was lower in 2016 with approximately 0.2. This means a drop of almost 5% relative to the mean rating in 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.048426835689519929"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(summary.loc['mean'][0] - summary.loc['mean'][1]) / summary.loc['mean'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the median is the same for both distributions, the mode is lower in 2016 by 0.5. Coupled with what we saw for the mean, the direction of the change we saw on the kernel density plot is confirmed: on average, popular movies released in 2016 were rated slightly lower than popular movies released in 2015.\n",
    "\n",
    "# Conclusion\n",
    "\n",
    "Our analysis showed that there is indeed a slight difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. We also determined that, on average, popular movies released in 2016 were rated lower on Fandango than popular movies released in 2015.\n",
    "\n",
    "We cannot be completely sure what caused the change, but the chances are very high that it was caused by Fandango fixing the biased rating system following Hickey's analysis."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
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}