Projects/step_5/1.statistics fundamentals/Guided Project_Investigating Fandango Movie Ratings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Is Fandango Still Inflating Ratings?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In October 2015, Walt Hickey from FiveThirtyEight published a [popular article](https://fivethirtyeight.com/features/fandango-movies-ratings/) where he presented strong evidence which suggest that Fandango's movie rating system was biased and dishonest. In this project, we'll analyze more recent movie ratings data to determine whether there has been any change in Fandango's rating system after Hickey's analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll work with two samples of movie ratings:the data in one sample was collected previous 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 getting familiar with their structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>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>4.5</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": [
    "\n",
    "Below we isolate only the columns that provide information about Fandango so we 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": {
      "text/html": [
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       "\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": 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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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",
       "  </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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_after.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to determine whether there has been any change in Fandango's rating system after Hickey's analysis. The population of interest for our analysis is made of all the movie ratings stored on Fandango's website, regardless of the releasing year.\n",
    "\n",
    "Because we want to find out whether the parameters of this population changed after Hickey's analysis, we're interested in sampling the population at two different periods in time — previous 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 previous to the analysis, and the other 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, wrong 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 to describe. 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 (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 (unclear how many from the README.md or from the data).\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Changing the Goal of our Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 has been any change in Fandango's rating system after 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": [
    "# 4. Isolating the Samples We Need"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this new research goal, we have two populations of interest:\n",
    "\n",
    "* All Fandango's ratings for popular movies released in 2015.\n",
    "* 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.\n",
    "\n",
    "Although one of the sampling criteria in our second sample is movie popularity, the sample doesn't provide information about the number of fan ratings. We should be skeptical once more and ask whether this sample is truly representative and contains popular movies (movies with over 30 fan ratings).\n",
    "\n",
    "One quick way to check the representativity of this sample is to sample randomly 10 movies from it and then check the number of fan ratings ourselves on Fandango's website. Ideally, at least 8 out of the 10 movies have 30 fan ratings or more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_after.sample(10, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Above we used a value of 1 as the random seed. This is good practice because it suggests that we weren't trying out various random seeds just to get a favorable sample.\n",
    "\n",
    "As of April 2018, these are the fan ratings we found:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90% of the movies in our sample are popular. This is enough and we move forward with a bit more confidence.\n",
    "\n",
    "Let's also double-check the other data set for popular movies. The documentation states clearly that there're only movies with at least 30 fan ratings, but it should take only a couple of seconds to double-check here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(fandango_previous['Fandango_votes'] < 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you explore the two data sets, you'll notice that there are movies with a releasing 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 data set 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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>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",
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       "\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": "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": "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our aim is to figure out whether there's any difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. One way to go about 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 for the plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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 striking on the figure above:\n",
    "\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 quite 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Relative Frequencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 data sets 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determining the Direction of the Change"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a couple of summary metrics to get a more precise picture about the direction of the change. In what follows, we'll compute the mean, the median, and the mode for both distributions and then 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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\n",
      "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": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "Our analysis showed that there's 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 after 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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}