0_data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1581d5c4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using a color-blind friendly palette with 10 colors\n",
    "color_blind_palette_10 = ['#cfcfcf', '#ffbc79', '#a2c8ec', '#898989', '#c85200',\n",
    "                          '#5f9ed1', '#595959', '#ababab', '#ff800e', '#006ba4']\n",
    "\n",
    "sns.palplot(color_blind_palette_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's take a look at the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the movie data into a dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_df = pd.read_csv('data/raw/u.item', sep='|', header=None, encoding='latin1', index_col=False,\n",
    "                   names=['movie_id',\n",
    "                          'movie_title',\n",
    "                          'release_date',\n",
    "                          'video_release_date',\n",
    "                          'imdb_url',\n",
    "                          'unknown',\n",
    "                          'action',\n",
    "                          'adventure',\n",
    "                          'animation',\n",
    "                          'children',\n",
    "                          'comedy',\n",
    "                          'crime',\n",
    "                          'documentary',\n",
    "                          'drama',\n",
    "                          'fantasy',\n",
    "                          'film_noir',\n",
    "                          'horror',\n",
    "                          'musical',\n",
    "                          'mystery'\n",
    "                          'romance',\n",
    "                          'sci_fi',\n",
    "                          'thriller',\n",
    "                          'war',\n",
    "                          'western'])\n",
    "\n",
    "movie_df.movie_id -= 1 # make this column zero-indexed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>unknown</th>\n",
       "      <th>action</th>\n",
       "      <th>adventure</th>\n",
       "      <th>animation</th>\n",
       "      <th>children</th>\n",
       "      <th>...</th>\n",
       "      <th>drama</th>\n",
       "      <th>fantasy</th>\n",
       "      <th>film_noir</th>\n",
       "      <th>horror</th>\n",
       "      <th>musical</th>\n",
       "      <th>mysteryromance</th>\n",
       "      <th>sci_fi</th>\n",
       "      <th>thriller</th>\n",
       "      <th>war</th>\n",
       "      <th>western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id        movie_title release_date  video_release_date  \\\n",
       "0         0   Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1         1   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "2         2  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
       "3         3  Get Shorty (1995)  01-Jan-1995                 NaN   \n",
       "4         4     Copycat (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            imdb_url  unknown  action  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n",
       "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n",
       "\n",
       "   adventure  animation  children   ...     drama  fantasy  film_noir  horror  \\\n",
       "0          0          1         1   ...         0        0          0       0   \n",
       "1          1          0         0   ...         0        0          0       0   \n",
       "2          0          0         0   ...         0        0          0       0   \n",
       "3          0          0         0   ...         1        0          0       0   \n",
       "4          0          0         0   ...         1        0          0       0   \n",
       "\n",
       "   musical  mysteryromance  sci_fi  thriller  war  western  \n",
       "0        0               0       0         0    0        0  \n",
       "1        0               0       0         0    1        0  \n",
       "2        0               0       0         0    1        0  \n",
       "3        0               0       0         0    0        0  \n",
       "4        0               0       0         0    1        0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the ratings data into a dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_df = pd.read_csv('data/raw/u.data', sep='\\t', header=None, names=['user_id','movie_id','rating','timestamp'])\n",
    "rating_df.user_id -= 1 # make this column zero-indexed\n",
    "rating_df.movie_id -= 1 # make this column zero-indexed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>195</td>\n",
       "      <td>241</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>185</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>376</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>243</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>165</td>\n",
       "      <td>345</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp\n",
       "0      195       241       3  881250949\n",
       "1      185       301       3  891717742\n",
       "2       21       376       1  878887116\n",
       "3      243        50       2  880606923\n",
       "4      165       345       1  886397596"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's focus on the 10 movies with the most ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_counts = rating_df.groupby('movie_id')['rating'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_movies = 10\n",
    "top_n_movies = rating_df.query('movie_id in {}'.format((movie_counts[:n_movies].index.values.tolist())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the movie title for ease of access when plotting\n",
    "# remove the timestamp column because we don't need it\n",
    "top_n_movies = top_n_movies.merge(movie_df[['movie_id','movie_title']], on='movie_id', how='left') \\\n",
    "                           .drop(columns=['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the year from the title\n",
    "top_n_movies.movie_title = top_n_movies.movie_title.str.replace('\\s+\\(.+\\)', '').str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>307</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Toy Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>250</td>\n",
       "      <td>99</td>\n",
       "      <td>4</td>\n",
       "      <td>Fargo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>180</td>\n",
       "      <td>5</td>\n",
       "      <td>Return of the Jedi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>Scream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>293</td>\n",
       "      <td>3</td>\n",
       "      <td>Liar Liar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating         movie_title\n",
       "0      307         0       4           Toy Story\n",
       "1      250        99       4               Fargo\n",
       "2       24       180       5  Return of the Jedi\n",
       "3       19       287       1              Scream\n",
       "4       31       293       3           Liar Liar"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the output for use in other notebooks\n",
    "top_n_movies.to_csv('data/top-n-movies_user-ratings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which are the top N movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Toy Story',\n",
       " 'Fargo',\n",
       " 'Return of the Jedi',\n",
       " 'Scream',\n",
       " 'Liar Liar',\n",
       " 'Contact',\n",
       " 'English Patient, The',\n",
       " 'Star Wars',\n",
       " 'Independence Day',\n",
       " 'Air Force One']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n_movies.movie_title.unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dictionary to map these movies to colors for consistency in plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Air Force One': '#cfcfcf',\n",
       " 'Contact': '#ffbc79',\n",
       " 'English Patient, The': '#a2c8ec',\n",
       " 'Fargo': '#898989',\n",
       " 'Independence Day': '#c85200',\n",
       " 'Liar Liar': '#5f9ed1',\n",
       " 'Return of the Jedi': '#595959',\n",
       " 'Scream': '#ababab',\n",
       " 'Star Wars': '#ff800e',\n",
       " 'Toy Story': '#006ba4'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_map = dict(zip(top_n_movies.movie_title.sort_values().unique().tolist(), color_blind_palette_10))\n",
    "color_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the actual rating distributions of the top N movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_counts_by_movie = top_n_movies.groupby(['movie_id','movie_title','rating'], as_index=False) \\\n",
    "                                        .size().to_frame('count') \\\n",
    "                                        .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>rating</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>4</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id movie_title  rating  count\n",
       "0         0   Toy Story       1      8\n",
       "1         0   Toy Story       2     27\n",
       "2         0   Toy Story       3     96\n",
       "3         0   Toy Story       4    202\n",
       "4         0   Toy Story       5    119"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_counts_by_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1581d8dfac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(color_blind_palette_10)\n",
    "\n",
    "rating_v_count_ax = sns.factorplot(data=rating_counts_by_movie, x='rating', y='count',\n",
    "                                   hue='movie_title', hue_order=color_map.keys(),\n",
    "                                   size=10, aspect=1.5, kind='bar', legend=False)\n",
    "\n",
    "plt.title('Rating Histogram for the 10 Movies with the Most Ratings')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Number of Ratings')\n",
    "\n",
    "plt.legend(title='')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_v_count_ax.savefig('output/images/rating_distributions.png', transparent=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's transform the ratings from a discrete range into a binary \"like\" or \"dislike\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# movie ratings above this threshold will be counted as a \"like\"\n",
    "reward_threshold = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_title</th>\n",
       "      <th>liked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Air Force One</th>\n",
       "      <th>False</th>\n",
       "      <td>1196</td>\n",
       "      <td>10</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>299</td>\n",
       "      <td>5</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Contact</th>\n",
       "      <th>False</th>\n",
       "      <td>1028</td>\n",
       "      <td>10</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Patient, The</th>\n",
       "      <th>False</th>\n",
       "      <td>1140</td>\n",
       "      <td>10</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            movie_id  rating  count\n",
       "movie_title          liked                         \n",
       "Air Force One        False      1196      10    346\n",
       "                     True        299       5     85\n",
       "Contact              False      1028      10    377\n",
       "                     True        257       5    132\n",
       "English Patient, The False      1140      10    350"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_counts_by_movie.eval('liked = rating > @reward_threshold') \\\n",
    "                      .groupby(['movie_title','liked']) \\\n",
    "                      .sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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_title</th>\n",
       "      <th>liked</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air Force One</td>\n",
       "      <td>False</td>\n",
       "      <td>1196</td>\n",
       "      <td>10</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air Force One</td>\n",
       "      <td>True</td>\n",
       "      <td>299</td>\n",
       "      <td>5</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Contact</td>\n",
       "      <td>False</td>\n",
       "      <td>1028</td>\n",
       "      <td>10</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Contact</td>\n",
       "      <td>True</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>English Patient, The</td>\n",
       "      <td>False</td>\n",
       "      <td>1140</td>\n",
       "      <td>10</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            movie_title  liked  movie_id  rating  count\n",
       "0         Air Force One  False      1196      10    346\n",
       "1         Air Force One   True       299       5     85\n",
       "2               Contact  False      1028      10    377\n",
       "3               Contact   True       257       5    132\n",
       "4  English Patient, The  False      1140      10    350"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_counts_by_movie.eval('liked = rating > @reward_threshold') \\\n",
    "                      .groupby(['movie_title','liked'], as_index=False) \\\n",
    "                      .sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_reward_counts = rating_counts_by_movie.eval('liked = rating > @reward_threshold') \\\n",
    "    .groupby(['movie_id','movie_title','liked'])['count'] \\\n",
    "    .sum() \\\n",
    "    .unstack('liked') \\\n",
    "    .reset_index() \\\n",
    "    .rename(columns={False:'disliked', True:'liked'}) \\\n",
    "    .eval('total = disliked + liked') \\\n",
    "    .eval('like_pct = 100 * liked / total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>liked</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>disliked</th>\n",
       "      <th>liked</th>\n",
       "      <th>total</th>\n",
       "      <th>like_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>258</td>\n",
       "      <td>325</td>\n",
       "      <td>583</td>\n",
       "      <td>55.746141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99</td>\n",
       "      <td>Fargo</td>\n",
       "      <td>281</td>\n",
       "      <td>227</td>\n",
       "      <td>508</td>\n",
       "      <td>44.685039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180</td>\n",
       "      <td>Return of the Jedi</td>\n",
       "      <td>336</td>\n",
       "      <td>171</td>\n",
       "      <td>507</td>\n",
       "      <td>33.727811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>285</td>\n",
       "      <td>English Patient, The</td>\n",
       "      <td>350</td>\n",
       "      <td>131</td>\n",
       "      <td>481</td>\n",
       "      <td>27.234927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>333</td>\n",
       "      <td>119</td>\n",
       "      <td>452</td>\n",
       "      <td>26.327434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>257</td>\n",
       "      <td>Contact</td>\n",
       "      <td>377</td>\n",
       "      <td>132</td>\n",
       "      <td>509</td>\n",
       "      <td>25.933202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>299</td>\n",
       "      <td>Air Force One</td>\n",
       "      <td>346</td>\n",
       "      <td>85</td>\n",
       "      <td>431</td>\n",
       "      <td>19.721578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>287</td>\n",
       "      <td>Scream</td>\n",
       "      <td>393</td>\n",
       "      <td>85</td>\n",
       "      <td>478</td>\n",
       "      <td>17.782427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120</td>\n",
       "      <td>Independence Day</td>\n",
       "      <td>353</td>\n",
       "      <td>76</td>\n",
       "      <td>429</td>\n",
       "      <td>17.715618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>293</td>\n",
       "      <td>Liar Liar</td>\n",
       "      <td>439</td>\n",
       "      <td>46</td>\n",
       "      <td>485</td>\n",
       "      <td>9.484536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "liked  movie_id           movie_title  disliked  liked  total   like_pct\n",
       "1            49             Star Wars       258    325    583  55.746141\n",
       "2            99                 Fargo       281    227    508  44.685039\n",
       "4           180    Return of the Jedi       336    171    507  33.727811\n",
       "6           285  English Patient, The       350    131    481  27.234927\n",
       "0             0             Toy Story       333    119    452  26.327434\n",
       "5           257               Contact       377    132    509  25.933202\n",
       "9           299         Air Force One       346     85    431  19.721578\n",
       "7           287                Scream       393     85    478  17.782427\n",
       "3           120      Independence Day       353     76    429  17.715618\n",
       "8           293             Liar Liar       439     46    485   9.484536"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_reward_counts.sort_values('like_pct', ascending=False, inplace=True)\n",
    "total_reward_counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1581f8046a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "like_probability_ax = sns.factorplot(size=10, aspect=1.5, x='like_pct', y='movie_title', kind='bar',\n",
    "                                     palette=total_reward_counts['movie_title'].apply(lambda x: color_map[x]),\n",
    "                                     data=total_reward_counts)\n",
    "\n",
    "plt.title('The 10 Movies with the Most Ratings')\n",
    "like_probability_ax.set_axis_labels('Probability of Being \"Liked\" (%)','')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "like_probability_ax.savefig('output/images/like_probabilities.png', transparent=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}