3.1.3_recommender_system.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommender- Yeah Money Money Money"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requirements\n",
    "```\n",
    "python 3.6\n",
    "\n",
    "numpy == '1.14.3'\n",
    "pandas == '0.23.0'\n",
    "tensorflow == '1.8.0'\n",
    "keras == '2.2.0'\n",
    "```\n",
    "Other versions of above library will probably work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare the csv data, from [movielens](https://grouplens.org/datasets/movielens/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %cd /data\n",
    "# !!wget http://files.grouplens.org/datasets/movielens/ml-latest-small.zip\n",
    "# !!unzip ml-latest-small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = \"/data/ml-latest-small/\"\n",
    "DIM = 100 # dimension for embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['links.csv', 'tags.csv', 'ratings.csv', 'README.txt', 'movies.csv']"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = os.listdir(DATA)\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dict()\n",
    "for f in files:\n",
    "    if f[-3:]==\"csv\":\n",
    "        data[f.split(\".\")[0]] = pd.read_csv(DATA+f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Sample Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'links'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>5198</td>\n",
       "      <td>81070</td>\n",
       "      <td>14807.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>6242</td>\n",
       "      <td>178868</td>\n",
       "      <td>2671.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8187</th>\n",
       "      <td>98083</td>\n",
       "      <td>1753968</td>\n",
       "      <td>65851.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6944</th>\n",
       "      <td>59315</td>\n",
       "      <td>371746</td>\n",
       "      <td>1726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2136</th>\n",
       "      <td>2669</td>\n",
       "      <td>53183</td>\n",
       "      <td>37305.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId   imdbId   tmdbId\n",
       "3994     5198    81070  14807.0\n",
       "4534     6242   178868   2671.0\n",
       "8187    98083  1753968  65851.0\n",
       "6944    59315   371746   1726.0\n",
       "2136     2669    53183  37305.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'tags'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>547</td>\n",
       "      <td>105504</td>\n",
       "      <td>toplist13</td>\n",
       "      <td>1386245656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>531</td>\n",
       "      <td>35836</td>\n",
       "      <td>funny</td>\n",
       "      <td>1243454588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>547</td>\n",
       "      <td>8494</td>\n",
       "      <td>dvd</td>\n",
       "      <td>1254390631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>179</td>\n",
       "      <td>260</td>\n",
       "      <td>nerdy</td>\n",
       "      <td>1436669973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>478</td>\n",
       "      <td>55247</td>\n",
       "      <td>road trip</td>\n",
       "      <td>1446622240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  movieId        tag   timestamp\n",
       "1179     547   105504  toplist13  1386245656\n",
       "819      531    35836      funny  1243454588\n",
       "975      547     8494        dvd  1254390631\n",
       "101      179      260      nerdy  1436669973\n",
       "698      478    55247  road trip  1446622240"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'ratings'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37501</th>\n",
       "      <td>270</td>\n",
       "      <td>1208</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1469278481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56144</th>\n",
       "      <td>405</td>\n",
       "      <td>4357</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1061360768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98876</th>\n",
       "      <td>664</td>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1343731830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23707</th>\n",
       "      <td>166</td>\n",
       "      <td>5445</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1064887945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66403</th>\n",
       "      <td>468</td>\n",
       "      <td>4545</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1296203102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       userId  movieId  rating   timestamp\n",
       "37501     270     1208     4.0  1469278481\n",
       "56144     405     4357     4.0  1061360768\n",
       "98876     664     3000     4.0  1343731830\n",
       "23707     166     5445     4.5  1064887945\n",
       "66403     468     4545     2.5  1296203102"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'movies'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>926</td>\n",
       "      <td>All About Eve (1950)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8045</th>\n",
       "      <td>94466</td>\n",
       "      <td>Black Mirror (2011)</td>\n",
       "      <td>Drama|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1804</th>\n",
       "      <td>2282</td>\n",
       "      <td>Pecker (1998)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4390</th>\n",
       "      <td>5943</td>\n",
       "      <td>Maid in Manhattan (2002)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>1179</td>\n",
       "      <td>Grifters, The (1990)</td>\n",
       "      <td>Crime|Drama|Film-Noir</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                     title                 genres\n",
       "746       926      All About Eve (1950)                  Drama\n",
       "8045    94466       Black Mirror (2011)           Drama|Sci-Fi\n",
       "1804     2282             Pecker (1998)           Comedy|Drama\n",
       "4390     5943  Maid in Manhattan (2002)         Comedy|Romance\n",
       "938      1179      Grifters, The (1990)  Crime|Drama|Film-Noir"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "list(display(k,v.sample(5)) for k,v in data.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's Collaborative Filtering Like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>161084</th>\n",
       "      <th>161155</th>\n",
       "      <th>161594</th>\n",
       "      <th>161830</th>\n",
       "      <th>161918</th>\n",
       "      <th>161944</th>\n",
       "      <th>162376</th>\n",
       "      <th>162542</th>\n",
       "      <th>162672</th>\n",
       "      <th>163949</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1       2       3       4       5       6       7       8       \\\n",
       "userId                                                                    \n",
       "1           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "3           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "4           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "5           NaN     NaN     4.0     NaN     NaN     NaN     NaN     NaN   \n",
       "6           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "7           3.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "8           NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "9           4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "10          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "11          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "12          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "13          5.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "14          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "15          2.0     2.0     NaN     NaN     4.5     4.0     NaN     NaN   \n",
       "16          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "17          NaN     NaN     NaN     NaN     NaN     4.5     NaN     NaN   \n",
       "18          NaN     NaN     NaN     NaN     3.0     4.0     3.0     NaN   \n",
       "19          3.0     3.0     3.0     3.0     NaN     3.0     3.0     NaN   \n",
       "20          3.5     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "21          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "22          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "23          3.0     NaN     NaN     NaN     NaN     3.5     NaN     NaN   \n",
       "24          NaN     NaN     NaN     NaN     NaN     5.0     NaN     NaN   \n",
       "25          NaN     NaN     3.0     NaN     NaN     NaN     NaN     NaN   \n",
       "26          5.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "27          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "28          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "29          NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "30          4.0     2.0     NaN     NaN     NaN     4.0     NaN     4.0   \n",
       "...         ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "642         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "643         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "644         NaN     NaN     NaN     1.0     NaN     NaN     NaN     NaN   \n",
       "645         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "646         5.0     NaN     5.0     NaN     NaN     NaN     NaN     NaN   \n",
       "647         4.0     NaN     NaN     NaN     NaN     4.0     NaN     NaN   \n",
       "648         NaN     NaN     NaN     NaN     NaN     4.0     NaN     NaN   \n",
       "649         4.0     NaN     NaN     3.0     NaN     NaN     NaN     NaN   \n",
       "650         NaN     NaN     3.0     1.0     3.0     NaN     NaN     NaN   \n",
       "651         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "652         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "653         4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "654         5.0     3.0     NaN     NaN     4.0     NaN     NaN     NaN   \n",
       "655         NaN     4.0     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "656         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "657         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "658         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "659         NaN     NaN     NaN     NaN     NaN     3.0     NaN     NaN   \n",
       "660         2.5     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "661         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "662         NaN     5.0     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "663         4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "664         3.5     NaN     NaN     NaN     NaN     4.0     NaN     NaN   \n",
       "665         NaN     3.0     3.0     NaN     3.0     NaN     NaN     NaN   \n",
       "666         NaN     NaN     NaN     NaN     NaN     5.0     NaN     NaN   \n",
       "667         NaN     NaN     NaN     NaN     NaN     4.0     NaN     NaN   \n",
       "668         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "669         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "670         4.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "671         5.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "movieId  9       10       ...    161084  161155  161594  161830  161918  \\\n",
       "userId                    ...                                             \n",
       "1           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "2           NaN     4.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "3           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "4           NaN     4.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "5           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "6           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "7           NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "8           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "9           NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "10          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "11          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "12          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "13          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "14          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "15          NaN     3.0   ...       NaN     0.5     NaN     NaN     NaN   \n",
       "16          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "17          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "18          3.0     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "19          3.0     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "20          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "21          NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "22          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "23          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "24          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "25          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "26          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "27          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "28          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "29          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "30          NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "...         ...     ...   ...       ...     ...     ...     ...     ...   \n",
       "642         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "643         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "644         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "645         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "646         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "647         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "648         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "649         NaN     5.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "650         NaN     4.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "651         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "652         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "653         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "654         NaN     4.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "655         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "656         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "657         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "658         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "659         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "660         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "661         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "662         NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "663         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "664         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "665         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "666         NaN     3.0   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "667         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "668         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "669         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "670         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "671         NaN     NaN   ...       NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "movieId  161944  162376  162542  162672  163949  \n",
       "userId                                           \n",
       "1           NaN     NaN     NaN     NaN     NaN  \n",
       "2           NaN     NaN     NaN     NaN     NaN  \n",
       "3           NaN     NaN     NaN     NaN     NaN  \n",
       "4           NaN     NaN     NaN     NaN     NaN  \n",
       "5           NaN     NaN     NaN     NaN     NaN  \n",
       "6           NaN     NaN     NaN     NaN     NaN  \n",
       "7           NaN     NaN     NaN     NaN     NaN  \n",
       "8           NaN     NaN     NaN     NaN     NaN  \n",
       "9           NaN     NaN     NaN     NaN     NaN  \n",
       "10          NaN     NaN     NaN     NaN     NaN  \n",
       "11          NaN     NaN     NaN     NaN     NaN  \n",
       "12          NaN     NaN     NaN     NaN     NaN  \n",
       "13          NaN     NaN     NaN     NaN     NaN  \n",
       "14          NaN     NaN     NaN     NaN     NaN  \n",
       "15          NaN     NaN     NaN     NaN     NaN  \n",
       "16          NaN     NaN     NaN     NaN     NaN  \n",
       "17          NaN     NaN     NaN     NaN     NaN  \n",
       "18          NaN     NaN     NaN     NaN     NaN  \n",
       "19          NaN     NaN     NaN     NaN     NaN  \n",
       "20          NaN     NaN     NaN     NaN     NaN  \n",
       "21          NaN     NaN     NaN     NaN     NaN  \n",
       "22          NaN     NaN     NaN     NaN     NaN  \n",
       "23          NaN     NaN     NaN     NaN     NaN  \n",
       "24          NaN     NaN     NaN     NaN     NaN  \n",
       "25          NaN     NaN     NaN     NaN     NaN  \n",
       "26          NaN     NaN     NaN     NaN     NaN  \n",
       "27          NaN     NaN     NaN     NaN     NaN  \n",
       "28          NaN     NaN     NaN     NaN     NaN  \n",
       "29          NaN     NaN     NaN     NaN     NaN  \n",
       "30          NaN     NaN     NaN     NaN     NaN  \n",
       "...         ...     ...     ...     ...     ...  \n",
       "642         NaN     NaN     NaN     NaN     NaN  \n",
       "643         NaN     NaN     NaN     NaN     NaN  \n",
       "644         NaN     NaN     NaN     NaN     NaN  \n",
       "645         NaN     NaN     NaN     NaN     NaN  \n",
       "646         NaN     NaN     NaN     NaN     NaN  \n",
       "647         NaN     NaN     NaN     NaN     NaN  \n",
       "648         NaN     NaN     NaN     NaN     NaN  \n",
       "649         NaN     NaN     NaN     NaN     NaN  \n",
       "650         NaN     NaN     NaN     NaN     NaN  \n",
       "651         NaN     NaN     NaN     NaN     NaN  \n",
       "652         NaN     NaN     NaN     NaN     NaN  \n",
       "653         NaN     NaN     NaN     NaN     NaN  \n",
       "654         NaN     NaN     NaN     NaN     NaN  \n",
       "655         NaN     NaN     NaN     NaN     NaN  \n",
       "656         NaN     NaN     NaN     NaN     NaN  \n",
       "657         NaN     NaN     NaN     NaN     NaN  \n",
       "658         NaN     NaN     NaN     NaN     NaN  \n",
       "659         NaN     NaN     NaN     NaN     NaN  \n",
       "660         NaN     NaN     NaN     NaN     NaN  \n",
       "661         NaN     NaN     NaN     NaN     NaN  \n",
       "662         NaN     NaN     NaN     NaN     NaN  \n",
       "663         NaN     NaN     NaN     NaN     NaN  \n",
       "664         NaN     NaN     NaN     NaN     NaN  \n",
       "665         NaN     NaN     NaN     NaN     NaN  \n",
       "666         NaN     NaN     NaN     NaN     NaN  \n",
       "667         NaN     NaN     NaN     NaN     NaN  \n",
       "668         NaN     NaN     NaN     NaN     NaN  \n",
       "669         NaN     NaN     NaN     NaN     NaN  \n",
       "670         NaN     NaN     NaN     NaN     NaN  \n",
       "671         NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[671 rows x 9066 columns]"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data[\"ratings\"].userId, data[\"ratings\"].movieId, data[\"ratings\"].rating, aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model On Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100004"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[\"ratings\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "671 9066\n"
     ]
    }
   ],
   "source": [
    "userId = list(set(data[\"ratings\"][\"userId\"]))\n",
    "movieId = list(set(data[\"ratings\"][\"movieId\"]))\n",
    "print(len(userId),len(movieId))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping\n",
    "user to index, movie to index, index to user, index to movie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use your own index for each appeared user, movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "u2i = dict((v,k) for k,v in enumerate(userId))\n",
    "m2i = dict((v,k) for k,v in enumerate(movieId))\n",
    "i2u = dict((k,v) for k,v in enumerate(userId))\n",
    "i2m = dict((k,v) for k,v in enumerate(movieId))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"ratings\"][\"movie_idx\"] = data[\"ratings\"][\"movieId\"].apply(lambda x:m2i[x])\n",
    "data[\"ratings\"][\"user_idx\"] = data[\"ratings\"][\"userId\"].apply(lambda x:u2i[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras accepts array, so we turn pandas objects into array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_array = data[\"ratings\"][\"user_idx\"].values.reshape(-1,1)\n",
    "movie_array = data[\"ratings\"][\"movie_idx\"].values.reshape(-1,1)\n",
    "rating_array = data[\"ratings\"][\"rating\"].values.reshape(-1,1)/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5],\n",
       "       [0.6],\n",
       "       [0.6],\n",
       "       ...,\n",
       "       [0.8],\n",
       "       [0.5],\n",
       "       [0.7]])"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check shapes on data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100004, 1), (100004, 1), (100004, 1))"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_array.shape,movie_array.shape,rating_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import *\n",
    "from keras.models import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "def idx2vec(leng,name,dim = DIM):\n",
    "    \"\"\"\n",
    "    leng: length of vector\n",
    "    name: name the input layer\n",
    "    dim: hidden dimension of embedding\n",
    "    return: input tensor, embedding layer\n",
    "    \"\"\"\n",
    "    ipt = Input((1,),name=name+\"_ipt\")\n",
    "    ebd = Embedding(leng,dim)(ipt)\n",
    "    ebd = SpatialDropout1D(.3)(ebd)\n",
    "    return ipt,ebd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipt_u, ebd_u = idx2vec(len(userId),\"user\")\n",
    "ipt_m, ebd_m = idx2vec(len(movieId),\"movie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "user2vec = Model(ipt_u,ebd_u)\n",
    "movie2vec = Model(ipt_m,ebd_m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check What's Embeding Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-3.30460444e-02, -2.59602908e-02, -1.15970857e-02,\n",
       "          2.40817778e-02, -1.02825873e-02, -4.36714999e-02,\n",
       "         -2.60997936e-03, -2.55534295e-02,  6.12873957e-03,\n",
       "         -4.28985432e-03,  4.22970913e-02, -1.29304826e-04,\n",
       "          2.07059048e-02, -4.12533656e-02, -3.29330713e-02,\n",
       "          8.31794739e-03, -4.42209505e-02, -1.93640590e-02,\n",
       "          2.84278132e-02, -3.13772447e-02, -4.35863622e-02,\n",
       "          3.19475792e-02, -4.98196743e-02,  3.09588350e-02,\n",
       "         -1.36613734e-02,  3.31640877e-02,  3.34727280e-02,\n",
       "         -1.59128532e-02,  1.42641105e-02, -1.93850994e-02,\n",
       "          2.89269947e-02, -4.93724123e-02,  3.35616581e-02,\n",
       "         -3.40440162e-02, -4.71961983e-02,  1.45145990e-02,\n",
       "         -2.31239926e-02, -2.75325309e-02, -3.26253660e-02,\n",
       "         -2.33692527e-02,  1.86637901e-02,  1.75088644e-06,\n",
       "         -3.58017311e-02,  4.80211638e-02,  8.66247341e-03,\n",
       "         -2.87798401e-02, -2.17184424e-02,  4.96555008e-02,\n",
       "         -4.87562530e-02,  4.12024744e-02, -1.15326419e-02,\n",
       "          1.75622739e-02, -2.31748950e-02,  4.33834307e-02,\n",
       "          3.50717567e-02, -3.20108533e-02,  3.41790803e-02,\n",
       "         -2.19332576e-02, -2.20697764e-02,  3.80755328e-02,\n",
       "          7.71669298e-03, -1.58548839e-02,  3.80399488e-02,\n",
       "          1.34961717e-02, -4.66373339e-02, -4.87845540e-02,\n",
       "          2.85569094e-02,  3.05749476e-04, -3.90954241e-02,\n",
       "          3.42399143e-02, -3.85838747e-02, -2.77378559e-02,\n",
       "         -4.76475358e-02, -1.22449547e-03,  5.00609726e-03,\n",
       "          1.39069557e-03,  1.27307810e-02, -1.27387047e-03,\n",
       "          3.47111374e-03, -3.14378962e-02, -1.63445584e-02,\n",
       "          2.93362774e-02,  1.74461119e-02, -1.43297687e-02,\n",
       "          1.66307203e-02,  2.52756961e-02,  7.27485493e-03,\n",
       "         -4.31477912e-02,  2.00841688e-02,  2.96922773e-03,\n",
       "          4.51513417e-02,  3.56480964e-02,  1.15491375e-02,\n",
       "          1.20573044e-02, -4.43863533e-02, -3.56390253e-02,\n",
       "          4.12232019e-02, -2.16464046e-02, -3.99747603e-02,\n",
       "          6.76150247e-03]],\n",
       "\n",
       "       [[-2.61906870e-02, -4.60656062e-02,  3.28912400e-02,\n",
       "          1.51119269e-02,  3.89645211e-02, -4.04976383e-02,\n",
       "          1.37291066e-02, -1.36324055e-02, -1.63157582e-02,\n",
       "         -4.09639850e-02, -4.33548801e-02, -1.48068666e-02,\n",
       "         -1.29504316e-02, -5.06992266e-03, -8.27088952e-05,\n",
       "          3.82054336e-02,  1.61604956e-03, -1.51328929e-02,\n",
       "          1.72339715e-02,  5.75188547e-03,  2.06904151e-02,\n",
       "         -2.00671796e-02, -7.22931698e-03,  3.89401652e-02,\n",
       "         -1.98240634e-02, -7.11420923e-03, -3.71961668e-03,\n",
       "          4.50419821e-02,  3.94353531e-02, -2.14068890e-02,\n",
       "          2.02964433e-02,  2.40995027e-02,  4.88841869e-02,\n",
       "         -4.15130965e-02,  1.25474595e-02,  2.79420353e-02,\n",
       "          4.88258898e-04, -4.48658131e-02,  2.67449059e-02,\n",
       "         -4.84723970e-03, -4.43841703e-02,  2.90829428e-02,\n",
       "          2.52962224e-02, -4.43883650e-02,  3.37433331e-02,\n",
       "          4.03819568e-02,  1.48763172e-02, -2.87613403e-02,\n",
       "          9.16301087e-03,  4.64424379e-02,  2.89123692e-02,\n",
       "         -3.52420434e-02,  4.68583032e-03,  1.78017057e-02,\n",
       "          3.91823314e-02,  3.11641209e-02,  3.39534022e-02,\n",
       "         -2.80497558e-02, -4.55006845e-02,  3.63623984e-02,\n",
       "          1.77045353e-02, -2.36941818e-02,  3.10234316e-02,\n",
       "          3.66838835e-02,  7.40788877e-04,  2.55859233e-02,\n",
       "         -4.37486917e-04,  1.96541585e-02,  3.29156779e-02,\n",
       "          1.95641443e-03, -2.59506591e-02,  6.66670874e-03,\n",
       "          1.62411369e-02,  3.75785865e-02, -6.72512129e-03,\n",
       "         -7.14626163e-03, -2.07630526e-02, -4.77231033e-02,\n",
       "          4.80413549e-02,  1.48946382e-02,  2.02152766e-02,\n",
       "         -2.49837637e-02,  2.92360894e-02, -6.35362789e-03,\n",
       "          1.15344152e-02, -2.20384244e-02,  2.42192037e-02,\n",
       "         -2.53617652e-02, -3.05821542e-02,  2.71608271e-02,\n",
       "          3.81080620e-02, -2.64008641e-02,  5.17153740e-03,\n",
       "         -4.75522280e-02,  2.91500725e-02, -2.99134851e-02,\n",
       "          2.75234841e-02,  2.34705843e-02, -3.67690437e-02,\n",
       "         -3.39887030e-02]]], dtype=float32)"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user2vec.predict(np.array([[0],[2]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Multiply()([ebd_u,ebd_m])\n",
    "x = Flatten()(x)\n",
    "x = Dense(1,)(x)\n",
    "\n",
    "cf = Model([ipt_u,ipt_m],x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf.compile(\"Adam\",loss=\"mae\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "user_ipt (InputLayer)           (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "movie_ipt (InputLayer)          (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_33 (Embedding)        (None, 1, 100)       67100       user_ipt[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "embedding_34 (Embedding)        (None, 1, 100)       906600      movie_ipt[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_29 (SpatialDr (None, 1, 100)       0           embedding_33[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_30 (SpatialDr (None, 1, 100)       0           embedding_34[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "multiply_16 (Multiply)          (None, 1, 100)       0           spatial_dropout1d_29[0][0]       \n",
      "                                                                 spatial_dropout1d_30[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "flatten_14 (Flatten)            (None, 100)          0           multiply_16[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_39 (Dense)                (None, 1)            101         flatten_14[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 973,801\n",
      "Trainable params: 973,801\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cf.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 70002 samples, validate on 30002 samples\n",
      "Epoch 1/5\n",
      "70002/70002 [==============================] - 4s 63us/step - loss: 0.5276 - val_loss: 0.5825\n",
      "Epoch 2/5\n",
      "70002/70002 [==============================] - 3s 40us/step - loss: 0.2006 - val_loss: 0.5236\n",
      "Epoch 3/5\n",
      "70002/70002 [==============================] - 3s 40us/step - loss: 0.1556 - val_loss: 0.4985\n",
      "Epoch 4/5\n",
      "70002/70002 [==============================] - 3s 40us/step - loss: 0.1393 - val_loss: 0.4794\n",
      "Epoch 5/5\n",
      "70002/70002 [==============================] - 3s 41us/step - loss: 0.1306 - val_loss: 0.4639\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a465ca940>"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf.fit([user_array,movie_array],rating_array,epochs=5,shuffle=True,validation_split=.3,batch_size=512)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipt_u, ebd_u = idx2vec(len(userId),\"user\")\n",
    "ipt_m, ebd_m = idx2vec(len(movieId),\"movie\")\n",
    "\n",
    "x = Multiply()([ebd_u,ebd_m])\n",
    "x = Flatten()(x)\n",
    "\n",
    "# The rest is multi-layer perceptron\n",
    "x = Dense(256,use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "x = Dropout(.5)(x)\n",
    "x = Dense(128,use_bias=False)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "x = Dropout(.5)(x)\n",
    "x = Dense(1,use_bias=False)(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_dnn = Model([ipt_u,ipt_m],x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "user_ipt (InputLayer)           (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "movie_ipt (InputLayer)          (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_37 (Embedding)        (None, 1, 100)       67100       user_ipt[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "embedding_38 (Embedding)        (None, 1, 100)       906600      movie_ipt[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_33 (SpatialDr (None, 1, 100)       0           embedding_37[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "spatial_dropout1d_34 (SpatialDr (None, 1, 100)       0           embedding_38[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "multiply_18 (Multiply)          (None, 1, 100)       0           spatial_dropout1d_33[0][0]       \n",
      "                                                                 spatial_dropout1d_34[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "flatten_16 (Flatten)            (None, 100)          0           multiply_18[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_43 (Dense)                (None, 256)          25600       flatten_16[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 256)          1024        dense_43[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 256)          0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_14 (Dropout)            (None, 256)          0           activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_44 (Dense)                (None, 128)          32768       dropout_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 128)          512         dense_44[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 128)          0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_15 (Dropout)            (None, 128)          0           activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_45 (Dense)                (None, 1)            128         dropout_15[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 1,033,732\n",
      "Trainable params: 1,032,964\n",
      "Non-trainable params: 768\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cf_dnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_dnn.compile(\"Adam\",loss=\"mse\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 90003 samples, validate on 10001 samples\n",
      "Epoch 1/3\n",
      "90003/90003 [==============================] - 10s 109us/step - loss: 0.2729 - val_loss: 0.0482\n",
      "Epoch 2/3\n",
      "90003/90003 [==============================] - 7s 80us/step - loss: 0.0622 - val_loss: 0.0468\n",
      "Epoch 3/3\n",
      "90003/90003 [==============================] - 7s 80us/step - loss: 0.0548 - val_loss: 0.0442\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a465d1358>"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_dnn.fit([user_array,movie_array],rating_array,epochs=3,shuffle=True,validation_split=.1,batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.622812]], dtype=float32)"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_dnn.predict([np.array([5]),np.array([4])])*5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(671, 100)\n",
      "(9066, 100)\n",
      "(100, 256)\n",
      "(256,)\n",
      "(256,)\n",
      "(256,)\n",
      "(256,)\n",
      "(256, 128)\n",
      "(128,)\n",
      "(128,)\n",
      "(128,)\n",
      "(128,)\n",
      "(128, 1)\n"
     ]
    }
   ],
   "source": [
    "cf_weights = cf_dnn.get_weights()\n",
    "\n",
    "for w in cf_weights:\n",
    "    print(w.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9066, 100)"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_vec = cf_weights[1]\n",
    "movie_vec.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# using principal component analysis to decompose the 100 dimensions\n",
    "# compressing to 3 dimensions\n",
    "pca = PCA(n_components=3,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 71.9 ms, sys: 9 ms, total: 80.9 ms\n",
      "Wall time: 42.7 ms\n"
     ]
    }
   ],
   "source": [
    "%time movie_3d = pca.fit_transform(movie_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02111751, -0.02028077, -0.02052776],\n",
       "       [-0.05166073,  0.00858772,  0.0260797 ],\n",
       "       [-0.02160499, -0.03896006, -0.0067747 ],\n",
       "       ...,\n",
       "       [-0.01275731, -0.00377434,  0.03801192],\n",
       "       [-0.02981995, -0.01292933, -0.01477792],\n",
       "       [-0.05492761,  0.01296559, -0.01981016]], dtype=float32)"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_watched = pd.DataFrame(data[\"ratings\"][\"movieId\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_watched = most_watched.reset_index().rename(columns={\"index\":\"mid\",\"movieId\":\"counts\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "mid2title = dict(zip(data[\"movies\"][\"movieId\"],data[\"movies\"][\"title\"]))\n",
    "mid2genres = dict(zip(data[\"movies\"][\"movieId\"],data[\"movies\"][\"genres\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Toy Story (1995)',\n",
       " 2: 'Jumanji (1995)',\n",
       " 3: 'Grumpier Old Men (1995)',\n",
       " 4: 'Waiting to Exhale (1995)',\n",
       " 5: 'Father of the Bride Part II (1995)',\n",
       " 6: 'Heat (1995)',\n",
       " 7: 'Sabrina (1995)',\n",
       " 8: 'Tom and Huck (1995)',\n",
       " 9: 'Sudden Death (1995)',\n",
       " 10: 'GoldenEye (1995)',\n",
       " 11: 'American President, The (1995)',\n",
       " 12: 'Dracula: Dead and Loving It (1995)',\n",
       " 13: 'Balto (1995)',\n",
       " 14: 'Nixon (1995)',\n",
       " 15: 'Cutthroat Island (1995)',\n",
       " 16: 'Casino (1995)',\n",
       " 17: 'Sense and Sensibility (1995)',\n",
       " 18: 'Four Rooms (1995)',\n",
       " 19: 'Ace Ventura: When Nature Calls (1995)',\n",
       " 20: 'Money Train (1995)',\n",
       " 21: 'Get Shorty (1995)',\n",
       " 22: 'Copycat (1995)',\n",
       " 23: 'Assassins (1995)',\n",
       " 24: 'Powder (1995)',\n",
       " 25: 'Leaving Las Vegas (1995)',\n",
       " 26: 'Othello (1995)',\n",
       " 27: 'Now and Then (1995)',\n",
       " 28: 'Persuasion (1995)',\n",
       " 29: 'City of Lost Children, The (Cité des enfants perdus, La) (1995)',\n",
       " 30: 'Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)',\n",
       " 31: 'Dangerous Minds (1995)',\n",
       " 32: 'Twelve Monkeys (a.k.a. 12 Monkeys) (1995)',\n",
       " 34: 'Babe (1995)',\n",
       " 35: 'Carrington (1995)',\n",
       " 36: 'Dead Man Walking (1995)',\n",
       " 37: 'Across the Sea of Time (1995)',\n",
       " 38: 'It Takes Two (1995)',\n",
       " 39: 'Clueless (1995)',\n",
       " 40: 'Cry, the Beloved Country (1995)',\n",
       " 41: 'Richard III (1995)',\n",
       " 42: 'Dead Presidents (1995)',\n",
       " 43: 'Restoration (1995)',\n",
       " 44: 'Mortal Kombat (1995)',\n",
       " 45: 'To Die For (1995)',\n",
       " 46: 'How to Make an American Quilt (1995)',\n",
       " 47: 'Seven (a.k.a. Se7en) (1995)',\n",
       " 48: 'Pocahontas (1995)',\n",
       " 49: 'When Night Is Falling (1995)',\n",
       " 50: 'Usual Suspects, The (1995)',\n",
       " 52: 'Mighty Aphrodite (1995)',\n",
       " 53: 'Lamerica (1994)',\n",
       " 54: 'Big Green, The (1995)',\n",
       " 55: 'Georgia (1995)',\n",
       " 57: 'Home for the Holidays (1995)',\n",
       " 58: 'Postman, The (Postino, Il) (1994)',\n",
       " 59: 'Confessional, The (Confessionnal, Le) (1995)',\n",
       " 60: 'Indian in the Cupboard, The (1995)',\n",
       " 61: 'Eye for an Eye (1996)',\n",
       " 62: \"Mr. Holland's Opus (1995)\",\n",
       " 63: \"Don't Be a Menace to South Central While Drinking Your Juice in the Hood (1996)\",\n",
       " 64: 'Two if by Sea (1996)',\n",
       " 65: 'Bio-Dome (1996)',\n",
       " 66: 'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
       " 68: 'French Twist (Gazon maudit) (1995)',\n",
       " 69: 'Friday (1995)',\n",
       " 70: 'From Dusk Till Dawn (1996)',\n",
       " 71: 'Fair Game (1995)',\n",
       " 72: 'Kicking and Screaming (1995)',\n",
       " 73: 'Misérables, Les (1995)',\n",
       " 74: 'Bed of Roses (1996)',\n",
       " 76: 'Screamers (1995)',\n",
       " 77: 'Nico Icon (1995)',\n",
       " 78: 'Crossing Guard, The (1995)',\n",
       " 79: 'Juror, The (1996)',\n",
       " 80: 'White Balloon, The (Badkonake sefid) (1995)',\n",
       " 81: \"Things to Do in Denver When You're Dead (1995)\",\n",
       " 82: \"Antonia's Line (Antonia) (1995)\",\n",
       " 83: 'Once Upon a Time... When We Were Colored (1995)',\n",
       " 84: 'Last Summer in the Hamptons (1995)',\n",
       " 85: 'Angels and Insects (1995)',\n",
       " 86: 'White Squall (1996)',\n",
       " 87: 'Dunston Checks In (1996)',\n",
       " 88: 'Black Sheep (1996)',\n",
       " 89: 'Nick of Time (1995)',\n",
       " 92: 'Mary Reilly (1996)',\n",
       " 93: 'Vampire in Brooklyn (1995)',\n",
       " 94: 'Beautiful Girls (1996)',\n",
       " 95: 'Broken Arrow (1996)',\n",
       " 96: 'In the Bleak Midwinter (1995)',\n",
       " 97: 'Hate (Haine, La) (1995)',\n",
       " 98: 'Shopping (1994)',\n",
       " 99: 'Heidi Fleiss: Hollywood Madam (1995)',\n",
       " 100: 'City Hall (1996)',\n",
       " 101: 'Bottle Rocket (1996)',\n",
       " 102: 'Mr. Wrong (1996)',\n",
       " 103: 'Unforgettable (1996)',\n",
       " 104: 'Happy Gilmore (1996)',\n",
       " 105: 'Bridges of Madison County, The (1995)',\n",
       " 107: 'Muppet Treasure Island (1996)',\n",
       " 108: 'Catwalk (1996)',\n",
       " 110: 'Braveheart (1995)',\n",
       " 111: 'Taxi Driver (1976)',\n",
       " 112: 'Rumble in the Bronx (Hont faan kui) (1995)',\n",
       " 113: 'Before and After (1996)',\n",
       " 114: \"Margaret's Museum (1995)\",\n",
       " 116: 'Anne Frank Remembered (1995)',\n",
       " 117: \"Young Poisoner's Handbook, The (1995)\",\n",
       " 118: 'If Lucy Fell (1996)',\n",
       " 119: 'Steal Big, Steal Little (1995)',\n",
       " 121: 'Boys of St. Vincent, The (1992)',\n",
       " 122: 'Boomerang (1992)',\n",
       " 123: 'Chungking Express (Chung Hing sam lam) (1994)',\n",
       " 124: \"Star Maker, The (Uomo delle stelle, L') (1995)\",\n",
       " 125: 'Flirting With Disaster (1996)',\n",
       " 126: 'NeverEnding Story III, The (1994)',\n",
       " 129: 'Pie in the Sky (1996)',\n",
       " 130: 'Angela (1995)',\n",
       " 131: 'Frankie Starlight (1995)',\n",
       " 132: 'Jade (1995)',\n",
       " 135: 'Down Periscope (1996)',\n",
       " 137: 'Man of the Year (1995)',\n",
       " 140: 'Up Close and Personal (1996)',\n",
       " 141: 'Birdcage, The (1996)',\n",
       " 144: 'Brothers McMullen, The (1995)',\n",
       " 145: 'Bad Boys (1995)',\n",
       " 146: 'Amazing Panda Adventure, The (1995)',\n",
       " 147: 'Basketball Diaries, The (1995)',\n",
       " 148: 'Awfully Big Adventure, An (1995)',\n",
       " 149: 'Amateur (1994)',\n",
       " 150: 'Apollo 13 (1995)',\n",
       " 151: 'Rob Roy (1995)',\n",
       " 152: 'Addiction, The (1995)',\n",
       " 153: 'Batman Forever (1995)',\n",
       " 154: 'Beauty of the Day (Belle de jour) (1967)',\n",
       " 155: 'Beyond Rangoon (1995)',\n",
       " 156: 'Blue in the Face (1995)',\n",
       " 157: 'Canadian Bacon (1995)',\n",
       " 158: 'Casper (1995)',\n",
       " 159: 'Clockers (1995)',\n",
       " 160: 'Congo (1995)',\n",
       " 161: 'Crimson Tide (1995)',\n",
       " 162: 'Crumb (1994)',\n",
       " 163: 'Desperado (1995)',\n",
       " 164: 'Devil in a Blue Dress (1995)',\n",
       " 165: 'Die Hard: With a Vengeance (1995)',\n",
       " 166: 'Doom Generation, The (1995)',\n",
       " 167: 'Feast of July (1995)',\n",
       " 168: 'First Knight (1995)',\n",
       " 169: 'Free Willy 2: The Adventure Home (1995)',\n",
       " 170: 'Hackers (1995)',\n",
       " 171: 'Jeffrey (1995)',\n",
       " 172: 'Johnny Mnemonic (1995)',\n",
       " 173: 'Judge Dredd (1995)',\n",
       " 174: 'Jury Duty (1995)',\n",
       " 175: 'Kids (1995)',\n",
       " 176: 'Living in Oblivion (1995)',\n",
       " 177: 'Lord of Illusions (1995)',\n",
       " 178: 'Love & Human Remains (1993)',\n",
       " 179: 'Mad Love (1995)',\n",
       " 180: 'Mallrats (1995)',\n",
       " 181: 'Mighty Morphin Power Rangers: The Movie (1995)',\n",
       " 183: 'Mute Witness (1994)',\n",
       " 184: 'Nadja (1994)',\n",
       " 185: 'Net, The (1995)',\n",
       " 186: 'Nine Months (1995)',\n",
       " 187: 'Party Girl (1995)',\n",
       " 188: 'Prophecy, The (1995)',\n",
       " 189: 'Reckless (1995)',\n",
       " 190: 'Safe (1995)',\n",
       " 191: 'Scarlet Letter, The (1995)',\n",
       " 193: 'Showgirls (1995)',\n",
       " 194: 'Smoke (1995)',\n",
       " 195: 'Something to Talk About (1995)',\n",
       " 196: 'Species (1995)',\n",
       " 198: 'Strange Days (1995)',\n",
       " 199: 'Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964)',\n",
       " 200: 'Tie That Binds, The (1995)',\n",
       " 201: 'Three Wishes (1995)',\n",
       " 202: 'Total Eclipse (1995)',\n",
       " 203: 'To Wong Foo, Thanks for Everything! Julie Newmar (1995)',\n",
       " 204: 'Under Siege 2: Dark Territory (1995)',\n",
       " 205: 'Unstrung Heroes (1995)',\n",
       " 206: 'Unzipped (1995)',\n",
       " 207: 'Walk in the Clouds, A (1995)',\n",
       " 208: 'Waterworld (1995)',\n",
       " 209: \"White Man's Burden (1995)\",\n",
       " 211: 'Browning Version, The (1994)',\n",
       " 213: 'Burnt by the Sun (Utomlyonnye solntsem) (1994)',\n",
       " 214: 'Before the Rain (Pred dozhdot) (1994)',\n",
       " 215: 'Before Sunrise (1995)',\n",
       " 216: 'Billy Madison (1995)',\n",
       " 217: 'Babysitter, The (1995)',\n",
       " 218: 'Boys on the Side (1995)',\n",
       " 219: 'Cure, The (1995)',\n",
       " 220: 'Castle Freak (1995)',\n",
       " 222: 'Circle of Friends (1995)',\n",
       " 223: 'Clerks (1994)',\n",
       " 224: 'Don Juan DeMarco (1995)',\n",
       " 225: 'Disclosure (1994)',\n",
       " 227: 'Drop Zone (1994)',\n",
       " 228: 'Destiny Turns on the Radio (1995)',\n",
       " 229: 'Death and the Maiden (1994)',\n",
       " 230: 'Dolores Claiborne (1995)',\n",
       " 231: 'Dumb & Dumber (Dumb and Dumber) (1994)',\n",
       " 232: 'Eat Drink Man Woman (Yin shi nan nu) (1994)',\n",
       " 233: 'Exotica (1994)',\n",
       " 234: 'Exit to Eden (1994)',\n",
       " 235: 'Ed Wood (1994)',\n",
       " 236: 'French Kiss (1995)',\n",
       " 237: 'Forget Paris (1995)',\n",
       " 238: 'Far From Home: The Adventures of Yellow Dog (1995)',\n",
       " 239: 'Goofy Movie, A (1995)',\n",
       " 240: 'Hideaway (1995)',\n",
       " 241: 'Fluke (1995)',\n",
       " 242: 'Farinelli: il castrato (1994)',\n",
       " 243: 'Gordy (1995)',\n",
       " 244: 'Gumby: The Movie (1995)',\n",
       " 245: 'The Glass Shield (1994)',\n",
       " 246: 'Hoop Dreams (1994)',\n",
       " 247: 'Heavenly Creatures (1994)',\n",
       " 248: 'Houseguest (1994)',\n",
       " 249: 'Immortal Beloved (1994)',\n",
       " 250: 'Heavyweights (Heavy Weights) (1995)',\n",
       " 251: 'Hunted, The (1995)',\n",
       " 252: 'I.Q. (1994)',\n",
       " 253: 'Interview with the Vampire: The Vampire Chronicles (1994)',\n",
       " 254: 'Jefferson in Paris (1995)',\n",
       " 255: 'Jerky Boys, The (1995)',\n",
       " 256: 'Junior (1994)',\n",
       " 257: 'Just Cause (1995)',\n",
       " 258: \"Kid in King Arthur's Court, A (1995)\",\n",
       " 259: 'Kiss of Death (1995)',\n",
       " 260: 'Star Wars: Episode IV - A New Hope (1977)',\n",
       " 261: 'Little Women (1994)',\n",
       " 262: 'Little Princess, A (1995)',\n",
       " 263: 'Ladybird Ladybird (1994)',\n",
       " 264: \"Enfer, L' (1994)\",\n",
       " 265: 'Like Water for Chocolate (Como agua para chocolate) (1992)',\n",
       " 266: 'Legends of the Fall (1994)',\n",
       " 267: 'Major Payne (1995)',\n",
       " 268: 'Little Odessa (1994)',\n",
       " 269: 'My Crazy Life (Mi vida loca) (1993)',\n",
       " 270: 'Love Affair (1994)',\n",
       " 271: 'Losing Isaiah (1995)',\n",
       " 272: 'Madness of King George, The (1994)',\n",
       " 273: \"Mary Shelley's Frankenstein (Frankenstein) (1994)\",\n",
       " 274: 'Man of the House (1995)',\n",
       " 275: 'Mixed Nuts (1994)',\n",
       " 276: 'Milk Money (1994)',\n",
       " 277: 'Miracle on 34th Street (1994)',\n",
       " 278: 'Miami Rhapsody (1995)',\n",
       " 279: 'My Family (1995)',\n",
       " 280: 'Murder in the First (1995)',\n",
       " 281: \"Nobody's Fool (1994)\",\n",
       " 282: 'Nell (1994)',\n",
       " 283: 'New Jersey Drive (1995)',\n",
       " 285: 'Beyond Bedlam (1993)',\n",
       " 287: 'Nina Takes a Lover (1994)',\n",
       " 288: 'Natural Born Killers (1994)',\n",
       " 289: 'Only You (1994)',\n",
       " 290: 'Once Were Warriors (1994)',\n",
       " 291: 'Poison Ivy II (1996)',\n",
       " 292: 'Outbreak (1995)',\n",
       " 293: 'Léon: The Professional (a.k.a. The Professional) (Léon) (1994)',\n",
       " 294: 'Perez Family, The (1995)',\n",
       " 295: \"Pyromaniac's Love Story, A (1995)\",\n",
       " 296: 'Pulp Fiction (1994)',\n",
       " 299: 'Priest (1994)',\n",
       " 300: 'Quiz Show (1994)',\n",
       " 301: 'Picture Bride (Bijo photo) (1994)',\n",
       " 302: 'Queen Margot (Reine Margot, La) (1994)',\n",
       " 303: 'Quick and the Dead, The (1995)',\n",
       " 304: 'Roommates (1995)',\n",
       " 305: 'Ready to Wear (Pret-A-Porter) (1994)',\n",
       " 306: 'Three Colors: Red (Trois couleurs: Rouge) (1994)',\n",
       " 307: 'Three Colors: Blue (Trois couleurs: Bleu) (1993)',\n",
       " 308: 'Three Colors: White (Trzy kolory: Bialy) (1994)',\n",
       " 309: 'Red Firecracker, Green Firecracker (Pao Da Shuang Deng) (1994)',\n",
       " 312: 'Stuart Saves His Family (1995)',\n",
       " 313: 'Swan Princess, The (1994)',\n",
       " 314: 'Secret of Roan Inish, The (1994)',\n",
       " 315: 'Specialist, The (1994)',\n",
       " 316: 'Stargate (1994)',\n",
       " 317: 'Santa Clause, The (1994)',\n",
       " 318: 'Shawshank Redemption, The (1994)',\n",
       " 319: 'Shallow Grave (1994)',\n",
       " 320: 'Suture (1993)',\n",
       " 321: 'Strawberry and Chocolate (Fresa y chocolate) (1993)',\n",
       " 322: 'Swimming with Sharks (1995)',\n",
       " 324: 'Sum of Us, The (1994)',\n",
       " 325: \"National Lampoon's Senior Trip (1995)\",\n",
       " 326: 'To Live (Huozhe) (1994)',\n",
       " 327: 'Tank Girl (1995)',\n",
       " 328: 'Tales from the Crypt Presents: Demon Knight (1995)',\n",
       " 329: 'Star Trek: Generations (1994)',\n",
       " 330: 'Tales from the Hood (1995)',\n",
       " 331: 'Tom & Viv (1994)',\n",
       " 332: 'Village of the Damned (1995)',\n",
       " 333: 'Tommy Boy (1995)',\n",
       " 334: 'Vanya on 42nd Street (1994)',\n",
       " 335: 'Underneath (1995)',\n",
       " 336: 'Walking Dead, The (1995)',\n",
       " 337: \"What's Eating Gilbert Grape (1993)\",\n",
       " 338: 'Virtuosity (1995)',\n",
       " 339: 'While You Were Sleeping (1995)',\n",
       " 340: 'War, The (1994)',\n",
       " 341: 'Double Happiness (1994)',\n",
       " 342: \"Muriel's Wedding (1994)\",\n",
       " 343: 'Baby-Sitters Club, The (1995)',\n",
       " 344: 'Ace Ventura: Pet Detective (1994)',\n",
       " 345: 'Adventures of Priscilla, Queen of the Desert, The (1994)',\n",
       " 346: 'Backbeat (1993)',\n",
       " 347: 'Bitter Moon (1992)',\n",
       " 348: 'Bullets Over Broadway (1994)',\n",
       " 349: 'Clear and Present Danger (1994)',\n",
       " 350: 'Client, The (1994)',\n",
       " 351: 'Corrina, Corrina (1994)',\n",
       " 352: 'Crooklyn (1994)',\n",
       " 353: 'Crow, The (1994)',\n",
       " 354: 'Cobb (1994)',\n",
       " 355: 'Flintstones, The (1994)',\n",
       " 356: 'Forrest Gump (1994)',\n",
       " 357: 'Four Weddings and a Funeral (1994)',\n",
       " 358: 'Higher Learning (1995)',\n",
       " 360: 'I Love Trouble (1994)',\n",
       " 361: 'It Could Happen to You (1994)',\n",
       " 362: 'Jungle Book, The (1994)',\n",
       " 363: 'Wonderful, Horrible Life of Leni Riefenstahl, The (Macht der Bilder: Leni Riefenstahl, Die) (1993)',\n",
       " 364: 'Lion King, The (1994)',\n",
       " 365: 'Little Buddha (1993)',\n",
       " 366: \"Wes Craven's New Nightmare (Nightmare on Elm Street Part 7: Freddy's Finale, A) (1994)\",\n",
       " 367: 'Mask, The (1994)',\n",
       " 368: 'Maverick (1994)',\n",
       " 369: 'Mrs. Parker and the Vicious Circle (1994)',\n",
       " 370: 'Naked Gun 33 1/3: The Final Insult (1994)',\n",
       " 371: 'Paper, The (1994)',\n",
       " 372: 'Reality Bites (1994)',\n",
       " 373: 'Red Rock West (1992)',\n",
       " 374: 'Richie Rich (1994)',\n",
       " 375: 'Safe Passage (1994)',\n",
       " 376: 'River Wild, The (1994)',\n",
       " 377: 'Speed (1994)',\n",
       " 378: 'Speechless (1994)',\n",
       " 379: 'Timecop (1994)',\n",
       " 380: 'True Lies (1994)',\n",
       " 381: 'When a Man Loves a Woman (1994)',\n",
       " 382: 'Wolf (1994)',\n",
       " 383: 'Wyatt Earp (1994)',\n",
       " 384: 'Bad Company (1995)',\n",
       " 387: 'Low Down Dirty Shame, A (1994)',\n",
       " 388: 'Boys Life (1995)',\n",
       " 389: 'Colonel Chabert, Le (1994)',\n",
       " 390: 'Faster Pussycat! Kill! Kill! (1965)',\n",
       " 391: \"Jason's Lyric (1994)\",\n",
       " 392: 'Secret Adventures of Tom Thumb, The (1993)',\n",
       " 393: 'Street Fighter (1994)',\n",
       " 401: 'Mirage (1995)',\n",
       " 405: 'Highlander III: The Sorcerer (a.k.a. Highlander: The Final Dimension) (1994)',\n",
       " 407: 'In the Mouth of Madness (1995)',\n",
       " 408: '8 Seconds (1994)',\n",
       " 409: 'Above the Rim (1994)',\n",
       " 410: 'Addams Family Values (1993)',\n",
       " 412: 'Age of Innocence, The (1993)',\n",
       " 413: 'Airheads (1994)',\n",
       " 414: 'Air Up There, The (1994)',\n",
       " 415: 'Another Stakeout (1993)',\n",
       " 416: 'Bad Girls (1994)',\n",
       " 417: 'Barcelona (1994)',\n",
       " 418: 'Being Human (1993)',\n",
       " 419: 'Beverly Hillbillies, The (1993)',\n",
       " 420: 'Beverly Hills Cop III (1994)',\n",
       " 421: 'Black Beauty (1994)',\n",
       " 422: 'Blink (1994)',\n",
       " 423: 'Blown Away (1994)',\n",
       " 424: 'Blue Chips (1994)',\n",
       " 425: 'Blue Sky (1994)',\n",
       " 426: 'Body Snatchers (1993)',\n",
       " 427: 'Boxing Helena (1993)',\n",
       " 428: 'Bronx Tale, A (1993)',\n",
       " 429: 'Cabin Boy (1994)',\n",
       " 431: \"Carlito's Way (1993)\",\n",
       " 432: \"City Slickers II: The Legend of Curly's Gold (1994)\",\n",
       " 433: 'Clean Slate (1994)',\n",
       " 434: 'Cliffhanger (1993)',\n",
       " 435: 'Coneheads (1993)',\n",
       " 436: 'Color of Night (1994)',\n",
       " 437: 'Cops and Robbersons (1994)',\n",
       " 438: 'Cowboy Way, The (1994)',\n",
       " 439: 'Dangerous Game (1993)',\n",
       " 440: 'Dave (1993)',\n",
       " 441: 'Dazed and Confused (1993)',\n",
       " 442: 'Demolition Man (1993)',\n",
       " 443: 'Endless Summer 2, The (1994)',\n",
       " 444: 'Even Cowgirls Get the Blues (1993)',\n",
       " 445: 'Fatal Instinct (1993)',\n",
       " 446: 'Farewell My Concubine (Ba wang bie ji) (1993)',\n",
       " 447: 'Favor, The (1994)',\n",
       " 448: 'Fearless (1993)',\n",
       " 449: 'Fear of a Black Hat (1994)',\n",
       " 450: 'With Honors (1994)',\n",
       " 451: 'Flesh and Bone (1993)',\n",
       " 452: \"Widows' Peak (1994)\",\n",
       " 453: 'For Love or Money (1993)',\n",
       " 454: 'Firm, The (1993)',\n",
       " 455: 'Free Willy (1993)',\n",
       " 456: 'Fresh (1994)',\n",
       " 457: 'Fugitive, The (1993)',\n",
       " 458: 'Geronimo: An American Legend (1993)',\n",
       " 459: 'Getaway, The (1994)',\n",
       " 460: 'Getting Even with Dad (1994)',\n",
       " 461: 'Go Fish (1994)',\n",
       " 463: 'Guilty as Sin (1993)',\n",
       " 464: 'Hard Target (1993)',\n",
       " 465: 'Heaven & Earth (1993)',\n",
       " 466: 'Hot Shots! Part Deux (1993)',\n",
       " 467: 'Live Nude Girls (1995)',\n",
       " 468: 'Englishman Who Went Up a Hill But Came Down a Mountain, The (1995)',\n",
       " 469: 'House of the Spirits, The (1993)',\n",
       " 470: 'House Party 3 (1994)',\n",
       " 471: 'Hudsucker Proxy, The (1994)',\n",
       " 472: \"I'll Do Anything (1994)\",\n",
       " 473: 'In the Army Now (1994)',\n",
       " 474: 'In the Line of Fire (1993)',\n",
       " 475: 'In the Name of the Father (1993)',\n",
       " 476: 'Inkwell, The (1994)',\n",
       " 477: \"What's Love Got to Do with It? (1993)\",\n",
       " 479: 'Judgment Night (1993)',\n",
       " 480: 'Jurassic Park (1993)',\n",
       " 481: 'Kalifornia (1993)',\n",
       " 482: 'Killing Zoe (1994)',\n",
       " 483: 'King of the Hill (1993)',\n",
       " 484: 'Lassie (1994)',\n",
       " 485: 'Last Action Hero (1993)',\n",
       " 486: 'Life with Mikey (1993)',\n",
       " 487: 'Lightning Jack (1994)',\n",
       " 488: 'M. Butterfly (1993)',\n",
       " 489: 'Made in America (1993)',\n",
       " 490: 'Malice (1993)',\n",
       " 491: 'Man Without a Face, The (1993)',\n",
       " 492: 'Manhattan Murder Mystery (1993)',\n",
       " 493: 'Menace II Society (1993)',\n",
       " 494: 'Executive Decision (1996)',\n",
       " 495: 'In the Realm of the Senses (Ai no corrida) (1976)',\n",
       " 496: 'What Happened Was... (1994)',\n",
       " 497: 'Much Ado About Nothing (1993)',\n",
       " 498: 'Mr. Jones (1993)',\n",
       " 499: 'Mr. Wonderful (1993)',\n",
       " 500: 'Mrs. Doubtfire (1993)',\n",
       " 501: 'Naked (1993)',\n",
       " 502: 'Next Karate Kid, The (1994)',\n",
       " 504: 'No Escape (1994)',\n",
       " 505: 'North (1994)',\n",
       " 506: 'Orlando (1992)',\n",
       " 507: 'Perfect World, A (1993)',\n",
       " 508: 'Philadelphia (1993)',\n",
       " 509: 'Piano, The (1993)',\n",
       " 510: 'Poetic Justice (1993)',\n",
       " 511: 'Program, The (1993)',\n",
       " 512: 'Puppet Masters, The (1994)',\n",
       " 513: 'Radioland Murders (1994)',\n",
       " 514: 'Ref, The (1994)',\n",
       " 515: 'Remains of the Day, The (1993)',\n",
       " 516: 'Renaissance Man (1994)',\n",
       " 517: 'Rising Sun (1993)',\n",
       " 518: 'Road to Wellville, The (1994)',\n",
       " 519: 'RoboCop 3 (1993)',\n",
       " 520: 'Robin Hood: Men in Tights (1993)',\n",
       " 521: 'Romeo Is Bleeding (1993)',\n",
       " 522: 'Romper Stomper (1992)',\n",
       " 523: 'Ruby in Paradise (1993)',\n",
       " 524: 'Rudy (1993)',\n",
       " 526: 'Savage Nights (Nuits fauves, Les) (1992)',\n",
       " 527: \"Schindler's List (1993)\",\n",
       " 528: 'Scout, The (1994)',\n",
       " 529: 'Searching for Bobby Fischer (1993)',\n",
       " 531: 'Secret Garden, The (1993)',\n",
       " 532: 'Serial Mom (1994)',\n",
       " 533: 'Shadow, The (1994)',\n",
       " 534: 'Shadowlands (1993)',\n",
       " 535: 'Short Cuts (1993)',\n",
       " 536: 'Simple Twist of Fate, A (1994)',\n",
       " 537: 'Sirens (1994)',\n",
       " 538: 'Six Degrees of Separation (1993)',\n",
       " 539: 'Sleepless in Seattle (1993)',\n",
       " 540: 'Sliver (1993)',\n",
       " 541: 'Blade Runner (1982)',\n",
       " 542: 'Son in Law (1993)',\n",
       " 543: 'So I Married an Axe Murderer (1993)',\n",
       " 544: 'Striking Distance (1993)',\n",
       " 546: 'Super Mario Bros. (1993)',\n",
       " 547: 'Surviving the Game (1994)',\n",
       " 548: 'Terminal Velocity (1994)',\n",
       " 549: 'Thirty-Two Short Films About Glenn Gould (1993)',\n",
       " 550: 'Threesome (1994)',\n",
       " 551: 'Nightmare Before Christmas, The (1993)',\n",
       " 552: 'Three Musketeers, The (1993)',\n",
       " 553: 'Tombstone (1993)',\n",
       " 554: 'Trial by Jury (1994)',\n",
       " 555: 'True Romance (1993)',\n",
       " 556: 'War Room, The (1993)',\n",
       " 558: 'Pagemaster, The (1994)',\n",
       " 559: 'Paris, France (1993)',\n",
       " 561: 'Killer (Bulletproof Heart) (1994)',\n",
       " 562: 'Welcome to the Dollhouse (1995)',\n",
       " 563: 'Germinal (1993)',\n",
       " 564: 'Chasers (1994)',\n",
       " 565: 'Cronos (1993)',\n",
       " 567: 'Kika (1993)',\n",
       " 568: 'Bhaji on the Beach (1993)',\n",
       " 569: 'Little Big League (1994)',\n",
       " 571: 'Wedding Gift, The (1994)',\n",
       " 573: 'Ciao, Professore! (Io speriamo che me la cavo) (1992)',\n",
       " 574: 'Spanking the Monkey (1994)',\n",
       " 575: 'Little Rascals, The (1994)',\n",
       " 577: 'Andre (1994)',\n",
       " 580: 'Princess Caraboo (1994)',\n",
       " 581: 'Celluloid Closet, The (1995)',\n",
       " 582: 'Métisse (Café au Lait) (1993)',\n",
       " 585: 'Brady Bunch Movie, The (1995)',\n",
       " 586: 'Home Alone (1990)',\n",
       " 587: 'Ghost (1990)',\n",
       " 588: 'Aladdin (1992)',\n",
       " 589: 'Terminator 2: Judgment Day (1991)',\n",
       " 590: 'Dances with Wolves (1990)',\n",
       " 592: 'Batman (1989)',\n",
       " 593: 'Silence of the Lambs, The (1991)',\n",
       " 594: 'Snow White and the Seven Dwarfs (1937)',\n",
       " 595: 'Beauty and the Beast (1991)',\n",
       " 596: 'Pinocchio (1940)',\n",
       " 597: 'Pretty Woman (1990)',\n",
       " 599: 'Wild Bunch, The (1969)',\n",
       " 600: 'Love and a .45 (1994)',\n",
       " 603: 'Bye Bye, Love (1995)',\n",
       " 605: 'One Fine Day (1996)',\n",
       " 606: 'Candyman: Farewell to the Flesh (1995)',\n",
       " 608: 'Fargo (1996)',\n",
       " 609: 'Homeward Bound II: Lost in San Francisco (1996)',\n",
       " 610: 'Heavy Metal (1981)',\n",
       " 611: 'Hellraiser: Bloodline (1996)',\n",
       " 612: 'Pallbearer, The (1996)',\n",
       " 613: 'Jane Eyre (1996)',\n",
       " 614: 'Loaded (1994)',\n",
       " 615: 'Bread and Chocolate (Pane e cioccolata) (1973)',\n",
       " 616: 'Aristocats, The (1970)',\n",
       " 617: 'Flower of My Secret, The (La flor de mi secreto) (1995)',\n",
       " 619: 'Ed (1996)',\n",
       " 620: 'Scream of Stone (Cerro Torre: Schrei aus Stein) (1991)',\n",
       " 621: 'My Favorite Season (1993)',\n",
       " 626: 'Thin Line Between Love and Hate, A (1996)',\n",
       " 627: 'Last Supper, The (1995)',\n",
       " 628: 'Primal Fear (1996)',\n",
       " 630: 'Carried Away (1996)',\n",
       " 631: 'All Dogs Go to Heaven 2 (1996)',\n",
       " 632: 'Land and Freedom (Tierra y libertad) (1995)',\n",
       " 633: 'Denise Calls Up (1995)',\n",
       " 635: 'Family Thing, A (1996)',\n",
       " 637: 'Sgt. Bilko (1996)',\n",
       " 638: 'Jack and Sarah (1995)',\n",
       " 639: 'Girl 6 (1996)',\n",
       " 640: 'Diabolique (1996)',\n",
       " 647: 'Courage Under Fire (1996)',\n",
       " 648: 'Mission: Impossible (1996)',\n",
       " 650: 'Moll Flanders (1996)',\n",
       " 651: 'Superweib, Das (1996)',\n",
       " 653: 'Dragonheart (1996)',\n",
       " 656: 'Eddie (1996)',\n",
       " 659: 'Purple Noon (Plein soleil) (1960)',\n",
       " 661: 'James and the Giant Peach (1996)',\n",
       " 662: 'Fear (1996)',\n",
       " 663: 'Kids in the Hall: Brain Candy (1996)',\n",
       " 664: 'Faithful (1996)',\n",
       " 665: 'Underground (1995)',\n",
       " 667: 'Bloodsport 2 (a.k.a. Bloodsport II: The Next Kumite) (1996)',\n",
       " 668: 'Song of the Little Road (Pather Panchali) (1955)',\n",
       " 670: 'World of Apu, The (Apur Sansar) (1959)',\n",
       " 671: 'Mystery Science Theater 3000: The Movie (1996)',\n",
       " 673: 'Space Jam (1996)',\n",
       " 674: 'Barbarella (1968)',\n",
       " 678: 'Some Folks Call It a Sling Blade (1993)',\n",
       " 679: 'Run of the Country, The (1995)',\n",
       " 680: 'Alphaville (Alphaville, une étrange aventure de Lemmy Caution) (1965)',\n",
       " 681: 'Coup de torchon (Clean Slate) (1981)',\n",
       " 685: \"It's My Party (1996)\",\n",
       " 687: 'Country Life (1994)',\n",
       " 688: 'Operation Dumbo Drop (1995)',\n",
       " 690: 'Promise, The (Versprechen, Das) (1995)',\n",
       " 691: 'Mrs. Winterbourne (1996)',\n",
       " 692: 'Solo (1996)',\n",
       " 694: 'Substitute, The (1996)',\n",
       " 695: 'True Crime (1996)',\n",
       " 696: 'Butterfly Kiss (1995)',\n",
       " 697: 'Feeling Minnesota (1996)',\n",
       " 698: 'Delta of Venus (1995)',\n",
       " 700: 'Angus (1995)',\n",
       " 702: 'Faces (1968)',\n",
       " 703: 'Boys (1996)',\n",
       " 704: 'Quest, The (1996)',\n",
       " 705: 'Cosi (1996)',\n",
       " 707: 'Mulholland Falls (1996)',\n",
       " 708: 'Truth About Cats & Dogs, The (1996)',\n",
       " 709: 'Oliver & Company (1988)',\n",
       " 710: 'Celtic Pride (1996)',\n",
       " 711: 'Flipper (1996)',\n",
       " 714: 'Dead Man (1995)',\n",
       " 715: 'Horseman on the Roof, The (Hussard sur le toit, Le) (1995)',\n",
       " 718: 'Visitors, The (Visiteurs, Les) (1993)',\n",
       " 719: 'Multiplicity (1996)',\n",
       " 720: 'Wallace & Gromit: The Best of Aardman Animation (1996)',\n",
       " 721: 'Halfmoon (Paul Bowles - Halbmond) (1995)',\n",
       " 722: 'Haunted World of Edward D. Wood Jr., The (1996)',\n",
       " 724: 'Craft, The (1996)',\n",
       " 725: 'Great White Hype, The (1996)',\n",
       " 726: 'Last Dance (1996)',\n",
       " 728: 'Cold Comfort Farm (1995)',\n",
       " 731: \"Heaven's Prisoners (1996)\",\n",
       " 733: 'Rock, The (1996)',\n",
       " 735: 'Cemetery Man (Dellamorte Dellamore) (1994)',\n",
       " 736: 'Twister (1996)',\n",
       " 737: 'Barb Wire (1996)',\n",
       " 741: 'Ghost in the Shell (Kôkaku kidôtai) (1995)',\n",
       " 742: 'Thinner (1996)',\n",
       " 743: 'Spy Hard (1996)',\n",
       " 745: 'Wallace & Gromit: A Close Shave (1995)',\n",
       " 746: 'Force of Evil (1948)',\n",
       " 747: 'Stupids, The (1996)',\n",
       " 748: 'Arrival, The (1996)',\n",
       " 750: 'Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964)',\n",
       " 753: 'Month by the Lake, A (1995)',\n",
       " 754: 'Gold Diggers: The Secret of Bear Mountain (1995)',\n",
       " 755: 'Kim (1950)',\n",
       " 756: 'Carmen Miranda: Bananas Is My Business (1994)',\n",
       " 757: 'Ashes of Time (Dung che sai duk) (1994)',\n",
       " 759: 'Maya Lin: A Strong Clear Vision (1994)',\n",
       " 760: 'Stalingrad (1993)',\n",
       " 761: 'Phantom, The (1996)',\n",
       " 762: 'Striptease (1996)',\n",
       " 764: 'Heavy (1995)',\n",
       " 765: 'Jack (1996)',\n",
       " 766: 'I Shot Andy Warhol (1996)',\n",
       " 767: 'Grass Harp, The (1995)',\n",
       " 769: 'Marlene Dietrich: Shadow and Light (1996)',\n",
       " 775: 'Spirits of the Dead (1968)',\n",
       " 778: 'Trainspotting (1996)',\n",
       " 779: \"'Til There Was You (1997)\",\n",
       " 780: 'Independence Day (a.k.a. ID4) (1996)',\n",
       " 781: 'Stealing Beauty (1996)',\n",
       " 782: 'Fan, The (1996)',\n",
       " 783: 'Hunchback of Notre Dame, The (1996)',\n",
       " 784: 'Cable Guy, The (1996)',\n",
       " 785: 'Kingpin (1996)',\n",
       " 786: 'Eraser (1996)',\n",
       " 787: 'Gate of Heavenly Peace, The (1995)',\n",
       " 788: 'Nutty Professor, The (1996)',\n",
       " 793: \"My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993)\",\n",
       " 798: 'Daylight (1996)',\n",
       " 799: 'Frighteners, The (1996)',\n",
       " 800: 'Lone Star (1996)',\n",
       " 801: 'Harriet the Spy (1996)',\n",
       " 802: 'Phenomenon (1996)',\n",
       " 803: 'Walking and Talking (1996)',\n",
       " 804: \"She's the One (1996)\",\n",
       " 805: 'Time to Kill, A (1996)',\n",
       " 806: 'American Buffalo (1996)',\n",
       " 808: 'Alaska (1996)',\n",
       " 809: 'Fled (1996)',\n",
       " 810: 'Kazaam (1996)',\n",
       " 813: 'Larger Than Life (1996)',\n",
       " 816: 'Two Deaths (1995)',\n",
       " 818: 'Very Brady Sequel, A (1996)',\n",
       " 820: 'Death in the Garden (Mort en ce jardin, La) (1956)',\n",
       " 824: 'Kaspar Hauser (1993)',\n",
       " 828: 'Adventures of Pinocchio, The (1996)',\n",
       " 829: \"Joe's Apartment (1996)\",\n",
       " 830: 'First Wives Club, The (1996)',\n",
       " 831: 'Stonewall (1995)',\n",
       " 832: 'Ransom (1996)',\n",
       " 833: 'High School High (1996)',\n",
       " 834: 'Phat Beach (1996)',\n",
       " 835: 'Foxfire (1996)',\n",
       " 836: 'Chain Reaction (1996)',\n",
       " 837: 'Matilda (1996)',\n",
       " 838: 'Emma (1996)',\n",
       " 839: 'Crow: City of Angels, The (1996)',\n",
       " 840: 'House Arrest (1996)',\n",
       " 841: 'Eyes Without a Face (Yeux sans visage, Les) (1959)',\n",
       " 842: 'Tales from the Crypt Presents: Bordello of Blood (1996)',\n",
       " 844: 'Story of Xinghua, The (Xinghua san yue tian) (1994)',\n",
       " 845: 'Day the Sun Turned Cold, The (Tianguo niezi) (1994)',\n",
       " 846: 'Flirt (1995)',\n",
       " 848: 'Spitfire Grill, The (1996)',\n",
       " 849: 'Escape from L.A. (1996)',\n",
       " 850: 'Cyclo (Xich lo) (1995)',\n",
       " 851: 'Basquiat (1996)',\n",
       " 852: 'Tin Cup (1996)',\n",
       " 854: 'Ballad of Narayama, The (Narayama Bushiko) (1958)',\n",
       " 858: 'Godfather, The (1972)',\n",
       " 861: 'Supercop (Police Story 3: Supercop) (Jing cha gu shi III: Chao ji jing cha) (1992)',\n",
       " 864: 'Wife, The (1995)',\n",
       " 865: 'Small Faces (1996)',\n",
       " 866: 'Bound (1996)',\n",
       " 867: 'Carpool (1996)',\n",
       " 869: 'Kansas City (1996)',\n",
       " 870: \"Gone Fishin' (1997)\",\n",
       " 872: \"Vive L'Amour (Ai qing wan sui) (1994)\",\n",
       " 875: 'Nothing to Lose (1994)',\n",
       " 876: 'Supercop 2 (Project S) (Chao ji ji hua) (1993)',\n",
       " 879: 'Relic, The (1997)',\n",
       " 880: 'Island of Dr. Moreau, The (1996)',\n",
       " 881: 'First Kid (1996)',\n",
       " 882: 'Trigger Effect, The (1996)',\n",
       " 885: 'Bogus (1996)',\n",
       " 886: 'Bulletproof (1996)',\n",
       " 888: 'Land Before Time III: The Time of the Great Giving (1995)',\n",
       " 889: '1-900 (06) (1994)',\n",
       " 891: 'Halloween: The Curse of Michael Myers (Halloween 6: The Curse of Michael Myers) (1995)',\n",
       " 892: 'Twelfth Night (1996)',\n",
       " 893: 'Mother Night (1996)',\n",
       " 896: 'Wild Reeds (Les roseaux sauvages) (1994)',\n",
       " 897: 'For Whom the Bell Tolls (1943)',\n",
       " 898: 'Philadelphia Story, The (1940)',\n",
       " 899: \"Singin' in the Rain (1952)\",\n",
       " 900: 'American in Paris, An (1951)',\n",
       " 901: 'Funny Face (1957)',\n",
       " 902: \"Breakfast at Tiffany's (1961)\",\n",
       " 903: 'Vertigo (1958)',\n",
       " 904: 'Rear Window (1954)',\n",
       " 905: 'It Happened One Night (1934)',\n",
       " 906: 'Gaslight (1944)',\n",
       " 907: 'Gay Divorcee, The (1934)',\n",
       " 908: 'North by Northwest (1959)',\n",
       " 909: 'Apartment, The (1960)',\n",
       " 910: 'Some Like It Hot (1959)',\n",
       " 911: 'Charade (1963)',\n",
       " 912: 'Casablanca (1942)',\n",
       " 913: 'Maltese Falcon, The (1941)',\n",
       " 914: 'My Fair Lady (1964)',\n",
       " 915: 'Sabrina (1954)',\n",
       " 916: 'Roman Holiday (1953)',\n",
       " 917: 'Little Princess, The (1939)',\n",
       " 918: 'Meet Me in St. Louis (1944)',\n",
       " 919: 'Wizard of Oz, The (1939)',\n",
       " 920: 'Gone with the Wind (1939)',\n",
       " 921: 'My Favorite Year (1982)',\n",
       " 922: 'Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)',\n",
       " 923: 'Citizen Kane (1941)',\n",
       " 924: '2001: A Space Odyssey (1968)',\n",
       " 926: 'All About Eve (1950)',\n",
       " 927: 'Women, The (1939)',\n",
       " 928: 'Rebecca (1940)',\n",
       " 929: 'Foreign Correspondent (1940)',\n",
       " 930: 'Notorious (1946)',\n",
       " 931: 'Spellbound (1945)',\n",
       " 932: 'Affair to Remember, An (1957)',\n",
       " 933: 'To Catch a Thief (1955)',\n",
       " 934: 'Father of the Bride (1950)',\n",
       " 935: 'Band Wagon, The (1953)',\n",
       " 936: 'Ninotchka (1939)',\n",
       " 937: 'Love in the Afternoon (1957)',\n",
       " 938: 'Gigi (1958)',\n",
       " 939: 'Reluctant Debutante, The (1958)',\n",
       " 940: 'Adventures of Robin Hood, The (1938)',\n",
       " 941: 'Mark of Zorro, The (1940)',\n",
       " 942: 'Laura (1944)',\n",
       " 943: 'Ghost and Mrs. Muir, The (1947)',\n",
       " 944: 'Lost Horizon (1937)',\n",
       " 945: 'Top Hat (1935)',\n",
       " 946: 'To Be or Not to Be (1942)',\n",
       " 947: 'My Man Godfrey (1936)',\n",
       " 948: 'Giant (1956)',\n",
       " 949: 'East of Eden (1955)',\n",
       " 950: 'Thin Man, The (1934)',\n",
       " 951: 'His Girl Friday (1940)',\n",
       " 952: 'Around the World in 80 Days (1956)',\n",
       " 953: \"It's a Wonderful Life (1946)\",\n",
       " 954: 'Mr. Smith Goes to Washington (1939)',\n",
       " 955: 'Bringing Up Baby (1938)',\n",
       " 956: 'Penny Serenade (1941)',\n",
       " 957: 'Scarlet Letter, The (1926)',\n",
       " 960: 'Angel on My Shoulder (1946)',\n",
       " 961: 'Little Lord Fauntleroy (1936)',\n",
       " 962: 'They Made Me a Criminal (1939)',\n",
       " 963: 'Inspector General, The (1949)',\n",
       " 964: 'Angel and the Badman (1947)',\n",
       " 965: '39 Steps, The (1935)',\n",
       " 966: 'A Walk in the Sun (1945)',\n",
       " 968: 'Night of the Living Dead (1968)',\n",
       " 969: 'African Queen, The (1951)',\n",
       " 970: 'Beat the Devil (1953)',\n",
       " 971: 'Cat on a Hot Tin Roof (1958)',\n",
       " 972: 'Last Time I Saw Paris, The (1954)',\n",
       " 973: 'Meet John Doe (1941)',\n",
       " 976: 'Farewell to Arms, A (1932)',\n",
       " 980: 'Yes, Madam (a.k.a. Police Assassins) (a.k.a. In the Line of Duty 2) (Huang gu shi jie) (1985)',\n",
       " 981: 'Dangerous Ground (1997)',\n",
       " 982: 'Picnic (1955)',\n",
       " 984: 'Pompatus of Love, The (1996)',\n",
       " 986: 'Fly Away Home (1996)',\n",
       " 987: 'Bliss (1997)',\n",
       " 988: 'Grace of My Heart (1996)',\n",
       " 990: 'Maximum Risk (1996)',\n",
       " 991: 'Michael Collins (1996)',\n",
       " 992: \"Rich Man's Wife, The (1996)\",\n",
       " 994: 'Big Night (1996)',\n",
       " 996: 'Last Man Standing (1996)',\n",
       " 997: 'Caught (1996)',\n",
       " 998: 'Set It Off (1996)',\n",
       " 999: '2 Days in the Valley (1996)',\n",
       " 1003: 'Extreme Measures (1996)',\n",
       " 1004: 'Glimmer Man, The (1996)',\n",
       " 1005: 'D3: The Mighty Ducks (1996)',\n",
       " 1006: 'Chamber, The (1996)',\n",
       " 1007: 'Apple Dumpling Gang, The (1975)',\n",
       " 1008: 'Davy Crockett, King of the Wild Frontier (1955)',\n",
       " 1009: 'Escape to Witch Mountain (1975)',\n",
       " 1010: 'Love Bug, The (1969)',\n",
       " 1011: 'Herbie Rides Again (1974)',\n",
       " 1012: 'Old Yeller (1957)',\n",
       " 1013: 'Parent Trap, The (1961)',\n",
       " 1014: 'Pollyanna (1960)',\n",
       " 1015: 'Homeward Bound: The Incredible Journey (1993)',\n",
       " 1016: 'Shaggy Dog, The (1959)',\n",
       " 1017: 'Swiss Family Robinson (1960)',\n",
       " 1018: 'That Darn Cat! (1965)',\n",
       " 1019: '20,000 Leagues Under the Sea (1954)',\n",
       " 1020: 'Cool Runnings (1993)',\n",
       " 1021: 'Angels in the Outfield (1994)',\n",
       " 1022: 'Cinderella (1950)',\n",
       " 1023: 'Winnie the Pooh and the Blustery Day (1968)',\n",
       " 1024: 'Three Caballeros, The (1945)',\n",
       " 1025: 'Sword in the Stone, The (1963)',\n",
       " 1026: 'So Dear to My Heart (1949)',\n",
       " 1027: 'Robin Hood: Prince of Thieves (1991)',\n",
       " 1028: 'Mary Poppins (1964)',\n",
       " 1029: 'Dumbo (1941)',\n",
       " 1030: \"Pete's Dragon (1977)\",\n",
       " 1031: 'Bedknobs and Broomsticks (1971)',\n",
       " 1032: 'Alice in Wonderland (1951)',\n",
       " 1033: 'Fox and the Hound, The (1981)',\n",
       " 1034: 'Freeway (1996)',\n",
       " 1035: 'Sound of Music, The (1965)',\n",
       " 1036: 'Die Hard (1988)',\n",
       " 1037: 'Lawnmower Man, The (1992)',\n",
       " 1040: 'Secret Agent, The (1996)',\n",
       " 1041: 'Secrets & Lies (1996)',\n",
       " 1042: 'That Thing You Do! (1996)',\n",
       " 1043: 'To Gillian on Her 37th Birthday (1996)',\n",
       " 1044: 'Surviving Picasso (1996)',\n",
       " 1046: 'Beautiful Thing (1996)',\n",
       " 1047: 'Long Kiss Goodnight, The (1996)',\n",
       " 1049: 'Ghost and the Darkness, The (1996)',\n",
       " 1050: 'Looking for Richard (1996)',\n",
       " 1051: 'Trees Lounge (1996)',\n",
       " 1053: 'Normal Life (1996)',\n",
       " 1054: 'Get on the Bus (1996)',\n",
       " 1055: 'Shadow Conspiracy (1997)',\n",
       " 1056: 'Jude (1996)',\n",
       " 1057: 'Everyone Says I Love You (1996)',\n",
       " 1059: \"William Shakespeare's Romeo + Juliet (1996)\",\n",
       " 1060: 'Swingers (1996)',\n",
       " 1061: 'Sleepers (1996)',\n",
       " 1063: 'Johns (1996)',\n",
       " 1064: 'Aladdin and the King of Thieves (1996)',\n",
       " 1066: 'Shall We Dance (1937)',\n",
       " 1067: 'Damsel in Distress, A (1937)',\n",
       " 1068: 'Crossfire (1947)',\n",
       " 1069: 'Murder, My Sweet (1944)',\n",
       " 1073: 'Willy Wonka & the Chocolate Factory (1971)',\n",
       " 1076: 'Innocents, The (1961)',\n",
       " 1077: 'Sleeper (1973)',\n",
       " 1078: 'Bananas (1971)',\n",
       " 1079: 'Fish Called Wanda, A (1988)',\n",
       " 1080: \"Monty Python's Life of Brian (1979)\",\n",
       " 1081: 'Victor/Victoria (1982)',\n",
       " 1082: 'Candidate, The (1972)',\n",
       " 1083: 'Great Race, The (1965)',\n",
       " 1084: 'Bonnie and Clyde (1967)',\n",
       " 1085: 'Old Man and the Sea, The (1958)',\n",
       " 1086: 'Dial M for Murder (1954)',\n",
       " 1087: 'Madame Butterfly (1995)',\n",
       " 1088: 'Dirty Dancing (1987)',\n",
       " 1089: 'Reservoir Dogs (1992)',\n",
       " 1090: 'Platoon (1986)',\n",
       " 1091: \"Weekend at Bernie's (1989)\",\n",
       " 1092: 'Basic Instinct (1992)',\n",
       " 1093: 'Doors, The (1991)',\n",
       " 1094: 'Crying Game, The (1992)',\n",
       " 1095: 'Glengarry Glen Ross (1992)',\n",
       " 1096: \"Sophie's Choice (1982)\",\n",
       " 1097: 'E.T. the Extra-Terrestrial (1982)',\n",
       " 1099: 'Christmas Carol, A (1938)',\n",
       " 1100: 'Days of Thunder (1990)',\n",
       " 1101: 'Top Gun (1986)',\n",
       " 1103: 'Rebel Without a Cause (1955)',\n",
       " 1104: 'Streetcar Named Desire, A (1951)',\n",
       " 1105: 'Children of the Corn IV: The Gathering (1996)',\n",
       " 1111: \"Microcosmos (Microcosmos: Le peuple de l'herbe) (1996)\",\n",
       " 1112: 'Palookaville (1996)',\n",
       " 1113: 'Associate, The (1996)',\n",
       " 1114: 'Funeral, The (1996)',\n",
       " 1120: 'People vs. Larry Flynt, The (1996)',\n",
       " 1123: 'Perfect Candidate, A (1996)',\n",
       " 1124: 'On Golden Pond (1981)',\n",
       " 1125: 'Return of the Pink Panther, The (1975)',\n",
       " 1126: 'Drop Dead Fred (1991)',\n",
       " 1127: 'Abyss, The (1989)',\n",
       " 1128: 'Fog, The (1980)',\n",
       " 1129: 'Escape from New York (1981)',\n",
       " 1130: 'Howling, The (1980)',\n",
       " 1131: 'Jean de Florette (1986)',\n",
       " 1132: 'Manon of the Spring (Manon des sources) (1986)',\n",
       " 1133: 'Talking About Sex (1994)',\n",
       " 1135: 'Private Benjamin (1980)',\n",
       " 1136: 'Monty Python and the Holy Grail (1975)',\n",
       " 1137: 'Hustler White (1996)',\n",
       " 1145: 'Snowriders (1996)',\n",
       " 1147: 'When We Were Kings (1996)',\n",
       " 1148: 'Wallace & Gromit: The Wrong Trousers (1993)',\n",
       " 1150: 'Return of Martin Guerre, The (Retour de Martin Guerre, Le) (1982)',\n",
       " 1151: 'Lesson Faust (1994)',\n",
       " 1152: 'He Walked by Night (1948)',\n",
       " 1153: 'Raw Deal (1948)',\n",
       " 1154: 'T-Men (1947)',\n",
       " 1161: 'Tin Drum, The (Blechtrommel, Die) (1979)',\n",
       " 1162: 'Ruling Class, The (1972)',\n",
       " 1163: 'Mina Tannenbaum (1994)',\n",
       " 1164: \"2 ou 3 choses que je sais d'elle (2 or 3 Things I Know About Her) (1967)\",\n",
       " 1165: 'Bloody Child, The (1996)',\n",
       " 1167: 'Dear God (1996)',\n",
       " 1168: 'Bad Moon (1996)',\n",
       " 1169: 'American Dream (1990)',\n",
       " 1171: 'Bob Roberts (1992)',\n",
       " 1172: 'Cinema Paradiso (Nuovo cinema Paradiso) (1989)',\n",
       " 1173: 'Cook the Thief His Wife & Her Lover, The (1989)',\n",
       " 1174: 'Dead Tired (Grosse Fatigue) (1994)',\n",
       " 1175: 'Delicatessen (1991)',\n",
       " 1176: 'Double Life of Veronique, The (Double Vie de Véronique, La) (1991)',\n",
       " 1177: 'Enchanted April (1992)',\n",
       " 1178: 'Paths of Glory (1957)',\n",
       " 1179: 'Grifters, The (1990)',\n",
       " 1180: 'Hear My Song (1991)',\n",
       " 1181: 'Shooter, The (1997)',\n",
       " 1183: 'English Patient, The (1996)',\n",
       " 1184: 'Mediterraneo (1991)',\n",
       " 1185: 'My Left Foot (1989)',\n",
       " 1186: 'Sex, Lies, and Videotape (1989)',\n",
       " 1187: 'Passion Fish (1992)',\n",
       " 1188: 'Strictly Ballroom (1992)',\n",
       " 1189: 'Thin Blue Line, The (1988)',\n",
       " 1190: 'Tie Me Up! Tie Me Down! (¡Átame!) (1990)',\n",
       " 1191: 'Madonna: Truth or Dare (1991)',\n",
       " 1192: 'Paris Is Burning (1990)',\n",
       " 1193: \"One Flew Over the Cuckoo's Nest (1975)\",\n",
       " 1194: \"Cheech and Chong's Up in Smoke (1978)\",\n",
       " 1196: 'Star Wars: Episode V - The Empire Strikes Back (1980)',\n",
       " 1197: 'Princess Bride, The (1987)',\n",
       " 1198: 'Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)',\n",
       " 1199: 'Brazil (1985)',\n",
       " 1200: 'Aliens (1986)',\n",
       " 1201: 'Good, the Bad and the Ugly, The (Buono, il brutto, il cattivo, Il) (1966)',\n",
       " 1202: 'Withnail & I (1987)',\n",
       " 1203: '12 Angry Men (1957)',\n",
       " 1204: 'Lawrence of Arabia (1962)',\n",
       " 1206: 'Clockwork Orange, A (1971)',\n",
       " 1207: 'To Kill a Mockingbird (1962)',\n",
       " 1208: 'Apocalypse Now (1979)',\n",
       " 1209: \"Once Upon a Time in the West (C'era una volta il West) (1968)\",\n",
       " 1210: 'Star Wars: Episode VI - Return of the Jedi (1983)',\n",
       " 1211: 'Wings of Desire (Himmel über Berlin, Der) (1987)',\n",
       " 1212: 'Third Man, The (1949)',\n",
       " 1213: 'Goodfellas (1990)',\n",
       " 1214: 'Alien (1979)',\n",
       " 1215: 'Army of Darkness (1993)',\n",
       " 1216: 'Big Blue, The (Grand bleu, Le) (1988)',\n",
       " 1217: 'Ran (1985)',\n",
       " 1218: 'Killer, The (Die xue shuang xiong) (1989)',\n",
       " 1219: 'Psycho (1960)',\n",
       " 1220: 'Blues Brothers, The (1980)',\n",
       " 1221: 'Godfather: Part II, The (1974)',\n",
       " 1222: 'Full Metal Jacket (1987)',\n",
       " 1223: 'Grand Day Out with Wallace and Gromit, A (1989)',\n",
       " 1224: 'Henry V (1989)',\n",
       " 1225: 'Amadeus (1984)',\n",
       " 1226: 'Quiet Man, The (1952)',\n",
       " 1227: 'Once Upon a Time in America (1984)',\n",
       " 1228: 'Raging Bull (1980)',\n",
       " 1230: 'Annie Hall (1977)',\n",
       " 1231: 'Right Stuff, The (1983)',\n",
       " 1232: 'Stalker (1979)',\n",
       " 1233: 'Boot, Das (Boat, The) (1981)',\n",
       " 1234: 'Sting, The (1973)',\n",
       " 1235: 'Harold and Maude (1971)',\n",
       " 1236: 'Trust (1990)',\n",
       " 1237: 'Seventh Seal, The (Sjunde inseglet, Det) (1957)',\n",
       " 1238: 'Local Hero (1983)',\n",
       " 1240: 'Terminator, The (1984)',\n",
       " 1241: 'Dead Alive (Braindead) (1992)',\n",
       " 1242: 'Glory (1989)',\n",
       " 1243: 'Rosencrantz and Guildenstern Are Dead (1990)',\n",
       " 1244: 'Manhattan (1979)',\n",
       " 1245: \"Miller's Crossing (1990)\",\n",
       " ...}"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mid2title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_watched[\"title\"] = most_watched[\"mid\"].apply(lambda x:mid2title[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "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>mid</th>\n",
       "      <th>counts</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>356</td>\n",
       "      <td>341</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>296</td>\n",
       "      <td>324</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>318</td>\n",
       "      <td>311</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>593</td>\n",
       "      <td>304</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>260</td>\n",
       "      <td>291</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>480</td>\n",
       "      <td>274</td>\n",
       "      <td>Jurassic Park (1993)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2571</td>\n",
       "      <td>259</td>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>247</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>527</td>\n",
       "      <td>244</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>589</td>\n",
       "      <td>237</td>\n",
       "      <td>Terminator 2: Judgment Day (1991)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1196</td>\n",
       "      <td>234</td>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>110</td>\n",
       "      <td>228</td>\n",
       "      <td>Braveheart (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1270</td>\n",
       "      <td>226</td>\n",
       "      <td>Back to the Future (1985)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>608</td>\n",
       "      <td>224</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2858</td>\n",
       "      <td>220</td>\n",
       "      <td>American Beauty (1999)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mid  counts                                              title\n",
       "0    356     341                                Forrest Gump (1994)\n",
       "1    296     324                                Pulp Fiction (1994)\n",
       "2    318     311                   Shawshank Redemption, The (1994)\n",
       "3    593     304                   Silence of the Lambs, The (1991)\n",
       "4    260     291          Star Wars: Episode IV - A New Hope (1977)\n",
       "5    480     274                               Jurassic Park (1993)\n",
       "6   2571     259                                 Matrix, The (1999)\n",
       "7      1     247                                   Toy Story (1995)\n",
       "8    527     244                            Schindler's List (1993)\n",
       "9    589     237                  Terminator 2: Judgment Day (1991)\n",
       "10  1196     234  Star Wars: Episode V - The Empire Strikes Back...\n",
       "11   110     228                                  Braveheart (1995)\n",
       "12  1270     226                          Back to the Future (1985)\n",
       "13   608     224                                       Fargo (1996)\n",
       "14  2858     220                             American Beauty (1999)"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_watched.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_watched_ebd_idx = most_watched[\"mid\"].apply(lambda x:m2i[x]).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_watched[\"vec_1\"] = movie_3d[most_watched_ebd_idx][:,0]\n",
    "most_watched[\"vec_2\"] = movie_3d[most_watched_ebd_idx][:,1]\n",
    "most_watched[\"vec_3\"] = movie_3d[most_watched_ebd_idx][:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "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>mid</th>\n",
       "      <th>counts</th>\n",
       "      <th>title</th>\n",
       "      <th>vec_1</th>\n",
       "      <th>vec_2</th>\n",
       "      <th>vec_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>508</td>\n",
       "      <td>86</td>\n",
       "      <td>Philadelphia (1993)</td>\n",
       "      <td>-0.064901</td>\n",
       "      <td>0.020036</td>\n",
       "      <td>-0.008784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1961</td>\n",
       "      <td>119</td>\n",
       "      <td>Rain Man (1988)</td>\n",
       "      <td>-0.064252</td>\n",
       "      <td>-0.017032</td>\n",
       "      <td>-0.047262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>903</td>\n",
       "      <td>69</td>\n",
       "      <td>Vertigo (1958)</td>\n",
       "      <td>-0.062546</td>\n",
       "      <td>-0.009131</td>\n",
       "      <td>0.005182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1258</td>\n",
       "      <td>101</td>\n",
       "      <td>Shining, The (1980)</td>\n",
       "      <td>-0.057882</td>\n",
       "      <td>0.027212</td>\n",
       "      <td>-0.017096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>1028</td>\n",
       "      <td>78</td>\n",
       "      <td>Mary Poppins (1964)</td>\n",
       "      <td>-0.055210</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>0.006531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1625</td>\n",
       "      <td>76</td>\n",
       "      <td>Game, The (1997)</td>\n",
       "      <td>-0.052415</td>\n",
       "      <td>-0.008257</td>\n",
       "      <td>-0.025971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2</td>\n",
       "      <td>107</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>-0.051661</td>\n",
       "      <td>0.008588</td>\n",
       "      <td>0.026080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>590</td>\n",
       "      <td>202</td>\n",
       "      <td>Dances with Wolves (1990)</td>\n",
       "      <td>-0.051624</td>\n",
       "      <td>-0.000504</td>\n",
       "      <td>-0.005979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>4226</td>\n",
       "      <td>132</td>\n",
       "      <td>Memento (2000)</td>\n",
       "      <td>-0.047003</td>\n",
       "      <td>0.017175</td>\n",
       "      <td>0.007325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1079</td>\n",
       "      <td>89</td>\n",
       "      <td>Fish Called Wanda, A (1988)</td>\n",
       "      <td>-0.046228</td>\n",
       "      <td>-0.020493</td>\n",
       "      <td>-0.033207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1089</td>\n",
       "      <td>132</td>\n",
       "      <td>Reservoir Dogs (1992)</td>\n",
       "      <td>-0.044298</td>\n",
       "      <td>-0.042439</td>\n",
       "      <td>0.009943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2028</td>\n",
       "      <td>191</td>\n",
       "      <td>Saving Private Ryan (1998)</td>\n",
       "      <td>-0.040851</td>\n",
       "      <td>0.020816</td>\n",
       "      <td>0.016023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>593</td>\n",
       "      <td>304</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>-0.040243</td>\n",
       "      <td>-0.039159</td>\n",
       "      <td>0.034969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>-0.039936</td>\n",
       "      <td>-0.003541</td>\n",
       "      <td>0.007280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1407</td>\n",
       "      <td>73</td>\n",
       "      <td>Scream (1996)</td>\n",
       "      <td>-0.038877</td>\n",
       "      <td>-0.045003</td>\n",
       "      <td>-0.026032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1259</td>\n",
       "      <td>112</td>\n",
       "      <td>Stand by Me (1986)</td>\n",
       "      <td>-0.038298</td>\n",
       "      <td>-0.011914</td>\n",
       "      <td>-0.022407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>1358</td>\n",
       "      <td>66</td>\n",
       "      <td>Sling Blade (1996)</td>\n",
       "      <td>-0.038271</td>\n",
       "      <td>-0.015899</td>\n",
       "      <td>-0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>4027</td>\n",
       "      <td>96</td>\n",
       "      <td>O Brother, Where Art Thou? (2000)</td>\n",
       "      <td>-0.037904</td>\n",
       "      <td>-0.008076</td>\n",
       "      <td>0.023101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>6377</td>\n",
       "      <td>122</td>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>-0.037734</td>\n",
       "      <td>0.037752</td>\n",
       "      <td>0.031624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1721</td>\n",
       "      <td>164</td>\n",
       "      <td>Titanic (1997)</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.029718</td>\n",
       "      <td>-0.027940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>442</td>\n",
       "      <td>95</td>\n",
       "      <td>Demolition Man (1993)</td>\n",
       "      <td>-0.035877</td>\n",
       "      <td>0.009768</td>\n",
       "      <td>0.017384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>454</td>\n",
       "      <td>110</td>\n",
       "      <td>Firm, The (1993)</td>\n",
       "      <td>-0.035534</td>\n",
       "      <td>-0.037616</td>\n",
       "      <td>-0.011030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5418</td>\n",
       "      <td>100</td>\n",
       "      <td>Bourne Identity, The (2002)</td>\n",
       "      <td>-0.035412</td>\n",
       "      <td>0.014292</td>\n",
       "      <td>-0.018449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1527</td>\n",
       "      <td>118</td>\n",
       "      <td>Fifth Element, The (1997)</td>\n",
       "      <td>-0.035342</td>\n",
       "      <td>0.029135</td>\n",
       "      <td>0.013153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1270</td>\n",
       "      <td>226</td>\n",
       "      <td>Back to the Future (1985)</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.022058</td>\n",
       "      <td>0.031957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>296</td>\n",
       "      <td>324</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>-0.035053</td>\n",
       "      <td>0.053959</td>\n",
       "      <td>0.021954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2174</td>\n",
       "      <td>104</td>\n",
       "      <td>Beetlejuice (1988)</td>\n",
       "      <td>-0.034805</td>\n",
       "      <td>-0.005558</td>\n",
       "      <td>-0.053593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>4896</td>\n",
       "      <td>105</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone (a.k.a. ...</td>\n",
       "      <td>-0.033940</td>\n",
       "      <td>0.016081</td>\n",
       "      <td>-0.017713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1230</td>\n",
       "      <td>80</td>\n",
       "      <td>Annie Hall (1977)</td>\n",
       "      <td>-0.033890</td>\n",
       "      <td>-0.016890</td>\n",
       "      <td>0.037617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1584</td>\n",
       "      <td>92</td>\n",
       "      <td>Contact (1997)</td>\n",
       "      <td>-0.033689</td>\n",
       "      <td>-0.030660</td>\n",
       "      <td>-0.016617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>161</td>\n",
       "      <td>80</td>\n",
       "      <td>Crimson Tide (1995)</td>\n",
       "      <td>0.036487</td>\n",
       "      <td>0.012929</td>\n",
       "      <td>0.034094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1097</td>\n",
       "      <td>160</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>0.037123</td>\n",
       "      <td>-0.017303</td>\n",
       "      <td>-0.005316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>5989</td>\n",
       "      <td>99</td>\n",
       "      <td>Catch Me If You Can (2002)</td>\n",
       "      <td>0.037845</td>\n",
       "      <td>0.021714</td>\n",
       "      <td>0.009158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1682</td>\n",
       "      <td>137</td>\n",
       "      <td>Truman Show, The (1998)</td>\n",
       "      <td>0.038027</td>\n",
       "      <td>0.055897</td>\n",
       "      <td>0.001822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1036</td>\n",
       "      <td>151</td>\n",
       "      <td>Die Hard (1988)</td>\n",
       "      <td>0.038211</td>\n",
       "      <td>0.027621</td>\n",
       "      <td>0.000862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>6016</td>\n",
       "      <td>69</td>\n",
       "      <td>City of God (Cidade de Deus) (2002)</td>\n",
       "      <td>0.039131</td>\n",
       "      <td>-0.020859</td>\n",
       "      <td>-0.014922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>780</td>\n",
       "      <td>218</td>\n",
       "      <td>Independence Day (a.k.a. ID4) (1996)</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.021458</td>\n",
       "      <td>-0.024229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>648</td>\n",
       "      <td>168</td>\n",
       "      <td>Mission: Impossible (1996)</td>\n",
       "      <td>0.041729</td>\n",
       "      <td>-0.036923</td>\n",
       "      <td>0.006806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>344</td>\n",
       "      <td>175</td>\n",
       "      <td>Ace Ventura: Pet Detective (1994)</td>\n",
       "      <td>0.042379</td>\n",
       "      <td>-0.014515</td>\n",
       "      <td>-0.013704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>594</td>\n",
       "      <td>71</td>\n",
       "      <td>Snow White and the Seven Dwarfs (1937)</td>\n",
       "      <td>0.042514</td>\n",
       "      <td>0.017075</td>\n",
       "      <td>0.005879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>16</td>\n",
       "      <td>88</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>0.043047</td>\n",
       "      <td>-0.005495</td>\n",
       "      <td>-0.026107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2858</td>\n",
       "      <td>220</td>\n",
       "      <td>American Beauty (1999)</td>\n",
       "      <td>0.043179</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>-0.016082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3578</td>\n",
       "      <td>161</td>\n",
       "      <td>Gladiator (2000)</td>\n",
       "      <td>0.043944</td>\n",
       "      <td>-0.004951</td>\n",
       "      <td>0.037815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>3949</td>\n",
       "      <td>69</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>0.046429</td>\n",
       "      <td>0.013529</td>\n",
       "      <td>0.003128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2542</td>\n",
       "      <td>74</td>\n",
       "      <td>Lock, Stock &amp; Two Smoking Barrels (1998)</td>\n",
       "      <td>0.047522</td>\n",
       "      <td>0.040110</td>\n",
       "      <td>-0.031752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1073</td>\n",
       "      <td>148</td>\n",
       "      <td>Willy Wonka &amp; the Chocolate Factory (1971)</td>\n",
       "      <td>0.048396</td>\n",
       "      <td>0.002313</td>\n",
       "      <td>-0.009590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>17</td>\n",
       "      <td>86</td>\n",
       "      <td>Sense and Sensibility (1995)</td>\n",
       "      <td>0.048765</td>\n",
       "      <td>0.015316</td>\n",
       "      <td>-0.049722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1196</td>\n",
       "      <td>234</td>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n",
       "      <td>0.051838</td>\n",
       "      <td>0.021919</td>\n",
       "      <td>0.021086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1517</td>\n",
       "      <td>107</td>\n",
       "      <td>Austin Powers: International Man of Mystery (1...</td>\n",
       "      <td>0.052353</td>\n",
       "      <td>-0.016959</td>\n",
       "      <td>0.024175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>1278</td>\n",
       "      <td>74</td>\n",
       "      <td>Young Frankenstein (1974)</td>\n",
       "      <td>0.057950</td>\n",
       "      <td>0.020931</td>\n",
       "      <td>-0.049185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>3977</td>\n",
       "      <td>79</td>\n",
       "      <td>Charlie's Angels (2000)</td>\n",
       "      <td>0.058200</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>-0.046252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2918</td>\n",
       "      <td>133</td>\n",
       "      <td>Ferris Bueller's Day Off (1986)</td>\n",
       "      <td>0.060227</td>\n",
       "      <td>0.011706</td>\n",
       "      <td>-0.011578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>440</td>\n",
       "      <td>90</td>\n",
       "      <td>Dave (1993)</td>\n",
       "      <td>0.061108</td>\n",
       "      <td>-0.015429</td>\n",
       "      <td>0.021205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1732</td>\n",
       "      <td>120</td>\n",
       "      <td>Big Lebowski, The (1998)</td>\n",
       "      <td>0.066197</td>\n",
       "      <td>0.036355</td>\n",
       "      <td>-0.054685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>778</td>\n",
       "      <td>124</td>\n",
       "      <td>Trainspotting (1996)</td>\n",
       "      <td>0.067248</td>\n",
       "      <td>-0.009262</td>\n",
       "      <td>-0.015278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1676</td>\n",
       "      <td>72</td>\n",
       "      <td>Starship Troopers (1997)</td>\n",
       "      <td>0.067311</td>\n",
       "      <td>0.002405</td>\n",
       "      <td>-0.002779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>3175</td>\n",
       "      <td>65</td>\n",
       "      <td>Galaxy Quest (1999)</td>\n",
       "      <td>0.075672</td>\n",
       "      <td>0.020371</td>\n",
       "      <td>-0.008864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>3147</td>\n",
       "      <td>103</td>\n",
       "      <td>Green Mile, The (1999)</td>\n",
       "      <td>0.078348</td>\n",
       "      <td>0.007206</td>\n",
       "      <td>-0.003115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1639</td>\n",
       "      <td>65</td>\n",
       "      <td>Chasing Amy (1997)</td>\n",
       "      <td>0.084180</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>-0.010945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>3623</td>\n",
       "      <td>81</td>\n",
       "      <td>Mission: Impossible II (2000)</td>\n",
       "      <td>0.105128</td>\n",
       "      <td>-0.002810</td>\n",
       "      <td>-0.004539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mid  counts                                              title  \\\n",
       "192   508      86                                Philadelphia (1993)   \n",
       "100  1961     119                                    Rain Man (1988)   \n",
       "273   903      69                                     Vertigo (1958)   \n",
       "147  1258     101                                Shining, The (1980)   \n",
       "226  1028      78                                Mary Poppins (1964)   \n",
       "229  1625      76                                   Game, The (1997)   \n",
       "127     2     107                                     Jumanji (1995)   \n",
       "21    590     202                          Dances with Wolves (1990)   \n",
       "72   4226     132                                     Memento (2000)   \n",
       "182  1079      89                        Fish Called Wanda, A (1988)   \n",
       "70   1089     132                              Reservoir Dogs (1992)   \n",
       "32   2028     191                         Saving Private Ryan (1998)   \n",
       "3     593     304                   Silence of the Lambs, The (1991)   \n",
       "92     10     122                                   GoldenEye (1995)   \n",
       "249  1407      73                                      Scream (1996)   \n",
       "114  1259     112                                 Stand by Me (1986)   \n",
       "291  1358      66                                 Sling Blade (1996)   \n",
       "159  4027      96                  O Brother, Where Art Thou? (2000)   \n",
       "94   6377     122                                Finding Nemo (2003)   \n",
       "42   1721     164                                     Titanic (1997)   \n",
       "164   442      95                              Demolition Man (1993)   \n",
       "120   454     110                                   Firm, The (1993)   \n",
       "150  5418     100                        Bourne Identity, The (2002)   \n",
       "102  1527     118                          Fifth Element, The (1997)   \n",
       "12   1270     226                          Back to the Future (1985)   \n",
       "1     296     324                                Pulp Fiction (1994)   \n",
       "139  2174     104                                 Beetlejuice (1988)   \n",
       "135  4896     105  Harry Potter and the Sorcerer's Stone (a.k.a. ...   \n",
       "211  1230      80                                  Annie Hall (1977)   \n",
       "171  1584      92                                     Contact (1997)   \n",
       "..    ...     ...                                                ...   \n",
       "216   161      80                                Crimson Tide (1995)   \n",
       "45   1097     160                  E.T. the Extra-Terrestrial (1982)   \n",
       "151  5989      99                         Catch Me If You Can (2002)   \n",
       "65   1682     137                            Truman Show, The (1998)   \n",
       "51   1036     151                                    Die Hard (1988)   \n",
       "277  6016      69                City of God (Cidade de Deus) (2002)   \n",
       "16    780     218               Independence Day (a.k.a. ID4) (1996)   \n",
       "40    648     168                         Mission: Impossible (1996)   \n",
       "38    344     175                  Ace Ventura: Pet Detective (1994)   \n",
       "263   594      71             Snow White and the Seven Dwarfs (1937)   \n",
       "183    16      88                                      Casino (1995)   \n",
       "14   2858     220                             American Beauty (1999)   \n",
       "44   3578     161                                   Gladiator (2000)   \n",
       "275  3949      69                         Requiem for a Dream (2000)   \n",
       "245  2542      74           Lock, Stock & Two Smoking Barrels (1998)   \n",
       "54   1073     148         Willy Wonka & the Chocolate Factory (1971)   \n",
       "190    17      86                       Sense and Sensibility (1995)   \n",
       "10   1196     234  Star Wars: Episode V - The Empire Strikes Back...   \n",
       "124  1517     107  Austin Powers: International Man of Mystery (1...   \n",
       "244  1278      74                          Young Frankenstein (1974)   \n",
       "220  3977      79                            Charlie's Angels (2000)   \n",
       "69   2918     133                    Ferris Bueller's Day Off (1986)   \n",
       "175   440      90                                        Dave (1993)   \n",
       "99   1732     120                           Big Lebowski, The (1998)   \n",
       "89    778     124                               Trainspotting (1996)   \n",
       "258  1676      72                           Starship Troopers (1997)   \n",
       "293  3175      65                                Galaxy Quest (1999)   \n",
       "144  3147     103                             Green Mile, The (1999)   \n",
       "298  1639      65                                 Chasing Amy (1997)   \n",
       "207  3623      81                      Mission: Impossible II (2000)   \n",
       "\n",
       "        vec_1     vec_2     vec_3  \n",
       "192 -0.064901  0.020036 -0.008784  \n",
       "100 -0.064252 -0.017032 -0.047262  \n",
       "273 -0.062546 -0.009131  0.005182  \n",
       "147 -0.057882  0.027212 -0.017096  \n",
       "226 -0.055210  0.001238  0.006531  \n",
       "229 -0.052415 -0.008257 -0.025971  \n",
       "127 -0.051661  0.008588  0.026080  \n",
       "21  -0.051624 -0.000504 -0.005979  \n",
       "72  -0.047003  0.017175  0.007325  \n",
       "182 -0.046228 -0.020493 -0.033207  \n",
       "70  -0.044298 -0.042439  0.009943  \n",
       "32  -0.040851  0.020816  0.016023  \n",
       "3   -0.040243 -0.039159  0.034969  \n",
       "92  -0.039936 -0.003541  0.007280  \n",
       "249 -0.038877 -0.045003 -0.026032  \n",
       "114 -0.038298 -0.011914 -0.022407  \n",
       "291 -0.038271 -0.015899 -0.000087  \n",
       "159 -0.037904 -0.008076  0.023101  \n",
       "94  -0.037734  0.037752  0.031624  \n",
       "42  -0.037059 -0.029718 -0.027940  \n",
       "164 -0.035877  0.009768  0.017384  \n",
       "120 -0.035534 -0.037616 -0.011030  \n",
       "150 -0.035412  0.014292 -0.018449  \n",
       "102 -0.035342  0.029135  0.013153  \n",
       "12  -0.035144  0.022058  0.031957  \n",
       "1   -0.035053  0.053959  0.021954  \n",
       "139 -0.034805 -0.005558 -0.053593  \n",
       "135 -0.033940  0.016081 -0.017713  \n",
       "211 -0.033890 -0.016890  0.037617  \n",
       "171 -0.033689 -0.030660 -0.016617  \n",
       "..        ...       ...       ...  \n",
       "216  0.036487  0.012929  0.034094  \n",
       "45   0.037123 -0.017303 -0.005316  \n",
       "151  0.037845  0.021714  0.009158  \n",
       "65   0.038027  0.055897  0.001822  \n",
       "51   0.038211  0.027621  0.000862  \n",
       "277  0.039131 -0.020859 -0.014922  \n",
       "16   0.041122  0.021458 -0.024229  \n",
       "40   0.041729 -0.036923  0.006806  \n",
       "38   0.042379 -0.014515 -0.013704  \n",
       "263  0.042514  0.017075  0.005879  \n",
       "183  0.043047 -0.005495 -0.026107  \n",
       "14   0.043179  0.005815 -0.016082  \n",
       "44   0.043944 -0.004951  0.037815  \n",
       "275  0.046429  0.013529  0.003128  \n",
       "245  0.047522  0.040110 -0.031752  \n",
       "54   0.048396  0.002313 -0.009590  \n",
       "190  0.048765  0.015316 -0.049722  \n",
       "10   0.051838  0.021919  0.021086  \n",
       "124  0.052353 -0.016959  0.024175  \n",
       "244  0.057950  0.020931 -0.049185  \n",
       "220  0.058200  0.001085 -0.046252  \n",
       "69   0.060227  0.011706 -0.011578  \n",
       "175  0.061108 -0.015429  0.021205  \n",
       "99   0.066197  0.036355 -0.054685  \n",
       "89   0.067248 -0.009262 -0.015278  \n",
       "258  0.067311  0.002405 -0.002779  \n",
       "293  0.075672  0.020371 -0.008864  \n",
       "144  0.078348  0.007206 -0.003115  \n",
       "298  0.084180  0.007418 -0.010945  \n",
       "207  0.105128 -0.002810 -0.004539  \n",
       "\n",
       "[300 rows x 6 columns]"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_watched.head(300).sort_values(by = \"vec_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "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>mid</th>\n",
       "      <th>counts</th>\n",
       "      <th>title</th>\n",
       "      <th>vec_1</th>\n",
       "      <th>vec_2</th>\n",
       "      <th>vec_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>368</td>\n",
       "      <td>71</td>\n",
       "      <td>Maverick (1994)</td>\n",
       "      <td>-0.019251</td>\n",
       "      <td>-0.081542</td>\n",
       "      <td>-0.007033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1391</td>\n",
       "      <td>80</td>\n",
       "      <td>Mars Attacks! (1996)</td>\n",
       "      <td>-0.012707</td>\n",
       "      <td>-0.080271</td>\n",
       "      <td>0.051152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2987</td>\n",
       "      <td>108</td>\n",
       "      <td>Who Framed Roger Rabbit? (1988)</td>\n",
       "      <td>0.015495</td>\n",
       "      <td>-0.075618</td>\n",
       "      <td>-0.023854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2395</td>\n",
       "      <td>68</td>\n",
       "      <td>Rushmore (1998)</td>\n",
       "      <td>0.013302</td>\n",
       "      <td>-0.068364</td>\n",
       "      <td>0.015651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>208</td>\n",
       "      <td>113</td>\n",
       "      <td>Waterworld (1995)</td>\n",
       "      <td>-0.006537</td>\n",
       "      <td>-0.057502</td>\n",
       "      <td>0.011053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1101</td>\n",
       "      <td>74</td>\n",
       "      <td>Top Gun (1986)</td>\n",
       "      <td>0.003654</td>\n",
       "      <td>-0.052434</td>\n",
       "      <td>-0.029896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2571</td>\n",
       "      <td>259</td>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>-0.004821</td>\n",
       "      <td>-0.051701</td>\n",
       "      <td>0.029076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>141</td>\n",
       "      <td>93</td>\n",
       "      <td>Birdcage, The (1996)</td>\n",
       "      <td>0.035209</td>\n",
       "      <td>-0.050537</td>\n",
       "      <td>-0.053623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>165</td>\n",
       "      <td>142</td>\n",
       "      <td>Die Hard: With a Vengeance (1995)</td>\n",
       "      <td>0.002055</td>\n",
       "      <td>-0.047892</td>\n",
       "      <td>-0.044821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2762</td>\n",
       "      <td>193</td>\n",
       "      <td>Sixth Sense, The (1999)</td>\n",
       "      <td>-0.002274</td>\n",
       "      <td>-0.047302</td>\n",
       "      <td>-0.000830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>235</td>\n",
       "      <td>78</td>\n",
       "      <td>Ed Wood (1994)</td>\n",
       "      <td>-0.020335</td>\n",
       "      <td>-0.045155</td>\n",
       "      <td>0.012206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1407</td>\n",
       "      <td>73</td>\n",
       "      <td>Scream (1996)</td>\n",
       "      <td>-0.038877</td>\n",
       "      <td>-0.045003</td>\n",
       "      <td>-0.026032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>36</td>\n",
       "      <td>104</td>\n",
       "      <td>Dead Man Walking (1995)</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>-0.044314</td>\n",
       "      <td>0.046912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1089</td>\n",
       "      <td>132</td>\n",
       "      <td>Reservoir Dogs (1992)</td>\n",
       "      <td>-0.044298</td>\n",
       "      <td>-0.042439</td>\n",
       "      <td>0.009943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>6365</td>\n",
       "      <td>82</td>\n",
       "      <td>Matrix Reloaded, The (2003)</td>\n",
       "      <td>-0.024057</td>\n",
       "      <td>-0.040628</td>\n",
       "      <td>-0.000779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>5816</td>\n",
       "      <td>81</td>\n",
       "      <td>Harry Potter and the Chamber of Secrets (2002)</td>\n",
       "      <td>0.011354</td>\n",
       "      <td>-0.040224</td>\n",
       "      <td>-0.017147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>2406</td>\n",
       "      <td>65</td>\n",
       "      <td>Romancing the Stone (1984)</td>\n",
       "      <td>0.010391</td>\n",
       "      <td>-0.039708</td>\n",
       "      <td>-0.014121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>47</td>\n",
       "      <td>201</td>\n",
       "      <td>Seven (a.k.a. Se7en) (1995)</td>\n",
       "      <td>0.028608</td>\n",
       "      <td>-0.039513</td>\n",
       "      <td>-0.045923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1393</td>\n",
       "      <td>106</td>\n",
       "      <td>Jerry Maguire (1996)</td>\n",
       "      <td>-0.010601</td>\n",
       "      <td>-0.039364</td>\n",
       "      <td>0.011430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>593</td>\n",
       "      <td>304</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>-0.040243</td>\n",
       "      <td>-0.039159</td>\n",
       "      <td>0.034969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>454</td>\n",
       "      <td>110</td>\n",
       "      <td>Firm, The (1993)</td>\n",
       "      <td>-0.035534</td>\n",
       "      <td>-0.037616</td>\n",
       "      <td>-0.011030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>6333</td>\n",
       "      <td>81</td>\n",
       "      <td>X2: X-Men United (2003)</td>\n",
       "      <td>-0.017739</td>\n",
       "      <td>-0.037600</td>\n",
       "      <td>0.014424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7153</td>\n",
       "      <td>176</td>\n",
       "      <td>Lord of the Rings: The Return of the King, The...</td>\n",
       "      <td>0.011489</td>\n",
       "      <td>-0.037440</td>\n",
       "      <td>0.036492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>648</td>\n",
       "      <td>168</td>\n",
       "      <td>Mission: Impossible (1996)</td>\n",
       "      <td>0.041729</td>\n",
       "      <td>-0.036923</td>\n",
       "      <td>0.006806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1200</td>\n",
       "      <td>125</td>\n",
       "      <td>Aliens (1986)</td>\n",
       "      <td>0.012479</td>\n",
       "      <td>-0.036668</td>\n",
       "      <td>-0.007642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2797</td>\n",
       "      <td>107</td>\n",
       "      <td>Big (1988)</td>\n",
       "      <td>0.006120</td>\n",
       "      <td>-0.035350</td>\n",
       "      <td>0.009346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>48516</td>\n",
       "      <td>84</td>\n",
       "      <td>Departed, The (2006)</td>\n",
       "      <td>-0.007310</td>\n",
       "      <td>-0.033603</td>\n",
       "      <td>0.030520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>509</td>\n",
       "      <td>78</td>\n",
       "      <td>Piano, The (1993)</td>\n",
       "      <td>0.009791</td>\n",
       "      <td>-0.032442</td>\n",
       "      <td>-0.017594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>953</td>\n",
       "      <td>72</td>\n",
       "      <td>It's a Wonderful Life (1946)</td>\n",
       "      <td>-0.007999</td>\n",
       "      <td>-0.032220</td>\n",
       "      <td>-0.022914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>5378</td>\n",
       "      <td>82</td>\n",
       "      <td>Star Wars: Episode II - Attack of the Clones (...</td>\n",
       "      <td>0.022232</td>\n",
       "      <td>-0.032098</td>\n",
       "      <td>0.000776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4963</td>\n",
       "      <td>130</td>\n",
       "      <td>Ocean's Eleven (2001)</td>\n",
       "      <td>-0.003114</td>\n",
       "      <td>0.036968</td>\n",
       "      <td>0.078091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>910</td>\n",
       "      <td>66</td>\n",
       "      <td>Some Like It Hot (1959)</td>\n",
       "      <td>-0.002676</td>\n",
       "      <td>0.036975</td>\n",
       "      <td>0.033701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>1544</td>\n",
       "      <td>67</td>\n",
       "      <td>Lost World: Jurassic Park, The (1997)</td>\n",
       "      <td>-0.033354</td>\n",
       "      <td>0.037269</td>\n",
       "      <td>0.005270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>6377</td>\n",
       "      <td>122</td>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>-0.037734</td>\n",
       "      <td>0.037752</td>\n",
       "      <td>0.031624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2640</td>\n",
       "      <td>75</td>\n",
       "      <td>Superman (1978)</td>\n",
       "      <td>-0.026832</td>\n",
       "      <td>0.037760</td>\n",
       "      <td>0.011586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>19</td>\n",
       "      <td>92</td>\n",
       "      <td>Ace Ventura: When Nature Calls (1995)</td>\n",
       "      <td>-0.001990</td>\n",
       "      <td>0.039373</td>\n",
       "      <td>-0.011534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2542</td>\n",
       "      <td>74</td>\n",
       "      <td>Lock, Stock &amp; Two Smoking Barrels (1998)</td>\n",
       "      <td>0.047522</td>\n",
       "      <td>0.040110</td>\n",
       "      <td>-0.031752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2692</td>\n",
       "      <td>80</td>\n",
       "      <td>Run Lola Run (Lola rennt) (1998)</td>\n",
       "      <td>0.006406</td>\n",
       "      <td>0.040332</td>\n",
       "      <td>0.036717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1387</td>\n",
       "      <td>108</td>\n",
       "      <td>Jaws (1975)</td>\n",
       "      <td>-0.003989</td>\n",
       "      <td>0.040995</td>\n",
       "      <td>-0.011278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "      <td>Mr. Holland's Opus (1995)</td>\n",
       "      <td>-0.014462</td>\n",
       "      <td>0.041079</td>\n",
       "      <td>-0.006090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>3408</td>\n",
       "      <td>85</td>\n",
       "      <td>Erin Brockovich (2000)</td>\n",
       "      <td>-0.007261</td>\n",
       "      <td>0.041125</td>\n",
       "      <td>-0.001977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>924</td>\n",
       "      <td>123</td>\n",
       "      <td>2001: A Space Odyssey (1968)</td>\n",
       "      <td>-0.001334</td>\n",
       "      <td>0.041748</td>\n",
       "      <td>-0.029961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4306</td>\n",
       "      <td>174</td>\n",
       "      <td>Shrek (2001)</td>\n",
       "      <td>-0.020349</td>\n",
       "      <td>0.043168</td>\n",
       "      <td>0.015726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>44191</td>\n",
       "      <td>73</td>\n",
       "      <td>V for Vendetta (2006)</td>\n",
       "      <td>0.033977</td>\n",
       "      <td>0.044460</td>\n",
       "      <td>-0.038937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2916</td>\n",
       "      <td>104</td>\n",
       "      <td>Total Recall (1990)</td>\n",
       "      <td>0.007339</td>\n",
       "      <td>0.044950</td>\n",
       "      <td>0.016176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>293</td>\n",
       "      <td>132</td>\n",
       "      <td>Léon: The Professional (a.k.a. The Professiona...</td>\n",
       "      <td>0.019084</td>\n",
       "      <td>0.045221</td>\n",
       "      <td>-0.039164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>185</td>\n",
       "      <td>102</td>\n",
       "      <td>Net, The (1995)</td>\n",
       "      <td>-0.002753</td>\n",
       "      <td>0.045515</td>\n",
       "      <td>0.008392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1608</td>\n",
       "      <td>70</td>\n",
       "      <td>Air Force One (1997)</td>\n",
       "      <td>-0.025653</td>\n",
       "      <td>0.049093</td>\n",
       "      <td>0.013789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2617</td>\n",
       "      <td>79</td>\n",
       "      <td>Mummy, The (1999)</td>\n",
       "      <td>-0.013756</td>\n",
       "      <td>0.049606</td>\n",
       "      <td>0.010179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>3753</td>\n",
       "      <td>65</td>\n",
       "      <td>Patriot, The (2000)</td>\n",
       "      <td>-0.024561</td>\n",
       "      <td>0.050297</td>\n",
       "      <td>0.017741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2081</td>\n",
       "      <td>78</td>\n",
       "      <td>Little Mermaid, The (1989)</td>\n",
       "      <td>0.009697</td>\n",
       "      <td>0.051070</td>\n",
       "      <td>0.004122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>296</td>\n",
       "      <td>324</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>-0.035053</td>\n",
       "      <td>0.053959</td>\n",
       "      <td>0.021954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>5445</td>\n",
       "      <td>115</td>\n",
       "      <td>Minority Report (2002)</td>\n",
       "      <td>-0.014115</td>\n",
       "      <td>0.054659</td>\n",
       "      <td>0.014136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1682</td>\n",
       "      <td>137</td>\n",
       "      <td>Truman Show, The (1998)</td>\n",
       "      <td>0.038027</td>\n",
       "      <td>0.055897</td>\n",
       "      <td>0.001822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>50</td>\n",
       "      <td>201</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>0.012029</td>\n",
       "      <td>0.056519</td>\n",
       "      <td>-0.023161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2353</td>\n",
       "      <td>67</td>\n",
       "      <td>Enemy of the State (1998)</td>\n",
       "      <td>-0.032643</td>\n",
       "      <td>0.060004</td>\n",
       "      <td>-0.015365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>4973</td>\n",
       "      <td>125</td>\n",
       "      <td>Amelie (Fabuleux destin d'Amélie Poulain, Le) ...</td>\n",
       "      <td>0.028604</td>\n",
       "      <td>0.064371</td>\n",
       "      <td>-0.031726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>555</td>\n",
       "      <td>70</td>\n",
       "      <td>True Romance (1993)</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.064654</td>\n",
       "      <td>-0.002820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>520</td>\n",
       "      <td>65</td>\n",
       "      <td>Robin Hood: Men in Tights (1993)</td>\n",
       "      <td>-0.001288</td>\n",
       "      <td>0.066902</td>\n",
       "      <td>0.012566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>1090</td>\n",
       "      <td>68</td>\n",
       "      <td>Platoon (1986)</td>\n",
       "      <td>0.029444</td>\n",
       "      <td>0.069270</td>\n",
       "      <td>0.015836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mid  counts                                              title  \\\n",
       "259    368      71                                    Maverick (1994)   \n",
       "217   1391      80                               Mars Attacks! (1996)   \n",
       "122   2987     108                    Who Framed Roger Rabbit? (1988)   \n",
       "280   2395      68                                    Rushmore (1998)   \n",
       "112    208     113                                  Waterworld (1995)   \n",
       "248   1101      74                                     Top Gun (1986)   \n",
       "6     2571     259                                 Matrix, The (1999)   \n",
       "170    141      93                               Birdcage, The (1996)   \n",
       "62     165     142                  Die Hard: With a Vengeance (1995)   \n",
       "31    2762     193                            Sixth Sense, The (1999)   \n",
       "223    235      78                                     Ed Wood (1994)   \n",
       "249   1407      73                                      Scream (1996)   \n",
       "138     36     104                            Dead Man Walking (1995)   \n",
       "70    1089     132                              Reservoir Dogs (1992)   \n",
       "203   6365      82                        Matrix Reloaded, The (2003)   \n",
       "208   5816      81     Harry Potter and the Chamber of Secrets (2002)   \n",
       "297   2406      65                         Romancing the Stone (1984)   \n",
       "23      47     201                        Seven (a.k.a. Se7en) (1995)   \n",
       "132   1393     106                               Jerry Maguire (1996)   \n",
       "3      593     304                   Silence of the Lambs, The (1991)   \n",
       "120    454     110                                   Firm, The (1993)   \n",
       "206   6333      81                            X2: X-Men United (2003)   \n",
       "36    7153     176  Lord of the Rings: The Return of the King, The...   \n",
       "40     648     168                         Mission: Impossible (1996)   \n",
       "88    1200     125                                      Aliens (1986)   \n",
       "125   2797     107                                         Big (1988)   \n",
       "197  48516      84                               Departed, The (2006)   \n",
       "224    509      78                                  Piano, The (1993)   \n",
       "256    953      72                       It's a Wonderful Life (1946)   \n",
       "204   5378      82  Star Wars: Episode II - Attack of the Clones (...   \n",
       "..     ...     ...                                                ...   \n",
       "75    4963     130                              Ocean's Eleven (2001)   \n",
       "288    910      66                            Some Like It Hot (1959)   \n",
       "285   1544      67              Lost World: Jurassic Park, The (1997)   \n",
       "94    6377     122                                Finding Nemo (2003)   \n",
       "236   2640      75                                    Superman (1978)   \n",
       "172     19      92              Ace Ventura: When Nature Calls (1995)   \n",
       "245   2542      74           Lock, Stock & Two Smoking Barrels (1998)   \n",
       "214   2692      80                   Run Lola Run (Lola rennt) (1998)   \n",
       "123   1387     108                                        Jaws (1975)   \n",
       "188     62      87                          Mr. Holland's Opus (1995)   \n",
       "194   3408      85                             Erin Brockovich (2000)   \n",
       "91     924     123                       2001: A Space Odyssey (1968)   \n",
       "39    4306     174                                       Shrek (2001)   \n",
       "250  44191      73                              V for Vendetta (2006)   \n",
       "142   2916     104                                Total Recall (1990)   \n",
       "71     293     132  Léon: The Professional (a.k.a. The Professiona...   \n",
       "145    185     102                                    Net, The (1995)   \n",
       "264   1608      70                               Air Force One (1997)   \n",
       "221   2617      79                                  Mummy, The (1999)   \n",
       "299   3753      65                                Patriot, The (2000)   \n",
       "222   2081      78                         Little Mermaid, The (1989)   \n",
       "1      296     324                                Pulp Fiction (1994)   \n",
       "106   5445     115                             Minority Report (2002)   \n",
       "65    1682     137                            Truman Show, The (1998)   \n",
       "22      50     201                         Usual Suspects, The (1995)   \n",
       "284   2353      67                          Enemy of the State (1998)   \n",
       "87    4973     125  Amelie (Fabuleux destin d'Amélie Poulain, Le) ...   \n",
       "271    555      70                                True Romance (1993)   \n",
       "295    520      65                   Robin Hood: Men in Tights (1993)   \n",
       "281   1090      68                                     Platoon (1986)   \n",
       "\n",
       "        vec_1     vec_2     vec_3  \n",
       "259 -0.019251 -0.081542 -0.007033  \n",
       "217 -0.012707 -0.080271  0.051152  \n",
       "122  0.015495 -0.075618 -0.023854  \n",
       "280  0.013302 -0.068364  0.015651  \n",
       "112 -0.006537 -0.057502  0.011053  \n",
       "248  0.003654 -0.052434 -0.029896  \n",
       "6   -0.004821 -0.051701  0.029076  \n",
       "170  0.035209 -0.050537 -0.053623  \n",
       "62   0.002055 -0.047892 -0.044821  \n",
       "31  -0.002274 -0.047302 -0.000830  \n",
       "223 -0.020335 -0.045155  0.012206  \n",
       "249 -0.038877 -0.045003 -0.026032  \n",
       "138 -0.000213 -0.044314  0.046912  \n",
       "70  -0.044298 -0.042439  0.009943  \n",
       "203 -0.024057 -0.040628 -0.000779  \n",
       "208  0.011354 -0.040224 -0.017147  \n",
       "297  0.010391 -0.039708 -0.014121  \n",
       "23   0.028608 -0.039513 -0.045923  \n",
       "132 -0.010601 -0.039364  0.011430  \n",
       "3   -0.040243 -0.039159  0.034969  \n",
       "120 -0.035534 -0.037616 -0.011030  \n",
       "206 -0.017739 -0.037600  0.014424  \n",
       "36   0.011489 -0.037440  0.036492  \n",
       "40   0.041729 -0.036923  0.006806  \n",
       "88   0.012479 -0.036668 -0.007642  \n",
       "125  0.006120 -0.035350  0.009346  \n",
       "197 -0.007310 -0.033603  0.030520  \n",
       "224  0.009791 -0.032442 -0.017594  \n",
       "256 -0.007999 -0.032220 -0.022914  \n",
       "204  0.022232 -0.032098  0.000776  \n",
       "..        ...       ...       ...  \n",
       "75  -0.003114  0.036968  0.078091  \n",
       "288 -0.002676  0.036975  0.033701  \n",
       "285 -0.033354  0.037269  0.005270  \n",
       "94  -0.037734  0.037752  0.031624  \n",
       "236 -0.026832  0.037760  0.011586  \n",
       "172 -0.001990  0.039373 -0.011534  \n",
       "245  0.047522  0.040110 -0.031752  \n",
       "214  0.006406  0.040332  0.036717  \n",
       "123 -0.003989  0.040995 -0.011278  \n",
       "188 -0.014462  0.041079 -0.006090  \n",
       "194 -0.007261  0.041125 -0.001977  \n",
       "91  -0.001334  0.041748 -0.029961  \n",
       "39  -0.020349  0.043168  0.015726  \n",
       "250  0.033977  0.044460 -0.038937  \n",
       "142  0.007339  0.044950  0.016176  \n",
       "71   0.019084  0.045221 -0.039164  \n",
       "145 -0.002753  0.045515  0.008392  \n",
       "264 -0.025653  0.049093  0.013789  \n",
       "221 -0.013756  0.049606  0.010179  \n",
       "299 -0.024561  0.050297  0.017741  \n",
       "222  0.009697  0.051070  0.004122  \n",
       "1   -0.035053  0.053959  0.021954  \n",
       "106 -0.014115  0.054659  0.014136  \n",
       "65   0.038027  0.055897  0.001822  \n",
       "22   0.012029  0.056519 -0.023161  \n",
       "284 -0.032643  0.060004 -0.015365  \n",
       "87   0.028604  0.064371 -0.031726  \n",
       "271  0.021578  0.064654 -0.002820  \n",
       "295 -0.001288  0.066902  0.012566  \n",
       "281  0.029444  0.069270  0.015836  \n",
       "\n",
       "[300 rows x 6 columns]"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_watched.head(300).sort_values(by = \"vec_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "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>mid</th>\n",
       "      <th>counts</th>\n",
       "      <th>title</th>\n",
       "      <th>vec_1</th>\n",
       "      <th>vec_2</th>\n",
       "      <th>vec_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>231</td>\n",
       "      <td>158</td>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber) (1994)</td>\n",
       "      <td>0.021526</td>\n",
       "      <td>0.016344</td>\n",
       "      <td>-0.101643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7361</td>\n",
       "      <td>106</td>\n",
       "      <td>Eternal Sunshine of the Spotless Mind (2004)</td>\n",
       "      <td>-0.023263</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-0.066678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3751</td>\n",
       "      <td>99</td>\n",
       "      <td>Chicken Run (2000)</td>\n",
       "      <td>-0.019505</td>\n",
       "      <td>0.006631</td>\n",
       "      <td>-0.064933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>111</td>\n",
       "      <td>118</td>\n",
       "      <td>Taxi Driver (1976)</td>\n",
       "      <td>-0.004734</td>\n",
       "      <td>-0.025892</td>\n",
       "      <td>-0.061280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>8360</td>\n",
       "      <td>81</td>\n",
       "      <td>Shrek 2 (2004)</td>\n",
       "      <td>0.001541</td>\n",
       "      <td>0.016775</td>\n",
       "      <td>-0.058592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1732</td>\n",
       "      <td>120</td>\n",
       "      <td>Big Lebowski, The (1998)</td>\n",
       "      <td>0.066197</td>\n",
       "      <td>0.036355</td>\n",
       "      <td>-0.054685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>141</td>\n",
       "      <td>93</td>\n",
       "      <td>Birdcage, The (1996)</td>\n",
       "      <td>0.035209</td>\n",
       "      <td>-0.050537</td>\n",
       "      <td>-0.053623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2174</td>\n",
       "      <td>104</td>\n",
       "      <td>Beetlejuice (1988)</td>\n",
       "      <td>-0.034805</td>\n",
       "      <td>-0.005558</td>\n",
       "      <td>-0.053593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>17</td>\n",
       "      <td>86</td>\n",
       "      <td>Sense and Sensibility (1995)</td>\n",
       "      <td>0.048765</td>\n",
       "      <td>0.015316</td>\n",
       "      <td>-0.049722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>1278</td>\n",
       "      <td>74</td>\n",
       "      <td>Young Frankenstein (1974)</td>\n",
       "      <td>0.057950</td>\n",
       "      <td>0.020931</td>\n",
       "      <td>-0.049185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1968</td>\n",
       "      <td>117</td>\n",
       "      <td>Breakfast Club, The (1985)</td>\n",
       "      <td>-0.007819</td>\n",
       "      <td>-0.012261</td>\n",
       "      <td>-0.049089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1653</td>\n",
       "      <td>84</td>\n",
       "      <td>Gattaca (1997)</td>\n",
       "      <td>0.002591</td>\n",
       "      <td>-0.013589</td>\n",
       "      <td>-0.048862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6539</td>\n",
       "      <td>141</td>\n",
       "      <td>Pirates of the Caribbean: The Curse of the Bla...</td>\n",
       "      <td>0.022992</td>\n",
       "      <td>0.003835</td>\n",
       "      <td>-0.048284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1961</td>\n",
       "      <td>119</td>\n",
       "      <td>Rain Man (1988)</td>\n",
       "      <td>-0.064252</td>\n",
       "      <td>-0.017032</td>\n",
       "      <td>-0.047262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1080</td>\n",
       "      <td>91</td>\n",
       "      <td>Monty Python's Life of Brian (1979)</td>\n",
       "      <td>0.034982</td>\n",
       "      <td>-0.000241</td>\n",
       "      <td>-0.046313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>3977</td>\n",
       "      <td>79</td>\n",
       "      <td>Charlie's Angels (2000)</td>\n",
       "      <td>0.058200</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>-0.046252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>47</td>\n",
       "      <td>201</td>\n",
       "      <td>Seven (a.k.a. Se7en) (1995)</td>\n",
       "      <td>0.028608</td>\n",
       "      <td>-0.039513</td>\n",
       "      <td>-0.045923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>596</td>\n",
       "      <td>66</td>\n",
       "      <td>Pinocchio (1940)</td>\n",
       "      <td>-0.013365</td>\n",
       "      <td>-0.004760</td>\n",
       "      <td>-0.044832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>589</td>\n",
       "      <td>237</td>\n",
       "      <td>Terminator 2: Judgment Day (1991)</td>\n",
       "      <td>0.009400</td>\n",
       "      <td>-0.015977</td>\n",
       "      <td>-0.044822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>165</td>\n",
       "      <td>142</td>\n",
       "      <td>Die Hard: With a Vengeance (1995)</td>\n",
       "      <td>0.002055</td>\n",
       "      <td>-0.047892</td>\n",
       "      <td>-0.044821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>8665</td>\n",
       "      <td>74</td>\n",
       "      <td>Bourne Supremacy, The (2004)</td>\n",
       "      <td>-0.027470</td>\n",
       "      <td>0.007433</td>\n",
       "      <td>-0.044307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>4246</td>\n",
       "      <td>71</td>\n",
       "      <td>Bridget Jones's Diary (2001)</td>\n",
       "      <td>-0.005212</td>\n",
       "      <td>-0.001099</td>\n",
       "      <td>-0.043708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1127</td>\n",
       "      <td>71</td>\n",
       "      <td>Abyss, The (1989)</td>\n",
       "      <td>-0.025511</td>\n",
       "      <td>0.015597</td>\n",
       "      <td>-0.041103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>7438</td>\n",
       "      <td>103</td>\n",
       "      <td>Kill Bill: Vol. 2 (2004)</td>\n",
       "      <td>-0.014891</td>\n",
       "      <td>-0.000579</td>\n",
       "      <td>-0.040971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>293</td>\n",
       "      <td>132</td>\n",
       "      <td>Léon: The Professional (a.k.a. The Professiona...</td>\n",
       "      <td>0.019084</td>\n",
       "      <td>0.045221</td>\n",
       "      <td>-0.039164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>44191</td>\n",
       "      <td>73</td>\n",
       "      <td>V for Vendetta (2006)</td>\n",
       "      <td>0.033977</td>\n",
       "      <td>0.044460</td>\n",
       "      <td>-0.038937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>480</td>\n",
       "      <td>274</td>\n",
       "      <td>Jurassic Park (1993)</td>\n",
       "      <td>-0.007602</td>\n",
       "      <td>-0.011922</td>\n",
       "      <td>-0.036630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>25</td>\n",
       "      <td>101</td>\n",
       "      <td>Leaving Las Vegas (1995)</td>\n",
       "      <td>0.009911</td>\n",
       "      <td>-0.018344</td>\n",
       "      <td>-0.036611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2959</td>\n",
       "      <td>202</td>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>0.002989</td>\n",
       "      <td>0.002469</td>\n",
       "      <td>-0.035691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>3176</td>\n",
       "      <td>66</td>\n",
       "      <td>Talented Mr. Ripley, The (1999)</td>\n",
       "      <td>0.009535</td>\n",
       "      <td>0.033543</td>\n",
       "      <td>-0.034278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1270</td>\n",
       "      <td>226</td>\n",
       "      <td>Back to the Future (1985)</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.022058</td>\n",
       "      <td>0.031957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>910</td>\n",
       "      <td>66</td>\n",
       "      <td>Some Like It Hot (1959)</td>\n",
       "      <td>-0.002676</td>\n",
       "      <td>0.036975</td>\n",
       "      <td>0.033701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>316</td>\n",
       "      <td>145</td>\n",
       "      <td>Stargate (1994)</td>\n",
       "      <td>-0.026495</td>\n",
       "      <td>0.005567</td>\n",
       "      <td>0.033722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>161</td>\n",
       "      <td>80</td>\n",
       "      <td>Crimson Tide (1995)</td>\n",
       "      <td>0.036487</td>\n",
       "      <td>0.012929</td>\n",
       "      <td>0.034094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>593</td>\n",
       "      <td>304</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>-0.040243</td>\n",
       "      <td>-0.039159</td>\n",
       "      <td>0.034969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>1035</td>\n",
       "      <td>80</td>\n",
       "      <td>Sound of Music, The (1965)</td>\n",
       "      <td>-0.024354</td>\n",
       "      <td>0.022842</td>\n",
       "      <td>0.035467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2700</td>\n",
       "      <td>75</td>\n",
       "      <td>South Park: Bigger, Longer and Uncut (1999)</td>\n",
       "      <td>0.007269</td>\n",
       "      <td>0.019838</td>\n",
       "      <td>0.035960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>58559</td>\n",
       "      <td>121</td>\n",
       "      <td>Dark Knight, The (2008)</td>\n",
       "      <td>0.017961</td>\n",
       "      <td>-0.018076</td>\n",
       "      <td>0.036209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>1374</td>\n",
       "      <td>72</td>\n",
       "      <td>Star Trek II: The Wrath of Khan (1982)</td>\n",
       "      <td>-0.016155</td>\n",
       "      <td>0.025827</td>\n",
       "      <td>0.036312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7153</td>\n",
       "      <td>176</td>\n",
       "      <td>Lord of the Rings: The Return of the King, The...</td>\n",
       "      <td>0.011489</td>\n",
       "      <td>-0.037440</td>\n",
       "      <td>0.036492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2692</td>\n",
       "      <td>80</td>\n",
       "      <td>Run Lola Run (Lola rennt) (1998)</td>\n",
       "      <td>0.006406</td>\n",
       "      <td>0.040332</td>\n",
       "      <td>0.036717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1230</td>\n",
       "      <td>80</td>\n",
       "      <td>Annie Hall (1977)</td>\n",
       "      <td>-0.033890</td>\n",
       "      <td>-0.016890</td>\n",
       "      <td>0.037617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3578</td>\n",
       "      <td>161</td>\n",
       "      <td>Gladiator (2000)</td>\n",
       "      <td>0.043944</td>\n",
       "      <td>-0.004951</td>\n",
       "      <td>0.037815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>3996</td>\n",
       "      <td>114</td>\n",
       "      <td>Crouching Tiger, Hidden Dragon (Wo hu cang lon...</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>-0.023263</td>\n",
       "      <td>0.038504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1394</td>\n",
       "      <td>70</td>\n",
       "      <td>Raising Arizona (1987)</td>\n",
       "      <td>-0.024404</td>\n",
       "      <td>0.032678</td>\n",
       "      <td>0.039485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>1288</td>\n",
       "      <td>82</td>\n",
       "      <td>This Is Spinal Tap (1984)</td>\n",
       "      <td>-0.008307</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0.040156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1221</td>\n",
       "      <td>135</td>\n",
       "      <td>Godfather: Part II, The (1974)</td>\n",
       "      <td>0.032463</td>\n",
       "      <td>-0.019792</td>\n",
       "      <td>0.040814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>802</td>\n",
       "      <td>72</td>\n",
       "      <td>Phenomenon (1996)</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>0.015479</td>\n",
       "      <td>0.043434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>339</td>\n",
       "      <td>101</td>\n",
       "      <td>While You Were Sleeping (1995)</td>\n",
       "      <td>-0.020611</td>\n",
       "      <td>-0.025509</td>\n",
       "      <td>0.043610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>357</td>\n",
       "      <td>122</td>\n",
       "      <td>Four Weddings and a Funeral (1994)</td>\n",
       "      <td>0.019736</td>\n",
       "      <td>-0.001861</td>\n",
       "      <td>0.044402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>36</td>\n",
       "      <td>104</td>\n",
       "      <td>Dead Man Walking (1995)</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>-0.044314</td>\n",
       "      <td>0.046912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>920</td>\n",
       "      <td>68</td>\n",
       "      <td>Gone with the Wind (1939)</td>\n",
       "      <td>-0.030518</td>\n",
       "      <td>-0.002464</td>\n",
       "      <td>0.047715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2329</td>\n",
       "      <td>107</td>\n",
       "      <td>American History X (1998)</td>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.024533</td>\n",
       "      <td>0.050793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1391</td>\n",
       "      <td>80</td>\n",
       "      <td>Mars Attacks! (1996)</td>\n",
       "      <td>-0.012707</td>\n",
       "      <td>-0.080271</td>\n",
       "      <td>0.051152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1240</td>\n",
       "      <td>158</td>\n",
       "      <td>Terminator, The (1984)</td>\n",
       "      <td>0.006055</td>\n",
       "      <td>-0.012378</td>\n",
       "      <td>0.053482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>288</td>\n",
       "      <td>107</td>\n",
       "      <td>Natural Born Killers (1994)</td>\n",
       "      <td>0.011650</td>\n",
       "      <td>0.026298</td>\n",
       "      <td>0.054863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1222</td>\n",
       "      <td>104</td>\n",
       "      <td>Full Metal Jacket (1987)</td>\n",
       "      <td>-0.008834</td>\n",
       "      <td>-0.009769</td>\n",
       "      <td>0.058233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>3948</td>\n",
       "      <td>88</td>\n",
       "      <td>Meet the Parents (2000)</td>\n",
       "      <td>0.012233</td>\n",
       "      <td>0.002458</td>\n",
       "      <td>0.075987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4963</td>\n",
       "      <td>130</td>\n",
       "      <td>Ocean's Eleven (2001)</td>\n",
       "      <td>-0.003114</td>\n",
       "      <td>0.036968</td>\n",
       "      <td>0.078091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>551</td>\n",
       "      <td>90</td>\n",
       "      <td>Nightmare Before Christmas, The (1993)</td>\n",
       "      <td>-0.031329</td>\n",
       "      <td>0.027721</td>\n",
       "      <td>0.085383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mid  counts                                              title  \\\n",
       "46     231     158             Dumb & Dumber (Dumb and Dumber) (1994)   \n",
       "129   7361     106       Eternal Sunshine of the Spotless Mind (2004)   \n",
       "152   3751      99                                 Chicken Run (2000)   \n",
       "101    111     118                                 Taxi Driver (1976)   \n",
       "209   8360      81                                     Shrek 2 (2004)   \n",
       "99    1732     120                           Big Lebowski, The (1998)   \n",
       "170    141      93                               Birdcage, The (1996)   \n",
       "139   2174     104                                 Beetlejuice (1988)   \n",
       "190     17      86                       Sense and Sensibility (1995)   \n",
       "244   1278      74                          Young Frankenstein (1974)   \n",
       "104   1968     117                         Breakfast Club, The (1985)   \n",
       "199   1653      84                                     Gattaca (1997)   \n",
       "63    6539     141  Pirates of the Caribbean: The Curse of the Bla...   \n",
       "100   1961     119                                    Rain Man (1988)   \n",
       "174   1080      91                Monty Python's Life of Brian (1979)   \n",
       "220   3977      79                            Charlie's Angels (2000)   \n",
       "23      47     201                        Seven (a.k.a. Se7en) (1995)   \n",
       "290    596      66                                   Pinocchio (1940)   \n",
       "9      589     237                  Terminator 2: Judgment Day (1991)   \n",
       "62     165     142                  Die Hard: With a Vengeance (1995)   \n",
       "247   8665      74                       Bourne Supremacy, The (2004)   \n",
       "260   4246      71                       Bridget Jones's Diary (2001)   \n",
       "262   1127      71                                  Abyss, The (1989)   \n",
       "143   7438     103                           Kill Bill: Vol. 2 (2004)   \n",
       "71     293     132  Léon: The Professional (a.k.a. The Professiona...   \n",
       "250  44191      73                              V for Vendetta (2006)   \n",
       "5      480     274                               Jurassic Park (1993)   \n",
       "148     25     101                           Leaving Las Vegas (1995)   \n",
       "20    2959     202                                  Fight Club (1999)   \n",
       "292   3176      66                    Talented Mr. Ripley, The (1999)   \n",
       "..     ...     ...                                                ...   \n",
       "12    1270     226                          Back to the Future (1985)   \n",
       "288    910      66                            Some Like It Hot (1959)   \n",
       "60     316     145                                    Stargate (1994)   \n",
       "216    161      80                                Crimson Tide (1995)   \n",
       "3      593     304                   Silence of the Lambs, The (1991)   \n",
       "213   1035      80                         Sound of Music, The (1965)   \n",
       "237   2700      75        South Park: Bigger, Longer and Uncut (1999)   \n",
       "95   58559     121                            Dark Knight, The (2008)   \n",
       "257   1374      72             Star Trek II: The Wrath of Khan (1982)   \n",
       "36    7153     176  Lord of the Rings: The Return of the King, The...   \n",
       "214   2692      80                   Run Lola Run (Lola rennt) (1998)   \n",
       "211   1230      80                                  Annie Hall (1977)   \n",
       "44    3578     161                                   Gladiator (2000)   \n",
       "110   3996     114  Crouching Tiger, Hidden Dragon (Wo hu cang lon...   \n",
       "267   1394      70                             Raising Arizona (1987)   \n",
       "202   1288      82                          This Is Spinal Tap (1984)   \n",
       "66    1221     135                     Godfather: Part II, The (1974)   \n",
       "255    802      72                                  Phenomenon (1996)   \n",
       "146    339     101                     While You Were Sleeping (1995)   \n",
       "93     357     122                 Four Weddings and a Funeral (1994)   \n",
       "138     36     104                            Dead Man Walking (1995)   \n",
       "278    920      68                          Gone with the Wind (1939)   \n",
       "126   2329     107                          American History X (1998)   \n",
       "217   1391      80                               Mars Attacks! (1996)   \n",
       "47    1240     158                             Terminator, The (1984)   \n",
       "128    288     107                        Natural Born Killers (1994)   \n",
       "140   1222     104                           Full Metal Jacket (1987)   \n",
       "184   3948      88                            Meet the Parents (2000)   \n",
       "75    4963     130                              Ocean's Eleven (2001)   \n",
       "177    551      90             Nightmare Before Christmas, The (1993)   \n",
       "\n",
       "        vec_1     vec_2     vec_3  \n",
       "46   0.021526  0.016344 -0.101643  \n",
       "129 -0.023263 -0.001144 -0.066678  \n",
       "152 -0.019505  0.006631 -0.064933  \n",
       "101 -0.004734 -0.025892 -0.061280  \n",
       "209  0.001541  0.016775 -0.058592  \n",
       "99   0.066197  0.036355 -0.054685  \n",
       "170  0.035209 -0.050537 -0.053623  \n",
       "139 -0.034805 -0.005558 -0.053593  \n",
       "190  0.048765  0.015316 -0.049722  \n",
       "244  0.057950  0.020931 -0.049185  \n",
       "104 -0.007819 -0.012261 -0.049089  \n",
       "199  0.002591 -0.013589 -0.048862  \n",
       "63   0.022992  0.003835 -0.048284  \n",
       "100 -0.064252 -0.017032 -0.047262  \n",
       "174  0.034982 -0.000241 -0.046313  \n",
       "220  0.058200  0.001085 -0.046252  \n",
       "23   0.028608 -0.039513 -0.045923  \n",
       "290 -0.013365 -0.004760 -0.044832  \n",
       "9    0.009400 -0.015977 -0.044822  \n",
       "62   0.002055 -0.047892 -0.044821  \n",
       "247 -0.027470  0.007433 -0.044307  \n",
       "260 -0.005212 -0.001099 -0.043708  \n",
       "262 -0.025511  0.015597 -0.041103  \n",
       "143 -0.014891 -0.000579 -0.040971  \n",
       "71   0.019084  0.045221 -0.039164  \n",
       "250  0.033977  0.044460 -0.038937  \n",
       "5   -0.007602 -0.011922 -0.036630  \n",
       "148  0.009911 -0.018344 -0.036611  \n",
       "20   0.002989  0.002469 -0.035691  \n",
       "292  0.009535  0.033543 -0.034278  \n",
       "..        ...       ...       ...  \n",
       "12  -0.035144  0.022058  0.031957  \n",
       "288 -0.002676  0.036975  0.033701  \n",
       "60  -0.026495  0.005567  0.033722  \n",
       "216  0.036487  0.012929  0.034094  \n",
       "3   -0.040243 -0.039159  0.034969  \n",
       "213 -0.024354  0.022842  0.035467  \n",
       "237  0.007269  0.019838  0.035960  \n",
       "95   0.017961 -0.018076  0.036209  \n",
       "257 -0.016155  0.025827  0.036312  \n",
       "36   0.011489 -0.037440  0.036492  \n",
       "214  0.006406  0.040332  0.036717  \n",
       "211 -0.033890 -0.016890  0.037617  \n",
       "44   0.043944 -0.004951  0.037815  \n",
       "110  0.006369 -0.023263  0.038504  \n",
       "267 -0.024404  0.032678  0.039485  \n",
       "202 -0.008307  0.000304  0.040156  \n",
       "66   0.032463 -0.019792  0.040814  \n",
       "255  0.013649  0.015479  0.043434  \n",
       "146 -0.020611 -0.025509  0.043610  \n",
       "93   0.019736 -0.001861  0.044402  \n",
       "138 -0.000213 -0.044314  0.046912  \n",
       "278 -0.030518 -0.002464  0.047715  \n",
       "126  0.002681  0.024533  0.050793  \n",
       "217 -0.012707 -0.080271  0.051152  \n",
       "47   0.006055 -0.012378  0.053482  \n",
       "128  0.011650  0.026298  0.054863  \n",
       "140 -0.008834 -0.009769  0.058233  \n",
       "184  0.012233  0.002458  0.075987  \n",
       "75  -0.003114  0.036968  0.078091  \n",
       "177 -0.031329  0.027721  0.085383  \n",
       "\n",
       "[300 rows x 6 columns]"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_watched.head(300).sort_values(by = \"vec_3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 71.4 ms, sys: 4.47 ms, total: 75.9 ms\n",
      "Wall time: 38 ms\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# using principal component analysis to decompose the 100 dimensions\n",
    "# compressing to 3 dimensions\n",
    "pca = PCA(n_components=2,)\n",
    "%time movie_2d = pca.fit_transform(movie_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_watched[\"vec_1\"] = movie_2d[most_watched_ebd_idx][:,0]\n",
    "most_watched[\"vec_2\"] = movie_2d[most_watched_ebd_idx][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = most_watched.head(100)[\"vec_1\"]\n",
    "Y = most_watched.head(100)[\"vec_2\"]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(most_watched[\"title\"], X, Y):\n",
    "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Run through this notebook\n",
    "* Try the [pytorch version here](experiments/collaborative_filtering_movie_lens.ipynb)\n",
    "* Try the [20 million larger version of data](https://grouplens.org/datasets/movielens/)(about 20 million ratings rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About Homework\n",
    "\n",
    "when the code mentioned ray, like from ray.matchbox import ....\n",
    "\n",
    "ray is a library I built for myself. Just put the repository inside your pathon path\n",
    "\n",
    "For example, if you are using anaconda3. you can do:\n",
    "\n",
    "```\n",
    "cd ~/anaconda3/lib/python3.6/site-packages/;\n",
    "git clone https://github.com/raynardj/ray\n",
    "```"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}