Movie Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movie Recommendation System using Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset : https://grouplens.org/datasets/movielens/\n",
    "### http://files.grouplens.org/datasets/movielens/ml-latest-small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, Markdown\n",
    "\n",
    "def head(df: pd.DataFrame, n_rows:int=1) -> None:\n",
    "    \"\"\"Pretty-print the head of a Pandas table in a Jupyter notebook and show its dimensions.\"\"\"\n",
    "    display(Markdown(\"**whole table (below):** {} rows × {} columns\".format(len(df), len(df.columns))))\n",
    "    display(df.head(n_rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**whole table (below):** 100836 rows × 4 columns"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>3.0</td>\n",
       "      <td>964982400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964980868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964984041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964984100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931\n",
       "5       1       70     3.0  964982400\n",
       "6       1      101     5.0  964980868\n",
       "7       1      110     4.0  964982176\n",
       "8       1      151     5.0  964984041\n",
       "9       1      157     5.0  964984100"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings = pd.read_csv('ratings.csv')\n",
    "head(ratings, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**whole table (below):** 9742 rows × 3 columns"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "5        6                         Heat (1995)   \n",
       "6        7                      Sabrina (1995)   \n",
       "7        8                 Tom and Huck (1995)   \n",
       "8        9                 Sudden Death (1995)   \n",
       "9       10                    GoldenEye (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  \n",
       "5                        Action|Crime|Thriller  \n",
       "6                               Comedy|Romance  \n",
       "7                           Adventure|Children  \n",
       "8                                       Action  \n",
       "9                    Action|Adventure|Thriller  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies = pd.read_csv('movies.csv')\n",
    "head(movies, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   userId  rating             title  \\\n",
      "0       1     4.0  Toy Story (1995)   \n",
      "1       5     4.0  Toy Story (1995)   \n",
      "2       7     4.5  Toy Story (1995)   \n",
      "3      15     2.5  Toy Story (1995)   \n",
      "4      17     4.5  Toy Story (1995)   \n",
      "\n",
      "                                        genres  \n",
      "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
      "1  Adventure|Animation|Children|Comedy|Fantasy  \n",
      "2  Adventure|Animation|Children|Comedy|Fantasy  \n",
      "3  Adventure|Animation|Children|Comedy|Fantasy  \n",
      "4  Adventure|Animation|Children|Comedy|Fantasy  \n",
      "(100836, 4)\n"
     ]
    }
   ],
   "source": [
    "data = pd.merge(ratings, movies, on='movieId')\n",
    "data = data.drop(['timestamp','movieId'], axis=1)\n",
    "print(data.head())\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                title  average rating  total ratings\n",
      "0                    Toy Story (1995)        3.920930            215\n",
      "1                      Jumanji (1995)        3.431818            110\n",
      "2             Grumpier Old Men (1995)        3.259615             52\n",
      "3            Waiting to Exhale (1995)        2.357143              7\n",
      "4  Father of the Bride Part II (1995)        3.071429             49\n"
     ]
    }
   ],
   "source": [
    "ratings = movies['title']\n",
    "avg_rating = data.groupby('title')['rating'].mean()\n",
    "ratings = pd.merge(ratings, avg_rating, on='title')\n",
    "count_rating = data.groupby('title')['rating'].count()\n",
    "ratings = pd.merge(ratings, count_rating, on='title')\n",
    "ratings.rename(columns={'rating_x':'average rating','rating_y':'total ratings'}, inplace=True)\n",
    "print(ratings.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "ratings['average rating'].hist(bins=50)\n",
    "plt.xlabel('Average Rating')\n",
    "plt.ylabel('Number of Ratings')\n",
    "plt.title('Distribution of Ratings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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27HXqeqpr6wNOiTGFy0tQeB0oAToDD+O0GJqdaCO3NdEwYFmjPg2jgJvc5zcBIyOW/0QcfXHGXPJcn5CO8FzNSeY7VmypzHxijDEmQF6CQjtVHQbUqOpkVf0ZTmuiRPoBPwYuEpH57uNyYCBwsYisBC52XwOMwZnAZxXwAvDLJD9Lxvxk2CweGLk4qMMbY0xgvIx9FJpyc7OIXIFTpNMx0Uaq+imxi/C/HWV9Be7wkJ60vDajafVI4+Kj6rp6Xp2+jv+7qpvfyTHGmJziJafwiIgcCfwW+B3wIs7UnDkpUcOUqSu3ZychSZq1dieLN1YEnYyCV1ev/HPGOmrqrF7CmGi8DIj3gfu0AndOZi89mvNKDjQg+sE/pgM28qrf3py1nvvfX8yeA7X84vyvBZ0cY3JOzJyCiDQXkf4i8jsR6eYuu1JEpgHPZC2F2RCuZ06uptlay+efiv01Df5mUsmW3Tw2tsT6UZi8Fi+nMAyniegsYLCIrAO+CQxQ1fezkThj8sn1z0+nssrJgbRt3TLo5BiTknhBoTfQQ1XrRaQ1sB04WVW3ZCdp2VNt5csmA+rd0XpteleTz+JVNFeraj2AqlYBKwoxIBSq+nq1YgxjTNLi5RS6ishC97kAX3NfC04L0h6+py4F8S6DZV/sy1o6gnbSH8Zw8zmdgk6GMSbPxAsKp2YtFVly8ZNTgk5CVr08rTToJBhj8ky8AfFSHgQvV+2vqcvo/qx0xhhTaLx0Xssr2azis5hgorG6HJPPCi4oZNuqbZV2ETCAtToyhSFe57UJ7t+/Zi85wUr22j63dCffeXIKrxRB2f2isgqGfbo27f1MWVHOe/PKMpAiY4wf4lU0dxCR84Hvi8gIGpXMqOo8X1OWBzZVOLO8LSyCMYu+98ynANzyrc5p7ecnw2cB8F+9Eo6paIwJQLyg8AAwAGdE1CcbvafARX4lKh1WkGOMMamL1/roHeAdEflfVf1TFtNUMEbO38jRhx3CeV9vH3RSTBZY3ZIpBF5GSf2TiHwfOM9dNCli5FQTx10j5gM28qkxJn8kbH0kIn8B7gKWuo+73GUF5S8fLku96CnHbhDtjjUY1vrIFAIvTVKvAC5W1eGqOhy41F1WUP4xeU3QSTDGmMB57adwVMTzI/1IiClsT45bQacBo4NOhjEmAS9zNP8F+FxEJuI0Sz0P+L2vqQqIZf79M3jCyrT3YcVixvjPS0XzmyIyCTgb57p5Xy4PoZ3OhcMuOcaYYuep+EhVN6vqKFUd6TUgiMhwEdkmIosjlj0kIhtFZL77uDzivd+LyCoRWS4i303+o5hCly8VuXZzYfKZn2MfvYxTKd3YU6ra032MARCR04AbgNPdbYaISHMf02ZMxuVHyDImPt+CgqpOAXZ6XP0qYISqHlDVtcAqoI9faTPGGBNd3KAgIs0ii38y5E4RWegWLx3tLjse2BCxTpm7LFqabhOROSIyJ8b7mU2tKUqrtlXSacBo5pR6va8xIZ0GjObPo5cGnQyTorhBwZ2jeYGIfDVDx3sO+BrQE9gMPOEuj3Ylj1o0q6pDVbW3qvbOUJoi953adhlOR7qskU76pqzYDsAHCzcHnJL89MLU9EfUNcHwUnzUAVgiIhNEZFTokcrBVHWrqta5weYFDhYRlQEnRKzaEdiU4jFS2Syumrp6Bk9Yyf7qzM7cZgqLxWJTCLz0U3g4UwcTkQ6qGrr1ugYIFU2NAt4QkSeBrwBdgFmpHOPedxZyfe8TEq+YhHfnlvHkuBXsra7l95cV3NTVJsOsANM/k5Zv49wu7WnezM6yXxLmFFR1MlAKtHSfzwYSzqUgIm8C04FTRKRMRG4BBonIIhFZCFwI3O0eYwnwL5yxlT4C7lDVnLktr3Lndq6KkVNYsGFXwn2oKtv3HMhoukzm1dTVs2tfdVr7sByDPyYt38bNL81myMRVQSeloHkZEO9W4B3gH+6i44H3E22nqv1VtYOqtlTVjqo6TFV/rKrdVbWHqn4/IteAqv5ZVb+mqqeo6oepfqAgrNm+N+E6r89cT+9HxvPZqu2+pWPYp2ut12+a7nxjHj3/b1xK29q9q7+2VTo3Vet27gs4JYXNS53CHUA/YDeAqq4EvuRnooKSzuW0Yl9N3PdfnOoMuHfjizPTOEp8f/pgKSu37fFt/8Vg7JKtAFgjNlOsvASFA6oazk+LSAuKOIe890Bt1OX1Ce7Qa+qyc8pqs3ScIFguyBj/eQkKk0XkD8ChInIx8DbwH3+TFYxYN4eRl6LTHxybjaQYY0wgvASFAUA5sAi4HRgD3O9notK1bkfiMv5UxOsYV6z3sIs3VqRdMeuVdUw0xn9eRkmtF5FXgJk4177lmuP5+PMfm5TSdok+VGVV9KIjL7J1PVM0qwHqyr9/yslfasP4e8737RgvTl1D29ZeWk9nx/7qOpo1g1Ytog/PFfp1hFqttW5pw3iZ/JHwlyYiVwDPA6txSlg6i8jt+dZCKBPenVeW8raFfJO7yufK7UdGLwPgrBOPTrBmdpz6wEccd0QrZv7hOw3faPQ/PvuR8VQeqLU5uk1e8VJ89ARwoapeoKrn4/QveMrfZOWWXLieX/n3qfz+vYVBJ8O4tu5O3OekMkajBJOinC6fKBxegsI2VY3sLbIG2OZTenKSl++i3yVqizfu5s1ZGxKvaIJjFy1TAGIWH4nIf7lPl4jIGJwexwpcj9Or2SRhw879QSfBZEmmigqnr97BIS2Es048JjM7zHe5kGUvAvHqFL4X8XwrEKpJLAdyo3A3w7wMVxGLtYzxX6K+IH4Isk1F/xdmAFidhMmqmEFBVX+azYTkgvoCyP4X8kiun693gva01Tvo3eng3XN1bT1//Pci7rnk63Q48tCMHCudEJ/bbfOMic9L66POwK+ATpHrq+r3/UtW/smVVrrXPT+dc7scG3QyfLW5omFR3KTl23h7bhlbdlfxnVOP48d9T6RZEKNoWmbRFAAvjb/fB4bh9GKu9zc5uSlHrveeTV3p36B7uWzqyu1MXbmdtq1b8F+9OgadHGPykpegUKWqg31PSYGprSvK+JkT9hZwEVqqqmrqrBOd8cRLk9SnReRBEfmmiPQKPXxPWQ5JpQ750qenZj4hJmM+WJjSxH55adqq7XT934+YsWZH0ElJT57l2POVl5xCd+DHwEUcLD5S97VxNf6+eu3lu2nXflo0t8LoZGSiOG/Dzv3sr67j0EMK/+55uhsMZq3dSd+T2gWcmvTZr8VfXoLCNcBJkcNnFxs/6xTOGfiJfzv3oLaunmYiwVTMpqjx/yPV5sC+jRJld7S+stPrLy/FRwuAo/xOiAnGyX/8kBvc9vCZMnlFOWvKg5vsJ6jwFuu4yzbvzmo6Clb+3LfkNS9B4TigRETGisio0MPvhOWCU+7/kJIt3n7Qmfq+qiqLN1ZkaG8HXR6njmPW2p0ZPdZNw2dx0ROTM7KvXGnqm47LrH7J5BEvxUcP+p6KHHWgtp7XZ6yn87GHJ1w3U5eut2ZvYMB7i3jp5rO5sGvmZj1dWkR3q5kMI8nsK//DV0Ofr/+C6Wt28MsLTg46KSaLvMynkJlbPtOAqvL2nKZDcZdsqQRg7fa9XJjtRJmwtIYtKZBijmuGTAPwNSis3FrJzr3VfKMAKsALRcLiIxGpFJHd7qNKROpEJOFtp4gMF5FtIrI4YtkxIjJORFa6f492l4uIDBaRVSKysBiavP5n4Wb+37s2FHYqElUQF8g1Oe/d8fo8Hh2zLO46Fz81hR8OzWydlklPwqCgqm1V9Qj30Rq4FnjGw75fBi5ttGwAMEFVuwAT3NcAlwFd3MdtwHPeku8/rzeMyRZ9V+yvib+/5HZnEti+5wAP/2cJtXV2ZrNl9KLNDJ2yJnM7tH9dVnipaG5AVd/HQx8FVZ0CNK7BvAp4xX3+CnB1xPJX1TEDOEpEOiSbtkJQSIOtVlbFD3zZ9OCoJbz0WSkTSrZ6Wj+tCu48u3ipKu/OLaO6Nj964RfQTyQneSk++q+Ix3UiMpDUv/bHqepmAPdvqCb1eCByBpkyd1m09NwmInNEZE6KaSh6D41akpXjDHh3kS/7bdJPwcM2dW4Owc/GTPl6sfpo8RZ++/YCBk9YGXRSTA7wklP4XsTju0Alzp19JkX7PUX9+arqUFXtraq9M5wG3/xzxromy2JdQLLRAvPlaaUArPa5L8GmCn8mFkqUm8pEbquY5sfY5RZlbt+TeIrRbPmkZCvvf74x6nt5lhHLO15aH2VyXoWtItJBVTe7xUOhaT3LgBMi1usI5MTgNEL6X8L731/c4PWT41bwpbat0txr+v77xZkZ3d/STbu5fHBm2+RHC5KSt/fkuS2XuoT87GWnIODqMyMKDOzfnhXxpuN8IM52qqp/SuF4o4CbgIHu35ERy+8UkRHAN4CKUDFTIXrp07Xcd1nXqO8lc4P6149K2LUv9XL7AxkuQ561tuGAa6FJcTItUeujXLq4JbKlooo2rVvQppWXLkP+sGutiRTvm7g3yrLDgVuAdkDcoCAibwIXAMeKSBlOJ7iBwL9E5BZgPc58zwBjgMuBVcA+IGdmffPj+qI4A+Gl4p8z1nF4q+Zcc2ZHnpu0OrMJyxNNxz4KJh3pqq9X+v5lAm1atWDxw98NOjn+jQVl8kq86TifCD0XkbbAXTgX6xHAE7G2i9i+f4y3vh1lXQXuSLTPIMxb/wUntkvco/nON+Yxc+1Oen31KN77Zb+466oqQxJc0B8fu5w/fbCUNY9e3mCwulBR1DVn5t4kMkFdUvwMEqnkOrxeXOeXOTmpPQdqkz9IEhJ9hkTnq7zyAO1zoLjTYlZ2xK1odjubPQIsxAkgvVT1PlXdFm+7QlJf7y17PdMdP2hemkUmofLy/TXORDF+/g7y9AY7K9eGbOQ+6nNkUvBEQeOHQ6f7ctxUmyzn6/c2X8QMCiLyGDAbp7VRd1V9SFW/yFrKcoSS+YtQvP1lMwufG5ck/6wp38Pzk7NfxJZrFeHpBrg15dFKktN37qCJvuzXpCdencJvgQPA/cAfI5roCU6JzxE+p80Q6kSVWxeZXLG6fA9VNXUxL3o3DJ3BtsoD/OgbXw0v8xp086myOl1B1cmk00jC+CdenULSvZ0LUbZ/L9m8y8zkkapq6ti6O/Pt3ONdm7/tDs897KboXVb2pzFXczaG7Pb7Yjxi1nqmrtrOSR5G+YX8CYR5ksy8FVw7uDzhS+ujAvxW/+Kfc5m0vDzoZCSUa0U7fhrwntOj/FcXxR/lNG/OSZ4kM99ZbiAPLNjgT3v/TMpmQPCzRVBIOj2a87VpZ7xU19UrX+ytZtrq7YBTSd5pwGieHm9DYxQaCwoJuBUoGd1nsheNq579LKPHB7hi8FR27ktv2u1tlVUZSk16El2/UwoiHjb6eMmWRulI71Z22KdreXDk4sQrJinh8BUekl1TV89Phs/iRy/MpLq2njr3/Pz9EwsKhcaCQgCSuUj5dc+5ZNPulC6W/1lwcPSRPn+ekMEU+cuPIpLbXpub0f396YOlvDJ9HarKMxm82L45a0P8FRp9D4ZOWc3GKJ0rl7sTQAWWE8rPDFjesaDgQTEMjnagNnql7PwNu9i6+2CO4Fdvfp6tJHnWdNRUb/+vjV80vPCNmLU+U0nyKHo65677gsc/XpHltDg27trPo2NKuOXl2Q2W51I9WLK/xj/8exGdfz/al7QUIgsKPpi2anvQSUjawA9Loi6/+tnPuOjxSdlNTKaErh4xLmgXPzWlwesXpjadECZ0V/zsxFVZKz+fu86f7kDLt1byg+ens6+6UQ/qiKtsqENdZZW/vayz6Y2Z6zMe1PZX11FRoE1qLSj44PY0ihUaZ0qydYe2fse+mO/tTaNpZ7q8lO37kZFrnDt8bOxynhqfnbv3XQlm5UvV6IWbmVW6k9ml0YOOKnxSEn2wgo279lFdV99g3WL2nScnc8b/fRx0MnxhQcGDoVOS6xWb6PeSD7+nhWW72LY7NyqSG0ulTDvZbbLRTyH2sf0/xsKyXXQaMJplm3c3KI55MMYETDcNnx11eTK6/HFM1iZ4SsVbs9fTacBo9noYiypanUuhsKDgQbKdshIOcBbnR58rd2Dff+YzLnI7h0UT5EXTq2gZCNgZcaAAABV2SURBVM9zbqdwvETzbnvld0WuqvLhYqflVGTOIPK4jf+/uxt9tlRyZzV1Gp7gKR1ezs7eA7X0HzqD0u3eh+gIjTq8rTJ3JhsKggWFHDP8s7UNXgfZ5t3v0TuDkGwsy5uOXWnI5YYUQ6esZkeoSW0SyfykZBvT1+zgsY+X+5OwAmZBIceVF/ldS1QJWhtNXrGtyZ1uqhf3xkG5qiZz9SuxrsXZCESZPsK+6lpmrtmReMUYlmyqiFpc+eiYEu7514J0kpa0fMgF+8mCQo67MEstf4K4WZxTupNfv/l5xoeQHrtka4O2+Q2KRTzmvGKdjmwM4pbVkXJjXAAT1otFrLB1dxWnPTCWHw6d0aD5cjKuGPxpzFFTwznWVE5LcV/fU2JBIQDJ/Ohr6nLvW/32nASdoTz66UuzGbVgE3saN5HMgD/8e1Fa2wd61rN48Ew0fx0ycVX4+b40WqrFmh62aa4vsRwuEct5FhQS8OPLlYu50/HLtnHyH8Z4WvfedxbS4+HsNMeLdqoaL5vhodgiFIjTLZrJxsUm1a/HiFlO65lYHRGj7X9i5JhVEW+kOltbPhe95HLdSjZZUEigmMZ8r02iGCeXOjf9Y0rTTmdw8EcemVavv/t07njTvS6memF9YpzTj8JLp6rI85DKpbA+ixf/WEeqrKqhpi567iKbdu2r5mcvzz5YIZ7nLCgk4Ed75Ez8nFZurczAXopDKjN8PTbWabWSTze+aQejJNZ90g1AydxIZMq01TuYtXYn3R/6mF8k6CiajfqZf85Yxycl25q0HMxXFhQCkIksduMhGvJNpwGjqYzR5LWyqoaFZf4MF55LF/lYd+hppzEDpSDxLqYDPyzhH5Oj5878tH6n0+t+4679/OAfzrzRE2L0wC6GpsR+CSQoiEipiCwSkfkiMsdddoyIjBORle7fo4NImwne7a/N5fvPfMbmiv38zz/nNXk/lZ976CIRurCkKhuXmtRjgsctPazWODBFFhe99Flpg/ciy+LTjWcHauuaNPutrVP2V9fxTESFtlepzb0RXW1dPfe8NT/pXPqLU9fwztyy5BMSkCBnXrtQVSNHjhsATFDVgSIywH19XzBJK16qGniF27z1TouYv3+yivHLtjZ5P50LT6Z6Hfsp1ZzC9j3e58eIvJMO/bvr4hQFZWv8q/MHTWJLo2atizZWcOoDH/l+7ETf+pItlbz3+UZKtjQMCon+X4+MXgbAdWd1TCN12ZNLxUdXAa+4z18Brg4wLUUriOKVWD/GN2ZGH8q6OkbTxXjCrY+SjHfZio8b0szBZMKoiLkygtI4IKRiTfkeyj1OADV33U5q3crqTH31l2yqoLIq928+YgkqKCjwsYjMFZHb3GXHqepmAPfvl6JtKCK3icicULFTPsqhYu0mstmqpLG6emXAuwupqol/0R+9aLOn/UW7oCd7jc9k8UM8kZXhXgZki8dLeXoht7686InJPPSfpQnXW1RWwbXPTU977orIc1lfr1wx+FNuednb5Snd/7UfggoK/VS1F3AZcIeInOd1Q1Udqqq9VbW3f8nzVy5VdjYWQGOSsCWbKhgxOzMd4xrL2OfycDFVVV5pNPDbpiRasb2Voc6B8UR+B6MFiBz+imZM+R4nN7F8y24gM/VFofM2Z91O53WcH/t788o4/cGxrMixloSBBAVV3eT+3Qb8G+gDbBWRDgDu3+jNCoyvMp1TOHfQJ57WK92+N/FcwhGS7Yk7bqlTN5GNoHfWI+ObDEEdrXjIr7qbqpo6lmyqiPm+l2aafo65dc2QzM85nknJ/gSirR9aNGPNzpjbhVpOLd9S5EFBRA4Xkbah58AlwGJgFHCTu9pNwMhsp81AdV19Rgd927DT2x3yBY9P4mces9zgtA3PhgnL8u/e5DdvzeeKwZ/GfP/etxdmtcK9cQX25+v9aW4MhOsHsuXz9V+EOw1GUx0nPaFbglTqyPwURE7hOOBTEVkAzAJGq+pHwEDgYhFZCVzsvjZZ1uOhj+n6v/639EhXtoZTyETFZ7rGeKxDCUmUi9qxt5rXfAqq0f4tv87ivN6XNOq/E5kerwMvJpOBu2bItLjvt2yWeGe/fTu7o8AmkvWgoKprVPUM93G6qv7ZXb5DVb+tql3cv7HzXaagpFKMksmQULp9L7v2eWvOuabc+6QtkdJJ76Tl/uZW0u3oFTlxzneebDoxk9eGAZmwJs6kOqGJhWLZXOHcAKRzv3HLK7Mb7KO5h6CQa4Lsp2CKxH3vLAw6CTEt3bSbywdP5dg2h3DkoS0Trn/D0BlRl8dr458N6RSnfeExIKbjxanZ7wENDetPErX02Z9msakgTIocYBBoFicoBN0fKJZc6qdgClQ2WtOk6trnnOx/Mh2/ojkvhfGVMun+9xenvG06g/95FerAFaT3528MOgl5wXIKJi95yeJ7uQ+LbG2Vzr1+ooETo6XX632i39UnQdywflLStKe636at3kHp9r10OvZwX/bfuFXXAyMX858c6BCYLAsKJnC5MgZ/qvUFfvP77AQxeFwyLc28mLR8G0MmrU64XuREPn5/7V6dHr9ILzcLj6z4yOSpXRlqUhlrtq+QTN1FpzOEc47EzJx280uzmbU2tbYpE5eXN7oxUSr21/D+5weLmyavKGfdjvhDkSQbXJP9bm3YuY+JMUaFzSTLKZi8NGVFecJ1vsjABEmJfrfbPI6xk45cyUnlqngd9RoLBedNu/Yzq/RgEGncDPvetxfw8dKtnNrhCE75cltuGj4r/N5yn3ogvz1nA89NWs0nv7sg6vuXPDWF/TV1lA68wpfjh1hQMIHL50tenz9P8LZiGh/S7/GocrQRjGfxOuo1puq0QjpnYMOe9o1zjKHmqdE6csZqaVaX5v/p3gSt9NJtHeWVFR8ZE0emmg2mc7nwv06hcEW7Tj/+8XLP2++t9j5g3eKN3nMskNnzXlVTx7tzyzKSq7ScgjFxZOqHG7X1kcedJ+oC4bWnbiz5nlOIp3Hd0w+enx5zxr9Ii9wL/P3/XhyzOKexTGXoGs9p8tS4FSzbvDvm+g+MXByu1N5aWcUvLzg5reNbUDCBe3ys9zu3bKutV6at3p54xQTSq2iOv+1zkxO3uilWjSufvQSESEEMc6LaMFA/PWFl3PUjWzkN+mg53Y8/knO7tE/5+FZ8ZAKXqOle0H70wsy095HOXWSibdMdYM7mM45tX3WdbxX9sYom0z1auk2rLSgYkwXp/NATVTRX7E+vN3YmR8UtBI1P99UJBr3L/PHTCwvpbm9BwZgsSOeHmmjT2aXJzS3R2KryPWltX+gWbPBvqO9oFBi/dCsfLIzeG9rvJsoWFIzJgmi/48nLE/e1gKY5hemrdyQcVsNkX6J6owdHLqbTgNHh15GFR5Ej4arCz1+dw51vRB9yPFFMiHz76fEr6TRgdFJzNlhQMCYLGl8w6uo17uQskRo3Lur/wgwufHxShlIGI+fn3/g8ueizVTvivv9K47qziKhw80uzw88TBZea+nrW7YhdbxAZNEKj01bVei8itKBgTACSKwJoum51bT1rrNinICX6ajzx8QrOf2xS3MCQ7D4jWVAwJgueazRYWzIhIVY3hB/GmNvBpCfoHvYPjjw4v/fHS5pODDR0inP3v3V39Hm0Q+m/84154Sa4Zzz8MRX7avh8/ReUxpmICKyfgjFZEVkZXFlVw94D3rPzsXIV1mooP23bXcWXjmgdsylw5PwjI+MMvf3AyMV89JvzYr7/wcKGM979z+tzmbY6fhEXWE7BmKzZsHMfqkr3hz6m7188jplEw5xC0DO8FYOde/2dia7Po87//t15ZWntp2RLZdShNSr21/DY2JImy9cmyCGEWE7BmCw5d9BEjjn8kKS3i2x9NH5ZxOQ0CuOWZn+ymkIXa8rVTIpshRTP6IXx57e+8u9NBwMcHKMHdGiQv0QsKBiTRanche6vruPCxyfxvTO+wtePaxNeXnmglltfzexkNcbkXFAQkUuBp4HmwIuqOjDgJBkTqDnrnPqIWHeAxmRSTtUpiEhz4FngMuA0oL+InBZsquL701WnB50EY4zJmJwKCkAfYJWqrlHVamAEcFWslTsedWjWEtZYm1YtGHJjL/6774mBpcEYYzIt14LC8cCGiNdl7rKojj78ELp+ua2nHR/Swvmox7ZpFV7W+djD427z6293afD6S20PbvvhXedyefcOiAitWzr7/uPlp8bd34ntDmuy7JyvtWvwutvxRzDouh60aBZ75Mpuxx8R9zipuOmbJ3LD2SdkZF/Nmwl3NTp3xpj8ILk0/6uIXA98V1V/7r7+MdBHVX8Vsc5twG0AX/3qV89aty63h102xphcIyJzVbV3tPdyLadQBkTernYEGvTeUNWhqtpbVXu3b5/6RBLGGGOayrWgMBvoIiKdReQQ4AZgVMBpMsaYopFTTVJVtVZE7gTG4jRJHa6qSxJsZowxJkNyKigAqOoYYEzQ6TDGmGKUa8VHxhhjAmRBwRhjTJgFBWOMMWEWFIwxxoTlVOe1ZIlIJbA86HTkmGOB7UEnIsfYOWnKzklTxXROTlTVqB29cq71UZKWx+qVV6xEZI6dk4bsnDRl56QpOycOKz4yxhgTZkHBGGNMWL4HhaFBJyAH2Tlpys5JU3ZOmrJzQp5XNBtjjMmsfM8pGGOMySALCsYYY8LyNiiIyKUislxEVonIgKDT4xcROUFEJorIMhFZIiJ3ucuPEZFxIrLS/Xu0u1xEZLB7XhaKSK+Ifd3krr9SRG4K6jNliog0F5HPReQD93VnEZnpfr633OHXEZFW7utV7vudIvbxe3f5chH5bjCfJDNE5CgReUdEStzvyzeL/XsiIne7v5vFIvKmiLQu9u9JQqqadw+cYbVXAycBhwALgNOCTpdPn7UD0Mt93hZYAZwGDAIGuMsHAH91n18OfAgI0BeY6S4/Bljj/j3afX500J8vzXNzD/AG8IH7+l/ADe7z54H/cZ//EnjefX4D8Jb7/DT3u9MK6Ox+p5oH/bnSOB+vAD93nx8CHFXM3xOcqXzXAodGfD9uLvbvSaJHvuYU+gCrVHWNqlYDI4CrAk6TL1R1s6rOc59XAstwvuxX4VwEcP9e7T6/CnhVHTOAo0SkA/BdYJyq7lTVL4BxwKVZ/CgZJSIdgSuAF93XAlwEvOOu0vichM7VO8C33fWvAkao6gFVXQuswvlu5R0ROQI4DxgGoKrVqrqLIv+e4HTQPVREWgCHAZsp4u+JF/kaFI4HNkS8LnOXFTQ3O3smMBM4TlU3gxM4gC+5q8U6N4V2zv4G/D+g3n3dDtilqrXu68jPF/7s7vsV7vqFdE5OAsqBl9witRdF5HCK+HuiqhuBx4H1OMGgAphLcX9PEsrXoCBRlhV021oRaQO8C/xGVXfHWzXKMo2zPO+IyJXANlWdG7k4yqqa4L2COSc4d8S9gOdU9UxgL05xUSwFf07c+pOrcIp8vgIcDlwWZdVi+p4klK9BoQw4IeJ1R2BTQGnxnYi0xAkIr6vqe+7irW52H/fvNnd5rHNTSOesH/B9ESnFKTq8CCfncJRbTAANP1/4s7vvHwnspLDOSRlQpqoz3dfv4ASJYv6efAdYq6rlqloDvAecQ3F/TxLK16AwG+jitiI4BKdSaFTAafKFW6Y5DFimqk9GvDUKCLUMuQkYGbH8J27rkr5AhVtsMBa4RESOdu+gLnGX5R1V/b2qdlTVTjj/+09U9UZgInCdu1rjcxI6V9e566u7/Aa31UlnoAswK0sfI6NUdQuwQUROcRd9G1hKEX9PcIqN+orIYe7vKHROivZ74knQNd2pPnBaT6zAaQnwx6DT4+Pn/BZOVnUhMN99XI5T1jkBWOn+PcZdX4Bn3fOyCOgdsa+f4VSSrQJ+GvRny9D5uYCDrY9OwvmxrgLeBlq5y1u7r1e5758Usf0f3XO1HLgs6M+T5rnoCcxxvyvv47QeKurvCfAwUAIsBl7DaUFU1N+TRA8b5sIYY0xYvhYfGWOM8YEFBWOMMWEWFIwxxoRZUDDGGBNmQcEYY0yYBQWTF0REReS1iNctRKQ8NEJqCvv7hYj8JIn1bxaRZ5I8xssicl3iNePu4yER+UujZT1FZFmCbX6XznFN8bKgYPLFXqCbiBzqvr4Y2JjqzlT1eVV9NSMp89ebwA8bLbsBZ3RYYzLOgoLJJx/ijIwK0B/nggmE55d4350bYIaI9BCRZiJSKiJHRay3SkSOi7ybFpGvichHIjJXRKaKSNd4iXBzAINFZJqIrAnlBtzewc+IyFIRGc3BwecQkbNEZLJ7jLEi0kFEjnTH5z/FXedNEbk18liquhzYJSLfiFj8A2CEiNwqIrNFZIGIvCsih0VJ6yQR6e0+P9YdGiQ0F8Vj7vYLReT2RCffFAcLCiafjMAZbqA10ANntNiQh4HPVbUH8AecYaHrcYYwuAbAvbCWqurWRvsdCvxKVc8CfgcM8ZCWDji9za8EBrrLrgFOAboDt+KMsxMau+rvwHXuMYYDf1bVCuBO4GURuQFn3oIXohzrTZzcAe6QFDtUdSXwnqqerapn4AypfouHdIfcgjO0xdnA2cCt7hAOpsi1SLyKMblBVRe6w4f3B8Y0evtbwLXuep+ISDsRORJ4C3gAeAl34pTIjdzRZ88B3naGxwGcoRASed8NOktF5Dh32XnAm6paB2wSkU/c5acA3YBx7jGa4wzljKqOE5HrcYacOCPGsUYA00Tkt+5nCOWQuonIIziT6bQhuTGKLgF6RNR5HIkzps/aJPZhCpAFBZNvRuGMkX8Bzrg+IbGGN54OnCwi7XEmU3mk0TrNcMbX75lkOg7EOHa0cWMEWKKq32zyhkgz4FRgP85sZ2WN11HVDW6xz/k4gS+0n5eBq1V1gYjcjHNOGqvlYIlA60Zp+pWq5utgd8YnVnxk8s1w4P9UdVGj5VOAGwFE5AJgu6ruVmdwr38DT+KMNLsjciN15qZY696th+oFYt2xJzIFp3iruTjDVF/oLl8OtBeRb7rHaCkip7vv3Y1T9NMfGO4WNUXzJvAUsFpVQ4GjLbDZ3ebGGNuVAme5zyNbQo0F/id0PBH5ujiT8pgiZzkFk1fcC+LTUd56CGfWsYXAPg4OgQxOkdFsnPl5o7kReE5E7gda4hTXLEghef/GmdthEc4IvpPdNFe7xTSD3SKtFsDfRKQG+DnQR1UrRWQKcD/wYJR9v43zuX8Vsex/cepV1rnHbBtlu8eBf4nIj4FPIpa/CHQC5rnDSpdzcFpKU8RslFRjjDFhVnxkjDEmzIKCMcaYMAsKxhhjwiwoGGOMCbOgYIwxJsyCgjHGmDALCsYYY8L+P5dj7bvcp2ztAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings[['title','total ratings']].plot(kind='line')\n",
    "plt.xlabel('Movie Index Value')\n",
    "plt.ylabel('Number of Ratings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 title  average rating  total ratings\n",
      "314                Forrest Gump (1994)        4.164134            329\n",
      "277   Shawshank Redemption, The (1994)        4.429022            317\n",
      "257                Pulp Fiction (1994)        4.197068            307\n",
      "510   Silence of the Lambs, The (1991)        4.161290            279\n",
      "1939                Matrix, The (1999)        4.192446            278\n"
     ]
    }
   ],
   "source": [
    "top_five = ratings.sort_values('total ratings', ascending=False)[:5]\n",
    "print(top_five)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**whole table (below):** 100836 rows × 4 columns"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "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>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  rating             title  \\\n",
       "0       1     4.0  Toy Story (1995)   \n",
       "1       5     4.0  Toy Story (1995)   \n",
       "2       7     4.5  Toy Story (1995)   \n",
       "3      15     2.5  Toy Story (1995)   \n",
       "4      17     4.5  Toy Story (1995)   \n",
       "5      18     3.5  Toy Story (1995)   \n",
       "6      19     4.0  Toy Story (1995)   \n",
       "7      21     3.5  Toy Story (1995)   \n",
       "8      27     3.0  Toy Story (1995)   \n",
       "9      31     5.0  Toy Story (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "2  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "3  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "4  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "5  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "6  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "7  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "8  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "9  Adventure|Animation|Children|Comedy|Fantasy  "
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     "output_type": "display_data"
    }
   ],
   "source": [
    "head(data, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**whole table (below):** 610 rows × 9719 columns"
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       "      <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>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 9719 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                                        \n",
       "1              NaN                                      NaN   \n",
       "2              NaN                                      NaN   \n",
       "3              NaN                                      NaN   \n",
       "4              NaN                                      NaN   \n",
       "5              NaN                                      NaN   \n",
       "6              NaN                                      NaN   \n",
       "7              NaN                                      NaN   \n",
       "8              NaN                                      NaN   \n",
       "9              NaN                                      NaN   \n",
       "10             NaN                                      NaN   \n",
       "11             NaN                                      NaN   \n",
       "12             NaN                                      NaN   \n",
       "13             NaN                                      NaN   \n",
       "14             NaN                                      NaN   \n",
       "15             NaN                                      NaN   \n",
       "16             NaN                                      NaN   \n",
       "17             NaN                                      NaN   \n",
       "18             NaN                                      NaN   \n",
       "19             NaN                                      NaN   \n",
       "20             NaN                                      NaN   \n",
       "\n",
       "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
       "userId                                                \n",
       "1                          NaN                  NaN   \n",
       "2                          NaN                  NaN   \n",
       "3                          NaN                  NaN   \n",
       "4                          NaN                  NaN   \n",
       "5                          NaN                  NaN   \n",
       "6                          NaN                  NaN   \n",
       "7                          NaN                  NaN   \n",
       "8                          NaN                  NaN   \n",
       "9                          NaN                  NaN   \n",
       "10                         NaN                  NaN   \n",
       "11                         NaN                  NaN   \n",
       "12                         NaN                  NaN   \n",
       "13                         NaN                  NaN   \n",
       "14                         NaN                  NaN   \n",
       "15                         NaN                  NaN   \n",
       "16                         NaN                  NaN   \n",
       "17                         NaN                  NaN   \n",
       "18                         NaN                  NaN   \n",
       "19                         NaN                  NaN   \n",
       "20                         NaN                  NaN   \n",
       "\n",
       "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
       "userId                                                               \n",
       "1                             NaN                              NaN   \n",
       "2                             NaN                              NaN   \n",
       "3                             NaN                              NaN   \n",
       "4                             NaN                              NaN   \n",
       "5                             NaN                              NaN   \n",
       "6                             NaN                              NaN   \n",
       "7                             NaN                              NaN   \n",
       "8                             NaN                              NaN   \n",
       "9                             NaN                              NaN   \n",
       "10                            NaN                              NaN   \n",
       "11                            NaN                              NaN   \n",
       "12                            NaN                              NaN   \n",
       "13                            NaN                              NaN   \n",
       "14                            NaN                              NaN   \n",
       "15                            NaN                              NaN   \n",
       "16                            NaN                              NaN   \n",
       "17                            NaN                              NaN   \n",
       "18                            NaN                              NaN   \n",
       "19                            NaN                              NaN   \n",
       "20                            NaN                              NaN   \n",
       "\n",
       "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
       "userId                                                                          \n",
       "1                      NaN                   NaN                          NaN   \n",
       "2                      NaN                   NaN                          NaN   \n",
       "3                      NaN                   NaN                          NaN   \n",
       "4                      NaN                   NaN                          NaN   \n",
       "5                      NaN                   NaN                          NaN   \n",
       "6                      NaN                   NaN                          NaN   \n",
       "7                      NaN                   NaN                          NaN   \n",
       "8                      NaN                   NaN                          NaN   \n",
       "9                      NaN                   NaN                          NaN   \n",
       "10                     NaN                   NaN                          NaN   \n",
       "11                     NaN                   NaN                          NaN   \n",
       "12                     5.0                   NaN                          NaN   \n",
       "13                     NaN                   NaN                          NaN   \n",
       "14                     NaN                   NaN                          NaN   \n",
       "15                     NaN                   NaN                          4.0   \n",
       "16                     NaN                   NaN                          NaN   \n",
       "17                     NaN                   NaN                          NaN   \n",
       "18                     NaN                   NaN                          4.0   \n",
       "19                     2.0                   NaN                          NaN   \n",
       "20                     NaN                   NaN                          NaN   \n",
       "\n",
       "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
       "userId                                  ...                              \n",
       "1                                  NaN  ...          NaN           NaN   \n",
       "2                                  NaN  ...          NaN           NaN   \n",
       "3                                  NaN  ...          NaN           NaN   \n",
       "4                                  NaN  ...          NaN           NaN   \n",
       "5                                  NaN  ...          NaN           NaN   \n",
       "6                                  NaN  ...          NaN           NaN   \n",
       "7                                  NaN  ...          NaN           NaN   \n",
       "8                                  NaN  ...          NaN           NaN   \n",
       "9                                  NaN  ...          NaN           NaN   \n",
       "10                                 NaN  ...          NaN           NaN   \n",
       "11                                 NaN  ...          NaN           NaN   \n",
       "12                                 NaN  ...          NaN           NaN   \n",
       "13                                 NaN  ...          NaN           NaN   \n",
       "14                                 NaN  ...          NaN           NaN   \n",
       "15                                 NaN  ...          NaN           NaN   \n",
       "16                                 NaN  ...          NaN           NaN   \n",
       "17                                 NaN  ...          NaN           NaN   \n",
       "18                                 NaN  ...          NaN           NaN   \n",
       "19                                 NaN  ...          NaN           NaN   \n",
       "20                                 NaN  ...          NaN           NaN   \n",
       "\n",
       "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
       "userId                                           \n",
       "1                 NaN                      NaN   \n",
       "2                 NaN                      NaN   \n",
       "3                 NaN                      NaN   \n",
       "4                 NaN                      NaN   \n",
       "5                 NaN                      NaN   \n",
       "6                 NaN                      NaN   \n",
       "7                 NaN                      NaN   \n",
       "8                 NaN                      NaN   \n",
       "9                 NaN                      NaN   \n",
       "10                NaN                      NaN   \n",
       "11                NaN                      NaN   \n",
       "12                NaN                      NaN   \n",
       "13                NaN                      NaN   \n",
       "14                NaN                      NaN   \n",
       "15                NaN                      NaN   \n",
       "16                NaN                      NaN   \n",
       "17                NaN                      NaN   \n",
       "18                NaN                      NaN   \n",
       "19                NaN                      NaN   \n",
       "20                NaN                      NaN   \n",
       "\n",
       "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "userId                                                           \n",
       "1                                                     NaN        \n",
       "2                                                     NaN        \n",
       "3                                                     NaN        \n",
       "4                                                     NaN        \n",
       "5                                                     NaN        \n",
       "6                                                     NaN        \n",
       "7                                                     NaN        \n",
       "8                                                     NaN        \n",
       "9                                                     NaN        \n",
       "10                                                    NaN        \n",
       "11                                                    NaN        \n",
       "12                                                    NaN        \n",
       "13                                                    NaN        \n",
       "14                                                    NaN        \n",
       "15                                                    NaN        \n",
       "16                                                    NaN        \n",
       "17                                                    NaN        \n",
       "18                                                    NaN        \n",
       "19                                                    NaN        \n",
       "20                                                    NaN        \n",
       "\n",
       "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
       "userId                                                                \n",
       "1                   NaN         NaN                             NaN   \n",
       "2                   NaN         NaN                             NaN   \n",
       "3                   NaN         NaN                             NaN   \n",
       "4                   NaN         NaN                             NaN   \n",
       "5                   NaN         NaN                             NaN   \n",
       "6                   NaN         NaN                             NaN   \n",
       "7                   NaN         NaN                             NaN   \n",
       "8                   NaN         NaN                             NaN   \n",
       "9                   NaN         1.0                             NaN   \n",
       "10                  NaN         NaN                             NaN   \n",
       "11                  NaN         NaN                             NaN   \n",
       "12                  NaN         NaN                             NaN   \n",
       "13                  NaN         NaN                             NaN   \n",
       "14                  NaN         NaN                             NaN   \n",
       "15                  NaN         NaN                             NaN   \n",
       "16                  NaN         NaN                             NaN   \n",
       "17                  NaN         NaN                             NaN   \n",
       "18                  NaN         NaN                             NaN   \n",
       "19                  NaN         NaN                             NaN   \n",
       "20                  NaN         0.5                             NaN   \n",
       "\n",
       "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
       "userId                                                                    \n",
       "1                         4.0                                        NaN  \n",
       "2                         NaN                                        NaN  \n",
       "3                         NaN                                        NaN  \n",
       "4                         NaN                                        NaN  \n",
       "5                         NaN                                        NaN  \n",
       "6                         NaN                                        NaN  \n",
       "7                         NaN                                        NaN  \n",
       "8                         NaN                                        NaN  \n",
       "9                         NaN                                        NaN  \n",
       "10                        NaN                                        NaN  \n",
       "11                        NaN                                        NaN  \n",
       "12                        NaN                                        NaN  \n",
       "13                        NaN                                        NaN  \n",
       "14                        NaN                                        NaN  \n",
       "15                        NaN                                        NaN  \n",
       "16                        NaN                                        NaN  \n",
       "17                        NaN                                        NaN  \n",
       "18                        NaN                                        NaN  \n",
       "19                        2.0                                        NaN  \n",
       "20                        NaN                                        NaN  \n",
       "\n",
       "[20 rows x 9719 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings_matrix = data.pivot_table(index='userId', columns='title', values='rating')\n",
    "head(ratings_matrix, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "'71 (2014)                                      NaN\n",
       "'Hellboy': The Seeds of Creation (2004)         NaN\n",
       "'Round Midnight (1986)                          NaN\n",
       "'Salem's Lot (2004)                             NaN\n",
       "'Til There Was You (1997)                       NaN\n",
       "'Tis the Season for Love (2015)                 NaN\n",
       "'burbs, The (1989)                        -0.187323\n",
       "'night Mother (1986)                            NaN\n",
       "(500) Days of Summer (2009)               -0.044034\n",
       "*batteries not included (1987)            -0.738549\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#input_movie = 'Toy Story (1995)'\n",
    "#input_movie = 'Jumanji (1995)'\n",
    "#input_movie = 'Forrest Gump (1994)'\n",
    "input_movie = 'Pulp Fiction (1994)'\n",
    "correlations = ratings_matrix.corrwith(ratings_matrix[input_movie])\n",
    "correlations.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Coffee Town (2013)                                        1.0\n",
       "Wolfman, The (2010)                                       1.0\n",
       "Swiss Army Man (2016)                                     1.0\n",
       "When in Rome (2010)                                       1.0\n",
       "Tinker, Tailor, Soldier, Spy (1979)                       1.0\n",
       "                                                         ... \n",
       "Zulu (2013)                                               NaN\n",
       "[REC]² (2009)                                             NaN\n",
       "[REC]³ 3 Génesis (2012)                                   NaN\n",
       "anohana: The Flower We Saw That Day - The Movie (2013)    NaN\n",
       "À nous la liberté (Freedom for Us) (1931)                 NaN\n",
       "Length: 9719, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index                                              title  Correlation  \\\n",
      "0   3456                                Pulp Fiction (1994)     1.000000   \n",
      "1   1487                                  Fight Club (1999)     0.543465   \n",
      "2   2358                           Kill Bill: Vol. 1 (2003)     0.504147   \n",
      "3   4462                               Trainspotting (1996)     0.437714   \n",
      "4   2359                           Kill Bill: Vol. 2 (2004)     0.421685   \n",
      "5   4572                         Usual Suspects, The (1995)     0.411700   \n",
      "6    185  Amelie (Fabuleux destin d'Amélie Poulain, Le) ...     0.402193   \n",
      "7   1372       Eternal Sunshine of the Spotless Mind (2004)     0.401534   \n",
      "8   3567                              Reservoir Dogs (1992)     0.394687   \n",
      "9   4511          Twelve Monkeys (a.k.a. 12 Monkeys) (1995)     0.391141   \n",
      "\n",
      "   average rating  total ratings  \n",
      "0        4.197068            307  \n",
      "1        4.272936            218  \n",
      "2        3.961832            131  \n",
      "3        4.039216            102  \n",
      "4        3.868182            110  \n",
      "5        4.237745            204  \n",
      "6        4.183333            120  \n",
      "7        4.160305            131  \n",
      "8        4.202290            131  \n",
      "9        3.983051            177  \n"
     ]
    }
   ],
   "source": [
    "recommendation = pd.DataFrame(correlations,columns=['Correlation'])\n",
    "recommendation.dropna(inplace=True)\n",
    "recommendation = pd.merge(recommendation, ratings, on='title')\n",
    "recommended = recommendation[recommendation['total ratings']>100].sort_values('Correlation',ascending=False).reset_index()\n",
    "print(recommended.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>index</th>\n",
       "      <th>title</th>\n",
       "      <th>Correlation</th>\n",
       "      <th>average rating</th>\n",
       "      <th>total ratings</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3456</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.197068</td>\n",
       "      <td>307</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1487</td>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>0.543465</td>\n",
       "      <td>4.272936</td>\n",
       "      <td>218</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2358</td>\n",
       "      <td>Kill Bill: Vol. 1 (2003)</td>\n",
       "      <td>0.504147</td>\n",
       "      <td>3.961832</td>\n",
       "      <td>131</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4462</td>\n",
       "      <td>Trainspotting (1996)</td>\n",
       "      <td>0.437714</td>\n",
       "      <td>4.039216</td>\n",
       "      <td>102</td>\n",
       "      <td>Comedy|Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2359</td>\n",
       "      <td>Kill Bill: Vol. 2 (2004)</td>\n",
       "      <td>0.421685</td>\n",
       "      <td>3.868182</td>\n",
       "      <td>110</td>\n",
       "      <td>Action|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>4572</td>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>0.411700</td>\n",
       "      <td>4.237745</td>\n",
       "      <td>204</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                       title  Correlation  average rating  \\\n",
       "0   3456         Pulp Fiction (1994)     1.000000        4.197068   \n",
       "1   1487           Fight Club (1999)     0.543465        4.272936   \n",
       "2   2358    Kill Bill: Vol. 1 (2003)     0.504147        3.961832   \n",
       "3   4462        Trainspotting (1996)     0.437714        4.039216   \n",
       "4   2359    Kill Bill: Vol. 2 (2004)     0.421685        3.868182   \n",
       "5   4572  Usual Suspects, The (1995)     0.411700        4.237745   \n",
       "\n",
       "   total ratings                       genres  \n",
       "0            307  Comedy|Crime|Drama|Thriller  \n",
       "1            218  Action|Crime|Drama|Thriller  \n",
       "2            131        Action|Crime|Thriller  \n",
       "3            102           Comedy|Crime|Drama  \n",
       "4            110        Action|Drama|Thriller  \n",
       "5            204       Crime|Mystery|Thriller  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended = pd.merge(recommended, movies[['title','genres']], on='title')\n",
    "recommended.head(6)"
   ]
  }
 ],
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