Movie Recommender with Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df = pd.read_csv('/cxldata/skin_disease_1/sudeep/recommended_system/movielen/movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_df = pd.read_csv('/cxldata/skin_disease_1/sudeep/recommended_system/movielen/ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
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       "      <td>964982931</td>\n",
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      ],
      "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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags_df = pd.read_csv('/cxldata/skin_disease_1/sudeep/recommended_system/movielen/tags.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>3678</td>\n",
       "      <td>606</td>\n",
       "      <td>7382</td>\n",
       "      <td>for katie</td>\n",
       "      <td>1171234019</td>\n",
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       "    <tr>\n",
       "      <td>3679</td>\n",
       "      <td>606</td>\n",
       "      <td>7936</td>\n",
       "      <td>austere</td>\n",
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       "    <tr>\n",
       "      <td>3680</td>\n",
       "      <td>610</td>\n",
       "      <td>3265</td>\n",
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       "      <td>1493843984</td>\n",
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       "<p>3683 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      userId  movieId               tag   timestamp\n",
       "0          2    60756             funny  1445714994\n",
       "1          2    60756   Highly quotable  1445714996\n",
       "2          2    60756      will ferrell  1445714992\n",
       "3          2    89774      Boxing story  1445715207\n",
       "4          2    89774               MMA  1445715200\n",
       "...      ...      ...               ...         ...\n",
       "3678     606     7382         for katie  1171234019\n",
       "3679     606     7936           austere  1173392334\n",
       "3680     610     3265            gun fu  1493843984\n",
       "3681     610     3265  heroic bloodshed  1493843978\n",
       "3682     610   168248  Heroic Bloodshed  1493844270\n",
       "\n",
       "[3683 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_tag_df = pd.merge(movies_df,ratings_df,on='movieId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
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       "      <td>3.5</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>21</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
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      "text/plain": [
       "   movieId             title                                       genres  \\\n",
       "0        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "2        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "3        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "4        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "5        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "6        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "7        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "8        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "9        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "\n",
       "   userId  rating   timestamp  \n",
       "0       1     4.0   964982703  \n",
       "1       5     4.0   847434962  \n",
       "2       7     4.5  1106635946  \n",
       "3      15     2.5  1510577970  \n",
       "4      17     4.5  1305696483  \n",
       "5      18     3.5  1455209816  \n",
       "6      19     4.0   965705637  \n",
       "7      21     3.5  1407618878  \n",
       "8      27     3.0   962685262  \n",
       "9      31     5.0   850466616  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_tag_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Karlson Returns (1970)                           5.0\n",
       "Winter in Prostokvashino (1984)                  5.0\n",
       "My Love (2006)                                   5.0\n",
       "Sorority House Massacre II (1990)                5.0\n",
       "Winnie the Pooh and the Day of Concern (1972)    5.0\n",
       "                                                ... \n",
       "The Beast of Hollow Mountain (1956)              0.5\n",
       "Follow Me, Boys! (1966)                          0.5\n",
       "The Butterfly Effect 3: Revelations (2009)       0.5\n",
       "The Emoji Movie (2017)                           0.5\n",
       "Rust and Bone (De rouille et d'os) (2012)        0.5\n",
       "Name: rating, Length: 9719, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_tag_df.groupby('title')['rating'].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Forrest Gump (1994)                                                               329\n",
       "Shawshank Redemption, The (1994)                                                  317\n",
       "Pulp Fiction (1994)                                                               307\n",
       "Silence of the Lambs, The (1991)                                                  279\n",
       "Matrix, The (1999)                                                                278\n",
       "                                                                                 ... \n",
       "Late Night Shopping (2001)                                                          1\n",
       "Late Night with Conan O'Brien: The Best of Triumph the Insult Comic Dog (2004)      1\n",
       "Late Shift, The (1996)                                                              1\n",
       "Latter Days (2003)                                                                  1\n",
       "'71 (2014)                                                                          1\n",
       "Name: rating, Length: 9719, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_tag_df.groupby('title')['rating'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_df = pd.DataFrame(movie_tag_df.groupby('title')['rating'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>'71 (2014)</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>'Hellboy': The Seeds of Creation (2004)</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>'Round Midnight (1986)</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>'Salem's Lot (2004)</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>'Til There Was You (1997)</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>eXistenZ (1999)</td>\n",
       "      <td>3.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>xXx (2002)</td>\n",
       "      <td>2.770833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>xXx: State of the Union (2005)</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>¡Three Amigos! (1986)</td>\n",
       "      <td>3.134615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>À nous la liberté (Freedom for Us) (1931)</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9719 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             rating\n",
       "title                                              \n",
       "'71 (2014)                                 4.000000\n",
       "'Hellboy': The Seeds of Creation (2004)    4.000000\n",
       "'Round Midnight (1986)                     3.500000\n",
       "'Salem's Lot (2004)                        5.000000\n",
       "'Til There Was You (1997)                  4.000000\n",
       "...                                             ...\n",
       "eXistenZ (1999)                            3.863636\n",
       "xXx (2002)                                 2.770833\n",
       "xXx: State of the Union (2005)             2.000000\n",
       "¡Three Amigos! (1986)                      3.134615\n",
       "À nous la liberté (Freedom for Us) (1931)  1.000000\n",
       "\n",
       "[9719 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_df['rating_count'] = pd.DataFrame(movie_tag_df.groupby('title')['rating'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>rating</th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>'71 (2014)</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>'Hellboy': The Seeds of Creation (2004)</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>'Round Midnight (1986)</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>'Salem's Lot (2004)</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>'Til There Was You (1997)</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>eXistenZ (1999)</td>\n",
       "      <td>3.863636</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>xXx (2002)</td>\n",
       "      <td>2.770833</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>xXx: State of the Union (2005)</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>¡Three Amigos! (1986)</td>\n",
       "      <td>3.134615</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>À nous la liberté (Freedom for Us) (1931)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9719 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             rating  rating_count\n",
       "title                                                            \n",
       "'71 (2014)                                 4.000000             1\n",
       "'Hellboy': The Seeds of Creation (2004)    4.000000             1\n",
       "'Round Midnight (1986)                     3.500000             2\n",
       "'Salem's Lot (2004)                        5.000000             1\n",
       "'Til There Was You (1997)                  4.000000             2\n",
       "...                                             ...           ...\n",
       "eXistenZ (1999)                            3.863636            22\n",
       "xXx (2002)                                 2.770833            24\n",
       "xXx: State of the Union (2005)             2.000000             5\n",
       "¡Three Amigos! (1986)                      3.134615            26\n",
       "À nous la liberté (Freedom for Us) (1931)  1.000000             1\n",
       "\n",
       "[9719 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f707864e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,4))\n",
    "rating_df['rating_count'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f706810b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,4))\n",
    "rating_df['rating'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f0f722633c8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.jointplot(x='rating',y='rating_count',data=rating_df,alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>4.164134</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>4.429022</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>4.197068</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>4.161290</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>4.192446</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>King Solomon's Mines (1950)</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>King Solomon's Mines (1937)</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>King Ralph (1991)</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>King Kong Lives (1986)</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>À nous la liberté (Freedom for Us) (1931)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9719 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             rating  rating_count\n",
       "title                                                            \n",
       "Forrest Gump (1994)                        4.164134           329\n",
       "Shawshank Redemption, The (1994)           4.429022           317\n",
       "Pulp Fiction (1994)                        4.197068           307\n",
       "Silence of the Lambs, The (1991)           4.161290           279\n",
       "Matrix, The (1999)                         4.192446           278\n",
       "...                                             ...           ...\n",
       "King Solomon's Mines (1950)                3.000000             1\n",
       "King Solomon's Mines (1937)                2.500000             1\n",
       "King Ralph (1991)                          1.500000             1\n",
       "King Kong Lives (1986)                     2.000000             1\n",
       "À nous la liberté (Freedom for Us) (1931)  1.000000             1\n",
       "\n",
       "[9719 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_df.sort_values('rating_count',ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>title</th>\n",
       "      <th>'71 (2014)</th>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <th>'Salem's Lot (2004)</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>'Tis the Season for Love (2015)</th>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <th>'night Mother (1986)</th>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <th>...</th>\n",
       "      <th>Zulu (2013)</th>\n",
       "      <th>[REC] (2007)</th>\n",
       "      <th>[REC]² (2009)</th>\n",
       "      <th>[REC]³ 3 Génesis (2012)</th>\n",
       "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <th>xXx (2002)</th>\n",
       "      <th>xXx: State of the Union (2005)</th>\n",
       "      <th>¡Three Amigos! (1986)</th>\n",
       "      <th>À nous la liberté (Freedom for Us) (1931)</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",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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",
       "      <td>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>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>5 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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\n",
       "[5 rows x 9719 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviemat = movie_tag_df.pivot_table(index='userId',columns='title',values='rating')\n",
    "moviemat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "forestgump_user_ratings = moviemat['Forrest Gump (1994)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId\n",
       "1    4.0\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "Name: Forrest Gump (1994), dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forestgump_user_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python3.6/site-packages/numpy/lib/function_base.py:2522: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "/usr/local/anaconda/lib/python3.6/site-packages/numpy/lib/function_base.py:2451: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    }
   ],
   "source": [
    "similar_to_forestgump = moviemat.corrwith(forestgump_user_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
       "                                               ...   \n",
       "eXistenZ (1999)                              0.011189\n",
       "xXx (2002)                                   0.306817\n",
       "xXx: State of the Union (2005)               0.383482\n",
       "¡Three Amigos! (1986)                        0.449692\n",
       "À nous la liberté (Freedom for Us) (1931)         NaN\n",
       "Length: 9719, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_to_forestgump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>'burbs, The (1989)</td>\n",
       "      <td>0.197712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(500) Days of Summer (2009)</td>\n",
       "      <td>0.234095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>*batteries not included (1987)</td>\n",
       "      <td>0.892710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...And Justice for All (1979)</td>\n",
       "      <td>0.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10 Cent Pistol (2015)</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Correlation\n",
       "title                                      \n",
       "'burbs, The (1989)                 0.197712\n",
       "(500) Days of Summer (2009)        0.234095\n",
       "*batteries not included (1987)     0.892710\n",
       "...And Justice for All (1979)      0.928571\n",
       "10 Cent Pistol (2015)             -1.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forestgump = pd.DataFrame(similar_to_forestgump,columns=['Correlation'])\n",
    "corr_forestgump.dropna(inplace=True)\n",
    "corr_forestgump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Lost &amp; Found (1999)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Century of the Self, The (2002)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>The 5th Wave (2016)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Play Time (a.k.a. Playtime) (1967)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Memories (Memorîzu) (1995)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Playing God (1997)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Killers (2010)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Girl Walks Home Alone at Night, A (2014)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tampopo (1985)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cercle Rouge, Le (Red Circle, The) (1970)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Correlation\n",
       "title                                                 \n",
       "Lost & Found (1999)                                1.0\n",
       "Century of the Self, The (2002)                    1.0\n",
       "The 5th Wave (2016)                                1.0\n",
       "Play Time (a.k.a. Playtime) (1967)                 1.0\n",
       "Memories (Memorîzu) (1995)                         1.0\n",
       "Playing God (1997)                                 1.0\n",
       "Killers (2010)                                     1.0\n",
       "Girl Walks Home Alone at Night, A (2014)           1.0\n",
       "Tampopo (1985)                                     1.0\n",
       "Cercle Rouge, Le (Red Circle, The) (1970)          1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forestgump.sort_values('Correlation',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>'burbs, The (1989)</td>\n",
       "      <td>0.197712</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(500) Days of Summer (2009)</td>\n",
       "      <td>0.234095</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>*batteries not included (1987)</td>\n",
       "      <td>0.892710</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...And Justice for All (1979)</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10 Cent Pistol (2015)</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Correlation  rating_count\n",
       "title                                                    \n",
       "'burbs, The (1989)                 0.197712            17\n",
       "(500) Days of Summer (2009)        0.234095            42\n",
       "*batteries not included (1987)     0.892710             7\n",
       "...And Justice for All (1979)      0.928571             3\n",
       "10 Cent Pistol (2015)             -1.000000             2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forestgump = corr_forestgump.join(rating_df['rating_count'])\n",
    "corr_forestgump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Good Will Hunting (1997)</td>\n",
       "      <td>0.484042</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>0.464268</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>American History X (1998)</td>\n",
       "      <td>0.457287</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Truman Show, The (1998)</td>\n",
       "      <td>0.432556</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Correlation  rating_count\n",
       "title                                               \n",
       "Forrest Gump (1994)           1.000000           329\n",
       "Good Will Hunting (1997)      0.484042           141\n",
       "Aladdin (1992)                0.464268           183\n",
       "American History X (1998)     0.457287           129\n",
       "Truman Show, The (1998)       0.432556           125"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forestgump[corr_forestgump['rating_count']>100].sort_values('Correlation',ascending=False).head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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