Movie_Recommender_System_Improved.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading Rating dataset\n",
    "rating_data = pd.read_csv(\"ratings.csv\")\n",
    "rating_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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": [
    "#Loading Movies Names DataSet\n",
    "movie_names = pd.read_csv(\"movies.csv\")\n",
    "movie_names.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
       "      <th>title</th>\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",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp             title  \\\n",
       "0       1        1     4.0   964982703  Toy Story (1995)   \n",
       "1       5        1     4.0   847434962  Toy Story (1995)   \n",
       "2       7        1     4.5  1106635946  Toy Story (1995)   \n",
       "3      15        1     2.5  1510577970  Toy Story (1995)   \n",
       "4      17        1     4.5  1305696483  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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now emerging both movies_name dataset and ratings dataset using \"Merge\" function\n",
    "\n",
    "movie_df = pd.merge(rating_data, movie_names, on='movieId')\n",
    "movie_df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "'71 (2014)                                 4.0\n",
       "'Hellboy': The Seeds of Creation (2004)    4.0\n",
       "'Round Midnight (1986)                     3.5\n",
       "'Salem's Lot (2004)                        5.0\n",
       "'Til There Was You (1997)                  4.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking average rating of each movie.\n",
    "#Group the dataset by the title of the movie and then calculate the mean of the rating for each movie.\n",
    "\n",
    "\n",
    "movie_df.groupby('title')['rating'].mean().head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
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       "      <td>964982703</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
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       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1510577970</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
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       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1305696483</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  movieId  rating   timestamp             title  \\\n",
       "0       1        1     4.0   964982703  Toy Story (1995)   \n",
       "1       5        1     4.0   847434962  Toy Story (1995)   \n",
       "2       7        1     4.5  1106635946  Toy Story (1995)   \n",
       "3      15        1     2.5  1510577970  Toy Story (1995)   \n",
       "4      17        1     4.5  1305696483  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  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort data set according to the rating\n",
    "movie_df.groupby('title')['rating'].count().sort_values(ascending=False) \n",
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         rating\n",
      "title                                          \n",
      "'71 (2014)                                  4.0\n",
      "'Hellboy': The Seeds of Creation (2004)     4.0\n",
      "'Round Midnight (1986)                      3.5\n",
      "'Salem's Lot (2004)                         5.0\n",
      "'Til There Was You (1997)                   4.0\n"
     ]
    }
   ],
   "source": [
    "#Creating new dataSet that contains average rating each movie and the number of ratings per movie.\n",
    "ratings_mean_count = pd.DataFrame(movie_df.groupby('title')['rating'].mean())\n",
    "print (ratings_mean_count.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         rating  rating_counts\n",
      "title                                                         \n",
      "'71 (2014)                                  4.0              1\n",
      "'Hellboy': The Seeds of Creation (2004)     4.0              1\n",
      "'Round Midnight (1986)                      3.5              2\n",
      "'Salem's Lot (2004)                         5.0              1\n",
      "'Til There Was You (1997)                   4.0              2\n"
     ]
    }
   ],
   "source": [
    "# Adding number of ratings of movie to ratings_mean_count\n",
    "ratings_mean_count['rating_counts'] = pd.DataFrame(movie_df.groupby('title')['rating'].count())\n",
    "print (ratings_mean_count.head())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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)  \\\n",
      "userId                                   \n",
      "1                                  NaN   \n",
      "2                                  NaN   \n",
      "3                                  NaN   \n",
      "4                                  NaN   \n",
      "5                                  NaN   \n",
      "\n",
      "title                     ...                      Zulu (2013)  [REC] (2007)  \\\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   [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]\n"
     ]
    }
   ],
   "source": [
    "#Creating Matrix of Movie names and ratings. So we could apply correlation between the ratings of a movies as the similarity metric.\n",
    "\n",
    "user_movie_rating = movie_df.pivot_table(index='userId', columns='title', values='rating')\n",
    "print(user_movie_rating.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "userId\n",
      "1    4.0\n",
      "2    NaN\n",
      "3    NaN\n",
      "4    NaN\n",
      "5    NaN\n",
      "Name: Forrest Gump (1994), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# as we know, each columns consists of all  user ratings for a particular. Now find all the user ratings for the movie \"Forrest Grump 1994\" and find the similar movie.\n",
    "forrest_grump_ratings = user_movie_rating['Forrest Gump (1994)']\n",
    "\n",
    "print(forrest_grump_ratings.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3175: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3109: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  c *= 1. / np.float64(fact)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                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\n"
     ]
    }
   ],
   "source": [
    "# Finding similar movies to \"Foresst Gump 1994\" using corrwith\n",
    "movies_like_forest_grump = user_movie_rating.corrwith(forrest_grump_ratings)\n",
    "\n",
    "corr_forrest_grump = pd.DataFrame(movies_like_forest_grump, columns=['Correlation'])\n",
    "corr_forrest_grump.dropna(inplace=True)\n",
    "print(corr_forrest_grump.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lost &amp; Found (1999)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cercle Rouge, Le (Red Circle, The) (1970)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Play Time (a.k.a. Playtime) (1967)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Killers (2010)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Playing God (1997)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Girl Walks Home Alone at Night, A (2014)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tampopo (1985)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Century of the Self, The (2002)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Welcome to the Jungle (2013)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poison Ivy: New Seduction (1997)</th>\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",
       "Cercle Rouge, Le (Red Circle, The) (1970)          1.0\n",
       "Play Time (a.k.a. Playtime) (1967)                 1.0\n",
       "Killers (2010)                                     1.0\n",
       "Playing God (1997)                                 1.0\n",
       "Girl Walks Home Alone at Night, A (2014)           1.0\n",
       "Tampopo (1985)                                     1.0\n",
       "Century of the Self, The (2002)                    1.0\n",
       "Welcome to the Jungle (2013)                       1.0\n",
       "Poison Ivy: New Seduction (1997)                   1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forrest_grump.sort_values('Correlation', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Correlation</th>\n",
       "      <th>rating_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>0.197712</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <td>0.234095</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <td>0.892710</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Cent Pistol (2015)</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Correlation  rating_counts\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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forrest_grump = corr_forrest_grump.join(ratings_mean_count['rating_counts'])  \n",
    "corr_forrest_grump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>rating_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Forrest Gump (1994)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr. Holland's Opus (1995)</th>\n",
       "      <td>0.652144</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pocahontas (1995)</th>\n",
       "      <td>0.550118</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grumpier Old Men (1995)</th>\n",
       "      <td>0.534682</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caddyshack (1980)</th>\n",
       "      <td>0.520328</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Correlation  rating_counts\n",
       "title                                                \n",
       "Forrest Gump (1994)           1.000000            329\n",
       "Mr. Holland's Opus (1995)     0.652144             80\n",
       "Pocahontas (1995)             0.550118             68\n",
       "Grumpier Old Men (1995)       0.534682             52\n",
       "Caddyshack (1980)             0.520328             52"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_forrest_grump[corr_forrest_grump ['rating_counts']>50].sort_values('Correlation', ascending=False).head()  "
   ]
  }
 ],
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