movierecommender.ipynb

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    "Problem statement\n\n",
    "The Dataset\n",
    "\n",
    "The movies dataset contains metadata for 45,000 movies released on or before July 2017 and listed in the MovieLens database. Features include genre, title, budget, revenue, release dates, languages, production countries and companies, countries, TMDB vote counts and vote averages, and other details. Below are the features information:\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "adult: Indicates if the movie is X-Rated or Adult.\n",
    "\n",
    "belongs_to_collection: A stringified dictionary that gives information on the movie series the particular film belongs to.\n",
    "\n",
    "budget: The budget of the movie in dollars.\n",
    "\n",
    "genres: A stringified list of dictionaries that list out all the genres associated with the movie.\n",
    "\n",
    "homepage: The Official Homepage of the move.\n",
    "\n",
    "id: The ID of the movie.\n",
    "\n",
    "imdb_id: The IMDB ID of the movie.\n",
    "\n",
    "original_language: The language in which the movie was originally shot in.\n",
    "\n",
    "original_title: The original title of the movie.\n",
    "\n",
    "overview: A brief blurb of the movie.\n",
    "\n",
    "popularity: The Popularity Score assigned by TMDB.\n",
    "\n",
    "poster_path: The URL of the poster image.\n",
    "\n",
    "production_companies: A stringified list of production companies involved with the making of the movie.\n",
    "\n",
    "production_countries: A stringified list of countries where the movie was shot/produced in.\n",
    "\n",
    "release_date: Theatrical Release Date of the movie.\n",
    "\n",
    "revenue: The total revenue of the movie in dollars.\n",
    "\n",
    "runtime: The runtime of the movie in minutes.\n",
    "\n",
    "spoken_languages: A stringified list of spoken languages in the film.\n",
    "\n",
    "status: The status of the movie (Released, To Be Released, Announced, etc.)\n",
    "\n",
    "tagline: The tagline of the movie.\n",
    "\n",
    "title: The Official Title of the movie.\n",
    "\n",
    "video: Indicates if there is a video present of the movie with TMDB.\n",
    "\n",
    "vote_average: The average rating of the movie.\n",
    "\n",
    "vote_count: The number of votes by users, as counted by TMDB.\n",
    "\n",
   
    "Dataset Source: https://www.kaggle.com/rounakbanik/the-movies-dataset"
   ]
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       "      <th></th>\n",
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       "      <th>tagline</th>\n",
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       "      <th>vote_average</th>\n",
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       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
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       "      <td>http://toystory.disney.com/toy-story</td>\n",
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       "      <td>tt0114709</td>\n",
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       "      <td>False</td>\n",
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       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
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       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15602</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31357</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11862</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>en</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Just when George Banks has recovered from his ...</td>\n",
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       "      <td>1995-02-10</td>\n",
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       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
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       "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>False</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
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       "<p>5 rows × 24 columns</p>\n",
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       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
       "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage     id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
       "1                                   NaN   8844  tt0113497                en   \n",
       "2                                   NaN  15602  tt0113228                en   \n",
       "3                                   NaN  31357  tt0114885                en   \n",
       "4                                   NaN  11862  tt0113041                en   \n",
       "\n",
       "                original_title  \\\n",
       "0                    Toy Story   \n",
       "1                      Jumanji   \n",
       "2             Grumpier Old Men   \n",
       "3            Waiting to Exhale   \n",
       "4  Father of the Bride Part II   \n",
       "\n",
       "                                            overview  ... release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  ...   1995-10-30   \n",
       "1  When siblings Judy and Peter discover an encha...  ...   1995-12-15   \n",
       "2  A family wedding reignites the ancient feud be...  ...   1995-12-22   \n",
       "3  Cheated on, mistreated and stepped on, the wom...  ...   1995-12-22   \n",
       "4  Just when George Banks has recovered from his ...  ...   1995-02-10   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
       "0  373554033.0    81.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "1  262797249.0   104.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
       "2          0.0   101.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "3   81452156.0   127.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "4   76578911.0   106.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
       "0                    Toy Story  False          7.7     5415.0  \n",
       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
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   ],
   "source": [
    "# Import the required modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Read the dataset and print first five records.\n",
    "url = \"https://drive.google.com/uc?id=1rPR-P45M2UWsbXc8vpyCzWcQAYUfgVJX\"\n",
    "movies_df = pd.read_csv(url)\n",
    "movies_df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45466 entries, 0 to 45465\n",
      "Data columns (total 24 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   adult                  45466 non-null  object \n",
      " 1   belongs_to_collection  4494 non-null   object \n",
      " 2   budget                 45466 non-null  object \n",
      " 3   genres                 45466 non-null  object \n",
      " 4   homepage               7782 non-null   object \n",
      " 5   id                     45466 non-null  object \n",
      " 6   imdb_id                45449 non-null  object \n",
      " 7   original_language      45455 non-null  object \n",
      " 8   original_title         45466 non-null  object \n",
      " 9   overview               44512 non-null  object \n",
      " 10  popularity             45461 non-null  object \n",
      " 11  poster_path            45080 non-null  object \n",
      " 12  production_companies   45463 non-null  object \n",
      " 13  production_countries   45463 non-null  object \n",
      " 14  release_date           45379 non-null  object \n",
      " 15  revenue                45460 non-null  float64\n",
      " 16  runtime                45203 non-null  float64\n",
      " 17  spoken_languages       45460 non-null  object \n",
      " 18  status                 45379 non-null  object \n",
      " 19  tagline                20412 non-null  object \n",
      " 20  title                  45460 non-null  object \n",
      " 21  video                  45460 non-null  object \n",
      " 22  vote_average           45460 non-null  float64\n",
      " 23  vote_count             45460 non-null  float64\n",
      "dtypes: float64(4), object(20)\n",
      "memory usage: 8.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# 2: Get the total number of rows and columns, data-types of columns and missing values in the dataset\n",
    "\n",
    "movies_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset consists of 45466 rows and 24 columns. A lot of features exhibit missing (or null) values in the dataset.\n",
    "\n",
    "Our aim is to build three simple recommender systems which recommends:\n",
    "\n",
    "Top 20 movies having highest popularity.\n",
    "\n",
    "Top 20 movies having highest average weighted rating.\n",
    "\n",
    "Top 20 movies of a genre having highest rating.\n",
    "\n",
    "As our dataset contains a lot of features that are not useful in building the above recommenders, let us create a subset of this dataset that consists of important features like:\n",
    "\n",
    "genres, id, popularity, title, vote_average, vote_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
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       "  <thead>\n",
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       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>862</td>\n",
       "      <td>21.946943</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>8844</td>\n",
       "      <td>17.015539</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>15602</td>\n",
       "      <td>11.7129</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>31357</td>\n",
       "      <td>3.859495</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>11862</td>\n",
       "      <td>8.387519</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              genres     id popularity  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...    862  21.946943   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   8844  17.015539   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...  15602    11.7129   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  31357   3.859495   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]  11862   8.387519   \n",
       "\n",
       "                         title  vote_average  vote_count  \n",
       "0                    Toy Story           7.7      5415.0  \n",
       "1                      Jumanji           6.9      2413.0  \n",
       "2             Grumpier Old Men           6.5        92.0  \n",
       "3            Waiting to Exhale           6.1        34.0  \n",
       "4  Father of the Bride Part II           5.7       173.0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3: Create a DataFrame with only required features.\n",
    "\n",
    "mov_subset_df = movies_df[['genres', 'id', 'popularity', 'title', 'vote_average', 'vote_count']]\n",
    "mov_subset_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...</td>\n",
       "      <td>105045</td>\n",
       "      <td>0.122178</td>\n",
       "      <td>The Promise</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...</td>\n",
       "      <td>105045</td>\n",
       "      <td>0.122178</td>\n",
       "      <td>The Promise</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44821</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>10991</td>\n",
       "      <td>6.480376</td>\n",
       "      <td>Pokémon: Spell of the Unknown</td>\n",
       "      <td>6.0</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4114</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>10991</td>\n",
       "      <td>10.264597</td>\n",
       "      <td>Pokémon: Spell of the Unknown</td>\n",
       "      <td>6.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5710</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>109962</td>\n",
       "      <td>12.180836</td>\n",
       "      <td>Rich and Famous</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20899</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>109962</td>\n",
       "      <td>10.396878</td>\n",
       "      <td>Rich and Famous</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23534</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>110428</td>\n",
       "      <td>0.110065</td>\n",
       "      <td>Camille Claudel 1915</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4356</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>110428</td>\n",
       "      <td>0.134014</td>\n",
       "      <td>Camille Claudel 1915</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24844</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>11115</td>\n",
       "      <td>6.880365</td>\n",
       "      <td>Deal</td>\n",
       "      <td>5.2</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14012</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>11115</td>\n",
       "      <td>6.880365</td>\n",
       "      <td>Deal</td>\n",
       "      <td>5.2</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21165</th>\n",
       "      <td>[{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'na...</td>\n",
       "      <td>119916</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>The Tempest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19890</th>\n",
       "      <td>[{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'na...</td>\n",
       "      <td>119916</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>The Tempest</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5535</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>12600</td>\n",
       "      <td>7.072301</td>\n",
       "      <td>Pokémon 4Ever: Celebi - Voice of the Forest</td>\n",
       "      <td>5.7</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44826</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>12600</td>\n",
       "      <td>6.080108</td>\n",
       "      <td>Pokémon 4Ever: Celebi - Voice of the Forest</td>\n",
       "      <td>5.7</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11342</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...</td>\n",
       "      <td>13209</td>\n",
       "      <td>1.52896</td>\n",
       "      <td>Offside</td>\n",
       "      <td>6.7</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15765</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...</td>\n",
       "      <td>13209</td>\n",
       "      <td>1.529879</td>\n",
       "      <td>Offside</td>\n",
       "      <td>6.7</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>132641</td>\n",
       "      <td>0.096079</td>\n",
       "      <td>Wife</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30001</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>132641</td>\n",
       "      <td>0.619388</td>\n",
       "      <td>Wife</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16764</th>\n",
       "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...</td>\n",
       "      <td>141971</td>\n",
       "      <td>0.411949</td>\n",
       "      <td>Blackout</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13261</th>\n",
       "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...</td>\n",
       "      <td>141971</td>\n",
       "      <td>0.411949</td>\n",
       "      <td>Blackout</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13375</th>\n",
       "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...</td>\n",
       "      <td>141971</td>\n",
       "      <td>0.411949</td>\n",
       "      <td>Blackout</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10419</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...</td>\n",
       "      <td>14788</td>\n",
       "      <td>3.185256</td>\n",
       "      <td>Bubble</td>\n",
       "      <td>6.4</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12066</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...</td>\n",
       "      <td>14788</td>\n",
       "      <td>3.008299</td>\n",
       "      <td>Bubble</td>\n",
       "      <td>6.4</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5130</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 10751...</td>\n",
       "      <td>15028</td>\n",
       "      <td>5.373623</td>\n",
       "      <td>Clockstoppers</td>\n",
       "      <td>4.9</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33743</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 10751...</td>\n",
       "      <td>15028</td>\n",
       "      <td>4.920175</td>\n",
       "      <td>Clockstoppers</td>\n",
       "      <td>4.9</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19925</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 878, 'nam...</td>\n",
       "      <td>152795</td>\n",
       "      <td>8.534039</td>\n",
       "      <td>The Congress</td>\n",
       "      <td>6.4</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21854</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 878, 'nam...</td>\n",
       "      <td>152795</td>\n",
       "      <td>8.534039</td>\n",
       "      <td>The Congress</td>\n",
       "      <td>6.4</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35798</th>\n",
       "      <td>[{'id': 99, 'name': 'Documentary'}]</td>\n",
       "      <td>159849</td>\n",
       "      <td>0.473322</td>\n",
       "      <td>Why We Fight: Divide and Conquer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13220</th>\n",
       "      <td>[{'id': 99, 'name': 'Documentary'}]</td>\n",
       "      <td>159849</td>\n",
       "      <td>0.473322</td>\n",
       "      <td>Why We Fight: Divide and Conquer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13603</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...</td>\n",
       "      <td>168538</td>\n",
       "      <td>1.276602</td>\n",
       "      <td>Nana, the True Key of Pleasure</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28860</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...</td>\n",
       "      <td>168538</td>\n",
       "      <td>1.276602</td>\n",
       "      <td>Nana, the True Key of Pleasure</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22151</th>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 27, 'nam...</td>\n",
       "      <td>18440</td>\n",
       "      <td>1.436085</td>\n",
       "      <td>Days of Darkness</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14000</th>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 27, 'nam...</td>\n",
       "      <td>18440</td>\n",
       "      <td>1.436085</td>\n",
       "      <td>Days of Darkness</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...</td>\n",
       "      <td>22649</td>\n",
       "      <td>1.914697</td>\n",
       "      <td>A Farewell to Arms</td>\n",
       "      <td>6.2</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15074</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...</td>\n",
       "      <td>22649</td>\n",
       "      <td>2.411191</td>\n",
       "      <td>A Farewell to Arms</td>\n",
       "      <td>6.2</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8068</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 16, '...</td>\n",
       "      <td>23305</td>\n",
       "      <td>1.967992</td>\n",
       "      <td>The Warrior</td>\n",
       "      <td>6.3</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9327</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 16, '...</td>\n",
       "      <td>23305</td>\n",
       "      <td>1.967992</td>\n",
       "      <td>The Warrior</td>\n",
       "      <td>6.3</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17229</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>25541</td>\n",
       "      <td>2.587911</td>\n",
       "      <td>Brotherhood</td>\n",
       "      <td>7.1</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23044</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
       "      <td>25541</td>\n",
       "      <td>2.587911</td>\n",
       "      <td>Brotherhood</td>\n",
       "      <td>7.1</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24164</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>265189</td>\n",
       "      <td>12.165685</td>\n",
       "      <td>Force Majeure</td>\n",
       "      <td>6.8</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45265</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>265189</td>\n",
       "      <td>12.165685</td>\n",
       "      <td>Force Majeure</td>\n",
       "      <td>6.8</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33184</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 14, 'name...</td>\n",
       "      <td>298721</td>\n",
       "      <td>2.535419</td>\n",
       "      <td>Cemetery of Splendour</td>\n",
       "      <td>4.4</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40040</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 14, 'name...</td>\n",
       "      <td>298721</td>\n",
       "      <td>2.535419</td>\n",
       "      <td>Cemetery of Splendour</td>\n",
       "      <td>4.4</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15702</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10769, 'n...</td>\n",
       "      <td>42495</td>\n",
       "      <td>0.187901</td>\n",
       "      <td>King Lear</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29374</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 10769, 'n...</td>\n",
       "      <td>42495</td>\n",
       "      <td>0.187901</td>\n",
       "      <td>King Lear</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33826</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 80, 'nam...</td>\n",
       "      <td>4912</td>\n",
       "      <td>7.645827</td>\n",
       "      <td>Confessions of a Dangerous Mind</td>\n",
       "      <td>6.6</td>\n",
       "      <td>281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5865</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 80, 'nam...</td>\n",
       "      <td>4912</td>\n",
       "      <td>11.331072</td>\n",
       "      <td>Confessions of a Dangerous Mind</td>\n",
       "      <td>6.6</td>\n",
       "      <td>281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7345</th>\n",
       "      <td>[{'id': 80, 'name': 'Crime'}, {'id': 18, 'name...</td>\n",
       "      <td>5511</td>\n",
       "      <td>9.091288</td>\n",
       "      <td>Le Samouraï</td>\n",
       "      <td>7.9</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9165</th>\n",
       "      <td>[{'id': 80, 'name': 'Crime'}, {'id': 18, 'name...</td>\n",
       "      <td>5511</td>\n",
       "      <td>9.091288</td>\n",
       "      <td>Le Samouraï</td>\n",
       "      <td>7.9</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26625</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 27, 'name...</td>\n",
       "      <td>69234</td>\n",
       "      <td>0.43849</td>\n",
       "      <td>The Phantom of the Opera</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9576</th>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 27, 'name...</td>\n",
       "      <td>69234</td>\n",
       "      <td>0.441872</td>\n",
       "      <td>The Phantom of the Opera</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20843</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 18, '...</td>\n",
       "      <td>77221</td>\n",
       "      <td>6.475665</td>\n",
       "      <td>Black Gold</td>\n",
       "      <td>5.9</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11155</th>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 18, '...</td>\n",
       "      <td>77221</td>\n",
       "      <td>6.652197</td>\n",
       "      <td>Black Gold</td>\n",
       "      <td>5.9</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>[{'id': 99, 'name': 'Documentary'}]</td>\n",
       "      <td>84198</td>\n",
       "      <td>0.501046</td>\n",
       "      <td>A Place at the Table</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21116</th>\n",
       "      <td>[{'id': 99, 'name': 'Documentary'}]</td>\n",
       "      <td>84198</td>\n",
       "      <td>1.673307</td>\n",
       "      <td>A Place at the Table</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13946</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>97995</td>\n",
       "      <td>0.141558</td>\n",
       "      <td>Seven Years Bad Luck</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40276</th>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>97995</td>\n",
       "      <td>0.141558</td>\n",
       "      <td>Seven Years Bad Luck</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38871</th>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...</td>\n",
       "      <td>99080</td>\n",
       "      <td>0.002362</td>\n",
       "      <td>The Viking</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16167</th>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...</td>\n",
       "      <td>99080</td>\n",
       "      <td>0.002362</td>\n",
       "      <td>The Viking</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  genres      id popularity  \\\n",
       "676    [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  105045   0.122178   \n",
       "1465   [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...  105045   0.122178   \n",
       "44821  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   10991   6.480376   \n",
       "4114   [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   10991  10.264597   \n",
       "5710                       [{'id': 18, 'name': 'Drama'}]  109962  12.180836   \n",
       "20899                      [{'id': 18, 'name': 'Drama'}]  109962  10.396878   \n",
       "23534                      [{'id': 18, 'name': 'Drama'}]  110428   0.110065   \n",
       "4356                       [{'id': 18, 'name': 'Drama'}]  110428   0.134014   \n",
       "24844  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   11115   6.880365   \n",
       "14012  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   11115   6.880365   \n",
       "21165  [{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'na...  119916   0.000018   \n",
       "19890  [{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'na...  119916   0.000018   \n",
       "5535   [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   12600   7.072301   \n",
       "44826  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   12600   6.080108   \n",
       "11342  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...   13209    1.52896   \n",
       "15765  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...   13209   1.529879   \n",
       "838                        [{'id': 18, 'name': 'Drama'}]  132641   0.096079   \n",
       "30001                      [{'id': 18, 'name': 'Drama'}]  132641   0.619388   \n",
       "16764  [{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...  141971   0.411949   \n",
       "13261  [{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...  141971   0.411949   \n",
       "13375  [{'id': 53, 'name': 'Thriller'}, {'id': 9648, ...  141971   0.411949   \n",
       "10419  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...   14788   3.185256   \n",
       "12066  [{'id': 18, 'name': 'Drama'}, {'id': 80, 'name...   14788   3.008299   \n",
       "5130   [{'id': 12, 'name': 'Adventure'}, {'id': 10751...   15028   5.373623   \n",
       "33743  [{'id': 12, 'name': 'Adventure'}, {'id': 10751...   15028   4.920175   \n",
       "19925  [{'id': 18, 'name': 'Drama'}, {'id': 878, 'nam...  152795   8.534039   \n",
       "21854  [{'id': 18, 'name': 'Drama'}, {'id': 878, 'nam...  152795   8.534039   \n",
       "35798                [{'id': 99, 'name': 'Documentary'}]  159849   0.473322   \n",
       "13220                [{'id': 99, 'name': 'Documentary'}]  159849   0.473322   \n",
       "13603  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...  168538   1.276602   \n",
       "28860  [{'id': 18, 'name': 'Drama'}, {'id': 35, 'name...  168538   1.276602   \n",
       "22151  [{'id': 28, 'name': 'Action'}, {'id': 27, 'nam...   18440   1.436085   \n",
       "14000  [{'id': 28, 'name': 'Action'}, {'id': 27, 'nam...   18440   1.436085   \n",
       "949    [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...   22649   1.914697   \n",
       "15074  [{'id': 18, 'name': 'Drama'}, {'id': 10749, 'n...   22649   2.411191   \n",
       "8068   [{'id': 12, 'name': 'Adventure'}, {'id': 16, '...   23305   1.967992   \n",
       "9327   [{'id': 12, 'name': 'Adventure'}, {'id': 16, '...   23305   1.967992   \n",
       "17229                      [{'id': 18, 'name': 'Drama'}]   25541   2.587911   \n",
       "23044                      [{'id': 18, 'name': 'Drama'}]   25541   2.587911   \n",
       "24164  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  265189  12.165685   \n",
       "45265  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  265189  12.165685   \n",
       "33184  [{'id': 18, 'name': 'Drama'}, {'id': 14, 'name...  298721   2.535419   \n",
       "40040  [{'id': 18, 'name': 'Drama'}, {'id': 14, 'name...  298721   2.535419   \n",
       "15702  [{'id': 18, 'name': 'Drama'}, {'id': 10769, 'n...   42495   0.187901   \n",
       "29374  [{'id': 18, 'name': 'Drama'}, {'id': 10769, 'n...   42495   0.187901   \n",
       "33826  [{'id': 35, 'name': 'Comedy'}, {'id': 80, 'nam...    4912   7.645827   \n",
       "5865   [{'id': 35, 'name': 'Comedy'}, {'id': 80, 'nam...    4912  11.331072   \n",
       "7345   [{'id': 80, 'name': 'Crime'}, {'id': 18, 'name...    5511   9.091288   \n",
       "9165   [{'id': 80, 'name': 'Crime'}, {'id': 18, 'name...    5511   9.091288   \n",
       "26625  [{'id': 18, 'name': 'Drama'}, {'id': 27, 'name...   69234    0.43849   \n",
       "9576   [{'id': 18, 'name': 'Drama'}, {'id': 27, 'name...   69234   0.441872   \n",
       "20843  [{'id': 12, 'name': 'Adventure'}, {'id': 18, '...   77221   6.475665   \n",
       "11155  [{'id': 12, 'name': 'Adventure'}, {'id': 18, '...   77221   6.652197   \n",
       "2564                 [{'id': 99, 'name': 'Documentary'}]   84198   0.501046   \n",
       "21116                [{'id': 99, 'name': 'Documentary'}]   84198   1.673307   \n",
       "13946                     [{'id': 35, 'name': 'Comedy'}]   97995   0.141558   \n",
       "40276                     [{'id': 35, 'name': 'Comedy'}]   97995   0.141558   \n",
       "38871  [{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...   99080   0.002362   \n",
       "16167  [{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...   99080   0.002362   \n",
       "\n",
       "                                             title  vote_average  vote_count  \n",
       "676                                    The Promise           5.0         1.0  \n",
       "1465                                   The Promise           5.0         1.0  \n",
       "44821                Pokémon: Spell of the Unknown           6.0       144.0  \n",
       "4114                 Pokémon: Spell of the Unknown           6.0       143.0  \n",
       "5710                               Rich and Famous           4.9         7.0  \n",
       "20899                              Rich and Famous           4.9         7.0  \n",
       "23534                         Camille Claudel 1915           7.0        20.0  \n",
       "4356                          Camille Claudel 1915           7.0        20.0  \n",
       "24844                                         Deal           5.2        22.0  \n",
       "14012                                         Deal           5.2        22.0  \n",
       "21165                                  The Tempest           0.0         0.0  \n",
       "19890                                  The Tempest           0.0         0.0  \n",
       "5535   Pokémon 4Ever: Celebi - Voice of the Forest           5.7        82.0  \n",
       "44826  Pokémon 4Ever: Celebi - Voice of the Forest           5.7        82.0  \n",
       "11342                                      Offside           6.7        27.0  \n",
       "15765                                      Offside           6.7        27.0  \n",
       "838                                           Wife           0.0         0.0  \n",
       "30001                                         Wife           0.0         0.0  \n",
       "16764                                     Blackout           6.7         3.0  \n",
       "13261                                     Blackout           6.7         3.0  \n",
       "13375                                     Blackout           6.7         3.0  \n",
       "10419                                       Bubble           6.4        36.0  \n",
       "12066                                       Bubble           6.4        36.0  \n",
       "5130                                 Clockstoppers           4.9        89.0  \n",
       "33743                                Clockstoppers           4.9        90.0  \n",
       "19925                                 The Congress           6.4       165.0  \n",
       "21854                                 The Congress           6.4       165.0  \n",
       "35798             Why We Fight: Divide and Conquer           5.0         1.0  \n",
       "13220             Why We Fight: Divide and Conquer           5.0         1.0  \n",
       "13603               Nana, the True Key of Pleasure           4.7         3.0  \n",
       "28860               Nana, the True Key of Pleasure           4.7         3.0  \n",
       "22151                             Days of Darkness           5.0         5.0  \n",
       "14000                             Days of Darkness           5.0         5.0  \n",
       "949                             A Farewell to Arms           6.2        29.0  \n",
       "15074                           A Farewell to Arms           6.2        29.0  \n",
       "8068                                   The Warrior           6.3        15.0  \n",
       "9327                                   The Warrior           6.3        15.0  \n",
       "17229                                  Brotherhood           7.1        21.0  \n",
       "23044                                  Brotherhood           7.1        21.0  \n",
       "24164                                Force Majeure           6.8       255.0  \n",
       "45265                                Force Majeure           6.8       255.0  \n",
       "33184                        Cemetery of Splendour           4.4        50.0  \n",
       "40040                        Cemetery of Splendour           4.4        50.0  \n",
       "15702                                    King Lear           8.0         3.0  \n",
       "29374                                    King Lear           8.0         3.0  \n",
       "33826              Confessions of a Dangerous Mind           6.6       281.0  \n",
       "5865               Confessions of a Dangerous Mind           6.6       281.0  \n",
       "7345                                   Le Samouraï           7.9       187.0  \n",
       "9165                                   Le Samouraï           7.9       187.0  \n",
       "26625                     The Phantom of the Opera           5.0         3.0  \n",
       "9576                      The Phantom of the Opera           5.0         3.0  \n",
       "20843                                   Black Gold           5.9        77.0  \n",
       "11155                                   Black Gold           5.9        77.0  \n",
       "2564                          A Place at the Table           6.9         7.0  \n",
       "21116                         A Place at the Table           6.9         7.0  \n",
       "13946                         Seven Years Bad Luck           5.6         4.0  \n",
       "40276                         Seven Years Bad Luck           5.6         4.0  \n",
       "38871                                   The Viking           0.0         0.0  \n",
       "16167                                   The Viking           0.0         0.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4: Identify and display the duplicate entries in the dataset\n",
    "\n",
    "mov_subset_df[mov_subset_df.duplicated('id', keep = False)].sort_values(by = ['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45436 entries, 0 to 45465\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   genres        45436 non-null  object \n",
      " 1   id            45436 non-null  object \n",
      " 2   popularity    45431 non-null  object \n",
      " 3   title         45430 non-null  object \n",
      " 4   vote_average  45430 non-null  float64\n",
      " 5   vote_count    45430 non-null  float64\n",
      "dtypes: float64(2), object(4)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# 5: Remove and verify the duplicate movie entries\n",
    "\n",
    "# Drop the duplicate rows on id column\n",
    "mov_subset_df = mov_subset_df.drop_duplicates(subset = ['id'])\n",
    "\n",
    "# Verify removal of duplicated entries on id column\n",
    "mov_subset_df[mov_subset_df.duplicated('id')]\n",
    "\n",
    "#  Get the total number of rows and columns and data-types of columns.\n",
    "mov_subset_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45430 entries, 0 to 45465\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   genres        45430 non-null  object \n",
      " 1   id            45430 non-null  object \n",
      " 2   popularity    45430 non-null  object \n",
      " 3   title         45430 non-null  object \n",
      " 4   vote_average  45430 non-null  float64\n",
      " 5   vote_count    45430 non-null  float64\n",
      "dtypes: float64(2), object(4)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# 6: Drop null values from the subset DataFrame\n",
    "mov_subset_df.dropna(inplace = True)\n",
    "mov_subset_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45430 entries, 0 to 45465\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   genres        45430 non-null  object \n",
      " 1   id            45430 non-null  int32  \n",
      " 2   popularity    45430 non-null  float64\n",
      " 3   title         45430 non-null  object \n",
      " 4   vote_average  45430 non-null  float64\n",
      " 5   vote_count    45430 non-null  float64\n",
      "dtypes: float64(3), int32(1), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# 7 : data-type conversion.\n",
    "\n",
    "# Convert the type of 'id' column to 'int' type.\n",
    "mov_subset_df['id'] = mov_subset_df['id'].astype('int')\n",
    "\n",
    "# Convert the type of 'popularity' column to 'float' type.\n",
    "mov_subset_df['popularity'] = mov_subset_df['popularity'].astype('float')\n",
    "\n",
    "# Verify the conversion.\n",
    "mov_subset_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Animation, Comedy, Family]</td>\n",
       "      <td>862</td>\n",
       "      <td>21.946943</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>8844</td>\n",
       "      <td>17.015539</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Romance, Comedy]</td>\n",
       "      <td>15602</td>\n",
       "      <td>11.712900</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>31357</td>\n",
       "      <td>3.859495</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>11862</td>\n",
       "      <td>8.387519</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         genres     id  popularity  \\\n",
       "0   [Animation, Comedy, Family]    862   21.946943   \n",
       "1  [Adventure, Fantasy, Family]   8844   17.015539   \n",
       "2             [Romance, Comedy]  15602   11.712900   \n",
       "3      [Comedy, Drama, Romance]  31357    3.859495   \n",
       "4                      [Comedy]  11862    8.387519   \n",
       "\n",
       "                         title  vote_average  vote_count  \n",
       "0                    Toy Story           7.7      5415.0  \n",
       "1                      Jumanji           6.9      2413.0  \n",
       "2             Grumpier Old Men           6.5        92.0  \n",
       "3            Waiting to Exhale           6.1        34.0  \n",
       "4  Father of the Bride Part II           5.7       173.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8:  obtain a list of genres.\n",
    "from ast import literal_eval\n",
    "mov_subset_df['genres'] = mov_subset_df['genres'].apply(lambda x: [i['name'] for i in literal_eval(x)])\n",
    "mov_subset_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30700</th>\n",
       "      <td>[Family, Animation, Adventure, Comedy]</td>\n",
       "      <td>211672</td>\n",
       "      <td>547.488298</td>\n",
       "      <td>Minions</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33356</th>\n",
       "      <td>[Action, Adventure, Fantasy]</td>\n",
       "      <td>297762</td>\n",
       "      <td>294.337037</td>\n",
       "      <td>Wonder Woman</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42222</th>\n",
       "      <td>[Family, Fantasy, Romance]</td>\n",
       "      <td>321612</td>\n",
       "      <td>287.253654</td>\n",
       "      <td>Beauty and the Beast</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43644</th>\n",
       "      <td>[Action, Crime]</td>\n",
       "      <td>339403</td>\n",
       "      <td>228.032744</td>\n",
       "      <td>Baby Driver</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24455</th>\n",
       "      <td>[Adventure, Family, Animation, Action, Comedy]</td>\n",
       "      <td>177572</td>\n",
       "      <td>213.849907</td>\n",
       "      <td>Big Hero 6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26564</th>\n",
       "      <td>[Action, Adventure, Comedy]</td>\n",
       "      <td>293660</td>\n",
       "      <td>187.860492</td>\n",
       "      <td>Deadpool</td>\n",
       "      <td>7.4</td>\n",
       "      <td>11444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26566</th>\n",
       "      <td>[Action, Adventure, Comedy, Science Fiction]</td>\n",
       "      <td>283995</td>\n",
       "      <td>185.330992</td>\n",
       "      <td>Guardians of the Galaxy Vol. 2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4858.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14551</th>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>19995</td>\n",
       "      <td>185.070892</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>12114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24351</th>\n",
       "      <td>[Action, Thriller]</td>\n",
       "      <td>245891</td>\n",
       "      <td>183.870374</td>\n",
       "      <td>John Wick</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23675</th>\n",
       "      <td>[Mystery, Thriller, Drama]</td>\n",
       "      <td>210577</td>\n",
       "      <td>154.801009</td>\n",
       "      <td>Gone Girl</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24873</th>\n",
       "      <td>[Science Fiction, Adventure, Thriller]</td>\n",
       "      <td>131631</td>\n",
       "      <td>147.098006</td>\n",
       "      <td>The Hunger Games: Mockingjay - Part 1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44274</th>\n",
       "      <td>[Drama, Science Fiction, War]</td>\n",
       "      <td>281338</td>\n",
       "      <td>146.161786</td>\n",
       "      <td>War for the Planet of the Apes</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26567</th>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>271110</td>\n",
       "      <td>145.882135</td>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>[Thriller, Crime]</td>\n",
       "      <td>680</td>\n",
       "      <td>140.950236</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26560</th>\n",
       "      <td>[Adventure, Action, Fantasy, Comedy]</td>\n",
       "      <td>166426</td>\n",
       "      <td>133.827820</td>\n",
       "      <td>Pirates of the Caribbean: Dead Men Tell No Tales</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2814.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>[Drama, Action, Crime, Thriller]</td>\n",
       "      <td>155</td>\n",
       "      <td>123.167259</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>8.3</td>\n",
       "      <td>12269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>[Science Fiction, Drama, Thriller]</td>\n",
       "      <td>78</td>\n",
       "      <td>96.272374</td>\n",
       "      <td>Blade Runner</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3833.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17818</th>\n",
       "      <td>[Science Fiction, Action, Adventure]</td>\n",
       "      <td>24428</td>\n",
       "      <td>89.887648</td>\n",
       "      <td>The Avengers</td>\n",
       "      <td>7.4</td>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43286</th>\n",
       "      <td>[Action, Animation, Comedy, Family]</td>\n",
       "      <td>268531</td>\n",
       "      <td>88.561239</td>\n",
       "      <td>Captain Underpants: The First Epic Movie</td>\n",
       "      <td>6.5</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33361</th>\n",
       "      <td>[Drama, Thriller, Science Fiction]</td>\n",
       "      <td>339988</td>\n",
       "      <td>88.439243</td>\n",
       "      <td>The Circle</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1015.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               genres      id  popularity  \\\n",
       "30700          [Family, Animation, Adventure, Comedy]  211672  547.488298   \n",
       "33356                    [Action, Adventure, Fantasy]  297762  294.337037   \n",
       "42222                      [Family, Fantasy, Romance]  321612  287.253654   \n",
       "43644                                 [Action, Crime]  339403  228.032744   \n",
       "24455  [Adventure, Family, Animation, Action, Comedy]  177572  213.849907   \n",
       "26564                     [Action, Adventure, Comedy]  293660  187.860492   \n",
       "26566    [Action, Adventure, Comedy, Science Fiction]  283995  185.330992   \n",
       "14551   [Action, Adventure, Fantasy, Science Fiction]   19995  185.070892   \n",
       "24351                              [Action, Thriller]  245891  183.870374   \n",
       "23675                      [Mystery, Thriller, Drama]  210577  154.801009   \n",
       "24873          [Science Fiction, Adventure, Thriller]  131631  147.098006   \n",
       "44274                   [Drama, Science Fiction, War]  281338  146.161786   \n",
       "26567            [Adventure, Action, Science Fiction]  271110  145.882135   \n",
       "292                                 [Thriller, Crime]     680  140.950236   \n",
       "26560            [Adventure, Action, Fantasy, Comedy]  166426  133.827820   \n",
       "12481                [Drama, Action, Crime, Thriller]     155  123.167259   \n",
       "536                [Science Fiction, Drama, Thriller]      78   96.272374   \n",
       "17818            [Science Fiction, Action, Adventure]   24428   89.887648   \n",
       "43286             [Action, Animation, Comedy, Family]  268531   88.561239   \n",
       "33361              [Drama, Thriller, Science Fiction]  339988   88.439243   \n",
       "\n",
       "                                                  title  vote_average  \\\n",
       "30700                                           Minions           6.4   \n",
       "33356                                      Wonder Woman           7.2   \n",
       "42222                              Beauty and the Beast           6.8   \n",
       "43644                                       Baby Driver           7.2   \n",
       "24455                                        Big Hero 6           7.8   \n",
       "26564                                          Deadpool           7.4   \n",
       "26566                    Guardians of the Galaxy Vol. 2           7.6   \n",
       "14551                                            Avatar           7.2   \n",
       "24351                                         John Wick           7.0   \n",
       "23675                                         Gone Girl           7.9   \n",
       "24873             The Hunger Games: Mockingjay - Part 1           6.6   \n",
       "44274                    War for the Planet of the Apes           6.7   \n",
       "26567                        Captain America: Civil War           7.1   \n",
       "292                                        Pulp Fiction           8.3   \n",
       "26560  Pirates of the Caribbean: Dead Men Tell No Tales           6.6   \n",
       "12481                                   The Dark Knight           8.3   \n",
       "536                                        Blade Runner           7.9   \n",
       "17818                                      The Avengers           7.4   \n",
       "43286          Captain Underpants: The First Epic Movie           6.5   \n",
       "33361                                        The Circle           5.4   \n",
       "\n",
       "       vote_count  \n",
       "30700      4729.0  \n",
       "33356      5025.0  \n",
       "42222      5530.0  \n",
       "43644      2083.0  \n",
       "24455      6289.0  \n",
       "26564     11444.0  \n",
       "26566      4858.0  \n",
       "14551     12114.0  \n",
       "24351      5499.0  \n",
       "23675      6023.0  \n",
       "24873      5767.0  \n",
       "44274      1675.0  \n",
       "26567      7462.0  \n",
       "292        8670.0  \n",
       "26560      2814.0  \n",
       "12481     12269.0  \n",
       "536        3833.0  \n",
       "17818     12000.0  \n",
       "43286       159.0  \n",
       "33361      1015.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_20_popular = mov_subset_df.sort_values(by = 'popularity', ascending = False).head(20)\n",
    "top_20_popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot top 20 popular movies\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.barplot(x='popularity', y= 'title', data = top_20_popular)\n",
    "plt.title(\"Top 20 popular movies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total movie records:  (93448, 6)\n",
      "Null values:  2442\n",
      "Percentage of null values: 2.613218046400137\n"
     ]
    }
   ],
   "source": [
    "#  Print the number of records and percentage of null values in the 'genres_subset_df' DataFrame\n",
    "genres_subset_df = mov_subset_df.explode('genres', ignore_index = True)\n",
    "print('Total movie records: ', genres_subset_df.shape)\n",
    "print('Null values: ', genres_subset_df['genres'].isna().sum())\n",
    "print('Percentage of null values:', genres_subset_df['genres'].isna().sum()  * 100 /len(genres_subset_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null values:  0\n"
     ]
    }
   ],
   "source": [
    "#Drop the movies with null genres\n",
    "genres_subset_df.dropna(inplace=True)\n",
    "print('Null values: ', genres_subset_df['genres'].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama              20243\n",
       "Comedy             13176\n",
       "Thriller            7618\n",
       "Romance             6730\n",
       "Action              6590\n",
       "Horror              4670\n",
       "Crime               4304\n",
       "Documentary         3930\n",
       "Adventure           3490\n",
       "Science Fiction     3042\n",
       "Family              2767\n",
       "Mystery             2464\n",
       "Fantasy             2309\n",
       "Animation           1930\n",
       "Foreign             1619\n",
       "Music               1597\n",
       "History             1398\n",
       "War                 1322\n",
       "Western             1042\n",
       "TV Movie             765\n",
       "Name: genres, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the count of movies falling into each genre\n",
    "genre_count = genres_subset_df['genres'].value_counts()\n",
    "genre_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a barplot with number of movies in each genre on x-axis and genre names on y-axis.\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x=genre_count.values, y=genre_count.index,color='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>genres</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>4.773008</td>\n",
       "      <td>5.584932</td>\n",
       "      <td>259.232777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>6.001218</td>\n",
       "      <td>5.687593</td>\n",
       "      <td>410.936390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>4.709924</td>\n",
       "      <td>6.275544</td>\n",
       "      <td>234.534715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.230777</td>\n",
       "      <td>5.715642</td>\n",
       "      <td>109.106937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crime</td>\n",
       "      <td>4.142982</td>\n",
       "      <td>5.877649</td>\n",
       "      <td>176.028346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>0.948341</td>\n",
       "      <td>5.823028</td>\n",
       "      <td>12.836641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drama</td>\n",
       "      <td>3.010777</td>\n",
       "      <td>5.905888</td>\n",
       "      <td>96.570074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Family</td>\n",
       "      <td>4.728138</td>\n",
       "      <td>5.753632</td>\n",
       "      <td>242.281171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>5.366410</td>\n",
       "      <td>5.790775</td>\n",
       "      <td>333.171503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Foreign</td>\n",
       "      <td>0.761960</td>\n",
       "      <td>5.735639</td>\n",
       "      <td>7.984558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>History</td>\n",
       "      <td>3.466370</td>\n",
       "      <td>6.154220</td>\n",
       "      <td>107.364092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Horror</td>\n",
       "      <td>3.565130</td>\n",
       "      <td>5.230493</td>\n",
       "      <td>100.883726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Music</td>\n",
       "      <td>2.558163</td>\n",
       "      <td>5.880150</td>\n",
       "      <td>67.833438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>3.963775</td>\n",
       "      <td>5.819521</td>\n",
       "      <td>165.995130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Romance</td>\n",
       "      <td>3.122609</td>\n",
       "      <td>5.803581</td>\n",
       "      <td>95.887816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>5.001141</td>\n",
       "      <td>5.344444</td>\n",
       "      <td>322.816897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TV Movie</td>\n",
       "      <td>2.046903</td>\n",
       "      <td>5.236601</td>\n",
       "      <td>20.835294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>4.487618</td>\n",
       "      <td>5.616093</td>\n",
       "      <td>188.170255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>War</td>\n",
       "      <td>3.333689</td>\n",
       "      <td>6.040998</td>\n",
       "      <td>120.445537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Western</td>\n",
       "      <td>2.629321</td>\n",
       "      <td>5.167179</td>\n",
       "      <td>75.356046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             genres  popularity  vote_average  vote_count\n",
       "0            Action    4.773008      5.584932  259.232777\n",
       "1         Adventure    6.001218      5.687593  410.936390\n",
       "2         Animation    4.709924      6.275544  234.534715\n",
       "3            Comedy    3.230777      5.715642  109.106937\n",
       "4             Crime    4.142982      5.877649  176.028346\n",
       "5       Documentary    0.948341      5.823028   12.836641\n",
       "6             Drama    3.010777      5.905888   96.570074\n",
       "7            Family    4.728138      5.753632  242.281171\n",
       "8           Fantasy    5.366410      5.790775  333.171503\n",
       "9           Foreign    0.761960      5.735639    7.984558\n",
       "10          History    3.466370      6.154220  107.364092\n",
       "11           Horror    3.565130      5.230493  100.883726\n",
       "12            Music    2.558163      5.880150   67.833438\n",
       "13          Mystery    3.963775      5.819521  165.995130\n",
       "14          Romance    3.122609      5.803581   95.887816\n",
       "15  Science Fiction    5.001141      5.344444  322.816897\n",
       "16         TV Movie    2.046903      5.236601   20.835294\n",
       "17         Thriller    4.487618      5.616093  188.170255\n",
       "18              War    3.333689      6.040998  120.445537\n",
       "19          Western    2.629321      5.167179   75.356046"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pivot table to determine the total count of parameters\n",
    "parameters_df = genres_subset_df[['genres', 'popularity', 'vote_average', 'vote_count']]\n",
    "genre_table = pd.pivot_table(parameters_df, index =['genres']).reset_index()\n",
    "genre_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lli01cuRIVapUSZUqVbrnmP4pMjJSHh4e5uLr65ujGAAAAAAAKCgPbaLj+PHj2rdvn0JDQyVJ9vb26tGjhxYtWiRJio+PV+PGjS36PProoxbXvXv3VkxMjPlsj+XLl6tDhw7y9PSUJB0+fFjR0dFydXU1l7Zt2yozM1Nnzpwxj1O7dm3z3waDQT4+Prp48WKePGeDBg0sru81pn8aO3askpKSzOX8+fN5EhsAAAAAAHnN3toBWMuiRYuUnp6u0qVLm+tMJpOMRqPmzp17T2M0bNhQlSpV0sqVK/X8889r3bp1Fud8pKSk6LnnntOLL76YpW+5cuXMfzs4OFjcMxgMyszMvOPcRYoUMW+zuSUtLS1LOxcXF4vre43pn4xGo4xG4x3jAQAAAACgMHgoEx3p6elaunSpZs2apccff9ziXpcuXfTRRx+pevXq+vbbbxUWFma+980332QZq3fv3lq+fLnKli2rIkWKWHymtl69ejp27JgqV66c61gdHR0l/X3+xz95eXkpMTHRfJ2RkaEffvhBwcHBdxwvL2ICAAAAAKCweii3rmzcuFGXL19WRESEatasaVG6deumRYsW6aWXXtLixYsVFRWln376SRMmTNDRo0ezjNW7d2999913evPNN/XUU09ZrHx45ZVXtGfPHg0ZMkSHDh3SiRMn9Omnn97x4M9/K1mypJycnLR161b99ttvSkpKkvT32RubNm3Spk2b9OOPP+r555/P8gWY7ORFTAAAAAAAFFYPZaJj0aJFat26tTw8PLLc69atmw4cOKDq1atr/Pjxevnll1W/fn2dO3dOzz//fJb2lStXVqNGjfT999+bv7ZyS+3atfX111/rp59+UrNmzVS3bl29/vrrFttl7sbe3l5z5szRggULVLp0aXXu3FmS1L9/f/Xt21dhYWEKCgpSxYoV77qaI69iAgAAAACgsDKY/n3QA3AXycnJ8vDwUODQ+bIzOlk7HAAAAABALsTNCLt7o0Li1u/QpKQkubu737HtQ7miAwAAAAAA2CYSHQAAAAAAwGaQ6AAAAAAAADbjofy8LPLGzimhd90bBQAAAABAQWJFBwAAAAAAsBkkOgAAAAAAgM0g0QEAAAAAAGwGZ3Qg15qP+0h2RidrhwEAAADgARc3I8zaIcCGsKIDAAAAAADYDBIdAAAAAADAZpDoAAAAAAAANoNEBwAAAAAAsBkkOu5TeHi4DAZDlnLy5Mk8GbtLly73HyQAAAAAAA8JvrqSB0JCQhQVFWVR5+XlZaVoAAAAAAB4eLGiIw8YjUb5+PhYlHfffVe1atWSi4uLfH19NXjwYKWkpJj7REdHy9PTU59//rmqV68uV1dXhYSEKDExUZI0ceJELVmyRJ9++ql5lUhMTIwk6ZVXXpG/v7+cnZ1VsWJFjR8/XmlpaeaxDx8+rODgYLm5ucnd3V3169fXgQMHdPXqVbm7u+uTTz6xiH/9+vVycXHRX3/9lf8vCwAAAACAfESiI58UKVJEc+bM0dGjR7VkyRJ99dVXevnlly3aXLt2TTNnztSyZcu0c+dOJSQkaNSoUZKkUaNGqXv37ubkR2Jiopo0aSJJcnNzU3R0tI4dO6Z3331XH3zwgd555x3zuL1791bZsmW1f/9+xcXFacyYMXJwcJCLi4t69uyZZfVJVFSUnnrqKbm5uWX7LKmpqUpOTrYoAAAAAAAURmxdyQMbN26Uq6ur+bpdu3ZavXq1+drPz09TpkzRoEGDNG/ePHN9Wlqa5s+fr0qVKkmShgwZojfeeEOS5OrqKicnJ6WmpsrHx8divnHjxlmMPWrUKK1cudKcSElISNDo0aNVrVo1SVKVKlXM7QcMGKAmTZooMTFRpUqV0sWLF7V582Zt27btts8XGRmpSZMm5fi9AAAAAABQ0FjRkQeCg4N16NAhc5kzZ462bdumVq1aqUyZMnJzc9MzzzyjS5cu6dq1a+Z+zs7O5iSHJHPi4W5WrVqlpk2bysfHR66urho3bpwSEhLM90eMGKEBAwaodevWmjZtmk6dOmW+16hRI9WoUUNLliyRJH344YcqX768mjdvftv5xo4dq6SkJHM5f/58jt4PAAAAAAAFhURHHnBxcVHlypXNJTU1VR07dlTt2rW1Zs0axcXF6b333pMk3bx509zPwcHBYhyDwSCTyXTHufbu3avevXurffv22rhxow4ePKjXXnvNYtyJEyfq6NGj6tChg7766isFBARo3bp15vsDBgxQdHS0pL+3rfTr108Gg+G2cxqNRrm7u1sUAAAAAAAKIxId+SAuLk6ZmZmaNWuW/vOf/8jf31+//vprjsdxdHRURkaGRd2ePXtUvnx5vfbaa2rQoIGqVKmic+fOZenr7++v4cOH64svvtCTTz5pcS5Hnz59dO7cOc2ZM0fHjh1T3759c/6QAAAAAAAUQiQ68kHlypWVlpam//73vzp9+rSWLVum+fPn53gcPz8/ff/99zp+/Lj++OMPpaWlqUqVKkpISNDKlSt16tQpzZkzx2K1xvXr1zVkyBDFxMTo3Llzio2N1f79+1W9enVzm2LFiunJJ5/U6NGj9fjjj6ts2bJ58twAAAAAAFgbiY58EBgYqLffflvTp09XzZo1tXz5ckVGRuZ4nIEDB6pq1apq0KCBvLy8FBsbqyeeeELDhw/XkCFDVKdOHe3Zs0fjx48397Gzs9OlS5cUFhYmf39/de/eXe3atctymGhERIRu3ryp/v373/fzAgAAAABQWBhMdzsUAjZp2bJlGj58uH799Vc5OjrmqG9ycrI8PDwUOHS+7IxO+RQhAAAAgIdF3Iwwa4eAQu7W79CkpKS7nhvJ52UfMteuXVNiYqKmTZum5557LsdJDgAAAAAACjO2rjxk3nrrLVWrVk0+Pj4aO3astcMBAAAAACBPkeh4yEycOFFpaWnavn27XF1drR0OAAAAAAB5iq0ryLWdU0LvujcKAAAAAICCxIoOAAAAAABgM0h0AAAAAAAAm0GiAwAAAAAA2AzO6ECuNR/3keyMTtYOAwAAAHioxM0Is3YIQKHGig4AAAAAAGAzSHQAAAAAAACbQaIDAAAAAADYDBIdAAAAAADAZjzQiY7o6Gh5enpaO4z70qJFCw0bNszqYwAAAAAAYAusmuj4/fff9fzzz6tcuXIyGo3y8fFR27ZtFRsbe0/9e/TooZ9++imfo7x/4eHhMhgMWcrJkye1du1aTZ48+Z7GiYmJkcFg0JUrVyzqczIGAAAAAAC2zKqfl+3WrZtu3rypJUuWqGLFivrtt9+0fft2Xbp06Z76Ozk5ycnpwfi8aUhIiKKioizqvLy8ZGdnd99jFy9e/L7HAAAAAADAFlhtRceVK1e0a9cuTZ8+XcHBwSpfvrwaNWqksWPH6oknnrBo99xzz8nb21tFixZVzZo1tXHjRknZb1359NNPVa9ePRUtWlQVK1bUpEmTlJ6ebr5vMBi0cOFCde3aVc7OzqpSpYo2bNhgMcbRo0fVsWNHubu7y83NTc2aNdOpU6fM9xcuXKjq1auraNGiqlatmubNm3fX5721YuWfxc7OLsu2k9TUVL3yyivy9fWV0WhU5cqVtWjRIp09e1bBwcGSpGLFislgMCg8PFxS1q0rly9fVlhYmIoVKyZnZ2e1a9dOJ06cMN+/9d4+//xzVa9eXa6urgoJCVFiYmK2saempio5OdmiAAAAAABQGFkt0eHq6ipXV1etX79eqamp2bbJzMxUu3btFBsbqw8//FDHjh3TtGnTbrsKYteuXQoLC9NLL72kY8eOacGCBYqOjtabb75p0W7SpEnq3r27vv/+e7Vv3169e/fWn3/+KUn65Zdf1Lx5cxmNRn311VeKi4tT//79zcmS5cuX6/XXX9ebb76p+Ph4TZ06VePHj9eSJUvy5L2EhYXpo48+0pw5cxQfH68FCxbI1dVVvr6+WrNmjSTp+PHjSkxM1LvvvpvtGOHh4Tpw4IA2bNigvXv3ymQyqX379kpLSzO3uXbtmmbOnKlly5Zp586dSkhI0KhRo7IdLzIyUh4eHubi6+ubJ88KAAAAAEBes9rWFXt7e0VHR2vgwIGaP3++6tWrp6CgIPXs2VO1a9eWJG3btk379u1TfHy8/P39JUkVK1a87ZiTJk3SmDFj1LdvX3PbyZMn6+WXX9aECRPM7cLDwxUaGipJmjp1qubMmaN9+/YpJCRE7733njw8PLRy5Uo5ODhIknluSZowYYJmzZqlJ598UpJUoUIFc1Ll1rzZ2bhxo1xdXc3X7dq10+rVqy3a/PTTT/r444/15ZdfqnXr1lme99YWlZIlS972ENYTJ05ow4YNio2NVZMmTST9nZzx9fXV+vXr9fTTT0uS0tLSNH/+fFWqVEmSNGTIEL3xxhvZjjl27FiNGDHCfJ2cnEyyAwAAAABQKFn9jI4OHTpo165d+uabb7Rlyxa99dZbWrhwocLDw3Xo0CGVLVvWItFwJ4cPH1ZsbKzFCo6MjAzduHFD165dk7OzsySZEymS5OLiInd3d128eFGSdOjQITVr1syc5Pinq1ev6tSpU4qIiNDAgQPN9enp6fLw8LhjbMHBwXr//fct5v23Q4cOyc7OTkFBQff0vNmJj4+Xvb29GjdubK4rUaKEqlatqvj4eHOds7OzOckhSaVKlTK/g38zGo0yGo25jgkAAAAAgIJi1USHJBUtWlRt2rRRmzZtNH78eA0YMEATJkxQeHh4jg8aTUlJ0aRJk8yrLf49zy3/TmIYDAZlZmZK0h3nTElJkSR98MEHFokESXc9VNTFxUWVK1e+Y5uCPFg1u3dgMpkKbH4AAAAAAPKDVT8vm52AgABdvXpV0t8rL37++ed7/oRsvXr1dPz4cVWuXDlLKVLk3h61du3a2rVrl8V5Frd4e3urdOnSOn36dJbxK1SocO8PeRu1atVSZmamvv7662zvOzo6Svp7lcrtVK9eXenp6fr222/NdZcuXdLx48cVEBBw3zECAAAAAFCYWS3RcenSJbVs2VIffvihvv/+e505c0arV6/WW2+9pc6dO0uSgoKC1Lx5c3Xr1k1ffvmlzpw5oy1btmjr1q3Zjvn6669r6dKlmjRpko4ePar4+HitXLlS48aNu+e4hgwZouTkZPXs2VMHDhzQiRMntGzZMh0/flzS3+eAREZGas6cOfrpp5905MgRRUVF6e23377vd+Ln56e+ffuqf//+Wr9+vc6cOaOYmBh9/PHHkqTy5cvLYDBo48aN+v33380rTP6pSpUq6ty5swYOHKjdu3fr8OHD6tOnj8qUKWN+rwAAAAAA2CqrfnWlcePGeuedd9S8eXPVrFlT48eP18CBAzV37lxzuzVr1qhhw4YKDQ1VQECAXn755duuaGjbtq02btyoL774Qg0bNtR//vMfvfPOOypfvvw9x1WiRAl99dVXSklJUVBQkOrXr68PPvjAvNVjwIABWrhwoaKiolSrVi0FBQUpOjo6T1Z0SNL777+vp556SoMHD1a1atU0cOBA8wqXMmXKmA9c9fb21pAhQ7IdIyoqSvXr11fHjh316KOPymQyafPmzdmeOwIAAAAAgC0xmDiYATmUnJwsDw8PBQ6dLztjwZ0rAgAAAECKmxFm7RCAAnfrd2hSUpLc3d3v2LbQndEBAAAAAACQWyQ6AAAAAACAzbD652Xx4No5JfSuS4YAAAAAAChIrOgAAAAAAAA2g0QHAAAAAACwGSQ6AAAAAACAzeCMDuRa83Ef8XlZAAAAPDT4rCvwYGBFBwAAAAAAsBkkOgAAAAAAgM0g0QEAAAAAAGwGiQ4AAAAAAGAzSHQ8wKKjo+Xp6WntMAAAAAAAKDRIdBSwvXv3ys7OTh06dMhRPz8/P82ePduirkePHvrpp5/yMDoAAAAAAB5sJDoK2KJFizR06FDt3LlTv/76632N5eTkpJIlS+ZRZAAAAAAAPPhIdBSglJQUrVq1Ss8//7w6dOig6Ohoi/ufffaZGjZsqKJFi+qRRx5R165dJUktWrTQuXPnNHz4cBkMBhkMBknZb115//33ValSJTk6Oqpq1apatmyZxX2DwaCFCxeqa9eucnZ2VpUqVbRhw4Z8e2YAAAAAAAoSiY4C9PHHH6tatWqqWrWq+vTpo8WLF8tkMkmSNm3apK5du6p9+/Y6ePCgtm/frkaNGkmS1q5dq7Jly+qNN95QYmKiEhMTsx1/3bp1eumllzRy5Ej98MMPeu6559SvXz/t2LHDot2kSZPUvXt3ff/992rfvr169+6tP//887Zxp6amKjk52aIAAAAAAFAYkegoQIsWLVKfPn0kSSEhIUpKStLXX38tSXrzzTfVs2dPTZo0SdWrV1dgYKDGjh0rSSpevLjs7Ozk5uYmHx8f+fj4ZDv+zJkzFR4ersGDB8vf318jRozQk08+qZkzZ1q0Cw8PV2hoqCpXrqypU6cqJSVF+/btu23ckZGR8vDwMBdfX9+8eB0AAAAAAOQ5Eh0F5Pjx49q3b59CQ0MlSfb29urRo4cWLVokSTp06JBatWp1X3PEx8eradOmFnVNmzZVfHy8RV3t2rXNf7u4uMjd3V0XL1687bhjx45VUlKSuZw/f/6+4gQAAAAAIL/YWzuAh8WiRYuUnp6u0qVLm+tMJpOMRqPmzp0rJyenAovFwcHB4tpgMCgzM/O27Y1Go4xGY36HBQAAAADAfWNFRwFIT0/X0qVLNWvWLB06dMhcDh8+rNKlS+ujjz5S7dq1tX379tuO4ejoqIyMjDvOU716dcXGxlrUxcbGKiAgIE+eAwAAAACAwo4VHQVg48aNunz5siIiIuTh4WFxr1u3blq0aJFmzJihVq1aqVKlSurZs6fS09O1efNmvfLKK5IkPz8/7dy5Uz179pTRaNQjjzySZZ7Ro0ere/fuqlu3rlq3bq3PPvtMa9eu1bZt2wrkOQEAAAAAsDZWdBSARYsWqXXr1lmSHNLfiY4DBw6oePHiWr16tTZs2KA6deqoZcuWFgeEvvHGGzp79qwqVaokLy+vbOfp0qWL3n33Xc2cOVM1atTQggULFBUVpRYtWuTXowEAAAAAUKgYTLe+bwrco+TkZHl4eChw6HzZGQvubBEAAADAmuJmhFk7BOChdet3aFJSktzd3e/YlhUdAAAAAADAZpDoAAAAAAAANoPDSJFrO6eE3nXJEAAAAAAABYkVHQAAAAAAwGaQ6AAAAAAAADaDRAcAAAAAALAZnNGBXGs+7iM+LwsAAACbwidkgQcfKzoAAAAAAIDNINEBAAAAAABsBokOAAAAAABgM0h0AAAAAAAAm0Giwwb5+flp9uzZ5muDwaD169dbLR4AAAAAAAoKiY4CEB4eLoPBkKWcPHkyX+bbv3+/nn322XwZGwAAAACAwozPyxaQkJAQRUVFWdR5eXnly1z5NS4AAAAAAIUdKzoKiNFolI+Pj0V59913VatWLbm4uMjX11eDBw9WSkqKuU90dLQ8PT21ceNGVa1aVc7Oznrqqad07do1LVmyRH5+fipWrJhefPFFZWRkmPv9e+vKP7Vs2VJDhgyxqPv999/l6Oio7du358uzAwAAAABQUEh0WFGRIkU0Z84cHT16VEuWLNFXX32ll19+2aLNtWvXNGfOHK1cuVJbt25VTEyMunbtqs2bN2vz5s1atmyZFixYoE8++eSe5hwwYIBWrFih1NRUc92HH36oMmXKqGXLltn2SU1NVXJyskUBAAAAAKAwItFRQDZu3ChXV1dzefrppzVs2DAFBwfLz89PLVu21JQpU/Txxx9b9EtLS9P777+vunXrqnnz5nrqqae0e/duLVq0SAEBAerYsaOCg4O1Y8eOe4rjySeflCR9+umn5rro6GjzOSLZiYyMlIeHh7n4+vrm8i0AAAAAAJC/OKOjgAQHB+v99983X7u4uGjbtm2KjIzUjz/+qOTkZKWnp+vGjRu6du2anJ2dJUnOzs6qVKmSuZ+3t7f8/Pzk6upqUXfx4sV7iqNo0aJ65plntHjxYnXv3l3fffedfvjhB23YsOG2fcaOHasRI0aYr5OTk0l2AAAAAAAKJVZ0FBAXFxdVrlzZXFJTU9WxY0fVrl1ba9asUVxcnN577z1J0s2bN839HBwcLMYxGAzZ1mVmZt5zLAMGDNCXX36pn3/+WVFRUWrZsqXKly9/2/ZGo1Hu7u4WBQAAAACAwogVHVYSFxenzMxMzZo1S0WK/J1v+ve2lfxSq1YtNWjQQB988IFWrFihuXPnFsi8AAAAAADkN1Z0WEnlypWVlpam//73vzp9+rSWLVum+fPnF9j8AwYM0LRp02QymdS1a9cCmxcAAAAAgPxEosNKAgMD9fbbb2v69OmqWbOmli9frsjIyAKbPzQ0VPb29goNDVXRokULbF4AAAAAAPKTwWQymawdBAre2bNnValSJe3fv1/16tXLUd/k5GR5eHgocOh82Rmd8ilCAAAAoODFzQizdggAsnHrd2hSUtJdz43kjI6HTFpami5duqRx48bpP//5T46THAAAAAAAFGZsXXnIxMbGqlSpUtq/f3+BngkCAAAAAEBBYOsKciwnS4YAAAAAALhfOfkdyooOAAAAAABgM0h0AAAAAAAAm0GiAwAAAAAA2AwSHQAAAAAAwGbweVnkWvNxH8nO6GTtMAAAAID7FjcjzNohAMgjrOgAAAAAAAA2g0QHAAAAAACwGSQ6AAAAAACAzSDRcZ8mTpyoOnXqWG1+g8Gg9evXW21+AAAAAAAKExIdkvbu3Ss7Ozt16NAhx31HjRql7du350NUlm6XUElMTFS7du3yfX4AAAAAAB4EJDokLVq0SEOHDtXOnTv166+/5qivq6urSpQokU+R3Z2Pj4+MRqPV5gcAAAAAoDB56BMdKSkpWrVqlZ5//nl16NBB0dHR5nsxMTEyGAzavn27GjRoIGdnZzVp0kTHjx83t/n3Sovw8HB16dJFU6dOlbe3tzw9PfXGG28oPT1do0ePVvHixVW2bFlFRUVZxPHKK6/I399fzs7OqlixosaPH6+0tDRJUnR0tCZNmqTDhw/LYDDIYDCY4/z31pUjR46oZcuWcnJyUokSJfTss88qJSUlS3wzZ85UqVKlVKJECb3wwgvmuQAAAAAAeJA99ImOjz/+WNWqVVPVqlXVp08fLV68WCaTyaLNa6+9plmzZunAgQOyt7dX//797zjmV199pV9//VU7d+7U22+/rQkTJqhjx44qVqyYvv32Ww0aNEjPPfecfv75Z3MfNzc3RUdH69ixY3r33Xf1wQcf6J133pEk9ejRQyNHjlSNGjWUmJioxMRE9ejRI8u8V69eVdu2bVWsWDHt379fq1ev1rZt2zRkyBCLdjt27NCpU6e0Y8cOLVmyRNHR0RYJnn9LTU1VcnKyRQEAAAAAoDB66BMdixYtUp8+fSRJISEhSkpK0tdff23R5s0331RQUJACAgI0ZswY7dmzRzdu3LjtmMWLF9ecOXNUtWpV9e/fX1WrVtW1a9f06quvqkqVKho7dqwcHR21e/duc59x48apSZMm8vPzU6dOnTRq1Ch9/PHHkiQnJye5urrK3t5ePj4+8vHxkZOTU5Z5V6xYoRs3bmjp0qWqWbOmWrZsqblz52rZsmX67bffzO2KFSumuXPnqlq1aurYsaM6dOhwx3NGIiMj5eHhYS6+vr739nIBAAAAAChgD3Wi4/jx49q3b59CQ0MlSfb29urRo4cWLVpk0a527drmv0uVKiVJunjx4m3HrVGjhooU+f9X6+3trVq1apmv7ezsVKJECYsxVq1apaZNm8rHx0eurq4aN26cEhIScvQ88fHxCgwMlIuLi7muadOmyszMtNhuU6NGDdnZ2Vk8052eZ+zYsUpKSjKX8+fP5yguAAAAAAAKir21A7CmRYsWKT09XaVLlzbXmUwmGY1GzZ0711zn4OBg/ttgMEiSMjMzbzvuP9vf6pNd3a0x9u7dq969e2vSpElq27atPDw8tHLlSs2aNSv3D3cHd4olO0ajkQNPAQAAAAAPhIc20ZGenq6lS5dq1qxZevzxxy3udenSRR999JGqVatWILHs2bNH5cuX12uvvWauO3funEUbR0dHZWRk3HGc6tWrKzo6WlevXjWv6oiNjVWRIkVUtWrVvA8cAAAAAIBC5qHdurJx40ZdvnxZERERqlmzpkXp1q1blu0r+alKlSpKSEjQypUrderUKc2ZM0fr1q2zaOPn56czZ87o0KFD+uOPP5SampplnN69e6to0aLq27evfvjhB+3YsUNDhw7VM888I29v74J6HAAAAAAArOahTXQsWrRIrVu3loeHR5Z73bp104EDB/T9998XSCxPPPGEhg8friFDhqhOnTras2ePxo8fnyWmkJAQBQcHy8vLSx999FGWcZydnfX555/rzz//VMOGDfXUU0+pVatWFttwAAAAAACwZQbTv7+lCtxFcnKyPDw8FDh0vuyMWb/+AgAAADxo4maEWTsEAHdw63doUlKS3N3d79j2oV3RAQAAAAAAbA+JDgAAAAAAYDMe2q+u4P7tnBJ61yVDAAAAAAAUJFZ0AAAAAAAAm0GiAwAAAAAA2AwSHQAAAAAAwGaQ6AAAAAAAADaDw0iRa83HfSQ7o5O1wwAAAMADLm5GmLVDAGBDWNEBAAAAAABsBokOAAAAAABgM0h0AAAAAAAAm0GiwwpiYmJkMBh05cqVHPdt0aKFhg0bZr728/PT7NmzzdcGg0Hr16+/7xgBAAAAAHgQcRhpHjMYDHe8P2HCBLVo0SLX469du1YODg657g8AAAAAgC0j0ZHHEhMTzX+vWrVKr7/+uo4fP26uc3V11YEDB3I87s2bN+Xo6KjixYvnSZx3mwcAAAAAgAcRW1fymI+Pj7l4eHjIYDBY1Lm6uprbxsXFqUGDBnJ2dlaTJk0sEiITJ05UnTp1tHDhQlWoUEFFixaVlHXryt2cP39e3bt3l6enp4oXL67OnTvr7Nmz5vvh4eHq0qWL3nzzTZUuXVpVq1a973cAAAAAAIC1kOiwotdee02zZs3SgQMHZG9vr/79+1vcP3nypNasWaO1a9fq0KFDOR4/LS1Nbdu2lZubm3bt2qXY2Fi5uroqJCREN2/eNLfbvn27jh8/ri+//FIbN27MMk5qaqqSk5MtCgAAAAAAhRFbV6zozTffVFBQkCRpzJgx6tChg27cuGFevXHz5k0tXbpUXl5euRp/1apVyszM1MKFC81nh0RFRcnT01MxMTF6/PHHJUkuLi5auHDhbbesREZGatKkSbmKAQAAAACAgsSKDiuqXbu2+e9SpUpJki5evGiuK1++fK6THJJ0+PBhnTx5Um5ubnJ1dZWrq6uKFy+uGzdu6NSpU+Z2tWrVuuO5HGPHjlVSUpK5nD9/PtcxAQAAAACQn1jRYUX//HrKrRUXmZmZ5joXF5f7Gj8lJUX169fX8uXLs9z7ZwLlbvMYjUYZjcb7igUAAAAAgIJAosOG1atXT6tWrVLJkiXl7u5u7XAAAAAAAMh3bF2xYb1799Yjjzyizp07a9euXTpz5oxiYmL04osv6ueff7Z2eAAAAAAA5DkSHTbM2dlZO3fuVLly5fTkk0+qevXqioiI0I0bN1jhAQAAAACwSQaTyWSydhB4sCQnJ8vDw0OBQ+fLzuhk7XAAAADwgIubEWbtEAAUcrd+hyYlJd31H+5Z0QEAAAAAAGwGiQ4AAAAAAGAz+OoKcm3nlFDO+gAAAAAAFCqs6AAAAAAAADaDRAcAAAAAALAZJDoAAAAAAIDNINEBAAAAAABsBoeRIteaj/tIdkYna4cBAADwUIqbEWbtEACgUGJFBwAAAAAAsBkkOgAAAAAAgM0g0QEAAAAAAGwGiY4HSExMjAwGg65cuWLtUAAAAAAAKJRIdFjJhQsXNHToUFWsWFFGo1G+vr7q1KmTtm/ffts+TZo0UWJiojw8PAowUgAAAAAAHhx8dcUKzp49q6ZNm8rT01MzZsxQrVq1lJaWps8//1wvvPCCfvzxxyx90tLS5OjoKB8fHytEDAAAAADAg4EVHVYwePBgGQwG7du3T926dZO/v79q1KihESNG6JtvvpEkGQwGvf/++3riiSfk4uKiN998M8vWlejoaHl6emrjxo2qWrWqnJ2d9dRTT+natWtasmSJ/Pz8VKxYMb344ovKyMgwz5+amqpRo0apTJkycnFxUePGjRUTE2OFNwEAAAAAQN5iRUcB+/PPP7V161a9+eabcnFxyXLf09PT/PfEiRM1bdo0zZ49W/b29jp9+nSW9teuXdOcOXO0cuVK/fXXX3ryySfVtWtXeXp6avPmzTp9+rS6deumpk2bqkePHpKkIUOG6NixY1q5cqVKly6tdevWKSQkREeOHFGVKlWyzJGamqrU1FTzdXJych68CQAAAAAA8h6JjgJ28uRJmUwmVatW7a5te/XqpX79+pmvs0t0pKWl6f3331elSpUkSU899ZSWLVum3377Ta6urgoICFBwcLB27NihHj16KCEhQVFRUUpISFDp0qUlSaNGjdLWrVsVFRWlqVOnZpkjMjJSkyZNyu0jAwAAAABQYEh0FDCTyXTPbRs0aHDXNs7OzuYkhyR5e3vLz89Prq6uFnUXL16UJB05ckQZGRny9/e3GCc1NVUlSpTIdo6xY8dqxIgR5uvk5GT5+vre83MAAAAAAFBQSHQUsCpVqshgMGR74Oi/Zbe15d8cHBwsrg0GQ7Z1mZmZkqSUlBTZ2dkpLi5OdnZ2Fu3+mRz5J6PRKKPReNdYAAAAAACwNg4jLWDFixdX27Zt9d577+nq1atZ7t86aDS/1K1bVxkZGbp48aIqV65sUfiiCwAAAADgQUeiwwree+89ZWRkqFGjRlqzZo1OnDih+Ph4zZkzR48++mi+zu3v76/evXsrLCxMa9eu1ZkzZ7Rv3z5FRkZq06ZN+To3AAAAAAD5ja0rVlCxYkV99913evPNNzVy5EglJibKy8tL9evX1/vvv5/v80dFRWnKlCkaOXKkfvnlFz3yyCP6z3/+o44dO+b73AAAAAAA5CeDKSenYwL6+zBSDw8PBQ6dLzujk7XDAQAAeCjFzQizdggAUGBu/Q5NSkqSu7v7HduydQUAAAAAANgMEh0AAAAAAMBmcEYHcm3nlNC7LhkCAAAAAKAgsaIDAAAAAADYDBIdAAAAAADAZpDoAAAAAAAANoNEBwAAAAAAsBkcRopcaz7uI9kZnawdBgAAQKERNyPM2iEAwEOPFR0AAAAAAMBmkOgAAAAAAAA2g0QHAAAAAACwGSQ6AAAAAACAzSDRcR/Cw8NlMBg0aNCgLPdeeOEFGQwGhYeH3/c80dHR8vT0vO9xAAAAAACwdSQ67pOvr69Wrlyp69evm+tu3LihFStWqFy5claMLKuMjAxlZmZaOwwAAAAAAPINiY77VK9ePfn6+mrt2rXmurVr16pcuXKqW7euJGnp0qUqUaKEUlNTLfp26dJFzzzzjCTp8OHDCg4Olpubm9zd3VW/fn0dOHBAMTEx6tevn5KSkmQwGGQwGDRx4kRJUmpqqkaNGqUyZcrIxcVFjRs3VkxMjHn8WytBNmzYoICAABmNRu3evVsODg66cOGCRSzDhg1Ts2bN8uENAQAAAABQcEh05IH+/fsrKirKfL148WL169fPfP30008rIyNDGzZsMNddvHhRmzZtUv/+/SVJvXv3VtmyZbV//37FxcVpzJgxcnBwUJMmTTR79my5u7srMTFRiYmJGjVqlCRpyJAh2rt3r1auXKnvv/9eTz/9tEJCQnTixAnzPNeuXdP06dO1cOFCHT16VA0aNFDFihW1bNkyc5u0tDQtX77cHMu/paamKjk52aIAAAAAAFAYkejIA3369NHu3bt17tw5nTt3TrGxserTp4/5vpOTk3r16mWRDPnwww9Vrlw5tWjRQpKUkJCg1q1bq1q1aqpSpYqefvppBQYGytHRUR4eHjIYDPLx8ZGPj49cXV2VkJCgqKgorV69Ws2aNVOlSpU0atQoPfbYYxbzpKWlad68eWrSpImqVq0qZ2dnRUREWLT57LPPdOPGDXXv3j3b54uMjJSHh4e5+Pr65vEbBAAAAAAgb5DoyANeXl7q0KGDoqOjFRUVpQ4dOuiRRx6xaDNw4EB98cUX+uWXXyT9va3k1mGmkjRixAgNGDBArVu31rRp03Tq1Kk7znnkyBFlZGTI399frq6u5vL1119b9HV0dFTt2rUt+oaHh+vkyZP65ptvzLF0795dLi4u2c41duxYJSUlmcv58+dz9oIAAAAAACgg9tYOwFb0799fQ4YMkSS99957We7XrVtXgYGBWrp0qR5//HEdPXpUmzZtMt+fOHGievXqpU2bNmnLli2aMGGCVq5cqa5du2Y7X0pKiuzs7BQXFyc7OzuLe66urua/nZyczMmUW0qWLKlOnTopKipKFSpU0JYtWyzO9vg3o9Eoo9F413cAAAAAAIC1kejIIyEhIbp586YMBoPatm2bbZsBAwZo9uzZ+uWXX9S6dessW0D8/f3l7++v4cOHKzQ0VFFRUeratascHR2VkZFh0bZu3brKyMjQxYsXc3WI6IABAxQaGqqyZcuqUqVKatq0aY7HAAAAAACgsGHrSh6xs7NTfHy8jh07lmWFxS29evXSzz//rA8++MDi4M/r169ryJAhiomJMZ/xsX//flWvXl2S5Ofnp5SUFG3fvl1//PGHrl27Jn9/f/Xu3VthYWFau3atzpw5o3379ikyMtJipcjttG3bVu7u7poyZYrFwakAAAAAADzISHTkIXd3d7m7u9/2voeHh7p16yZXV1d16dLFXG9nZ6dLly4pLCxM/v7+6t69u9q1a6dJkyZJkpo0aaJBgwapR48e8vLy0ltvvSVJioqKUlhYmEaOHKmqVauqS5cu2r9/v8qVK3fXWIsUKaLw8HBlZGQoLCzs/h4cAAAAAIBCwmAymUzWDuJh0qpVK9WoUUNz5syxdiiKiIjQ77//bvHZ23uRnJwsDw8PBQ6dLzujUz5FBwAA8OCJm8E/IAFAfrj1OzQpKemOCwwkzugoMJcvX1ZMTIxiYmI0b948q8aSlJSkI0eOaMWKFTlOcgAAAAAAUJiR6CggdevW1eXLlzV9+nRVrVrVqrF07txZ+/bt06BBg9SmTRurxgIAAAAAQF5i6wpyLCdLhgAAAAAAuF85+R3KYaQAAAAAAMBmkOgAAAAAAAA2g0QHAAAAAACwGSQ6AAAAAACAzcjVV1fOnz8vg8GgsmXLSpL27dunFStWKCAgQM8++2yeBojCq/m4j2RndLJ2GAAA4AEQNyPM2iEAAB4SuVrR0atXL+3YsUOSdOHCBbVp00b79u3Ta6+9pjfeeCNPAwQAAAAAALhXuUp0/PDDD2rUqJEk6eOPP1bNmjW1Z88eLV++XNHR0XkZHwAAAAAAwD3LVaIjLS1NRqNRkrRt2zY98cQTkqRq1aopMTEx76IDAAAAAADIgVwlOmrUqKH58+dr165d+vLLLxUSEiJJ+vXXX1WiRIk8DRAAAAAAAOBe5SrRMX36dC1YsEAtWrRQaGioAgMDJUkbNmwwb2mBpfDwcHXp0iVLfUxMjAwGg65cuVLgMQEAAAAAYGty9dWVFi1a6I8//lBycrKKFStmrn/22Wfl7OycZ8Hh7m7evClHR0eLuoyMDBkMBhUpkrM8Vm77AQAAAABQWOT6F63JZFJcXJwWLFigv/76S5Lk6OhIouM+rVmzRjVq1JDRaJSfn59mzZplcd/Pz0+TJ09WWFiY3N3d9eyzzyo6Olqenp7asGGDAgICZDQalZCQoMuXLyssLEzFihWTs7Oz2rVrpxMnTpjHul0/AAAAAAAeVLlKdJw7d061atVS586d9cILL+j333+X9PeWllGjRuVpgA+TuLg4de/eXT179tSRI0c0ceJEjR8/PsuXbGbOnKnAwEAdPHhQ48ePlyRdu3ZN06dP18KFC3X06FGVLFlS4eHhOnDggDZs2KC9e/fKZDKpffv2SktLM4+VXb9/S01NVXJyskUBAAAAAKAwytXWlZdeekkNGjTQ4cOHLQ4f7dq1qwYOHJhnwdmajRs3ytXV1aIuIyPD/Pfbb7+tVq1amZMX/v7+OnbsmGbMmKHw8HBzu5YtW2rkyJHm6127diktLU3z5s0zn5dy4sQJbdiwQbGxsWrSpIkkafny5fL19dX69ev19NNPS1KWftmJjIzUpEmT7u/hAQAAAAAoALla0bFr1y6NGzcuy9kQfn5++uWXX/IkMFsUHBysQ4cOWZSFCxea78fHx6tp06YWfZo2baoTJ05YJEQaNGiQZWxHR0fVrl3bYix7e3s1btzYXFeiRAlVrVpV8fHxt+2XnbFjxyopKclczp8/f+8PDQAAAABAAcrVio7MzEyLH963/Pzzz3Jzc7vvoGyVi4uLKleubFH3888/52qcf3NycpLBYMjxWPfSz2g0ymg05nhsAAAAAAAKWq5WdDz++OOaPXu2+dpgMCglJUUTJkxQ+/bt8yq2h0716tUVGxtrURcbGyt/f3/Z2dnleKz09HR9++235rpLly7p+PHjCggIyJN4AQAAAAAobHKV6Jg5c6ZiY2MVEBCgGzduqFevXuZtK9OnT8/rGB8aI0eO1Pbt2zV58mT99NNPWrJkiebOnZurA16rVKmizp07a+DAgdq9e7cOHz6sPn36qEyZMurcuXM+RA8AAAAAgPXlauuKr6+vDh8+rFWrVunw4cNKSUlRRESEevfuLScnp7yO8aFRr149ffzxx3r99dc1efJklSpVSm+88YbFQaQ5ERUVpZdeekkdO3bUzZs31bx5c23evFkODg55GzgAAAAAAIWEwWQymXLSIS0tTdWqVdPGjRtVvXr1/IoLhVhycrI8PDwUOHS+7IwktgAAwN3FzQizdggAgAfYrd+hSUlJcnd3v2PbHG9dcXBw0I0bN3IdHAAAAAAAQH7J1RkdL7zwgqZPn6709PS8jgcAAAAAACDXcrx1RZK6du2q7du3y9XVVbVq1cryudO1a9fmWYAofHKyZAgAAAAAgPuVk9+huTqM1NPTU926dctVcAAAAAAAAPklV4mOqKiovI4DAAAAAADgvuXqjA4AAAAAAIDCKFeJjt9++03PPPOMSpcuLXt7e9nZ2VkUAAAAAAAAa8jV1pXw8HAlJCRo/PjxKlWqlAwGQ17HhQdA83Efyc7oZO0wAABAAYmbEWbtEAAAuKtcJTp2796tXbt2qU6dOnkcDgAAAAAAQO7lauuKr6+vcvFVWgAAAAAAgHyVq0TH7NmzNWbMGJ09ezaPwwEAAAAAAMi9XCU6evTooZiYGFWqVElubm4qXry4RcHdRUdHy9PT09phAAAAAABgU3J1Rsfs2bPzOAzbEh4eritXrmj9+vUW9TExMQoODtbly5fVo0cPtW/f/p7Gi46O1rBhw3TlypW8DxYAAAAAABuSq0RH37598zqOh46Tk5OcnAr2iyUZGRkyGAwqUiRXC3kAAAAAACj0cv2L99SpUxo3bpxCQ0N18eJFSdKWLVt09OjRPAvOlv1768rhw4cVHBwsNzc3ubu7q379+jpw4IBiYmLUr18/JSUlyWAwyGAwaOLEiZKky5cvKywsTMWKFZOzs7PatWunEydOZJljw4YNCggIkNFo1O7du+Xg4KALFy5YxDNs2DA1a9asIB4dAAAAAIB8k6tEx9dff61atWrp22+/1dq1a5WSkiLp7x/rEyZMyNMAHxa9e/dW2bJltX//fsXFxWnMmDFycHBQkyZNNHv2bLm7uysxMVGJiYkaNWqUpL+3yBw4cEAbNmzQ3r17ZTKZ1L59e6WlpZnHvXbtmqZPn66FCxfq6NGjatCggSpWrKhly5aZ26SlpWn58uXq379/trGlpqYqOTnZogAAAAAAUBjlauvKmDFjNGXKFI0YMUJubm7m+pYtW2ru3Ll5FtyDbOPGjXJ1dbWoy8jIuG37hIQEjR49WtWqVZMkValSxXzPw8NDBoNBPj4+5roTJ05ow4YNio2NVZMmTSRJy5cvl6+vr9avX6+nn35a0t9JjHnz5ikwMNDcNyIiQlFRURo9erQk6bPPPtONGzfUvXv3bGOLjIzUpEmTcvL4AAAAAABYRa5WdBw5ckRdu3bNUl+yZEn98ccf9x2ULQgODtahQ4csysKFC2/bfsSIERowYIBat26tadOm6dSpU3ccPz4+Xvb29mrcuLG5rkSJEqpatari4+PNdY6Ojqpdu7ZF3/DwcJ08eVLffPONpL+3uHTv3l0uLi7ZzjV27FglJSWZy/nz5+/6/AAAAAAAWEOuEh2enp5KTEzMUn/w4EGVKVPmvoOyBS4uLqpcubJFudO7mThxoo4ePaoOHTroq6++UkBAgNatW3ffcTg5OclgMFjUlSxZUp06dVJUVJR+++03bdmy5bbbViTJaDTK3d3dogAAAAAAUBjlKtHRs2dPvfLKK7pw4YIMBoMyMzMVGxurUaNGKSwsLK9jfGj4+/tr+PDh+uKLL/Tkk08qKipK0t+rMv697aV69epKT0/Xt99+a667dOmSjh8/roCAgLvONWDAAK1atUr/+9//VKlSJTVt2jRvHwYAAAAAACvIVaJj6tSpqlatmnx9fZWSkqKAgAA1a9ZMTZo00bhx4/I6Rpt3/fp1DRkyRDExMTp37pxiY2O1f/9+Va9eXZLk5+enlJQUbd++XX/88YeuXbumKlWqqHPnzho4cKB2796tw4cPq0+fPipTpow6d+581znbtm0rd3d3TZkyRf369cvvRwQAAAAAoEDkKtHh6OioDz74QKdPn9bGjRv14Ycf6vjx41q2bJns7OzyOkabZ2dnp0uXLiksLEz+/v7q3r272rVrZz4AtEmTJho0aJB69OghLy8vvfXWW5KkqKgo1a9fXx07dtSjjz4qk8mkzZs3y8HB4a5zFilSROHh4crIyGAVDgAAAADAZhhMJpMpp51GjBiR/WAGg4oWLarKlSurc+fOKl68+H0HiPwTERGh33//XRs2bMhRv+TkZHl4eChw6HzZGZ3yKToAAFDYxM3gH0cAANZx63doUlLSXc+NzNXnZQ8ePKjvvvtOGRkZqlq1qiTpp59+kp2dnapVq6Z58+Zp5MiR2r179z2dF4GClZSUpCNHjmjFihU5TnIAAAAAAFCY5WrrSufOndW6dWv9+uuviouLU1xcnH7++We1adNGoaGh+uWXX9S8eXMNHz48r+NFHujcubMef/xxDRo0SG3atLF2OAAAAAAA5JlcbV0pU6aMvvzyyyyrNY4eParHH39cv/zyi7777js9/vjj+uOPP/IsWBQObF0BAODhxNYVAIC15PvWlaSkJF28eDFLouP3339XcnKyJMnT01M3b97MzfB4QOycEnrX/8AAAAAAAChIud660r9/f61bt04///yzfv75Z61bt04RERHq0qWLJGnfvn3y9/fPy1gBAAAAAADuKFdbV1JSUjR8+HAtXbpU6enpkiR7e3v17dtX77zzjlxcXHTo0CFJUp06dfIyXhQCOVkyBAAAAADA/crJ79BcJTpuSUlJ0enTpyVJFStWlKura26HwgOERAcAAAAAoCDl+xkdt7i6uqp27dr3MwQeYM3HfcRhpAAAFGIcHgoAeBjl6owOAAAAAACAwohEBwAAAAAAsBkkOgAAAAAAgM0g0QEAAAAAAGwGiY4HxPz58+Xm5mb+nK/091dvHBwc1KJFC4u2MTExMhgMOnXqVAFHCQAAAACAdZHoeEAEBwcrJSVFBw4cMNft2rVLPj4++vbbb3Xjxg1z/Y4dO1SuXDlVqlQpR3OYTCaLRAoAAAAAAA8aEh0PiKpVq6pUqVKKiYkx18XExKhz586qUKGCvvnmG4v64OBgLVu2TA0aNJCbm5t8fHzUq1cvXbx40aKdwWDQli1bVL9+fRmNRu3evbsgHwsAAAAAgDxFouMBEhwcrB07dpivd+zYoRYtWigoKMhcf/36dX377bcKDg5WWlqaJk+erMOHD2v9+vU6e/aswsPDs4w7ZswYTZs2TfHx8apdu3aW+6mpqUpOTrYoAAAAAAAURvbWDgD3Ljg4WMOGDVN6erquX7+ugwcPKigoSGlpaZo/f74kae/evUpNTVVwcLDKlStn7luxYkXNmTNHDRs2VEpKilxdXc333njjDbVp0+a280ZGRmrSpEn592AAAAAAAOQRVnQ8QFq0aKGrV69q//792rVrl/z9/eXl5aWgoCDzOR0xMTGqWLGiypUrp7i4OHXq1EnlypWTm5ubgoKCJEkJCQkW4zZo0OCO844dO1ZJSUnmcv78+Xx7RgAAAAAA7gcrOh4glStXVtmyZbVjxw5dvnzZnLgoXbq0fH19tWfPHu3YsUMtW7bU1atX1bZtW7Vt21bLly+Xl5eXEhIS1LZtW928edNiXBcXlzvOazQaZTQa8+25AAAAAADIK6zoeMAEBwcrJiZGMTExFp+Vbd68ubZs2aJ9+/YpODhYP/74oy5duqRp06apWbNmqlatmsVBpAAAAAAA2CISHQ+Y4OBg7d69W4cOHTKv6JCkoKAgLViwQDdv3jSfz+Ho6Kj//ve/On36tDZs2KDJkydbMXIAAAAAAPIfiY4HTHBwsK5fv67KlSvL29vbXB8UFKS//vrL/BlaLy8vRUdHa/Xq1QoICNC0adM0c+ZMK0YOAAAAAED+M5hMJpO1g8CDJTk5WR4eHgocOl92RidrhwMAAG4jbkaYtUMAACBP3PodmpSUJHd39zu2ZUUHAAAAAACwGSQ6AAAAAACAzSDRAQAAAAAAbIa9tQPAg2vnlNC77o0CAAAAAKAgsaIDAAAAAADYDBIdAAAAAADAZpDoAAAAAAAANoMzOpBrzcd9JDujk7XDAADApsXNCLN2CAAAPFBY0QEAAAAAAGwGiQ4AAAAAAGAzSHQAAAAAAACbQaIDAAAAAADYDBIdD4GJEyeqTp061g4DAAAAAIB8R6Ijly5cuKChQ4eqYsWKMhqN8vX1VadOnbR9+3ZrhwYAAAAAwEOLz8vmwtmzZ9W0aVN5enpqxowZqlWrltLS0vT555/rhRde0I8//mjtEAEAAAAAeCixoiMXBg8eLIPBoH379qlbt27y9/dXjRo1NGLECH3zzTeSpISEBHXu3Fmurq5yd3dX9+7d9dtvv5nHuLWdZPHixSpXrpxcXV01ePBgZWRk6K233pKPj49KliypN99802LuK1euaMCAAfLy8pK7u7tatmypw4cPW7SZNm2avL295ebmpoiICN24ccN8b+fOnXJwcNCFCxcs+gwbNkzNmjXL9nlTU1OVnJxsUQAAAAAAKIxIdOTQn3/+qa1bt+qFF16Qi4tLlvuenp7KzMxU586d9eeff+rrr7/Wl19+qdOnT6tHjx4WbU+dOqUtW7Zo69at+uijj7Ro0SJ16NBBP//8s77++mtNnz5d48aN07fffmvu8/TTT+vixYvasmWL4uLiVK9ePbVq1Up//vmnJOnjjz/WxIkTNXXqVB04cEClSpXSvHnzzP2bN2+uihUratmyZea6tLQ0LV++XP3798/2mSMjI+Xh4WEuvr6+9/UOAQAAAADILyQ6cujkyZMymUyqVq3abdts375dR44c0YoVK1S/fn01btxYS5cu1ddff639+/eb22VmZmrx4sUKCAhQp06dFBwcrOPHj2v27NmqWrWq+vXrp6pVq2rHjh2SpN27d2vfvn1avXq1GjRooCpVqmjmzJny9PTUJ598IkmaPXu2IiIiFBERoapVq2rKlCkKCAiwiC8iIkJRUVHm688++0w3btxQ9+7ds32esWPHKikpyVzOnz+f6/cHAAAAAEB+ItGRQyaT6a5t4uPj5evra7HyISAgQJ6enoqPjzfX+fn5yc3NzXzt7e2tgIAAFSlSxKLu4sWLkqTDhw8rJSVFJUqUkKurq7mcOXNGp06dMs/duHFji3geffRRi+vw8HCdPHnSvM0mOjpa3bt3z3aFiiQZjUa5u7tbFAAAAAAACiMOI82hKlWqyGAw5MmBow4ODhbXBoMh27rMzExJUkpKikqVKqWYmJgsY3l6et7zvCVLllSnTp0UFRWlChUqaMuWLdmOCQAAAADAg4YVHTlUvHhxtW3bVu+9956uXr2a5f6VK1dUvXp1nT9/3mKLx7Fjx3TlypUs20hyol69erpw4YLs7e1VuXJli/LII49IkqpXr25xpock88qNfxowYIBWrVql//3vf6pUqZKaNm2a67gAAAAAACgsSHTkwnvvvaeMjAw1atRIa9as0YkTJxQfH685c+bo0UcfVevWrVWrVi317t1b3333nfbt26ewsDAFBQWpQYMGuZ63devWevTRR9WlSxd98cUXOnv2rPbs2aPXXntNBw4ckCS99NJLWrx4saKiovTTTz9pwoQJOnr0aJax2rZtK3d3d02ZMkX9+vXLdUwAAAAAABQmJDpyoWLFivruu+8UHByskSNHqmbNmmrTpo22b9+u999/XwaDQZ9++qmKFSum5s2bq3Xr1qpYsaJWrVp1X/MaDAZt3rxZzZs3V79+/eTv76+ePXvq3Llz8vb2liT16NFD48eP18svv6z69evr3Llzev7557OMVaRIEYWHhysjI0NhYWH3FRcAAAAAAIWFwXQvp2vCJkVEROj333/Xhg0bctQvOTlZHh4eChw6X3ZGp3yKDgAASFLcDP5BAgCAW79Dk5KS7vqBDA4jfQglJSWZP3+b0yQHAAAAAACFGYmOh1Dnzp21b98+DRo0SG3atLF2OAAAAAAA5Bm2riDHcrJkCAAAAACA+5WT36EcRgoAAAAAAGwGiQ4AAAAAAGAzSHQAAAAAAACbwWGkyLXm4z7i87IAANwFn4cFAKBgsaIDAAAAAADYDBIdAAAAAADAZpDoAAAAAAAANoNEBwAAAAAAsBkkOgAAAAAAgM0g0XEb4eHhMhgMMhgMcnBwUIUKFfTyyy/rxo0b1g4NAAAAAADcBp+XvYOQkBBFRUUpLS1NcXFx6tu3rwwGg6ZPn27t0AAAAAAAQDZY0XEHRqNRPj4+8vX1VZcuXdS6dWt9+eWXkqTU1FS9+OKLKlmypIoWLarHHntM+/fvN/eNiYmRwWDQ559/rrp168rJyUktW7bUxYsXtWXLFlWvXl3u7u7q1auXrl27Zu63detWPfbYY/L09FSJEiXUsWNHnTp1ynz/7NmzMhgMWrt2rYKDg+Xs7KzAwEDt3bvXIvbY2Fi1aNFCzs7OKlasmNq2bavLly9LkjIzMxUZGakKFSrIyclJgYGB+uSTT/LzVQIAAAAAUCBIdNyjH374QXv27JGjo6Mk6eWXX9aaNWu0ZMkSfffdd6pcubLatm2rP//806LfxIkTNXfuXO3Zs0fnz59X9+7dNXv2bK1YsUKbNm3SF198of/+97/m9levXtWIESN04MABbd++XUWKFFHXrl2VmZlpMe5rr72mUaNG6dChQ/L391doaKjS09MlSYcOHVKrVq0UEBCgvXv3avfu3erUqZMyMjIkSZGRkVq6dKnmz5+vo0ePavjw4erTp4++/vrrbJ89NTVVycnJFgUAAAAAgMLIYDKZTNYOojAKDw/Xhx9+qKJFiyo9PV2pqakqUqSIPv74Y4WEhKhYsWKKjo5Wr169JElpaWny8/PTsGHDNHr0aMXExCg4OFjbtm1Tq1atJEnTpk3T2LFjderUKVWsWFGSNGjQIJ09e1Zbt27NNo4//vhDXl5eOnLkiGrWrKmzZ8+qQoUKWrhwoSIiIiRJx44dU40aNRQfH69q1aqpV69eSkhI0O7du7OMl5qaquLFi2vbtm169NFHzfUDBgzQtWvXtGLFiix9Jk6cqEmTJmWpDxw6X3ZGpxy+WQAAHi5xM8KsHQIAAA+85ORkeXh4KCkpSe7u7ndsy4qOOwgODtahQ4f07bffqm/fvurXr5+6deumU6dOKS0tTU2bNjW3dXBwUKNGjRQfH28xRu3atc1/e3t7y9nZ2ZzkuFV38eJF8/WJEycUGhqqihUryt3dXX5+fpKkhISE245bqlQpSTKPc2tFR3ZOnjypa9euqU2bNnJ1dTWXpUuXWmyR+aexY8cqKSnJXM6fP3/bdwYAAAAAgDVxGOkduLi4qHLlypKkxYsXKzAwUIsWLVLDhg3veQwHBwfz37e+4PJPBoPBYltKp06dVL58eX3wwQcqXbq0MjMzVbNmTd28efOO40oyj+PkdPtVFikpKZKkTZs2qUyZMhb3jEZjtn2MRuNt7wEAAAAAUJiwouMeFSlSRK+++qrGjRunSpUqydHRUbGxseb7aWlp2r9/vwICAnI9x6VLl3T8+HGNGzdOrVq1UvXq1c0HiOZE7dq1tX379mzvBQQEyGg0KiEhQZUrV7Yovr6+uY4dAAAAAIDCgBUdOfD0009r9OjRev/99/X8889r9OjRKl68uMqVK6e33npL165dM5+bkRvFihVTiRIl9L///U+lSpVSQkKCxowZk+Nxxo4dq1q1amnw4MEaNGiQHB0dtWPHDj399NN65JFHNGrUKA0fPlyZmZl67LHHlJSUpNjYWLm7u6tv3765jh8AAAAAAGsj0ZED9vb2GjJkiN566y2dOXNGmZmZeuaZZ/TXX3+pQYMG+vzzz1WsWLFcj1+kSBGtXLlSL774omrWrKmqVatqzpw5atGiRY7G8ff31xdffKFXX31VjRo1kpOTkxo3bqzQ0FBJ0uTJk+Xl5aXIyEidPn1anp6eqlevnl599dVcxw4AAAAAQGHAV1eQY7dOu+WrKwAA3B1fXQEA4P7x1RUAAAAAAPBQItEBAAAAAABsBmd0INd2Tgm965IhAAAAAAAKEis6AAAAAACAzSDRAQAAAAAAbAaJDgAAAAAAYDM4owO51nzcR3xeFgBg8/g8LAAADxZWdAAAAAAAAJtBogMAAAAAANgMEh0AAAAAAMBmkOgAAAAAAAA2g0QHAAAAAACwGSQ68lF4eLgMBoMMBoMcHBzk7e2tNm3aaPHixcrMzLR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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot average popularity score arranged in descending order vs genres\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Popular Genres\")\n",
    "sns.barplot(data = genre_table.sort_values('popularity', ascending = False),\n",
    "            y = 'genres',\n",
    "            x = 'popularity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot average  `vote_count` arranged in descending order vs genres\n",
    "\n",
    "plt.figure(figsize = (12, 6))\n",
    "plt.title(\"Most Voted Genres\")\n",
    "sns.barplot(data = genre_table.sort_values('vote_count', ascending = False),\n",
    "            y = 'genres', x = 'vote_count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot mean `'vote_average` arranged in descending order vs genres\n",
    "plt.figure(figsize = (12, 6))\n",
    "plt.title(\"Highest Vote Average Genres\")\n",
    "sns.barplot(data = genre_table.sort_values('vote_average', ascending = False),\n",
    "            y = 'genres', x = 'vote_average')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41646</th>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>401593</td>\n",
       "      <td>0.248238</td>\n",
       "      <td>Big Jay Oakerson: Live at Webster Hall</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33389</th>\n",
       "      <td>[]</td>\n",
       "      <td>134782</td>\n",
       "      <td>0.059830</td>\n",
       "      <td>The Italian Key</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11385</th>\n",
       "      <td>[Music, Documentary]</td>\n",
       "      <td>68736</td>\n",
       "      <td>0.035284</td>\n",
       "      <td>Joe Cocker - Mad Dogs &amp; Englishmen</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41879</th>\n",
       "      <td>[]</td>\n",
       "      <td>190605</td>\n",
       "      <td>0.066255</td>\n",
       "      <td>Panorama of Esplanade by Night</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22395</th>\n",
       "      <td>[Documentary]</td>\n",
       "      <td>230864</td>\n",
       "      <td>0.371755</td>\n",
       "      <td>Marvin Hamlisch: What He Did For Love</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22396</th>\n",
       "      <td>[Documentary]</td>\n",
       "      <td>72123</td>\n",
       "      <td>0.091080</td>\n",
       "      <td>Meat the Truth</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11050</th>\n",
       "      <td>[Drama, Comedy]</td>\n",
       "      <td>43147</td>\n",
       "      <td>0.090803</td>\n",
       "      <td>Blessed Event</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33627</th>\n",
       "      <td>[]</td>\n",
       "      <td>267178</td>\n",
       "      <td>0.027705</td>\n",
       "      <td>Mad As Hell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22578</th>\n",
       "      <td>[Fantasy, Drama]</td>\n",
       "      <td>89445</td>\n",
       "      <td>0.055097</td>\n",
       "      <td>Eggshells</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42136</th>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>163839</td>\n",
       "      <td>0.153100</td>\n",
       "      <td>The Black Sheep of Whitehall</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25689</th>\n",
       "      <td>[Animation, Family, Adventure]</td>\n",
       "      <td>153936</td>\n",
       "      <td>0.040437</td>\n",
       "      <td>Back to School with Franklin</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33280</th>\n",
       "      <td>[]</td>\n",
       "      <td>123691</td>\n",
       "      <td>0.061678</td>\n",
       "      <td>Dream Deceivers: The Story Behind James Vance ...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42179</th>\n",
       "      <td>[Action, Drama, Horror, Thriller]</td>\n",
       "      <td>295011</td>\n",
       "      <td>1.083454</td>\n",
       "      <td>Patient Zero</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10456</th>\n",
       "      <td>[Comedy, Music, Family]</td>\n",
       "      <td>34662</td>\n",
       "      <td>0.611118</td>\n",
       "      <td>Li'l Abner</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42211</th>\n",
       "      <td>[Drama]</td>\n",
       "      <td>376188</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>Demidovy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23191</th>\n",
       "      <td>[Drama]</td>\n",
       "      <td>197611</td>\n",
       "      <td>0.646465</td>\n",
       "      <td>Dragonfly</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>[Comedy, Drama, Family]</td>\n",
       "      <td>78373</td>\n",
       "      <td>0.567444</td>\n",
       "      <td>Dancer, Texas Pop. 81</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42438</th>\n",
       "      <td>[]</td>\n",
       "      <td>312137</td>\n",
       "      <td>0.036798</td>\n",
       "      <td>Has the Film Already Started?</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41845</th>\n",
       "      <td>[]</td>\n",
       "      <td>170078</td>\n",
       "      <td>0.017521</td>\n",
       "      <td>Maula Jat</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41804</th>\n",
       "      <td>[Documentary]</td>\n",
       "      <td>299909</td>\n",
       "      <td>0.052588</td>\n",
       "      <td>Bella Vita</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  genres      id  popularity  \\\n",
       "41646                           [Comedy]  401593    0.248238   \n",
       "33389                                 []  134782    0.059830   \n",
       "11385               [Music, Documentary]   68736    0.035284   \n",
       "41879                                 []  190605    0.066255   \n",
       "22395                      [Documentary]  230864    0.371755   \n",
       "22396                      [Documentary]   72123    0.091080   \n",
       "11050                    [Drama, Comedy]   43147    0.090803   \n",
       "33627                                 []  267178    0.027705   \n",
       "22578                   [Fantasy, Drama]   89445    0.055097   \n",
       "42136                           [Comedy]  163839    0.153100   \n",
       "25689     [Animation, Family, Adventure]  153936    0.040437   \n",
       "33280                                 []  123691    0.061678   \n",
       "42179  [Action, Drama, Horror, Thriller]  295011    1.083454   \n",
       "10456            [Comedy, Music, Family]   34662    0.611118   \n",
       "42211                            [Drama]  376188    0.000220   \n",
       "23191                            [Drama]  197611    0.646465   \n",
       "1761             [Comedy, Drama, Family]   78373    0.567444   \n",
       "42438                                 []  312137    0.036798   \n",
       "41845                                 []  170078    0.017521   \n",
       "41804                      [Documentary]  299909    0.052588   \n",
       "\n",
       "                                                   title  vote_average  \\\n",
       "41646             Big Jay Oakerson: Live at Webster Hall          10.0   \n",
       "33389                                    The Italian Key          10.0   \n",
       "11385                 Joe Cocker - Mad Dogs & Englishmen          10.0   \n",
       "41879                     Panorama of Esplanade by Night          10.0   \n",
       "22395              Marvin Hamlisch: What He Did For Love          10.0   \n",
       "22396                                     Meat the Truth          10.0   \n",
       "11050                                      Blessed Event          10.0   \n",
       "33627                                        Mad As Hell          10.0   \n",
       "22578                                          Eggshells          10.0   \n",
       "42136                       The Black Sheep of Whitehall          10.0   \n",
       "25689                       Back to School with Franklin          10.0   \n",
       "33280  Dream Deceivers: The Story Behind James Vance ...          10.0   \n",
       "42179                                       Patient Zero          10.0   \n",
       "10456                                         Li'l Abner          10.0   \n",
       "42211                                           Demidovy          10.0   \n",
       "23191                                          Dragonfly          10.0   \n",
       "1761                               Dancer, Texas Pop. 81          10.0   \n",
       "42438                      Has the Film Already Started?          10.0   \n",
       "41845                                          Maula Jat          10.0   \n",
       "41804                                         Bella Vita          10.0   \n",
       "\n",
       "       vote_count  \n",
       "41646         1.0  \n",
       "33389         1.0  \n",
       "11385         1.0  \n",
       "41879         1.0  \n",
       "22395         1.0  \n",
       "22396         1.0  \n",
       "11050         1.0  \n",
       "33627         1.0  \n",
       "22578         1.0  \n",
       "42136         1.0  \n",
       "25689         1.0  \n",
       "33280         1.0  \n",
       "42179         2.0  \n",
       "10456         1.0  \n",
       "42211         1.0  \n",
       "23191         1.0  \n",
       "1761          1.0  \n",
       "42438         1.0  \n",
       "41845         1.0  \n",
       "41804         1.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recommend top 20 movies as per average rating of the movie\n",
    "top_20_va = mov_subset_df.sort_values(by = 'vote_average', ascending = False).head(20)\n",
    "top_20_va"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above results, you may observe that many of the highest rated movies (vote_average = 10.0) has vote_count = 1.0. This means that the rating 10 was given by only 1 subscriber to these movies.\n",
    "\n",
    "Hence, we cannot proclaim these movies as highest rated movies as the rating was given by very few subscribers. Thus, we cannot recommend movies to a new subscriber by simply considering vote_average column.\n",
    "\n",
    "To overcome these shortcomings, we have to consider both average rating and the number of voters. For this we will use the weighted rating formula used by IMDB.\n",
    "\n",
    "The weighted rating approach ensures that a film with only a few ratings, all at 10, would not rank above \"the Godfather\", for example, with a 9.2 average from over 500,000 ratings. It is mathematically represented as:\n",
    "\n",
    "W=(Rv+Cm)/(v+m )\n",
    "\n",
    "where,\n",
    "\n",
    "W  is the Weighted Rating.\n",
    "R  is the average rating of the movie.\n",
    "v  is the number of votes for the movie.\n",
    "m  is the minimum votes required to be listed in the top movies list.\n",
    "C  denotes the mean vote across the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.618329297820823"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate mean of 'vote_average' column\n",
    "C = mov_subset_df['vote_average'].mean()\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the minimum number of votes for a movie in order to qualify it for recommendation\n",
    "m = mov_subset_df['vote_count'].quantile(0.9)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
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       "      <td>[Animation, Comedy, Family]</td>\n",
       "      <td>862</td>\n",
       "      <td>21.946943</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>8844</td>\n",
       "      <td>17.015539</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>11862</td>\n",
       "      <td>8.387519</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>949</td>\n",
       "      <td>17.924927</td>\n",
       "      <td>Heat</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[Action, Adventure, Thriller]</td>\n",
       "      <td>9091</td>\n",
       "      <td>5.231580</td>\n",
       "      <td>Sudden Death</td>\n",
       "      <td>5.5</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             genres     id  popularity  \\\n",
       "0       [Animation, Comedy, Family]    862   21.946943   \n",
       "1      [Adventure, Fantasy, Family]   8844   17.015539   \n",
       "4                          [Comedy]  11862    8.387519   \n",
       "5  [Action, Crime, Drama, Thriller]    949   17.924927   \n",
       "8     [Action, Adventure, Thriller]   9091    5.231580   \n",
       "\n",
       "                         title  vote_average  vote_count  \n",
       "0                    Toy Story           7.7      5415.0  \n",
       "1                      Jumanji           6.9      2413.0  \n",
       "4  Father of the Bride Part II           5.7       173.0  \n",
       "5                         Heat           7.7      1886.0  \n",
       "8                 Sudden Death           5.5       174.0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame of movies having vote_count more than minimum number of votes\n",
    "\n",
    "qualified_movies_df = mov_subset_df.copy().loc[mov_subset_df['vote_count'] >= m]\n",
    "qualified_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Function to computes the weighted rating of each movie\n",
    "def weighted_rating(df):\n",
    "    v = df['vote_count']\n",
    "    R = df['vote_average']\n",
    "\n",
    "    # Calculation based on the Weighted Rating formula\n",
    "    W = ((R * v) + (C * m)) / (v + m)\n",
    "    return W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>genres</th>\n",
       "      <th>id</th>\n",
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       "      <th>0</th>\n",
       "      <td>[Animation, Comedy, Family]</td>\n",
       "      <td>862</td>\n",
       "      <td>21.946943</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "      <td>7.640257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>8844</td>\n",
       "      <td>17.015539</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "      <td>6.820300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>11862</td>\n",
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       "      <td>Father of the Bride Part II</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "      <td>5.660759</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>949</td>\n",
       "      <td>17.924927</td>\n",
       "      <td>Heat</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>7.537211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[Action, Adventure, Thriller]</td>\n",
       "      <td>9091</td>\n",
       "      <td>5.231580</td>\n",
       "      <td>Sudden Death</td>\n",
       "      <td>5.5</td>\n",
       "      <td>174.0</td>\n",
       "      <td>5.556685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             genres     id  popularity  \\\n",
       "0       [Animation, Comedy, Family]    862   21.946943   \n",
       "1      [Adventure, Fantasy, Family]   8844   17.015539   \n",
       "4                          [Comedy]  11862    8.387519   \n",
       "5  [Action, Crime, Drama, Thriller]    949   17.924927   \n",
       "8     [Action, Adventure, Thriller]   9091    5.231580   \n",
       "\n",
       "                         title  vote_average  vote_count  weighted_rating  \n",
       "0                    Toy Story           7.7      5415.0         7.640257  \n",
       "1                      Jumanji           6.9      2413.0         6.820300  \n",
       "4  Father of the Bride Part II           5.7       173.0         5.660759  \n",
       "5                         Heat           7.7      1886.0         7.537211  \n",
       "8                 Sudden Death           5.5       174.0         5.556685  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append a column for the weighted rating of the movies in the DataFrame\n",
    "qualified_movies_df['weighted_rating'] = qualified_movies_df.apply(weighted_rating, axis= 1)\n",
    "qualified_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>314</th>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>278</td>\n",
       "      <td>51.645403</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8358.0</td>\n",
       "      <td>8.445871</td>\n",
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       "      <th>834</th>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>238</td>\n",
       "      <td>41.109264</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>8.5</td>\n",
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       "      <th>10309</th>\n",
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       "      <td>19404</td>\n",
       "      <td>34.457024</td>\n",
       "      <td>Dilwale Dulhania Le Jayenge</td>\n",
       "      <td>9.1</td>\n",
       "      <td>661.0</td>\n",
       "      <td>8.421477</td>\n",
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       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>[Drama, Action, Crime, Thriller]</td>\n",
       "      <td>155</td>\n",
       "      <td>123.167259</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>8.3</td>\n",
       "      <td>12269.0</td>\n",
       "      <td>8.265479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2843</th>\n",
       "      <td>[Drama]</td>\n",
       "      <td>550</td>\n",
       "      <td>63.869599</td>\n",
       "      <td>Fight Club</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9678.0</td>\n",
       "      <td>8.256387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>[Thriller, Crime]</td>\n",
       "      <td>680</td>\n",
       "      <td>140.950236</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8670.0</td>\n",
       "      <td>8.251408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>[Drama, History, War]</td>\n",
       "      <td>424</td>\n",
       "      <td>41.725123</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>8.3</td>\n",
       "      <td>4436.0</td>\n",
       "      <td>8.206643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23673</th>\n",
       "      <td>[Drama]</td>\n",
       "      <td>244786</td>\n",
       "      <td>64.299990</td>\n",
       "      <td>Whiplash</td>\n",
       "      <td>8.3</td>\n",
       "      <td>4376.0</td>\n",
       "      <td>8.205408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5481</th>\n",
       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
       "      <td>129</td>\n",
       "      <td>41.048867</td>\n",
       "      <td>Spirited Away</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3968.0</td>\n",
       "      <td>8.196059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "      <td>637</td>\n",
       "      <td>39.394970</td>\n",
       "      <td>Life Is Beautiful</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3643.0</td>\n",
       "      <td>8.187177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>240</td>\n",
       "      <td>36.629307</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>8.180082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>[Drama]</td>\n",
       "      <td>510</td>\n",
       "      <td>35.529554</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>8.164262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>13</td>\n",
       "      <td>48.307194</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8147.0</td>\n",
       "      <td>8.150275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>1891</td>\n",
       "      <td>19.470959</td>\n",
       "      <td>The Empire Strikes Back</td>\n",
       "      <td>8.2</td>\n",
       "      <td>5998.0</td>\n",
       "      <td>8.132922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>[Drama, Horror, Thriller]</td>\n",
       "      <td>539</td>\n",
       "      <td>36.826309</td>\n",
       "      <td>Psycho</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>8.132722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18465</th>\n",
       "      <td>[Drama, Comedy]</td>\n",
       "      <td>77338</td>\n",
       "      <td>16.086919</td>\n",
       "      <td>The Intouchables</td>\n",
       "      <td>8.2</td>\n",
       "      <td>5410.0</td>\n",
       "      <td>8.125841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40251</th>\n",
       "      <td>[Romance, Animation, Drama]</td>\n",
       "      <td>372058</td>\n",
       "      <td>34.461252</td>\n",
       "      <td>Your Name.</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>8.112548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>[Thriller, Crime, Drama]</td>\n",
       "      <td>101</td>\n",
       "      <td>20.477329</td>\n",
       "      <td>Leon: The Professional</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4293.0</td>\n",
       "      <td>8.107238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3030</th>\n",
       "      <td>[Fantasy, Drama, Crime]</td>\n",
       "      <td>497</td>\n",
       "      <td>19.966780</td>\n",
       "      <td>The Green Mile</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>8.104515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>769</td>\n",
       "      <td>15.424092</td>\n",
       "      <td>GoodFellas</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>8.077464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>[Drama]</td>\n",
       "      <td>73</td>\n",
       "      <td>18.157166</td>\n",
       "      <td>American History X</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3120.0</td>\n",
       "      <td>8.074065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15480</th>\n",
       "      <td>[Action, Thriller, Science Fiction, Mystery, A...</td>\n",
       "      <td>27205</td>\n",
       "      <td>29.108149</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14075.0</td>\n",
       "      <td>8.072106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22879</th>\n",
       "      <td>[Adventure, Drama, Science Fiction]</td>\n",
       "      <td>157336</td>\n",
       "      <td>32.213481</td>\n",
       "      <td>Interstellar</td>\n",
       "      <td>8.1</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>8.065007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7000</th>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>122</td>\n",
       "      <td>29.324358</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8226.0</td>\n",
       "      <td>8.052651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>11</td>\n",
       "      <td>42.149697</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>8.1</td>\n",
       "      <td>6778.0</td>\n",
       "      <td>8.042769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  genres      id  popularity  \\\n",
       "314                                       [Drama, Crime]     278   51.645403   \n",
       "834                                       [Drama, Crime]     238   41.109264   \n",
       "10309                           [Comedy, Drama, Romance]   19404   34.457024   \n",
       "12481                   [Drama, Action, Crime, Thriller]     155  123.167259   \n",
       "2843                                             [Drama]     550   63.869599   \n",
       "292                                    [Thriller, Crime]     680  140.950236   \n",
       "522                                [Drama, History, War]     424   41.725123   \n",
       "23673                                            [Drama]  244786   64.299990   \n",
       "5481             [Fantasy, Adventure, Animation, Family]     129   41.048867   \n",
       "2211                                     [Comedy, Drama]     637   39.394970   \n",
       "1178                                      [Drama, Crime]     240   36.629307   \n",
       "1152                                             [Drama]     510   35.529554   \n",
       "351                             [Comedy, Drama, Romance]      13   48.307194   \n",
       "1154                [Adventure, Action, Science Fiction]    1891   19.470959   \n",
       "1176                           [Drama, Horror, Thriller]     539   36.826309   \n",
       "18465                                    [Drama, Comedy]   77338   16.086919   \n",
       "40251                        [Romance, Animation, Drama]  372058   34.461252   \n",
       "289                             [Thriller, Crime, Drama]     101   20.477329   \n",
       "3030                             [Fantasy, Drama, Crime]     497   19.966780   \n",
       "1170                                      [Drama, Crime]     769   15.424092   \n",
       "2216                                             [Drama]      73   18.157166   \n",
       "15480  [Action, Thriller, Science Fiction, Mystery, A...   27205   29.108149   \n",
       "22879                [Adventure, Drama, Science Fiction]  157336   32.213481   \n",
       "7000                        [Adventure, Fantasy, Action]     122   29.324358   \n",
       "256                 [Adventure, Action, Science Fiction]      11   42.149697   \n",
       "\n",
       "                                               title  vote_average  \\\n",
       "314                         The Shawshank Redemption           8.5   \n",
       "834                                    The Godfather           8.5   \n",
       "10309                    Dilwale Dulhania Le Jayenge           9.1   \n",
       "12481                                The Dark Knight           8.3   \n",
       "2843                                      Fight Club           8.3   \n",
       "292                                     Pulp Fiction           8.3   \n",
       "522                                 Schindler's List           8.3   \n",
       "23673                                       Whiplash           8.3   \n",
       "5481                                   Spirited Away           8.3   \n",
       "2211                               Life Is Beautiful           8.3   \n",
       "1178                          The Godfather: Part II           8.3   \n",
       "1152                 One Flew Over the Cuckoo's Nest           8.3   \n",
       "351                                     Forrest Gump           8.2   \n",
       "1154                         The Empire Strikes Back           8.2   \n",
       "1176                                          Psycho           8.3   \n",
       "18465                               The Intouchables           8.2   \n",
       "40251                                     Your Name.           8.5   \n",
       "289                           Leon: The Professional           8.2   \n",
       "3030                                  The Green Mile           8.2   \n",
       "1170                                      GoodFellas           8.2   \n",
       "2216                              American History X           8.2   \n",
       "15480                                      Inception           8.1   \n",
       "22879                                   Interstellar           8.1   \n",
       "7000   The Lord of the Rings: The Return of the King           8.1   \n",
       "256                                        Star Wars           8.1   \n",
       "\n",
       "       vote_count  weighted_rating  \n",
       "314        8358.0         8.445871  \n",
       "834        6024.0         8.425442  \n",
       "10309       661.0         8.421477  \n",
       "12481     12269.0         8.265479  \n",
       "2843       9678.0         8.256387  \n",
       "292        8670.0         8.251408  \n",
       "522        4436.0         8.206643  \n",
       "23673      4376.0         8.205408  \n",
       "5481       3968.0         8.196059  \n",
       "2211       3643.0         8.187177  \n",
       "1178       3418.0         8.180082  \n",
       "1152       3001.0         8.164262  \n",
       "351        8147.0         8.150275  \n",
       "1154       5998.0         8.132922  \n",
       "1176       2405.0         8.132722  \n",
       "18465      5410.0         8.125841  \n",
       "40251      1030.0         8.112548  \n",
       "289        4293.0         8.107238  \n",
       "3030       4166.0         8.104515  \n",
       "1170       3211.0         8.077464  \n",
       "2216       3120.0         8.074065  \n",
       "15480     14075.0         8.072106  \n",
       "22879     11187.0         8.065007  \n",
       "7000       8226.0         8.052651  \n",
       "256        6778.0         8.042769  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the top 25 movies.\n",
    "qualified_movies_df.sort_values(by= 'weighted_rating', ascending = False, inplace = True)\n",
    "qualified_movies_df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>weighted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Drama</td>\n",
       "      <td>278</td>\n",
       "      <td>51.645403</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8358.0</td>\n",
       "      <td>8.445871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Crime</td>\n",
       "      <td>278</td>\n",
       "      <td>51.645403</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8358.0</td>\n",
       "      <td>8.445871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Drama</td>\n",
       "      <td>238</td>\n",
       "      <td>41.109264</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6024.0</td>\n",
       "      <td>8.425442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Crime</td>\n",
       "      <td>238</td>\n",
       "      <td>41.109264</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6024.0</td>\n",
       "      <td>8.425442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>19404</td>\n",
       "      <td>34.457024</td>\n",
       "      <td>Dilwale Dulhania Le Jayenge</td>\n",
       "      <td>9.1</td>\n",
       "      <td>661.0</td>\n",
       "      <td>8.421477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genres     id  popularity                        title  vote_average  \\\n",
       "0   Drama    278   51.645403     The Shawshank Redemption           8.5   \n",
       "1   Crime    278   51.645403     The Shawshank Redemption           8.5   \n",
       "2   Drama    238   41.109264                The Godfather           8.5   \n",
       "3   Crime    238   41.109264                The Godfather           8.5   \n",
       "4  Comedy  19404   34.457024  Dilwale Dulhania Le Jayenge           9.1   \n",
       "\n",
       "   vote_count  weighted_rating  \n",
       "0      8358.0         8.445871  \n",
       "1      8358.0         8.445871  \n",
       "2      6024.0         8.425442  \n",
       "3      6024.0         8.425442  \n",
       "4       661.0         8.421477  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_based_df = qualified_movies_df.explode('genres', ignore_index = True)\n",
    "genre_based_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def genre_recommender(fav_genre):\n",
    "  recommended_df = genre_based_df[genre_based_df['genres'] == fav_genre]\n",
    "  return(recommended_df.head(25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>weighted_rating</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>155</td>\n",
       "      <td>123.167259</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>8.3</td>\n",
       "      <td>12269.0</td>\n",
       "      <td>8.265479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>680</td>\n",
       "      <td>140.950236</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8670.0</td>\n",
       "      <td>8.251408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>539</td>\n",
       "      <td>36.826309</td>\n",
       "      <td>Psycho</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>8.132722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>101</td>\n",
       "      <td>20.477329</td>\n",
       "      <td>Leon: The Professional</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4293.0</td>\n",
       "      <td>8.107238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>27205</td>\n",
       "      <td>29.108149</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14075.0</td>\n",
       "      <td>8.072106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>807</td>\n",
       "      <td>18.457430</td>\n",
       "      <td>Se7en</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5915.0</td>\n",
       "      <td>8.034639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>274</td>\n",
       "      <td>4.307222</td>\n",
       "      <td>The Silence of the Lambs</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4549.0</td>\n",
       "      <td>8.015679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>77</td>\n",
       "      <td>15.450789</td>\n",
       "      <td>Memento</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4168.0</td>\n",
       "      <td>8.008256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>694</td>\n",
       "      <td>19.611589</td>\n",
       "      <td>The Shining</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3890.0</td>\n",
       "      <td>8.001959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>500</td>\n",
       "      <td>12.220340</td>\n",
       "      <td>Reservoir Dogs</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3821.0</td>\n",
       "      <td>8.000259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>629</td>\n",
       "      <td>16.302466</td>\n",
       "      <td>The Usual Suspects</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3334.0</td>\n",
       "      <td>7.986357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>264644</td>\n",
       "      <td>12.443291</td>\n",
       "      <td>Room</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>7.967556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>567</td>\n",
       "      <td>17.911314</td>\n",
       "      <td>Rear Window</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>7.955726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>205596</td>\n",
       "      <td>31.595940</td>\n",
       "      <td>The Imitation Game</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5895.0</td>\n",
       "      <td>7.937066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>1124</td>\n",
       "      <td>16.945560</td>\n",
       "      <td>The Prestige</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4510.0</td>\n",
       "      <td>7.918401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>111</td>\n",
       "      <td>11.299673</td>\n",
       "      <td>Scarface</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3017.0</td>\n",
       "      <td>7.880054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>16869</td>\n",
       "      <td>16.895640</td>\n",
       "      <td>Inglourious Basterds</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6598.0</td>\n",
       "      <td>7.845980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>210577</td>\n",
       "      <td>154.801009</td>\n",
       "      <td>Gone Girl</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6023.0</td>\n",
       "      <td>7.840956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>670</td>\n",
       "      <td>10.616859</td>\n",
       "      <td>Oldboy</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.823580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>348</td>\n",
       "      <td>23.377420</td>\n",
       "      <td>Alien</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4564.0</td>\n",
       "      <td>7.822721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>1422</td>\n",
       "      <td>18.515448</td>\n",
       "      <td>The Departed</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>7.820895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>78</td>\n",
       "      <td>96.272374</td>\n",
       "      <td>Blade Runner</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3833.0</td>\n",
       "      <td>7.808573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>146233</td>\n",
       "      <td>11.962620</td>\n",
       "      <td>Prisoners</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3183.0</td>\n",
       "      <td>7.790796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>11324</td>\n",
       "      <td>15.813629</td>\n",
       "      <td>Shutter Island</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6559.0</td>\n",
       "      <td>7.748048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>426</td>\n",
       "      <td>18.208220</td>\n",
       "      <td>Vertigo</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>7.711749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genres      id  popularity                     title  vote_average  \\\n",
       "10   Thriller     155  123.167259           The Dark Knight           8.3   \n",
       "12   Thriller     680  140.950236              Pulp Fiction           8.3   \n",
       "35   Thriller     539   36.826309                    Psycho           8.3   \n",
       "41   Thriller     101   20.477329    Leon: The Professional           8.2   \n",
       "51   Thriller   27205   29.108149                 Inception           8.1   \n",
       "66   Thriller     807   18.457430                     Se7en           8.1   \n",
       "70   Thriller     274    4.307222  The Silence of the Lambs           8.1   \n",
       "78   Thriller      77   15.450789                   Memento           8.1   \n",
       "80   Thriller     694   19.611589               The Shining           8.1   \n",
       "82   Thriller     500   12.220340            Reservoir Dogs           8.1   \n",
       "87   Thriller     629   16.302466        The Usual Suspects           8.1   \n",
       "89   Thriller  264644   12.443291                      Room           8.1   \n",
       "100  Thriller     567   17.911314               Rear Window           8.2   \n",
       "111  Thriller  205596   31.595940        The Imitation Game           8.0   \n",
       "120  Thriller    1124   16.945560              The Prestige           8.0   \n",
       "128  Thriller     111   11.299673                  Scarface           8.0   \n",
       "143  Thriller   16869   16.895640      Inglourious Basterds           7.9   \n",
       "149  Thriller  210577  154.801009                 Gone Girl           7.9   \n",
       "170  Thriller     670   10.616859                    Oldboy           8.0   \n",
       "175  Thriller     348   23.377420                     Alien           7.9   \n",
       "178  Thriller    1422   18.515448              The Departed           7.9   \n",
       "185  Thriller      78   96.272374              Blade Runner           7.9   \n",
       "200  Thriller  146233   11.962620                 Prisoners           7.9   \n",
       "224  Thriller   11324   15.813629            Shutter Island           7.8   \n",
       "246  Thriller     426   18.208220                   Vertigo           8.0   \n",
       "\n",
       "     vote_count  weighted_rating  \n",
       "10      12269.0         8.265479  \n",
       "12       8670.0         8.251408  \n",
       "35       2405.0         8.132722  \n",
       "41       4293.0         8.107238  \n",
       "51      14075.0         8.072106  \n",
       "66       5915.0         8.034639  \n",
       "70       4549.0         8.015679  \n",
       "78       4168.0         8.008256  \n",
       "80       3890.0         8.001959  \n",
       "82       3821.0         8.000259  \n",
       "87       3334.0         7.986357  \n",
       "89       2838.0         7.967556  \n",
       "100      1531.0         7.955726  \n",
       "111      5895.0         7.937066  \n",
       "120      4510.0         7.918401  \n",
       "128      3017.0         7.880054  \n",
       "143      6598.0         7.845980  \n",
       "149      6023.0         7.840956  \n",
       "170      2000.0         7.823580  \n",
       "175      4564.0         7.822721  \n",
       "178      4455.0         7.820895  \n",
       "185      3833.0         7.808573  \n",
       "200      3183.0         7.790796  \n",
       "224      6559.0         7.748048  \n",
       "246      1162.0         7.711749  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#: Recommend 'Thriller' movies\n",
    "\n",
    "genre_recommender('Thriller')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>weighted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Horror</td>\n",
       "      <td>539</td>\n",
       "      <td>36.826309</td>\n",
       "      <td>Psycho</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>8.132722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Horror</td>\n",
       "      <td>694</td>\n",
       "      <td>19.611589</td>\n",
       "      <td>The Shining</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3890.0</td>\n",
       "      <td>8.001959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Horror</td>\n",
       "      <td>348</td>\n",
       "      <td>23.377420</td>\n",
       "      <td>Alien</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4564.0</td>\n",
       "      <td>7.822721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>Horror</td>\n",
       "      <td>1091</td>\n",
       "      <td>16.831250</td>\n",
       "      <td>The Thing</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>7.604881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Horror</td>\n",
       "      <td>679</td>\n",
       "      <td>21.761179</td>\n",
       "      <td>Aliens</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3282.0</td>\n",
       "      <td>7.603234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Horror</td>\n",
       "      <td>396535</td>\n",
       "      <td>22.685169</td>\n",
       "      <td>Train to Busan</td>\n",
       "      <td>7.7</td>\n",
       "      <td>984.0</td>\n",
       "      <td>7.408857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>Horror</td>\n",
       "      <td>578</td>\n",
       "      <td>19.726114</td>\n",
       "      <td>Jaws</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2628.0</td>\n",
       "      <td>7.392013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>Horror</td>\n",
       "      <td>747</td>\n",
       "      <td>14.902948</td>\n",
       "      <td>Shaun of the Dead</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>7.385916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>Horror</td>\n",
       "      <td>9552</td>\n",
       "      <td>12.137595</td>\n",
       "      <td>The Exorcist</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>7.363523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>Horror</td>\n",
       "      <td>138843</td>\n",
       "      <td>14.901690</td>\n",
       "      <td>The Conjuring</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3169.0</td>\n",
       "      <td>7.314368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>Horror</td>\n",
       "      <td>1933</td>\n",
       "      <td>11.046007</td>\n",
       "      <td>The Others</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1708.0</td>\n",
       "      <td>7.247394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>Horror</td>\n",
       "      <td>13310</td>\n",
       "      <td>7.274237</td>\n",
       "      <td>Let the Right One In</td>\n",
       "      <td>7.5</td>\n",
       "      <td>997.0</td>\n",
       "      <td>7.239786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>Horror</td>\n",
       "      <td>805</td>\n",
       "      <td>11.504558</td>\n",
       "      <td>Rosemary's Baby</td>\n",
       "      <td>7.5</td>\n",
       "      <td>892.0</td>\n",
       "      <td>7.213814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>Horror</td>\n",
       "      <td>765</td>\n",
       "      <td>13.520764</td>\n",
       "      <td>Evil Dead II</td>\n",
       "      <td>7.5</td>\n",
       "      <td>760.0</td>\n",
       "      <td>7.172753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>Horror</td>\n",
       "      <td>948</td>\n",
       "      <td>13.672759</td>\n",
       "      <td>Halloween</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>7.167482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>Horror</td>\n",
       "      <td>374430</td>\n",
       "      <td>24.910782</td>\n",
       "      <td>Black Mirror: White Christmas</td>\n",
       "      <td>8.3</td>\n",
       "      <td>211.0</td>\n",
       "      <td>7.143484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>Horror</td>\n",
       "      <td>49797</td>\n",
       "      <td>8.435562</td>\n",
       "      <td>I Saw the Devil</td>\n",
       "      <td>7.6</td>\n",
       "      <td>534.0</td>\n",
       "      <td>7.143131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>Horror</td>\n",
       "      <td>19908</td>\n",
       "      <td>11.063029</td>\n",
       "      <td>Zombieland</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3655.0</td>\n",
       "      <td>7.133665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>Horror</td>\n",
       "      <td>419430</td>\n",
       "      <td>36.894806</td>\n",
       "      <td>Get Out</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2978.0</td>\n",
       "      <td>7.119354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>Horror</td>\n",
       "      <td>63311</td>\n",
       "      <td>9.782542</td>\n",
       "      <td>The Skin I Live In</td>\n",
       "      <td>7.4</td>\n",
       "      <td>797.0</td>\n",
       "      <td>7.102124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>Horror</td>\n",
       "      <td>10331</td>\n",
       "      <td>14.384198</td>\n",
       "      <td>Night of the Living Dead</td>\n",
       "      <td>7.5</td>\n",
       "      <td>591.0</td>\n",
       "      <td>7.099111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>Horror</td>\n",
       "      <td>246741</td>\n",
       "      <td>10.504198</td>\n",
       "      <td>What We Do in the Shadows</td>\n",
       "      <td>7.4</td>\n",
       "      <td>779.0</td>\n",
       "      <td>7.096414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>Horror</td>\n",
       "      <td>176</td>\n",
       "      <td>23.508433</td>\n",
       "      <td>Saw</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>7.095210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>Horror</td>\n",
       "      <td>653</td>\n",
       "      <td>7.435873</td>\n",
       "      <td>Nosferatu</td>\n",
       "      <td>7.7</td>\n",
       "      <td>373.0</td>\n",
       "      <td>7.075108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>Horror</td>\n",
       "      <td>36685</td>\n",
       "      <td>8.699428</td>\n",
       "      <td>The Rocky Horror Picture Show</td>\n",
       "      <td>7.4</td>\n",
       "      <td>703.0</td>\n",
       "      <td>7.069679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      genres      id  popularity                          title  vote_average  \\\n",
       "34    Horror     539   36.826309                         Psycho           8.3   \n",
       "79    Horror     694   19.611589                    The Shining           8.1   \n",
       "173   Horror     348   23.377420                          Alien           7.9   \n",
       "324   Horror    1091   16.831250                      The Thing           7.8   \n",
       "328   Horror     679   21.761179                         Aliens           7.7   \n",
       "593   Horror  396535   22.685169                 Train to Busan           7.7   \n",
       "645   Horror     578   19.726114                           Jaws           7.5   \n",
       "652   Horror     747   14.902948              Shaun of the Dead           7.5   \n",
       "676   Horror    9552   12.137595                   The Exorcist           7.5   \n",
       "799   Horror  138843   14.901690                  The Conjuring           7.4   \n",
       "919   Horror    1933   11.046007                     The Others           7.4   \n",
       "935   Horror   13310    7.274237           Let the Right One In           7.5   \n",
       "1013  Horror     805   11.504558                Rosemary's Baby           7.5   \n",
       "1107  Horror     765   13.520764                   Evil Dead II           7.5   \n",
       "1119  Horror     948   13.672759                      Halloween           7.4   \n",
       "1192  Horror  374430   24.910782  Black Mirror: White Christmas           8.3   \n",
       "1198  Horror   49797    8.435562                I Saw the Devil           7.6   \n",
       "1211  Horror   19908   11.063029                     Zombieland           7.2   \n",
       "1242  Horror  419430   36.894806                        Get Out           7.2   \n",
       "1296  Horror   63311    9.782542             The Skin I Live In           7.4   \n",
       "1301  Horror   10331   14.384198       Night of the Living Dead           7.5   \n",
       "1306  Horror  246741   10.504198      What We Do in the Shadows           7.4   \n",
       "1307  Horror     176   23.508433                            Saw           7.2   \n",
       "1378  Horror     653    7.435873                      Nosferatu           7.7   \n",
       "1387  Horror   36685    8.699428  The Rocky Horror Picture Show           7.4   \n",
       "\n",
       "      vote_count  weighted_rating  \n",
       "34        2405.0         8.132722  \n",
       "79        3890.0         8.001959  \n",
       "173       4564.0         7.822721  \n",
       "324       1629.0         7.604881  \n",
       "328       3282.0         7.603234  \n",
       "593        984.0         7.408857  \n",
       "645       2628.0         7.392013  \n",
       "652       2479.0         7.385916  \n",
       "676       2046.0         7.363523  \n",
       "799       3169.0         7.314368  \n",
       "919       1708.0         7.247394  \n",
       "935        997.0         7.239786  \n",
       "1013       892.0         7.213814  \n",
       "1107       760.0         7.172753  \n",
       "1119      1066.0         7.167482  \n",
       "1192       211.0         7.143484  \n",
       "1198       534.0         7.143131  \n",
       "1211      3655.0         7.133665  \n",
       "1242      2978.0         7.119354  \n",
       "1296       797.0         7.102124  \n",
       "1301       591.0         7.099111  \n",
       "1306       779.0         7.096414  \n",
       "1307      2255.0         7.095210  \n",
       "1378       373.0         7.075108  \n",
       "1387       703.0         7.069679  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#: Recommend 'Horror' movies\n",
    "\n",
    "genre_recommender('Horror')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we observe that the system has correctly recommended top movies in the adventure genre. Hence, we have successfully designed the basic recommendation engine for our new users.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Let us understand the collaborative filters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collaborative filtering builds a model from the user's past behaviour (i.e. items purchased or searched by the user) as well as similar decisions made by other users. This model is then used to predict items that users may have an interest in.\n",
    "\n",
    "\n",
    "We will build an intelligent recommender that would recommend movies to a customer say X based on the customer's watch history.\n",
    "First, we need to find other sets of users who have watched same movies along with some other movies and suggest customer X the movies which were appreciated by those sets of users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasets\n",
    "We will use following three datasets to set up a recommender system that will recommend movies to a user based on ratings given by other users:\n",
    "\n",
    "1. The movie_metadata.csv file:\n",
    "\n",
    "This is the main Movies Metadata file.\n",
    "\n",
    "It contains information on 45,000 movies featured in the Full MovieLens database.\n",
    "\n",
    "2. The links.csv file:\n",
    "\n",
    "This file contains the TMDB and IMDB IDs of all the movies featured in the Full MovieLens dataset.\n",
    "\n",
    "3. The ratings_small.csv file:\n",
    "\n",
    "This file is a subset of 100,000 ratings from 700 users on 9,000 movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>Toy Story</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>Jumanji</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15602</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>Grumpier Old Men</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31357</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11862</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id    imdb_id                        title\n",
       "0    862  tt0114709                    Toy Story\n",
       "1   8844  tt0113497                      Jumanji\n",
       "2  15602  tt0113228             Grumpier Old Men\n",
       "3  31357  tt0114885            Waiting to Exhale\n",
       "4  11862  tt0113041  Father of the Bride Part II"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Create 'movies_df' DataFrame consisting of columns: 'id', 'imdb_id', 'title'\n",
    "movies_df = movies_df[['id', 'imdb_id', 'title']]\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45443 entries, 0 to 45465\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   id       45443 non-null  object\n",
      " 1   imdb_id  45443 non-null  object\n",
      " 2   title    45443 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#  Drop missing values from the DataFrame.\n",
    "movies_df.dropna(inplace = True)\n",
    "movies_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>862.0</td>\n",
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       "      <td>Toy Story</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>tt0113497</td>\n",
       "      <td>Jumanji</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15602.0</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31357.0</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11862.0</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id    imdb_id                        title\n",
       "0    862.0  tt0114709                    Toy Story\n",
       "1   8844.0  tt0113497                      Jumanji\n",
       "2  15602.0  tt0113228             Grumpier Old Men\n",
       "3  31357.0  tt0114885            Waiting to Exhale\n",
       "4  11862.0  tt0113041  Father of the Bride Part II"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Convert data type of 'id' column to float\n",
    "movies_df['id'] = movies_df['id'].astype('float')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   movieId  imdbId   tmdbId\n",
       "0        1  114709    862.0\n",
       "1        2  113497   8844.0\n",
       "2        3  113228  15602.0\n",
       "3        4  114885  31357.0\n",
       "4        5  113041  11862.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
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   ],
   "source": [
    "#  Load 'links.csv' file into 'links_df' DataFrame.\n",
    "links_df = pd.read_csv('https://drive.google.com/uc?id=1bcxKJJMhU15qH77BmmdPf4lZwi5k5Cnb')\n",
    "links_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>113041</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>45500</th>\n",
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       "      <td>tt6209470</td>\n",
       "      <td>Subdue</td>\n",
       "      <td>176269</td>\n",
       "      <td>6209470</td>\n",
       "      <td>439050.0</td>\n",
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       "    <tr>\n",
       "      <th>45501</th>\n",
       "      <td>111109.0</td>\n",
       "      <td>tt2028550</td>\n",
       "      <td>Century of Birthing</td>\n",
       "      <td>176271</td>\n",
       "      <td>2028550</td>\n",
       "      <td>111109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45502</th>\n",
       "      <td>67758.0</td>\n",
       "      <td>tt0303758</td>\n",
       "      <td>Betrayal</td>\n",
       "      <td>176273</td>\n",
       "      <td>303758</td>\n",
       "      <td>67758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45503</th>\n",
       "      <td>227506.0</td>\n",
       "      <td>tt0008536</td>\n",
       "      <td>Satan Triumphant</td>\n",
       "      <td>176275</td>\n",
       "      <td>8536</td>\n",
       "      <td>227506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45504</th>\n",
       "      <td>461257.0</td>\n",
       "      <td>tt6980792</td>\n",
       "      <td>Queerama</td>\n",
       "      <td>176279</td>\n",
       "      <td>6980792</td>\n",
       "      <td>461257.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45505 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "             id    imdb_id                        title  movieId   imdbId  \\\n",
       "0         862.0  tt0114709                    Toy Story        1   114709   \n",
       "1        8844.0  tt0113497                      Jumanji        2   113497   \n",
       "2       15602.0  tt0113228             Grumpier Old Men        3   113228   \n",
       "3       31357.0  tt0114885            Waiting to Exhale        4   114885   \n",
       "4       11862.0  tt0113041  Father of the Bride Part II        5   113041   \n",
       "...         ...        ...                          ...      ...      ...   \n",
       "45500  439050.0  tt6209470                       Subdue   176269  6209470   \n",
       "45501  111109.0  tt2028550          Century of Birthing   176271  2028550   \n",
       "45502   67758.0  tt0303758                     Betrayal   176273   303758   \n",
       "45503  227506.0  tt0008536             Satan Triumphant   176275     8536   \n",
       "45504  461257.0  tt6980792                     Queerama   176279  6980792   \n",
       "\n",
       "         tmdbId  \n",
       "0         862.0  \n",
       "1        8844.0  \n",
       "2       15602.0  \n",
       "3       31357.0  \n",
       "4       11862.0  \n",
       "...         ...  \n",
       "45500  439050.0  \n",
       "45501  111109.0  \n",
       "45502   67758.0  \n",
       "45503  227506.0  \n",
       "45504  461257.0  \n",
       "\n",
       "[45505 rows x 6 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Merge 'movies_df' and 'links_df' DataFrames\n",
    "m_links_df = pd.merge(movies_df, links_df, left_on ='id', right_on ='tmdbId')\n",
    "m_links_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45500</th>\n",
       "      <td>176269</td>\n",
       "      <td>Subdue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45501</th>\n",
       "      <td>176271</td>\n",
       "      <td>Century of Birthing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45502</th>\n",
       "      <td>176273</td>\n",
       "      <td>Betrayal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45503</th>\n",
       "      <td>176275</td>\n",
       "      <td>Satan Triumphant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45504</th>\n",
       "      <td>176279</td>\n",
       "      <td>Queerama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45505 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                        title\n",
       "0            1                    Toy Story\n",
       "1            2                      Jumanji\n",
       "2            3             Grumpier Old Men\n",
       "3            4            Waiting to Exhale\n",
       "4            5  Father of the Bride Part II\n",
       "...        ...                          ...\n",
       "45500   176269                       Subdue\n",
       "45501   176271          Century of Birthing\n",
       "45502   176273                     Betrayal\n",
       "45503   176275             Satan Triumphant\n",
       "45504   176279                     Queerama\n",
       "\n",
       "[45505 rows x 2 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the final DataFrame consisting of only 'movieId' and 'title' columns.\n",
    "m_df = m_links_df[['movieId', 'title']]\n",
    "m_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Load 'ratings_small.csv' file into 'ratings_df' DataFrame.\n",
    "ratings_df = pd.read_csv('https://drive.google.com/uc?id=1DKT6CcjHsdKY9TKKAfk50ic2khf9JbJA')\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1       31     2.5\n",
       "1       1     1029     3.0\n",
       "2       1     1061     3.0\n",
       "3       1     1129     2.0\n",
       "4       1     1172     4.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Drop 'timestamp' column from 'ratings_df' DataFrame.\n",
    "ratings_df = ratings_df.drop('timestamp', axis=1)\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>userId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>13</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>19</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId      title  userId  rating\n",
       "0        1  Toy Story       7     3.0\n",
       "1        1  Toy Story       9     4.0\n",
       "2        1  Toy Story      13     5.0\n",
       "3        1  Toy Story      15     2.0\n",
       "4        1  Toy Story      19     3.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#: Merge 'm_df' and 'ratings_df' DataFrames.\n",
    "final_movies_df = pd.merge(m_df, ratings_df, on = 'movieId')\n",
    "final_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Female Perversions    5.0\n",
       "Lake of Fire          5.0\n",
       "Lamerica              5.0\n",
       "The Family Stone      5.0\n",
       "Riding Giants         5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Print top 5 movies having highest mean rating.\n",
    "final_movies_df.groupby('title')['rating'].mean().sort_values(ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Forrest Gump                341\n",
       "Pulp Fiction                324\n",
       "The Shawshank Redemption    311\n",
       "The Silence of the Lambs    304\n",
       "Star Wars                   291\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Print top 5 movies having highest count of ratings.\n",
    "final_movies_df.groupby('title')['rating'].count().sort_values(ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>num of ratings</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>$9.99</th>\n",
       "      <td>3.833333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Neath the Arizona Skies</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'night, Mother</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(500) Days of Summer</th>\n",
       "      <td>3.755556</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And God Created Woman</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>À Nous la Liberté</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Æon Flux</th>\n",
       "      <td>2.538462</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>İtirazım Var</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Želary</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>’Round Midnight</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8754 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            rating  num of ratings\n",
       "title                                             \n",
       "$9.99                     3.833333               3\n",
       "'Neath the Arizona Skies  0.500000               1\n",
       "'night, Mother            5.000000               3\n",
       "(500) Days of Summer      3.755556              45\n",
       "...And God Created Woman  5.000000               1\n",
       "...                            ...             ...\n",
       "À Nous la Liberté         4.500000               1\n",
       "Æon Flux                  2.538462              13\n",
       "İtirazım Var              3.500000               1\n",
       "Želary                    5.000000               1\n",
       "’Round Midnight           2.250000               2\n",
       "\n",
       "[8754 rows x 2 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# : Create a DataFrame with average rating and number of ratings for each movie.\n",
    "all_movies_ratings = pd.DataFrame(final_movies_df.groupby('title')['rating'].mean())\n",
    "all_movies_ratings['num of ratings'] = pd.DataFrame(final_movies_df.groupby('title')['rating'].count())\n",
    "all_movies_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>userId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>13</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>19</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId      title  userId  rating\n",
       "0        1  Toy Story       7     3.0\n",
       "1        1  Toy Story       9     4.0\n",
       "2        1  Toy Story      13     5.0\n",
       "3        1  Toy Story      15     2.0\n",
       "4        1  Toy Story      19     3.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Print first 5 rows of final movies DataFrame.\n",
    "final_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "title   $9.99  'Neath the Arizona Skies  'night, Mother  (500) Days of Summer  \\\n",
       "userId                                                                          \n",
       "1         NaN                       NaN             NaN                   NaN   \n",
       "2         NaN                       NaN             NaN                   NaN   \n",
       "3         NaN                       NaN             NaN                   NaN   \n",
       "4         NaN                       NaN             NaN                   NaN   \n",
       "5         NaN                       NaN             NaN                   NaN   \n",
       "...       ...                       ...             ...                   ...   \n",
       "667       NaN                       NaN             NaN                   NaN   \n",
       "668       NaN                       NaN             NaN                   NaN   \n",
       "669       NaN                       NaN             NaN                   NaN   \n",
       "670       NaN                       NaN             NaN                   NaN   \n",
       "671       NaN                       NaN             NaN                   NaN   \n",
       "\n",
       "title   ...And God Created Woman  ...And Justice for All  1-900  10  \\\n",
       "userId                                                                \n",
       "1                            NaN                     NaN    NaN NaN   \n",
       "2                            NaN                     NaN    NaN NaN   \n",
       "3                            NaN                     NaN    NaN NaN   \n",
       "4                            NaN                     NaN    NaN NaN   \n",
       "5                            NaN                     NaN    NaN NaN   \n",
       "...                          ...                     ...    ...  ..   \n",
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       "669                          NaN                     NaN    NaN NaN   \n",
       "670                          NaN                     NaN    NaN NaN   \n",
       "671                          NaN                     NaN    NaN NaN   \n",
       "\n",
       "title   10 Attitudes  10 Cloverfield Lane  ...  eXistenZ  \\\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",
       "667              NaN                  NaN  ...       NaN   \n",
       "668              NaN                  NaN  ...       NaN   \n",
       "669              NaN                  NaN  ...       NaN   \n",
       "670              NaN                  NaN  ...       NaN   \n",
       "671              NaN                  NaN  ...       NaN   \n",
       "\n",
       "title   loudQUIETloud: A Film About the Pixies  xXx  xXx: State of the Union  \\\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",
       "667                                        NaN  NaN                      NaN   \n",
       "668                                        NaN  NaN                      NaN   \n",
       "669                                        NaN  NaN                      NaN   \n",
       "670                                        NaN  NaN                      NaN   \n",
       "671                                        NaN  NaN                      NaN   \n",
       "\n",
       "title   ¡Three Amigos!  À Nous la Liberté  Æon Flux  İtirazım Var  Želary  \\\n",
       "userId                                                                      \n",
       "1                  NaN                NaN       NaN           NaN     NaN   \n",
       "2                  NaN                NaN       NaN           NaN     NaN   \n",
       "3                  NaN                NaN       NaN           NaN     NaN   \n",
       "4                  NaN                NaN       NaN           NaN     NaN   \n",
       "5                  NaN                NaN       NaN           NaN     NaN   \n",
       "...                ...                ...       ...           ...     ...   \n",
       "667                NaN                NaN       NaN           NaN     NaN   \n",
       "668                NaN                NaN       NaN           NaN     NaN   \n",
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       "670                NaN                NaN       NaN           NaN     NaN   \n",
       "671                NaN                NaN       NaN           NaN     NaN   \n",
       "\n",
       "title   ’Round Midnight  \n",
       "userId                   \n",
       "1                   NaN  \n",
       "2                   NaN  \n",
       "3                   NaN  \n",
       "4                   NaN  \n",
       "5                   NaN  \n",
       "...                 ...  \n",
       "667                 NaN  \n",
       "668                 NaN  \n",
       "669                 NaN  \n",
       "670                 NaN  \n",
       "671                 NaN  \n",
       "\n",
       "[671 rows x 8754 columns]"
      ]
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     "execution_count": 102,
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   ],
   "source": [
    "# Create a pivot table with index ='userId', columns ='title', values ='rating'\n",
    "user_ratings = final_movies_df.pivot_table(index ='userId', columns ='title', values ='rating')\n",
    "user_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, each cell consist of the rating the user gave to that movie. The NaN values indicate that these movies were not watched or rated by that particular user.\n",
    "\n",
    "To obtain recommendation for similar movies based on the ratings given by other users, we will compute a similarity score. Collaborative filters can use a variety of similarity scores, for example:\n",
    "\n",
    "Pearson Correlation Coefficient\n",
    "\n",
    "Cosine Similarity\n",
    "\n",
    "\n",
    "\n",
    "Collaborative Filtering I - Pearson Correlation Coefficient\n",
    "\n",
    "For our recommender, we will use Pearson Correlation Coefficient to obtain the similarity score of the movies. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
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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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(500) Days of Summer</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.327327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.188982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.424179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.617213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And God Created Woman</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>À Nous la Liberté</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Æon Flux</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.251952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>İtirazım Var</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Želary</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>’Round Midnight</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>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>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8754 rows × 8754 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title                     $9.99  'Neath the Arizona Skies  'night, Mother  \\\n",
       "title                                                                       \n",
       "$9.99                       1.0                       NaN             NaN   \n",
       "'Neath the Arizona Skies    NaN                       NaN             NaN   \n",
       "'night, Mother              NaN                       NaN             NaN   \n",
       "(500) Days of Summer        1.0                       NaN             NaN   \n",
       "...And God Created Woman    NaN                       NaN             NaN   \n",
       "...                         ...                       ...             ...   \n",
       "À Nous la Liberté           NaN                       NaN             NaN   \n",
       "Æon Flux                    NaN                       NaN             NaN   \n",
       "İtirazım Var                NaN                       NaN             NaN   \n",
       "Želary                      NaN                       NaN             NaN   \n",
       "’Round Midnight             NaN                       NaN             NaN   \n",
       "\n",
       "title                     (500) Days of Summer  ...And God Created Woman  \\\n",
       "title                                                                      \n",
       "$9.99                                 1.000000                       NaN   \n",
       "'Neath the Arizona Skies                   NaN                       NaN   \n",
       "'night, Mother                             NaN                       NaN   \n",
       "(500) Days of Summer                  1.000000                       NaN   \n",
       "...And God Created Woman                   NaN                       NaN   \n",
       "...                                        ...                       ...   \n",
       "À Nous la Liberté                          NaN                       NaN   \n",
       "Æon Flux                              0.866025                       NaN   \n",
       "İtirazım Var                               NaN                       NaN   \n",
       "Želary                                     NaN                       NaN   \n",
       "’Round Midnight                       1.000000                       NaN   \n",
       "\n",
       "title                     ...And Justice for All  1-900        10  \\\n",
       "title                                                               \n",
       "$9.99                                        NaN    NaN       NaN   \n",
       "'Neath the Arizona Skies                     NaN    NaN       NaN   \n",
       "'night, Mother                               NaN    NaN       NaN   \n",
       "(500) Days of Summer                   -0.327327    NaN -0.188982   \n",
       "...And God Created Woman                     NaN    NaN       NaN   \n",
       "...                                          ...    ...       ...   \n",
       "À Nous la Liberté                            NaN    NaN       NaN   \n",
       "Æon Flux                                     NaN    NaN       NaN   \n",
       "İtirazım Var                                 NaN    NaN       NaN   \n",
       "Želary                                       NaN    NaN       NaN   \n",
       "’Round Midnight                              NaN    NaN       NaN   \n",
       "\n",
       "title                     10 Attitudes  10 Cloverfield Lane  ...  eXistenZ  \\\n",
       "title                                                        ...             \n",
       "$9.99                              NaN                  NaN  ...       NaN   \n",
       "'Neath the Arizona Skies           NaN                  NaN  ...       NaN   \n",
       "'night, Mother                     NaN                  NaN  ...       NaN   \n",
       "(500) Days of Summer               NaN                 -0.5  ...       NaN   \n",
       "...And God Created Woman           NaN                  NaN  ...       NaN   \n",
       "...                                ...                  ...  ...       ...   \n",
       "À Nous la Liberté                  NaN                  NaN  ...       NaN   \n",
       "Æon Flux                           NaN                  1.0  ...       NaN   \n",
       "İtirazım Var                       NaN                  NaN  ...       NaN   \n",
       "Želary                             NaN                  NaN  ...       NaN   \n",
       "’Round Midnight                    NaN                  NaN  ...       NaN   \n",
       "\n",
       "title                     loudQUIETloud: A Film About the Pixies       xXx  \\\n",
       "title                                                                        \n",
       "$9.99                                                        NaN       NaN   \n",
       "'Neath the Arizona Skies                                     NaN       NaN   \n",
       "'night, Mother                                               NaN       NaN   \n",
       "(500) Days of Summer                                         NaN  0.424179   \n",
       "...And God Created Woman                                     NaN       NaN   \n",
       "...                                                          ...       ...   \n",
       "À Nous la Liberté                                            NaN       NaN   \n",
       "Æon Flux                                                     NaN  0.251952   \n",
       "İtirazım Var                                                 NaN       NaN   \n",
       "Želary                                                       NaN       NaN   \n",
       "’Round Midnight                                              NaN       NaN   \n",
       "\n",
       "title                     xXx: State of the Union  ¡Three Amigos!  \\\n",
       "title                                                               \n",
       "$9.99                                         NaN             NaN   \n",
       "'Neath the Arizona Skies                      NaN             NaN   \n",
       "'night, Mother                                NaN             NaN   \n",
       "(500) Days of Summer                          NaN       -0.617213   \n",
       "...And God Created Woman                      NaN             NaN   \n",
       "...                                           ...             ...   \n",
       "À Nous la Liberté                             NaN             NaN   \n",
       "Æon Flux                                      NaN        1.000000   \n",
       "İtirazım Var                                  NaN             NaN   \n",
       "Želary                                        NaN             NaN   \n",
       "’Round Midnight                               NaN             NaN   \n",
       "\n",
       "title                     À Nous la Liberté  Æon Flux  İtirazım Var  Želary  \\\n",
       "title                                                                         \n",
       "$9.99                                   NaN       NaN           NaN     NaN   \n",
       "'Neath the Arizona Skies                NaN       NaN           NaN     NaN   \n",
       "'night, Mother                          NaN       NaN           NaN     NaN   \n",
       "(500) Days of Summer                    NaN  0.866025           NaN     NaN   \n",
       "...And God Created Woman                NaN       NaN           NaN     NaN   \n",
       "...                                     ...       ...           ...     ...   \n",
       "À Nous la Liberté                       NaN       NaN           NaN     NaN   \n",
       "Æon Flux                                NaN  1.000000           NaN     NaN   \n",
       "İtirazım Var                            NaN       NaN           NaN     NaN   \n",
       "Želary                                  NaN       NaN           NaN     NaN   \n",
       "’Round Midnight                         NaN       NaN           NaN     NaN   \n",
       "\n",
       "title                     ’Round Midnight  \n",
       "title                                      \n",
       "$9.99                                 NaN  \n",
       "'Neath the Arizona Skies              NaN  \n",
       "'night, Mother                        NaN  \n",
       "(500) Days of Summer                  1.0  \n",
       "...And God Created Woman              NaN  \n",
       "...                                   ...  \n",
       "À Nous la Liberté                     NaN  \n",
       "Æon Flux                              NaN  \n",
       "İtirazım Var                          NaN  \n",
       "Želary                                NaN  \n",
       "’Round Midnight                       1.0  \n",
       "\n",
       "[8754 rows x 8754 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Calculate correlation coefficient between each pair of movies using 'corr()' function.\n",
    "similarity_df = user_ratings.corr()\n",
    "similarity_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Create a user-defined function recommend_movies and pass the movie_name string as input. Inside this function:\n",
    "\n",
    "Obtain the correlation coefficients of all the movies with the input movie_name by specifying the movie_name as index to the correlation table similarity_df.\n",
    "\n",
    "This will return a pandas series containing the correlation coefficients. Store this pandas Series in a new DataFrame. Rename the column name to 'correlation'.\n",
    "\n",
    "Add the number of ratings for each movie to the new DataFrame obtained in the above step using join() function.\n",
    "\n",
    "Return top 20 highly correlated/similar movies but only those whose num of ratings is greater than 100 ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define 'recommend_movies()' function.\n",
    "def recommend_movies(movie_name):\n",
    "  similar_movies = similarity_df[movie_name]\n",
    "  similar_movies_df = pd.DataFrame(similar_movies)\n",
    "  similar_movies_df.rename(columns = {similar_movies_df.columns[0]: 'correlation'}, inplace = True)\n",
    "  corr_num_ratings = similar_movies_df.join(all_movies_ratings['num of ratings'])\n",
    "  return corr_num_ratings[corr_num_ratings['num of ratings'] > 100].sort_values('correlation',ascending = False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>num of ratings</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>Star Wars</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Return of the Jedi</th>\n",
       "      <td>0.747774</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Empire Strikes Back</th>\n",
       "      <td>0.700790</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Dark Knight</th>\n",
       "      <td>0.549486</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Lord of the Rings: The Fellowship of the Ring</th>\n",
       "      <td>0.477582</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raiders of the Lost Ark</th>\n",
       "      <td>0.476442</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Incredibles</th>\n",
       "      <td>0.450914</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Lord of the Rings: The Two Towers</th>\n",
       "      <td>0.448153</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E.T. the Extra-Terrestrial</th>\n",
       "      <td>0.428289</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Trek: Generations</th>\n",
       "      <td>0.413682</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monsters, Inc.</th>\n",
       "      <td>0.403180</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Big</th>\n",
       "      <td>0.393398</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Lord of the Rings: The Return of the King</th>\n",
       "      <td>0.383217</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy Story 2</th>\n",
       "      <td>0.374929</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Babe</th>\n",
       "      <td>0.374293</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Men in Black</th>\n",
       "      <td>0.341665</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spider-Man</th>\n",
       "      <td>0.341194</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Lion King</th>\n",
       "      <td>0.337286</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade Runner</th>\n",
       "      <td>0.333179</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoldenEye</th>\n",
       "      <td>0.329020</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   correlation  num of ratings\n",
       "title                                                                         \n",
       "Star Wars                                             1.000000             291\n",
       "Return of the Jedi                                    0.747774             217\n",
       "The Empire Strikes Back                               0.700790             234\n",
       "The Dark Knight                                       0.549486             121\n",
       "The Lord of the Rings: The Fellowship of the Ring     0.477582             200\n",
       "Raiders of the Lost Ark                               0.476442             220\n",
       "The Incredibles                                       0.450914             126\n",
       "The Lord of the Rings: The Two Towers                 0.448153             188\n",
       "E.T. the Extra-Terrestrial                            0.428289             160\n",
       "Star Trek: Generations                                0.413682             114\n",
       "Monsters, Inc.                                        0.403180             130\n",
       "Big                                                   0.393398             107\n",
       "The Lord of the Rings: The Return of the King         0.383217             176\n",
       "Toy Story 2                                           0.374929             125\n",
       "Babe                                                  0.374293             148\n",
       "Men in Black                                          0.341665             190\n",
       "Spider-Man                                            0.341194             134\n",
       "The Lion King                                         0.337286             200\n",
       "Blade Runner                                          0.333179             146\n",
       "GoldenEye                                             0.329020             122"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call 'recommend_movies()' function and pass 'Star Wars' as input.\n",
    "recommend_movies('Star Wars')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the top recommendations are pretty good. The movie that has the highest/full correlation to Star Wars is Star Wars itself. The movies such as Return of the Jedi, The Empire Strikes Back and The Dark Knight show high correlation with Star Wars.\n",
    "\n",
    "Thus, we have successfully built a movie recommender by performing collaborative filtering using Pearson correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collaborative Filtering II - Cosine Similarity\n",
    "\n",
    "To personalize the recommendation engine for any User X having a watch history of M movies:\n",
    "\n",
    "Step 1. We will get the most similar users of the User X.\n",
    "\n",
    "Step 2. We will get the most similar movies of the movie set M.\n",
    "\n",
    "Step 3. Then we combine the ratings for each item of the movies for similar users, and the similarity score of the similar users.\n",
    "\n",
    "Step 4. We will recommend only the items that have higher ratings given by the similar users.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hotel Transylvania 2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indiana Jones and the Temple of Doom</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indiana Jones and the Kingdom of the Crystal S...</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Men in Black II</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  rating\n",
       "0                               Hotel Transylvania 2     4.0\n",
       "1               Indiana Jones and the Temple of Doom     4.5\n",
       "2  Indiana Jones and the Kingdom of the Crystal S...     4.0\n",
       "3                                    Men in Black II     4.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predefined user watch history\n",
    "user_history = [\n",
    "            {'title':'Hotel Transylvania 2', 'rating':4},\n",
    "            {'title':\"Indiana Jones and the Temple of Doom\", 'rating':4.5},\n",
    "            {'title':\"Indiana Jones and the Kingdom of the Crystal Skull\", 'rating':4},\n",
    "            {'title':'Men in Black II', 'rating':4}\n",
    "         ]\n",
    "user_history_df = pd.DataFrame(user_history)\n",
    "user_history_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we observe that the user_history_df consists of the movie title and rating columns. In order to correlate the user watch history with our database, we need to assign the movieId to the respective movies from the database.\n",
    "\n",
    "First let's look for the title from the user_history_df, find it in moveis_df. For this we will use:\n",
    "\n",
    "tolist() function from Pandas\n",
    "\n",
    "DataFrame.isin() method from Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variable watch_list is of:  <class 'list'>\n",
      "['Hotel Transylvania 2', 'Indiana Jones and the Temple of Doom', 'Indiana Jones and the Kingdom of the Crystal Skull', 'Men in Black II']\n"
     ]
    }
   ],
   "source": [
    "#  Obtain  the list of movie titles the user has watched\n",
    "watch_list = user_history_df['title'].tolist()\n",
    "print('The variable watch_list is of: ',type(watch_list))\n",
    "print(watch_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will search for the movie titles appearing in watch_list in the master dataset we obtained in the previous class: m_links_df to obtain the movieId for the corresponding movies. For this we will use Pandas.isin() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>87.0</td>\n",
       "      <td>tt0087469</td>\n",
       "      <td>Indiana Jones and the Temple of Doom</td>\n",
       "      <td>2115</td>\n",
       "      <td>87469</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5345</th>\n",
       "      <td>608.0</td>\n",
       "      <td>tt0120912</td>\n",
       "      <td>Men in Black II</td>\n",
       "      <td>5459</td>\n",
       "      <td>120912</td>\n",
       "      <td>608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12668</th>\n",
       "      <td>217.0</td>\n",
       "      <td>tt0367882</td>\n",
       "      <td>Indiana Jones and the Kingdom of the Crystal S...</td>\n",
       "      <td>59615</td>\n",
       "      <td>367882</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33301</th>\n",
       "      <td>159824.0</td>\n",
       "      <td>tt2510894</td>\n",
       "      <td>Hotel Transylvania 2</td>\n",
       "      <td>142997</td>\n",
       "      <td>2510894</td>\n",
       "      <td>159824.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id    imdb_id                                              title  \\\n",
       "2014       87.0  tt0087469               Indiana Jones and the Temple of Doom   \n",
       "5345      608.0  tt0120912                                    Men in Black II   \n",
       "12668     217.0  tt0367882  Indiana Jones and the Kingdom of the Crystal S...   \n",
       "33301  159824.0  tt2510894                               Hotel Transylvania 2   \n",
       "\n",
       "       movieId   imdbId    tmdbId  \n",
       "2014      2115    87469      87.0  \n",
       "5345      5459   120912     608.0  \n",
       "12668    59615   367882     217.0  \n",
       "33301   142997  2510894  159824.0  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain a DataFrame consisting of movieId based on user watch history\n",
    "user_history_id = m_links_df[m_links_df['title'].isin(watch_list)]\n",
    "user_history_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the output we observe that the user_history_id DataFrame now consists of the movieId of the movies the user has watched.\n",
    "\n",
    "However, to work with collaborative filters we need the user ratings as well. So, Merge the DataFrames: user_history_id, user_history_df to obtain the workable dataset, say: watched_movies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87.0</td>\n",
       "      <td>tt0087469</td>\n",
       "      <td>Indiana Jones and the Temple of Doom</td>\n",
       "      <td>2115</td>\n",
       "      <td>87469</td>\n",
       "      <td>87.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608.0</td>\n",
       "      <td>tt0120912</td>\n",
       "      <td>Men in Black II</td>\n",
       "      <td>5459</td>\n",
       "      <td>120912</td>\n",
       "      <td>608.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>217.0</td>\n",
       "      <td>tt0367882</td>\n",
       "      <td>Indiana Jones and the Kingdom of the Crystal S...</td>\n",
       "      <td>59615</td>\n",
       "      <td>367882</td>\n",
       "      <td>217.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159824.0</td>\n",
       "      <td>tt2510894</td>\n",
       "      <td>Hotel Transylvania 2</td>\n",
       "      <td>142997</td>\n",
       "      <td>2510894</td>\n",
       "      <td>159824.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id    imdb_id                                              title  \\\n",
       "0      87.0  tt0087469               Indiana Jones and the Temple of Doom   \n",
       "1     608.0  tt0120912                                    Men in Black II   \n",
       "2     217.0  tt0367882  Indiana Jones and the Kingdom of the Crystal S...   \n",
       "3  159824.0  tt2510894                               Hotel Transylvania 2   \n",
       "\n",
       "   movieId   imdbId    tmdbId  rating  \n",
       "0     2115    87469      87.0     4.5  \n",
       "1     5459   120912     608.0     4.0  \n",
       "2    59615   367882     217.0     4.0  \n",
       "3   142997  2510894  159824.0     4.0  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the movieId and rating DataFrame for the user watch history\n",
    "\n",
    "watched_movies_df = pd.merge(user_history_id, user_history_df)\n",
    "watched_movies_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The watch history dataset watched_movies_df from user now consist of the movie title, rating, and movieId for all the movies user X has watched.\n",
    "\n",
    "As per the recommendation engine logic , we need to find the subset of other users who have watched and rated the movies from the user X watch history: watched_movies_df.\n",
    "This can be done in a similar way we searched for the movieId:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the users_subset_df DataFrame:  (173, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>4</td>\n",
       "      <td>2115</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>15</td>\n",
       "      <td>2115</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>15</td>\n",
       "      <td>5459</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2258</th>\n",
       "      <td>15</td>\n",
       "      <td>59615</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>22</td>\n",
       "      <td>2115</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3965</th>\n",
       "      <td>22</td>\n",
       "      <td>5459</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4295</th>\n",
       "      <td>23</td>\n",
       "      <td>2115</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5442</th>\n",
       "      <td>30</td>\n",
       "      <td>2115</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7032</th>\n",
       "      <td>41</td>\n",
       "      <td>5459</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7493</th>\n",
       "      <td>48</td>\n",
       "      <td>5459</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  movieId  rating\n",
       "285        4     2115     5.0\n",
       "1407      15     2115     4.0\n",
       "1927      15     5459     1.0\n",
       "2258      15    59615     1.0\n",
       "3876      22     2115     3.5\n",
       "3965      22     5459     3.5\n",
       "4295      23     2115     4.0\n",
       "5442      30     2115     4.0\n",
       "7032      41     5459     3.5\n",
       "7493      48     5459     3.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Creating subset of users that has watched movies from the user watched movies dataframe\n",
    "watch_list_movieid = watched_movies_df['movieId'].tolist()\n",
    "users_subset_df = ratings_df[ratings_df['movieId'].isin(watch_list_movieid)]\n",
    "print('Shape of the users_subset_df DataFrame: ',users_subset_df.shape)\n",
    "users_subset_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we observe that we have obtained a DataFrame for the users subset that has watched movies from the user watch history. For example:\n",
    "\n",
    "userId 4 has watched the movieId 2115 (Indiana Jones and the Temple of Doom) and rated it 5.0 out of 5.\n",
    "\n",
    "Similarly, userId 15 has watched 3 movies, with movieId 2115, 5459, and 59615 respectively.\n",
    "\n",
    "\n",
    "Next, let's group this dataset with respect to users and sort it based on the most number of common movies the subset users have watched. For this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(624,\n",
       "         userId  movieId  rating\n",
       "  93859     624     2115     4.0\n",
       "  94331     624     5459     2.0\n",
       "  94639     624    59615     2.0\n",
       "  95254     624   142997     2.5),\n",
       " (15,\n",
       "        userId  movieId  rating\n",
       "  1407      15     2115     4.0\n",
       "  1927      15     5459     1.0\n",
       "  2258      15    59615     1.0),\n",
       " (73,\n",
       "         userId  movieId  rating\n",
       "  10591      73     2115     5.0\n",
       "  10992      73     5459     2.0\n",
       "  11435      73    59615     1.5),\n",
       " (88,\n",
       "         userId  movieId  rating\n",
       "  13636      88     2115     2.0\n",
       "  13704      88     5459     2.5\n",
       "  13754      88    59615     1.0),\n",
       " (213,\n",
       "         userId  movieId  rating\n",
       "  29493     213     2115     3.5\n",
       "  29663     213     5459     2.5\n",
       "  29928     213    59615     2.0)]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Create users subset group based on userId and sort on base of highest number of common movies watched.\n",
    "users_subset_group = users_subset_df.groupby(['userId'])\n",
    "users_subset_group = sorted(users_subset_group,  key=lambda u: len(u[1]), reverse=True)\n",
    "users_subset_group[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the users subset ready. Next we will proceed with similarity measure using cosine_similarity()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary to store the similarity scores of users subset with respect to target user X\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "# Generate an empty dictionary\n",
    "cosine_similarity_dict = {}\n",
    "\n",
    "# Iterate through individual users and the movie corresponding user has watched from the user subset group\n",
    "for user, group in users_subset_group:\n",
    "\n",
    "    # Sorting the target user and current user group to prevent mismatch in movieId field\n",
    "    group = group.sort_values(by='movieId')\n",
    "    input_movies = watched_movies_df.sort_values(by='movieId')\n",
    "\n",
    "    # Obtain the rating for the movies that they both have in common\n",
    "    temp_df = input_movies[input_movies['movieId'].isin(group['movieId'].tolist())]\n",
    "\n",
    "    # Store these ratings them in a temporary buffer list for similarity calculations\n",
    "    temp_rating_list = temp_df['rating'].tolist()\n",
    "\n",
    "    # Also put the corresponding user group rating in a temporary buffer list for similarity calculations\n",
    "    temp_group_list = group['rating'].tolist()\n",
    "\n",
    "    # Obtain the similarity scores and append to dictionary with respect to corresponding user\n",
    "    similarity = cosine_similarity([temp_rating_list], [temp_group_list])\n",
    "    cosine_similarity_dict[user] = similarity.reshape(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above code cell we are creating a dictionary which will store the userId as key and similarity scores as value which will be used later to obtain the recommendation.\n",
    "\n",
    "The similarity scores are obtained based on the ratings provided by the corresponding user in the user_subset_group and ratings provided by the target user X. The dictionary cosine_similarity_dict has these similarity scores for all the users from the users_subset_group with respect to our target user X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([(624, array([0.96836405])), (15, array([0.84780105])), (73, array([0.90328481])), (88, array([0.94865528])), (213, array([0.98432694])), (468, array([0.97421458])), (471, array([0.99734724])), (481, array([0.97825343])), (580, array([0.97332853])), (664, array([0.98422368])), (22, array([0.99827437])), (93, array([0.99434562])), (134, array([0.99865342])), (150, array([0.98591396])), (176, array([0.99827437])), (212, array([0.9904049])), (287, array([0.99827437])), (294, array([1.])), (311, array([0.9856839])), (324, array([0.99778516])), (346, array([0.87373206])), (355, array([0.95281498])), (384, array([0.99996948])), (402, array([0.99705449])), (426, array([0.99705449])), (452, array([0.99949111])), (475, array([0.9486833])), (553, array([0.99328922])), (574, array([0.99827437])), (607, array([0.99784915])), (654, array([0.9904049])), (4, array([1.])), (23, array([1.])), (30, array([1.])), (41, array([1.])), (48, array([1.])), (49, array([1.])), (56, array([1.])), (57, array([1.])), (60, array([1.])), (61, array([1.])), (75, array([1.])), (77, array([1.])), (84, array([1.])), (94, array([1.])), (105, array([1.])), (111, array([1.])), (119, array([1.])), (125, array([1.])), (130, array([1.])), (157, array([1.])), (165, array([1.])), (175, array([1.])), (187, array([1.])), (195, array([1.])), (201, array([1.])), (205, array([1.])), (210, array([1.])), (214, array([1.])), (217, array([1.])), (228, array([1.])), (232, array([1.])), (236, array([1.])), (242, array([1.])), (243, array([1.])), (244, array([1.])), (247, array([1.])), (251, array([1.])), (253, array([1.])), (262, array([1.])), (268, array([1.])), (272, array([1.])), (285, array([1.])), (290, array([1.])), (292, array([1.])), (295, array([1.])), (303, array([1.])), (306, array([1.])), (313, array([1.])), (316, array([1.])), (350, array([1.])), (352, array([1.])), (353, array([1.])), (362, array([1.])), (365, array([1.])), (367, array([1.])), (388, array([1.])), (394, array([1.])), (398, array([1.])), (405, array([1.])), (408, array([1.])), (418, array([1.])), (423, array([1.])), (428, array([1.])), (442, array([1.])), (456, array([1.])), (457, array([1.])), (461, array([1.])), (463, array([1.])), (466, array([1.])), (483, array([1.])), (497, array([1.])), (498, array([1.])), (501, array([1.])), (505, array([1.])), (509, array([1.])), (512, array([1.])), (518, array([1.])), (520, array([1.])), (522, array([1.])), (523, array([1.])), (525, array([1.])), (527, array([1.])), (528, array([1.])), (544, array([1.])), (550, array([1.])), (561, array([1.])), (562, array([1.])), (564, array([1.])), (570, array([1.])), (572, array([1.])), (575, array([1.])), (592, array([1.])), (596, array([1.])), (598, array([1.])), (599, array([1.])), (601, array([1.])), (605, array([1.])), (615, array([1.])), (623, array([1.])), (652, array([1.]))])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Display the key value pairs of the similarity score dictionary\n",
    "\n",
    "cosine_similarity_dict.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here,  the similarity score of userId 624 and user X is 0.968 and so on. We have now obtained the individual users form the users_subset_group and their similarity scores with respect to the target user X.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        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>similarity score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>0.968364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.847801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.903285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.948655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>0.984327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     similarity score\n",
       "624          0.968364\n",
       "15           0.847801\n",
       "73           0.903285\n",
       "88           0.948655\n",
       "213          0.984327"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain a DataFrame from the similarity score dictionary\n",
    "\n",
    "cosine_df = pd.DataFrame.from_dict(cosine_similarity_dict, orient='index')\n",
    "cosine_df.columns = ['similarity score']\n",
    "cosine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>similarity score</th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>624</td>\n",
       "      <td>0.968364</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73</td>\n",
       "      <td>0.903285</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88</td>\n",
       "      <td>0.948655</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>213</td>\n",
       "      <td>0.984327</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  similarity score  userId\n",
       "0    624          0.968364     624\n",
       "1     15          0.847801      15\n",
       "2     73          0.903285      73\n",
       "3     88          0.948655      88\n",
       "4    213          0.984327     213"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Append a column for userId and reset the index of the DataFrame\n",
    "cosine_df['userId'] = cosine_df.index\n",
    "cosine_df.reset_index(inplace=True)\n",
    "cosine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>similarity score</th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>244</td>\n",
       "      <td>1.0</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>463</td>\n",
       "      <td>1.0</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>457</td>\n",
       "      <td>1.0</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>456</td>\n",
       "      <td>1.0</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>442</td>\n",
       "      <td>1.0</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  similarity score  userId\n",
       "65    244               1.0     244\n",
       "98    463               1.0     463\n",
       "96    457               1.0     457\n",
       "95    456               1.0     456\n",
       "94    442               1.0     442"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new DataFrame for top users by sorting the users based on similarity scores\n",
    "top_users = cosine_df.sort_values(by ='similarity score', ascending=False)\n",
    "top_users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>userId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>13</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>19</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99845</th>\n",
       "      <td>161918</td>\n",
       "      <td>Sharknado 4: The 4th Awakens</td>\n",
       "      <td>624</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99846</th>\n",
       "      <td>161944</td>\n",
       "      <td>The Last Brickmaker in America</td>\n",
       "      <td>287</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99847</th>\n",
       "      <td>162542</td>\n",
       "      <td>Rustom</td>\n",
       "      <td>611</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99848</th>\n",
       "      <td>162672</td>\n",
       "      <td>Mohenjo Daro</td>\n",
       "      <td>611</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99849</th>\n",
       "      <td>163949</td>\n",
       "      <td>The Beatles: Eight Days a Week - The Touring Y...</td>\n",
       "      <td>547</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99850 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                              title  userId  \\\n",
       "0            1                                          Toy Story       7   \n",
       "1            1                                          Toy Story       9   \n",
       "2            1                                          Toy Story      13   \n",
       "3            1                                          Toy Story      15   \n",
       "4            1                                          Toy Story      19   \n",
       "...        ...                                                ...     ...   \n",
       "99845   161918                       Sharknado 4: The 4th Awakens     624   \n",
       "99846   161944                     The Last Brickmaker in America     287   \n",
       "99847   162542                                             Rustom     611   \n",
       "99848   162672                                       Mohenjo Daro     611   \n",
       "99849   163949  The Beatles: Eight Days a Week - The Touring Y...     547   \n",
       "\n",
       "       rating  \n",
       "0         3.0  \n",
       "1         4.0  \n",
       "2         5.0  \n",
       "3         2.0  \n",
       "4         3.0  \n",
       "...       ...  \n",
       "99845     1.5  \n",
       "99846     5.0  \n",
       "99847     5.0  \n",
       "99848     3.0  \n",
       "99849     5.0  \n",
       "\n",
       "[99850 rows x 4 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the final movies DataFrame\n",
    "\n",
    "final_movies_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above code cell output we can verify that the DataFrame final_movies_df consists of the movies every user has watched and rated.\n",
    "\n",
    "\n",
    "Next, create a DataFrame top_users_rating which consists of the movies and the respective ratings provided by the userId in the top_users DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>similarity score</th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>110</td>\n",
       "      <td>Braveheart</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>260</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>318</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>457</td>\n",
       "      <td>The Fugitive</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49522</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>160271</td>\n",
       "      <td>Central Intelligence</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49523</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>160563</td>\n",
       "      <td>The Legend of Tarzan</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49524</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>160565</td>\n",
       "      <td>The Purge: Election Year</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49525</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>160567</td>\n",
       "      <td>Mike and Dave Need Wedding Dates</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49526</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>161155</td>\n",
       "      <td>Sunspring</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49527 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  similarity score  userId  movieId  \\\n",
       "0        244          1.000000     244      110   \n",
       "1        244          1.000000     244      260   \n",
       "2        244          1.000000     244      318   \n",
       "3        244          1.000000     244      356   \n",
       "4        244          1.000000     244      457   \n",
       "...      ...               ...     ...      ...   \n",
       "49522     15          0.847801      15   160271   \n",
       "49523     15          0.847801      15   160563   \n",
       "49524     15          0.847801      15   160565   \n",
       "49525     15          0.847801      15   160567   \n",
       "49526     15          0.847801      15   161155   \n",
       "\n",
       "                                  title  rating  \n",
       "0                            Braveheart     3.5  \n",
       "1                             Star Wars     5.0  \n",
       "2              The Shawshank Redemption     5.0  \n",
       "3                          Forrest Gump     5.0  \n",
       "4                          The Fugitive     0.5  \n",
       "...                                 ...     ...  \n",
       "49522              Central Intelligence     2.5  \n",
       "49523              The Legend of Tarzan     1.0  \n",
       "49524          The Purge: Election Year     2.0  \n",
       "49525  Mike and Dave Need Wedding Dates     4.0  \n",
       "49526                         Sunspring     0.5  \n",
       "\n",
       "[49527 rows x 6 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the movies watched and ratings provided by users of top users DataFrame\n",
    "top_users_rating = top_users.merge(final_movies_df, on = 'userId', how ='inner')\n",
    "top_users_rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the top_users_rating now consist of the movies watched and rated by the users similar to our target user X.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>index</th>\n",
       "      <th>similarity score</th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "      <th>weighted rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>110</td>\n",
       "      <td>Braveheart</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>260</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>318</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244</td>\n",
       "      <td>457</td>\n",
       "      <td>The Fugitive</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49522</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>160271</td>\n",
       "      <td>Central Intelligence</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.119503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49523</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>160563</td>\n",
       "      <td>The Legend of Tarzan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.847801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49524</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>160565</td>\n",
       "      <td>The Purge: Election Year</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.695602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49525</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>160567</td>\n",
       "      <td>Mike and Dave Need Wedding Dates</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.391204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49526</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847801</td>\n",
       "      <td>15</td>\n",
       "      <td>161155</td>\n",
       "      <td>Sunspring</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.423901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49527 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  similarity score  userId  movieId  \\\n",
       "0        244          1.000000     244      110   \n",
       "1        244          1.000000     244      260   \n",
       "2        244          1.000000     244      318   \n",
       "3        244          1.000000     244      356   \n",
       "4        244          1.000000     244      457   \n",
       "...      ...               ...     ...      ...   \n",
       "49522     15          0.847801      15   160271   \n",
       "49523     15          0.847801      15   160563   \n",
       "49524     15          0.847801      15   160565   \n",
       "49525     15          0.847801      15   160567   \n",
       "49526     15          0.847801      15   161155   \n",
       "\n",
       "                                  title  rating  weighted rating  \n",
       "0                            Braveheart     3.5         3.500000  \n",
       "1                             Star Wars     5.0         5.000000  \n",
       "2              The Shawshank Redemption     5.0         5.000000  \n",
       "3                          Forrest Gump     5.0         5.000000  \n",
       "4                          The Fugitive     0.5         0.500000  \n",
       "...                                 ...     ...              ...  \n",
       "49522              Central Intelligence     2.5         2.119503  \n",
       "49523              The Legend of Tarzan     1.0         0.847801  \n",
       "49524          The Purge: Election Year     2.0         1.695602  \n",
       "49525  Mike and Dave Need Wedding Dates     4.0         3.391204  \n",
       "49526                         Sunspring     0.5         0.423901  \n",
       "\n",
       "[49527 rows x 7 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the weighted rating by combining the similarity score and movie rating for top users\n",
    "top_users_rating['weighted rating'] = top_users_rating['similarity score']*top_users_rating['rating']\n",
    "top_users_rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a weighted rating based on the similarity score and rating provided by individual users.\n",
    "\n",
    "\n",
    "The final step is to obtain similar movies with the: highest ratings given by the similar users.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>cumulative similarity score</th>\n",
       "      <th>cumulative weighted rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.435396</td>\n",
       "      <td>291.002862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.378484</td>\n",
       "      <td>142.941369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.938193</td>\n",
       "      <td>42.326552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.781033</td>\n",
       "      <td>46.119740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161155</th>\n",
       "      <td>0.847801</td>\n",
       "      <td>0.423901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161594</th>\n",
       "      <td>0.903285</td>\n",
       "      <td>2.709854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161830</th>\n",
       "      <td>0.968364</td>\n",
       "      <td>0.968364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161918</th>\n",
       "      <td>0.968364</td>\n",
       "      <td>1.452546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161944</th>\n",
       "      <td>0.998274</td>\n",
       "      <td>4.991372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7474 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         cumulative similarity score  cumulative weighted rating\n",
       "movieId                                                         \n",
       "1                          79.435396                  291.002862\n",
       "2                          45.378484                  142.941369\n",
       "3                          14.938193                   42.326552\n",
       "4                           2.000000                    2.500000\n",
       "5                          15.781033                   46.119740\n",
       "...                              ...                         ...\n",
       "161155                      0.847801                    0.423901\n",
       "161594                      0.903285                    2.709854\n",
       "161830                      0.968364                    0.968364\n",
       "161918                      0.968364                    1.452546\n",
       "161944                      0.998274                    4.991372\n",
       "\n",
       "[7474 rows x 2 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Obtain the cumulative similarity scores and weighted rating for similar movies\n",
    "temp_top_users_rating = top_users_rating.groupby('movieId').sum()[['similarity score','weighted rating']]\n",
    "temp_top_users_rating.columns = ['cumulative similarity score','cumulative weighted rating']\n",
    "temp_top_users_rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can observe that we have now obtained the cumulative scores for individual movies.\n",
    "\n",
    "The last job of the recommendation engine is to sort these movies based on the descending values of the cumulative score and Bingo! we have successfully designed the recommendation engine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>cumulative similarity score</th>\n",
       "      <th>cumulative weighted rating</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>104.258973</td>\n",
       "      <td>450.521202</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>104.312532</td>\n",
       "      <td>442.914943</td>\n",
       "      <td>1198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>102.276437</td>\n",
       "      <td>439.599611</td>\n",
       "      <td>1196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>100.261682</td>\n",
       "      <td>415.656374</td>\n",
       "      <td>2571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>100.398112</td>\n",
       "      <td>401.945802</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4993</th>\n",
       "      <td>92.270852</td>\n",
       "      <td>394.684642</td>\n",
       "      <td>4993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>95.274002</td>\n",
       "      <td>385.832857</td>\n",
       "      <td>1270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>95.441718</td>\n",
       "      <td>381.202445</td>\n",
       "      <td>1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>86.422818</td>\n",
       "      <td>378.314396</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>88.328291</td>\n",
       "      <td>371.348200</td>\n",
       "      <td>1210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>97.317175</td>\n",
       "      <td>366.317735</td>\n",
       "      <td>2115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5952</th>\n",
       "      <td>85.270343</td>\n",
       "      <td>346.109002</td>\n",
       "      <td>5952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1580</th>\n",
       "      <td>94.264953</td>\n",
       "      <td>341.478297</td>\n",
       "      <td>1580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>89.298245</td>\n",
       "      <td>337.869644</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>76.473325</td>\n",
       "      <td>337.458644</td>\n",
       "      <td>858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cumulative similarity score  cumulative weighted rating    Id\n",
       "movieId                                                               \n",
       "260                       104.258973                  450.521202   260\n",
       "1198                      104.312532                  442.914943  1198\n",
       "1196                      102.276437                  439.599611  1196\n",
       "2571                      100.261682                  415.656374  2571\n",
       "356                       100.398112                  401.945802   356\n",
       "4993                       92.270852                  394.684642  4993\n",
       "1270                       95.274002                  385.832857  1270\n",
       "1291                       95.441718                  381.202445  1291\n",
       "296                        86.422818                  378.314396   296\n",
       "1210                       88.328291                  371.348200  1210\n",
       "2115                       97.317175                  366.317735  2115\n",
       "5952                       85.270343                  346.109002  5952\n",
       "1580                       94.264953                  341.478297  1580\n",
       "480                        89.298245                  337.869644   480\n",
       "858                        76.473325                  337.458644   858"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the movies recommended from the cumulative ratings obtained from the top users\n",
    "recommendation_df = temp_top_users_rating.copy()\n",
    "recommendation_df['Id'] = recommendation_df.index\n",
    "recommendation_df = recommendation_df.sort_values(by = 'cumulative weighted rating' , ascending=False)\n",
    "recommendation_df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above DataFrame does not convey information on movie title. In order to link title to the respective movieId let's merge: recommednation_df and m_links_df. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain the movie title for recommended movies and save it to a DataFrame\n",
    "\n",
    "recommend_movies = pd.merge(recommendation_df, m_links_df, left_on ='Id', right_on ='movieId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can display the recommended movies based on the watch history of user X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                     Star Wars\n",
      "1       Raiders of the Lost Ark\n",
      "2       The Empire Strikes Back\n",
      "3                    The Matrix\n",
      "4                  Forrest Gump\n",
      "                 ...           \n",
      "7481            The Conspirator\n",
      "7482                   Trespass\n",
      "7483                Miles Ahead\n",
      "7484          Jupiter Ascending\n",
      "7485    Approaching the Unknown\n",
      "Name: title, Length: 7486, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Display the recommended movies sorted by our recommendation engine\n",
    "print(recommend_movies.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bingo!** We now have a list to recommend to the user X which matches with the user's choice of movies.\n",
    "\n",
    "\n",
    "<center>\n",
    "<img src=https://s3-whjr-v2-prod-bucket.whjr.online/whjr-v2-prod-bucket/34c112ff-f2f8-4cad-bcc0-0c2930f3562d.jpg width=450>\n",
    "\n",
    "Image Source: Photo by Andrea Piacquadio from Pexels </center>\n"
   ]
  }
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