movie-recommender-system.ipynb

{
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   "id": "0ed54592-5102-4a96-9d99-076626f2fabb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87f3b884-d9f8-4e00-be60-25268f96a403",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv(\"tmdb_5000_movies.csv\")\n",
    "credits = pd.read_csv(\"tmdb_5000_credits.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5d4e554-47a7-4189-ad86-d45199a76501",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</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>237000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2009-12-10</td>\n",
       "      <td>2787965087</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "      budget                                             genres  \\\n",
       "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                      homepage     id  \\\n",
       "0  http://www.avatarmovie.com/  19995   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "\n",
       "  original_title                                           overview  \\\n",
       "0         Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
       "\n",
       "   popularity                               production_companies  \\\n",
       "0  150.437577  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
       "\n",
       "                                production_countries release_date     revenue  \\\n",
       "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
       "\n",
       "   runtime                                   spoken_languages    status  \\\n",
       "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "\n",
       "                       tagline   title  vote_average  vote_count  \n",
       "0  Enter the World of Pandora.  Avatar           7.2       11800  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03466a42-8a3a-45fe-bba6-855c78996fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4803, 20)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c403d09c-b847-4e52-b560-0c24c6702742",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
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      "text/plain": [
       "   movie_id   title                                               cast  \\\n",
       "0     19995  Avatar  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credits.head(1)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "51b579f1-79a2-4c5c-98aa-2f3ff11593df",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
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       "      <th>0</th>\n",
       "      <td>237000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
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       "      <td>...</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300000000</td>\n",
       "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
       "      <td>285</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
       "      <td>...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "      <td>285</td>\n",
       "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>245000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
       "      <td>206647</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>en</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
       "      <td>...</td>\n",
       "      <td>148.0</td>\n",
       "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
       "      <td>Released</td>\n",
       "      <td>A Plan No One Escapes</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "      <td>206647</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>http://www.thedarkknightrises.com/</td>\n",
       "      <td>49026</td>\n",
       "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
       "      <td>en</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
       "      <td>...</td>\n",
       "      <td>165.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Legend Ends</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "      <td>49026</td>\n",
       "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>260000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://movies.disney.com/john-carter</td>\n",
       "      <td>49529</td>\n",
       "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Lost in our world, found in another.</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "      <td>49529</td>\n",
       "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
       "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>4804</th>\n",
       "      <td>220000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9367</td>\n",
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       "      <td>es</td>\n",
       "      <td>El Mariachi</td>\n",
       "      <td>El Mariachi just wants to play his guitar and ...</td>\n",
       "      <td>14.269792</td>\n",
       "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}]</td>\n",
       "      <td>...</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{\"iso_639_1\": \"es\", \"name\": \"Espa\\u00f1ol\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>He didn't come looking for trouble, but troubl...</td>\n",
       "      <td>El Mariachi</td>\n",
       "      <td>6.6</td>\n",
       "      <td>238</td>\n",
       "      <td>9367</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"El Mariachi\", \"c...</td>\n",
       "      <td>[{\"credit_id\": \"52fe44eec3a36847f80b280b\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>9000</td>\n",
       "      <td>[{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 10749, \"...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72766</td>\n",
       "      <td>[]</td>\n",
       "      <td>en</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>A newlywed couple's honeymoon is upended by th...</td>\n",
       "      <td>0.642552</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>Released</td>\n",
       "      <td>A newlywed couple's honeymoon is upended by th...</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5</td>\n",
       "      <td>72766</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"Buzzy\", \"credit_...</td>\n",
       "      <td>[{\"credit_id\": \"52fe487dc3a368484e0fb013\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>0</td>\n",
       "      <td>[{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 18, \"nam...</td>\n",
       "      <td>http://www.hallmarkchannel.com/signedsealeddel...</td>\n",
       "      <td>231617</td>\n",
       "      <td>[{\"id\": 248, \"name\": \"date\"}, {\"id\": 699, \"nam...</td>\n",
       "      <td>en</td>\n",
       "      <td>Signed, Sealed, Delivered</td>\n",
       "      <td>\"Signed, Sealed, Delivered\" introduces a dedic...</td>\n",
       "      <td>1.444476</td>\n",
       "      <td>[{\"name\": \"Front Street Pictures\", \"id\": 3958}...</td>\n",
       "      <td>...</td>\n",
       "      <td>120.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Signed, Sealed, Delivered</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "      <td>231617</td>\n",
       "      <td>[{\"cast_id\": 8, \"character\": \"Oliver O\\u2019To...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4df3c3a36847f8275ecf\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4807</th>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>http://shanghaicalling.com/</td>\n",
       "      <td>126186</td>\n",
       "      <td>[]</td>\n",
       "      <td>en</td>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>When ambitious New York attorney Sam is sent t...</td>\n",
       "      <td>0.857008</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>A New Yorker in Shanghai</td>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7</td>\n",
       "      <td>126186</td>\n",
       "      <td>[{\"cast_id\": 3, \"character\": \"Sam\", \"credit_id...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4ad9c3a368484e16a36b\", \"de...</td>\n",
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       "    <tr>\n",
       "      <th>4808</th>\n",
       "      <td>0</td>\n",
       "      <td>[{\"id\": 99, \"name\": \"Documentary\"}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25975</td>\n",
       "      <td>[{\"id\": 1523, \"name\": \"obsession\"}, {\"id\": 224...</td>\n",
       "      <td>en</td>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>Ever since the second grade when he first saw ...</td>\n",
       "      <td>1.929883</td>\n",
       "      <td>[{\"name\": \"rusty bear entertainment\", \"id\": 87...</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>6.3</td>\n",
       "      <td>16</td>\n",
       "      <td>25975</td>\n",
       "      <td>[{\"cast_id\": 3, \"character\": \"Herself\", \"credi...</td>\n",
       "      <td>[{\"credit_id\": \"58ce021b9251415a390165d9\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4809 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         budget                                             genres  \\\n",
       "0     237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "1     300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
       "2     245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "3     250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
       "4     260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "...         ...                                                ...   \n",
       "4804     220000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
       "4805       9000  [{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 10749, \"...   \n",
       "4806          0  [{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 18, \"nam...   \n",
       "4807          0                                                 []   \n",
       "4808          0                [{\"id\": 99, \"name\": \"Documentary\"}]   \n",
       "\n",
       "                                               homepage      id  \\\n",
       "0                           http://www.avatarmovie.com/   19995   \n",
       "1          http://disney.go.com/disneypictures/pirates/     285   \n",
       "2           http://www.sonypictures.com/movies/spectre/  206647   \n",
       "3                    http://www.thedarkknightrises.com/   49026   \n",
       "4                  http://movies.disney.com/john-carter   49529   \n",
       "...                                                 ...     ...   \n",
       "4804                                                NaN    9367   \n",
       "4805                                                NaN   72766   \n",
       "4806  http://www.hallmarkchannel.com/signedsealeddel...  231617   \n",
       "4807                        http://shanghaicalling.com/  126186   \n",
       "4808                                                NaN   25975   \n",
       "\n",
       "                                               keywords original_language  \\\n",
       "0     [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "1     [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
       "2     [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
       "3     [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
       "4     [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
       "...                                                 ...               ...   \n",
       "4804  [{\"id\": 5616, \"name\": \"united states\\u2013mexi...                es   \n",
       "4805                                                 []                en   \n",
       "4806  [{\"id\": 248, \"name\": \"date\"}, {\"id\": 699, \"nam...                en   \n",
       "4807                                                 []                en   \n",
       "4808  [{\"id\": 1523, \"name\": \"obsession\"}, {\"id\": 224...                en   \n",
       "\n",
       "                                original_title  \\\n",
       "0                                       Avatar   \n",
       "1     Pirates of the Caribbean: At World's End   \n",
       "2                                      Spectre   \n",
       "3                        The Dark Knight Rises   \n",
       "4                                  John Carter   \n",
       "...                                        ...   \n",
       "4804                               El Mariachi   \n",
       "4805                                 Newlyweds   \n",
       "4806                 Signed, Sealed, Delivered   \n",
       "4807                          Shanghai Calling   \n",
       "4808                         My Date with Drew   \n",
       "\n",
       "                                               overview  popularity  \\\n",
       "0     In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1     Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "2     A cryptic message from Bond’s past sends him o...  107.376788   \n",
       "3     Following the death of District Attorney Harve...  112.312950   \n",
       "4     John Carter is a war-weary, former military ca...   43.926995   \n",
       "...                                                 ...         ...   \n",
       "4804  El Mariachi just wants to play his guitar and ...   14.269792   \n",
       "4805  A newlywed couple's honeymoon is upended by th...    0.642552   \n",
       "4806  \"Signed, Sealed, Delivered\" introduces a dedic...    1.444476   \n",
       "4807  When ambitious New York attorney Sam is sent t...    0.857008   \n",
       "4808  Ever since the second grade when he first saw ...    1.929883   \n",
       "\n",
       "                                   production_companies  ... runtime  \\\n",
       "0     [{\"name\": \"Ingenious Film Partners\", \"id\": 289...  ...   162.0   \n",
       "1     [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...  ...   169.0   \n",
       "2     [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...  ...   148.0   \n",
       "3     [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...  ...   165.0   \n",
       "4           [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]  ...   132.0   \n",
       "...                                                 ...  ...     ...   \n",
       "4804           [{\"name\": \"Columbia Pictures\", \"id\": 5}]  ...    81.0   \n",
       "4805                                                 []  ...    85.0   \n",
       "4806  [{\"name\": \"Front Street Pictures\", \"id\": 3958}...  ...   120.0   \n",
       "4807                                                 []  ...    98.0   \n",
       "4808  [{\"name\": \"rusty bear entertainment\", \"id\": 87...  ...    90.0   \n",
       "\n",
       "                                       spoken_languages    status  \\\n",
       "0     [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "1              [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "2     [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
       "3              [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "4              [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "...                                                 ...       ...   \n",
       "4804      [{\"iso_639_1\": \"es\", \"name\": \"Espa\\u00f1ol\"}]  Released   \n",
       "4805                                                 []  Released   \n",
       "4806           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "4807           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "4808           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "\n",
       "                                                tagline  \\\n",
       "0                           Enter the World of Pandora.   \n",
       "1        At the end of the world, the adventure begins.   \n",
       "2                                 A Plan No One Escapes   \n",
       "3                                       The Legend Ends   \n",
       "4                  Lost in our world, found in another.   \n",
       "...                                                 ...   \n",
       "4804  He didn't come looking for trouble, but troubl...   \n",
       "4805  A newlywed couple's honeymoon is upended by th...   \n",
       "4806                                                NaN   \n",
       "4807                           A New Yorker in Shanghai   \n",
       "4808                                                NaN   \n",
       "\n",
       "                                         title vote_average vote_count  \\\n",
       "0                                       Avatar          7.2      11800   \n",
       "1     Pirates of the Caribbean: At World's End          6.9       4500   \n",
       "2                                      Spectre          6.3       4466   \n",
       "3                        The Dark Knight Rises          7.6       9106   \n",
       "4                                  John Carter          6.1       2124   \n",
       "...                                        ...          ...        ...   \n",
       "4804                               El Mariachi          6.6        238   \n",
       "4805                                 Newlyweds          5.9          5   \n",
       "4806                 Signed, Sealed, Delivered          7.0          6   \n",
       "4807                          Shanghai Calling          5.7          7   \n",
       "4808                         My Date with Drew          6.3         16   \n",
       "\n",
       "     movie_id                                               cast  \\\n",
       "0       19995  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "1         285  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
       "2      206647  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
       "3       49026  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
       "4       49529  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
       "...       ...                                                ...   \n",
       "4804     9367  [{\"cast_id\": 1, \"character\": \"El Mariachi\", \"c...   \n",
       "4805    72766  [{\"cast_id\": 1, \"character\": \"Buzzy\", \"credit_...   \n",
       "4806   231617  [{\"cast_id\": 8, \"character\": \"Oliver O\\u2019To...   \n",
       "4807   126186  [{\"cast_id\": 3, \"character\": \"Sam\", \"credit_id...   \n",
       "4808    25975  [{\"cast_id\": 3, \"character\": \"Herself\", \"credi...   \n",
       "\n",
       "                                                   crew  \n",
       "0     [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1     [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2     [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  \n",
       "3     [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
       "4     [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  \n",
       "...                                                 ...  \n",
       "4804  [{\"credit_id\": \"52fe44eec3a36847f80b280b\", \"de...  \n",
       "4805  [{\"credit_id\": \"52fe487dc3a368484e0fb013\", \"de...  \n",
       "4806  [{\"credit_id\": \"52fe4df3c3a36847f8275ecf\", \"de...  \n",
       "4807  [{\"credit_id\": \"52fe4ad9c3a368484e16a36b\", \"de...  \n",
       "4808  [{\"credit_id\": \"58ce021b9251415a390165d9\", \"de...  \n",
       "\n",
       "[4809 rows x 23 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging on the basis of 'title' because it is not feasible to work on two different dataset. Since \"title\" column is common in both, we can merge them on the basis of 'title'\n",
    "movies.merge(credits,on='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4376964-acad-4eb4-8107-5cd182ab4328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4809, 23)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " movies.merge(credits,on='title').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f3157f38-773a-48a1-8f26-4ba4839afa53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have to store this merged table in some variable and therefore we are merging and storing it in the 'movies' variable itself.\n",
    "movies = movies.merge(credits,on='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7296c5ef-541e-4dee-b24d-c616a6835e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4809, 23)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee4948ba-fa13-46ab-8d58-a066cc276f67",
   "metadata": {},
   "source": [
    "Since we have 23 columns, we will remove those columns that are not necessary for our recommendation system. Below is the list of all those columns\n",
    "which we are keeping\n",
    "1. genres\n",
    "2. id or movie_id\n",
    "3. keywords\n",
    "4. title       (we can also keep 'original_title' but we are not because original_title might have a title that is in a regional language)\n",
    "5. overview\n",
    "\n",
    "           (we can keep popularity column but since we are building a recommender system based on tags creation, this column will not fit in) \n",
    "7. cast        (because some people watch some movies just because of the actor) \n",
    "8. crew        (some people want to watch movies of a particular director, so crew is an important column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17288528-10cd-444e-a85f-0346c575b910",
   "metadata": {
    "scrolled": true
   },
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
       "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
       "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
       "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</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>4804</th>\n",
       "      <td>9367</td>\n",
       "      <td>El Mariachi</td>\n",
       "      <td>El Mariachi just wants to play his guitar and ...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>[{\"id\": 5616, \"name\": \"united states\\u2013mexi...</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"El Mariachi\", \"c...</td>\n",
       "      <td>[{\"credit_id\": \"52fe44eec3a36847f80b280b\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>72766</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>A newlywed couple's honeymoon is upended by th...</td>\n",
       "      <td>[{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 10749, \"...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"Buzzy\", \"credit_...</td>\n",
       "      <td>[{\"credit_id\": \"52fe487dc3a368484e0fb013\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>231617</td>\n",
       "      <td>Signed, Sealed, Delivered</td>\n",
       "      <td>\"Signed, Sealed, Delivered\" introduces a dedic...</td>\n",
       "      <td>[{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 18, \"nam...</td>\n",
       "      <td>[{\"id\": 248, \"name\": \"date\"}, {\"id\": 699, \"nam...</td>\n",
       "      <td>[{\"cast_id\": 8, \"character\": \"Oliver O\\u2019To...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4df3c3a36847f8275ecf\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4807</th>\n",
       "      <td>126186</td>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>When ambitious New York attorney Sam is sent t...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{\"cast_id\": 3, \"character\": \"Sam\", \"credit_id...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4ad9c3a368484e16a36b\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4808</th>\n",
       "      <td>25975</td>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>Ever since the second grade when he first saw ...</td>\n",
       "      <td>[{\"id\": 99, \"name\": \"Documentary\"}]</td>\n",
       "      <td>[{\"id\": 1523, \"name\": \"obsession\"}, {\"id\": 224...</td>\n",
       "      <td>[{\"cast_id\": 3, \"character\": \"Herself\", \"credi...</td>\n",
       "      <td>[{\"credit_id\": \"58ce021b9251415a390165d9\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4809 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id                                     title  \\\n",
       "0        19995                                    Avatar   \n",
       "1          285  Pirates of the Caribbean: At World's End   \n",
       "2       206647                                   Spectre   \n",
       "3        49026                     The Dark Knight Rises   \n",
       "4        49529                               John Carter   \n",
       "...        ...                                       ...   \n",
       "4804      9367                               El Mariachi   \n",
       "4805     72766                                 Newlyweds   \n",
       "4806    231617                 Signed, Sealed, Delivered   \n",
       "4807    126186                          Shanghai Calling   \n",
       "4808     25975                         My Date with Drew   \n",
       "\n",
       "                                               overview  \\\n",
       "0     In the 22nd century, a paraplegic Marine is di...   \n",
       "1     Captain Barbossa, long believed to be dead, ha...   \n",
       "2     A cryptic message from Bond’s past sends him o...   \n",
       "3     Following the death of District Attorney Harve...   \n",
       "4     John Carter is a war-weary, former military ca...   \n",
       "...                                                 ...   \n",
       "4804  El Mariachi just wants to play his guitar and ...   \n",
       "4805  A newlywed couple's honeymoon is upended by th...   \n",
       "4806  \"Signed, Sealed, Delivered\" introduces a dedic...   \n",
       "4807  When ambitious New York attorney Sam is sent t...   \n",
       "4808  Ever since the second grade when he first saw ...   \n",
       "\n",
       "                                                 genres  \\\n",
       "0     [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "1     [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
       "2     [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "3     [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
       "4     [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "...                                                 ...   \n",
       "4804  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
       "4805  [{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 10749, \"...   \n",
       "4806  [{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 18, \"nam...   \n",
       "4807                                                 []   \n",
       "4808                [{\"id\": 99, \"name\": \"Documentary\"}]   \n",
       "\n",
       "                                               keywords  \\\n",
       "0     [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
       "1     [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...   \n",
       "2     [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...   \n",
       "3     [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...   \n",
       "4     [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...   \n",
       "...                                                 ...   \n",
       "4804  [{\"id\": 5616, \"name\": \"united states\\u2013mexi...   \n",
       "4805                                                 []   \n",
       "4806  [{\"id\": 248, \"name\": \"date\"}, {\"id\": 699, \"nam...   \n",
       "4807                                                 []   \n",
       "4808  [{\"id\": 1523, \"name\": \"obsession\"}, {\"id\": 224...   \n",
       "\n",
       "                                                   cast  \\\n",
       "0     [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "1     [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
       "2     [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
       "3     [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
       "4     [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
       "...                                                 ...   \n",
       "4804  [{\"cast_id\": 1, \"character\": \"El Mariachi\", \"c...   \n",
       "4805  [{\"cast_id\": 1, \"character\": \"Buzzy\", \"credit_...   \n",
       "4806  [{\"cast_id\": 8, \"character\": \"Oliver O\\u2019To...   \n",
       "4807  [{\"cast_id\": 3, \"character\": \"Sam\", \"credit_id...   \n",
       "4808  [{\"cast_id\": 3, \"character\": \"Herself\", \"credi...   \n",
       "\n",
       "                                                   crew  \n",
       "0     [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1     [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2     [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  \n",
       "3     [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
       "4     [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  \n",
       "...                                                 ...  \n",
       "4804  [{\"credit_id\": \"52fe44eec3a36847f80b280b\", \"de...  \n",
       "4805  [{\"credit_id\": \"52fe487dc3a368484e0fb013\", \"de...  \n",
       "4806  [{\"credit_id\": \"52fe4df3c3a36847f8275ecf\", \"de...  \n",
       "4807  [{\"credit_id\": \"52fe4ad9c3a368484e16a36b\", \"de...  \n",
       "4808  [{\"credit_id\": \"58ce021b9251415a390165d9\", \"de...  \n",
       "\n",
       "[4809 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[['movie_id','title','overview','genres','keywords','cast','crew']]\n",
    "# storing these columns in 'movies' dataframe itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8af2d70-c68e-4711-aa36-71e3870a432f",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = movies[['movie_id','title','overview','genres','keywords','cast','crew']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "25fe890f-4335-45c0-8f5e-e0d7b5ec4793",
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id   title                                           overview  \\\n",
       "0     19995  Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8bf1b8e5-b955-4365-a756-08655201d196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id    0\n",
       "title       0\n",
       "overview    3\n",
       "genres      0\n",
       "keywords    0\n",
       "cast        0\n",
       "crew        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091495b3-8089-4f83-abd5-0ab3179acae4",
   "metadata": {},
   "source": [
    "Now the next step is that we will create a new dataframe that will have a new column 'tags' created by merging 'overview', 'genres', 'keywords', 'cast', 'crew'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f41e8323-5a91-48f1-9b20-146258c86c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the null values row because these are just 3 rows and its not a large value. won't make much impact on the recommendation if not there\n",
    "movies.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f761128-93c7-4065-9042-6d8a279f56a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id    0\n",
       "title       0\n",
       "overview    0\n",
       "genres      0\n",
       "keywords    0\n",
       "cast        0\n",
       "crew        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ff47232-b383-45fc-ba0f-14a69e77507b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bad4bd7c-ecf9-4585-b328-0b4cc17cbe9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.iloc[0].genres"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d41026d-aaf7-4998-93f5-5831a0a8f2d6",
   "metadata": {},
   "source": [
    "The output is in weird format. It is a list of dictionaries.\n",
    "We want the above output in the given format:\n",
    "['Action','Adventure','Fantasy','Science Fiction']\n",
    "For this we will create a helper function"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5787e013-8de5-4596-b23c-ae3bcc9853c2",
   "metadata": {},
   "source": [
    "def convert(obj):\n",
    "    L = []               # creating a list\n",
    "    for i in obj:\n",
    "        L.append(i['name'])         # appending the value to the key 'name' from the dictionary to the list\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e467fa1e-9152-458a-93f0-1452f9fe589e",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "string indices must be integers, not 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[28], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m[\u001b[39;49m\u001b[38;5;124;43m{\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m: 28, \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m: \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAction\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m}, \u001b[39;49m\u001b[38;5;124;43m{\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m: 12, \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m: \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAdventure\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m}, \u001b[39;49m\u001b[38;5;124;43m{\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m: 14, \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m: \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mFantasy\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m}, \u001b[39;49m\u001b[38;5;124;43m{\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m: 878, \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m: \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mScience Fiction\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m}]\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[25], line 4\u001b[0m, in \u001b[0;36mconvert\u001b[1;34m(obj)\u001b[0m\n\u001b[0;32m      2\u001b[0m L \u001b[38;5;241m=\u001b[39m []               \u001b[38;5;66;03m# creating a list\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m obj:\n\u001b[1;32m----> 4\u001b[0m     L\u001b[38;5;241m.\u001b[39mappend(\u001b[43mi\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)         \u001b[38;5;66;03m# appending the value to the key 'name' from the dictionary to the list\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m L\n",
      "\u001b[1;31mTypeError\u001b[0m: string indices must be integers, not 'str'"
     ]
    }
   ],
   "source": [
    "convert('[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174ea718-c4f5-4572-ac13-227f6dc142d3",
   "metadata": {},
   "source": [
    "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'\n",
    "\n",
    "this is a string and that is why it is not working. So we first have to convert this string of list into list.\n",
    "For this we have a module called ast in python which has a function literal_eval() which does this work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ce5b3a08-b008-4107-b20d-384ae36e46f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 28, 'name': 'Action'},\n",
       " {'id': 12, 'name': 'Adventure'},\n",
       " {'id': 14, 'name': 'Fantasy'},\n",
       " {'id': 878, 'name': 'Science Fiction'}]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ast\n",
    "ast.literal_eval('[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6e0b0391-826e-41d3-93ce-bd06091f9ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# therefore I am creating the above function again which implements literal_eval() function\n",
    "def convert(obj):\n",
    "    L = []               # creating a list\n",
    "    for i in ast.literal_eval(obj):\n",
    "        L.append(i['name'])         # appending the value to the key 'name' from the dictionary to the list\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1fdc3d0e-1b34-4012-b090-273ea812ae49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Action', 'Adventure', 'Fantasy', 'Science Fiction']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert('[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df2562e-a9df-404b-b43e-2c9fa63b967b",
   "metadata": {},
   "source": [
    "the above function is working fine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6bcac4ac-2540-4409-b34c-6f331a26e980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Action, Adventure, Fantasy, Science Fiction]\n",
       "1                        [Adventure, Fantasy, Action]\n",
       "2                          [Action, Adventure, Crime]\n",
       "3                    [Action, Crime, Drama, Thriller]\n",
       "4                [Action, Adventure, Science Fiction]\n",
       "                            ...                      \n",
       "4804                        [Action, Crime, Thriller]\n",
       "4805                                [Comedy, Romance]\n",
       "4806               [Comedy, Drama, Romance, TV Movie]\n",
       "4807                                               []\n",
       "4808                                    [Documentary]\n",
       "Name: genres, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['genres'].apply(convert)       # apply the convert function to the genres column of movies dataframe to convert list of dictionaries into list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5f2e35c5-1dd9-4451-8e5b-d02f38440482",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the above result in the same genres column\n",
    "movies['genres'] = movies['genres'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "de5a6b28-6a85-4e0a-aaac-c52663d972fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                            overview  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...   \n",
       "1  Captain Barbossa, long believed to be dead, ha...   \n",
       "2  A cryptic message from Bond’s past sends him o...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
       "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...   \n",
       "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
       "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a964935-22b9-4c6b-971f-0064e1270844",
   "metadata": {},
   "source": [
    "#### now we'll do the same thing in the 'keywords' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "971e95f6-6b48-4515-9d19-a13698b16edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"space war\"}, {\"id\": 3388, \"name\": \"space colony\"}, {\"id\": 3679, \"name\": \"society\"}, {\"id\": 3801, \"name\": \"space travel\"}, {\"id\": 9685, \"name\": \"futuristic\"}, {\"id\": 9840, \"name\": \"romance\"}, {\"id\": 9882, \"name\": \"space\"}, {\"id\": 9951, \"name\": \"alien\"}, {\"id\": 10148, \"name\": \"tribe\"}, {\"id\": 10158, \"name\": \"alien planet\"}, {\"id\": 10987, \"name\": \"cgi\"}, {\"id\": 11399, \"name\": \"marine\"}, {\"id\": 13065, \"name\": \"soldier\"}, {\"id\": 14643, \"name\": \"battle\"}, {\"id\": 14720, \"name\": \"love affair\"}, {\"id\": 165431, \"name\": \"anti war\"}, {\"id\": 193554, \"name\": \"power relations\"}, {\"id\": 206690, \"name\": \"mind and soul\"}, {\"id\": 209714, \"name\": \"3d\"}]'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.iloc[0].keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "febe4ca2-f431-40e1-9096-c616cb78b0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [culture clash, future, space war, space colon...\n",
       "1       [ocean, drug abuse, exotic island, east india ...\n",
       "2       [spy, based on novel, secret agent, sequel, mi...\n",
       "3       [dc comics, crime fighter, terrorist, secret i...\n",
       "4       [based on novel, mars, medallion, space travel...\n",
       "                              ...                        \n",
       "4804    [united states–mexico barrier, legs, arms, pap...\n",
       "4805                                                   []\n",
       "4806    [date, love at first sight, narration, investi...\n",
       "4807                                                   []\n",
       "4808            [obsession, camcorder, crush, dream girl]\n",
       "Name: keywords, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['keywords'].apply(convert)  # apply the convert function to the keywords column of movies dataframe to convert list of dictionaries into list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f6818178-addb-4783-b027-a685f599791a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the above result in the same keywords column\n",
    "movies['keywords'] = movies['keywords'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "80181d16-c2e0-4bbc-9a61-e0fdd1317085",
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[culture clash, future, space war, space colon...</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
       "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                            overview  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...   \n",
       "1  Captain Barbossa, long believed to be dead, ha...   \n",
       "2  A cryptic message from Bond’s past sends him o...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "1  [ocean, drug abuse, exotic island, east india ...   \n",
       "2  [spy, based on novel, secret agent, sequel, mi...   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
       "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4766c2a4-5d76-4dae-ba7f-e4ea6320ef96",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"credit_id\": \"5602a8a7c3a3685532001c9a\", \"gender\": 2, \"id\": 65731, \"name\": \"Sam Worthington\", \"order\": 0}, {\"cast_id\": 3, \"character\": \"Neytiri\", \"credit_id\": \"52fe48009251416c750ac9cb\", \"gender\": 1, \"id\": 8691, \"name\": \"Zoe Saldana\", \"order\": 1}, {\"cast_id\": 25, \"character\": \"Dr. Grace Augustine\", \"credit_id\": \"52fe48009251416c750aca39\", \"gender\": 1, \"id\": 10205, \"name\": \"Sigourney Weaver\", \"order\": 2}, {\"cast_id\": 4, \"character\": \"Col. Quaritch\", \"credit_id\": \"52fe48009251416c750ac9cf\", \"gender\": 2, \"id\": 32747, \"name\": \"Stephen Lang\", \"order\": 3}, {\"cast_id\": 5, \"character\": \"Trudy Chacon\", \"credit_id\": \"52fe48009251416c750ac9d3\", \"gender\": 1, \"id\": 17647, \"name\": \"Michelle Rodriguez\", \"order\": 4}, {\"cast_id\": 8, \"character\": \"Selfridge\", \"credit_id\": \"52fe48009251416c750ac9e1\", \"gender\": 2, \"id\": 1771, \"name\": \"Giovanni Ribisi\", \"order\": 5}, {\"cast_id\": 7, \"character\": \"Norm Spellman\", \"credit_id\": \"52fe48009251416c750ac9dd\", \"gender\": 2, \"id\": 59231, \"name\": \"Joel David Moore\", \"order\": 6}, {\"cast_id\": 9, \"character\": \"Moat\", \"credit_id\": \"52fe48009251416c750ac9e5\", \"gender\": 1, \"id\": 30485, \"name\": \"CCH Pounder\", \"order\": 7}, {\"cast_id\": 11, \"character\": \"Eytukan\", \"credit_id\": \"52fe48009251416c750ac9ed\", \"gender\": 2, \"id\": 15853, \"name\": \"Wes Studi\", \"order\": 8}, {\"cast_id\": 10, \"character\": \"Tsu\\'Tey\", \"credit_id\": \"52fe48009251416c750ac9e9\", \"gender\": 2, \"id\": 10964, \"name\": \"Laz Alonso\", \"order\": 9}, {\"cast_id\": 12, \"character\": \"Dr. Max Patel\", \"credit_id\": \"52fe48009251416c750ac9f1\", \"gender\": 2, \"id\": 95697, \"name\": \"Dileep Rao\", \"order\": 10}, {\"cast_id\": 13, \"character\": \"Lyle Wainfleet\", \"credit_id\": \"52fe48009251416c750ac9f5\", \"gender\": 2, \"id\": 98215, \"name\": \"Matt Gerald\", \"order\": 11}, {\"cast_id\": 32, \"character\": \"Private Fike\", \"credit_id\": \"52fe48009251416c750aca5b\", \"gender\": 2, \"id\": 154153, \"name\": \"Sean Anthony Moran\", \"order\": 12}, {\"cast_id\": 33, \"character\": \"Cryo Vault Med Tech\", \"credit_id\": \"52fe48009251416c750aca5f\", \"gender\": 2, \"id\": 397312, \"name\": \"Jason Whyte\", \"order\": 13}, {\"cast_id\": 34, \"character\": \"Venture Star Crew Chief\", \"credit_id\": \"52fe48009251416c750aca63\", \"gender\": 2, \"id\": 42317, \"name\": \"Scott Lawrence\", \"order\": 14}, {\"cast_id\": 35, \"character\": \"Lock Up Trooper\", \"credit_id\": \"52fe48009251416c750aca67\", \"gender\": 2, \"id\": 986734, \"name\": \"Kelly Kilgour\", \"order\": 15}, {\"cast_id\": 36, \"character\": \"Shuttle Pilot\", \"credit_id\": \"52fe48009251416c750aca6b\", \"gender\": 0, \"id\": 1207227, \"name\": \"James Patrick Pitt\", \"order\": 16}, {\"cast_id\": 37, \"character\": \"Shuttle Co-Pilot\", \"credit_id\": \"52fe48009251416c750aca6f\", \"gender\": 0, \"id\": 1180936, \"name\": \"Sean Patrick Murphy\", \"order\": 17}, {\"cast_id\": 38, \"character\": \"Shuttle Crew Chief\", \"credit_id\": \"52fe48009251416c750aca73\", \"gender\": 2, \"id\": 1019578, \"name\": \"Peter Dillon\", \"order\": 18}, {\"cast_id\": 39, \"character\": \"Tractor Operator / Troupe\", \"credit_id\": \"52fe48009251416c750aca77\", \"gender\": 0, \"id\": 91443, \"name\": \"Kevin Dorman\", \"order\": 19}, {\"cast_id\": 40, \"character\": \"Dragon Gunship Pilot\", \"credit_id\": \"52fe48009251416c750aca7b\", \"gender\": 2, \"id\": 173391, \"name\": \"Kelson Henderson\", \"order\": 20}, {\"cast_id\": 41, \"character\": \"Dragon Gunship Gunner\", \"credit_id\": \"52fe48009251416c750aca7f\", \"gender\": 0, \"id\": 1207236, \"name\": \"David Van Horn\", \"order\": 21}, {\"cast_id\": 42, \"character\": \"Dragon Gunship Navigator\", \"credit_id\": \"52fe48009251416c750aca83\", \"gender\": 0, \"id\": 215913, \"name\": \"Jacob Tomuri\", \"order\": 22}, {\"cast_id\": 43, \"character\": \"Suit #1\", \"credit_id\": \"52fe48009251416c750aca87\", \"gender\": 0, \"id\": 143206, \"name\": \"Michael Blain-Rozgay\", \"order\": 23}, {\"cast_id\": 44, \"character\": \"Suit #2\", \"credit_id\": \"52fe48009251416c750aca8b\", \"gender\": 2, \"id\": 169676, \"name\": \"Jon Curry\", \"order\": 24}, {\"cast_id\": 46, \"character\": \"Ambient Room Tech\", \"credit_id\": \"52fe48009251416c750aca8f\", \"gender\": 0, \"id\": 1048610, \"name\": \"Luke Hawker\", \"order\": 25}, {\"cast_id\": 47, \"character\": \"Ambient Room Tech / Troupe\", \"credit_id\": \"52fe48009251416c750aca93\", \"gender\": 0, \"id\": 42288, \"name\": \"Woody Schultz\", \"order\": 26}, {\"cast_id\": 48, \"character\": \"Horse Clan Leader\", \"credit_id\": \"52fe48009251416c750aca97\", \"gender\": 2, \"id\": 68278, \"name\": \"Peter Mensah\", \"order\": 27}, {\"cast_id\": 49, \"character\": \"Link Room Tech\", \"credit_id\": \"52fe48009251416c750aca9b\", \"gender\": 0, \"id\": 1207247, \"name\": \"Sonia Yee\", \"order\": 28}, {\"cast_id\": 50, \"character\": \"Basketball Avatar / Troupe\", \"credit_id\": \"52fe48009251416c750aca9f\", \"gender\": 1, \"id\": 1207248, \"name\": \"Jahnel Curfman\", \"order\": 29}, {\"cast_id\": 51, \"character\": \"Basketball Avatar\", \"credit_id\": \"52fe48009251416c750acaa3\", \"gender\": 0, \"id\": 89714, \"name\": \"Ilram Choi\", \"order\": 30}, {\"cast_id\": 52, \"character\": \"Na\\'vi Child\", \"credit_id\": \"52fe48009251416c750acaa7\", \"gender\": 0, \"id\": 1207249, \"name\": \"Kyla Warren\", \"order\": 31}, {\"cast_id\": 53, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acaab\", \"gender\": 0, \"id\": 1207250, \"name\": \"Lisa Roumain\", \"order\": 32}, {\"cast_id\": 54, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acaaf\", \"gender\": 1, \"id\": 83105, \"name\": \"Debra Wilson\", \"order\": 33}, {\"cast_id\": 57, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acabb\", \"gender\": 0, \"id\": 1207253, \"name\": \"Chris Mala\", \"order\": 34}, {\"cast_id\": 55, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acab3\", \"gender\": 0, \"id\": 1207251, \"name\": \"Taylor Kibby\", \"order\": 35}, {\"cast_id\": 56, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acab7\", \"gender\": 0, \"id\": 1207252, \"name\": \"Jodie Landau\", \"order\": 36}, {\"cast_id\": 58, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acabf\", \"gender\": 0, \"id\": 1207254, \"name\": \"Julie Lamm\", \"order\": 37}, {\"cast_id\": 59, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acac3\", \"gender\": 0, \"id\": 1207257, \"name\": \"Cullen B. Madden\", \"order\": 38}, {\"cast_id\": 60, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acac7\", \"gender\": 0, \"id\": 1207259, \"name\": \"Joseph Brady Madden\", \"order\": 39}, {\"cast_id\": 61, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acacb\", \"gender\": 0, \"id\": 1207262, \"name\": \"Frankie Torres\", \"order\": 40}, {\"cast_id\": 62, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acacf\", \"gender\": 1, \"id\": 1158600, \"name\": \"Austin Wilson\", \"order\": 41}, {\"cast_id\": 63, \"character\": \"Troupe\", \"credit_id\": \"52fe48019251416c750acad3\", \"gender\": 1, \"id\": 983705, \"name\": \"Sara Wilson\", \"order\": 42}, {\"cast_id\": 64, \"character\": \"Troupe\", \"credit_id\": \"52fe48019251416c750acad7\", \"gender\": 0, \"id\": 1207263, \"name\": \"Tamica Washington-Miller\", \"order\": 43}, {\"cast_id\": 65, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acadb\", \"gender\": 1, \"id\": 1145098, \"name\": \"Lucy Briant\", \"order\": 44}, {\"cast_id\": 66, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acadf\", \"gender\": 2, \"id\": 33305, \"name\": \"Nathan Meister\", \"order\": 45}, {\"cast_id\": 67, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acae3\", \"gender\": 0, \"id\": 1207264, \"name\": \"Gerry Blair\", \"order\": 46}, {\"cast_id\": 68, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acae7\", \"gender\": 2, \"id\": 33311, \"name\": \"Matthew Chamberlain\", \"order\": 47}, {\"cast_id\": 69, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acaeb\", \"gender\": 0, \"id\": 1207265, \"name\": \"Paul Yates\", \"order\": 48}, {\"cast_id\": 70, \"character\": \"Op Center Duty Officer\", \"credit_id\": \"52fe48019251416c750acaef\", \"gender\": 0, \"id\": 1207266, \"name\": \"Wray Wilson\", \"order\": 49}, {\"cast_id\": 71, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acaf3\", \"gender\": 2, \"id\": 54492, \"name\": \"James Gaylyn\", \"order\": 50}, {\"cast_id\": 72, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acaf7\", \"gender\": 0, \"id\": 1207267, \"name\": \"Melvin Leno Clark III\", \"order\": 51}, {\"cast_id\": 73, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acafb\", \"gender\": 0, \"id\": 1207268, \"name\": \"Carvon Futrell\", \"order\": 52}, {\"cast_id\": 74, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acaff\", \"gender\": 0, \"id\": 1207269, \"name\": \"Brandon Jelkes\", \"order\": 53}, {\"cast_id\": 75, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb03\", \"gender\": 0, \"id\": 1207270, \"name\": \"Micah Moch\", \"order\": 54}, {\"cast_id\": 76, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb07\", \"gender\": 0, \"id\": 1207271, \"name\": \"Hanniyah Muhammad\", \"order\": 55}, {\"cast_id\": 77, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb0b\", \"gender\": 0, \"id\": 1207272, \"name\": \"Christopher Nolen\", \"order\": 56}, {\"cast_id\": 78, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb0f\", \"gender\": 0, \"id\": 1207273, \"name\": \"Christa Oliver\", \"order\": 57}, {\"cast_id\": 79, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb13\", \"gender\": 0, \"id\": 1207274, \"name\": \"April Marie Thomas\", \"order\": 58}, {\"cast_id\": 80, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb17\", \"gender\": 0, \"id\": 1207275, \"name\": \"Bravita A. Threatt\", \"order\": 59}, {\"cast_id\": 81, \"character\": \"Mining Chief (uncredited)\", \"credit_id\": \"52fe48019251416c750acb1b\", \"gender\": 0, \"id\": 1207276, \"name\": \"Colin Bleasdale\", \"order\": 60}, {\"cast_id\": 82, \"character\": \"Veteran Miner (uncredited)\", \"credit_id\": \"52fe48019251416c750acb1f\", \"gender\": 0, \"id\": 107969, \"name\": \"Mike Bodnar\", \"order\": 61}, {\"cast_id\": 83, \"character\": \"Richard (uncredited)\", \"credit_id\": \"52fe48019251416c750acb23\", \"gender\": 0, \"id\": 1207278, \"name\": \"Matt Clayton\", \"order\": 62}, {\"cast_id\": 84, \"character\": \"Nav\\'i (uncredited)\", \"credit_id\": \"52fe48019251416c750acb27\", \"gender\": 1, \"id\": 147898, \"name\": \"Nicole Dionne\", \"order\": 63}, {\"cast_id\": 85, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb2b\", \"gender\": 0, \"id\": 1207280, \"name\": \"Jamie Harrison\", \"order\": 64}, {\"cast_id\": 86, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb2f\", \"gender\": 0, \"id\": 1207281, \"name\": \"Allan Henry\", \"order\": 65}, {\"cast_id\": 87, \"character\": \"Ground Technician (uncredited)\", \"credit_id\": \"52fe48019251416c750acb33\", \"gender\": 2, \"id\": 1207282, \"name\": \"Anthony Ingruber\", \"order\": 66}, {\"cast_id\": 88, \"character\": \"Flight Crew Mechanic (uncredited)\", \"credit_id\": \"52fe48019251416c750acb37\", \"gender\": 0, \"id\": 1207283, \"name\": \"Ashley Jeffery\", \"order\": 67}, {\"cast_id\": 14, \"character\": \"Samson Pilot\", \"credit_id\": \"52fe48009251416c750ac9f9\", \"gender\": 0, \"id\": 98216, \"name\": \"Dean Knowsley\", \"order\": 68}, {\"cast_id\": 89, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb3b\", \"gender\": 0, \"id\": 1201399, \"name\": \"Joseph Mika-Hunt\", \"order\": 69}, {\"cast_id\": 90, \"character\": \"Banshee (uncredited)\", \"credit_id\": \"52fe48019251416c750acb3f\", \"gender\": 0, \"id\": 236696, \"name\": \"Terry Notary\", \"order\": 70}, {\"cast_id\": 91, \"character\": \"Soldier (uncredited)\", \"credit_id\": \"52fe48019251416c750acb43\", \"gender\": 0, \"id\": 1207287, \"name\": \"Kai Pantano\", \"order\": 71}, {\"cast_id\": 92, \"character\": \"Blast Technician (uncredited)\", \"credit_id\": \"52fe48019251416c750acb47\", \"gender\": 0, \"id\": 1207288, \"name\": \"Logan Pithyou\", \"order\": 72}, {\"cast_id\": 93, \"character\": \"Vindum Raah (uncredited)\", \"credit_id\": \"52fe48019251416c750acb4b\", \"gender\": 0, \"id\": 1207289, \"name\": \"Stuart Pollock\", \"order\": 73}, {\"cast_id\": 94, \"character\": \"Hero (uncredited)\", \"credit_id\": \"52fe48019251416c750acb4f\", \"gender\": 0, \"id\": 584868, \"name\": \"Raja\", \"order\": 74}, {\"cast_id\": 95, \"character\": \"Ops Centreworker (uncredited)\", \"credit_id\": \"52fe48019251416c750acb53\", \"gender\": 0, \"id\": 1207290, \"name\": \"Gareth Ruck\", \"order\": 75}, {\"cast_id\": 96, \"character\": \"Engineer (uncredited)\", \"credit_id\": \"52fe48019251416c750acb57\", \"gender\": 0, \"id\": 1062463, \"name\": \"Rhian Sheehan\", \"order\": 76}, {\"cast_id\": 97, \"character\": \"Col. Quaritch\\'s Mech Suit (uncredited)\", \"credit_id\": \"52fe48019251416c750acb5b\", \"gender\": 0, \"id\": 60656, \"name\": \"T. J. Storm\", \"order\": 77}, {\"cast_id\": 98, \"character\": \"Female Marine (uncredited)\", \"credit_id\": \"52fe48019251416c750acb5f\", \"gender\": 0, \"id\": 1207291, \"name\": \"Jodie Taylor\", \"order\": 78}, {\"cast_id\": 99, \"character\": \"Ikran Clan Leader (uncredited)\", \"credit_id\": \"52fe48019251416c750acb63\", \"gender\": 1, \"id\": 1186027, \"name\": \"Alicia Vela-Bailey\", \"order\": 79}, {\"cast_id\": 100, \"character\": \"Geologist (uncredited)\", \"credit_id\": \"52fe48019251416c750acb67\", \"gender\": 0, \"id\": 1207292, \"name\": \"Richard Whiteside\", \"order\": 80}, {\"cast_id\": 101, \"character\": \"Na\\'vi (uncredited)\", \"credit_id\": \"52fe48019251416c750acb6b\", \"gender\": 0, \"id\": 103259, \"name\": \"Nikie Zambo\", \"order\": 81}, {\"cast_id\": 102, \"character\": \"Ambient Room Tech / Troupe\", \"credit_id\": \"52fe48019251416c750acb6f\", \"gender\": 1, \"id\": 42286, \"name\": \"Julene Renee\", \"order\": 82}]'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.iloc[0].cast         # or we can also use movies['cast'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afcfa991-69dc-4728-8835-10879b56af86",
   "metadata": {},
   "source": [
    "###### here we will take top 3 actors and not all the cast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ecde6452-2a26-4525-bd5d-f4776711abcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# {\"cast_id\": 242, \"character\": \"Jake Sully\", \"credit_id\": \"5602a8a7c3a3685532001c9a\", \"gender\": 2, \"id\": 65731, \"name\": \"Sam Worthington\", \"order\": 0}\n",
    "# in this \"Jake Sully\" is the name of character in the movie and \"Sam Worthington\" is the actual name of the actor\n",
    "# therefore, we will take the the actual name. The same above function is repeated with some minor changes\n",
    "\n",
    "def convert3(obj):\n",
    "    L = []               # creating a list\n",
    "    counter = 0            # to take maximum of 3 casts\n",
    "    for i in ast.literal_eval(obj):\n",
    "        if counter != 3:\n",
    "            L.append(i['name'])         # appending the value to the key 'name' from the dictionary to the list\n",
    "            counter+=1\n",
    "        else:\n",
    "            break\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f455ef5d-4f84-4459-9e68-bb09d0dd3419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [Sam Worthington, Zoe Saldana, Sigourney Weaver]\n",
       "1           [Johnny Depp, Orlando Bloom, Keira Knightley]\n",
       "2            [Daniel Craig, Christoph Waltz, Léa Seydoux]\n",
       "3            [Christian Bale, Michael Caine, Gary Oldman]\n",
       "4          [Taylor Kitsch, Lynn Collins, Samantha Morton]\n",
       "                              ...                        \n",
       "4804    [Carlos Gallardo, Jaime de Hoyos, Peter Marqua...\n",
       "4805         [Edward Burns, Kerry Bishé, Marsha Dietlein]\n",
       "4806           [Eric Mabius, Kristin Booth, Crystal Lowe]\n",
       "4807            [Daniel Henney, Eliza Coupe, Bill Paxton]\n",
       "4808    [Drew Barrymore, Brian Herzlinger, Corey Feldman]\n",
       "Name: cast, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['cast'].apply(convert3)  # apply the convert function to the cast column of movies dataframe to get top 3 casts and to convert list of dictionaries into list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "27d2b8c7-660c-4d36-81c2-c62b86cc2f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the above result in the same cast column\n",
    "movies['cast'] = movies['cast'].apply(convert3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "370272ac-7235-4d5b-af45-f8bd450a794d",
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[culture clash, future, space war, space colon...</td>\n",
       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
       "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
       "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux]</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                            overview  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...   \n",
       "1  Captain Barbossa, long believed to be dead, ha...   \n",
       "2  A cryptic message from Bond’s past sends him o...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "1  [ocean, drug abuse, exotic island, east india ...   \n",
       "2  [spy, based on novel, secret agent, sequel, mi...   \n",
       "\n",
       "                                               cast  \\\n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]   \n",
       "1     [Johnny Depp, Orlando Bloom, Keira Knightley]   \n",
       "2      [Daniel Craig, Christoph Waltz, Léa Seydoux]   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e969f5f0-452f-48e1-b8e7-1895aa797796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"credit_id\": \"52fe48009251416c750aca23\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1721, \"job\": \"Editor\", \"name\": \"Stephen E. Rivkin\"}, {\"credit_id\": \"539c47ecc3a36810e3001f87\", \"department\": \"Art\", \"gender\": 2, \"id\": 496, \"job\": \"Production Design\", \"name\": \"Rick Carter\"}, {\"credit_id\": \"54491c89c3a3680fb4001cf7\", \"department\": \"Sound\", \"gender\": 0, \"id\": 900, \"job\": \"Sound Designer\", \"name\": \"Christopher Boyes\"}, {\"credit_id\": \"54491cb70e0a267480001bd0\", \"department\": \"Sound\", \"gender\": 0, \"id\": 900, \"job\": \"Supervising Sound Editor\", \"name\": \"Christopher Boyes\"}, {\"credit_id\": \"539c4a4cc3a36810c9002101\", \"department\": \"Production\", \"gender\": 1, \"id\": 1262, \"job\": \"Casting\", \"name\": \"Mali Finn\"}, {\"credit_id\": \"5544ee3b925141499f0008fc\", \"department\": \"Sound\", \"gender\": 2, \"id\": 1729, \"job\": \"Original Music Composer\", \"name\": \"James Horner\"}, {\"credit_id\": \"52fe48009251416c750ac9c3\", \"department\": \"Directing\", \"gender\": 2, \"id\": 2710, \"job\": \"Director\", \"name\": \"James Cameron\"}, {\"credit_id\": \"52fe48009251416c750ac9d9\", \"department\": \"Writing\", \"gender\": 2, \"id\": 2710, \"job\": \"Writer\", \"name\": \"James Cameron\"}, {\"credit_id\": \"52fe48009251416c750aca17\", \"department\": \"Editing\", \"gender\": 2, \"id\": 2710, \"job\": \"Editor\", \"name\": \"James Cameron\"}, {\"credit_id\": \"52fe48009251416c750aca29\", \"department\": \"Production\", \"gender\": 2, \"id\": 2710, \"job\": \"Producer\", \"name\": \"James Cameron\"}, {\"credit_id\": \"52fe48009251416c750aca3f\", \"department\": \"Writing\", \"gender\": 2, \"id\": 2710, \"job\": \"Screenplay\", \"name\": \"James Cameron\"}, {\"credit_id\": \"539c4987c3a36810ba0021a4\", \"department\": \"Art\", \"gender\": 2, \"id\": 7236, \"job\": \"Art Direction\", \"name\": \"Andrew Menzies\"}, {\"credit_id\": \"549598c3c3a3686ae9004383\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 6690, \"job\": \"Visual Effects Producer\", \"name\": \"Jill Brooks\"}, {\"credit_id\": \"52fe48009251416c750aca4b\", \"department\": \"Production\", \"gender\": 1, \"id\": 6347, \"job\": \"Casting\", \"name\": \"Margery Simkin\"}, {\"credit_id\": \"570b6f419251417da70032fe\", \"department\": \"Art\", \"gender\": 2, \"id\": 6878, \"job\": \"Supervising Art Director\", \"name\": \"Kevin Ishioka\"}, {\"credit_id\": \"5495a0fac3a3686ae9004468\", \"department\": \"Sound\", \"gender\": 0, \"id\": 6883, \"job\": \"Music Editor\", \"name\": \"Dick Bernstein\"}, {\"credit_id\": \"54959706c3a3686af3003e81\", \"department\": \"Sound\", \"gender\": 0, \"id\": 8159, \"job\": \"Sound Effects Editor\", \"name\": \"Shannon Mills\"}, {\"credit_id\": \"54491d58c3a3680fb1001ccb\", \"department\": \"Sound\", \"gender\": 0, \"id\": 8160, \"job\": \"Foley\", \"name\": \"Dennie Thorpe\"}, {\"credit_id\": \"54491d6cc3a3680fa5001b2c\", \"department\": \"Sound\", \"gender\": 0, \"id\": 8163, \"job\": \"Foley\", \"name\": \"Jana Vance\"}, {\"credit_id\": \"52fe48009251416c750aca57\", \"department\": \"Costume & Make-Up\", \"gender\": 1, \"id\": 8527, \"job\": \"Costume Design\", \"name\": \"Deborah Lynn Scott\"}, {\"credit_id\": \"52fe48009251416c750aca2f\", \"department\": \"Production\", \"gender\": 2, \"id\": 8529, \"job\": \"Producer\", \"name\": \"Jon Landau\"}, {\"credit_id\": \"539c4937c3a36810ba002194\", \"department\": \"Art\", \"gender\": 0, \"id\": 9618, \"job\": \"Art Direction\", \"name\": \"Sean Haworth\"}, {\"credit_id\": \"539c49b6c3a36810c10020e6\", \"department\": \"Art\", \"gender\": 1, \"id\": 12653, \"job\": \"Set Decoration\", \"name\": \"Kim Sinclair\"}, {\"credit_id\": \"570b6f2f9251413a0e00020d\", \"department\": \"Art\", \"gender\": 1, \"id\": 12653, \"job\": \"Supervising Art Director\", \"name\": \"Kim Sinclair\"}, {\"credit_id\": \"54491a6c0e0a26748c001b19\", \"department\": \"Art\", \"gender\": 2, \"id\": 14350, \"job\": \"Set Designer\", \"name\": \"Richard F. Mays\"}, {\"credit_id\": \"56928cf4c3a3684cff0025c4\", \"department\": \"Production\", \"gender\": 1, \"id\": 20294, \"job\": \"Executive Producer\", \"name\": \"Laeta Kalogridis\"}, {\"credit_id\": \"52fe48009251416c750aca51\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 17675, \"job\": \"Costume Design\", \"name\": \"Mayes C. 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      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['crew'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b5ecbe-0299-4001-98d4-441a704280f6",
   "metadata": {},
   "source": [
    "For us only those dictionaries matters where the values of job is director. So we'll make a function for this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "57fffee6-d3d2-4fb0-bf24-152631d65fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_director(obj):\n",
    "    L = []               # creating a list\n",
    "    for i in ast.literal_eval(obj):\n",
    "        if i['job'] == 'Director':\n",
    "            L.append(i['name'])         # appending the value to the key 'name' from the dictionary to the list\n",
    "            break\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4682e20e-fbeb-4b66-851c-8e8ecabd9028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           [James Cameron]\n",
       "1          [Gore Verbinski]\n",
       "2              [Sam Mendes]\n",
       "3       [Christopher Nolan]\n",
       "4          [Andrew Stanton]\n",
       "               ...         \n",
       "4804     [Robert Rodriguez]\n",
       "4805         [Edward Burns]\n",
       "4806          [Scott Smith]\n",
       "4807          [Daniel Hsia]\n",
       "4808     [Brian Herzlinger]\n",
       "Name: crew, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['crew'].apply(fetch_director) # apply the convert function to the crew column of movies dataframe to fetch the directors and to convert list of dictionaries into list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9d27c94e-345a-4ca5-ad37-9d50cf137f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the above result in the same crew column\n",
    "movies['crew'] = movies['crew'].apply(fetch_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4888edc4-4c2a-448d-a464-bb00aaf4aecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[culture clash, future, space war, space colon...</td>\n",
       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
       "      <td>[James Cameron]</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
       "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
       "      <td>[Gore Verbinski]</td>\n",
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       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
       "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux]</td>\n",
       "      <td>[Sam Mendes]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                            overview  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...   \n",
       "1  Captain Barbossa, long believed to be dead, ha...   \n",
       "2  A cryptic message from Bond’s past sends him o...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "1  [ocean, drug abuse, exotic island, east india ...   \n",
       "2  [spy, based on novel, secret agent, sequel, mi...   \n",
       "\n",
       "                                               cast              crew  \n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]   [James Cameron]  \n",
       "1     [Johnny Depp, Orlando Bloom, Keira Knightley]  [Gore Verbinski]  \n",
       "2      [Daniel Craig, Christoph Waltz, Léa Seydoux]      [Sam Mendes]  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4e09e414-4677-46d0-ae97-1cc9250c54a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization.'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now coming to the 'overview' column\n",
    "movies['overview'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0492b65-f497-4003-b922-7d91e25e2ddb",
   "metadata": {},
   "source": [
    "Now this is a string. We will convert it into a list so that we can concatenate it with the other lists in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3e6905d5-0783-4980-b208-4c86a81bf1b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [In, the, 22nd, century,, a, paraplegic, Marin...\n",
       "1       [Captain, Barbossa,, long, believed, to, be, d...\n",
       "2       [A, cryptic, message, from, Bond’s, past, send...\n",
       "3       [Following, the, death, of, District, Attorney...\n",
       "4       [John, Carter, is, a, war-weary,, former, mili...\n",
       "                              ...                        \n",
       "4804    [El, Mariachi, just, wants, to, play, his, gui...\n",
       "4805    [A, newlywed, couple's, honeymoon, is, upended...\n",
       "4806    [\"Signed,, Sealed,, Delivered\", introduces, a,...\n",
       "4807    [When, ambitious, New, York, attorney, Sam, is...\n",
       "4808    [Ever, since, the, second, grade, when, he, fi...\n",
       "Name: overview, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['overview'].apply(lambda x:x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f8e592d7-798d-4a9d-860f-3d01193835a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging the above obtained lists into the same column\n",
    "movies['overview'] = movies['overview'].apply(lambda x:x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b9fe0eaf-506c-4514-b626-ef9e50ef7340",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
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       "  <tbody>\n",
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       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[culture clash, future, space war, space colon...</td>\n",
       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
       "      <td>[James Cameron]</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
       "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
       "      <td>[Gore Verbinski]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
       "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux]</td>\n",
       "      <td>[Sam Mendes]</td>\n",
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      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                            overview  \\\n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "1  [ocean, drug abuse, exotic island, east india ...   \n",
       "2  [spy, based on novel, secret agent, sequel, mi...   \n",
       "\n",
       "                                               cast              crew  \n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]   [James Cameron]  \n",
       "1     [Johnny Depp, Orlando Bloom, Keira Knightley]  [Gore Verbinski]  \n",
       "2      [Daniel Craig, Christoph Waltz, Léa Seydoux]      [Sam Mendes]  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619de8fc-8924-4e04-99fa-e68d0c4ba8e1",
   "metadata": {},
   "source": [
    "Now since these columns  ('overview', 'genres', 'keywords', 'cast', 'crew') are a list, we will concatenate them to make a big list and store them in a column called 'tags'. Then we'll convert this list in a string to make it a paragraph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e761c319-c0a5-4a35-a162-f595d798db8b",
   "metadata": {},
   "source": [
    "We have to remove spaces from ('genres', 'keywords', 'cast', 'crew') columns. For that we have to apply transformation on these columns.\n",
    "For exammple, we have to convert 'Sam Worthington' to 'SamWorthington'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c3f1fc22-ebbf-4d85-ab46-6dd0ad0b6ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Action, Adventure, Fantasy, ScienceFiction]\n",
       "1                       [Adventure, Fantasy, Action]\n",
       "2                         [Action, Adventure, Crime]\n",
       "3                   [Action, Crime, Drama, Thriller]\n",
       "4                [Action, Adventure, ScienceFiction]\n",
       "                            ...                     \n",
       "4804                       [Action, Crime, Thriller]\n",
       "4805                               [Comedy, Romance]\n",
       "4806               [Comedy, Drama, Romance, TVMovie]\n",
       "4807                                              []\n",
       "4808                                   [Documentary]\n",
       "Name: genres, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first we'll try to apply this on genres column\n",
    "movies['genres'].apply(lambda x:[i.replace(\" \",\"\") for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bcf6f6dd-55c9-4987-8262-ad6ea6404221",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the above result in the same genres column\n",
    "movies['genres'] = movies['genres'].apply(lambda x:[i.replace(\" \",\"\") for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a267ba61-ec52-44de-9db2-c0517d23301d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now doing the same for remaining 3 columns\n",
    "movies['keywords'] = movies['keywords'].apply(lambda x:[i.replace(\" \",\"\") for i in x])\n",
    "movies['cast'] = movies['cast'].apply(lambda x:[i.replace(\" \",\"\") for i in x])\n",
    "movies['crew'] = movies['crew'].apply(lambda x:[i.replace(\" \",\"\") for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "26897ca5-bb03-4c4d-b701-77586733a30d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
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       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
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       "      <td>Avatar</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
       "      <td>[Action, Adventure, Fantasy, ScienceFiction]</td>\n",
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       "      <td>[SamWorthington, ZoeSaldana, SigourneyWeaver]</td>\n",
       "      <td>[JamesCameron]</td>\n",
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       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drugabuse, exoticisland, eastindiatrad...</td>\n",
       "      <td>[JohnnyDepp, OrlandoBloom, KeiraKnightley]</td>\n",
       "      <td>[GoreVerbinski]</td>\n",
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       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
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       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, basedonnovel, secretagent, sequel, mi6, ...</td>\n",
       "      <td>[DanielCraig, ChristophWaltz, LéaSeydoux]</td>\n",
       "      <td>[SamMendes]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                            overview  \\\n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
       "\n",
       "                                         genres  \\\n",
       "0  [Action, Adventure, Fantasy, ScienceFiction]   \n",
       "1                  [Adventure, Fantasy, Action]   \n",
       "2                    [Action, Adventure, Crime]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [cultureclash, future, spacewar, spacecolony, ...   \n",
       "1  [ocean, drugabuse, exoticisland, eastindiatrad...   \n",
       "2  [spy, basedonnovel, secretagent, sequel, mi6, ...   \n",
       "\n",
       "                                            cast             crew  \n",
       "0  [SamWorthington, ZoeSaldana, SigourneyWeaver]   [JamesCameron]  \n",
       "1     [JohnnyDepp, OrlandoBloom, KeiraKnightley]  [GoreVerbinski]  \n",
       "2      [DanielCraig, ChristophWaltz, LéaSeydoux]      [SamMendes]  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a1b981ec-9afc-4855-81bf-68e992792d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenating the above decided columns and forming a new column\n",
    "movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d0b35b5b-9f5b-4a38-9389-4473b0d9522b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Pirates of the Caribbean: At World's End</td>\n",
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       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drugabuse, exoticisland, eastindiatrad...</td>\n",
       "      <td>[JohnnyDepp, OrlandoBloom, KeiraKnightley]</td>\n",
       "      <td>[GoreVerbinski]</td>\n",
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       "      <td>[DanielCraig, ChristophWaltz, LéaSeydoux]</td>\n",
       "      <td>[SamMendes]</td>\n",
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      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                            overview  \\\n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
       "\n",
       "                                         genres  \\\n",
       "0  [Action, Adventure, Fantasy, ScienceFiction]   \n",
       "1                  [Adventure, Fantasy, Action]   \n",
       "2                    [Action, Adventure, Crime]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [cultureclash, future, spacewar, spacecolony, ...   \n",
       "1  [ocean, drugabuse, exoticisland, eastindiatrad...   \n",
       "2  [spy, basedonnovel, secretagent, sequel, mi6, ...   \n",
       "\n",
       "                                            cast             crew  \\\n",
       "0  [SamWorthington, ZoeSaldana, SigourneyWeaver]   [JamesCameron]   \n",
       "1     [JohnnyDepp, OrlandoBloom, KeiraKnightley]  [GoreVerbinski]   \n",
       "2      [DanielCraig, ChristophWaltz, LéaSeydoux]      [SamMendes]   \n",
       "\n",
       "                                                tags  \n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...  \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...  \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6f9f6e0a-707f-4381-b367-0a4dfd5a5cf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = movies[['movie_id','title','tags']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "386033a0-52ea-4dce-91b7-07ed92e14a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                                tags  \n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...  \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...  \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "06bd77a5-dcb6-425e-8c69-f5ddb93ec5b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       In the 22nd century, a paraplegic Marine is di...\n",
       "1       Captain Barbossa, long believed to be dead, ha...\n",
       "2       A cryptic message from Bond’s past sends him o...\n",
       "3       Following the death of District Attorney Harve...\n",
       "4       John Carter is a war-weary, former military ca...\n",
       "                              ...                        \n",
       "4804    El Mariachi just wants to play his guitar and ...\n",
       "4805    A newlywed couple's honeymoon is upended by th...\n",
       "4806    \"Signed, Sealed, Delivered\" introduces a dedic...\n",
       "4807    When ambitious New York attorney Sam is sent t...\n",
       "4808    Ever since the second grade when he first saw ...\n",
       "Name: tags, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we'll convert the lists in tags column to string\n",
    "new_df['tags'].apply(lambda x:\" \".join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e840eabd-6ab8-4954-a943-ba05f2e64b49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_6532\\1942866522.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['tags'] = new_df['tags'].apply(lambda x:\" \".join(x))\n"
     ]
    }
   ],
   "source": [
    "# saving this in tags column\n",
    "new_df['tags'] = new_df['tags'].apply(lambda x:\" \".join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "607e634b-e892-46c9-8d3d-a8c2be4a8b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>19995</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
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      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                                tags  \n",
       "0  In the 22nd century, a paraplegic Marine is di...  \n",
       "1  Captain Barbossa, long believed to be dead, ha...  \n",
       "2  A cryptic message from Bond’s past sends him o...  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5909cd12-fd8e-4a65-b779-fc341a77915b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       in the 22nd century, a paraplegic marine is di...\n",
       "1       captain barbossa, long believed to be dead, ha...\n",
       "2       a cryptic message from bond’s past sends him o...\n",
       "3       following the death of district attorney harve...\n",
       "4       john carter is a war-weary, former military ca...\n",
       "                              ...                        \n",
       "4804    el mariachi just wants to play his guitar and ...\n",
       "4805    a newlywed couple's honeymoon is upended by th...\n",
       "4806    \"signed, sealed, delivered\" introduces a dedic...\n",
       "4807    when ambitious new york attorney sam is sent t...\n",
       "4808    ever since the second grade when he first saw ...\n",
       "Name: tags, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting the string into lowercase\n",
    "new_df['tags'].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3334c81b-025c-4140-9fbc-9050aa4b498b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_6532\\2284866200.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['tags'] = new_df['tags'].apply(lambda x:x.lower())\n"
     ]
    }
   ],
   "source": [
    "# saving the result in the same column\n",
    "new_df['tags'] = new_df['tags'].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "eb0f90cb-48b5-4940-9de9-93c7e506636e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe 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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
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       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>captain barbossa, long believed to be dead, ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>a cryptic message from bond’s past sends him o...</td>\n",
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      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "\n",
       "                                                tags  \n",
       "0  in the 22nd century, a paraplegic marine is di...  \n",
       "1  captain barbossa, long believed to be dead, ha...  \n",
       "2  a cryptic message from bond’s past sends him o...  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1b6c72-04fe-4acc-9160-785a9ac3d597",
   "metadata": {},
   "source": [
    "# Vectorization\n",
    "Here we will check the similarities between tags so that we could recommend similar movies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f19811c-6989-4853-a8ee-1dc54ae343d1",
   "metadata": {},
   "source": [
    "For this, we will combine all the tags and create a large_text. For example, large_text = tag1 + tag2 + ..... +tag5000\n",
    "\n",
    "Now, from this large_text, we will take out lets say 5000 most common words and create vectors with them.\n",
    "\n",
    "We will use CountVectorizer from Sklearn.feature_extraction.text for this. Here we will use the stopwords functionality. Stopwords are the words which are used in the sentence formation. For example: are, is, in, the, etc.\n",
    "We will neglect these stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9b34b5a4-fa7d-45bc-a732-381c2f84dc9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ca86c604-58c8-4610-a29f-cb336e75a5ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e0ba03a0-0a7f-49e1-9db1-e1b1571d5024",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem(text):\n",
    "    y = []\n",
    "    for i in text.split():\n",
    "        y.append(ps.stem(i))\n",
    "    return\" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "10ca93eb-f962-4f2a-b1b7-0c987fa806ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love love love'"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stem('loved loving love')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "966fea65-5409-4171-bc74-07fcf49540ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'danc danc danc'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stem('dance dancing danced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "59c089bc-5f60-4b38-9f4f-09c9e54993b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       in the 22nd century, a parapleg marin is dispa...\n",
       "1       captain barbossa, long believ to be dead, ha c...\n",
       "2       a cryptic messag from bond’ past send him on a...\n",
       "3       follow the death of district attorney harvey d...\n",
       "4       john carter is a war-weary, former militari ca...\n",
       "                              ...                        \n",
       "4804    el mariachi just want to play hi guitar and ca...\n",
       "4805    a newlyw couple' honeymoon is upend by the arr...\n",
       "4806    \"signed, sealed, delivered\" introduc a dedic q...\n",
       "4807    when ambiti new york attorney sam is sent to s...\n",
       "4808    ever sinc the second grade when he first saw h...\n",
       "Name: tags, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['tags'].apply(stem)  # performing stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "396bd884-ac46-4dce-8a6d-64643ce81ef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_6532\\3827281523.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['tags'] = new_df['tags'].apply(stem)\n"
     ]
    }
   ],
   "source": [
    "# storing the above result in the same column\n",
    "new_df['tags'] = new_df['tags'].apply(stem)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e1289db-218d-4d2f-8b7e-b509500920c6",
   "metadata": {},
   "source": [
    "now to remove words which have similar meaning\n",
    "for example ['loved','loving','love']    =>  ['love','love','love']\n",
    "we will use stemming for this\n",
    "\n",
    "we will use nltk library for this.\n",
    "\n",
    "\n",
    "we have created a stem function above for this thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6cd4de4f-baa1-413c-a64e-122931b634b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "da0c1473-040f-4893-a266-20a3bc5e909d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer(max_features=5000, stop_words='english')    # max_features=5000 these are the words taken out from large_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ad4c3bb7-9395-4e01-a6a2-13cf0e53f98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.fit_transform(new_df['tags']).toarray()      # explicitly converting tags to numpy array as cv returns a sparse matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4f8ec495-e97a-4569-ad82-ad442c7d44b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the above result in a new variable\n",
    "vectors = cv.fit_transform(new_df['tags']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2bc1b531-9ba6-4ced-81a8-40dddeb90157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4806, 5000)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "537234b9-cae8-43b2-9a99-ace1ae1f5597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['000', '007', '10', ..., 'zone', 'zoo', 'zooeydeschanel'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check 5000 most frequent words\n",
    "cv.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad694ad2-41c1-4f54-8850-5727e5897dc2",
   "metadata": {},
   "source": [
    "##### now we have 4806 vectors which have 5000 numbers each\n",
    "##### we need to calculate the distance between each and every movie to each and every other movie \n",
    "##### the more the distance, lesser the similarity between those 2 movies\n",
    "##### here we will not calculate the euclean distance, instead we will calculate the cosine distance\n",
    "##### i.e., euclean distance is sqrt((x2-x1)^2 - (y2-y1)^2) and cosine distance is the angle between the two vectors\n",
    "### Note: If you are working with high dimensional data, as in now as we have 4806x5000, euclean distance fails. It is not a reliable measure for higher dimensional data.\n",
    "\n",
    "##### for cosine similarity, we have a function in sklearn.metrics.pairwise known as cosine_similarity which ranges between 0 to 1. If it is 1 then similarity is more else not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "388dc891-490a-45f7-95a3-feeba6609fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "aa89904a-6705-4785-b38f-f0cb79ebf240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.08346223, 0.0860309 , ..., 0.04499213, 0.        ,\n",
       "        0.        ],\n",
       "       [0.08346223, 1.        , 0.06063391, ..., 0.02378257, 0.        ,\n",
       "        0.02615329],\n",
       "       [0.0860309 , 0.06063391, 1.        , ..., 0.02451452, 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.04499213, 0.02378257, 0.02451452, ..., 1.        , 0.03962144,\n",
       "        0.04229549],\n",
       "       [0.        , 0.        , 0.        , ..., 0.03962144, 1.        ,\n",
       "        0.08714204],\n",
       "       [0.        , 0.02615329, 0.        , ..., 0.04229549, 0.08714204,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarity(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c3dd9200-d093-4b40-9500-cc1fbfff9fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4806, 4806)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarity(vectors).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "770469c3-6410-454c-9e16-976272e4f400",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing all these distances in a variable\n",
    "similarity = cosine_similarity(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "9f07331e-4874-4d99-afbd-7a6ed8f8f141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.08346223, 0.0860309 , ..., 0.04499213, 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "037a09c9-140e-4c30-a1f5-c3b1f3eb1d96",
   "metadata": {},
   "source": [
    "it has 4806 items as the distance is calculated between the 1st movie and all the other 4806 movies \n",
    "similarity[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "d6809ea4-5f8a-4f58-aaf4-b88c4a843ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08346223, 1.        , 0.06063391, ..., 0.02378257, 0.        ,\n",
       "       0.02615329])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similarity score of 1st movie is 1 as it is the maximum similarity.. similarly it goes for 2nd movie\n",
    "similarity[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274e9a36-e85e-4ae8-b143-65e8325f6ec2",
   "metadata": {},
   "source": [
    "now the next step is to create a function which takes movie as an input and returns 5 similar movies.\n",
    "\n",
    "To do this, first a movie is taken as input. Then we have to find the index of the movie in the data. Corresponding to that index, we will go to the similarity matrix which contains the distance of that movie with other movies. Then we will sort the similarity score in descending order i.e., more similar movies will come in the beginning. (more the similarity score, more similar the movie). Then we will fetch the top 5 movies and display them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19480e07-195e-4e01-9330-6e74b9d61bf5",
   "metadata": {},
   "source": [
    "### this is how we will take out the index of the movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "bc0da4d7-8de1-4aba-9169-7c28605804ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "4804    False\n",
       "4805    False\n",
       "4806    False\n",
       "4807    False\n",
       "4808    False\n",
       "Name: title, Length: 4806, dtype: bool"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['title'] == 'Avatar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "85d0893f-2412-40d0-aaac-db53ba4d8866",
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>in the 22nd century, a parapleg marin is dispa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id   title                                               tags\n",
       "0     19995  Avatar  in the 22nd century, a parapleg marin is dispa..."
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['title'] == 'Avatar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "1725cdd5-b8ee-4d13-a829-2c0399c2c4ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([0], dtype='int64')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['title'] == 'Avatar'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5dd536ec-5d6b-454d-9f84-7d3d82497e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['title'] == 'Avatar'].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "1a4ce3f7-299a-4cc5-9af1-af9a9b057a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['title'] == 'Spectre'].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "d5dd99d2-5db7-4f95-bff3-26af88521eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0860309 , 0.06063391, 1.        , ..., 0.02451452, 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is how we'll fetch the similarity scores from similarity matrix.\n",
    "similarity[new_df[new_df['title'] == 'Spectre'].index[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "926c9af0-ba80-4bc8-9f9e-8568aa1db236",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       " 0.05661385170722978,\n",
       " ...]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is how we will sort the score\n",
    "sorted(similarity[2],reverse = True)   # if we omit reverse attribute or write false in it then the sorting will be in ascending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "f2020f40-f764-4a56-8c04-d0e5faf7ade3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       " (999, 0.06454972243679027),\n",
       " ...]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we have to hold the positions of distance as after sorting, the indexes will change and we won't be able to find out which score at which index belongs to which movie\n",
    "# for this we will use enumerate() function which returns an object which when converted to list given the following result\n",
    "list(enumerate(similarity[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "65df02a2-9dfc-4b2d-a921-c3820fbbb4ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       " (2945, 0.058123819371909635),\n",
       " (3318, 0.058123819371909635),\n",
       " (3673, 0.058123819371909635),\n",
       " (4063, 0.058123819371909635),\n",
       " (4093, 0.058123819371909635),\n",
       " (4113, 0.058123819371909635),\n",
       " (3380, 0.057863756235784464),\n",
       " (3730, 0.057863756235784464),\n",
       " (16, 0.057353933467640436),\n",
       " (268, 0.057353933467640436),\n",
       " (322, 0.057353933467640436),\n",
       " (411, 0.057353933467640436),\n",
       " (548, 0.057353933467640436),\n",
       " (714, 0.057353933467640436),\n",
       " (955, 0.057353933467640436),\n",
       " (965, 0.057353933467640436),\n",
       " (1004, 0.057353933467640436),\n",
       " (1183, 0.057353933467640436),\n",
       " (1445, 0.057353933467640436),\n",
       " (2220, 0.057353933467640436),\n",
       " (2795, 0.057353933467640436),\n",
       " (2854, 0.057353933467640436),\n",
       " (3603, 0.057353933467640436),\n",
       " (4140, 0.057353933467640436),\n",
       " (4162, 0.057353933467640436),\n",
       " (4316, 0.057353933467640436),\n",
       " (4691, 0.057353933467640436),\n",
       " (1307, 0.05718696387472674),\n",
       " (2028, 0.05685735326841775),\n",
       " (75, 0.05661385170722978),\n",
       " (280, 0.05661385170722978),\n",
       " (500, 0.05661385170722978),\n",
       " (922, 0.05661385170722978),\n",
       " (1206, 0.05661385170722978),\n",
       " (1382, 0.05661385170722978),\n",
       " ...]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we can sort the above data\n",
    "sorted(list(enumerate(similarity[2])),reverse=True,key=lambda x:x[1])    # if key was not written, sorting would have been done on the basis of index values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "cadd6e4e-b1e6-4373-a963-b032edaab03b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(11, 0.3609974361905776),\n",
       " (29, 0.3307189138830738),\n",
       " (1344, 0.32639560491693337),\n",
       " (4075, 0.2611164839335467),\n",
       " (1745, 0.2390457218668787)]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fetching top 5 movies\n",
    "sorted(list(enumerate(similarity[2])),reverse=True,key=lambda x:x[1])[1:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecc3bca-1662-4c33-b05b-f987c3bf1b6f",
   "metadata": {},
   "source": [
    "## Now to implement all these functionalities discussed above, we will create a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "ea600d73-b792-421c-bd95-c69de8b32f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function which takes movie as an input and returns 5 similar movies\n",
    "def recommend(movie):\n",
    "    movie_index = new_df[new_df['title'] == movie].index[0]\n",
    "    distances = similarity[movie_index]\n",
    "    movie_list = sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])[1:6]\n",
    "\n",
    "    for i in movie_list:\n",
    "        print(new_df.iloc[i[0]].title)     # new_df.iloc[i[0]].title will fetch 5 movies name from the data. i[0] will give the top 5 movies index and corresponding to that index we will fetch the movie name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "055cf81f-6c86-464e-ac6a-607527c8035e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aliens vs Predator: Requiem\n",
      "Aliens\n",
      "Falcon Rising\n",
      "Independence Day\n",
      "Titan A.E.\n"
     ]
    }
   ],
   "source": [
    "recommend('Avatar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "6b1d5397-54da-4546-a26a-70abf89aec47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iron Man 3\n",
      "Avengers: Age of Ultron\n",
      "Captain America: Civil War\n",
      "Captain America: The First Avenger\n",
      "Iron Man\n"
     ]
    }
   ],
   "source": [
    "recommend('The Avengers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "221a4d26-fc86-4277-a35d-529bbadad1f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batman\n",
      "Batman & Robin\n",
      "Batman Begins\n",
      "Batman Returns\n",
      "The R.M.\n"
     ]
    }
   ],
   "source": [
    "recommend('Batman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "450a58ab-c0f4-482e-8748-6c1f1cd8fa22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "86e8e4e2-209b-4e81-8df3-4ce4b33483bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(new_df.to_dict(),open('movies-recommender/movie_dict.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "9440b4af-8567-4701-b872-71e34ffab3a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(similarity,open('movies-recommender/similarity.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1408ff89-ba21-45c0-99e1-d1fbfa659c35",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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