ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries to perform basic operations on dataset\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4803, 20), (4803, 4))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading dataset using pandas\n",
    "df_movies = pd.read_csv(filepath_or_buffer='.\\\\dataset\\\\tmdb_5000_movies.csv')\n",
    "df_credits = pd.read_csv(filepath_or_buffer=\".\\\\dataset\\\\tmdb_5000_credits.csv\")\n",
    "# shape of the datasets\n",
    "df_movies.shape, df_credits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking over common columns(dataset 1) to merge datasets\n",
    "df_movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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>[{\"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                                               cast  \\\n",
       "0     19995  Avatar  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking over common columns(dataset 2) to merge datasets\n",
    "df_credits.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understanding column (genres) \n",
    "df_movies['genres'][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understanding column (cast)\n",
    "df_credits['cast'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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. Rubeo\"}, {\"credit_id\": \"52fe48009251416c750aca11\", \"department\": \"Camera\", \"gender\": 2, \"id\": 18265, \"job\": \"Director of Photography\", \"name\": \"Mauro Fiore\"}, {\"credit_id\": \"5449194d0e0a26748f001b39\", \"department\": \"Art\", \"gender\": 0, \"id\": 42281, \"job\": \"Set Designer\", \"name\": \"Scott Herbertson\"}, {\"credit_id\": \"52fe48009251416c750aca05\", \"department\": \"Crew\", \"gender\": 0, \"id\": 42288, \"job\": \"Stunts\", \"name\": \"Woody Schultz\"}, {\"credit_id\": \"5592aefb92514152de0010f5\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 29067, \"job\": \"Makeup Artist\", \"name\": \"Linda DeVetta\"}, {\"credit_id\": \"5592afa492514152de00112c\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 29067, \"job\": \"Hairstylist\", \"name\": \"Linda DeVetta\"}, {\"credit_id\": \"54959ed592514130fc002e5d\", \"department\": \"Camera\", \"gender\": 2, \"id\": 33302, \"job\": \"Camera Operator\", \"name\": \"Richard Bluck\"}, {\"credit_id\": \"539c4891c3a36810ba002147\", \"department\": \"Art\", \"gender\": 2, \"id\": 33303, \"job\": \"Art Direction\", \"name\": \"Simon Bright\"}, {\"credit_id\": \"54959c069251417a81001f3a\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 113145, \"job\": \"Visual Effects Supervisor\", \"name\": \"Richard Martin\"}, {\"credit_id\": \"54959a0dc3a3680ff5002c8d\", \"department\": \"Crew\", \"gender\": 2, \"id\": 58188, \"job\": \"Visual Effects Editor\", \"name\": \"Steve R. Moore\"}, {\"credit_id\": \"52fe48009251416c750aca1d\", \"department\": \"Editing\", \"gender\": 2, \"id\": 58871, \"job\": \"Editor\", \"name\": \"John Refoua\"}, {\"credit_id\": \"54491a4dc3a3680fc30018ca\", \"department\": \"Art\", \"gender\": 0, \"id\": 92359, \"job\": \"Set Designer\", \"name\": \"Karl J. Martin\"}, {\"credit_id\": \"52fe48009251416c750aca35\", \"department\": \"Camera\", \"gender\": 1, \"id\": 72201, \"job\": \"Director of Photography\", \"name\": \"Chiling Lin\"}, {\"credit_id\": \"52fe48009251416c750ac9ff\", \"department\": \"Crew\", \"gender\": 0, \"id\": 89714, \"job\": \"Stunts\", \"name\": \"Ilram Choi\"}, {\"credit_id\": \"54959c529251416e2b004394\", \"department\": \"Visual Effects\", \"gender\": 2, \"id\": 93214, \"job\": \"Visual Effects Supervisor\", \"name\": \"Steven Quale\"}, {\"credit_id\": \"54491edf0e0a267489001c37\", \"department\": \"Crew\", \"gender\": 1, \"id\": 122607, \"job\": \"Dialect Coach\", \"name\": \"Carla Meyer\"}, {\"credit_id\": \"539c485bc3a368653d001a3a\", \"department\": \"Art\", \"gender\": 2, \"id\": 132585, \"job\": \"Art Direction\", \"name\": \"Nick Bassett\"}, {\"credit_id\": \"539c4903c3a368653d001a74\", \"department\": \"Art\", \"gender\": 0, \"id\": 132596, \"job\": \"Art Direction\", \"name\": \"Jill Cormack\"}, {\"credit_id\": \"539c4967c3a368653d001a94\", \"department\": \"Art\", \"gender\": 0, \"id\": 132604, \"job\": \"Art Direction\", \"name\": \"Andy McLaren\"}, {\"credit_id\": \"52fe48009251416c750aca45\", \"department\": \"Crew\", \"gender\": 0, \"id\": 236696, \"job\": \"Motion Capture Artist\", \"name\": \"Terry Notary\"}, {\"credit_id\": \"54959e02c3a3680fc60027d2\", \"department\": \"Crew\", \"gender\": 2, \"id\": 956198, \"job\": \"Stunt Coordinator\", \"name\": \"Garrett Warren\"}, {\"credit_id\": \"54959ca3c3a3686ae300438c\", \"department\": \"Visual Effects\", \"gender\": 2, \"id\": 957874, \"job\": \"Visual Effects Supervisor\", \"name\": \"Jonathan Rothbart\"}, {\"credit_id\": \"570b6f519251412c74001b2f\", \"department\": \"Art\", \"gender\": 0, \"id\": 957889, \"job\": \"Supervising Art Director\", \"name\": \"Stefan Dechant\"}, {\"credit_id\": \"570b6f62c3a3680b77007460\", \"department\": \"Art\", \"gender\": 2, \"id\": 959555, \"job\": \"Supervising Art Director\", \"name\": \"Todd Cherniawsky\"}, {\"credit_id\": \"539c4a3ac3a36810da0021cc\", \"department\": \"Production\", \"gender\": 0, \"id\": 1016177, \"job\": \"Casting\", \"name\": \"Miranda Rivers\"}, {\"credit_id\": \"539c482cc3a36810c1002062\", \"department\": \"Art\", \"gender\": 0, \"id\": 1032536, \"job\": \"Production Design\", \"name\": \"Robert Stromberg\"}, {\"credit_id\": \"539c4b65c3a36810c9002125\", \"department\": \"Costume & Make-Up\", \"gender\": 2, \"id\": 1071680, \"job\": \"Costume Design\", \"name\": \"John Harding\"}, {\"credit_id\": \"54959e6692514130fc002e4e\", \"department\": \"Camera\", \"gender\": 0, \"id\": 1177364, \"job\": \"Steadicam Operator\", \"name\": \"Roberto De Angelis\"}, {\"credit_id\": \"539c49f1c3a368653d001aac\", \"department\": \"Costume & Make-Up\", \"gender\": 2, \"id\": 1202850, \"job\": \"Makeup Department Head\", \"name\": \"Mike Smithson\"}, {\"credit_id\": \"5495999ec3a3686ae100460c\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1204668, \"job\": \"Visual Effects Producer\", \"name\": \"Alain Lalanne\"}, {\"credit_id\": \"54959cdfc3a3681153002729\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1206410, \"job\": \"Visual Effects Supervisor\", \"name\": \"Lucas Salton\"}, {\"credit_id\": \"549596239251417a81001eae\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1234266, \"job\": \"Post Production Supervisor\", \"name\": \"Janace Tashjian\"}, {\"credit_id\": \"54959c859251416e1e003efe\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1271932, \"job\": \"Visual Effects Supervisor\", \"name\": \"Stephen Rosenbaum\"}, {\"credit_id\": \"5592af28c3a368775a00105f\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1310064, \"job\": \"Makeup Artist\", \"name\": \"Frankie Karena\"}, {\"credit_id\": \"539c4adfc3a36810e300203b\", \"department\": \"Costume & Make-Up\", \"gender\": 1, \"id\": 1319844, \"job\": \"Costume Supervisor\", \"name\": \"Lisa Lovaas\"}, {\"credit_id\": \"54959b579251416e2b004371\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1327028, \"job\": \"Visual Effects Supervisor\", \"name\": \"Jonathan Fawkner\"}, {\"credit_id\": \"539c48a7c3a36810b5001fa7\", \"department\": \"Art\", \"gender\": 0, \"id\": 1330561, \"job\": \"Art Direction\", \"name\": \"Robert Bavin\"}, {\"credit_id\": \"539c4a71c3a36810da0021e0\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1330567, \"job\": \"Costume Supervisor\", \"name\": \"Anthony Almaraz\"}, {\"credit_id\": \"539c4a8ac3a36810ba0021e4\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1330570, \"job\": \"Costume Supervisor\", \"name\": \"Carolyn M. Fenton\"}, {\"credit_id\": \"539c4ab6c3a36810da0021f0\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1330574, \"job\": \"Costume Supervisor\", \"name\": \"Beth Koenigsberg\"}, {\"credit_id\": \"54491ab70e0a267480001ba2\", \"department\": \"Art\", \"gender\": 0, \"id\": 1336191, \"job\": \"Set Designer\", \"name\": \"Sam Page\"}, {\"credit_id\": \"544919d9c3a3680fc30018bd\", \"department\": \"Art\", \"gender\": 0, \"id\": 1339441, \"job\": \"Set Designer\", \"name\": \"Tex Kadonaga\"}, {\"credit_id\": \"54491cf50e0a267483001b0c\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1352422, \"job\": \"Dialogue Editor\", \"name\": \"Kim Foscato\"}, {\"credit_id\": \"544919f40e0a26748c001b09\", \"department\": \"Art\", \"gender\": 0, \"id\": 1352962, \"job\": \"Set Designer\", \"name\": \"Tammy S. Lee\"}, {\"credit_id\": \"5495a115c3a3680ff5002d71\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1357070, \"job\": \"Transportation Coordinator\", \"name\": \"Denny Caira\"}, {\"credit_id\": \"5495a12f92514130fc002e94\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1357071, \"job\": \"Transportation Coordinator\", \"name\": \"James Waitkus\"}, {\"credit_id\": \"5495976fc3a36811530026b0\", \"department\": \"Sound\", \"gender\": 0, \"id\": 1360103, \"job\": \"Supervising Sound Editor\", \"name\": \"Addison Teague\"}, {\"credit_id\": \"54491837c3a3680fb1001c5a\", \"department\": \"Art\", \"gender\": 2, \"id\": 1376887, \"job\": \"Set Designer\", \"name\": \"C. Scott Baker\"}, {\"credit_id\": \"54491878c3a3680fb4001c9d\", \"department\": \"Art\", \"gender\": 0, \"id\": 1376888, \"job\": \"Set Designer\", \"name\": \"Luke Caska\"}, {\"credit_id\": \"544918dac3a3680fa5001ae0\", \"department\": \"Art\", \"gender\": 0, \"id\": 1376889, \"job\": \"Set Designer\", \"name\": \"David Chow\"}, {\"credit_id\": \"544919110e0a267486001b68\", \"department\": \"Art\", \"gender\": 0, \"id\": 1376890, \"job\": \"Set Designer\", \"name\": \"Jonathan Dyer\"}, {\"credit_id\": \"54491967c3a3680faa001b5e\", \"department\": \"Art\", \"gender\": 0, \"id\": 1376891, \"job\": \"Set Designer\", \"name\": \"Joseph Hiura\"}, {\"credit_id\": \"54491997c3a3680fb1001c8a\", \"department\": \"Art\", \"gender\": 0, \"id\": 1376892, \"job\": \"Art Department Coordinator\", \"name\": \"Rebecca Jellie\"}, {\"credit_id\": \"544919ba0e0a26748f001b42\", \"department\": \"Art\", \"gender\": 0, \"id\": 1376893, \"job\": \"Set Designer\", \"name\": \"Robert Andrew Johnson\"}, {\"credit_id\": \"54491b1dc3a3680faa001b8c\", \"department\": \"Art\", \"gender\": 0, \"id\": 1376895, \"job\": \"Assistant Art Director\", \"name\": \"Mike Stassi\"}, {\"credit_id\": \"54491b79c3a3680fbb001826\", \"department\": \"Art\", \"gender\": 0, \"id\": 1376897, \"job\": \"Construction Coordinator\", \"name\": \"John Villarino\"}, {\"credit_id\": \"54491baec3a3680fb4001ce6\", \"department\": \"Art\", \"gender\": 2, \"id\": 1376898, \"job\": \"Assistant Art Director\", \"name\": \"Jeffrey Wisniewski\"}, {\"credit_id\": \"54491d2fc3a3680fb4001d07\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1376899, \"job\": \"Dialogue Editor\", \"name\": \"Cheryl Nardi\"}, {\"credit_id\": \"54491d86c3a3680fa5001b2f\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1376901, \"job\": \"Dialogue Editor\", \"name\": \"Marshall Winn\"}, {\"credit_id\": \"54491d9dc3a3680faa001bb0\", \"department\": \"Sound\", \"gender\": 0, \"id\": 1376902, \"job\": \"Supervising Sound Editor\", \"name\": \"Gwendolyn Yates Whittle\"}, {\"credit_id\": \"54491dc10e0a267486001bce\", \"department\": \"Sound\", \"gender\": 0, \"id\": 1376903, \"job\": \"Sound Re-Recording Mixer\", \"name\": \"William Stein\"}, {\"credit_id\": \"54491f500e0a26747c001c07\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1376909, \"job\": \"Choreographer\", \"name\": \"Lula Washington\"}, {\"credit_id\": \"549599239251412c4e002a2e\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1391692, \"job\": \"Visual Effects Producer\", \"name\": \"Chris Del Conte\"}, {\"credit_id\": \"54959d54c3a36831b8001d9a\", \"department\": \"Visual Effects\", \"gender\": 2, \"id\": 1391695, \"job\": \"Visual Effects Supervisor\", \"name\": \"R. Christopher White\"}, {\"credit_id\": \"54959bdf9251412c4e002a66\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1394070, \"job\": \"Visual Effects Supervisor\", \"name\": \"Dan Lemmon\"}, {\"credit_id\": \"5495971d92514132ed002922\", \"department\": \"Sound\", \"gender\": 0, \"id\": 1394129, \"job\": \"Sound Effects Editor\", \"name\": \"Tim Nielsen\"}, {\"credit_id\": \"5592b25792514152cc0011aa\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1394286, \"job\": \"CG Supervisor\", \"name\": \"Michael Mulholland\"}, {\"credit_id\": \"54959a329251416e2b004355\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1394750, \"job\": \"Visual Effects Editor\", \"name\": \"Thomas Nittmann\"}, {\"credit_id\": \"54959d6dc3a3686ae9004401\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1394755, \"job\": \"Visual Effects Supervisor\", \"name\": \"Edson Williams\"}, {\"credit_id\": \"5495a08fc3a3686ae300441c\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1394953, \"job\": \"Digital Intermediate\", \"name\": \"Christine Carr\"}, {\"credit_id\": \"55402d659251413d6d000249\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1395269, \"job\": \"Visual Effects Supervisor\", \"name\": \"John Bruno\"}, {\"credit_id\": \"54959e7b9251416e1e003f3e\", \"department\": \"Camera\", \"gender\": 0, \"id\": 1398970, \"job\": \"Steadicam Operator\", \"name\": \"David Emmerichs\"}, {\"credit_id\": \"54959734c3a3686ae10045e0\", \"department\": \"Sound\", \"gender\": 0, \"id\": 1400906, \"job\": \"Sound Effects Editor\", \"name\": \"Christopher Scarabosio\"}, {\"credit_id\": \"549595dd92514130fc002d79\", \"department\": \"Production\", \"gender\": 0, \"id\": 1401784, \"job\": \"Production Supervisor\", \"name\": \"Jennifer Teves\"}, {\"credit_id\": \"549596009251413af70028cc\", \"department\": \"Production\", \"gender\": 0, \"id\": 1401785, \"job\": \"Production Manager\", \"name\": \"Brigitte Yorke\"}, {\"credit_id\": \"549596e892514130fc002d99\", \"department\": \"Sound\", \"gender\": 0, \"id\": 1401786, \"job\": \"Sound Effects Editor\", \"name\": \"Ken Fischer\"}, {\"credit_id\": \"549598229251412c4e002a1c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1401787, \"job\": \"Special Effects Coordinator\", \"name\": \"Iain Hutton\"}, {\"credit_id\": \"549598349251416e2b00432b\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1401788, \"job\": \"Special Effects Coordinator\", \"name\": \"Steve Ingram\"}, {\"credit_id\": \"54959905c3a3686ae3004324\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401789, \"job\": \"Visual Effects Producer\", \"name\": \"Joyce Cox\"}, {\"credit_id\": \"5495994b92514132ed002951\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401790, \"job\": \"Visual Effects Producer\", \"name\": \"Jenny Foster\"}, {\"credit_id\": \"549599cbc3a3686ae1004613\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1401791, \"job\": \"Visual Effects Editor\", \"name\": \"Christopher Marino\"}, {\"credit_id\": \"549599f2c3a3686ae100461e\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1401792, \"job\": \"Visual Effects Editor\", \"name\": \"Jim Milton\"}, {\"credit_id\": \"54959a51c3a3686af3003eb5\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401793, \"job\": \"Visual Effects Producer\", \"name\": \"Cyndi Ochs\"}, {\"credit_id\": \"54959a7cc3a36811530026f4\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1401794, \"job\": \"Visual Effects Editor\", \"name\": \"Lucas Putnam\"}, {\"credit_id\": \"54959b91c3a3680ff5002cb4\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401795, \"job\": \"Visual Effects Supervisor\", \"name\": \"Anthony \\'Max\\' Ivins\"}, {\"credit_id\": \"54959bb69251412c4e002a5f\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401796, \"job\": \"Visual Effects Supervisor\", \"name\": \"John Knoll\"}, {\"credit_id\": \"54959cbbc3a3686ae3004391\", \"department\": \"Visual Effects\", \"gender\": 2, \"id\": 1401799, \"job\": \"Visual Effects Supervisor\", \"name\": \"Eric Saindon\"}, {\"credit_id\": \"54959d06c3a3686ae90043f6\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401800, \"job\": \"Visual Effects Supervisor\", \"name\": \"Wayne Stables\"}, {\"credit_id\": \"54959d259251416e1e003f11\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401801, \"job\": \"Visual Effects Supervisor\", \"name\": \"David Stinnett\"}, {\"credit_id\": \"54959db49251413af7002975\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401803, \"job\": \"Visual Effects Supervisor\", \"name\": \"Guy Williams\"}, {\"credit_id\": \"54959de4c3a3681153002750\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1401804, \"job\": \"Stunt Coordinator\", \"name\": \"Stuart Thorp\"}, {\"credit_id\": \"54959ef2c3a3680fc60027f2\", \"department\": \"Lighting\", \"gender\": 0, \"id\": 1401805, \"job\": \"Best Boy Electric\", \"name\": \"Giles Coburn\"}, {\"credit_id\": \"54959f07c3a3680fc60027f9\", \"department\": \"Camera\", \"gender\": 2, \"id\": 1401806, \"job\": \"Still Photographer\", \"name\": \"Mark Fellman\"}, {\"credit_id\": \"54959f47c3a3681153002774\", \"department\": \"Lighting\", \"gender\": 0, \"id\": 1401807, \"job\": \"Lighting Technician\", \"name\": \"Scott Sprague\"}, {\"credit_id\": \"54959f8cc3a36831b8001df2\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401808, \"job\": \"Animation Director\", \"name\": \"Jeremy Hollobon\"}, {\"credit_id\": \"54959fa0c3a36831b8001dfb\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401809, \"job\": \"Animation Director\", \"name\": \"Orlando Meunier\"}, {\"credit_id\": \"54959fb6c3a3686af3003f54\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401810, \"job\": \"Animation Director\", \"name\": \"Taisuke Tanimura\"}, {\"credit_id\": \"54959fd2c3a36831b8001e02\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1401812, \"job\": \"Set Costumer\", \"name\": \"Lilia Mishel Acevedo\"}, {\"credit_id\": \"54959ff9c3a3686ae300440c\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1401814, \"job\": \"Set Costumer\", \"name\": \"Alejandro M. Hernandez\"}, {\"credit_id\": \"5495a0ddc3a3686ae10046fe\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1401815, \"job\": \"Digital Intermediate\", \"name\": \"Marvin Hall\"}, {\"credit_id\": \"5495a1f7c3a3686ae3004443\", \"department\": \"Production\", \"gender\": 0, \"id\": 1401816, \"job\": \"Publicist\", \"name\": \"Judy Alley\"}, {\"credit_id\": \"5592b29fc3a36869d100002f\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1418381, \"job\": \"CG Supervisor\", \"name\": \"Mike Perry\"}, {\"credit_id\": \"5592b23a9251415df8001081\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1426854, \"job\": \"CG Supervisor\", \"name\": \"Andrew Morley\"}, {\"credit_id\": \"55491e1192514104c40002d8\", \"department\": \"Art\", \"gender\": 0, \"id\": 1438901, \"job\": \"Conceptual Design\", \"name\": \"Seth Engstrom\"}, {\"credit_id\": \"5525d5809251417276002b06\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1447362, \"job\": \"Visual Effects Art Director\", \"name\": \"Eric Oliver\"}, {\"credit_id\": \"554427ca925141586500312a\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1447503, \"job\": \"Modeling\", \"name\": \"Matsune Suzuki\"}, {\"credit_id\": \"551906889251415aab001c88\", \"department\": \"Art\", \"gender\": 0, \"id\": 1447524, \"job\": \"Art Department Manager\", \"name\": \"Paul Tobin\"}, {\"credit_id\": \"5592af8492514152cc0010de\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1452643, \"job\": \"Hairstylist\", \"name\": \"Roxane Griffin\"}, {\"credit_id\": \"553d3c109251415852001318\", \"department\": \"Lighting\", \"gender\": 0, \"id\": 1453938, \"job\": \"Lighting Artist\", \"name\": \"Arun Ram-Mohan\"}, {\"credit_id\": \"5592af4692514152d5001355\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1457305, \"job\": \"Makeup Artist\", \"name\": \"Georgia Lockhart-Adams\"}, {\"credit_id\": \"5592b2eac3a36877470012a5\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1466035, \"job\": \"CG Supervisor\", \"name\": \"Thrain Shadbolt\"}, {\"credit_id\": \"5592b032c3a36877450015f1\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483220, \"job\": \"CG Supervisor\", \"name\": \"Brad Alexander\"}, {\"credit_id\": \"5592b05592514152d80012f6\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483221, \"job\": \"CG Supervisor\", \"name\": \"Shadi Almassizadeh\"}, {\"credit_id\": \"5592b090c3a36877570010b5\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483222, \"job\": \"CG Supervisor\", \"name\": \"Simon Clutterbuck\"}, {\"credit_id\": \"5592b0dbc3a368774b00112c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483223, \"job\": \"CG Supervisor\", \"name\": \"Graeme Demmocks\"}, {\"credit_id\": \"5592b0fe92514152db0010c1\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483224, \"job\": \"CG Supervisor\", \"name\": \"Adrian Fernandes\"}, {\"credit_id\": \"5592b11f9251415df8001059\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483225, \"job\": \"CG Supervisor\", \"name\": \"Mitch Gates\"}, {\"credit_id\": \"5592b15dc3a3687745001645\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483226, \"job\": \"CG Supervisor\", \"name\": \"Jerry Kung\"}, {\"credit_id\": \"5592b18e925141645a0004ae\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483227, \"job\": \"CG Supervisor\", \"name\": \"Andy Lomas\"}, {\"credit_id\": \"5592b1bfc3a368775d0010e7\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483228, \"job\": \"CG Supervisor\", \"name\": \"Sebastian Marino\"}, {\"credit_id\": \"5592b2049251415df8001078\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483229, \"job\": \"CG Supervisor\", \"name\": \"Matthias Menz\"}, {\"credit_id\": \"5592b27b92514152d800136a\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483230, \"job\": \"CG Supervisor\", \"name\": \"Sergei Nevshupov\"}, {\"credit_id\": \"5592b2c3c3a36869e800003c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483231, \"job\": \"CG Supervisor\", \"name\": \"Philippe Rebours\"}, {\"credit_id\": \"5592b317c3a36877470012af\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483232, \"job\": \"CG Supervisor\", \"name\": \"Michael Takarangi\"}, {\"credit_id\": \"5592b345c3a36877470012bb\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483233, \"job\": \"CG Supervisor\", \"name\": \"David Weitzberg\"}, {\"credit_id\": \"5592b37cc3a368775100113b\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483234, \"job\": \"CG Supervisor\", \"name\": \"Ben White\"}, {\"credit_id\": \"573c8e2f9251413f5d000094\", \"department\": \"Crew\", \"gender\": 1, \"id\": 1621932, \"job\": \"Stunts\", \"name\": \"Min Windle\"}]'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understanding column (crew)\n",
    "df_credits['crew'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4809, 23)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging datasets on \"title\" column\n",
    "df = df_movies.merge(right=df_credits, on='title')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</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",
       "      <th>movie_id</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4763</th>\n",
       "      <td>0</td>\n",
       "      <td>[{\"id\": 99, \"name\": \"Documentary\"}]</td>\n",
       "      <td>http://www.bendingsteelmovie.com/</td>\n",
       "      <td>174362</td>\n",
       "      <td>[]</td>\n",
       "      <td>en</td>\n",
       "      <td>Bending Steel</td>\n",
       "      <td>The Cyclone, The Freakshow, The Mermaid Parade...</td>\n",
       "      <td>0.048726</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Limiting Factor Is The Mind</td>\n",
       "      <td>Bending Steel</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>174362</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{\"credit_id\": \"52fe4d49c3a36847f8258e35\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                               genres  \\\n",
       "4763       0  [{\"id\": 99, \"name\": \"Documentary\"}]   \n",
       "\n",
       "                               homepage      id keywords original_language  \\\n",
       "4763  http://www.bendingsteelmovie.com/  174362       []                en   \n",
       "\n",
       "     original_title                                           overview  \\\n",
       "4763  Bending Steel  The Cyclone, The Freakshow, The Mermaid Parade...   \n",
       "\n",
       "      popularity production_companies  ... runtime  \\\n",
       "4763    0.048726                   []  ...    93.0   \n",
       "\n",
       "                              spoken_languages    status  \\\n",
       "4763  [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "\n",
       "                              tagline          title vote_average vote_count  \\\n",
       "4763  The Limiting Factor Is The Mind  Bending Steel          5.0          1   \n",
       "\n",
       "     movie_id  cast                                               crew  \n",
       "4763   174362    []  [{\"credit_id\": \"52fe4d49c3a36847f8258e35\", \"de...  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking\n",
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4809 entries, 0 to 4808\n",
      "Data columns (total 23 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   budget                4809 non-null   int64  \n",
      " 1   genres                4809 non-null   object \n",
      " 2   homepage              1713 non-null   object \n",
      " 3   id                    4809 non-null   int64  \n",
      " 4   keywords              4809 non-null   object \n",
      " 5   original_language     4809 non-null   object \n",
      " 6   original_title        4809 non-null   object \n",
      " 7   overview              4806 non-null   object \n",
      " 8   popularity            4809 non-null   float64\n",
      " 9   production_companies  4809 non-null   object \n",
      " 10  production_countries  4809 non-null   object \n",
      " 11  release_date          4808 non-null   object \n",
      " 12  revenue               4809 non-null   int64  \n",
      " 13  runtime               4807 non-null   float64\n",
      " 14  spoken_languages      4809 non-null   object \n",
      " 15  status                4809 non-null   object \n",
      " 16  tagline               3965 non-null   object \n",
      " 17  title                 4809 non-null   object \n",
      " 18  vote_average          4809 non-null   float64\n",
      " 19  vote_count            4809 non-null   int64  \n",
      " 20  movie_id              4809 non-null   int64  \n",
      " 21  cast                  4809 non-null   object \n",
      " 22  crew                  4809 non-null   object \n",
      "dtypes: float64(3), int64(5), object(15)\n",
      "memory usage: 864.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# looking for more details\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>overview</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</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>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>Following the death of District Attorney Harve...</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>El Mariachi just wants to play his guitar and ...</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>[{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 10749, \"...</td>\n",
       "      <td>A newlywed couple's honeymoon is upended by th...</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>[{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 18, \"nam...</td>\n",
       "      <td>\"Signed, Sealed, Delivered\" introduces a dedic...</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>[]</td>\n",
       "      <td>When ambitious New York attorney Sam is sent t...</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>[{\"id\": 99, \"name\": \"Documentary\"}]</td>\n",
       "      <td>Ever since the second grade when he first saw ...</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": [
       "          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",
       "                                                 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",
       "                                               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",
       "                                               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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting only needed columns (-> dimensionality reduction )\n",
    "df = df.loc[:,[ 'id', 'title', 'genres', 'overview','keywords', 'cast', 'crew']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id          0\n",
       "title       0\n",
       "genres      0\n",
       "overview    3\n",
       "keywords    0\n",
       "cast        0\n",
       "crew        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values on each columns\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping null values records\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id          0\n",
       "title       0\n",
       "genres      0\n",
       "overview    0\n",
       "keywords    0\n",
       "cast        0\n",
       "crew        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking ..\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking for duplicate records\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature engineering \n",
    "\"\"\"genre is an important column which can help my model to\n",
    "recommend movie's based on user genre interest\"\"\"\n",
    "df.loc[0]['genres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing ast library to deal with string object\n",
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking\\demonstration\n",
    "x = '[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'\n",
    "ast.literal_eval(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['action', 'adventure', 'fantasy', 'sciencefiction']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to convert string to original object/structure \n",
    "# returns the list of genre's\n",
    "def extract_important(obj):\n",
    "    l = []\n",
    "    for i in ast.literal_eval(obj):\n",
    "        l.append(i['name'].lower().replace(\" \", ''))\n",
    "    return l[:]\n",
    "\n",
    "# testing function\n",
    "extract_important(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying function on genre column\n",
    "df['genres'] = df['genres'].apply(func=extract_important)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying function on keywords column\n",
    "\"\"\"The keyword column is also helps my machine learning model\n",
    "to recommend movies based on matching keywords.\"\"\"\n",
    "df['keywords'] = df['keywords'].apply(func=extract_important)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['samworthington', 'zoesaldana', 'sigourneyweaver']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This function first convert a string to its original structure then return top 3 strings inside list.\n",
    "\"\"\"Mainly this function is designed to extract top 3 casts\n",
    "from the cast column so, the machine learning model can recommend \n",
    "movies based on the cast's of the movie\"\"\"\n",
    "def extract_important_top3(obj):\n",
    "    l = []\n",
    "    for i in ast.literal_eval(obj):\n",
    "        if len(l)<3:\n",
    "            l.append(i['name'].lower().replace(\" \", ''))\n",
    "    return l[:]\n",
    "\n",
    "# testing function\n",
    "extract_important_top3(df['cast'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying function on cast column\n",
    "df['cast'] = df['cast'].apply(func=extract_important_top3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['jamescameron']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This function will returns directors name inside a list.\n",
    "\"\"\"The crew column can also helps my machine learning model \n",
    "to recommend movies based on the name of directors.\"\"\"\n",
    "def extract_director(obj):\n",
    "    l = []\n",
    "    for i in ast.literal_eval(obj):\n",
    "        if i['job'] == 'Director':\n",
    "            l.append(i['name'].lower().replace(\" \", ''))\n",
    "    return l\n",
    "\n",
    "# testing function\n",
    "extract_director(df['crew'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying function on crew column\n",
    "df['crew'] = df['crew'].apply(func=extract_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying function on overview column\n",
    "df['overview'] = df['overview'].apply(func=lambda x : x.lower().split(sep=' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenating ['genre', 'overview', 'keywords', 'cast', 'crew'] columns\n",
    "# into a single column called 'tags'.\n",
    "\"\"\"concatenating columns to create a new column called 'tags' will\n",
    "help my machine learning model to recommend movies by searching similar\n",
    "movies by only searching similarities in 'tags' column this is very helpful. \"\"\"\n",
    "df['tags'] = df['genres']+df['overview']+df['keywords']+df['cast']+df['crew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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>[action, adventure, fantasy, sciencefiction, i...</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>[adventure, fantasy, action, captain, barbossa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[action, adventure, crime, a, cryptic, message...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[action, crime, drama, thriller, following, th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[action, adventure, sciencefiction, john, cart...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>[action, crime, thriller, el, mariachi, just, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>72766</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>[comedy, romance, a, newlywed, couple's, honey...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>231617</td>\n",
       "      <td>Signed, Sealed, Delivered</td>\n",
       "      <td>[comedy, drama, romance, tvmovie, \"signed,, se...</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...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4808</th>\n",
       "      <td>25975</td>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>[documentary, ever, since, the, second, grade,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4806 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          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",
       "                                                   tags  \n",
       "0     [action, adventure, fantasy, sciencefiction, i...  \n",
       "1     [adventure, fantasy, action, captain, barbossa...  \n",
       "2     [action, adventure, crime, a, cryptic, message...  \n",
       "3     [action, crime, drama, thriller, following, th...  \n",
       "4     [action, adventure, sciencefiction, john, cart...  \n",
       "...                                                 ...  \n",
       "4804  [action, crime, thriller, el, mariachi, just, ...  \n",
       "4805  [comedy, romance, a, newlywed, couple's, honey...  \n",
       "4806  [comedy, drama, romance, tvmovie, \"signed,, se...  \n",
       "4807  [when, ambitious, new, york, attorney, sam, is...  \n",
       "4808  [documentary, ever, since, the, second, grade,...  \n",
       "\n",
       "[4806 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking ..\n",
    "df = df.loc[:,['id','title','tags']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['tags'] = df['tags'].apply(lambda x: (\" \".join(x)).lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing PorterStremmer from nltk library for Steming \n",
    "from nltk.stem.porter import PorterStemmer\n",
    "# PorterStremmer Object \n",
    "stemer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function will return stemmed words\n",
    "def stem_data(obj):\n",
    "    l = []\n",
    "    for i in obj:\n",
    "        l.append(stemer.stem(i))\n",
    "    # joining stemmed words for ML model\n",
    "    return \" \".join(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying function on tags column\n",
    "df['tags'] = df['tags'].apply(func= stem_data )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"adventur fantasi action captain barbossa, long believ to be dead, ha come back to life and is head to the edg of the earth with will turner and elizabeth swann. but noth is quit as it seems. ocean drugabus exoticisland eastindiatradingcompani loveofone'slif traitor shipwreck strongwoman ship allianc calypso afterlif fighter pirat swashbuckl aftercreditssting johnnydepp orlandobloom keiraknightley goreverbinski\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking ...\n",
    "df['tags'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing CountVectorizer from Sklearn library \n",
    "\"\"\"Count Vectorizer is a popular technique in Natural Language Processing (NLP)\n",
    "used to convert text data into numerical features that can be processed by machine learning algorithms.\"\"\" \n",
    "# In simple words Converting text to vectors to plot in multidimensional space and find patterns.\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CountVectorizer object\n",
    "cv = CountVectorizer(max_features=10000, stop_words= 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting into vectors\n",
    "vectors = cv.fit_transform(df['tags']).toarray()\n",
    "vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing cosine_similarity from sklearn package to\n",
    "# calculate cosine similarity between two vectors\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.07194017, 0.07396705, ..., 0.03933686, 0.        ,\n",
       "        0.        ],\n",
       "       [0.07194017, 1.        , 0.055577  , ..., 0.02216755, 0.        ,\n",
       "        0.02423931],\n",
       "       [0.07396705, 0.055577  , 1.        , ..., 0.02279212, 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.03933686, 0.02216755, 0.02279212, ..., 1.        , 0.03739788,\n",
       "        0.03976214],\n",
       "       [0.        , 0.        , 0.        , ..., 0.03739788, 1.        ,\n",
       "        0.08178608],\n",
       "       [0.        , 0.02423931, 0.        , ..., 0.03976214, 0.08178608,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute cosine similarities between \"tags\".\n",
    "similarities = cosine_similarity(vectors)\n",
    "similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_values(['The Iceman', 'Riddick', 'Split Second'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys([68812, 87421, 13006])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction model function\n",
    "def recommendation_system(movie:str):\n",
    "    movie_index = df[df['title']==movie].index[0]\n",
    "    movie_similarities = similarities[movie_index]\n",
    "    top_3 = sorted(enumerate(iterable=movie_similarities), reverse=True, key=lambda x:x[1])[1:4]\n",
    "    recommend_movies = dict()\n",
    "    for i in top_3:\n",
    "        recommend_movies[df.loc[i[0],'id']] = df.loc[i[0],'title']\n",
    "    print(recommend_movies.values())\n",
    "    return recommend_movies.keys()\n",
    "\n",
    "# TESTING ......\n",
    "recommendation_system('John Carter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing pickle library to save the model in the form of binary file\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dumping similarities \n",
    "pickle.dump(similarities, open('similarities.pkl', \"wb\"))\n",
    "pickle.dump(df, open(\"data.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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>action adventur fantasi sciencefict in the 22n...</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>adventur fantasi action captain barbossa, long...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>action adventur crime a cryptic messag from bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>action crime drama thriller follow the death o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>action adventur sciencefict john carter is a w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>action crime thriller el mariachi just want to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>72766</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>comedi romanc a newlyw couple' honeymoon is up...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>231617</td>\n",
       "      <td>Signed, Sealed, Delivered</td>\n",
       "      <td>comedi drama romanc tvmovi \"signed, sealed, de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4807</th>\n",
       "      <td>126186</td>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>when ambiti new york attorney sam is sent to s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4808</th>\n",
       "      <td>25975</td>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>documentari ever sinc the second grade when he...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4806 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          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",
       "                                                   tags  \n",
       "0     action adventur fantasi sciencefict in the 22n...  \n",
       "1     adventur fantasi action captain barbossa, long...  \n",
       "2     action adventur crime a cryptic messag from bo...  \n",
       "3     action crime drama thriller follow the death o...  \n",
       "4     action adventur sciencefict john carter is a w...  \n",
       "...                                                 ...  \n",
       "4804  action crime thriller el mariachi just want to...  \n",
       "4805  comedi romanc a newlyw couple' honeymoon is up...  \n",
       "4806  comedi drama romanc tvmovi \"signed, sealed, de...  \n",
       "4807  when ambiti new york attorney sam is sent to s...  \n",
       "4808  documentari ever sinc the second grade when he...  \n",
       "\n",
       "[4806 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Iceman'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 'the iceman'\n",
    "x.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Avatar'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>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>action adventur fantasi sciencefict in the 22n...</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>adventur fantasi action captain barbossa, long...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>action adventur crime a cryptic messag from bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>action crime drama thriller follow the death o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>action adventur sciencefict john carter is a w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>action crime thriller el mariachi just want to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>72766</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>comedi romanc a newlyw couple' honeymoon is up...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>231617</td>\n",
       "      <td>Signed, Sealed, Delivered</td>\n",
       "      <td>comedi drama romanc tvmovi \"signed, sealed, de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4807</th>\n",
       "      <td>126186</td>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>when ambiti new york attorney sam is sent to s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4808</th>\n",
       "      <td>25975</td>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>documentari ever sinc the second grade when he...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4806 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          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",
       "                                                   tags  \n",
       "0     action adventur fantasi sciencefict in the 22n...  \n",
       "1     adventur fantasi action captain barbossa, long...  \n",
       "2     action adventur crime a cryptic messag from bo...  \n",
       "3     action crime drama thriller follow the death o...  \n",
       "4     action adventur sciencefict john carter is a w...  \n",
       "...                                                 ...  \n",
       "4804  action crime thriller el mariachi just want to...  \n",
       "4805  comedi romanc a newlyw couple' honeymoon is up...  \n",
       "4806  comedi drama romanc tvmovi \"signed, sealed, de...  \n",
       "4807  when ambiti new york attorney sam is sent to s...  \n",
       "4808  documentari ever sinc the second grade when he...  \n",
       "\n",
       "[4806 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  }
 ],
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