imdb-test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  1  142M    1 2635k    0     0  1678k      0  0:01:26  0:00:01  0:01:25 1678k\n",
      "  3  142M    3 5697k    0     0  2216k      0  0:01:05  0:00:02  0:01:03 2216k\n",
      "  5  142M    5 8720k    0     0  2444k      0  0:00:59  0:00:03  0:00:56 2444k\n",
      "  7  142M    7 10.9M    0     0  2460k      0  0:00:59  0:00:04  0:00:55 2460k\n",
      "  9  142M    9 13.2M    0     0  2435k      0  0:00:59  0:00:05  0:00:54 2793k\n",
      " 11  142M   11 16.1M    0     0  2526k      0  0:00:57  0:00:06  0:00:51 2791k\n",
      " 13  142M   13 19.2M    0     0  2596k      0  0:00:56  0:00:07  0:00:49 2791k\n",
      " 15  142M   15 22.1M    0     0  2646k      0  0:00:55  0:00:08  0:00:47 2790k\n",
      " 17  142M   17 25.1M    0     0  2686k      0  0:00:54  0:00:09  0:00:45 2893k\n",
      " 19  142M   19 28.0M    0     0  2717k      0  0:00:53  0:00:10  0:00:43 3029k\n",
      " 21  142M   21 31.0M    0     0  2748k      0  0:00:52  0:00:11  0:00:41 3041k\n",
      " 23  142M   23 33.7M    0     0  2751k      0  0:00:52  0:00:12  0:00:40 2987k\n",
      " 25  142M   25 36.7M    0     0  2773k      0  0:00:52  0:00:13  0:00:39 2990k\n",
      " 27  142M   27 39.7M    0     0  2791k      0  0:00:52  0:00:14  0:00:38 2992k\n",
      " 30  142M   30 42.6M    0     0  2807k      0  0:00:51  0:00:15  0:00:36 2998k\n",
      " 32  142M   32 45.6M    0     0  2822k      0  0:00:51  0:00:16  0:00:35 2991k\n",
      " 34  142M   34 48.4M    0     0  2823k      0  0:00:51  0:00:17  0:00:34 3005k\n",
      " 36  142M   36 51.6M    0     0  2848k      0  0:00:51  0:00:18  0:00:33 3053k\n",
      " 38  142M   38 54.1M    0     0  2833k      0  0:00:51  0:00:19  0:00:32 2956k\n",
      " 39  142M   39 56.1M    0     0  2798k      0  0:00:52  0:00:20  0:00:32 2769k\n",
      " 40  142M   40 58.1M    0     0  2757k      0  0:00:52  0:00:21  0:00:31 2542k\n",
      " 42  142M   42 60.7M    0     0  2755k      0  0:00:52  0:00:22  0:00:30 2514k\n",
      " 44  142M   44 63.7M    0     0  2768k      0  0:00:52  0:00:23  0:00:29 2468k\n",
      " 46  142M   46 66.6M    0     0  2779k      0  0:00:52  0:00:24  0:00:28 2566k\n",
      " 48  142M   48 69.6M    0     0  2789k      0  0:00:52  0:00:25  0:00:27 2754k\n",
      " 50  142M   50 72.3M    0     0  2787k      0  0:00:52  0:00:26  0:00:26 2916k\n",
      " 52  142M   52 75.2M    0     0  2796k      0  0:00:52  0:00:27  0:00:25 2979k\n",
      " 54  142M   54 77.5M    0     0  2779k      0  0:00:52  0:00:28  0:00:24 2832k\n",
      " 56  142M   56 80.5M    0     0  2790k      0  0:00:52  0:00:29  0:00:23 2842k\n",
      " 58  142M   58 83.5M    0     0  2798k      0  0:00:52  0:00:30  0:00:22 2843k\n",
      " 60  142M   60 86.4M    0     0  2804k      0  0:00:51  0:00:31  0:00:20 2895k\n",
      " 62  142M   62 89.1M    0     0  2804k      0  0:00:51  0:00:32  0:00:19 2847k\n",
      " 65  142M   65 92.4M    0     0  2821k      0  0:00:51  0:00:33  0:00:18 3058k\n",
      " 67  142M   67 95.3M    0     0  2824k      0  0:00:51  0:00:34  0:00:17 3026k\n",
      " 68  142M   68 97.5M    0     0  2808k      0  0:00:51  0:00:35  0:00:16 2868k\n",
      " 70  142M   70  100M    0     0  2813k      0  0:00:51  0:00:36  0:00:15 2872k\n",
      " 72  142M   72  103M    0     0  2819k      0  0:00:51  0:00:37  0:00:14 2921k\n",
      " 74  142M   74  106M    0     0  2825k      0  0:00:51  0:00:38  0:00:13 2855k\n",
      " 76  142M   76  109M    0     0  2831k      0  0:00:51  0:00:39  0:00:12 2879k\n",
      " 79  142M   79  112M    0     0  2830k      0  0:00:51  0:00:40  0:00:11 2987k\n",
      " 81  142M   81  115M    0     0  2839k      0  0:00:51  0:00:41  0:00:10 3030k\n",
      " 83  142M   83  118M    0     0  2843k      0  0:00:51  0:00:42  0:00:09 3020k\n",
      " 85  142M   85  121M    0     0  2844k      0  0:00:51  0:00:43  0:00:08 2994k\n",
      " 87  142M   87  123M    0     0  2848k      0  0:00:51  0:00:44  0:00:07 2982k\n",
      " 88  142M   88  126M    0     0  2834k      0  0:00:51  0:00:45  0:00:06 2867k\n",
      " 90  142M   90  128M    0     0  2833k      0  0:00:51  0:00:46  0:00:05 2780k\n",
      " 92  142M   92  131M    0     0  2828k      0  0:00:51  0:00:47  0:00:04 2706k\n",
      " 94  142M   94  133M    0     0  2822k      0  0:00:51  0:00:48  0:00:03 2623k\n",
      " 96  142M   96  137M    0     0  2830k      0  0:00:51  0:00:49  0:00:02 2676k\n",
      " 98  142M   98  139M    0     0  2831k      0  0:00:51  0:00:50  0:00:01 2803k\n",
      "100  142M  100  142M    0     0  2838k      0  0:00:51  0:00:51 --:--:-- 2891k\n"
     ]
    }
   ],
   "source": [
    "!curl -O https://datasets.imdbws.com/title.basics.tsv.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  0 55.3M    0  492k    0     0  1276k      0  0:00:44 --:--:--  0:00:44 1276k\n",
      "  5 55.3M    5 3376k    0     0  2336k      0  0:00:24  0:00:01  0:00:23 2336k\n",
      " 11 55.3M   11 6429k    0     0  2705k      0  0:00:20  0:00:02  0:00:18 2705k\n",
      " 16 55.3M   16 9448k    0     0  2797k      0  0:00:20  0:00:03  0:00:17 2797k\n",
      " 21 55.3M   21 12.1M    0     0  2831k      0  0:00:20  0:00:04  0:00:16 2831k\n",
      " 27 55.3M   27 15.1M    0     0  2893k      0  0:00:19  0:00:05  0:00:14 3018k\n",
      " 31 55.3M   31 17.6M    0     0  2828k      0  0:00:20  0:00:06  0:00:14 2972k\n",
      " 37 55.3M   37 20.6M    0     0  2860k      0  0:00:19  0:00:07  0:00:12 2935k\n",
      " 42 55.3M   42 23.7M    0     0  2909k      0  0:00:19  0:00:08  0:00:11 2984k\n",
      " 48 55.3M   48 26.7M    0     0  2919k      0  0:00:19  0:00:09  0:00:10 2996k\n",
      " 53 55.3M   53 29.7M    0     0  2934k      0  0:00:19  0:00:10  0:00:09 2978k\n",
      " 58 55.3M   58 32.2M    0     0  2901k      0  0:00:19  0:00:11  0:00:08 2994k\n",
      " 63 55.3M   63 35.3M    0     0  2922k      0  0:00:19  0:00:12  0:00:07 3014k\n",
      " 69 55.3M   69 38.2M    0     0  2929k      0  0:00:19  0:00:13  0:00:06 2964k\n",
      " 74 55.3M   74 41.0M    0     0  2921k      0  0:00:19  0:00:14  0:00:05 2927k\n",
      " 79 55.3M   79 43.9M    0     0  2922k      0  0:00:19  0:00:15  0:00:04 2898k\n",
      " 84 55.3M   84 46.5M    0     0  2901k      0  0:00:19  0:00:16  0:00:03 2901k\n",
      " 89 55.3M   89 49.5M    0     0  2919k      0  0:00:19  0:00:17  0:00:02 2911k\n",
      " 94 55.3M   94 52.5M    0     0  2926k      0  0:00:19  0:00:18  0:00:01 2917k\n",
      "100 55.3M  100 55.3M    0     0  2931k      0  0:00:19  0:00:19 --:--:-- 2961k\n"
     ]
    }
   ],
   "source": [
    "!curl -O https://datasets.imdbws.com/title.crew.tsv.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  9 5813k    9  527k    0     0   571k      0  0:00:10 --:--:--  0:00:10  571k\n",
      " 43 5813k   43 2553k    0     0  1758k      0  0:00:03  0:00:01  0:00:02 1757k\n",
      " 96 5813k   96 5628k    0     0  2287k      0  0:00:02  0:00:02 --:--:-- 2287k\n",
      "100 5813k  100 5813k    0     0  2296k      0  0:00:02  0:00:02 --:--:-- 2295k\n"
     ]
    }
   ],
   "source": [
    "!curl -O https://datasets.imdbws.com/title.ratings.tsv.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usrer\\Anaconda3\\envs\\tf_gpu_2\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3063: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "basics = pd.read_csv(\"title.basics.tsv.gz\" ,compression='gzip', sep='\\t', dtype={\"genres\": str}, na_values = '\\\\N' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "crew = pd.read_csv(\"title.crew.tsv.gz\",compression='gzip', sep='\\t', dtype={\"directors\": str,\"writers\": str}, na_values = '\\\\N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = pd.read_csv(\"title.ratings.tsv.gz\",compression='gzip', sep='\\t', na_values = '\\\\N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = basics.join(crew.set_index(\"tconst\"), on=\"tconst\", how=\"inner\")\n",
    "df = df.join(ratings.set_index(\"tconst\"), on=\"tconst\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1193466 entries, 0 to 8514999\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count    Dtype  \n",
      "---  ------          --------------    -----  \n",
      " 0   tconst          1193466 non-null  object \n",
      " 1   titleType       1193466 non-null  object \n",
      " 2   primaryTitle    1193466 non-null  object \n",
      " 3   originalTitle   1193466 non-null  object \n",
      " 4   isAdult         1193466 non-null  float64\n",
      " 5   startYear       1193264 non-null  float64\n",
      " 6   endYear         35350 non-null    float64\n",
      " 7   runtimeMinutes  862288 non-null   object \n",
      " 8   genres          1173527 non-null  object \n",
      " 9   directors       1008491 non-null  object \n",
      " 10  writers         926480 non-null   object \n",
      " 11  averageRating   1193466 non-null  float64\n",
      " 12  numVotes        1193466 non-null  int64  \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 127.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['genres'] = df['genres'].fillna('None')\n",
    "#df['directors'] = df['directors'].fillna('None')\n",
    "#df['writers'] = df['writers'].fillna('None')\n",
    "df = df.dropna(subset=['primaryTitle', 'startYear', 'genres','directors','writers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 856006 entries, 8 to 8514959\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          856006 non-null  object \n",
      " 1   titleType       856006 non-null  object \n",
      " 2   primaryTitle    856006 non-null  object \n",
      " 3   originalTitle   856006 non-null  object \n",
      " 4   isAdult         856006 non-null  float64\n",
      " 5   startYear       856006 non-null  float64\n",
      " 6   endYear         26992 non-null   float64\n",
      " 7   runtimeMinutes  670587 non-null  object \n",
      " 8   genres          856006 non-null  object \n",
      " 9   directors       856006 non-null  object \n",
      " 10  writers         856006 non-null  object \n",
      " 11  averageRating   856006 non-null  float64\n",
      " 12  numVotes        856006 non-null  int64  \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 91.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    " df = df[df['numVotes'] >= 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 259126 entries, 8 to 8514812\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          259126 non-null  object \n",
      " 1   titleType       259126 non-null  object \n",
      " 2   primaryTitle    259126 non-null  object \n",
      " 3   originalTitle   259126 non-null  object \n",
      " 4   isAdult         259126 non-null  float64\n",
      " 5   startYear       259126 non-null  float64\n",
      " 6   endYear         13238 non-null   float64\n",
      " 7   runtimeMinutes  242612 non-null  object \n",
      " 8   genres          259126 non-null  object \n",
      " 9   directors       259126 non-null  object \n",
      " 10  writers         259126 non-null  object \n",
      " 11  averageRating   259126 non-null  float64\n",
      " 12  numVotes        259126 non-null  int64  \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 27.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['titleType'] != 'tvEpisode']\n",
    "df = df[df['titleType'] != 'videoGame']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 154086 entries, 8 to 8514812\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          154086 non-null  object \n",
      " 1   titleType       154086 non-null  object \n",
      " 2   primaryTitle    154086 non-null  object \n",
      " 3   originalTitle   154086 non-null  object \n",
      " 4   isAdult         154086 non-null  float64\n",
      " 5   startYear       154086 non-null  float64\n",
      " 6   endYear         13238 non-null   float64\n",
      " 7   runtimeMinutes  146688 non-null  object \n",
      " 8   genres          154086 non-null  object \n",
      " 9   directors       154086 non-null  object \n",
      " 10  writers         154086 non-null  object \n",
      " 11  averageRating   154086 non-null  float64\n",
      " 12  numVotes        154086 non-null  int64  \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 16.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 154086 entries, 0 to 154085\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          154086 non-null  object \n",
      " 1   titleType       154086 non-null  object \n",
      " 2   primaryTitle    154086 non-null  object \n",
      " 3   originalTitle   154086 non-null  object \n",
      " 4   isAdult         154086 non-null  float64\n",
      " 5   startYear       154086 non-null  float64\n",
      " 6   endYear         13238 non-null   float64\n",
      " 7   runtimeMinutes  146688 non-null  object \n",
      " 8   genres          154086 non-null  object \n",
      " 9   directors       154086 non-null  object \n",
      " 10  writers         154086 non-null  object \n",
      " 11  averageRating   154086 non-null  float64\n",
      " 12  numVotes        154086 non-null  int64  \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 15.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "column_names_to_normalize = ['startYear']\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(df[column_names_to_normalize].values)\n",
    "df_temp = pd.DataFrame(x_scaled, columns=column_names_to_normalize, index = df.index)\n",
    "df[['startYear_norm']] = df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 154086 entries, 0 to 154085\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          154086 non-null  object \n",
      " 1   titleType       154086 non-null  object \n",
      " 2   primaryTitle    154086 non-null  object \n",
      " 3   originalTitle   154086 non-null  object \n",
      " 4   isAdult         154086 non-null  float64\n",
      " 5   startYear       154086 non-null  float64\n",
      " 6   endYear         13238 non-null   float64\n",
      " 7   runtimeMinutes  146688 non-null  object \n",
      " 8   genres          154086 non-null  object \n",
      " 9   directors       154086 non-null  object \n",
      " 10  writers         154086 non-null  object \n",
      " 11  averageRating   154086 non-null  float64\n",
      " 12  numVotes        154086 non-null  int64  \n",
      " 13  startYear_norm  154086 non-null  float64\n",
      "dtypes: float64(5), int64(1), object(8)\n",
      "memory usage: 16.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('imdb_test_df_data.pickle', 'wb') as f:\n",
    "    pickle.dump([df], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "\n",
    "with open('imdb_test_df_data.pickle', 'rb') as f:\n",
    "    [df] = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 154086 entries, 0 to 154085\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          154086 non-null  object \n",
      " 1   titleType       154086 non-null  object \n",
      " 2   primaryTitle    154086 non-null  object \n",
      " 3   originalTitle   154086 non-null  object \n",
      " 4   isAdult         154086 non-null  float64\n",
      " 5   startYear       154086 non-null  float64\n",
      " 6   endYear         13238 non-null   float64\n",
      " 7   runtimeMinutes  146688 non-null  object \n",
      " 8   genres          154086 non-null  object \n",
      " 9   directors       154086 non-null  object \n",
      " 10  writers         154086 non-null  object \n",
      " 11  averageRating   154086 non-null  float64\n",
      " 12  numVotes        154086 non-null  int64  \n",
      " 13  startYear_norm  154086 non-null  float64\n",
      "dtypes: float64(5), int64(1), object(8)\n",
      "memory usage: 16.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "primaryTitle_col = df['primaryTitle']\n",
    "genres_col = df['genres']\n",
    "directors_col = df['directors']\n",
    "writers_col = df['writers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
       " PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),\n",
       " PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'),\n",
       " PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_physical_devices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.eager.context._EagerDeviceContext at 0x20db8860cc8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.device(\"gpu:0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_primaryTitle_length = 40\n",
    "max_genres_length = 3\n",
    "max_directors_length = 50\n",
    "max_writers_length = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "import re\n",
    "\n",
    "def custom_normalize(input_string):\n",
    "    lowercase = tf.strings.lower(input_string)\n",
    "    return tf.strings.regex_replace(lowercase, \"[%s]\" % re.escape(string.punctuation), \"\")\n",
    "\n",
    "def custom_standardization(input_data):\n",
    "    return tf.strings.regex_replace(input_data, \"[%s]\" % re.escape(string.punctuation), \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_addons as tfa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model():\n",
    "\t# creating book embedding path\n",
    "\tprimaryTitle_input = tf.keras.Input(shape=[1], name=\"primaryTitle-Input\", dtype=tf.string)\n",
    "\n",
    "\t# Create the layer.\n",
    "\tprimaryTitle_vectorize_layer = tf.keras.layers.experimental.preprocessing.TextVectorization(\n",
    "\t standardize=custom_normalize,\n",
    "\t output_mode='int',\n",
    "\t output_sequence_length=max_primaryTitle_length)\n",
    "\n",
    "\tprimaryTitle_vectorize_layer.adapt(primaryTitle_col.to_numpy())\n",
    "\n",
    "\tn_primaryTitle = len(primaryTitle_vectorize_layer.get_vocabulary())\n",
    "\n",
    "\tprimaryTitle_embedding = tf.keras.layers.Embedding(n_primaryTitle, 20, name=\"primaryTitle-Embedding\")(\n",
    "\t\tprimaryTitle_vectorize_layer(primaryTitle_input)\n",
    "\t)\n",
    "\tprimaryTitle_vec = tf.keras.layers.Flatten(name=\"Flatten-primaryTitle\")(primaryTitle_embedding)\n",
    "\n",
    "\n",
    "\n",
    "\t# creating book embedding path\n",
    "\tgenres_input = tf.keras.Input(shape=[1], name=\"genres-Input\", dtype=tf.string)\n",
    "\n",
    "\t# Create the layer.\n",
    "\tgenres_vectorize_layer = tf.keras.layers.experimental.preprocessing.TextVectorization(\n",
    "\t standardize=custom_standardization,\n",
    "\t output_mode='int',\n",
    "\t output_sequence_length=max_genres_length)\n",
    "\n",
    "\tgenres_vectorize_layer.adapt(genres_col.to_numpy())\n",
    "\n",
    "\tn_genres = len(genres_vectorize_layer.get_vocabulary())\n",
    "\n",
    "\tgenres_embedding = tf.keras.layers.Embedding(n_genres, 4, name=\"genres-Embedding\")(genres_vectorize_layer(genres_input))\n",
    "\tgenres_vec = tf.keras.layers.Flatten(name=\"Flatten-genres\")(genres_embedding)\n",
    "\n",
    "\n",
    "\t# creating book embedding path\n",
    "\tdirectors_input = tf.keras.Input(shape=[1], name=\"directors-Input\", dtype=tf.string)\n",
    "\n",
    "\t# Create the layer.\n",
    "\tdirectors_vectorize_layer = tf.keras.layers.experimental.preprocessing.TextVectorization(\n",
    "\t standardize=custom_standardization,\n",
    "\t output_mode='int',\n",
    "\t output_sequence_length=max_directors_length)\n",
    "\n",
    "\tdirectors_vectorize_layer.adapt(directors_col.to_numpy())\n",
    "\n",
    "\tn_directors = len(directors_vectorize_layer.get_vocabulary())\n",
    "\n",
    "\tdirectors_embedding = tf.keras.layers.Embedding(n_directors, 24, name=\"directors-Embedding\")(directors_vectorize_layer(directors_input))\n",
    "\tdirectors_vec = tf.keras.layers.Flatten(name=\"Flatten-directors\")(directors_embedding)\n",
    "\n",
    "\n",
    "\t# creating writers embedding path\n",
    "\twriters_input = tf.keras.Input(shape=[1], name=\"writers-Input\", dtype=tf.string)\n",
    "\n",
    "\t# Create the layer.\n",
    "\twriters_vectorize_layer = tf.keras.layers.experimental.preprocessing.TextVectorization(\n",
    "\t standardize=custom_standardization,\n",
    "\t output_mode='int',\n",
    "\t output_sequence_length=max_writers_length)\n",
    "\n",
    "\twriters_vectorize_layer.adapt(writers_col.to_numpy())\n",
    "\n",
    "\tn_writers = len(writers_vectorize_layer.get_vocabulary())\n",
    "\n",
    "\twriters_embedding = tf.keras.layers.Embedding(n_writers, 24, name=\"writers-Embedding\")(writers_vectorize_layer(writers_input))\n",
    "\twriters_vec = tf.keras.layers.Flatten(name=\"Flatten-writers\")(writers_embedding)\n",
    "\n",
    "    \n",
    "\t# creating writers embedding path\n",
    "\tstartYear_input = tf.keras.Input(shape=[1], name=\"startYear-Input\", dtype=tf.float32)\n",
    "    \n",
    "\n",
    "\t# concatenate features\n",
    "\tconc = tf.keras.layers.Concatenate()([primaryTitle_vec, genres_vec, directors_vec, writers_vec, startYear_input])\n",
    "\t# add fully-connected-layers\n",
    "\tfc1 = tf.keras.layers.Dense(256, activation='tanh', name = 'properties')(tf.keras.layers.Dropout(0.2)(conc))\n",
    "\t#fc2 = tf.keras.layers.Dense(256, activation='relu')(fc1)\n",
    "\tfc3 = tf.keras.layers.Dense(64, activation='relu', name = 'features')(tf.keras.layers.Dropout(0.2)(fc1))\n",
    "\t#out = tf.keras.layers.Dense(1, activation=\"linear\", name='output')(fc3)\n",
    "\tout = tfa.layers.maxout.Maxout(num_units=1, name=\"maxout_1\")(tf.keras.layers.Dropout(0.2)(fc3))\n",
    "\t# Create model and compile it\n",
    "\tmodel2 = tf.keras.Model([primaryTitle_input, genres_input, writers_input, directors_input, startYear_input], out)\n",
    "\tmodel2.compile('adam', 'mean_squared_error')\n",
    "    \n",
    "\treturn model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = make_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "primaryTitle-Input (InputLayer) [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "genres-Input (InputLayer)       [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "directors-Input (InputLayer)    [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "writers-Input (InputLayer)      [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "text_vectorization (TextVectori (None, 40)           0           primaryTitle-Input[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "text_vectorization_1 (TextVecto (None, 3)            0           genres-Input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "text_vectorization_2 (TextVecto (None, 50)           0           directors-Input[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "text_vectorization_3 (TextVecto (None, 80)           0           writers-Input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "primaryTitle-Embedding (Embeddi (None, 40, 20)       1356780     text_vectorization[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "genres-Embedding (Embedding)    (None, 3, 4)         136         text_vectorization_1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "directors-Embedding (Embedding) (None, 50, 24)       1921464     text_vectorization_2[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "writers-Embedding (Embedding)   (None, 80, 24)       4006344     text_vectorization_3[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Flatten-primaryTitle (Flatten)  (None, 800)          0           primaryTitle-Embedding[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Flatten-genres (Flatten)        (None, 12)           0           genres-Embedding[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Flatten-directors (Flatten)     (None, 1200)         0           directors-Embedding[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Flatten-writers (Flatten)       (None, 1920)         0           writers-Embedding[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "startYear-Input (InputLayer)    [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 3933)         0           Flatten-primaryTitle[0][0]       \n",
      "                                                                 Flatten-genres[0][0]             \n",
      "                                                                 Flatten-directors[0][0]          \n",
      "                                                                 Flatten-writers[0][0]            \n",
      "                                                                 startYear-Input[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 3933)         0           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "properties (Dense)              (None, 256)          1007104     dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 256)          0           properties[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "features (Dense)                (None, 64)           16448       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 64)           0           features[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "maxout_1 (Maxout)               (None, 1)            0           dropout_2[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 8,308,276\n",
      "Trainable params: 8,308,276\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "4816/4816 [==============================] - 264s 55ms/step - loss: 1.4066\n",
      "Epoch 2/10\n",
      "4816/4816 [==============================] - 266s 55ms/step - loss: 0.9724\n",
      "Epoch 3/10\n",
      "4816/4816 [==============================] - 265s 55ms/step - loss: 0.7553\n",
      "Epoch 4/10\n",
      "4816/4816 [==============================] - 267s 55ms/step - loss: 0.6310\n",
      "Epoch 5/10\n",
      "4816/4816 [==============================] - 263s 55ms/step - loss: 0.5485\n",
      "Epoch 6/10\n",
      "4816/4816 [==============================] - 264s 55ms/step - loss: 0.4957\n",
      "Epoch 7/10\n",
      "4816/4816 [==============================] - 270s 56ms/step - loss: 0.4531\n",
      "Epoch 8/10\n",
      "4816/4816 [==============================] - 264s 55ms/step - loss: 0.4220\n",
      "Epoch 9/10\n",
      "4816/4816 [==============================] - 263s 55ms/step - loss: 0.3989\n",
      "Epoch 10/10\n",
      "4816/4816 [==============================] - 262s 54ms/step - loss: 0.3781\n"
     ]
    }
   ],
   "source": [
    "history = model.fit([df.primaryTitle, df.genres, df.directors, df.writers, df.startYear_norm], df.averageRating, epochs=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model2.save('imdb_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('imdb_model_weights_small/imdb_model_weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "4816/4816 [==============================] - 280s 58ms/step - loss: 0.3625\n",
      "Epoch 2/10\n",
      "4816/4816 [==============================] - 275s 57ms/step - loss: 0.3452\n",
      "Epoch 3/10\n",
      "4816/4816 [==============================] - 276s 57ms/step - loss: 0.3315\n",
      "Epoch 4/10\n",
      "4816/4816 [==============================] - 279s 58ms/step - loss: 0.3194\n",
      "Epoch 5/10\n",
      "4816/4816 [==============================] - 278s 58ms/step - loss: 0.3078\n",
      "Epoch 6/10\n",
      "4816/4816 [==============================] - 263s 55ms/step - loss: 0.2969\n",
      "Epoch 7/10\n",
      "4816/4816 [==============================] - 273s 57ms/step - loss: 0.2902\n",
      "Epoch 8/10\n",
      "4816/4816 [==============================] - 266s 55ms/step - loss: 0.2792\n",
      "Epoch 9/10\n",
      "4816/4816 [==============================] - 271s 56ms/step - loss: 0.2727\n",
      "Epoch 10/10\n",
      "4816/4816 [==============================] - 272s 56ms/step - loss: 0.2656\n"
     ]
    }
   ],
   "source": [
    "history = model.fit([df.primaryTitle, df.genres, df.directors, df.writers, df.startYear_norm], df.averageRating, epochs=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('imdb_model_weights_small_2/imdb_model_weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x20dd1d2e348>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model.load_weights('imdb_model_weights_small_2/imdb_model_weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_concatenate = tf.keras.Model(inputs = model.input, outputs = model.get_layer('concatenate').output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = []\n",
    "for index in range(len(df)):\n",
    "    row = df.iloc[[index]]\n",
    "    y_row = model_concatenate.predict_on_batch([row.primaryTitle, row.genres, row.directors, row.writers, row.startYear_norm])\n",
    "    predictions.append(y_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "predictions = np.concatenate(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('imdb_test_concatenate_predictions.pickle', 'wb') as f:\n",
    "    pickle.dump([predictions], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "predictions = preprocessing.normalize(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = 200\n",
    "whiten = False\n",
    "random_state = 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=n_components, whiten=whiten, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_PCA = pca.fit_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance Explained by principal components:  0.9410778239028812\n"
     ]
    }
   ],
   "source": [
    "print(\"Variance Explained by principal components: \", sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KDTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "kdtree = KDTree(X_train_PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('imdb_test_object_data.pickle', 'wb') as f:\n",
    "    pickle.dump([df, X_train_PCA, kdtree], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findNearest(tconst):\n",
    "    idx = df[df[\"tconst\"] == tconst].index[0]\n",
    "    _, ind = kdtree.query(X_train_PCA[idx:idx+1], k=51)\n",
    "    return df.iloc[ind[0][0:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>directors</th>\n",
       "      <th>writers</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>startYear_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44984</th>\n",
       "      <td>tt0120338</td>\n",
       "      <td>movie</td>\n",
       "      <td>Titanic</td>\n",
       "      <td>Titanic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194</td>\n",
       "      <td>Drama,Romance</td>\n",
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       "      <td>nm0000116</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1107195</td>\n",
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       "    <tr>\n",
       "      <th>30268</th>\n",
       "      <td>tt0088247</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Terminator</td>\n",
       "      <td>The Terminator</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>nm0000116</td>\n",
       "      <td>nm0000116,nm0005036,nm0936537</td>\n",
       "      <td>8.0</td>\n",
       "      <td>825868</td>\n",
       "      <td>0.708661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70269</th>\n",
       "      <td>tt0379354</td>\n",
       "      <td>short</td>\n",
       "      <td>Lilly</td>\n",
       "      <td>Lilly</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>Drama,Short</td>\n",
       "      <td>nm1441566</td>\n",
       "      <td>nm1441566,nm0406926</td>\n",
       "      <td>7.7</td>\n",
       "      <td>226</td>\n",
       "      <td>0.842520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116236</th>\n",
       "      <td>tt2322822</td>\n",
       "      <td>tvMovie</td>\n",
       "      <td>The Elisted</td>\n",
       "      <td>The Elisted</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>nm1938104</td>\n",
       "      <td>nm1938104</td>\n",
       "      <td>8.5</td>\n",
       "      <td>144</td>\n",
       "      <td>0.929134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34239</th>\n",
       "      <td>tt0096754</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Abyss</td>\n",
       "      <td>The Abyss</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140</td>\n",
       "      <td>Adventure,Drama,Mystery</td>\n",
       "      <td>nm0000116</td>\n",
       "      <td>nm0000116</td>\n",
       "      <td>7.5</td>\n",
       "      <td>171937</td>\n",
       "      <td>0.748031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59200</th>\n",
       "      <td>tt0251488</td>\n",
       "      <td>short</td>\n",
       "      <td>Xenogenesis</td>\n",
       "      <td>Xenogenesis</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>Sci-Fi,Short</td>\n",
       "      <td>nm0000116,nm0289696</td>\n",
       "      <td>nm0000116,nm0289696</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1137</td>\n",
       "      <td>0.661417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37149</th>\n",
       "      <td>tt0103064</td>\n",
       "      <td>movie</td>\n",
       "      <td>Terminator 2: Judgment Day</td>\n",
       "      <td>Terminator 2: Judgment Day</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>nm0000116</td>\n",
       "      <td>nm0000116,nm0936537</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1043539</td>\n",
       "      <td>0.763780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116467</th>\n",
       "      <td>tt2338151</td>\n",
       "      <td>movie</td>\n",
       "      <td>PK</td>\n",
       "      <td>PK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153</td>\n",
       "      <td>Comedy,Drama,Sci-Fi</td>\n",
       "      <td>nm0386246</td>\n",
       "      <td>nm0386246,nm0430785</td>\n",
       "      <td>8.1</td>\n",
       "      <td>174233</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104711</th>\n",
       "      <td>tt1592812</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>Shararat</td>\n",
       "      <td>Shararat</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>30</td>\n",
       "      <td>Comedy,Fantasy,Romance</td>\n",
       "      <td>nm0576491</td>\n",
       "      <td>nm0576491</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1504</td>\n",
       "      <td>0.858268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98792</th>\n",
       "      <td>tt13491806</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>Esperança</td>\n",
       "      <td>Esperança</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>nm1659256</td>\n",
       "      <td>nm1659256,nm1761005,nm2143612</td>\n",
       "      <td>8.6</td>\n",
       "      <td>172</td>\n",
       "      <td>0.992126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63364</th>\n",
       "      <td>tt0292167</td>\n",
       "      <td>movie</td>\n",
       "      <td>Pakshe</td>\n",
       "      <td>Pakshe</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>nm0595896</td>\n",
       "      <td>nm1015055</td>\n",
       "      <td>7.1</td>\n",
       "      <td>257</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148610</th>\n",
       "      <td>tt8080648</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ring of Silence</td>\n",
       "      <td>Ring of Silence</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm5029090</td>\n",
       "      <td>nm5029090,nm6176210,nm4085033,nm8513216</td>\n",
       "      <td>8.9</td>\n",
       "      <td>508</td>\n",
       "      <td>0.984252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124680</th>\n",
       "      <td>tt3396910</td>\n",
       "      <td>movie</td>\n",
       "      <td>Banshee</td>\n",
       "      <td>Banshee</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>Action</td>\n",
       "      <td>nm4409868</td>\n",
       "      <td>nm4409868</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2883</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94897</th>\n",
       "      <td>tt12369754</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>Staged</td>\n",
       "      <td>Staged</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>22</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>nm1124670</td>\n",
       "      <td>nm1124670,nm4813799</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5687</td>\n",
       "      <td>0.992126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56676</th>\n",
       "      <td>tt0220640</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Drunkard</td>\n",
       "      <td>O methystakas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>nm0419485</td>\n",
       "      <td>nm0419485</td>\n",
       "      <td>7.6</td>\n",
       "      <td>326</td>\n",
       "      <td>0.440945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89441</th>\n",
       "      <td>tt10919240</td>\n",
       "      <td>movie</td>\n",
       "      <td>Malik</td>\n",
       "      <td>Malik</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162</td>\n",
       "      <td>Action,Biography,Drama</td>\n",
       "      <td>nm3525879</td>\n",
       "      <td>nm3525879</td>\n",
       "      <td>8.2</td>\n",
       "      <td>11174</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95988</th>\n",
       "      <td>tt1272016</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Beauty</td>\n",
       "      <td>Grazuole</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>Drama,Family</td>\n",
       "      <td>nm0954066</td>\n",
       "      <td>nm0945086</td>\n",
       "      <td>7.9</td>\n",
       "      <td>464</td>\n",
       "      <td>0.590551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116693</th>\n",
       "      <td>tt2354667</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>Battlefield Friends</td>\n",
       "      <td>Battlefield Friends</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Animation,Comedy</td>\n",
       "      <td>nm0873051</td>\n",
       "      <td>nm2410776,nm11921311,nm11921310,nm1751917,nm16...</td>\n",
       "      <td>8.5</td>\n",
       "      <td>605</td>\n",
       "      <td>0.929134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44341</th>\n",
       "      <td>tt0119174</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Game</td>\n",
       "      <td>The Game</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129</td>\n",
       "      <td>Drama,Mystery,Thriller</td>\n",
       "      <td>nm0000399</td>\n",
       "      <td>nm0104335,nm0274905</td>\n",
       "      <td>7.7</td>\n",
       "      <td>373741</td>\n",
       "      <td>0.811024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143273</th>\n",
       "      <td>tt6722972</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>Hot Dog</td>\n",
       "      <td>Tskheli Dzaghli</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>50</td>\n",
       "      <td>Comedy,Crime,Thriller</td>\n",
       "      <td>nm0882203</td>\n",
       "      <td>nm0229100,nm0789394</td>\n",
       "      <td>9.4</td>\n",
       "      <td>403</td>\n",
       "      <td>0.858268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16637</th>\n",
       "      <td>tt0058487</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Return</td>\n",
       "      <td>Povratak</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm0667870</td>\n",
       "      <td>nm0417732,nm2130243</td>\n",
       "      <td>7.1</td>\n",
       "      <td>133</td>\n",
       "      <td>0.566929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58837</th>\n",
       "      <td>tt0247896</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>Darkness</td>\n",
       "      <td>Tamas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>297</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm0631539</td>\n",
       "      <td>nm0756380</td>\n",
       "      <td>8.9</td>\n",
       "      <td>377</td>\n",
       "      <td>0.740157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26235</th>\n",
       "      <td>tt0079382</td>\n",
       "      <td>movie</td>\n",
       "      <td>Jurmana</td>\n",
       "      <td>Jurmana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>nm0611531</td>\n",
       "      <td>nm0244887,nm0713592</td>\n",
       "      <td>6.9</td>\n",
       "      <td>189</td>\n",
       "      <td>0.669291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144432</th>\n",
       "      <td>tt7003872</td>\n",
       "      <td>short</td>\n",
       "      <td>Valentine's Day</td>\n",
       "      <td>Valentine's Day</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Drama,Short</td>\n",
       "      <td>nm4414683</td>\n",
       "      <td>nm3330045</td>\n",
       "      <td>9.3</td>\n",
       "      <td>418</td>\n",
       "      <td>0.968504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93459</th>\n",
       "      <td>tt11933162</td>\n",
       "      <td>short</td>\n",
       "      <td>Brave</td>\n",
       "      <td>Valiente</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>Drama,Short</td>\n",
       "      <td>nm6458895</td>\n",
       "      <td>nm6458895</td>\n",
       "      <td>8.8</td>\n",
       "      <td>115</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15146</th>\n",
       "      <td>tt0055276</td>\n",
       "      <td>movie</td>\n",
       "      <td>Parakh</td>\n",
       "      <td>Parakh</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>nm0746950</td>\n",
       "      <td>nm0787589,nm0006005</td>\n",
       "      <td>6.9</td>\n",
       "      <td>133</td>\n",
       "      <td>0.519685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35993</th>\n",
       "      <td>tt0100511</td>\n",
       "      <td>movie</td>\n",
       "      <td>Romeo</td>\n",
       "      <td>Romeo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm0395483</td>\n",
       "      <td>nm0395483</td>\n",
       "      <td>6.9</td>\n",
       "      <td>125</td>\n",
       "      <td>0.755906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75492</th>\n",
       "      <td>tt0443706</td>\n",
       "      <td>movie</td>\n",
       "      <td>Zodiac</td>\n",
       "      <td>Zodiac</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>Crime,Drama,Mystery</td>\n",
       "      <td>nm0000399</td>\n",
       "      <td>nm0888743,nm1172196</td>\n",
       "      <td>7.7</td>\n",
       "      <td>505512</td>\n",
       "      <td>0.889764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52410</th>\n",
       "      <td>tt0180342</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>Blockbusters</td>\n",
       "      <td>Blockbusters</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>30</td>\n",
       "      <td>Game-Show</td>\n",
       "      <td>nm0804973</td>\n",
       "      <td>nm1740865,nm0595763,nm0606777,nm0851432</td>\n",
       "      <td>7.4</td>\n",
       "      <td>100</td>\n",
       "      <td>0.677165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124255</th>\n",
       "      <td>tt3327644</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>A Foreign Exchange Love</td>\n",
       "      <td>A Foreign Exchange Love</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>nm1938104</td>\n",
       "      <td>nm1938104,nm2952021</td>\n",
       "      <td>7.5</td>\n",
       "      <td>140</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122944</th>\n",
       "      <td>tt3147798</td>\n",
       "      <td>tvMovie</td>\n",
       "      <td>Yunona i Avos</td>\n",
       "      <td>Yunona i Avos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>Drama,Music,Musical</td>\n",
       "      <td>nm0952272</td>\n",
       "      <td>nm0903942</td>\n",
       "      <td>7.9</td>\n",
       "      <td>171</td>\n",
       "      <td>0.700787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30164</th>\n",
       "      <td>tt0088021</td>\n",
       "      <td>movie</td>\n",
       "      <td>Dissolved and Effused</td>\n",
       "      <td>Rozpustený a vypustený</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>Comedy,Crime</td>\n",
       "      <td>nm0810602</td>\n",
       "      <td>nm0810602,nm0006664</td>\n",
       "      <td>7.8</td>\n",
       "      <td>529</td>\n",
       "      <td>0.716535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90771</th>\n",
       "      <td>tt11243342</td>\n",
       "      <td>movie</td>\n",
       "      <td>Invasion</td>\n",
       "      <td>Invasion</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>Drama,Fantasy,Horror</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>8.2</td>\n",
       "      <td>188</td>\n",
       "      <td>0.984252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148599</th>\n",
       "      <td>tt8078816</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>Something in the Rain</td>\n",
       "      <td>Bap Jal Sajuneun Yeppeun Nuna</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>nm2300117</td>\n",
       "      <td>nm9665676</td>\n",
       "      <td>8.1</td>\n",
       "      <td>5021</td>\n",
       "      <td>0.976378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85843</th>\n",
       "      <td>tt10121378</td>\n",
       "      <td>short</td>\n",
       "      <td>Ivan the Terrible</td>\n",
       "      <td>Ivan the Terrible</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Fantasy,History,Horror</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>8.9</td>\n",
       "      <td>118</td>\n",
       "      <td>0.984252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89404</th>\n",
       "      <td>tt10913716</td>\n",
       "      <td>movie</td>\n",
       "      <td>Chernobyl</td>\n",
       "      <td>Chernobyl</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>Adventure,Comedy,Horror</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>7.7</td>\n",
       "      <td>179</td>\n",
       "      <td>0.984252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121077</th>\n",
       "      <td>tt2899460</td>\n",
       "      <td>short</td>\n",
       "      <td>The Vehicle</td>\n",
       "      <td>The Vehicle</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>Short,Thriller</td>\n",
       "      <td>nm1938104</td>\n",
       "      <td>nm5663650</td>\n",
       "      <td>9.4</td>\n",
       "      <td>124</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151911</th>\n",
       "      <td>tt9077854</td>\n",
       "      <td>short</td>\n",
       "      <td>Bodzilla</td>\n",
       "      <td>Bodzilla</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Action,Comedy,Drama</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>8.5</td>\n",
       "      <td>150</td>\n",
       "      <td>0.976378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86156</th>\n",
       "      <td>tt10192576</td>\n",
       "      <td>tvSeries</td>\n",
       "      <td>One Spring Night</td>\n",
       "      <td>Bombam</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>35</td>\n",
       "      <td>Drama,Romance</td>\n",
       "      <td>nm2300117</td>\n",
       "      <td>nm9665676</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2096</td>\n",
       "      <td>0.984252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30289</th>\n",
       "      <td>tt0088295</td>\n",
       "      <td>tvMovie</td>\n",
       "      <td>Treffer</td>\n",
       "      <td>Treffer</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm0333705</td>\n",
       "      <td>nm0296356</td>\n",
       "      <td>6.9</td>\n",
       "      <td>145</td>\n",
       "      <td>0.708661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88128</th>\n",
       "      <td>tt10618286</td>\n",
       "      <td>movie</td>\n",
       "      <td>Mank</td>\n",
       "      <td>Mank</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>Biography,Comedy,Drama</td>\n",
       "      <td>nm0000399</td>\n",
       "      <td>nm10827638</td>\n",
       "      <td>6.9</td>\n",
       "      <td>68072</td>\n",
       "      <td>0.992126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51288</th>\n",
       "      <td>tt0169547</td>\n",
       "      <td>movie</td>\n",
       "      <td>American Beauty</td>\n",
       "      <td>American Beauty</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm0005222</td>\n",
       "      <td>nm0050332</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1112754</td>\n",
       "      <td>0.826772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51659</th>\n",
       "      <td>tt0173684</td>\n",
       "      <td>tvMiniSeries</td>\n",
       "      <td>Bogach, bednyak...</td>\n",
       "      <td>Bogach, bednyak...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm0954066</td>\n",
       "      <td>nm0464971,nm0789758</td>\n",
       "      <td>7.2</td>\n",
       "      <td>130</td>\n",
       "      <td>0.692913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93760</th>\n",
       "      <td>tt12021598</td>\n",
       "      <td>movie</td>\n",
       "      <td>Coronavirus. Apocalypse</td>\n",
       "      <td>Coronavirus. Apocalypse</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>Fantasy,Sci-Fi,Thriller</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>8.0</td>\n",
       "      <td>153</td>\n",
       "      <td>0.992126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96849</th>\n",
       "      <td>tt1295059</td>\n",
       "      <td>movie</td>\n",
       "      <td>Eep!</td>\n",
       "      <td>Iep!</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82</td>\n",
       "      <td>Drama,Family,Fantasy</td>\n",
       "      <td>nm0395483</td>\n",
       "      <td>nm1174527,nm3783203</td>\n",
       "      <td>6.3</td>\n",
       "      <td>448</td>\n",
       "      <td>0.913386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20799</th>\n",
       "      <td>tt0067555</td>\n",
       "      <td>movie</td>\n",
       "      <td>Parwana</td>\n",
       "      <td>Parwana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>Romance,Thriller</td>\n",
       "      <td>nm0841918</td>\n",
       "      <td>nm0440589,nm1504569</td>\n",
       "      <td>6.8</td>\n",
       "      <td>204</td>\n",
       "      <td>0.606299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144047</th>\n",
       "      <td>tt6909846</td>\n",
       "      <td>movie</td>\n",
       "      <td>El-Asliyyin</td>\n",
       "      <td>El-Asliyyin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125</td>\n",
       "      <td>Drama,Mystery,Thriller</td>\n",
       "      <td>nm1441566</td>\n",
       "      <td>nm13050140,nm13043663,nm6211716</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413</td>\n",
       "      <td>0.968504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103809</th>\n",
       "      <td>tt15523028</td>\n",
       "      <td>movie</td>\n",
       "      <td>Bekhudi</td>\n",
       "      <td>Bekhudi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Romance,Thriller</td>\n",
       "      <td>nm8348429</td>\n",
       "      <td>nm8348429</td>\n",
       "      <td>9.2</td>\n",
       "      <td>598</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35751</th>\n",
       "      <td>tt0100002</td>\n",
       "      <td>movie</td>\n",
       "      <td>Lekin...</td>\n",
       "      <td>Lekin...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171</td>\n",
       "      <td>Drama,Fantasy,Musical</td>\n",
       "      <td>nm0347899</td>\n",
       "      <td>nm0347899</td>\n",
       "      <td>7.5</td>\n",
       "      <td>352</td>\n",
       "      <td>0.755906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142432</th>\n",
       "      <td>tt6498526</td>\n",
       "      <td>movie</td>\n",
       "      <td>Purgatory</td>\n",
       "      <td>Chistilishche</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>Drama,Horror,Thriller</td>\n",
       "      <td>nm5954636</td>\n",
       "      <td>nm5954636,nm8083112,nm5572825</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1602</td>\n",
       "      <td>0.968504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23544</th>\n",
       "      <td>tt0073391</td>\n",
       "      <td>movie</td>\n",
       "      <td>Mili</td>\n",
       "      <td>Mili</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150</td>\n",
       "      <td>Drama,Family,Musical</td>\n",
       "      <td>nm0611531</td>\n",
       "      <td>nm0244887,nm0713592,nm1100176</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1315</td>\n",
       "      <td>0.637795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tconst     titleType                primaryTitle  \\\n",
       "44984    tt0120338         movie                     Titanic   \n",
       "30268    tt0088247         movie              The Terminator   \n",
       "70269    tt0379354         short                       Lilly   \n",
       "116236   tt2322822       tvMovie                 The Elisted   \n",
       "34239    tt0096754         movie                   The Abyss   \n",
       "59200    tt0251488         short                 Xenogenesis   \n",
       "37149    tt0103064         movie  Terminator 2: Judgment Day   \n",
       "116467   tt2338151         movie                          PK   \n",
       "104711   tt1592812      tvSeries                    Shararat   \n",
       "98792   tt13491806      tvSeries                   Esperança   \n",
       "63364    tt0292167         movie                      Pakshe   \n",
       "148610   tt8080648         movie             Ring of Silence   \n",
       "124680   tt3396910         movie                     Banshee   \n",
       "94897   tt12369754      tvSeries                      Staged   \n",
       "56676    tt0220640         movie                The Drunkard   \n",
       "89441   tt10919240         movie                       Malik   \n",
       "95988    tt1272016         movie                  The Beauty   \n",
       "116693   tt2354667      tvSeries         Battlefield Friends   \n",
       "44341    tt0119174         movie                    The Game   \n",
       "143273   tt6722972      tvSeries                     Hot Dog   \n",
       "16637    tt0058487         movie                  The Return   \n",
       "58837    tt0247896      tvSeries                    Darkness   \n",
       "26235    tt0079382         movie                     Jurmana   \n",
       "144432   tt7003872         short             Valentine's Day   \n",
       "93459   tt11933162         short                       Brave   \n",
       "15146    tt0055276         movie                      Parakh   \n",
       "35993    tt0100511         movie                       Romeo   \n",
       "75492    tt0443706         movie                      Zodiac   \n",
       "52410    tt0180342      tvSeries                Blockbusters   \n",
       "124255   tt3327644      tvSeries     A Foreign Exchange Love   \n",
       "122944   tt3147798       tvMovie               Yunona i Avos   \n",
       "30164    tt0088021         movie       Dissolved and Effused   \n",
       "90771   tt11243342         movie                    Invasion   \n",
       "148599   tt8078816      tvSeries       Something in the Rain   \n",
       "85843   tt10121378         short           Ivan the Terrible   \n",
       "89404   tt10913716         movie                   Chernobyl   \n",
       "121077   tt2899460         short                 The Vehicle   \n",
       "151911   tt9077854         short                    Bodzilla   \n",
       "86156   tt10192576      tvSeries            One Spring Night   \n",
       "30289    tt0088295       tvMovie                     Treffer   \n",
       "88128   tt10618286         movie                        Mank   \n",
       "51288    tt0169547         movie             American Beauty   \n",
       "51659    tt0173684  tvMiniSeries          Bogach, bednyak...   \n",
       "93760   tt12021598         movie     Coronavirus. Apocalypse   \n",
       "96849    tt1295059         movie                        Eep!   \n",
       "20799    tt0067555         movie                     Parwana   \n",
       "144047   tt6909846         movie                 El-Asliyyin   \n",
       "103809  tt15523028         movie                     Bekhudi   \n",
       "35751    tt0100002         movie                    Lekin...   \n",
       "142432   tt6498526         movie                   Purgatory   \n",
       "23544    tt0073391         movie                        Mili   \n",
       "\n",
       "                        originalTitle  isAdult  startYear  endYear  \\\n",
       "44984                         Titanic      0.0     1997.0      NaN   \n",
       "30268                  The Terminator      0.0     1984.0      NaN   \n",
       "70269                           Lilly      0.0     2001.0      NaN   \n",
       "116236                    The Elisted      0.0     2012.0      NaN   \n",
       "34239                       The Abyss      0.0     1989.0      NaN   \n",
       "59200                     Xenogenesis      0.0     1978.0      NaN   \n",
       "37149      Terminator 2: Judgment Day      0.0     1991.0      NaN   \n",
       "116467                             PK      0.0     2014.0      NaN   \n",
       "104711                       Shararat      0.0     2003.0   2006.0   \n",
       "98792                       Esperança      0.0     2020.0      NaN   \n",
       "63364                          Pakshe      0.0     1994.0      NaN   \n",
       "148610                Ring of Silence      0.0     2019.0      NaN   \n",
       "124680                        Banshee      0.0     2013.0      NaN   \n",
       "94897                          Staged      0.0     2020.0   2021.0   \n",
       "56676                   O methystakas      0.0     1950.0      NaN   \n",
       "89441                           Malik      0.0     2021.0      NaN   \n",
       "95988                        Grazuole      0.0     1969.0      NaN   \n",
       "116693            Battlefield Friends      0.0     2012.0      NaN   \n",
       "44341                        The Game      0.0     1997.0      NaN   \n",
       "143273                Tskheli Dzaghli      0.0     2003.0   2013.0   \n",
       "16637                        Povratak      0.0     1966.0      NaN   \n",
       "58837                           Tamas      0.0     1988.0      NaN   \n",
       "26235                         Jurmana      0.0     1979.0      NaN   \n",
       "144432                Valentine's Day      0.0     2017.0      NaN   \n",
       "93459                        Valiente      0.0     2021.0      NaN   \n",
       "15146                          Parakh      0.0     1960.0      NaN   \n",
       "35993                           Romeo      0.0     1990.0      NaN   \n",
       "75492                          Zodiac      0.0     2007.0      NaN   \n",
       "52410                    Blockbusters      0.0     1980.0   1982.0   \n",
       "124255        A Foreign Exchange Love      0.0     2014.0      NaN   \n",
       "122944                  Yunona i Avos      0.0     1983.0      NaN   \n",
       "30164          Rozpustený a vypustený      0.0     1985.0      NaN   \n",
       "90771                        Invasion      0.0     2019.0      NaN   \n",
       "148599  Bap Jal Sajuneun Yeppeun Nuna      0.0     2018.0   2018.0   \n",
       "85843               Ivan the Terrible      0.0     2019.0      NaN   \n",
       "89404                       Chernobyl      0.0     2019.0      NaN   \n",
       "121077                    The Vehicle      0.0     2013.0      NaN   \n",
       "151911                       Bodzilla      0.0     2018.0      NaN   \n",
       "86156                          Bombam      0.0     2019.0   2019.0   \n",
       "30289                         Treffer      0.0     1984.0      NaN   \n",
       "88128                            Mank      0.0     2020.0      NaN   \n",
       "51288                 American Beauty      0.0     1999.0      NaN   \n",
       "51659              Bogach, bednyak...      0.0     1982.0      NaN   \n",
       "93760         Coronavirus. Apocalypse      0.0     2020.0      NaN   \n",
       "96849                            Iep!      0.0     2010.0      NaN   \n",
       "20799                         Parwana      0.0     1971.0      NaN   \n",
       "144047                    El-Asliyyin      0.0     2017.0      NaN   \n",
       "103809                        Bekhudi      0.0     2021.0      NaN   \n",
       "35751                        Lekin...      0.0     1990.0      NaN   \n",
       "142432                  Chistilishche      0.0     2017.0      NaN   \n",
       "23544                            Mili      0.0     1975.0      NaN   \n",
       "\n",
       "       runtimeMinutes                   genres            directors  \\\n",
       "44984             194            Drama,Romance            nm0000116   \n",
       "30268             107            Action,Sci-Fi            nm0000116   \n",
       "70269              40              Drama,Short            nm1441566   \n",
       "116236            NaN                   Comedy            nm1938104   \n",
       "34239             140  Adventure,Drama,Mystery            nm0000116   \n",
       "59200              12             Sci-Fi,Short  nm0000116,nm0289696   \n",
       "37149             137            Action,Sci-Fi            nm0000116   \n",
       "116467            153      Comedy,Drama,Sci-Fi            nm0386246   \n",
       "104711             30   Comedy,Fantasy,Romance            nm0576491   \n",
       "98792             NaN                   Comedy            nm1659256   \n",
       "63364             154            Drama,Romance            nm0595896   \n",
       "148610             95                    Drama            nm5029090   \n",
       "124680             50                   Action            nm4409868   \n",
       "94897              22             Comedy,Drama            nm1124670   \n",
       "56676             105     Comedy,Drama,Romance            nm0419485   \n",
       "89441             162   Action,Biography,Drama            nm3525879   \n",
       "95988              71             Drama,Family            nm0954066   \n",
       "116693            NaN         Animation,Comedy            nm0873051   \n",
       "44341             129   Drama,Mystery,Thriller            nm0000399   \n",
       "143273             50    Comedy,Crime,Thriller            nm0882203   \n",
       "16637              70                    Drama            nm0667870   \n",
       "58837             297                    Drama            nm0631539   \n",
       "26235             139            Drama,Romance            nm0611531   \n",
       "144432            NaN              Drama,Short            nm4414683   \n",
       "93459               8              Drama,Short            nm6458895   \n",
       "15146             139     Comedy,Drama,Romance            nm0746950   \n",
       "35993              94                    Drama            nm0395483   \n",
       "75492             157      Crime,Drama,Mystery            nm0000399   \n",
       "52410              30                Game-Show            nm0804973   \n",
       "124255             25                   Comedy            nm1938104   \n",
       "122944             83      Drama,Music,Musical            nm0952272   \n",
       "30164              85             Comedy,Crime            nm0810602   \n",
       "90771              64     Drama,Fantasy,Horror            nm5954636   \n",
       "148599             75            Drama,Romance            nm2300117   \n",
       "85843               5   Fantasy,History,Horror            nm5954636   \n",
       "89404              62  Adventure,Comedy,Horror            nm5954636   \n",
       "121077             30           Short,Thriller            nm1938104   \n",
       "151911             10      Action,Comedy,Drama            nm5954636   \n",
       "86156              35            Drama,Romance            nm2300117   \n",
       "30289             110                    Drama            nm0333705   \n",
       "88128             131   Biography,Comedy,Drama            nm0000399   \n",
       "51288             122                    Drama            nm0005222   \n",
       "51659             NaN                    Drama            nm0954066   \n",
       "93760              66  Fantasy,Sci-Fi,Thriller            nm5954636   \n",
       "96849              82     Drama,Family,Fantasy            nm0395483   \n",
       "20799              93         Romance,Thriller            nm0841918   \n",
       "144047            125   Drama,Mystery,Thriller            nm1441566   \n",
       "103809            NaN         Romance,Thriller            nm8348429   \n",
       "35751             171    Drama,Fantasy,Musical            nm0347899   \n",
       "142432             55    Drama,Horror,Thriller            nm5954636   \n",
       "23544             150     Drama,Family,Musical            nm0611531   \n",
       "\n",
       "                                                  writers  averageRating  \\\n",
       "44984                                           nm0000116            7.8   \n",
       "30268                       nm0000116,nm0005036,nm0936537            8.0   \n",
       "70269                                 nm1441566,nm0406926            7.7   \n",
       "116236                                          nm1938104            8.5   \n",
       "34239                                           nm0000116            7.5   \n",
       "59200                                 nm0000116,nm0289696            5.4   \n",
       "37149                                 nm0000116,nm0936537            8.5   \n",
       "116467                                nm0386246,nm0430785            8.1   \n",
       "104711                                          nm0576491            7.5   \n",
       "98792                       nm1659256,nm1761005,nm2143612            8.6   \n",
       "63364                                           nm1015055            7.1   \n",
       "148610            nm5029090,nm6176210,nm4085033,nm8513216            8.9   \n",
       "124680                                          nm4409868            8.5   \n",
       "94897                                 nm1124670,nm4813799            8.6   \n",
       "56676                                           nm0419485            7.6   \n",
       "89441                                           nm3525879            8.2   \n",
       "95988                                           nm0945086            7.9   \n",
       "116693  nm2410776,nm11921311,nm11921310,nm1751917,nm16...            8.5   \n",
       "44341                                 nm0104335,nm0274905            7.7   \n",
       "143273                                nm0229100,nm0789394            9.4   \n",
       "16637                                 nm0417732,nm2130243            7.1   \n",
       "58837                                           nm0756380            8.9   \n",
       "26235                                 nm0244887,nm0713592            6.9   \n",
       "144432                                          nm3330045            9.3   \n",
       "93459                                           nm6458895            8.8   \n",
       "15146                                 nm0787589,nm0006005            6.9   \n",
       "35993                                           nm0395483            6.9   \n",
       "75492                                 nm0888743,nm1172196            7.7   \n",
       "52410             nm1740865,nm0595763,nm0606777,nm0851432            7.4   \n",
       "124255                                nm1938104,nm2952021            7.5   \n",
       "122944                                          nm0903942            7.9   \n",
       "30164                                 nm0810602,nm0006664            7.8   \n",
       "90771                                           nm5954636            8.2   \n",
       "148599                                          nm9665676            8.1   \n",
       "85843                                           nm5954636            8.9   \n",
       "89404                                           nm5954636            7.7   \n",
       "121077                                          nm5663650            9.4   \n",
       "151911                                          nm5954636            8.5   \n",
       "86156                                           nm9665676            7.9   \n",
       "30289                                           nm0296356            6.9   \n",
       "88128                                          nm10827638            6.9   \n",
       "51288                                           nm0050332            8.3   \n",
       "51659                                 nm0464971,nm0789758            7.2   \n",
       "93760                                           nm5954636            8.0   \n",
       "96849                                 nm1174527,nm3783203            6.3   \n",
       "20799                                 nm0440589,nm1504569            6.8   \n",
       "144047                    nm13050140,nm13043663,nm6211716            6.9   \n",
       "103809                                          nm8348429            9.2   \n",
       "35751                                           nm0347899            7.5   \n",
       "142432                      nm5954636,nm8083112,nm5572825            8.5   \n",
       "23544                       nm0244887,nm0713592,nm1100176            7.5   \n",
       "\n",
       "        numVotes  startYear_norm  \n",
       "44984    1107195        0.811024  \n",
       "30268     825868        0.708661  \n",
       "70269        226        0.842520  \n",
       "116236       144        0.929134  \n",
       "34239     171937        0.748031  \n",
       "59200       1137        0.661417  \n",
       "37149    1043539        0.763780  \n",
       "116467    174233        0.944882  \n",
       "104711      1504        0.858268  \n",
       "98792        172        0.992126  \n",
       "63364        257        0.787402  \n",
       "148610       508        0.984252  \n",
       "124680      2883        0.937008  \n",
       "94897       5687        0.992126  \n",
       "56676        326        0.440945  \n",
       "89441      11174        1.000000  \n",
       "95988        464        0.590551  \n",
       "116693       605        0.929134  \n",
       "44341     373741        0.811024  \n",
       "143273       403        0.858268  \n",
       "16637        133        0.566929  \n",
       "58837        377        0.740157  \n",
       "26235        189        0.669291  \n",
       "144432       418        0.968504  \n",
       "93459        115        1.000000  \n",
       "15146        133        0.519685  \n",
       "35993        125        0.755906  \n",
       "75492     505512        0.889764  \n",
       "52410        100        0.677165  \n",
       "124255       140        0.944882  \n",
       "122944       171        0.700787  \n",
       "30164        529        0.716535  \n",
       "90771        188        0.984252  \n",
       "148599      5021        0.976378  \n",
       "85843        118        0.984252  \n",
       "89404        179        0.984252  \n",
       "121077       124        0.937008  \n",
       "151911       150        0.976378  \n",
       "86156       2096        0.984252  \n",
       "30289        145        0.708661  \n",
       "88128      68072        0.992126  \n",
       "51288    1112754        0.826772  \n",
       "51659        130        0.692913  \n",
       "93760        153        0.992126  \n",
       "96849        448        0.913386  \n",
       "20799        204        0.606299  \n",
       "144047      2413        0.968504  \n",
       "103809       598        1.000000  \n",
       "35751        352        0.755906  \n",
       "142432      1602        0.968504  \n",
       "23544       1315        0.637795  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findNearest(\"tt0120338\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>directors</th>\n",
       "      <th>writers</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>startYear_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41780</th>\n",
       "      <td>tt0113568</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell</td>\n",
       "      <td>Kôkaku Kidôtai</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>Action,Animation,Crime</td>\n",
       "      <td>nm0651900</td>\n",
       "      <td>nm0794385,nm0411872</td>\n",
       "      <td>8.0</td>\n",
       "      <td>137202</td>\n",
       "      <td>0.795276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95608</th>\n",
       "      <td>tt1260502</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell 2.0</td>\n",
       "      <td>Kôkaku kidôtai 2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>Action,Adventure,Animation</td>\n",
       "      <td>nm0651900</td>\n",
       "      <td>nm0794385,nm0411872</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14256</td>\n",
       "      <td>0.897638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67736</th>\n",
       "      <td>tt0347246</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell 2: Innocence</td>\n",
       "      <td>Inosensu: Innocence</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>Animation,Drama,Mystery</td>\n",
       "      <td>nm0651900</td>\n",
       "      <td>nm0794385,nm0651900,nm0258268,nm0556856</td>\n",
       "      <td>7.5</td>\n",
       "      <td>37590</td>\n",
       "      <td>0.866142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139613</th>\n",
       "      <td>tt5973984</td>\n",
       "      <td>tvMovie</td>\n",
       "      <td>Ghost in the Shell Arise: Border 5 - Pyrophori...</td>\n",
       "      <td>Ghost in the Shell Arise: Border 5 - Pyrophori...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>Action,Animation,Sci-Fi</td>\n",
       "      <td>nm0457124</td>\n",
       "      <td>nm0794385,nm2396196</td>\n",
       "      <td>7.3</td>\n",
       "      <td>651</td>\n",
       "      <td>0.952756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130778</th>\n",
       "      <td>tt4337072</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell: The New Movie</td>\n",
       "      <td>Kôkaku Kidôtai Shin Gekijôban</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>Action,Animation,Sci-Fi</td>\n",
       "      <td>nm0457124,nm2420940</td>\n",
       "      <td>nm0794385,nm0121423,nm1727020,nm2396196</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3737</td>\n",
       "      <td>0.952756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126070</th>\n",
       "      <td>tt3579524</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell Arise: Border 3 - Ghost Tears</td>\n",
       "      <td>Ghost in the Shell Arise: Border 3 - Ghost Tears</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>Action,Animation,Sci-Fi</td>\n",
       "      <td>nm0457124</td>\n",
       "      <td>nm0794385,nm2396196,nm0926075</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2642</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128926</th>\n",
       "      <td>tt4016942</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell Arise: Border 4 - Ghost Sta...</td>\n",
       "      <td>Ghost in the Shell Arise: Border 4 - Ghost Sta...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>Action,Animation,Sci-Fi</td>\n",
       "      <td>nm1556115,nm0457124</td>\n",
       "      <td>nm0794385,nm2396196</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2070</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119506</th>\n",
       "      <td>tt2636124</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell: Arise - Border 1: Ghost Pain</td>\n",
       "      <td>Ghost in the Shell Arise: Border 1 - Ghost Pain</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>Action,Animation,Sci-Fi</td>\n",
       "      <td>nm1496044,nm0457124</td>\n",
       "      <td>nm0794385,nm2396196,nm0121423,nm1727020</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3991</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>tt3017864</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell Arise: Border 2 - Ghost Whi...</td>\n",
       "      <td>Ghost in the Shell Arise: Border 2 - Ghost Whi...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>Action,Animation,Sci-Fi</td>\n",
       "      <td>nm0847591,nm0457124</td>\n",
       "      <td>nm0794385,nm2396196</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2917</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82649</th>\n",
       "      <td>tt0856797</td>\n",
       "      <td>tvMovie</td>\n",
       "      <td>Ghost in the Shell: Stand Alone Complex - Soli...</td>\n",
       "      <td>Kôkaku kidôtai: Stand Alone Complex Solid Stat...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108</td>\n",
       "      <td>Action,Adventure,Animation</td>\n",
       "      <td>nm0436784</td>\n",
       "      <td>nm0794385,nm0436784,nm1591046,nm1371924</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6953</td>\n",
       "      <td>0.881890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109750</th>\n",
       "      <td>tt1856022</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell S.A.C. Solid State Society 3D</td>\n",
       "      <td>Kôkaku kidôtai S.A.C. Solid State Society 3D</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130</td>\n",
       "      <td>Action,Animation,Fantasy</td>\n",
       "      <td>nm0436784</td>\n",
       "      <td>nm0794385,nm0436784,nm1591046,nm1371924</td>\n",
       "      <td>7.6</td>\n",
       "      <td>491</td>\n",
       "      <td>0.921260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94279</th>\n",
       "      <td>tt1219827</td>\n",
       "      <td>movie</td>\n",
       "      <td>Ghost in the Shell</td>\n",
       "      <td>Ghost in the Shell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>nm2782185</td>\n",
       "      <td>nm0794385,nm1150145,nm0923980,nm0472567</td>\n",
       "      <td>6.3</td>\n",
       "      <td>207774</td>\n",
       "      <td>0.968504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75683</th>\n",
       "      <td>tt0446488</td>\n",
       "      <td>video</td>\n",
       "      <td>A Touch of Greece</td>\n",
       "      <td>A Touch of Greece</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>nm1583975</td>\n",
       "      <td>nm1583975</td>\n",
       "      <td>7.7</td>\n",
       "      <td>138</td>\n",
       "      <td>0.866142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73152</th>\n",
       "      <td>tt0416449</td>\n",
       "      <td>movie</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117</td>\n",
       "      <td>Action,Drama</td>\n",
       "      <td>nm0811583</td>\n",
       "      <td>nm0811583,nm0426500,nm1290582,nm0588340,nm2241067</td>\n",
       "      <td>7.6</td>\n",
       "      <td>762539</td>\n",
       "      <td>0.881890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121416</th>\n",
       "      <td>tt2945970</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Last American Guido</td>\n",
       "      <td>The Last American Guido</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>nm2766988</td>\n",
       "      <td>nm2766988</td>\n",
       "      <td>7.3</td>\n",
       "      <td>109</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68786</th>\n",
       "      <td>tt0363226</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Blind Swordsman: Zatoichi</td>\n",
       "      <td>Zatôichi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116</td>\n",
       "      <td>Action,Comedy,Crime</td>\n",
       "      <td>nm0001429</td>\n",
       "      <td>nm0001429,nm0793753</td>\n",
       "      <td>7.5</td>\n",
       "      <td>48800</td>\n",
       "      <td>0.858268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59284</th>\n",
       "      <td>tt0252315</td>\n",
       "      <td>movie</td>\n",
       "      <td>Man in a Shell</td>\n",
       "      <td>Chelovek v futlyare</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm0029363</td>\n",
       "      <td>nm0029363,nm0155009</td>\n",
       "      <td>7.6</td>\n",
       "      <td>134</td>\n",
       "      <td>0.354331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130093</th>\n",
       "      <td>tt4208318</td>\n",
       "      <td>video</td>\n",
       "      <td>No Joke: Don't Drink and Drive</td>\n",
       "      <td>No Joke: Don't Drink and Drive</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Crime,Short</td>\n",
       "      <td>nm4891189</td>\n",
       "      <td>nm4891189</td>\n",
       "      <td>6.9</td>\n",
       "      <td>246</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2366</th>\n",
       "      <td>tt0023871</td>\n",
       "      <td>movie</td>\n",
       "      <td>A Song of Lisbon</td>\n",
       "      <td>A Canção de Lisboa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1933.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>Comedy,Musical</td>\n",
       "      <td>nm0183018</td>\n",
       "      <td>nm0183018,nm0302040</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1432</td>\n",
       "      <td>0.307087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153560</th>\n",
       "      <td>tt9695156</td>\n",
       "      <td>movie</td>\n",
       "      <td>27 Steps of May</td>\n",
       "      <td>27 Steps of May</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm1068687</td>\n",
       "      <td>nm1142012</td>\n",
       "      <td>8.1</td>\n",
       "      <td>418</td>\n",
       "      <td>0.976378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105739</th>\n",
       "      <td>tt1647977</td>\n",
       "      <td>short</td>\n",
       "      <td>A Hollywood Love Story</td>\n",
       "      <td>A Hollywood Love Story</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>Comedy,Drama,Short</td>\n",
       "      <td>nm3892014</td>\n",
       "      <td>nm3892014</td>\n",
       "      <td>8.8</td>\n",
       "      <td>192</td>\n",
       "      <td>0.913386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123164</th>\n",
       "      <td>tt3178292</td>\n",
       "      <td>movie</td>\n",
       "      <td>A: Film by Upendra</td>\n",
       "      <td>A: Film by Upendra</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150</td>\n",
       "      <td>Drama</td>\n",
       "      <td>nm1962192</td>\n",
       "      <td>nm1962192</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1951</td>\n",
       "      <td>0.818898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93557</th>\n",
       "      <td>tt1195489</td>\n",
       "      <td>short</td>\n",
       "      <td>Love</td>\n",
       "      <td>Love</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>Drama,Short</td>\n",
       "      <td>nm1131369</td>\n",
       "      <td>nm1131369</td>\n",
       "      <td>7.9</td>\n",
       "      <td>121</td>\n",
       "      <td>0.897638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143124</th>\n",
       "      <td>tt6683920</td>\n",
       "      <td>short</td>\n",
       "      <td>The Bliss of Being No One</td>\n",
       "      <td>The Bliss of Being No One</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>Drama,Short</td>\n",
       "      <td>nm3453844</td>\n",
       "      <td>nm3453844</td>\n",
       "      <td>7.7</td>\n",
       "      <td>242</td>\n",
       "      <td>0.960630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136058</th>\n",
       "      <td>tt5310090</td>\n",
       "      <td>movie</td>\n",
       "      <td>Saala Khadoos</td>\n",
       "      <td>Saala Khadoos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109</td>\n",
       "      <td>Action,Drama,Sport</td>\n",
       "      <td>nm1464314</td>\n",
       "      <td>nm1464314,nm0534856,nm8260883,nm3555217,nm8245...</td>\n",
       "      <td>7.6</td>\n",
       "      <td>11926</td>\n",
       "      <td>0.960630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23528</th>\n",
       "      <td>tt0073341</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Man Who Would Be King</td>\n",
       "      <td>The Man Who Would Be King</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129</td>\n",
       "      <td>Adventure,History,War</td>\n",
       "      <td>nm0001379</td>\n",
       "      <td>nm0001379,nm0384283,nm0456017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>47519</td>\n",
       "      <td>0.637795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33067</th>\n",
       "      <td>tt0094210</td>\n",
       "      <td>movie</td>\n",
       "      <td>Un zoo la nuit</td>\n",
       "      <td>Un zoo la nuit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115</td>\n",
       "      <td>Crime,Drama,Thriller</td>\n",
       "      <td>nm0491682</td>\n",
       "      <td>nm0491682</td>\n",
       "      <td>7.2</td>\n",
       "      <td>715</td>\n",
       "      <td>0.732283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118940</th>\n",
       "      <td>tt2562232</td>\n",
       "      <td>movie</td>\n",
       "      <td>Birdman or (The Unexpected Virtue of Ignorance)</td>\n",
       "      <td>Birdman or (The Unexpected Virtue of Ignorance)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>nm0327944</td>\n",
       "      <td>nm0327944,nm1481172,nm5409486,nm3174584,nm0142577</td>\n",
       "      <td>7.7</td>\n",
       "      <td>606119</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94143</th>\n",
       "      <td>tt12152704</td>\n",
       "      <td>short</td>\n",
       "      <td>We Are One Family</td>\n",
       "      <td>We Are One Family</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Short</td>\n",
       "      <td>nm0659243</td>\n",
       "      <td>nm0659243</td>\n",
       "      <td>8.8</td>\n",
       "      <td>129</td>\n",
       "      <td>0.992126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124614</th>\n",
       "      <td>tt3386444</td>\n",
       "      <td>tvMovie</td>\n",
       "      <td>That Day We Sang</td>\n",
       "      <td>That Day We Sang</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>Musical</td>\n",
       "      <td>nm0940064</td>\n",
       "      <td>nm0940064</td>\n",
       "      <td>7.7</td>\n",
       "      <td>342</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75425</th>\n",
       "      <td>tt0443424</td>\n",
       "      <td>short</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>Adventure,Animation,Family</td>\n",
       "      <td>nm0009942</td>\n",
       "      <td>nm0009942</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6620</td>\n",
       "      <td>0.874016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86676</th>\n",
       "      <td>tt10306206</td>\n",
       "      <td>tvMovie</td>\n",
       "      <td>A Heist with Markiplier</td>\n",
       "      <td>A Heist with Markiplier</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adventure,Comedy,Crime</td>\n",
       "      <td>nm6179964</td>\n",
       "      <td>nm6179964</td>\n",
       "      <td>9.2</td>\n",
       "      <td>562</td>\n",
       "      <td>0.984252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55788</th>\n",
       "      <td>tt0211691</td>\n",
       "      <td>short</td>\n",
       "      <td>Tulip</td>\n",
       "      <td>Tulip</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>Drama,Short</td>\n",
       "      <td>nm0341737</td>\n",
       "      <td>nm0341737</td>\n",
       "      <td>8.1</td>\n",
       "      <td>110</td>\n",
       "      <td>0.818898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38339</th>\n",
       "      <td>tt0105888</td>\n",
       "      <td>movie</td>\n",
       "      <td>And Life Goes On</td>\n",
       "      <td>Zendegi va digar hich</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95</td>\n",
       "      <td>Adventure,Drama</td>\n",
       "      <td>nm0452102</td>\n",
       "      <td>nm0452102</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5582</td>\n",
       "      <td>0.771654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80054</th>\n",
       "      <td>tt0758916</td>\n",
       "      <td>short</td>\n",
       "      <td>K-7</td>\n",
       "      <td>K-7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>Comedy,Short</td>\n",
       "      <td>nm0002814</td>\n",
       "      <td>nm0002814</td>\n",
       "      <td>7.3</td>\n",
       "      <td>145</td>\n",
       "      <td>0.881890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83917</th>\n",
       "      <td>tt0921766</td>\n",
       "      <td>short</td>\n",
       "      <td>Death to the Tinman</td>\n",
       "      <td>Death to the Tinman</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>Adventure,Comedy,Fantasy</td>\n",
       "      <td>nm0864092</td>\n",
       "      <td>nm0864092</td>\n",
       "      <td>7.8</td>\n",
       "      <td>546</td>\n",
       "      <td>0.889764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102365</th>\n",
       "      <td>tt14927356</td>\n",
       "      <td>short</td>\n",
       "      <td>Far from the Tree</td>\n",
       "      <td>Far from the Tree</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>nm8824925</td>\n",
       "      <td>nm8824925</td>\n",
       "      <td>7.6</td>\n",
       "      <td>986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105909</th>\n",
       "      <td>tt1656622</td>\n",
       "      <td>short</td>\n",
       "      <td>Change for a Dollar</td>\n",
       "      <td>Change for a Dollar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>Drama,Family,Short</td>\n",
       "      <td>nm3120888</td>\n",
       "      <td>nm3120888</td>\n",
       "      <td>8.3</td>\n",
       "      <td>124</td>\n",
       "      <td>0.913386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92467</th>\n",
       "      <td>tt11683950</td>\n",
       "      <td>short</td>\n",
       "      <td>The George Foreskin Grill</td>\n",
       "      <td>The George Foreskin Grill</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Comedy,Short</td>\n",
       "      <td>nm1518150</td>\n",
       "      <td>nm1518150</td>\n",
       "      <td>8.7</td>\n",
       "      <td>134</td>\n",
       "      <td>0.984252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112108</th>\n",
       "      <td>tt2048726</td>\n",
       "      <td>movie</td>\n",
       "      <td>Daruchini Dwip</td>\n",
       "      <td>Daruchini Dwip</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>nm2609716</td>\n",
       "      <td>nm0014152</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3041</td>\n",
       "      <td>0.889764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136268</th>\n",
       "      <td>tt5343524</td>\n",
       "      <td>short</td>\n",
       "      <td>First is the Worst</td>\n",
       "      <td>First is the Worst</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Crime,Drama,Short</td>\n",
       "      <td>nm1469684</td>\n",
       "      <td>nm1469684</td>\n",
       "      <td>8.7</td>\n",
       "      <td>113</td>\n",
       "      <td>0.960630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96968</th>\n",
       "      <td>tt1298641</td>\n",
       "      <td>tvMovie</td>\n",
       "      <td>Britain's Got the Pop Factor... and Possibly a...</td>\n",
       "      <td>Britain's Got the Pop Factor... and Possibly a...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120</td>\n",
       "      <td>Comedy,Music</td>\n",
       "      <td>nm0443153</td>\n",
       "      <td>nm0443153,nm1844473</td>\n",
       "      <td>6.8</td>\n",
       "      <td>727</td>\n",
       "      <td>0.897638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120102</th>\n",
       "      <td>tt2730162</td>\n",
       "      <td>short</td>\n",
       "      <td>Fool's Day</td>\n",
       "      <td>Fool's Day</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>Comedy,Drama,Short</td>\n",
       "      <td>nm2913326</td>\n",
       "      <td>nm2913326,nm3229298</td>\n",
       "      <td>8.1</td>\n",
       "      <td>430</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113932</th>\n",
       "      <td>tt2172935</td>\n",
       "      <td>movie</td>\n",
       "      <td>Metallica Through the Never</td>\n",
       "      <td>Metallica Through the Never</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>Music</td>\n",
       "      <td>nm0030735</td>\n",
       "      <td>nm0030735,nm0381723,nm0005515,nm0004987,nm0874235</td>\n",
       "      <td>7.2</td>\n",
       "      <td>17227</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76982</th>\n",
       "      <td>tt0461857</td>\n",
       "      <td>movie</td>\n",
       "      <td>A Stranger of Mine</td>\n",
       "      <td>Unmei janai hito</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>nm1932020</td>\n",
       "      <td>nm1932020</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1131</td>\n",
       "      <td>0.874016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86399</th>\n",
       "      <td>tt1024215</td>\n",
       "      <td>video</td>\n",
       "      <td>Ghost in the Shell: Stand Alone Complex - The ...</td>\n",
       "      <td>Kôkaku kidôtai: Stand alone complex - The laug...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160</td>\n",
       "      <td>Action,Animation,Drama</td>\n",
       "      <td>nm0436784</td>\n",
       "      <td>nm1825288,nm0794385,nm1371924,nm0766199,nm1591046</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2038</td>\n",
       "      <td>0.874016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126798</th>\n",
       "      <td>tt3681442</td>\n",
       "      <td>movie</td>\n",
       "      <td>Mundasupatti</td>\n",
       "      <td>Mundaasupatti</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>nm6442107</td>\n",
       "      <td>nm6442107</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1379</td>\n",
       "      <td>0.944882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114338</th>\n",
       "      <td>tt2194499</td>\n",
       "      <td>movie</td>\n",
       "      <td>About Time</td>\n",
       "      <td>About Time</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>nm0193485</td>\n",
       "      <td>nm0193485</td>\n",
       "      <td>7.8</td>\n",
       "      <td>327067</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40920</th>\n",
       "      <td>tt0111579</td>\n",
       "      <td>movie</td>\n",
       "      <td>Burnt by the Sun</td>\n",
       "      <td>Utomlennye solntsem</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135</td>\n",
       "      <td>Drama,History,War</td>\n",
       "      <td>nm0586482</td>\n",
       "      <td>nm0586482,nm0406534</td>\n",
       "      <td>7.9</td>\n",
       "      <td>15083</td>\n",
       "      <td>0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129592</th>\n",
       "      <td>tt4128132</td>\n",
       "      <td>short</td>\n",
       "      <td>TSA America: Just Relax</td>\n",
       "      <td>TSA America: Just Relax</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>Comedy,Short</td>\n",
       "      <td>nm0172557</td>\n",
       "      <td>nm0172557,nm3889751,nm2943251</td>\n",
       "      <td>9.1</td>\n",
       "      <td>114</td>\n",
       "      <td>0.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143635</th>\n",
       "      <td>tt6818902</td>\n",
       "      <td>short</td>\n",
       "      <td>Pfa: Love Mom &amp; Dad</td>\n",
       "      <td>Pfa: Love Mom &amp; Dad</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>Drama,Short</td>\n",
       "      <td>nm7219937</td>\n",
       "      <td>nm7219937</td>\n",
       "      <td>7.5</td>\n",
       "      <td>596</td>\n",
       "      <td>0.968504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tconst titleType  \\\n",
       "41780    tt0113568     movie   \n",
       "95608    tt1260502     movie   \n",
       "67736    tt0347246     movie   \n",
       "139613   tt5973984   tvMovie   \n",
       "130778   tt4337072     movie   \n",
       "126070   tt3579524     movie   \n",
       "128926   tt4016942     movie   \n",
       "119506   tt2636124     movie   \n",
       "121958   tt3017864     movie   \n",
       "82649    tt0856797   tvMovie   \n",
       "109750   tt1856022     movie   \n",
       "94279    tt1219827     movie   \n",
       "75683    tt0446488     video   \n",
       "73152    tt0416449     movie   \n",
       "121416   tt2945970     movie   \n",
       "68786    tt0363226     movie   \n",
       "59284    tt0252315     movie   \n",
       "130093   tt4208318     video   \n",
       "2366     tt0023871     movie   \n",
       "153560   tt9695156     movie   \n",
       "105739   tt1647977     short   \n",
       "123164   tt3178292     movie   \n",
       "93557    tt1195489     short   \n",
       "143124   tt6683920     short   \n",
       "136058   tt5310090     movie   \n",
       "23528    tt0073341     movie   \n",
       "33067    tt0094210     movie   \n",
       "118940   tt2562232     movie   \n",
       "94143   tt12152704     short   \n",
       "124614   tt3386444   tvMovie   \n",
       "75425    tt0443424     short   \n",
       "86676   tt10306206   tvMovie   \n",
       "55788    tt0211691     short   \n",
       "38339    tt0105888     movie   \n",
       "80054    tt0758916     short   \n",
       "83917    tt0921766     short   \n",
       "102365  tt14927356     short   \n",
       "105909   tt1656622     short   \n",
       "92467   tt11683950     short   \n",
       "112108   tt2048726     movie   \n",
       "136268   tt5343524     short   \n",
       "96968    tt1298641   tvMovie   \n",
       "120102   tt2730162     short   \n",
       "113932   tt2172935     movie   \n",
       "76982    tt0461857     movie   \n",
       "86399    tt1024215     video   \n",
       "126798   tt3681442     movie   \n",
       "114338   tt2194499     movie   \n",
       "40920    tt0111579     movie   \n",
       "129592   tt4128132     short   \n",
       "143635   tt6818902     short   \n",
       "\n",
       "                                             primaryTitle  \\\n",
       "41780                                  Ghost in the Shell   \n",
       "95608                              Ghost in the Shell 2.0   \n",
       "67736                     Ghost in the Shell 2: Innocence   \n",
       "139613  Ghost in the Shell Arise: Border 5 - Pyrophori...   \n",
       "130778                  Ghost in the Shell: The New Movie   \n",
       "126070   Ghost in the Shell Arise: Border 3 - Ghost Tears   \n",
       "128926  Ghost in the Shell Arise: Border 4 - Ghost Sta...   \n",
       "119506   Ghost in the Shell: Arise - Border 1: Ghost Pain   \n",
       "121958  Ghost in the Shell Arise: Border 2 - Ghost Whi...   \n",
       "82649   Ghost in the Shell: Stand Alone Complex - Soli...   \n",
       "109750   Ghost in the Shell S.A.C. Solid State Society 3D   \n",
       "94279                                  Ghost in the Shell   \n",
       "75683                                   A Touch of Greece   \n",
       "73152                                                 300   \n",
       "121416                            The Last American Guido   \n",
       "68786                       The Blind Swordsman: Zatoichi   \n",
       "59284                                      Man in a Shell   \n",
       "130093                     No Joke: Don't Drink and Drive   \n",
       "2366                                     A Song of Lisbon   \n",
       "153560                                    27 Steps of May   \n",
       "105739                             A Hollywood Love Story   \n",
       "123164                                 A: Film by Upendra   \n",
       "93557                                                Love   \n",
       "143124                          The Bliss of Being No One   \n",
       "136058                                      Saala Khadoos   \n",
       "23528                           The Man Who Would Be King   \n",
       "33067                                      Un zoo la nuit   \n",
       "118940    Birdman or (The Unexpected Virtue of Ignorance)   \n",
       "94143                                   We Are One Family   \n",
       "124614                                   That Day We Sang   \n",
       "75425                                                   9   \n",
       "86676                             A Heist with Markiplier   \n",
       "55788                                               Tulip   \n",
       "38339                                    And Life Goes On   \n",
       "80054                                                 K-7   \n",
       "83917                                 Death to the Tinman   \n",
       "102365                                  Far from the Tree   \n",
       "105909                                Change for a Dollar   \n",
       "92467                           The George Foreskin Grill   \n",
       "112108                                     Daruchini Dwip   \n",
       "136268                                 First is the Worst   \n",
       "96968   Britain's Got the Pop Factor... and Possibly a...   \n",
       "120102                                         Fool's Day   \n",
       "113932                        Metallica Through the Never   \n",
       "76982                                  A Stranger of Mine   \n",
       "86399   Ghost in the Shell: Stand Alone Complex - The ...   \n",
       "126798                                       Mundasupatti   \n",
       "114338                                         About Time   \n",
       "40920                                    Burnt by the Sun   \n",
       "129592                            TSA America: Just Relax   \n",
       "143635                                Pfa: Love Mom & Dad   \n",
       "\n",
       "                                            originalTitle  isAdult  startYear  \\\n",
       "41780                                      Kôkaku Kidôtai      0.0     1995.0   \n",
       "95608                                  Kôkaku kidôtai 2.0      0.0     2008.0   \n",
       "67736                                 Inosensu: Innocence      0.0     2004.0   \n",
       "139613  Ghost in the Shell Arise: Border 5 - Pyrophori...      0.0     2015.0   \n",
       "130778                      Kôkaku Kidôtai Shin Gekijôban      0.0     2015.0   \n",
       "126070   Ghost in the Shell Arise: Border 3 - Ghost Tears      0.0     2014.0   \n",
       "128926  Ghost in the Shell Arise: Border 4 - Ghost Sta...      0.0     2014.0   \n",
       "119506    Ghost in the Shell Arise: Border 1 - Ghost Pain      0.0     2013.0   \n",
       "121958  Ghost in the Shell Arise: Border 2 - Ghost Whi...      0.0     2013.0   \n",
       "82649   Kôkaku kidôtai: Stand Alone Complex Solid Stat...      0.0     2006.0   \n",
       "109750       Kôkaku kidôtai S.A.C. Solid State Society 3D      0.0     2011.0   \n",
       "94279                                  Ghost in the Shell      0.0     2017.0   \n",
       "75683                                   A Touch of Greece      0.0     2004.0   \n",
       "73152                                                 300      0.0     2006.0   \n",
       "121416                            The Last American Guido      0.0     2014.0   \n",
       "68786                                            Zatôichi      0.0     2003.0   \n",
       "59284                                 Chelovek v futlyare      0.0     1939.0   \n",
       "130093                     No Joke: Don't Drink and Drive      0.0     2014.0   \n",
       "2366                                   A Canção de Lisboa      0.0     1933.0   \n",
       "153560                                    27 Steps of May      0.0     2018.0   \n",
       "105739                             A Hollywood Love Story      0.0     2010.0   \n",
       "123164                                 A: Film by Upendra      0.0     1998.0   \n",
       "93557                                                Love      0.0     2008.0   \n",
       "143124                          The Bliss of Being No One      0.0     2016.0   \n",
       "136058                                      Saala Khadoos      0.0     2016.0   \n",
       "23528                           The Man Who Would Be King      0.0     1975.0   \n",
       "33067                                      Un zoo la nuit      0.0     1987.0   \n",
       "118940    Birdman or (The Unexpected Virtue of Ignorance)      0.0     2014.0   \n",
       "94143                                   We Are One Family      0.0     2020.0   \n",
       "124614                                   That Day We Sang      0.0     2014.0   \n",
       "75425                                                   9      0.0     2005.0   \n",
       "86676                             A Heist with Markiplier      0.0     2019.0   \n",
       "55788                                               Tulip      0.0     1998.0   \n",
       "38339                               Zendegi va digar hich      0.0     1992.0   \n",
       "80054                                                 K-7      0.0     2006.0   \n",
       "83917                                 Death to the Tinman      0.0     2007.0   \n",
       "102365                                  Far from the Tree      0.0     2021.0   \n",
       "105909                                Change for a Dollar      0.0     2010.0   \n",
       "92467                           The George Foreskin Grill      0.0     2019.0   \n",
       "112108                                     Daruchini Dwip      0.0     2007.0   \n",
       "136268                                 First is the Worst      0.0     2016.0   \n",
       "96968   Britain's Got the Pop Factor... and Possibly a...      0.0     2008.0   \n",
       "120102                                         Fool's Day      0.0     2013.0   \n",
       "113932                        Metallica Through the Never      0.0     2013.0   \n",
       "76982                                    Unmei janai hito      0.0     2005.0   \n",
       "86399   Kôkaku kidôtai: Stand alone complex - The laug...      0.0     2005.0   \n",
       "126798                                      Mundaasupatti      0.0     2014.0   \n",
       "114338                                         About Time      0.0     2013.0   \n",
       "40920                                 Utomlennye solntsem      0.0     1994.0   \n",
       "129592                            TSA America: Just Relax      0.0     2013.0   \n",
       "143635                                Pfa: Love Mom & Dad      0.0     2017.0   \n",
       "\n",
       "        endYear runtimeMinutes                      genres  \\\n",
       "41780       NaN             83      Action,Animation,Crime   \n",
       "95608       NaN             83  Action,Adventure,Animation   \n",
       "67736       NaN            100     Animation,Drama,Mystery   \n",
       "139613      NaN             46     Action,Animation,Sci-Fi   \n",
       "130778      NaN            100     Action,Animation,Sci-Fi   \n",
       "126070      NaN             55     Action,Animation,Sci-Fi   \n",
       "128926      NaN             59     Action,Animation,Sci-Fi   \n",
       "119506      NaN             58     Action,Animation,Sci-Fi   \n",
       "121958      NaN             57     Action,Animation,Sci-Fi   \n",
       "82649       NaN            108  Action,Adventure,Animation   \n",
       "109750      NaN            130    Action,Animation,Fantasy   \n",
       "94279       NaN            107          Action,Crime,Drama   \n",
       "75683       NaN             93                   Adventure   \n",
       "73152       NaN            117                Action,Drama   \n",
       "121416      NaN             99                      Comedy   \n",
       "68786       NaN            116         Action,Comedy,Crime   \n",
       "59284       NaN             84                       Drama   \n",
       "130093      NaN              1                 Crime,Short   \n",
       "2366        NaN             85              Comedy,Musical   \n",
       "153560      NaN            112                       Drama   \n",
       "105739      NaN             17          Comedy,Drama,Short   \n",
       "123164      NaN            150                       Drama   \n",
       "93557       NaN             13                 Drama,Short   \n",
       "143124      NaN             26                 Drama,Short   \n",
       "136058      NaN            109          Action,Drama,Sport   \n",
       "23528       NaN            129       Adventure,History,War   \n",
       "33067       NaN            115        Crime,Drama,Thriller   \n",
       "118940      NaN            119                Comedy,Drama   \n",
       "94143       NaN              5                       Short   \n",
       "124614      NaN             89                     Musical   \n",
       "75425       NaN             11  Adventure,Animation,Family   \n",
       "86676       NaN            NaN      Adventure,Comedy,Crime   \n",
       "55788       NaN             15                 Drama,Short   \n",
       "38339       NaN             95             Adventure,Drama   \n",
       "80054       NaN             18                Comedy,Short   \n",
       "83917       NaN             12    Adventure,Comedy,Fantasy   \n",
       "102365      NaN              7  Adventure,Animation,Comedy   \n",
       "105909      NaN             11          Drama,Family,Short   \n",
       "92467       NaN              2                Comedy,Short   \n",
       "112108      NaN            128        Comedy,Drama,Romance   \n",
       "136268      NaN              7           Crime,Drama,Short   \n",
       "96968       NaN            120                Comedy,Music   \n",
       "120102      NaN             19          Comedy,Drama,Short   \n",
       "113932      NaN             93                       Music   \n",
       "76982       NaN             98        Comedy,Drama,Romance   \n",
       "86399       NaN            160      Action,Animation,Drama   \n",
       "126798      NaN            148        Comedy,Drama,Romance   \n",
       "114338      NaN            123        Comedy,Drama,Fantasy   \n",
       "40920       NaN            135           Drama,History,War   \n",
       "129592      NaN              8                Comedy,Short   \n",
       "143635      NaN             15                 Drama,Short   \n",
       "\n",
       "                  directors  \\\n",
       "41780             nm0651900   \n",
       "95608             nm0651900   \n",
       "67736             nm0651900   \n",
       "139613            nm0457124   \n",
       "130778  nm0457124,nm2420940   \n",
       "126070            nm0457124   \n",
       "128926  nm1556115,nm0457124   \n",
       "119506  nm1496044,nm0457124   \n",
       "121958  nm0847591,nm0457124   \n",
       "82649             nm0436784   \n",
       "109750            nm0436784   \n",
       "94279             nm2782185   \n",
       "75683             nm1583975   \n",
       "73152             nm0811583   \n",
       "121416            nm2766988   \n",
       "68786             nm0001429   \n",
       "59284             nm0029363   \n",
       "130093            nm4891189   \n",
       "2366              nm0183018   \n",
       "153560            nm1068687   \n",
       "105739            nm3892014   \n",
       "123164            nm1962192   \n",
       "93557             nm1131369   \n",
       "143124            nm3453844   \n",
       "136058            nm1464314   \n",
       "23528             nm0001379   \n",
       "33067             nm0491682   \n",
       "118940            nm0327944   \n",
       "94143             nm0659243   \n",
       "124614            nm0940064   \n",
       "75425             nm0009942   \n",
       "86676             nm6179964   \n",
       "55788             nm0341737   \n",
       "38339             nm0452102   \n",
       "80054             nm0002814   \n",
       "83917             nm0864092   \n",
       "102365            nm8824925   \n",
       "105909            nm3120888   \n",
       "92467             nm1518150   \n",
       "112108            nm2609716   \n",
       "136268            nm1469684   \n",
       "96968             nm0443153   \n",
       "120102            nm2913326   \n",
       "113932            nm0030735   \n",
       "76982             nm1932020   \n",
       "86399             nm0436784   \n",
       "126798            nm6442107   \n",
       "114338            nm0193485   \n",
       "40920             nm0586482   \n",
       "129592            nm0172557   \n",
       "143635            nm7219937   \n",
       "\n",
       "                                                  writers  averageRating  \\\n",
       "41780                                 nm0794385,nm0411872            8.0   \n",
       "95608                                 nm0794385,nm0411872            8.0   \n",
       "67736             nm0794385,nm0651900,nm0258268,nm0556856            7.5   \n",
       "139613                                nm0794385,nm2396196            7.3   \n",
       "130778            nm0794385,nm0121423,nm1727020,nm2396196            6.7   \n",
       "126070                      nm0794385,nm2396196,nm0926075            7.2   \n",
       "128926                                nm0794385,nm2396196            7.2   \n",
       "119506            nm0794385,nm2396196,nm0121423,nm1727020            7.1   \n",
       "121958                                nm0794385,nm2396196            7.2   \n",
       "82649             nm0794385,nm0436784,nm1591046,nm1371924            8.0   \n",
       "109750            nm0794385,nm0436784,nm1591046,nm1371924            7.6   \n",
       "94279             nm0794385,nm1150145,nm0923980,nm0472567            6.3   \n",
       "75683                                           nm1583975            7.7   \n",
       "73152   nm0811583,nm0426500,nm1290582,nm0588340,nm2241067            7.6   \n",
       "121416                                          nm2766988            7.3   \n",
       "68786                                 nm0001429,nm0793753            7.5   \n",
       "59284                                 nm0029363,nm0155009            7.6   \n",
       "130093                                          nm4891189            6.9   \n",
       "2366                                  nm0183018,nm0302040            7.7   \n",
       "153560                                          nm1142012            8.1   \n",
       "105739                                          nm3892014            8.8   \n",
       "123164                                          nm1962192            8.8   \n",
       "93557                                           nm1131369            7.9   \n",
       "143124                                          nm3453844            7.7   \n",
       "136058  nm1464314,nm0534856,nm8260883,nm3555217,nm8245...            7.6   \n",
       "23528                       nm0001379,nm0384283,nm0456017            7.8   \n",
       "33067                                           nm0491682            7.2   \n",
       "118940  nm0327944,nm1481172,nm5409486,nm3174584,nm0142577            7.7   \n",
       "94143                                           nm0659243            8.8   \n",
       "124614                                          nm0940064            7.7   \n",
       "75425                                           nm0009942            7.8   \n",
       "86676                                           nm6179964            9.2   \n",
       "55788                                           nm0341737            8.1   \n",
       "38339                                           nm0452102            7.9   \n",
       "80054                                           nm0002814            7.3   \n",
       "83917                                           nm0864092            7.8   \n",
       "102365                                          nm8824925            7.6   \n",
       "105909                                          nm3120888            8.3   \n",
       "92467                                           nm1518150            8.7   \n",
       "112108                                          nm0014152            7.9   \n",
       "136268                                          nm1469684            8.7   \n",
       "96968                                 nm0443153,nm1844473            6.8   \n",
       "120102                                nm2913326,nm3229298            8.1   \n",
       "113932  nm0030735,nm0381723,nm0005515,nm0004987,nm0874235            7.2   \n",
       "76982                                           nm1932020            7.6   \n",
       "86399   nm1825288,nm0794385,nm1371924,nm0766199,nm1591046            8.3   \n",
       "126798                                          nm6442107            7.6   \n",
       "114338                                          nm0193485            7.8   \n",
       "40920                                 nm0586482,nm0406534            7.9   \n",
       "129592                      nm0172557,nm3889751,nm2943251            9.1   \n",
       "143635                                          nm7219937            7.5   \n",
       "\n",
       "        numVotes  startYear_norm  \n",
       "41780     137202        0.795276  \n",
       "95608      14256        0.897638  \n",
       "67736      37590        0.866142  \n",
       "139613       651        0.952756  \n",
       "130778      3737        0.952756  \n",
       "126070      2642        0.944882  \n",
       "128926      2070        0.944882  \n",
       "119506      3991        0.937008  \n",
       "121958      2917        0.937008  \n",
       "82649       6953        0.881890  \n",
       "109750       491        0.921260  \n",
       "94279     207774        0.968504  \n",
       "75683        138        0.866142  \n",
       "73152     762539        0.881890  \n",
       "121416       109        0.944882  \n",
       "68786      48800        0.858268  \n",
       "59284        134        0.354331  \n",
       "130093       246        0.944882  \n",
       "2366        1432        0.307087  \n",
       "153560       418        0.976378  \n",
       "105739       192        0.913386  \n",
       "123164      1951        0.818898  \n",
       "93557        121        0.897638  \n",
       "143124       242        0.960630  \n",
       "136058     11926        0.960630  \n",
       "23528      47519        0.637795  \n",
       "33067        715        0.732283  \n",
       "118940    606119        0.944882  \n",
       "94143        129        0.992126  \n",
       "124614       342        0.944882  \n",
       "75425       6620        0.874016  \n",
       "86676        562        0.984252  \n",
       "55788        110        0.818898  \n",
       "38339       5582        0.771654  \n",
       "80054        145        0.881890  \n",
       "83917        546        0.889764  \n",
       "102365       986        1.000000  \n",
       "105909       124        0.913386  \n",
       "92467        134        0.984252  \n",
       "112108      3041        0.889764  \n",
       "136268       113        0.960630  \n",
       "96968        727        0.897638  \n",
       "120102       430        0.937008  \n",
       "113932     17227        0.937008  \n",
       "76982       1131        0.874016  \n",
       "86399       2038        0.874016  \n",
       "126798      1379        0.944882  \n",
       "114338    327067        0.937008  \n",
       "40920      15083        0.787402  \n",
       "129592       114        0.937008  \n",
       "143635       596        0.968504  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findNearest(\"tt0113568\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'simple-3d-scatter-x.html'"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import *\n",
    "\n",
    "idx = df[df[\"tconst\"] == \"tt0113568\"].index[0]\n",
    "_, ind = kdtree.query(X_train_PCA[idx:idx+1], k=100)\n",
    "nearest3d =  X_train_PCA[ind[0]]\n",
    "\n",
    "tsne = TSNE(n_components=3).fit_transform(nearest3d)\n",
    "\n",
    "texts = df.iloc[ind[0]][\"primaryTitle\"]\n",
    "\n",
    "li = []\n",
    "for x in range(100):\n",
    "    li.append(x)\n",
    "\n",
    "scatter = go.Scatter3d(\n",
    "    x=tsne[:,0],\n",
    "    y=tsne[:,1],\n",
    "    z=tsne[:,2],\n",
    "    mode='markers',\n",
    "    text=texts,\n",
    "    marker=dict(\n",
    "        size=8,\n",
    "        color=li,                # set color to an array/list of desired values\n",
    "        colorscale='Viridis',   # choose a colorscale\n",
    "        opacity=0.8\n",
    "    ))\n",
    "\n",
    "fig = go.Figure(data=[scatter])\n",
    "\n",
    "plot(fig, filename='simple-3d-scatter-x.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'simple-2d-scatter-x.html'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne2 = TSNE(n_components=2).fit_transform(nearest3d)\n",
    "\n",
    "\n",
    "scatter2 = go.Scatter(\n",
    "    x=tsne[:,0],\n",
    "    y=tsne[:,1],\n",
    "    mode='markers',\n",
    "    text=texts,\n",
    "    marker=dict(\n",
    "        size=8,\n",
    "        color=li,                # set color to an array/list of desired values\n",
    "        colorscale='Viridis',   # choose a colorscale\n",
    "        opacity=0.8\n",
    "    ))\n",
    "\n",
    "fig2 = go.Figure(data=[scatter2])\n",
    "\n",
    "plot(fig2, filename='simple-2d-scatter-x.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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