{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('tmdb_5000_movies.csv')\n",
    "credits = pd.read_csv('tmdb_5000_credits.csv')\n",
    "df = pd.merge(movies,credits,left_on=['id','title'],right_on=['movie_id','title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "def load_json_columns(col):\n",
    "    col = col.apply(json.loads)\n",
    "load_json_columns(df['genres'])\n",
    "# df['genres'] = df['genres'].apply(json.loads)\n",
    "# df['keywords'] = df['keywords'].apply(json.loads)\n",
    "# df['production_companies'] = df['production_companies'].apply(json.loads)\n",
    "# df['production_countries'] = df['production_countries'].apply(json.loads)\n",
    "# df['cast'] = df['cast'].apply(json.loads)\n",
    "# df['crew'] = df['crew'].apply(json.loads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 1463, 'name': 'culture clash'},\n",
       " {'id': 2964, 'name': 'future'},\n",
       " {'id': 3386, 'name': 'space war'},\n",
       " {'id': 3388, 'name': 'space colony'},\n",
       " {'id': 3679, 'name': 'society'},\n",
       " {'id': 3801, 'name': 'space travel'},\n",
       " {'id': 9685, 'name': 'futuristic'},\n",
       " {'id': 9840, 'name': 'romance'},\n",
       " {'id': 9882, 'name': 'space'},\n",
       " {'id': 9951, 'name': 'alien'},\n",
       " {'id': 10148, 'name': 'tribe'},\n",
       " {'id': 10158, 'name': 'alien planet'},\n",
       " {'id': 10987, 'name': 'cgi'},\n",
       " {'id': 11399, 'name': 'marine'},\n",
       " {'id': 13065, 'name': 'soldier'},\n",
       " {'id': 14643, 'name': 'battle'},\n",
       " {'id': 14720, 'name': 'love affair'},\n",
       " {'id': 165431, 'name': 'anti war'},\n",
       " {'id': 193554, 'name': 'power relations'},\n",
       " {'id': 206690, 'name': 'mind and soul'},\n",
       " {'id': 209714, 'name': '3d'}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['keywords'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}