movie api.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from requests import get\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "\n",
    "from pandasql import sqldf\n",
    "pysqldf = lambda q: sqldf(q, globals())\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tconst                         0\n",
       "titleType                      0\n",
       "primaryTitle                   0\n",
       "originalTitle                  0\n",
       "isAdult                        0\n",
       "startYear                      0\n",
       "endYear                        0\n",
       "runtimeMinutes                 0\n",
       "genres                         0\n",
       "title                          0\n",
       "check                          0\n",
       "averageRating                  0\n",
       "numVotes                       0\n",
       "budget_LCY                  1011\n",
       "gross_USA_USD                930\n",
       "cumm_worldwide_gross_USD     629\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imdb = pd.read_csv('2016_2019_USmovies.csv')\n",
    "df_imdb.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# http://www.omdbapi.com/?i=tt3896198&apikey=81f8cc38\n",
    "\n",
    "base_url = \"http://www.omdbapi.com/?i=\"\n",
    "key1 = \"&apikey=81f8cc38\"\n",
    "key2 = \"&apikey=a7bcd2ed\"\n",
    "key3 = \"&apikey=7af14aee\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = base_url+df_imdb.tconst.iloc[0]+end_url\n",
    "r = get(url)\n",
    "print(r.status_code)\n",
    "response = r.json()\n",
    "type(r.status_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Title': 'The Other Side of the Wind',\n",
       " 'Year': '2018',\n",
       " 'Rated': 'R',\n",
       " 'Released': '02 Nov 2018',\n",
       " 'Runtime': '122 min',\n",
       " 'Genre': 'Drama',\n",
       " 'Director': 'Orson Welles',\n",
       " 'Writer': 'Orson Welles, Oja Kodar',\n",
       " 'Actors': 'John Huston, Oja Kodar, Peter Bogdanovich, Susan Strasberg',\n",
       " 'Plot': 'A Hollywood director emerges from semi-exile with plans to complete work on an innovative motion picture.',\n",
       " 'Language': 'English, German',\n",
       " 'Country': 'France, Iran, USA',\n",
       " 'Awards': 'N/A',\n",
       " 'Poster': 'https://m.media-amazon.com/images/M/MV5BNDg5NTQ0NDU1Nl5BMl5BanBnXkFtZTgwMTExNzIyNjM@._V1_SX300.jpg',\n",
       " 'Ratings': [{'Source': 'Internet Movie Database', 'Value': '6.9/10'},\n",
       "  {'Source': 'Rotten Tomatoes', 'Value': '83%'},\n",
       "  {'Source': 'Metacritic', 'Value': '78/100'}],\n",
       " 'Metascore': '78',\n",
       " 'imdbRating': '6.9',\n",
       " 'imdbVotes': '4,936',\n",
       " 'imdbID': 'tt0069049',\n",
       " 'Type': 'movie',\n",
       " 'DVD': '02 Nov 2018',\n",
       " 'BoxOffice': 'N/A',\n",
       " 'Production': 'Netflix',\n",
       " 'Website': 'N/A',\n",
       " 'Response': 'True'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2018-11-02'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "date_string = response[\"Released\"]\n",
    "datetime.datetime.strptime(date_string, '%d %b %Y').month\n",
    "str(datetime.datetime.strptime(date_string, '%d %b %Y').date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83\n"
     ]
    }
   ],
   "source": [
    "for i in response[\"Ratings\"]:\n",
    "    if list(i.values())[0] == \"Rotten Tomatoes\":\n",
    "        print(str(list(i.values())[1]).replace(\"%\", \"\"))\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'78'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response[\"Metascore\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'John Huston, Oja Kodar, Peter Bogdanovich, Susan Strasberg'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response[\"Actors\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Netflix'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response[\"Production\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymongo\n",
    "myclient = pymongo.MongoClient(\"mongodb://127.0.0.1:27017/\")\n",
    "db = myclient[\"Project3\"]\n",
    "movie = db[\"Movies\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pymongo.results.DeleteResult at 0x11c54bcc8>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#movie.insert(response)\n",
    "movie.delete_many({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = movie.find({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "for line in query:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imdb[\"Released_Date\"] = pd.Series([])\n",
    "df_imdb[\"Released_Month\"] = pd.Series([])\n",
    "df_imdb[\"Rotten_Tomatoes\"] = pd.Series([])\n",
    "df_imdb[\"Metascore\"] = pd.Series([])\n",
    "df_imdb[\"Actors\"] = pd.Series([])\n",
    "df_imdb[\"Production\"] = pd.Series([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: insert is deprecated. Use insert_one or insert_many instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for i in range(200, 2015):\n",
    "    url = base_url+df_imdb.tconst.iloc[i]+key2\n",
    "    r = get(url)\n",
    "    if r.status_code == 200:\n",
    "        response = r.json()\n",
    "        movie.insert(response)\n",
    "        df_imdb[\"Released_Date\"].iloc[i] = response[\"Released\"]\n",
    "        df_imdb[\"Metascore\"].iloc[i] = response[\"Metascore\"]\n",
    "        df_imdb[\"Actors\"].iloc[i] = response[\"Actors\"]\n",
    "        df_imdb[\"Production\"].iloc[i] = response[\"Production\"]\n",
    "        for j in response[\"Ratings\"]:\n",
    "            if list(j.values())[0] == \"Rotten Tomatoes\":\n",
    "                df_imdb[\"Rotten_Tomatoes\"].iloc[i] = list(j.values())[1]\n",
    "            else:\n",
    "                continue\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(1000):\n",
    "#     url = base_url+df_imdb.tconst.iloc[i]+key1\n",
    "#     r = get(url)\n",
    "#     if r.status_code == 200:\n",
    "#         response = r.json()\n",
    "#         movie.insert(response)\n",
    "#     else:\n",
    "#         continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_imdb[\"Released_Date\"].iloc[i] = str(datetime.datetime.strptime(response[\"Released\"], '%d %b %Y').date()) if response[\"Released\"] != \"N/A\" else \"N/A\"\n",
    "# df_imdb[\"Released_Month\"].iloc[i] = datetime.datetime.strptime(response[\"Released\"], '%d %b %Y').month if response[\"Released\"] != \"N/A\" else \"N/A\"\n",
    "# df_imdb[\"Metascore\"].iloc[i] = int(response[\"Metascore\"]) if response[\"Metascore\"] != \"N/A\" else \"N/A\"\n",
    "# df_imdb[\"Actors\"].iloc[i] = response[\"Actors\"]\n",
    "# df_imdb[\"Production\"].iloc[i] = response[\"Production\"]\n",
    "# for j in response[\"Ratings\"]:\n",
    "#     if list(j.values())[0] == \"Rotten Tomatoes\":\n",
    "#         df_imdb[\"Rotten_Tomatoes\"].iloc[i] = int((list(j.values())[1]).replace(\"%\", \"\"))\n",
    "#     else:\n",
    "#         continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2015 entries, 0 to 2014\n",
      "Data columns (total 22 columns):\n",
      "tconst                      2015 non-null object\n",
      "titleType                   2015 non-null object\n",
      "primaryTitle                2015 non-null object\n",
      "originalTitle               2015 non-null object\n",
      "isAdult                     2015 non-null int64\n",
      "startYear                   2015 non-null int64\n",
      "endYear                     2015 non-null object\n",
      "runtimeMinutes              2015 non-null int64\n",
      "genres                      2015 non-null object\n",
      "title                       2015 non-null object\n",
      "check                       2015 non-null int64\n",
      "averageRating               2015 non-null float64\n",
      "numVotes                    2015 non-null int64\n",
      "budget_LCY                  1004 non-null object\n",
      "gross_USA_USD               1085 non-null float64\n",
      "cumm_worldwide_gross_USD    1386 non-null float64\n",
      "Released_Date               1000 non-null object\n",
      "Released_Month              1000 non-null object\n",
      "Rotten_Tomatoes             787 non-null object\n",
      "Metascore                   1000 non-null object\n",
      "Actors                      1000 non-null object\n",
      "Production                  1000 non-null object\n",
      "dtypes: float64(3), int64(5), object(14)\n",
      "memory usage: 346.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_imdb[\"Released_Date\"].iloc[i] = str(datetime.datetime.strptime(response[\"Released\"], '%d %b %Y').date()) if response[\"Released\"] != \"N/A\" else \"N/A\"\n",
    "df_imdb[\"Released_Month\"].iloc[i] = datetime.datetime.strptime(response[\"Released\"], '%d %b %Y').month if response[\"Released\"] != \"N/A\" else \"N/A\"\n",
    "df_imdb[\"Metascore\"].iloc[i] = int(response[\"Metascore\"]) if response[\"Metascore\"] != \"N/A\" else \"N/A\"\n",
    "df_imdb[\"Actors\"].iloc[i] = response[\"Actors\"]\n",
    "df_imdb[\"Production\"].iloc[i] = response[\"Production\"]\n",
    "for j in response[\"Ratings\"]:\n",
    "    if list(j.values())[0] == \"Rotten Tomatoes\":\n",
    "        df_imdb[\"Rotten_Tomatoes\"].iloc[i] = int((list(j.values())[1]).replace(\"%\", \"\"))\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1289, 1444, 1655, 1852, 2009, 2010, 2011, 2012, 2013, 2014]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_batch = list(df_imdb[df_imdb.Production.isna()].index)\n",
    "last_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: insert is deprecated. Use insert_one or insert_many instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for i in last_batch:\n",
    "    url = base_url+df_imdb.tconst.iloc[i]+key2\n",
    "    r = get(url)\n",
    "    if r.status_code == 200:\n",
    "        response = r.json()\n",
    "        movie.insert(response)\n",
    "        df_imdb[\"Released_Date\"].iloc[i] = response[\"Released\"]\n",
    "        df_imdb[\"Metascore\"].iloc[i] = response[\"Metascore\"]\n",
    "        df_imdb[\"Actors\"].iloc[i] = response[\"Actors\"]\n",
    "        df_imdb[\"Production\"].iloc[i] = response[\"Production\"]\n",
    "        for j in response[\"Ratings\"]:\n",
    "            if list(j.values())[0] == \"Rotten Tomatoes\":\n",
    "                df_imdb[\"Rotten_Tomatoes\"].iloc[i] = list(j.values())[1]\n",
    "            else:\n",
    "                continue\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>title</th>\n",
       "      <th>...</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>budget_LCY</th>\n",
       "      <th>gross_USA_USD</th>\n",
       "      <th>cumm_worldwide_gross_USD</th>\n",
       "      <th>Released_Date</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Rotten_Tomatoes</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Actors</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0069049</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>122</td>\n",
       "      <td>Drama</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>...</td>\n",
       "      <td>4989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02 Nov 2018</td>\n",
       "      <td>11</td>\n",
       "      <td>83%</td>\n",
       "      <td>78</td>\n",
       "      <td>John Huston, Oja Kodar, Peter Bogdanovich, Sus...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0339736</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Horror</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>...</td>\n",
       "      <td>2572</td>\n",
       "      <td>$6000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30 Aug 2017</td>\n",
       "      <td>8</td>\n",
       "      <td>100%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Sean Patrick Flanery, Dina Meyer, Frederick Ko...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0360556</td>\n",
       "      <td>movie</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>100</td>\n",
       "      <td>Drama,Sci-Fi,Thriller</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>...</td>\n",
       "      <td>15295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20 May 2018</td>\n",
       "      <td>5</td>\n",
       "      <td>32%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Michael B. Jordan, Aaron Davis, Cindy Katz, Mi...</td>\n",
       "      <td>HBO Films</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0365545</td>\n",
       "      <td>movie</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>...</td>\n",
       "      <td>6718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21 Sep 2018</td>\n",
       "      <td>9</td>\n",
       "      <td>71%</td>\n",
       "      <td>63</td>\n",
       "      <td>Sanaa Lathan, Ricky Whittle, Lyriq Bent, Lynn ...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>movie</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>\\N</td>\n",
       "      <td>124</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>...</td>\n",
       "      <td>549806</td>\n",
       "      <td>$150000000</td>\n",
       "      <td>652.27</td>\n",
       "      <td>1670.4</td>\n",
       "      <td>12 Jun 2015</td>\n",
       "      <td>6</td>\n",
       "      <td>72%</td>\n",
       "      <td>59</td>\n",
       "      <td>Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...</td>\n",
       "      <td>Universal Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>tt9562694</td>\n",
       "      <td>movie</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>88</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>...</td>\n",
       "      <td>2003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05 Apr 2019</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Clayton Snyder, David B. Meadows, Daniel Washi...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011</td>\n",
       "      <td>tt9664078</td>\n",
       "      <td>movie</td>\n",
       "      <td>American Son</td>\n",
       "      <td>American Son</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>90</td>\n",
       "      <td>Drama</td>\n",
       "      <td>American Son</td>\n",
       "      <td>...</td>\n",
       "      <td>2233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01 Nov 2019</td>\n",
       "      <td>11</td>\n",
       "      <td>47%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Kerry Washington, Jeremy Jordan, Steven Pasqua...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012</td>\n",
       "      <td>tt9815714</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>92</td>\n",
       "      <td>Action</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>...</td>\n",
       "      <td>1717</td>\n",
       "      <td>$3000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05 Mar 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Michael Jai White, Luke Goss, Randy Couture, M...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>tt9831136</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>89</td>\n",
       "      <td>Comedy,Horror,Sci-Fi</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>...</td>\n",
       "      <td>1668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13 Aug 2019</td>\n",
       "      <td>8</td>\n",
       "      <td>67%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Dani Kind, Steve Lund, Celina Martin, Finlay W...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>tt9860728</td>\n",
       "      <td>movie</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Romance</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>...</td>\n",
       "      <td>9569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29 Aug 2019</td>\n",
       "      <td>8</td>\n",
       "      <td>61%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Christina Milian, Adam Demos, Jeffrey Bowyer-C...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2015 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tconst titleType                primaryTitle  \\\n",
       "0     tt0069049     movie  The Other Side of the Wind   \n",
       "1     tt0339736     movie             The Evil Within   \n",
       "2     tt0360556     movie              Fahrenheit 451   \n",
       "3     tt0365545     movie          Nappily Ever After   \n",
       "4     tt0369610     movie              Jurassic World   \n",
       "...         ...       ...                         ...   \n",
       "2010  tt9562694     movie               Alien Warfare   \n",
       "2011  tt9664078     movie                American Son   \n",
       "2012  tt9815714     movie                The Hard Way   \n",
       "2013  tt9831136     movie     The Banana Splits Movie   \n",
       "2014  tt9860728     movie            Falling Inn Love   \n",
       "\n",
       "                   originalTitle  isAdult  startYear endYear  runtimeMinutes  \\\n",
       "0     The Other Side of the Wind        0       2018      \\N             122   \n",
       "1                The Evil Within        0       2017      \\N              98   \n",
       "2                 Fahrenheit 451        0       2018      \\N             100   \n",
       "3             Nappily Ever After        0       2018      \\N              98   \n",
       "4                 Jurassic World        0       2015      \\N             124   \n",
       "...                          ...      ...        ...     ...             ...   \n",
       "2010               Alien Warfare        0       2019      \\N              88   \n",
       "2011                American Son        0       2019      \\N              90   \n",
       "2012                The Hard Way        0       2019      \\N              92   \n",
       "2013     The Banana Splits Movie        0       2019      \\N              89   \n",
       "2014            Falling Inn Love        0       2019      \\N              98   \n",
       "\n",
       "                       genres                       title  ...  numVotes  \\\n",
       "0                       Drama  The Other Side of the Wind  ...      4989   \n",
       "1                      Horror             The Evil Within  ...      2572   \n",
       "2       Drama,Sci-Fi,Thriller              Fahrenheit 451  ...     15295   \n",
       "3        Comedy,Drama,Romance          Nappily Ever After  ...      6718   \n",
       "4     Action,Adventure,Sci-Fi              Jurassic World  ...    549806   \n",
       "...                       ...                         ...  ...       ...   \n",
       "2010            Action,Sci-Fi               Alien Warfare  ...      2003   \n",
       "2011                    Drama                American Son  ...      2233   \n",
       "2012                   Action                The Hard Way  ...      1717   \n",
       "2013     Comedy,Horror,Sci-Fi     The Banana Splits Movie  ...      1668   \n",
       "2014           Comedy,Romance            Falling Inn Love  ...      9569   \n",
       "\n",
       "      budget_LCY  gross_USA_USD cumm_worldwide_gross_USD  Released_Date  \\\n",
       "0            NaN            NaN                      NaN    02 Nov 2018   \n",
       "1       $6000000            NaN                      NaN    30 Aug 2017   \n",
       "2            NaN            NaN                      NaN    20 May 2018   \n",
       "3            NaN            NaN                      NaN    21 Sep 2018   \n",
       "4     $150000000         652.27                   1670.4    12 Jun 2015   \n",
       "...          ...            ...                      ...            ...   \n",
       "2010         NaN            NaN                      NaN    05 Apr 2019   \n",
       "2011         NaN            NaN                      NaN    01 Nov 2019   \n",
       "2012    $3000000            NaN                      NaN    05 Mar 2019   \n",
       "2013         NaN            NaN                      NaN    13 Aug 2019   \n",
       "2014         NaN            NaN                      NaN    29 Aug 2019   \n",
       "\n",
       "      Released_Month Rotten_Tomatoes Metascore  \\\n",
       "0                 11             83%        78   \n",
       "1                  8            100%       N/A   \n",
       "2                  5             32%       N/A   \n",
       "3                  9             71%        63   \n",
       "4                  6             72%        59   \n",
       "...              ...             ...       ...   \n",
       "2010               4             NaN       N/A   \n",
       "2011              11             47%       N/A   \n",
       "2012               3             NaN       N/A   \n",
       "2013               8             67%       N/A   \n",
       "2014               8             61%       N/A   \n",
       "\n",
       "                                                 Actors          Production  \n",
       "0     John Huston, Oja Kodar, Peter Bogdanovich, Sus...             Netflix  \n",
       "1     Sean Patrick Flanery, Dina Meyer, Frederick Ko...                 N/A  \n",
       "2     Michael B. Jordan, Aaron Davis, Cindy Katz, Mi...           HBO Films  \n",
       "3     Sanaa Lathan, Ricky Whittle, Lyriq Bent, Lynn ...             Netflix  \n",
       "4     Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...  Universal Pictures  \n",
       "...                                                 ...                 ...  \n",
       "2010  Clayton Snyder, David B. Meadows, Daniel Washi...                 N/A  \n",
       "2011  Kerry Washington, Jeremy Jordan, Steven Pasqua...             Netflix  \n",
       "2012  Michael Jai White, Luke Goss, Randy Couture, M...             Netflix  \n",
       "2013  Dani Kind, Steve Lund, Celina Martin, Finlay W...                 N/A  \n",
       "2014  Christina Milian, Adam Demos, Jeffrey Bowyer-C...                 N/A  \n",
       "\n",
       "[2015 rows x 22 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imdb[\"Released_Month\"] = df_imdb[\"Released_Month\"].map(lambda x: datetime.datetime.strptime(x, '%d %b %Y').month if x != \"N/A\" else \"N/A\")\n",
    "df_imdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>title</th>\n",
       "      <th>...</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>budget_LCY</th>\n",
       "      <th>gross_USA_USD</th>\n",
       "      <th>cumm_worldwide_gross_USD</th>\n",
       "      <th>Released_Date</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Rotten_Tomatoes</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Actors</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0069049</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>122</td>\n",
       "      <td>Drama</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>...</td>\n",
       "      <td>4989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02 Nov 2018</td>\n",
       "      <td>11</td>\n",
       "      <td>83%</td>\n",
       "      <td>78</td>\n",
       "      <td>John Huston, Oja Kodar, Peter Bogdanovich, Sus...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0339736</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Horror</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>...</td>\n",
       "      <td>2572</td>\n",
       "      <td>$6000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30 Aug 2017</td>\n",
       "      <td>8</td>\n",
       "      <td>100%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Sean Patrick Flanery, Dina Meyer, Frederick Ko...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0360556</td>\n",
       "      <td>movie</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>100</td>\n",
       "      <td>Drama,Sci-Fi,Thriller</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>...</td>\n",
       "      <td>15295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20 May 2018</td>\n",
       "      <td>5</td>\n",
       "      <td>32%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Michael B. Jordan, Aaron Davis, Cindy Katz, Mi...</td>\n",
       "      <td>HBO Films</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0365545</td>\n",
       "      <td>movie</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>...</td>\n",
       "      <td>6718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21 Sep 2018</td>\n",
       "      <td>9</td>\n",
       "      <td>71%</td>\n",
       "      <td>63</td>\n",
       "      <td>Sanaa Lathan, Ricky Whittle, Lyriq Bent, Lynn ...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>movie</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>\\N</td>\n",
       "      <td>124</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>...</td>\n",
       "      <td>549806</td>\n",
       "      <td>$150000000</td>\n",
       "      <td>652.27</td>\n",
       "      <td>1670.4</td>\n",
       "      <td>12 Jun 2015</td>\n",
       "      <td>6</td>\n",
       "      <td>72%</td>\n",
       "      <td>59</td>\n",
       "      <td>Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...</td>\n",
       "      <td>Universal Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>tt9562694</td>\n",
       "      <td>movie</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>88</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>...</td>\n",
       "      <td>2003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05 Apr 2019</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Clayton Snyder, David B. Meadows, Daniel Washi...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011</td>\n",
       "      <td>tt9664078</td>\n",
       "      <td>movie</td>\n",
       "      <td>American Son</td>\n",
       "      <td>American Son</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>90</td>\n",
       "      <td>Drama</td>\n",
       "      <td>American Son</td>\n",
       "      <td>...</td>\n",
       "      <td>2233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01 Nov 2019</td>\n",
       "      <td>11</td>\n",
       "      <td>47%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Kerry Washington, Jeremy Jordan, Steven Pasqua...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012</td>\n",
       "      <td>tt9815714</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>92</td>\n",
       "      <td>Action</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>...</td>\n",
       "      <td>1717</td>\n",
       "      <td>$3000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05 Mar 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Michael Jai White, Luke Goss, Randy Couture, M...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>tt9831136</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>89</td>\n",
       "      <td>Comedy,Horror,Sci-Fi</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>...</td>\n",
       "      <td>1668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13 Aug 2019</td>\n",
       "      <td>8</td>\n",
       "      <td>67%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Dani Kind, Steve Lund, Celina Martin, Finlay W...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>tt9860728</td>\n",
       "      <td>movie</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Romance</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>...</td>\n",
       "      <td>9569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29 Aug 2019</td>\n",
       "      <td>8</td>\n",
       "      <td>61%</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Christina Milian, Adam Demos, Jeffrey Bowyer-C...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2015 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tconst titleType                primaryTitle  \\\n",
       "0     tt0069049     movie  The Other Side of the Wind   \n",
       "1     tt0339736     movie             The Evil Within   \n",
       "2     tt0360556     movie              Fahrenheit 451   \n",
       "3     tt0365545     movie          Nappily Ever After   \n",
       "4     tt0369610     movie              Jurassic World   \n",
       "...         ...       ...                         ...   \n",
       "2010  tt9562694     movie               Alien Warfare   \n",
       "2011  tt9664078     movie                American Son   \n",
       "2012  tt9815714     movie                The Hard Way   \n",
       "2013  tt9831136     movie     The Banana Splits Movie   \n",
       "2014  tt9860728     movie            Falling Inn Love   \n",
       "\n",
       "                   originalTitle  isAdult  startYear endYear  runtimeMinutes  \\\n",
       "0     The Other Side of the Wind        0       2018      \\N             122   \n",
       "1                The Evil Within        0       2017      \\N              98   \n",
       "2                 Fahrenheit 451        0       2018      \\N             100   \n",
       "3             Nappily Ever After        0       2018      \\N              98   \n",
       "4                 Jurassic World        0       2015      \\N             124   \n",
       "...                          ...      ...        ...     ...             ...   \n",
       "2010               Alien Warfare        0       2019      \\N              88   \n",
       "2011                American Son        0       2019      \\N              90   \n",
       "2012                The Hard Way        0       2019      \\N              92   \n",
       "2013     The Banana Splits Movie        0       2019      \\N              89   \n",
       "2014            Falling Inn Love        0       2019      \\N              98   \n",
       "\n",
       "                       genres                       title  ...  numVotes  \\\n",
       "0                       Drama  The Other Side of the Wind  ...      4989   \n",
       "1                      Horror             The Evil Within  ...      2572   \n",
       "2       Drama,Sci-Fi,Thriller              Fahrenheit 451  ...     15295   \n",
       "3        Comedy,Drama,Romance          Nappily Ever After  ...      6718   \n",
       "4     Action,Adventure,Sci-Fi              Jurassic World  ...    549806   \n",
       "...                       ...                         ...  ...       ...   \n",
       "2010            Action,Sci-Fi               Alien Warfare  ...      2003   \n",
       "2011                    Drama                American Son  ...      2233   \n",
       "2012                   Action                The Hard Way  ...      1717   \n",
       "2013     Comedy,Horror,Sci-Fi     The Banana Splits Movie  ...      1668   \n",
       "2014           Comedy,Romance            Falling Inn Love  ...      9569   \n",
       "\n",
       "      budget_LCY  gross_USA_USD cumm_worldwide_gross_USD  Released_Date  \\\n",
       "0            NaN            NaN                      NaN    02 Nov 2018   \n",
       "1       $6000000            NaN                      NaN    30 Aug 2017   \n",
       "2            NaN            NaN                      NaN    20 May 2018   \n",
       "3            NaN            NaN                      NaN    21 Sep 2018   \n",
       "4     $150000000         652.27                   1670.4    12 Jun 2015   \n",
       "...          ...            ...                      ...            ...   \n",
       "2010         NaN            NaN                      NaN    05 Apr 2019   \n",
       "2011         NaN            NaN                      NaN    01 Nov 2019   \n",
       "2012    $3000000            NaN                      NaN    05 Mar 2019   \n",
       "2013         NaN            NaN                      NaN    13 Aug 2019   \n",
       "2014         NaN            NaN                      NaN    29 Aug 2019   \n",
       "\n",
       "      Released_Month Rotten_Tomatoes Metascore  \\\n",
       "0                 11             83%        78   \n",
       "1                  8            100%       N/A   \n",
       "2                  5             32%       N/A   \n",
       "3                  9             71%        63   \n",
       "4                  6             72%        59   \n",
       "...              ...             ...       ...   \n",
       "2010               4             NaN       N/A   \n",
       "2011              11             47%       N/A   \n",
       "2012               3             NaN       N/A   \n",
       "2013               8             67%       N/A   \n",
       "2014               8             61%       N/A   \n",
       "\n",
       "                                                 Actors          Production  \n",
       "0     John Huston, Oja Kodar, Peter Bogdanovich, Sus...             Netflix  \n",
       "1     Sean Patrick Flanery, Dina Meyer, Frederick Ko...                 N/A  \n",
       "2     Michael B. Jordan, Aaron Davis, Cindy Katz, Mi...           HBO Films  \n",
       "3     Sanaa Lathan, Ricky Whittle, Lyriq Bent, Lynn ...             Netflix  \n",
       "4     Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...  Universal Pictures  \n",
       "...                                                 ...                 ...  \n",
       "2010  Clayton Snyder, David B. Meadows, Daniel Washi...                 N/A  \n",
       "2011  Kerry Washington, Jeremy Jordan, Steven Pasqua...             Netflix  \n",
       "2012  Michael Jai White, Luke Goss, Randy Couture, M...             Netflix  \n",
       "2013  Dani Kind, Steve Lund, Celina Martin, Finlay W...                 N/A  \n",
       "2014  Christina Milian, Adam Demos, Jeffrey Bowyer-C...                 N/A  \n",
       "\n",
       "[2015 rows x 22 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imdb[\"Metascore\"] = df_imdb[\"Metascore\"].map(lambda x: int(x) if x != \"N/A\" else \"N/A\")\n",
    "df_imdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>title</th>\n",
       "      <th>...</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>budget_LCY</th>\n",
       "      <th>gross_USA_USD</th>\n",
       "      <th>cumm_worldwide_gross_USD</th>\n",
       "      <th>Released_Date</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Rotten_Tomatoes</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Actors</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0069049</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>122</td>\n",
       "      <td>Drama</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>...</td>\n",
       "      <td>4989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02 Nov 2018</td>\n",
       "      <td>11</td>\n",
       "      <td>83</td>\n",
       "      <td>78</td>\n",
       "      <td>John Huston, Oja Kodar, Peter Bogdanovich, Sus...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0339736</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Horror</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>...</td>\n",
       "      <td>2572</td>\n",
       "      <td>$6000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30 Aug 2017</td>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Sean Patrick Flanery, Dina Meyer, Frederick Ko...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0360556</td>\n",
       "      <td>movie</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>100</td>\n",
       "      <td>Drama,Sci-Fi,Thriller</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>...</td>\n",
       "      <td>15295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20 May 2018</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Michael B. Jordan, Aaron Davis, Cindy Katz, Mi...</td>\n",
       "      <td>HBO Films</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0365545</td>\n",
       "      <td>movie</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>...</td>\n",
       "      <td>6718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21 Sep 2018</td>\n",
       "      <td>9</td>\n",
       "      <td>71</td>\n",
       "      <td>63</td>\n",
       "      <td>Sanaa Lathan, Ricky Whittle, Lyriq Bent, Lynn ...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>movie</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>\\N</td>\n",
       "      <td>124</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>...</td>\n",
       "      <td>549806</td>\n",
       "      <td>$150000000</td>\n",
       "      <td>652.27</td>\n",
       "      <td>1670.4</td>\n",
       "      <td>12 Jun 2015</td>\n",
       "      <td>6</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...</td>\n",
       "      <td>Universal Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>tt9562694</td>\n",
       "      <td>movie</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>88</td>\n",
       "      <td>Action,Sci-Fi</td>\n",
       "      <td>Alien Warfare</td>\n",
       "      <td>...</td>\n",
       "      <td>2003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05 Apr 2019</td>\n",
       "      <td>4</td>\n",
       "      <td>nan</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Clayton Snyder, David B. Meadows, Daniel Washi...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011</td>\n",
       "      <td>tt9664078</td>\n",
       "      <td>movie</td>\n",
       "      <td>American Son</td>\n",
       "      <td>American Son</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>90</td>\n",
       "      <td>Drama</td>\n",
       "      <td>American Son</td>\n",
       "      <td>...</td>\n",
       "      <td>2233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01 Nov 2019</td>\n",
       "      <td>11</td>\n",
       "      <td>47</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Kerry Washington, Jeremy Jordan, Steven Pasqua...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012</td>\n",
       "      <td>tt9815714</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>92</td>\n",
       "      <td>Action</td>\n",
       "      <td>The Hard Way</td>\n",
       "      <td>...</td>\n",
       "      <td>1717</td>\n",
       "      <td>$3000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05 Mar 2019</td>\n",
       "      <td>3</td>\n",
       "      <td>nan</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Michael Jai White, Luke Goss, Randy Couture, M...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>tt9831136</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>89</td>\n",
       "      <td>Comedy,Horror,Sci-Fi</td>\n",
       "      <td>The Banana Splits Movie</td>\n",
       "      <td>...</td>\n",
       "      <td>1668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13 Aug 2019</td>\n",
       "      <td>8</td>\n",
       "      <td>67</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Dani Kind, Steve Lund, Celina Martin, Finlay W...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>tt9860728</td>\n",
       "      <td>movie</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Romance</td>\n",
       "      <td>Falling Inn Love</td>\n",
       "      <td>...</td>\n",
       "      <td>9569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29 Aug 2019</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Christina Milian, Adam Demos, Jeffrey Bowyer-C...</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2015 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         tconst titleType                primaryTitle  \\\n",
       "0     tt0069049     movie  The Other Side of the Wind   \n",
       "1     tt0339736     movie             The Evil Within   \n",
       "2     tt0360556     movie              Fahrenheit 451   \n",
       "3     tt0365545     movie          Nappily Ever After   \n",
       "4     tt0369610     movie              Jurassic World   \n",
       "...         ...       ...                         ...   \n",
       "2010  tt9562694     movie               Alien Warfare   \n",
       "2011  tt9664078     movie                American Son   \n",
       "2012  tt9815714     movie                The Hard Way   \n",
       "2013  tt9831136     movie     The Banana Splits Movie   \n",
       "2014  tt9860728     movie            Falling Inn Love   \n",
       "\n",
       "                   originalTitle  isAdult  startYear endYear  runtimeMinutes  \\\n",
       "0     The Other Side of the Wind        0       2018      \\N             122   \n",
       "1                The Evil Within        0       2017      \\N              98   \n",
       "2                 Fahrenheit 451        0       2018      \\N             100   \n",
       "3             Nappily Ever After        0       2018      \\N              98   \n",
       "4                 Jurassic World        0       2015      \\N             124   \n",
       "...                          ...      ...        ...     ...             ...   \n",
       "2010               Alien Warfare        0       2019      \\N              88   \n",
       "2011                American Son        0       2019      \\N              90   \n",
       "2012                The Hard Way        0       2019      \\N              92   \n",
       "2013     The Banana Splits Movie        0       2019      \\N              89   \n",
       "2014            Falling Inn Love        0       2019      \\N              98   \n",
       "\n",
       "                       genres                       title  ...  numVotes  \\\n",
       "0                       Drama  The Other Side of the Wind  ...      4989   \n",
       "1                      Horror             The Evil Within  ...      2572   \n",
       "2       Drama,Sci-Fi,Thriller              Fahrenheit 451  ...     15295   \n",
       "3        Comedy,Drama,Romance          Nappily Ever After  ...      6718   \n",
       "4     Action,Adventure,Sci-Fi              Jurassic World  ...    549806   \n",
       "...                       ...                         ...  ...       ...   \n",
       "2010            Action,Sci-Fi               Alien Warfare  ...      2003   \n",
       "2011                    Drama                American Son  ...      2233   \n",
       "2012                   Action                The Hard Way  ...      1717   \n",
       "2013     Comedy,Horror,Sci-Fi     The Banana Splits Movie  ...      1668   \n",
       "2014           Comedy,Romance            Falling Inn Love  ...      9569   \n",
       "\n",
       "      budget_LCY  gross_USA_USD cumm_worldwide_gross_USD  Released_Date  \\\n",
       "0            NaN            NaN                      NaN    02 Nov 2018   \n",
       "1       $6000000            NaN                      NaN    30 Aug 2017   \n",
       "2            NaN            NaN                      NaN    20 May 2018   \n",
       "3            NaN            NaN                      NaN    21 Sep 2018   \n",
       "4     $150000000         652.27                   1670.4    12 Jun 2015   \n",
       "...          ...            ...                      ...            ...   \n",
       "2010         NaN            NaN                      NaN    05 Apr 2019   \n",
       "2011         NaN            NaN                      NaN    01 Nov 2019   \n",
       "2012    $3000000            NaN                      NaN    05 Mar 2019   \n",
       "2013         NaN            NaN                      NaN    13 Aug 2019   \n",
       "2014         NaN            NaN                      NaN    29 Aug 2019   \n",
       "\n",
       "      Released_Month Rotten_Tomatoes Metascore  \\\n",
       "0                 11              83        78   \n",
       "1                  8             100       N/A   \n",
       "2                  5              32       N/A   \n",
       "3                  9              71        63   \n",
       "4                  6              72        59   \n",
       "...              ...             ...       ...   \n",
       "2010               4             nan       N/A   \n",
       "2011              11              47       N/A   \n",
       "2012               3             nan       N/A   \n",
       "2013               8              67       N/A   \n",
       "2014               8              61       N/A   \n",
       "\n",
       "                                                 Actors          Production  \n",
       "0     John Huston, Oja Kodar, Peter Bogdanovich, Sus...             Netflix  \n",
       "1     Sean Patrick Flanery, Dina Meyer, Frederick Ko...                 N/A  \n",
       "2     Michael B. Jordan, Aaron Davis, Cindy Katz, Mi...           HBO Films  \n",
       "3     Sanaa Lathan, Ricky Whittle, Lyriq Bent, Lynn ...             Netflix  \n",
       "4     Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...  Universal Pictures  \n",
       "...                                                 ...                 ...  \n",
       "2010  Clayton Snyder, David B. Meadows, Daniel Washi...                 N/A  \n",
       "2011  Kerry Washington, Jeremy Jordan, Steven Pasqua...             Netflix  \n",
       "2012  Michael Jai White, Luke Goss, Randy Couture, M...             Netflix  \n",
       "2013  Dani Kind, Steve Lund, Celina Martin, Finlay W...                 N/A  \n",
       "2014  Christina Milian, Adam Demos, Jeffrey Bowyer-C...                 N/A  \n",
       "\n",
       "[2015 rows x 22 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imdb[\"Rotten_Tomatoes\"] = df_imdb[\"Rotten_Tomatoes\"].map(lambda x: str(x).replace(\"%\", \"\") if ((x != \"N/A\") & (x != \"NaN\")) else \"N/A\")\n",
    "df_imdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imdb.to_csv('2016_2019_USmovies_API.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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