ETL_wiki_movies_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_dir = '/Users/amylio/Desktop/UC\\ Berkeley\\ Class/JAN21/Movies-ETL'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open ('wikipedia-movies.json', mode='r') as file:\n",
    "    wiki_movies_raw = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7311"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wiki_movies_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'url': 'https://en.wikipedia.org/wiki/The_Adventures_of_Ford_Fairlane',\n",
       "  'year': 1990,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0098987/',\n",
       "  'title': 'The Adventures of Ford Fairlane',\n",
       "  'Directed by': 'Renny Harlin',\n",
       "  'Produced by': ['Steve Perry', 'Joel Silver'],\n",
       "  'Screenplay by': ['David Arnott', 'James Cappe', 'Daniel Waters'],\n",
       "  'Story by': ['David Arnott', 'James Cappe'],\n",
       "  'Based on': ['Characters', 'by Rex Weiner'],\n",
       "  'Starring': ['Andrew Dice Clay',\n",
       "   'Wayne Newton',\n",
       "   'Priscilla Presley',\n",
       "   'Lauren Holly',\n",
       "   'Morris Day',\n",
       "   'Robert Englund',\n",
       "   \"Ed O'Neill\"],\n",
       "  'Narrated by': 'Andrew \"Dice\" Clay',\n",
       "  'Music by': ['Cliff Eidelman', 'Yello'],\n",
       "  'Cinematography': 'Oliver Wood',\n",
       "  'Edited by': 'Michael Tronick',\n",
       "  'Productioncompany ': 'Silver Pictures',\n",
       "  'Distributed by': '20th Century Fox',\n",
       "  'Release date': ['July 11, 1990', '(', '1990-07-11', ')'],\n",
       "  'Running time': '102 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$20 million',\n",
       "  'Box office': '$21.4 million'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/After_Dark,_My_Sweet',\n",
       "  'year': 1990,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0098994/',\n",
       "  'title': 'After Dark, My Sweet',\n",
       "  'Directed by': 'James Foley',\n",
       "  'Produced by': ['Ric Kidney', 'Robert Redlin'],\n",
       "  'Screenplay by': ['James Foley', 'Robert Redlin'],\n",
       "  'Based on': ['the novel', 'After Dark, My Sweet', 'by', 'Jim Thompson'],\n",
       "  'Starring': ['Jason Patric',\n",
       "   'Rachel Ward',\n",
       "   'Bruce Dern',\n",
       "   'George Dickerson'],\n",
       "  'Music by': 'Maurice Jarre',\n",
       "  'Cinematography': 'Mark Plummer',\n",
       "  'Edited by': 'Howard E. Smith',\n",
       "  'Productioncompany ': 'Avenue Pictures',\n",
       "  'Distributed by': 'Avenue Pictures',\n",
       "  'Release date': ['May 17, 1990',\n",
       "   '(',\n",
       "   '1990-05-17',\n",
       "   ')',\n",
       "   '(Cannes Film Market)',\n",
       "   'August 24, 1990',\n",
       "   '(',\n",
       "   '1990-08-24',\n",
       "   ')',\n",
       "   '(United States)'],\n",
       "  'Running time': '114 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$6 million',\n",
       "  'Box office': '$2.7 million'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/Air_America_(film)',\n",
       "  'year': 1990,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0099005/',\n",
       "  'title': 'Air America',\n",
       "  'Directed by': 'Roger Spottiswoode',\n",
       "  'Produced by': 'Daniel Melnick',\n",
       "  'Screenplay by': ['John Eskow', 'Richard Rush'],\n",
       "  'Based on': ['Air America', 'by', 'Christopher Robbins'],\n",
       "  'Starring': ['Mel Gibson',\n",
       "   'Robert Downey Jr.',\n",
       "   'Nancy Travis',\n",
       "   'David Marshall Grant',\n",
       "   'Lane Smith'],\n",
       "  'Music by': 'Charles Gross',\n",
       "  'Cinematography': 'Roger Deakins',\n",
       "  'Edited by': ['John Bloom', 'Lois Freeman-Fox'],\n",
       "  'Productioncompany ': ['Carolco Pictures', 'IndieProd Company'],\n",
       "  'Distributed by': 'TriStar Pictures',\n",
       "  'Release date': ['August 10, 1990', '(', '1990-08-10', ')'],\n",
       "  'Running time': '113 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': ['English', 'Lao'],\n",
       "  'Budget': '$35 million',\n",
       "  'Box office': '$57,718,089'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/Alice_(1990_film)',\n",
       "  'year': 1990,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0099012/',\n",
       "  'title': 'Alice',\n",
       "  'Directed by': 'Woody Allen',\n",
       "  'Produced by': 'Robert Greenhut',\n",
       "  'Written by': 'Woody Allen',\n",
       "  'Starring': ['Alec Baldwin',\n",
       "   'Blythe Danner',\n",
       "   'Judy Davis',\n",
       "   'Mia Farrow',\n",
       "   'William Hurt',\n",
       "   'Keye Luke',\n",
       "   'Joe Mantegna',\n",
       "   'Bernadette Peters'],\n",
       "  'Cinematography': 'Carlo Di Palma',\n",
       "  'Edited by': 'Susan E. Morse',\n",
       "  'Distributed by': 'Orion Pictures',\n",
       "  'Release date': ['December 25, 1990', '(', '1990-12-25', ')'],\n",
       "  'Running time': '106 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$12 million',\n",
       "  'Box office': '$7,331,647'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/Almost_an_Angel',\n",
       "  'year': 1990,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0099018/',\n",
       "  'title': 'Almost an Angel',\n",
       "  'Directed by': 'John Cornell',\n",
       "  'Produced by': 'John Cornell',\n",
       "  'Written by': 'Paul Hogan',\n",
       "  'Starring': ['Paul Hogan', 'Elias Koteas', 'Linda Kozlowski'],\n",
       "  'Music by': 'Maurice Jarre',\n",
       "  'Cinematography': 'Russell Boyd',\n",
       "  'Edited by': 'David Stiven',\n",
       "  'Distributed by': 'Paramount Pictures',\n",
       "  'Release date': 'December 19, 1990',\n",
       "  'Running time': '95 minutes',\n",
       "  'Country': 'US',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$25 million',\n",
       "  'Box office': '$6,939,946 (USA)'}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First 5 records\n",
    "wiki_movies_raw[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'url': 'https://en.wikipedia.org/wiki/Holmes_%26_Watson',\n",
       " 'year': 2018,\n",
       " 'imdb_link': 'https://www.imdb.com/title/tt1255919/',\n",
       " 'title': 'Holmes & Watson',\n",
       " 'Directed by': 'Etan Cohen',\n",
       " 'Produced by': ['Will Ferrell',\n",
       "  'Adam McKay',\n",
       "  'Jimmy Miller',\n",
       "  'Clayton Townsend'],\n",
       " 'Screenplay by': 'Etan Cohen',\n",
       " 'Based on': ['Sherlock Holmes',\n",
       "  'and',\n",
       "  'Dr. Watson',\n",
       "  'by',\n",
       "  'Sir Arthur Conan Doyle'],\n",
       " 'Starring': ['Will Ferrell',\n",
       "  'John C. Reilly',\n",
       "  'Rebecca Hall',\n",
       "  'Rob Brydon',\n",
       "  'Steve Coogan',\n",
       "  'Ralph Fiennes'],\n",
       " 'Music by': 'Mark Mothersbaugh',\n",
       " 'Cinematography': 'Oliver Wood',\n",
       " 'Edited by': 'Dean Zimmerman',\n",
       " 'Productioncompanies ': ['Columbia Pictures',\n",
       "  'Gary Sanchez Productions',\n",
       "  'Mosaic Media Group',\n",
       "  'Mimran Schur Pictures'],\n",
       " 'Distributed by': 'Sony Pictures Releasing',\n",
       " 'Release date': ['December 25, 2018',\n",
       "  '(',\n",
       "  '2018-12-25',\n",
       "  ')',\n",
       "  '(United States)'],\n",
       " 'Running time': '90 minutes',\n",
       " 'Country': 'United States',\n",
       " 'Language': 'English',\n",
       " 'Budget': '$42 million',\n",
       " 'Box office': '$41.9 million'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last 5 records\n",
    "wiki_movies_raw[-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'url': 'https://en.wikipedia.org/wiki/Benji:_Off_the_Leash!',\n",
       "  'year': 2004,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0315273/',\n",
       "  'title': 'Benji: Off the Leash!',\n",
       "  'Directed by': 'Joe Camp',\n",
       "  'Written by': 'Joe Camp',\n",
       "  'Starring': ['Benji', 'Nick Whitaker', 'Shaggy', 'Gypsy the Cockatoo'],\n",
       "  'Music by': 'Antonio di Lorenzo',\n",
       "  'Productioncompany ': 'Mulberry Square Productions',\n",
       "  'Distributed by': 'Mulberry Square Productions',\n",
       "  'Release date': ['March 26, 2004', '(', '2004-03-26', ')'],\n",
       "  'Running time': '97 min',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Box office': '$3,817,362'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/The_Best_Thief_in_the_World',\n",
       "  'year': 2004,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0389796/',\n",
       "  'title': 'The Best Thief in the World',\n",
       "  'Directed by': 'Jacob Kornbluth',\n",
       "  'Produced by': ['Tim Perrell', 'Nicola Usborne'],\n",
       "  'Written by': 'Jacob Kornbluth',\n",
       "  'Starring': ['Marc Rozendaal',\n",
       "   'Michael Silverman',\n",
       "   'David Warshofsky',\n",
       "   'Audra McDonald',\n",
       "   'Lois Smith'],\n",
       "  'Music by': ['Prince Paul', 'and', 'Don Newkirk'],\n",
       "  'Cinematography': 'Ben Kutchins',\n",
       "  'Edited by': 'Stephanie Sterner',\n",
       "  'Distributed by': 'Showtime Networks',\n",
       "  'Release date': ['January 16, 2004', '(', '2004-01-16', ')'],\n",
       "  'Running time': '93 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/Beyond_the_Sea_(2004_film)',\n",
       "  'year': 2004,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0363473/',\n",
       "  'title': 'Beyond the Sea',\n",
       "  'Directed by': 'Kevin Spacey',\n",
       "  'Produced by': ['Kevin Spacey',\n",
       "   'Phillip Barry',\n",
       "   'Dana Brunetti',\n",
       "   'Arthur Friedman',\n",
       "   'Jan Fantl',\n",
       "   'Andy Paterson'],\n",
       "  'Written by': ['Lewis Colick', 'Kevin Spacey'],\n",
       "  'Starring': ['Kevin Spacey', 'Kate Bosworth', 'Bob Hoskins', 'John Goodman'],\n",
       "  'Music by': 'Bobby Darin',\n",
       "  'Cinematography': 'Eduardo Serra',\n",
       "  'Edited by': 'Trevor Waite',\n",
       "  'Productioncompany ': 'Trigger Street Productions',\n",
       "  'Distributed by': ['Lionsgate Films',\n",
       "   '(USA)',\n",
       "   'Entertainment Film Distributors',\n",
       "   '(UK)',\n",
       "   'GAGA',\n",
       "   '(Japan)',\n",
       "   'Maple Pictures',\n",
       "   '(Canada)'],\n",
       "  'Release date': ['December 17, 2004', '(', '2004-12-17', ')'],\n",
       "  'Running time': '118 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$25 million',\n",
       "  'Box office': '$8.4 million'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/The_Big_Bounce_(2004_film)',\n",
       "  'year': 2004,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0315824/',\n",
       "  'title': 'The Big Bounce',\n",
       "  'Directed by': 'George Armitage',\n",
       "  'Produced by': ['George Armitage', 'Steve Bing'],\n",
       "  'Screenplay by': 'Sebastian Gutierrez',\n",
       "  'Based on': ['The Big Bounce', 'by', 'Elmore Leonard'],\n",
       "  'Starring': ['Owen Wilson',\n",
       "   'Charlie Sheen',\n",
       "   'Morgan Freeman',\n",
       "   'Sara Foster',\n",
       "   'Gary Sinise'],\n",
       "  'Music by': 'George S. Clinton',\n",
       "  'Cinematography': 'Jeffrey L. Kimball',\n",
       "  'Edited by': 'Barry Malkin',\n",
       "  'Productioncompany ': 'Shangri-La Entertainment',\n",
       "  'Distributed by': 'Warner Bros. Pictures',\n",
       "  'Release date': ['January 30, 2004', '(', '2004-01-30', ')'],\n",
       "  'Running time': '88 minutes',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$50 million',\n",
       "  'Box office': '$6.8 million'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/Birth_(film)',\n",
       "  'year': 2004,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt0337876/',\n",
       "  'title': 'Birth',\n",
       "  'Directed by': 'Jonathan Glazer',\n",
       "  'Produced by': ['Lizie Gower', 'Nick Morris', 'Jean-Louis Piel'],\n",
       "  'Written by': ['Jean-Claude Carrière', 'Milo Addica', 'Jonathan Glazer'],\n",
       "  'Starring': ['Nicole Kidman',\n",
       "   'Cameron Bright',\n",
       "   'Danny Huston',\n",
       "   'Lauren Bacall'],\n",
       "  'Music by': 'Alexandre Desplat',\n",
       "  'Cinematography': 'Harris Savides',\n",
       "  'Edited by': ['Sam Sneade', 'Claus Wehlisch'],\n",
       "  'Distributed by': 'New Line Cinema',\n",
       "  'Release date': ['November 8, 2004', '(', '2004-11-08', ')'],\n",
       "  'Running time': '96 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$20 million',\n",
       "  'Box office': '$23.9 million'}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some records in the middle\n",
    "wiki_movies_raw[3600:3605]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26024289"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata = pd.read_csv('movies_metadata.csv', low_memory=False)\n",
    "ratings = pd.read_csv('ratings.csv')\n",
    "len(ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-10-30</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15602</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31357</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
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       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
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       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
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       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
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       "\n",
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       "4  Father of the Bride Part II   \n",
       "\n",
       "                                            overview  ... release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  ...   1995-10-30   \n",
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       "2  A family wedding reignites the ancient feud be...  ...   1995-12-22   \n",
       "3  Cheated on, mistreated and stepped on, the wom...  ...   1995-12-22   \n",
       "4  Just when George Banks has recovered from his ...  ...   1995-02-10   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
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       "1  262797249.0   104.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
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       "3   81452156.0   127.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "4   76578911.0   106.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
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       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
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       "19229                     [{'id': 35, 'name': 'Comedy'}]      NaN   56909   \n",
       "38249                                                 []      NaN  153423   \n",
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       "\n",
       "           revenue runtime                           spoken_languages  \\\n",
       "8877           0.0    93.0   [{'iso_639_1': 'en', 'name': 'English'}]   \n",
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    "wiki_movies_df = pd.DataFrame(wiki_movies_raw)"
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       "    .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>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title</th>\n",
       "      <th>Directed by</th>\n",
       "      <th>Produced by</th>\n",
       "      <th>Screenplay by</th>\n",
       "      <th>Story by</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>...</th>\n",
       "      <th>Predecessor</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Area served</th>\n",
       "      <th>Products</th>\n",
       "      <th>Services</th>\n",
       "      <th>Russian</th>\n",
       "      <th>Hebrew</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Operating income</th>\n",
       "      <th>Polish</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Adventures_o...</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0098987/</td>\n",
       "      <td>The Adventures of Ford Fairlane</td>\n",
       "      <td>Renny Harlin</td>\n",
       "      <td>[Steve Perry, Joel Silver]</td>\n",
       "      <td>[David Arnott, James Cappe, Daniel Waters]</td>\n",
       "      <td>[David Arnott, James Cappe]</td>\n",
       "      <td>[Characters, by Rex Weiner]</td>\n",
       "      <td>[Andrew Dice Clay, Wayne Newton, Priscilla Pre...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://en.wikipedia.org/wiki/After_Dark,_My_S...</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0098994/</td>\n",
       "      <td>After Dark, My Sweet</td>\n",
       "      <td>James Foley</td>\n",
       "      <td>[Ric Kidney, Robert Redlin]</td>\n",
       "      <td>[James Foley, Robert Redlin]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[the novel, After Dark, My Sweet, by, Jim Thom...</td>\n",
       "      <td>[Jason Patric, Rachel Ward, Bruce Dern, George...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Air_America_(film)</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0099005/</td>\n",
       "      <td>Air America</td>\n",
       "      <td>Roger Spottiswoode</td>\n",
       "      <td>Daniel Melnick</td>\n",
       "      <td>[John Eskow, Richard Rush]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Air America, by, Christopher Robbins]</td>\n",
       "      <td>[Mel Gibson, Robert Downey Jr., Nancy Travis, ...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Alice_(1990_film)</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0099012/</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>Robert Greenhut</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Alec Baldwin, Blythe Danner, Judy Davis, Mia ...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Almost_an_Angel</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>https://www.imdb.com/title/tt0099018/</td>\n",
       "      <td>Almost an Angel</td>\n",
       "      <td>John Cornell</td>\n",
       "      <td>John Cornell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Paul Hogan, Elias Koteas, Linda Kozlowski]</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url    year  \\\n",
       "0  https://en.wikipedia.org/wiki/The_Adventures_o...  1990.0   \n",
       "1  https://en.wikipedia.org/wiki/After_Dark,_My_S...  1990.0   \n",
       "2   https://en.wikipedia.org/wiki/Air_America_(film)  1990.0   \n",
       "3    https://en.wikipedia.org/wiki/Alice_(1990_film)  1990.0   \n",
       "4      https://en.wikipedia.org/wiki/Almost_an_Angel  1990.0   \n",
       "\n",
       "                               imdb_link                            title  \\\n",
       "0  https://www.imdb.com/title/tt0098987/  The Adventures of Ford Fairlane   \n",
       "1  https://www.imdb.com/title/tt0098994/             After Dark, My Sweet   \n",
       "2  https://www.imdb.com/title/tt0099005/                      Air America   \n",
       "3  https://www.imdb.com/title/tt0099012/                            Alice   \n",
       "4  https://www.imdb.com/title/tt0099018/                  Almost an Angel   \n",
       "\n",
       "          Directed by                  Produced by  \\\n",
       "0        Renny Harlin   [Steve Perry, Joel Silver]   \n",
       "1         James Foley  [Ric Kidney, Robert Redlin]   \n",
       "2  Roger Spottiswoode               Daniel Melnick   \n",
       "3         Woody Allen              Robert Greenhut   \n",
       "4        John Cornell                 John Cornell   \n",
       "\n",
       "                                Screenplay by                     Story by  \\\n",
       "0  [David Arnott, James Cappe, Daniel Waters]  [David Arnott, James Cappe]   \n",
       "1                [James Foley, Robert Redlin]                          NaN   \n",
       "2                  [John Eskow, Richard Rush]                          NaN   \n",
       "3                                         NaN                          NaN   \n",
       "4                                         NaN                          NaN   \n",
       "\n",
       "                                            Based on  \\\n",
       "0                        [Characters, by Rex Weiner]   \n",
       "1  [the novel, After Dark, My Sweet, by, Jim Thom...   \n",
       "2             [Air America, by, Christopher Robbins]   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "                                            Starring  ... Predecessor  \\\n",
       "0  [Andrew Dice Clay, Wayne Newton, Priscilla Pre...  ...         NaN   \n",
       "1  [Jason Patric, Rachel Ward, Bruce Dern, George...  ...         NaN   \n",
       "2  [Mel Gibson, Robert Downey Jr., Nancy Travis, ...  ...         NaN   \n",
       "3  [Alec Baldwin, Blythe Danner, Judy Davis, Mia ...  ...         NaN   \n",
       "4        [Paul Hogan, Elias Koteas, Linda Kozlowski]  ...         NaN   \n",
       "\n",
       "  Founders Area served Products Services Russian Hebrew Revenue  \\\n",
       "0      NaN         NaN      NaN      NaN     NaN    NaN     NaN   \n",
       "1      NaN         NaN      NaN      NaN     NaN    NaN     NaN   \n",
       "2      NaN         NaN      NaN      NaN     NaN    NaN     NaN   \n",
       "3      NaN         NaN      NaN      NaN     NaN    NaN     NaN   \n",
       "4      NaN         NaN      NaN      NaN     NaN    NaN     NaN   \n",
       "\n",
       "  Operating income Polish  \n",
       "0              NaN    NaN  \n",
       "1              NaN    NaN  \n",
       "2              NaN    NaN  \n",
       "3              NaN    NaN  \n",
       "4              NaN    NaN  \n",
       "\n",
       "[5 rows x 193 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['url',\n",
       " 'year',\n",
       " 'imdb_link',\n",
       " 'title',\n",
       " 'Directed by',\n",
       " 'Produced by',\n",
       " 'Screenplay by',\n",
       " 'Story by',\n",
       " 'Based on',\n",
       " 'Starring',\n",
       " 'Narrated by',\n",
       " 'Music by',\n",
       " 'Cinematography',\n",
       " 'Edited by',\n",
       " 'Productioncompany ',\n",
       " 'Distributed by',\n",
       " 'Release date',\n",
       " 'Running time',\n",
       " 'Country',\n",
       " 'Language',\n",
       " 'Budget',\n",
       " 'Box office',\n",
       " 'Written by',\n",
       " 'Genre',\n",
       " 'Theme music composer',\n",
       " 'Country of origin',\n",
       " 'Original language(s)',\n",
       " 'Producer(s)',\n",
       " 'Editor(s)',\n",
       " 'Production company(s)',\n",
       " 'Original network',\n",
       " 'Original release',\n",
       " 'Productioncompanies ',\n",
       " 'Executive producer(s)',\n",
       " 'Production location(s)',\n",
       " 'Distributor',\n",
       " 'Picture format',\n",
       " 'Audio format',\n",
       " 'Voices of',\n",
       " 'Followed by',\n",
       " 'Composer(s)',\n",
       " 'Created by',\n",
       " 'Also known as',\n",
       " 'Opening theme',\n",
       " 'No. of episodes',\n",
       " 'Preceded by',\n",
       " 'Author',\n",
       " 'Publisher',\n",
       " 'Publication date',\n",
       " 'Media type',\n",
       " 'Pages',\n",
       " 'ISBN',\n",
       " 'OCLC',\n",
       " 'LC Class',\n",
       " 'Cover artist',\n",
       " 'Series',\n",
       " 'Set in',\n",
       " 'Adaptation by',\n",
       " 'Suggested by',\n",
       " 'Biographical data',\n",
       " 'Born',\n",
       " 'Died',\n",
       " 'Resting place',\n",
       " 'Occupation',\n",
       " 'Years active',\n",
       " 'Spouse(s)',\n",
       " 'Children',\n",
       " 'Parent(s)',\n",
       " 'Genres',\n",
       " 'Instruments',\n",
       " 'Labels',\n",
       " 'Website',\n",
       " 'Traditional',\n",
       " 'Mandarin',\n",
       " 'Type',\n",
       " 'Industry',\n",
       " 'Fate',\n",
       " 'Founded',\n",
       " 'Founder',\n",
       " 'Headquarters',\n",
       " 'Parent',\n",
       " 'Released',\n",
       " 'Recorded',\n",
       " 'Venue',\n",
       " 'Length',\n",
       " 'Label',\n",
       " 'Director',\n",
       " 'Producer',\n",
       " 'Area',\n",
       " 'Coordinates',\n",
       " 'Status',\n",
       " 'Opening date',\n",
       " 'Closing date',\n",
       " 'Replaced',\n",
       " 'Replaced by',\n",
       " 'Name',\n",
       " 'Attraction type',\n",
       " 'Music',\n",
       " 'Duration',\n",
       " 'Simplified Chinese',\n",
       " 'Traditional Chinese',\n",
       " 'Hanyu Pinyin',\n",
       " 'Literal meaning',\n",
       " 'Transcriptions',\n",
       " 'Bopomofo',\n",
       " 'Gwoyeu Romatzyh',\n",
       " 'Wade–Giles',\n",
       " 'IPA',\n",
       " 'Yale Romanization',\n",
       " 'Jyutping',\n",
       " 'Hokkien POJ',\n",
       " 'Animation by',\n",
       " 'Color process',\n",
       " 'Engine(s)',\n",
       " 'Genre(s)',\n",
       " 'Actor control',\n",
       " 'Production company',\n",
       " 'Release(s)',\n",
       " 'Format(s)',\n",
       " 'Simplified',\n",
       " 'Characters',\n",
       " 'Date premiered',\n",
       " 'Place premiered',\n",
       " 'Setting',\n",
       " 'Original language',\n",
       " 'Subject',\n",
       " 'Published',\n",
       " 'Dewey Decimal',\n",
       " 'Text',\n",
       " 'Illustrator',\n",
       " 'Original title',\n",
       " 'Published in English',\n",
       " 'French',\n",
       " 'Developed by',\n",
       " 'Ending theme',\n",
       " 'No. of seasons',\n",
       " 'Nationality',\n",
       " 'Portrayed by',\n",
       " 'Alias',\n",
       " 'Species',\n",
       " 'Gender',\n",
       " 'Family',\n",
       " 'Alma mater',\n",
       " 'Camera setup',\n",
       " 'Novel(s)',\n",
       " 'Comics',\n",
       " 'Film(s)',\n",
       " 'Screen story by',\n",
       " 'Hangul',\n",
       " 'Revised Romanization',\n",
       " 'McCune–Reischauer',\n",
       " 'Developer(s)',\n",
       " 'Publisher(s)',\n",
       " 'Designer(s)',\n",
       " 'Programmer(s)',\n",
       " 'Artist(s)',\n",
       " 'Writer(s)',\n",
       " 'Engine',\n",
       " 'Platform(s)',\n",
       " 'Release',\n",
       " 'Mode(s)',\n",
       " 'Original work',\n",
       " 'Television series',\n",
       " 'Japanese',\n",
       " 'Hepburn',\n",
       " 'Literally',\n",
       " 'Cantonese',\n",
       " 'Full name',\n",
       " 'Height',\n",
       " 'Seasons',\n",
       " 'Chinese',\n",
       " 'Other names',\n",
       " 'Relatives',\n",
       " 'Yiddish',\n",
       " 'Formerly',\n",
       " 'Key people',\n",
       " 'Total assets',\n",
       " 'Owner',\n",
       " 'Number of employees',\n",
       " 'Divisions',\n",
       " 'Subsidiaries',\n",
       " 'Arabic',\n",
       " 'Romanized',\n",
       " 'Predecessor',\n",
       " 'Founders',\n",
       " 'Area served',\n",
       " 'Products',\n",
       " 'Services',\n",
       " 'Russian',\n",
       " 'Hebrew',\n",
       " 'Revenue',\n",
       " 'Operating income',\n",
       " 'Polish']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7080"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies = [movie for movie in wiki_movies_raw\n",
    "               if('Director' in movie or 'Directed by' in movie) \n",
    "               and 'imdb_link' in movie]\n",
    "len(wiki_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7076"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies = [movie for movie in wiki_movies_raw\n",
    "               if ('Director' in movie or 'Directed by' in movie)\n",
    "                   and 'imdb_link' in movie\n",
    "                   and 'No. of episodes' not in movie]\n",
    "len(wiki_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_movie(movie):\n",
    "    movie = dict(movie) #create a non-destructive copy\n",
    "    return movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title</th>\n",
       "      <th>Directed by</th>\n",
       "      <th>Produced by</th>\n",
       "      <th>Screenplay by</th>\n",
       "      <th>Story by</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>...</th>\n",
       "      <th>Predecessor</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Area served</th>\n",
       "      <th>Products</th>\n",
       "      <th>Services</th>\n",
       "      <th>Russian</th>\n",
       "      <th>Hebrew</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Operating income</th>\n",
       "      <th>Polish</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7060</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Insult_(film)</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>https://www.imdb.com/title/tt7048622/</td>\n",
       "      <td>The Insult</td>\n",
       "      <td>Ziad Doueiri</td>\n",
       "      <td>[Rachid Bouchareb, Jean Bréhat, Julie Gayet, A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Adel Karam, Kamel El Basha]</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7293</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Capernaum_(film)</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>https://www.imdb.com/title/tt8267604/</td>\n",
       "      <td>Capernaum</td>\n",
       "      <td>Nadine Labaki</td>\n",
       "      <td>[Michel Merkt, Khaled Mouzanar]</td>\n",
       "      <td>[Nadine Labaki, Jihad Hojaily, Michelle Keserw...</td>\n",
       "      <td>[Georges Khabbaz, Nadine Labaki, Michelle Kese...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Zain Al Rafeea, Yordanos Shiferaw, Boluwatife...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  url    year  \\\n",
       "7060  https://en.wikipedia.org/wiki/The_Insult_(film)  2018.0   \n",
       "7293   https://en.wikipedia.org/wiki/Capernaum_(film)  2018.0   \n",
       "\n",
       "                                  imdb_link       title    Directed by  \\\n",
       "7060  https://www.imdb.com/title/tt7048622/  The Insult   Ziad Doueiri   \n",
       "7293  https://www.imdb.com/title/tt8267604/   Capernaum  Nadine Labaki   \n",
       "\n",
       "                                            Produced by  \\\n",
       "7060  [Rachid Bouchareb, Jean Bréhat, Julie Gayet, A...   \n",
       "7293                    [Michel Merkt, Khaled Mouzanar]   \n",
       "\n",
       "                                          Screenplay by  \\\n",
       "7060                                                NaN   \n",
       "7293  [Nadine Labaki, Jihad Hojaily, Michelle Keserw...   \n",
       "\n",
       "                                               Story by Based on  \\\n",
       "7060                                                NaN      NaN   \n",
       "7293  [Georges Khabbaz, Nadine Labaki, Michelle Kese...      NaN   \n",
       "\n",
       "                                               Starring  ... Predecessor  \\\n",
       "7060                       [Adel Karam, Kamel El Basha]  ...         NaN   \n",
       "7293  [Zain Al Rafeea, Yordanos Shiferaw, Boluwatife...  ...         NaN   \n",
       "\n",
       "     Founders Area served Products Services Russian Hebrew Revenue  \\\n",
       "7060      NaN         NaN      NaN      NaN     NaN    NaN     NaN   \n",
       "7293      NaN         NaN      NaN      NaN     NaN    NaN     NaN   \n",
       "\n",
       "     Operating income Polish  \n",
       "7060              NaN    NaN  \n",
       "7293              NaN    NaN  \n",
       "\n",
       "[2 rows x 193 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df[wiki_movies_df['Arabic'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7060    https://en.wikipedia.org/wiki/The_Insult_(film)\n",
       "7293     https://en.wikipedia.org/wiki/Capernaum_(film)\n",
       "Name: url, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df[wiki_movies_df['Arabic'].notnull()]['url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Actor control',\n",
       " 'Adaptation by',\n",
       " 'Alias',\n",
       " 'Alma mater',\n",
       " 'Also known as',\n",
       " 'Animation by',\n",
       " 'Arabic',\n",
       " 'Area',\n",
       " 'Area served',\n",
       " 'Artist(s)',\n",
       " 'Attraction type',\n",
       " 'Audio format',\n",
       " 'Author',\n",
       " 'Based on',\n",
       " 'Biographical data',\n",
       " 'Bopomofo',\n",
       " 'Born',\n",
       " 'Box office',\n",
       " 'Budget',\n",
       " 'Camera setup',\n",
       " 'Cantonese',\n",
       " 'Characters',\n",
       " 'Children',\n",
       " 'Chinese',\n",
       " 'Cinematography',\n",
       " 'Closing date',\n",
       " 'Color process',\n",
       " 'Comics',\n",
       " 'Composer(s)',\n",
       " 'Coordinates',\n",
       " 'Country',\n",
       " 'Country of origin',\n",
       " 'Cover artist',\n",
       " 'Created by',\n",
       " 'Date premiered',\n",
       " 'Designer(s)',\n",
       " 'Developed by',\n",
       " 'Developer(s)',\n",
       " 'Dewey Decimal',\n",
       " 'Died',\n",
       " 'Directed by',\n",
       " 'Director',\n",
       " 'Distributed by',\n",
       " 'Distributor',\n",
       " 'Divisions',\n",
       " 'Duration',\n",
       " 'Edited by',\n",
       " 'Editor(s)',\n",
       " 'Ending theme',\n",
       " 'Engine',\n",
       " 'Engine(s)',\n",
       " 'Executive producer(s)',\n",
       " 'Family',\n",
       " 'Fate',\n",
       " 'Film(s)',\n",
       " 'Followed by',\n",
       " 'Format(s)',\n",
       " 'Formerly',\n",
       " 'Founded',\n",
       " 'Founder',\n",
       " 'Founders',\n",
       " 'French',\n",
       " 'Full name',\n",
       " 'Gender',\n",
       " 'Genre',\n",
       " 'Genre(s)',\n",
       " 'Genres',\n",
       " 'Gwoyeu Romatzyh',\n",
       " 'Hangul',\n",
       " 'Hanyu Pinyin',\n",
       " 'Headquarters',\n",
       " 'Hebrew',\n",
       " 'Height',\n",
       " 'Hepburn',\n",
       " 'Hokkien POJ',\n",
       " 'IPA',\n",
       " 'ISBN',\n",
       " 'Illustrator',\n",
       " 'Industry',\n",
       " 'Instruments',\n",
       " 'Japanese',\n",
       " 'Jyutping',\n",
       " 'Key people',\n",
       " 'LC Class',\n",
       " 'Label',\n",
       " 'Labels',\n",
       " 'Language',\n",
       " 'Length',\n",
       " 'Literal meaning',\n",
       " 'Literally',\n",
       " 'Mandarin',\n",
       " 'McCune–Reischauer',\n",
       " 'Media type',\n",
       " 'Mode(s)',\n",
       " 'Music',\n",
       " 'Music by',\n",
       " 'Name',\n",
       " 'Narrated by',\n",
       " 'Nationality',\n",
       " 'No. of episodes',\n",
       " 'No. of seasons',\n",
       " 'Novel(s)',\n",
       " 'Number of employees',\n",
       " 'OCLC',\n",
       " 'Occupation',\n",
       " 'Opening date',\n",
       " 'Opening theme',\n",
       " 'Operating income',\n",
       " 'Original language',\n",
       " 'Original language(s)',\n",
       " 'Original network',\n",
       " 'Original release',\n",
       " 'Original title',\n",
       " 'Original work',\n",
       " 'Other names',\n",
       " 'Owner',\n",
       " 'Pages',\n",
       " 'Parent',\n",
       " 'Parent(s)',\n",
       " 'Picture format',\n",
       " 'Place premiered',\n",
       " 'Platform(s)',\n",
       " 'Polish',\n",
       " 'Portrayed by',\n",
       " 'Preceded by',\n",
       " 'Predecessor',\n",
       " 'Produced by',\n",
       " 'Producer',\n",
       " 'Producer(s)',\n",
       " 'Production company',\n",
       " 'Production company(s)',\n",
       " 'Production location(s)',\n",
       " 'Productioncompanies ',\n",
       " 'Productioncompany ',\n",
       " 'Products',\n",
       " 'Programmer(s)',\n",
       " 'Publication date',\n",
       " 'Published',\n",
       " 'Published in English',\n",
       " 'Publisher',\n",
       " 'Publisher(s)',\n",
       " 'Recorded',\n",
       " 'Relatives',\n",
       " 'Release',\n",
       " 'Release date',\n",
       " 'Release(s)',\n",
       " 'Released',\n",
       " 'Replaced',\n",
       " 'Replaced by',\n",
       " 'Resting place',\n",
       " 'Revenue',\n",
       " 'Revised Romanization',\n",
       " 'Romanized',\n",
       " 'Running time',\n",
       " 'Russian',\n",
       " 'Screen story by',\n",
       " 'Screenplay by',\n",
       " 'Seasons',\n",
       " 'Series',\n",
       " 'Services',\n",
       " 'Set in',\n",
       " 'Setting',\n",
       " 'Simplified',\n",
       " 'Simplified Chinese',\n",
       " 'Species',\n",
       " 'Spouse(s)',\n",
       " 'Starring',\n",
       " 'Status',\n",
       " 'Story by',\n",
       " 'Subject',\n",
       " 'Subsidiaries',\n",
       " 'Suggested by',\n",
       " 'Television series',\n",
       " 'Text',\n",
       " 'Theme music composer',\n",
       " 'Total assets',\n",
       " 'Traditional',\n",
       " 'Traditional Chinese',\n",
       " 'Transcriptions',\n",
       " 'Type',\n",
       " 'Venue',\n",
       " 'Voices of',\n",
       " 'Wade–Giles',\n",
       " 'Website',\n",
       " 'Writer(s)',\n",
       " 'Written by',\n",
       " 'Yale Romanization',\n",
       " 'Years active',\n",
       " 'Yiddish',\n",
       " 'imdb_link',\n",
       " 'title',\n",
       " 'url',\n",
       " 'year']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(wiki_movies_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_movie(movie):\n",
    "    movie = dict(movie) # create a non-destructive copy\n",
    "    alt_titles={}\n",
    "    \n",
    "    return movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_movie(movie):\n",
    "    movie = dict(movie) # create a non-destructive copy\n",
    "    alt_titles = {}\n",
    "    for key in ['Also known as', 'Arabic', 'Cantonese', 'Chinese', 'French', \n",
    "                'Hangul', 'Hebrew', 'Hepburn','Japanese', 'Literally', 'Mandarin', \n",
    "                'McCune–Reischauer', 'Original title', 'Polish', 'Revised Romanization', \n",
    "                'Romanized', 'Russian', 'Simplified', 'Traditional', 'Yiddish']:\n",
    "        return movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_movie(movie):\n",
    "    movie = dict(movie) # create a non-destructive copy\n",
    "    alt_titles = {}\n",
    "    for key in ['Also known as', 'Arabic', 'Cantonese', 'Chinese', 'French', \n",
    "                'Hangul', 'Hebrew', 'Hepburn','Japanese', 'Literally', 'Mandarin', \n",
    "                'McCune–Reischauer', 'Original title', 'Polish', 'Revised Romanization', \n",
    "                'Romanized', 'Russian', 'Simplified', 'Traditional', 'Yiddish']:\n",
    "        if key in movie:\n",
    "            alt_titles[key] = movie[key]\n",
    "            movies.pop(key)\n",
    "    return movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_movie(movie):\n",
    "    movie = dict(movie) # create a non-destructive copy\n",
    "    alt_titles = {}\n",
    "    # combine alternate titles into one list\n",
    "    for key in ['Also known as', 'Arabic', 'Cantonese', 'Chinese', 'French', \n",
    "                'Hangul', 'Hebrew', 'Hepburn','Japanese', 'Literally', 'Mandarin', \n",
    "                'McCune–Reischauer', 'Original title', 'Polish', 'Revised Romanization', \n",
    "                'Romanized', 'Russian', 'Simplified', 'Traditional', 'Yiddish']:\n",
    "        if key in movie:\n",
    "            alt_titles[key] = movie[key]\n",
    "            movie.pop(key) \n",
    "    if len(alt_titles)>0:\n",
    "                movie['alt_titles'] = alt_titles\n",
    "            \n",
    "    # merge column names      \n",
    "    def change_column_name(old_name, new_name):\n",
    "        if old_name in movie:\n",
    "            movie[new_name] = movie.pop(old_name)\n",
    "            \n",
    "    change_column_name('Adaptation by', 'Writer(s)')\n",
    "    change_column_name('Country of origin', 'Country')\n",
    "    change_column_name('Directed by', 'Director')\n",
    "    change_column_name('Distributed by', 'Distributor')\n",
    "    change_column_name('Edited by', 'Editor(s)')\n",
    "    change_column_name('Length', 'Running time')\n",
    "    change_column_name('Original release', 'Release date')\n",
    "    change_column_name('Music by', 'Composer(s)')\n",
    "    change_column_name('Produced by', 'Producer(s)')\n",
    "    change_column_name('Producer', 'Producer(s)')\n",
    "    change_column_name('Productioncompanies ', 'Production company(s)')\n",
    "    change_column_name('Productioncompany ', 'Production company(s)')\n",
    "    change_column_name('Released', 'Release Date')\n",
    "    change_column_name('Release Date', 'Release date')\n",
    "    change_column_name('Screen story by', 'Writer(s)')\n",
    "    change_column_name('Screenplay by', 'Writer(s)')\n",
    "    change_column_name('Story by', 'Writer(s)')\n",
    "    change_column_name('Theme music composer', 'Composer(s)')\n",
    "    change_column_name('Written by', 'Writer(s)')\n",
    "            \n",
    "    return movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_movies = [clean_movie(movie) for movie in wiki_movies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Animation by',\n",
       " 'Audio format',\n",
       " 'Based on',\n",
       " 'Box office',\n",
       " 'Budget',\n",
       " 'Cinematography',\n",
       " 'Color process',\n",
       " 'Composer(s)',\n",
       " 'Country',\n",
       " 'Created by',\n",
       " 'Director',\n",
       " 'Distributor',\n",
       " 'Editor(s)',\n",
       " 'Executive producer(s)',\n",
       " 'Followed by',\n",
       " 'Genre',\n",
       " 'Label',\n",
       " 'Language',\n",
       " 'Narrated by',\n",
       " 'Original language(s)',\n",
       " 'Original network',\n",
       " 'Picture format',\n",
       " 'Preceded by',\n",
       " 'Producer(s)',\n",
       " 'Production company(s)',\n",
       " 'Production location(s)',\n",
       " 'Recorded',\n",
       " 'Release date',\n",
       " 'Running time',\n",
       " 'Starring',\n",
       " 'Suggested by',\n",
       " 'Venue',\n",
       " 'Voices of',\n",
       " 'Writer(s)',\n",
       " 'alt_titles',\n",
       " 'imdb_link',\n",
       " 'title',\n",
       " 'url',\n",
       " 'year']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df = pd.DataFrame(clean_movies)\n",
    "sorted(wiki_movies_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7076\n",
      "7033\n"
     ]
    },
    {
     "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>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Narrated by</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Running time</th>\n",
       "      <th>...</th>\n",
       "      <th>Created by</th>\n",
       "      <th>Preceded by</th>\n",
       "      <th>Suggested by</th>\n",
       "      <th>alt_titles</th>\n",
       "      <th>Recorded</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Label</th>\n",
       "      <th>Animation by</th>\n",
       "      <th>Color process</th>\n",
       "      <th>imdb_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Adventures_o...</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0098987/</td>\n",
       "      <td>The Adventures of Ford Fairlane</td>\n",
       "      <td>[Characters, by Rex Weiner]</td>\n",
       "      <td>[Andrew Dice Clay, Wayne Newton, Priscilla Pre...</td>\n",
       "      <td>Andrew \"Dice\" Clay</td>\n",
       "      <td>Oliver Wood</td>\n",
       "      <td>[July 11, 1990, (, 1990-07-11, )]</td>\n",
       "      <td>102 minutes</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0098987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://en.wikipedia.org/wiki/After_Dark,_My_S...</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0098994/</td>\n",
       "      <td>After Dark, My Sweet</td>\n",
       "      <td>[the novel, After Dark, My Sweet, by, Jim Thom...</td>\n",
       "      <td>[Jason Patric, Rachel Ward, Bruce Dern, George...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mark Plummer</td>\n",
       "      <td>[May 17, 1990, (, 1990-05-17, ), (Cannes Film ...</td>\n",
       "      <td>114 minutes</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0098994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Air_America_(film)</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099005/</td>\n",
       "      <td>Air America</td>\n",
       "      <td>[Air America, by, Christopher Robbins]</td>\n",
       "      <td>[Mel Gibson, Robert Downey Jr., Nancy Travis, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Deakins</td>\n",
       "      <td>[August 10, 1990, (, 1990-08-10, )]</td>\n",
       "      <td>113 minutes</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0099005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Alice_(1990_film)</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099012/</td>\n",
       "      <td>Alice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Alec Baldwin, Blythe Danner, Judy Davis, Mia ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Carlo Di Palma</td>\n",
       "      <td>[December 25, 1990, (, 1990-12-25, )]</td>\n",
       "      <td>106 minutes</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0099012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Almost_an_Angel</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099018/</td>\n",
       "      <td>Almost an Angel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Paul Hogan, Elias Koteas, Linda Kozlowski]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Russell Boyd</td>\n",
       "      <td>December 19, 1990</td>\n",
       "      <td>95 minutes</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tt0099018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  year  \\\n",
       "0  https://en.wikipedia.org/wiki/The_Adventures_o...  1990   \n",
       "1  https://en.wikipedia.org/wiki/After_Dark,_My_S...  1990   \n",
       "2   https://en.wikipedia.org/wiki/Air_America_(film)  1990   \n",
       "3    https://en.wikipedia.org/wiki/Alice_(1990_film)  1990   \n",
       "4      https://en.wikipedia.org/wiki/Almost_an_Angel  1990   \n",
       "\n",
       "                               imdb_link                            title  \\\n",
       "0  https://www.imdb.com/title/tt0098987/  The Adventures of Ford Fairlane   \n",
       "1  https://www.imdb.com/title/tt0098994/             After Dark, My Sweet   \n",
       "2  https://www.imdb.com/title/tt0099005/                      Air America   \n",
       "3  https://www.imdb.com/title/tt0099012/                            Alice   \n",
       "4  https://www.imdb.com/title/tt0099018/                  Almost an Angel   \n",
       "\n",
       "                                            Based on  \\\n",
       "0                        [Characters, by Rex Weiner]   \n",
       "1  [the novel, After Dark, My Sweet, by, Jim Thom...   \n",
       "2             [Air America, by, Christopher Robbins]   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "                                            Starring         Narrated by  \\\n",
       "0  [Andrew Dice Clay, Wayne Newton, Priscilla Pre...  Andrew \"Dice\" Clay   \n",
       "1  [Jason Patric, Rachel Ward, Bruce Dern, George...                 NaN   \n",
       "2  [Mel Gibson, Robert Downey Jr., Nancy Travis, ...                 NaN   \n",
       "3  [Alec Baldwin, Blythe Danner, Judy Davis, Mia ...                 NaN   \n",
       "4        [Paul Hogan, Elias Koteas, Linda Kozlowski]                 NaN   \n",
       "\n",
       "   Cinematography                                       Release date  \\\n",
       "0     Oliver Wood                  [July 11, 1990, (, 1990-07-11, )]   \n",
       "1    Mark Plummer  [May 17, 1990, (, 1990-05-17, ), (Cannes Film ...   \n",
       "2   Roger Deakins                [August 10, 1990, (, 1990-08-10, )]   \n",
       "3  Carlo Di Palma              [December 25, 1990, (, 1990-12-25, )]   \n",
       "4    Russell Boyd                                  December 19, 1990   \n",
       "\n",
       "  Running time  ... Created by Preceded by Suggested by alt_titles Recorded  \\\n",
       "0  102 minutes  ...        NaN         NaN          NaN        NaN      NaN   \n",
       "1  114 minutes  ...        NaN         NaN          NaN        NaN      NaN   \n",
       "2  113 minutes  ...        NaN         NaN          NaN        NaN      NaN   \n",
       "3  106 minutes  ...        NaN         NaN          NaN        NaN      NaN   \n",
       "4   95 minutes  ...        NaN         NaN          NaN        NaN      NaN   \n",
       "\n",
       "  Venue Label Animation by Color process    imdb_id  \n",
       "0   NaN   NaN          NaN           NaN  tt0098987  \n",
       "1   NaN   NaN          NaN           NaN  tt0098994  \n",
       "2   NaN   NaN          NaN           NaN  tt0099005  \n",
       "3   NaN   NaN          NaN           NaN  tt0099012  \n",
       "4   NaN   NaN          NaN           NaN  tt0099018  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df['imdb_id'] = wiki_movies_df['imdb_link'].str.extract(r'(tt\\d{7})')\n",
    "print(len(wiki_movies_df))\n",
    "wiki_movies_df.drop_duplicates(subset='imdb_id', inplace=True)\n",
    "print(len(wiki_movies_df))\n",
    "wiki_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['url', 0],\n",
       " ['year', 0],\n",
       " ['imdb_link', 0],\n",
       " ['title', 1],\n",
       " ['Based on', 4852],\n",
       " ['Starring', 184],\n",
       " ['Narrated by', 6752],\n",
       " ['Cinematography', 691],\n",
       " ['Release date', 32],\n",
       " ['Running time', 139],\n",
       " ['Country', 236],\n",
       " ['Language', 244],\n",
       " ['Budget', 2295],\n",
       " ['Box office', 1548],\n",
       " ['Director', 0],\n",
       " ['Distributor', 357],\n",
       " ['Editor(s)', 548],\n",
       " ['Composer(s)', 518],\n",
       " ['Producer(s)', 202],\n",
       " ['Production company(s)', 1678],\n",
       " ['Writer(s)', 199],\n",
       " ['Genre', 6923],\n",
       " ['Original language(s)', 6875],\n",
       " ['Original network', 6908],\n",
       " ['Executive producer(s)', 6936],\n",
       " ['Production location(s)', 6986],\n",
       " ['Picture format', 6969],\n",
       " ['Audio format', 6972],\n",
       " ['Voices of', 7031],\n",
       " ['Followed by', 7024],\n",
       " ['Created by', 7023],\n",
       " ['Preceded by', 7023],\n",
       " ['Suggested by', 7032],\n",
       " ['alt_titles', 7012],\n",
       " ['Recorded', 7031],\n",
       " ['Venue', 7032],\n",
       " ['Label', 7031],\n",
       " ['Animation by', 7031],\n",
       " ['Color process', 7032],\n",
       " ['imdb_id', 0]]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[column,wiki_movies_df[column].isnull().sum()] for column in wiki_movies_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['url',\n",
       " 'year',\n",
       " 'imdb_link',\n",
       " 'title',\n",
       " 'Based on',\n",
       " 'Starring',\n",
       " 'Cinematography',\n",
       " 'Release date',\n",
       " 'Running time',\n",
       " 'Country',\n",
       " 'Language',\n",
       " 'Budget',\n",
       " 'Box office',\n",
       " 'Director',\n",
       " 'Distributor',\n",
       " 'Editor(s)',\n",
       " 'Composer(s)',\n",
       " 'Producer(s)',\n",
       " 'Production company(s)',\n",
       " 'Writer(s)',\n",
       " 'imdb_id']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[column for column in wiki_movies_df.columns if wiki_movies_df[column].isnull().sum() < len(wiki_movies_df) * 0.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_columns_to_keep = [column for column in wiki_movies_df.columns if wiki_movies_df[column].isnull().sum() < len(wiki_movies_df) * 0.9]\n",
    "wiki_movies_df = wiki_movies_df[wiki_columns_to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                      object\n",
       "year                      int64\n",
       "imdb_link                object\n",
       "title                    object\n",
       "Based on                 object\n",
       "Starring                 object\n",
       "Cinematography           object\n",
       "Release date             object\n",
       "Running time             object\n",
       "Country                  object\n",
       "Language                 object\n",
       "Budget                   object\n",
       "Box office               object\n",
       "Director                 object\n",
       "Distributor              object\n",
       "Editor(s)                object\n",
       "Composer(s)              object\n",
       "Producer(s)              object\n",
       "Production company(s)    object\n",
       "Writer(s)                object\n",
       "imdb_id                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "box_office = wiki_movies_df['Box office'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5485"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(box_office)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34                           [US$, 4,212,828]\n",
       "54      [$6,698,361 (, United States, ), [2]]\n",
       "74                    [$6,488,144, (US), [1]]\n",
       "126                [US$1,531,489, (domestic)]\n",
       "130                          [US$, 4,803,039]\n",
       "                        ...                  \n",
       "6980               [$99.6, million, [4], [5]]\n",
       "6994                   [$365.6, million, [1]]\n",
       "6995                         [$53.8, million]\n",
       "7015                     [$435, million, [7]]\n",
       "7048                   [$529.3, million, [4]]\n",
       "Name: Box office, Length: 135, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_not_a_string(x):\n",
    "    return type(x) !=str\n",
    "box_office[box_office.map(is_not_a_string)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34                           [US$, 4,212,828]\n",
       "54      [$6,698,361 (, United States, ), [2]]\n",
       "74                    [$6,488,144, (US), [1]]\n",
       "126                [US$1,531,489, (domestic)]\n",
       "130                          [US$, 4,803,039]\n",
       "                        ...                  \n",
       "6980               [$99.6, million, [4], [5]]\n",
       "6994                   [$365.6, million, [1]]\n",
       "6995                         [$53.8, million]\n",
       "7015                     [$435, million, [7]]\n",
       "7048                   [$529.3, million, [4]]\n",
       "Name: Box office, Length: 135, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lambda x: type(x) != str\n",
    "box_office[box_office.map(lambda x: type(x) !=str)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          $21.4 million\n",
       "1           $2.7 million\n",
       "2            $57,718,089\n",
       "3             $7,331,647\n",
       "4       $6,939,946 (USA)\n",
       "              ...       \n",
       "7070       $19.4 million\n",
       "7071       $41.9 million\n",
       "7072       $76.1 million\n",
       "7073       $38.4 million\n",
       "7074        $5.5 million\n",
       "Name: Box office, Length: 5485, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office = box_office.apply(lambda x: ' '.join(x) if type(x) == list else x)\n",
    "box_office"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "form_one = r'\\$\\d+\\.?\\d*\\s*[mb]illion'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3896"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office.str.contains(form_one,flags=re.IGNORECASE).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "form_two = r'\\$\\d{1,3}(?:,\\d{3})+'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1544"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office.str.contains(form_two,flags=re.IGNORECASE).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_form_one = box_office.str.contains(form_one, flags=re.IGNORECASE)\n",
    "matches_form_two = box_office.str.contains(form_two, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34                         US$ 4,212,828\n",
       "79                              $335.000\n",
       "110                   $4.35-4.37 million\n",
       "130                        US$ 4,803,039\n",
       "600                           $5000 (US)\n",
       "731                         $ 11,146,270\n",
       "957                             $ 50,004\n",
       "1070                          35,254,617\n",
       "1147    $ 407,618 (U.S.) (sub-total) [1]\n",
       "1446                        $ 11,829,959\n",
       "1480                          £3 million\n",
       "1611                            $520.000\n",
       "1865                        ¥1.1 billion\n",
       "2032                                 N/A\n",
       "2091                                $309\n",
       "2130               US$ 171.8 million [9]\n",
       "2257                   US$ 3,395,581 [1]\n",
       "2263            $ 1,223,034 ( domestic )\n",
       "2347                            $282.175\n",
       "2638            $ 104,883 (US sub-total)\n",
       "2665         926,423 admissions (France)\n",
       "2697      $ 1.7 million (US) (sub-total)\n",
       "2823                            $414.000\n",
       "2924                            $621.000\n",
       "3088           $32 [2] –33.1 million [1]\n",
       "3631                                 TBA\n",
       "3859                  $38.9–40.3 million\n",
       "3879            CN¥3.650 million (China)\n",
       "4116                          £7,385,434\n",
       "4123                            $161.000\n",
       "4261                  $20.7–23.9 million\n",
       "4306                              $20-30\n",
       "4492                        $47.7 millon\n",
       "4561             $45.2k (only in Turkey)\n",
       "4662                USD$ 8.2 million [2]\n",
       "5362                   $ 142 million [3]\n",
       "5447                               £2.56\n",
       "5784                            413 733$\n",
       "6013                             Unknown\n",
       "6145                  $17.5–18.4 million\n",
       "6234                  $41.8–41.9 million\n",
       "6369                               $111k\n",
       "6370                                $588\n",
       "6593                      less than $372\n",
       "6829                    $ 41 million [3]\n",
       "6843                             8 crore\n",
       "6904                         $6.9 millon\n",
       "Name: Box office, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this will throw an error!\n",
    "#box_office[(not matches_form_one) and (not matches_forms_two)]\n",
    "#box_office[~matches_form_one & ~matches_form_two]\n",
    "box_office[~matches_form_one & ~matches_form_two]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "form_one = r'\\$\\s*\\d+\\.?\\d*\\s*[mb]illi?on'\n",
    "form_two = r'\\$\\s*\\d{1,3}(?:[,\\.]\\d{3})+(?!\\s[mb]illion)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "box_office = box_office.str.replace(r'\\$.*[-—–](?![a-z])','$',regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$21.4 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$2.7 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$57,718,089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$7,331,647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$6,939,946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7070</th>\n",
       "      <td>$19.4 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7071</th>\n",
       "      <td>$41.9 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7072</th>\n",
       "      <td>$76.1 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7073</th>\n",
       "      <td>$38.4 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7074</th>\n",
       "      <td>$5.5 million</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5485 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "0     $21.4 million\n",
       "1      $2.7 million\n",
       "2       $57,718,089\n",
       "3        $7,331,647\n",
       "4        $6,939,946\n",
       "...             ...\n",
       "7070  $19.4 million\n",
       "7071  $41.9 million\n",
       "7072  $76.1 million\n",
       "7073  $38.4 million\n",
       "7074   $5.5 million\n",
       "\n",
       "[5485 rows x 1 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office.str.extract(f'({form_one}|{form_two})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_dollars(s):\n",
    "    # if s is not a string, return NaN\n",
    "    if type(s) != str:\n",
    "        return np.nan\n",
    "\n",
    "    # if input is of the form $###.# million\n",
    "    if re.match(r'\\$\\s*\\d+\\.?\\d*\\s*milli?on', s, flags=re.IGNORECASE):\n",
    "\n",
    "        # remove dollar sign and \" million\"\n",
    "        s = re.sub('\\$|\\s|[a-zA-Z]','', s)\n",
    "\n",
    "        # convert to float and multiply by a million\n",
    "        value = float(s) * 10**6\n",
    "\n",
    "        # return value\n",
    "        return value\n",
    "\n",
    "    # if input is of the form $###.# billion\n",
    "    elif re.match(r'\\$\\s*\\d+\\.?\\d*\\s*billi?on', s, flags=re.IGNORECASE):\n",
    "\n",
    "        # remove dollar sign and \" billion\"\n",
    "        s = re.sub('\\$|\\s|[a-zA-Z]','', s)\n",
    "\n",
    "        # convert to float and multiply by a billion\n",
    "        value = float(s) * 10**9\n",
    "\n",
    "        # return value\n",
    "        return value\n",
    "\n",
    "    # if input is of the form $###,###,###\n",
    "    elif re.match(r'\\$\\s*\\d{1,3}(?:[,\\.]\\d{3})+(?!\\s[mb]illion)', s, flags=re.IGNORECASE):\n",
    "\n",
    "        # remove dollar sign and commas\n",
    "        s = re.sub('\\$|,','', s)\n",
    "\n",
    "        # convert to float\n",
    "        value = float(s)\n",
    "\n",
    "        # return value\n",
    "        return value\n",
    "\n",
    "    # otherwise, return NaN\n",
    "    else:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       21400000.0\n",
       "1        2700000.0\n",
       "2       57718089.0\n",
       "3        7331647.0\n",
       "4        6939946.0\n",
       "           ...    \n",
       "7071    41900000.0\n",
       "7072    76100000.0\n",
       "7073    38400000.0\n",
       "7074     5500000.0\n",
       "7075           NaN\n",
       "Name: box_office, Length: 7033, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df['box_office'] = box_office.str.extract(f'({form_one}|{form_two})', flags=re.IGNORECASE)[0].apply(parse_dollars)\n",
    "wiki_movies_df['box_office']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_movies_df.drop('Box office', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "budget = wiki_movies_df['Budget'].dropna()\n",
    "budget = budget.map(lambda x: ' '.join(x) if type(x) == list else x)\n",
    "budget = budget.str.replace(r'\\$.*[-—–](?![a-z])', '$', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136                         Unknown\n",
       "204     60 million Norwegian Kroner\n",
       "478                         Unknown\n",
       "973             $34 [3] [4] million\n",
       "1126               $120 [4] million\n",
       "1226                        Unknown\n",
       "1278                            HBO\n",
       "1374                     £6,000,000\n",
       "1397                     13 million\n",
       "1480                   £2.8 million\n",
       "1734                   CAD2,000,000\n",
       "1913     PHP 85 million (estimated)\n",
       "1948                    102,888,900\n",
       "1953                   3,500,000 DM\n",
       "1973                     ₤2,300,874\n",
       "2281                     $14 milion\n",
       "2451                     ₤6,350,000\n",
       "3144                   € 40 million\n",
       "3360               $150 [6] million\n",
       "3418                        $218.32\n",
       "3802                   £4.2 million\n",
       "3906                            N/A\n",
       "3959                    760,000 USD\n",
       "4470                       19 crore\n",
       "4641                    £17 million\n",
       "5034              $$200 [4] million\n",
       "5055           $155 [2] [3] million\n",
       "5419                $40 [4] million\n",
       "5424                            N/A\n",
       "5447                     £4 million\n",
       "5671                    €14 million\n",
       "5687                   $ dead link]\n",
       "6385               £ 12 million [3]\n",
       "6593                     £3 million\n",
       "6821                  £12.9 million\n",
       "6843                      3.5 crore\n",
       "6895                        919,000\n",
       "7070                   €4.3 million\n",
       "Name: Budget, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_form_one = budget.str.contains(form_one, flags=re.IGNORECASE)\n",
    "matches_form_two = budget.str.contains(form_two, flags=re.IGNORECASE)\n",
    "budget[~matches_form_one & ~matches_form_two]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136                         Unknown\n",
       "204     60 million Norwegian Kroner\n",
       "478                         Unknown\n",
       "973                     $34 million\n",
       "1126                   $120 million\n",
       "1226                        Unknown\n",
       "1278                            HBO\n",
       "1374                     £6,000,000\n",
       "1397                     13 million\n",
       "1480                   £2.8 million\n",
       "1734                   CAD2,000,000\n",
       "1913     PHP 85 million (estimated)\n",
       "1948                    102,888,900\n",
       "1953                   3,500,000 DM\n",
       "1973                     ₤2,300,874\n",
       "2281                     $14 milion\n",
       "2451                     ₤6,350,000\n",
       "3144                   € 40 million\n",
       "3360                   $150 million\n",
       "3418                        $218.32\n",
       "3802                   £4.2 million\n",
       "3906                            N/A\n",
       "3959                    760,000 USD\n",
       "4470                       19 crore\n",
       "4641                    £17 million\n",
       "5034                  $$200 million\n",
       "5055                   $155 million\n",
       "5419                    $40 million\n",
       "5424                            N/A\n",
       "5447                     £4 million\n",
       "5671                    €14 million\n",
       "5687                   $ dead link]\n",
       "6385                  £ 12 million \n",
       "6593                     £3 million\n",
       "6821                  £12.9 million\n",
       "6843                      3.5 crore\n",
       "6895                        919,000\n",
       "7070                   €4.3 million\n",
       "Name: Budget, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budget = budget.str.replace(r'\\[\\d+\\]\\s*', '')\n",
    "budget[~matches_form_one & ~matches_form_two]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_movies_df['budget'] = budget.str.extract(f'({form_one}|{form_two})', flags=re.IGNORECASE)[0].apply(parse_dollars)\n",
    "wiki_movies_df.drop('Budget', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "release_date = wiki_movies_df['Release date'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_form_one = r'(?:January|February|March|April|May|June|July|August|September|October|November|December)\\s[123]\\d,\\s\\d{4}'\n",
    "date_form_two = r'\\d{4}.[01]\\d.[123]\\d'\n",
    "date_form_three = r'(?:January|February|March|April|May|June|July|August|September|October|November|December)\\s\\d{4}'\n",
    "date_form_four = r'\\d{4}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>July 11, 1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May 17, 1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>August 10, 1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>December 25, 1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>December 19, 1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7071</th>\n",
       "      <td>December 25, 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7072</th>\n",
       "      <td>December 11, 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7073</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7074</th>\n",
       "      <td>August 31, 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7075</th>\n",
       "      <td>December 2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7001 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0\n",
       "0         July 11, 1990\n",
       "1          May 17, 1990\n",
       "2       August 10, 1990\n",
       "3     December 25, 1990\n",
       "4     December 19, 1990\n",
       "...                 ...\n",
       "7071  December 25, 2018\n",
       "7072  December 11, 2018\n",
       "7073               2018\n",
       "7074    August 31, 2018\n",
       "7075      December 2018\n",
       "\n",
       "[7001 rows x 1 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "release_date.str.extract(f'({date_form_one}|{date_form_two}|{date_form_three}|{date_form_four})', flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_movies_df['release_date'] = pd.to_datetime(release_date.str.extract(f'({date_form_one}|{date_form_two}|{date_form_three}|{date_form_four})')[0], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "running_time = wiki_movies_df['Running time'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6528"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_time.str.contains(r'^\\d*\\s*minutes$', flags=re.IGNORECASE).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9                                                 102 min\n",
       "26                                                 93 min\n",
       "28                                                32 min.\n",
       "34                                                101 min\n",
       "35                                                 97 min\n",
       "                              ...                        \n",
       "6500       114 minutes [1] 120 minutes (extended edition)\n",
       "6643                                             104 mins\n",
       "6709    90 minutes (theatrical) [1] 91 minutes (unrate...\n",
       "7057    108 minutes (Original cut) 98 minutes (UK cut)...\n",
       "7075                Variable; 90 minutes for default path\n",
       "Name: Running time, Length: 366, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_time[running_time.str.contains(r'^\\d*\\s*minutes$', flags=re.IGNORECASE) != True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6877"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_time.str.contains(r'^\\d*\\s*m', flags=re.IGNORECASE).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "668                     UK:84 min (DVD version) US:86 min\n",
       "727                         78-102 min (depending on cut)\n",
       "840                       Varies (79 [3] –84 [1] minutes)\n",
       "1347                                              25 : 03\n",
       "1443    United States: 77 minutes Argentina: 94 minute...\n",
       "1499                                            1hr 35min\n",
       "1551                                               varies\n",
       "1774                    Netherlands:96 min, Canada:95 min\n",
       "1777                                       approx. 14 min\n",
       "2273                                           1 h 43 min\n",
       "2993                                               1h 48m\n",
       "3925                                              4 hours\n",
       "4425    US domestic version: 86 minutes Original versi...\n",
       "4967    Theatrical cut: 97 minutes Unrated cut: 107 mi...\n",
       "5424                    115 [1] /123 [2] /128 [3] minutes\n",
       "5447                                    1 hour 32 minutes\n",
       "7075                Variable; 90 minutes for default path\n",
       "Name: Running time, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_time[running_time.str.contains(r'^\\d*\\s*m', flags=re.IGNORECASE) != True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "running_time_extract = running_time.str.extract(r'(\\d+)\\s*ho?u?r?s?\\s*(\\d*)|(\\d+)\\s*m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "running_time_extract = running_time_extract.apply(lambda col: pd.to_numeric(col, errors='coerce')).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_movies_df['running_time'] = running_time_extract.apply(lambda row: row[0]*60 + row[1] if row[2] == 0 else row[2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_movies_df.drop('Running time', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adult                     object\n",
       "belongs_to_collection     object\n",
       "budget                    object\n",
       "genres                    object\n",
       "homepage                  object\n",
       "id                        object\n",
       "imdb_id                   object\n",
       "original_language         object\n",
       "original_title            object\n",
       "overview                  object\n",
       "popularity                object\n",
       "poster_path               object\n",
       "production_companies      object\n",
       "production_countries      object\n",
       "release_date              object\n",
       "revenue                  float64\n",
       "runtime                  float64\n",
       "spoken_languages          object\n",
       "status                    object\n",
       "tagline                   object\n",
       "title                     object\n",
       "video                     object\n",
       "vote_average             float64\n",
       "vote_count               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False                                                                                                                             45454\n",
       "True                                                                                                                                  9\n",
       " - Written by Ørnås                                                                                                                   1\n",
       " Avalanche Sharks tells the story of a bikini contest that turns into a horrifying affair when it is hit by a shark avalanche.        1\n",
       " Rune Balot goes to a casino connected to the October corporation to try to wrap up her case once and for all.                        1\n",
       "Name: adult, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata['adult'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19730</th>\n",
       "      <td>- Written by Ørnås</td>\n",
       "      <td>0.065736</td>\n",
       "      <td>/ff9qCepilowshEtG2GYWwzt2bs4.jpg</td>\n",
       "      <td>[{'name': 'Carousel Productions', 'id': 11176}...</td>\n",
       "      <td>[{'iso_3166_1': 'CA', 'name': 'Canada'}, {'iso...</td>\n",
       "      <td>1997-08-20</td>\n",
       "      <td>0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29503</th>\n",
       "      <td>Rune Balot goes to a casino connected to the ...</td>\n",
       "      <td>1.931659</td>\n",
       "      <td>/zV8bHuSL6WXoD6FWogP9j4x80bL.jpg</td>\n",
       "      <td>[{'name': 'Aniplex', 'id': 2883}, {'name': 'Go...</td>\n",
       "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
       "      <td>2012-09-29</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>[{'iso_639_1': 'ja', 'name': '日本語'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35587</th>\n",
       "      <td>Avalanche Sharks tells the story of a bikini ...</td>\n",
       "      <td>2.185485</td>\n",
       "      <td>/zaSf5OG7V8X8gqFvly88zDdRm46.jpg</td>\n",
       "      <td>[{'name': 'Odyssey Media', 'id': 17161}, {'nam...</td>\n",
       "      <td>[{'iso_3166_1': 'CA', 'name': 'Canada'}]</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   adult  \\\n",
       "19730                                 - Written by Ørnås   \n",
       "29503   Rune Balot goes to a casino connected to the ...   \n",
       "35587   Avalanche Sharks tells the story of a bikini ...   \n",
       "\n",
       "      belongs_to_collection                            budget  \\\n",
       "19730              0.065736  /ff9qCepilowshEtG2GYWwzt2bs4.jpg   \n",
       "29503              1.931659  /zV8bHuSL6WXoD6FWogP9j4x80bL.jpg   \n",
       "35587              2.185485  /zaSf5OG7V8X8gqFvly88zDdRm46.jpg   \n",
       "\n",
       "                                                  genres  \\\n",
       "19730  [{'name': 'Carousel Productions', 'id': 11176}...   \n",
       "29503  [{'name': 'Aniplex', 'id': 2883}, {'name': 'Go...   \n",
       "35587  [{'name': 'Odyssey Media', 'id': 17161}, {'nam...   \n",
       "\n",
       "                                                homepage          id imdb_id  \\\n",
       "19730  [{'iso_3166_1': 'CA', 'name': 'Canada'}, {'iso...  1997-08-20       0   \n",
       "29503  [{'iso_3166_1': 'US', 'name': 'United States o...  2012-09-29       0   \n",
       "35587           [{'iso_3166_1': 'CA', 'name': 'Canada'}]  2014-01-01       0   \n",
       "\n",
       "      original_language                            original_title  overview  \\\n",
       "19730             104.0  [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "29503              68.0      [{'iso_639_1': 'ja', 'name': '日本語'}]  Released   \n",
       "35587              82.0  [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "\n",
       "       ... release_date revenue runtime spoken_languages status  tagline  \\\n",
       "19730  ...            1     NaN     NaN              NaN    NaN      NaN   \n",
       "29503  ...           12     NaN     NaN              NaN    NaN      NaN   \n",
       "35587  ...           22     NaN     NaN              NaN    NaN      NaN   \n",
       "\n",
       "       title video vote_average vote_count  \n",
       "19730    NaN   NaN          NaN        NaN  \n",
       "29503    NaN   NaN          NaN        NaN  \n",
       "35587    NaN   NaN          NaN        NaN  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata[~kaggle_metadata['adult'].isin(['True','False'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_metadata = kaggle_metadata[kaggle_metadata['adult'] == 'False']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    45358\n",
       "True        93\n",
       "Name: video, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata['video'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "45461    False\n",
       "45462    False\n",
       "45463    False\n",
       "45464    False\n",
       "45465    False\n",
       "Name: video, Length: 45454, dtype: bool"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata['video'] == 'True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_metadata['video'] = kaggle_metadata['video']  == 'True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_metadata['budget'] = kaggle_metadata['budget'].astype(int)\n",
    "kaggle_metadata['id'] = pd.to_numeric(kaggle_metadata['id'], errors='raise')\n",
    "kaggle_metadata['popularity'] = pd.to_numeric(kaggle_metadata['popularity'], errors='raise')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_metadata['release_date'] = pd.to_datetime(kaggle_metadata['release_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26024289 entries, 0 to 26024288\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count     Dtype  \n",
      "---  ------     --------------     -----  \n",
      " 0   userId     26024289 non-null  int64  \n",
      " 1   movieId    26024289 non-null  int64  \n",
      " 2   rating     26024289 non-null  float64\n",
      " 3   timestamp  26024289 non-null  int64  \n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 794.2 MB\n"
     ]
    }
   ],
   "source": [
    "ratings.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          2015-03-09 22:52:09\n",
       "1          2015-03-09 23:07:15\n",
       "2          2015-03-09 22:52:03\n",
       "3          2015-03-09 22:52:26\n",
       "4          2015-03-09 22:52:36\n",
       "                   ...        \n",
       "26024284   2009-10-31 23:26:04\n",
       "26024285   2009-10-31 23:33:52\n",
       "26024286   2009-10-31 23:29:24\n",
       "26024287   2009-11-01 00:06:30\n",
       "26024288   2009-10-31 23:30:58\n",
       "Name: timestamp, Length: 26024289, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(ratings['timestamp'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          26,024,289.00\n",
       "mean                    3.53\n",
       "std                     1.07\n",
       "min                     0.50\n",
       "25%                     3.00\n",
       "50%                     3.50\n",
       "75%                     4.00\n",
       "max                     5.00\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.options.display.float_format = '{:20,.2f}'.format\n",
    "ratings['rating'].plot(kind='hist')\n",
    "ratings['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df = pd.merge(wiki_movies_df, kaggle_metadata, on='imdb_id', suffixes=['_wiki','_kaggle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Competing data:\n",
    "# Wiki                     Movielens                Resolution\n",
    "#--------------------------------------------------------------------------\n",
    "# title_wiki               title_kaggle\n",
    "# running_time             runtime\n",
    "# budget_wiki              budget_kaggle\n",
    "# box_office               revenue\n",
    "# release_date_wiki        release_date_kaggle\n",
    "# Language                 original_language\n",
    "# Production company(s)    production_companies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>title_wiki</th>\n",
       "      <th>title_kaggle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Adventures of Ford Fairlane</td>\n",
       "      <td>The Adventures of Ford Fairlane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>After Dark, My Sweet</td>\n",
       "      <td>After Dark, My Sweet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air America</td>\n",
       "      <td>Air America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alice</td>\n",
       "      <td>Alice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almost an Angel</td>\n",
       "      <td>Almost an Angel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6047</th>\n",
       "      <td>A Fantastic Woman</td>\n",
       "      <td>A Fantastic Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6048</th>\n",
       "      <td>Permission</td>\n",
       "      <td>Permission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6049</th>\n",
       "      <td>Loveless</td>\n",
       "      <td>Loveless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6050</th>\n",
       "      <td>Gemini</td>\n",
       "      <td>Gemini</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>How to Talk to Girls at Parties</td>\n",
       "      <td>How to Talk to Girls at Parties</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6052 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           title_wiki                     title_kaggle\n",
       "0     The Adventures of Ford Fairlane  The Adventures of Ford Fairlane\n",
       "1                After Dark, My Sweet             After Dark, My Sweet\n",
       "2                         Air America                      Air America\n",
       "3                               Alice                            Alice\n",
       "4                     Almost an Angel                  Almost an Angel\n",
       "...                               ...                              ...\n",
       "6047                A Fantastic Woman                A Fantastic Woman\n",
       "6048                       Permission                       Permission\n",
       "6049                         Loveless                         Loveless\n",
       "6050                           Gemini                           Gemini\n",
       "6051  How to Talk to Girls at Parties  How to Talk to Girls at Parties\n",
       "\n",
       "[6052 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Columns\n",
    "# First, just take a quick look at some of the titles.\n",
    "movies_df[['title_wiki','title_kaggle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>title_wiki</th>\n",
       "      <th>title_kaggle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>China Cry</td>\n",
       "      <td>China Cry: A True Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Daddy's Dyin' ...Who's Got the Will?</td>\n",
       "      <td>Daddy's Dyin'... Who's Got the Will?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>The Dark Side of the Moon</td>\n",
       "      <td>The Dark Side of The Moon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Delta Force 2</td>\n",
       "      <td>Delta Force 2: The Colombian Connection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>DuckTales the Movie:Treasure of the Lost Lamp</td>\n",
       "      <td>DuckTales: The Movie - Treasure of the Lost Lamp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5956</th>\n",
       "      <td>Chips</td>\n",
       "      <td>CHiPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5971</th>\n",
       "      <td>Spark</td>\n",
       "      <td>Spark: A Space Tail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>Pirates of the Caribbean:Dead Men Tell No Tales</td>\n",
       "      <td>Pirates of the Caribbean: Dead Men Tell No Tales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6023</th>\n",
       "      <td>Valerian and the Cityof a Thousand Planets</td>\n",
       "      <td>Valerian and the City of a Thousand Planets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6028</th>\n",
       "      <td>An Inconvenient Sequel:Truth to Power</td>\n",
       "      <td>An Inconvenient Sequel: Truth to Power</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>438 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           title_wiki  \\\n",
       "27                                          China Cry   \n",
       "36               Daddy's Dyin' ...Who's Got the Will?   \n",
       "38                          The Dark Side of the Moon   \n",
       "42                                      Delta Force 2   \n",
       "48      DuckTales the Movie:Treasure of the Lost Lamp   \n",
       "...                                               ...   \n",
       "5956                                            Chips   \n",
       "5971                                            Spark   \n",
       "5994  Pirates of the Caribbean:Dead Men Tell No Tales   \n",
       "6023       Valerian and the Cityof a Thousand Planets   \n",
       "6028            An Inconvenient Sequel:Truth to Power   \n",
       "\n",
       "                                          title_kaggle  \n",
       "27                             China Cry: A True Story  \n",
       "36                Daddy's Dyin'... Who's Got the Will?  \n",
       "38                           The Dark Side of The Moon  \n",
       "42             Delta Force 2: The Colombian Connection  \n",
       "48    DuckTales: The Movie - Treasure of the Lost Lamp  \n",
       "...                                                ...  \n",
       "5956                                             CHiPS  \n",
       "5971                               Spark: A Space Tail  \n",
       "5994  Pirates of the Caribbean: Dead Men Tell No Tales  \n",
       "6023       Valerian and the City of a Thousand Planets  \n",
       "6028            An Inconvenient Sequel: Truth to Power  \n",
       "\n",
       "[438 rows x 2 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# They both seem pretty consistent, which we'd expect. Look at the rows where the titles don't match.\n",
    "movies_df[movies_df['title_wiki'] != movies_df['title_kaggle']][['title_wiki','title_kaggle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title_wiki</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date_kaggle</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title_kaggle</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [url, year, imdb_link, title_wiki, Based on, Starring, Cinematography, Release date, Country, Language, Director, Distributor, Editor(s), Composer(s), Producer(s), Production company(s), Writer(s), imdb_id, box_office, budget_wiki, release_date_wiki, running_time, adult, belongs_to_collection, budget_kaggle, genres, homepage, id, original_language, original_title, overview, popularity, poster_path, production_companies, production_countries, release_date_kaggle, revenue, runtime, spoken_languages, status, tagline, title_kaggle, video, vote_average, vote_count]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 45 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Both options look pretty good, but the Kaggle data looks just a little bit more consistent. Let's confirm there aren't any missing titles \n",
    "# in the Kaggle data with the following code:\n",
    "\n",
    "# Show any rows where title_kaggle is empty\n",
    "movies_df[(movies_df['title_kaggle'] == '') | (movies_df['title_kaggle'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No results were returned, so we can just drop the Wikipedia titles. Note that for now, we’re merely noting the resolution.\n",
    "\n",
    "Next, look at running_time versus runtime. A scatter plot is a great way to give us a sense of how similar the columns are to each other. If the two columns were exactly the same, we'd see a scatter plot of a perfectly straight line. Any wildly different values will show up as dots far from that central line, and if one column is missing data, those values will fall on the x-axis or y-axis.\n",
    "\n",
    "**CAUTION**\n",
    "Because we're dealing with merged data, we should expect there to be missing values. Scatter plots won't show null values, so we need to fill them in with zeros when we're making our plots to get the whole picture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='running_time', ylabel='runtime'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The following code will fill in missing values with zero and make the scatter plot:\n",
    "\n",
    "movies_df.fillna(0).plot(x='running_time', y='runtime', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that there are more data points on the origin of the Y axis than on the origin of the X axis. Since the X axis is Wikipedia and the Y axis is Kaggle, this means there are more missing entries in the Wikipedia data set than in the Kaggle data set. Also, most of the runtimes are pretty close to each other but the Wikipedia data has some outliers, so the Kaggle data is probably a better choice here. However, we can also see from the scatter plot that there are movies where Kaggle has 0 for the runtime but Wikipedia has data, so we'll fill in the gaps with Wikipedia data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='budget_wiki', ylabel='budget_kaggle'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since budget_wiki and budget_kaggle are numeric, we'll make another scatter plot to compare the values:\n",
    "movies_df.fillna(0).plot(x='budget_wiki',y='budget_kaggle', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**\n",
    "Here are some questions to consider when interpreting this scatter plot:\n",
    "\n",
    "* Which dataset seems to have more outliers?\n",
    "* Which dataset seems to have more missing data points?\n",
    "* If we were to fill in the missing data points of one set with the other, which would be more likely to give us consistent data?\n",
    "* Is it better to start with a base of consistent data and fill in missing points with possible outliers? Or is it better to start with a base of data with outliers and fill in missing points with more consistent data?\n",
    "\n",
    "The Wikipedia data appears to have more outliers compared to the Kaggle data. However, there are quite a few movies with no data in the Kaggle column, while Wikipedia does have budget data. Therefore, we'll fill in the gaps with Wikipedia's data.\n",
    "\n",
    "### Box Office"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='box_office', ylabel='revenue'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The box_office and revenue columns are numeric, so we'll make another scatter plot.\n",
    "\n",
    "movies_df.fillna(0).plot(x='box_office', y='revenue', kind='scatter')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks pretty close, but we might be getting thrown off by the scale of that large data point. Let's look at the scatter plot for everything less than $1 billion in box_office."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='box_office', ylabel='revenue'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies_df.fillna(0)[movies_df['box_office'] < 10**9].plot(x='box_office', y='revenue', kind='scatter')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks similar to what we've seen for budget, so we'll make the same decision: keep the Kaggle data, but fill in the zeros with Wikipedia data.\n",
    "\n",
    "### Release Date\n",
    "\n",
    "For release_date_wiki and release_date_kaggle, we can't directly make a scatter plot, because the scatter plot only works on numeric data. However, there's a tricky workaround that we can use. We'll use the regular line plot (which can plot date data), and change the style to only put dots by adding style='.' to the plot() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='release_date_wiki'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zXimv/8DdmFTSN0nRtX6e9J1zaWXRqo18XlGFATt3GbNffK/R5xDRxF7tc7I4+8jCVrG4SUvxpO+cSxtlazax5P1P4s/E2EAS3HfeURl5kbYhPOk759JCdVlnR0Vicx6LaNrjP7fyWTCbmyd951zKDb/ySdZv3ZnQsXt3yqX056OSHFHb5UnfuQxWe5RMKh77LwtWJrxUIcB/jRqQxKjaPk/6zmWo2FEyudlZzP5uSYsl/qbOcz9w3zxys7M4fXjrWp82HXjSdy5Dxc41X1FZxaJVG1ss6TdlzP15x/b30ThN4BOuOZehSvoXkJudRTtBTnYWJf0LWuRxC6c9kvCxAvL2zEleMBnIe/rOZaiivvnM/m5Js9f0R139DO+s30bXjjn8ZPTBjT7+zBF9uH9xORWVVS365tRWyWz3BTVJvYFZwL5AFTDDzK6T1BX4G1AIrAZOM7NN4ZhLgHOBXcCPzOzx0F7EFytnPQpcaPUEUFxcbKWlpQn+ec65VBp19TOsWL8t4eNzs7OY870SgJRdcG6tJJWZWXHt9ob09CuBn5jZYkl5QJmkJ4GzgafMbLqkacA04GJJhwATgEOB/YD5kg4ys13ATcAUYBFR0h+DL5noXKvSmBE/iST8vPbZDNw3jwP3yfvStMee7JOjIcslrgXWhttbJC0DegKnACPDbncAzwAXh/a7zexz4F1J7wBHVK+ba2YLASTNAsbiSd+5VqMhI36aUrMHOPmw/fhNG1yQPF006kKupEJgGPAisE94Q6h+Y+gedusJvB9zWHlo6xlu1253zrUS8Ub8xGpqws/NzuLbbXRB8nTR4Au5kjoBc4Efm9mnUl1rpcddRN120x7vsaYQlYHo08fH4DqXLqpH/MReVJ1064u8tPpjDt4nr9Hna5cFE4b34dD9urBp+06v2beABvX0JeUQJfzZZnZ/aP5IUo+wvQewLrSXA71jDu8FfBjae8Vp/wozm2FmxWZW3K1bt4b+Lc65ZlbUN58jCruSk53FEYVduW7+2yxYsYEdFVUsKd/c4PN02qMdRxTm0y4rizkvvcevH37DE34LqTfpK+rS3wosM7NrYjb9HZgcbk8GHoxpnyBpD0n9gAOBl0IJaIukknDOSTHHOOdagR/f/UpNkl+wYgMLVmxo1PHtssRvTx3M0l+N4bgB3ancVXepyDWPhpR3jgbOAl6XtCS0XQpMB+6RdC7wHjAewMzekHQP8CbRyJ8LwsgdgKl8MWTzMfwirnOtyuNvfNSk4684ZVDNtAnxSkWu+dU7Tj/VfJy+c+kh0SULjyjM5/PKqrjz5KRywre2rinj9J1zGaJ2Eh775+d4rXwz7XOy2C+/Q6PP99tTB+92QrSivvme7FuYJ33nHBAl/DP+Eo3Bb5clenTeg/JPdgCwvaKKd9ZtbdT5Rh2yj8+AmYZ8wjXnHAD3Ly5nZ2W0atWuKqtJ+Ilon5PFecftn6zQXBJ50nfOAbDioy1JOU+7LPHf/3Zo3LJN2ZpN3PD0O5St2ZSUx3KN5+Ud5xwAL61OPBHnZImK6tVQzNi0/atLH6Zy0Rb3BU/6zmWwsX9+jtc/2MxeCc5RP7wwn3vPO6omoe9u+GUqF21xX/Ck71wGiR2dc8VDb9R8i3bj9opGnytLMO2b0QpWDZmb38flpwcfp+9chogtr2QJwjXbRuuZvycjD+r2pWmPGxODj8tvGT5O37kMF/vFqkQWI4doVM71E4YlnLB9XH7qedJ3LgM0Zcrjrh1zOfbAvTlwnzzvobcBnvSda6OmP7qMf7zxL8Ycum/C5zhzRB9+6wuatCme9J1rg6Y/uoybF6wCqPndGAL2yMlinC9o0uZ40neuDUok0VcbXpjPyAHdvZTTRnnSd64NSHQGzFjVvftp3xzoyb4N86TvXCuXaMLP26Md23buwgzatROnF/dOaBima1086TvXSo26+hlWbtjGnjntGn2sgPNGHkBJ/wIfN59hGrJc4m2S1klaGtN2mKSFkl6X9JCkzqE9V9LM0P6qpJExxxSF9nckXa/drKzunNu9UVc/w4r126gy2LZzV/0H1FL9jdiivvlccPwBnvAzSENm2bwdGFOr7RZgmpkNBuYBF4X27wGE9lHA1ZKqH+MmYArRmrkHxjmnc66BVqzf1qj9zzu2P4f16sIRhflMHNGHOd/zyc4yVb3lHTNbIKmwVvMAYEG4/STwOHA5cAjwVDhunaRPgGJJ7wOdzWwhgKRZwFh8jVzn4krmdAVzpx7lCd7VSLSmvxQ4GXiQaEH03qH9VeAUSXeHtqLwuwoojzm+HOiZ4GM716bFm4IYqHkTeOtfDZv3PjtL/PqUQZ7w3ZckmvTPAa6X9N/A34HqybNvAwYCpcAa4AWgkui6UW11zv4haQpRKYg+fXy5NZdZak9BPHdxOfeVvk/FLiOnnehT0LHec0wc0cdH4ri4Ekr6ZrYcGA0g6SDgpNBeCfxn9X6SXgBWAJuA2K/29QI+3M35ZwAzIJplM5EYnWutSvoXkJ0lKnYZ7bLEhi2fs3NX9N9g5y7jkzgLlMQ6sFtHfuNTJ7g6JLRcoqTu4XcW8HPg5nC/g6SO4fYooNLM3jSztcAWSSVh1M4kotKQcy6e6sFtcQa5xVvwpGuHHNoJhvbqwpM/GdnMwbnWrN6evqQ5wEhgb0nlwC+ATpIuCLvcD8wMt7sDj0uqAj4Azoo51VSikUB7El3A9Yu4zsWxaNVGKndVYcCuXVXsnbfHlxYfOeeY/vzy70tryj1zphzpZRzXYA0ZvXNGHZuui7PvaqKRPfHOUwoMakxwzmWi2itMjTu8F+MO7/Wl0TwD9s3zL1W5hPjKWc6lIV9hyjWVr5zlXJqKl+B9hSnXXDzpO5dC8cbke7J3zSmh0TvOueSoPSZ/0aqNqQ7JtXGe9J1LoeqLtu30xSRozjUnL+84l0JFffOZ/d0Sv2jrWownfedSzC/aupbk5R3nnMsgnvSdcy6DeNJ3zrkM4knfOecyiCd955zLIJ70nXMug3jSd865DOJJ3znnMognfeecyyD1Jn1Jt0laJ2lpTNthkhZKel3SQ5I6h/YcSXeE9mWSLok5pii0vyPp+rBsonPOuRbUkJ7+7cCYWm23ANPMbDAwD7gotI8H9gjtRcD3JRWGbTcBU4ADw0/tczrnnGtm9SZ9M1sAfFyreQCwINx+EhhXvTvQUVI20Vq4O4FPJfUAOpvZQouW6poFjG16+M455xoj0Zr+UuDkcHs80Dvcvg/YBqwF3gP+YGYfAz2B8pjjy0Obc865FpRo0j8HuEBSGZBH1KMHOALYBewH9AN+Iqk/EK9+X+fivJKmSCqVVLp+/foEQ3TOOVdbQknfzJab2WgzKwLmACvDpjOBf5hZhZmtA54Hiol69r1iTtEL+HA3559hZsVmVtytW7dEQnTOORdHQklfUvfwOwv4OXBz2PQecIIiHYESYLmZrQW2SCoJo3YmAQ82OXrnnHON0pAhm3OAhcAASeWSzgXOkPQ2sJyoxz4z7H4D0Imo5v8yMNPMXgvbphKN+nmH6JPBY8n8Q5xzztWv3pWzzOyMOjZdF2ffrUQXduOdpxQY1KjonHPOJZV/I9c55zKIJ33nnMsgnvSdcy6DeNJ3zrkM4knfOecyiCd955zLIJ70nXMug3jSd865DOJJ3znnMognfeecyyCe9J1zLoN40nfOuQziSd855zKIJ33nnMsgnvSdcy6DeNJ3zrkM4knfOecySEOWS7xN0jpJS2PaDpO0UNLrkh6S1Dm0T5S0JOanStLQsK0o7P+OpOvDWrnOOedaUEN6+rcDY2q13QJMM7PBwDzgIgAzm21mQ81sKHAWsNrMloRjbgKmAAeGn9rndM4518zqTfpmtgD4uFbzAGBBuP0kMC7OoWcAcwAk9QA6m9lCMzNgFjA2wZidc84lKNGa/lLg5HB7PNA7zj6nE5I+0BMoj9lWHtrikjRFUqmk0vXr1ycYonPOudoSTfrnABdIKgPygJ2xGyWNALabWfV1gHj1e6vr5GY2w8yKzay4W7duCYbonHOutuxEDjKz5cBoAEkHASfV2mUCX/TyIerZ94q53wv4MJHHds45l7iEevqSuoffWcDPgZtjtmURlXzurm4zs7XAFkklYdTOJODBJsTtnHMuAQ0ZsjkHWAgMkFQu6VzgDElvA8uJeuwzYw45Fig3s1W1TjWVaNTPO8BK4LEkxO+cc64RFA2mSV/FxcVWWlqa6jCcc65VkVRmZsW12/0buc45l0E86TvnXAbxpO+ccxnEk75zzmUQT/rOOZdBPOk751wG8aTvnHMZxJO+c85lEE/6zjmXQTzpO+dcBvGk75xzGcSTvnPOZRBP+s45l0E86bcxZWs2ccPT71C2ZlOqQ3HOpaGEVs5y6alszSYm3rKInZVV5GZnMfu7JRT1zU91WM65NOI9/TZk0aqN7KysosqgorKKRas2pjok51yaacjKWbdJWidpaUzbYZIWSnpd0kOSOsdsGxK2vRG2tw/tReH+O5KuD8smuiQq6V9AbnYW7QQ52VmU9C9IdUjOuTTTkJ7+7cCYWm23ANPMbDAwD7gIQFI2cCdwnpkdCowEKsIxNwFTgAPDT+1zuiYq6pvP7O+W8F+jB3hpxzkXV701fTNbIKmwVvMAYEG4/STwOHA5MBp4zcxeDcduBJDUA+hsZgvD/VnAWHyd3KQr6pvvyd45V6dEa/pLgZPD7fFA73D7IMAkPS5psaSfhfaeQHnM8eWhLS5JUySVSipdv359giE655yrLdGkfw5wgaQyIA/YGdqzgWOAieH3qZJOBOLV7+tckd3MZphZsZkVd+vWLcEQ2wYfgumcS6aEhmya2XKiUg6SDgJOCpvKgWfNbEPY9ihwOFGdv1fMKXoBHyYYc8bwIZjOuWRLqKcvqXv4nQX8HLg5bHocGCKpQ7ioexzwppmtBbZIKgmjdiYBDzY5+jbOh2A655KtIUM25wALgQGSyiWdC5wh6W1gOVGPfSaAmW0CrgFeBpYAi83skXCqqUSjft4BVuIXcevlQzCdc8kmszpL62mhuLjYSktLUx1GypSt2cSiVRsp6V/gpR3nXINJKjOz4trtPg1DmvMhmM65ZPJpGJxzLoN40nfOuQziSd855zKIJ33nnMsgnvSdcy6DeNJ3zrkM4knfOecyiCd951yb4JMTNox/OauJ/BuzzqWeT07YcJ70m8BfaM6lh3iTE/r/xfi8vNMEPgumc+nBJydsOO/pN0H1C62isspfaM6lUPX60F5qrZ/PstlEXtN3zqUjn2WzmfgsmM651sRr+s45l0EasnLWbZLWSVoa03aYpIWSXpf0kKTOob1Q0meSloSfm2OOKQr7vyPp+rBsonPOuRbUkJ7+7cCYWm23ANPMbDAwD7goZttKMxsafs6Lab8JmAIcGH5qn9M551wzqzfpm9kC4ONazQOABeH2k8C43Z1DUg+gs5kttOjK8SxgbKOjdc451ySJ1vSXAieH2+OB3jHb+kl6RdKzkr4W2noC5TH7lIe2uCRNkVQqqXT9+vUJhuicc662RJP+OcAFksqAPGBnaF8L9DGzYcB/AXeFen+8+n2dY0XNbIaZFZtZcbdu3RIM0TnnXG0JDdk0s+XAaABJBwEnhfbPgc/D7TJJK4GDiHr2vWJO0Qv4MPGwnXPOJSKhnr6k7uF3FvBz4OZwv5ukduF2f6ILtqvMbC2wRVJJGLUzCXgwCfE755xrhHp7+pLmACOBvSWVA78AOkm6IOxyPzAz3D4W+LWkSmAXcJ6ZVV8Enko0EmhP4LHw45xzrgW12WkYfHoE51wmy6hpGMrWbOKMvyyqmQhtzvd8ymPnnIM2Og3D/YvL2VlZhQE7K6u4f3F5vcc451wmaJNJv3bBKr0LWM4513LaZNIfd3gvctsJAbntxLjDe9V7jHPOZYI2WdMv6pvPnClH+oVc55yrpU0mffB57p1zLp42Wd5xzjkXnyd955zLIJ70nXMug3jSd865DJL20zBIWg+sacGH3BvY0IKP1xQea/PwWJOvtcQJbSfWvmb2lbnp0z7ptzRJpfHmq0hHHmvz8FiTr7XECW0/Vi/vOOdcBvGk75xzGcST/lfNSHUAjeCxNg+PNflaS5zQxmP1mr5zzmUQ7+k751wG8aTvnHMZxJO+cy4pJCnVMTRUJseasUk/k//Rm5PH2jxaSaw5qQ6gETI21oxK+pIOlTQSwNL8CrbH2jw81uSTdKSke4E/SDpEUrtUx1QXjzVDRu9IygL+DJwAvAe8CDxoZqWSssysKqUBxvBYm4fH2jwkdQceI4q3N9ATKDWzv0hSOr1ZeayRTOnp5wN5wEBgIrAR+ImkTun0HyhoTbF2ofXE2pqe172ATrSOWA8D3jazmcDVwP3AKZIOMjNLs7LUIFpPrM32vLbZpC9plKRR4W5n4Eigg5mtB+YCHwMXhH1T+o8t6d8lnR/upnus35b0x3C3gPSO9XBJB4W7XYCjSN9Y+0lqH+52JU1jlXSGpF9JOjk0vQIUS9rfzLYBLwOlwPchtWUpScdJGhHT9CpRrP3TMNaxki6VdFJoWkIzPa9tLumHOujdwKXAJgAzexd4Hvhx2G0t0TvnMEn7peofW1InSXOBnwKbJGWncayHSLoLuBy4MMTyDrAwDWPtJ+kR4Abgr5JGmdkq4IU0jLVQ0mPALcBsSYeE53UB8F/pEqsi5wE/A1YDV0n6LrAVmAVcGHb9BJgPdJDUo6XjBJCUJ+l+YB7wfUn5AGa2Efgb8KM0irWbpAeI/q0/BmZK+veYN/sfJjvWNpH0q3s+kroS/Wf52MyON7PSmN1uB46W1M/MKoGPgB3AnqmINegNfGRmJWY2B9hVK9b+6RCrpGOBvwCLzGwYcB1Q3YO6lfR7Xn8KLDGzI4EHgXNC+21pGuuLZnYi8DTwK0mHEL0GSlL9GqgW3miOBKaHksMFwEjgROBR4ABJXw/lp41ENejNqYgV2An8E/gP4ENgfMy2ucDBkk5Mk1j3B543s2PN7GbgJ8B/hm1zaIZY20TSB9oDmNnHwFXAHgCSzpb0DUl9zexpoo+iV4V9lwJ9gc9TEWswBOgVYj0f+IWkY4A3iXqlf4CUxlqdYN4ERpvZ9ZJygQOA6tryq8Bi4H9SHGt7qEmo24CK0N4ZWCbpAKJPUC+T+ue1OtbscP+NEM+fgSOACUTJ6mVS+LxKmhRKJF1D0zKgZ/hEOh9YCpQA64G7gGvD83wiICA3BbHuZWafE31ymg+8TVQmGRB2fRW4Ow1iHSmpA1BG9EkJRaNz3gw/AK+HWK9LZqytOumHuv2TRB81J4Tm64DhktYCJwP/D3hI0v7Ar4hetH+StJRocZbNLVEjjYn1fySdEZoXA2sl3UbUi/oEuAw4BbgG6C7pzymOdYKZbTCzbZLam9lOohfjRAAz2wT8GuiV4uf1KkmnhR7pc8CBkl4BxgDtiJLSccDvgB4pfl6rY60k+kg/TNJhkg4jSqT9QsxX0sLPayjj9JD0NDCZ6N/5T5I6A+8D3Yne9CFKSIOAAjO7E5gNTCN60/qZmX3SXHHuJtYbJO1tZjvCa3UhsA44DcDMqszsduCvwCUpjPVMok/PXczsI0ntzGwX0cX7LrVinUUyn1cza5U/RC+8F4kS5DCiF9ylYdu3gMkx+94G/D7c3ofoItnJKY71J0A20ZX5MiAn7HsWMCPc7p4Gsd4Z87xWx3hcaO8Wc1y3NIj1LuCnYdsA4P6YfS8H/pxGr4E5wPlEI4ouBx4merMqDn/Hj1s6VqBd+H0QcGe4nQ3cCNxB9CWh28JrtEvYfjvwm5hz5LbQ81lXrH8C5tba99TwNxwAdASy0ijW+2vtMws4Ldzetzme1+qPl62CovHLWFTfGgGUmdmDYdtTwDWSbjGzh6r3D/s+Anw9HPsRUX00lbH+kyjZ30pUcx5GVHe8i+jj57gQ+zqiXkqqY61+XqtjySHqjWyqPodFF57WpzjW+SHWvxL1oN+XNNDMlhHVy38sSWnyGphP9Bq418yuCLX7VWHb83xRxlkX4m3OOLOJPq21k/QoUUlsV4i9UtIPiC4mH0L0Gh1LVJb8HVGZ74Xqc1nUu05lrD8CPpR0nJk9G9rnSRoI/INoKOzxwLJ0jJXowvi7kn4NfFvSGDMrT2asraa8I+k7QDlwRWh6HThDUmG4nwOsJNRrIfrPJmky8Auif/B0iTUbeBf4HzNbAFxLNA77YqKPzM+F87REySGR53U+UY/0qOaOL1YDY10Vtm8hGvr4I0kXAv9LVONNl1iziZ7X6uGv74bjpgDnEpX+sNDNa8Y4jyP6pJkPvBPirQCOl3REiKGKKHn9PvzbzwCOkfRiOO6Z5oyxkbFaiPWXMceNJyqbPg0MCZ2AtItVUU3/HOA+ojeI482sPOnBtcTHmyR8POoEPEA0LGwxcHBov5boY/LzROWGwUS9+u5EY8ivInpBDk/TWB8lfIQDhhONwT0yTWN9JCbWHGAKUJimsT5G9DF+INGQtzuAkjSN9RFgn7D9x0QXb1vy9fo14KyY+zcCU4GziT6ZQNQ53Be4t/rfnOgLZD1bKs4EYr0H6Bdz3NfSPNa+RCN5rgUOb9bYWvKJaOKT2Cf8ng78LdxuR9SbOybc701UY8wOP31bQaztW8nzOhPYo5XEegctVK9N0mtgj3C/Qwri7EA02q26pjwR+F24vQT4YbhdDMxJ8XPaVmO9uyVjazXlHTN7L9y8Fugn6RsWXe3ebGbPhW3nAdvD/pVmtqblI210rBVxTtFiGhHrZ0BlCkKs0YhYt/HFdx5SopGvgcpwzPYUxLndzD4PsQGM4otrM98BBkp6mOgTyuKWji9WG461DFrwm9apfDdswrvo94FnY+4fQXRBtKZcki4/HqvH2hpiJfoUkkVUGjsgtB1AVMY5hhYu5XiszffT6mbZrB6RI+k+ohEFnxNdoFthZitTG92XeazNw2NNvtDLzCX6UtM8oguKG4nKEJ+mMrbaPNamaVVDNqFmRE4Hoou1I4Ffm1mLjcxpDI+1eXisyWdmJmkYUe25HzDTzG5NcVhxeaxN0+qSfnA+Uc1ulEVfuU5nHmvz8FiTr5xoaOM1aR4neKwJa3XlHfjSl67SnsfaPDxW5xLTKpO+c865xLSaIZvOOeeazpO+c85lEE/6zjmXQTzpO+dcBvGk71olSVtTHUO1+mKRtJe+WPi+OR7/0fAYhYoWW6m9vVjS9c31+K518dE7Lm2FbzMq3nBHSVvNrFMKwvqK+mIJ0yk/bGaDmjmOFnkc17p5T9+lldBbXSbpRqIvNF0u6WVJr0n6VR3HXBRvH0kPSCqT9EaYpx5J7STdLmmppNcl/Wdo31/SP8L+/yfp4N3E2E/SwvCYV8S0d5L0lKTF4dynhE3Tgf0lLZF01e5ijvNYP1O02AaS/qhoURsknSjpznB7taS9ax3XX9IrkoYrWo/14bqfdZdJPOm7dDSAaNm4i4GeRBOUDQWKJB0bu6Ok0cCBdexzjpkVEU1f+yNJBWGfnmY2yMwGE00ZDdHCID8M+/+UaP7zulwH3GRmw4F/xbTvAE41s8OJVme6OnxamQasNLOhZnZRPTHXtoBobnbC39FJUg7RZF3/F+8ARYuAzwW+Y2Yv7+bvcBmotU7D4Nq2NWa2SNIfgNHAK6G9E1GyXBCz7+jd7PMjSaeG9t6h/S2gv6Q/ES1g8oSkTkSrgN0bM7vtHruJ72hgXLj9V+D34baA34YEXkX0hrVPnON3F3NtZURvCnlEk7UtJkr+XwN+FGf/bkQzeI4zszd28ze4DOVJ36WjbeG3iBae+N/d7Bt3H0kjidZFPtLMtkt6hmjBmk2SDgO+AVwAnEa0YtUnZja0ETHGuxg2kSjpFplZhaTVQPuGxhz3Qb44z3eI1qJ9jehTxP5AvGX/NgPvE70xedJ3X+HlHZfOHgfOCT1xJPWU1L2B+3QBNoWEfzBQErbvDWSZ2VzgcqKl6T4lWox6fNhH4Y2hLs8DE8LtiTHtXYgWMq+QdDzREngQrdeb18i/K9YCopLTAqKSznnAEos/CmMn0cLlkySduZtzugzlPX2XtszsCUkDgYWh7LIV+A9gXQP2+QdwnqTXiEo6i8IhPYGZkqo7PJeE3xOBmyT9nGgN4LuBV+sI7ULgLkULrs+NaZ8NPCSplGhJvOUhxo2Sng/DKR8Ldf3d/l21/B/RLI0LzWybpB3UUc8Pj7dN0r8BT0raRtT7dw7wIZvOOZdRvLzjnHMZxMs7ztVB0mXA+FrN95rZb5rhsQqAp+JsOtHMNib78Vzm8vKOc85lEC/vOOdcBvGk75xzGcSTvnPOZRBP+s45l0E86TvnXAb5/+Naf1VoIbGyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies_df[['release_date_wiki','release_date_kaggle']].plot(x='release_date_wiki', y='release_date_kaggle', style='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should investigate that wild outlier around 2006. We're just going to choose some rough cutoff dates to single out that one movie. We'll look for any movie whose release date according to Wikipedia is after 1996, but whose release date according to Kaggle is before 1965. Here's what your code should look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title_wiki</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date_kaggle</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title_kaggle</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Holiday</td>\n",
       "      <td>2006</td>\n",
       "      <td>https://www.imdb.com/title/tt00457939/</td>\n",
       "      <td>The Holiday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Kate Winslet, Cameron Diaz, Jude Law, Jack Bl...</td>\n",
       "      <td>Dean Cundey</td>\n",
       "      <td>[December 8, 2006, (, 2006-12-08, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>1953-08-28</td>\n",
       "      <td>30,500,000.00</td>\n",
       "      <td>118.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Pouring out of impassioned pages...brawling th...</td>\n",
       "      <td>From Here to Eternity</td>\n",
       "      <td>False</td>\n",
       "      <td>7.20</td>\n",
       "      <td>137.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            url  year  \\\n",
       "3607  https://en.wikipedia.org/wiki/The_Holiday  2006   \n",
       "\n",
       "                                   imdb_link   title_wiki Based on  \\\n",
       "3607  https://www.imdb.com/title/tt00457939/  The Holiday      NaN   \n",
       "\n",
       "                                               Starring Cinematography  \\\n",
       "3607  [Kate Winslet, Cameron Diaz, Jude Law, Jack Bl...    Dean Cundey   \n",
       "\n",
       "                              Release date        Country Language  ...  \\\n",
       "3607  [December 8, 2006, (, 2006-12-08, )]  United States  English  ...   \n",
       "\n",
       "     release_date_kaggle              revenue              runtime  \\\n",
       "3607          1953-08-28        30,500,000.00               118.00   \n",
       "\n",
       "                              spoken_languages    status  \\\n",
       "3607  [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "\n",
       "                                                tagline  \\\n",
       "3607  Pouring out of impassioned pages...brawling th...   \n",
       "\n",
       "               title_kaggle  video         vote_average           vote_count  \n",
       "3607  From Here to Eternity  False                 7.20               137.00  \n",
       "\n",
       "[1 rows x 45 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df[(movies_df['release_date_wiki'] > '1996-01-01') & (movies_df['release_date_kaggle'] < '1965-01-01')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the output, it looks like somehow The Holiday in the Wikipedia data got merged with From Here to Eternity. We'll have to drop that row from our DataFrame. We'll get the index of that row with the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3607], dtype='int64')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df[(movies_df['release_date_wiki'] > '1996-01-01') & (movies_df['release_date_kaggle'] < '1965-01-01')].index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows\n",
    "movies_df = movies_df.drop(movies_df[(movies_df['release_date_wiki'] > '1996-01-01') & (movies_df['release_date_kaggle'] < '1965-01-01')].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title_wiki</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date_kaggle</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title_kaggle</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Black_Scorpion_(...</td>\n",
       "      <td>1995</td>\n",
       "      <td>https://www.imdb.com/title/tt0112519/</td>\n",
       "      <td>Black Scorpion</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Joan Severance, Bruce Abbott, Garrett Morris]</td>\n",
       "      <td>Geoff George</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-08-22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>92.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Justice has a nasty sting.</td>\n",
       "      <td>Black Scorpion</td>\n",
       "      <td>False</td>\n",
       "      <td>6.10</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Flirt_(1995_film)</td>\n",
       "      <td>1995</td>\n",
       "      <td>https://www.imdb.com/title/tt0113080/</td>\n",
       "      <td>Flirt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Bill Sage, Dwight Ewell, Miho Nikaido]</td>\n",
       "      <td>Michael Spiller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[United States, Germany, Japan, [1]]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-09-14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>85.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Flirt</td>\n",
       "      <td>False</td>\n",
       "      <td>7.50</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Let_It_Be_Me_(19...</td>\n",
       "      <td>1995</td>\n",
       "      <td>https://www.imdb.com/title/tt0113638/</td>\n",
       "      <td>Let It Be Me</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miroslav Ondrícek</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-11-17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>[]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Let It Be Me</td>\n",
       "      <td>False</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>https://en.wikipedia.org/wiki/A_Brooklyn_State...</td>\n",
       "      <td>1997</td>\n",
       "      <td>https://www.imdb.com/title/tt0118782/</td>\n",
       "      <td>A Brooklyn State of Mind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ken Kelsch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A Brooklyn State of Mind</td>\n",
       "      <td>False</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Highball_(film)</td>\n",
       "      <td>1997</td>\n",
       "      <td>https://www.imdb.com/title/tt0119291/</td>\n",
       "      <td>Highball</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Justine Bateman, Peter Bogdanovich, Chris Eig...</td>\n",
       "      <td>Steven Bernstein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Highball</td>\n",
       "      <td>False</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Velocity_Trap</td>\n",
       "      <td>1997</td>\n",
       "      <td>https://www.imdb.com/title/tt0120435/</td>\n",
       "      <td>Velocity Trap</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Olivier Gruner, Alicia Coppola, Ken Olandt]</td>\n",
       "      <td>Philip D. Schwartz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1999-02-22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>86.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Crime at the Speed of Light</td>\n",
       "      <td>Velocity Trap</td>\n",
       "      <td>False</td>\n",
       "      <td>4.70</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2386</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Visit_(2000_...</td>\n",
       "      <td>2000</td>\n",
       "      <td>https://www.imdb.com/title/tt0199129/</td>\n",
       "      <td>The Visit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Hill Harper, Billy Dee Williams, Obba Babatun...</td>\n",
       "      <td>John L. Demps Jr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Rumored</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Visit</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2786</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Stevie_(2002_film)</td>\n",
       "      <td>2002</td>\n",
       "      <td>https://www.imdb.com/title/tt0334416/</td>\n",
       "      <td>Stevie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Dana Kupper, Gordon Quinn, Peter Gilbert]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>2002-09-09</td>\n",
       "      <td>97,000.00</td>\n",
       "      <td>140.00</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stevie</td>\n",
       "      <td>False</td>\n",
       "      <td>6.70</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3174</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Return_to_Sender...</td>\n",
       "      <td>2004</td>\n",
       "      <td>https://www.imdb.com/title/tt0396190/</td>\n",
       "      <td>Return to Sender</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Aidan Quinn, Connie Nielsen, Mark Holton]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Denmark, USA, UK]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2004-09-17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>109.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>How can you save someone, who won't save herself?</td>\n",
       "      <td>Return to Sender</td>\n",
       "      <td>False</td>\n",
       "      <td>5.90</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Live_Free_or_Die...</td>\n",
       "      <td>2006</td>\n",
       "      <td>https://www.imdb.com/title/tt0432318/</td>\n",
       "      <td>Live Free or Die</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Aaron Stanford, Paul Schneider, Ebon Moss-Bac...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>2006-03-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Live Free or Die</td>\n",
       "      <td>False</td>\n",
       "      <td>5.50</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4967</th>\n",
       "      <td>https://en.wikipedia.org/wiki/For_the_Love_of_...</td>\n",
       "      <td>2012</td>\n",
       "      <td>https://www.imdb.com/title/tt1730294/</td>\n",
       "      <td>For the Love of Money</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Yehuda Levi, Edward Furlong, James Caan, Jeff...</td>\n",
       "      <td>Andrzej Sekuła</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>English/Hebrew</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-06-08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>For the love of money is the root of all evil.</td>\n",
       "      <td>For the Love of Money</td>\n",
       "      <td>False</td>\n",
       "      <td>5.60</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  year  \\\n",
       "1008  https://en.wikipedia.org/wiki/Black_Scorpion_(...  1995   \n",
       "1061    https://en.wikipedia.org/wiki/Flirt_(1995_film)  1995   \n",
       "1121  https://en.wikipedia.org/wiki/Let_It_Be_Me_(19...  1995   \n",
       "1564  https://en.wikipedia.org/wiki/A_Brooklyn_State...  1997   \n",
       "1633      https://en.wikipedia.org/wiki/Highball_(film)  1997   \n",
       "1775        https://en.wikipedia.org/wiki/Velocity_Trap  1997   \n",
       "2386  https://en.wikipedia.org/wiki/The_Visit_(2000_...  2000   \n",
       "2786   https://en.wikipedia.org/wiki/Stevie_(2002_film)  2002   \n",
       "3174  https://en.wikipedia.org/wiki/Return_to_Sender...  2004   \n",
       "3651  https://en.wikipedia.org/wiki/Live_Free_or_Die...  2006   \n",
       "4967  https://en.wikipedia.org/wiki/For_the_Love_of_...  2012   \n",
       "\n",
       "                                  imdb_link                title_wiki  \\\n",
       "1008  https://www.imdb.com/title/tt0112519/            Black Scorpion   \n",
       "1061  https://www.imdb.com/title/tt0113080/                     Flirt   \n",
       "1121  https://www.imdb.com/title/tt0113638/              Let It Be Me   \n",
       "1564  https://www.imdb.com/title/tt0118782/  A Brooklyn State of Mind   \n",
       "1633  https://www.imdb.com/title/tt0119291/                  Highball   \n",
       "1775  https://www.imdb.com/title/tt0120435/             Velocity Trap   \n",
       "2386  https://www.imdb.com/title/tt0199129/                 The Visit   \n",
       "2786  https://www.imdb.com/title/tt0334416/                    Stevie   \n",
       "3174  https://www.imdb.com/title/tt0396190/          Return to Sender   \n",
       "3651  https://www.imdb.com/title/tt0432318/          Live Free or Die   \n",
       "4967  https://www.imdb.com/title/tt1730294/     For the Love of Money   \n",
       "\n",
       "     Based on                                           Starring  \\\n",
       "1008      NaN     [Joan Severance, Bruce Abbott, Garrett Morris]   \n",
       "1061      NaN            [Bill Sage, Dwight Ewell, Miho Nikaido]   \n",
       "1121      NaN                                                NaN   \n",
       "1564      NaN                                                NaN   \n",
       "1633      NaN  [Justine Bateman, Peter Bogdanovich, Chris Eig...   \n",
       "1775      NaN       [Olivier Gruner, Alicia Coppola, Ken Olandt]   \n",
       "2386      NaN  [Hill Harper, Billy Dee Williams, Obba Babatun...   \n",
       "2786      NaN                                                NaN   \n",
       "3174      NaN         [Aidan Quinn, Connie Nielsen, Mark Holton]   \n",
       "3651      NaN  [Aaron Stanford, Paul Schneider, Ebon Moss-Bac...   \n",
       "4967      NaN  [Yehuda Levi, Edward Furlong, James Caan, Jeff...   \n",
       "\n",
       "                                  Cinematography Release date  \\\n",
       "1008                                Geoff George          NaN   \n",
       "1061                             Michael Spiller          NaN   \n",
       "1121                           Miroslav Ondrícek          NaN   \n",
       "1564                                  Ken Kelsch          NaN   \n",
       "1633                            Steven Bernstein          NaN   \n",
       "1775                          Philip D. Schwartz          NaN   \n",
       "2386                           John L. Demps Jr.          NaN   \n",
       "2786  [Dana Kupper, Gordon Quinn, Peter Gilbert]          NaN   \n",
       "3174                                         NaN          NaN   \n",
       "3651                                         NaN          NaN   \n",
       "4967                              Andrzej Sekuła          NaN   \n",
       "\n",
       "                                   Country        Language  ...  \\\n",
       "1008                         United States             NaN  ...   \n",
       "1061  [United States, Germany, Japan, [1]]             NaN  ...   \n",
       "1121                                   NaN             NaN  ...   \n",
       "1564                                   NaN             NaN  ...   \n",
       "1633                         United States         English  ...   \n",
       "1775                         United States             NaN  ...   \n",
       "2386                                   NaN         English  ...   \n",
       "2786                         United States         English  ...   \n",
       "3174                    [Denmark, USA, UK]             NaN  ...   \n",
       "3651                         United States         English  ...   \n",
       "4967                         United States  English/Hebrew  ...   \n",
       "\n",
       "     release_date_kaggle              revenue              runtime  \\\n",
       "1008          1995-08-22                 0.00                92.00   \n",
       "1061          1995-09-14                 0.00                85.00   \n",
       "1121          1995-11-17                 0.00                95.00   \n",
       "1564          1998-01-01                 0.00                89.00   \n",
       "1633          1997-01-01                 0.00               110.00   \n",
       "1775          1999-02-22                 0.00                86.00   \n",
       "2386          2000-01-01                 0.00               107.00   \n",
       "2786          2002-09-09            97,000.00               140.00   \n",
       "3174          2004-09-17                 0.00               109.00   \n",
       "3651          2006-03-11                 0.00                89.00   \n",
       "4967          2012-06-08                 0.00                93.00   \n",
       "\n",
       "                                       spoken_languages    status  \\\n",
       "1008           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1061  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "1121                                                 []  Released   \n",
       "1564           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1633           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1775           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "2386  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   Rumored   \n",
       "2786                                                 []       NaN   \n",
       "3174           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "3651           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "4967  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "\n",
       "                                                tagline  \\\n",
       "1008                         Justice has a nasty sting.   \n",
       "1061                                                NaN   \n",
       "1121                                                NaN   \n",
       "1564                                                NaN   \n",
       "1633                                                NaN   \n",
       "1775                        Crime at the Speed of Light   \n",
       "2386                                                NaN   \n",
       "2786                                                NaN   \n",
       "3174  How can you save someone, who won't save herself?   \n",
       "3651                                                NaN   \n",
       "4967     For the love of money is the root of all evil.   \n",
       "\n",
       "                  title_kaggle  video         vote_average  \\\n",
       "1008            Black Scorpion  False                 6.10   \n",
       "1061                     Flirt  False                 7.50   \n",
       "1121              Let It Be Me  False                 8.00   \n",
       "1564  A Brooklyn State of Mind  False                 5.00   \n",
       "1633                  Highball  False                 4.50   \n",
       "1775             Velocity Trap  False                 4.70   \n",
       "2386                 The Visit  False                 0.00   \n",
       "2786                    Stevie  False                 6.70   \n",
       "3174          Return to Sender  False                 5.90   \n",
       "3651          Live Free or Die  False                 5.50   \n",
       "4967     For the Love of Money  False                 5.60   \n",
       "\n",
       "               vote_count  \n",
       "1008                 7.00  \n",
       "1061                 4.00  \n",
       "1121                 2.00  \n",
       "1564                 1.00  \n",
       "1633                 4.00  \n",
       "1775                 6.00  \n",
       "2386                 0.00  \n",
       "2786                13.00  \n",
       "3174                13.00  \n",
       "3651                 8.00  \n",
       "4967                 9.00  \n",
       "\n",
       "[11 rows x 45 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check null values\n",
    "movies_df[movies_df['release_date_wiki'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the Kaggle data isn't missing any release dates. In this case, we'll just drop the Wikipedia data.\n",
    "\n",
    "### Language\n",
    "\n",
    "For the language data, we'll compare the value counts of each. However, consider the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.map_locations\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'\n",
      "Traceback (most recent call last):\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 1709, in pandas._libs.hashtable.PyObjectHashTable.map_locations\n",
      "TypeError: unhashable type: 'list'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "English                           5479\n",
       "[English, Spanish]                  68\n",
       "[English, French]                   35\n",
       "[English, Japanese]                 25\n",
       "[English, Russian]                  23\n",
       "                                  ... \n",
       "[Spanish, Quechua]                   1\n",
       "French/English                       1\n",
       "[English, Scottish Gaelic]           1\n",
       "[English, French, Kinyarwanda]       1\n",
       "[English, Filipino]                  1\n",
       "Name: Language, Length: 197, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['Language'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code throws an error because some of the language data points are stored as lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "English                                                 5479\n",
       "NaN                                                      134\n",
       "(English, Spanish)                                        68\n",
       "(English, French)                                         35\n",
       "(English, Japanese)                                       25\n",
       "                                                        ... \n",
       "(English, Mandarin, (, Nanjing dialect, ), Japanese)       1\n",
       "(English, Spanish, French, Japanese, Arabic)               1\n",
       "(English, Spanish, French, Russian)                        1\n",
       "(English, Māori, British Sign Language)                    1\n",
       "(French, English, Persian, German)                         1\n",
       "Name: Language, Length: 198, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We need to convert the lists in Language to tuples so that the value_counts() method will work. See the following code:\n",
    "\n",
    "movies_df['Language'].apply(lambda x: tuple(x) if type(x) == list else x).value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "en    5987\n",
       "fr      16\n",
       "es      10\n",
       "it       8\n",
       "de       6\n",
       "zh       4\n",
       "pt       4\n",
       "ja       4\n",
       "hi       2\n",
       "da       2\n",
       "he       1\n",
       "ab       1\n",
       "ko       1\n",
       "ru       1\n",
       "tr       1\n",
       "cn       1\n",
       "sv       1\n",
       "ar       1\n",
       "Name: original_language, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the Kaggle data, there are no lists, so we can just run value_counts() on it.\n",
    "movies_df['original_language'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**\n",
    "There's a trade-off here between the Wikipedia language data and the Kaggle language data. While the Wikipedia data has more information about multiple languages, the Kaggle data is already in a consistent and usable format. Parsing the Wikipedia data may create too many difficulties to make it worthwhile, though.\n",
    "\n",
    "This is another judgment call; there's no clear-cut answer here. However, for better or for worse, decisions that save time are usually the ones that win, so we'll use the Kaggle data here.\n",
    "\n",
    "## Production Companies"
   ]
  },
  {
   "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>Production company(s)</th>\n",
       "      <th>production_companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Silver Pictures</td>\n",
       "      <td>[{'name': 'Twentieth Century Fox Film Corporat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Avenue Pictures</td>\n",
       "      <td>[{'name': 'Avenue Pictures Productions', 'id':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Carolco Pictures, IndieProd Company]</td>\n",
       "      <td>[{'name': 'IndieProd Company Productions', 'id...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'Orion Pictures', 'id': 41}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'name': 'Paramount Pictures', 'id': 4}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6047</th>\n",
       "      <td>[Fabula, Komplizen Film]</td>\n",
       "      <td>[{'name': 'Komplizen Film', 'id': 1618}, {'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6048</th>\n",
       "      <td>Ball &amp; Chain Productions</td>\n",
       "      <td>[{'name': 'Ball &amp; Chain Productions', 'id': 74...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6049</th>\n",
       "      <td>[Arte France Cinéma, Why Not Productions]</td>\n",
       "      <td>[{'name': 'ARTE France Cinéma', 'id': 94}, {'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6050</th>\n",
       "      <td>[Film Science, Rough House Pictures, Syncopate...</td>\n",
       "      <td>[{'name': 'Film Science', 'id': 1976}, {'name'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>[HanWay Films, Little Punk, See-Saw Films]</td>\n",
       "      <td>[{'name': 'HanWay Films', 'id': 2395}, {'name'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6051 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Production company(s)  \\\n",
       "0                                       Silver Pictures   \n",
       "1                                       Avenue Pictures   \n",
       "2                 [Carolco Pictures, IndieProd Company]   \n",
       "3                                                   NaN   \n",
       "4                                                   NaN   \n",
       "...                                                 ...   \n",
       "6047                           [Fabula, Komplizen Film]   \n",
       "6048                           Ball & Chain Productions   \n",
       "6049          [Arte France Cinéma, Why Not Productions]   \n",
       "6050  [Film Science, Rough House Pictures, Syncopate...   \n",
       "6051         [HanWay Films, Little Punk, See-Saw Films]   \n",
       "\n",
       "                                   production_companies  \n",
       "0     [{'name': 'Twentieth Century Fox Film Corporat...  \n",
       "1     [{'name': 'Avenue Pictures Productions', 'id':...  \n",
       "2     [{'name': 'IndieProd Company Productions', 'id...  \n",
       "3                [{'name': 'Orion Pictures', 'id': 41}]  \n",
       "4             [{'name': 'Paramount Pictures', 'id': 4}]  \n",
       "...                                                 ...  \n",
       "6047  [{'name': 'Komplizen Film', 'id': 1618}, {'nam...  \n",
       "6048  [{'name': 'Ball & Chain Productions', 'id': 74...  \n",
       "6049  [{'name': 'ARTE France Cinéma', 'id': 94}, {'n...  \n",
       "6050  [{'name': 'Film Science', 'id': 1976}, {'name'...  \n",
       "6051  [{'name': 'HanWay Films', 'id': 2395}, {'name'...  \n",
       "\n",
       "[6051 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df[['Production company(s)','production_companies']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Kaggle data is much more consistent, and it would be difficult, if not impossible, to translate the Wikipedia data into the same format.\n",
    "\n",
    "We'll drop the Wikipedia data in this case.\n",
    "\n",
    "### Pull it Altogether"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, we'll drop the title_wiki, release_date_wiki, Language, and Production company(s)\n",
    "# columns\n",
    "\n",
    "movies_df.drop(columns=['title_wiki','release_date_wiki','Language','Production company(s)'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next, to save a little time, we'll make a function that fills in missing data for \n",
    "# a column pair and then drops the redundant column.\n",
    "\n",
    "def fill_missing_kaggle_data(df, kaggle_column, wiki_column):\n",
    "    df[kaggle_column] = df.apply(\n",
    "        lambda row: row[wiki_column] if row[kaggle_column] == 0 else row[kaggle_column]\n",
    "        , axis=1)\n",
    "    df.drop(columns=wiki_column, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Director</th>\n",
       "      <th>Distributor</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date_kaggle</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title_kaggle</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Adventures_o...</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0098987/</td>\n",
       "      <td>[Characters, by Rex Weiner]</td>\n",
       "      <td>[Andrew Dice Clay, Wayne Newton, Priscilla Pre...</td>\n",
       "      <td>Oliver Wood</td>\n",
       "      <td>[July 11, 1990, (, 1990-07-11, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>Renny Harlin</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-07-11</td>\n",
       "      <td>20,423,389.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Kojak. Columbo. Dirty Harry. Wimps.</td>\n",
       "      <td>The Adventures of Ford Fairlane</td>\n",
       "      <td>False</td>\n",
       "      <td>6.20</td>\n",
       "      <td>72.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://en.wikipedia.org/wiki/After_Dark,_My_S...</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0098994/</td>\n",
       "      <td>[the novel, After Dark, My Sweet, by, Jim Thom...</td>\n",
       "      <td>[Jason Patric, Rachel Ward, Bruce Dern, George...</td>\n",
       "      <td>Mark Plummer</td>\n",
       "      <td>[May 17, 1990, (, 1990-05-17, ), (Cannes Film ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>James Foley</td>\n",
       "      <td>Avenue Pictures</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-08-24</td>\n",
       "      <td>2,700,000.00</td>\n",
       "      <td>114.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>All they risked was everything.</td>\n",
       "      <td>After Dark, My Sweet</td>\n",
       "      <td>False</td>\n",
       "      <td>6.50</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Air_America_(film)</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099005/</td>\n",
       "      <td>[Air America, by, Christopher Robbins]</td>\n",
       "      <td>[Mel Gibson, Robert Downey Jr., Nancy Travis, ...</td>\n",
       "      <td>Roger Deakins</td>\n",
       "      <td>[August 10, 1990, (, 1990-08-10, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>Roger Spottiswoode</td>\n",
       "      <td>TriStar Pictures</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-08-10</td>\n",
       "      <td>33,461,269.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>The few. The proud. The totally insane.</td>\n",
       "      <td>Air America</td>\n",
       "      <td>False</td>\n",
       "      <td>5.30</td>\n",
       "      <td>146.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Alice_(1990_film)</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099012/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Alec Baldwin, Blythe Danner, Judy Davis, Mia ...</td>\n",
       "      <td>Carlo Di Palma</td>\n",
       "      <td>[December 25, 1990, (, 1990-12-25, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>Orion Pictures</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-12-25</td>\n",
       "      <td>7,331,647.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alice</td>\n",
       "      <td>False</td>\n",
       "      <td>6.30</td>\n",
       "      <td>57.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Almost_an_Angel</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099018/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Paul Hogan, Elias Koteas, Linda Kozlowski]</td>\n",
       "      <td>Russell Boyd</td>\n",
       "      <td>December 19, 1990</td>\n",
       "      <td>US</td>\n",
       "      <td>John Cornell</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-12-21</td>\n",
       "      <td>6,939,946.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Who does he think he is?</td>\n",
       "      <td>Almost an Angel</td>\n",
       "      <td>False</td>\n",
       "      <td>5.60</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <th>6047</th>\n",
       "      <td>https://en.wikipedia.org/wiki/A_Fantastic_Woman</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt5639354/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Daniela Vega, Francisco Reyes]</td>\n",
       "      <td>Benjamín Echazarreta</td>\n",
       "      <td>[12 February 2017, (, 2017-02-12, ), (, Berlin...</td>\n",
       "      <td>[Chile, Germany, Spain, United States, [2]]</td>\n",
       "      <td>Sebastián Lelio</td>\n",
       "      <td>[Participant Media (Chile), Piffl Medien (Germ...</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>3,700,000.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>[{'iso_639_1': 'es', 'name': 'Español'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A Fantastic Woman</td>\n",
       "      <td>False</td>\n",
       "      <td>7.20</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6048</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Permission_(film)</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt5390066/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Rebecca Hall, Dan Stevens, Morgan Spector, Fr...</td>\n",
       "      <td>Adam Bricker</td>\n",
       "      <td>[April 22, 2017, (, 2017-04-22, ), (, Tribeca ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>Brian Crano</td>\n",
       "      <td>Good Deed Entertainment</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>nan</td>\n",
       "      <td>96.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Permission</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6049</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Loveless_(film)</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt6304162/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Maryana Spivak, Aleksey Rozin, Matvey Novikov...</td>\n",
       "      <td>Mikhail Krichman</td>\n",
       "      <td>[18 May 2017, (, 2017-05-18, ), (, Cannes, ), ...</td>\n",
       "      <td>[Russia, France, Belgium, Germany, [3]]</td>\n",
       "      <td>Andrey Zvyagintsev</td>\n",
       "      <td>[Sony Pictures Releasing, (Russia), [1]]</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>4,800,000.00</td>\n",
       "      <td>128.00</td>\n",
       "      <td>[{'iso_639_1': 'ru', 'name': 'Pусский'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Loveless</td>\n",
       "      <td>False</td>\n",
       "      <td>7.80</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6050</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Gemini_(2017_film)</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt5795086/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Lola Kirke, Zoë Kravitz, Greta Lee, Michelle ...</td>\n",
       "      <td>Andrew Reed</td>\n",
       "      <td>[March 12, 2017, (, 2017-03-12, ), (, SXSW, ),...</td>\n",
       "      <td>United States</td>\n",
       "      <td>Aaron Katz</td>\n",
       "      <td>Neon</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-03-12</td>\n",
       "      <td>200,340.00</td>\n",
       "      <td>92.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Post Production</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gemini</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>https://en.wikipedia.org/wiki/How_to_Talk_to_G...</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt3859310/</td>\n",
       "      <td>[\", How to Talk to Girls at Parties, \", by, Ne...</td>\n",
       "      <td>[Elle Fanning, Alex Sharp, Nicole Kidman, Ruth...</td>\n",
       "      <td>Frank G. DeMarco</td>\n",
       "      <td>[May 21, 2017, (, 2017-05-21, ), (, Cannes, ),...</td>\n",
       "      <td>[United Kingdom, United States]</td>\n",
       "      <td>John Cameron Mitchell</td>\n",
       "      <td>[A24, StudioCanal UK]</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>382,053.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Some girls are out of this world.</td>\n",
       "      <td>How to Talk to Girls at Parties</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6051 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  year  \\\n",
       "0     https://en.wikipedia.org/wiki/The_Adventures_o...  1990   \n",
       "1     https://en.wikipedia.org/wiki/After_Dark,_My_S...  1990   \n",
       "2      https://en.wikipedia.org/wiki/Air_America_(film)  1990   \n",
       "3       https://en.wikipedia.org/wiki/Alice_(1990_film)  1990   \n",
       "4         https://en.wikipedia.org/wiki/Almost_an_Angel  1990   \n",
       "...                                                 ...   ...   \n",
       "6047    https://en.wikipedia.org/wiki/A_Fantastic_Woman  2018   \n",
       "6048    https://en.wikipedia.org/wiki/Permission_(film)  2018   \n",
       "6049      https://en.wikipedia.org/wiki/Loveless_(film)  2018   \n",
       "6050   https://en.wikipedia.org/wiki/Gemini_(2017_film)  2018   \n",
       "6051  https://en.wikipedia.org/wiki/How_to_Talk_to_G...  2018   \n",
       "\n",
       "                                  imdb_link  \\\n",
       "0     https://www.imdb.com/title/tt0098987/   \n",
       "1     https://www.imdb.com/title/tt0098994/   \n",
       "2     https://www.imdb.com/title/tt0099005/   \n",
       "3     https://www.imdb.com/title/tt0099012/   \n",
       "4     https://www.imdb.com/title/tt0099018/   \n",
       "...                                     ...   \n",
       "6047  https://www.imdb.com/title/tt5639354/   \n",
       "6048  https://www.imdb.com/title/tt5390066/   \n",
       "6049  https://www.imdb.com/title/tt6304162/   \n",
       "6050  https://www.imdb.com/title/tt5795086/   \n",
       "6051  https://www.imdb.com/title/tt3859310/   \n",
       "\n",
       "                                               Based on  \\\n",
       "0                           [Characters, by Rex Weiner]   \n",
       "1     [the novel, After Dark, My Sweet, by, Jim Thom...   \n",
       "2                [Air America, by, Christopher Robbins]   \n",
       "3                                                   NaN   \n",
       "4                                                   NaN   \n",
       "...                                                 ...   \n",
       "6047                                                NaN   \n",
       "6048                                                NaN   \n",
       "6049                                                NaN   \n",
       "6050                                                NaN   \n",
       "6051  [\", How to Talk to Girls at Parties, \", by, Ne...   \n",
       "\n",
       "                                               Starring        Cinematography  \\\n",
       "0     [Andrew Dice Clay, Wayne Newton, Priscilla Pre...           Oliver Wood   \n",
       "1     [Jason Patric, Rachel Ward, Bruce Dern, George...          Mark Plummer   \n",
       "2     [Mel Gibson, Robert Downey Jr., Nancy Travis, ...         Roger Deakins   \n",
       "3     [Alec Baldwin, Blythe Danner, Judy Davis, Mia ...        Carlo Di Palma   \n",
       "4           [Paul Hogan, Elias Koteas, Linda Kozlowski]          Russell Boyd   \n",
       "...                                                 ...                   ...   \n",
       "6047                    [Daniela Vega, Francisco Reyes]  Benjamín Echazarreta   \n",
       "6048  [Rebecca Hall, Dan Stevens, Morgan Spector, Fr...          Adam Bricker   \n",
       "6049  [Maryana Spivak, Aleksey Rozin, Matvey Novikov...      Mikhail Krichman   \n",
       "6050  [Lola Kirke, Zoë Kravitz, Greta Lee, Michelle ...           Andrew Reed   \n",
       "6051  [Elle Fanning, Alex Sharp, Nicole Kidman, Ruth...      Frank G. DeMarco   \n",
       "\n",
       "                                           Release date  \\\n",
       "0                     [July 11, 1990, (, 1990-07-11, )]   \n",
       "1     [May 17, 1990, (, 1990-05-17, ), (Cannes Film ...   \n",
       "2                   [August 10, 1990, (, 1990-08-10, )]   \n",
       "3                 [December 25, 1990, (, 1990-12-25, )]   \n",
       "4                                     December 19, 1990   \n",
       "...                                                 ...   \n",
       "6047  [12 February 2017, (, 2017-02-12, ), (, Berlin...   \n",
       "6048  [April 22, 2017, (, 2017-04-22, ), (, Tribeca ...   \n",
       "6049  [18 May 2017, (, 2017-05-18, ), (, Cannes, ), ...   \n",
       "6050  [March 12, 2017, (, 2017-03-12, ), (, SXSW, ),...   \n",
       "6051  [May 21, 2017, (, 2017-05-21, ), (, Cannes, ),...   \n",
       "\n",
       "                                          Country               Director  \\\n",
       "0                                   United States           Renny Harlin   \n",
       "1                                   United States            James Foley   \n",
       "2                                   United States     Roger Spottiswoode   \n",
       "3                                   United States            Woody Allen   \n",
       "4                                              US           John Cornell   \n",
       "...                                           ...                    ...   \n",
       "6047  [Chile, Germany, Spain, United States, [2]]        Sebastián Lelio   \n",
       "6048                                United States            Brian Crano   \n",
       "6049      [Russia, France, Belgium, Germany, [3]]     Andrey Zvyagintsev   \n",
       "6050                                United States             Aaron Katz   \n",
       "6051              [United Kingdom, United States]  John Cameron Mitchell   \n",
       "\n",
       "                                            Distributor  ...  \\\n",
       "0                                      20th Century Fox  ...   \n",
       "1                                       Avenue Pictures  ...   \n",
       "2                                      TriStar Pictures  ...   \n",
       "3                                        Orion Pictures  ...   \n",
       "4                                    Paramount Pictures  ...   \n",
       "...                                                 ...  ...   \n",
       "6047  [Participant Media (Chile), Piffl Medien (Germ...  ...   \n",
       "6048                            Good Deed Entertainment  ...   \n",
       "6049           [Sony Pictures Releasing, (Russia), [1]]  ...   \n",
       "6050                                               Neon  ...   \n",
       "6051                              [A24, StudioCanal UK]  ...   \n",
       "\n",
       "     release_date_kaggle              revenue              runtime  \\\n",
       "0             1990-07-11        20,423,389.00               104.00   \n",
       "1             1990-08-24         2,700,000.00               114.00   \n",
       "2             1990-08-10        33,461,269.00               112.00   \n",
       "3             1990-12-25         7,331,647.00               102.00   \n",
       "4             1990-12-21         6,939,946.00                95.00   \n",
       "...                  ...                  ...                  ...   \n",
       "6047          2017-04-06         3,700,000.00               104.00   \n",
       "6048          2017-04-22                  nan                96.00   \n",
       "6049          2017-06-01         4,800,000.00               128.00   \n",
       "6050          2017-03-12           200,340.00                92.00   \n",
       "6051          2017-12-27           382,053.00               102.00   \n",
       "\n",
       "                                       spoken_languages           status  \\\n",
       "0              [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "1              [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "2     [{'iso_639_1': 'en', 'name': 'English'}, {'iso...         Released   \n",
       "3              [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "4              [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "...                                                 ...              ...   \n",
       "6047           [{'iso_639_1': 'es', 'name': 'Español'}]         Released   \n",
       "6048           [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "6049           [{'iso_639_1': 'ru', 'name': 'Pусский'}]         Released   \n",
       "6050           [{'iso_639_1': 'en', 'name': 'English'}]  Post Production   \n",
       "6051           [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "\n",
       "                                      tagline  \\\n",
       "0         Kojak. Columbo. Dirty Harry. Wimps.   \n",
       "1             All they risked was everything.   \n",
       "2     The few. The proud. The totally insane.   \n",
       "3                                         NaN   \n",
       "4                    Who does he think he is?   \n",
       "...                                       ...   \n",
       "6047                                      NaN   \n",
       "6048                                      NaN   \n",
       "6049                                      NaN   \n",
       "6050                                      NaN   \n",
       "6051        Some girls are out of this world.   \n",
       "\n",
       "                         title_kaggle  video         vote_average  \\\n",
       "0     The Adventures of Ford Fairlane  False                 6.20   \n",
       "1                After Dark, My Sweet  False                 6.50   \n",
       "2                         Air America  False                 5.30   \n",
       "3                               Alice  False                 6.30   \n",
       "4                     Almost an Angel  False                 5.60   \n",
       "...                               ...    ...                  ...   \n",
       "6047                A Fantastic Woman  False                 7.20   \n",
       "6048                       Permission  False                 0.00   \n",
       "6049                         Loveless  False                 7.80   \n",
       "6050                           Gemini  False                 0.00   \n",
       "6051  How to Talk to Girls at Parties  False                 0.00   \n",
       "\n",
       "               vote_count  \n",
       "0                   72.00  \n",
       "1                   17.00  \n",
       "2                  146.00  \n",
       "3                   57.00  \n",
       "4                   23.00  \n",
       "...                   ...  \n",
       "6047                13.00  \n",
       "6048                 1.00  \n",
       "6049                26.00  \n",
       "6050                 0.00  \n",
       "6051                10.00  \n",
       "\n",
       "[6051 rows x 38 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we can run the function for the three column pairs that we decided to fill in zeros.\n",
    "fill_missing_kaggle_data(movies_df, 'runtime', 'running_time')\n",
    "fill_missing_kaggle_data(movies_df, 'budget_kaggle', 'budget_wiki')\n",
    "fill_missing_kaggle_data(movies_df, 'revenue', 'box_office')\n",
    "movies_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we've merged our data and filled in values, it's good to check that there aren't any columns with only one value, since that doesn't really provide any information. Don't forget, we need to convert lists to tuples for value_counts() to work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adult\n",
      "video\n"
     ]
    }
   ],
   "source": [
    "for col in movies_df.columns:\n",
    "    lists_to_tuples = lambda x: tuple(x) if type(x) == list else x\n",
    "    value_counts = movies_df[col].apply(lists_to_tuples).value_counts(dropna=False)\n",
    "    num_values = len(value_counts)\n",
    "    if num_values == 1:\n",
    "        print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    6051\n",
       "Name: video, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['video'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26024289"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it's false for every row, we don't need to include this column.\n",
    "\n",
    "We should reorder the columns to make the dataset easier to read for the hackathon participants. Having similar columns near each other helps people looking through the data get a better sense of what information is available. One way to reorder them would be to consider the columns roughly in groups, like this:\n",
    "\n",
    "* Identifying information (IDs, titles, URLs, etc.)\n",
    "* Quantitative facts (runtime, budget, revenue, etc.)\n",
    "* Qualitative facts (genres, languages, country, etc.)\n",
    "* Business data (production companies, distributors, etc.)\n",
    "* People (producers, director, cast, writers, etc.)\n",
    "\n",
    "The following code is one way to reorder the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df = movies_df.loc[:, ['imdb_id','id','title_kaggle','original_title','tagline','belongs_to_collection','url','imdb_link',\n",
    "                       'runtime','budget_kaggle','revenue','release_date_kaggle','popularity','vote_average','vote_count',\n",
    "                       'genres','original_language','overview','spoken_languages','Country',\n",
    "                       'production_companies','production_countries','Distributor',\n",
    "                       'Producer(s)','Director','Starring','Cinematography','Editor(s)','Writer(s)','Composer(s)','Based on'\n",
    "                      ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally, we need to rename the columns to be consistent.\n",
    "\n",
    "movies_df.rename({'id':'kaggle_id',\n",
    "                  'title_kaggle':'title',\n",
    "                  'url':'wikipedia_url',\n",
    "                  'budget_kaggle':'budget',\n",
    "                  'release_date_kaggle':'release_date',\n",
    "                  'Country':'country',\n",
    "                  'Distributor':'distributor',\n",
    "                  'Producer(s)':'producers',\n",
    "                  'Director':'director',\n",
    "                  'Starring':'starring',\n",
    "                  'Cinematography':'cinematography',\n",
    "                  'Editor(s)':'editors',\n",
    "                  'Writer(s)':'writers',\n",
    "                  'Composer(s)':'composers',\n",
    "                  'Based on':'based_on'\n",
    "                 }, axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**\n",
    "If you did not use .loc to reorder the columns and instead passed a list of column names to the indexing operator (i.e. movies_df = movies_df[[‘imdb_id’, ‘title_kaggle’, … ]]), you may receive a SettingWithCopyWarning. Don't panic! This isn't an error, so your code will continue to work, but it is a warning that your code may not behave as you expect. In this case, your code will work fine, but for best practices, use .loc instead to avoid this warning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.4.2 Transform and Merge Rating Data\n",
    "\n",
    "For each movie, Britta wants to include the rating data, but the rating dataset has so much information that it's too unwieldy to use all of it. We could calculate some basic statistics like the mean and median rating for each movie, but a more useful summary is just to count how many times a movie received a given rating. This way, someone who wants to calculate statistics for the dataset would have all the information they need. \n",
    "\n",
    "We'll include the raw ratings data if the hackathon participants want to do more in-depth analysis, such as comparing across users, but having the rating counts for each movie is easy enough to do. Plus, it will enable the hackathon participants to calculate statistics on their own without having to work with a dataset containing 26-million rows.\n",
    "\n",
    "First, we need to use a groupby on the \"movieId\" and \"rating\" columns and take the count for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>userId</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>441</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>804</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>438</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2083</td>\n",
       "      <td>2083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1584</td>\n",
       "      <td>1584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239376</th>\n",
       "      <td>176267</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239377</th>\n",
       "      <td>176269</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239378</th>\n",
       "      <td>176271</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239379</th>\n",
       "      <td>176273</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239380</th>\n",
       "      <td>176275</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239381 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movieId               rating  userId  timestamp\n",
       "0             1                 0.50     441        441\n",
       "1             1                 1.00     804        804\n",
       "2             1                 1.50     438        438\n",
       "3             1                 2.00    2083       2083\n",
       "4             1                 2.50    1584       1584\n",
       "...         ...                  ...     ...        ...\n",
       "239376   176267                 4.00       1          1\n",
       "239377   176269                 3.50       1          1\n",
       "239378   176271                 5.00       1          1\n",
       "239379   176273                 1.00       1          1\n",
       "239380   176275                 3.00       1          1\n",
       "\n",
       "[239381 rows x 4 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_counts = ratings.groupby(['movieId','rating'], as_index=False).count()\n",
    "rating_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The choice of renaming \"userId\" to \"count\" is arbitrary. Both \"userId\" and \"timestamp\" \n",
    "# have the same information, so we could use either one.\n",
    "rating_counts = ratings.groupby(['movieId','rating'], as_index=False).count() \\\n",
    "                .rename({'userId':'count'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pivot this data so that movieId is the index, the columns will be all the rating values, \n",
    "# and the rows will be the counts for each rating value.\n",
    "\n",
    "rating_counts = ratings.groupby(['movieId','rating'], as_index=False).count() \\\n",
    "                .rename({'userId':'count'}, axis=1) \\\n",
    "                .pivot(index='movieId',columns='rating', values='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to rename the columns so they're easier to understand. We'll prepend rating_ \n",
    "# to each column with a list comprehension:\n",
    "\n",
    "rating_counts.columns = ['rating_' + str(col) for col in rating_counts.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This time, we need to use a left merge, since we want to keep everything in movies_df\n",
    "movies_with_ratings_df = pd.merge(movies_df, rating_counts, left_on='kaggle_id', right_index=True, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally, because not every movie got a rating for each rating level, there will be missing values instead of zeros. We have to fill those \n",
    "# in ourselves, like this:\n",
    "\n",
    "movies_with_ratings_df[rating_counts.columns] = movies_with_ratings_df[rating_counts.columns].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect Pandas and SQL\n",
    "\n",
    "Now that we've extracted and transformed our data, it's time to load it into a SQL database. We're going to create a new database and use the built-in to_sql() method in Pandas to create a table for our merged movie data. We'll also import the raw ratings data into its own table.\n",
    "\n",
    "**Create a Database**\n",
    "Start pgAdmin and expand your local servers in the left-hand pane so you can see the Databases section. Right-click on Databases and select Create followed by Database.\n",
    "\n",
    "Name the database \"movie_data\" and click Save.\n",
    "\n",
    "**Import Modules**\n",
    "Go back to your Jupyter Notebook. We need to import create_engine from the sqlalchemy module. Don't forget to add this import to the first cell so that all your imports are in one spot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "from config import db_password\n",
    "db_string = f\"postgres://postgres:{db_password}@127.0.0.1:5432/movie_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(db_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#pip install psycopg2-binary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the Movie Data\n",
    "To save the movies_df DataFrame to a SQL table, we only have to specify the name of the table and the engine in the to_sql() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df.to_sql(name='movies', con=engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the Ratings Data\n",
    "The ratings data is too large to import in one statement, so it has to be divided into \"chunks\" of data. To do so, we'll need to reimport the CSV using the chunksize= parameter in read_csv(). This creates an iterable object, so we can make a for loop and append the chunks of data to the new rows to the target SQL table.\n",
    "\n",
    "**CAUTION**\n",
    "The to_sql() method also has a chunksize= parameter, but that won't help us with memory concerns. The chunksize= parameter in to_sql() creates smaller transactions sent to SQL to prevent the SQL instance from getting locked up with a large transaction.\n",
    "\n",
    "The simplest way to do this is with two lines:\n",
    "\n",
    "**Do not run this yet!**\n",
    "for data in pd.read_csv(f'{file_dir}ratings.csv', chunksize=1000000):\n",
    "    data.to_sql(name='ratings', con=engine, if_exists='append')\n",
    "    \n",
    "This can take quite a long time to run (more than an hour). It's a really good idea to print out some information about how it's running.\n",
    "\n",
    "Let's add functionality to this code to print out:\n",
    "\n",
    "* How many rows have been imported\n",
    "* How much time has elapsed\n",
    "\n",
    "### Step 1: Print Number of Imported Rows\n",
    "Below is the previous block of code, with comments added for refactoring:\n",
    "\n",
    "**create a variable for the number of rows imported**\n",
    "rows_imported = 0\n",
    "for data in pd.read_csv(f'{file_dir}ratings.csv', chunksize=1000000):\n",
    "\n",
    "    # print out the range of rows that are being imported\n",
    "    print(f'importing rows {rows_imported} to {rows_imported + len(data)}...', end='')\n",
    "\n",
    "    data.to_sql(name='ratings', con=engine, if_exists='append')\n",
    "\n",
    "    # increment the number of rows imported by the size of 'data'\n",
    "    rows_imported += len(data)\n",
    "\n",
    "    # print that the rows have finished importing\n",
    "    print('Done.')\n",
    "\n",
    "### Step 2: Print Elapsed Time\n",
    "This is an optional step, but it's a good idea when running a long process. We're going to print the total amount of time elapsed at every step. This is useful to estimate how long the process is going to take.\n",
    "\n",
    "We'll use the built-in time module in Python. time.time() returns the current time whenever it is called. Subtracting two time values gives the difference in seconds. By setting a variable at the beginning to the time at the start, inside the loop we can easily calculate elapsed time and print it out.\n",
    "\n",
    "First, we'll add the following to our import cell and rerun it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing rows 0 to 1000000...Done. 525.4634711742401 total seconds elapsed\n",
      "importing rows 1000000 to 2000000...Done. 988.7244353294373 total seconds elapsed\n",
      "importing rows 2000000 to 3000000...Done. 1359.0365262031555 total seconds elapsed\n",
      "importing rows 3000000 to 4000000...Done. 1874.4942111968994 total seconds elapsed\n",
      "importing rows 4000000 to 5000000...Done. 2383.777595281601 total seconds elapsed\n",
      "importing rows 5000000 to 6000000...Done. 2898.9499311447144 total seconds elapsed\n",
      "importing rows 6000000 to 7000000...Done. 3306.66321516037 total seconds elapsed\n",
      "importing rows 7000000 to 8000000...Done. 3628.2320880889893 total seconds elapsed\n",
      "importing rows 8000000 to 9000000...Done. 3985.622810125351 total seconds elapsed\n",
      "importing rows 9000000 to 10000000...Done. 4390.494742155075 total seconds elapsed\n",
      "importing rows 10000000 to 11000000...Done. 4746.692054033279 total seconds elapsed\n",
      "importing rows 11000000 to 12000000...Done. 5060.136583328247 total seconds elapsed\n",
      "importing rows 12000000 to 13000000...Done. 5495.694656133652 total seconds elapsed\n",
      "importing rows 13000000 to 14000000...Done. 5861.32293510437 total seconds elapsed\n",
      "importing rows 14000000 to 15000000...Done. 6281.164924144745 total seconds elapsed\n",
      "importing rows 15000000 to 16000000...Done. 6675.091168165207 total seconds elapsed\n",
      "importing rows 16000000 to 17000000...Done. 6998.6474051475525 total seconds elapsed\n",
      "importing rows 17000000 to 18000000...Done. 7290.534757137299 total seconds elapsed\n",
      "importing rows 18000000 to 19000000...Done. 7618.330251216888 total seconds elapsed\n",
      "importing rows 19000000 to 20000000...Done. 7927.608329296112 total seconds elapsed\n",
      "importing rows 20000000 to 21000000...Done. 8285.843659162521 total seconds elapsed\n",
      "importing rows 21000000 to 22000000...Done. 8732.849701166153 total seconds elapsed\n",
      "importing rows 22000000 to 23000000...Done. 9173.822847366333 total seconds elapsed\n",
      "importing rows 23000000 to 24000000...Done. 9707.116415262222 total seconds elapsed\n",
      "importing rows 24000000 to 25000000...Done. 10145.051285028458 total seconds elapsed\n",
      "importing rows 25000000 to 26000000...Done. 10507.377743244171 total seconds elapsed\n",
      "importing rows 26000000 to 26024289...Done. 10515.650385141373 total seconds elapsed\n"
     ]
    }
   ],
   "source": [
    "rows_imported = 0\n",
    "# get the start_time from time.time()\n",
    "start_time = time.time()\n",
    "for data in pd.read_csv('ratings.csv', chunksize=1000000):\n",
    "    print(f'importing rows {rows_imported} to {rows_imported + len(data)}...', end='')\n",
    "    data.to_sql(name='ratings', con=engine, if_exists='append')\n",
    "    rows_imported += len(data)\n",
    "\n",
    "    # add elapsed time to final print out\n",
    "    print(f'Done. {time.time() - start_time} total seconds elapsed')"
   ]
  }
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