exploratory.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\n",
    "import time\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "from config import db_password"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_dir = '.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load wikimedia json file\n",
    "with open(f'{file_dir}/data/wikipedia_movies.json', mode='r') as file:\n",
    "    wiki_movies_raw = json.load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data exploration"
   ]
  },
  {
   "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'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/Vice_(2018_film)',\n",
       "  'year': 2018,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt6266538/',\n",
       "  'title': 'Vice',\n",
       "  'Directed by': 'Adam McKay',\n",
       "  'Produced by': ['Brad Pitt',\n",
       "   'Dede Gardner',\n",
       "   'Jeremy Kleiner',\n",
       "   'Kevin J. Messick',\n",
       "   'Will Ferrell',\n",
       "   'Adam McKay'],\n",
       "  'Written by': 'Adam McKay',\n",
       "  'Starring': ['Christian Bale',\n",
       "   'Amy Adams',\n",
       "   'Steve Carell',\n",
       "   'Sam Rockwell',\n",
       "   'Tyler Perry',\n",
       "   'Alison Pill',\n",
       "   'Lily Rabe',\n",
       "   'Jesse Plemons'],\n",
       "  'Music by': 'Nicholas Britell',\n",
       "  'Cinematography': 'Greig Fraser',\n",
       "  'Edited by': 'Hank Corwin',\n",
       "  'Productioncompany ': ['Plan B Entertainment',\n",
       "   'Gary Sanchez Productions',\n",
       "   'Annapurna Pictures'],\n",
       "  'Distributed by': 'Mirror Releasing',\n",
       "  'Release date': ['December 11, 2018',\n",
       "   '(',\n",
       "   '2018-12-11',\n",
       "   ')',\n",
       "   '(',\n",
       "   'Samuel Goldwyn Theater',\n",
       "   ')',\n",
       "   'December 25, 2018',\n",
       "   '(',\n",
       "   '2018-12-25',\n",
       "   ')',\n",
       "   '(United States)'],\n",
       "  'Running time': '132 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$60 million',\n",
       "  'Box office': '$76.1 million'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/On_the_Basis_of_Sex',\n",
       "  'year': 2018,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt4669788/',\n",
       "  'title': 'On the Basis of Sex',\n",
       "  'Directed by': 'Mimi Leder',\n",
       "  'Produced by': 'Robert W. Cort',\n",
       "  'Written by': 'Daniel Stiepleman',\n",
       "  'Starring': ['Felicity Jones',\n",
       "   'Armie Hammer',\n",
       "   'Justin Theroux',\n",
       "   'Sam Waterston',\n",
       "   'Kathy Bates'],\n",
       "  'Music by': 'Mychael Danna',\n",
       "  'Cinematography': 'Michael Grady',\n",
       "  'Edited by': 'Michelle Tesoro',\n",
       "  'Productioncompanies ': ['Focus Features',\n",
       "   '[1]',\n",
       "   'Participant Media',\n",
       "   '[1]',\n",
       "   'Robert Cort Productions',\n",
       "   '[1]',\n",
       "   'Alibaba Pictures',\n",
       "   '[2]'],\n",
       "  'Distributed by': 'Focus Features',\n",
       "  'Release date': ['November 8, 2018',\n",
       "   '(',\n",
       "   '2018-11-08',\n",
       "   ')',\n",
       "   '(',\n",
       "   'AFI Fest',\n",
       "   ')',\n",
       "   'December 25, 2018',\n",
       "   '(',\n",
       "   '2018-12-25',\n",
       "   ')',\n",
       "   '(United States)'],\n",
       "  'Running time': '120 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$20 million',\n",
       "  'Box office': '$38.4 million'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/Destroyer_(2018_film)',\n",
       "  'year': 2018,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt7137380/',\n",
       "  'title': 'Destroyer',\n",
       "  'Directed by': 'Karyn Kusama',\n",
       "  'Produced by': ['Fred Berger', 'Phil Hay', 'Matt Manfredi'],\n",
       "  'Written by': ['Phil Hay', 'Matt Manfredi'],\n",
       "  'Starring': ['Nicole Kidman',\n",
       "   'Sebastian Stan',\n",
       "   'Toby Kebbell',\n",
       "   'Tatiana Maslany',\n",
       "   'Bradley Whitford',\n",
       "   'Jade Pettyjohn',\n",
       "   'Scoot McNairy'],\n",
       "  'Music by': 'Theodore Shapiro',\n",
       "  'Cinematography': 'Julie Kirkwood',\n",
       "  'Edited by': 'Plummy Tucker',\n",
       "  'Productioncompany ': ['30West',\n",
       "   'Automatik Entertainment',\n",
       "   'Annapurna Pictures'],\n",
       "  'Distributed by': 'Mirror Releasing',\n",
       "  'Release date': ['August 31, 2018',\n",
       "   '(',\n",
       "   '2018-08-31',\n",
       "   ')',\n",
       "   '(',\n",
       "   'Telluride',\n",
       "   ')',\n",
       "   'December 25, 2018',\n",
       "   '(',\n",
       "   '2018-12-25',\n",
       "   ')',\n",
       "   '(United States)'],\n",
       "  'Running time': '123 minutes',\n",
       "  'Country': 'United States',\n",
       "  'Language': 'English',\n",
       "  'Budget': '$9 million',\n",
       "  'Box office': '$5.5 million'},\n",
       " {'url': 'https://en.wikipedia.org/wiki/Black_Mirror:_Bandersnatch',\n",
       "  'year': 2018,\n",
       "  'imdb_link': 'https://www.imdb.com/title/tt9495224/',\n",
       "  'title': 'Bandersnatch',\n",
       "  'Directed by': 'David Slade',\n",
       "  'Produced by': 'Russell McLean',\n",
       "  'Written by': 'Charlie Brooker',\n",
       "  'Starring': ['Fionn Whitehead',\n",
       "   'Will Poulter',\n",
       "   'Craig Parkinson',\n",
       "   'Alice Lowe',\n",
       "   'Asim Chaudhry'],\n",
       "  'Music by': 'Brian Reitzell',\n",
       "  'Cinematography': ['Aaron Morton', 'Jake Polonsky'],\n",
       "  'Edited by': 'Tony Kearns',\n",
       "  'Productioncompany ': ['House of Tomorrow', 'Netflix'],\n",
       "  'Distributed by': 'Netflix',\n",
       "  'Release date': ['28 December 2018', '(', '2018-12-28', ')'],\n",
       "  'Running time': 'Variable; 90 minutes for default path',\n",
       "  'Country': 'United Kingdom',\n",
       "  'Language': 'English'}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Last 5 records\n",
    "wiki_movies_raw[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": [],
   "source": [
    "# Load metadata and ratings from Kaggle\n",
    "kaggle_metadata = pd.read_csv(f'{file_dir}/data/movies_metadata.csv',low_memory=False)\n",
    "ratings = pd.read_csv(f'{file_dir}/data/ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load wikimedia JSON file into dataframe\n",
    "wiki_movies_df = pd.DataFrame(wiki_movies_raw)\n",
    "wiki_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Begin clean-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7076"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduce list based on if items have directors, imdb link, and not episodic (TV)\n",
    "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df[wiki_movies_df['Arabic'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(wiki_movies_df.columns.to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to combined alt-titles and merge duplicate columns\n",
    "\n",
    "def clean_movie(movie):\n",
    "    movie = dict(movie)\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',\n",
    "                'Mandarin','McCune–Reischauer','Original title','Polish',\n",
    "                'Revised Romanization','Romanized','Russian',\n",
    "                '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": 15,
   "metadata": {},
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_movies = [clean_movie(movie) for movie in wiki_movies]\n",
    "\n",
    "# Convert \"clean_movies\" into wiki_movies dataframe\n",
    "wiki_movies_df = pd.DataFrame(clean_movies)\n",
    "sorted(wiki_movies_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wiki_movies_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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": 17,
     "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": 18,
   "metadata": {},
   "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": 18,
     "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": 19,
   "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": 19,
     "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": 20,
   "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": 21,
   "metadata": {
    "scrolled": true
   },
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Office clean-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "box_office = wiki_movies_df['Box office'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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                   [$529.3, million, [4]]\n",
       "Name: Box office, Length: 135, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_not_a_string(x):\n",
    "    return type(x) != str\n",
    "\n",
    "box_office[box_office.map(is_not_a_string)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34                           [US$, 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                   [$529.3, million, [4]]\n",
       "Name: Box office, Length: 135, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office[box_office.map(lambda x: type(x) != str)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "box_office = box_office.apply(lambda x: ' '.join(x) if type(x)==list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3896"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "form_one = r'\\$\\d+\\.?\\d*\\s*[mb]illion'\n",
    "box_office.str.contains(form_one, flags=re.IGNORECASE).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1544"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "form_two = r'\\$\\d{1,3}(?:,\\d{3})+'\n",
    "box_office.str.contains(form_two, flags=re.IGNORECASE).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {},
   "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                         $6.9 millon\n",
       "Name: Box office, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office[~matches_form_one & ~matches_form_two]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adjusting forms to account for: spaces between dollar sign and number,\n",
    "# periods instead of commas within the number\n",
    "# forms with 4 digits and mill/bill\n",
    "# values given as ranges\n",
    "# misspelled m/billion\n",
    "form_one = r'\\$\\s*\\d+\\.?\\d*\\s*[mb]ill?i?on'\n",
    "form_two = r'\\$\\s*\\d{1,3}(?:[,\\.]\\d{3})+(?!\\s[mb]illion)'\n",
    "\n",
    "# Correct values with citation references\n",
    "box_office = box_office.str.replace(r'\\[\\d+\\]\\s*', '')\n",
    "\n",
    "# Correct values given as ranges\n",
    "box_office = box_office.str.replace(r'\\$.*[-—–](?![a-z])', '$', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse_dollars function\n",
    "\n",
    "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",
    "        # convert to float and multiply by a million\n",
    "        value = float(s)*10**6\n",
    "        # return value\n",
    "        return value\n",
    "    # if input is of the form $###.# billion\n",
    "    elif re.match(r'\\$\\s*\\d+\\.?\\d*\\s*billi?on', s, flags=re.IGNORECASE):\n",
    "        # remove dollar sign and \" billion\"\n",
    "        s = re.sub('\\$|\\s|[a-zA-Z]','', s)\n",
    "        # convert to float and multiply by a billion\n",
    "        value = float(s)*10**9\n",
    "        # return value\n",
    "        return value\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",
    "        # remove dollar sign and commas\n",
    "        s = re.sub('\\$|,','', s)\n",
    "        # convert to float\n",
    "        value = float(s)\n",
    "        # return value\n",
    "        return value\n",
    "    # otherwise, return NaN\n",
    "    else:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_movies_df.drop('Box office',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Budget clean-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     $20 million\n",
       "1      $6 million\n",
       "2     $35 million\n",
       "3     $12 million\n",
       "4     $25 million\n",
       "10    $50 million\n",
       "11    $22 million\n",
       "13    $29 million\n",
       "14    $40 million\n",
       "15     $7 million\n",
       "Name: Budget, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budget.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Budget, dtype: object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budget[budget.map(lambda x: type(x) != str)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136                        Unknown\n",
       "204     60 millionNorwegian 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     PHP85 million (estimated)\n",
       "1948                   102,888,900\n",
       "1953                  3,500,000 DM\n",
       "1973                    ₤2,300,874\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": 37,
     "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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136                        Unknown\n",
       "204     60 millionNorwegian Kroner\n",
       "478                        Unknown\n",
       "973                     $34million\n",
       "1126                   $120million\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     PHP85 million (estimated)\n",
       "1948                   102,888,900\n",
       "1953                  3,500,000 DM\n",
       "1973                    ₤2,300,874\n",
       "2451                    ₤6,350,000\n",
       "3144                   €40 million\n",
       "3360                   $150million\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                  $$200million\n",
       "5055                   $155million\n",
       "5419                    $40million\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": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       20000000.0\n",
       "1        6000000.0\n",
       "2       35000000.0\n",
       "3       12000000.0\n",
       "4       25000000.0\n",
       "           ...    \n",
       "7071    42000000.0\n",
       "7072    60000000.0\n",
       "7073    20000000.0\n",
       "7074     9000000.0\n",
       "7075           NaN\n",
       "Name: budget, Length: 7033, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df['budget']=budget.str.extract(f'({form_one}|{form_two})',flags=re.IGNORECASE)[0].apply(parse_dollars)\n",
    "wiki_movies_df['budget']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_movies_df.drop('Budget',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Release Date clean-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                            July 11, 1990 ( 1990-07-11 )\n",
       "1       May 17, 1990 ( 1990-05-17 ) (Cannes Film Marke...\n",
       "2                          August 10, 1990 ( 1990-08-10 )\n",
       "3                        December 25, 1990 ( 1990-12-25 )\n",
       "4                                       December 19, 1990\n",
       "                              ...                        \n",
       "7071     December 25, 2018 ( 2018-12-25 ) (United States)\n",
       "7072    December 11, 2018 ( 2018-12-11 ) ( Samuel Gold...\n",
       "7073    November 8, 2018 ( 2018-11-08 ) ( AFI Fest ) D...\n",
       "7074    August 31, 2018 ( 2018-08-31 ) ( Telluride ) D...\n",
       "7075                      28 December 2018 ( 2018-12-28 )\n",
       "Name: Release date, Length: 7001, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "release_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "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": 44,
     "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": 45,
   "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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki_movies_df.drop('Release date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                      7033\n",
       "year                     7033\n",
       "imdb_link                7033\n",
       "title                    7032\n",
       "Based on                 2181\n",
       "Starring                 6849\n",
       "Cinematography           6342\n",
       "Running time             6894\n",
       "Country                  6797\n",
       "Language                 6789\n",
       "Director                 7033\n",
       "Distributor              6676\n",
       "Editor(s)                6485\n",
       "Composer(s)              6515\n",
       "Producer(s)              6831\n",
       "Production company(s)    5355\n",
       "Writer(s)                6834\n",
       "imdb_id                  7033\n",
       "box_office               5466\n",
       "budget                   4706\n",
       "release_date             7001\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview check-up\n",
    "wiki_movies_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Time clean-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                 102 minutes\n",
       "1                                 114 minutes\n",
       "2                                 113 minutes\n",
       "3                                 106 minutes\n",
       "4                                  95 minutes\n",
       "                        ...                  \n",
       "7071                               90 minutes\n",
       "7072                              132 minutes\n",
       "7073                              120 minutes\n",
       "7074                              123 minutes\n",
       "7075    Variable; 90 minutes for default path\n",
       "Name: Running time, Length: 6894, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_time = wiki_movies_df['Running time'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)\n",
    "running_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 49,
     "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6877"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_time.str.contains(r'^\\d*\\s*m', flags=re.IGNORECASE).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allowing for multiple patters of running time\n",
    "running_time_extract = running_time.str.extract(r'(\\d+)\\s*ho?u?r?s?\\s*(\\d*)|(\\d+)\\s*m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting running time to numeric values\n",
    "running_time_extract = running_time_extract.apply(lambda col: pd.to_numeric(col, errors='coerce')).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert capture groups to minutes\n",
    "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": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop 'Running Time'\n",
    "wiki_movies_df.drop('Running time', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Kaggle metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False                                                                                                                             45454\n",
       "True                                                                                                                                  9\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",
       " - Written by Ørnås                                                                                                                   1\n",
       "Name: adult, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata['adult'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata[~kaggle_metadata['adult'].isin(['True','False'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep only data where 'adult' == False\n",
    "kaggle_metadata = kaggle_metadata[kaggle_metadata['adult'] == 'False'].drop('adult',axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    45358\n",
       "True        93\n",
       "Name: video, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_metadata['video'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_metadata['video'] = kaggle_metadata['video'] == 'True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_metadata['release_date'] = pd.to_datetime(kaggle_metadata['release_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 65,
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(ratings['timestamp'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to unix time\n",
    "ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HrmMaVNV3qurm0fZ9wEbmf9m8z6l59492Dxi99uleVZKjgLXA+4aupXcGvJpKsgY4Cbhx4FIGMxqOuAXYAlxTVfvsvRh5J/AG4GcD1zEtCvhMkg2jpVsmxoBXM0keB1wBXFBV24auZyhV9WBVncj8r7lPSbLPDuElORvYUlUbhq5lipxaVSczv/Lua0bDvRNhwKuJ0XjzFcCHqurjQ9czDarqXuA64MxhKxnUqcA5o3HnjwBnJPngsCUNq6ruGf3dAlzJ/Eq8E2HAa+JGXyy+H9hYVW8fup4hJZlJcvho+yDgucCdgxY1oKq6qKqOqqo1zC9f8p9V9YqByxpMkkNGExFIcgjwfGBiM/EM+AlJchnwReC4JJuTnDd0TQM6FXgl872zW0avFwxd1ECOBK5Ncivz6zNdU1X7/NRA/dwTgC8k+QpwE3BVVV09qYs7TVKSOmUPXpI6ZcBLUqcMeEnqlAEvSZ0y4CWpUwa8JHXKgJekTv0/JIS6v0mqqDQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build out a histogram of ratings\n",
    "pd.options.display.float_format = '{:20,.2f}'.format\n",
    "ratings['rating'].plot(kind='hist')\n",
    "ratings['rating'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging wikipedia and kaggle metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge and add suffixes to duplicate columns\n",
    "movies_df = pd.merge(wiki_movies_df, kaggle_metadata, on='imdb_id', suffixes=['_wiki','_kaggle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Based on',\n",
       " 'Cinematography',\n",
       " 'Composer(s)',\n",
       " 'Country',\n",
       " 'Director',\n",
       " 'Distributor',\n",
       " 'Editor(s)',\n",
       " 'Language',\n",
       " 'Producer(s)',\n",
       " 'Production company(s)',\n",
       " 'Starring',\n",
       " 'Writer(s)',\n",
       " 'belongs_to_collection',\n",
       " 'box_office',\n",
       " 'budget_kaggle',\n",
       " 'budget_wiki',\n",
       " 'genres',\n",
       " 'homepage',\n",
       " 'id',\n",
       " 'imdb_id',\n",
       " 'imdb_link',\n",
       " 'original_language',\n",
       " 'original_title',\n",
       " 'overview',\n",
       " 'popularity',\n",
       " 'poster_path',\n",
       " 'production_companies',\n",
       " 'production_countries',\n",
       " 'release_date_kaggle',\n",
       " 'release_date_wiki',\n",
       " 'revenue',\n",
       " 'running_time',\n",
       " 'runtime',\n",
       " 'spoken_languages',\n",
       " 'status',\n",
       " 'tagline',\n",
       " 'title_kaggle',\n",
       " 'title_wiki',\n",
       " 'url',\n",
       " 'video',\n",
       " 'vote_average',\n",
       " 'vote_count',\n",
       " 'year']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display list of all columns in merged dataframe\n",
    "movies_df.columns.sort_values().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Competing data:\n",
    "# Wiki                     Movielens                Resolution\n",
    "#--------------------------------------------------------------------------\n",
    "# title_wiki               title_kaggle             Drop Wikipedia\n",
    "# running_time             runtime                  Keep Kaggle; fill in zeros with Wikipedia data.\n",
    "# budget_wiki              budget_kaggle            Keep Kaggle; fill in zeros with Wikipedia data.\n",
    "# box_office               revenue                  Keep Kaggle; fill in zeros with Wikipedia data.\n",
    "# release_date_wiki        release_date_kaggle      Drop Wikipedia\n",
    "# Language                 original_language        Drop Wikipedia\n",
    "# Production company(s)    production_companies     Drop Wikipedia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing columns\n",
    "### Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df[['title_wiki','title_kaggle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df[movies_df['title_wiki'] != movies_df['title_kaggle']][['title_wiki','title_kaggle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Director</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 × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [url, year, imdb_link, title_wiki, Based on, Starring, Cinematography, 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, 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 43 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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": [
    "### Runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='running_time', ylabel='runtime'>"
      ]
     },
     "execution_count": 74,
     "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": [
    "# Fill in missing values with 0 and plot with scatter plot\n",
    "movies_df.fillna(0).plot(x='running_time', y='runtime', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='budget_wiki', ylabel='budget_kaggle'>"
      ]
     },
     "execution_count": 75,
     "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": [
    "# Compare budget numbers from competing columns, fill in blanks with 0\n",
    "movies_df.fillna(0).plot(x='budget_wiki',y='budget_kaggle', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Office"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='box_office', ylabel='revenue'>"
      ]
     },
     "execution_count": 76,
     "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": [
    "# Scatter plot compare the two budget columns, fill blanks with 0\n",
    "movies_df.fillna(0).plot(x='box_office', y='revenue', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='box_office', ylabel='revenue'>"
      ]
     },
     "execution_count": 77,
     "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": [
    "# look at everything less than 1 billion\n",
    "movies_df.fillna(0)[movies_df['box_office'] < 10**9].plot(x='box_office', y='revenue', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Release Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='release_date_wiki'>"
      ]
     },
     "execution_count": 78,
     "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": [
    "# Compare release dates on scatter plot\n",
    "movies_df[['release_date_wiki','release_date_kaggle']].plot(x='release_date_wiki', y='release_date_kaggle', style='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Director</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>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>Nancy Meyers</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 × 43 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",
       "            Country Language      Director  ... release_date_kaggle  \\\n",
       "3607  United States  English  Nancy Meyers  ...          1953-08-28   \n",
       "\n",
       "                  revenue              runtime  \\\n",
       "3607        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 43 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the outlier around (2006, 1955)\n",
    "movies_df[(movies_df['release_date_wiki'] > '1996-01-01') & (movies_df['release_date_kaggle'] < '1965-01-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3607], dtype='int64')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the index of that row\n",
    "movies_df[(movies_df['release_date_wiki'] > '1996-01-01') & (movies_df['release_date_kaggle'] < '1965-01-01')].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the corrupted row\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": 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>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>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Director</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>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jonathan Winfrey</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>[United States, Germany, Japan, [1]]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hal Hartley</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>Eleanor Bergstein</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>Frank Rainone</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>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>[Noah Baumbach, (as Ernie Fusco)]</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>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Phillip J. Roth</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>English</td>\n",
       "      <td>Jordan Walker-Pearlman</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>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>Steve James</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>[Denmark, USA, UK]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bille August</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>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>[Gregg Kavet, Andy Robin]</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>United States</td>\n",
       "      <td>English/Hebrew</td>\n",
       "      <td>Ellie Kanner-Zuckerman</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 × 43 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  \\\n",
       "1008                                Geoff George   \n",
       "1061                             Michael Spiller   \n",
       "1121                           Miroslav Ondrícek   \n",
       "1564                                  Ken Kelsch   \n",
       "1633                            Steven Bernstein   \n",
       "1775                          Philip D. Schwartz   \n",
       "2386                           John L. Demps Jr.   \n",
       "2786  [Dana Kupper, Gordon Quinn, Peter Gilbert]   \n",
       "3174                                         NaN   \n",
       "3651                                         NaN   \n",
       "4967                              Andrzej Sekuła   \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",
       "                               Director  ... release_date_kaggle  \\\n",
       "1008                   Jonathan Winfrey  ...          1995-08-22   \n",
       "1061                        Hal Hartley  ...          1995-09-14   \n",
       "1121                  Eleanor Bergstein  ...          1995-11-17   \n",
       "1564                      Frank Rainone  ...          1998-01-01   \n",
       "1633  [Noah Baumbach, (as Ernie Fusco)]  ...          1997-01-01   \n",
       "1775                    Phillip J. Roth  ...          1999-02-22   \n",
       "2386             Jordan Walker-Pearlman  ...          2000-01-01   \n",
       "2786                        Steve James  ...          2002-09-09   \n",
       "3174                       Bille August  ...          2004-09-17   \n",
       "3651          [Gregg Kavet, Andy Robin]  ...          2006-03-11   \n",
       "4967             Ellie Kanner-Zuckerman  ...          2012-06-08   \n",
       "\n",
       "                  revenue              runtime  \\\n",
       "1008                 0.00                92.00   \n",
       "1061                 0.00                85.00   \n",
       "1121                 0.00                95.00   \n",
       "1564                 0.00                89.00   \n",
       "1633                 0.00               110.00   \n",
       "1775                 0.00                86.00   \n",
       "2386                 0.00               107.00   \n",
       "2786            97,000.00               140.00   \n",
       "3174                 0.00               109.00   \n",
       "3651                 0.00                89.00   \n",
       "4967                 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 43 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "movies_df[movies_df['release_date_wiki'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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, /, Irish)                                1\n",
       "(English, Polish, Italian)                         1\n",
       "(English, Vietnamese, French)                      1\n",
       "English, Inuktitut                                 1\n",
       "(English,, Bosnian, ,, Croatian, ,, Serbian)       1\n",
       "Name: Language, Length: 198, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert lists in the languages field to tuples\n",
    "movies_df['Language'].apply(lambda x: tuple(x) if type(x) == list else x).value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "en    5987\n",
       "fr      16\n",
       "es      10\n",
       "it       8\n",
       "de       6\n",
       "pt       4\n",
       "zh       4\n",
       "ja       4\n",
       "hi       2\n",
       "da       2\n",
       "he       1\n",
       "tr       1\n",
       "ar       1\n",
       "ru       1\n",
       "cn       1\n",
       "sv       1\n",
       "ab       1\n",
       "ko       1\n",
       "Name: original_language, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No lists in kaggle data, so...\n",
    "movies_df['original_language'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Production Companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review samples\n",
    "movies_df[['Production company(s)','production_companies']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping un-needed columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df.drop(columns=['title_wiki','release_date_wiki','Language','Production company(s)'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 88,
   "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>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Country</th>\n",
       "      <th>Director</th>\n",
       "      <th>Distributor</th>\n",
       "      <th>Editor(s)</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>United States</td>\n",
       "      <td>Renny Harlin</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>Michael Tronick</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>United States</td>\n",
       "      <td>James Foley</td>\n",
       "      <td>Avenue Pictures</td>\n",
       "      <td>Howard E. Smith</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>United States</td>\n",
       "      <td>Roger Spottiswoode</td>\n",
       "      <td>TriStar Pictures</td>\n",
       "      <td>[John Bloom, Lois Freeman-Fox]</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>United States</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>Orion Pictures</td>\n",
       "      <td>Susan E. Morse</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>US</td>\n",
       "      <td>John Cornell</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>David Stiven</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>[Chile, Germany, Spain, United States, [2]]</td>\n",
       "      <td>Sebastián Lelio</td>\n",
       "      <td>[Participant Media (Chile), Piffl Medien (Germ...</td>\n",
       "      <td>Soledad Salfate</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>United States</td>\n",
       "      <td>Brian Crano</td>\n",
       "      <td>Good Deed Entertainment</td>\n",
       "      <td>Matt Friedman</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>[Russia, France, Belgium, Germany, [3]]</td>\n",
       "      <td>Andrey Zvyagintsev</td>\n",
       "      <td>[Sony Pictures Releasing, (Russia), [1]]</td>\n",
       "      <td>Anna Mass</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>United States</td>\n",
       "      <td>Aaron Katz</td>\n",
       "      <td>Neon</td>\n",
       "      <td>Aaron Katz</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>[United Kingdom, United States]</td>\n",
       "      <td>John Cameron Mitchell</td>\n",
       "      <td>[A24, StudioCanal UK]</td>\n",
       "      <td>Brian A. Kates</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 × 36 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",
       "                                          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",
       "                           Editor(s)  ... release_date_kaggle  \\\n",
       "0                    Michael Tronick  ...          1990-07-11   \n",
       "1                    Howard E. Smith  ...          1990-08-24   \n",
       "2     [John Bloom, Lois Freeman-Fox]  ...          1990-08-10   \n",
       "3                     Susan E. Morse  ...          1990-12-25   \n",
       "4                       David Stiven  ...          1990-12-21   \n",
       "...                              ...  ...                 ...   \n",
       "6047                 Soledad Salfate  ...          2017-04-06   \n",
       "6048                   Matt Friedman  ...          2017-04-22   \n",
       "6049                       Anna Mass  ...          2017-06-01   \n",
       "6050                      Aaron Katz  ...          2017-03-12   \n",
       "6051                  Brian A. Kates  ...          2017-12-27   \n",
       "\n",
       "                  revenue              runtime  \\\n",
       "0           20,423,389.00               104.00   \n",
       "1            2,700,000.00               114.00   \n",
       "2           33,461,269.00               112.00   \n",
       "3            7,331,647.00               102.00   \n",
       "4            6,939,946.00                95.00   \n",
       "...                   ...                  ...   \n",
       "6047         3,700,000.00               104.00   \n",
       "6048                  nan                96.00   \n",
       "6049         4,800,000.00               128.00   \n",
       "6050           200,340.00                92.00   \n",
       "6051           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 36 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "video\n"
     ]
    }
   ],
   "source": [
    "# Convert lists to tuples so we can run value_count\n",
    "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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    6051\n",
       "Name: video, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['video'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code to reorder columns\n",
    "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": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the columns\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": [
    "## Transform and Merge rating data\n",
    "### (from loaded CSV early in program)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_counts = ratings.groupby(['movieId','rating'], as_index=False).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename userID column to count\n",
    "rating_counts = ratings.groupby(['movieId','rating'], as_index=False).count().rename({'userId':'count'},axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pivot the data so 'movieId' is index, columns will be rating values, rows will be counts for each rating\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": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepend 'rating_' to each column with list comprehension\n",
    "rating_counts.columns = ['rating_' + str(col) for col in rating_counts.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge ratings with 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": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill in missing values\n",
    "movies_with_ratings_df[rating_counts.columns] = movies_with_ratings_df[rating_counts.columns].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect data to SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connection string\n",
    "db_string = f\"postgres://postgres:{db_password}@127.0.0.1:5432/movie_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(db_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import movie data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df.to_sql(name='movies', con=engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import ratings data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing rows 0 to 1000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 1000000 to 2000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 2000000 to 3000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 3000000 to 4000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 4000000 to 5000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 5000000 to 6000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 6000000 to 7000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 7000000 to 8000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 8000000 to 9000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 9000000 to 10000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 10000000 to 11000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 11000000 to 12000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 12000000 to 13000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 13000000 to 14000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 14000000 to 15000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 15000000 to 16000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 16000000 to 17000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 17000000 to 18000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 18000000 to 19000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 19000000 to 20000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 20000000 to 21000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 21000000 to 22000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 22000000 to 23000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 23000000 to 24000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 24000000 to 25000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 25000000 to 26000000...Done. {time.time()-start_time} total seconds elapsed\n",
      "importing rows 26000000 to 26024289...Done. {time.time()-start_time} total seconds elapsed\n"
     ]
    }
   ],
   "source": [
    "# reimporting ratings.csv in chunks because of size/memory concerns\n",
    "\n",
    "\n",
    "# create a variable for the number of rows imported\n",
    "rows_imported = 0\n",
    "# get the start_time from time.time()\n",
    "start_time = time.time()\n",
    "\n",
    "for data in pd.read_csv(f'{file_dir}/data/ratings.csv', chunksize=1000000):\n",
    "\n",
    "    # print out 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='replace')\n",
    "    \n",
    "    # increment the number of rows imported by the chunksize\n",
    "    rows_imported += len(data)\n",
    "    \n",
    "    # print that the rows have finished importing, with elapsed time\n",
    "    print(f'Done. {time.time() - start_time} total seconds elapsed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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