ETL-Working.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f2a5a9a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load dependencies\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Import the library for python module for regular expressions\n",
    "import re\n",
    "from sqlalchemy import create_engine\n",
    "from config import db_password\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53db96d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Created a variable for the directory that holds the data (If files are moved only this path needs to be updated)\n",
    "file_dir = 'C:/Users/cassi/Documents/Data Science Bootcamp/Class Folder/ETL/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cab66da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Format to open a file in the directory but the file we need is messy so I cannot use this method currently\n",
    "#f'{file_dir}filename'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61bfe8b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the raw JSON into a list of dictionaries using load()\n",
    "#When opening files in Python use 'with' statement to handle the file resource\n",
    "#'json.load() saves the data to a new variable'\n",
    "with open(f'{file_dir}/wikipedia-movies.json', mode='r') as file:\n",
    "    wiki_movies_raw = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8447b372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7311"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#'len()' checks how many records we pulled in.\n",
    "len(wiki_movies_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ddeff4a5",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at individual records to check how data came through\n",
    "#This is an list not a DataFrame yet so we use index slices to select specific chunkcs.\n",
    "#First five records\n",
    "wiki_movies_raw[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6f4751a",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Last five records\n",
    "wiki_movies_raw[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d5e80d5",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some records in the middle\n",
    "wiki_movies_raw[3600:3605]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "930916d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#They are flat-files so we can pull them into Pandas DataFrame directly and give them a variable name.\n",
    "kaggle_metadata = pd.read_csv(f'{file_dir}movies_metadata.csv', low_memory=False)\n",
    "ratings = pd.read_csv(f'{file_dir}ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1a65c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title</th>\n",
       "      <th>Directed by</th>\n",
       "      <th>Produced by</th>\n",
       "      <th>Screenplay by</th>\n",
       "      <th>Story by</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>...</th>\n",
       "      <th>Predecessor</th>\n",
       "      <th>Founders</th>\n",
       "      <th>Area served</th>\n",
       "      <th>Products</th>\n",
       "      <th>Services</th>\n",
       "      <th>Russian</th>\n",
       "      <th>Hebrew</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Operating income</th>\n",
       "      <th>Polish</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a Data Frame and printing it\n",
    "wiki_movies_df = pd.DataFrame(wiki_movies_raw)\n",
    "wiki_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fe756a1c",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Because there are so many columns we need to convert them to a list to see all the columns\n",
    "wiki_movies_df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97e35a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7076"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering data and finding if there is director or directed by keys in the current dict. Then also checking that if there is it has an IMBD link.\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": 13,
   "id": "8762971e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Call the function clean_movie and have it take movie as a parameter.\n",
    "#Create a new local variable called movie and assign it a new copy of the parameter movie.\n",
    "#Cleaning and sorting data\n",
    "def clean_movie(movie):\n",
    "    movie = dict(movie) #create a non-destructive copy\n",
    "    alt_titles = {}\n",
    "    for key in ['Also known as','Arabic','Cantonese','Chinese','French',\n",
    "                'Hangul','Hebrew','Hepburn','Japanese','Literally',\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",
    "    return movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b295a35a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a list of cleaned movies with a list comprehension\n",
    "clean_movies = [clean_movie(movie) for movie in wiki_movies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e48ffd5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Adaptation by',\n",
       " 'Animation by',\n",
       " 'Audio format',\n",
       " 'Based on',\n",
       " 'Box office',\n",
       " 'Budget',\n",
       " 'Cinematography',\n",
       " 'Color process',\n",
       " 'Composer(s)',\n",
       " 'Country',\n",
       " 'Country of origin',\n",
       " 'Created by',\n",
       " 'Directed by',\n",
       " 'Director',\n",
       " 'Distributed by',\n",
       " 'Distributor',\n",
       " 'Edited by',\n",
       " 'Editor(s)',\n",
       " 'Executive producer(s)',\n",
       " 'Followed by',\n",
       " 'Genre',\n",
       " 'Label',\n",
       " 'Language',\n",
       " 'Length',\n",
       " 'Music by',\n",
       " 'Narrated by',\n",
       " 'Original language(s)',\n",
       " 'Original network',\n",
       " 'Original release',\n",
       " 'Picture format',\n",
       " 'Preceded by',\n",
       " 'Produced by',\n",
       " 'Producer',\n",
       " 'Producer(s)',\n",
       " 'Production company(s)',\n",
       " 'Production location(s)',\n",
       " 'Productioncompanies ',\n",
       " 'Productioncompany ',\n",
       " 'Recorded',\n",
       " 'Release date',\n",
       " 'Released',\n",
       " 'Running time',\n",
       " 'Screen story by',\n",
       " 'Screenplay by',\n",
       " 'Starring',\n",
       " 'Story by',\n",
       " 'Suggested by',\n",
       " 'Theme music composer',\n",
       " 'Venue',\n",
       " 'Voices of',\n",
       " 'Written by',\n",
       " 'alt_titles',\n",
       " 'imdb_link',\n",
       " 'title',\n",
       " 'url',\n",
       " 'year']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting the wiki_movies_df to be the DataFrame created from clean_movies\n",
    "# Print out list of columns\n",
    "wiki_movies_df = pd.DataFrame(clean_movies)\n",
    "sorted(wiki_movies_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d150502a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cleaning recap with column rename\n",
    "def clean_movie(movie):\n",
    "    movie = dict(movie) #create a non-destructive copy\n",
    "    alt_titles = {}\n",
    "    # combine alternate titles into one list\n",
    "    for key in ['Also known as','Arabic','Cantonese','Chinese','French',\n",
    "                'Hangul','Hebrew','Hepburn','Japanese','Literally',\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",
    "    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": 17,
   "id": "a1ca941a",
   "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",
       " 'McCune–Reischauer',\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rerun list comprehension to clean wiki_movies and recreate the data frame\n",
    "clean_movies = [clean_movie(movie) for movie in wiki_movies]\n",
    "wiki_movies_df = pd.DataFrame(clean_movies)\n",
    "sorted(wiki_movies_df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97001725",
   "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>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>McCune–Reischauer</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 × 41 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  ... Preceded by Suggested by alt_titles Recorded Venue Label  \\\n",
       "0  102 minutes  ...         NaN          NaN        NaN      NaN   NaN   NaN   \n",
       "1  114 minutes  ...         NaN          NaN        NaN      NaN   NaN   NaN   \n",
       "2  113 minutes  ...         NaN          NaN        NaN      NaN   NaN   NaN   \n",
       "3  106 minutes  ...         NaN          NaN        NaN      NaN   NaN   NaN   \n",
       "4   95 minutes  ...         NaN          NaN        NaN      NaN   NaN   NaN   \n",
       "\n",
       "  Animation by Color process McCune–Reischauer    imdb_id  \n",
       "0          NaN           NaN               NaN  tt0098987  \n",
       "1          NaN           NaN               NaN  tt0098994  \n",
       "2          NaN           NaN               NaN  tt0099005  \n",
       "3          NaN           NaN               NaN  tt0099012  \n",
       "4          NaN           NaN               NaN  tt0099018  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code to extract the IMDb ID \n",
    "wiki_movies_df['imdb_id'] = wiki_movies_df['imdb_link'].str.extract(r'(tt\\d{7})')\n",
    "# Drop duplicates\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": 19,
   "id": "cb15f988",
   "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",
       " ['McCune–Reischauer', 7031],\n",
       " ['imdb_id', 0]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the null values for each column using a list comprehension\n",
    "[[column,wiki_movies_df[column].isnull().sum()] for column in wiki_movies_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a076be6",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tweak comprehension list to have 90% less null values so we can trim down dataset\n",
    "[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": 21,
   "id": "e94b9a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we have the columns we want to keep and can select them from the Pandas DataFrame\n",
    "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": 22,
   "id": "946cd84a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Starting with the Box Office data making a data series that drops missing values\n",
    "box_office = wiki_movies_df['Box office'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "17dc63aa",
   "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                   [$529.3, million, [4]]\n",
       "Name: Box office, Length: 135, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding Box Office values that are not strings\n",
    "def is_not_a_string(x):\n",
    "    return type(x) != str\n",
    "box_office[box_office.map(lambda x: type(x) != str)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "042d783f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.<lambda>(x)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda x: type(x) != str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3b4b454f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Makes concatenates list items into one string \n",
    "box_office = box_office.apply(lambda x: ' '.join(x) if type(x) == list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ce7d04fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3903"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a variable for the regular expression string\n",
    "form_one = r'\\$\\s*\\d+\\.?\\d*\\s*[mb]illi?on'\n",
    "# Counting up how many box office values match.\n",
    "box_office.str.contains(form_one, flags=re.IGNORECASE, na=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fa167c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1559"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "form_two = r'\\$\\s*\\d{1,3}(?:[,\\.]\\d{3})+(?!\\s[mb]illion)'\n",
    "box_office.str.contains(form_two, flags=re.IGNORECASE, na=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9550a2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create boolean series and select the box office values that do not match either\n",
    "matches_form_one = box_office.str.contains(form_one, flags=re.IGNORECASE, na=False)\n",
    "matches_form_two = box_office.str.contains(form_two, flags=re.IGNORECASE, na=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97a616c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110              $4.35-4.37 million\n",
       "600                      $5000 (US)\n",
       "1070                     35,254,617\n",
       "1480                     £3 million\n",
       "1865                   ¥1.1 billion\n",
       "2032                            N/A\n",
       "2091                           $309\n",
       "2665    926,423 admissions (France)\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",
       "4261             $20.7–23.9 million\n",
       "4306                         $20-30\n",
       "4561        $45.2k (only in Turkey)\n",
       "5447                          £2.56\n",
       "5784                       413 733$\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",
       "6843                        8 crore\n",
       "Name: Box office, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting the values that do not match\n",
    "box_office[~matches_form_one & ~matches_form_two]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0ba2f930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$21.4 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$2.7 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$57,718,089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$7,331,647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$6,939,946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7070</th>\n",
       "      <td>$19.4 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7071</th>\n",
       "      <td>$41.9 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7072</th>\n",
       "      <td>$76.1 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7073</th>\n",
       "      <td>$38.4 million</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7074</th>\n",
       "      <td>$5.5 million</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5485 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "0     $21.4 million\n",
       "1      $2.7 million\n",
       "2       $57,718,089\n",
       "3        $7,331,647\n",
       "4        $6,939,946\n",
       "...             ...\n",
       "7070  $19.4 million\n",
       "7071  $41.9 million\n",
       "7072  $76.1 million\n",
       "7073  $38.4 million\n",
       "7074   $5.5 million\n",
       "\n",
       "[5485 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract only the parts of the strings that match.\n",
    "box_office.str.extract(f'({form_one}|{form_two})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5ae5570",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to turn the extracted values into a numeric value.\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",
    "\n",
    "        # convert to float and multiply by a million\n",
    "        value = float(s) * 10**6\n",
    "\n",
    "        # return value\n",
    "        return value\n",
    "\n",
    "    # if input is of the form $###.# billion\n",
    "    elif re.match(r'\\$\\s*\\d+\\.?\\d*\\s*billi?on', s, flags=re.IGNORECASE):\n",
    "\n",
    "        # remove dollar sign and \" billion\"\n",
    "        s = re.sub('\\$|\\s|[a-zA-Z]','', s)\n",
    "\n",
    "        # convert to float and multiply by a billion\n",
    "        value = float(s) * 10**9\n",
    "\n",
    "        # return value\n",
    "        return value\n",
    "\n",
    "    # if input is of the form $###,###,###\n",
    "    elif re.match(r'\\$\\s*\\d{1,3}(?:[,\\.]\\d{3})+(?!\\s[mb]illion)', s, flags=re.IGNORECASE):\n",
    "\n",
    "        # remove dollar sign and commas\n",
    "        s = re.sub('\\$|,','', s)\n",
    "\n",
    "        # convert to float\n",
    "        value = float(s)\n",
    "\n",
    "        # return value\n",
    "        return value\n",
    "\n",
    "    # otherwise, return NaN\n",
    "    else:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c27b4079",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract values \n",
    "wiki_movies_df['box_office'] = box_office.str.extract(f'({form_one}|{form_two})', flags=re.IGNORECASE)[0].apply(parse_dollars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "61d838a0",
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki_movies_df['box_office']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c8d96206",
   "metadata": {},
   "outputs": [],
   "source": [
    "# No longer need Box office column, drop it\n",
    "wiki_movies_df.drop('Box office', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e019a3c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a budget variable \n",
    "budget = wiki_movies_df['Budget'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "42fb856c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert any lists to strings\n",
    "budget = budget.map(lambda x: ' '.join(x) if type(x) == list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2d985c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove any values between a dollar sign and hyphen\n",
    "budget = budget.str.replace(r'\\$.*[-—–](?![a-z])', '$', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5ea1ee15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136                         Unknown\n",
       "204     60 million Norwegian Kroner\n",
       "478                         Unknown\n",
       "973             $34 [3] [4] million\n",
       "1126               $120 [4] million\n",
       "1226                        Unknown\n",
       "1278                            HBO\n",
       "1374                     £6,000,000\n",
       "1397                     13 million\n",
       "1480                   £2.8 million\n",
       "1734                   CAD2,000,000\n",
       "1913     PHP 85 million (estimated)\n",
       "1948                    102,888,900\n",
       "1953                   3,500,000 DM\n",
       "1973                     ₤2,300,874\n",
       "2281                     $14 milion\n",
       "2451                     ₤6,350,000\n",
       "3144                   € 40 million\n",
       "3360               $150 [6] million\n",
       "3418                        $218.32\n",
       "3802                   £4.2 million\n",
       "3906                            N/A\n",
       "3959                    760,000 USD\n",
       "4470                       19 crore\n",
       "4641                    £17 million\n",
       "5034              $$200 [4] million\n",
       "5055           $155 [2] [3] million\n",
       "5419                $40 [4] million\n",
       "5424                            N/A\n",
       "5447                     £4 million\n",
       "5671                    €14 million\n",
       "5687                   $ dead link]\n",
       "6385               £ 12 million [3]\n",
       "6593                     £3 million\n",
       "6821                  £12.9 million\n",
       "6843                      3.5 crore\n",
       "6895                        919,000\n",
       "7070                   €4.3 million\n",
       "Name: Budget, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parsing through the budget data\n",
    "matches_form_one = budget.str.contains(form_one, flags=re.IGNORECASE, na=False)\n",
    "matches_form_two = budget.str.contains(form_two, flags=re.IGNORECASE, na=False)\n",
    "budget[~matches_form_one & ~matches_form_two]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "605b1d53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cassi\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "136                         Unknown\n",
       "204     60 million Norwegian Kroner\n",
       "478                         Unknown\n",
       "973                     $34 million\n",
       "1126                   $120 million\n",
       "1226                        Unknown\n",
       "1278                            HBO\n",
       "1374                     £6,000,000\n",
       "1397                     13 million\n",
       "1480                   £2.8 million\n",
       "1734                   CAD2,000,000\n",
       "1913     PHP 85 million (estimated)\n",
       "1948                    102,888,900\n",
       "1953                   3,500,000 DM\n",
       "1973                     ₤2,300,874\n",
       "2281                     $14 milion\n",
       "2451                     ₤6,350,000\n",
       "3144                   € 40 million\n",
       "3360                   $150 million\n",
       "3418                        $218.32\n",
       "3802                   £4.2 million\n",
       "3906                            N/A\n",
       "3959                    760,000 USD\n",
       "4470                       19 crore\n",
       "4641                    £17 million\n",
       "5034                  $$200 million\n",
       "5055                   $155 million\n",
       "5419                    $40 million\n",
       "5424                            N/A\n",
       "5447                     £4 million\n",
       "5671                    €14 million\n",
       "5687                   $ dead link]\n",
       "6385                  £ 12 million \n",
       "6593                     £3 million\n",
       "6821                  £12.9 million\n",
       "6843                      3.5 crore\n",
       "6895                        919,000\n",
       "7070                   €4.3 million\n",
       "Name: Budget, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the citation references\n",
    "budget = budget.str.replace(r'\\[\\d+\\]\\s*', '')\n",
    "budget[~matches_form_one & ~matches_form_two]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f05cf474",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing the values \n",
    "wiki_movies_df['budget'] = budget.str.extract(f'({form_one}|{form_two})', flags=re.IGNORECASE)[0].apply(parse_dollars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ab698a04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the original budget column\n",
    "wiki_movies_df.drop('Budget', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f65f221b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a variable that holds the non-null values of Release data and converting list to stings\n",
    "release_date = wiki_movies_df['Release date'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cee4801d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing forms\n",
    "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.[0123]\\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,
   "id": "ad22f34a",
   "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>November 8, 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   November 8, 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": [
    "# Extract the dates\n",
    "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,
   "id": "d0f4dc47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using built in function to parse dates\n",
    "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,
   "id": "e42d4592",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a variable that holds the non-null values of Release date converting list to strings\n",
    "running_time = wiki_movies_df['Running time'].dropna().apply(lambda x: ' '.join(x) if type(x) == list else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8db1507c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6528"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seeing how many running times look the same\n",
    "running_time.str.contains(r'^\\d*\\s*minutes$', flags=re.IGNORECASE, na=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e9fd5a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9                                                 102 min\n",
       "26                                                 93 min\n",
       "28                                                32 min.\n",
       "34                                                101 min\n",
       "35                                                 97 min\n",
       "                              ...                        \n",
       "6500       114 minutes [1] 120 minutes (extended edition)\n",
       "6643                                             104 mins\n",
       "6709    90 minutes (theatrical) [1] 91 minutes (unrate...\n",
       "7057    108 minutes (Original cut) 98 minutes (UK cut)...\n",
       "7075                Variable; 90 minutes for default path\n",
       "Name: Running time, Length: 366, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting a sense of what the other 366 enteries look like\n",
    "running_time[running_time.str.contains(r'^\\d*\\s*minutes$', flags=re.IGNORECASE, na=False) != True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "002b5acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6877"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Marking the beginning of the string and accepting other abbreviations \n",
    "running_time.str.contains(r'^\\d*\\s*m', flags=re.IGNORECASE, na=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c7eb18bf",
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_time[running_time.str.contains(r'^\\d*\\s*m', flags=re.IGNORECASE, na=False) != True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e8ddf34c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract values \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": 52,
   "id": "efdb81a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to numeric values from strings\n",
    "running_time_extract = running_time_extract.apply(lambda col: pd.to_numeric(col, errors='coerce')).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6137bdae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert the hour and minute capture groups and save output\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": 54,
   "id": "59b77fae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>...</th>\n",
       "      <th>Editor(s)</th>\n",
       "      <th>Composer(s)</th>\n",
       "      <th>Producer(s)</th>\n",
       "      <th>Production company(s)</th>\n",
       "      <th>Writer(s)</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>box_office</th>\n",
       "      <th>budget</th>\n",
       "      <th>release_date</th>\n",
       "      <th>running_time</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>Oliver Wood</td>\n",
       "      <td>[July 11, 1990, (, 1990-07-11, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>Michael Tronick</td>\n",
       "      <td>[Cliff Eidelman, Yello]</td>\n",
       "      <td>[Steve Perry, Joel Silver]</td>\n",
       "      <td>Silver Pictures</td>\n",
       "      <td>[David Arnott, James Cappe]</td>\n",
       "      <td>tt0098987</td>\n",
       "      <td>21400000.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>1990-07-11</td>\n",
       "      <td>102.0</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>Mark Plummer</td>\n",
       "      <td>[May 17, 1990, (, 1990-05-17, ), (Cannes Film ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>Howard E. Smith</td>\n",
       "      <td>Maurice Jarre</td>\n",
       "      <td>[Ric Kidney, Robert Redlin]</td>\n",
       "      <td>Avenue Pictures</td>\n",
       "      <td>[James Foley, Robert Redlin]</td>\n",
       "      <td>tt0098994</td>\n",
       "      <td>2700000.0</td>\n",
       "      <td>6000000.0</td>\n",
       "      <td>1990-05-17</td>\n",
       "      <td>114.0</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>Roger Deakins</td>\n",
       "      <td>[August 10, 1990, (, 1990-08-10, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>[English, Lao]</td>\n",
       "      <td>...</td>\n",
       "      <td>[John Bloom, Lois Freeman-Fox]</td>\n",
       "      <td>Charles Gross</td>\n",
       "      <td>Daniel Melnick</td>\n",
       "      <td>[Carolco Pictures, IndieProd Company]</td>\n",
       "      <td>[John Eskow, Richard Rush]</td>\n",
       "      <td>tt0099005</td>\n",
       "      <td>57718089.0</td>\n",
       "      <td>35000000.0</td>\n",
       "      <td>1990-08-10</td>\n",
       "      <td>113.0</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>Carlo Di Palma</td>\n",
       "      <td>[December 25, 1990, (, 1990-12-25, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>Susan E. Morse</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Robert Greenhut</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>tt0099012</td>\n",
       "      <td>7331647.0</td>\n",
       "      <td>12000000.0</td>\n",
       "      <td>1990-12-25</td>\n",
       "      <td>106.0</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>Russell Boyd</td>\n",
       "      <td>December 19, 1990</td>\n",
       "      <td>US</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>David Stiven</td>\n",
       "      <td>Maurice Jarre</td>\n",
       "      <td>John Cornell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paul Hogan</td>\n",
       "      <td>tt0099018</td>\n",
       "      <td>6939946.0</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>1990-12-19</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 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  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",
       "                                        Release date        Country  \\\n",
       "0                  [July 11, 1990, (, 1990-07-11, )]  United States   \n",
       "1  [May 17, 1990, (, 1990-05-17, ), (Cannes Film ...  United States   \n",
       "2                [August 10, 1990, (, 1990-08-10, )]  United States   \n",
       "3              [December 25, 1990, (, 1990-12-25, )]  United States   \n",
       "4                                  December 19, 1990             US   \n",
       "\n",
       "         Language  ...                       Editor(s)  \\\n",
       "0         English  ...                 Michael Tronick   \n",
       "1         English  ...                 Howard E. Smith   \n",
       "2  [English, Lao]  ...  [John Bloom, Lois Freeman-Fox]   \n",
       "3         English  ...                  Susan E. Morse   \n",
       "4         English  ...                    David Stiven   \n",
       "\n",
       "               Composer(s)                  Producer(s)  \\\n",
       "0  [Cliff Eidelman, Yello]   [Steve Perry, Joel Silver]   \n",
       "1            Maurice Jarre  [Ric Kidney, Robert Redlin]   \n",
       "2            Charles Gross               Daniel Melnick   \n",
       "3                      NaN              Robert Greenhut   \n",
       "4            Maurice Jarre                 John Cornell   \n",
       "\n",
       "                   Production company(s)                     Writer(s)  \\\n",
       "0                        Silver Pictures   [David Arnott, James Cappe]   \n",
       "1                        Avenue Pictures  [James Foley, Robert Redlin]   \n",
       "2  [Carolco Pictures, IndieProd Company]    [John Eskow, Richard Rush]   \n",
       "3                                    NaN                   Woody Allen   \n",
       "4                                    NaN                    Paul Hogan   \n",
       "\n",
       "     imdb_id  box_office      budget  release_date  running_time  \n",
       "0  tt0098987  21400000.0  20000000.0    1990-07-11         102.0  \n",
       "1  tt0098994   2700000.0   6000000.0    1990-05-17         114.0  \n",
       "2  tt0099005  57718089.0  35000000.0    1990-08-10         113.0  \n",
       "3  tt0099012   7331647.0  12000000.0    1990-12-25         106.0  \n",
       "4  tt0099018   6939946.0  25000000.0    1990-12-19          95.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop running time from the dataset\n",
    "wiki_movies_df.drop('Running time', axis=1, inplace=True)\n",
    "wiki_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6f66e151",
   "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",
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       "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that all columns came in as the correct data types.\n",
    "kaggle_metadata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6cc2d43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False                                                                                                                             45454\n",
       "True                                                                                                                                  9\n",
       " - Written by Ørnås                                                                                                                   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",
       " 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",
       "Name: adult, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that values are T/F\n",
    "kaggle_metadata['adult'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6a085b01",
   "metadata": {},
   "outputs": [
    {
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       "      <th>tagline</th>\n",
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       "      <th>19730</th>\n",
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       "      <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",
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      "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove bad data\n",
    "kaggle_metadata[~kaggle_metadata['adult'].isin(['True','False'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "03a37bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep rows where the adult column is False, and drop the adult column\n",
    "kaggle_metadata = kaggle_metadata[kaggle_metadata['adult'] == 'False'].drop('adult',axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b3ac9c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    45358\n",
       "True        93\n",
       "Name: video, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the values in video column\n",
    "kaggle_metadata['video'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c7d3d7da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data types\n",
    "kaggle_metadata['video'] == 'True'\n",
    "kaggle_metadata['video'] = kaggle_metadata['video'] == 'True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3662e9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean numeric columns\n",
    "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": 62,
   "id": "6e4e7655",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to standard format\n",
    "kaggle_metadata['release_date'] = pd.to_datetime(kaggle_metadata['release_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dc34b9f6",
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cassi\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: null_counts is deprecated. Use show_counts instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Looking at ratatings data\n",
    "ratings.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7567e493",
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the origin is unix and time unit is seconds\n",
    "pd.to_datetime(ratings['timestamp'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "448e93b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign it to the timestamp column\n",
    "ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e463640a",
   "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": 66,
     "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 stats of actual ratings and see if there are any errors using a histogram\n",
    "pd.options.display.float_format = '{:20,.2f}'.format\n",
    "ratings['rating'].plot(kind='hist')\n",
    "ratings['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "31e00ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print out list of columns to identify which ones are redundant\n",
    "movies_df = pd.merge(wiki_movies_df, kaggle_metadata, on='imdb_id', suffixes=['_wiki','_kaggle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b139e398",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Competing data:\n",
    "# Wiki                     Movielens                Resolution\n",
    "#--------------------------------------------------------------------------\n",
    "# title_wiki               title_kaggle\n",
    "# running_time             runtime\n",
    "# budget_wiki              budget_kaggle\n",
    "# box_office               revenue\n",
    "# release_date_wiki        release_date_kaggle\n",
    "# Language                 original_language\n",
    "# Production company(s)    production_companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "02310ac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at titles\n",
    "movies_df[['title_wiki','title_kaggle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "99a6b69d",
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at rows where the titles don't match\n",
    "movies_df[movies_df['title_wiki'] != movies_df['title_kaggle']][['title_wiki','title_kaggle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e9575a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title_wiki</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date_kaggle</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title_kaggle</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [url, year, imdb_link, title_wiki, Based on, Starring, Cinematography, Release date, Country, Language, Director, Distributor, Editor(s), Composer(s), Producer(s), Production company(s), Writer(s), imdb_id, box_office, budget_wiki, release_date_wiki, running_time, 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 44 columns]"
      ]
     },
     "execution_count": 71,
     "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": "code",
   "execution_count": 72,
   "id": "4859f98f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='running_time', ylabel='runtime'>"
      ]
     },
     "execution_count": 72,
     "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 zeros and make scatter plot\n",
    "movies_df.fillna(0).plot(x='running_time', y='runtime', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3a088fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='budget_wiki', ylabel='budget_kaggle'>"
      ]
     },
     "execution_count": 73,
     "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": [
    "# Scatterplot to compare values\n",
    "movies_df.fillna(0).plot(x='budget_wiki',y='budget_kaggle', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "febff584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='box_office', ylabel='revenue'>"
      ]
     },
     "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": [
    "movies_df.fillna(0).plot(x='box_office', y='revenue', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8c7d8ce7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='box_office', ylabel='revenue'>"
      ]
     },
     "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": [
    "# Scatterplot for les than $1 bill in box office\n",
    "movies_df.fillna(0)[movies_df['box_office'] < 10**9].plot(x='box_office', y='revenue', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b0eec8c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='release_date_wiki'>"
      ]
     },
     "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": [
    "# Regular line plot instead because scatterplot only works for numeric\n",
    "movies_df[['release_date_wiki','release_date_kaggle']].plot(x='release_date_wiki', y='release_date_kaggle', style='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "15568471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title_wiki</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date_kaggle</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title_kaggle</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Holiday</td>\n",
       "      <td>2006</td>\n",
       "      <td>https://www.imdb.com/title/tt00457939/</td>\n",
       "      <td>The Holiday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Kate Winslet, Cameron Diaz, Jude Law, Jack Bl...</td>\n",
       "      <td>Dean Cundey</td>\n",
       "      <td>[December 8, 2006, (, 2006-12-08, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>1953-08-28</td>\n",
       "      <td>30,500,000.00</td>\n",
       "      <td>118.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Pouring out of impassioned pages...brawling th...</td>\n",
       "      <td>From Here to Eternity</td>\n",
       "      <td>False</td>\n",
       "      <td>7.20</td>\n",
       "      <td>137.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            url  year  \\\n",
       "3607  https://en.wikipedia.org/wiki/The_Holiday  2006   \n",
       "\n",
       "                                   imdb_link   title_wiki Based on  \\\n",
       "3607  https://www.imdb.com/title/tt00457939/  The Holiday      NaN   \n",
       "\n",
       "                                               Starring Cinematography  \\\n",
       "3607  [Kate Winslet, Cameron Diaz, Jude Law, Jack Bl...    Dean Cundey   \n",
       "\n",
       "                              Release date        Country Language  ...  \\\n",
       "3607  [December 8, 2006, (, 2006-12-08, )]  United States  English  ...   \n",
       "\n",
       "     release_date_kaggle              revenue              runtime  \\\n",
       "3607          1953-08-28        30,500,000.00               118.00   \n",
       "\n",
       "                              spoken_languages    status  \\\n",
       "3607  [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "\n",
       "                                                tagline  \\\n",
       "3607  Pouring out of impassioned pages...brawling th...   \n",
       "\n",
       "               title_kaggle  video         vote_average           vote_count  \n",
       "3607  From Here to Eternity  False                 7.20               137.00  \n",
       "\n",
       "[1 rows x 44 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Investigating outliers\n",
    "movies_df[(movies_df['release_date_wiki'] > '1996-01-01') & (movies_df['release_date_kaggle'] < '1965-01-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5aa27c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3607], dtype='int64')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the index of 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": 79,
   "id": "e23e246a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 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": 80,
   "id": "bcd46264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>title_wiki</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date_kaggle</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title_kaggle</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Black_Scorpion_(...</td>\n",
       "      <td>1995</td>\n",
       "      <td>https://www.imdb.com/title/tt0112519/</td>\n",
       "      <td>Black Scorpion</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Joan Severance, Bruce Abbott, Garrett Morris]</td>\n",
       "      <td>Geoff George</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-08-22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>92.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Justice has a nasty sting.</td>\n",
       "      <td>Black Scorpion</td>\n",
       "      <td>False</td>\n",
       "      <td>6.10</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Flirt_(1995_film)</td>\n",
       "      <td>1995</td>\n",
       "      <td>https://www.imdb.com/title/tt0113080/</td>\n",
       "      <td>Flirt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Bill Sage, Dwight Ewell, Miho Nikaido]</td>\n",
       "      <td>Michael Spiller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[United States, Germany, Japan, [1]]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-09-14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>85.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Flirt</td>\n",
       "      <td>False</td>\n",
       "      <td>7.50</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Let_It_Be_Me_(19...</td>\n",
       "      <td>1995</td>\n",
       "      <td>https://www.imdb.com/title/tt0113638/</td>\n",
       "      <td>Let It Be Me</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miroslav Ondrícek</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-11-17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>[]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Let It Be Me</td>\n",
       "      <td>False</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>https://en.wikipedia.org/wiki/A_Brooklyn_State...</td>\n",
       "      <td>1997</td>\n",
       "      <td>https://www.imdb.com/title/tt0118782/</td>\n",
       "      <td>A Brooklyn State of Mind</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ken Kelsch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A Brooklyn State of Mind</td>\n",
       "      <td>False</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Highball_(film)</td>\n",
       "      <td>1997</td>\n",
       "      <td>https://www.imdb.com/title/tt0119291/</td>\n",
       "      <td>Highball</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Justine Bateman, Peter Bogdanovich, Chris Eig...</td>\n",
       "      <td>Steven Bernstein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Highball</td>\n",
       "      <td>False</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Velocity_Trap</td>\n",
       "      <td>1997</td>\n",
       "      <td>https://www.imdb.com/title/tt0120435/</td>\n",
       "      <td>Velocity Trap</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Olivier Gruner, Alicia Coppola, Ken Olandt]</td>\n",
       "      <td>Philip D. Schwartz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1999-02-22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>86.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Crime at the Speed of Light</td>\n",
       "      <td>Velocity Trap</td>\n",
       "      <td>False</td>\n",
       "      <td>4.70</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2386</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Visit_(2000_...</td>\n",
       "      <td>2000</td>\n",
       "      <td>https://www.imdb.com/title/tt0199129/</td>\n",
       "      <td>The Visit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Hill Harper, Billy Dee Williams, Obba Babatun...</td>\n",
       "      <td>John L. Demps Jr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Rumored</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Visit</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2786</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Stevie_(2002_film)</td>\n",
       "      <td>2002</td>\n",
       "      <td>https://www.imdb.com/title/tt0334416/</td>\n",
       "      <td>Stevie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Dana Kupper, Gordon Quinn, Peter Gilbert]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>2002-09-09</td>\n",
       "      <td>97,000.00</td>\n",
       "      <td>140.00</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stevie</td>\n",
       "      <td>False</td>\n",
       "      <td>6.70</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3174</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Return_to_Sender...</td>\n",
       "      <td>2004</td>\n",
       "      <td>https://www.imdb.com/title/tt0396190/</td>\n",
       "      <td>Return to Sender</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Aidan Quinn, Connie Nielsen, Mark Holton]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Denmark, USA, UK]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2004-09-17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>109.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>How can you save someone, who won't save herself?</td>\n",
       "      <td>Return to Sender</td>\n",
       "      <td>False</td>\n",
       "      <td>5.90</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Live_Free_or_Die...</td>\n",
       "      <td>2006</td>\n",
       "      <td>https://www.imdb.com/title/tt0432318/</td>\n",
       "      <td>Live Free or Die</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Aaron Stanford, Paul Schneider, Ebon Moss-Bac...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>English</td>\n",
       "      <td>...</td>\n",
       "      <td>2006-03-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Live Free or Die</td>\n",
       "      <td>False</td>\n",
       "      <td>5.50</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4967</th>\n",
       "      <td>https://en.wikipedia.org/wiki/For_the_Love_of_...</td>\n",
       "      <td>2012</td>\n",
       "      <td>https://www.imdb.com/title/tt1730294/</td>\n",
       "      <td>For the Love of Money</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Yehuda Levi, Edward Furlong, James Caan, Jeff...</td>\n",
       "      <td>Andrzej Sekuła</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>English/Hebrew</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-06-08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>For the love of money is the root of all evil.</td>\n",
       "      <td>For the Love of Money</td>\n",
       "      <td>False</td>\n",
       "      <td>5.60</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  year  \\\n",
       "1008  https://en.wikipedia.org/wiki/Black_Scorpion_(...  1995   \n",
       "1061    https://en.wikipedia.org/wiki/Flirt_(1995_film)  1995   \n",
       "1121  https://en.wikipedia.org/wiki/Let_It_Be_Me_(19...  1995   \n",
       "1564  https://en.wikipedia.org/wiki/A_Brooklyn_State...  1997   \n",
       "1633      https://en.wikipedia.org/wiki/Highball_(film)  1997   \n",
       "1775        https://en.wikipedia.org/wiki/Velocity_Trap  1997   \n",
       "2386  https://en.wikipedia.org/wiki/The_Visit_(2000_...  2000   \n",
       "2786   https://en.wikipedia.org/wiki/Stevie_(2002_film)  2002   \n",
       "3174  https://en.wikipedia.org/wiki/Return_to_Sender...  2004   \n",
       "3651  https://en.wikipedia.org/wiki/Live_Free_or_Die...  2006   \n",
       "4967  https://en.wikipedia.org/wiki/For_the_Love_of_...  2012   \n",
       "\n",
       "                                  imdb_link                title_wiki  \\\n",
       "1008  https://www.imdb.com/title/tt0112519/            Black Scorpion   \n",
       "1061  https://www.imdb.com/title/tt0113080/                     Flirt   \n",
       "1121  https://www.imdb.com/title/tt0113638/              Let It Be Me   \n",
       "1564  https://www.imdb.com/title/tt0118782/  A Brooklyn State of Mind   \n",
       "1633  https://www.imdb.com/title/tt0119291/                  Highball   \n",
       "1775  https://www.imdb.com/title/tt0120435/             Velocity Trap   \n",
       "2386  https://www.imdb.com/title/tt0199129/                 The Visit   \n",
       "2786  https://www.imdb.com/title/tt0334416/                    Stevie   \n",
       "3174  https://www.imdb.com/title/tt0396190/          Return to Sender   \n",
       "3651  https://www.imdb.com/title/tt0432318/          Live Free or Die   \n",
       "4967  https://www.imdb.com/title/tt1730294/     For the Love of Money   \n",
       "\n",
       "     Based on                                           Starring  \\\n",
       "1008      NaN     [Joan Severance, Bruce Abbott, Garrett Morris]   \n",
       "1061      NaN            [Bill Sage, Dwight Ewell, Miho Nikaido]   \n",
       "1121      NaN                                                NaN   \n",
       "1564      NaN                                                NaN   \n",
       "1633      NaN  [Justine Bateman, Peter Bogdanovich, Chris Eig...   \n",
       "1775      NaN       [Olivier Gruner, Alicia Coppola, Ken Olandt]   \n",
       "2386      NaN  [Hill Harper, Billy Dee Williams, Obba Babatun...   \n",
       "2786      NaN                                                NaN   \n",
       "3174      NaN         [Aidan Quinn, Connie Nielsen, Mark Holton]   \n",
       "3651      NaN  [Aaron Stanford, Paul Schneider, Ebon Moss-Bac...   \n",
       "4967      NaN  [Yehuda Levi, Edward Furlong, James Caan, Jeff...   \n",
       "\n",
       "                                  Cinematography Release date  \\\n",
       "1008                                Geoff George          NaN   \n",
       "1061                             Michael Spiller          NaN   \n",
       "1121                           Miroslav Ondrícek          NaN   \n",
       "1564                                  Ken Kelsch          NaN   \n",
       "1633                            Steven Bernstein          NaN   \n",
       "1775                          Philip D. Schwartz          NaN   \n",
       "2386                           John L. Demps Jr.          NaN   \n",
       "2786  [Dana Kupper, Gordon Quinn, Peter Gilbert]          NaN   \n",
       "3174                                         NaN          NaN   \n",
       "3651                                         NaN          NaN   \n",
       "4967                              Andrzej Sekuła          NaN   \n",
       "\n",
       "                                   Country        Language  ...  \\\n",
       "1008                         United States             NaN  ...   \n",
       "1061  [United States, Germany, Japan, [1]]             NaN  ...   \n",
       "1121                                   NaN             NaN  ...   \n",
       "1564                                   NaN             NaN  ...   \n",
       "1633                         United States         English  ...   \n",
       "1775                         United States             NaN  ...   \n",
       "2386                                   NaN         English  ...   \n",
       "2786                         United States         English  ...   \n",
       "3174                    [Denmark, USA, UK]             NaN  ...   \n",
       "3651                         United States         English  ...   \n",
       "4967                         United States  English/Hebrew  ...   \n",
       "\n",
       "     release_date_kaggle              revenue              runtime  \\\n",
       "1008          1995-08-22                 0.00                92.00   \n",
       "1061          1995-09-14                 0.00                85.00   \n",
       "1121          1995-11-17                 0.00                95.00   \n",
       "1564          1998-01-01                 0.00                89.00   \n",
       "1633          1997-01-01                 0.00               110.00   \n",
       "1775          1999-02-22                 0.00                86.00   \n",
       "2386          2000-01-01                 0.00               107.00   \n",
       "2786          2002-09-09            97,000.00               140.00   \n",
       "3174          2004-09-17                 0.00               109.00   \n",
       "3651          2006-03-11                 0.00                89.00   \n",
       "4967          2012-06-08                 0.00                93.00   \n",
       "\n",
       "                                       spoken_languages    status  \\\n",
       "1008           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1061  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "1121                                                 []  Released   \n",
       "1564           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1633           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1775           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "2386  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   Rumored   \n",
       "2786                                                 []       NaN   \n",
       "3174           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "3651           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "4967  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "\n",
       "                                                tagline  \\\n",
       "1008                         Justice has a nasty sting.   \n",
       "1061                                                NaN   \n",
       "1121                                                NaN   \n",
       "1564                                                NaN   \n",
       "1633                                                NaN   \n",
       "1775                        Crime at the Speed of Light   \n",
       "2386                                                NaN   \n",
       "2786                                                NaN   \n",
       "3174  How can you save someone, who won't save herself?   \n",
       "3651                                                NaN   \n",
       "4967     For the love of money is the root of all evil.   \n",
       "\n",
       "                  title_kaggle  video         vote_average  \\\n",
       "1008            Black Scorpion  False                 6.10   \n",
       "1061                     Flirt  False                 7.50   \n",
       "1121              Let It Be Me  False                 8.00   \n",
       "1564  A Brooklyn State of Mind  False                 5.00   \n",
       "1633                  Highball  False                 4.50   \n",
       "1775             Velocity Trap  False                 4.70   \n",
       "2386                 The Visit  False                 0.00   \n",
       "2786                    Stevie  False                 6.70   \n",
       "3174          Return to Sender  False                 5.90   \n",
       "3651          Live Free or Die  False                 5.50   \n",
       "4967     For the Love of Money  False                 5.60   \n",
       "\n",
       "               vote_count  \n",
       "1008                 7.00  \n",
       "1061                 4.00  \n",
       "1121                 2.00  \n",
       "1564                 1.00  \n",
       "1633                 4.00  \n",
       "1775                 6.00  \n",
       "2386                 0.00  \n",
       "2786                13.00  \n",
       "3174                13.00  \n",
       "3651                 8.00  \n",
       "4967                 9.00  \n",
       "\n",
       "[11 rows x 44 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "movies_df[movies_df['release_date_wiki'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "55157e68",
   "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 /, Zulu)                 1\n",
       "(English, Hebrew, Spanish)        1\n",
       "(English, Japanese, French)       1\n",
       "(English, Spanish, Apache)        1\n",
       "Russian                           1\n",
       "Name: Language, Length: 198, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare value count\n",
    "# Convert lists in language 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": 82,
   "id": "e7fc4b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "en    5987\n",
       "fr      16\n",
       "es      10\n",
       "it       8\n",
       "de       6\n",
       "ja       4\n",
       "pt       4\n",
       "zh       4\n",
       "da       2\n",
       "hi       2\n",
       "sv       1\n",
       "ko       1\n",
       "ab       1\n",
       "tr       1\n",
       "he       1\n",
       "ar       1\n",
       "cn       1\n",
       "ru       1\n",
       "Name: original_language, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['original_language'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c63b73e8",
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove small number samples\n",
    "movies_df[['Production company(s)','production_companies']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1b9cd916",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns\n",
    "movies_df.drop(columns=['title_wiki','release_date_wiki','Language','Production company(s)'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "352cb451",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create function that fills in missin data for a column pair and then drop redundant columns\n",
    "def fill_missing_kaggle_data(df, kaggle_column, wiki_column):\n",
    "    df[kaggle_column] = df.apply(\n",
    "        lambda row: row[wiki_column] if row[kaggle_column] == 0 else row[kaggle_column]\n",
    "        , axis=1)\n",
    "    df.drop(columns=wiki_column, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c93b638b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>year</th>\n",
       "      <th>imdb_link</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Director</th>\n",
       "      <th>Distributor</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date_kaggle</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title_kaggle</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Adventures_o...</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0098987/</td>\n",
       "      <td>[Characters, by Rex Weiner]</td>\n",
       "      <td>[Andrew Dice Clay, Wayne Newton, Priscilla Pre...</td>\n",
       "      <td>Oliver Wood</td>\n",
       "      <td>[July 11, 1990, (, 1990-07-11, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>Renny Harlin</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-07-11</td>\n",
       "      <td>20,423,389.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Kojak. Columbo. Dirty Harry. Wimps.</td>\n",
       "      <td>The Adventures of Ford Fairlane</td>\n",
       "      <td>False</td>\n",
       "      <td>6.20</td>\n",
       "      <td>72.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://en.wikipedia.org/wiki/After_Dark,_My_S...</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0098994/</td>\n",
       "      <td>[the novel, After Dark, My Sweet, by, Jim Thom...</td>\n",
       "      <td>[Jason Patric, Rachel Ward, Bruce Dern, George...</td>\n",
       "      <td>Mark Plummer</td>\n",
       "      <td>[May 17, 1990, (, 1990-05-17, ), (Cannes Film ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>James Foley</td>\n",
       "      <td>Avenue Pictures</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-08-24</td>\n",
       "      <td>2,700,000.00</td>\n",
       "      <td>114.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>All they risked was everything.</td>\n",
       "      <td>After Dark, My Sweet</td>\n",
       "      <td>False</td>\n",
       "      <td>6.50</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Air_America_(film)</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099005/</td>\n",
       "      <td>[Air America, by, Christopher Robbins]</td>\n",
       "      <td>[Mel Gibson, Robert Downey Jr., Nancy Travis, ...</td>\n",
       "      <td>Roger Deakins</td>\n",
       "      <td>[August 10, 1990, (, 1990-08-10, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>Roger Spottiswoode</td>\n",
       "      <td>TriStar Pictures</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-08-10</td>\n",
       "      <td>33,461,269.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>The few. The proud. The totally insane.</td>\n",
       "      <td>Air America</td>\n",
       "      <td>False</td>\n",
       "      <td>5.30</td>\n",
       "      <td>146.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Alice_(1990_film)</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099012/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Alec Baldwin, Blythe Danner, Judy Davis, Mia ...</td>\n",
       "      <td>Carlo Di Palma</td>\n",
       "      <td>[December 25, 1990, (, 1990-12-25, )]</td>\n",
       "      <td>United States</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>Orion Pictures</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-12-25</td>\n",
       "      <td>7,331,647.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alice</td>\n",
       "      <td>False</td>\n",
       "      <td>6.30</td>\n",
       "      <td>57.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Almost_an_Angel</td>\n",
       "      <td>1990</td>\n",
       "      <td>https://www.imdb.com/title/tt0099018/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Paul Hogan, Elias Koteas, Linda Kozlowski]</td>\n",
       "      <td>Russell Boyd</td>\n",
       "      <td>December 19, 1990</td>\n",
       "      <td>US</td>\n",
       "      <td>John Cornell</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>...</td>\n",
       "      <td>1990-12-21</td>\n",
       "      <td>6,939,946.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Who does he think he is?</td>\n",
       "      <td>Almost an Angel</td>\n",
       "      <td>False</td>\n",
       "      <td>5.60</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6047</th>\n",
       "      <td>https://en.wikipedia.org/wiki/A_Fantastic_Woman</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt5639354/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Daniela Vega, Francisco Reyes]</td>\n",
       "      <td>Benjamín Echazarreta</td>\n",
       "      <td>[12 February 2017, (, 2017-02-12, ), (, Berlin...</td>\n",
       "      <td>[Chile, Germany, Spain, United States, [2]]</td>\n",
       "      <td>Sebastián Lelio</td>\n",
       "      <td>[Participant Media (Chile), Piffl Medien (Germ...</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>3,700,000.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>[{'iso_639_1': 'es', 'name': 'Español'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A Fantastic Woman</td>\n",
       "      <td>False</td>\n",
       "      <td>7.20</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6048</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Permission_(film)</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt5390066/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Rebecca Hall, Dan Stevens, Morgan Spector, Fr...</td>\n",
       "      <td>Adam Bricker</td>\n",
       "      <td>[April 22, 2017, (, 2017-04-22, ), (, Tribeca ...</td>\n",
       "      <td>United States</td>\n",
       "      <td>Brian Crano</td>\n",
       "      <td>Good Deed Entertainment</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Permission</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6049</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Loveless_(film)</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt6304162/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Maryana Spivak, Aleksey Rozin, Matvey Novikov...</td>\n",
       "      <td>Mikhail Krichman</td>\n",
       "      <td>[18 May 2017, (, 2017-05-18, ), (, Cannes, ), ...</td>\n",
       "      <td>[Russia, France, Belgium, Germany, [3]]</td>\n",
       "      <td>Andrey Zvyagintsev</td>\n",
       "      <td>[Sony Pictures Releasing, (Russia), [1]]</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>4,800,000.00</td>\n",
       "      <td>128.00</td>\n",
       "      <td>[{'iso_639_1': 'ru', 'name': 'Pусский'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Loveless</td>\n",
       "      <td>False</td>\n",
       "      <td>7.80</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6050</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Gemini_(2017_film)</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt5795086/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Lola Kirke, Zoë Kravitz, Greta Lee, Michelle ...</td>\n",
       "      <td>Andrew Reed</td>\n",
       "      <td>[March 12, 2017, (, 2017-03-12, ), (, SXSW, ),...</td>\n",
       "      <td>United States</td>\n",
       "      <td>Aaron Katz</td>\n",
       "      <td>Neon</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-03-12</td>\n",
       "      <td>200,340.00</td>\n",
       "      <td>92.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Post Production</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gemini</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6051</th>\n",
       "      <td>https://en.wikipedia.org/wiki/How_to_Talk_to_G...</td>\n",
       "      <td>2018</td>\n",
       "      <td>https://www.imdb.com/title/tt3859310/</td>\n",
       "      <td>[\", How to Talk to Girls at Parties, \", by, Ne...</td>\n",
       "      <td>[Elle Fanning, Alex Sharp, Nicole Kidman, Ruth...</td>\n",
       "      <td>Frank G. DeMarco</td>\n",
       "      <td>[May 21, 2017, (, 2017-05-21, ), (, Cannes, ),...</td>\n",
       "      <td>[United Kingdom, United States]</td>\n",
       "      <td>John Cameron Mitchell</td>\n",
       "      <td>[A24, StudioCanal UK]</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>382,053.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Some girls are out of this world.</td>\n",
       "      <td>How to Talk to Girls at Parties</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6051 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  year  \\\n",
       "0     https://en.wikipedia.org/wiki/The_Adventures_o...  1990   \n",
       "1     https://en.wikipedia.org/wiki/After_Dark,_My_S...  1990   \n",
       "2      https://en.wikipedia.org/wiki/Air_America_(film)  1990   \n",
       "3       https://en.wikipedia.org/wiki/Alice_(1990_film)  1990   \n",
       "4         https://en.wikipedia.org/wiki/Almost_an_Angel  1990   \n",
       "...                                                 ...   ...   \n",
       "6047    https://en.wikipedia.org/wiki/A_Fantastic_Woman  2018   \n",
       "6048    https://en.wikipedia.org/wiki/Permission_(film)  2018   \n",
       "6049      https://en.wikipedia.org/wiki/Loveless_(film)  2018   \n",
       "6050   https://en.wikipedia.org/wiki/Gemini_(2017_film)  2018   \n",
       "6051  https://en.wikipedia.org/wiki/How_to_Talk_to_G...  2018   \n",
       "\n",
       "                                  imdb_link  \\\n",
       "0     https://www.imdb.com/title/tt0098987/   \n",
       "1     https://www.imdb.com/title/tt0098994/   \n",
       "2     https://www.imdb.com/title/tt0099005/   \n",
       "3     https://www.imdb.com/title/tt0099012/   \n",
       "4     https://www.imdb.com/title/tt0099018/   \n",
       "...                                     ...   \n",
       "6047  https://www.imdb.com/title/tt5639354/   \n",
       "6048  https://www.imdb.com/title/tt5390066/   \n",
       "6049  https://www.imdb.com/title/tt6304162/   \n",
       "6050  https://www.imdb.com/title/tt5795086/   \n",
       "6051  https://www.imdb.com/title/tt3859310/   \n",
       "\n",
       "                                               Based on  \\\n",
       "0                           [Characters, by Rex Weiner]   \n",
       "1     [the novel, After Dark, My Sweet, by, Jim Thom...   \n",
       "2                [Air America, by, Christopher Robbins]   \n",
       "3                                                   NaN   \n",
       "4                                                   NaN   \n",
       "...                                                 ...   \n",
       "6047                                                NaN   \n",
       "6048                                                NaN   \n",
       "6049                                                NaN   \n",
       "6050                                                NaN   \n",
       "6051  [\", How to Talk to Girls at Parties, \", by, Ne...   \n",
       "\n",
       "                                               Starring        Cinematography  \\\n",
       "0     [Andrew Dice Clay, Wayne Newton, Priscilla Pre...           Oliver Wood   \n",
       "1     [Jason Patric, Rachel Ward, Bruce Dern, George...          Mark Plummer   \n",
       "2     [Mel Gibson, Robert Downey Jr., Nancy Travis, ...         Roger Deakins   \n",
       "3     [Alec Baldwin, Blythe Danner, Judy Davis, Mia ...        Carlo Di Palma   \n",
       "4           [Paul Hogan, Elias Koteas, Linda Kozlowski]          Russell Boyd   \n",
       "...                                                 ...                   ...   \n",
       "6047                    [Daniela Vega, Francisco Reyes]  Benjamín Echazarreta   \n",
       "6048  [Rebecca Hall, Dan Stevens, Morgan Spector, Fr...          Adam Bricker   \n",
       "6049  [Maryana Spivak, Aleksey Rozin, Matvey Novikov...      Mikhail Krichman   \n",
       "6050  [Lola Kirke, Zoë Kravitz, Greta Lee, Michelle ...           Andrew Reed   \n",
       "6051  [Elle Fanning, Alex Sharp, Nicole Kidman, Ruth...      Frank G. DeMarco   \n",
       "\n",
       "                                           Release date  \\\n",
       "0                     [July 11, 1990, (, 1990-07-11, )]   \n",
       "1     [May 17, 1990, (, 1990-05-17, ), (Cannes Film ...   \n",
       "2                   [August 10, 1990, (, 1990-08-10, )]   \n",
       "3                 [December 25, 1990, (, 1990-12-25, )]   \n",
       "4                                     December 19, 1990   \n",
       "...                                                 ...   \n",
       "6047  [12 February 2017, (, 2017-02-12, ), (, Berlin...   \n",
       "6048  [April 22, 2017, (, 2017-04-22, ), (, Tribeca ...   \n",
       "6049  [18 May 2017, (, 2017-05-18, ), (, Cannes, ), ...   \n",
       "6050  [March 12, 2017, (, 2017-03-12, ), (, SXSW, ),...   \n",
       "6051  [May 21, 2017, (, 2017-05-21, ), (, Cannes, ),...   \n",
       "\n",
       "                                          Country               Director  \\\n",
       "0                                   United States           Renny Harlin   \n",
       "1                                   United States            James Foley   \n",
       "2                                   United States     Roger Spottiswoode   \n",
       "3                                   United States            Woody Allen   \n",
       "4                                              US           John Cornell   \n",
       "...                                           ...                    ...   \n",
       "6047  [Chile, Germany, Spain, United States, [2]]        Sebastián Lelio   \n",
       "6048                                United States            Brian Crano   \n",
       "6049      [Russia, France, Belgium, Germany, [3]]     Andrey Zvyagintsev   \n",
       "6050                                United States             Aaron Katz   \n",
       "6051              [United Kingdom, United States]  John Cameron Mitchell   \n",
       "\n",
       "                                            Distributor  ...  \\\n",
       "0                                      20th Century Fox  ...   \n",
       "1                                       Avenue Pictures  ...   \n",
       "2                                      TriStar Pictures  ...   \n",
       "3                                        Orion Pictures  ...   \n",
       "4                                    Paramount Pictures  ...   \n",
       "...                                                 ...  ...   \n",
       "6047  [Participant Media (Chile), Piffl Medien (Germ...  ...   \n",
       "6048                            Good Deed Entertainment  ...   \n",
       "6049           [Sony Pictures Releasing, (Russia), [1]]  ...   \n",
       "6050                                               Neon  ...   \n",
       "6051                              [A24, StudioCanal UK]  ...   \n",
       "\n",
       "     release_date_kaggle              revenue              runtime  \\\n",
       "0             1990-07-11        20,423,389.00               104.00   \n",
       "1             1990-08-24         2,700,000.00               114.00   \n",
       "2             1990-08-10        33,461,269.00               112.00   \n",
       "3             1990-12-25         7,331,647.00               102.00   \n",
       "4             1990-12-21         6,939,946.00                95.00   \n",
       "...                  ...                  ...                  ...   \n",
       "6047          2017-04-06         3,700,000.00               104.00   \n",
       "6048          2017-04-22                  NaN                96.00   \n",
       "6049          2017-06-01         4,800,000.00               128.00   \n",
       "6050          2017-03-12           200,340.00                92.00   \n",
       "6051          2017-12-27           382,053.00               102.00   \n",
       "\n",
       "                                       spoken_languages           status  \\\n",
       "0              [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "1              [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "2     [{'iso_639_1': 'en', 'name': 'English'}, {'iso...         Released   \n",
       "3              [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "4              [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "...                                                 ...              ...   \n",
       "6047           [{'iso_639_1': 'es', 'name': 'Español'}]         Released   \n",
       "6048           [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "6049           [{'iso_639_1': 'ru', 'name': 'Pусский'}]         Released   \n",
       "6050           [{'iso_639_1': 'en', 'name': 'English'}]  Post Production   \n",
       "6051           [{'iso_639_1': 'en', 'name': 'English'}]         Released   \n",
       "\n",
       "                                      tagline  \\\n",
       "0         Kojak. Columbo. Dirty Harry. Wimps.   \n",
       "1             All they risked was everything.   \n",
       "2     The few. The proud. The totally insane.   \n",
       "3                                         NaN   \n",
       "4                    Who does he think he is?   \n",
       "...                                       ...   \n",
       "6047                                      NaN   \n",
       "6048                                      NaN   \n",
       "6049                                      NaN   \n",
       "6050                                      NaN   \n",
       "6051        Some girls are out of this world.   \n",
       "\n",
       "                         title_kaggle  video         vote_average  \\\n",
       "0     The Adventures of Ford Fairlane  False                 6.20   \n",
       "1                After Dark, My Sweet  False                 6.50   \n",
       "2                         Air America  False                 5.30   \n",
       "3                               Alice  False                 6.30   \n",
       "4                     Almost an Angel  False                 5.60   \n",
       "...                               ...    ...                  ...   \n",
       "6047                A Fantastic Woman  False                 7.20   \n",
       "6048                       Permission  False                 0.00   \n",
       "6049                         Loveless  False                 7.80   \n",
       "6050                           Gemini  False                 0.00   \n",
       "6051  How to Talk to Girls at Parties  False                 0.00   \n",
       "\n",
       "               vote_count  \n",
       "0                   72.00  \n",
       "1                   17.00  \n",
       "2                  146.00  \n",
       "3                   57.00  \n",
       "4                   23.00  \n",
       "...                   ...  \n",
       "6047                13.00  \n",
       "6048                 1.00  \n",
       "6049                26.00  \n",
       "6050                 0.00  \n",
       "6051                10.00  \n",
       "\n",
       "[6051 rows x 37 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run function and fill in zeros\n",
    "fill_missing_kaggle_data(movies_df, 'runtime', 'running_time')\n",
    "fill_missing_kaggle_data(movies_df, 'budget_kaggle', 'budget_wiki')\n",
    "fill_missing_kaggle_data(movies_df, 'revenue', 'box_office')\n",
    "movies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9f4743fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "video\n"
     ]
    }
   ],
   "source": [
    "# Check\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": 88,
   "id": "16408e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    6051\n",
       "Name: video, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['video'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ebac765a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reorder the 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']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bf3511e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename 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": "code",
   "execution_count": 91,
   "id": "8ed2d1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_counts = ratings.groupby(['movieId','rating'], as_index=False).count() \\\n",
    "                .rename({'userId':'count'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7bb0621e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pivot the data\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": 93,
   "id": "a79aaf60",
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_counts.columns = ['rating_' + str(col) for col in rating_counts.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e9c5621b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# left merge\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": 95,
   "id": "599534d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling in values\n",
    "movies_with_ratings_df[rating_counts.columns] = movies_with_ratings_df[rating_counts.columns].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "54e293b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a connection string to SQLAlchemy\n",
    "db_string = f\"postgresql://postgres:{db_password}@127.0.0.1:5432/movie_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "096cf151",
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(db_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d66bade6",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Table 'movies' already exists.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_22032\\489159978.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmovies_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'movies'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(self, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)\u001b[0m\n\u001b[0;32m   2880\u001b[0m             \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2881\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2882\u001b[1;33m             \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2883\u001b[0m         )\n\u001b[0;32m   2884\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method, engine, **engine_kwargs)\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    727\u001b[0m         \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 728\u001b[1;33m         \u001b[1;33m**\u001b[0m\u001b[0mengine_kwargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    729\u001b[0m     )\n\u001b[0;32m    730\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method, engine, **engine_kwargs)\u001b[0m\n\u001b[0;32m   1756\u001b[0m             \u001b[0mindex_label\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex_label\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1757\u001b[0m             \u001b[0mschema\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mschema\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1758\u001b[1;33m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1759\u001b[0m         )\n\u001b[0;32m   1760\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mprep_table\u001b[1;34m(self, frame, name, if_exists, index, index_label, schema, dtype)\u001b[0m\n\u001b[0;32m   1648\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1649\u001b[0m         )\n\u001b[1;32m-> 1650\u001b[1;33m         \u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1651\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1652\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    856\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    857\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mif_exists\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"fail\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 858\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Table '{self.name}' already exists.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    859\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mif_exists\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    860\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpd_sql\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Table 'movies' already exists."
     ]
    }
   ],
   "source": [
    "movies_df.to_sql(name='movies', con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "46026758",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing rows 0 to 1000000..."
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_22032\\301130270.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'importing rows {rows_imported} to {rows_imported + len(data)}...'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ratings'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mif_exists\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'append'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;31m# increment the number of rows imported by the size of 'data'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(self, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)\u001b[0m\n\u001b[0;32m   2880\u001b[0m             \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2881\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2882\u001b[1;33m             \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2883\u001b[0m         )\n\u001b[0;32m   2884\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method, engine, **engine_kwargs)\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    727\u001b[0m         \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 728\u001b[1;33m         \u001b[1;33m**\u001b[0m\u001b[0mengine_kwargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    729\u001b[0m     )\n\u001b[0;32m    730\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mto_sql\u001b[1;34m(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method, engine, **engine_kwargs)\u001b[0m\n\u001b[0;32m   1768\u001b[0m             \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1769\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1770\u001b[1;33m             \u001b[1;33m**\u001b[0m\u001b[0mengine_kwargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1771\u001b[0m         )\n\u001b[0;32m   1772\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36minsert_records\u001b[1;34m(self, table, con, frame, name, index, schema, chunksize, method, **engine_kwargs)\u001b[0m\n\u001b[0;32m   1338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1339\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1340\u001b[1;33m             \u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1341\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSQLAlchemyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1342\u001b[0m             \u001b[1;31m# GH34431\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36minsert\u001b[1;34m(self, chunksize, method)\u001b[0m\n\u001b[0;32m    965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    966\u001b[0m                 \u001b[0mchunk_iter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstart_i\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mend_i\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata_list\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 967\u001b[1;33m                 \u001b[0mexec_insert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunk_iter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    968\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    969\u001b[0m     def _query_iterator(\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36m_execute_insert\u001b[1;34m(self, conn, keys, data_iter)\u001b[0m\n\u001b[0;32m    880\u001b[0m         \"\"\"\n\u001b[0;32m    881\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata_iter\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 882\u001b[1;33m         \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    884\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_execute_insert_multi\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_iter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[0;32m   1293\u001b[0m             )\n\u001b[0;32m   1294\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1295\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_EMPTY_EXECUTION_OPTS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1296\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1297\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_execute_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecution_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sqlalchemy\\sql\\elements.py\u001b[0m in \u001b[0;36m_execute_on_connection\u001b[1;34m(self, connection, multiparams, params, execution_options, _force)\u001b[0m\n\u001b[0;32m    324\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0m_force\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupports_execution\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m             return connection._execute_clauseelement(\n\u001b[1;32m--> 326\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecution_options\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    327\u001b[0m             )\n\u001b[0;32m    328\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_clauseelement\u001b[1;34m(self, elem, multiparams, params, execution_options)\u001b[0m\n\u001b[0;32m   1495\u001b[0m             \u001b[0melem\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1496\u001b[0m             \u001b[0mextracted_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1497\u001b[1;33m             \u001b[0mcache_hit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache_hit\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1498\u001b[0m         )\n\u001b[0;32m   1499\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhas_events\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m   1850\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1851\u001b[0m             self._handle_dbapi_exception(\n\u001b[1;32m-> 1852\u001b[1;33m                 \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1853\u001b[0m             )\n\u001b[0;32m   1854\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[1;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[0;32m   2034\u001b[0m                 )\n\u001b[0;32m   2035\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2036\u001b[1;33m                 \u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwith_traceback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2037\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2038\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sqlalchemy\\util\\compat.py\u001b[0m in \u001b[0;36mraise_\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m    205\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    206\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 207\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    208\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    209\u001b[0m             \u001b[1;31m# credit to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m   1787\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1788\u001b[0m                     self.dialect.do_executemany(\n\u001b[1;32m-> 1789\u001b[1;33m                         \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1790\u001b[0m                     )\n\u001b[0;32m   1791\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mparameters\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_parameters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sqlalchemy\\dialects\\postgresql\\psycopg2.py\u001b[0m in \u001b[0;36mdo_executemany\u001b[1;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[0;32m    955\u001b[0m                 \u001b[0mtemplate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexecutemany_values\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    956\u001b[0m                 \u001b[0mfetch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompiled\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreturning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 957\u001b[1;33m                 \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    958\u001b[0m             )\n\u001b[0;32m    959\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\site-packages\\psycopg2\\extras.py\u001b[0m in \u001b[0;36mexecute_values\u001b[1;34m(cur, sql, argslist, template, page_size, fetch)\u001b[0m\n\u001b[0;32m   1290\u001b[0m             \u001b[0mparts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1291\u001b[0m         \u001b[0mparts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpost\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1292\u001b[1;33m         \u001b[0mcur\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb''\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1293\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1294\u001b[0m             \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcur\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetchall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonData\\lib\\encodings\\utf_8.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(input, errors)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mencode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutf_8_encode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'strict'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutf_8_decode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# create a variable for the number of rows imported\n",
    "rows_imported = 0\n",
    "for data in pd.read_csv(f'{file_dir}ratings.csv', chunksize=1000000):\n",
    "\n",
    "    # print out the range of rows that are being imported\n",
    "    print(f'importing rows {rows_imported} to {rows_imported + len(data)}...', end='')\n",
    "\n",
    "    data.to_sql(name='ratings', con=engine, if_exists='append')\n",
    "\n",
    "    # increment the number of rows imported by the size of 'data'\n",
    "    rows_imported += len(data)\n",
    "\n",
    "    # print that the rows have finished importing\n",
    "    print('Done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "257a748b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows_imported = 0\n",
    "# get the start_time from time.time()\n",
    "start_time = time.time()\n",
    "for data in pd.read_csv(f'{file_dir}ratings.csv', chunksize=1000000):\n",
    "    print(f'importing rows {rows_imported} to {rows_imported + len(data)}...', end='')\n",
    "    data.to_sql(name='ratings', con=engine, if_exists='append')\n",
    "    rows_imported += len(data)\n",
    "\n",
    "    # add elapsed time to final print out\n",
    "    print(f'Done. {time.time() - start_time} total seconds elapsed')"
   ]
  }
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