Fortune Global 500 - Bar Chart Race Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fortune 500 (Global) Companies - Visual Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "1. [Setup and Data Pre-Processing](#setup) \n",
    "  \n",
    "  \n",
    "2. [Global Fortune 500 Bar Chart Race](#bar_chart_race)  \n",
    "  \n",
    "  \n",
    "3. [Top-ranked Fortune 500 company over the years](#top_rank)  \n",
    "  \n",
    "  \n",
    "4. [Bottom-ranked Fortune 500 company over the years](#bottom_rank)  \n",
    "  \n",
    "  \n",
    "5. [Total revenue of Fortune 500 companies over the years](#total_revenue)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<a name=\"setup\"></a>\n",
    "### 1) Setup and Data Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from openpyxl import load_workbook\n",
    "import bar_chart_race as bcr\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset from Excel file\n",
    "wb = load_workbook('Data/Fortune 500 Global (By Year) Original.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_years = wb.sheetnames\n",
    "len(list_of_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']\n"
     ]
    }
   ],
   "source": [
    "print(list_of_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine all sheets in Excel document into a single dataframe\n",
    "master_df = pd.DataFrame()\n",
    "\n",
    "for year in list_of_years:\n",
    "    df = pd.DataFrame(wb[year].values)\n",
    "    df.columns = df.iloc[0]\n",
    "    df.drop(df.index[0], inplace = True)\n",
    "    df.dropna(how = 'all', inplace = True)\n",
    "    df['Revenue'] = df['Revenue'].astype(float).round(0).astype(int)\n",
    "    df['Year'] = year\n",
    "    master_df = master_df.append(df, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8969</th>\n",
       "      <td>156</td>\n",
       "      <td>ConAgra Foods, Incorporated</td>\n",
       "      <td>27194</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6019</th>\n",
       "      <td>116</td>\n",
       "      <td>Indian Oil Corporation Limited</td>\n",
       "      <td>57427</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10700</th>\n",
       "      <td>442</td>\n",
       "      <td>Gillette Company</td>\n",
       "      <td>10056</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5486</th>\n",
       "      <td>56</td>\n",
       "      <td>Tesco PLC</td>\n",
       "      <td>94300</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4458</th>\n",
       "      <td>479</td>\n",
       "      <td>Fresenius SE &amp; Co. KGaA</td>\n",
       "      <td>22973</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10234</th>\n",
       "      <td>460</td>\n",
       "      <td>Pohang Iron &amp; Steel Co. Ltd.</td>\n",
       "      <td>10684</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12564</th>\n",
       "      <td>374</td>\n",
       "      <td>Bayerische Landesbank</td>\n",
       "      <td>10259</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11319</th>\n",
       "      <td>81</td>\n",
       "      <td>Compagnie G_n_rale des Eaux</td>\n",
       "      <td>32429</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7434</th>\n",
       "      <td>88</td>\n",
       "      <td>Johnson &amp; Johnson</td>\n",
       "      <td>47348</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9030</th>\n",
       "      <td>218</td>\n",
       "      <td>Bristol-Myers Squibb Company</td>\n",
       "      <td>21717</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0     Rank                            Name  Revenue  Year\n",
       "8969   156     ConAgra Foods, Incorporated    27194  2002\n",
       "6019   116  Indian Oil Corporation Limited    57427  2008\n",
       "10700  442                Gillette Company    10056  1999\n",
       "5486    56                       Tesco PLC    94300  2009\n",
       "4458   479         Fresenius SE & Co. KGaA    22973  2012\n",
       "10234  460    Pohang Iron & Steel Co. Ltd.    10684  2000\n",
       "12564  374           Bayerische Landesbank    10259  1995\n",
       "11319   81     Compagnie G_n_rale des Eaux    32429  1997\n",
       "7434    88               Johnson & Johnson    47348  2005\n",
       "9030   218    Bristol-Myers Squibb Company    21717  2002"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round 1 Cleaning - General pre-processing\n",
    "master_df['Name'].replace(' Co., Ltd.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Corporation','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Corp.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Company','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' AG','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' A.G.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' SpA','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' SE','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' SPA','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' S.P.A.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' S.p.a.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' S.p.A.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Spa','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' SA/NV','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' SA','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' S.A.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' SA/NV','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' N.V.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' N. V.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' NV','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Aktiengesellschaft','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' , Inc.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' ,Inc.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Inc.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' plc','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' p.l.c.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' PLC','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Plc','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Ltd.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' , L.P.','',regex = True, inplace = True)\n",
    "master_df['Name'].replace(' Limited','',regex = True, inplace = True)\n",
    "master_df['Name'] = master_df['Name'].str.rstrip(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-\n",
      ".\n",
      "3M\n",
      "A.P. Moller-Maersk Group\n",
      "A.P. Møller-Mærsk A/S\n",
      "A.P. Møller-Mærsk Group\n",
      "AB Electrolux\n",
      "AB Volvo\n",
      "ABB\n",
      "ABB Asea Brown Boveri\n",
      "ABB Asea Brown Boveri Ltd\n",
      "ABN AMRO Holding\n",
      "ABN-AMRO Holding\n",
      "ACS\n",
      "AEGON\n",
      "AEON\n",
      "AES\n",
      "AFLAC\n",
      "AFLACrporated\n",
      "AIA Group\n",
      "AIG\n",
      "AMP\n",
      "AMR\n",
      "AOL Time Warner\n",
      "AREVA Group\n",
      "ASDA Group\n",
      "ASUSTek Computer\n",
      "AT&T\n",
      "AT&T Wireless Services\n",
      "AXA\n",
      "A_rospatiale\n",
      "A_rospatiale Matra\n",
      "AbbVie\n",
      "Abbey National\n",
      "Abbott Laboratories\n",
      "Accenture\n",
      "Acciona\n",
      "Acer\n",
      "Achmea\n",
      "Actividades de Construccion y Servicios\n",
      "Adecco\n",
      "Adecco Group\n",
      "Adidas\n",
      "AdvancePCS\n",
      "Aegon\n",
      "Aetna\n",
      "Aetna Life & Casualty Co.\n",
      "Aflac\n",
      "Aflacrporated\n",
      "Ageas\n",
      "Agricultural Bank of China\n",
      "Agricultural Development Bank of China\n",
      "Air France-KLM\n",
      "Air France-KLM Group\n",
      "Air Liquide\n",
      "Airbus\n",
      "Airbus Group\n",
      "Aisin Seiki\n",
      "Akzo Nobel\n",
      "Albertson's\n",
      "Albertsons\n",
      "Albertsons Cos.\n",
      "Alcan\n",
      "Alcan Aluminium\n",
      "Alcatel\n",
      "Alcatel Alsthom\n",
      "Alcatel Alsthom Group\n",
      "Alcatel-Lucent\n",
      "Alco Standard\n",
      "Alcoa\n",
      "Alfresa Holdings\n",
      "Algemene Maatschappij Voor Nuver\n",
      "Alibaba Group Holding\n",
      "Alimentation Couche-Tard\n",
      "All Nippon Airways\n",
      "Allegheny Energyrporated\n",
      "Alliance Boots\n",
      "Alliance Boots GMbH\n",
      "Alliance Boots, GMbH\n",
      "Alliance Unichem\n",
      "Allianz\n",
      "Allied Domecq\n",
      "AlliedSignal\n",
      "Allstate\n",
      "Alphabet\n",
      "Alstom\n",
      "Altadis\n",
      "Altice\n",
      "Altria Group\n",
      "Aluminum of America\n",
      "Aluminum of China\n",
      "Amazon\n",
      "Amazon.com\n",
      "Amer International Group\n",
      "Amer International Group Co.\n",
      "Amerada Hess\n",
      "AmeriSource Health\n",
      "America Movil\n",
      "American Airlines Group\n",
      "American Brands\n",
      "American Electric Power\n",
      "American Express\n",
      "American General\n",
      "American Home Products\n",
      "American International Group\n",
      "American Stores\n",
      "American electric Power\n",
      "AmerisourceBergen\n",
      "Ameritech\n",
      "Amgen\n",
      "Amoco\n",
      "América Móvil\n",
      "América Telecom.S.A. de C.V.\n",
      "Anbang Insurance Group\n",
      "Andeavor\n",
      "Anglo American\n",
      "Anheuser-Busch\n",
      "Anheuser-Busch Companies\n",
      "Anheuser-Busch InBev\n",
      "Anhui Conch Group\n",
      "Ansteel Group\n",
      "AntarChile\n",
      "Anthem\n",
      "Anthem,Inc.\n",
      "Apple\n",
      "Apple Computer\n",
      "Aquila\n",
      "Arbed\n",
      "Arcandor\n",
      "Arcelor\n",
      "ArcelorMittal\n",
      "Archer Daniels Midland\n",
      "Archer-Daniels-Midland\n",
      "Arconic\n",
      "Argyll Group\n",
      "Arrow Electronics\n",
      "Arrow Electronics, Inc\n",
      "Asahi Bank\n",
      "Asahi Chemical Industry\n",
      "Asahi Glass\n",
      "Asahi Kasei\n",
      "Asahi Mutual Life Insurance Co.\n",
      "Ashland\n",
      "Asko Deutsches Kaufhaus Aktien.\n",
      "Assicurazioni Generali\n",
      "Associates First Capital\n",
      "Assurances G_n_rales de France\n",
      "Assurances Generales de France\n",
      "AstraZeneca\n",
      "Atlantic Richfield\n",
      "Auchan Holding\n",
      "Australia & New Zeal. Bnkg.\n",
      "Australia & New Zealand Banking\n",
      "Australia & New Zealand Banking Group\n",
      "Australia and New Zealand Banking Group\n",
      "AutoNation\n",
      "Autonation\n",
      "Aventis\n",
      "Aviation Industry of China\n",
      "Aviva\n",
      "Aviva,plc\n",
      "Avnet\n",
      "B.A.T Industries\n",
      "B.A.T. Industries\n",
      "BAE Systems\n",
      "BASF\n",
      "BCE\n",
      "BG\n",
      "BG Group\n",
      "BHP Billiton\n",
      "BHP Group\n",
      "BHP Limite\n",
      "BMW\n",
      "BMW Group\n",
      "BNP Paribas\n",
      "BP\n",
      "BP Amoco\n",
      "BT Group\n",
      "BTR\n",
      "Bailian Group\n",
      "Baker Hughes\n",
      "Banc One\n",
      "Banca Commerciale Italiana\n",
      "Banca Di Roma\n",
      "Banca Intesa\n",
      "Banca Monte Dei Paschi Di Siena\n",
      "Banca Nazionale del Lavoro\n",
      "Banco Bilbao Vizcaya\n",
      "Banco Bilbao Vizcaya Argentaria\n",
      "Banco Bradesco\n",
      "Banco Central HispanoAmericano\n",
      "Banco De Santander\n",
      "Banco Do Brasil\n",
      "Banco Santander\n",
      "Banco do Brasil\n",
      "Bank Of China\n",
      "Bank One\n",
      "Bank of America\n",
      "Bank of China\n",
      "Bank of Communications\n",
      "Bank of Ireland Group\n",
      "Bank of Montreal\n",
      "Bank of Nova Scotia\n",
      "Bank of Scotland\n",
      "BankAmerica\n",
      "Bankers Trust\n",
      "Bankers Trust New York\n",
      "Bankgesellschaft Berlin\n",
      "Banque Nationale de Paris\n",
      "Baosteel Group\n",
      "Barclays\n",
      "Barclays Bank\n",
      "Baxter International\n",
      "Bayer\n",
      "Bayerische Hypo-und Vereinsbank\n",
      "Bayerische Hypotheken- und Wechsel-Bank\n",
      "Bayerische Hypotheken-und Wechsel-Bank\n",
      "Bayerische Landesbank\n",
      "Bayerische Landesbank Girozentrale\n",
      "Bayerische Motoren Werke\n",
      "Bayerische Vereinsbank\n",
      "Beijing Automotive Group\n",
      "Bell Atlantic\n",
      "BellSouth\n",
      "Bergen Brunswig\n",
      "Berkshire Hathaway\n",
      "Bertelsmann\n",
      "Best Buy\n",
      "Best Buy Co.\n",
      "Bestfoods\n",
      "Bharat Petroleum\n",
      "Boehringer Ingelheim\n",
      "Boeing\n",
      "Bohai Steel Group\n",
      "Bombardier\n",
      "Bosch\n",
      "Bosch Group\n",
      "Bouygues\n",
      "Brazilian Distribution\n",
      "Bridgestone\n",
      "Bristol-Myers Squibb\n",
      "British Aerospace\n",
      "British Aerospace Public\n",
      "British Airways\n",
      "British American Tobacco\n",
      "British Gas\n",
      "British Petroleum\n",
      "British Post Office\n",
      "British Steel\n",
      "British Telecommunications\n",
      "Broken Hill Proprietary\n",
      "Brookfield Asset Management\n",
      "Bunge\n",
      "Burlington Northern Santa Fe\n",
      "CBS\n",
      "CEA-Industrie\n",
      "CEFC China Energy\n",
      "CFE\n",
      "CGNU\n",
      "CGU\n",
      "CHS\n",
      "CIGNA\n",
      "CITIC Group\n",
      "CInergy\n",
      "CJ\n",
      "CK Hutchison Holdings\n",
      "CMA CGM\n",
      "CMS Energy\n",
      "CNP Assurances\n",
      "COFCO\n",
      "CPC\n",
      "CPC International\n",
      "CPC, Taiwan\n",
      "CRH\n",
      "CRRC\n",
      "CRRC Group\n",
      "CS Holding\n",
      "CSX\n",
      "CVS\n",
      "CVS Caremark\n",
      "CVS Health\n",
      "Cable and Wireless\n",
      "Caisse Centrale des Assurances Mutuellas Agricoles\n",
      "Caisse Centrale des Assurances Mutuelles Agricoles\n",
      "Caixa Econômica Federal\n",
      "Caltex Australia\n",
      "Canadian Imperial Bank of Commerce\n",
      "Canadian Pacific\n",
      "Canon\n",
      "Capital One Financial\n",
      "Cardinal Health\n",
      "Caremark Rx\n",
      "Cariplo\n",
      "Carrefour\n",
      "Carso Global Telecom de C.V.\n",
      "Carso Global TelecomB. de C.V.\n",
      "Caterpillar\n",
      "Cathay Financial Holding\n",
      "Cathay Financial Holdings\n",
      "Cathay Life Insurance\n",
      "Cathay Life Insurance Co.\n",
      "Ceconomy\n",
      "Cedar Holdings Group\n",
      "Cemex, de C.V.\n",
      "Cendant\n",
      "Centene\n",
      "Centex\n",
      "Central Japan Railway\n",
      "Centrica\n",
      "Charter Communications\n",
      "Chase Manhattan\n",
      "Chase Manhattan Corp\n",
      "ChemChina\n",
      "Chemical Banking\n",
      "Chevron\n",
      "ChevronTexaco\n",
      "China Aerospace Science & Industry\n",
      "China Aerospace Science & Technology\n",
      "China Baowu Steel Group\n",
      "China COSCO Shipping\n",
      "China Communications Construction\n",
      "China Communications Construction Group\n",
      "China Construction Bank\n",
      "China Datang\n",
      "China Development Bank\n",
      "China Electronics\n",
      "China Electronics Technology Group\n",
      "China Energy Engineering\n",
      "China Energy Engineering Group\n",
      "China Energy Investment\n",
      "China Everbright Group\n",
      "China Evergrande Group\n",
      "China FAW Group\n",
      "China First Automotive Works Group\n",
      "China General Technology\n",
      "China Guodian\n",
      "China Huadian\n",
      "China Huaneng Group\n",
      "China Life Insurance\n",
      "China Life Insurance (Group)\n",
      "China Life Insurance(Group)\n",
      "China Merchants Bank\n",
      "China Merchants Group\n",
      "China Metallurgical Group\n",
      "China Minmetals\n",
      "China Minsheng Banking\n",
      "China Minsheng Bankingtion\n",
      "China Mobile Communications\n",
      "China Nat'l Cereals, Oils & Foodstuffs Import & Export\n",
      "China National Aviation Fuel Group\n",
      "China National Bldg. Materials Group\n",
      "China National Building Material Group\n",
      "China National Building Materials Group\n",
      "China National Chemical\n",
      "China National Chemicals Import & Export\n",
      "China National Chemicals Import and Export\n",
      "China National Coal Group\n",
      "China National Machinery Industry\n",
      "China National Nuclear\n",
      "China National Offshore Oil\n",
      "China National Petroleum\n",
      "China National PetroleumCorporation\n",
      "China National Pharmaceutical Group\n",
      "China Nonferrous Metal Mining\n",
      "China Nonferrous Metal Mining (Group)\n",
      "China North Industries\n",
      "China North Industries Group\n",
      "China Ocean Shipping\n",
      "China Ocean Shipping (Group)\n",
      "China Ocean Shipping Group\n",
      "China Pacific Insurance\n",
      "China Pacific Insurance (Group)\n",
      "China Petro-Chemical\n",
      "China Petrochemical\n",
      "China Poly Group\n",
      "China Post Group\n",
      "China Power Investment\n",
      "China Railway Construction\n",
      "China Railway Engineering\n",
      "China Railway Engineering Group\n",
      "China Railway Group\n",
      "China Railway Materials\n",
      "China Railway Materials Commercial\n",
      "China Resources\n",
      "China Resources National\n",
      "China Shipbuilding Industry\n",
      "China South Industries Group\n",
      "China Southern Power Grid\n",
      "China State Construction Engineering\n",
      "China State Shipbuilding\n",
      "China Taiping Insurance Group\n",
      "China Telecommunications\n",
      "China TelecommunicationsCorporation\n",
      "China United Network Communications\n",
      "China United Telecommunications\n",
      "China Vanke\n",
      "Chinese Petroleum\n",
      "Christian Dior\n",
      "Chrysler\n",
      "Chrysler Group LLC\n",
      "Chubb\n",
      "Chubu Electric Power\n",
      "Chubu Electric Power Co.\n",
      "Ciba-Geigy\n",
      "Cigna\n",
      "Circuit City Stores\n",
      "Cisco Systems\n",
      "Citic Group\n",
      "Citicorp\n",
      "Citicorp/Citibank\n",
      "Citigroup\n",
      "Co. Generale Michelin\n",
      "Coca-Cola\n",
      "Coca-Cola Enterprises\n",
      "Coles Group\n",
      "Coles Myer\n",
      "Colgate-Palmolive\n",
      "College Retirement Equities Fund\n",
      "Columbia/HCA Healthcare\n",
      "Comcast\n",
      "Comision Federal De Electricidad\n",
      "Commercial Union\n",
      "Commerzbank\n",
      "Commonwealth Bank\n",
      "Commonwealth Bank of Australia\n",
      "Community Health Systems\n",
      "Compagnie De Suez\n",
      "Compagnie G_n_rale des Eaux\n",
      "Compagnie Nationale A Portefeuille\n",
      "Compagnie Nationale Air France\n",
      "Compagnie de Saint-Gobain\n",
      "Compal Electronics\n",
      "Companhia Vale Do Rio Doce\n",
      "Compania Espanola de Petroleos\n",
      "Compaq Computer\n",
      "Compart\n",
      "Compass Group\n",
      "Computer Sciences\n",
      "ConAgra\n",
      "ConAgra Foods,rporated\n",
      "Conoco\n",
      "ConocoPhillips\n",
      "Consignia\n",
      "Constellation Energy Group\n",
      "Continental\n",
      "Coop\n",
      "Coop Group\n",
      "Coop Schweiz\n",
      "Corus Group\n",
      "Cosmo Oil\n",
      "Costco\n",
      "Costco Companies\n",
      "Costco Wholesale\n",
      "Country Garden Holdings\n",
      "Countrywide Financial\n",
      "Cr_dit Agricole Group\n",
      "Cr_dit Lyonais\n",
      "Cr_dit Lyonnais\n",
      "Cr_dit Nationale de Cr_dit Agricole\n",
      "Credit Agricole\n",
      "Credit Agricole Group\n",
      "Credit Lyonnais\n",
      "Credit Suisse Group\n",
      "Credito Italiano\n",
      "Crédit Agricole\n",
      "Crédit Nationale de Crédit Agricole\n",
      "D.R. Horton\n",
      "DDI\n",
      "DIRECTV\n",
      "DXC Technology\n",
      "DZ Bank\n",
      "DZ Bank Deutsche Zentral-Genossenschaftsbank Aktiengesellsch\n",
      "Daewoo\n",
      "Daewoo Group\n",
      "Dai Nippon Printing\n",
      "Dai-ichi Life Holdings\n",
      "Dai-ichi Life Insurance\n",
      "Daido Life Insurance\n",
      "Daihatsu Motor\n",
      "Daihyaku Mutual Life Insurance Co.\n",
      "Daimler\n",
      "Daimler-Benz\n",
      "DaimlerChrysler\n",
      "Dainippon Ink & Chemicals\n",
      "Daiwa Bank Holdings\n",
      "Daiwa House Industry\n",
      "Dalian Wanda Group\n",
      "Dana\n",
      "Danone\n",
      "Danone Group\n",
      "Danske Bank Aktieselskab\n",
      "Datong Coal Mine Group\n",
      "Datong Coal Mine Group Co.\n",
      "Dayton Hudson\n",
      "Deere\n",
      "Deere &\n",
      "Degussa\n",
      "Delhaize Group\n",
      "Dell\n",
      "Dell Computer\n",
      "Dell Technologies\n",
      "Delphi\n",
      "Delphi Automotive Systems\n",
      "Delta Air Lines\n",
      "Den Norske Stats Oljeselskap AS\n",
      "Den norske stats oljeselskap a.s\n",
      "Denso\n",
      "Dentsu\n",
      "Deutsche Bahn\n",
      "Deutsche Bank\n",
      "Deutsche Genossenschaftsbank\n",
      "Deutsche Lufthansa\n",
      "Deutsche Lufthansa Group\n",
      "Deutsche Post\n",
      "Deutsche Post DHL Group\n",
      "Deutsche Telekom\n",
      "Dexia\n",
      "Dexia Group\n",
      "Diageo\n",
      "Digital Equipment\n",
      "DirecTV\n",
      "Disney\n",
      "Dollar General\n",
      "Dominion Resources\n",
      "Dongfeng Motor\n",
      "Dongfeng Motor Group\n",
      "Doosan\n",
      "Dow\n",
      "Dow Chemical\n",
      "DowDuPont\n",
      "Dresdner Bank\n",
      "DuPont\n",
      "Duke Energy\n",
      "Dynegy\n",
      "E. ON\n",
      "E.I. Du Pont de Nemours and\n",
      "E.I. du Pont de Nemours\n",
      "E.I. du Pont de Nemours and\n",
      "E.ON\n",
      "EDP-Energias de Portugal\n",
      "EMC\n",
      "ENEOS Holdings\n",
      "ENI\n",
      "EXOR\n",
      "EXOR Group\n",
      "East Japan Railway\n",
      "Eastman Kodak\n",
      "Ecopetrol\n",
      "Edeka Zentrale\n",
      "Edison\n",
      "Edison International\n",
      "Eiffage\n",
      "El Paso\n",
      "El Paso Energy\n",
      "El Paso Corporation\n",
      "Electricite De France\n",
      "Electricite de France\n",
      "Electricité de France\n",
      "Electrolux\n",
      "Electrolux AB\n",
      "Electronic Data Systems\n",
      "Elf Aquitaine\n",
      "Eli Lilly &\n",
      "Eli Lilly and\n",
      "Emerson Electric\n",
      "Emerson Electric Co.\n",
      "Emirates Group\n",
      "EnCana\n",
      "Enbridge\n",
      "Endesa\n",
      "Enel\n",
      "Energie Baden-Wurttemberg\n",
      "Energie Baden-Württemberg\n",
      "Energy Transfer\n",
      "Energy Transfer Equity\n",
      "Engie\n",
      "Enron\n",
      "Entergy\n",
      "Enterprise GP Holdings, L.P.\n",
      "Enterprise Products\n",
      "Enterprise Products Partners\n",
      "Enterprise Products Partners L.P.\n",
      "Enterprise Products Partners, L.P.\n",
      "Equinor\n",
      "Ericsson\n",
      "Erste Bank der oesterreichischenrkassen\n",
      "Erste Group Bank\n",
      "Eurohypo\n",
      "European Aeronautic Defence andce EADS\n",
      "Evergrande Real Estate Group\n",
      "Evonik Industries\n",
      "Evraz Group\n",
      "Exelon\n",
      "Express Scripts\n",
      "Express Scripts Holding\n",
      "Exxon\n",
      "Exxon Mobil\n",
      "FDX\n",
      "Facebook\n",
      "Fannie Mae\n",
      "Farmland Industries\n",
      "Faros\n",
      "FedEx\n",
      "Federal Express\n",
      "Federal Home Loan Mortgage\n",
      "Federal National Mortgage Association\n",
      "Federated Depart_x0004_ment Stores\n",
      "Federated Department Stores\n",
      "Ferruzzi Finanziaria\n",
      "Fiat\n",
      "Financière de l'Odet\n",
      "Finatis\n",
      "Finmeccanica\n",
      "First Chicago NBD\n",
      "First Union\n",
      "FirstEnergy\n",
      "Fleet Financial Group\n",
      "FleetBoston Financial\n",
      "Fleming Companies\n",
      "Flex\n",
      "Flextronics International\n",
      "Fluor\n",
      "Fomento Economico Mexicano\n",
      "Fomento Económico Mexicano\n",
      "Fomento de Construcciones y Contratas\n",
      "Foncière Euris\n",
      "Ford Motor\n",
      "Formosa Petrochemical\n",
      "Fortis\n",
      "Fortum\n",
      "France T_l_com\n",
      "France Télécom\n",
      "Franz Haniel\n",
      "Franz Haniel & Cie. GmbH\n",
      "Franz Haniel & Cie.GmbH\n",
      "Fred Meyer\n",
      "Freddie Mac\n",
      "Freeport-McMoRan Copper & Gold\n",
      "Fresenius\n",
      "Fresenius & Co. KGaA\n",
      "Fried. Krupp\n",
      "Fried. Krupp Hoesch-Krupp\n",
      "Friends Provident\n",
      "Fubon Financial Holding\n",
      "Fuji Electric\n",
      "Fuji Heavy Industries\n",
      "Fuji Photo Film\n",
      "Fujifilm Holdings\n",
      "Fujifilm Holdingsraton\n",
      "Fujitsu\n",
      "Fukoku Mutual Life Insurance Co.\n",
      "GDF Suez\n",
      "GEC (General Electric Co.)\n",
      "GEC (General Electric Co.,)\n",
      "GMAC\n",
      "GMAC LLC\n",
      "GS Caltex\n",
      "GS Holdings\n",
      "GTE\n",
      "GUS\n",
      "Galp Energia, SGPS\n",
      "Gan\n",
      "Gas Natural Fenosa\n",
      "Gas Natural SDG\n",
      "GasTerra\n",
      "GasTerra B.V.\n",
      "Gasunie Trade & Supply B.V.\n",
      "Gaz de France\n",
      "Gazprom\n",
      "General Accident\n",
      "General Dynamics\n",
      "General Electric\n",
      "General Mills\n",
      "General Motors\n",
      "Generale Bank\n",
      "George Weston\n",
      "Georgia-Pacific\n",
      "Georgia-Pacific Group\n",
      "Gilead Sciences\n",
      "Gillette\n",
      "Glaxo Wellcome\n",
      "GlaxoSmithKline\n",
      "Glencore\n",
      "Glencore International\n",
      "Glencore Xstrata\n",
      "Goldman Sachs Group\n",
      "Goodyear Tire & Rubber\n",
      "Google\n",
      "Grand Metropolitan\n",
      "Great Atlantic & Pacific Tea\n",
      "Gree Electric Appliances\n",
      "Greenergy Fuels Holdings\n",
      "Greenland Holding Group\n",
      "Groupama\n",
      "Groupe Air France\n",
      "Groupe Auchan\n",
      "Groupe BPCE\n",
      "Groupe Casino\n",
      "Groupe Danone\n",
      "Groupe Docks De France\n",
      "Groupe Docks de France\n",
      "Groupe Pinault-Printemps\n",
      "Grupo Ferrovial\n",
      "Gruppo Cassa Di Risparmio Di Roma\n",
      "Guangxi Investment Group\n",
      "Guangzhou Automobile Industry Group\n",
      "H & M Hennes & Mauritz\n",
      "H.J. Heinz\n",
      "HAVAS\n",
      "HBIS Group\n",
      "HBOS\n",
      "HBOSplc\n",
      "HCA\n",
      "HCA Healthcare\n",
      "HCA Holdings\n",
      "HCA Inc\n",
      "HCA, Inc\n",
      "HCA-The Healthcare\n",
      "HNA Group\n",
      "HP\n",
      "HSBC Holdings\n",
      "Haier Smart Home\n",
      "Hailiang Group\n",
      "Halifax\n",
      "Halifax Building Society\n",
      "Halifax Group\n",
      "Halliburton\n",
      "Hanson\n",
      "Hanwha\n",
      "Hartford Financial Services Group\n",
      "HeBei Iron & Steel Group\n",
      "HeSteel Group\n",
      "Health Net\n",
      "Hebei Iron & Steel Group\n",
      "HeidelbergCement\n",
      "Heineken Holding\n",
      "Henan Coal & Chemical Industry Group\n",
      "Henan Energy & Chemical\n",
      "Hengli Group\n",
      "Henkel\n",
      "Henkel KGaA\n",
      "Heraeus Holding\n",
      "Heraeus Holding GmbH\n",
      "Hess\n",
      "Hewlett Packard Enterprise\n",
      "Hewlett-Packard\n",
      "Hilton Group\n",
      "Hindustan Petroleum\n",
      "Hitachi\n",
      "Hochteif\n",
      "Hochtief\n",
      "Hoechst\n",
      "Holcim\n",
      "Holcim Ltd\n",
      "Home Depot\n",
      "Hon Hai Precision Industry\n",
      "Honda Motor\n",
      "Honeywell International\n",
      "Household International\n",
      "Hsbc Holdings\n",
      "Huawei Investment & Holding\n",
      "Huawei Investment and Holding\n",
      "Huawei Technologies\n",
      "Huaxia Life Insurance\n",
      "Humana\n",
      "Husky Energy\n",
      "Hutchison Whampoa\n",
      "Hypo Real Estate Holding\n",
      "Hyundai\n",
      "Hyundai Heavy Industries\n",
      "Hyundai Mobis\n",
      "Hyundai Motor\n",
      "IBM\n",
      "IBP\n",
      "ING Groep\n",
      "ING Group\n",
      "INI (Instituto Nacional de Industria)\n",
      "INTL FCStone\n",
      "IRI\n",
      "ITT\n",
      "ITT Hartford Group\n",
      "ITT Industries\n",
      "Iberdrola\n",
      "Idemitsu Kosan\n",
      "Ikon Office Solutions\n",
      "Imperial Chemical Industries\n",
      "Imperial Chemical Industries PlC\n",
      "Imperial Tobacco Group\n",
      "Inbev\n",
      "Inchcape\n",
      "Indian Oil\n",
      "Inditex\n",
      "Industrial & Commer. Bank of China\n",
      "Industrial & Commercial Bank of China\n",
      "Industrial Bank\n",
      "Industrial and Commercial Bank of China\n",
      "Ineos Group Holdings\n",
      "Ingram Micro\n",
      "Intel\n",
      "International Airlines Group\n",
      "International Assets Holding\n",
      "International Business Machines\n",
      "International Consolidated Airlines Group\n",
      "International Paper\n",
      "International Petroleum Investment\n",
      "International Petroleum Investment PJSC\n",
      "Internationale Nederlanden Group\n",
      "Intesa Sanpaolo\n",
      "IntesaBci\n",
      "Invensys\n",
      "Ishikawajima-Harima HeavyIndustries\n",
      "Israel\n",
      "Istituto Bancario San Paolo Di Torino\n",
      "Istituto Per La Ricostruzione Industriale\n",
      "Istituto Por La Ricostruzione Industriale\n",
      "Isuzu Motors\n",
      "Itau Unibanco Holding\n",
      "Itaú Unibanco Holding\n",
      "Itaúsa-Investimentos Itaú\n",
      "Ito-Yokado\n",
      "Itochu\n",
      "J Sainsbury\n",
      "J. Sainsbury\n",
      "J.C. Penney\n",
      "J.P. Morgan & Co.\n",
      "J.P. Morgan & Co.rporated\n",
      "J.P. Morgan Chase\n",
      "J.P. Morgan Chase & Co.\n",
      "JBS\n",
      "JD.com\n",
      "JFE Holdings\n",
      "JP Morgan Chase\n",
      "JPMorgan Chase\n",
      "JPMorgan Chase & Co.\n",
      "JX Holdings\n",
      "JXTG Holdings\n",
      "Japan Airlines\n",
      "Japan Airlines System\n",
      "Japan Energy\n",
      "Japan Post\n",
      "Japan Post Holdings\n",
      "Japan Postal Service\n",
      "Japan Telecom Co.\n",
      "Japan Tobacco\n",
      "Japan Travel Bureau\n",
      "Jardine Matheson\n",
      "Jardine Matheson Holdings\n",
      "Jiangsu Shagang Group\n",
      "Jiangsu Shagang Group Co.\n",
      "Jiangxi Copper\n",
      "Jinchuan Group\n",
      "Jinneng Group\n",
      "Jizhong Energy Group\n",
      "John Hancock Mutual Life Insurance\n",
      "Johnson & Johnson\n",
      "Johnson Controls\n",
      "Johnson Controls International\n",
      "Jusco\n",
      "KB Financial Group\n",
      "KBC Group\n",
      "KDDI\n",
      "KFW Bankengruppe\n",
      "KT\n",
      "KTporation\n",
      "Kailuan (Group) Liability\n",
      "Kailuan Group\n",
      "Kajima\n",
      "Kanematsu\n",
      "Kansai Electric Power\n",
      "Kao\n",
      "Karstadt\n",
      "Karstadt Group\n",
      "Karstadt Quelle\n",
      "KarstadtQuelle\n",
      "Kaufhof\n",
      "Kawasaki Heavy Industries\n",
      "Kawasaki Steel\n",
      "Kawasho\n",
      "Kia Motors\n",
      "Kimberly-Clark\n",
      "Kingfisher\n",
      "Kinki Nippon Railway\n",
      "Kintetsu\n",
      "Kirin Brewery\n",
      "Kirin Holdings\n",
      "Kmart\n",
      "Kmart Holding\n",
      "Kobe Steel\n",
      "Koc Holding\n",
      "Kohl's\n",
      "Komatsu\n",
      "Koninklijke Ahold\n",
      "Kookmin Bank\n",
      "Korea Electric Power\n",
      "Korea Gas\n",
      "Koç Holding\n",
      "Kraft Foods\n",
      "Kraft Heinz\n",
      "Kreditanstalt fur Wiederaufbau\n",
      "Kroger\n",
      "Kubota\n",
      "Kumagai Gumi\n",
      "Kuraya Sanseido\n",
      "Kv_rner Group\n",
      "Kyobo Life Insurance\n",
      "Kyocera\n",
      "Kyushu Electric Power Co.\n",
      "L'Air Liquide\n",
      "L'Or_al\n",
      "L'Oreal\n",
      "L'Oréal\n",
      "L.M. Ericsson\n",
      "LG\n",
      "LG Chem\n",
      "LG Display\n",
      "LG Electronics\n",
      "LG International\n",
      "LM Ericsson\n",
      "LVMH M_et Hennessy Louis Vuitton\n",
      "LVMH Moet Hennessy Louis Vuitton\n",
      "La Poste\n",
      "Ladbrokes\n",
      "Lafarge\n",
      "LafargeHolcim\n",
      "Landesbank Baden-Württemberg\n",
      "Lear\n",
      "Legal & General\n",
      "Legal & General Group\n",
      "Lehman Brothers Holdings\n",
      "Lennar\n",
      "Lenovo Group\n",
      "Les Caisses d'Epargne et de Pr_voyance\n",
      "Les Caisses d'Épargne et de Prévoyance\n",
      "Liberty Mutual Holding\n",
      "Liberty Mutual Insurance Group\n",
      "Linde\n",
      "Lloyds Bank\n",
      "Lloyds Banking Group\n",
      "Lloyds TSB Group\n",
      "Lockheed\n",
      "Lockheed Martin\n",
      "Loews\n",
      "Lotte Shopping\n",
      "Louis Dreyfus\n",
      "Louis Dreyfus Commodities\n",
      "Lowe's\n",
      "Lowe's Companies\n",
      "Lucent Technologies\n",
      "Lufthansa Group\n",
      "Lukoil\n",
      "Lyondell Chemical\n",
      "LyondellBasell Industries\n",
      "LyondellBasell Industries AF S.C.A.\n",
      "Lyonnaise des Eaux\n",
      "M&G\n",
      "MAN\n",
      "MBNA\n",
      "MCI\n",
      "MCI Communications\n",
      "MCI WorldCom\n",
      "MOL Hungarian Oil and Gas\n",
      "MS&AD Insurance\n",
      "MS&AD Insurance Group Holdings\n",
      "MYCAL CORPORATION\n",
      "Macy's\n",
      "Maersk Group\n",
      "Magna International\n",
      "Man\n",
      "Mannesmann\n",
      "Manpower\n",
      "Manulife Financial\n",
      "Mapfre\n",
      "Mapfre Group\n",
      "Marathon Oil\n",
      "Marathon Petroleum\n",
      "Marks & Spencer\n",
      "Marks & Spencer Group\n",
      "Marquard & Bahls\n",
      "Marriott International\n",
      "Marsh & McLennan Companies\n",
      "Martin Marietta\n",
      "Marubeni\n",
      "Maruha\n",
      "Maruhan\n",
      "Masco\n",
      "Massachusetts Financial Group\n",
      "Massachusetts Mutual Life\n",
      "Massachusetts Mutual Life Insurance\n",
      "Matsushita Electric Industrial\n",
      "Matsushita Electric Works\n",
      "Mazda Motor\n",
      "McDonald's\n",
      "McDonnell Douglas\n",
      "McKesson\n",
      "McKesson HBOC\n",
      "Medco Health Solutions\n",
      "Mediceo Holdings Co.\n",
      "Mediceo Paltac Holdings\n",
      "Mediolanum\n",
      "Medipal Holdings\n",
      "Medtronic\n",
      "Meiji Life Insurance\n",
      "Meiji Yasuda Life Insurance\n",
      "Melville\n",
      "Mercantil Servicios Financieros\n",
      "Merck\n",
      "Merck & Co.\n",
      "Merrill Lynch &\n",
      "Merrill Lynch & Co.\n",
      "MetLife\n",
      "Metallgesellschaft\n",
      "Metro\n",
      "Metro Holding\n",
      "Metropolitan Life Insurance Co.\n",
      "Michelin\n",
      "Micron Technology\n",
      "Microsoft\n",
      "Midea Group\n",
      "Migros\n",
      "Migros Genossenschafts Bund\n",
      "Migros Genossenschafts-Bund\n",
      "Migros Group\n",
      "Migros-Genossenschafts-Bund\n",
      "Millea Holdings\n",
      "Minnesota Mining & Manufacturing\n",
      "Minnesota Mining & Manufacturing Company\n",
      "Minnesota Mining and Manufacturing\n",
      "Mirant\n",
      "Mitsubishi\n",
      "Mitsubishi Chemical\n",
      "Mitsubishi Chemical Holdings\n",
      "Mitsubishi Electric\n",
      "Mitsubishi Heavy Industries\n",
      "Mitsubishi Materials\n",
      "Mitsubishi Motors\n",
      "Mitsubishi Oil\n",
      "Mitsubishi Tokyo Financial Group\n",
      "Mitsubishi Tokyo Financial Group,Inc.\n",
      "Mitsubishi UFJ Financial Group\n",
      "Mitsui\n",
      "Mitsui &\n",
      "Mitsui Fudosan\n",
      "Mitsui Life Insurance\n",
      "Mitsui Marine & Fire Insurance\n",
      "Mitsui Mutual Life Insurance Co.\n",
      "Mitsui O.S.K. Lines\n",
      "Mitsui Sumitomo Insurance\n",
      "Mitsui Sumitomo Insurance Group Holdings\n",
      "Mitsukoshi\n",
      "Mittal Steel\n",
      "Mizuho Financial Group\n",
      "Mizuho Financial Group.\n",
      "Mizuho Holdings\n",
      "Mobil\n",
      "Mol Hungarian Oil and Gas\n",
      "Mondelez International\n",
      "Monsanto\n",
      "Monte Dei Paschi Di Siena\n",
      "Montedison\n",
      "Morgan Stanley\n",
      "Morgan Stanley Dean Witter & Co.\n",
      "Morgan Stanley Group\n",
      "Morrisons\n",
      "Motorola\n",
      "Motorola Solutions\n",
      "Munich Re\n",
      "Munich Re Group\n",
      "Murphy Oil\n",
      "Münchener Rück Rückversicherungs-Gesellschaft\n",
      "N.V. Nederlandse Gasunie\n",
      "NEC\n",
      "NGC\n",
      "NKK\n",
      "NKSJ Holdings\n",
      "NTT (Nippon Telegraph & Telephone)\n",
      "NYNEX\n",
      "Nabisco Group Holdings\n",
      "National Australia Bank\n",
      "National Grid\n",
      "National Grid Transco\n",
      "National Westminster Bank\n",
      "NationsBank\n",
      "Nationwide\n",
      "Nationwide Building Society\n",
      "Nationwide Insurance Enterprise\n",
      "Nationwide Mutual Insurance Co.\n",
      "Naturgy Energy Group\n",
      "Neste\n",
      "Neste Oil\n",
      "Neste Oy\n",
      "Nestl_\n",
      "Nestle\n",
      "Nestlé\n",
      "New China Life Insurance\n",
      "New Oji Paper\n",
      "New York Life Insurance\n",
      "News\n",
      "Nextel Communications\n",
      "Nichii Co. \n",
      "Nichimen\n",
      "Nike\n",
      "Nippon Dantai Life Insurance\n",
      "Nippon Express\n",
      "Nippon Life Insurance\n",
      "Nippon Mining Holdings\n",
      "Nippon Mitsubishi Oil\n",
      "Nippon Oil\n",
      "Nippon Paper Industries\n",
      "Nippon Steel\n",
      "Nippon Steel & Sumitomo Metal\n",
      "Nippon Tel. & Tel.\n",
      "Nippon Telegraph & Telephone\n",
      "Nippon Yusen Kabushiki Kaisha\n",
      "Nippondenso\n",
      "Nissan Motor\n",
      "Nissho Iwai\n",
      "Nittetsu Shoji\n",
      "Noble Group\n",
      "Nokia\n",
      "Nomura Holdings\n",
      "Norddeutsche Landesbank Girozen.\n",
      "Nordea AB\n",
      "Nordea Bank AB\n",
      "Norsk Hydro\n",
      "Norsk Hydro ASA\n",
      "Norsk Hydro a.s\n",
      "Nortel Networks\n",
      "Northern Telecom\n",
      "Northrop Grumman\n",
      "Northwest Airlines\n",
      "Northwestern Mutual\n",
      "Northwestern Mutual Life Insurance\n",
      "Northwestern Mutual Life Insurance,Inc.\n",
      "Norwest\n",
      "Norwich Union\n",
      "Novartis\n",
      "Novartis Group\n",
      "Nucor\n",
      "OAO Gazprom\n",
      "OAO Lukoil\n",
      "OAO Lukoil0\n",
      "OAO NK Yukos\n",
      "OAO SeverStal\n",
      "OAO TNK-BP Holding\n",
      "OMV Group\n",
      "Obayashi\n",
      "Occidental Petroleum\n",
      "Office Depot\n",
      "OfficeMaxrporated\n",
      "Oil & Natural Gas\n",
      "Oil and Natural Gas\n",
      "Oji Paper\n",
      "Old Mutual\n",
      "Olivetti\n",
      "Onex\n",
      "Oracle\n",
      "Orange\n",
      "Otto GmbH & Co. KG\n",
      "Otto Versand (GmbH & Co.)\n",
      "Otto Versand Group\n",
      "PBF Energy\n",
      "PDVSA\n",
      "PG&E\n",
      "PHOENIX Pharmahandel GmbH & Co. KG\n",
      "PKN ORLEN Group\n",
      "PKN Orlen Group\n",
      "POSCO\n",
      "PPR\n",
      "PSA Peugeot Citroën\n",
      "PT Pertamina (Persero)\n",
      "PTT\n",
      "PTT Public\n",
      "PTT Suisses\n",
      "Paccar\n",
      "PacifiCare Health Systems\n",
      "Pacific Construction Group\n",
      "Pacific Gas & Electric\n",
      "Pacific Telesis Group\n",
      "Panasonic\n",
      "Pechiney\n",
      "Pegatron\n",
      "Pemex\n",
      "Peninsular & Oriental Steam Navigation\n",
      "People's Insurance Co. of China\n",
      "People's Insurance of China\n",
      "PepsiCo\n",
      "Peregrine Investments Holdings\n",
      "Pertamina\n",
      "Perusahaan Listrik Negara\n",
      "Petro-Canada\n",
      "PetroFina\n",
      "Petrobras\n",
      "Petrofina\n",
      "Petroleos de Venezuela\n",
      "Petroliam Nasional Berhad\n",
      "Petronas\n",
      "Petroplus Holdings\n",
      "Petróleo Brasileiro\n",
      "Petróleos Mexicanos\n",
      "Peugeot\n",
      "Peugeot S. A.\n",
      "Pfizer\n",
      "Pharmacia\n",
      "Philip Morris Companies\n",
      "Philip Morris International\n",
      "Philips Electronics\n",
      "Phillips 66\n",
      "Phillips Petroleum\n",
      "Phoenix Group Holdings\n",
      "Phoenix Pharma\n",
      "Phoenix Pharmahandel\n",
      "Pinault-Printemps-Redoute\n",
      "Ping An Insurance\n",
      "Ping An Insurance (Group) of China\n",
      "Plains All American Pipeline, L.P.\n",
      "Plains GP Holdings\n",
      "Pohang Iron & Steel Co.\n",
      "Polski Koncern Naftowy ORLEN Spólka Akcyjna Group\n",
      "Poste Italiane\n",
      "Power China\n",
      "Power Construction of China\n",
      "Power of Canada\n",
      "PowerChina\n",
      "Premafin Finanziaria\n",
      "Premcor\n",
      "Preussag\n",
      "Price/Costco\n",
      "Procter & Gamble\n",
      "Progressive\n",
      "Prudential\n",
      "Prudential Financial\n",
      "Prudential Financial (U.S.)\n",
      "Prudential Insurance of America\n",
      "Public Service Enterprise Group\n",
      "Publix Super Markets\n",
      "Pulte Homes\n",
      "Qingdao Haier\n",
      "Qualcomm\n",
      "Quanta Computer\n",
      "Quelle Group\n",
      "Qwest Communications International\n",
      "R. J. Reynolds Tobacco Holdings\n",
      "RAG\n",
      "RAG Ag.\n",
      "RAO Gazprom\n",
      "RJR Nabisco Holdings\n",
      "RWE\n",
      "RWE Group\n",
      "Rabobank\n",
      "Rabobank Group\n",
      "Rajesh Exports\n",
      "Randstad\n",
      "Randstad Holding\n",
      "Raytheon\n",
      "Raytheon Technologies\n",
      "Reliance Industries\n",
      "Reliant Energy\n",
      "Reliant Energy,rporated\n",
      "Reliant Resources\n",
      "Renault\n",
      "Repsol\n",
      "Repsol YPF\n",
      "Repsol YPF,S.A.\n",
      "Repsol,S.A.\n",
      "Republic Industries\n",
      "Research In Motion\n",
      "Resona Holdings\n",
      "Rexel\n",
      "Ricoh\n",
      "Rio Tinto Group\n",
      "Rite Aid\n",
      "Robert Bosch\n",
      "Robert Bosch GmbH\n",
      "Roche Group\n",
      "Roche Holding\n",
      "Rockwell International\n",
      "Rolls-Royce Holdings\n",
      "Rosneft Oil\n",
      "Royal & Sun Alliance\n",
      "Royal & Sun Alliance Insurance Group\n",
      "Royal Ahold\n",
      "Royal Ahold Delhaize\n",
      "Royal Bank of Canada\n",
      "Royal Bank of Scotland Group\n",
      "Royal Dutch Shell\n",
      "Royal Dutch/Shell Group\n",
      "Royal Insurance Holdings\n",
      "Royal KPN\n",
      "Royal Mail Group\n",
      "Royal Mail Holdings\n",
      "Royal PTT Nederland\n",
      "Royal Philips\n",
      "Royal Philips Electronics\n",
      "Royal Ptt Nederland\n",
      "Ruhrkohle\n",
      "Russian Grids\n",
      "Rwe Group\n",
      "S-Oil\n",
      "SABIC\n",
      "SABMiller\n",
      "SAIC Motor\n",
      "SAP\n",
      "SBC Communications\n",
      "SCE\n",
      "SHV Holdings\n",
      "SK\n",
      "SK Corp\n",
      "SK Global\n",
      "SK Holdings\n",
      "SK Hynix\n",
      "SK Networks\n",
      "SNCF\n",
      "SNCF Group\n",
      "SNCF Mobilites\n",
      "SNCF Mobilités\n",
      "SPAR Handels\n",
      "SSE\n",
      "SYSCO\n",
      "Sabic\n",
      "Safeway\n",
      "Safran\n",
      "Sainsbury's\n",
      "Saint-Gobain\n",
      "Salomon\n",
      "Samsung\n",
      "Samsung C&T\n",
      "Samsung Electronics\n",
      "Samsung Life Insurance\n",
      "San Paolo IMI\n",
      "Sandoz\n",
      "Sanofi\n",
      "Sanofi-Aventis\n",
      "Santander Central Hispano Group\n",
      "Santander Group\n",
      "Sanyo Electric\n",
      "Sara Lee\n",
      "Saudi Aramco\n",
      "Saudi Basic Industries\n",
      "Savings Bank of the Russian Federation\n",
      "Sberbank\n",
      "Schering-Plough\n",
      "Schlumberger\n",
      "Schneider\n",
      "Schneider Electric\n",
      "Scottish & Southern Energy\n",
      "Scottish Power\n",
      "Scottish Widows Fund & Life Assurance\n",
      "Scottish and Southern Energy\n",
      "Sears Holdings\n",
      "Sears Roebuck and\n",
      "Sears, Roebuck &\n",
      "Sears, Roebuck and\n",
      "Sears, Roebuck and Co.\n",
      "Seiko Epson\n",
      "Sekisui Chemical\n",
      "Sekisui House\n",
      "Seven & I Holdings\n",
      "Shaanxi Coal & Chemical Industry\n",
      "Shaanxi Yanchang Petroleum\n",
      "Shaanxi Yanchang Petroleum (Group)\n",
      "Shandong Energy Group\n",
      "Shandong Energy Group Co.\n",
      "Shandong Iron & Steel Group\n",
      "Shandong Weiqiao Pioneering\n",
      "Shandong Weiqiao Pioneering Group\n",
      "Shanghai Automotive Industry\n",
      "Shanghai Baosteel Group\n",
      "Shanghai Construction Group\n",
      "Shanghai Pharmaceuticals Holding\n",
      "Shanghai Pudong Devel. Bank\n",
      "Shanghai Pudong Development Bank\n",
      "Shanxi Coal Transportation and Sales Group\n",
      "Shanxi Coking Coal Group\n",
      "Shanxi Jincheng Anthracite Coal Mining\n",
      "Shanxi Jincheng Anthracite Coal Mining Group\n",
      "Shanxi Jincheng Anthracite Coal Mining Group Co.\n",
      "Shanxi LuAn Mining Group\n",
      "Sharp\n",
      "Shenghong Holding Group\n",
      "Shenhua Group\n",
      "Shenhua Group (now China Energy Investment)\n",
      "Shenhua Group of China\n",
      "Shenzhen Investment Holdings\n",
      "Shimizu\n",
      "Shinhan Financial Group\n",
      "Shougang Group\n",
      "Showa Shell Sekiyu\n",
      "Showa Shell Sekiyu K.K.\n",
      "Siemens\n",
      "Sinochem\n",
      "Sinochem Group\n",
      "Sinomach\n",
      "Sinopec Group\n",
      "Sinopharm\n",
      "Sinosteel\n",
      "Sistema\n",
      "Sistema JSFC\n",
      "Skandia Group\n",
      "Skandinaviska Enskilda Banken\n",
      "Skanska AB\n",
      "Smithkline Beecham\n",
      "Snow Brand Milk Products\n",
      "Soci_t_ G_n_rale\n",
      "Sociedad Estatal De Participaciones Industriales\n",
      "Societe Generale\n",
      "Societe Nationale des Chemins de Fer Francais\n",
      "Société Générale\n",
      "Sodexho Alliance\n",
      "Sodexo\n",
      "SoftBank Group\n",
      "Softbank\n",
      "Solectron\n",
      "Solvay\n",
      "Somerfield\n",
      "Sompo Holdings\n",
      "Sompo Japan Insurance\n",
      "Sompo Japan Nipponkoa\n",
      "Sompo Japan Nipponkoa Holdings\n",
      "Sony\n",
      "Southern\n",
      "Sprint\n",
      "Sprint Nextel\n",
      "Ssangyong Business Group\n",
      "Standard Chartered\n",
      "Standard Chartered Bank\n",
      "Standard Life\n",
      "Standard Life Assurance Co.\n",
      "Staples\n",
      "Starbucks\n",
      "State Bank of India\n",
      "State Farm Group\n",
      "State Farm Insurance\n",
      "State Farm Insurance Companies\n",
      "State Farm Insurance Cos.\n",
      "State Grid\n",
      "State Grid of China\n",
      "State Power Investment\n",
      "State Power of China\n",
      "Statoil\n",
      "Statoil ASA\n",
      "Statoil Hydro ASA\n",
      "StoneX Group\n",
      "Stora Enso AB\n",
      "Stora Enso Oyj\n",
      "Strabag\n",
      "Subaru\n",
      "Suez\n",
      "Suez Environnement\n",
      "Suez Lyonnaise des Eaux\n",
      "Sumikin Bussan\n",
      "Sumikin Bussan Kaisha\n",
      "Sumitomo\n",
      "Sumitomo Chemical\n",
      "Sumitomo Electric Industries\n",
      "Sumitomo Life Insurance\n",
      "Sumitomo Metal Industries\n",
      "Sumitomo Mitsui Banking Corp\n",
      "Sumitomo Mitsui Financial Group\n",
      "Sun\n",
      "Sun Life Assurance of Canada *\n",
      "Sun Life Financial\n",
      "Sun Life Financial Services of Canada\n",
      "Sun Microsystems\n",
      "Suncor Energy\n",
      "Suning Commerce Group\n",
      "Suning.com Group\n",
      "Sunkyong\n",
      "Sunoco\n",
      "Suntory\n",
      "Supervalu\n",
      "Surgutneftegas\n",
      "Suzuken\n",
      "Suzuki Motor\n",
      "Svenska Cellulosa Aktiebolaget SCA\n",
      "Swiss Bank\n",
      "Swiss Life\n",
      "Swiss Life Holding\n",
      "Swiss Life Insurance & Pension Co\n",
      "Swiss Post\n",
      "Swiss Re\n",
      "Swiss Reinsurance\n",
      "Swisscom\n",
      "Sysco\n",
      "T&D Holdings\n",
      "TIAA\n",
      "TIAA-CREF\n",
      "TJX\n",
      "TNK-BP International\n",
      "TNT\n",
      "TOTAL\n",
      "TPG\n",
      "TRW\n",
      "TUI\n",
      "TXU\n",
      "T_neo\n",
      "Taikang Insurance Group\n",
      "Taisei\n",
      "Taiwan Power\n",
      "Taiwan Semiconductor\n",
      "Taiwan Semiconductor Manufacturing\n",
      "Takashimaya\n",
      "Takeda Pharmaceutical\n",
      "Takenaka\n",
      "Talanx\n",
      "Target\n",
      "Tata Motors\n",
      "Tata Steel\n",
      "Teachers Insurance & Annuity Association of America\n",
      "Teachers Insurance and Annuity Association College Retiremen\n",
      "Tech Data\n",
      "Tel_fonos De M_xico, de C.V.\n",
      "Telecom Italia\n",
      "Telecom Italia Group\n",
      "Telecomunicacoes Brasileiras\n",
      "Telefonaktiebolaget LM Ericsson\n",
      "Telefonaktiebolaget Lm Ericsson\n",
      "Telefonica\n",
      "Telefónica\n",
      "Telstra\n",
      "Tencent Holdings\n",
      "Tenet Healthcare\n",
      "Tenneco\n",
      "Tesco\n",
      "Tesoro\n",
      "Teva Pharmaceutical Industries\n",
      "Tewoo Group\n",
      "Texaco\n",
      "Texas Instruments\n",
      "Texas Instrumentsrporated\n",
      "Texas Utilities\n",
      "Textron\n",
      "Thales Group\n",
      "The\n",
      "The Allstate\n",
      "The Asahi Bank\n",
      "The Bank of Nova Scotia\n",
      "The Bank of Tokyo\n",
      "The Bank of Tokyo-Mitsubishi\n",
      "The Boeing\n",
      "The British Petroleum\n",
      "The Broken Hill Proprietary\n",
      "The Chase Manhattan\n",
      "The Chiyoda Mutual Life Insurance Co.\n",
      "The Chubb\n",
      "The Chugoku Electric Power\n",
      "The Chugoku Electric Power Co.\n",
      "The Co-operative Group\n",
      "The Coastal\n",
      "The Coca-Cola\n",
      "The Dai-Ichi Kangyo Bank \n",
      "The Dai-ichi Life Insurance\n",
      "The Dai-ichi Life Insurance Co.\n",
      "The Dai-ichi Mutual Life Insurance Co.\n",
      "The Daiei\n",
      "The Daiwa Bank\n",
      "The DirecTV Group\n",
      "The Dow Chemical\n",
      "The Fuji Bank\n",
      "The Gap\n",
      "The Goldman Sachs Group\n",
      "The Goodyear Tire & Rubber\n",
      "The Goodyear Tire & Rubber and Subsidiaries\n",
      "The Great Atlantic & Pacific Tea\n",
      "The Great Atlantic & Pacific Tea, I\n",
      "The Great Universal Stores P.L.C.\n",
      "The Hartford Financial Services Group\n",
      "The Home Depot\n",
      "The Industrial Bank of Japan\n",
      "The Kansai Electric Power Co.\n",
      "The Kroger\n",
      "The Kyoei Life Insurance\n",
      "The Linde Group\n",
      "The Long-Term Credit Bank of Japan\n",
      "The May Department Stores\n",
      "The Meiji Mutual Life Insurance Co.\n",
      "The Meiji Mutual Life Life Insurance Co.\n",
      "The Mitsubishi Bank \n",
      "The Mitsubishi Trust & Banking\n",
      "The Mitsui Trust & Banking.\n",
      "The News\n",
      "The Nippon Credit Bank \n",
      "The Nomura Securities Co.\n",
      "The Norinchukin Bank\n",
      "The PNC Financial Services Group\n",
      "The People's Insurance Co. (Group) of China\n",
      "The Principal Financial Group\n",
      "The Principal Mutual Life Insurance\n",
      "The Procter & Gamble\n",
      "The Progressive\n",
      "The Royal Bank of Scotland Group\n",
      "The Sakura Bank\n",
      "The Sakura Bank \n",
      "The Sanwa Bank\n",
      "The Seagram\n",
      "The Seiyu\n",
      "The Shoko Chukin Bank\n",
      "The Southern\n",
      "The St. Paul Companies\n",
      "The St. Paul Travelers Companies\n",
      "The Standard Life Assurance\n",
      "The Standard Life Assurance0000000000000000000000000\n",
      "The Sumitomo Bank\n",
      "The Sumitomo Marine & Fire Insurance\n",
      "The Sumitomo Trust & Banking Co.,Ltd.\n",
      "The TJX Companies\n",
      "The Taiyo Life Insurance Co.\n",
      "The Taiyo Mutual Life Insurance Co.\n",
      "The Tokai Bank\n",
      "The Tokio Marine & Fire Insurance\n",
      "The Tokyo Electric Power\n",
      "The Tokyo Electric Power Co.\n",
      "The Toronto-Dominion Bank\n",
      "The Travelers\n",
      "The Travelers Companies\n",
      "The Travelers Group\n",
      "The Walt Disney\n",
      "The Williams Companies\n",
      "The Yasuda Fire & Marine Insurance Co. \n",
      "The Yasuda Mutual Life Insurance\n",
      "The Yasuda Mutual Life Insurance Co.\n",
      "The Yasuda Trust & Banking Co. \n",
      "Thermo Fisher Scientific\n",
      "Thomson\n",
      "Thyssen\n",
      "Thyssen Krupp\n",
      "ThyssenKrupp\n",
      "Time Warner\n",
      "Time Warner Cable\n",
      "Toho Mutual Life Insurance\n",
      "Tohoku Electric Power Co.\n",
      "Tokio Marine Holdings\n",
      "Tokyo Electric Power\n",
      "Tokyo Gas\n",
      "Tokyu\n",
      "Tomen\n",
      "TonenGeneral Sekiyu\n",
      "TongLing Nonferrous Metals Group\n",
      "Toppan Printing\n",
      "Toray Industries\n",
      "Toronto-Dominion Bank\n",
      "Tosco\n",
      "Toshiba\n",
      "Toshoku\n",
      "Total\n",
      "Total Fina\n",
      "Total Fina Elf\n",
      "Toyo Seikan Kaisha\n",
      "Toyota Industries\n",
      "Toyota Motor\n",
      "Toyota Tsusho\n",
      "Tractebel\n",
      "Trafigura Beheer\n",
      "Trafigura Group\n",
      "TransCanada Pipelines\n",
      "Travelers\n",
      "Travelers Cos.\n",
      "Travelers Group\n",
      "Travelers Property Casualty\n",
      "Tsingshan Holding Group\n",
      "Twenty-First Century Fox\n",
      "Tyco International\n",
      "Tyson Foods\n",
      "U S WEST\n",
      "U S West\n",
      "U.S. Bancorp\n",
      "U.S. Postal Service\n",
      "UAL\n",
      "UBS\n",
      "UBS Group\n",
      "UFJ Holdings\n",
      "UPM-Kymmene Group\n",
      "UPS\n",
      "US Foods Holding\n",
      "US West\n",
      "USAA\n",
      "USX\n",
      "Ultramar Diamond Shamrock\n",
      "Ultrapar Holdings\n",
      "UniCredit Group\n",
      "UniCredito Italiano\n",
      "Unified Energy System of Russia\n",
      "Unilever\n",
      "Unilever/ Unilever\n",
      "Union Bank of Switzerland\n",
      "Union Des Assurances de Paris\n",
      "Union Pacific\n",
      "Uniper\n",
      "Unipol\n",
      "Unipol Group\n",
      "United Airlines Holdings\n",
      "United Continental Holdings\n",
      "United Health Care\n",
      "United HealthCare\n",
      "United Parcel Service\n",
      "United Parcel Service of America\n",
      "United Services Auto. Assn.\n",
      "United Services Automobile Association\n",
      "United States Postal Service\n",
      "United States Steel\n",
      "United Technologies\n",
      "United Technologies (now Raytheon)\n",
      "UnitedHealth Group\n",
      "UnitedHealth Group,rporated\n",
      "UnitedHealth Grouprporated\n",
      "Uny\n",
      "Usinor\n",
      "Usinor-Sacilor\n",
      "UtiliCorp United\n",
      "VIAG\n",
      "VTB Bank\n",
      "Vale\n",
      "Valero Energy\n",
      "Valero Energy Corp\n",
      "Vattenfall\n",
      "Vattenfall AB\n",
      "Veba\n",
      "Veolia Environnement\n",
      "Verizon\n",
      "Verizon Communications\n",
      "Viacom\n",
      "ViacomCBS\n",
      "Viag\n",
      "Vinci\n",
      "Visteon\n",
      "Vivendi\n",
      "Vivendi Universal\n",
      "Vodafone Airtouch\n",
      "Vodafone Group\n",
      "Volkswagen\n",
      "Volvo\n",
      "Volvo AB\n",
      "WH Group\n",
      "WMX Technologies\n",
      "Wachovia\n",
      "Wal-Mart Stores\n",
      "Walgreen\n",
      "Walgreen Co.\n",
      "Walgreens\n",
      "Walgreens Boots Alliance\n",
      "Walmart\n",
      "Walt Disney\n",
      "Warner-Lambert\n",
      "Washington Mutual\n",
      "Waste Management\n",
      "WellPoint\n",
      "WellPoint Health Networks\n",
      "Wells Fargo\n",
      "Wells Fargo &\n",
      "Wells Fargo & Co.\n",
      "Wesfarmers\n",
      "West Japan Railway\n",
      "WestLB\n",
      "Westdeutsche Landesbank\n",
      "Westdeutsche Landesbank Girozentrale\n",
      "Westdeutsche Landesbank Girozentrale Girozentrale\n",
      "Westinghouse Electric\n",
      "Weston\n",
      "Westpac Banking\n",
      "Weyerhaeuser\n",
      "Whirlpool\n",
      "William Hill\n",
      "William Morrison Supermarkets\n",
      "Wilmar International\n",
      "Winn-Dixie Stores\n",
      "Winterthur Group\n",
      "Winterthur Gruppe\n",
      "Wistron\n",
      "Wm Morrison Supermarkets\n",
      "Wm. Morrison Supermarkets\n",
      "Wolseley\n",
      "Woolworth\n",
      "Woolworths\n",
      "Woolworths Group\n",
      "Woori Finance Holdings\n",
      "World Fuel Services\n",
      "WorldCom\n",
      "Wuchan Zhongda Group\n",
      "Wuhan Iron & Steel\n",
      "Wuhan Iron and Steel (Group)\n",
      "Wyeth\n",
      "X5 Retail Group\n",
      "XMXYG\n",
      "Xcel Energy\n",
      "Xerox\n",
      "Xiamen C&D\n",
      "Xiamen ITG Holding Group\n",
      "Xiamen Xiangyu Group\n",
      "Xiaomi\n",
      "Xinjiang Guanghui Industry Investment\n",
      "Xinxing Cathay International\n",
      "Xinxing Cathay International Group\n",
      "Xstrata\n",
      "Yamada Denki\n",
      "Yamaha Motor\n",
      "Yango Financial Holding\n",
      "Yango Longking Group\n",
      "Yangquan Coal Industry (Group) Co.\n",
      "Yangquan Coal Industry Group\n",
      "Yankuang Group\n",
      "ZF Friedrichshafen\n",
      "Zhejiang Geely Holding Group\n",
      "Zhejiang Materials Industry Group\n",
      "Zurich Financial Services\n",
      "Zurich Insurance Co. Group\n",
      "Zurich Insurance Group\n",
      "_x0004_Franz Haniel & Cie. GmbH\n",
      "_x0005__x000F_\n",
      "kuraya\n",
      "nan\n",
      "us\n",
      "AMP\n",
      "British American Tobacco\n",
      "Électricite de France\n",
      "Électricité de France\n"
     ]
    }
   ],
   "source": [
    "# Display company names after round 1 of cleaning\n",
    "for i in sorted(list(master_df['Name'].unique().astype(str))):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round 2 cleaning - Specific companies\n",
    "master_df['Name'].replace('Apple Computer','Apple',inplace = True)\n",
    "master_df['Name'].replace('Amazon.com','Amazon',inplace = True)\n",
    "master_df['Name'].replace('American International Group','AIG',inplace = True)\n",
    "master_df['Name'].replace('AT&T Wireless Services','AT&T',inplace = True)\n",
    "master_df['Name'].replace('BP Amoco','BP',inplace = True)\n",
    "master_df['Name'].replace('British Petroleum','BP',inplace = True)\n",
    "master_df['Name'].replace('ChevronTexaco','Chevron',inplace = True)\n",
    "master_df['Name'].replace('China Petrochemical','Sinopec Group',inplace = True)\n",
    "master_df['Name'].replace('China Petro-Chemical','Sinopec Group',inplace = True)\n",
    "master_df['Name'].replace('China National PetroleumCorporation','China National Petroleum',inplace = True)\n",
    "master_df['Name'].replace('CVS','CVS Health',inplace = True)\n",
    "master_df['Name'].replace('CVS Caremark','CVS Health',inplace = True)\n",
    "master_df['Name'].replace('Citicorp','Citigroup',inplace = True)\n",
    "master_df['Name'].replace('Citicorp/Citibank','Citigroup',inplace = True)\n",
    "master_df['Name'].replace('Daimler-Benz','Daimler',inplace = True)\n",
    "master_df['Name'].replace('DaimlerChrysler','Daimler',inplace = True)\n",
    "master_df['Name'].replace('EXOR Group','EXOR',inplace = True)\n",
    "master_df['Name'].replace('E. ON','E.ON',inplace = True)\n",
    "master_df['Name'].replace('Federal National Mortgage Association','Fannie Mae',inplace = True)\n",
    "master_df['Name'].replace('Glencore International','Glencore',inplace = True)\n",
    "master_df['Name'].replace('Glencore Xstrata','Glencore',inplace = True)\n",
    "master_df['Name'].replace('Industrial & Commer. Bank of China','Industrial & Commercial Bank of China',inplace = True)\n",
    "master_df['Name'].replace('International Business Machines','IBM',inplace = True)\n",
    "master_df['Name'].replace('ING Groep','ING Group',inplace = True)\n",
    "master_df['Name'].replace('Internationale Nederlanden Group','ING Group',inplace = True)\n",
    "master_df['Name'].replace('Industrial and Commercial Bank of China','Industrial & Commercial Bank of China',inplace = True)\n",
    "master_df['Name'].replace('Japan Postal Service','Japan Post Holdings',inplace = True)\n",
    "master_df['Name'].replace('Japan Post','Japan Post Holdings',inplace = True)\n",
    "master_df['Name'].replace('Mitsui &','Mitsui',inplace = True)\n",
    "master_df['Name'].replace('McKesson HBOC','McKesson',inplace = True)\n",
    "master_df['Name'].replace('Melville','CVS Health',inplace = True)\n",
    "master_df['Name'].replace('NationsBank','Bank of America',inplace = True)\n",
    "master_df['Name'].replace('NTT (Nippon Telegraph & Telephone)','NTT',inplace = True)\n",
    "master_df['Name'].replace('Nippon Telegraph & Telephone','NTT',inplace = True)\n",
    "master_df['Name'].replace('Nippon Tel. & Tel.','NTT',inplace = True)\n",
    "master_df['Name'].replace('OAO Gazprom','Gazprom',inplace = True)\n",
    "master_df['Name'].replace('RAO Gazprom','Gazprom',inplace = True)\n",
    "master_df['Name'].replace('Royal Dutch/Shell Group','Royal Dutch Shell',inplace = True)\n",
    "master_df['Name'].replace('State Grid of China','State Grid',inplace = True)\n",
    "master_df['Name'].replace('State Power of China','State Grid',inplace = True)\n",
    "master_df['Name'].replace('The British Petroleum','BP',inplace = True)\n",
    "master_df['Name'].replace('Total Fina Elf','Total',inplace = True)\n",
    "master_df['Name'].replace('Total Fina','Total',inplace = True)\n",
    "master_df['Name'].replace('TOTAL','Total',inplace = True)\n",
    "master_df['Name'].replace('United HealthCare','UnitedHealth Group',inplace = True)\n",
    "master_df['Name'].replace('United Health Care','UnitedHealth Group',inplace = True)\n",
    "master_df['Name'].replace('UnitedHealth Group,rporated','UnitedHealth Group',inplace = True)\n",
    "master_df['Name'].replace('UnitedHealth Grouprporated','UnitedHealth Group',inplace = True)\n",
    "master_df['Name'].replace('Veba','E.ON',inplace = True)\n",
    "master_df['Name'].replace('Wal-Mart Stores','Walmart',inplace = True)\n",
    "master_df['Name'].replace('Phillips Petroleum','ConocoPhillips',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AT&T', 'AXA', 'Allianz', 'Amazon', 'Apple', 'BP', 'Bank of America', 'Berkshire Hathaway', 'CVS Health', 'Chevron', 'China National Petroleum', 'Citigroup', 'ConocoPhillips', 'Daimler', 'E.ON', 'Enron', 'Exxon', 'Exxon Mobil', 'Fannie Mae', 'Ford Motor', 'Fortis', 'Gazprom', 'General Electric', 'General Motors', 'Glencore', 'Hitachi', 'IBM', 'ING Group', 'Industrial & Commercial Bank of China', 'Itochu', 'Japan Post Holdings', 'Marubeni', 'McKesson', 'Mitsubishi', 'Mitsui', 'NTT', 'Nippon Life Insurance', 'Nissho Iwai', 'Royal Dutch Shell', 'Samsung Electronics', 'Saudi Aramco', 'Sinopec Group', 'State Grid', 'Sumitomo', 'Total', 'Toyota Motor', 'UnitedHealth Group', 'Volkswagen', 'Walmart']\n"
     ]
    }
   ],
   "source": [
    "# Get list of companies who were in Top 15 at least once\n",
    "top15_companies_df = master_df[master_df['Rank'] <= 15]\n",
    "top15_companies = top15_companies_df['Name'].unique().tolist()\n",
    "top15_companies.sort()\n",
    "print(top15_companies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "We will need to generate a dataframe in a format that is suitable for bar chart race visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "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>Name</th>\n",
       "      <th>AT&amp;T</th>\n",
       "      <th>AXA</th>\n",
       "      <th>Allianz</th>\n",
       "      <th>Amazon</th>\n",
       "      <th>Apple</th>\n",
       "      <th>BP</th>\n",
       "      <th>Bank of America</th>\n",
       "      <th>Berkshire Hathaway</th>\n",
       "      <th>CVS Health</th>\n",
       "      <th>Chevron</th>\n",
       "      <th>...</th>\n",
       "      <th>Samsung Electronics</th>\n",
       "      <th>Saudi Aramco</th>\n",
       "      <th>Sinopec Group</th>\n",
       "      <th>State Grid</th>\n",
       "      <th>Sumitomo</th>\n",
       "      <th>Total</th>\n",
       "      <th>Toyota Motor</th>\n",
       "      <th>UnitedHealth Group</th>\n",
       "      <th>Volkswagen</th>\n",
       "      <th>Walmart</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>75094</td>\n",
       "      <td>22426</td>\n",
       "      <td>40415</td>\n",
       "      <td>0</td>\n",
       "      <td>9189</td>\n",
       "      <td>50737</td>\n",
       "      <td>13126</td>\n",
       "      <td>0</td>\n",
       "      <td>11286</td>\n",
       "      <td>31064</td>\n",
       "      <td>...</td>\n",
       "      <td>14578</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>162476</td>\n",
       "      <td>24653</td>\n",
       "      <td>88159</td>\n",
       "      <td>0</td>\n",
       "      <td>49350</td>\n",
       "      <td>83412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>79609</td>\n",
       "      <td>26173</td>\n",
       "      <td>46045</td>\n",
       "      <td>0</td>\n",
       "      <td>11062</td>\n",
       "      <td>56982</td>\n",
       "      <td>16298</td>\n",
       "      <td>0</td>\n",
       "      <td>11516</td>\n",
       "      <td>32094</td>\n",
       "      <td>...</td>\n",
       "      <td>24151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>167531</td>\n",
       "      <td>27226</td>\n",
       "      <td>111052</td>\n",
       "      <td>0</td>\n",
       "      <td>61489</td>\n",
       "      <td>93627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>74525</td>\n",
       "      <td>32681</td>\n",
       "      <td>56577</td>\n",
       "      <td>0</td>\n",
       "      <td>9833</td>\n",
       "      <td>69852</td>\n",
       "      <td>17509</td>\n",
       "      <td>10500</td>\n",
       "      <td>0</td>\n",
       "      <td>38691</td>\n",
       "      <td>...</td>\n",
       "      <td>24710</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>119281</td>\n",
       "      <td>34513</td>\n",
       "      <td>108702</td>\n",
       "      <td>10074</td>\n",
       "      <td>66528</td>\n",
       "      <td>106147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>53261</td>\n",
       "      <td>76874</td>\n",
       "      <td>56785</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71194</td>\n",
       "      <td>21734</td>\n",
       "      <td>10430</td>\n",
       "      <td>13086</td>\n",
       "      <td>36376</td>\n",
       "      <td>...</td>\n",
       "      <td>23810</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102395</td>\n",
       "      <td>32741</td>\n",
       "      <td>95137</td>\n",
       "      <td>11794</td>\n",
       "      <td>65328</td>\n",
       "      <td>119299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>53588</td>\n",
       "      <td>78729</td>\n",
       "      <td>64875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68304</td>\n",
       "      <td>50777</td>\n",
       "      <td>13832</td>\n",
       "      <td>15274</td>\n",
       "      <td>26801</td>\n",
       "      <td>...</td>\n",
       "      <td>18394</td>\n",
       "      <td>0</td>\n",
       "      <td>34025</td>\n",
       "      <td>0</td>\n",
       "      <td>89021</td>\n",
       "      <td>27059</td>\n",
       "      <td>99740</td>\n",
       "      <td>17355</td>\n",
       "      <td>76307</td>\n",
       "      <td>139208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>62391</td>\n",
       "      <td>87646</td>\n",
       "      <td>74178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83566</td>\n",
       "      <td>51392</td>\n",
       "      <td>24028</td>\n",
       "      <td>18098</td>\n",
       "      <td>32676</td>\n",
       "      <td>...</td>\n",
       "      <td>26992</td>\n",
       "      <td>0</td>\n",
       "      <td>41883</td>\n",
       "      <td>36076</td>\n",
       "      <td>95702</td>\n",
       "      <td>44990</td>\n",
       "      <td>115671</td>\n",
       "      <td>19562</td>\n",
       "      <td>80073</td>\n",
       "      <td>166809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>65981</td>\n",
       "      <td>92782</td>\n",
       "      <td>71022</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148062</td>\n",
       "      <td>57747</td>\n",
       "      <td>33976</td>\n",
       "      <td>20088</td>\n",
       "      <td>48069</td>\n",
       "      <td>...</td>\n",
       "      <td>38491</td>\n",
       "      <td>0</td>\n",
       "      <td>45346</td>\n",
       "      <td>42549</td>\n",
       "      <td>91168</td>\n",
       "      <td>105870</td>\n",
       "      <td>121416</td>\n",
       "      <td>21122</td>\n",
       "      <td>78852</td>\n",
       "      <td>193295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>59142</td>\n",
       "      <td>65580</td>\n",
       "      <td>85929</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174218</td>\n",
       "      <td>52641</td>\n",
       "      <td>37668</td>\n",
       "      <td>22241</td>\n",
       "      <td>99699</td>\n",
       "      <td>...</td>\n",
       "      <td>35969</td>\n",
       "      <td>0</td>\n",
       "      <td>40388</td>\n",
       "      <td>48374</td>\n",
       "      <td>77140</td>\n",
       "      <td>94312</td>\n",
       "      <td>120814</td>\n",
       "      <td>23454</td>\n",
       "      <td>79287</td>\n",
       "      <td>219812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>31179</td>\n",
       "      <td>62051</td>\n",
       "      <td>101930</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>178721</td>\n",
       "      <td>45732</td>\n",
       "      <td>42353</td>\n",
       "      <td>24182</td>\n",
       "      <td>92043</td>\n",
       "      <td>...</td>\n",
       "      <td>47606</td>\n",
       "      <td>0</td>\n",
       "      <td>44503</td>\n",
       "      <td>0</td>\n",
       "      <td>75745</td>\n",
       "      <td>96945</td>\n",
       "      <td>131754</td>\n",
       "      <td>25020</td>\n",
       "      <td>82204</td>\n",
       "      <td>246525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>25612</td>\n",
       "      <td>111912</td>\n",
       "      <td>114950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>232571</td>\n",
       "      <td>48065</td>\n",
       "      <td>63859</td>\n",
       "      <td>26588</td>\n",
       "      <td>112937</td>\n",
       "      <td>...</td>\n",
       "      <td>54400</td>\n",
       "      <td>0</td>\n",
       "      <td>55062</td>\n",
       "      <td>58348</td>\n",
       "      <td>15126</td>\n",
       "      <td>118441</td>\n",
       "      <td>153111</td>\n",
       "      <td>28823</td>\n",
       "      <td>98637</td>\n",
       "      <td>263009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>30537</td>\n",
       "      <td>121606</td>\n",
       "      <td>118937</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285059</td>\n",
       "      <td>63324</td>\n",
       "      <td>74382</td>\n",
       "      <td>30594</td>\n",
       "      <td>147967</td>\n",
       "      <td>...</td>\n",
       "      <td>71556</td>\n",
       "      <td>0</td>\n",
       "      <td>75077</td>\n",
       "      <td>71290</td>\n",
       "      <td>19068</td>\n",
       "      <td>152610</td>\n",
       "      <td>172616</td>\n",
       "      <td>37218</td>\n",
       "      <td>110649</td>\n",
       "      <td>287989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>43862</td>\n",
       "      <td>129839</td>\n",
       "      <td>121406</td>\n",
       "      <td>0</td>\n",
       "      <td>13931</td>\n",
       "      <td>267600</td>\n",
       "      <td>83980</td>\n",
       "      <td>81663</td>\n",
       "      <td>37006</td>\n",
       "      <td>189481</td>\n",
       "      <td>...</td>\n",
       "      <td>78717</td>\n",
       "      <td>0</td>\n",
       "      <td>98785</td>\n",
       "      <td>86984</td>\n",
       "      <td>22800</td>\n",
       "      <td>152361</td>\n",
       "      <td>185805</td>\n",
       "      <td>45365</td>\n",
       "      <td>118377</td>\n",
       "      <td>315654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>118928</td>\n",
       "      <td>162762</td>\n",
       "      <td>140618</td>\n",
       "      <td>0</td>\n",
       "      <td>24006</td>\n",
       "      <td>291438</td>\n",
       "      <td>119190</td>\n",
       "      <td>118245</td>\n",
       "      <td>76330</td>\n",
       "      <td>210783</td>\n",
       "      <td>...</td>\n",
       "      <td>106006</td>\n",
       "      <td>0</td>\n",
       "      <td>159260</td>\n",
       "      <td>132885</td>\n",
       "      <td>32144</td>\n",
       "      <td>187280</td>\n",
       "      <td>230201</td>\n",
       "      <td>75431</td>\n",
       "      <td>149054</td>\n",
       "      <td>378799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>118928</td>\n",
       "      <td>162762</td>\n",
       "      <td>140618</td>\n",
       "      <td>0</td>\n",
       "      <td>24006</td>\n",
       "      <td>291438</td>\n",
       "      <td>119190</td>\n",
       "      <td>118245</td>\n",
       "      <td>76330</td>\n",
       "      <td>210783</td>\n",
       "      <td>...</td>\n",
       "      <td>106006</td>\n",
       "      <td>0</td>\n",
       "      <td>159260</td>\n",
       "      <td>132885</td>\n",
       "      <td>32144</td>\n",
       "      <td>187280</td>\n",
       "      <td>230201</td>\n",
       "      <td>75431</td>\n",
       "      <td>149054</td>\n",
       "      <td>378799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>124028</td>\n",
       "      <td>80257</td>\n",
       "      <td>142395</td>\n",
       "      <td>19166</td>\n",
       "      <td>32479</td>\n",
       "      <td>367053</td>\n",
       "      <td>113106</td>\n",
       "      <td>107786</td>\n",
       "      <td>87472</td>\n",
       "      <td>263159</td>\n",
       "      <td>...</td>\n",
       "      <td>110350</td>\n",
       "      <td>0</td>\n",
       "      <td>207814</td>\n",
       "      <td>164136</td>\n",
       "      <td>34954</td>\n",
       "      <td>234674</td>\n",
       "      <td>204352</td>\n",
       "      <td>81186</td>\n",
       "      <td>166579</td>\n",
       "      <td>405607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>123018</td>\n",
       "      <td>175257</td>\n",
       "      <td>125999</td>\n",
       "      <td>24509</td>\n",
       "      <td>36537</td>\n",
       "      <td>246138</td>\n",
       "      <td>150450</td>\n",
       "      <td>112493</td>\n",
       "      <td>98729</td>\n",
       "      <td>163527</td>\n",
       "      <td>...</td>\n",
       "      <td>108927</td>\n",
       "      <td>0</td>\n",
       "      <td>187518</td>\n",
       "      <td>184496</td>\n",
       "      <td>31063</td>\n",
       "      <td>155887</td>\n",
       "      <td>204106</td>\n",
       "      <td>87138</td>\n",
       "      <td>146205</td>\n",
       "      <td>408214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>124629</td>\n",
       "      <td>162236</td>\n",
       "      <td>127379</td>\n",
       "      <td>34204</td>\n",
       "      <td>65225</td>\n",
       "      <td>308928</td>\n",
       "      <td>134194</td>\n",
       "      <td>136185</td>\n",
       "      <td>96413</td>\n",
       "      <td>196337</td>\n",
       "      <td>...</td>\n",
       "      <td>133780</td>\n",
       "      <td>0</td>\n",
       "      <td>273422</td>\n",
       "      <td>226294</td>\n",
       "      <td>36218</td>\n",
       "      <td>186055</td>\n",
       "      <td>221760</td>\n",
       "      <td>94155</td>\n",
       "      <td>168041</td>\n",
       "      <td>421849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>126723</td>\n",
       "      <td>142712</td>\n",
       "      <td>134168</td>\n",
       "      <td>48077</td>\n",
       "      <td>108249</td>\n",
       "      <td>386463</td>\n",
       "      <td>115074</td>\n",
       "      <td>143688</td>\n",
       "      <td>107750</td>\n",
       "      <td>245621</td>\n",
       "      <td>...</td>\n",
       "      <td>148944</td>\n",
       "      <td>0</td>\n",
       "      <td>375214</td>\n",
       "      <td>259142</td>\n",
       "      <td>41301</td>\n",
       "      <td>231580</td>\n",
       "      <td>235364</td>\n",
       "      <td>101862</td>\n",
       "      <td>221550</td>\n",
       "      <td>446950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>127434</td>\n",
       "      <td>154571</td>\n",
       "      <td>130775</td>\n",
       "      <td>61093</td>\n",
       "      <td>156508</td>\n",
       "      <td>388285</td>\n",
       "      <td>100078</td>\n",
       "      <td>162463</td>\n",
       "      <td>123133</td>\n",
       "      <td>233899</td>\n",
       "      <td>...</td>\n",
       "      <td>178555</td>\n",
       "      <td>0</td>\n",
       "      <td>428167</td>\n",
       "      <td>298449</td>\n",
       "      <td>36322</td>\n",
       "      <td>234278</td>\n",
       "      <td>265702</td>\n",
       "      <td>110618</td>\n",
       "      <td>247613</td>\n",
       "      <td>469162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>128752</td>\n",
       "      <td>165893</td>\n",
       "      <td>134636</td>\n",
       "      <td>74452</td>\n",
       "      <td>170910</td>\n",
       "      <td>396217</td>\n",
       "      <td>101697</td>\n",
       "      <td>182150</td>\n",
       "      <td>126761</td>\n",
       "      <td>220356</td>\n",
       "      <td>...</td>\n",
       "      <td>208938</td>\n",
       "      <td>0</td>\n",
       "      <td>457201</td>\n",
       "      <td>333386</td>\n",
       "      <td>33114</td>\n",
       "      <td>227882</td>\n",
       "      <td>256454</td>\n",
       "      <td>122489</td>\n",
       "      <td>261539</td>\n",
       "      <td>476294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>132447</td>\n",
       "      <td>161173</td>\n",
       "      <td>136846</td>\n",
       "      <td>88988</td>\n",
       "      <td>182795</td>\n",
       "      <td>358678</td>\n",
       "      <td>95181</td>\n",
       "      <td>194673</td>\n",
       "      <td>139367</td>\n",
       "      <td>203784</td>\n",
       "      <td>...</td>\n",
       "      <td>195845</td>\n",
       "      <td>0</td>\n",
       "      <td>446811</td>\n",
       "      <td>339426</td>\n",
       "      <td>34218</td>\n",
       "      <td>212018</td>\n",
       "      <td>247702</td>\n",
       "      <td>130474</td>\n",
       "      <td>268566</td>\n",
       "      <td>485651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>146801</td>\n",
       "      <td>129250</td>\n",
       "      <td>122948</td>\n",
       "      <td>107006</td>\n",
       "      <td>233715</td>\n",
       "      <td>225982</td>\n",
       "      <td>93056</td>\n",
       "      <td>210821</td>\n",
       "      <td>153290</td>\n",
       "      <td>131118</td>\n",
       "      <td>...</td>\n",
       "      <td>177440</td>\n",
       "      <td>0</td>\n",
       "      <td>294344</td>\n",
       "      <td>329601</td>\n",
       "      <td>33409</td>\n",
       "      <td>143421</td>\n",
       "      <td>236592</td>\n",
       "      <td>157107</td>\n",
       "      <td>236600</td>\n",
       "      <td>482130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>163786</td>\n",
       "      <td>143722</td>\n",
       "      <td>122196</td>\n",
       "      <td>135987</td>\n",
       "      <td>215639</td>\n",
       "      <td>186606</td>\n",
       "      <td>93662</td>\n",
       "      <td>223604</td>\n",
       "      <td>177526</td>\n",
       "      <td>107567</td>\n",
       "      <td>...</td>\n",
       "      <td>173957</td>\n",
       "      <td>0</td>\n",
       "      <td>267518</td>\n",
       "      <td>315199</td>\n",
       "      <td>36888</td>\n",
       "      <td>127925</td>\n",
       "      <td>254694</td>\n",
       "      <td>184840</td>\n",
       "      <td>240264</td>\n",
       "      <td>485873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>160546</td>\n",
       "      <td>149461</td>\n",
       "      <td>123532</td>\n",
       "      <td>177866</td>\n",
       "      <td>229234</td>\n",
       "      <td>244582</td>\n",
       "      <td>100264</td>\n",
       "      <td>242137</td>\n",
       "      <td>184765</td>\n",
       "      <td>134533</td>\n",
       "      <td>...</td>\n",
       "      <td>211940</td>\n",
       "      <td>0</td>\n",
       "      <td>326953</td>\n",
       "      <td>348903</td>\n",
       "      <td>43570</td>\n",
       "      <td>149099</td>\n",
       "      <td>265172</td>\n",
       "      <td>201159</td>\n",
       "      <td>260028</td>\n",
       "      <td>500343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>170756</td>\n",
       "      <td>125578</td>\n",
       "      <td>126800</td>\n",
       "      <td>232887</td>\n",
       "      <td>265595</td>\n",
       "      <td>303738</td>\n",
       "      <td>110584</td>\n",
       "      <td>247837</td>\n",
       "      <td>194579</td>\n",
       "      <td>166339</td>\n",
       "      <td>...</td>\n",
       "      <td>221579</td>\n",
       "      <td>355905</td>\n",
       "      <td>414650</td>\n",
       "      <td>387056</td>\n",
       "      <td>48156</td>\n",
       "      <td>184106</td>\n",
       "      <td>272612</td>\n",
       "      <td>226247</td>\n",
       "      <td>278342</td>\n",
       "      <td>514405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>181193</td>\n",
       "      <td>148984</td>\n",
       "      <td>130359</td>\n",
       "      <td>280522</td>\n",
       "      <td>260174</td>\n",
       "      <td>282616</td>\n",
       "      <td>113589</td>\n",
       "      <td>254616</td>\n",
       "      <td>256776</td>\n",
       "      <td>146516</td>\n",
       "      <td>...</td>\n",
       "      <td>197705</td>\n",
       "      <td>329784</td>\n",
       "      <td>407009</td>\n",
       "      <td>383906</td>\n",
       "      <td>48746</td>\n",
       "      <td>176249</td>\n",
       "      <td>275288</td>\n",
       "      <td>242155</td>\n",
       "      <td>282760</td>\n",
       "      <td>523964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name    AT&T     AXA  Allianz  Amazon   Apple      BP  Bank of America  \\\n",
       "Year                                                                     \n",
       "1995   75094   22426    40415       0    9189   50737            13126   \n",
       "1996   79609   26173    46045       0   11062   56982            16298   \n",
       "1997   74525   32681    56577       0    9833   69852            17509   \n",
       "1998   53261   76874    56785       0       0   71194            21734   \n",
       "1999   53588   78729    64875       0       0   68304            50777   \n",
       "2000   62391   87646    74178       0       0   83566            51392   \n",
       "2001   65981   92782    71022       0       0  148062            57747   \n",
       "2002   59142   65580    85929       0       0  174218            52641   \n",
       "2003   31179   62051   101930       0       0  178721            45732   \n",
       "2004   25612  111912   114950       0       0  232571            48065   \n",
       "2005   30537  121606   118937       0       0  285059            63324   \n",
       "2006   43862  129839   121406       0   13931  267600            83980   \n",
       "2007  118928  162762   140618       0   24006  291438           119190   \n",
       "2008  118928  162762   140618       0   24006  291438           119190   \n",
       "2009  124028   80257   142395   19166   32479  367053           113106   \n",
       "2010  123018  175257   125999   24509   36537  246138           150450   \n",
       "2011  124629  162236   127379   34204   65225  308928           134194   \n",
       "2012  126723  142712   134168   48077  108249  386463           115074   \n",
       "2013  127434  154571   130775   61093  156508  388285           100078   \n",
       "2014  128752  165893   134636   74452  170910  396217           101697   \n",
       "2015  132447  161173   136846   88988  182795  358678            95181   \n",
       "2016  146801  129250   122948  107006  233715  225982            93056   \n",
       "2017  163786  143722   122196  135987  215639  186606            93662   \n",
       "2018  160546  149461   123532  177866  229234  244582           100264   \n",
       "2019  170756  125578   126800  232887  265595  303738           110584   \n",
       "2020  181193  148984   130359  280522  260174  282616           113589   \n",
       "\n",
       "Name  Berkshire Hathaway  CVS Health  Chevron  ...  Samsung Electronics  \\\n",
       "Year                                           ...                        \n",
       "1995                   0       11286    31064  ...                14578   \n",
       "1996                   0       11516    32094  ...                24151   \n",
       "1997               10500           0    38691  ...                24710   \n",
       "1998               10430       13086    36376  ...                23810   \n",
       "1999               13832       15274    26801  ...                18394   \n",
       "2000               24028       18098    32676  ...                26992   \n",
       "2001               33976       20088    48069  ...                38491   \n",
       "2002               37668       22241    99699  ...                35969   \n",
       "2003               42353       24182    92043  ...                47606   \n",
       "2004               63859       26588   112937  ...                54400   \n",
       "2005               74382       30594   147967  ...                71556   \n",
       "2006               81663       37006   189481  ...                78717   \n",
       "2007              118245       76330   210783  ...               106006   \n",
       "2008              118245       76330   210783  ...               106006   \n",
       "2009              107786       87472   263159  ...               110350   \n",
       "2010              112493       98729   163527  ...               108927   \n",
       "2011              136185       96413   196337  ...               133780   \n",
       "2012              143688      107750   245621  ...               148944   \n",
       "2013              162463      123133   233899  ...               178555   \n",
       "2014              182150      126761   220356  ...               208938   \n",
       "2015              194673      139367   203784  ...               195845   \n",
       "2016              210821      153290   131118  ...               177440   \n",
       "2017              223604      177526   107567  ...               173957   \n",
       "2018              242137      184765   134533  ...               211940   \n",
       "2019              247837      194579   166339  ...               221579   \n",
       "2020              254616      256776   146516  ...               197705   \n",
       "\n",
       "Name  Saudi Aramco  Sinopec Group  State Grid  Sumitomo   Total  Toyota Motor  \\\n",
       "Year                                                                            \n",
       "1995             0              0           0    162476   24653         88159   \n",
       "1996             0              0           0    167531   27226        111052   \n",
       "1997             0              0           0    119281   34513        108702   \n",
       "1998             0              0           0    102395   32741         95137   \n",
       "1999             0          34025           0     89021   27059         99740   \n",
       "2000             0          41883       36076     95702   44990        115671   \n",
       "2001             0          45346       42549     91168  105870        121416   \n",
       "2002             0          40388       48374     77140   94312        120814   \n",
       "2003             0          44503           0     75745   96945        131754   \n",
       "2004             0          55062       58348     15126  118441        153111   \n",
       "2005             0          75077       71290     19068  152610        172616   \n",
       "2006             0          98785       86984     22800  152361        185805   \n",
       "2007             0         159260      132885     32144  187280        230201   \n",
       "2008             0         159260      132885     32144  187280        230201   \n",
       "2009             0         207814      164136     34954  234674        204352   \n",
       "2010             0         187518      184496     31063  155887        204106   \n",
       "2011             0         273422      226294     36218  186055        221760   \n",
       "2012             0         375214      259142     41301  231580        235364   \n",
       "2013             0         428167      298449     36322  234278        265702   \n",
       "2014             0         457201      333386     33114  227882        256454   \n",
       "2015             0         446811      339426     34218  212018        247702   \n",
       "2016             0         294344      329601     33409  143421        236592   \n",
       "2017             0         267518      315199     36888  127925        254694   \n",
       "2018             0         326953      348903     43570  149099        265172   \n",
       "2019        355905         414650      387056     48156  184106        272612   \n",
       "2020        329784         407009      383906     48746  176249        275288   \n",
       "\n",
       "Name  UnitedHealth Group  Volkswagen  Walmart  \n",
       "Year                                           \n",
       "1995                   0       49350    83412  \n",
       "1996                   0       61489    93627  \n",
       "1997               10074       66528   106147  \n",
       "1998               11794       65328   119299  \n",
       "1999               17355       76307   139208  \n",
       "2000               19562       80073   166809  \n",
       "2001               21122       78852   193295  \n",
       "2002               23454       79287   219812  \n",
       "2003               25020       82204   246525  \n",
       "2004               28823       98637   263009  \n",
       "2005               37218      110649   287989  \n",
       "2006               45365      118377   315654  \n",
       "2007               75431      149054   378799  \n",
       "2008               75431      149054   378799  \n",
       "2009               81186      166579   405607  \n",
       "2010               87138      146205   408214  \n",
       "2011               94155      168041   421849  \n",
       "2012              101862      221550   446950  \n",
       "2013              110618      247613   469162  \n",
       "2014              122489      261539   476294  \n",
       "2015              130474      268566   485651  \n",
       "2016              157107      236600   482130  \n",
       "2017              184840      240264   485873  \n",
       "2018              201159      260028   500343  \n",
       "2019              226247      278342   514405  \n",
       "2020              242155      282760   523964  \n",
       "\n",
       "[26 rows x 49 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_companies_df = pd.DataFrame()\n",
    "\n",
    "for company in top15_companies:\n",
    "    df = master_df.drop(columns=['Rank'])\n",
    "    df['Name'] = df['Name'].str.strip()\n",
    "    df_company = df[df['Name'] == company]\n",
    "    top_companies_df = top_companies_df.append(df_company, ignore_index=True)\n",
    "    \n",
    "bcr_df = top_companies_df.pivot_table(values = 'Revenue',index = ['Year'], columns = 'Name')\n",
    "bcr_df.fillna(0, inplace=True)\n",
    "bcr_df.sort_values(list(bcr_df.columns),inplace=True)\n",
    "bcr_df = bcr_df.sort_index()\n",
    "#pd.set_option(\"display.max_columns\", 101)\n",
    "bcr_df = bcr_df.astype(int)\n",
    "bcr_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Note: There is an error in the original data sources of the Fortune 500 website, where the datasets for 2007 and 2008 are duplicates of each other. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export for visualization in Flourish Studio\n",
    "bcr_df.to_excel('Data/Fortune 500 (Global) - Bar Chart Race Dataset.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used Flourish studio to do the bar chart race visualization (in addition to doing it in Python). Flourish has a host of visualization templates that can be easily created and customized. Here is the link to the Fortune 500 Bar Chart Race:\n",
    "- https://public.flourish.studio/visualisation/5287940/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<a name=\"bar_chart_race\"></a>\n",
    "### 2) Bar Chart Race in Python\n",
    "Using the Python `bar_chart_race` package (built on Matplotlib) to generate bar chart race.\n",
    "\n",
    "Official documentation: https://www.dexplo.org/bar_chart_race/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcr.bar_chart_race(df = bcr_df, \n",
    "                   n_bars = 10, \n",
    "                   sort='desc',\n",
    "                   title='Top 10 Fortune 500 (Global) Companies (1995-2020)',\n",
    "                   filename = 'Top 10 Fortune 500 (Global) Companies (1995-2020).mp4',\n",
    "                   period_length = 1600,\n",
    "                   bar_label_size=6,\n",
    "                   tick_label_size=6,\n",
    "                   steps_per_period = 70,\n",
    "                   dpi = 400,\n",
    "                   cmap='dark24',\n",
    "                   fixed_max=True,\n",
    "                   filter_column_colors = True,\n",
    "                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<a name=\"top_rank\"></a>\n",
    "### 3) Top ranked Fortune 500 company over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Year</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>175836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996</td>\n",
       "      <td>1</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>184365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1997</td>\n",
       "      <td>1</td>\n",
       "      <td>General Motors</td>\n",
       "      <td>168369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>General Motors</td>\n",
       "      <td>178174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>General Motors</td>\n",
       "      <td>161315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>General Motors</td>\n",
       "      <td>176558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>210392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>219812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>246525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>263009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>287989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>339938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>378799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>378799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Dutch Shell</td>\n",
       "      <td>458361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>408214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>421849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Dutch Shell</td>\n",
       "      <td>484489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Dutch Shell</td>\n",
       "      <td>481700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>476294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>485651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>482130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>485873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>500343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>514405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>523964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0   Year Rank               Name  Revenue\n",
       "0   1995    1         Mitsubishi   175836\n",
       "1   1996    1         Mitsubishi   184365\n",
       "2   1997    1     General Motors   168369\n",
       "3   1998    1     General Motors   178174\n",
       "4   1999    1     General Motors   161315\n",
       "5   2000    1     General Motors   176558\n",
       "6   2001    1        Exxon Mobil   210392\n",
       "7   2002    1            Walmart   219812\n",
       "8   2003    1            Walmart   246525\n",
       "9   2004    1            Walmart   263009\n",
       "10  2005    1            Walmart   287989\n",
       "11  2006    1        Exxon Mobil   339938\n",
       "12  2007    1            Walmart   378799\n",
       "13  2008    1            Walmart   378799\n",
       "14  2009    1  Royal Dutch Shell   458361\n",
       "15  2010    1            Walmart   408214\n",
       "16  2011    1            Walmart   421849\n",
       "17  2012    1  Royal Dutch Shell   484489\n",
       "18  2013    1  Royal Dutch Shell   481700\n",
       "19  2014    1            Walmart   476294\n",
       "20  2015    1            Walmart   485651\n",
       "21  2016    1            Walmart   482130\n",
       "22  2017    1            Walmart   485873\n",
       "23  2018    1            Walmart   500343\n",
       "24  2019    1            Walmart   514405\n",
       "25  2020    1            Walmart   523964"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_df = master_df.copy()\n",
    "top_df = top_df[top_df['Rank']==1].reset_index(drop=True).sort_values(by=['Year'])\n",
    "top_df = top_df[['Year','Rank','Name','Revenue']]\n",
    "top_df = top_df.reset_index(drop = True)\n",
    "top_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<a name=\"bottom_rank\"></a>\n",
    "### 4) Bottom ranked Fortune 500 company over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
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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>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500</td>\n",
       "      <td>Toyo Seikan Kaisha</td>\n",
       "      <td>7844</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "      <td>Telecomunicacoes Brasileiras</td>\n",
       "      <td>8862</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500</td>\n",
       "      <td>Banca Monte Dei Paschi Di Siena</td>\n",
       "      <td>9169</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500</td>\n",
       "      <td>Sun</td>\n",
       "      <td>8968</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Northrop Grumman</td>\n",
       "      <td>8902</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>500</td>\n",
       "      <td>The</td>\n",
       "      <td>9723</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>500</td>\n",
       "      <td>Sodexho Alliance</td>\n",
       "      <td>10306</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>500</td>\n",
       "      <td>Takenaka</td>\n",
       "      <td>10096</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>500</td>\n",
       "      <td>Kawasaki Heavy Industries</td>\n",
       "      <td>10173</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>500</td>\n",
       "      <td>The Toronto-Dominion Bank</td>\n",
       "      <td>10827</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>500</td>\n",
       "      <td>Masco</td>\n",
       "      <td>12431</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>500</td>\n",
       "      <td>Nike</td>\n",
       "      <td>13740</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>500</td>\n",
       "      <td>Fluor</td>\n",
       "      <td>16691</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>500</td>\n",
       "      <td>Fluor</td>\n",
       "      <td>16691</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>500</td>\n",
       "      <td>Mitsui O.S.K. Lines</td>\n",
       "      <td>18572</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>500</td>\n",
       "      <td>Dai Nippon Printing</td>\n",
       "      <td>17053</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>500</td>\n",
       "      <td>Wistron</td>\n",
       "      <td>19538</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>500</td>\n",
       "      <td>Manpower</td>\n",
       "      <td>22006</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>500</td>\n",
       "      <td>Ricoh</td>\n",
       "      <td>23175</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>500</td>\n",
       "      <td>Raytheon</td>\n",
       "      <td>23706</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>500</td>\n",
       "      <td>Wuhan Iron &amp; Steel</td>\n",
       "      <td>23721</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>500</td>\n",
       "      <td>Old Mutual</td>\n",
       "      <td>20923</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>500</td>\n",
       "      <td>AutoNation</td>\n",
       "      <td>21609</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>500</td>\n",
       "      <td>Ericsson</td>\n",
       "      <td>23556</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>500</td>\n",
       "      <td>Air Liquide</td>\n",
       "      <td>24797</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>500</td>\n",
       "      <td>Shanxi Jincheng Anthracite Coal Mining Group</td>\n",
       "      <td>25386</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0  Rank                                          Name  Revenue  Year\n",
       "0   500                            Toyo Seikan Kaisha     7844  1995\n",
       "1   500                  Telecomunicacoes Brasileiras     8862  1996\n",
       "2   500               Banca Monte Dei Paschi Di Siena     9169  1997\n",
       "3   500                                           Sun     8968  1998\n",
       "4   500                              Northrop Grumman     8902  1999\n",
       "5   500                                           The     9723  2000\n",
       "6   500                              Sodexho Alliance    10306  2001\n",
       "7   500                                      Takenaka    10096  2002\n",
       "8   500                     Kawasaki Heavy Industries    10173  2003\n",
       "9   500                     The Toronto-Dominion Bank    10827  2004\n",
       "10  500                                         Masco    12431  2005\n",
       "11  500                                          Nike    13740  2006\n",
       "12  500                                         Fluor    16691  2007\n",
       "13  500                                         Fluor    16691  2008\n",
       "14  500                           Mitsui O.S.K. Lines    18572  2009\n",
       "15  500                           Dai Nippon Printing    17053  2010\n",
       "16  500                                       Wistron    19538  2011\n",
       "17  500                                      Manpower    22006  2012\n",
       "18  500                                         Ricoh    23175  2013\n",
       "19  500                                      Raytheon    23706  2014\n",
       "20  500                            Wuhan Iron & Steel    23721  2015\n",
       "21  500                                    Old Mutual    20923  2016\n",
       "22  500                                    AutoNation    21609  2017\n",
       "23  500                                      Ericsson    23556  2018\n",
       "24  500                                   Air Liquide    24797  2019\n",
       "25  500  Shanxi Jincheng Anthracite Coal Mining Group    25386  2020"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_df = master_df.copy()\n",
    "bottom_df = bottom_df[bottom_df['Rank']==500].sort_values(by=['Year']).reset_index(drop=True)\n",
    "bottom_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "line": {
          "color": "red"
         },
         "name": "Bottom Ranked Company",
         "opacity": 0.6,
         "type": "scatter",
         "x": [
          "1995",
          "1996",
          "1997",
          "1998",
          "1999",
          "2000",
          "2001",
          "2002",
          "2003",
          "2004",
          "2005",
          "2006",
          "2007",
          "2008",
          "2009",
          "2010",
          "2011",
          "2012",
          "2013",
          "2014",
          "2015",
          "2016",
          "2017",
          "2018",
          "2019",
          "2020"
         ],
         "y": [
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Plot time series\n",
    "trace_bottom = go.Scatter(x = bottom_df['Year'],\n",
    "                          y = bottom_df['Revenue'],\n",
    "                          name = \"Bottom Ranked Company\",\n",
    "                          line = dict(color = 'red'),\n",
    "                          opacity = 0.6)\n",
    "\n",
    "trace_top = go.Scatter(x = top_df['Year'],\n",
    "                       y = top_df['Revenue'],\n",
    "                       name = \"Top Ranked Company\",\n",
    "                       line = dict(color = 'green'),\n",
    "                       opacity = 0.6)\n",
    "\n",
    "layout = dict(title='Rank and Revenue',\n",
    "              width=1040, \n",
    "              height=670,\n",
    "              xaxis_title=\"Year\",\n",
    "              yaxis_title=\"Revenue ($M)\",\n",
    "              legend=dict(\n",
    "                    yanchor=\"top\",\n",
    "                    y=0.99,\n",
    "                    xanchor=\"left\",\n",
    "                    x=0.01)\n",
    "             )\n",
    "\n",
    "fig = dict(data=[trace_bottom, trace_top], \n",
    "           layout=layout)\n",
    "iplot(fig)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<a name=\"total_revenue\"></a>\n",
    "### 5) Total revenue of Fortune 500 companies over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_revenue_df = pd.DataFrame()\n",
    "\n",
    "for year in list_of_years:\n",
    "    df = pd.DataFrame(wb[year].values)\n",
    "    df.columns = df.iloc[0]\n",
    "    df.drop(df.index[0], inplace = True)\n",
    "    df.dropna(how = 'all', inplace = True)\n",
    "    df['Revenue'] = df['Revenue'].astype(float).round(0).astype(int)\n",
    "    total_revenue = df['Revenue'].sum()\n",
    "    revenue_dict = {}\n",
    "    revenue_dict['year'] = year\n",
    "    revenue_dict['total_revenue'] = total_revenue\n",
    "    revenue_df = pd.DataFrame([revenue_dict])\n",
    "    total_revenue_df = total_revenue_df.append(revenue_df, ignore_index = True)"
   ]
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   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
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   "outputs": [],
   "source": [
    "total_revenue_df = total_revenue_df.sort_values(by='year').reset_index(drop=True)"
   ]
  },
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   "execution_count": 22,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time series\n",
    "trace = go.Scatter(x = total_revenue_df['year'],\n",
    "                          y = total_revenue_df['total_revenue'],\n",
    "                          name = \"Total Revenue\",\n",
    "                          line = dict(color = 'black'),\n",
    "                          opacity = 0.6)\n",
    "\n",
    "layout = dict(title='Total Revenue of Fortune 500 Companies',\n",
    "              width=1050, \n",
    "              height=410,\n",
    "              xaxis_title=\"Year\",\n",
    "              yaxis_title=\"Total Revenue ($M)\",\n",
    "              legend=dict(\n",
    "                    yanchor=\"top\",\n",
    "                    y=0.99,\n",
    "                    xanchor=\"left\",\n",
    "                    x=0.01)\n",
    "             )\n",
    "\n",
    "fig = dict(data=[trace], \n",
    "           layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "#### References\n",
    "- https://www.wikihow.com/Install-FFmpeg-on-Windows  \n",
    "- https://medium.com/dunder-data/create-a-bar-chart-race-animation-in-python-with-matplotlib-477ed1590096\n",
    "- https://www.dexplo.org/bar_chart_race/tutorial/"
   ]
  }
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