forbes_2000.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualizing correlation tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "from numpy import nan as NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>country</th>\n",
       "      <th>category</th>\n",
       "      <th>sales</th>\n",
       "      <th>profits</th>\n",
       "      <th>assets</th>\n",
       "      <th>marketvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Citigroup</td>\n",
       "      <td>United States</td>\n",
       "      <td>Banking</td>\n",
       "      <td>94.71</td>\n",
       "      <td>17.85</td>\n",
       "      <td>1264.03</td>\n",
       "      <td>255.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>General Electric</td>\n",
       "      <td>United States</td>\n",
       "      <td>Conglomerates</td>\n",
       "      <td>134.19</td>\n",
       "      <td>15.59</td>\n",
       "      <td>626.93</td>\n",
       "      <td>328.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>American Intl Group</td>\n",
       "      <td>United States</td>\n",
       "      <td>Insurance</td>\n",
       "      <td>76.66</td>\n",
       "      <td>6.46</td>\n",
       "      <td>647.66</td>\n",
       "      <td>194.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ExxonMobil</td>\n",
       "      <td>United States</td>\n",
       "      <td>Oil &amp; gas operations</td>\n",
       "      <td>222.88</td>\n",
       "      <td>20.96</td>\n",
       "      <td>166.99</td>\n",
       "      <td>277.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>BP</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Oil &amp; gas operations</td>\n",
       "      <td>232.57</td>\n",
       "      <td>10.27</td>\n",
       "      <td>177.57</td>\n",
       "      <td>173.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Bank of America</td>\n",
       "      <td>United States</td>\n",
       "      <td>Banking</td>\n",
       "      <td>49.01</td>\n",
       "      <td>10.81</td>\n",
       "      <td>736.45</td>\n",
       "      <td>117.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>HSBC Group</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Banking</td>\n",
       "      <td>44.33</td>\n",
       "      <td>6.66</td>\n",
       "      <td>757.60</td>\n",
       "      <td>177.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Toyota Motor</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Consumer durables</td>\n",
       "      <td>135.82</td>\n",
       "      <td>7.99</td>\n",
       "      <td>171.71</td>\n",
       "      <td>115.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Fannie Mae</td>\n",
       "      <td>United States</td>\n",
       "      <td>Diversified financials</td>\n",
       "      <td>53.13</td>\n",
       "      <td>6.48</td>\n",
       "      <td>1019.17</td>\n",
       "      <td>76.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Wal-Mart Stores</td>\n",
       "      <td>United States</td>\n",
       "      <td>Retailing</td>\n",
       "      <td>256.33</td>\n",
       "      <td>9.05</td>\n",
       "      <td>104.91</td>\n",
       "      <td>243.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>UBS</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Diversified financials</td>\n",
       "      <td>48.95</td>\n",
       "      <td>5.15</td>\n",
       "      <td>853.23</td>\n",
       "      <td>85.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>ING Group</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Diversified financials</td>\n",
       "      <td>94.72</td>\n",
       "      <td>4.73</td>\n",
       "      <td>752.49</td>\n",
       "      <td>54.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Royal Dutch/Shell Group</td>\n",
       "      <td>Netherlands/ United Kingdom</td>\n",
       "      <td>Oil &amp; gas operations</td>\n",
       "      <td>133.50</td>\n",
       "      <td>8.40</td>\n",
       "      <td>100.72</td>\n",
       "      <td>163.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>United States</td>\n",
       "      <td>Insurance</td>\n",
       "      <td>56.22</td>\n",
       "      <td>6.95</td>\n",
       "      <td>172.24</td>\n",
       "      <td>141.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>JP Morgan Chase</td>\n",
       "      <td>United States</td>\n",
       "      <td>Banking</td>\n",
       "      <td>44.39</td>\n",
       "      <td>4.47</td>\n",
       "      <td>792.70</td>\n",
       "      <td>81.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>IBM</td>\n",
       "      <td>United States</td>\n",
       "      <td>Technology hardware &amp; equipment</td>\n",
       "      <td>89.13</td>\n",
       "      <td>7.58</td>\n",
       "      <td>104.46</td>\n",
       "      <td>171.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Total</td>\n",
       "      <td>France</td>\n",
       "      <td>Oil &amp; gas operations</td>\n",
       "      <td>131.64</td>\n",
       "      <td>8.84</td>\n",
       "      <td>87.84</td>\n",
       "      <td>116.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>BNP Paribas</td>\n",
       "      <td>France</td>\n",
       "      <td>Banking</td>\n",
       "      <td>47.74</td>\n",
       "      <td>4.73</td>\n",
       "      <td>745.09</td>\n",
       "      <td>59.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Royal Bank of Scotland</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Banking</td>\n",
       "      <td>35.65</td>\n",
       "      <td>4.95</td>\n",
       "      <td>663.45</td>\n",
       "      <td>90.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Freddie Mac</td>\n",
       "      <td>United States</td>\n",
       "      <td>Diversified financials</td>\n",
       "      <td>46.26</td>\n",
       "      <td>10.09</td>\n",
       "      <td>752.25</td>\n",
       "      <td>44.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>DaimlerChrysler</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Consumer durables</td>\n",
       "      <td>157.13</td>\n",
       "      <td>5.12</td>\n",
       "      <td>195.58</td>\n",
       "      <td>47.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Altria Group</td>\n",
       "      <td>United States</td>\n",
       "      <td>Food drink &amp; tobacco</td>\n",
       "      <td>60.70</td>\n",
       "      <td>9.20</td>\n",
       "      <td>96.18</td>\n",
       "      <td>111.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>ChevronTexaco</td>\n",
       "      <td>United States</td>\n",
       "      <td>Oil &amp; gas operations</td>\n",
       "      <td>112.94</td>\n",
       "      <td>7.43</td>\n",
       "      <td>82.36</td>\n",
       "      <td>92.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Pfizer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Drugs &amp; biotechnology</td>\n",
       "      <td>40.36</td>\n",
       "      <td>6.20</td>\n",
       "      <td>120.06</td>\n",
       "      <td>285.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Wells Fargo</td>\n",
       "      <td>United States</td>\n",
       "      <td>Banking</td>\n",
       "      <td>31.80</td>\n",
       "      <td>6.20</td>\n",
       "      <td>387.80</td>\n",
       "      <td>97.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Verizon Commun</td>\n",
       "      <td>United States</td>\n",
       "      <td>Telecommunications services</td>\n",
       "      <td>67.75</td>\n",
       "      <td>2.57</td>\n",
       "      <td>165.97</td>\n",
       "      <td>103.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Barclays</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Banking</td>\n",
       "      <td>33.69</td>\n",
       "      <td>4.90</td>\n",
       "      <td>791.54</td>\n",
       "      <td>61.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>Morgan Stanley</td>\n",
       "      <td>United States</td>\n",
       "      <td>Diversified financials</td>\n",
       "      <td>33.00</td>\n",
       "      <td>3.64</td>\n",
       "      <td>580.63</td>\n",
       "      <td>64.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>General Motors</td>\n",
       "      <td>United States</td>\n",
       "      <td>Consumer durables</td>\n",
       "      <td>185.52</td>\n",
       "      <td>3.82</td>\n",
       "      <td>450.00</td>\n",
       "      <td>27.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>Nippon Tel &amp; Tel</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Telecommunications services</td>\n",
       "      <td>92.41</td>\n",
       "      <td>2.17</td>\n",
       "      <td>150.87</td>\n",
       "      <td>73.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank                     name                      country  \\\n",
       "1      1                Citigroup                United States   \n",
       "2      2         General Electric                United States   \n",
       "3      3      American Intl Group                United States   \n",
       "4      4               ExxonMobil                United States   \n",
       "5      5                       BP               United Kingdom   \n",
       "6      6          Bank of America                United States   \n",
       "7      7               HSBC Group               United Kingdom   \n",
       "8      8             Toyota Motor                        Japan   \n",
       "9      9               Fannie Mae                United States   \n",
       "10    10          Wal-Mart Stores                United States   \n",
       "11    11                      UBS                  Switzerland   \n",
       "12    12                ING Group                  Netherlands   \n",
       "13    13  Royal Dutch/Shell Group  Netherlands/ United Kingdom   \n",
       "14    14       Berkshire Hathaway                United States   \n",
       "15    15          JP Morgan Chase                United States   \n",
       "16    16                      IBM                United States   \n",
       "17    17                    Total                       France   \n",
       "18    18              BNP Paribas                       France   \n",
       "19    19   Royal Bank of Scotland               United Kingdom   \n",
       "20    20              Freddie Mac                United States   \n",
       "21    21          DaimlerChrysler                      Germany   \n",
       "22    22             Altria Group                United States   \n",
       "23    23            ChevronTexaco                United States   \n",
       "24    24                   Pfizer                United States   \n",
       "25    25              Wells Fargo                United States   \n",
       "26    26           Verizon Commun                United States   \n",
       "27    27                 Barclays               United Kingdom   \n",
       "28    28           Morgan Stanley                United States   \n",
       "29    29           General Motors                United States   \n",
       "30    30         Nippon Tel & Tel                        Japan   \n",
       "\n",
       "                           category   sales  profits   assets  marketvalue  \n",
       "1                           Banking   94.71    17.85  1264.03       255.30  \n",
       "2                     Conglomerates  134.19    15.59   626.93       328.54  \n",
       "3                         Insurance   76.66     6.46   647.66       194.87  \n",
       "4              Oil & gas operations  222.88    20.96   166.99       277.02  \n",
       "5              Oil & gas operations  232.57    10.27   177.57       173.54  \n",
       "6                           Banking   49.01    10.81   736.45       117.55  \n",
       "7                           Banking   44.33     6.66   757.60       177.96  \n",
       "8                 Consumer durables  135.82     7.99   171.71       115.40  \n",
       "9            Diversified financials   53.13     6.48  1019.17        76.84  \n",
       "10                        Retailing  256.33     9.05   104.91       243.74  \n",
       "11           Diversified financials   48.95     5.15   853.23        85.07  \n",
       "12           Diversified financials   94.72     4.73   752.49        54.59  \n",
       "13             Oil & gas operations  133.50     8.40   100.72       163.45  \n",
       "14                        Insurance   56.22     6.95   172.24       141.14  \n",
       "15                          Banking   44.39     4.47   792.70        81.94  \n",
       "16  Technology hardware & equipment   89.13     7.58   104.46       171.54  \n",
       "17             Oil & gas operations  131.64     8.84    87.84       116.64  \n",
       "18                          Banking   47.74     4.73   745.09        59.29  \n",
       "19                          Banking   35.65     4.95   663.45        90.21  \n",
       "20           Diversified financials   46.26    10.09   752.25        44.25  \n",
       "21                Consumer durables  157.13     5.12   195.58        47.43  \n",
       "22             Food drink & tobacco   60.70     9.20    96.18       111.02  \n",
       "23             Oil & gas operations  112.94     7.43    82.36        92.49  \n",
       "24            Drugs & biotechnology   40.36     6.20   120.06       285.27  \n",
       "25                          Banking   31.80     6.20   387.80        97.53  \n",
       "26      Telecommunications services   67.75     2.57   165.97       103.97  \n",
       "27                          Banking   33.69     4.90   791.54        61.33  \n",
       "28           Diversified financials   33.00     3.64   580.63        64.81  \n",
       "29                Consumer durables  185.52     3.82   450.00        27.47  \n",
       "30      Telecommunications services   92.41     2.17   150.87        73.00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Forbes2000.csv', nrows=30, index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>country</th>\n",
       "      <th>category</th>\n",
       "      <th>sales</th>\n",
       "      <th>profits</th>\n",
       "      <th>assets</th>\n",
       "      <th>marketvalue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</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>BP</th>\n",
       "      <td>5</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Oil &amp; gas operations</td>\n",
       "      <td>232.57</td>\n",
       "      <td>10.27</td>\n",
       "      <td>177.57</td>\n",
       "      <td>173.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSBC Group</th>\n",
       "      <td>7</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Banking</td>\n",
       "      <td>44.33</td>\n",
       "      <td>6.66</td>\n",
       "      <td>757.60</td>\n",
       "      <td>177.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royal Bank of Scotland</th>\n",
       "      <td>19</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Banking</td>\n",
       "      <td>35.65</td>\n",
       "      <td>4.95</td>\n",
       "      <td>663.45</td>\n",
       "      <td>90.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barclays</th>\n",
       "      <td>27</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Banking</td>\n",
       "      <td>33.69</td>\n",
       "      <td>4.90</td>\n",
       "      <td>791.54</td>\n",
       "      <td>61.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        rank         country              category   sales  \\\n",
       "name                                                                         \n",
       "BP                         5  United Kingdom  Oil & gas operations  232.57   \n",
       "HSBC Group                 7  United Kingdom               Banking   44.33   \n",
       "Royal Bank of Scotland    19  United Kingdom               Banking   35.65   \n",
       "Barclays                  27  United Kingdom               Banking   33.69   \n",
       "\n",
       "                        profits  assets  marketvalue  \n",
       "name                                                  \n",
       "BP                        10.27  177.57       173.54  \n",
       "HSBC Group                 6.66  757.60       177.96  \n",
       "Royal Bank of Scotland     4.95  663.45        90.21  \n",
       "Barclays                   4.90  791.54        61.33  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting one country by name as its index.\n",
    "gb = df.groupby(\"country\")\n",
    "united_kingdom = gb.get_group(\"United Kingdom\").set_index('name')\n",
    "united_kingdom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\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>country</th>\n",
       "      <th>category</th>\n",
       "      <th>sales</th>\n",
       "      <th>profits</th>\n",
       "      <th>assets</th>\n",
       "      <th>marketvalue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</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>Total</th>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>Oil &amp; gas operations</td>\n",
       "      <td>131.64</td>\n",
       "      <td>8.84</td>\n",
       "      <td>87.84</td>\n",
       "      <td>116.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNP Paribas</th>\n",
       "      <td>18</td>\n",
       "      <td>France</td>\n",
       "      <td>Banking</td>\n",
       "      <td>47.74</td>\n",
       "      <td>4.73</td>\n",
       "      <td>745.09</td>\n",
       "      <td>59.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             rank country              category   sales  profits  assets  \\\n",
       "name                                                                       \n",
       "Total          17  France  Oil & gas operations  131.64     8.84   87.84   \n",
       "BNP Paribas    18  France               Banking   47.74     4.73  745.09   \n",
       "\n",
       "             marketvalue  \n",
       "name                      \n",
       "Total             116.64  \n",
       "BNP Paribas        59.29  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting another country\n",
    "france = gb.get_group(\"France\").set_index('name')\n",
    "france"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>sales</th>\n",
       "      <th>profits</th>\n",
       "      <th>assets</th>\n",
       "      <th>marketvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.500000</td>\n",
       "      <td>86.560000</td>\n",
       "      <td>6.695000</td>\n",
       "      <td>597.540000</td>\n",
       "      <td>125.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.376255</td>\n",
       "      <td>97.449744</td>\n",
       "      <td>2.519848</td>\n",
       "      <td>285.174878</td>\n",
       "      <td>58.942898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>33.690000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>177.570000</td>\n",
       "      <td>61.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>35.160000</td>\n",
       "      <td>4.937500</td>\n",
       "      <td>541.980000</td>\n",
       "      <td>82.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>39.990000</td>\n",
       "      <td>5.805000</td>\n",
       "      <td>710.525000</td>\n",
       "      <td>131.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>91.390000</td>\n",
       "      <td>7.562500</td>\n",
       "      <td>766.085000</td>\n",
       "      <td>174.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>232.570000</td>\n",
       "      <td>10.270000</td>\n",
       "      <td>791.540000</td>\n",
       "      <td>177.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rank       sales    profits      assets  marketvalue\n",
       "count   4.000000    4.000000   4.000000    4.000000     4.000000\n",
       "mean   14.500000   86.560000   6.695000  597.540000   125.760000\n",
       "std    10.376255   97.449744   2.519848  285.174878    58.942898\n",
       "min     5.000000   33.690000   4.900000  177.570000    61.330000\n",
       "25%     6.500000   35.160000   4.937500  541.980000    82.990000\n",
       "50%    13.000000   39.990000   5.805000  710.525000   131.875000\n",
       "75%    21.000000   91.390000   7.562500  766.085000   174.645000\n",
       "max    27.000000  232.570000  10.270000  791.540000   177.960000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic statistics about the united_kingdom's dataframe.\n",
    "united_kingdom.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>sales</th>\n",
       "      <th>profits</th>\n",
       "      <th>assets</th>\n",
       "      <th>marketvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>89.690000</td>\n",
       "      <td>6.785000</td>\n",
       "      <td>416.465000</td>\n",
       "      <td>87.965000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>59.326259</td>\n",
       "      <td>2.906209</td>\n",
       "      <td>464.745932</td>\n",
       "      <td>40.552574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>47.740000</td>\n",
       "      <td>4.730000</td>\n",
       "      <td>87.840000</td>\n",
       "      <td>59.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.250000</td>\n",
       "      <td>68.715000</td>\n",
       "      <td>5.757500</td>\n",
       "      <td>252.152500</td>\n",
       "      <td>73.627500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>89.690000</td>\n",
       "      <td>6.785000</td>\n",
       "      <td>416.465000</td>\n",
       "      <td>87.965000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.750000</td>\n",
       "      <td>110.665000</td>\n",
       "      <td>7.812500</td>\n",
       "      <td>580.777500</td>\n",
       "      <td>102.302500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>131.640000</td>\n",
       "      <td>8.840000</td>\n",
       "      <td>745.090000</td>\n",
       "      <td>116.640000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rank       sales   profits      assets  marketvalue\n",
       "count   2.000000    2.000000  2.000000    2.000000     2.000000\n",
       "mean   17.500000   89.690000  6.785000  416.465000    87.965000\n",
       "std     0.707107   59.326259  2.906209  464.745932    40.552574\n",
       "min    17.000000   47.740000  4.730000   87.840000    59.290000\n",
       "25%    17.250000   68.715000  5.757500  252.152500    73.627500\n",
       "50%    17.500000   89.690000  6.785000  416.465000    87.965000\n",
       "75%    17.750000  110.665000  7.812500  580.777500   102.302500\n",
       "max    18.000000  131.640000  8.840000  745.090000   116.640000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic statistics about the france's dataframe.\n",
    "france.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>sales</th>\n",
       "      <th>profits</th>\n",
       "      <th>assets</th>\n",
       "      <th>marketvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.305147</td>\n",
       "      <td>-0.668235</td>\n",
       "      <td>-0.308350</td>\n",
       "      <td>-0.595729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>-0.305147</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423953</td>\n",
       "      <td>-0.461601</td>\n",
       "      <td>0.354148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profits</th>\n",
       "      <td>-0.668235</td>\n",
       "      <td>0.423953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060933</td>\n",
       "      <td>0.696713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assets</th>\n",
       "      <td>-0.308350</td>\n",
       "      <td>-0.461601</td>\n",
       "      <td>0.060933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.139405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketvalue</th>\n",
       "      <td>-0.595729</td>\n",
       "      <td>0.354148</td>\n",
       "      <td>0.696713</td>\n",
       "      <td>-0.139405</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 rank     sales   profits    assets  marketvalue\n",
       "rank         1.000000 -0.305147 -0.668235 -0.308350    -0.595729\n",
       "sales       -0.305147  1.000000  0.423953 -0.461601     0.354148\n",
       "profits     -0.668235  0.423953  1.000000  0.060933     0.696713\n",
       "assets      -0.308350 -0.461601  0.060933  1.000000    -0.139405\n",
       "marketvalue -0.595729  0.354148  0.696713 -0.139405     1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation of the whole dataframe.\n",
    "# Replacing all instances of 0 for NAN.\n",
    "df.replace(0, np.NAN).dropna(axis=1).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>sales</th>\n",
       "      <th>profits</th>\n",
       "      <th>assets</th>\n",
       "      <th>marketvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.646233</td>\n",
       "      <td>-0.815785</td>\n",
       "      <td>0.620792</td>\n",
       "      <td>-0.986887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>-0.646233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.959993</td>\n",
       "      <td>-0.979858</td>\n",
       "      <td>0.579635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profits</th>\n",
       "      <td>-0.815785</td>\n",
       "      <td>0.959993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.913928</td>\n",
       "      <td>0.778024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assets</th>\n",
       "      <td>0.620792</td>\n",
       "      <td>-0.979858</td>\n",
       "      <td>-0.913928</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.526573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketvalue</th>\n",
       "      <td>-0.986887</td>\n",
       "      <td>0.579635</td>\n",
       "      <td>0.778024</td>\n",
       "      <td>-0.526573</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 rank     sales   profits    assets  marketvalue\n",
       "rank         1.000000 -0.646233 -0.815785  0.620792    -0.986887\n",
       "sales       -0.646233  1.000000  0.959993 -0.979858     0.579635\n",
       "profits     -0.815785  0.959993  1.000000 -0.913928     0.778024\n",
       "assets       0.620792 -0.979858 -0.913928  1.000000    -0.526573\n",
       "marketvalue -0.986887  0.579635  0.778024 -0.526573     1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# United_Kingdom's Correlation Table:\n",
    "united_kingdom_corr = united_kingdom.corr()\n",
    "united_kingdom_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualizing United Kingdom's Correlation Table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [c[:2] for c in united_kingdom_corr.columns]\n",
    "\n",
    "fig = plt.figure(figsize=(14,14))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.matshow(united_kingdom_corr, cmap=plt.cm.RdYlGn)\n",
    "\n",
    "ax.set_xticks(np.arange(len(labels)))\n",
    "ax.set_yticks(np.arange(len(labels)))\n",
    "\n",
    "ax.set_yticklabels(labels)\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>sales</th>\n",
       "      <th>profits</th>\n",
       "      <th>assets</th>\n",
       "      <th>marketvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profits</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assets</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketvalue</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             rank  sales  profits  assets  marketvalue\n",
       "rank          1.0   -1.0     -1.0     1.0         -1.0\n",
       "sales        -1.0    1.0      1.0    -1.0          1.0\n",
       "profits      -1.0    1.0      1.0    -1.0          1.0\n",
       "assets        1.0   -1.0     -1.0     1.0         -1.0\n",
       "marketvalue  -1.0    1.0      1.0    -1.0          1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# France's Correlation Table:\n",
    "france.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualizing France's Correlation Table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [c[:2] for c in france.corr().columns]\n",
    "\n",
    "fig = plt.figure(figsize=(14,14))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.matshow(france.corr(), cmap=plt.cm.RdYlGn)\n",
    "\n",
    "ax.set_xticks(np.arange(len(labels)))\n",
    "ax.set_yticks(np.arange(len(labels)))\n",
    "\n",
    "ax.set_yticklabels(labels)\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}