Homicide data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Location</th>\n",
       "      <th>Region</th>\n",
       "      <th>Subregion</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2474</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>2.1</td>\n",
       "      <td>61</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>1.3</td>\n",
       "      <td>580</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1217</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Location  Region        Subregion  Rate  Count  Year\n",
       "0  Afghanistan    Asia    Southern Asia   6.7   2474  2018\n",
       "1      Albania  Europe  Southern Europe   2.1     61  2020\n",
       "2      Algeria  Africa  Northern Africa   1.3    580  2020\n",
       "3      Andorra  Europe  Southern Europe   2.6      2  2020\n",
       "4       Angola  Africa    Middle Africa   4.8   1217  2012"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\james\\OneDrive\\Documents\\DATA ANALYST PORTFOLIO\\Python Projects\\Homicide Data Analysis\\Dataset\\homicide_by_countries.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 6)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location     0\n",
       "Region       0\n",
       "Subregion    0\n",
       "Rate         0\n",
       "Count        0\n",
       "Year         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location      object\n",
       "Region        object\n",
       "Subregion     object\n",
       "Rate         float64\n",
       "Count          int64\n",
       "Year           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the data types\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert data type for rate\n",
    "\n",
    "df['Rate'] = df['Rate'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location     object\n",
       "Region       object\n",
       "Subregion    object\n",
       "Rate          int32\n",
       "Count         int64\n",
       "Year          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the converted data types\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Region'].replace('Americas', 'N/S America', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Region</th>\n",
       "      <th>Subregion</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>22</td>\n",
       "      <td>47722</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "      <td>22</td>\n",
       "      <td>44200</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>3</td>\n",
       "      <td>40651</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Central America</td>\n",
       "      <td>28</td>\n",
       "      <td>36579</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>United States</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>6</td>\n",
       "      <td>21570</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Southern Africa</td>\n",
       "      <td>33</td>\n",
       "      <td>19846</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>28</td>\n",
       "      <td>15299</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>22</td>\n",
       "      <td>11520</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>7</td>\n",
       "      <td>10697</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>36</td>\n",
       "      <td>10598</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>3</td>\n",
       "      <td>8490</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>8</td>\n",
       "      <td>8153</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>7525</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>4</td>\n",
       "      <td>4764</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>9</td>\n",
       "      <td>4460</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>3830</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Central America</td>\n",
       "      <td>36</td>\n",
       "      <td>3598</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>6</td>\n",
       "      <td>3439</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>10</td>\n",
       "      <td>3339</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Guatemala</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Central America</td>\n",
       "      <td>17</td>\n",
       "      <td>3129</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Iraq (excluding Kurdistan)</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>10</td>\n",
       "      <td>3029</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>6</td>\n",
       "      <td>2751</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>6</td>\n",
       "      <td>2474</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Peru</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>7</td>\n",
       "      <td>2452</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>5</td>\n",
       "      <td>2416</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Central America</td>\n",
       "      <td>37</td>\n",
       "      <td>2398</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>2</td>\n",
       "      <td>2207</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>4</td>\n",
       "      <td>2151</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>2095</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Iran</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>1795</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Saint Martin (French part)</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Malta</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>São Tomé and Príncipe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Cayman Islands</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Greenland</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Macau</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Turks and Caicos Islands</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brunei</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>British Virgin Islands</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Gibraltar</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Montserrat</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Isle of Man</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Holy See</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Channel Islands</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Saint Helena</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Location       Region           Subregion  Rate  Count  \\\n",
       "26                       Brazil  N/S America       South America    22  47722   \n",
       "125                     Nigeria       Africa      Western Africa    22  44200   \n",
       "78                        India         Asia       Southern Asia     3  40651   \n",
       "111                      Mexico  N/S America     Central America    28  36579   \n",
       "186               United States  N/S America    Northern America     6  21570   \n",
       "162                South Africa       Africa     Southern Africa    33  19846   \n",
       "118                     Myanmar         Asia  South-Eastern Asia    28  15299   \n",
       "41                     Colombia  N/S America       South America    22  11520   \n",
       "143                      Russia       Europe      Eastern Europe     7  10697   \n",
       "190                   Venezuela  N/S America       South America    36  10598   \n",
       "129                    Pakistan         Asia       Southern Asia     3   8490   \n",
       "57                     Ethiopia       Africa      Eastern Africa     8   8153   \n",
       "40                        China         Asia        Eastern Asia     0   7525   \n",
       "133                 Philippines         Asia  South-Eastern Asia     4   4764   \n",
       "181                      Uganda       Africa      Eastern Africa     9   4460   \n",
       "15                   Bangladesh         Asia       Southern Asia     2   3830   \n",
       "74                     Honduras  N/S America     Central America    36   3598   \n",
       "185                    Tanzania       Africa      Eastern Africa     6   3439   \n",
       "81                         Iraq         Asia        Western Asia    10   3339   \n",
       "69                    Guatemala  N/S America     Central America    17   3129   \n",
       "82   Iraq (excluding Kurdistan)         Asia        Western Asia    10   3029   \n",
       "182                     Ukraine       Europe      Eastern Europe     6   2751   \n",
       "0                   Afghanistan         Asia       Southern Asia     6   2474   \n",
       "132                        Peru  N/S America       South America     7   2452   \n",
       "7                     Argentina  N/S America       South America     5   2416   \n",
       "53                  El Salvador  N/S America     Central America    37   2398   \n",
       "52                        Egypt       Africa     Northern Africa     2   2207   \n",
       "91                        Kenya       Africa      Eastern Africa     4   2151   \n",
       "178                      Turkey         Asia        Western Asia     2   2095   \n",
       "80                         Iran         Asia       Southern Asia     2   1795   \n",
       "..                          ...          ...                 ...   ...    ...   \n",
       "159                   Singapore         Asia  South-Eastern Asia     0     10   \n",
       "148  Saint Martin (French part)  N/S America           Caribbean    27     10   \n",
       "157                  Seychelles       Africa      Eastern Africa    10     10   \n",
       "6           Antigua and Barbuda  N/S America           Caribbean     9      9   \n",
       "107                       Malta       Europe     Southern Europe     1      7   \n",
       "152       São Tomé and Príncipe       Africa       Middle Africa     3      6   \n",
       "36               Cayman Islands  N/S America           Caribbean     8      5   \n",
       "77                      Iceland       Europe     Northern Europe     1      5   \n",
       "5                      Anguilla  N/S America           Caribbean    28      4   \n",
       "106                    Maldives         Asia       Southern Asia     0      3   \n",
       "66                    Greenland  N/S America    Northern America     5      3   \n",
       "103                       Macau         Asia        Eastern Asia     0      2   \n",
       "3                       Andorra       Europe     Southern Europe     2      2   \n",
       "14                      Bahrain         Asia        Western Asia     0      2   \n",
       "180    Turks and Caicos Islands  N/S America           Caribbean     5      2   \n",
       "9                         Aruba  N/S America           Caribbean     1      2   \n",
       "28                       Brunei         Asia  South-Eastern Asia     0      2   \n",
       "27       British Virgin Islands  N/S America           Caribbean     8      2   \n",
       "64                    Gibraltar       Europe     Southern Europe     3      1   \n",
       "149   Saint Pierre and Miquelon  N/S America    Northern America    15      1   \n",
       "100               Liechtenstein       Europe      Western Europe     2      1   \n",
       "102                  Luxembourg       Europe      Western Europe     0      1   \n",
       "115                  Montserrat  N/S America           Caribbean    20      1   \n",
       "84                  Isle of Man       Europe     Northern Europe     0      0   \n",
       "112                      Monaco       Europe      Western Europe     0      0   \n",
       "73                     Holy See       Europe     Southern Europe     0      0   \n",
       "38              Channel Islands       Europe     Northern Europe     0      0   \n",
       "145                Saint Helena       Africa      Western Africa     0      0   \n",
       "151                  San Marino       Europe     Southern Europe     0      0   \n",
       "21                      Bermuda  N/S America    Northern America     0      0   \n",
       "\n",
       "     Year  \n",
       "26   2020  \n",
       "125  2019  \n",
       "78   2020  \n",
       "111  2020  \n",
       "186  2020  \n",
       "162  2020  \n",
       "118  2021  \n",
       "41   2020  \n",
       "143  2020  \n",
       "190  2018  \n",
       "129  2020  \n",
       "57   2012  \n",
       "40   2018  \n",
       "133  2019  \n",
       "181  2020  \n",
       "15   2018  \n",
       "74   2020  \n",
       "185  2016  \n",
       "81   2013  \n",
       "69   2020  \n",
       "82   2014  \n",
       "182  2017  \n",
       "0    2018  \n",
       "132  2018  \n",
       "7    2020  \n",
       "53   2019  \n",
       "52   2012  \n",
       "91   2020  \n",
       "178  2020  \n",
       "80   2018  \n",
       "..    ...  \n",
       "159  2020  \n",
       "148  2016  \n",
       "157  2019  \n",
       "6    2020  \n",
       "107  2020  \n",
       "152  2011  \n",
       "36   2014  \n",
       "77   2020  \n",
       "5    2014  \n",
       "106  2019  \n",
       "66   2016  \n",
       "103  2020  \n",
       "3    2020  \n",
       "14   2019  \n",
       "180  2014  \n",
       "9    2014  \n",
       "28   2013  \n",
       "27   2006  \n",
       "64   2010  \n",
       "149  2009  \n",
       "100  2020  \n",
       "102  2020  \n",
       "115  2012  \n",
       "84   2016  \n",
       "112  2015  \n",
       "73   2015  \n",
       "38   2010  \n",
       "145  2009  \n",
       "151  2011  \n",
       "21   2019  \n",
       "\n",
       "[195 rows x 6 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extracting columns that we will use for visualization\n",
    "\n",
    "df1 = df.sort_values('Count', ascending = False)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>Location</th>\n",
       "      <th>Count</th>\n",
       "      <th>Perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>47722</td>\n",
       "      <td>25.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>44200</td>\n",
       "      <td>23.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>India</td>\n",
       "      <td>40651</td>\n",
       "      <td>21.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>36579</td>\n",
       "      <td>19.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>United States</td>\n",
       "      <td>21570</td>\n",
       "      <td>11.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Location  Count   Perc\n",
       "26          Brazil  47722  25.02\n",
       "125        Nigeria  44200  23.18\n",
       "78           India  40651  21.31\n",
       "111         Mexico  36579  19.18\n",
       "186  United States  21570  11.31"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extracting the first 5 rows we will use for visualization\n",
    "\n",
    "df1 = df[['Location', 'Count']].sort_values(by = 'Count', ascending = False).head(5)\n",
    "df1['Perc'] = (df1['Count'] *100 / df1['Count'].sum()).round(2)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RZewl7Jr9PHXL3sHVyVn2udsZ3yF36OHrWu1bs5gd7z0GoRB5J52L59TLAah58z6atqxGXG6y+h5Lj/N+iLgzqP90DrWznseVm0evab/EndsF/87N7ProWXpdklT3qw0CrgcesF1IOhGj858d4PVcg3N3ddKqazJMfqaeW0/PZlpR5kHP3TOrkYaA4c6zclrc94OqAL+b28Rb3zrwy37aS3v5+cQsxvfP4L45jXy6PcQTFx/cLXXnB42M6uNCBJ5d5mdAFxf3n5eNS+y3Ki5vvL1yoRmRUKueBep2EKzbQXafYYQa97J55o/pNe2X7K2chWTm4hk/LeK+JhRk0+Pfp+AbvyIjvwebZ/6Enhf/jKyeA9n3xUJyhowFYNub95EzYCT5o7/KludupuCKu6iv+AgT9NNlzEXUvHEvXSddRWb3Y+L1sqNlGzC0asbU3bYLSRfa3bSfs2b17bbL6Ah/0PD1l/dyVXHmYQEB8K3iTF6tCLT6eDX1IZZtDTK+v9Pg/MbITOauDx60zaY9IRZuCnLJCGe84qXLcsnOgPfWBFs6pA0J9y4oI6872X2GAeDK7kRmjwEE92xv1b5Nmz8jo2tfMrv2QdyZdC46g32fzwcgd+g4RAQRIbvvsQT2bHN2Ehcm6McEGhGXm4b1K3B37paMAQHQE12cKK40JA74b2Cg7SLayxjDNW80UNTTzU2nHei2/Xz7gV/Wb3waYETP1v+Td8sVahvgs/Ax/vlFgKJeB+9/278bufss53z7As4Yhktgrz8xfjcn+sB1oHYrTVvXkN3vOAD2fPIWm576IdvefoBgQ93h2+/ZTkaXXv/52p3fk2DdwQFjggHqV75P7uCTAfBM/CbVL99OQ9VSOh8/mdq5L+GZ+M0YvqqYu6mwtLy37SLShY5JAHg9ucCttsvoiDnrgzy33E9xgYtRZc4vl9+cnc2TS/x8ui2ES2BQVxdlU52upkWbgpQtavpP19HpT9dTuS1EXZOh/+/38OTFuZw3LIPHL8rh6y/vwyXQLUd46pIDXU1LNjvhMTo8YH7N6EyKH61nQBfhjsk6vng0oaZ91Lz2G7qffS2u7E7kj/4qnglXggi7Zv2Fnf9+gp5f/XErjnRwt96Odx8hu/8J5AxwphzLHTya3MGjAajzvUfu0LEEtm9gx8d/x5WTR7dz/htXZstdkAmqM86szDplThzomASA13MduqBQSrqi8bZVH5uihFsS0wQDVL9yJ7mDT6bLKZce9nygdivVr9xJv2seOejxxo0V7Jr9Ar2/4dzrWTvvZQA8p10BwK7ZL9BUvYZel/4CkYNbfSF/A9Wv3EXvK+5i68u3UTDtNupXfQDiIn/U+TF4lTFVAwyomjG15Wu6VdRod5PjetsFqNhIxO4mYwzb/+9BMnsMOCggAnU7/vP53s/mkdlz0GH7ZvU9lsDOTfh3bcEE/dRXfETusPEA7Fn2Dg1rP6HnRT89LCAAdi94lS5jL0bcGRh/k/OguDCBpPw92wv4lu0i0oF2N3k9E4ATbZeh0kfjxlXUr3yfzF6FbHr6BsC53LW+4iOatq4BETI8BXQ/z1nnKrBnO9v/8Ud6X34n4nLT/SvXUf3y7WBC5BV/haxeTpjseOdPZHgK2PIXZ1y307ET6Boeewjs2U7TltV0nXQVAF1OuZQtz92MK6czvab9Mt7fgmj5EfC07SJSnXY3eT3PAv9luwwVG99o/OWqBeb4hOtuUlFzVtWMqR/YLiKVpXd3k9fTHbjcdhkqdkTS/E1Q6vuR7QJSXXqHBFwNJNVlHUqpg1xUWFrez3YRqSx9Q8LrEeD7tstQMadNidTmBr5nu4hUlr4hAVOAY20XoWLL/sQgKg6uKSwt13/qGEnnkLjOdgFKqagoBM61XUSqSs+Q8Hr6AJfYLkPFXiLeJ6Fi4lrbBaSq9AwJuAY4fAY8lXK0DyJtXFxYWl5gu4hUlK4hcZXtApRSUZUJXGy7iFSUfiHh9QwHimyXoeJG+5vSh4ZEDKRfSOgPUlrRMYm0ck5hafnhC7WrDtGQUEqlilzgHNtFpJr0Cgmvpwcw0XYZKq60KZFe9E1glKVXSMBUnDs0VZoQjF7glF4u1BvroivdQkLfZaQfbUmklz7AONtFpJL0CQmvJxs4z3YZKr504Dot6ZvBKEqfkICzgDzbRSilYu4i2wWkknQKCX13kZ60KZF+TiwsLT987VfVLukUEvruIg3pwHXa0jeFUZIeIeH1FAP9bZehlIobfVMYJekREjDedgHKDtHupnQ1qbC0XC93j4J0CQm9JC5N6dVNaSsXON52EalAQ0IplarG2C4gFaR+SHg9OUCx7TKUNdqUSF8aElGQ+iEBo4EM20UoO/TqprSmIREF6RAS2tWkVHoapYPXHachoVKddjelr1x0gbEO05BQKU2vbkp72uXUQakdEl6PBzjWdhlKKWs0JDootUMCxgI6cJnetCmR3jQkOijVQ0K7mtKcXt2U9nTwuoNSPSRG2i5A2aXTcqS9TsAI20Uks1QPicG2C1B2aTNCAaNsF5DMNCSUUqlugO0CklnqhoQzHUcf22Uo24x2N6l+tgtIZqkbElCI9jakPdGfAQV9bReQzFI2JIJOSCillIZEB6TsxHejBg8ciDG12cZUe0KhXb0DwX0DA4HQkCZ/xlC/P2+I39/tGH+gTxZk265VxZJ2NykNiY5I2ZAA+iHiaRTxVLtcVGdk4Ds0D4wxAjW5xmzrGgzt7hsINAwKBBjW5M8c4vfnF/r9PfoEgr3doNdZJynta1JoSHRIq0JCRCYaY+Yc7bEEc/TBKhEx0GuvSK+9LhebMjNYfOg2xgRdsLlTyGzrEQrW9fMHmgb7Awz1+7OH+P1dBvn9vXoGQz2171uphJVbWFretWrG1F22C0lGrW1JPASc3IrHEkl03j2IuEPQt84tfevcLtZlZjLv0G2MacyArZ1DoR29gsH6/v6Af7A/4Brq9+cOafJ7BgYCBZ5QqGtU6lFtpN1NCnB+H2hItMMRQ0JETgMmAL1E5KZmT3Uh8btg4nfZm0h2AAbWut0Da91uVmdl8cGh2xhTnwVb84OhXQXBYP1Avz842B9wD/X7Ow1u8ncbEAj07mRM57jVnCa0hafC+gIVtotIRkdrSWQBeeHt8ps9vhu4LFZFRUmB7QIOItK5CYZsz3CzPcNNRXbW4ZsYs2v/QHufQLBhoN8fGuoPZA71+zsXNvm79wsE+mQ5/yZKqbbReyXa6YghYYz5EPhQRJ4xxqyLU03R0sl2AW1lRLo2iHRtcLnYmpHBspzDB9pdUJ1rzLZuzkB74yC/n6H+QNaQJn9eod/fq08wWOBK4Uub20rnblJhOnjdTq0dk8gWkcdw7j34zz7GmCmxKCpKUu/SVhEJQUG9SEG9y8WGzAwW5uYcvI0xAbcz0L69RzBYd0wg0FTo98swZ3wkf5A/0KtHKNTTzguIP110SIVpSLRTa0Pib0AZ8AQQjF05UZVz9E1SkEhGEI7Z45Zj9rhdVGVlMofcg7cxpiEDtuaHQjt6OgPtgSF+v3tYkz93sD/gGRDwF3QJGY+dF6BUTKTNG6Noa21IBIwxj8a0kigqnlnsJvEH1u0RyQnAoJ1u96CdbjefZ2Xx/qHbGLMny1DdJRTaWRAM7BvoD4SG+v3uoU3+ToP9gW79A4E+OcbktnT4BKNNCQWQabuAZNXakHhTRH4AvAY07n/QGLMjJlV1XHq2IqJJJL9JyN/mcrMtw82q7MN778SYHdnG1HQNhWr7BIINhX5/aIjfnzm0yZ9X6A/06BsI9M60/J9TFx1SYRoS7dTakCgJ//3TZo8ZYEh0y4ma1BuPSEBGpHuDSPctLhdbMjJYevhAe8gFW3ON2dY9GNzTLxBsLPT7GdLkzxrq93cZ5A/0KIj9QLu2JBSk9uwSMdWqb5wxJtnWZdCQSAQirhD0rhfpXe9ysT4zkwWHD7T73bCpcyi0o2cwtOeYQMA/2O93DW3y5wzx+7sM9Ad6dQ+FerS7BM0I5dCQaKfWTsvxnZYeN8Y8G91yoka7m5KFSGYQ+u92u/vvdrtZk5XJrMMH2vdlwpb8UGhnr0Bwb/+AM9A+tMmfO9jv7zrQH+idZ0x+yydQCtCQaLfWfuPGNfs8Bzgb+ARI1JDQlkQqEcn1w+AdbvfgHW43n2Zn8d6h24Rn/O0SCtX2Dgb3DfQHgkP8/oysLzNri3AfNiWXSi9BzBbbNSSr1nY33dD8axHxAM/FpKLo0JZEugnP+FvjclGTkcGK7Gyym0z9k+++WbvglNFb/Vn5o22XqKxK1ItsEl57Bwz3AsOjWUiU6eWvivMXm2VZgb39Js77xci8ug2zbdejrEqW+7sSTqtCQkTeFJE3wh/lwKfA67EtrUPqbBeg7PvqwlA2gMuEMk9ZdM+k3lsXfmi7JmVNwHYByaq1YxK/a/Z5AFhnjNkQg3qiZbftApRdXetMTdd6RjV/7ISKZybn1W2Y88WQr41FRMet0ouGRDu1qiURnuivEmcm2G5AUyyLigINiTQ3bU6oQlrodhy0/l8TT/Q9Wkni3giqYkNDop1a2910BfAxcDlwBbBARBJ2qnBfia8e7YNMa5N9plek53ruWHnS+IW/2i2hQLLNbKzaL9Hf2Cas1g5c3wqMM8aUGGO+A5wC3Ba7sqJij+0ClB0Dq82aXD9FR9qm894thRPn/SI/w1+/PF51Kau22S4gWbU2JFzGmOpmX29vw762aJdTmrrio9D61myX5a/vPmnuL47rVL9lbqxrUtZttV1AsmrtL/p/iMg7InK1iFwNlANvx66sqNCQSEfGmJO/MK2eU8xlAtnjF959Ws+apR/EsCpln4ZEOx0xJERkmIhMNMb8FPgzcCJwEjAPeCwO9XWEhkQaGrXGrMgIMaAt+wjIiSsfP3Pw2rdmY4w/VrUpq6qPvolqydFaEg8Q7ts3xvzdGHOTMeYnOK2IB2JdXAdpSKShy2eHdrV338Hr/m9S8crHVmBMbTRrUglBWxLtdLSQKDTGHDawZ4xZhLOUaSLTkEgzGUHTNGwTIztyjF7blo8et+iebRIKtmpcQyUNDYl2OlpIHGkOpERflazGdgEqviYvN0vFuY+nQ/LrNw6dMO/WHHdg38po1KUSgoZEOx0tJBaKyLWHPigi1wCJPrPmGtsFqPi6ZH4oFK1jZfv39Jo095Yhuftq5kXrmMqa3dPLpjTYLiJZHW1ajh8Dr4nIVRwIhbFAFnBpLAuLgi9sF6Dip1ODqe29i6jO9OoO+XNPXeA9dVnxDz7Y0eOEM6N5bBVXOmjdAUdsSRhjthpjJgB3AlXhjzuNMacZk/Dzs6+2XYCKn4sWhHwSg3VEBGSU75EzB617ZxbG6NQOyUm7mjqgtetJvA+8H+Naom0NzvrGYrsQFXtfWRLblemGrn3j9Ly6jYtXHv/d4Yh0ieW5VNRpSHRAot813W6+Et8+YJPtOlTs9dplNuXv48RYn6d3zeIxYxf/dquEghtjfS4VVWttF5DMUjYkwrTLKQ1cPjv0ucSpxdilbv3w0+bfnuEONFTE43wqKny2C0hmqR4SOnidBiZUmGPieb6cpl29J829ZVBOw/YF8TyvajedxLEDUj0ktCWR4oZvNJ9lBRgW7/O6Q02dTpt/x7iuOz/V1e4SWwBYZbuIZJbqIaEtiRR3xazQZlvnFozr5GV/nDxg/b8/xBhdvyQxfTa9bEqj7SKSWaqHhLYkUpgYExpZZY6zXcfwL16dXFT53CcYo2urJx7tauqg1q5xnawqAD+QabsQFX3jPzXL3Ca6N9C1V9+tC8bl7quu/GT0TV0RV594nfcvH9zHinXzyc/tyq1XPAnAJ198yNuLZ7J155fcPO1PDOp1eI76A0088MaPCQT9BE2Q0YPPYOq4qwH4cMX/8r7vVbbt3sSM7/ydvFwPAEvWfET5omfonJ3PtefdRV6Oh5raTby58Em+d07CrkGmIdFBKd2SCF8Gu8x2HSo2ps0J7bVdQ3Ndd68dcdr8O4wr2PhpvM556rHnMf2r9xz0WL/uhVx77p0M7Rv5quAMdyY3XnQ/t1z+OLd8/TFWbVjI2q1O1/2QPidww4X30T2v90H7/Hv537j5aw9zyrHnsoXj5DMAAB3BSURBVGj1vwF4a+FTXDjuu1F+VVGlIdFBKR0SYTr3TgrK8pt9g6pjf29EW+U27ug7ae4tx2Q17loUj/MN63cinXIOvrevT7dB9O565CU1RITsTGeOzmAoQDAUQMJXEQ/oOZwe+Yc3hkRcBIJ+mgINuF0ZrN68nC6dulPg6R+lVxMTGhIdlA4hoUtTpqCvLDFLBWJ6l3V7ZQQb8ybO++XoLrVrPrJdy5GEQkHueeW/KX3264w4ZgyFvY+4LDgXjPkv/vT2z/l0wyeMHXoW//jkeS4Y819xqrZddk4vm6JTvndQqo9JgIZESpr6cchtu4YjEYx77JL7z6gcfuWHm/pNOh2RhHtD5nK5ueWyx9jbWMfj797Oph1r6dd9cMTti/qPpaj/WADmf/oOJww8ha271vPespfplJ3PZROmk5V5pNUF4k5voouChPvBjTZfie9LQKdRSCFd6s32HnsSY8D6aEZ8/uLk4z7760KMSajxk+Y6ZecxvO8oVq1f2Krtm/wNfPzZu5xx/CW88fETXHXmTxnQ61gWrn4vxpW22Se2C0gFKR8SYToukUIumR9aKUl0xdoxm+eMH730wSpMKGEWwtqzbxd7G50rdpsCjXy6cfFRxzH2++eylzizeBpudwb+QBMS/tMUSLglGxIutZKRGGNs1xBzxTOLfwL83nYdKjqe/n1gRefGji1TasO+nJ4bFoy7tSHkzoraHeJP/+tXfL55GXUNtXTJ7cZXx5bQObsLf5vzEHX7asnN7swxPYbxw6m/ZVf9Nl748H5+8NV72Lj9C557/15CJogxhpOHTuaCMd8B4APf3/nXspfYvXcHebndOGHgKVw1+WYAdtVv468f/Z7rL/gNcOBy205ZeVx73l3k53aN1kvrqCag+/SyKfW2C0l26RIS44H5tutQHddvu1n3wGPBQbbraC+/O7d2wfjbVzdldRlju5YU9+H0siln2i4iFaRLd9MSIOHawqrtrvgoVGW7ho7IDO7zTJh360n5e9bNsl1LinvXdgGpIi1CwlfiawJaNyqnEtq4z0zStiL2c5lQxrjF957eZ8v8D0iHprwdGhJRkhYhEVZuuwDVMSdUhVZmhii0XUe0HF/53JnDV78yH2O0lRtd29Erm6ImnULiNdsFqI65YnZou+0aom3Axg9OG7X84c8xoW22a0kh700vmxKyXUSqSJuQ8JX4PsOZ8E8lIVfIBI5bz/G264iF7jsri0/9+O69EvLrMpvRoV1NUZQOd1w397/AkecesKxpexMbH99IoDYAAt3O7EbPc3tS+3Et1f9bTePmRobePpTcwbmH7RtqCrH2nrWYgMEEDV3GdaH3pc4kbdv/tZ3t726nqbqJEQ+NICPf+aevXVhL9WvVuPPcDLxxIBl5GTRWN1L9SjUDftC66+bjYdJKs8QF42zXESud9lUPnDT3F7vmj79jqT8zb5TtepKchkQUpU1LIizhu5zELfS5sg/D7xnOkNuGsOO9HTRsbCC7fzYDbxhIp2M7Rd43Uyj8eSHD7h7GsLuGUeerY+9q50bfTsM7UfjTQjJ7HHwP2rZ3tjHktiF0ndCV2nm1AFS/Wk3BtILYvch2uHRuyG+7hljLDOztOnHuL47vXLdxtu1aklilztcUXekWEouADbaLOJLMrpnkFjqtBHeum+x+2QR2Bsjpl0N23+wj7isiuHOcKY1M0GlNhCf2JHdQLlm9slrcxwQMoaYQ4hbqP60no2sG2X2OfK54ym00e/rtSI5pODrKZYJZ4xf9ZlJB9WJdFrV93rFdQKpJq5DwlfgM8LrtOlqrqaaJhnUN5A49vGspEhMyrL5tNZU3VpJ3Qh6dhkZueQAUXFJA1e+qqF9Zj+dUDzVv1lBwcWK1Ii5YZJYLtP6bkAJGrnpq8tAvXpuDMU22a0kyL9kuINWk25gEOF1O020XcTTBhiBfPvwlfb7VB3du6yc8FZcw7O5hBOuDfPnQlzRsaCCnf+SZOfNG5jFspDNLxM7ZO8k7MY/GzY1s+8c23J3c9L2qL65su+8lzl8USquA2G/Q+n9N7Lx3y7LlI68biEg32/UkgbXTy6boPG1RllYtibAPgZ22izgSEzCsf3g9XU/rimesp13HcHd203lEZ+p8rVt2OdQYYtecXfSY0oOtr2zlmGuOIbcwl13zdrXr/NHSfbfZ6tlL2g7k9ty+4qRTFv56l4QC62zXkgResF1AKkq7kPCV+ALAW7briMQYw8anNpLdN5ue5/ds076B3QGC9UHAudKpblUdWX0PH4doSc3bNfT4Sg8kQwg1hS8xd3Hgc0u+PidUKWn4c9pc3t7NgyfOuzUvw79XV1k7sudtF5CK0mKCv0MVzyw+lwQd4Kr/rJ61v1lLdv9sRJxR596X9cYEDJv+songniCuTi5yB+ZSeHMh/p1+Nj69kcKbCmlY38CGxzdgQgYMeE7xUHCJM76w/Z/bqXm7hkBtgIwuGeSfmM8x3zsGAP9OP5ue2cSgnzgzXuy/3NbdKXxZbBd7vZLP/i7waY6f46wVkEBCktH48bhbFu/t1GeC7VoS0JLpZVNOtl1EKkrXkBDgc2Co7VpUZIO3mNW/fToYtWm1U4EB4xv5/Y+29Txxsu1aEsxPppdNeaAtO4hIIfCWMWZks8e8QJ0x5ndH2G8s8B1jzI0icibQZIxp0wqYIlIFjDXGbDvk8e8BPwEMTgv6VmPM6yJyNfCuMWbTUY7bqu3aIi2b8eGrnP5suw51ZFfMCumKgocQkBNX/Hny4LXlszEm5e8daaUm4Ll4ncwYs8gYc2P4yzOBqLTsRKQ/cCswyRhzInAqsL+L8WqgXysO09rtWi0tQyLsaaDRdhEqAmPMqC+MtiIiGLzu7UkjVz6+AmNqbdeSAF6fXjYl6vN6icgHIvJbEflYRD4TkdPDj58pIm+FWyLXAT8RkaUicrqI9BKRV0VkYfhjYnifHiLyrogsEZE/8587mA5SAOwB6gCMMXXGmLUichkwFng+fJ5cEbk9fPwVIvKYOFraboyIfCgii0XkHRHpG67nRhFZJSLLReTFI30f0jYkfCW+bcArtutQLRuz2ix3G46xXUciK9i2bPS4Rfdsk1AwoW8QjYMnYnjsDGPMKcCPgTuaP2GMqQLKgD8YY0YZY2YBD4a/Hgd8vVltdwCzjTGjgTeAgS2caxmwFVgrIk+LyEXh87yCcyPwVeHz7AMeNsaMC3eV5QIXHrodEAAeAi4zxowBngJ+HT5XKTA63GK57kjfgLQNibBHbRegWnbZ7NBu2zUkg/z6jUMnzLs12x3Yt9J2LZZ8CfyrnftGGpBt/vjfw38vhlZNU38O8LCILMUJgy4ikg+cAfwFwBhTTguX4RtjgsD5wGXAZ8AfwmMkLTlLRBaIiA+YApzQwjbHASOBf4br+SXQP/zccpwWx7dxwiSitA4JX4lvDuCzXYc6WEbANA7Zwom260gW2f49vSbNvWVI7r6adFyi98kOTAu+HTj0JsXuQPPB5P1d0kFad/OxCzgt/I5/lDHmGGPMnvBzR71KyDg+NsbcA1yJ0xo5iIjkAI/gtBCKgceBlu6YFWBls1qKjTHnhp+bCvwJGAMsFpGIry2tQyKszHYB6mBTlpklAu27izBNuUP+3FMXeMd337EqneZ8qgMebu/Oxpg6YLOInA0gIt1x3sm3ZYLFPUB+s6/fBX64/wsR2X8j6EfAVeHHLuDwcEJE+olI88t4RwH7b6Jsfp79gbBNRPJwWh4t1fMp0EtETgsfP1NEThARFzDAGPM+8DOgK5AX6QVqSDhXRbTutmQVFxcvCLU0qKeOQkBGLf/T5IFfvvsRxhyxCyFFPDa9bMqODh7jO8Avw90x/wbuNMZ80Yb93wQu3T9wDdwIjA0PCK/iQH//ncAZIvIJcC5ON9mhMoHfiUhluJ5vAD8KP/cMUBZ+vBGn9eDDWf6g+dLMzbdz4wTIb0VkGbAU50osN/CXcFfVEpwxlIhTK6TlfRKHKp5ZXAZ833YdCvL2mV1PPhDsJNC6W8VVi7YWjFm0sui7x+H0h6eiRmDI9LIpUbsfQLVMWxKOBwBd7jABXDw/5NOA6Lje1YvHjv3kvs2YYKr+Ep2pAREfGhKAr8RXCfzVdh0KzllqdCwiSrrsWXfshHm3u93BxlRbtjcI/NZ2EelCQ+KAO3F++JQlvXeYDZ0bKLZdRyrJadrVe9Kc0oHZDTs+tl1LFL08vWzKGttFpAsNiTBfie9z4FnbdaSzy2eHvpCW70RVHeAONXWeMP/2sV13fZ4KVz4Z4B7bRaQTDYmD3Q3ofDiWnFpp+h99K9UegnGdvPSByf03vP8hzk1byeqt6WVT9N6mONKQaMZX4luLM6eTirPj1puKrKDOyhtrx65+ZXLRp39ZjDH1tmtpp18ffRMVTRoSh/sVOvFf3F0xK1Rtu4Z00XfL/FNOXvL7LzGhLbZraaP3p5dNWWC7iHSjIXEIX4lvPbGdMEwdwhUywRO+NCNs15FOuu5eU3Ta/DuMK9j0me1aWsngTEqn4kxDomW/ARpsF5EuTq00y1yG3rbrSDe5jTv6Tppb2jercdci27W0wjPTy6ak0hVaSUNDogW+Et8mdIbYuJk2N7TPdg3pKiPYmD9x3i9Hd6ld+5HtWo6gFm1FWKMhEdmvOHg2SBUDWX6zd0ANJ9muI50Jxj12ye/O6Ldp9ocYk4gzD9wxvWyKjllZoiERga/EtwN99xJz5y02S+UIM1Cq+Bnx2V8nH/v5Sx/jLGqTKFbiTGmtLNGQOLKngHScoz9upi4M6TxNCaT/plmnjl724BpMqMZ2LWE3TC+bkg4z2iYsDYkj8JX4DPADdPK/mPDUmZpudYw6+pYqnrrt+vyE0xbc2egK+tsyZXYs/G162ZT3LdeQ9jQkjsJX4luCswqUirJL54UqpHWrfak4y23Y1n/i3Ft6Zjbt+cRSCXuB/7F0btWMhkTr3Aqk+2LzUXfmctPTdg0qsszgPs/Eeb8oztuzfpaF098zvWzKegvnVYfQkGgFX4lvN063k4qS/jVmbacmjrddhzoylwllnrJ4xul9tiz4kPitULYGuC9O51JHkdAhISJtWlZURM4UkbfCn18sIlG7OslX4nsTeDlax0t335gVamn5xpi5dfNmJq3+nIvXHphhurKhgW+uq+KStWv5wYb11AVbnveupX0BKhoauHJdFZdWreXyqiqW73MuCnp3z24uWruGb3+5jl3hY37Z1MT/bNoYo1cXe8dXPjt52BevzsOYWN9kGgK+O71sik6NkyASOiQ6whjzhjFmRpQPewPQ0TV1FTDmczM4nue71OPhsf4DDnrs9q1buKlXAa8PHszZ+fk8tbPlf9qW9gW4v6aaH/ToyWuFg/lhz57cX+Ncyv/Mjp28OGgQl3Tx8NbuWgD+uK2GG3r2ivKriq+BG96fcNLyP32OCW2P4WlmTC+bksg39qWdpAiJcAvhAxF5JbxI+PMiIuHnzg8/NhuY1myfq0Xk4fDnF4nIAhFZIiL/EpF2TQHhK/FVAz+MxmtKZyeuCa3ICDEwnucc26kTHvfBP+5rm5oYm5sLwIROnXl3z55W7wsgCPUh58K3ulCIgoxMAFwCTSFDgwmRIcKivXvplZFBYVbyX+3bY2dF8fiPf1UnocDaGBz+Y+COGBxXdUBShETYaODHwPHAEGCiiOQAjwMXAacDfSLsOxs41RgzGngR+Fl7i/CV+P6KTgDYIZfPDiVEa2x4Vjb/rnN6NN/Zs4ct/rZdjl9aUMB9NdVM+WI199VU8+NeTkvhBz16cu2G9cyrr2dqfhf+vH071/VInTH6zvu2Dpo09xddM/z1y6J42D3At/SeiMSTTCHxsTFmg3GmDVgKFAIjgLXGmM+NM6j2lwj79gfeEREf8FPghA7WciOgC5+0gzto/MM3MtJ2HQC/6tOHv+7ayWVVa6kPhchs45p4L+7aRWlBAf8eOoyf9yrgti2bAZjQuTOvFA7mkf4DeK+ujjPyOlPV1MSPN27k9i2b2RdK/ttuMgP13SbNvaWoc/2mOVE65A3Ty6bYvi9DtSCZQqL5QFaQA9fXt+aKi4eAh40xxcD3gZyOFOIr8e0DrgCSdeEWa85YYZa4oLvtOgCGZGfzxICBvFI4mKldujCwjd1Br++u5St5+QCcn5+Pr+HgMd19oRCv767lyq7d+ENNDb/q04fjc3J4a/fuqL0Gm1wmmDV+4a8nFlR/0tFlUV+cXjZlZlSKUlGXTCHRkkpgsIjsX9HsmxG28wD7Ly0picaJfSW+SvSy2Db72rxQwiyduT3g9GyEjKFs+zau6Nq1TfsXZGSwcN9eAObv3cugzMyDnn9yx3a+3bUbmSI0mBAi4ML5PJWMXPXk5CFrXp+DMU3t2H0dcF20a1LRk9QhYZzL8f4bKA8PXK+LsKkX+JuIzCKKM7v6SnzPAs9E63ipLrfB7O6zk9E2zn3zpo18c906qpqaOOuL1by6axdv79nNBWu+YOraNRRkZDCtiweA6oCf729Yf8R9Ae7s3Yd7q6u5tGotD2yr4c4+ff+zT3XAz8qGBs7Od1oaV3fvzpXr1vF6bS1T87vE8ZXHR+GX704sXvHnVRizqw27BYFvTy+bUhurulTHSfzuj0lNxTOLOwELQW8MO5rLZwVnXz7bTLJdh4qdus791i4c83OXcWUMasXmd00vm6JXMyW4pG5JJAJfiW8vzvhEIk2vnJDOW2x0SvAUl1e/afDEebfmZfj3Hu3Cjg+Bu+JRk+oYDYko8JX4VuLcaKci6FlrNufv40TbdajYy/LX9Zg475bhuXu3zo2wyRfA16eXTUmY8SkVmYZElPhKfE8CD9uuI1FdNjv0mejPW9pwhwI5p35812k9ti0/9Mqn3cBF08umxPKubRVF+p82un4EvGa7iEQ0cZXpZ7sGFV8CctKKP08urHp7FsYEcAaqvzG9bEqF7dpU62lIRJGvxBcCrgIiNbPT0tBN5vPsAMNt16HsGFJVfvrIVU8udwWbfjS9bMo/bNej2kZDIsrCN9pdDHxqu5ZEccWs0CbbNSi7CmqWzLr+8fN1reokpCERA74S33bgAmCr7VpsE2NCJ641x9quQ1n1KnCT7SJU+2hIxIivxLcWmAq0aU2MVDPuM7Pcbeh79C1VipoDfLuosiK1bjNPIxoSMeQr8S0GLgfSdmbLr88JpXVIprmVwMVFlRWxXqhIxZCGRIz5Snz/wJlUMO1kBkxD4Va9NyJNrQSmFFVWJMS08Kr9NCTiwFfiewr4H9t1xNvZS80SgdSbqEgdzf6AqLZdiOo4DYk48ZX4fg9cT+umNk8JFy0IuW3XoOJuJXCWBkTq0JCII1+Jrwy4GuemopSWv9fs6Lnbzoyvypr9AVFjuxAVPRoScRaeXvybgN92LbF08fzQSoHMo2+pUsQKNCBSkoaEBb4S39+AS4GUverjnKWmbSv4qGS2AmcMQgMiBWlIWOIr8ZUDF5KCS6D22WHWd26k2HYdKi4+QQMipWlIWOQr8b0HnIczM2bKuGJWaI3tGlRcvAGcoQGR2jQkLPOV+OYAU4CU+Y82/lMz0HYNKuYeBC4tqqxIuZawOpiGRAII35k9Dlhiu5aOOv5LsyozyGDbdaiYCQI3FFVW/Fin2kgPGhIJwlfiWwdMAl60XUtHXD4ruM12DSpm6nCm2dDFtdKIGJM293YljeKZxT8D7iHJQtwVMoEX7g3udBl62a5FRd1GYGpRZcUy24Wo+EqqX0LpwlfiuxdnBtldtmtpiwmrzFINiJS0BBivAZGeNCQSVHhiwFOAVbZraa1pc0NNtmtQUVcGTCiqrNhouxBlh4ZEAvOV+D4HTgVet13L0eQ0mbpjtjPKdh0qanYBlxdVVlyvU32nNw2JBOcr8e3BuTv7dhJ4XYrzF5llAp1s16GiYj4wuqiy4hXbhSj7NCSSgK/EZ3wlvruB04AK2/W05IJFoRzbNagOM8BvgdOLKiuqLNeiEoSGRBLxlfgWAScD9wMJc416tz2mumu9djUluWrg/KLKitKiyoqEbbGq+NOQSDK+El+Dr8R3MzAZ+MJ2PQDT5oYqBHTtiOT1DnBSUWXFu7YLUYlHQyJJ+Up8s4GTgEewvJDRGT7T2+b5VbtVA98uqqw4v6iyYovtYlRi0pvpUkDxzOJzgKeAAfE+96Ct5ov7ngoOjfd5VYcY4EngZ0WVFTttF6MSm7YkUoCvxPcvoBjnP35cU/+KWaEN8Tyf6rBVODO3XqsBoVpDWxIppnhm8Ricge3JMT+ZMeaFe4MbM0L0j/m5VEc1AL8C7i2qrEjpVRFVdGlIpKjimcWXAvcCw2J1jtGrQ8tv+VvoxFgdX0XNv4DriyorVtsuRCUf7W5KUb4S32vACcD/EKM5oC6bHaqNxXFV1HwCXFBUWfEVDQjVXtqSSAPFM4t7AHcA1wMZ0ThmRtA0PX9vcK+ArmWdeD4FbgNeKaqs0P/gqkM0JNJI8czi44D7gIs6eqxzloQW/Pc/QuM7XpWKoi+BO4GZRZUVQdvFqNSgIZGGimcWnwL8FJhGO7scH3o0ML/3Lk6NamGqvWqAXwNlRZUVjbaLUalFQyKNFc8sHgrcBHwXyG3tfp33mdqnHgjmCGTHrDjVGhuBh4GHiyor6mwXo1KThoSieGZxT2A68EOg59G2v/LD4Kxpc83pMS9MRTIPeBB4VedZUrGmIaH+o3hmcS5wNc4VURHvon7yD4Fl+Q2cFK+6FABNwMvAH4sqKxbaLkalDw0JdZjimcUunDUsfohzU57sf65gl9n40KPBftLsMRVT1Tirwz2q8yspGzQk1BEVzywuBErCH4Onvxn8cPIKE/u7udNbAHgXeAHnMlYdjFbWaEioVimeWSzAGY88HLiw5x7+H3p/RLSFgFnAX3GCYbvlepQCNCRUO1SMKMoCzgO+CVwMdLZbUVJbiBMMLxdVVmy0XYxSh9KQUB1SMaIoFzgLJzTOA46zW1HCCwIfA+XAi0WVFQmxcJRSkWhIqKiqGFE0CDgXJzDORrulwLkT+l2cFeDe0ym6VTLRkFAxUzGiyA2cghMYU4DRQJ7VouJjPfDB/o+iyoo1VqtRqgM0JFTcVIwocgHHAieHP8bgBIfHZl0dEMJZZ3xZs4+lRZUV661WpVQUaUgoqypGFAkwhAPBMQIYBAwEelgsrTkDbAM+5+BA8BVVVtTbLEypWNOQUAmrYkRRZ5ywGNTs70E4a3l3xbmqqvlHWyYrDOGs1tYA7MGZB2lD+O9DP99YVFnR1PFXpFTy0ZBQKSN8pVXz0MjFmc6i4dAPXcJTqdbRkFBKKRWRLl+qlFIqIg0JpZRSEWlIKGWBiBgRea7Z1xkiUiMib7XzeBeLSGn0KlTKkWG7AKXSVD0wUkRyjTH7gK/gXEnVLsaYN4A3olWcUvtpS0Ipe/4PmBr+/Js4E/0BICKdReQpEVkoIktE5JLw4zeJyFPhz4tFZIWIdBKRq0Xk4fDjvUXkNRFZFv6Y0GzfFeGPH8f1laqkpSGhlD0vAleKSA5wIrCg2XO3Av82xozDmUDxPhHpDDwADBORS4Gnge8bY/Yectw/Ah8aY07CuUFxpYiMwVnLfDxwKnCtiIyO4WtTKUJDQilLjDHLgUKcVsTbhzx9LlAqIktx5oDKAQYaY0I4S8w+hxMEc1o49BTg0fA5gsaYWmAS8Joxpt4YUwf8HdB1ytVR6ZiEUna9AfwOOJODpyER4OvGmE9b2Gc4UAf0a8N5dLlZ1S7aklDKrqeAu4wxvkMefwe4QUQEYH/XkIh4gAeBM4AeInJZC8d8D7g+vL1bRLoAHwFfC49fdMZZw3xWLF6QSi0aEkpZZIzZYIx5sIWn7gYygeUisiL8NcAfgEeMMZ8B1wAzRKTgkH1/BJwlIj5gMXCCMeYT4BmcBY8WAE8YY5ZE/QWplKPTciillIpIWxJKKaUi0pBQSikVkYaEUkqpiDQklFJKRaQhoZRSKiINCaWUUhFpSCillIpIQ0IppVREGhJKKaUi0pBQSikVkYaEUkqpiDQklFJKRaQhoZRSKiINCaWUUhFpSCillIpIQ0IppVREGhJKKaUi0pBQSikVkYaEUkqpiDQklFJKRaQhoZRSKiINCaWUUhFpSCillIpIQ0IppVREGhJKKaUi0pBQSikVkYaEUkqpiP4/cjOgimve3mAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the extracted data in a pie chart\n",
    "\n",
    "df1.plot(x = 'Location', y = 'Count', kind = 'pie', labels = df1.Location, autopct = '%1.2f%%')\n",
    "plt.legend().set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "N/S America    153597\n",
       "Asia           105552\n",
       "Africa          99481\n",
       "Europe          19869\n",
       "Oceania           347\n",
       "Name: Count, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total count of homicide people by region\n",
    "\n",
    "df2 = df.groupby('Region')['Count'].sum().sort_values(ascending = False)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(kind = 'bar')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>Location</th>\n",
       "      <th>Region</th>\n",
       "      <th>Subregion</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>6</td>\n",
       "      <td>2474</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>1</td>\n",
       "      <td>580</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>4</td>\n",
       "      <td>1217</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>5</td>\n",
       "      <td>2416</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Australia, New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>230</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>18</td>\n",
       "      <td>73</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>3830</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>14</td>\n",
       "      <td>41</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>225</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Belize</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Central America</td>\n",
       "      <td>25</td>\n",
       "      <td>102</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>7</td>\n",
       "      <td>806</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Southern Africa</td>\n",
       "      <td>15</td>\n",
       "      <td>303</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>22</td>\n",
       "      <td>47722</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>British Virgin Islands</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Brunei</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>3</td>\n",
       "      <td>742</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Palestine</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>5</td>\n",
       "      <td>1702</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>192</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Central Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>1787</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>East Timor</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>38</td>\n",
       "      <td>538</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>4</td>\n",
       "      <td>562</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>2095</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Central Asia</td>\n",
       "      <td>4</td>\n",
       "      <td>203</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Turks and Caicos Islands</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>9</td>\n",
       "      <td>4460</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>6</td>\n",
       "      <td>2751</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>755</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>6</td>\n",
       "      <td>3439</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>United States</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>6</td>\n",
       "      <td>21570</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>U.S. Virgin Islands</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>49</td>\n",
       "      <td>52</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>9</td>\n",
       "      <td>337</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Central Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>396</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>N/S America</td>\n",
       "      <td>South America</td>\n",
       "      <td>36</td>\n",
       "      <td>10598</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>1358</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>6</td>\n",
       "      <td>1703</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>5</td>\n",
       "      <td>853</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>7</td>\n",
       "      <td>981</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Location       Region               Subregion  Rate  \\\n",
       "0                 Afghanistan         Asia           Southern Asia     6   \n",
       "1                     Albania       Europe         Southern Europe     2   \n",
       "2                     Algeria       Africa         Northern Africa     1   \n",
       "3                     Andorra       Europe         Southern Europe     2   \n",
       "4                      Angola       Africa           Middle Africa     4   \n",
       "5                    Anguilla  N/S America               Caribbean    28   \n",
       "6         Antigua and Barbuda  N/S America               Caribbean     9   \n",
       "7                   Argentina  N/S America           South America     5   \n",
       "8                     Armenia         Asia            Western Asia     1   \n",
       "9                       Aruba  N/S America               Caribbean     1   \n",
       "10                  Australia      Oceania  Australia, New Zealand     0   \n",
       "11                    Austria       Europe          Western Europe     0   \n",
       "12                 Azerbaijan         Asia            Western Asia     2   \n",
       "13                    Bahamas  N/S America               Caribbean    18   \n",
       "14                    Bahrain         Asia            Western Asia     0   \n",
       "15                 Bangladesh         Asia           Southern Asia     2   \n",
       "16                   Barbados  N/S America               Caribbean    14   \n",
       "17                    Belarus       Europe          Eastern Europe     2   \n",
       "18                    Belgium       Europe          Western Europe     1   \n",
       "19                     Belize  N/S America         Central America    25   \n",
       "20                      Benin       Africa          Western Africa     1   \n",
       "21                    Bermuda  N/S America        Northern America     0   \n",
       "22                     Bhutan         Asia           Southern Asia     2   \n",
       "23                    Bolivia  N/S America           South America     7   \n",
       "24     Bosnia and Herzegovina       Europe         Southern Europe     1   \n",
       "25                   Botswana       Africa         Southern Africa    15   \n",
       "26                     Brazil  N/S America           South America    22   \n",
       "27     British Virgin Islands  N/S America               Caribbean     8   \n",
       "28                     Brunei         Asia      South-Eastern Asia     0   \n",
       "29                   Bulgaria       Europe          Eastern Europe     1   \n",
       "..                        ...          ...                     ...   ...   \n",
       "165                 Sri Lanka         Asia           Southern Asia     3   \n",
       "166                 Palestine         Asia            Western Asia     1   \n",
       "167                     Sudan       Africa         Northern Africa     5   \n",
       "168                  Suriname  N/S America           South America     9   \n",
       "169                    Sweden       Europe         Northern Europe     1   \n",
       "170               Switzerland       Europe          Western Europe     0   \n",
       "171                     Syria         Asia            Western Asia     0   \n",
       "172                    Taiwan         Asia            Eastern Asia     0   \n",
       "173                Tajikistan         Asia            Central Asia     0   \n",
       "174                  Thailand         Asia      South-Eastern Asia     2   \n",
       "175                East Timor         Asia      South-Eastern Asia     4   \n",
       "176       Trinidad and Tobago  N/S America               Caribbean    38   \n",
       "177                   Tunisia       Africa         Northern Africa     4   \n",
       "178                    Turkey         Asia            Western Asia     2   \n",
       "179              Turkmenistan         Asia            Central Asia     4   \n",
       "180  Turks and Caicos Islands  N/S America               Caribbean     5   \n",
       "181                    Uganda       Africa          Eastern Africa     9   \n",
       "182                   Ukraine       Europe          Eastern Europe     6   \n",
       "183      United Arab Emirates         Asia            Western Asia     0   \n",
       "184            United Kingdom       Europe         Northern Europe     1   \n",
       "185                  Tanzania       Africa          Eastern Africa     6   \n",
       "186             United States  N/S America        Northern America     6   \n",
       "187       U.S. Virgin Islands  N/S America               Caribbean    49   \n",
       "188                   Uruguay  N/S America           South America     9   \n",
       "189                Uzbekistan         Asia            Central Asia     1   \n",
       "190                 Venezuela  N/S America           South America    36   \n",
       "191                   Vietnam         Asia      South-Eastern Asia     1   \n",
       "192                     Yemen         Asia            Western Asia     6   \n",
       "193                    Zambia       Africa          Eastern Africa     5   \n",
       "194                  Zimbabwe       Africa          Eastern Africa     7   \n",
       "\n",
       "     Count  Year  \n",
       "0     2474  2018  \n",
       "1       61  2020  \n",
       "2      580  2020  \n",
       "3        2  2020  \n",
       "4     1217  2012  \n",
       "5        4  2014  \n",
       "6        9  2020  \n",
       "7     2416  2020  \n",
       "8       52  2020  \n",
       "9        2  2014  \n",
       "10     221  2020  \n",
       "11      65  2020  \n",
       "12     230  2020  \n",
       "13      73  2020  \n",
       "14       2  2019  \n",
       "15    3830  2018  \n",
       "16      41  2020  \n",
       "17     225  2019  \n",
       "18     193  2017  \n",
       "19     102  2020  \n",
       "20     126  2017  \n",
       "21       0  2019  \n",
       "22      19  2020  \n",
       "23     806  2019  \n",
       "24      43  2020  \n",
       "25     303  2010  \n",
       "26   47722  2020  \n",
       "27       2  2006  \n",
       "28       2  2013  \n",
       "29      69  2020  \n",
       "..     ...   ...  \n",
       "165    742  2019  \n",
       "166     48  2020  \n",
       "167   1702  2008  \n",
       "168     55  2020  \n",
       "169    124  2020  \n",
       "170     47  2020  \n",
       "171    149  2018  \n",
       "172    192  2015  \n",
       "173     87  2020  \n",
       "174   1787  2017  \n",
       "175     49  2015  \n",
       "176    538  2019  \n",
       "177    562  2020  \n",
       "178   2095  2020  \n",
       "179    203  2006  \n",
       "180      2  2014  \n",
       "181   4460  2020  \n",
       "182   2751  2017  \n",
       "183     64  2019  \n",
       "184    755  2018  \n",
       "185   3439  2016  \n",
       "186  21570  2020  \n",
       "187     52  2012  \n",
       "188    337  2020  \n",
       "189    396  2019  \n",
       "190  10598  2018  \n",
       "191   1358  2011  \n",
       "192   1703  2013  \n",
       "193    853  2015  \n",
       "194    981  2012  \n",
       "\n",
       "[195 rows x 6 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subregion\n",
       "South America             78872\n",
       "Southern Asia             58631\n",
       "Central America           47371\n",
       "Western Africa            46318\n",
       "South-Eastern Asia        25314\n",
       "Eastern Africa            23669\n",
       "Northern America          22317\n",
       "Southern Africa           21479\n",
       "Eastern Europe            14604\n",
       "Western Asia              11638\n",
       "Eastern Asia               8563\n",
       "Northern Africa            5538\n",
       "Caribbean                  5037\n",
       "Middle Africa              2477\n",
       "Northern Europe            2097\n",
       "Western Europe             2075\n",
       "Central Asia               1406\n",
       "Southern Europe            1093\n",
       "Australia, New Zealand      347\n",
       "Name: Count, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group dataframe by subregion\n",
    "\n",
    "df3 = df.groupby('Subregion')['Count'].sum().sort_values(ascending = False)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['South America', 'Southern Asia', 'Central America', 'Western Africa',\n",
       "       'South-Eastern Asia', 'Eastern Africa', 'Northern America',\n",
       "       'Southern Africa', 'Eastern Europe', 'Western Asia', 'Eastern Asia',\n",
       "       'Northern Africa', 'Caribbean', 'Middle Africa', 'Northern Europe',\n",
       "       'Western Europe', 'Central Asia', 'Southern Europe',\n",
       "       'Australia, New Zealand'],\n",
       "      dtype='object', name='Subregion')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate index\n",
    "\n",
    "df3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([78872, 58631, 47371, 46318, 25314, 23669, 22317, 21479, 14604,\n",
       "       11638,  8563,  5538,  5037,  2477,  2097,  2075,  1406,  1093,\n",
       "         347], dtype=int64)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate Values\n",
    "\n",
    "df3.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the data in a bar plot using seaborn\n",
    "\n",
    "sns.barplot(x = df3.index, y = df3.values)\n",
    "plt.xticks(rotation = 'vertical')\n",
    "xlabel = None\n",
    "from matplotlib import rcParams\n",
    "\n",
    "# figure size in inches\n",
    "rcParams['figure.figsize'] = 6,6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    94\n",
       "2019    20\n",
       "2018    13\n",
       "2012    11\n",
       "2017    10\n",
       "2016     9\n",
       "2015     9\n",
       "2009     6\n",
       "2014     5\n",
       "2013     5\n",
       "2011     5\n",
       "2010     3\n",
       "2006     2\n",
       "2021     1\n",
       "2008     1\n",
       "2007     1\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Count of Asia and Europe over years\n",
    "\n",
    "df4 = df[df['Region'].isin(['Asia', 'Europe'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df4[df4['Year']> 2016][['Region', 'Year', 'Count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>Region</th>\n",
       "      <th>Year</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2018</td>\n",
       "      <td>2474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2018</td>\n",
       "      <td>3830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2019</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2017</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2018</td>\n",
       "      <td>7525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2017</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>40651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2018</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>8490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>4764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2019</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>10697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2018</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2020</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2018</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2017</td>\n",
       "      <td>1787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2020</td>\n",
       "      <td>2095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2017</td>\n",
       "      <td>2751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Europe</td>\n",
       "      <td>2018</td>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2019</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region  Year  Count\n",
       "0      Asia  2018   2474\n",
       "1    Europe  2020     61\n",
       "3    Europe  2020      2\n",
       "8      Asia  2020     52\n",
       "11   Europe  2020     65\n",
       "12     Asia  2020    230\n",
       "14     Asia  2019      2\n",
       "15     Asia  2018   3830\n",
       "17   Europe  2019    225\n",
       "18   Europe  2017    193\n",
       "22     Asia  2020     19\n",
       "24   Europe  2020     43\n",
       "29   Europe  2020     69\n",
       "40     Asia  2018   7525\n",
       "43   Europe  2020     39\n",
       "46     Asia  2020     15\n",
       "47   Europe  2020     77\n",
       "48   Europe  2020     55\n",
       "54   Europe  2017    726\n",
       "55   Europe  2020     42\n",
       "58   Europe  2020     91\n",
       "59   Europe  2020    879\n",
       "61     Asia  2019     77\n",
       "62   Europe  2020    782\n",
       "65   Europe  2020     78\n",
       "75     Asia  2020     22\n",
       "76   Europe  2020     80\n",
       "77   Europe  2020      5\n",
       "78     Asia  2020  40651\n",
       "79     Asia  2018   1150\n",
       "..      ...   ...    ...\n",
       "128    Asia  2020     14\n",
       "129    Asia  2020   8490\n",
       "133    Asia  2019   4764\n",
       "134  Europe  2020    268\n",
       "135  Europe  2020     93\n",
       "137    Asia  2020     12\n",
       "138    Asia  2020    308\n",
       "139  Europe  2020     93\n",
       "140  Europe  2019     24\n",
       "142  Europe  2020    281\n",
       "143  Europe  2020  10697\n",
       "153    Asia  2019    285\n",
       "154  Europe  2018     61\n",
       "156  Europe  2020     89\n",
       "159    Asia  2020     10\n",
       "160  Europe  2020     63\n",
       "161  Europe  2020     11\n",
       "164  Europe  2020    298\n",
       "165    Asia  2019    742\n",
       "166    Asia  2020     48\n",
       "169  Europe  2020    124\n",
       "170  Europe  2020     47\n",
       "171    Asia  2018    149\n",
       "173    Asia  2020     87\n",
       "174    Asia  2017   1787\n",
       "178    Asia  2020   2095\n",
       "182  Europe  2017   2751\n",
       "183    Asia  2019     64\n",
       "184  Europe  2018    755\n",
       "189    Asia  2019    396\n",
       "\n",
       "[83 rows x 3 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region  Year\n",
       "Asia    2017     1787\n",
       "        2018    16923\n",
       "        2019     6458\n",
       "        2020    53516\n",
       "        2021    15299\n",
       "Europe  2017     3670\n",
       "        2018      883\n",
       "        2019      249\n",
       "        2020    15066\n",
       "Name: Count, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4.groupby(['Region', 'Year']).sum()['Count']\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>Region</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1787.0</td>\n",
       "      <td>3670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>16923.0</td>\n",
       "      <td>883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>6458.0</td>\n",
       "      <td>249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>53516.0</td>\n",
       "      <td>15066.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>15299.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Region     Asia   Europe\n",
       "Year                    \n",
       "2017     1787.0   3670.0\n",
       "2018    16923.0    883.0\n",
       "2019     6458.0    249.0\n",
       "2020    53516.0  15066.0\n",
       "2021    15299.0      NaN"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert the data to rows and columns\n",
    "\n",
    "df_unstacked = df4.unstack(level = 0)\n",
    "df_unstacked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_unstacked.index = df_unstacked.index.astype(int).astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qx1w2uko4jw1LYm3WAZ6cklZGCeWDDz7AzEhNPfbQ+uDBg9m9e3cZpQouFX0iIiKl7Mmp6VSJCOOWfm1LtHzv1nFceXpzXv5mNQvX7QpyOgF45513OPPMM3n33XePudxnn31GrVq1yihVcKnoExERKUXLNu5h0uJNXHdmS+rFVC3xendd0I5GNaO4c/wScvI0zBtM2dnZfPPNN7z88ss/Fn2bN2+mb9++dO3alU6dOvH1118DEB8fz44dOwAYOnQoPXr0oGPHjrz44ou+5T9Z+pw+ERGRUjRuchq1q0Vyfd9WJ7ReTFQkfx3WmWv+M5+/T88o9o7fyuTBScv5YdPeUt1mh8Y1uf/ijsdd7sMPP2TQoEEkJCRQp04dFi5cyIwZMxg4cCD33nsvBQUFHDhw4GfrvfLKK9SpU4eDBw/Ss2dPhg8fTlxcXKkeQzCpp09ERKSUfLtyB7PSt3PzuW2oGXXij1c7J7E+I3o05YWZq1i2cU8QEgoEhnavuOIKAK644greeecdevbsyX/+8x8eeOABli5dSkxMzM/We/bZZ+nSpQu9evVi/fr1ZGRklHX0U6KePhERkVLgnGPcF2k0io3iql4tTno7913YgZnp2xk9fgkf/74PkeGVs3+mJD1ywZCVlcX06dNZtmwZZkZBQQFmxrhx45g1axaffvopV199NaNHj2bkyJE/rvfVV18xbdo05syZQ7Vq1TjnnHPIycnx5RhOVuX8ThIRESljk5dvZdH63dx2fgJRkeEnvZ3YapE8MrQTKzbv5YWvVpZiQgEYP348I0eOZO3ataxZs4b169fTsmVLZs2aRf369bn++uu57rrrWLhw4U/W27NnD7Vr16ZatWqkpqYyd+5cn47g5AW16DOzNWa21MwWmVmK11bHzKaaWYb3b22v3czsWTPLNLMlZta9yHZGectnmNmoIu09vO1neutaMI9HRESkOPkFhTwxJY3W9aozrHuTU97egI4NubhLY56dnkH61n2lkFAOe+edd7j00kt/0jZ8+HCuueYaunbtSrdu3ZgwYQK33nrrT5YZNGgQ+fn5JCUlcd9999GrV6+yjF0qzDkXvI2brQGSnXM7irSNA3Y65x4zs7uA2s65MWY2GLgFGAycDjzjnDvdzOoAKUAy4IAFQA/n3C4zmwfcCswFPgOedc59fqxMycnJLiUlpdSPVUREQtf7Keu5c/wSXriqB4M6NSyVbWZl59L/qVk0qx3NhBvPIKISDPOuWLGC9u3b+x2j3Cru/JjZAudccmls34/voEuA17zXrwFDi7S/7gLmArXMrBEwEJjqnNvpnNsFTAUGefNqOufmuEDl+nqRbYmIiJSJnLwCnp6aTpdmtRjYsUGpbTeuRlUeGNKRxRv28Mo3q0ttuxK6gl30OWCKmS0wsxu8tgbOuc0A3r/1vfYmwPoi627w2o7VvqGY9p8xsxvMLMXMUrZv336KhyQiIvI/b85dy6Y9OYwZlEhpX2V0cVIj+ndowJNT0lm1PbtUty2hJ9hFXx/nXHfgAuBmM+t7jGWL+0lxJ9H+80bnXnTOJTvnkuvVq3e8zCIiIiWyNyeP52Zk0jehHme0rlvq2zczHh7aiaoRYYyZsITCwuBdkiWVX1CLPufcJu/fbcAHwGnAVm9oFu/fbd7iG4BmRVZvCmw6TnvTYtpFRETKxEuzVrHrQB53DkwM2j4a1Izivos6MH/NLt78bm3Q9iOVX9CKPjOrbmYxh18DA4BlwMfA4TtwRwEfea8/BkZ6d/H2AvZ4w7+TgQFmVtu703cAMNmbt8/Menl37Y4ssi0REZGg2r4vl39/vZqLkhrRqUlsUPc1okdT+ibU47HPU1m/8+dPihApiWD29DUAZpvZYmAe8Klz7gvgMaC/mWUA/b33ELj7dhWQCbwE3ATgnNsJPATM96a/eG0ANwL/9tZZCRzzzl0REZHS8o/pGeQVFPLHAcHr5TvMzHh0WGcMuHviUoL5yRtSeQXtiRzOuVVAl2Las4Dziml3wM1H2dYrwCvFtKcAnU45rIiIyAlYl3WAt+et4/KezYivW71M9tmkVjR3D27Pnz5cxvsp67m8Z/My2W9lEx4eTufOnX98f8UVV3DXXXf5mKjs6DFsIiIiJ+hvU9MIDzP+77y2ZbrfX53WnEmLN/HwJys4O6E+DWOjynT/lUF0dDSLFi06qXXz8/OJiKi4pVPF/6RHERGRMvTDpr18tHgT1/ZpSYOaZVt0hYUZY4cnkVdYyL0faJi3NMXHx7NjR+BZEikpKZxzzjkAPPDAA9xwww0MGDCAkSNHkpOTw69//Ws6d+5Mt27dmDFjBgCvvvoql1xyCYMGDSIxMZEHH3zwx22/+eabnHbaaXTt2pXf/va3FBQUlPnxgXr6RERETsjjk1OpGRXJb89u7cv+4+tW548DEnn40xV8tGgTQ7ud+mPffPH5XbBlaelus2FnuOCxYy5y8OBBunbt+uP7u+++m8svv/yY6yxYsIDZs2cTHR3Nk08+CcDSpUtJTU1lwIABpKenAzBv3jyWLVtGtWrV6NmzJxdeeCHVq1fnvffe45tvviEyMpKbbrqJt956i5EjR57iwZ44FX0iIiIl9N2qLGakbeeuC9oRGx3pW45f92nJp0s388Ck5fRpU5d6MVV9y1LRnMzw7pAhQ4iOjgZg9uzZ3HLLLQC0a9eOFi1a/Fj09e/fn7i4OACGDRvG7NmziYiIYMGCBfTs2RMIFJ3169cvZi/Bp6JPRESkBJxzjJucRoOaVbnmjHhfs4SHGY+PSGLwM7N54OPlPHdld1/znJTj9MiVtYiICAoLCwHIycn5ybzq1f93s86xhtSPfCKLmeGcY9SoUTz66KOlmPbk6Jo+ERGREpi2YhsL1u7iD+cnEBUZ7ncc2tSP4dbz2/Lp0s18vnSz33EqvPj4eBYsWADAhAkTjrpc3759eeuttwBIT09n3bp1JCYGPrZn6tSp7Ny5k4MHD/Lhhx/Sp08fzjvvPMaPH8+2bYFnUezcuZO1a/35kG0VfSIiIsdRUOh4fHIqrepW57IeTY+/Qhm5oW8rOjWpyX0fLWfX/kN+x6kQDl/Td3g6/HEt999/P7feeitnnXUW4eFHL+pvuukmCgoK6Ny5M5dffjmvvvoqVasGhtfPPPNMrr76arp27crw4cNJTk6mQ4cOPPzwwwwYMICkpCT69+/P5s3+FOkWanf+JCcnu5SUFL9jiIhIBTJhwQbu+O9inr+yO4M7N/I7zk/8sGkvQ/4xmyFdGvO3y7sefwUfrVixgvbt2/sdIyheffVVUlJS+Mc//nHS2yju/JjZAudc8qnmA/X0iYiIHFNufgF/m5pO5yaxXNCpod9xfqZD45rcdE5rJn6/kempW/2OI+WYij4REZFjeGvuOjbuPsiYQe1+dqF+eXFzvzYkNKjBPROXsTcnz+84Iemaa645pV6+sqCiT0RE5Ciyc/P5x4xMzmxTlzPb1vU7zlFVjQhn3IgubNuXw6OfrfA7jpRTKvpERESO4qVZq9i5/xCjByb6HeW4ujarxfVnteKdeev5JnOH33GOKtTuJSipsjgvKvpERESKsSM7l39/vYrBnRvSpVktv+OUyG39E2hZtzp3TVzC/tx8v+P8TFRUFFlZWSr8juCcIysri6io4D7WTx/OLCIiUoznZmSSk1/IHQPKfy/fYVGR4YwbkcQv/jWHxyen8cCQjn5H+ommTZuyYcMGtm/f7neUcicqKoqmTYP7cUAq+kRERI6wfucB3pq7jl8kN6V1vRp+xzkhPePrMKp3PK/NWcOFSY3oGV/H70g/ioyMpGXLln7HCFka3hURETnCU9PSMYNbz0vwO8pJGT0wkSa1ohkzfgk5eQV+x5FyQkWfiIhIEWlb9vHB9xu5pk88DWODe41VsFSvGsFjw5JYtWM/T01L9zuOlBMq+kRERIp4fHIqNapGcOPZrf2OckrObFuXK3o246VZq1i8frffcaQcUNEnIiLiSVmzk2krtvG7s1tTq1oVv+OcsnsubE/9mCjuHL+EQ/mFfscRn6noExERIfCxGWO/SKV+TFWu7VM5bjaoGRXJI5d2Im3rPp6bkel3HPGZij4RERFgRto25q/Zxf+d15boKuF+xyk157VvwKXdmvDcjExWbN7rdxzxkYo+EREJeYWFjnFfpBEfV43LezbzO06p+/NFHahVLZLR4xeTX6Bh3lClok9ERELex4s3kbplH3cMSCQyvPL9aqxdvQoPXdKJZRv38uLXq/yOIz6pfN/ZIiIiJ+BQfiFPTk2jY+OaXNi5kd9xguaCzo0Y3LkhT0/LIHPbPr/jiA9U9ImISEh7Z9461u88yJ2D2hEWZn7HCaoHh3SiWpVw7hy/hIJCPf821KjoExGRkLU/N5+/T8+gd6s4+rat63ecoKsXU5X7L+7AwnW7efXbNX7HkTKmok9ERELWy7NXsyP7EHcOSsSscvfyHTa0axP6tavP45NTWZu13+84UoZU9ImISEjauf8QL85axcCODejWvLbfccqMmfHIpZ2IDAvjrglLKdQwb8hQ0SciIiHp+RmZHDiUz+iBiX5HKXONYqO598L2zFmVxTvz1/kdR8qIij4REQk5G3cf5PU5axnRoylt6sf4HccXl/dsRp82cTz6WSobdx/0O46UARV9IiIScp6emg4Gfzg/we8ovjEzHhuWRKFz3DNxKc5pmLeyU9EnIiIhJWPrPiYs3MCo3i1oXCva7zi+alanGncOTGRm+nYmLNzodxwJMhV9IiISUh6fnEb1KhHcdE4bv6OUCyN7x5PcojZ/mbScbXtz/I4jQaSiT0REQsbCdbuY8sNWbujbitrVq/gdp1wICzPGjUgiN7+Qez9cpmHeSkxFn4iIhATnHGM/T6Vujapce2ZLv+OUK63q1eD2/glM/WErnyzZ7HccCRIVfSIiEhJmpm/nu9U7+b/z2lC9aoTfccqd685sSZemsTzw8XKysnP9jiNBoKJPREQqvcJCx7gv0mhepxpX9Gzud5xyKSI8jHEjurA3J48HJ/3gdxwJAhV9IiJS6X2ydDM/bN7LHQMSqBKhX31Hk9gwhlv6teXjxZuYsnyL33GklOk7X0REKrVD+YU8OSWNdg1juDipsd9xyr0bz2lN+0Y1+dOHy9hzIM/vOFKKVPSJiEil9l7KetZmHWDMoHaEhZnfccq9yPAwHh+RRNb+Qzz8qYZ5KxMVfSIiUmkdOJTPs19mcFrLOpyTWM/vOBVGpyax/LZvK/67YAMz07f7HUdKiYo+ERGptP7zzRq278tlzKBEzNTLdyL+77y2tK5XnbsnLGFfjoZ5KwMVfSIiUint2n+IF75ayfntG9CjRR2/41Q4UZHhjBvRhc17cxj7RarfcaQUqOgTEZFK6YWZK8k+lM+dgxL9jlJh9WhRm2v7tOTNueuYuyrL7zhyilT0iYhIpbN5z0Fe/XYNw7o1JaFBjN9xKrQ/DkikRVw1xkxYwsFDBX7HkVOgok9ERCqdZ6Zl4Bzc1r+t31EqvOgq4Tw2LIm1WQd4ckqa33HkFAS96DOzcDP73sw+8d63NLPvzCzDzN4zsypee1XvfaY3P77INu722tPMbGCR9kFeW6aZ3RXsYxERkfIvc1s276es56peLWhau5rfcSqF3q3juKpXc17+ZjUL1+3yO46cpLLo6bsVWFHk/VjgKedcW2AXcJ3Xfh2wyznXBnjKWw4z6wBcAXQEBgHPe4VkOPAccAHQAfilt6yIiISwJ6ekER0Zzs3ntvY7SqUyZlA7GtWM4s7xS8jJ0zBvRRTUos/MmgIXAv/23hvQDxjvLfIaMNR7fYn3Hm/+ed7ylwDvOudynXOrgUzgNG/KdM6tcs4dAt71lhURkRC1eP1uPl+2hev7tiKuRlW/41QqMVGR/HVYZzK3ZfP36Rl+x5GTEOyevqeBO4FC730csNs5l++93wA08V43AdYDePP3eMv/2H7EOkdrFxGREDVucipx1avwm7Na+R2lUjonsT4jejTlhZmrWLZxj99x5AQFregzs4uAbc65BUWbi1nUHWfeibYXl+UGM0sxs5Tt2/XJ4iIildHXGdv5JjOL3/drQ42qEX7HqbTuu7ADdapXYfT4JeQVFCv/u+wAACAASURBVB5/BSk3gtnT1wcYYmZrCAy99iPQ81fLzA7/NDYFNnmvNwDNALz5scDOou1HrHO09p9xzr3onEt2ziXXq6fH8IiIVDaFhY5xX6TRtHY0vzq9ud9xKrXYapE8MrQTKzbv5YWvVvodR05A0Io+59zdzrmmzrl4AjdiTHfOXQnMAEZ4i40CPvJef+y9x5s/3TnnvPYrvLt7WwJtgXnAfKCtdzdwFW8fHwfreEREpPz6fNkWlm7cw+39E6gaEe53nEpvQMeGXNylMc9OzyB96z6/40gJ+fE5fWOA280sk8A1ey977S8DcV777cBdAM655cD7wA/AF8DNzrkC77q/3wOTCdwd/L63rIiIhJC8gkKemJJGYoMYLumqS7vLygMXdyAmKpLR/11MvoZ5KwQLdKaFjuTkZJeSkuJ3DBERKSVvf7eOez5Yyr9HJnN+hwZ+xwkpkxZv4pZ3vueewe24oa8+IicYzGyBcy65NLalJ3KIiEiFdfBQAc98mU5yi9qc176+33FCzkVJjejfoQFPTkln1fZsv+PIcajoExGRCuvVb9ewdW8uYy5oR+CjXaUsmRkPD+1E1YgwxkxYQmFhaI0eVjQq+kREpELacyCPf36VSb929ekZX8fvOCGrQc0o7ruoA/PX7OKNuWv9jiPHoKJPREQqpBdmrWRfbj6jByb6HSXkjejRlL4J9Rj7RSrrdx7wO44chYo+ERGpcLbuzeE/36xmaNcmtG9U0+84Ic/MeHRYZwy4e+JSQu0m0YpCRZ+IiFQ4z3yZQUGh4/b+CX5HEU+TWtHcPbg9szN38H7K+uOvIGVORZ+IiFQoq3fs573567ny9BY0q1PN7zhSxK9Oa06vVnV4+JMVbNmT43ccOYKKPhERqVCemJJG1Ygwbj63jd9R5AhhYcbY4UnkFRZy7wca5i1vVPSJiEiFsXTDHj5dspnfnNmSejFV/Y4jxWgRV50/Dkjky9RtfLRok99xpAgVfSIiUmGMm5xK7WqRXN+3ld9R5Bh+3acl3ZrX4oFJy9m+L9fvOOJR0SciIhXCt5k7+DpjBzef24aYqEi/48gxhIcZj49I4kBuAfd/vMzvOOJR0SciIuWec46xX6TSODaKq3q18DuOlECb+jHcen5bPlu6hc+XbvY7jqCiT0REKoDJy7eweMMebuufQFRkuN9xpIRu6NuKTk1qct9Hy9m1/5DfcUKeij4RESnX8gsKGTc5jbb1azCse1O/48gJiAwPY9zwLuw+cIiHPvnB7zghT0WfiIiUaxMWbmDV9v38cWAi4WHmdxw5QR0a1+Smc9sw8fuNTE/d6neckKaiT0REyq2cvAKenpZBt+a1GNChgd9x5CT9/tw2JDSowT0Tl7E3J8/vOCFLRZ+IiJRbr89Zw+Y9OYwZ1A4z9fJVVFUiwhg3ogvb9uXw6Gcr/I4TslT0iYhIubTnYB7PzVjJ2Qn16NUqzu84coq6NqvF9We14p156/kmc4ffcUKSij4RESmXXpq1ij0H8xg9MNHvKFJKbuufQMu61blr4hL25+b7HSfkqOgTEZFyZ9veHF6evZohXRrTqUms33GklERFhjNuRBIbdh3k8clpfscJOSr6RESk3Pn79EzyCgq5vX+C31GklPWMr8Oo3vG8NmcN89fs9DtOSFHRJyIi5crarP28M28dV5zWjPi61f2OI0EwemAiTWpFM2b8EnLyCvyOEzJU9ImISLny5JR0IsPD+L9+bf2OIkFSvWoEY4cnsWrHfp6alu53nJChok9ERMqN5Zv28PHiTVx7Zjz1a0b5HUeCqE+bulzRsxkvzVrF4vW7/Y4TElT0iYhIufH45DRioyO5oW9rv6NIGbjnwvbUj4li9PjF5OZrmDfYVPSJiEi5MHdVFl+lbefmc1sTGx3pdxwpAzWjInnk0k6kb83muRkr/Y5T6anoExER3znnGPtFKg1rRjGyd7zfcaQMnde+AZd2a8LzMzJZsXmv33EqNRV9IiLiu6k/bOX7dbv5w/ltiYoM9zuOlLE/X9SBWtUiGT1+MfkFhX7HqbRU9ImIiK8KCh2PT06jVb3qjOjR1O844oPa1avw0CWdWLZxLy9+vcrvOJWWij4REfHVxIUbyNiWzegBiUSE69dSqLqgcyMGd27I09MyyNy2z+84lZJ+ukRExDc5eQU8PS2DLk1jGdSpod9xxGcPDulEtSrh3Dl+CQWFzu84lY6KPhER8c2bc9eycfdBxgxqh5n5HUd8Vi+mKvdf3IGF63bz6rdr/I5T6ajoExERX+zLyeO5GZmc1bYuZ7Sp63ccKSeGdm1Cv3b1eXxyKmuz9vsdp1JR0SciIr546evV7DqQx50D2/kdRcoRM+ORSzsRGRbGmAlLKNQwb6lR0SciImVu+75c/v31Ki5MakTnprF+x5FyplFsNPde2J65q3byzvx1fsepNFT0iYhImXtuRia5+YXc0T/B7yhSTl3esxl92sTx6GepbNx90O84lYKKPhERKVPrdx7gre/W8ovkZrSqV8PvOFJOmRmPDUui0DnumbgU5zTMe6pU9ImISJn629R0wsz4w/lt/Y4i5VyzOtUYM6gdM9O3M2HhRr/jVHgq+kREpMys2LyXDxdt5Nd9WtKgZpTfcaQCuLpXC3rG1+Yvk5azbW+O33EqNBV9IiJSZp6YnEZM1QhuPLu131GkgggLM8YOTyI3v5B7P1ymYd5ToKJPRETKxPw1O/kydRs3ntOG2GqRfseRCqRVvRrc3j+BqT9s5ZMlm/2OU2Gp6BMRkaBzzjH281Tqx1TlmjPi/Y4jFdB1Z7akS9NY7v94OVnZuX7HqZBU9ImISNBNT91Gytpd3Hp+W6KrhPsdRyqgiPAwxo3owr6cPB6c9IPfcSokFX0iIhJUBYWOcV+k0bJudX6R3MzvOFKBJTaM4ZZ+bfl48SamLN/id5wKR0WfiIgE1UeLNpK2dR93DEggMly/duTU3HhOa9o3qsmfPlzGngN5fsepUPTTJyIiQZObX8DfpqbTqUlNBndq5HccqQQiw8N4fEQSWfsP8fCnGuY9ESr6REQkaN7+bh0bdh1kzKB2hIWZ33GkkujUJJbfnd2K/y7YwMz07X7HqTCCVvSZWZSZzTOzxWa23Mwe9Npbmtl3ZpZhZu+ZWRWvvar3PtObH19kW3d77WlmNrBI+yCvLdPM7grWsYiIyInLzs3nH9MzOaN1HGe2qet3HKlkbunXltb1qnP3hCXsy9Ewb0kEs6cvF+jnnOsCdAUGmVkvYCzwlHOuLbALuM5b/jpgl3OuDfCUtxxm1gG4AugIDAKeN7NwMwsHngMuADoAv/SWFRGRcuDlr1eTtf8Qdw5qh5l6+aR0RUWGM25EFzbvzWHsF6l+x6kQglb0uYBs722kNzmgHzDea38NGOq9vsR7jzf/PAv8L3EJ8K5zLtc5txrIBE7zpkzn3Crn3CHgXW9ZERHxWVZ2Li/OWskFnRrStVktv+NIJdWjRW2u7dOSN+euY+6qLL/jlHtBvabP65FbBGwDpgIrgd3OuXxvkQ1AE+91E2A9gDd/DxBXtP2IdY7WXlyOG8wsxcxStm/X2L+ISLA9N2MlB/MKuGNAot9RpJL744BEWsRVY8yEJRw8VOB3nHItqEWfc67AOdcVaEqgZ659cYt5/xbX9+9Oor24HC8655Kdc8n16tU7fnARETlpG3Yd4M25a7msRzPa1K/hdxyp5KKrhPPYsCTWZh3gySlpfscp18rk7l3n3G7gK6AXUMvMIrxZTYFN3usNQDMAb34ssLNo+xHrHK1dRER89NTUDDD4Q/+2fkeRENG7dRxX9WrOy9+sZuG6XX7HKbeCefduPTOr5b2OBs4HVgAzgBHeYqOAj7zXH3vv8eZPd845r/0K7+7elkBbYB4wH2jr3Q1chcDNHh8H63hEROT40rbsY+L3G7jmjHgaxUb7HUdCyF0XtKdxbDR3jl9CTp6GeYsTzJ6+RsAMM1tCoECb6pz7BBgD3G5mmQSu2XvZW/5lIM5rvx24C8A5txx4H/gB+AK42Rs2zgd+D0wmUEy+7y0rIiI+eWJKGjWqRHDj2a39jiIhpkbVCP46rDOZ27L5+/QMv+OUSxHHXwTMrI9z7pvjtRXlnFsCdCumfRWB6/uObM8BLjvKth4BHimm/TPgs+MegIiIBN2CtTuZ+sNWRg9MpHb1Kn7HkRB0dkI9RvRoygszV3FBp0Z0ahLrd6RypaQ9fX8vYZuIiIQg5xxjP0+jbo2q/LpPvN9xJITdd2EH6lSvwujxS8grKPQ7TrlyzKLPzHqb2R1APTO7vcj0ABBeJglFRKTc+yp9O/PW7OTW89pQrUqJBpFEgiK2WiSPDO3Eis17eeGrlX7HKVeO19NXBahBYBg4psi0l//djCEiIiGssNAx7os0WsRV44rTmvsdR4QBHRtycZfGPDs9g/St+/yOU24c888x59xMYKaZveqcW1tGmUREpAKZtGQTKzbv5ZkruhIZXiafBCZyXA9c3IFvMncw+r+LmXDjGUToe7PE1/RVNbMXzWyKmU0/PAU1mYiIlHuH8gt5cko6HRrV5OKkxn7HEflRXI2qPDikI4s37OGVb1b7HadcKOmFF/8FXgD+DejDb0REBIB3569j3c4DvPrrnoSFFfegJBH/XJTUiEmLN/HklHTOb9+AVvVC+wkxJe3py3fO/dM5N885t+DwFNRkIiJSru3PzefZLzM5vWUdzk7QIy6l/DEzHh7aiaoRYYyZsITCwmKf1hoySlr0TTKzm8yskZnVOTwFNZmIiJRr//lmNTuycxlzQTvM1Msn5VP9mlHcd1EH5q/ZxRtzQ/v2hJIO7x5+PNroIm0OaFW6cUREpCLYtf8Q/5q5igEdGtC9eW2/44gc04geTZm0ZDNjv0ilX7v6NKtTze9IvihRT59zrmUxkwo+EZEQ9fxXmew/lM8fByb6HUXkuMyMR4d1xoC7Jy7FudAc5i3pY9hGFtfunHu9dOOIiEh5t2n3QV6bs5Zh3ZuS0CDG7zgiJdKkVjR3D27Pnz5cxvsp67m8Z+h9pmRJr+nrWWQ6C3gAGBKkTCIiUo49PS0dHNzWP8HvKCIn5FenNadXqzo8/MkKtuzJ8TtOmSvp8O4tRabrgW4EntYhIiIhJHPbPsYv2MDVvVvQpFa033FETkhYmDF2eBJ5hYXc+0HoDfOe7MdTHwDalmYQEREp/56YnE61KhHcfG4bv6OInJQWcdUZPbAdX6Zu46NFm/yOU6ZKek3fJAJ36wKEA+2B94MVSkREyp/v1+3ii+VbuL1/AnWqa7BHKq5rzojn0yWbeGDScvq0qUu9mKp+RyoTJe3pewJ40pv+CvR1zt0VtFQiIlKuOOcY+0UqcdWrcN2ZLf2OI3JKwsOMcSOSOJBbwP0fL/M7Tpkp6TV9M4FUIAaoDRwKZigRESlfvs7YwdxVO7mlXxuqVy3pR7yKlF9t6sdw6/lt+WzpFj5futnvOGWiREWfmf0CmAdcBvwC+M7MRgQzmIiIlA+FhYFevqa1o/nV6S38jiNSam7o24pOTWpy30fL2bW/8vdnlXR4916gp3NulHNuJHAacF/wYomISHnx6dLNLN+0lzsGJFAl4mTv/xMpfyLDwxg3vAu7DxzioU9+8DtO0JX0pzfMObetyPusE1hXREQqqLyCQp6ckka7hjEM6dLE7zgipa5D45rcdG4bJn6/kempW/2OE1QlLdy+MLPJZnaNmV0DfAp8FrxYIiJSHrw3fz1rsg5w56BEwsPM7zgiQfH7c9uQ2CCGeyYuY29Ont9xguaYRZ+ZtTGzPs650cC/gCSgCzAHeLEM8omIiE8OHirgmS8z6Blfm3MT6/sdRyRoqkSEMW5EEtv25fDoZyv8jhM0x+vpexrYB+Ccm+icu905dxuBXr6ngx1ORET8859vV7N9Xy5jBrXDTL18Url1aVaL689qxTvz1jM7Y4ffcYLieEVfvHNuyZGNzrkUID4oiURExHe7Dxzin1+t5Pz29UmOr+N3HJEycVv/BFrWrc5dE5ewPzff7zil7nhFX9Qx5umhiyIildQ/Z64kOzefPw5M9DuKSJmJigxn3IgkNu4+yOOT0/yOU+qOV/TNN7Prj2w0s+uABcGJJCIiftqyJ4dXv1nDpV2b0K5hTb/jiJSpnvF1GNU7ntfmrGH+mp1+xylVxyv6/gD82sy+MrMnvWkm8Bvg1uDHExGRsvbMl+kUOsdt/RP8jiLii9EDE2lSK5ox45eQk1fgd5xSc8yizzm31Tl3BvAgsMabHnTO9XbObQl+PBERKUsrt2fzfsoGrjy9Bc3qVPM7jogvqleNYOzwJFbt2M9T09L9jlNqSvQARefcDGBGkLOIiIjP/jYlnaiIMH7fr43fUUR81adNXX55WjNemrWKwZ0a0aVZLb8jnTI9VUNERABYsmE3ny7dzG/OakXdGlX9jiPiu7sHt6d+TBSjxy8mN7/iD/Oq6BMREQDGfZFGnepV+M1ZLf2OIlIu1IyK5K/DOpG+NZvnZqz0O84pU9EnIiLMztjB7Mwd3HxuG2KiIv2OI1Ju9GvXgEu7NeH5GZn8sGmv33FOiYo+EZEQ55xj3ORUmtSK5qpezf2OI1Lu/PmiDtSqFsmdExaTX1Dod5yTpqJPRCTEfb5sC0s27OG2/glUjQj3O45IuVO7ehUeuqQTyzbu5cWvV/kd56Sp6BMRCWH5BYU8MTmNhAY1uLRbE7/jiJRbF3RuxODODXl6WgaZ2/b5HeekqOgTEQlh/12wgVU79jN6YDvCw8zvOCLl2oNDOlGtSjh3jl9CQaHzO84JU9EnIhKicvIKeHpaOt2b1+L89vX9jiNS7tWLqcoDF3dk4brdvPrtGr/jnDAVfSIiIeq1b9ewdW8uYwa1w0y9fCIlcUnXxvRrV5/HJ6eyNmu/33FOiIo+EZEQtOdgHs9/tZJzEutxeqs4v+OIVBhmxiOXdiIyLIwxE5ZQWIGGeVX0iYiEoH/NXMmeg3ncObCd31FEKpxGsdHce2F75q7aydvz1vkdp8RU9ImIhJhte3N45ZvVXNK1MR0a1/Q7jkiFdHnPZvRpE8djn6eycfdBv+OUiIo+EZEQ88yXGeQXOG7vn+B3FJEKy8x4bFgShc5xz8SlOFf+h3lV9ImIhJDVO/bz7vz1/Or05rSIq+53HJEKrVmdaowZ1I6Z6duZsHCj33GOS0WfiEgI+dvUdKqEh/H7fm38jiJSKVzdqwU942vzl0nL2bY3x+84x6SiT0QkRCzbuIdJizdx3ZktqR8T5XcckUohLMwYOzyJ3PxC7v1wWbke5lXRJyISIsZNTqNWtUhuOLuV31FEKpVW9Wpwe/8Epv6wlU+WbPY7zlGp6BMRCQHfrtzBrPTt3HxOG2pGRfodR6TSue7MlnRpGsv9Hy8nKzvX7zjFClrRZ2bNzGyGma0ws+VmdqvXXsfMpppZhvdvba/dzOxZM8s0syVm1r3ItkZ5y2eY2agi7T3MbKm3zrOmj5QXEfkZ5xxjv0ijUWwUV/du4XcckUopIjyMcSO6sC8njwcn/eB3nGIFs6cvH7jDOdce6AXcbGYdgLuAL51zbYEvvfcAFwBtvekG4J8QKBKB+4HTgdOA+w8Xit4yNxRZb1AQj0dEpEKavHwri9fv5rbzE4iKDPc7jkilldgwhlv6teXjxZuYsnyL33F+JmhFn3Nus3Nuofd6H7ACaAJcArzmLfYaMNR7fQnwuguYC9Qys0bAQGCqc26nc24XMBUY5M2r6Zyb4wJXTb5eZFsiIgLkFxTyxJQ0WterzrDuTfyOI1Lp3XhOa9o3qsmfPlzGngN5fsf5iTK5ps/M4oFuwHdAA+fcZggUhkB9b7EmwPoiq23w2o7VvqGY9uL2f4OZpZhZyvbt20/1cEREKoyJCzeSuS2b0QMTiQjXZdwiwRYZHsbjI5LI2n+Ihz8tX8O8Qf8fwMxqABOAPzjn9h5r0WLa3Em0/7zRuRedc8nOueR69eodL7KISKWQk1fAU9PS6dKsFgM7NvQ7jkjI6NQklt+d3Yr/LtjAzPTy09kU1KLPzCIJFHxvOecmes1bvaFZvH+3ee0bgGZFVm8KbDpOe9Ni2kVEBHhz7lo278lhzKBEdJ+bSNm6pV9b2tSvwd0TlrAvp3wM8wbz7l0DXgZWOOf+VmTWx8DhO3BHAR8VaR/p3cXbC9jjDf9OBgaYWW3vBo4BwGRv3j4z6+Xta2SRbYmIhLS9OXn8Y0YmZ7Wtyxmt6/odRyTkREWGM3Z4Epv35jD2i1S/4wDB7enrA1wN9DOzRd40GHgM6G9mGUB/7z3AZ8AqIBN4CbgJwDm3E3gImO9Nf/HaAG4E/u2tsxL4PIjHIyJSYbw0axW7D+QxZlA7v6OIhKweLWpzbZ+WvDl3HXNWZvkdByvPjwsJhuTkZJeSkuJ3DBGRoNm+L5e+42ZwXvv6/ONX3Y+/gogEzcFDBQx6ZhYAX9zal+gqJ/axSWa2wDmXXBpZdCuXiEgl8/fpGeQVFHLHgES/o4iEvOgq4Tw2LIm1WQd4ckqar1lU9ImIVCLrsg7w9nfruLxnM1rWre53HBEBereO46pezXn5m9UsXLfLtxwq+kREKpG/TU0jItz4v/Pa+h1FRIq464L2NI6N5s7xS8jJK/Alg4o+EZFK4odNe/lo8SZ+3aclDWpG+R1HRIqoUTWCvw7rTOa2bP4+PcOXDCr6REQqiccnp1IzKpLfnd3a7ygiUoyzE+pxWY+mvDBzFcs27inz/avoExGpBL5blcWMtO3ceE5rYqMj/Y4jIkfxpws7UKd6Ff7438Ucyi8s032r6BMRqeCcc4z9IpUGNasyqne833FE5Bhiq0XyyNBOpG7ZxwszV5bpvlX0iYhUcNNWbGPhut384fyEE/4MMBEpewM6NuTiLo35+/QM0rfuK7P9qugTEanACgodj09OpVXd6lzWo+nxVxCRcuGBizsQExXJ6P8uJr+gbIZ5VfSJiFRgH3y/kfSt2dwxIJGIcP2XLlJRxNWoyoNDOrJ4wx5e+WZ1mexT/0OIHKGw0PHRoo38++tV7MjO9TuOyFHl5hfw1NR0OjeJZXDnhn7HEZETdFFSIwZ0aMCTU9JZtT076PtT0SdSxA+b9vKLf83h1ncX8fCnKzjj0en84d3vWbB2F6H2nGop/96au46Nuw8yZlA7zMzvOCJygsyMh4d2ompEGGMmLKGwMLi/Z1T0iQB7c/J4cNJyLvr716zasZ9xI5KYdntffnV6c75csY3h//yWi/4+m3fnrePgIX8+SV2kqH05efxjRiZ92sRxZtu6fscRkZNUv2YUf764I/PX7OKNuWuDui8Ltd6L5ORkl5KS4ncMKSecc3y0aBOPfLaCHdm5XHl6c/44IJFa1ar8uMz+3Hw+XLSRN+asJXXLPmpGRXBZcjOu7tWCeD3bVHzy1NR0nvkyg49u7kOXZrX8jiMip8A5xzX/mc/8NTuZ/Ie+NKtT7cd5ZrbAOZdcGvtR0SchK33rPu77cBnfrd5Jl6axPDS0E0lNj/7L0zn3419iny/dTH6ho29CPUb2asG57eoTHqbhNSkbO7JzOXvcDM5OrMfzV/bwO46IlIKNuw8y4G8z6da8Nm9cd9qPl2yUZtEXURobEalIsnPzefbLDF6ZvZoaURH89dLOXNGzGWHHKdrMjNNa1uG0lnXYdlF73p23nre/W8dvXk+hae1orjy9Bb9IbkpcjapldCQSqv4xPZOc/ELuGJDodxQRKSVNakVz9+D2/OnDZbyfsp7LezYv9X2op09ChnOOT5du5uFPVrBlbw6XJzdjzAXtqFO9yvFXPoq8gkKm/bCV1+esZc6qLKpEhHFR50Zc3bsFXZvV0sX1UurW7zxAvye/YkSPpjw6LMnvOCJSigoLHb/691yWb9zL1NvPpmFslIZ3T4WKvtC0cns293+0nNmZO+jYuCYPDe1E9+a1S3UfGVv38cbctUxcuJHs3Hw6N4nl6t4tGNKlMVGRekqClI7b31/Ep0s289Xoc2gUG+13HBEpZWuz9jPw6Vn0aV2Xf49KJiwsTEXfyVLRF1oOHMrnH9MzeenrVURFhjN6YCJXnt4iqNffZefm88HCDbw+Zy0Z27KpVS2SXyQ346rTW9A8rtrxNyByFKlb9nLBM19zw1mtuHtwe7/jiEiQvDx7NQ998gNPX96VS7s3VdF3slT0hQbnHJOXb+WhT35g4+6DDO/elLsuaEe9mLK73s45x3erd/LGnLV8sXwLhc5xTkI9RvaO5+yEese9hlDkSL95bT7frd7J13ee+5M7zEWkcikodFz2wres2rGfxfcP1I0cIkezNms/93+8nK/SttOuYQzv/7Y3p7WsU+Y5zIxereLo1SqOrXtzePu7dbwzbx2/fnU+zepEc9XpLfhFcjNqn8I1hRI65q/ZybQV2xg9MFEFX0W2byvsWg3Ne/mdRMqx8DBj3Igkhj73baluVz19Umnk5BXw/FcreWHmSqqEh3Fb/wRG9W5Rrp5HmldQyOTlW3h9zlrmrd5J1YgwLu7SmJG9Wxzz42IktDnnuOyFOazdeYCZo8+hWhX9vV7hFOTBdy/AV2OhWm245XsI19dRjm3PwTxqVauinj6Ror5csZUHJi1n/c6DXNK1MfcMbk+DmlF+x/qZyPAwLkpqzEVJjUndspc35qzlg+83Mn7BBro0q8XIXi24MKmRbvyQn5iRto2Utbt4eGgnFXwV0crp8PkY2JEObQfAoMdU8EmJxP5/e/cdH1WV/3/8ddIh1IQektB77wGlKVJE0LUrxfZ119X9uuuubRUFcV1FXdtvdXXXBljXBiodAUFCr6HXkEBIqAkhkDbn98cMXyMEaZPcKe/n4zGPxJuZO597vUzeOeeecyqEe3V/aukTv5Z2OI9x325kzqZMmtSqxDPDW9OzsX8tSZVzspCvVqYzaUkqp8tkZQAAIABJREFUOw4cJyY6gpu6xHN794RfzMouwanYZbn69YWcKCxmzkN9CPehlms5h6N7YOYTsGkqVG/oDnvNBzldlfgZTc4sQS+/qJh3Fuzk/83bTmiI4bHBLbirV0MiwvzvF2KVqHDu6NWQ0T0bkLzjEBOTU/n3wp28/eMOrmhRixE9EundVAM/gtXUtXvZvP8Yr9/aUYHPXxSegJ9eh0WvuP+7/5OQ9AcI973eBwkuCn3idxZsPcDTU1LYfSiPIW3r8OTVrahXzf/nKzPG0LNJDXo2qUFG9gnPwI805mxaToPYiozokciNneOpWtG7zf3iuwqKXLw8ayut61VhaNu6Tpcj52ItbJkGMx6Ho6nQ6lq46lmoFu90ZSKAunfFj+w7eoLx321kesp+GtaIZtyw1vRuVtPpsspUQZGL6SkZTEpOZUXqEaLCQxjePo6RSYm0iavqdHlSxj74aRdjv93Ih3d1o0+AX+t+7+A29317O+ZCzZYw+AVo1MfpqiQAqHtXgkpBkYt3F+3i9bnbsFgeHticey5vSGRY4A92iAgLYXiHOIZ3iGPjvhwmLdnNN6v38dmKNDolVGNUUgMGt60TFOci2OTmF/HGD9vp0SiG3k396z7VoJJ/DH58EZLfhPAKMPDv0O1/IFQt8uJ71NInPm3x9oM8NXUD27NyuapVbcYMbRX0gxuyTxTy5cp0Ji9JZefB48RGR3BLt3hu655IXAB0c4vb63O38Y/ZW/n69z3p6OUlA8ULrIX1X8DsMXAsAzqMgCufhkq1nK5MAozW3r0ECn3+ITPnJM9+v4lv1+4jIaYiY4e1on+L2k6X5VNcLstPOw4yMTmVuZsyAbiiZW1GJSXSq3ENDfzwY4dy8+nz4nx6NYnl7ZFe+awXb9q/HqY9AnsWQ72OMPhFiO/qdFUSoNS9KwGrsNjFh4t388rsrRS6LA9e0ZT7+jbWvHWlCAkxXN60Jpc3rUn6kTw+XrqHz5anMXtjJo1qRDOiRyLXd67v9XmepOy9OX8HeQVFPDywudOlSEl5h2Hec7DiXahQHa55HTqOhBCNqhb/oJY+8RlLdx7iqSkb2JJ5jH7NazJ2WGsSY6OdLsuv5BcVM219BhOTU1m95ygVwkO5tmMco5ISaVm3itPlyXnYe/QE/V6cz7Ud6zHhhvZOlyMArmJYPQnmjIOTR6HrPdDvr+7gJ1LG1NInAeXAsXz+Pm0TX63eS1y1CrwzsjMDWtXGGHVPXqjIsFCu61if6zrWJ2VvNhOTd/PVqnQ+WbaHLonVGZmUyOA2df1yPsNg8crsrWDgwSubOV2KAKQth2l/gYw1kNAThkyAOm2drkrkoqilTxxTVOzio6V7eGnWFk4WFnNv70Y80K8pFSLUletNR/MK+MKz4kfqoTxqVIrk1m7x3NY9gbpVNfDDl2zNPMagV3/krl4NeXJoK6fLCW65WTBnLKz5CCrXdc+31+Z60B+jUs40kOMSKPT5hpWpRxjzTQobM3K4vGkNxg1rTaOalZwuK6C5XJYftx1gUnIqP2zJIsQYBngGfiQ1jlXLqg+4d+IKkncc4sdH+lE9OsLpcoJTcSEsewfmP+9eWSPpfuj9METq80mcoe5d8VuHcvN5YcZmPl+RTp0qUbx5eycGt6mjwFEOQkIMfZvXom/zWqQdzmPy0lQ+X57GjA37aVKrEiN7JPKbTnFUjtLADyesTD3CrI2Z/HlAMwU+p+xcANMfgQObocmV7rVyazR1uioRr1FLn5SLYpfl0+V7mDBjC8fzi7j78ob8b/+mREfq7w4nnSws5rt1GUxK3s3a9GyiI0K5rlMcI3s0oHmdyk6XFzSstdz8zhJ2HjjOgof76t9FeTuaBrOegI1ToFqiO+w1H6yuXPEJaukTv7I27ShjpqSwLj2bHo1iGD+8DU1rK1D4gqjwUG7oXJ8bOtdnbdpRJi1J5fMV6UxesoduDWMYlZTIwNZ1CA/VwI+ytGDrAZbtOswzw1sr8JWnwpOw+A1Y+DJgod8T0PMP7pU1RAKQWvqkzBzNK2DCzC18smwPNSpF8uTVLRnWvp66cn3ckeMFfL4ijclLU0k7fIJalSO5tVsCt3VPoHaVKKfLCzgul+XqNxaRm1/I3If6amR1ebAWts6AGY/Bkd3QchgM/BtUS3C6MpEzqKVPfJrLZfliZTrPz9hM9olC7uzZkD8NaKp7xfxE9egIftunMfdc3ogFW7OYmJzK6z9s45/ztjOwdR1GJiXSvWGMwruXfLtuH5sycnj15g4KfOXh0A6Y/ihsnw01msPIb6BxP6erEikXCn3iVRv2ZTPmmxRW7TlK1wbVeWZ4G00K7KdCQwz9W9Smf4vapB46zmRP1+/36zNoVrsSI5MacF3HOCqpO/KiFRS5eHnWVlrUqcyw9vWcLiew5efCwpcg+Z8QGglX/Q26/xZC9ceoBA9174pXZJ8o5JXZW5mYvJvqFSN4fEhLru8Up9agAHOioJhv1+5j4pLdpOzNoVJkGL/p5F7xo0kt3ad5oSYl72bMlA28f0dX+rWo5XQ5gclaSPkSZo2BY/ug/a1w5TiorLW8xT+oe1d8hrWWr1fv5blpmzl8PJ8RPRL581XNtd5rgKoQEcpNXeO5sUt91qQdZVJyKp8uS2NicipJjWIZlZTIgFa1CdPAj3PKKyjitbnb6dYghr7NazpdTmDan+Luyk1dBHXbw40fQEJ3p6sScYxCn1y0zftzeOqbDSzbfZgO8dX44M6utImr6nRZUg6MMXRMqE7HhOo8cXVLPluRxkdL9nDfR6uoUyWK27oncEu3eGpV1sCPs3n/p90czM3n7ZGd1CLubSeOwLy/w/J/Q1RVGPoKdBoNIVrtR4Kbunflgh07Wchrc7bx/uLdVIkK49FBLbipSzwhIfrFFcyKXZYfNmcxMXk3C7cdJCzEMKhNHUYlNaBrg+oKNiUcOV5A7wnz6N4olv+M9kqvjQC4XLB6Eswd5w5+Xe5yT8NSMcbpykQumrp3xRHWWr5dl8Gz323kQG4+t3RN4JGBzbV6gADugR8DWtVmQKva7DyQy+Qle/jvyjS+W5dBizqVGZmUyLUd4jQPHfDWgh3kFhTx8MDmTpcSONJXwrS/wL5VEN8DhrwIdds5XZWITymzlj5jzHvAUCDLWtvGsy0G+AxoAOwGbrLWHjHuJoDXgCFAHnCHtXaV5zWjgSc9u33WWvuhZ3tn4AOgAjANeNCex8Gope/ibM86xlNTNrB4xyHaxlVl/LVt6BBfzemyxMflFRQxZc0+Jiansikjh8qRYVzfuT4jkxJpHKRrLWdkn6DPi/O5pl09Xr6pvdPl+L/cAzB3LKyeDJXqwFXjoe2NWk1DAoY3W/rKMvT1BnKBiSVC3wTgsLX2eWPMY0B1a+2jxpghwB9wh77uwGvW2u6ekLgC6AJYYCXQ2RMUlwEPAktwh77XrbXTz1WXQt+FySso4vW523l30U4qhIfy8KAW3NYtgVB15coFsNayas8RJianMm19BoXFlsua1GBkUiJXtKgVVAM/Hv1iHV+v3svcP/chPqai0+X4r+Ii9z178/4Ohcehx++hzyMQqVHkElj8onvXWvujMabBaZuHA309338IzAce9Wyf6GmpW2KMqWaMqet57mxr7WEAY8xsYJAxZj5QxVqb7Nk+EbgWOGfok/NjrWVGyn7Gf7eRfdknubFzfR4b3ILYSpFOlyZ+yBhD58QYOifG8OTVrfhs+R4+WrqH305aSb2qUdzeI5Gbu8ZTI8Cvr+1Zufx3ZRqjezZQ4LsUuxbC9EcgayM07g+DXoCazZyuSsTnlffNNbWttRkA1toMY8ypianigLQSz0v3bPu17emlbBcv2HXwOE9P3cCPWw/Qsm4VXr+1I10a6EZo8Y6alSN5oH9TftenMXM2ZTF5SSovztzCq3O2MqRtXUYlJdIpITAHfrw8awsVwkN5oF8Tp0vxT9npMOtJ2PC1e8m0mz+CFlerK1fkPPnKHdWl/Yu1F7G99J0bcy9wL0BCgtZWPJsTBcW8OX87by/YSWRYCE9f04qRPRKDqutNyk9YaAiD2tRhUJs6bM/KZfKSVL5cmc6UNftoVbcKo5ISGd4hjgoRgTHNxpq0o0xP2c8fr2yqFvMLVZQPi9+AhS+DdUHfx6HXgxBewenKRPxKeYe+TGNMXU8rX10gy7M9HYgv8bz6wD7P9r6nbZ/v2V6/lOeXylr7DvAOuO/pu7RDCEyzN2Yy7tsNpB85wXUd43h8SAvNsSblpkmtSowd1pqHBzbnmzV7mZScymNfree5aZu4sUs8I3ok0rBGtNNlXjRrLS9M30xsdAT3XN7I6XL8y9aZ7gmWj+yCFkNh4HNQPdHpqkT8UnmHvqnAaOB5z9cpJbY/YIz5FPdAjmxPMJwJPGeMqe553lXA49baw8aYY8aYHsBSYBTwRnkeSKDYcyiPcd9uYO7mLJrVrsSn9/agR6NYp8uSIBUdGcbt3RO5rVsCy3cfYWLybj5cvJt3F+2id7OajOqRSL8WtfxuINGi7QdJ3nmIp69ppbWKz9ehHTDjcdg2E2o0g5Ffu+/fE5GLVmafPsaYT3C30tUwxqQDT+MOe58bY+4G9gA3ep4+DffI3e24p2y5E8AT7sYDyz3Pe+bUoA7gPn6esmU6GsRxQU4WFvP2gp28OX87YSGGJ4a05I5eDQhXV674AGMM3RrG0K1hDFk5J/lkWRofL0vlnokriKtWgdt7JHBzl3i/6CZ1uSwvzNhMXLUK3NZdt5ecU8Fxdzfu4jcgNAIGjIfuv4MwzQcqcqm0IkcQmrcli7FTN5B6KI+h7ery5NWtqFNVXbni2wqLXczZmMnE5FSSdx4iIiyEoW3rMjIpkQ7x1Xx24Md36/bxwMerefnG9lzfuf65XxCsrIUNX8GsMZCzF9rdAgPGQeU6Tlcm4ii/mLJFfM/eoyd45tsNzNyQSaOa0Uy+uzuXNa3hdFki5yU8NITBbesyuG1dtmUeY5Jn4MdXq/fSNq4qI5MSGda+HlHhvjPwo7DYxcuzttK8dmWu7agJBs4qc6N7CpbdC6FOW7jhPUjo4XRVIgFHLX1BoKDIxb8X7uSNH7ZhMPzhiibcc1kjIsLUlSv+LTe/iK9XpTMxOZVtWblUqxjOTV3iub17Aomxzg/8+GhpKk98ncJ/RnXhyla1nS7H95w4CvOfh2XvQFQV6D8GOt8BIb4T3EWc5hcrcviqYAt9i7Yd5KmpKew8cJxBresw5ppWxFXTNAcSWKy1LNl5mElLdjNzQyYua+nTrCajkhLp08yZgR8nCorp8+I8EmIq8t/fJfls97MjXC5Y8xHMGQt5h6DLne7AV1HzgYqcTt27ck4Z2Sd49vtNfL8ug8TYinxwZ1f6Nq917heK+CFjDEmNY0lqHMv+7JN8vGwPnyzbw10frCA+pgIjuidyU5d4qkeX32CADxbvJutYPv+8vZMCX0l7V8K0h91f47vDiC+hXgenqxIJCmrpCzCFxS7e/2kXr87ZRrHLcn+/Jtzbu5FP3eckUh4Ki13M3LCficmpLNt1mMiwEK5pX49RSYm0q1+tTN87O6+Qyyf8QJcGMbx3R9cyfS+/cfygu2Vv9WSIrglXjYd2N2s1DZFzUEuflGrJzkM8NSWFrZm5XNGiFk9f05qEWK3vKcEpPDSEoe3qMbRdPTbvz2FScipfr97LFyvTaR9fjZE9Ehnarm6Z/EH01oIdHMsv4uGBzb2+b79TXAQr3oV5f3NPx5J0P/R51H0Pn4iUK7X0BYCsYyd57vtNfLNmH/WrV2DsNa1107hIKXJOFvLVynQmLUllx4HjVK8Yzk1d4xnRPZH4GO/8gbQ/+yR9XpzH4DZ1ePWWjl7Zp9/avQimPQJZG6BRXxg8AWoqCItcCLX0CQBFxS4mJqfyyuyt5Be5+N/+Tbivb5OAWatUxNuqRIVzR6+GjO7ZgMU7DjExeTf//nEn7/y4k/7NazEyKZHeTWsScgkDP16buw2XtTw0IIjDTfZemD0GUr6Eqglw0yRoeY26ckUcptDnp1bsPsyT36Swef8xejerybhhrf16bVKR8mSMoVeTGvRqUoN9R0/w8dI9fLp8D3Pfz6JBbEVG9Ejkxs7xVK0YfkH73Xkgl89XpDGie0Jw3lpRlA/J/4QfXwJXkbsbt9cfISIIz4WID1L3rp85mJvP89M388XKdOpVjeKpa1oxsHUdjQ4UuUQFRS6mp2QwKTmVFalHiAoPYXj7OEYmJdImrup57eP+j1cxb3MWCx7uR83Kvr9EnFdtnQUzHoPDO6DFUBj4N6jewOmqRPyeuneDULHL8vHSVF6cuYUThcXc17cxf+jfhIoR+l8o4g0RYSEM7xDH8A5xbNiXzeQlqXyzeh+frUijU0I1RiYlMqRtXSLDSr99Yn16Nt+vy+B/+zcJrsB3eCfM+CtsnQ6xTdxTsDS50umqRKQUaunzA2vSjjLmmxTW782mZ+NYnhnehia1KjldlkjAyz5RyBcr05m8JJVdB48TGx3BzV3jub1H4hmTnI98dykpe7NZ8Eg/qkRdWLewXyrIg0X/gJ9eh9Bw6PMIdL8PwspvLkSRYKCWviBx5HgBE2Zu5tPladSqHMkbt3ZkaLu66soVKSdVK4Rz92UNubNnAxZtP8jE5FT+tWAH/1qwgyta1mZUUiK9GtcgeechFm47yJNXtwz8wGctbPwGZj4JOenQ9iYY8AxUqet0ZSJyDgp9Psjlsny+Io0XZmwm52QR91zWkAevbEalSP3vEnFCSIihd7Oa9G5Wk/QjeZ6BH2nM3phJoxrRWKBe1ShG9Eh0utSylbUJpj8Cu36E2m3h+n9DYk+nqxKR86QU4WNS9mbz5DcprEk7SreGMYwf3obmdSo7XZaIeNSvXpFHBrXgwSubMm19BhOTU1m95yj/uKl94K58czIb5j8PS9+GyMow5CXofCeE6leIiD/Rv1gfkZ1XyEuztjB5aSqx0ZG8cnN7ru0Qp65cER8VGRbKdR3rc13H+hzKzSe2UgAO3nC5YO0nMOdp9zJqnUdD/6cgOtbpykTkIij0Oczlsny5Kp3np2/mSF4Bo5Ma8KcBzahaIcDvCxIJIAEZ+Paucnflpi+H+l3h9v9CvSBfYUTEzyn0OWhTRg5jvklhReoROiVUY+Ld3Whd7/zmAxMRKRPHD8HccbBqIkTXhGvfgna3QEiI05WJyCVS6HPAsZOFvDJ7Gx8m76ZqhXAm3NCOGzrVv6Sln0RELklxEax8H34YD/m50OP30PdRiNIfoiKBQqGvHFlrmbp2H89+v4mDufnc1i2Bhwc2p1pFzWslIg5KXQzTHobMFGjYBwZPgFotnK5KRLxMoa+cbMs8xpgpKSzZeZj29avy7ugutKtfzemyRCSY5eyD2U/B+v9Clfpw44fQajhoAJlIQFLoK2PH84t4fe423l20i+jIMJ67ri03d40nVF25IuKUogJY8iYsmACuIuj9CFz2J4io6HRlIlKGFPrKiLWWaev3M/67jezPOcnNXeJ5dHALYqLVlSsiDto2B2Y8Coe2Q/MhMPA5iGnodFUiUg4U+srAjgO5jJ26gYXbDtK6XhXeHNGJTgnVnS5LRILZ4V0w8wnY8j3ENIbbv4CmA5yuSkTKkUKfF50oKOaNH7bx74U7iQoP5Znhrbm9e6K6ckXEOQV5sOgV+Ok1CAmDK8e6R+aGBeDcgiLyqxT6vMBay6yNmTzz7Ub2Hj3BbzrF8fjgltSsrA9VEXGItbBpqrt1LzsN2twAV42HKvWcrkxEHKLQd4lSDx1n7NQNzNtygOa1K/P5b5Po1jDG6bJEJJhlbXavprFrAdRqDXd8Dw0uc7oqEXGYQt9FOllYzFvzd/DWgh1EhIbw5NUtGd2zAeGhmrVeRBxyMgcWvABL/wUR0TD4RehyF4Tqo15EFPouyg+bMxk7dSN7DucxrH09nri6JbWrRDldlogEK5cL1n3mnnPv+AHoNBKueBqiazhdmYj4kOALfYd2wJT7oUrcz4+qce77XM6x3FDa4Tye+W4jszdm0qRWJT7+n+70bKwPVRFx0L417tU00pdBXBe47VOI6+x0VSLig4Iv9LmK3PNU5WYC9pc/i6jsDn+nQmCV+lClHgWV6vLFVhcvLzvOCRPNY4NbcFevhkSEqStXRBySdxjmPgMrP3C36A3/J7S/DUL0uSQipTPW2nM/K4B06dLFrlixwj0jfe5+yN4LOace+yA73f01Zy/kZnF6MHRFVCKkan1PKPw5GLqDoucRVcWZgxORwOcqhpXvw9zxkH8Muv8W+jwKFbSso0ggMsastNZ28ca+gq+l75SwCKiW4H6UIiP7BM99u5bVGzbTsVoev+8YRcvoY4RklwiImRtKDYZEVC7RWnhaF/KpkKhgKCIXKjUZpj8M+9dDg8th8ASo3crpqkTETwRv6DuLgiIX7/20i9fnbsNlLQ8M6MX/9G5EZFho6S8oKoBjGT+3Dubs/WXrYeYGT1fyaSKrlAiF9RQMReTsju13D9JY95n7s+KG96H1dWA08buInD+FvhIW7zjIU1M2sD0rlwGtavPU0FbEx5xjAfKwCKie6H6czf8Fw1K6kHP2QmbK+QXD/+tWLtF6GFn50g5aRHxXUQEsfQsWTIDiArj8L3D5Q+7pWERELpBCH5CZc5K/fb+JqWv3ER9Tgffu6EL/FrW99wbeCIb718PxrDNfF1mlRGuhgqFIwNg+F6Y/Coe2QbNBMPA5iG3sdFUi4seCOvQVFrv4cPFuXp2zjYJiFw9e0ZT7+jYmKvwsXbll6byD4T5PGLzIYFhywEnJkKhgKOIbjux2L522+TuIaQS3fQ7NBjpdlYgEgKANfct2HeapKSls3n+Mfs1rMnZYaxJjfbzLJCwCqjdwP86mZDAsbWTyWYNh1RKthQqGIuWu8AQsehV+ehVMCFzxFCQ9AGFaw1tEvCPoQl+Ry/LQ52v4atVe4qpV4J2RnRnQqjYmUG6IPq9gmO/uSs72hMGc9F+2Hp4rGJ4+4KRqiYCoYChyYax1t+rN+Ctk74HWv4Grxrv/2BIR8aKgC31b9h/j+Np93N+vMQ/0a0qFCAe6cp0WFnnxwfBU62HGWvdyT6c7ZzCMg8hKZXVkIv7lwFaY/gjsnAe1WsHo76Dh5U5XJSIBKugmZ45t0NIuW76cxjUVPC5ZUf7PLYSlBcOcvWcPhueax1DBUALZyRz4cQIseQvCo6HfX6HrPRAadH+Hi8g5aHLmS9CwRrQCn7eERUJMQ/fjbH4RDEvOY+gJiWdrMYyq+ss5DBUMJRBYC+s+h9lj3NM0dRwJVzwNlWo6XZmIBIGgC31Szs4nGBaePMsE1+cbDM8yj6GCofiSjLUw7RFIWwL1OsEtn0D9zk5XJSJBRKFPnBcedQHB8CzzGGasubhgWDVOE91K2co7DD88614vt0IMDHsDOoyAkBCnKxORIKPQJ/7BG8Fw32rIO3jm66Kqlj4SuWIshEa4WytDI35+hEVCaDiEnvrq2RYSpmWx5GeuYlj5Afww3n0PX7d7oe/jUKGa05WJSJBS6JPAcd7B8CzzGP5aMDwvpkQwjPj1oHhGkDz1/ek//7XXlNznud4zQi1L5WnPUpj2F9i/DhIvgyEToHZrp6sSkSCn0CfBJTzKvcpBTKOzP+dUMDxxBIoL3YNRiguhON+9/mlRgftrcf5pPz99W8HPj9Nfk3f8LPs89fx8wMsj60PCTguSFxNOz/bzCwifpf08UFpJj+2H2U/Duk+hcj244T33vHuBcGwi4vf8PvQZYwYBrwGhwH+stc87XJL4u1PB0GnFRRcWJE//eamvKRlgz7LPgjwoPvJzkC3tfVyF3j/es4XCiw2S5xVeT29dLW2fkeduJS0uhKX/gvkvuM/TZQ/B5X/WQCIR8Sl+HfqMMaHAP4EBQDqw3Bgz1Vq70dnKRLwgNMwzb1tFpys5k7XnHz5Pb8U8I3yepaWz1HBaCIVHz/KaEnVYl3eP14T+etDMO+wead70Khj0PMQ29u77i4h4gV+HPqAbsN1auxPAGPMpMBxQ6BMpS8a4Q1BYJPji0rCu4l9p5bzY8Fny56dtqxIHV78MzQc5feQiImfl76EvDkgr8d/pQPfTn2SMuRe4FyAhIaF8KhMR54SEQkRFfLKVVETEIf4+nK+0u6PPuPvdWvuOtbaLtbZLzZqa+V5ERESCj7+HvnQgvsR/1wf2OVSLiIiIiM/y99C3HGhqjGlojIkAbgGmOlyTiIiIiM/x63v6rLVFxpgHgJm4p2x5z1q7weGyRERERHyOX4c+AGvtNGCa03WIiIiI+DJ/794VERERkfOg0CciIiISBBT6RERERIKAQp+IiIhIEFDoExEREQkCCn0iIiIiQUChT0RERCQIKPSJiIiIBAFjrXW6hnJljDkGbHG6Dh9TAzjodBE+SOeldDovpdN5OZPOSel0Xkqn81K65tbayt7Ykd+vyHERtlhruzhdhC8xxqzQOTmTzkvpdF5Kp/NyJp2T0um8lE7npXTGmBXe2pe6d0VERESCgEKfiIiISBAIxtD3jtMF+CCdk9LpvJRO56V0Oi9n0jkpnc5L6XReSue18xJ0AzlEREREglEwtvSJiIiIBB2/D33GmHhjzDxjzCZjzAZjzIOe7THGmNnGmG2er9U921sYY5KNMfnGmL+U2E9zY8yaEo8cY8wfnTou8T5vXSuen/3Js48UY8wnxpgoJ45Jyo6Xr5cHPdfKBn2uBJ6LuFZuN8as8zwWG2Pal9jXIGPMFmPMdmPMY04dk5QdL18v7xljsowxKef13v7evWuMqQvUtdauMsZUBlYC1wJ3AIettc97/uFUt9Y+aoypBSR6nnPEWvtSKfsMBfYC3a21qeV1LFK2vHWtGGPigEVAK2vtCWPM58A0a+0H5X9UUla8eL20AT4FugEFwAzgPmvttnKJf0cEAAAEpklEQVQ/KCkTF3Gt9AQ2WWuPGGMGA2Ottd09v3u2AgOAdGA5cKu1dqMTxyVlw1vXi2dfvYFcYKK1ts253tvvW/qstRnW2lWe748Bm4A4YDjwoedpH+I+oVhrs6y1y4HCX9ntFcAOBb7A4uVrJQyoYIwJAyoC+8q4fClnXrxeWgJLrLV51toiYAFwXTkcgpSTi7hWFltrj3i2LwHqe77vBmy31u601hbg/mNhePkchZQXL14vWGt/BA6f73v7fegryRjTAOgILAVqW2szwH2CgVoXsKtbgE+8XZ/4jku5Vqy1e4GXgD1ABpBtrZ1VlvWKsy7xsyUF6G2MiTXGVASGAPFlV6046SKulbuB6Z7v44C0Ej9L92yTAHWJ18sFC5jQZ4ypBHwJ/NFam3MJ+4kAhgH/9VZt4lsu9Vrx3GcxHGgI1AOijTEjvFul+IpLvV6stZuAF4DZuLt21wJFXi1SfMKFXivGmH64f4k/empTKU/z73uw5Ky8cL1csIAIfcaYcNwn7iNr7VeezZmefvNT/edZ57m7wcAqa22m9ysVp3npWrkS2GWtPWCtLQS+AnqWVc3iHG99tlhr37XWdrLW9sbdFaP7+QLMhV4rxph2wH+A4dbaQ57N6fyyFbg+unUkIHnperlgfh/6jDEGeBf3TY7/KPGjqcBoz/ejgSnnuctbUdduQPLitbIH6GGMqejZ5xW478mQAOLNzxbPIA+MMQnAb9BnTEC50GvFcx18BYy01m4t8fzlQFNjTENPr9Mtnn1IAPHi9XLh7x0Ao3cvAxYC6wGXZ/NfcfePfw4k4P4lfaO19rAxpg6wAqjieX4u7lGYOZ77bdKARtba7PI9EilrXr5WxgE34+6mWw3cY63NL8/jkbLl5etlIRCLe5DHQ9baueV6MFKmLuJa+Q9wPXBqsGCRtbaLZ19DgFeBUOA9a+3fyu1ApFx4+Xr5BOgL1AAygaette+e9b39PfSJiIiIyLn5ffeuiIiIiJybQp+IiIhIEFDoExEREQkCCn0iIiIiQUChT0RERCQIKPSJiJTCuC3yLHB+attNxpgZTtYlInKxNGWLiMhZGGPa4F6SsSPuedPWAIOstTsuYZ9h1lotwyYi5U6hT0TkVxhjJgDHgWjgmLV2vDFmNHA/EAEsBh6w1rqMMe8AnYAKwGfW2mc8+0gH3gYGAa9aa7W2t4iUuzCnCxAR8XHjgFVAAdDF0/p3HdDTWlvkCXq3AB8Dj3lm0A8D5hljvrDWbvTs57i1tpcTByAiAgp9IiK/ylp73BjzGZBrrc03xlwJdAVWuJfQpALu5RsBbjXG3I37s7Ue0Ao4Ffo+K9/KRUR+SaFPROTcXPy8RqbBvSbqmJJPMMY0BR4EullrjxpjJgNRJZ5yvFwqFRE5C43eFRG5MHOAm4wxNQCMMbHGmASgCnAMyDHG1AUGOlijiMgZ1NInInIBrLXrjTHjgDnGmBCgEPgdsAJ3V24KsBP4ybkqRUTOpNG7IiIiIkFA3bsiIiIiQUChT0RERCQIKPSJiIiIBAGFPhEREZEgoNAnIiIiEgQU+kRERESCgEKfiIiISBBQ6BMREREJAv8fx2bvC6yhlCcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Count of Asia and Europe over years in line graph\n",
    "\n",
    "df_unstacked.plot(kind = 'line', figsize = (10,6))\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count of Asia and Europe over years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2020    564\n",
       "2019    153\n",
       "2012    113\n",
       "2016     76\n",
       "2018     64\n",
       "2015     55\n",
       "2014     52\n",
       "2009     36\n",
       "2021     28\n",
       "2017     27\n",
       "2013     20\n",
       "2007     19\n",
       "2010     18\n",
       "2006     12\n",
       "2011      8\n",
       "2008      5\n",
       "Name: Rate, dtype: int32"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sum of rate values by each year\n",
    "\n",
    "df5 = df.groupby(['Year'])['Rate'].sum().sort_values(ascending = False)\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize Sum of Rate by Region and Year in bar chart\n",
    "\n",
    "df5.plot(kind = 'bar', figsize = (7,3), color = 'skyblue', edgecolor = 'black')\n",
    "plt.xlabel('Region, Year')\n",
    "plt.ylabel('Sum')\n",
    "plt.title('Sum of Rate by Region and Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <th>Asia</th>\n",
       "      <td>15299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020</th>\n",
       "      <th>Oceania</th>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N/S America</th>\n",
       "      <td>134784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>15066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>53516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>28460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
       "      <th>Europe</th>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>44210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>6458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N/S America</th>\n",
       "      <td>4276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2018</th>\n",
       "      <th>N/S America</th>\n",
       "      <td>13793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>16923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2017</th>\n",
       "      <th>Europe</th>\n",
       "      <td>3670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>1787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>1467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2016</th>\n",
       "      <th>Asia</th>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N/S America</th>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>4987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
       "      <th>Asia</th>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>2206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>N/S America</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>3029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <th>Asia</th>\n",
       "      <td>5782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012</th>\n",
       "      <th>Africa</th>\n",
       "      <td>15264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N/S America</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2011</th>\n",
       "      <th>Asia</th>\n",
       "      <td>1626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2010</th>\n",
       "      <th>Europe</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2009</th>\n",
       "      <th>N/S America</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <th>Africa</th>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <th>N/S America</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <th>N/S America</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Count\n",
       "Year Region             \n",
       "2021 Asia          15299\n",
       "2020 Oceania         221\n",
       "     N/S America  134784\n",
       "     Europe        15066\n",
       "     Asia          53516\n",
       "     Africa        28460\n",
       "2019 Europe          249\n",
       "     Africa        44210\n",
       "     Asia           6458\n",
       "     N/S America    4276\n",
       "     Oceania         126\n",
       "2018 N/S America   13793\n",
       "     Europe          883\n",
       "     Asia          16923\n",
       "2017 Europe         3670\n",
       "     Asia           1787\n",
       "     Africa         1467\n",
       "2016 Asia            627\n",
       "     N/S America     608\n",
       "     Europe            0\n",
       "     Africa         4987\n",
       "2015 Asia            241\n",
       "     Africa         2206\n",
       "     Europe            0\n",
       "2014 N/S America      13\n",
       "     Asia           3029\n",
       "2013 Asia           5782\n",
       "2012 Africa        15264\n",
       "     N/S America      53\n",
       "     Asia             61\n",
       "2011 Asia           1626\n",
       "     Africa          855\n",
       "     Europe            0\n",
       "2010 Europe            1\n",
       "     Africa          303\n",
       "2009 N/S America      42\n",
       "     Africa           27\n",
       "2008 Africa         1702\n",
       "2007 N/S America      26\n",
       "2006 N/S America       2"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sum of count values for each specific year within each region\n",
    "\n",
    "df6 = df[['Year', 'Region', 'Count']]\n",
    "df6 = df6.groupby(['Year', 'Region']).sum().sort_values(by = 'Year', ascending = False).head(40)\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize Sum of Count by year and Region in bar chat\n",
    "\n",
    "df6.plot(kind = 'bar', figsize = (12,6), colormap = 'viridis')\n",
    "plt.xlabel('Year,Region')\n",
    "plt.ylabel('Sum of Count')\n",
    "plt.title('Sum of Count by Year and Region')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subregion\n",
       "Southern Asia             6514.56\n",
       "South America             6067.08\n",
       "Central America           5921.38\n",
       "Northern America          4463.40\n",
       "Southern Africa           4295.80\n",
       "Western Africa            4210.73\n",
       "South-Eastern Asia        2531.40\n",
       "Eastern Africa            1577.93\n",
       "Eastern Europe            1460.40\n",
       "Eastern Asia              1223.29\n",
       "Northern Africa           1107.60\n",
       "Middle Africa              619.25\n",
       "Western Asia               581.90\n",
       "Central Asia               281.20\n",
       "Western Europe             230.56\n",
       "Caribbean                  201.48\n",
       "Australia, New Zealand     173.50\n",
       "Northern Europe            139.80\n",
       "Southern Europe             64.29\n",
       "Name: Count, dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group the dataframe by Subregion column\n",
    "\n",
    "df7 = df.groupby('Subregion')['Count'].mean().sort_values(ascending = False).round(2)\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Southern Asia', 'South America', 'Central America', 'Northern America',\n",
       "       'Southern Africa', 'Western Africa', 'South-Eastern Asia',\n",
       "       'Eastern Africa', 'Eastern Europe', 'Eastern Asia', 'Northern Africa',\n",
       "       'Middle Africa', 'Western Asia', 'Central Asia', 'Western Europe',\n",
       "       'Caribbean', 'Australia, New Zealand', 'Northern Europe',\n",
       "       'Southern Europe'],\n",
       "      dtype='object', name='Subregion')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate index\n",
    "\n",
    "df7.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6514.56, 6067.08, 5921.38, 4463.4 , 4295.8 , 4210.73, 2531.4 ,\n",
       "       1577.93, 1460.4 , 1223.29, 1107.6 ,  619.25,  581.9 ,  281.2 ,\n",
       "        230.56,  201.48,  173.5 ,  139.8 ,   64.29])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate Values\n",
    "\n",
    "df7.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Value</th>\n",
       "      <th>Info</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>6514.56</td>\n",
       "      <td>6514.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South America</td>\n",
       "      <td>6067.08</td>\n",
       "      <td>6067.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central America</td>\n",
       "      <td>5921.38</td>\n",
       "      <td>5921.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>4463.40</td>\n",
       "      <td>4463.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Southern Africa</td>\n",
       "      <td>4295.80</td>\n",
       "      <td>4295.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Western Africa</td>\n",
       "      <td>4210.73</td>\n",
       "      <td>4210.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2531.40</td>\n",
       "      <td>2531.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>1577.93</td>\n",
       "      <td>1577.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>1460.40</td>\n",
       "      <td>1460.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>1223.29</td>\n",
       "      <td>1223.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>1107.60</td>\n",
       "      <td>1107.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>619.25</td>\n",
       "      <td>619.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Western Asia</td>\n",
       "      <td>581.90</td>\n",
       "      <td>581.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Central Asia</td>\n",
       "      <td>281.20</td>\n",
       "      <td>281.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Western Europe</td>\n",
       "      <td>230.56</td>\n",
       "      <td>230.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Caribbean</td>\n",
       "      <td>201.48</td>\n",
       "      <td>201.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Australia, New Zealand</td>\n",
       "      <td>173.50</td>\n",
       "      <td>173.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>139.80</td>\n",
       "      <td>139.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>64.29</td>\n",
       "      <td>64.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "                  Category    Value     Info\n",
       "0            Southern Asia  6514.56  6514.56\n",
       "1            South America  6067.08  6067.08\n",
       "2          Central America  5921.38  5921.38\n",
       "3         Northern America  4463.40  4463.40\n",
       "4          Southern Africa  4295.80  4295.80\n",
       "5           Western Africa  4210.73  4210.73\n",
       "6       South-Eastern Asia  2531.40  2531.40\n",
       "7           Eastern Africa  1577.93  1577.93\n",
       "8           Eastern Europe  1460.40  1460.40\n",
       "9             Eastern Asia  1223.29  1223.29\n",
       "10         Northern Africa  1107.60  1107.60\n",
       "11           Middle Africa   619.25   619.25\n",
       "12            Western Asia   581.90   581.90\n",
       "13            Central Asia   281.20   281.20\n",
       "14          Western Europe   230.56   230.56\n",
       "15               Caribbean   201.48   201.48\n",
       "16  Australia, New Zealand   173.50   173.50\n",
       "17         Northern Europe   139.80   139.80\n",
       "18         Southern Europe    64.29    64.29"
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   "source": [
    "#Create the Dataframe\n",
    "\n",
    "data = {\n",
    "    'Category':df7.index,\n",
    "    'Value' :df7.values,\n",
    "    'Info' : df7.values\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "#Visualize the data in a Treemap\n",
    "\n",
    "fig = px.treemap(df, path = ['Category'], values = 'Value', title = 'Treemap')\n",
    "fig.update_traces(hovertemplate = 'Category: %{label}<br>Value: %{value}')\n",
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