Investigate_a_Dataset_pg.2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='top'></a>\n",
    "# Society Data\n",
    "## Investigate how a countries societal scores may have been affected by their rapid growth. For example:\n",
    "1. How did the perception of corruption change during expansive growth, for the top 10 countries of the world?\n",
    "1. How did the GINI coefficient change, if any, during expansive growth?\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "<ul>\n",
    "    <li><a href=\"#data_wrangling\">Data Wrangling</a></li>\n",
    "    <li>EDA Continued</li>\n",
    "    <ul>\n",
    "        <li><a href=\"#question_1\">Question 1</a></li>\n",
    "        <li><a href=\"#question_2\">Question 2</a></li>\n",
    "    </ul>\n",
    "    <li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='data_wrangling'></a>\n",
    "# Data Wrangling\n",
    "\n",
    "Utilizing an economic growth dataset, **GINI coefficient** from gapminder.com/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "mpl.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load corruption data\n",
    "corruption_df = pd.read_csv(\"data_political/corruption_perception_index_cpi.csv\")\n",
    "\n",
    "# Using the results from the \"investigate_GDP per capita\" notebook, create a list of the top 10 fastest countries\n",
    "top10 = ['Myanmar', 'China','Azerbaijan','Turkmenistan','Armenia','Ethiopia','Georgia','Cambodia','Bhutan','Mongolia']\n",
    "top10index = list(range(0,10,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>country</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  rank\n",
       "0     Myanmar     0\n",
       "1       China     1\n",
       "2  Azerbaijan     2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add in their country name & rank. Here, I could have used the index as their rank but I suspected it would be safer to\n",
    "# designate them in a dedicated column named 'rank'\n",
    "top10_df = pd.DataFrame({'country':top10, 'rank':top10index})\n",
    "top10_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of           country  2012  2013  2014  2015  2016  2017\n",
       "5         Armenia  34.0  36.0  37.0  35.0  33.0    35\n",
       "8      Azerbaijan  27.0  28.0  29.0  29.0  30.0    31\n",
       "16         Bhutan  63.0  63.0  65.0  65.0  65.0    67\n",
       "25       Cambodia  22.0  20.0  21.0  21.0  21.0    21\n",
       "32          China  39.0  40.0  36.0  37.0  40.0    41\n",
       "53       Ethiopia  33.0  33.0  33.0  33.0  34.0    35\n",
       "58        Georgia  52.0  49.0  52.0  52.0  57.0    56\n",
       "103      Mongolia  36.0  38.0  39.0  39.0  38.0    36\n",
       "107       Myanmar  15.0  21.0  21.0  22.0  28.0    30\n",
       "163  Turkmenistan  17.0  17.0  17.0  18.0  22.0    19>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is desirable to work with the fastest growing countries. Pull out the items according to the top10 list\n",
    "corruption_top10 = corruption_df.loc[corruption_df.country.isin(top10)]\n",
    "corruption_top10.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  2012  2013  2014  2015  2016  2017  rank\n",
       "0     Armenia  34.0  36.0  37.0  35.0  33.0    35     4\n",
       "1  Azerbaijan  27.0  28.0  29.0  29.0  30.0    31     2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add fastest growing rank to corruption data\n",
    "corruption_top10a = corruption_top10.merge(top10_df, on='country')\n",
    "corruption_top10a.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>country</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>52.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>36.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  2012  2013  2014  2015  2016  2017\n",
       "rank                                                  \n",
       "4          Armenia  34.0  36.0  37.0  35.0  33.0    35\n",
       "2       Azerbaijan  27.0  28.0  29.0  29.0  30.0    31\n",
       "8           Bhutan  63.0  63.0  65.0  65.0  65.0    67\n",
       "7         Cambodia  22.0  20.0  21.0  21.0  21.0    21\n",
       "1            China  39.0  40.0  36.0  37.0  40.0    41\n",
       "5         Ethiopia  33.0  33.0  33.0  33.0  34.0    35\n",
       "6          Georgia  52.0  49.0  52.0  52.0  57.0    56\n",
       "9         Mongolia  36.0  38.0  39.0  39.0  38.0    36\n",
       "0          Myanmar  15.0  21.0  21.0  22.0  28.0    30\n",
       "3     Turkmenistan  17.0  17.0  17.0  18.0  22.0    19"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set rank as index\n",
    "corruption_top10a = corruption_top10a.set_index('rank')\n",
    "corruption_top10a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>rank</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>8</th>\n",
       "      <th>7</th>\n",
       "      <th>1</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>9</th>\n",
       "      <th>0</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>Bhutan</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>China</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>Mongolia</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>Turkmenistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "      <td>63</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>52</td>\n",
       "      <td>36</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>36</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>33</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>57</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>67</td>\n",
       "      <td>21</td>\n",
       "      <td>41</td>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "rank           4           2       8         7      1         5        6  \\\n",
       "country  Armenia  Azerbaijan  Bhutan  Cambodia  China  Ethiopia  Georgia   \n",
       "2012          34          27      63        22     39        33       52   \n",
       "2013          36          28      63        20     40        33       49   \n",
       "2014          37          29      65        21     36        33       52   \n",
       "2015          35          29      65        21     37        33       52   \n",
       "2016          33          30      65        21     40        34       57   \n",
       "2017          35          31      67        21     41        35       56   \n",
       "\n",
       "rank            9        0             3  \n",
       "country  Mongolia  Myanmar  Turkmenistan  \n",
       "2012           36       15            17  \n",
       "2013           38       21            17  \n",
       "2014           39       21            17  \n",
       "2015           39       22            18  \n",
       "2016           38       28            22  \n",
       "2017           36       30            19  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corruption_top10a = corruption_top10a.transpose()\n",
    "corruption_top10a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>Azerbaijan</th>\n",
       "      <th>Bhutan</th>\n",
       "      <th>Cambodia</th>\n",
       "      <th>China</th>\n",
       "      <th>Ethiopia</th>\n",
       "      <th>Georgia</th>\n",
       "      <th>Mongolia</th>\n",
       "      <th>Myanmar</th>\n",
       "      <th>Turkmenistan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "      <td>63</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>52</td>\n",
       "      <td>36</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>36</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country  year Armenia Azerbaijan Bhutan Cambodia China Ethiopia Georgia  \\\n",
       "1        2012      34         27     63       22    39       33      52   \n",
       "2        2013      36         28     63       20    40       33      49   \n",
       "3        2014      37         29     65       21    36       33      52   \n",
       "\n",
       "country Mongolia Myanmar Turkmenistan  \n",
       "1             36      15           17  \n",
       "2             38      21           17  \n",
       "3             39      21           17  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename all column names with the first row of the dataframe with vectorizing\n",
    "corruption_top10a.columns = corruption_top10a.iloc[0]\n",
    "corruption_top10a = corruption_top10a.reset_index()\n",
    "corruption_top10a.drop(0,inplace=True)\n",
    "corruption_top10a = corruption_top10a.rename(columns={'index':'year'})\n",
    "corruption_top10a.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='question_1'></a>\n",
    "# #1 How did the perception of corruption change during expansive growth, for the top 10 countries of the world?\n",
    "\n",
    "<a href=\"#top\">Back to top</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "corruption_top10a.plot(x='year', y='Myanmar', kind='line', figsize=(14,8), ax=ax)\n",
    "corruption_top10a.plot(x='year', y='China', kind='line', figsize=(14,8), ax=ax)\n",
    "corruption_top10a.plot(x='year', y='Azerbaijan', kind='line', figsize=(14,8), ax=ax)\n",
    "corruption_top10a.plot(x='year', y='Turkmenistan', kind='line', figsize=(14,8), ax=ax)\n",
    "corruption_top10a.plot(x='year', y='Armenia', kind='line', figsize=(14,8), ax=ax)\n",
    "\n",
    "plt.title(\"Corruption Perception Trend,\\n Top 5 fastest growing countries from 2012-2017\", fontsize=24)\n",
    "plt.ylabel(\"Perception index\", fontsize=20)\n",
    "plt.xlabel(\"Years\", fontsize=20)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis:\n",
    "There was an overall increase in the perception of corruption for the top 5 fastest growing countries but one cannot concluded that rapid growth is the contributing factor in that perception. Additional data such wealth gap increase, wages/capita decrease, home ownership decrease, etc. may contribute in determining correlation and/or causality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2wAAAIBCAYAAAA1cTxhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxkVZn/8U/2PZ1u0lu6mzTrAYSGhm4VBfdBUFHcShEGHcd1FMd1xgVHXMZxHX6jzrgNjjPqqOUoOi6o4wKIC3QjsnPYbJZegDS9Jens9fvjObfqplKVVFUqqUrn+3698qoktd26de+55znbU5NKpRAREREREZHqU1vpDRAREREREZHcFLCJiIiIiIhUKQVsIiIiIiIiVUoBm4iIiIiISJVSwCYiIiIiIlKlFLCJiIiIiIhUqfpyvZBzbg3wOuBs4DigFXgMuBH4b+Ab3vvxcr3fQuKcawJ6vPd/jv3vVcB/ADd47zdVattycc5dCnwgz93jwEHgIeCXwKfjn2sxc84d772/I+t/Ud6Mk7z3t1Zgs6Zwzm0Deot82tXe+6eVfWPmiHPuzcBnmcV2O+fWA6Uc2x/03l9aynuWm3NuFfBe4LnAGqAfuBb4iPd+axlevxb4PXCU9757hsdeBLwZOAkYBv4E/LP3/n9LeN9nAh8JrzWOfc/PL/Z1ShU+99He+7vm6z2r6f0rzTn3VeCV2PXnnRXenENeKcebc+5pwK+B3TOVDfPNObcR+CSwGagD7vbeb6zsVpWHc24Z8A7gBcCRQAq4B/gOcJn3fiDP854EXAI8CWgMz/kP4DOF1N0LvRY451qAtwAJ4FgsDrkf+F/gE977vsI+6ZTXPQu4GHgC0IXFH78DPuW9/9002/Ju4HysTrQXuAq7Pt5S4Ps+D/ghcLH3/nM57i8md9oR3vtt+e4sSw+bc+412Jf7D8AmYB9wC9AAPBv4T+A659zacrzfQuKc+wvgVqzCtNDsB36b9bMFeBA4CngTcEsomBct59wq59y3gf+q9LYUaAtTv9d4MJl932+x83mxGSL3vngw3J/r/Pgt8MC8b2kOzrlTgduwi9hy4A7sQvwC4HfOuReU4W0+Ajy+gG35GHYd2IRdKx4Fngr8wDn3/mLe0Dl3FPAT4InAIHAXpQXWJXHObcLOodfN13tW0/vL4nKoHW/OuS7gF8AzgQmsXLy3ohtVJs65I7CGsPcCx2PXql3AicCHgd875w7L8bznAtcA52BBiwceB/wz8BPnXCGdOzNeC0Iw+QfgY8BGoA/YhtUn3wX80Tl3dAHvlf26HwF+BjwPiztuA1qAFwK/cc69McdzGoGfYnHL4VgdJ4UFkteHuvtM79sLfHGGh+WqI8R/ogD1MWzf5zXrHjbn3MeBvwNGgY9j0Wxf7P7nAP8CnAb80jn3eO/9vtm+7wLyPiDXAXgFduAenN/NKcqN+XonnHPrsM9wGvA159yx3vtq/ixz6WzsJL8hx33Hh9v75m9zpue9f2n2/2KtoXjvz5jvbapG3vtdwJR9EeuBznt+VJpzrh1rsVwGfAN4o/f+QOjt/wxW+fqKc+4I7/3+El6/BrvQvaeAxz4P+HvsgnS2935L+P8LgG8DH3TO/cp7/9sC3/48LPC8FzjRez9U7PbP0puBUwnnSwVU+v2rwXuwSt/uSm/IIlDq8XY9dv0bK/sWzc4zsXJxP9ZreEgcQ6FM/iawDutZeoX3/v5w34lAEhuR8GXgRbHnrcLK4Vrgtd77fw//Px74MXAWVsf/6DTvW9C1APg8sAG4E0hEvVjOucOxkXhPBr7tnNvkvS+oZ8o5dw5Wzx7Derm+EP5fjwWuHwQ+65y7znv/x9hTPwo8BWuMeL73fpdzri48/n3Afzvnjsp3fXTOHYM1HPZMt33T1afCa9yIBYoXeO+nDdhm1cMWuiDfhQ1JeYX3/t3Z3Zne+59gLal9WPfnpbN5z0OF936f9/7O6IRaaLz3DwIXYgfaWmDehiMtJOE7vtN7P1LpbZFF5R3YEMjfARd57w8AeO+HsZ7x+7BKy3nFvnC4wF9B4WX5e8Ptu6NgLWzLD7CLYw2FXewjy8PtlgoEa1IFvPc7Q7n6aKW3RXLz3g+G7+ieSm9Llqj8uP1QCdaCJ2HDAQewYChdtwzTMRLhzxeGnqHIW4A24JtRsBaecwdwQfjzHaGxb5JirgVhhN1LsV7NC+JDDr33DwAvwYbsn4oFUoV6e7j9XBSshdcc895/CPgfbNjrxbFtWQa8IWzLRaFxFu/9uPf+EuDnQDd5epWdcy/DAr2iewNjr9GABaltwOe99z+d6TklB2xhvOq/YRfbr3rv/yffY733O7DWMIDXOufaSn1fqR7e+zuBu8Ofmyu5LSIyyYXh9r3e+4n4Hd77MSygewc2dKRgoZHuLmxY5S5mCLTC8JbTsREY/53jIZeH27PCUKVCRCNDhgt8vIhI5FAtP54Wbn/vvd+efWcI2raFP0+N3fWX4fY/czzn98DtWOPes+L3FXstwDpuaoD7snq6ovfaBUTzqk/Nvj+XEIdEPVj5YpAf5XjNF2GB0nWhHpstClxfluM9fwZ8C1gSHpdzflwBLsamCBSy74DZDYl8KjbuFODTBTz+q8Aj2OTwSZMew4X6rdhOPBqLej22Uz6XPdQutmjC0cA/AucCI8APvfcXFXH/ud77H5HFOdcHHAY83Xt/Vfjfq7AJmF8EPoRNWD0LaA7b+kXg8qhyFB9iFnzWOfdZwoIE0y06UsL+uAr7PjYD7diXvzls253YQfWF7IpbmUTDWzuy7whDnv4GOyjbgO3YyfMx7/3OrMdeig0zexfWQvQ+7Du4FzgvaqVzzp0E/C02rKEnvP+14TWvz7ENR2CTSs8Kjz+ADUW9zHv/yxyPT2EFeSvwNuD12BCDndjwgH8KDRDR47eRWcDjtPD8+73362OvBzkWHXHOvSi8/uawf3ZhY+s/nj25O3a8/CvWmnUp1qu5EjuvfgR8KHu/llNsEY7bsNa6y7Fx6I8BH40m3IaWuDdirXPHYQ1DHquwfy67VyS2gMBLsQaAf8Ba2Dqw7/8b2OICUy6wzrlTsGPlDKATG17wkfJ96tLM4hw+HWvZuwQbvrIfuyB81Hufa8htrvc+PLznXmxewhTe++8X/aHMCVgZ8zXs/Dhphsc/IdzenGuyu/f+Eefcfdjk+Cdicwpyip0DkVc6514ZXqcm9rgzsNbTJ2PnB9j5+0tsUvukFv8wpOfVWJDrsMrJI1i5cllsCOd6Js+Ve4dz7h3Af3rvXxV7vRXYEKJzsbkRQ8AfsVbUKZWKcr9/PmG4z19hrcbHYUOIrgLej517HyC2YE5WmfMr4BPYaIoHgVdFQ1jDsJ53kiljB7Dh4V/y3n8n9v6vB74AXOO9f2rWtp1ApvHgWdlls3Puu9i5dJH3/msux6IjsWvuj7GK1vuwcmotdi78Aisjpyyc4Zxbih3PCaw83431HHwA+B52bqbrAzMJreevxSrDDptPcw9WBl6Wowxsxq6VL8fOsVqsF/wKbGGePVmPvypsU76FDrZi0xX+ynv/1VL2z0zH20zHBzaXKOeiI6GifRF23G8AmsJ7fRebVjNl2kwo6/8ufK5ebP7qLdj14SuhISqvHHWyp8auz0cA62P752PY8L1jsevy27333w2vsxo73p8XtmM4bMd/Av+RvR2xOsJSbE2HqNw8CFwN/L33/h7n3JHYtesvsGufBz7pvf/6dJ8rJoldLx+b5jGt4bY+bNsa7DsDWzAklz9gx+SZ2L6JFHstuJpMD1s+UWdOobFJLfBirI6Wb1G3XK8ZXZfyBVt/CLenOufasq5dT8CO8b/13l8RzsWihPLmkvDnJYVOS5jNkMhnhNudPmtlvFy897u9918LXZ9pobC/CSsYTyAzgXwjVgD8IXS75vJ1rBv1Luzikz28cKb7S7EauA54BTZxfjtWgHwJG3sbHRT7sAmF0RdxHwUsSDDL/XERVnCeGd5nX3jevwKXFfk5CxUF7dFCDDjnapxzXwK+j13Eh7GTaSXW/X6zs4nMubwY67kdx1qD2gnzv5xzf4l1Q/81tgrQLViLzQuB34YWnzTn3LPDY14HrMAqBAexBWB+4ZzLtxIm2Pf5qfA+t2Hf+5uBLWFsd2QLmV7GfjILs+TlnKt1zn0duzidFZ53M1agvxq4KQRzufRglb+/wY7pu7EC9w3YhOJCeylmYwk2wfdErPWtC5u4HQ01uAY73k7FLnb3YBflTwLXuhyTnoNnYPMezg3Pexg7B/4RuxhN4px7IVawvgQrjG8HTgauJEfL2HyZ5Tn8l9jcsw3YPq3HKqq/d85NmXuYx4nh9k7vfco5t8E598/OuZ85537gnHurc6512lfI73rgVO/9RQUOJ4qGjEy3KEhULs80vORh7PyKWo8fITNxGwDn3CXAb7DGgibs3H0Yq5C9FrjBOeeyXvfzWKPWU7EGnVuxcud8bL9Hi7NEi9A8Ev7eHv5OBwDOFnq5Beu97A337caO7e84574SArQ5ef98QgDxbWz+yubw3O3YkNgt2HCqfJ4Qntse3mtV+Iw4587DjvXXYQ0NN2PXvGcBSefcf4dAETKVvSfmGGXzjNjv2cFcPdZAN47NGZlJBxbsvgc7Bu4I23YBdu4dmfX6PVjF7f1Yw8HtWI/wm7Fr/XKKECpjv8auu0/Ejr97sflcHwV+FL6P6PHLw/t8GmvcfACrrDusUvcn59xxxWzDDArdP4Ueb3mPj1xCg97/YsHemViweAdWl7gEW3gi+zt6KlbWn0/m2r8Ha9j7ItYQNpOoThbNJ48vGhUPoI/AjrM1YbtWYcc4zrnTsfPz7dj5fRt2rXoyVmf4mbP5w7l8NGzneuy63YGV7b9xzj0Fu66/CNgRtm0Dtj7AhTlfLYv3/m7v/be89z/PdX/Y9hXhz9vDbVTm9kXD5nPIVz4XdS3w3j/kvf8f7/338mxfL3b9jm/fTK855r3/iff+i7mC/CAqP+OvOdN1aTtWv6rFjoe4twPOe39FIduYx/uw+t5dWGdWQWYTsEUFSMlLlYdC64dYK+TVwHrv/Sne+5PC69+MHbRTKmvBRuBMb8ux9mAVwmLuL8XzsVaKp3rvT/DeH4ddTPZjFcc3Anjvb/Q22fDG8LzLvPdneO+/ku+Fy7A/LsYKwVXe+1Owivynwn1vmqaSWJJQkCwLf8YvpG/DKkc7sNbSntCLuBILxrqB7znnpvTKYRe4/4d99uOBTd77iVDJ+jJ2gfkQsCK8Zg+2klE9FjC3hW1bj+2nNmx1pKXe+1O99+uwE3g/cGmocGRrwoLCj2HpGDZh38mvw/v9V1Tp8raARzQZ14fveKaK9SXYxXEf1st7uPd+c9g/l2E9o99wNlE42wuxoHOT9/5I7/2J2MViELuAvHaG9y6HtWEbjvLenxr+/lW476vYSlG/A4713h/jvT8ZK/R+gzVuXD7lFc0bgf8D1nnvT/Le92K9qQDPd86lh92GXoyvYt/Vx4HVYR+uxi6KFVk4pQzn8N9g4+fXxY7vy7DW6q+E1t2ZHB5u+5xz78YqAm/DGgeeH17vZmerLRbFe/877/2finhKVNmd7oIetQhPu/S39/7KUKZGlbMrw/l2BoBzbgM2Jy6aU93jvd/svT8CuxY8hPXCRscUzrnHYb3cj2K94C7s99VYWVWHlS9473eF97oyPP1b4f0/Gl5rCdZItQILwJZ770/23h+NHY87sB6uOXn/Gfwt1hjWB5zhvT8+HJMbwnZNtyLapvC51nnvN2BLT+93zh2LLXLQgqXQWBn293oyq82dT5jf4r1/CDv+G5k6R+WZsd+fmnXf6Vgj0R8KbCR4ClbJfpb3fl24/p+IVayXYsF03Jewc/N6rEw7LRwzL8Yq7ScU8J5xl2Fl8l3Yd3p8KKc3Yj29z8R6iiLfwr6HW4HHhXrFRqw8/xV2Pv9v6IUrh4L2TxHHW87jY5r3/yTWaHoHVuFfH64jPVhv5pFY40a8fvoprKx/J6Gs994fhZVpQ8CLnXNnTvehY3WyqPH6xqj88GEOU3ACFhAeHupRa0IP2FKsbF+GLZHfE44Vh13zHsQaHj6fZxPeiC2+tCa87slYb/QqrG5xPbYPT8aOuyggeHuO1ypKaDSJ6oI3ee+j3uySy+cSrgUz+ThWNjyMjYaYNefc2dgxAtYTG5n2c3sbjRYFgNmf+yt+FgvshXpvNDfu076IdGezCdiilvzZTNp8OdaK9DDwglCgA+CtW/652AF9prOVYLJ9z9sYW7z3ozkKiZnuL9Vfee/TQ42897/Chj8BvDtHC2qhZrs/bgdeE33OcCC8D6vM11HA8tszcc7VO+d6nXNvwSoUAFf4MCY5XFSiRQYu9LGhLd77Ae/9m7DWxHVYb1K2EeD9PqwQ5DOL2LwDK7CT3vsPeO9Hw/2jWCF+C3ZMnhse/y6scvZf3vt/8LFFP7zlfXp3+DNfL9sV3vv3+DC8wdvk9pdgrXqbmL6Ck1cIKKO8Qa/3sSG53vuD3vu3Az/AgrZ8y51f5GNjwL3lGPlm+PP0UrarBJ/y3j8S3v8xbz05m7D9vxsbxppeKtnbIjUvwXoQXuCcOznHaz4GvNR7/3DseZ8hs+Ry/LO9Aft+r/K22FH0PfVjQ6UqNdF9tufwg8CLo4ppKLfejgV/7cQmTk8jagh5IvBPWIXwWOyYehIWwB2FtfSXqxKYT0u4ne4CF93XMs1jCvEXWM/Id7z334zf4b2/iczyy/EKeDSM53exSky0OMu7sAaEX03Tah73Oqxcuxp4nY+1WHsbPvia8Od7Yj0s5Xz/nEIPVTRH4q99bDVOb8O0X4QtHjWd98XK3KhM/nvsmPqp9/4t3vvB2Ov+FAtOwYbSRb3qUS9bOkALFfOnYefsw8ATso7L6Dz54QzbGPfWrGvPnViPF8TKEWf5uKJz8rxQTkXP+R6TA6sZhSFmF2H780U+Ngze2yILbwl//mV4/JlYJX8IeJ6PjVbyNvT+PKyh4Rgy+7McCto/Rch1fEwRejPfgF3nX+S9jxq0CWXeBVgP46lkruWQOU/S007Cc/4PG7XwbaxRq1w+GK4l8c/zZmyaxi1Yg1B66KG3YcsvIKz252yIb7ZfeO8/EVXQwz7/QbhvGHhZqGdE9Zp/DvdtmEWdMvL/sLI/Rab+AfNbPuflnHsnmVEx7/VlWEwqNPJ/Lfx5DZl9DZX93K/CrtGPUmQqqNkEbFHhPJt5cFFusm/4HN2ZobITdZ8+L8fz8425LfT+Utzvcyd6/Qa2T3qwlrRSzHZ//NRnLYUaApVt4c/OIrfnqc65VPwHqxBtw1I1dGDj3uOB15OxQu1h732+ZYCjFvJcFdZbo4IyS7RvpvRQhs98HtY6Fb12VNh/M/vxsW1IAafk6bX4TI73eYzp938hziRzsn4nz2Oi9z4nNpwo8pj3/rocz/HhttjvuFS5zq1o6MEvfI7V20KAF/XE5frur8rTcpXrs50dbr+R9djomP9a9v/nyWzP4a/43IlNo0nQhRx3UWW3GysTLvQ2XGY4NGA9G+v9OA67eMylQubNRtehYhKMTuG9/zR2cX1VnodE16z4cNCoMeC5zrl3hQpl9HqD3vuzvPevzVMmZYtWyv1Wdjkc/BRr8FmB9TSX+/3zeRLWK7CLHEGP9/5mbIhcPo/53AmTnxNu/y3HfdE8yQew7yQa8hiNxIgvYHAa1tj2a2x4ZhOZOSaQOdcLDdhSZHqFJm1SuI2XI9F14kc+9/zfyylucYrnYMP0r4sH4DE/AE4hU0eIyouf+BwrRoegP5q3Weo1J1sx+6cQ+Y6PXM7BAqsbfY7FHkJF/YrYYyPRefI159ymeAATGnBf7q3hvFxyXd+i7+rLPsd8uRB8Xot9/7ny7uba59F3vtVnzVPEGi/AGttLHcKOc+6TWLAJ8I/e+1/E7p638jkf59zFZEa/fd1PMwqtiNd0WC9dN1buXZBVJlfyc/9NuP1CsYHpbIKtqAt5Nhnsjw23N07zmD9irVHH5rhvpgUW5mIBhq25/um9H3HO3Y11cx+NbXexZrs/pqwMFESV4OzK/0z2M3Us+ihW2bsTqwxenXV/1LLU7pzLVwmIhlFmzyWBHN9ZaG2NKjI5h+B67++LPb4Da+kG+GiY15LLOHYOHJvjfXN+z7H3L3U51+h7u9nnXwQmOnY6sOES8e+13N9xqXKdW9F3f+Y03300HjzXd1/MZzsm3OYb635znv/Ptdmew+U47uJB74ey7/Te9znnvoz14JyLLQQxV6JAY7qevGi56FnncQw9vSnn3DOwxK9HYsdKNCweYg2V3vstzrkrsKHGnwA+4Zy7GZuj+SPg2mnO02zR8f+WaeadNIZbhw3xK+f75xPNub0lTyAJdrzmG1KWq0zuxMqm6Ln53IgN6YuO9d9jPekbnHPLQ8NOFMz9Gpvf8zxsWOTVzrmVWIDzZ+99QfNagH6fezRNrnIk2jc5ywvv/UHnnMeGLBYiGmac7zo1SpgPFRRaXsQfO1vF7J9CFFPPis6Ro6e5RkQNqPFrxPuwOd/PCz+POuf+DwuCfuRnyF9VpAGfez5Xod/VmRReP4tG/uRKTzEa+73oHrbQs/55Mj37X8IW9Iqb1/I5m7N1BC4Nf/4Ym4oy29fcHF5rOTba59nxkS5BRT53mOYSTSfLtWrytGYTsEUtKgWP73a2wt+dUdc5maE7+SY7QmbH5prvNFN0Ohc5erJbQeKiz7GkxNee7f6YKddXsSd9KYmBo9a5Nqy3bTq59lOu7yy+SEUhLc3xFsJCejuzt2Nsmhbt+fyO44+PlPs7LlWu7yna7z3MkEyS3PuvmM8WPT9XbxRYo0IlzPYczle+RK/X4pxriJWhucQ/e77J/1Hr/xGQHhr22TyPvTg+dKlI0ZD5ZdM8Jjq/Z51Tyzn3OqxSsib271GsknUzmd6auJdi80tegzW4bQg/7wLud879rbeccTOJjv/jp32UiR//5Xr/fKL9m+9cgemP11znevzYLfhY996PO1sW+3xsWOS3yAyPvAoL2CAzj+1s7LwvZjhkMeXIbPdNtuj1Cu0RnW15UYpyX0OKqWdF58hhFFE/8N5/3zn3ZGwqw7Oxyvgrws+wc+6LwDtnKBcLle/zzPa7mu4YK2svjrNFpZJkevr+FSvHs99nXsvn2PbVYQ2FUTB5BfByn5Wv1jn3anJPncmZkNo59xxs5FIr1kP5Fz6W7y1m2s8dti86/sqZ6zHq0b81Vw/zTGYTsP0Ym8S4wjl3Up6dkhbG4G8FRpxzL/He/4zMwT1dF3y002YzJCSffAXTdN3P090XfY68Y7hnUOn9UQ5RofQj7/250z6ycIOx39vITAadaRsAun3xyTHrp6kUz+d3HH/8QhDt93eG4WlzKRpalm9uz5yNt5/BbM/hfOVL9Hr7C6iU+Njv+SoCUa9N9FpLyF+BKrVxAqwnHjIV8VyitBizmnfonHstmXlqX8OG390K3O29Hw7B3JSALcwp+RzwOefcOiyAOAur7PQC33XOPcHPnFZhANtXmwp47Fy8/3TbBdNX+IsNBuLHbif5y+Rcx/qPCQGbc+572IIsd3rvdzrnHsUqxKc75xrJfF9T0u+USbn3TXStKjTX7GzLi1LqMJUU7e/Pee8LmY+b5r3/A3BeCEaegp0n52I9cW/ByrS3lXFbs/VjQ3erun7mbKXoH5NZ+TWdqiOHqHxe4Zxr9bF5qDFlKZ9j29eENdREC759BZvzm2vxjcOZObCPXvcVWGqFemwl0LN8bB59ljux42d9nvvXhNeZILOqaDlEw5rz5q2eTskBm/f+Tufc7VgP29uZeULsq7DhICksRwtYxSIaz51vzkk01r+ciwhE449zZW7vYvrKXs4exTBsL+oGL3ToRrZK7Y9yipa4z9vKHFZwXIUlUHwk3+Mi3vs9znLjdWP7f0f2Y0Jl7GXYggNfCBf+5WE7pgy9CC0oT8fm4/05R2FxApOHrkSKWnY2h6gyvcE5V5tnuFP0HQ+S47NWsUK++41YGXBvnmEnhfJYwHYKsWXdYwrp5ZgLsz2HT8AWmshWzHH3J6wVvRFbICd72DJkhpTeB+Atv9Rc9M5GQzxPds41Z4/Zd7ba5xHYhXHadBgFeFe4vdR7/8Ec96/N/kcY2nccNuf2fm+LTnwV+GpY9fFabAW9l5O5buVzN7a/j8/3WGe5oHZhZd9Imd8/n+iYOdE5V5NnWGSuFWnz8t7vc87twsrxjcTSukTCPKNohEP8WP8p9n0/E0sx0IL1ruG9HwtD5c7BFr84Cwvgch3D5XA7VnHMmUMqVC6PyXVfHtOOPArD1K7GhhG+icz1YLqRILnKi+nqMDVkhqtWm0KuEcdjAe+94dpfjw01Xeq9/0MIKn4aft7lnPsQtkDXhcxtwOaxuZUbsRWPc6lo/SwEs1GwNoH1quWcYwrp4fH3Y0HZE5icpy7yxHCba+58sdtXi807j4K1j3vv353v8SHQvLSA130RtoBHHVZvO9tPXvkzW3RdemKe+6P/31zsPLNptrGBzPGRb32Hac1m0RHIzI+4yDn3/HwPcpZTI7qAftlnVt2JVox6Rbg4ZT9vLZmFDHLmlihRNOwo1zyamXqFTnG2fHS2CwmJqr338RbuqEJeSGWoUvujnK7BWpeOcs49K89jLsfmMhTTC/OzcHtR9h3hAvUqbC5ENCY5mtz+hjyvdwFWMf4TuXtpXpnjfbrJFDTxHBzFfMfXYnMDu7GhULm8Kdz+sgzzV+ZTdPy+OOyrScIx/UtseFpilu8VDRF7tZu8/HN0UfjLWb5+qWZ7Dv9l9opg4e9obP+MuV9CIBxNcH9r9v2hcSkaZjKbXDIz8t5vw77vJqyMzBYNiflJjkn3xVofbqcM33TOtWC9OjC5ofITWEXkPdnPCYvGREFkfF5PvvM9+u5fl/0dhm04E7tQ306m1bqc75/Pb7B5YyvJsdiPs/QO2UvpFyIqY9+Y5/7zsJbqMWIVlDDi4TosUI/K2XgFJvr977EhSz/LHipVRlE58pzQeJDtfIrrrf851iB1urN8jNmeiVWmn4aN0oiOmec4y0M1SZiPHV3z4uXFdAG5ZfMAACAASURBVHWYM7Dl+cul2ONtOleG13taWBhikhCcfR877qPl7DdhPSJXutyr2kaLaMz1/O3ou3qNy+TbTXPOnUamol+p+tkXyQRrr5ouWIuJFsGaMn/MOfckLLh+lPJ8pg9i6TLAVhbNG6wVyllqlG9g3//1wNNmCNbAhliPAWfkOg7J7ItCk5YX4gTsOjhBiY1vswrYvPffxibO1WJ5tT4Ur6g5SxD8IqySugxrfXpv7CW+jbVarAS+Hyoz0XOPxU6QVqxyP5sx/Nmi7OYXu1hCyhBgFJJgOhm2L3re2WSWYM1eij3qGp9SGOdQqf1RNqGyGO3Db8SDNudci3PuMiywGseWmi3UJ7HhWxc6W02tLrxmA7Z0+enYuORomdRPYGPRL3DO/WO8oHeWYPtz4c8v+9wJFy8O46ej56zCCrYlwI+999fHHht9x6vDMJ68wty4KFD9onMuvZqUc67ZOffP2GpzI+RPOVCVQi/NNdiwkR8759ILZDhb/e77WEViJyVMuM3yZWwS96nAl0KFPApGPo/1ck3hnFvinDvOlTcRbdxsz+HTgH+LjtfQwv8ZrBK2k8IXCHk/dr6c55z7ROz12rAGkzVYeZwvH1w5/VO4/XToYSJsy/Ox+WYpLAfPbEW9GxeHnqvofY7CLtDR8Riv9EXH4WuccxfGAy1nyXqjRpX4Cm/5yvR/wyrhZwKXxwN2ZykvohVsf+C9j3oayvn+OYUW4mgVtsudc+kVGMO+uYLSKrufxCbkn+2c+xcXS8YerolRvsXLfCxVRxBVfl8Vbq+K3Ze9kuxcDYcklOM/wxrtvudiq3Q65/6C4q5RhO/1O1id6LtZZeAGMkN2P+O9H/fe/wYLUJuwNBvx+khUZq7BesK/HHurqA7zCmdzu6LnnIoNCyunoo636YTFwaLK9Y/C9gLp0U1fx0YqDZAp667DPn8X1vO8NPac5WQW0si1CmM5fZ6QLxGr26TnPzlb6OJ7WFD7HR9LuzNfwvEaNYp9wHtf6ErJ/w87jy9wzr01KoPCsRgFLP/iZ5F7LLze8WQapi73heWPLMSXsDJ9F5bXdsb566HR6EvYefqdqLHEOVfnnPsw1rO/m8nn3GxFo2Tu8blXgp7RbOawRf4K+7L/GqskvMc5tw2b+H4UmZae64EX+sm5aUacJS7+Gdbi9Gfn3G3Yyfw47OC/GctPUXByuQJchvWwrARuCe/ZibX4/R4bgnFWnufuxIZi3eGcuwWrfEUtaZ/y3mePTb0Z67V7q3PumVgesX8ihwruj3L7EDbM56XA/znrcu/D9lNUkXp9kfM8bnI27PHfsWDs78JxFh1jB4nlRvHe3+6cuwgblvZerBLnsWGS0YXnF1grbs63xCo3H8Qmr56EDTG7manJqW/FKp2rgbudcw/mmhAb849Yq9XLsYvWA+E9jsPmSwxi+fRKXeihks7HWuIeD9zlbNj0OPbZGrHexefMtvD3lrj3ZWRWlnqxs1Vaj8Eu7D8kd2/5C8ksk132IYBlOIdvw3qFXx4+z9HY8b0nPKegXJLe+1ucc6/EKm/vAl7rnLsHqwx1Ao8A55druMcM2/Id59xXsF69X4djooFMufk+7/10y8oX6h+wuQHPArY75+7Czqejsf3+K6yxaJULQwO999eERpK3Y2XFp8P52E2mx+5L3vI9RaIVBV8crgFXee8v9t4/4px7MRaI/xVwfvjul5AJFm8mNn2gnO8/w775FBZIPgf4g3PuDqxR6ESsQn5P2MYpy5XnE6ZFXIhVwN+C9XbfgV0fozI2ia3wl+3HwEew4+C2rKHxN2L1hy6sNfonU59eVq/GhlU/GdgW9mkntj9uJlN2Fbpv3oCtTroJ8OEYaMSO91psKN/HYo9/BVZmngTcnvXd1GNLv0+qO2Hzfi7G6iy/CedUXdjWu7GGgFcUvgumVcrxNp03YcfHU4AbQjnXj/UWtmINTS/23m+H9Mqv52NDSV8GPD+UZTXYd9SMTW14F3MoDB98IXZtSWD5RG/FypioAf/XZJIiz7d4QvjnhEaTfP7Re38lgPf+AefcG7Hr4mVY3sQ+7HiswwLhj+V9pcL9LZmGoY0u/yqhYOltZlze3zn3RDJz9caxRpd8D9/pvY+Panov1tC/Eau33YI1jqzEjsGXFHq9LVC0+umU4eOFmu2QSLz3I97712Af/N+xgn811sI9ghW2FwBP8pYIMvv5d4bHfgSrJB+LLcm+BRvO8wQfS2ZZDmGYzmasMtOHVZ5HsbGyT2f6ZTx3YJXR72MX1OXYMK9zvfe5CoyPhffZjxWm084VqMT+KDdvOUpehgUk/4cVaCdjPV5XAE/x3l+e/xXyvu5XsX3/LezieTKWI+cbwGne+59nPf472L68nLCUNFYRivblc6YZanMR1sM1hlW07wUuAc7wWfl6vOWgeU14zCpsOOjKaT7HOHYxfRkWNHaEbXsEa6Xf6LMS/y4U4Rx/PJZw9gbswnw8dt58ETjZe/+nMr3Xb7Eeqf/ALvgnYRfuC8iTG2o+zPIc/gJ2bGzDPs8BbL+dGlrji9mOb2IXo29gZdoG7Dz4HHa+zGcr8GuwivH1WLm5FmscO79cLa3eEh2fiQXLB7DPuwQrg17svX8mdhwuwSqLkXdiPT1XYRXkU7Ael59iF+3XZ73VV7GKzSNYJTydBN57fw32vf0LdmF+XPist2DlyZNztACX7f3zCWXyC7A5Pjdj38HhWK/AZjIT64tqSAn7PF7GnkxIpo3t85f5HIvkhDIgWub8qqz7JrCeerB8ZuVcpW2KUGadhvU0bMeu0c3YPo4fJwXtmzC090zse/0T1qjYG35/M5YgeyT2+F3YULq/w4LVXiwQuR1rBN/oLVde/D32YXOOPosdZ8dgQzcvw77Pcu6zr1Lk8TadEHg+C3g9NvpqBXbO7MHKqk3eFqWLP+d67PN+HftsDjuG78IaiE/OVb8st3DNiZ/fJ2KrKF6DlW/PKqSHZ47EG4lPxxog8v1Mqp947/8TO9avxOYPnoAF/pdgjQXl6CCIb9+pM2zf4SW85poZXnNz/InhHDoT+DDwZ+y7bAT+F4tXrir4kxUmWm0zX/qiGdWkUnOSC++Q45x7FVYxvMF7v6nCmyNzxFlycICTvPc5c+nIwuYsR9ePvPdVs5Kac+4qbB7Rxd77z83wcJGycs5dhzW0XOi9n5KMfrEKQ62jlfPWRr0+IiLzrRxDIkVEFpIzgTsqvREi88FZSh2P9UY812ct3R3m50ZzPhfiMOySOefOwXqpfu69/5scD4mGlT2qYE1EKmnWQyJFRBaKsOjFu8mscCtySAsLHe3B5lR+JGsBprXYQjmNwG+896WmK1mobsSG1r3eOffy+B3OucdjCYeh8MV+RETmhHrYRGQx2YLNoZxtzi+RheSt2GqLb8MWB7kXW+DhaKwecA85UqYc6rz3u5xzl2LzWL4ZFoDZzuRFX67E5qOKiFSMethEZNHw3g8oWJPFxnv/C2wBlM9jAYnDFge7FVvYYlNYjGvR8d5/BJs/egW2sMhJWDB7Fbai56RFQkREKkGLjoiIiIiIiFSpQ2lIpCJPEREREREpRNnzsc6VQylgY8eOOU/DUbDu7m76+voqvRmywOi4kVLouJFS6LiRUui4kVJU23HT09NT6U0oiuawiYiIiIiIVCkFbCIiIiIiIlVKAZuIiIiIiEiVUsAmIiIiIiJSpRSwiYiIiIiIVCkFbCIiIiIiIlVKAZuIiIiIiEiVUsAmIiIiIiJSpRSwiYiIiIiIVCkFbCIiIiIiIlVKAZuIiIiIiEiVUsAmIiIiIiJSpRSwiYiIiIiIVCkFbCIiIiIiIlVKAZuIiIiIiEiVUsAmIiIiIiJSpRSwiYiIiIjInJiYmGBiYqLSm7Gg1Vd6A0REREREZGEaGRnhwIEDeX/6+/s5+uijOeeccyq9qQuWAjYREREREZkilUoxMDAwbUA2PDw86Tk1NTW0t7fT0dFBT08PHR0dnHzyyRX6BIcGBWwiIiIiIovQ6Ogo/f39HDhwgP3790/6Peodyx7O2NjYSEdHBx0dHaxevTr9e/TT1tZGbe3kWVfd3d309fXN50c7pChgExERERE5xKRSKQ4ePJi3Z2z//v0MDQ1Nek5NTQ1tbW10dHSwatWqKcFYR0cHTU1NFfpEi5cCNhERERGRBWZsbCzdI5bvZ3x8fNJz6uvr6ejooLOzkxUrVuTsHaurq6vQJ5J8FLCJiIiIiFSRVCrF0NDQtMHY4ODglOe1tbXR3t5Od3c3Rx55JB0dHen5ZJ2dnTQ1NVFTU1OBTySzoYBNRERERGQejY+PMzAwMGmuWPbcsdHR0UnPqaurS/eErV+/fkrvWHt7O/X1qtofivStioiIiIiU0fDw8LRzxwYGBqY8p6WlhY6ODpYtW0Zvb++UgKylpUW9Y4uUAjYRERERkQJNTEzMuNT9yMjIpOfU1tamA6/DDz8852Ie6h2TfHRkiIiIiIgEhSSCTqVSk57T3NxMe3s7nZ2drF27Nj1EsbOzk46ODlpbW9U7JiVTwCYiIiIii0IqlWJwcHDavGO5lrrPTgTd2dmZ/l9HRweNjY0V+kSyGChgExEREZFDwtjY2LS9YwcOHChLImiR+aSATURERESq3kyJoA8cOMDBgwcnPSeeCHrlypUcffTRSgQtC44CNhERERGpOCWCFslNAZuIiIiIzKlUKpVe6j7f3LFcS90rEbSIAjYRERERmaUoEfR0vWPTJYLOlXdMiaBFjM4CEZEFKN5a3d/fz759+yq9SbLA6LiRUuzevZvt27dPCcYGBgamLHUfJYJeunRpztxjSgQtUhgFbCIiVShKzBoNFyqktVpEZL7EE0GvW7dOiaBF5pDOJBGRCshOzJo9pyNXa3VzczMdHR10dXVNqiCtWLGC/fv3V+iTyELV2dmp40aK1t3dzcTEhBJBi8wjBWwiImU2MTGRTsyaLygbHh6e9Jza2tr0RPq1a9fmbK1uaGjI+X7d3d309fXNx0eTQ4iOGymFjhuR+aeATUSkSKOjo9NOrO/v75+SmLWpqSkdePX09EwJxlpbW5WYVURERKZQwCYiEpNKpXL2jsV/hoaGJj0nSsza2dnJ6tWraW9vp7Ozc9JKZ0rMKiIiIqVQwCYii0o8MWs8/0/89+zErA0NDenga9WqVemhi1EeoLa2NvWOiYiIyJxQwCYih4xUKsXQ0NC0c8cGBwenPK+trS29eMdRRx01ZbiiErOKiIhIpShgE5EFY3x8PB145fsZGxub9Jz6+vp04NXd3Z0zMWtdXV2FPpGIiIjI9BSwiUhViCeCzvczMDAw5Xmtra10dHSwbNky1q9fPyUga25uVu+YiIiILFgK2ERkXkSJoPPNHcuVCDqemLW3tzc9dyxa0KO9vV2JWUVEROSQppqOiJRFORNBx5e6V++YiIiILGYK2ERkRqlUKt07lu+nnImgRURERMQoYBORWSWCbm9vVyJoERERkTmigE3kEJdKpTh48OCkuWKlJILOnjumRNAiIiIic08Bm8gCF08EnWvu2EyJoFeuXDmpZ0yJoEVERESqhwI2kSqWLxF0/EeJoEVEREQOXQrY5sDu3bu599576e/vr/SmyAJTV1fHww8/rETQIiIiIgIoYJsTN954I7fffnulN0MWqJaWFjo7O1m2bBm9vb3peWNKBC0iIiKy+ChgmwPPeMYzOPfcc9m9e3elN0UWmNWrV7Nv375Kb4aIiIiIVAkFbHOgtraWtrY2Dh48WOlNkQVGeclEREREJE7LwImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlVLAJiIiIiIiUqUUsImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlVLAJiIiIiIiUqUUsImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlVLAJiIiIiIiUqUUsImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlVLAJiIiIiIiUqUUsImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlaqv9AYkEomTgM8CS4Bx4PXAn4BPA2dj2/ipZDL5hYptpIiIiIiISAVUtIctkUi0Aj8HPpFMJjcCHwa+gQVtxwInApuBtyYSicdXbENFREREREQqoNJDIs8C7k0mkz8Jf/8vkABeCPxHMpkcSyaTe4BvARdWaBtFREREREQqotJDIo8FdiUSicuBk4G9wN8B64AHY497CNgw/5snIiIiIiJSOZUO2BqA5wBPTyaT1yUSiRcAPwEOAqnY42qw+W3T6u7unpONLEV9fX1VbY8sDDpupBQ6bqQUOm6kFDpupBQ6bman0gHbDuCOZDJ5HUAymfxBIpH4d+A+oCf2uB6sl21afX19c7KRpeju7q6q7ZGFQceNlELHjZRCx42UQseNlKLajpuenp6ZH1RFKj2H7UrgiEQicRpAIpF4Ctaz9n3g1YlEoj6RSHQBLw//ExERERERWTQqGrAlk8ldwHnAvyUSiVuBy4AXAZ8H7gVuArYAlyeTyasrtqEiIiIiIiIVUOkhkSSTyWuAJ+S4663zvS0iIiIiIiLVpNJDIkVERERERCQPBWwiIiIiIiJVSgGbiIiIiIhIlVLAJiIiIiIiUqUUsImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlVLAJiIiIiIiUqUUsImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlVLAJiIiIiIiUqUUsImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlVLAJiIiIiIiUqUUsImIiIiIiFQpBWwiIiIiIiJVSgGbiIiIiIhIlaqv9AaIiIiISHWaSKW4Z/cQW3f0s3V7P48O3kNLfQ2tDbW0NdTS2liX/r0t+j3ctmb9r62hlsa6Gmpqair9sUQWFAVsIiIiIpI2ODrOTTsH2bK9nxt29LN3aJzaGjiuu4VnHrOUPQcGGRidYHB0nEcHRhkYmWBgdJyDoxNMpKZ/7boaaA3B2+SArpbWhszvbQ11tEa3DbXp39saa2ms0wAxWVwUsImIiIgscjsPjLB1u/Wi3frIIGMT0NZYy2mr29m0po2NPe10NtXR3d1NX19fztdIpVIcHJtgcHSCwRDEDYzY3wMj4/b/8PvA6ASD4X+7+kfTvw+OTjBDzEd9bU3o3bMgry0W0Nlt+H9jVlAYu79BQZ8sIArYRERERBaZsYkUdz56kC0hSHto/wgAazsbOdctY/Oado5b3kJdbeHDF2tqakIvWR20lrZdE6kUB0enBnn90e8hEMwOCvfuHwlB4AQHxyZmfJ+G2pp0r14U2GUHeVEvYKZHsC70/tn/6ovYNyKzoYBNREREZBHYPzTGH3cOsGV7PzfuGGBgdIL62hpOXNnKOcd2samnnVUdjRXdxtqaGguWGutY3tZQ0muMT1jQlx3YWRAYfo96/sLvA6Pj7B4cCwHiOENjM/XzQWNdTTp4m3SbYx7fpF7A2GOLCYhl8VLAJiIiInIISqVS3L93mK3bB9i6ox/fd5CJFCxtruP0wzvYvKadk1e10dJwaA0PrKutob2pjvamupJfY3wiNamXLxPYWUCXPdQzGuL56IAN7xwYnWBkfOagr7m+ZtLcvUwv3uS5e9Hv7WFYZzTEs0VB36KggE1ERETkEDE8NsGtDw+mhzo+OjgGwFHLmkmceBib1rRz1LJmarVS47TqamvoaKqjYxZB3+h4ioOjFrxZgDd57l70e/z+/pFxHu4fTQeFozOt4gI019dmhmrmGOLZlhXkRfP4ol7AloZaHQ9VTgGbiIiIyAK2e3CUrdttqONNuwYYGU/RXF/DyavaeNlJ7Zy2pp1lLaryzbeGuhoa6urpbC79NUbHJ9Jz8zLDOjPDOzNBYKYXcO/QODsOjKT/N9OUvhqgpSET0KUDvhzz+NpiaRxaYyt8ttTXKl3DHNLZKyIiIrKATKRS3L17KL2q4317hgFY0dbAXxzdxaaeNk5c2arl7w8BDXW1dNXV0lVi0JdKpRgZT00a1hn9ng74wv+i3wdGJ3js4BgPpRdyGWem0Z21NRb0Rb182ekZnnJsDccvKe0ziAI2ERERkao3ODrOn3YOsGX7ADfs6GdfLDfaK09Zzqa17azrbFQvh0xSU1NDU30NTfW1LC2xlzUK+vqzUjNMWsEz3tsXgry+wTHuHxlhcHScsZoGjt90WJk/3eKhgE1ERESkCu08MMKW7f1s2d7P7SE3WntjLaf2tLN5TTsbV7fNao6VSCHiQV+pIdd0+ftkZgrYRERERKrA2ESK2x8ZtKGOOwbYHnKjHb6kkecft4xNa9o5rru43GgisvApYBMRERGpkP1DY9ywI+RG2znAYMiNdtLKVp577FI2rWljZXtlc6OJSGUpYBMRERGZJ1FuNBvqOMBdfQdJYbnRnhxyo204BHOjiUjpFLCJiIiIzKHhsQluediGOm7Z3k9fyI12zGHNvPykbjataefIZU3KhSUiOSlgExERESmzvsHR9LL7N+0aTOdGO2V1G+dvaOe0nvaSV+0TkcVFJYWIiIjILI1PpLjnsSG2PNTP1h39/DnkRlvZ3sBZR3exaU07J65ooUG50USkSArYREREREowMGK50bbu6OeG7QPsG7bcaMcvb+GVG5ezeU07a5UbTURmSQGbiIiISIF27LfcaFu393PbI4OMp6Aj5EbbtKadU1e30a7caCJSRgrYRERERPIYHU9xx6OD6SBtx4FRAHqXNHHe8ZYbzSk3mojMIQVsIiIiIjH7Qm60rbHcaA0hN9rz3DLlRhOReaWATURERBa1VCrFtpAbbev2fu7qG7LcaC31nNHbwaY17Zy8qo3mei0YIiLzTwGbiIiILDrDYxPcvCsMddzRz+54brQN3Wxe086RS5u0YIiIVJwCNhEREVkUHh3I5Ea7+eEoN1otG1e3skm50USkSqlUEhERkUPS+ESKu3cPpYc6bttrudFWtTfw7JAb7XHKjSYiVU4Bm4iIiBwyBkbGuXHnAFu29/PHHQPsD7nRTljRyqtCbrQ1yo0mIguIAjYRERFZsFKpFNsPjIShjgPcHsuNdlrIjbaxp432RuVGE5GFSQGbiIiILCij4yluj+VG2xnlRutq4oUnHMamnjaOVW40ETlEKGATERGRqrd3aIwbtvezZfsAf9o5wMExy422YVUrzz9uGZt62lnR3lDpzRQRKTsFbCIiIlJ1UqkUf94zzNbt/WzZ3s/duy032rKWep6yvpNNa9rYoNxoIrIIKGATERGRqjA8NsFNuwbYun2Ardv72X1wjBosN9orNnSzaU07Ryg3mogsMgrYREREpGIe6R9l6w6bi3ZLyI3WUl/LKavb2LymjdN62ulSbjQRWcRUAoqIiMi8GZ9IcVffQbbusKX374/nRjumi81r2jlheSsNdepFExEBBWwiIiIyx/pHxrlxhw1zvGHnAAeGx6kLudFefeoKTlvTxpoO5UYTEclFAZuIiIiUVSqVYvv+kfSy+7c/epCJFHQ01XFaTxub17RzymrlRhMRKYQCNhEREZm10fEUtz0ymF7VcVe/5UZb39XEi044jM1r2jnmsGblRhMRKZICNhERESnJ3oNj6QVDbtw5yNDYBI11NWxY2cp5xy9j05p2lrcpN5qIyGwoYBMREZGCpFIp7tsznB7qePfuIQAOa6nnqes72bymnQ2rWmlSbjQRkbJRwCYiIiJ5DaVzo/WzdfsAj4XcaMd2N3PByd1sXtPO+i7lRhMRmSsK2ERERGSSh/tH0smrb3l4kNGJFK0NtWxc3camNe2c2tNGV7OqECIi80GlrYiIyCI3PpHC9x1ky/Z+btg+wP37LDdaT0cD5xxrudGOV240EZGKKDhgSyQST04mk7+d4TENwKXJZPJ9s94yERERmTP7h8a4Ztt+tm7v5487+jkwMkFdDTxuRSuvPmoFm9a0s6azsdKbKSKy6BXTw3ZVIpH4JPAPyWRyLPvORCKxCfgP4ARgUQds376lj607H6SnvY71XU30djWxfmkzS5vrNMZfREqWSqV47OAY9+8dZtveYe4PPwOj9zE+MVHpzZOFJAV7h8YYT0FnUx2b17azqcdyo7UpN5qISFUpJmC7E3g3cE4ikbgwmUzeBpBIJBqBDwFvB+qAr5R9KxeY3q4m7t03xk27+rnqz/vT/+9oigVw4fbwriaatZqWiGQZGpvggVhgFt0eGB5PP2ZZSz29XU2csLqN4eHhCm6tLERrDuvkcUtrOXqZcqOJiFSzYgK2U7HA7J3A1kQicQnwe+DfgeOAO4DXJ5PJa8u+lQvME9d18LyNR9DX18f+obEpFa7/u2cvw+MpAGqAVR0NsUCumd6uJla2N+gCKrIIjE+keLh/lG17hyaVE7sOjJIKj2mqq+HwriaeuLY99Ng30dvVTGeT9YR0d3fT19dXuQ8hC5KOGxGRhaHggC2ZTI4C70kkEldgvWifCHcdBC4BPhkeIzGdzfVsWFXPhlVt6f9NpEIFbU8UyFlF7Q8P9k+poMV749Z3NdGpVblEFqxcDTj37x1mJNaAs7qjgfVdzTxt/RJ6l9p5v7K9gVoNpxYREVmUSqn9DwK7sbpFZBTQBIoC1dbUsLqjkdUdjZx+eEf6/0NjEzy4bzgWyA1z3UP9/OLefenHLA1DoOJB3LoljTTUaVilSLUYGZ/goX0jk4OzPUPsGcoMZ+wMQ6SffUxXZoj0kiYlHBYREZFJilklshX4IPCW8Lx/A74bbj8GXJBIJF6fTCavm4sNXQya62s55rAWjjmsJf2/VCrFnqFxq/TtyQyZ+pEfZGzCWuVra2BNZ2NWINfM8rZ6LXIiModSqRSPDIxO6jHbtmeYHQdGCKcnDbU1rFvSyMaetknDnru0CJGIiIgUoJgettuBdcCfgb9OJpNXAyQSiVOADwNvA36bSCS+mEwm31T2LV2k1pysbgAAIABJREFUampqWNZSz7KWejauzgyrHJ9Isf3ACPfHeuPu6hvi2vsPpB/T2lBLb9awyt6uJq0AJlKC/pHxyYuA7BnmgX3DDI5mBhesaGtg/dImTl/XEeaZNdHT0aj5qCIiIlKyYgK2dcBngfckk8mD0T+TyeQw8HeJROJ/sGX93wAoYJtjdbU1HL7EhlCdGfv/4Oh4ujIZzY/5zbb9/DRWqVzeWp9etCAK5no6G6lXpVKEsYkU2/ePTOrVvn/vMI8OZrKZtIXGkKeu70wHZr1dTbQ2qDFEREREyquYgO2p060AmUwmr08kEhuBS2e9VVKy1oY6jl/eyvHLW9P/S6VS9A2OTQrktu0d4o87BghrHVAfhm31LmlKL3TQ29XEshYNq5RD06ScZrGe6of2DzMW2jfqamBtZxPHL2/l7Nh50d2q80JERETmR00qlZr5UTkkEonVwLJkMnlbIpGoz5VMe56lduzYUeFNyFgIyyWPjk/wULonIVNhfexg5qvsaKyld2nzlGGVyh03NxbCcbMQHRyd4IF9kxcAuX/vMAdGMj3Ph0UL+izNDCNe09lEQ131B2Y6bqQUOm6kFDpupBTVdtz09PTA5AUUq1pRq0QmEokW4APAXwHdQCq8xjsSicSzgTcmk0lf9q2UOdFQV8sRS5s5YmkzHJH5//7hce7fmxkKtm3PML+8dy9DY5mlx1e2N0yq2PZ2NbNKueOkwsYnUuyK5zQLDRG7+jMZR5rra+jtauL0wzvSC4D0djXR0aThjCIiIlJ9ilklsh24Ckug/SBwH3BkuLsVeBrwm0Qi8fhkMrmtrFsp86qzqY6TVrZx0sqpueOyV8O7/qH+9Gp4jXU2r25yINfEEuWOkzmwL57TLARmD+zL5DSrrYHVHY0cuayZZxy5JH1MrlBOMxEREVlAiqlJX4IFa28B/hXraXs/QDKZ/EAikbgHW3Tk/cBfl3k7pcLiueOeuC6TO254LGuo2d5htmTnjmuuC0PNMkMr1y5ppFG546QAI+MTPLhvZEpqi72xnGZLmuroXdrE2cd0pZfOX7ekUTnNREREZMErJmBLAD9NJpOfA0gkEpMmvyWTya8lEokXA08v4/ZJlWvKkTsOYO/BWO9HGJ72Y7+H0VjuuJ6Oxilzhla0NWgxh0VqIpXi0YHRMMcs0wAQz2nWWGeL45za0z4peXxXi3pxRURE5NBUTC2nB/jWDI/xwLOL2YBEIvFp4KXAY7HXeAXwaeDssI2fSiaTXyjmdaWyulrqOaWlnlOycsftPDASC+SGueexIX77QCZ3XEt9LHdcbLn0duWOO6T0D49PHl67d5gH9g5zcCyzCMiq9gZ6u5p40uEd6eBstXKaiYiIyCJTTMD2KHD8DI85MTyuGE8CXp5MJn8X/SORSPwNcGx4vQ7g94lE4o/JZPL6Il9bqkhdbQ1rlzSxdkkTZ/Rm/j84Os4De0cm9cZd+8B+fnZPpvLe3Vo/aV7c+qXNrFHuuKo3Op5i+/7hKcHZ7lhOs/ZGC9KfcWQnvV3NrF/axLoljcppJiIiIkJxAdsPgdclEolzksnkldl3huGQ5wBfKvQFE4lEE7ARS7x9FHAX8DbghcCXQqqAPYlE4lvAhYACtkNQa0Mdxy1v4bjlmWGVqVSK3QfHJqUbuH/vMH/aGc8dZzmyJgdyyh1XCdH3FR/KuG3vMNtjOc3qa2FNZxMnrmjNfGdLmzhM35eIiIhIXsUEbB8EzgV+mEgkfgIcBpBIJC4FNmHB2iPAh4t4zR7gV9iCJrcB7wR+gK06+WDscQ8BG4p4XVngampq6G5toLu1gU1r2tP/j3psoqDg/r3D3PrIIFdv259+THtj7aSeuN6uJg5f0kRLgxagKIcop5kF00Pp76I/ntOstZ71XU2c1tOW/i4WSk4zERERkWpSVOLsRCKxDvgCNrcsu+Z1DfC6ZDJ5V6kbk0gkaoB9QAtwRjKZvC78/7XAWclk8qXTPD01MjJS6luXXX19PWNjlc4lvnjsHxrjvt0D3NM3wH19g3a7e5CDo5mVBHuWNHN0dytHHtbG0d1tHNndytolLVU1J6qajpvxiRQP7TvIvX2D3Ns3YD+7B9mxbyj9mJaGOo7qbuWow9rstruNIw9ro1OpHOZVNR03snDouJFS6LiRUlTbcdPY2AiHauLsZDL5IPDcRCKxClvivwvoB24uJfdaIpHYAJycTCa/Fvt3DXA11vsW6cF62aZVTRnUqy2j+2KwtgnWrmnkaWsagS4mUikeycodd++j/Vx732NZqw5OHlLZ29VEV4UCjkodN3uHpg4/fTArp1lPRyNHdDXx9N52epfaPlvelp3TbJSR/r309c/7R1jUVN5IKXTcSCl03Egpqu246enpmflBVaSkWmkymdwF/KQM7z8BfCaRSFybTCb/DLwRuBkbFvnqRCLxQ6AdeDnwhjK8nywitTU1rOpoZFVHI0/Iyh1neb0yw/lu2NHPL+/L5I7rinLHdUUrVR4aeb2izx4t7hIFaPtiOc26mutY39XEOcd0pYeUru1c+J9dREREZCHKG7AlEomLSn3RZDL5XwU+7tZEInExNi+uDutFOx/YARwF3AQ0Al9MJpNXl7o9InFN9bUcfVgzRx/WPOn/e4fGQnLmTCBz5d17p/QyZS9yMrWXqfKyexejwHRnVk6zw5c0samnfVIKhUr1LoqIiIjIVNPVzL4KxCe41eT4O5I9Ea6ggA0gmUx+Hfh6jrveWuhriJRDV3M9XavqOXlVVu64/pFJgdy9WbnjmkPuuHgi596uJtqb5mdZ+gMhp1k8LcL9e0cYypHT7IzejrCNzaxqb6iq+XsiIiIiMtV0Advbsv6uw1ZxXAL8J/A7LNl1O7AZ+GugD3hv+TdTpDLqamtY29nE2s4mnnx45v+Do+NhWOUw2/ZYkPTbrNxx0UqJvbFAbjYrJeZaIXPbnmF2H8xM4u1orKV3aTPPPGpJ+r21QqaIiIjIwpU3YEsmk/8S/zuRSLwX6ATOTCaTf8x+eCKRuBz4A7bE//+Ue0NFqklrQx2uuwXXPTl33GM5csfdtGtgSi6y7GGV8VxkqVSKvsGxSa9x/55hHto/PCkH3bolTZy0sjW9AEhvl3LQiYiIiBxqipms8gbguzmCNQCSyeSdiUTiO8ArgXeXY+NEFpKamhoOa23gsNYGTovljhubSLF9/+TeuNsfGeSaWO64tpA7rr5+B/f09TMQy2m2vLWe3q4mNq9tTwd6PZ2N1Gs4o4iIiMghr5iAbRkwUMDjWkvcFpFDUn1tTXpY5FPWd6b/3z88zv37hifNj6tNpTiztzMdmB3e1UR74/zMhRMRERGR6lNMwHYL8MJEIvHhZDK5M/vORCJxDPASYEu5Nk7kUNbeVMfjVrTyuBWZNo5qy1MiIiIiIpVVTMD2MeAK4PeJROJfgBuAA9giJE8G3oL1rl1a5m0UERERERFZlApeOi6ZTP4AeC0WoH0a+DWwFfgl8OHwsJcmk8lry72RIiIiIiIii1FRGXKTyeTliUTif4DnAhuApcAerLftJ8lkspA5biIiIiIiIlKAogI2gGQyuQ/47/AjIiIiIiIic6TogC2RSDwNOBZoAnKuK55MJj8zu80SERERERGRggO2RCLRC/wMOCb8K18SqBSggE1ERERERGSWiulh+zjWs/Zz4EpgHxaciYiIiIiIyBwoJmA7C7g6mUyePVcbIyIiIiIiIhnFBGwNwHVztSEiIiIiImJSqRRDQ0NMTExQU5NvJtLC8PDDDzM8PDyv75lKpaitraW5uXnB779iArYbgNPmakNERERERMQMDQ3R0NBAfX3RawRWnfr6eurq6ub9fcfGxhgaGqKlpWXe37ucCk6czf9v787j5Krq/P+/aum9Q7YOS4iJQZYJErYZBUWRRZQRB5cZj4774IwgMgwaEXABARW+XzYFRsQZNTLKOMdxAWd+8nMBozg4qEOAEVCWYAwBkg4JWbrT3VV1v39Udad6X1LdVd15PR+PPLrq3lu3ThWXqnrfz7nnwEXAK0MIHw4hTP8jR5IkSapRhUJhRoS1aspmsxQKhWo3Y7eN5yj4O+B3wFXAZSGEPwBD1TaTGKOVOEmSJGmCpns3vloxE97H8QS295bdbgaWDbOdI0dKkiRJ09zq1au59NJLWbJkSd+yOXPm8O53v5tt27ZxxBFH8La3vY1bbrmF+vr6vm3uvfdenn32Wf7iL/5iXM938cUXc9lll1Ws/TPFmANbjHE83SclSZIkTXNHHXUUF198cb9lK1euZN68eRxxxBFDPualL33phJ7LsDY0O8ZKkiRJNazwX3eS/OLHFd1n6rhXk375SeN+3MaNG7njjjuoq6vjoIMOAuC6667j6aefBuDyyy/nF7/4BWvXruX9738/MUbuuusu0uk0hx9+OGeeeSYrV65k7dq1bNmyhW3btnHuueeyfPly3vzmN/Od73yH1atXc8sttwDFwVcuuugiXvCCF1TuxU8zwwa2EMLpwCMxxt+X3R+TGOPtFWibJEmSpCq67777OO+88/ruH3vssZx66qnMmzePZcuKV0i97nWvY/ny5Vx55ZX8+te/7tv2iSee4K677uKmm24iSRIuueQS7rnnHgAaGhq49tprWbNmDZ/+9Kf58pe/3Pe4J598ko997GO0tbXx9a9/nVWrVvHOd75zil5x7RmpwvY94FPAZWX3R7s+LVXaZurH7ZQkSZJmoPTLT4IJVMMqYbgukeUOPvhgAObNm9dvvrW1a9dy6KGHks1myeVyLF++nCeffBKAo48+GoClS5eyefPmfvtra2vjhhtuoKmpifb2dg477LAKv6rpZaTAdimwquz+ZTigiCRJkrRHS6VS/YbLH24kxsWLFxNjJJfLkSQJDzzwAK95zWt4/PHH+f3vf88pp5zCmjVraGtr6/e4q6++mltvvZXm5mauuOIKkmTPjiDDBrYY46UD7n9q0lsjSZIkqWYM7BIJ8Na3vpWbb7653+iRQznggAM44YQTOPvssykUCixfvpxXvOIVPP744zz66KN8+MMfZufOnaxYsaLf40455RTOPvtsWltbmTdvHps2bar465pOUjMosSbr16+vdhv6tLW10d7eXu1maJrxuNFEeNxoIjxuNBEeN1Ono6OD5ubmajejInq7RPbqHWXy9NPHPETGhA31Pi5cuBCKl3JNCw7VL0mSJEk1ymH9JUmSJE2Z9773vdVuwrRihU2SJEmSapSBTZIkSZJqlIFNkiRJkmrUuK9hCyEsBQ4CGhhmdJUY4+272S5JkiRJ2uONObCFEOYD/wqcPMJmKYqTa2d2s12SJEmSqmj9+vXcfPPNbNy4kcbGRurr6znzzDNZunRpRZ/njjvuYNasWRx33HEV3e9MMZ4K2xXAq4HfAj8GnqcYziRJkiTNIDt37uTjH/84H/nIR3jxi18MwMMPP8znP/95Pve5z1X0uU499dSK7m+mGU9gewNwH/DSGGN+ktojSZIkqcydTzzPTx7fUtF9nvyiOZx0wOxh1//Xf/0XRx11VF9YA1i2bBnXXXcdGzZs4JprrqG7u5v6+npWrFjB3nvvTYyRO++8k0wmw+GHH86ZZ57JypUreeihh+jo6OD8889n1apV3H333cyePZuuri7OOOMMVq9ezbx58zjttNO49tpr2bBhA1u3buWYY47hjDPOqOjrno7GE9hmAT80rEmSJEkz2zPPPMP+++/fd//jH/84O3bs4LnnnqOtrY23vvWtHHPMMfzmN7/hS1/6Em9/+9u56667uPHGG8lkMlxyySXcc889ACxZsoQPfvCDPPbYY9x7773cdNNN5HK5QWFs48aNHHrooZx//vl0d3fzlre8xcDG+ALbg8CfTFZDJEmSJA120gGzR6yGTYYFCxbw+9//vu/+Zz7zGQDOPvtsHnroIb7xjW/wr//6ryRJQl1dHWvXruXQQw8lmy3Gi+XLl/Pkk08CsHjxYgDWrl3Ln/zJn5DJZMhkMhxyyCH9nnPWrFk88sgj3HfffbS0tNDT0zMFr7T2jWdY/08Drw8hvHmyGiNJkiSp+l7xilfwm9/8hoceeqhv2VNPPcXGjRtZtmwZ73//+/nc5z7HihUrOP7441m8eDEPP/ww+XyeJEl44IEHWLRoEQCpVHFg+Re+8IU88sgjFAoFuru7eeyxx/o95x133EFrayuf+MQnCCHQ1dVFkjhkxngqbEcB9wPfCiE8Afwe6BpiuyTG+JeVaJwkSZKkqdfU1MRnP/tZvvSlL7Fp0yby+TyZTIYVK1awePFirrvuOrq7u+nu7uacc87hgAMO4IQTTuCcc84hSRKWL1/OK17xCh5//PG+fR5wwAEce+yxfPCDH2T27Nl9lbZeRx99NJdffjkPPvggjY2N7L///rS3t7NgwYJqvAU1IzXW1BpCKIxxn0mMsRrD+ifr16+vwtMOra2tjfb29mo3Q9OMx40mwuNGE+Fxo4nwuJk6HR0dNDc3V7sZFZHNZsnlcmzevJlVq1bxxje+ke7ubv7mb/6Ga6+9ln322WfSnnuo93HhwoUwzHzStWg8FbbKTrggSZIkaY8xe/Zsfve733HWWWeRSqU47bTTJjWszRRjDmwxxj9MZkMkSZIkzVzpdJoLLrig2s2YdsZTYQMghPBC4N3AEUAzsIniZNrfjDGuqWjrJEmSJGkPNp5RIgkhnAX8DvgU8CbgtcDbgc8AD4cQzqx0AyVJkiRpTzXmwBZCeDXwj0A7cA5wJLA3cAjwfmAD8I8hhOMnoZ2SJEmStMcZT5fIjwLPA8fFGJ8sW94OPBpC+AnwG+B84GcVa6EkSZIk7aHG0yXypcBtA8Jan9L1a7cBx1agXZIkSZKqaM2aNVx44YV86EMf4qyzzuKrX/3qhCeyXr16NZdddtmEHnv22WfzzDPPcMcdd/CLX/xiQvuYzsZTYasHto+yzXaKA5FIkiRJmqa2b9/O5ZdfzmWXXcaiRYvI5/NceumlfP/73+f000+vSptOPfXUqjxvtY0nsD0CnBpCaIoxdg5cGUJoBv6c4qAkkiRJkirgj2u6+eOaroru8wVLG3jB0vph1999990cddRRLFq0CIBMJsNFF11EJpPhqquuYsOGDWzdupVjjjmGM844gyuvvJJsNsuzzz5Ld3c3J510Evfccw/PPvssV155JQDr1q3j/PPPZ+vWrZx++umcdtppPProo1x//fWk02nq6+v5yEc+wj777MM///M/c++997L33nvz/PPPA7By5UrmzZvHaaedxrXXXjuoDTPVeLpE/hPwIuDfQwhLyleEEA6l2B1yKfDlyjVPkiRJ0lTbtGkT++23X79lTU1NPPfccxx66KFcddVV3HDDDdx222196/fdd1+uuuoqlixZwtNPP82VV17J8ccf39eNMZ/P89nPfpbrr7+eb37zm2zZsoWrr76af/iHf+Dzn/88b3jDG/jCF77AmjVreOCBB/jiF7/IRRddRGdn/1rRxo0bh23DTDSeCtsXgROBvwKeCCE8RXEQkoXAHCAFfDvG+I8Vb6UkSZK0h3rB0voRq2GTYZ999uHRRx/tt+zpp59mw4YNPPLII9x33320tLTQ09PTt/6ggw4CoLW1lcWLFwMwa9Ysuru7AVi2bBl1dXUALFmyhGeeeYZNmzZx4IEHAnDEEUfwT//0T6xZs4ZDDjmEdDpNS0sLS5cu7deOWbNmDduGmWjMFbYYYwK8FXgP8FOK16odUlr9U+A9Mca3VLh9kiRJkqbYy172Mu69916eeuopAHK5HF/4whd47LHHaG1t5ROf+AQhBLq6uvoGIkmlUiPu87HHHiOfz9PZ2cnatWtZuHAh8+fP5/HHHwfg/vvvZ9GiRSxevJiHH36YQqFAZ2cnf/jDH/rt54477hi2DTPReCpsvaHtX0r/JEmSJM1ALS0tXHjhhVxzzTUUCgU6Ojp4+ctfztFHH83ll1/Ogw8+SGNjI/vvvz/t7e1j2md9fT0XXHAB27dv5z3veQ977bUXH/nIR7j++utJkoRMJsP555/PwoULOeGEEzjrrLNoa2tjzpw5/fYzXBsWLFgwGW9F1aWGS6MhhL2AnTHG7rL7YxJj3FqZ5o1Lsn79+io87dDa2trGfPBKvTxuNBEeN5oIjxtNhMfN1Ono6KC5eWYMvp7NZsnlclV57qHex4ULF0Lxcq5pYaQK22bgUqB3woQtwFhqjcko+5UkSZIkjcFIwernwJNl93/G2AKbJEmSJKkChg1sMcYTRrovSZIkSZpcYx4lMoRwcQjh+FG2eX0I4Uu73yxJkiRJ0ngmzv4U8KpRtnkd8K4Jt0aSJEmS1GfYLpEhhA8C7xuw+AMhhDcN85B64E+ANRVqmyRJkiTt0UYadORfgIuB3gkNEmDf0r+h9ABrgXMr1jpJkiRJVbF69WouvfRSlixZQpIk5PN5PvrRj3Lttdfy4Q9/mMWLF4+6j2effZYnn3ySY445ZgpaPDONNOjIVmCf3vshhALwqRjjZcM9RpIkSdLMcdRRR3HxxRcD8Ktf/YqbbrppXI+/7777WLdunYFtN4xnvrQT6T/MvyRJkqRJ9vDDD/PQQw9VdJ+HHnooy5YtG9djtm3bxr777suaNWv42te+xubNm+ns7OSTn/wkGzZs4Pbbb+8Ld29+85v51re+xa233kpXVxfLli2jpaWFW265BYCdO3dy0UUXUVdXx+WXX86CBQtYv349y5Yt40Mf+lBFX+t0N+bAFmNcBRBC2As4DTgCmA20A/8N/DDG2D0ZjZQkSZI09e677z7OO+88enp6eOKJJ7jiiitYs2YNxx57LKeccgorV65k1apVQ4a/TCbD29/+dtatW8dxxx3H9773PT72sY/R1tbG17/+dVatWsWrX/1q1q1bx1VXXUVDQwPveMc7eO6555g3b14VXm1tGk+FjRDCe4BrgTlAqmxVAqwLIfxdjPGHFWyfJEmStEdbtmzZuKthlVLeJXLt2rWcc8457L///hx88MEAzJs3j+eee25M+2pra+OGG26gqamJ9vZ2DjvsMAAWLlxIc3Nz3/66u60BlRvPPGynAV+hGNQ+A5wOvBR4DXAp0AzcFkJ4ySS0U5IkSVIVlVe9UqlUv3X19fVs2rQJgGeeeYatW7f2bVcoFAC4+uqrueCCC7jwwguZP38+SZIMuS/1N54K28eB54BjYoxPDFj34xDCNyl2jfwUxS6TkiRJkqax3i6R6XSazs5Ozj77bO64445B2x1yyCG0trbygQ98gCVLlrDffvsBcMABB/CNb3yDAw88kFNOOYWzzz6b1tZW5s2b1xfwNLJUb7IdTQhhG/D1GOMHRtjmn4G/jDHOrVD7xiNZv359FZ52aG1tbbS3t1e7GZpmPG40ER43mgiPG02Ex83U6ejo6OsmON1ls1lyuVxVnnuo93HhwoXQ//KumjbmLpHA80BmlG0SYOfEmyNJkiRJ6jWewPYl4B3DXaMWQjgEeEtpO0mSJEnSbhrPNWy/BB4GfhFC+FfgZ8BTQBPwEuD9QDewOYRwbvkDY4zXV6a5kiRJkrTnGE9gK7+68F2lf70XwJX3Ab2GwUP+G9gkSZIkaZzGE9j+ZtJaIUmSJEkaZMyBLcb4tclsiCRJkiSpv/EMOtInhLAshPDXIYQPlu4vDiG0VrZpkiRJkqpl9erVnHjiidx55539lr/vfe/jyiuvnLTnXblyJbfffjuPPfYYX/uaNaNxBbYQwqEhhF8C/wt8nV3Xpv0NsC6E8NYKt0+SJElSlSxevLhfYHviiSfYuXNqZvE68MADec973jMlz1XLxtwlMoSwlOLIkLOAW4F9gZNKq9dQDH/fCCE8FWO8ezyNCCG8EfiXGOOsEEKG4sAlp5bad3WM8Yvj2Z8kSZI0UzRu/R8at/66ovvcudefsXOvo0fd7kUvehHr1q1j+/bttLa28qMf/YiTTz6ZDRs28KMf/Yhvf/vb1NXVsWjRIlasWMGPf/xjfvnLX9LV1cX69ev567/+a17/+tfz8MMP8/nPf57m5mbmzJlDfX09F154ITFG7rzzTjKZDIcffjhnnnlm33OvXr2a22+/nYsvvpjvfve7/PznPyeXy9HS0sJll11GXV1dRd+TWjWeCtvlQDPwshjju4C+UBZjvAU4FugALhpPA0IIBwFXs2tkyTOBg4HDKE4XcF4I4aXj2ackSZKkynjlK1/Jz3/+c5Ik4ZFHHuGwww5j69atrFy5kmuvvZYbbriB1tZWvv/97wOwY8cOrrjiCj7zmc9w6623AnDddddxwQUXcO2117Jw4UKgWK276667uPHGG7nxxht56qmnuOeeewY9f6FQYOvWrVx99dVcf/315HI5Hnnkkal7A6psPKNEngLEGOP/DLUyxvhQCOFbwOvGusMQQjPFrpUfpli1A3gT8KUYY47inG7fBN4J3DuOtkqSJEkzws69jh5TNWyynHzyyVx33XXst99+LF++HCiGqBe+8IU0NzcDcPjhh/PrX/+aZcuWceCBBwKw9957093dDUB7eztLly7t2/bOO+9k7dq1HHrooWSzxUiyfPlynnzyyUHPn06nyWazXH755TQ1NbFx40by+fxkv+yaMZ4K217AhlG22QLMHsc+by79e6Bs2QuAP5bdXwcsGsc+JUmSJFXIwoUL2blzJ9/5znc45ZRTAEilUvzhD3+gs7MTgPvvv59Fixb1rRto77337gtjDz30EFC8Pu7hhx8mn8+TJAkPPPBA3z7KPf7449x9991ccsklnHvuuSRJQpIkg7abqcZTYXsCeOVwK0MIKeCE0najCiGcDeRijF8JIbywbFWaXRNyQ7Gr5JgidFtb21g2mxLZbLam2qPpweNGE+Fxo4nwuNFEeNxMnWeffbav8lQtmUyGVCpFNpvl5JNP5oc//CFLly5lw4YNzJkzh9e85jWsWLGCVCrF/vtKBMQAAAAgAElEQVTvzwc+8AF+8pOf9FXE8vl8X3hbsWIFV111FU1NTdTV1dHW1sbBBx/MSSedxN///d+TJAnLly/nhBNOYM2aNaTT6b7nX7JkCc3NzZx11ll9j928efOY3p+GhoZpf8ymxppOQwgXAZ8BrgQ+CXwCuDjGmAkhNJSWnwtcEmP89Bj2dy/Fa+JyQD1wCPAg8BxwQ4zxu6XtLgHmxBg/NMouk/Xr14/ptUyFtrY22tvbq90MTTMeN5oIjxtNhMeNJsLjZup0dHT0dTec7rLZLN/61rc48cQTmTNnDl/+8pfJZrNTMgLkUO9j6Rq6wWXAGjWe2H418GrgQuAsoBMghPBT4MXAfOC/gavGsrMYY99AIqUK2//GGI8MIfw9cEYI4ftAK/C20vNJkiRJmobmzZvH+eefT1NTEy0tLVx44YXVbtK0MebAFmPsCSG8FvgQ8D6KIzkCHA+sBf4RuDLG2LWbbboJeBFwP8XK280xxlW7uU9JkiRJVfKqV72KV73qVdVuxrQ05i6RA4UQWigOMLI9xri1oq2aGLtEatrzuNFEeNxoIjxuNBEeN1NnpnWJzOVyVXnuPa1LZG9I+zvgtzHGHwE7SsvvAH4EfL40HL8kSZKkCdqTRkGcTDPhfRzzsP4hhDbgv4BrgJPKljcDrwD+L/DzEMKsSjdSkiRJ2pOk0+mqVaVmilwuRzo9nlnMatN4KmyfApYDHwNu6F0YY+wIIcwFzgP+D3B56bYkaXckCSQ9pAs7SZX+pfO7bqcKO4vrdtTTUpoHRxqrVEcrzV0FCukmknQjSbqBQrqRJNNIkm6kkG6CVB0MMZ+SpMnX2NjIzp076erqGnJes+mkoaGBrq7dHeZifJIkIZ1O09jYOKXPOxnGE9heB9wWY7xy4IoYYw9wVQjhVcBfYWCTJCj07ApV5QGrsJNUfpjlA0MZhRGfIiEF6TqaZ0CXD02xLTlaGfm4SUiTpBtKAa4Y5JJME4XSsoHL+0Jf2TpDnzQxqVSKpqamajejIrz2cfeMJ7DtAzw+yjYPA6dMvDmSVCOS3NhD1VChLL+TFPlRn6b/D98mCplZ5OsWlP0IbixVQIb5gZyqp23BAr8INW5t8+fTvnH9ruN4jMd7uuc5sn3rukiNKfQ1UcjsOob7Hce9tzPDLE83Qrpuit4VSao94wlsayleqzaSY4B1E2+OJFVAku8fnIYLVUP8SO1bl4x+3UAhVV+qKhR/WBYyLSR188t+aDaRZAZXHJK+Lmj1kJr+fes1TaVSpRMBDRQHfZ6ApEAq6e77/2y4/8cGrsv0bOoLfenC6N2kEjKlkxfDBL5M4/BhsLSO1LjGWZOkmjGeT68IfCKEcDXwsRhjd++KEEIWuBg4jjFOnC1JQ0ryxbP2AwLXkKFquKpA0jPq0xRS9WU/8BooZJpI6uYWKwFly3f94Gsa8EOwwbAlpdIkqVIggjHUlIeQFEgVukkVOoevVg/6DOgi27Nx17qke/SnSWX7BbmxVPZ2Vbl7Q19mIq9QknbLeALblcDpwIeB94UQVgNbgVnAkcAc4AHg05VupKRpIin0D1vDdaka5gfZ2H941Q0KVfm6OWU/tvp3M+z/o6vBH15SLUmli6Eps7uhr/ezp3OIz50uUoXOQd08s7mtu5aP4URP/8+e4QPf8Osa/OyRNG5jDmwxxs4QwsuBi4C3AeVTlf8RuAn4bIxxR2WbKGlK9J3lHhiqSj9m8l0D1nWSKnT1r3QlY+jalMqWhapimMrX71VW6er9YdM04AdQg12bJA0tlS52T840UWDuxPZR6krd97mWH6riVwx+vZX9dKGTVG7LrqA41q7UY6jsDd/N0+q+tKcZ86+eEMLLgPtijBcDF4cQGoF5wPYY49bJaqCkMUiS4nUk/boPdfb/8dF3Vnno5WMbPCAz6IdErq6trNLV1G9doXx56QeKYUtSTUplSDItJJmWUcZmHUGSK6vslZ/kGlj121UNTOd3kOrZVAqDY71+tmFcXTn7D1bUSJLy+llpOhnPL6dvA78B/gIgxrgTWD8ZjZL2OElCqtABHd1kO58uBanyroVlZ3rzQ/0IGOtIbf3P3Obr5vWrdPX7Qk83lM3P1Ds8d9bhuSVpOKksSaaVfKZ14vsYNB1IeU+Gzn7Le7dL57eR6tlIuvf7YZSOpQm9A84McXJtpJNu6UboqS/OEel3gTRlxhPY5gC/nayGSDNakiOd20omt4VMzxbSuS1kcltI9xT/ZnJb+q6fmDfUw0kNOktaqJtDLr3vqCOjOReSJE0j6TqSdB15Zk3s8UlS/M4ZeLKv0DVgAJfedaXgl99GuntjX3f3YeeA/CMsSGXJZ2dTyM4hn51Lvm7X7UJ2NvnsbKdikCpoPIHtNuDNIYRrYowbJ6tB0rSTJMVhqnOlIFYWyHpDWTq/bVAFrJBpJZ+dTa5+H7qbDyZfN4eWOQvZuqNnQKWrt/uKYUuSNIpUClJ1FNJ1sFuhr2eI7vRdzGpK6NzyVN8JyPqO35HJbxu0i3xmVim8zaFQN5f8gNtJutnvNWmMxhPYVgEnAE+EEO4GngQ6h9guiTGu2P2mSTUiyZPObRs5kA0YbCMhUzrjOJfu5oOKX1p1c4tfVqUvraHOPra0tdGNEyBLkqoolYJUPYV0PbBXv1Wz2trYkR3wPZXkyOSe7+s10vcd2bOFbPczZDoeGXRtXiFVX6zK9VXn5pTuz+n7rnRETaloPIHtC2W3XzvCdglgYNO0kSp0Dfkl09dtMbd1UNeQQrq5+KVSN5/upheVzhru+sIpZFq8oFuStGdIZcnXzSdfN58hJ0dIElKFHWTKv2vLvmfrup4mnd/e/yGkKGRm9X23Dg50c0gyTVPy8qRqG09gO3HSWiFNlqRAOr+978thqC+KdKF/oTgh3VcF625aOujMXyE7uzissiRJGl0qRZJpJZdpJceiobcp9JS+o58nk9tc+p4u3s52raNh+28HDaZSSDf0fUcPPHFa/DvLKp1mhPHMw7ZqMhsiTUihm0zpAz3d83zpw35zcVnpw37wB3xj3wd6T9OSQR/2hcwsq2OSJE2ldB35+gXk6xcMU6UrOwGbe550z+bSpQnF23U71w5zAnavIatzxROwczwBq2lh3BMihRDmAwE4EpgbYwwhhOOATIzxZ5VuoPZgSUIqv6N4pi33fKkytrl0xq1UJSv0n6c9IdX34dzT+ALy2eWDulMkmcYqvSBJkjQhqWL4KmT3YriZ6lKFrrJeNKVKXW4L6Z7nqdv5Bxq2PzDEJQ5N/apzAwNdIdPqSVxV3bgCWwjh7cDNQDOQgr5h714PfDSEcFOM8ZzKNlEzVpLr96Fa/JDtXyUb8iLl0odqrmH/4odrXXn3h73s/iBJ0h4oSTeQr9+HfP0+w2xQKE5f0LNl0MngTM9m6jrXkC7s7P8QMqWBw4a+jm64QcSkShpzYAshnADcAjwB/B/gWOCM0urbgD8HPhBCuDfGeEuF26nppjQR9FDdFvr6qA87DPAccg0L6Wo5tN9Zrnx2Dkm6yWGAJUnS+KWK16gXsrPJsWToTfI7y0aCLr/cYgv1nY+XBiIbOE1Py5DVud5lSabF3y7aLeOpsH0C2AAcE2PcHELou2o0xvjLEMIrgf8FzqYY7DSTJfnSRNCbh6ySpXNbSCfd/R+SyvZNqtndsm+/s1P5urml6ti4e+lKkiRVRJJpJJ/Zl3zDvsNs0Pv7Z8ugAc2y3RtJdzw6zO+f8t88ZT2D6uYUJxr3949GMJ6j4yXAyhjj5qFWxhi3hRC+C7ynIi1TVRXPMPVWxraUDXv/fPE6shEngl5Aofmgsg+kueTrZpOkPcMkSZKmsVSGQt1cCnVzh16fJKQKnQN+O+2aMmjkicYHV+d6LwOxh9GebTyBLU3xurWRNIxzn6qGgRNBl32o9C5LF4abCHpO2UTQc/oqZvbhliRJe7xUiiTTTC7TDA0Lh95mpInGu54ms+PhESYaH6ZS5zX8M9p4wtWDwGkhhPNjjF0DV4YQZgGnAfdXqnGamP6jJI11Iugm8nVzSxNBH1AMYqUyvaMkSZIkVchYJhrP7+h3Ir3/RONPkc4PP0p28UT6XAp1s0uVurmOkj3NjSewXQf8G/CfIYSPUqymEUJIA0eX1u8PXFjpRqrMgImgd10YW1Z6H2Ei6J6mFxa7KPb+D10314mgJUmSakUqRZJtJZcdy0Tjg0/MjzzR+Fzypd+Ehbq5fSfm83VznYe2ho1n4uxvhRCWAx8HflW2aieQodhd8voY462VbeIeZoT/AUeaCLpYEp9dNhH07L4qmf8DSpIkzSDjmWh8iBP8I080PrA6N9uJxqtsXNebxRgvDiH8B8Xh/I8G5gDbgQcoDkjy04q3cCYZpcSdyW0ZscS9ayLo2WVVMkvckiRJKlM+0Xjj4qE3GeoSmt5A1/kkDcNdQlNWnRtYqfMSmskx7gFCYoz3AvdOQltmjHRuK2z4Hc1b1g2qkjkRtCRJkqptTBONl09hUD5IXc8m6joeJ50MP0hdeXWO1j9j9LELNZxRA1sI4QXAa4H5wFPAD2KMmya7YdNZa/t/kN7+IK2UTwS9H10tywbMu+EwrZIkSapBqXTx0pq6OcNv0jfR+OZSoOudl/d56jsf65toPOl8APZ53xQ2fmYZMbCFEC6jOIhIeYmnM4SwIsZ486S2bBrbuvdbqD/wbbRvKzgRoiRJkmaksU40Pm/BItiyY+htNKphO5mGEN4BfALoAm4FrgK+C9QDXwghnDwlLZyO0nXQuLdhTZIkSXuu0kTjZJuq3ZJpbaRE8bfAFuAlMcbHexeGEP4M+BnwQeAnk9s8SZIkSdpzjTSMy3Igloc1igt+DfwH8NLJbJgkSZIk7elGCmx7ARuGWfd7oK3yzZEkSZIk9RopsGWB3DDreoC6yjdHkiRJktTLme0kSZIkqUY5jKEkSZKkESU93dC5Azo6oLOjeLuzg6Rje+l+2bLOHdCxo2/Z1j99Ofzle6v9Eqat0QLbkSGEdw+1HCCE8C6GmLY8xnhLBdomSZIkaTcluZ5+gaoYpnaQ9C4rC2FJv23KHpPrGf2JmppL/1qKf2fPJbXvIuoOeTHdk/8yZ6zRAtsbSv8G6g1pK4dYngAGNkmSJGk3Jfn8rtBUVrlKOncMCmG7lg0IZz1jiEsNTbsCV3MLzJpNau/9doWvpmZoboWmZlK9y5p7w1kLNDaRSg99tVVTWxs72tsr/M7sOUYKbJdOWSskSZKkGSYp5KGzs1/Q6lfZKqt29Q9hZcu7u0Z/ovqGYmhqbukLXKn5e++qdvUub2ohVV4Bay6FraYmUunM5L8hmpBhA1uM0cAmSZKkPVJSKMDOsmpVqdtgMVSVB7ABIax8eVfn6E9UV98vUNHUDHPnF6tYA5anBgWtZmhsJpV1WIqZzP+6kiRJmlGSQgG6dvbvStjZQVJWzdpV4SoLYeXLd3ZCkoz8RNm6/kGruQVmzxtQxeqtbJWHrbIQlnWmLI3MwCZJkqSakSRJKWwNd31W/+u5+nUvLL89WtjKZPsPktHcAnvvtytYlQWswctKIayufmreFO3RDGySJEmqiCRJoLu7LGhtL3UjHBy0hhs4g50dUCiM/ETp9ODrsNr2GfL6rH7LyrsZ1tWTSg0a7FyqOQY2SZIkAaW5toboNpiUAtZ2EgrPtQ9R2Sp7TD4/8pOk0tDUtGt0weZmmLegFKyaoam1VMEaMEhGeVfC+gbDlvYYBjZJkqQZIOnpGXwdVueOvrDVb9lQ82x17oBcbsTn2JFKQWNz/66Epbm2Bg2c0dRMqm9wjLKuhA1Nhi1pHAxskiRJVZbkcgO6Bg7TlXDQwBll68cy11ZjU79AxazZpPZZOCBotQwIW7uWtS16AZuee27y3xBJfQxskiRJuyHJ54vXXQ3oSph0lAeq8sEzhuhK2D2WiY0b+1+H1TqL1IJ9Bw+cUT78e7+/uz/X1nATI0uaPAY2SZK0x+o3sXF5FWvARMe91awhl3ftHP2J6uv7dw1saiE1b8GAboS9la0hBslobCaVcWJjaU9kYJMkSdNScWLjzjFdszVsV8KdY5zYeMC1WcyZX+oy2D9cpQZOatw315Y/uSRNjJ8ekiRpyhXn2uqEvm6D2/uNRtivu2DHEMO/94atUSc2zg4KT8yeO3io92Gu2SrOteXExpKqx8AmSZLGpTjXVtfga7YGzLG1q3thR1/w2nU9Vycko8y1lckMvg5rwX5lVayyod+HqHbR7MTGkqY/A9skSAoFCtu3kuzYXu2maJopNNZ73GjcPG40Efk0JE+vG8fw7wMqX6NNbJxKl01eXApQ8xeQanph/8mO+8211QzNrWVzbTmxsSQZ2CZB8i//yMa7f1TtZmga2ljtBmha8rjRRLSPtDKVGnzN1tw2UguHuT5r4KTGTc3Q0GjYkqQKMLBNgtTJr6f14EPZvmNHtZuiaaa1pcXjRuPmcaOJaJ0zh+35ZOiuhA2NDt8uSTXCwDYJUouW0nzkS+hoH/H8pTRIc1ubx43GzeNGE+FxI0nTg6fPJEmSJKlGGdgkSZIkqUYZ2CRJkiSpRhnYJEmSJKlGGdgkSZIkqUYZ2CRJkiSpRhnYJEmSJKlGGdgkSZIkqUYZ2CRJkiSpRhnYJEmSJKlGGdgkSZIkqUYZ2CRJkiSpRhnYJEmSJKlGGdgkSZIkqUYZ2CRJkiSpRhnYJEmSJKlGGdgkSZIkqUYZ2CRJkiSpRhnYJEmSJKlGZavdgBDCOcAHgAR4HPg7YBNwDXAqxTZeHWP8YtUaKUmSJElVUNUKWwjhT4GPAC+PMR4GPApcDpwJHAwcBrwEOC+E8NKqNVSSJEmSqqCqgS3G+BvgoBjj8yGERmB/itW1NwFfjTHmYoybgW8C76xiUyVJkiRpylX9GrYYY08I4Y3AOuB44KvAC4A/lm22DlhUheZJkiRJUtVU/Ro2gBjj94DvhRD+Dvj/gRzFa9p6pYD8aPtpa2ubnAZOQDabran2aHrwuNFEeNxoIjxuNBEeN5oIj5vdU9XAFkI4ENg3xnh3adFXgC8CPwMWlm26kGKVbUTt7e0Vb+NEtbW11VR7ND143GgiPG40ER43mgiPG01ErR03CxcuHH2jGlLtLpH7Ad8MIfRG7ncA/wt8BzgjhJANIcwB3gZ8r0ptlCRJkqSqqPagIz8HPgP8NISwmmIweyNwE8Uh/u8HfgV8Oca4qmoNlSRJkqQqqPo1bDHGmygGtIHOm+q2SJIkSVItqXaXSEmSJEnSMAxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUo7LVbsBM1L4hx7onnyNJumlqSdPcnKKxKU06k6p20yRJkiRNIwa2SfDsUz088fvtg5Y3NqVoak4X/7Wk+243t6Rpak6RrUuRShnqJEmSJBUZ2CbBi49q4rgT92fdHzfS2VGgc0eh+LcjoXNHgec353nmqR4Khf6Py2bpF+QGhrqGxhTptIFOkiRJ2lMY2CZJNpumdVaG1lmZIdcnSULXzqQU5PqHuo4dBTZv6qGnO+n3mFSqVKUrD3WlYNdcup2tM9BJkiRJM4WBrUpSqRSNTcVr2+bOH3qbXC4ZEOZ23X6uPc/Ojh6S/pmOuvoUTc2Du142l243NNrtUpIkSZouDGw1LJtNMWuvDLP2GqZKV0jYOWSVrnj7uY15enoGVOnS0NTUG+RSg6p0jc1pslkDnSRJklQLDGzTWCq9q5o2nJ6epH+QK4W5jo4C7Rvy7OxMYECVrr6hLMg1pwZV6eobrNJJkiRJU8HANsPV1aWom5NhrzlDV+kKhYSdnUN0vewosH1bno3PFsjn+j8mnSmv0vUOirIr5DU2p8k4hYEkSZK02wxse7h0OkVzS4rmljQsGLw+SZKyKt3gYLfh6R66diaDHtfQONQUBqnSFAZp6uqt0km7I0kS8nnI9SRs29pDx/Z8tZukaWZbvceNxs/jRhORm1MYfSMNy8CmEaVSKerrU9TXp5k9d+ht8vmEnZ2F/qGuo0DHjgJbn8/z7NM9FAZ8tmeyDBrpsrxa19iUdgoDzVhJklDIF7ss9/Qk5Mr/dg+433ebQet2DTq0tZovR9PWtmo3QNOSx43Gb7/9c/zZKxqq3Yxpy8Cm3ZbJpGhpzdDSOvwUBt1dAwdHSfpC3fObe+juGlyl65tofMA1dL3Brq7eQKfqyOfLglN3MkLwGiaU9SQkYzjZmK0rdWuuS5EtjQCbrUsX7/cur0sxe84stm/bPvkvXDNK66xWjxuNm8eNJuKFB8wnnxj2J8rApkmXSqVoaEzR0Jhmzryht8nnEjo7B450WQx1W57L88y6ISYarxtcpWsuu+1E4xpKIT90gNpVvWJAZWtw1WvgsTiUbB39QlVDY4rWWcW5Euvq+weu8r+967JZxtxtuK1tL9rbu3fzndGexuNGE+Fxo4mYO7+B9nYD20QZ2FQTMtnUhCYa7ygFOyca3zMUCoOrWH3dBXvGUPXqSQZ1zx1KJku/EFXfkKJl1uDKVt2Q4as4JUfKkwWSJKkCDGyaFsY00XjPUFW6YqgbeaLxXXPSNbf0D3ZONF45SSGhJ1cephj12q3+oaw4yMZoMhn6ham6+uJgN/1C1RAhq/d2ts7KrCRJqh1VD2whhHcC51OcDawDOBe4D7gGOJViG6+OMX6xao3UtJCtSzGrbmITjXfsKLBpY45cT//HDDXRePOAil1mD5hoPEkGdxccGKZGClo9Pcmg6SGGks4wqIrV2JQeJmjRb3mdYUuSJM1AVQ1sIYRDgKuAo2OMT4cQXgd8B7gSOBg4DJgF3BNC+J8Y473Va62muzFNNN6dDJpkvLfrZfuzeXbuHGGi8SGmL2hqrv5E40mSkMsxqIo10uiEA9cNDLJDSacZVMVqKIWtft0FBwSs8r9p5++TJEnqp9oVti7gb2OMT5fu/xrYF3gL8IUYYw7YHEL4JvBOwMCmSVXsQjeGicYHVOc6Owps35pn4zM9Q0803jxwpMtdIa+xafiJxpOkWJkaPMT7CF0IB13DNfrrTqX7V7bq6lI0tKZ3VbKGGCSjd+TC3mVOli5JklR5VQ1sMcYngScBQggp4FrgdoqVtT+WbboOOHyKmycN0m+i8SEkSVJWpUsGDI5S4NlRJhpvbNxJR0dPWWUrGXTd3UCp1ODKVlNrir2GGoVwmOCVzox9REJJkiRNnWpX2AAIIbQAK4EXULxu7V76dzxLAaMON9DW1jYZzZuQbDZbU+1R7cjnE3Zsz7FjWw/bt+fYsS3H9m3F+4UEZs9poL4+Q31Dmrr6NPX16QG3M/2WZbMOjLKn8/NGE+Fxo4nwuNFEeNzsnqoHthDCYuD7wMPAiTHGzhDCWmBh2WYLKVbZRtTe3j45jZyAtra2mmqPak9dI8xthLltAGmgYYjjJqF4rqL/+YpcAXKdQOdUtVa1zM8bTYTHjSbC40YTUWvHzcKFC0ffqIZUe9CRWcBPga/FGC8tW3UbcEYI4ftAK/A24Kypb6EkSZIkVU+1K2znAEuAN4UQ3lS2/LXAi4D7gXrg5hjjqiq0T5IkSZKqptqDjlwBXDHM6vOmsi2SJEmSVGuGn5BKkiRJklRVBjZJkiRJqlEGNkmSJEmqUQY2SZIkSapRBjZJkiRJqlEGNkmSJEmqUQY2SZIkSapRBjZJkiRJqlEGNkmSJEmqUQY2SZIkSapRBjZJkiRJqlEGNkmSJEmqUQY2SZIkSapRBjZJkiRJqlEGNkmSJEmqUakkSardhkqZMS9EkiRJ0qRKVbsBY5WtdgMqaNq86ZIkSZI0FnaJlCRJkqQaZWCTJEmSpBplYJMkSZKkGmVgkyRJkqQaZWCTJEmSpBo1k0aJnFQhhHcC51OcPqADOBe4D7gGOJXie3l1jPGLAx53BvCmGONflC1bAZwB5ICNwJkxxsen4nVoalXquAkhpIDLgL8sbfIr4AMxxo6peB2aWpX8vClbdx7wtzHGwya5+aqSCn9PfRs4AtheWnRXjPFDk/4iNOUqfNwcD/xfoAl4HnhvjPGJqXgdmloV/H1zIfC2sk0WALNijHtN+ouYRqywjUEI4RDgKuDUGOORwKeB7wBnAgcDhwEvAc4LIby09Jh5IYQvAp+nbMqBEMKrgfcBL4sxHlHaz1en8OVoilTyuAHeBLwWOBJ4MdAM/MMUvRRNoQofN737PA746NS8AlXDJBw3LwOOjzEeWfpnWJuBKvz7ZhHwXeDs0u+bbwNfmMKXoylSyeMmxnhl7+cMcAKwA3jrFL6cacHANjZdFM9MP126/2tgX+AtwFdjjLkY42bgm8A7S9sEYD3wkQH7eoZiZWRr2b6WTGbjVTUVO25ijJ0f2VEAAAe5SURBVN8BjosxdgOzgL2BTZP/ElQFlfy8IYSwD3AjxTOhmrkqdtyEEJZS/Jz5pxDCgyGEr4YQ5k3Fi9CUq+TnzV8BP4gx/k/p/s3AeZPZeFVNRb+nylxN8Rj6weQ0e/qyS+QYxBifBJ6Evq5p1wK3UzyD8MeyTdcBh5ce88XS9u8dsK//7b0dQmgArgS+NVltV/VU8rgpresJIZxD8UzWUxTPZGqGqeRxE0LIALdSrK71TGrDVVUV/rzZG/gxxS5O64HPAV8B3jhJzVeVVPi4ORjYEUL4JnAIsBawMjsDVfr3TWn5oRQ/Y140Oa2e3qywjUMIoQWIwIHA31J8/5KyTVJAfoz7WgD8kOL1AR+rbEtVSyp53MQYbwTmUgxr/17ZlqqWVOi4uQL4WYzxR5PSSNWcShw3Mcb/jjG+Kcb4xxhjHvgUcFoIoX5yWq1qq9DnTR3wBuCTMcajgJ9Q7CanGaqSv28oVmNvjDE+X9FGzhAGtjEKISwG/ovigXdijHELxbNHC8s2W0jxbMJo+zqc4qAR/0PxwsvuyrdYtaBSx00I4YgQwlEAMcYE+Gfg6ElptKqugp837wLeHEJYTfGYeVHptmagCn7evDKEcHrZohRQYOw/vDSNVPDzZj3wixjjo6X7XwaOCCE0VbjJqgEV/l2coTio2srKt3RmsEvkGIQQZgE/Bb4WY7y0bNVtwBkhhO8DrRRHuTlrlH0tAu4EPhpj/MrktFi1oJLHDcUuBStCCC8vjQz5borHkWaYSh43Mcb9yvZ7AsWzl0dWus2qvgp/3rQCN4QQ7o4xPkfx+sd/L1XbNINU+Lj5LnBmCGFpjHEN8GbgtzHGzsq3XNVU4eMGYDmwudTVUkMwsI3NORQHBnlTCOFNZctfS7Gv7f1APXBzjHHVKPv6JNACnBtCOLe0rCvGeEyF26zqq9hxE2P8lxDCgcCvQwg54LcURxvVzFPJzxvtOSr5efODEML1wC9CCGngQeDvJqfZqrJKHjerQwhnA98NIdQBmykOQqGZp9LfUwdRuiZOQ0slSTL6VpIkSZKkKec1bJIkSZJUowxskiRJklSjDGySJEmSVKMMbJIkSZJUowxskiRJklSjHNZfklQVIYQbKA4PfUeM8c9H2O6NFOd4+j1wpPM6SZL2JFbYJEnVchHwR+DUEMLbhtqgNEHrDUABOMOwJkna0xjYJElVEWPcDpxduntdCGHOEJt9BlgE3BBj/MWUNU6SpBphYJMkVU2M8T+AfwP2Ba4sXxdCeAnwQeBx4GNT3zpJkqovlSRJtdsgSdqDhRD2AR4C5gIvjzH+MoSQAX4FHAmcGGNcVdr2JIpdKV9K8TrsB4BrYoz/PsR+3w2cARwBtACbgDuBT8YYnyjbLgG+RvEauY+WFl8aY7wuhPAa4AJgOdBKMTzeWnrO7oq+EZIkDcHAJkmquhDC3wBfAf4beBnFytoNwI0xxr8vbfO3wJeAjcBtwHbgDcABwMdjjJ8t29/VwArgfuAuIAFeBRxN8bq5Q3qvhysFtmeBJmAlxWrf54AM8BOgHfgO0AmcQjFEfiXG+L5JeTMkSSpjYJMk1YQQwk+Ak4BzgUuBLcDyGOOOEMIi4DHgCeCVMcZNpcc0AT8GjgWOiDH+bwhhf2AtcDdwUowxX/Yc/wm8DnhtjPGHpWW9X4Snxxi/X7btvwN/CRwQY1xTWlYH3Eux4jY/xvj85LwbkiQVeQ2bJKlWnEmxinU9MAf42xjjjtK6dwINwMW9YQ2gVCW7hOL32XtKi3cC7wL+oTyslawq/d17wPJO4P8bsKz3O/IVZc/XA/w5hjVJ0hRxHjZJUk2IMT4WQrgKuBj4eozxzrLVf1r6e3II4bABD20t/T2ytJ9NwK0hhHRp22UUu00eAby6tG1mwD7+OES4+yfgjcAtIYRPAj8o/bvT69ckSVPFCpskqZasKf19YsDy3iH/z6JYUSv/t6K0bl7vxiGENwOPAA8CEfgEsJDiNW0AqQH7HzS/W4zxB8CJwH8Ciyl21fwB8HQI4e/H+bokSZoQK2ySpOlge+nvi8pHeBxKCOEY4FvAOuCvKY42+USMMQkhXMCuKtuoSqNTrgohtACvBF5Psevl9SGEx0qhTpKkSWNgkyRNBw9Q7J74ZwyovoUQDqJ4/duq0qAhb6PYg+TsGON/DtjPstLfgRW2QUII/wC0xRg/WbqW7g7gjhDCfwO3UAxwBjZJ0qSyS6QkaTr4OpAHPhNC2Ld3YQghS3H4/xXA/NLinaW/+5TvIIRwMvD20t26MTzna4GPhxCOHbD8haW/fxhr4yVJmigrbJKkmhdjfDSE8FHgGuC3IYTbgM0UR2xcBvwHxVAH8G8UA9wXQgivAp4GDqcYwNopjhA5n9FdQvEatrtCCN8CngIOBf4CeLjs+SRJmjRW2CRJ00KM8VrgNGA1xfnRzgR6KIazv4ox5krbraY419pvKHajfD/FybAvpjhSZKG0frTn+xVwPPBDivPDfZhi8Ps8xbngdozwcEmSKsKJsyVJkiSpRllhkyRJkqQaZWCTJEmSpBplYJMkSZKkGmVgkyRJkqQaZWCTJEmSpBplYJMkSZKkGmVgkyRJkqQaZWCTJEmSpBplYJMkSZKkGmVgkyRJkqQa9f8AgL/ZGnQft08AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "corruption_top10a.plot(x='year', y='Ethiopia', kind='line', figsize=(14,8), ax=ax)\n",
    "corruption_top10a.plot(x='year', y='Georgia', kind='line', figsize=(14,8), ax=ax)\n",
    "corruption_top10a.plot(x='year', y='Cambodia', kind='line', figsize=(14,8), ax=ax)\n",
    "corruption_top10a.plot(x='year', y='Bhutan', kind='line', figsize=(14,8), ax=ax)\n",
    "corruption_top10a.plot(x='year', y='Mongolia', kind='line', figsize=(14,8), ax=ax)\n",
    "\n",
    "plt.title(\"Corruption Perception Trend, Top 6-10 fastest growing countries from 2012-2017\", fontsize=24)\n",
    "plt.ylabel(\"Perception index\", fontsize=20)\n",
    "plt.xlabel(\"Years\", fontsize=20)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis:\n",
    "There is little to no change in the corruption perception trend for the top 6-10 fastest growing countries. Focusing on the top 10 countries is short sited in making any conclusion. It would have been wiser to look at all countries and the changes in their growth to determine any correlations about their perception of corruption."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gini Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1806</th>\n",
       "      <th>1807</th>\n",
       "      <th>1808</th>\n",
       "      <th>...</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2033</th>\n",
       "      <th>2034</th>\n",
       "      <th>2035</th>\n",
       "      <th>2036</th>\n",
       "      <th>2037</th>\n",
       "      <th>2038</th>\n",
       "      <th>2039</th>\n",
       "      <th>2040</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>...</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>...</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.2</td>\n",
       "      <td>...</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "      <td>42.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>...</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>42.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>...</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.7</td>\n",
       "      <td>...</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.4</td>\n",
       "      <td>...</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  1800  1801  1802  1803  1804  1805  1806  1807  1808  \\\n",
       "0          Afghanistan  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5   \n",
       "1              Albania  38.9  38.9  38.9  38.9  38.9  38.9  38.9  38.9  38.9   \n",
       "2              Algeria  56.2  56.2  56.2  56.2  56.2  56.2  56.2  56.2  56.2   \n",
       "3              Andorra  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0   \n",
       "4               Angola  57.2  57.2  57.2  57.2  57.2  57.2  57.2  57.2  57.2   \n",
       "5  Antigua and Barbuda  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0   \n",
       "6            Argentina  47.7  47.7  47.7  47.7  47.7  47.7  47.7  47.7  47.7   \n",
       "7              Armenia  31.5  31.5  31.5  31.5  31.5  31.5  31.5  31.5  31.5   \n",
       "8            Australia  38.7  38.7  38.7  38.7  38.7  38.7  38.7  38.7  38.7   \n",
       "9              Austria  33.4  33.4  33.4  33.4  33.4  33.4  33.4  33.4  33.4   \n",
       "\n",
       "   ...  2031  2032  2033  2034  2035  2036  2037  2038  2039  2040  \n",
       "0  ...  36.8  36.8  36.8  36.8  36.8  36.8  36.8  36.8  36.8  36.8  \n",
       "1  ...  29.0  29.0  29.0  29.0  29.0  29.0  29.0  29.0  29.0  29.0  \n",
       "2  ...  27.6  27.6  27.6  27.6  27.6  27.6  27.6  27.6  27.6  27.6  \n",
       "3  ...  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  \n",
       "4  ...  42.6  42.6  42.6  42.6  42.6  42.6  42.6  42.6  42.6  42.6  \n",
       "5  ...  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  40.0  \n",
       "6  ...  42.4  42.4  42.4  42.4  42.4  42.4  42.4  42.4  42.4  42.4  \n",
       "7  ...  32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  \n",
       "8  ...  32.3  32.3  32.3  32.3  32.3  32.3  32.3  32.3  32.3  32.3  \n",
       "9  ...  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  30.5  \n",
       "\n",
       "[10 rows x 242 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read CSV data for GINI\n",
    "gini_df = pd.read_csv(\"data_political/gini.csv\")\n",
    "gini_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the results from the \"investigate_GDP per capita\" notebook, create a list of the top 10 fastest countries\n",
    "top10 = ['Myanmar', 'China','Azerbaijan','Turkmenistan','Armenia','Ethiopia','Georgia','Cambodia','Bhutan','Mongolia']\n",
    "top10index = list(range(0,10,1))\n",
    "top10index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  rank\n",
       "0       Myanmar     0\n",
       "1         China     1\n",
       "2    Azerbaijan     2\n",
       "3  Turkmenistan     3\n",
       "4       Armenia     4\n",
       "5      Ethiopia     5\n",
       "6       Georgia     6\n",
       "7      Cambodia     7\n",
       "8        Bhutan     8\n",
       "9      Mongolia     9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add in their country name & rank. Here, I could have used the index as their rank but I suspected it would be safer to\n",
    "# designate them in a dedicated column named 'rank'\n",
    "top10_df = pd.DataFrame({'country':top10, 'rank':top10index})\n",
    "top10_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>country</th>\n",
       "      <th>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1806</th>\n",
       "      <th>1807</th>\n",
       "      <th>1808</th>\n",
       "      <th>...</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2033</th>\n",
       "      <th>2034</th>\n",
       "      <th>2035</th>\n",
       "      <th>2036</th>\n",
       "      <th>2037</th>\n",
       "      <th>2038</th>\n",
       "      <th>2039</th>\n",
       "      <th>2040</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>...</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>70.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>...</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>...</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>China</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>...</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>...</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>26.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>...</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>...</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  1800  1801  1802  1803  1804  1805  1806  1807  1808  ...  \\\n",
       "7         Armenia  31.5  31.5  31.5  31.5  31.5  31.5  31.5  31.5  31.5  ...   \n",
       "10     Azerbaijan  70.5  70.5  70.5  70.5  70.5  70.5  70.5  70.5  70.5  ...   \n",
       "19         Bhutan  40.9  40.9  40.9  40.9  40.9  40.9  40.9  40.9  40.9  ...   \n",
       "28       Cambodia  42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  ...   \n",
       "35          China  37.1  37.1  37.1  37.1  37.1  37.1  37.1  37.1  37.1  ...   \n",
       "56       Ethiopia  47.7  47.7  47.7  47.7  47.7  47.7  47.7  47.7  47.7  ...   \n",
       "62        Georgia  26.9  26.9  26.9  26.9  26.9  26.9  26.9  26.9  26.9  ...   \n",
       "113      Mongolia  32.0  32.0  32.0  32.0  32.0  32.0  32.0  32.0  32.0  ...   \n",
       "117       Myanmar  26.2  26.2  26.2  26.2  26.2  26.2  26.2  26.2  26.2  ...   \n",
       "180  Turkmenistan  35.0  35.0  35.0  35.0  35.0  35.0  35.0  35.0  35.0  ...   \n",
       "\n",
       "     2031  2032  2033  2034  2035  2036  2037  2038  2039  2040  \n",
       "7    32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  32.6  \n",
       "10   32.4  32.4  32.4  32.4  32.4  32.4  32.4  32.4  32.4  32.4  \n",
       "19   37.4  37.4  37.4  37.4  37.4  37.4  37.4  37.4  37.4  37.4  \n",
       "28   42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  \n",
       "35   39.1  39.1  39.1  39.1  39.1  39.1  39.1  39.1  39.1  39.1  \n",
       "56   39.1  39.1  39.1  39.1  39.1  39.1  39.1  39.1  39.1  39.1  \n",
       "62   36.5  36.5  36.5  36.5  36.5  36.5  36.5  36.5  36.5  36.5  \n",
       "113  32.4  32.4  32.4  32.4  32.4  32.4  32.4  32.4  32.4  32.4  \n",
       "117  38.1  38.1  38.1  38.1  38.1  38.1  38.1  38.1  38.1  38.1  \n",
       "180  46.4  46.4  46.4  46.4  46.4  46.4  46.4  46.4  46.4  46.4  \n",
       "\n",
       "[10 rows x 242 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is desirable to work with the fastest growing countries. Pull out the items according to the top10 list\n",
    "ginitop10 = gini_df.loc[gini_df.country.isin(top10)]\n",
    "ginitop10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a copy as a stop gap should future alterations derrange the df\n",
    "ginitop10_2 = ginitop10.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>543</td>\n",
       "      <td>529.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2240</td>\n",
       "      <td>2450</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>2890.0</td>\n",
       "      <td>3060.0</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>3490.0</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>3930.0</td>\n",
       "      <td>4090</td>\n",
       "      <td>4210.0</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>4330.0</td>\n",
       "      <td>4410.0</td>\n",
       "      <td>4520.0</td>\n",
       "      <td>4680.0</td>\n",
       "      <td>4870.0</td>\n",
       "      <td>5080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3560</td>\n",
       "      <td>3620</td>\n",
       "      <td>3770.0</td>\n",
       "      <td>3990.0</td>\n",
       "      <td>4110.0</td>\n",
       "      <td>4290.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>4380.0</td>\n",
       "      <td>4410.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>4480</td>\n",
       "      <td>4520.0</td>\n",
       "      <td>4590.0</td>\n",
       "      <td>4620.0</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>4780.0</td>\n",
       "      <td>4830.0</td>\n",
       "      <td>4790.0</td>\n",
       "      <td>4760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40800</td>\n",
       "      <td>41400</td>\n",
       "      <td>42400.0</td>\n",
       "      <td>45500.0</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>48800.0</td>\n",
       "      <td>49700.0</td>\n",
       "      <td>48700.0</td>\n",
       "      <td>43900.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>39700</td>\n",
       "      <td>38200.0</td>\n",
       "      <td>38200.0</td>\n",
       "      <td>39100.0</td>\n",
       "      <td>40800.0</td>\n",
       "      <td>41800.0</td>\n",
       "      <td>42900.0</td>\n",
       "      <td>43900.0</td>\n",
       "      <td>44600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2200</td>\n",
       "      <td>2210</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>2420.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2880.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>3410.0</td>\n",
       "      <td>3650.0</td>\n",
       "      <td>3550.0</td>\n",
       "      <td>3590</td>\n",
       "      <td>3580.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>3530.0</td>\n",
       "      <td>3410.0</td>\n",
       "      <td>3230.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    2000   2001     2002     2003     2004     2005     2006     2007  \\\n",
       "0    330    330    330.0    343.0    333.0    357.0    365.0    406.0   \n",
       "1   2240   2450   2570.0   2730.0   2890.0   3060.0   3260.0   3490.0   \n",
       "2   3560   3620   3770.0   3990.0   4110.0   4290.0   4300.0   4380.0   \n",
       "3  40800  41400  42400.0  45500.0  47000.0  48800.0  49700.0  48700.0   \n",
       "4   2200   2210   2430.0   2420.0   2600.0   2880.0   3100.0   3410.0   \n",
       "\n",
       "      2008     2009   2010     2011     2012     2013     2014     2015  \\\n",
       "0    412.0    488.0    543    529.0    576.0    588.0    584.0    574.0   \n",
       "1   3780.0   3930.0   4090   4210.0   4280.0   4330.0   4410.0   4520.0   \n",
       "2   4410.0   4400.0   4480   4520.0   4590.0   4620.0   4700.0   4780.0   \n",
       "3  43900.0  42000.0  39700  38200.0  38200.0  39100.0  40800.0  41800.0   \n",
       "4   3650.0   3550.0   3590   3580.0   3750.0   3800.0   3840.0   3750.0   \n",
       "\n",
       "      2016     2017     2018  \n",
       "0    571.0    572.0    564.0  \n",
       "1   4680.0   4870.0   5080.0  \n",
       "2   4830.0   4790.0   4760.0  \n",
       "3  42900.0  43900.0  44600.0  \n",
       "4   3530.0   3410.0   3230.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdps_df = pd.read_csv('gdps_only.csv')\n",
    "gdps_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdps2005 = gdps_df['2005']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      36.8\n",
       "1      30.7\n",
       "2      30.8\n",
       "3      40.0\n",
       "4      47.3\n",
       "       ... \n",
       "190    49.3\n",
       "191    36.3\n",
       "192    34.8\n",
       "193    50.7\n",
       "194    43.2\n",
       "Name: 2005, Length: 195, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini2005 = gini_df['2005']\n",
    "gini2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "#sns.distplot(gdps2005, color='skyblue', bins=20, kde=False, rug=True, label=\"GDP/capita '2005\");\n",
    "sns.distplot(gini2005, color='red', bins=20, kde=False, rug=True, label=\"GINI '2005\");\n",
    "plt.legend()\n",
    "\n",
    "plt.xlabel('GINI coefficient', fontsize=20)\n",
    "plt.title('Year 2005 - GINI for all countries', fontsize=24)\n",
    "plt.ylabel('Number of Countries', fontsize=20)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the top 10 fastest growing countries were obtained by calculations made on data from 2000-2018\n",
    "# the gini data will do the same\n",
    "years20 = [str(x) for x in range(2000,2019,1)]\n",
    "years20.append('country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2000',\n",
       " '2001',\n",
       " '2002',\n",
       " '2003',\n",
       " '2004',\n",
       " '2005',\n",
       " '2006',\n",
       " '2007',\n",
       " '2008',\n",
       " '2009',\n",
       " '2010',\n",
       " '2011',\n",
       " '2012',\n",
       " '2013',\n",
       " '2014',\n",
       " '2015',\n",
       " '2016',\n",
       " '2017',\n",
       " '2018',\n",
       " 'country']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter columns according to the years20 list above\n",
    "ginitop10_2 = ginitop10_2.loc[:,years20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36.2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>35.3</td>\n",
       "      <td>34.2</td>\n",
       "      <td>33.5</td>\n",
       "      <td>32.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>29.6</td>\n",
       "      <td>29.6</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>30.2</td>\n",
       "      <td>30.7</td>\n",
       "      <td>31.3</td>\n",
       "      <td>31.9</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.5</td>\n",
       "      <td>32.6</td>\n",
       "      <td>Armenia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.8</td>\n",
       "      <td>35.5</td>\n",
       "      <td>34.9</td>\n",
       "      <td>34.1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>31.9</td>\n",
       "      <td>31.7</td>\n",
       "      <td>31.8</td>\n",
       "      <td>32.1</td>\n",
       "      <td>32.2</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.4</td>\n",
       "      <td>Azerbaijan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.8</td>\n",
       "      <td>40.5</td>\n",
       "      <td>40.1</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>38.4</td>\n",
       "      <td>38.4</td>\n",
       "      <td>38.5</td>\n",
       "      <td>38.6</td>\n",
       "      <td>38.6</td>\n",
       "      <td>38.5</td>\n",
       "      <td>38.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>37.6</td>\n",
       "      <td>37.5</td>\n",
       "      <td>Bhutan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Cambodia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>39.3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.4</td>\n",
       "      <td>40.7</td>\n",
       "      <td>40.9</td>\n",
       "      <td>41.1</td>\n",
       "      <td>41.5</td>\n",
       "      <td>41.9</td>\n",
       "      <td>42.3</td>\n",
       "      <td>42.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>41.9</td>\n",
       "      <td>41.3</td>\n",
       "      <td>40.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>39.2</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>32.1</td>\n",
       "      <td>30.7</td>\n",
       "      <td>29.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.9</td>\n",
       "      <td>31.5</td>\n",
       "      <td>32.1</td>\n",
       "      <td>32.7</td>\n",
       "      <td>33.5</td>\n",
       "      <td>34.5</td>\n",
       "      <td>35.5</td>\n",
       "      <td>36.7</td>\n",
       "      <td>37.7</td>\n",
       "      <td>38.4</td>\n",
       "      <td>38.8</td>\n",
       "      <td>39.1</td>\n",
       "      <td>Ethiopia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>39.7</td>\n",
       "      <td>38.8</td>\n",
       "      <td>38.1</td>\n",
       "      <td>37.4</td>\n",
       "      <td>36.9</td>\n",
       "      <td>37.1</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.7</td>\n",
       "      <td>38.3</td>\n",
       "      <td>38.8</td>\n",
       "      <td>38.9</td>\n",
       "      <td>39.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>38.1</td>\n",
       "      <td>37.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>Georgia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>31.6</td>\n",
       "      <td>32.2</td>\n",
       "      <td>32.8</td>\n",
       "      <td>33.3</td>\n",
       "      <td>33.9</td>\n",
       "      <td>34.4</td>\n",
       "      <td>34.8</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>Mongolia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>Myanmar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>44.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>Turkmenistan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "7    36.2  35.0  35.3  35.3  34.2  33.5  32.7  30.8  29.6  29.6  29.2  29.5   \n",
       "10   35.8  35.5  34.9  34.1  33.0  32.3  31.9  31.7  31.8  32.1  32.2  32.4   \n",
       "19   40.9  40.9  40.8  40.5  40.1  39.5  39.0  38.6  38.4  38.4  38.5  38.6   \n",
       "28   42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0  42.0   \n",
       "35   39.3  40.0  40.4  40.7  40.9  41.1  41.5  41.9  42.3  42.5  42.5  41.9   \n",
       "56   32.1  30.7  29.9  29.9  30.0  30.2  30.5  30.9  31.5  32.1  32.7  33.5   \n",
       "62   39.7  38.8  38.1  37.4  36.9  37.1  37.4  37.7  38.3  38.8  38.9  39.0   \n",
       "113  31.6  32.2  32.8  33.3  33.9  34.4  34.8  35.0  34.7  34.5  34.2  33.6   \n",
       "117  38.1  38.1  38.1  38.1  38.1  38.1  38.1  38.1  38.1  38.1  38.1  38.1   \n",
       "180  44.9  46.0  46.4  46.4  46.4  46.4  46.4  46.4  46.4  46.4  46.4  46.4   \n",
       "\n",
       "     2012  2013  2014  2015  2016  2017  2018       country  \n",
       "7    30.2  30.7  31.3  31.9  32.3  32.5  32.6       Armenia  \n",
       "10   32.4  32.4  32.4  32.4  32.4  32.4  32.4    Azerbaijan  \n",
       "19   38.6  38.5  38.3  38.0  37.8  37.6  37.5        Bhutan  \n",
       "28   42.0  42.0  42.0  42.0  42.0  42.0  42.0      Cambodia  \n",
       "35   41.3  40.6  40.0  39.4  39.2  39.1  39.1         China  \n",
       "56   34.5  35.5  36.7  37.7  38.4  38.8  39.1      Ethiopia  \n",
       "62   38.8  38.1  37.5  37.0  36.6  36.5  36.5       Georgia  \n",
       "113  33.1  33.0  32.6  32.4  32.3  32.3  32.4      Mongolia  \n",
       "117  38.1  38.1  38.1  38.1  38.1  38.1  38.1       Myanmar  \n",
       "180  46.4  46.4  46.4  46.4  46.4  46.4  46.4  Turkmenistan  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ginitop10_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_ser = pd.Series([0],index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define function that returns delta percentage\n",
    "\n",
    "def percent_delta(row):\n",
    "    country = row['country']\n",
    "    percent_change = (((row['2000'] - row['2018']) / row['2000']) * 100)\n",
    "    return (country,percent_change)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run precent_delta for each countr\n",
    "delta_ser = ginitop10_2.apply(lambda row: percent_delta(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7           (Armenia, 9.944751381215474)\n",
       "10       (Azerbaijan, 9.497206703910612)\n",
       "19            (Bhutan, 8.31295843520782)\n",
       "28                       (Cambodia, 0.0)\n",
       "35            (China, 0.508905852417292)\n",
       "56       (Ethiopia, -21.806853582554517)\n",
       "62          (Georgia, 8.060453400503786)\n",
       "113       (Mongolia, -2.531645569620244)\n",
       "117                       (Myanmar, 0.0)\n",
       "180    (Turkmenistan, -3.34075723830735)\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create another saved location to return to should calculations go awry\n",
    "ginitop10_3 = ginitop10_2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>7</th>\n",
       "      <th>10</th>\n",
       "      <th>19</th>\n",
       "      <th>28</th>\n",
       "      <th>35</th>\n",
       "      <th>56</th>\n",
       "      <th>62</th>\n",
       "      <th>113</th>\n",
       "      <th>117</th>\n",
       "      <th>180</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>36.2</td>\n",
       "      <td>35.8</td>\n",
       "      <td>40.9</td>\n",
       "      <td>42</td>\n",
       "      <td>39.3</td>\n",
       "      <td>32.1</td>\n",
       "      <td>39.7</td>\n",
       "      <td>31.6</td>\n",
       "      <td>38.1</td>\n",
       "      <td>44.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>35</td>\n",
       "      <td>35.5</td>\n",
       "      <td>40.9</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>30.7</td>\n",
       "      <td>38.8</td>\n",
       "      <td>32.2</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>35.3</td>\n",
       "      <td>34.9</td>\n",
       "      <td>40.8</td>\n",
       "      <td>42</td>\n",
       "      <td>40.4</td>\n",
       "      <td>29.9</td>\n",
       "      <td>38.1</td>\n",
       "      <td>32.8</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>35.3</td>\n",
       "      <td>34.1</td>\n",
       "      <td>40.5</td>\n",
       "      <td>42</td>\n",
       "      <td>40.7</td>\n",
       "      <td>29.9</td>\n",
       "      <td>37.4</td>\n",
       "      <td>33.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>34.2</td>\n",
       "      <td>33</td>\n",
       "      <td>40.1</td>\n",
       "      <td>42</td>\n",
       "      <td>40.9</td>\n",
       "      <td>30</td>\n",
       "      <td>36.9</td>\n",
       "      <td>33.9</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>33.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>39.5</td>\n",
       "      <td>42</td>\n",
       "      <td>41.1</td>\n",
       "      <td>30.2</td>\n",
       "      <td>37.1</td>\n",
       "      <td>34.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>32.7</td>\n",
       "      <td>31.9</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>41.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>37.4</td>\n",
       "      <td>34.8</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>30.8</td>\n",
       "      <td>31.7</td>\n",
       "      <td>38.6</td>\n",
       "      <td>42</td>\n",
       "      <td>41.9</td>\n",
       "      <td>30.9</td>\n",
       "      <td>37.7</td>\n",
       "      <td>35</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>29.6</td>\n",
       "      <td>31.8</td>\n",
       "      <td>38.4</td>\n",
       "      <td>42</td>\n",
       "      <td>42.3</td>\n",
       "      <td>31.5</td>\n",
       "      <td>38.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>29.6</td>\n",
       "      <td>32.1</td>\n",
       "      <td>38.4</td>\n",
       "      <td>42</td>\n",
       "      <td>42.5</td>\n",
       "      <td>32.1</td>\n",
       "      <td>38.8</td>\n",
       "      <td>34.5</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>29.2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>38.5</td>\n",
       "      <td>42</td>\n",
       "      <td>42.5</td>\n",
       "      <td>32.7</td>\n",
       "      <td>38.9</td>\n",
       "      <td>34.2</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>29.5</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.6</td>\n",
       "      <td>42</td>\n",
       "      <td>41.9</td>\n",
       "      <td>33.5</td>\n",
       "      <td>39</td>\n",
       "      <td>33.6</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>30.2</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.6</td>\n",
       "      <td>42</td>\n",
       "      <td>41.3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>38.8</td>\n",
       "      <td>33.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>30.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.5</td>\n",
       "      <td>42</td>\n",
       "      <td>40.6</td>\n",
       "      <td>35.5</td>\n",
       "      <td>38.1</td>\n",
       "      <td>33</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>31.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.3</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>36.7</td>\n",
       "      <td>37.5</td>\n",
       "      <td>32.6</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>31.9</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38</td>\n",
       "      <td>42</td>\n",
       "      <td>39.4</td>\n",
       "      <td>37.7</td>\n",
       "      <td>37</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>32.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>42</td>\n",
       "      <td>39.2</td>\n",
       "      <td>38.4</td>\n",
       "      <td>36.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>32.5</td>\n",
       "      <td>32.4</td>\n",
       "      <td>37.6</td>\n",
       "      <td>42</td>\n",
       "      <td>39.1</td>\n",
       "      <td>38.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>32.6</td>\n",
       "      <td>32.4</td>\n",
       "      <td>37.5</td>\n",
       "      <td>42</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>36.5</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>Bhutan</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>China</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>Mongolia</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>Turkmenistan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             7           10      19        28     35        56       62   \\\n",
       "2000        36.2        35.8    40.9        42   39.3      32.1     39.7   \n",
       "2001          35        35.5    40.9        42     40      30.7     38.8   \n",
       "2002        35.3        34.9    40.8        42   40.4      29.9     38.1   \n",
       "2003        35.3        34.1    40.5        42   40.7      29.9     37.4   \n",
       "2004        34.2          33    40.1        42   40.9        30     36.9   \n",
       "2005        33.5        32.3    39.5        42   41.1      30.2     37.1   \n",
       "2006        32.7        31.9      39        42   41.5      30.5     37.4   \n",
       "2007        30.8        31.7    38.6        42   41.9      30.9     37.7   \n",
       "2008        29.6        31.8    38.4        42   42.3      31.5     38.3   \n",
       "2009        29.6        32.1    38.4        42   42.5      32.1     38.8   \n",
       "2010        29.2        32.2    38.5        42   42.5      32.7     38.9   \n",
       "2011        29.5        32.4    38.6        42   41.9      33.5       39   \n",
       "2012        30.2        32.4    38.6        42   41.3      34.5     38.8   \n",
       "2013        30.7        32.4    38.5        42   40.6      35.5     38.1   \n",
       "2014        31.3        32.4    38.3        42     40      36.7     37.5   \n",
       "2015        31.9        32.4      38        42   39.4      37.7       37   \n",
       "2016        32.3        32.4    37.8        42   39.2      38.4     36.6   \n",
       "2017        32.5        32.4    37.6        42   39.1      38.8     36.5   \n",
       "2018        32.6        32.4    37.5        42   39.1      39.1     36.5   \n",
       "country  Armenia  Azerbaijan  Bhutan  Cambodia  China  Ethiopia  Georgia   \n",
       "\n",
       "              113      117           180  \n",
       "2000         31.6     38.1          44.9  \n",
       "2001         32.2     38.1            46  \n",
       "2002         32.8     38.1          46.4  \n",
       "2003         33.3     38.1          46.4  \n",
       "2004         33.9     38.1          46.4  \n",
       "2005         34.4     38.1          46.4  \n",
       "2006         34.8     38.1          46.4  \n",
       "2007           35     38.1          46.4  \n",
       "2008         34.7     38.1          46.4  \n",
       "2009         34.5     38.1          46.4  \n",
       "2010         34.2     38.1          46.4  \n",
       "2011         33.6     38.1          46.4  \n",
       "2012         33.1     38.1          46.4  \n",
       "2013           33     38.1          46.4  \n",
       "2014         32.6     38.1          46.4  \n",
       "2015         32.4     38.1          46.4  \n",
       "2016         32.3     38.1          46.4  \n",
       "2017         32.3     38.1          46.4  \n",
       "2018         32.4     38.1          46.4  \n",
       "country  Mongolia  Myanmar  Turkmenistan  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the columns must be transposed with the row in order to create meaningful visualizations\n",
    "ginitop10_3 = ginitop10_3.transpose()\n",
    "ginitop10_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ginitop10_3.columns = ginitop10_3.iloc[19,:]\n",
    "ginitop10_3.drop('country',inplace=True)\n",
    "ginitop10_3 = ginitop10_3.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>Azerbaijan</th>\n",
       "      <th>Bhutan</th>\n",
       "      <th>Cambodia</th>\n",
       "      <th>China</th>\n",
       "      <th>Ethiopia</th>\n",
       "      <th>Georgia</th>\n",
       "      <th>Mongolia</th>\n",
       "      <th>Myanmar</th>\n",
       "      <th>Turkmenistan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>36.2</td>\n",
       "      <td>35.8</td>\n",
       "      <td>40.9</td>\n",
       "      <td>42</td>\n",
       "      <td>39.3</td>\n",
       "      <td>32.1</td>\n",
       "      <td>39.7</td>\n",
       "      <td>31.6</td>\n",
       "      <td>38.1</td>\n",
       "      <td>44.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>35</td>\n",
       "      <td>35.5</td>\n",
       "      <td>40.9</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>30.7</td>\n",
       "      <td>38.8</td>\n",
       "      <td>32.2</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>35.3</td>\n",
       "      <td>34.9</td>\n",
       "      <td>40.8</td>\n",
       "      <td>42</td>\n",
       "      <td>40.4</td>\n",
       "      <td>29.9</td>\n",
       "      <td>38.1</td>\n",
       "      <td>32.8</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>35.3</td>\n",
       "      <td>34.1</td>\n",
       "      <td>40.5</td>\n",
       "      <td>42</td>\n",
       "      <td>40.7</td>\n",
       "      <td>29.9</td>\n",
       "      <td>37.4</td>\n",
       "      <td>33.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>34.2</td>\n",
       "      <td>33</td>\n",
       "      <td>40.1</td>\n",
       "      <td>42</td>\n",
       "      <td>40.9</td>\n",
       "      <td>30</td>\n",
       "      <td>36.9</td>\n",
       "      <td>33.9</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>33.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>39.5</td>\n",
       "      <td>42</td>\n",
       "      <td>41.1</td>\n",
       "      <td>30.2</td>\n",
       "      <td>37.1</td>\n",
       "      <td>34.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>32.7</td>\n",
       "      <td>31.9</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>41.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>37.4</td>\n",
       "      <td>34.8</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>30.8</td>\n",
       "      <td>31.7</td>\n",
       "      <td>38.6</td>\n",
       "      <td>42</td>\n",
       "      <td>41.9</td>\n",
       "      <td>30.9</td>\n",
       "      <td>37.7</td>\n",
       "      <td>35</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>29.6</td>\n",
       "      <td>31.8</td>\n",
       "      <td>38.4</td>\n",
       "      <td>42</td>\n",
       "      <td>42.3</td>\n",
       "      <td>31.5</td>\n",
       "      <td>38.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>29.6</td>\n",
       "      <td>32.1</td>\n",
       "      <td>38.4</td>\n",
       "      <td>42</td>\n",
       "      <td>42.5</td>\n",
       "      <td>32.1</td>\n",
       "      <td>38.8</td>\n",
       "      <td>34.5</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>29.2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>38.5</td>\n",
       "      <td>42</td>\n",
       "      <td>42.5</td>\n",
       "      <td>32.7</td>\n",
       "      <td>38.9</td>\n",
       "      <td>34.2</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>29.5</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.6</td>\n",
       "      <td>42</td>\n",
       "      <td>41.9</td>\n",
       "      <td>33.5</td>\n",
       "      <td>39</td>\n",
       "      <td>33.6</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>30.2</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.6</td>\n",
       "      <td>42</td>\n",
       "      <td>41.3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>38.8</td>\n",
       "      <td>33.1</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>30.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.5</td>\n",
       "      <td>42</td>\n",
       "      <td>40.6</td>\n",
       "      <td>35.5</td>\n",
       "      <td>38.1</td>\n",
       "      <td>33</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>31.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.3</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>36.7</td>\n",
       "      <td>37.5</td>\n",
       "      <td>32.6</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>31.9</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38</td>\n",
       "      <td>42</td>\n",
       "      <td>39.4</td>\n",
       "      <td>37.7</td>\n",
       "      <td>37</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>42</td>\n",
       "      <td>39.2</td>\n",
       "      <td>38.4</td>\n",
       "      <td>36.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>32.5</td>\n",
       "      <td>32.4</td>\n",
       "      <td>37.6</td>\n",
       "      <td>42</td>\n",
       "      <td>39.1</td>\n",
       "      <td>38.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>32.6</td>\n",
       "      <td>32.4</td>\n",
       "      <td>37.5</td>\n",
       "      <td>42</td>\n",
       "      <td>39.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>36.5</td>\n",
       "      <td>32.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country  year Armenia Azerbaijan Bhutan Cambodia China Ethiopia Georgia  \\\n",
       "0        2000    36.2       35.8   40.9       42  39.3     32.1    39.7   \n",
       "1        2001      35       35.5   40.9       42    40     30.7    38.8   \n",
       "2        2002    35.3       34.9   40.8       42  40.4     29.9    38.1   \n",
       "3        2003    35.3       34.1   40.5       42  40.7     29.9    37.4   \n",
       "4        2004    34.2         33   40.1       42  40.9       30    36.9   \n",
       "5        2005    33.5       32.3   39.5       42  41.1     30.2    37.1   \n",
       "6        2006    32.7       31.9     39       42  41.5     30.5    37.4   \n",
       "7        2007    30.8       31.7   38.6       42  41.9     30.9    37.7   \n",
       "8        2008    29.6       31.8   38.4       42  42.3     31.5    38.3   \n",
       "9        2009    29.6       32.1   38.4       42  42.5     32.1    38.8   \n",
       "10       2010    29.2       32.2   38.5       42  42.5     32.7    38.9   \n",
       "11       2011    29.5       32.4   38.6       42  41.9     33.5      39   \n",
       "12       2012    30.2       32.4   38.6       42  41.3     34.5    38.8   \n",
       "13       2013    30.7       32.4   38.5       42  40.6     35.5    38.1   \n",
       "14       2014    31.3       32.4   38.3       42    40     36.7    37.5   \n",
       "15       2015    31.9       32.4     38       42  39.4     37.7      37   \n",
       "16       2016    32.3       32.4   37.8       42  39.2     38.4    36.6   \n",
       "17       2017    32.5       32.4   37.6       42  39.1     38.8    36.5   \n",
       "18       2018    32.6       32.4   37.5       42  39.1     39.1    36.5   \n",
       "\n",
       "country Mongolia Myanmar Turkmenistan  \n",
       "0           31.6    38.1         44.9  \n",
       "1           32.2    38.1           46  \n",
       "2           32.8    38.1         46.4  \n",
       "3           33.3    38.1         46.4  \n",
       "4           33.9    38.1         46.4  \n",
       "5           34.4    38.1         46.4  \n",
       "6           34.8    38.1         46.4  \n",
       "7             35    38.1         46.4  \n",
       "8           34.7    38.1         46.4  \n",
       "9           34.5    38.1         46.4  \n",
       "10          34.2    38.1         46.4  \n",
       "11          33.6    38.1         46.4  \n",
       "12          33.1    38.1         46.4  \n",
       "13            33    38.1         46.4  \n",
       "14          32.6    38.1         46.4  \n",
       "15          32.4    38.1         46.4  \n",
       "16          32.3    38.1         46.4  \n",
       "17          32.3    38.1         46.4  \n",
       "18          32.4    38.1         46.4  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ginitop10_3 = ginitop10_3.rename(columns={'index':'year'})\n",
    "ginitop10_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='question_2'></a>\n",
    "# #2 How did the countries rapid growth affect their gini score?\n",
    "\n",
    "<a href=\"#top\">Back to top</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7           (Armenia, 9.944751381215474)\n",
       "10       (Azerbaijan, 9.497206703910612)\n",
       "19            (Bhutan, 8.31295843520782)\n",
       "28                       (Cambodia, 0.0)\n",
       "35            (China, 0.508905852417292)\n",
       "56       (Ethiopia, -21.806853582554517)\n",
       "62          (Georgia, 8.060453400503786)\n",
       "113       (Mongolia, -2.531645569620244)\n",
       "117                       (Myanmar, 0.0)\n",
       "180    (Turkmenistan, -3.34075723830735)\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "ginitop10_3.plot(x='year', y='Myanmar', kind='line', figsize=(14,8), ax=ax)\n",
    "ginitop10_3.plot(x='year', y='China', kind='line', figsize=(14,8), ax=ax)\n",
    "ginitop10_3.plot(x='year', y='Azerbaijan', kind='line', figsize=(14,8), ax=ax)\n",
    "ginitop10_3.plot(x='year', y='Turkmenistan', kind='line', figsize=(14,8), ax=ax)\n",
    "ginitop10_3.plot(x='year', y='Armenia', kind='line', figsize=(14,8), ax=ax)\n",
    "\n",
    "plt.title(\"GINI Score for fastest 1-5 growing countries from 2000-2018\", fontsize=24)\n",
    "plt.ylabel(\"GINI\", fontsize=20)\n",
    "plt.xlabel(\"Years\", fontsize=20)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "\n",
    "One cannot make any definitive conclusions looking at the trends between rapid growth and inequality solely. Should this analysis continue, additional data is needed to make any meaningful conclusions. \n",
    "\n",
    "Some additional observations between the perception of corruption & the actual inequality. Myanmar's perception of corruption doubled from 2012 to 2017 but inequality as measured by GINI remained constant as distinguished by the data collected.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "\n",
    "ginitop10_3.plot(x='year', y='Ethiopia', kind='line', figsize=(14,8), ax=ax)\n",
    "ginitop10_3.plot(x='year', y='Georgia', kind='line', figsize=(14,8), ax=ax)\n",
    "ginitop10_3.plot(x='year', y='Cambodia', kind='line', figsize=(14,8), ax=ax)\n",
    "ginitop10_3.plot(x='year', y='Bhutan', kind='line', figsize=(14,8), ax=ax)\n",
    "ginitop10_3.plot(x='year', y='Mongolia', kind='line', figsize=(14,8), ax=ax)\n",
    "\n",
    "plt.title(\"GINI Score for fastest 6-10 growing countries from 2000-2018\", fontsize=24)\n",
    "plt.ylabel(\"GINI\", fontsize=20)\n",
    "plt.xlabel(\"Years\", fontsize=20)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "# Conclusions\n",
    "\n",
    "### During the expansive growth from 2000-2018:\n",
    "\n",
    "**1. How did the perception of corruption change during expansive growth, for the top 10 countries of the world?**\n",
    "Visualization The top 5 fastest growing countries perception of corruption was greatest. Countries 6-10 had much less change in perception and 3 countries, Cambodia, Mongolia, & Ethopia had little to no change.\n",
    "\n",
    "**2. How did the GINI coefficient change, if any, during expansive growth?**\n",
    "Armenia, Azerbaijan, Georgia, & Bhutan's are the only countries who's GINI coefficient decreased, with Armenia having the greatest decrease at nearly **10%**. \n",
    "***\n",
    "Cambodia, China, & Myanmar all remained the same, with China's score increasing until 2010, and then decreasing nearly the same. These countries also have the highest GINI coefficients of all 10 countries.\n",
    "***\n",
    "Ethiopia, Mongolia, & Turkmenistan's score all increased, with Ethiopia having the greatest increase in inequality.\n",
    "***\n",
    "\n",
    "<a href=\"#top\">Back to top</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limitations\n",
    "\n",
    "The limitations of this analysis are significant. There can be no correlations or causalities made between the significant growth of a country, its popluation's perception of corruption, and the overall inequality. At a minimum, it is advised to include all countries and the changes in their growth to determine any correlations about their perception of corruption as well as the inequality as a whole. Additionally, it would have also been good to look at the relationship between the perception of corruption and actual inequality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}