Exploring The World Happiness Reports by Region.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring The World Happiness Reports by Region\n",
    "\n",
    "What is happiness to you?\n",
    "\n",
    "The member states of the United Nations, through its [Sustainable Development Solutions Network (SDSN)](https://www.unsdsn.org/) launched in 2012, has begun the process of quantifying happiness in each country around the world, in an effort to collect information that will help achieve the [Sustainable Development Goals (SDGs)](https://sdgs.un.org/goals) of the 21st Century. This process is known as [The World Happiness Report](https://worldhappiness.report/), and a new report is scheduled to arrive every year for the foreseeable future.\n",
    "\n",
    "To the United Nations, quantifying happiness involves measuring and aggregating 6 explanatory factors. The aggregate of these factors is known as the \"Happiness Score\". The 6 explanatory factors that the UN has agreed upon are:\n",
    "\n",
    "* GDP per capita\n",
    "* Social support\n",
    "* Healthy life expectancy\n",
    "* Freedom to make life choices\n",
    "* Generosity\n",
    "* Perception of curruption.\n",
    "\n",
    "The UN has chosen these factors because the most current research has shown that these 6 factors most accurately explain national-level differences in life evaluations. The 6 factors are not meant to be interpreted as specific causal agents, but rather to be understood as proven correlates to the happiness and well-being of a nation. They are seen as trusted metrics in the emerging science of happiness and well-being. ([The 2019 World Happiness Report FAQ](https://worldhappiness.report/faq/))\n",
    "\n",
    "## Goal\n",
    "\n",
    "I would like to know how Happiness has changed in each region of the world since the beginning of the UN's publishing of The World Happiness Report. To achieve this goal, I would like to make use of every World Happiness Report that has been published since the report's inception, 2012. However, since the UN has not made public the datasets that were used in 2012 and 2013, and it did not publish a report in 2014, all of the next most recent World Happiness Reports will have to suffice.\n",
    "\n",
    "### Research Question\n",
    "\n",
    "*How has Happiness changed in each region of the world since 2015, the year that the UN began publicizing its World Happiness Reports' raw datasets?*\n",
    "\n",
    "To answer this question, I will analyze the 6 most recent World Happiness Reports:\n",
    "\n",
    "* [The 2021 World Happiness Report](https://worldhappiness.report/ed/2021/)\n",
    "* [The 2020 World Happiness Report](https://worldhappiness.report/ed/2020/)\n",
    "* [The 2019 World Happiness Report](https://worldhappiness.report/ed/2019/)\n",
    "* [The 2018 World Happiness Report](https://worldhappiness.report/ed/2018/)\n",
    "* [The 2017 World Happiness Report](https://worldhappiness.report/ed/2017/)\n",
    "* [The 2016 World Happiness Report](https://worldhappiness.report/ed/2016/)\n",
    "* [The 2015 World Happiness Report](https://worldhappiness.report/ed/2015/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents:\n",
    "* [Introducing the Data](#introducing)\n",
    "* [Cleaning the Data](#cleaning)\n",
    "    * [Standardizing the column names](#column_names)\n",
    "        * [Standardizing with string methods](#string_methods)\n",
    "        * [Dropping the raw score columns from the 2020 dataframe](#raw_score)\n",
    "        * [Standardizing with renaming dictionary ](#renaming)\n",
    "    * [Adding the `year` column](#year)\n",
    "    * [Adding the `happiness` column](#happiness)\n",
    "    * [Adding the `region` column](#region)\n",
    "    * [Adding the `rank` column](#rank)\n",
    "    * [Standardizing the column order](#column_order)\n",
    "    * [Concatenating the dataframes](#concatenating)\n",
    "    * [Filling the `region` column](#filling)\n",
    "* [Calculating the Aggregate Data](#aggregate)\n",
    "    * [Calculating the Regional Mean Happiness Scores](#calculating)\n",
    "    * [Analyzing the Regional Mean Happiness Scores](#analyzing)\n",
    "* [Data Visualization](#visualization)\n",
    "    * [Visualizing the Highest Happiness Scores](#highest)\n",
    "    * [Visualizing the Medium Happiness Scores](#medium)\n",
    "    * [Visualizing the Lowest Happiness Scores](#lowest)\n",
    "    * [Visualizing all Plots in one Figure](#plots)\n",
    "* [Conclusion](#conclusion)\n",
    "    * [Further Research](#further)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing the Data <a class=\"anchor\" id=\"introducing\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to read in each World Happiness Report file from 2015 to 2019\n",
    "\n",
    "def read_file(string):\n",
    "    \n",
    "    df = pd.read_csv('World Happiness Report {} Agg Data.csv'.format(str(string)))\n",
    "    \n",
    "    return df\n",
    "\n",
    "years = [2015,2016,2017,2018,2019, 2020, 2021]\n",
    "\n",
    "# Reading in the csv files as dataframes and saving them into a list\n",
    "\n",
    "dfs = []\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    df = read_file(year)\n",
    "    \n",
    "    dfs.append(df)\n",
    "    \n",
    "# Converting the list of dataframes into a dataframe of dataframes, indexed by year\n",
    "    \n",
    "happy = pd.DataFrame({'df': dfs}, index=years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015 World Happiness Report:\n",
      "\n",
      "(158, 9) (Countries, Features)\n",
      "\n",
      "\n",
      "2016 World Happiness Report:\n",
      "\n",
      "(157, 11) (Countries, Features)\n",
      "\n",
      "\n",
      "2017 World Happiness Report:\n",
      "\n",
      "(155, 11) (Countries, Features)\n",
      "\n",
      "\n",
      "2018 World Happiness Report:\n",
      "\n",
      "(156, 11) (Countries, Features)\n",
      "\n",
      "\n",
      "2019 World Happiness Report:\n",
      "\n",
      "(156, 11) (Countries, Features)\n",
      "\n",
      "\n",
      "2020 World Happiness Report:\n",
      "\n",
      "(153, 20) (Countries, Features)\n",
      "\n",
      "\n",
      "2021 World Happiness Report:\n",
      "\n",
      "(149, 20) (Countries, Features)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Viewing the number of rows & columns for each dataframe \n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    print(str(year) + ' World Happiness Report:\\n\\n' + str(happy.loc[year].df.shape) + ' (Countries, Features)\\n\\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>Dystopia + residual</th>\n",
       "      <th>Explained by: Log GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "      <th>Regional indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2.517383</td>\n",
       "      <td>1.396505</td>\n",
       "      <td>1.349505</td>\n",
       "      <td>0.941432</td>\n",
       "      <td>0.665573</td>\n",
       "      <td>0.296775</td>\n",
       "      <td>0.419777</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2.702013</td>\n",
       "      <td>1.302324</td>\n",
       "      <td>1.402231</td>\n",
       "      <td>0.947844</td>\n",
       "      <td>0.628772</td>\n",
       "      <td>0.436297</td>\n",
       "      <td>0.141451</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2.492042</td>\n",
       "      <td>1.325478</td>\n",
       "      <td>1.360581</td>\n",
       "      <td>0.874641</td>\n",
       "      <td>0.649384</td>\n",
       "      <td>0.341386</td>\n",
       "      <td>0.483573</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>2.465312</td>\n",
       "      <td>1.458997</td>\n",
       "      <td>1.330955</td>\n",
       "      <td>0.885209</td>\n",
       "      <td>0.669732</td>\n",
       "      <td>0.346989</td>\n",
       "      <td>0.365034</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2.451756</td>\n",
       "      <td>1.326292</td>\n",
       "      <td>1.322608</td>\n",
       "      <td>0.905631</td>\n",
       "      <td>0.632968</td>\n",
       "      <td>0.458109</td>\n",
       "      <td>0.329573</td>\n",
       "      <td>North America and ANZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  Dystopia + residual  Explained by: Log GDP per capita  \\\n",
       "0  Switzerland             2.517383                          1.396505   \n",
       "1      Iceland             2.702013                          1.302324   \n",
       "2      Denmark             2.492042                          1.325478   \n",
       "3       Norway             2.465312                          1.458997   \n",
       "4       Canada             2.451756                          1.326292   \n",
       "\n",
       "   Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                      1.349505                               0.941432   \n",
       "1                      1.402231                               0.947844   \n",
       "2                      1.360581                               0.874641   \n",
       "3                      1.330955                               0.885209   \n",
       "4                      1.322608                               0.905631   \n",
       "\n",
       "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                    0.665573                  0.296775   \n",
       "1                                    0.628772                  0.436297   \n",
       "2                                    0.649384                  0.341386   \n",
       "3                                    0.669732                  0.346989   \n",
       "4                                    0.632968                  0.458109   \n",
       "\n",
       "   Explained by: Perceptions of corruption     Regional indicator  \n",
       "0                                 0.419777         Western Europe  \n",
       "1                                 0.141451         Western Europe  \n",
       "2                                 0.483573         Western Europe  \n",
       "3                                 0.365034         Western Europe  \n",
       "4                                 0.329573  North America and ANZ  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the first five rows for each of the 5 dataframes\n",
    "\n",
    "happy.loc[2015].df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Happiness score</th>\n",
       "      <th>Whisker-high</th>\n",
       "      <th>Whisker-low</th>\n",
       "      <th>Explained by: GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "      <th>Dystopia (2.33) + residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.526</td>\n",
       "      <td>7.592</td>\n",
       "      <td>7.460</td>\n",
       "      <td>1.441784</td>\n",
       "      <td>1.163745</td>\n",
       "      <td>0.795040</td>\n",
       "      <td>0.579413</td>\n",
       "      <td>0.361710</td>\n",
       "      <td>0.444532</td>\n",
       "      <td>2.739390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>7.509</td>\n",
       "      <td>7.590</td>\n",
       "      <td>7.428</td>\n",
       "      <td>1.527334</td>\n",
       "      <td>1.145236</td>\n",
       "      <td>0.863029</td>\n",
       "      <td>0.585572</td>\n",
       "      <td>0.280825</td>\n",
       "      <td>0.412033</td>\n",
       "      <td>2.694632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.501</td>\n",
       "      <td>7.669</td>\n",
       "      <td>7.333</td>\n",
       "      <td>1.426655</td>\n",
       "      <td>1.183259</td>\n",
       "      <td>0.867333</td>\n",
       "      <td>0.566242</td>\n",
       "      <td>0.476784</td>\n",
       "      <td>0.149747</td>\n",
       "      <td>2.831373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>7.498</td>\n",
       "      <td>7.575</td>\n",
       "      <td>7.421</td>\n",
       "      <td>1.577440</td>\n",
       "      <td>1.126896</td>\n",
       "      <td>0.795789</td>\n",
       "      <td>0.596086</td>\n",
       "      <td>0.378948</td>\n",
       "      <td>0.357758</td>\n",
       "      <td>2.664645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.413</td>\n",
       "      <td>7.475</td>\n",
       "      <td>7.351</td>\n",
       "      <td>1.405979</td>\n",
       "      <td>1.134640</td>\n",
       "      <td>0.810914</td>\n",
       "      <td>0.571037</td>\n",
       "      <td>0.254919</td>\n",
       "      <td>0.410040</td>\n",
       "      <td>2.825965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness score  Whisker-high  Whisker-low  \\\n",
       "0      Denmark            7.526         7.592        7.460   \n",
       "1  Switzerland            7.509         7.590        7.428   \n",
       "2      Iceland            7.501         7.669        7.333   \n",
       "3       Norway            7.498         7.575        7.421   \n",
       "4      Finland            7.413         7.475        7.351   \n",
       "\n",
       "   Explained by: GDP per capita  Explained by: Social support  \\\n",
       "0                      1.441784                      1.163745   \n",
       "1                      1.527334                      1.145236   \n",
       "2                      1.426655                      1.183259   \n",
       "3                      1.577440                      1.126896   \n",
       "4                      1.405979                      1.134640   \n",
       "\n",
       "   Explained by: Healthy life expectancy  \\\n",
       "0                               0.795040   \n",
       "1                               0.863029   \n",
       "2                               0.867333   \n",
       "3                               0.795789   \n",
       "4                               0.810914   \n",
       "\n",
       "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                    0.579413                  0.361710   \n",
       "1                                    0.585572                  0.280825   \n",
       "2                                    0.566242                  0.476784   \n",
       "3                                    0.596086                  0.378948   \n",
       "4                                    0.571037                  0.254919   \n",
       "\n",
       "   Explained by: Perceptions of corruption  Dystopia (2.33) + residual  \n",
       "0                                 0.444532                    2.739390  \n",
       "1                                 0.412033                    2.694632  \n",
       "2                                 0.149747                    2.831373  \n",
       "3                                 0.357758                    2.664645  \n",
       "4                                 0.410040                    2.825965  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy.loc[2016].df.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>Happiness score</th>\n",
       "      <th>Whisker-high</th>\n",
       "      <th>Whisker-low</th>\n",
       "      <th>Explained by: GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "      <th>Dystopia (1.85) + residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>7.537</td>\n",
       "      <td>7.594</td>\n",
       "      <td>7.480</td>\n",
       "      <td>1.616</td>\n",
       "      <td>1.534</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.316</td>\n",
       "      <td>2.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.522</td>\n",
       "      <td>7.582</td>\n",
       "      <td>7.462</td>\n",
       "      <td>1.482</td>\n",
       "      <td>1.551</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.401</td>\n",
       "      <td>2.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.504</td>\n",
       "      <td>7.622</td>\n",
       "      <td>7.386</td>\n",
       "      <td>1.481</td>\n",
       "      <td>1.611</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.154</td>\n",
       "      <td>2.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>7.562</td>\n",
       "      <td>7.426</td>\n",
       "      <td>1.565</td>\n",
       "      <td>1.517</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.367</td>\n",
       "      <td>2.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.469</td>\n",
       "      <td>7.528</td>\n",
       "      <td>7.410</td>\n",
       "      <td>1.444</td>\n",
       "      <td>1.540</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.383</td>\n",
       "      <td>2.430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness score  Whisker-high  Whisker-low  \\\n",
       "0       Norway            7.537         7.594        7.480   \n",
       "1      Denmark            7.522         7.582        7.462   \n",
       "2      Iceland            7.504         7.622        7.386   \n",
       "3  Switzerland            7.494         7.562        7.426   \n",
       "4      Finland            7.469         7.528        7.410   \n",
       "\n",
       "   Explained by: GDP per capita  Explained by: Social support  \\\n",
       "0                         1.616                         1.534   \n",
       "1                         1.482                         1.551   \n",
       "2                         1.481                         1.611   \n",
       "3                         1.565                         1.517   \n",
       "4                         1.444                         1.540   \n",
       "\n",
       "   Explained by: Healthy life expectancy  \\\n",
       "0                                  0.797   \n",
       "1                                  0.793   \n",
       "2                                  0.834   \n",
       "3                                  0.858   \n",
       "4                                  0.809   \n",
       "\n",
       "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                       0.635                     0.362   \n",
       "1                                       0.626                     0.355   \n",
       "2                                       0.627                     0.476   \n",
       "3                                       0.620                     0.291   \n",
       "4                                       0.618                     0.245   \n",
       "\n",
       "   Explained by: Perceptions of corruption  Dystopia (1.85) + residual  \n",
       "0                                    0.316                       2.277  \n",
       "1                                    0.401                       2.314  \n",
       "2                                    0.154                       2.323  \n",
       "3                                    0.367                       2.277  \n",
       "4                                    0.383                       2.430  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy.loc[2017].df.head()"
   ]
  },
  {
   "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>Happiness score</th>\n",
       "      <th>Whisker-high</th>\n",
       "      <th>Whisker-low</th>\n",
       "      <th>Dystopia (1.92) + residual</th>\n",
       "      <th>Explained by: GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.632</td>\n",
       "      <td>7.695</td>\n",
       "      <td>7.569</td>\n",
       "      <td>2.595</td>\n",
       "      <td>1.305</td>\n",
       "      <td>1.592</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>7.594</td>\n",
       "      <td>7.657</td>\n",
       "      <td>7.530</td>\n",
       "      <td>2.383</td>\n",
       "      <td>1.456</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.555</td>\n",
       "      <td>7.623</td>\n",
       "      <td>7.487</td>\n",
       "      <td>2.370</td>\n",
       "      <td>1.351</td>\n",
       "      <td>1.590</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.495</td>\n",
       "      <td>7.593</td>\n",
       "      <td>7.398</td>\n",
       "      <td>2.426</td>\n",
       "      <td>1.343</td>\n",
       "      <td>1.644</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>7.487</td>\n",
       "      <td>7.570</td>\n",
       "      <td>7.405</td>\n",
       "      <td>2.320</td>\n",
       "      <td>1.420</td>\n",
       "      <td>1.549</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness score  Whisker-high  Whisker-low  \\\n",
       "0      Finland            7.632         7.695        7.569   \n",
       "1       Norway            7.594         7.657        7.530   \n",
       "2      Denmark            7.555         7.623        7.487   \n",
       "3      Iceland            7.495         7.593        7.398   \n",
       "4  Switzerland            7.487         7.570        7.405   \n",
       "\n",
       "   Dystopia (1.92) + residual  Explained by: GDP per capita  \\\n",
       "0                       2.595                         1.305   \n",
       "1                       2.383                         1.456   \n",
       "2                       2.370                         1.351   \n",
       "3                       2.426                         1.343   \n",
       "4                       2.320                         1.420   \n",
       "\n",
       "   Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                         1.592                                  0.874   \n",
       "1                         1.582                                  0.861   \n",
       "2                         1.590                                  0.868   \n",
       "3                         1.644                                  0.914   \n",
       "4                         1.549                                  0.927   \n",
       "\n",
       "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                       0.681                     0.192   \n",
       "1                                       0.686                     0.286   \n",
       "2                                       0.683                     0.284   \n",
       "3                                       0.677                     0.353   \n",
       "4                                       0.660                     0.256   \n",
       "\n",
       "   Explained by: Perceptions of corruption  \n",
       "0                                    0.393  \n",
       "1                                    0.340  \n",
       "2                                    0.408  \n",
       "3                                    0.138  \n",
       "4                                    0.357  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy.loc[2018].df.head()"
   ]
  },
  {
   "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></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness score</th>\n",
       "      <th>Whisker-high</th>\n",
       "      <th>Whisker-low</th>\n",
       "      <th>Dystopia (1.88) + residual</th>\n",
       "      <th>Explained by: GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>7.830</td>\n",
       "      <td>7.708</td>\n",
       "      <td>2.714</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>7.667</td>\n",
       "      <td>7.534</td>\n",
       "      <td>2.393</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>7.616</td>\n",
       "      <td>7.492</td>\n",
       "      <td>2.241</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>7.613</td>\n",
       "      <td>7.374</td>\n",
       "      <td>2.401</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>7.542</td>\n",
       "      <td>7.433</td>\n",
       "      <td>2.393</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness score  Whisker-high  Whisker-low  \\\n",
       "0      Finland            7.769         7.830        7.708   \n",
       "1      Denmark            7.600         7.667        7.534   \n",
       "2       Norway            7.554         7.616        7.492   \n",
       "3      Iceland            7.494         7.613        7.374   \n",
       "4  Netherlands            7.488         7.542        7.433   \n",
       "\n",
       "   Dystopia (1.88) + residual  Explained by: GDP per capita  \\\n",
       "0                       2.714                         1.340   \n",
       "1                       2.393                         1.383   \n",
       "2                       2.241                         1.488   \n",
       "3                       2.401                         1.380   \n",
       "4                       2.393                         1.396   \n",
       "\n",
       "   Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                         1.587                                  0.986   \n",
       "1                         1.573                                  0.996   \n",
       "2                         1.582                                  1.028   \n",
       "3                         1.624                                  1.026   \n",
       "4                         1.522                                  0.999   \n",
       "\n",
       "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                       0.596                     0.153   \n",
       "1                                       0.592                     0.252   \n",
       "2                                       0.603                     0.271   \n",
       "3                                       0.591                     0.354   \n",
       "4                                       0.557                     0.322   \n",
       "\n",
       "   Explained by: Perceptions of corruption  \n",
       "0                                    0.393  \n",
       "1                                    0.410  \n",
       "2                                    0.341  \n",
       "3                                    0.118  \n",
       "4                                    0.298  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy.loc[2019].df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 name</th>\n",
       "      <th>Regional indicator</th>\n",
       "      <th>Ladder score</th>\n",
       "      <th>Standard error of ladder score</th>\n",
       "      <th>upperwhisker</th>\n",
       "      <th>lowerwhisker</th>\n",
       "      <th>Logged GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Ladder score in Dystopia</th>\n",
       "      <th>Explained by: Log GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "      <th>Dystopia + residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.8087</td>\n",
       "      <td>0.031156</td>\n",
       "      <td>7.869766</td>\n",
       "      <td>7.747634</td>\n",
       "      <td>10.639267</td>\n",
       "      <td>0.954330</td>\n",
       "      <td>71.900825</td>\n",
       "      <td>0.949172</td>\n",
       "      <td>-0.059482</td>\n",
       "      <td>0.195445</td>\n",
       "      <td>1.972317</td>\n",
       "      <td>1.285190</td>\n",
       "      <td>1.499526</td>\n",
       "      <td>0.961271</td>\n",
       "      <td>0.662317</td>\n",
       "      <td>0.159670</td>\n",
       "      <td>0.477857</td>\n",
       "      <td>2.762835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.6456</td>\n",
       "      <td>0.033492</td>\n",
       "      <td>7.711245</td>\n",
       "      <td>7.579955</td>\n",
       "      <td>10.774001</td>\n",
       "      <td>0.955991</td>\n",
       "      <td>72.402504</td>\n",
       "      <td>0.951444</td>\n",
       "      <td>0.066202</td>\n",
       "      <td>0.168489</td>\n",
       "      <td>1.972317</td>\n",
       "      <td>1.326949</td>\n",
       "      <td>1.503449</td>\n",
       "      <td>0.979333</td>\n",
       "      <td>0.665040</td>\n",
       "      <td>0.242793</td>\n",
       "      <td>0.495260</td>\n",
       "      <td>2.432741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.5599</td>\n",
       "      <td>0.035014</td>\n",
       "      <td>7.628528</td>\n",
       "      <td>7.491272</td>\n",
       "      <td>10.979933</td>\n",
       "      <td>0.942847</td>\n",
       "      <td>74.102448</td>\n",
       "      <td>0.921337</td>\n",
       "      <td>0.105911</td>\n",
       "      <td>0.303728</td>\n",
       "      <td>1.972317</td>\n",
       "      <td>1.390774</td>\n",
       "      <td>1.472403</td>\n",
       "      <td>1.040533</td>\n",
       "      <td>0.628954</td>\n",
       "      <td>0.269056</td>\n",
       "      <td>0.407946</td>\n",
       "      <td>2.350267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.5045</td>\n",
       "      <td>0.059616</td>\n",
       "      <td>7.621347</td>\n",
       "      <td>7.387653</td>\n",
       "      <td>10.772559</td>\n",
       "      <td>0.974670</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.948892</td>\n",
       "      <td>0.246944</td>\n",
       "      <td>0.711710</td>\n",
       "      <td>1.972317</td>\n",
       "      <td>1.326502</td>\n",
       "      <td>1.547567</td>\n",
       "      <td>1.000843</td>\n",
       "      <td>0.661981</td>\n",
       "      <td>0.362330</td>\n",
       "      <td>0.144541</td>\n",
       "      <td>2.460688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.4880</td>\n",
       "      <td>0.034837</td>\n",
       "      <td>7.556281</td>\n",
       "      <td>7.419719</td>\n",
       "      <td>11.087804</td>\n",
       "      <td>0.952487</td>\n",
       "      <td>73.200783</td>\n",
       "      <td>0.955750</td>\n",
       "      <td>0.134533</td>\n",
       "      <td>0.263218</td>\n",
       "      <td>1.972317</td>\n",
       "      <td>1.424207</td>\n",
       "      <td>1.495173</td>\n",
       "      <td>1.008072</td>\n",
       "      <td>0.670201</td>\n",
       "      <td>0.287985</td>\n",
       "      <td>0.434101</td>\n",
       "      <td>2.168266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country name Regional indicator  Ladder score  \\\n",
       "0      Finland     Western Europe        7.8087   \n",
       "1      Denmark     Western Europe        7.6456   \n",
       "2  Switzerland     Western Europe        7.5599   \n",
       "3      Iceland     Western Europe        7.5045   \n",
       "4       Norway     Western Europe        7.4880   \n",
       "\n",
       "   Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
       "0                        0.031156      7.869766      7.747634   \n",
       "1                        0.033492      7.711245      7.579955   \n",
       "2                        0.035014      7.628528      7.491272   \n",
       "3                        0.059616      7.621347      7.387653   \n",
       "4                        0.034837      7.556281      7.419719   \n",
       "\n",
       "   Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0              10.639267        0.954330                71.900825   \n",
       "1              10.774001        0.955991                72.402504   \n",
       "2              10.979933        0.942847                74.102448   \n",
       "3              10.772559        0.974670                73.000000   \n",
       "4              11.087804        0.952487                73.200783   \n",
       "\n",
       "   Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
       "0                      0.949172   -0.059482                   0.195445   \n",
       "1                      0.951444    0.066202                   0.168489   \n",
       "2                      0.921337    0.105911                   0.303728   \n",
       "3                      0.948892    0.246944                   0.711710   \n",
       "4                      0.955750    0.134533                   0.263218   \n",
       "\n",
       "   Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
       "0                  1.972317                          1.285190   \n",
       "1                  1.972317                          1.326949   \n",
       "2                  1.972317                          1.390774   \n",
       "3                  1.972317                          1.326502   \n",
       "4                  1.972317                          1.424207   \n",
       "\n",
       "   Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                      1.499526                               0.961271   \n",
       "1                      1.503449                               0.979333   \n",
       "2                      1.472403                               1.040533   \n",
       "3                      1.547567                               1.000843   \n",
       "4                      1.495173                               1.008072   \n",
       "\n",
       "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                    0.662317                  0.159670   \n",
       "1                                    0.665040                  0.242793   \n",
       "2                                    0.628954                  0.269056   \n",
       "3                                    0.661981                  0.362330   \n",
       "4                                    0.670201                  0.287985   \n",
       "\n",
       "   Explained by: Perceptions of corruption  Dystopia + residual  \n",
       "0                                 0.477857             2.762835  \n",
       "1                                 0.495260             2.432741  \n",
       "2                                 0.407946             2.350267  \n",
       "3                                 0.144541             2.460688  \n",
       "4                                 0.434101             2.168266  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy.loc[2020].df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 name</th>\n",
       "      <th>Regional indicator</th>\n",
       "      <th>Ladder score</th>\n",
       "      <th>Standard error of ladder score</th>\n",
       "      <th>upperwhisker</th>\n",
       "      <th>lowerwhisker</th>\n",
       "      <th>Logged GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Ladder score in Dystopia</th>\n",
       "      <th>Explained by: Log GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "      <th>Dystopia + residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.842</td>\n",
       "      <td>0.032</td>\n",
       "      <td>7.904</td>\n",
       "      <td>7.780</td>\n",
       "      <td>10.775</td>\n",
       "      <td>0.954</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.949</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>0.186</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.446</td>\n",
       "      <td>1.106</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.481</td>\n",
       "      <td>3.253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.620</td>\n",
       "      <td>0.035</td>\n",
       "      <td>7.687</td>\n",
       "      <td>7.552</td>\n",
       "      <td>10.933</td>\n",
       "      <td>0.954</td>\n",
       "      <td>72.7</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.179</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.502</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.485</td>\n",
       "      <td>2.868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.571</td>\n",
       "      <td>0.036</td>\n",
       "      <td>7.643</td>\n",
       "      <td>7.500</td>\n",
       "      <td>11.117</td>\n",
       "      <td>0.942</td>\n",
       "      <td>74.4</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.292</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.566</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.413</td>\n",
       "      <td>2.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.554</td>\n",
       "      <td>0.059</td>\n",
       "      <td>7.670</td>\n",
       "      <td>7.438</td>\n",
       "      <td>10.878</td>\n",
       "      <td>0.983</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.673</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.482</td>\n",
       "      <td>1.172</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.170</td>\n",
       "      <td>2.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.464</td>\n",
       "      <td>0.027</td>\n",
       "      <td>7.518</td>\n",
       "      <td>7.410</td>\n",
       "      <td>10.932</td>\n",
       "      <td>0.942</td>\n",
       "      <td>72.4</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.338</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.501</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.384</td>\n",
       "      <td>2.798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country name Regional indicator  Ladder score  \\\n",
       "0      Finland     Western Europe         7.842   \n",
       "1      Denmark     Western Europe         7.620   \n",
       "2  Switzerland     Western Europe         7.571   \n",
       "3      Iceland     Western Europe         7.554   \n",
       "4  Netherlands     Western Europe         7.464   \n",
       "\n",
       "   Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
       "0                           0.032         7.904         7.780   \n",
       "1                           0.035         7.687         7.552   \n",
       "2                           0.036         7.643         7.500   \n",
       "3                           0.059         7.670         7.438   \n",
       "4                           0.027         7.518         7.410   \n",
       "\n",
       "   Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0                 10.775           0.954                     72.0   \n",
       "1                 10.933           0.954                     72.7   \n",
       "2                 11.117           0.942                     74.4   \n",
       "3                 10.878           0.983                     73.0   \n",
       "4                 10.932           0.942                     72.4   \n",
       "\n",
       "   Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
       "0                         0.949      -0.098                      0.186   \n",
       "1                         0.946       0.030                      0.179   \n",
       "2                         0.919       0.025                      0.292   \n",
       "3                         0.955       0.160                      0.673   \n",
       "4                         0.913       0.175                      0.338   \n",
       "\n",
       "   Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
       "0                      2.43                             1.446   \n",
       "1                      2.43                             1.502   \n",
       "2                      2.43                             1.566   \n",
       "3                      2.43                             1.482   \n",
       "4                      2.43                             1.501   \n",
       "\n",
       "   Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                         1.106                                  0.741   \n",
       "1                         1.108                                  0.763   \n",
       "2                         1.079                                  0.816   \n",
       "3                         1.172                                  0.772   \n",
       "4                         1.079                                  0.753   \n",
       "\n",
       "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                       0.691                     0.124   \n",
       "1                                       0.686                     0.208   \n",
       "2                                       0.653                     0.204   \n",
       "3                                       0.698                     0.293   \n",
       "4                                       0.647                     0.302   \n",
       "\n",
       "   Explained by: Perceptions of corruption  Dystopia + residual  \n",
       "0                                    0.481                3.253  \n",
       "1                                    0.485                2.868  \n",
       "2                                    0.413                2.839  \n",
       "3                                    0.170                2.967  \n",
       "4                                    0.384                2.798  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy.loc[2021].df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 9 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   country                                     158 non-null    object \n",
      " 1   Dystopia + residual                         158 non-null    float64\n",
      " 2   Explained by: Log GDP per capita            158 non-null    float64\n",
      " 3   Explained by: Social support                158 non-null    float64\n",
      " 4   Explained by: Healthy life expectancy       158 non-null    float64\n",
      " 5   Explained by: Freedom to make life choices  158 non-null    float64\n",
      " 6   Explained by: Generosity                    158 non-null    float64\n",
      " 7   Explained by: Perceptions of corruption     158 non-null    float64\n",
      " 8   Regional indicator                          158 non-null    object \n",
      "dtypes: float64(7), object(2)\n",
      "memory usage: 11.2+ KB\n",
      "None\n",
      "\n",
      "2016\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157 entries, 0 to 156\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Country                                     157 non-null    object \n",
      " 1   Happiness score                             157 non-null    float64\n",
      " 2   Whisker-high                                157 non-null    float64\n",
      " 3   Whisker-low                                 157 non-null    float64\n",
      " 4   Explained by: GDP per capita                157 non-null    float64\n",
      " 5   Explained by: Social support                157 non-null    float64\n",
      " 6   Explained by: Healthy life expectancy       157 non-null    float64\n",
      " 7   Explained by: Freedom to make life choices  157 non-null    float64\n",
      " 8   Explained by: Generosity                    157 non-null    float64\n",
      " 9   Explained by: Perceptions of corruption     157 non-null    float64\n",
      " 10  Dystopia (2.33) + residual                  157 non-null    float64\n",
      "dtypes: float64(10), object(1)\n",
      "memory usage: 13.6+ KB\n",
      "None\n",
      "\n",
      "2017\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 155 entries, 0 to 154\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Country                                     155 non-null    object \n",
      " 1   Happiness score                             155 non-null    float64\n",
      " 2   Whisker-high                                155 non-null    float64\n",
      " 3   Whisker-low                                 155 non-null    float64\n",
      " 4   Explained by: GDP per capita                155 non-null    float64\n",
      " 5   Explained by: Social support                155 non-null    float64\n",
      " 6   Explained by: Healthy life expectancy       155 non-null    float64\n",
      " 7   Explained by: Freedom to make life choices  155 non-null    float64\n",
      " 8   Explained by: Generosity                    155 non-null    float64\n",
      " 9   Explained by: Perceptions of corruption     155 non-null    float64\n",
      " 10  Dystopia (1.85) + residual                  155 non-null    float64\n",
      "dtypes: float64(10), object(1)\n",
      "memory usage: 13.4+ KB\n",
      "None\n",
      "\n",
      "2018\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Country                                     156 non-null    object \n",
      " 1   Happiness score                             156 non-null    float64\n",
      " 2   Whisker-high                                156 non-null    float64\n",
      " 3   Whisker-low                                 156 non-null    float64\n",
      " 4   Dystopia (1.92) + residual                  156 non-null    float64\n",
      " 5   Explained by: GDP per capita                156 non-null    float64\n",
      " 6   Explained by: Social support                156 non-null    float64\n",
      " 7   Explained by: Healthy life expectancy       156 non-null    float64\n",
      " 8   Explained by: Freedom to make life choices  156 non-null    float64\n",
      " 9   Explained by: Generosity                    156 non-null    float64\n",
      " 10  Explained by: Perceptions of corruption     156 non-null    float64\n",
      "dtypes: float64(10), object(1)\n",
      "memory usage: 13.5+ KB\n",
      "None\n",
      "\n",
      "2019\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Country                                     156 non-null    object \n",
      " 1   Happiness score                             156 non-null    float64\n",
      " 2   Whisker-high                                156 non-null    float64\n",
      " 3   Whisker-low                                 156 non-null    float64\n",
      " 4   Dystopia (1.88) + residual                  156 non-null    float64\n",
      " 5   Explained by: GDP per capita                156 non-null    float64\n",
      " 6   Explained by: Social support                156 non-null    float64\n",
      " 7   Explained by: Healthy life expectancy       156 non-null    float64\n",
      " 8   Explained by: Freedom to make life choices  156 non-null    float64\n",
      " 9   Explained by: Generosity                    156 non-null    float64\n",
      " 10  Explained by: Perceptions of corruption     156 non-null    float64\n",
      "dtypes: float64(10), object(1)\n",
      "memory usage: 13.5+ KB\n",
      "None\n",
      "\n",
      "2020\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 153 entries, 0 to 152\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Country name                                153 non-null    object \n",
      " 1   Regional indicator                          153 non-null    object \n",
      " 2   Ladder score                                153 non-null    float64\n",
      " 3   Standard error of ladder score              153 non-null    float64\n",
      " 4   upperwhisker                                153 non-null    float64\n",
      " 5   lowerwhisker                                153 non-null    float64\n",
      " 6   Logged GDP per capita                       153 non-null    float64\n",
      " 7   Social support                              153 non-null    float64\n",
      " 8   Healthy life expectancy                     153 non-null    float64\n",
      " 9   Freedom to make life choices                153 non-null    float64\n",
      " 10  Generosity                                  153 non-null    float64\n",
      " 11  Perceptions of corruption                   153 non-null    float64\n",
      " 12  Ladder score in Dystopia                    153 non-null    float64\n",
      " 13  Explained by: Log GDP per capita            153 non-null    float64\n",
      " 14  Explained by: Social support                153 non-null    float64\n",
      " 15  Explained by: Healthy life expectancy       153 non-null    float64\n",
      " 16  Explained by: Freedom to make life choices  153 non-null    float64\n",
      " 17  Explained by: Generosity                    153 non-null    float64\n",
      " 18  Explained by: Perceptions of corruption     153 non-null    float64\n",
      " 19  Dystopia + residual                         153 non-null    float64\n",
      "dtypes: float64(18), object(2)\n",
      "memory usage: 24.0+ KB\n",
      "None\n",
      "\n",
      "2021\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149 entries, 0 to 148\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Country name                                149 non-null    object \n",
      " 1   Regional indicator                          149 non-null    object \n",
      " 2   Ladder score                                149 non-null    float64\n",
      " 3   Standard error of ladder score              149 non-null    float64\n",
      " 4   upperwhisker                                149 non-null    float64\n",
      " 5   lowerwhisker                                149 non-null    float64\n",
      " 6   Logged GDP per capita                       149 non-null    float64\n",
      " 7   Social support                              149 non-null    float64\n",
      " 8   Healthy life expectancy                     149 non-null    float64\n",
      " 9   Freedom to make life choices                149 non-null    float64\n",
      " 10  Generosity                                  149 non-null    float64\n",
      " 11  Perceptions of corruption                   149 non-null    float64\n",
      " 12  Ladder score in Dystopia                    149 non-null    float64\n",
      " 13  Explained by: Log GDP per capita            149 non-null    float64\n",
      " 14  Explained by: Social support                149 non-null    float64\n",
      " 15  Explained by: Healthy life expectancy       149 non-null    float64\n",
      " 16  Explained by: Freedom to make life choices  149 non-null    float64\n",
      " 17  Explained by: Generosity                    149 non-null    float64\n",
      " 18  Explained by: Perceptions of corruption     149 non-null    float64\n",
      " 19  Dystopia + residual                         149 non-null    float64\n",
      "dtypes: float64(18), object(2)\n",
      "memory usage: 23.4+ KB\n",
      "None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Viewing the columns, datatypes, and number of non-null values for each dataframe\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    print(str(year) + '\\n')\n",
    "\n",
    "    print(str(happy.loc[year].df.info()) + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In all of the dataframes it looks like the columns need to be cleaned up and standardized. Luckily, this should not take long. All of the columns appear to be of the correct datatypes, and no null values exist in any of them.\n",
    "\n",
    "To clean up and standardize the columns, I will focus on those which I choose to analyze. The columns that I will keep in each are the country columns, the happiness score columns, the regional indicator columns, and the 7 columns that are used to calculate the happiness score (6 \"Explained by\" factor columns + the \"Dystopia + residual\" column. The whisker columns will not be used in this analysis, nor will the 6 \"raw score\" factor columns or the ladder columns in the 2020 dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Data <a class=\"anchor\" id=\"cleaning\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizing the column names <a class=\"anchor\" id=\"column_names\"></a>\n",
    "\n",
    "#### Standardizing with string methods <a class=\"anchor\" id=\"string_methods\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using string methods to standardize the format of each column name\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    happy.loc[year].df.columns = happy.loc[year].df.columns.str.strip().str.lower()\n",
    "    \n",
    "    happy.loc[year].df.columns = happy.loc[year].df.columns.str.replace('\\(\\d+\\.\\d+\\)', '').str.replace('[+:]','')\n",
    "    \n",
    "    happy.loc[year].df.columns = happy.loc[year].df.columns.str.replace('\\s+','_').str.replace('-','_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n",
      "\n",
      "Index(['country', 'dystopia_residual', 'explained_by_log_gdp_per_capita',\n",
      "       'explained_by_social_support', 'explained_by_healthy_life_expectancy',\n",
      "       'explained_by_freedom_to_make_life_choices', 'explained_by_generosity',\n",
      "       'explained_by_perceptions_of_corruption', 'regional_indicator'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2016\n",
      "\n",
      "Index(['country', 'happiness_score', 'whisker_high', 'whisker_low',\n",
      "       'explained_by_gdp_per_capita', 'explained_by_social_support',\n",
      "       'explained_by_healthy_life_expectancy',\n",
      "       'explained_by_freedom_to_make_life_choices', 'explained_by_generosity',\n",
      "       'explained_by_perceptions_of_corruption', 'dystopia_residual'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2017\n",
      "\n",
      "Index(['country', 'happiness_score', 'whisker_high', 'whisker_low',\n",
      "       'explained_by_gdp_per_capita', 'explained_by_social_support',\n",
      "       'explained_by_healthy_life_expectancy',\n",
      "       'explained_by_freedom_to_make_life_choices', 'explained_by_generosity',\n",
      "       'explained_by_perceptions_of_corruption', 'dystopia_residual'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2018\n",
      "\n",
      "Index(['country', 'happiness_score', 'whisker_high', 'whisker_low',\n",
      "       'dystopia_residual', 'explained_by_gdp_per_capita',\n",
      "       'explained_by_social_support', 'explained_by_healthy_life_expectancy',\n",
      "       'explained_by_freedom_to_make_life_choices', 'explained_by_generosity',\n",
      "       'explained_by_perceptions_of_corruption'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2019\n",
      "\n",
      "Index(['country', 'happiness_score', 'whisker_high', 'whisker_low',\n",
      "       'dystopia_residual', 'explained_by_gdp_per_capita',\n",
      "       'explained_by_social_support', 'explained_by_healthy_life_expectancy',\n",
      "       'explained_by_freedom_to_make_life_choices', 'explained_by_generosity',\n",
      "       'explained_by_perceptions_of_corruption'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2020\n",
      "\n",
      "Index(['country_name', 'regional_indicator', 'ladder_score',\n",
      "       'standard_error_of_ladder_score', 'upperwhisker', 'lowerwhisker',\n",
      "       'logged_gdp_per_capita', 'social_support', 'healthy_life_expectancy',\n",
      "       'freedom_to_make_life_choices', 'generosity',\n",
      "       'perceptions_of_corruption', 'ladder_score_in_dystopia',\n",
      "       'explained_by_log_gdp_per_capita', 'explained_by_social_support',\n",
      "       'explained_by_healthy_life_expectancy',\n",
      "       'explained_by_freedom_to_make_life_choices', 'explained_by_generosity',\n",
      "       'explained_by_perceptions_of_corruption', 'dystopia_residual'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2021\n",
      "\n",
      "Index(['country_name', 'regional_indicator', 'ladder_score',\n",
      "       'standard_error_of_ladder_score', 'upperwhisker', 'lowerwhisker',\n",
      "       'logged_gdp_per_capita', 'social_support', 'healthy_life_expectancy',\n",
      "       'freedom_to_make_life_choices', 'generosity',\n",
      "       'perceptions_of_corruption', 'ladder_score_in_dystopia',\n",
      "       'explained_by_log_gdp_per_capita', 'explained_by_social_support',\n",
      "       'explained_by_healthy_life_expectancy',\n",
      "       'explained_by_freedom_to_make_life_choices', 'explained_by_generosity',\n",
      "       'explained_by_perceptions_of_corruption', 'dystopia_residual'],\n",
      "      dtype='object')\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Viewing the column names after the first cleaning process\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    print(str(year) + '\\n\\n' + str(happy.loc[year].df.columns) + '\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping the raw score columns from the 2020 dataframe <a class=\"anchor\" id=\"raw_score\"></a>\n",
    "\n",
    "I have to drop the raw score factor columns from the 2020 dataframe now because this will make renaming the \"Explained by\" columns in the dataframe much easier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the raw score columns in the 2020 and 2021 dataframes\n",
    "\n",
    "happy.loc[2020].df = happy.loc[2020].df.drop(happy.loc[2020].df.columns[2:13], axis=1)\n",
    "happy.loc[2021].df = happy.loc[2021].df.drop(happy.loc[2021].df.columns[2:13], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standardizing with a renaming dictionary <a class=\"anchor\" id=\"renaming\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell is dedicated to using a renaming dictionary to standardize all of the relevant column names\n",
    "\n",
    "# Preparing the renaming dictionary\n",
    "\n",
    "rename_dict = {'country_name': 'country',\n",
    "               'regional_indicator': 'region',\n",
    "               'explained_by_log_gdp_per_capita': 'gdp',\n",
    "               'explained_by_social_support': 'social',\n",
    "               'explained_by_healthy_life_expectancy': 'life_expectancy',\n",
    "               'explained_by_freedom_to_make_life_choices': 'freedom',\n",
    "               'explained_by_generosity': 'generosity',\n",
    "               'explained_by_perceptions_of_corruption': 'corruption',\n",
    "               'happiness_score': 'happiness',\n",
    "               'dystopia_residual' : 'dystopia',\n",
    "               'explained_by_gdp_per_capita': 'gdp'}\n",
    "\n",
    "# Standardizing all of the column names with the renaming dictionary\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    happy.loc[year].df.rename(rename_dict, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n",
      "\n",
      "Index(['country', 'dystopia', 'gdp', 'social', 'life_expectancy', 'freedom',\n",
      "       'generosity', 'corruption', 'region'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2016\n",
      "\n",
      "Index(['country', 'happiness', 'whisker_high', 'whisker_low', 'gdp', 'social',\n",
      "       'life_expectancy', 'freedom', 'generosity', 'corruption', 'dystopia'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2017\n",
      "\n",
      "Index(['country', 'happiness', 'whisker_high', 'whisker_low', 'gdp', 'social',\n",
      "       'life_expectancy', 'freedom', 'generosity', 'corruption', 'dystopia'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2018\n",
      "\n",
      "Index(['country', 'happiness', 'whisker_high', 'whisker_low', 'dystopia',\n",
      "       'gdp', 'social', 'life_expectancy', 'freedom', 'generosity',\n",
      "       'corruption'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2019\n",
      "\n",
      "Index(['country', 'happiness', 'whisker_high', 'whisker_low', 'dystopia',\n",
      "       'gdp', 'social', 'life_expectancy', 'freedom', 'generosity',\n",
      "       'corruption'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2020\n",
      "\n",
      "Index(['country', 'region', 'gdp', 'social', 'life_expectancy', 'freedom',\n",
      "       'generosity', 'corruption', 'dystopia'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "2021\n",
      "\n",
      "Index(['country', 'region', 'gdp', 'social', 'life_expectancy', 'freedom',\n",
      "       'generosity', 'corruption', 'dystopia'],\n",
      "      dtype='object')\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Viewing the standardized column names\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    print(str(year) + '\\n\\n' + str(happy.loc[year].df.columns) + '\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding the `year` column <a class=\"anchor\" id=\"year\"></a>\n",
    "\n",
    "If I am going to concatenate all of the dataframes into one, then I should make a `year` column that will show which year each datum is from.\n",
    "\n",
    "I will do this now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a `year` column to each dataframe\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    happy.loc[year].df['year'] = year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding the `happiness` column <a class=\"anchor\" id=\"happiness\"></a>\n",
    "\n",
    "The 2015 and 2020 dataframes do not have a `happiness` column. I will calculate the happiness score for these years and add the column to their respective dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the happiness score for the 2015, 2020, and 2021 dataframes by summing the relevant columns\n",
    "\n",
    "# Returning the result as `happiness` column\n",
    "\n",
    "happy.loc[2015].df['happiness'] = happy.loc[2015].df.loc[:, 'dystopia':'corruption'].sum(axis=1)\n",
    "\n",
    "happy.loc[2020].df['happiness'] = happy.loc[2020].df.loc[:, 'gdp':'dystopia'].sum(axis=1)\n",
    "\n",
    "happy.loc[2021].df['happiness'] = happy.loc[2021].df.loc[:, 'gdp':'dystopia'].sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding the `region` columns <a class=\"anchor\" id=\"region\"></a>\n",
    "\n",
    "In order to see how happiness has changed in each region over time, I need a `region` column in each dataframe. Two of the dataframes that we have currently contain full `region` columns: the 2015 dataframe and the 2020 dataframe. These dataframes display a different number of rows, so they must contain different countries in them. My goal here is to create a mapping dictionary that can map `country`:`region` key:value pairs onto the new `region` column of all of the necessary dataframes. To do this, I will have to use both of these dataframes.\n",
    "\n",
    "It is necessary to use both of these dataframes rather than just the larger one because although the 2015 dataframe is larger, the 2020 dataframe may still contain countries that the 2015 dataframe does not. This is likely true since this \"science of happiness\" and The World Happiness Report are new enough that the scope of countries included in each year is probably more variable than desired. Therefore, both dataframes probably have valuable `country`:`region` pairs for our other dataframes.\n",
    "\n",
    "To employ both dataframes, I will have to create a mapping dictionary for the `country`:`region` pairs of both the 2015 and the 2020 dataframes, and then I will have to merge those dictionaries together as one comprehensive mapping dictionary. I will do that now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell is dedicated to creating the dictionary-merging fuction\n",
    "# This function will be called when it is time to merge the two mapping dictionaries\n",
    "\n",
    "# Defining the function that will merge these two dictionaries\n",
    "\n",
    "def merge_dictionaries(left: dict, right: dict) -> dict:\n",
    "\n",
    "    \"\"\"Merge two dictionaries using a shallow copy.\"\"\"\n",
    "\n",
    "    temp: dict = left.copy()\n",
    "\n",
    "    temp.update(right)\n",
    "\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell is dedicated to mapping the `region` column to the `country` column in each necessary dataframe\n",
    "\n",
    "# Creating dataframes containing the `country` and `region` columns of the 2015 and 2020 dataframes\n",
    "\n",
    "regions_2015 = happy.loc[2015].df[['country','region']]\n",
    "\n",
    "regions_2020 = happy.loc[2020].df[['country','region']]\n",
    "\n",
    "# Creating the 2015 country-region mapping dictionary using the 2015 country-region dataframe\n",
    "\n",
    "regions_mapping_2015 = {}\n",
    "\n",
    "for i, row in regions_2015.iterrows():\n",
    "    \n",
    "    regions_mapping_2015[row[0]] = row[1]\n",
    "\n",
    "# Creating the 2020 country-region mapping dictionary using the 2020 country-region dataframe\n",
    "\n",
    "regions_mapping_2020 = {}\n",
    "\n",
    "for i, row in regions_2020.iterrows():\n",
    "    \n",
    "    regions_mapping_2020[row[0]] = row[1]\n",
    "\n",
    "# Merging the two mapping dictionaries into one regions mapping dictionary\n",
    "\n",
    "regions_mapping = merge_dictionaries(regions_mapping_2015, regions_mapping_2020)\n",
    "    \n",
    "# Adding the `region` column to each dataframe using the country-region mapping dictionary\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    happy.loc[year].df['region'] = happy.loc[year].df['country'].map(regions_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding the `rank` column <a class=\"anchor\" id=\"rank\"></a>\n",
    "\n",
    "In our analysis, it may be useful to know the rank of each country each year. To ensure that this information is retained, I will add a `rank` column to each dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the `rank` column by making it equal to +1 of the index of each row\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    happy.loc[year].df['rank'] = happy.loc[year].df.index + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizing the column order <a class=\"anchor\" id=\"column_order\"></a>\n",
    "\n",
    "Data is easier to understand when columns are ordered in an insightful manner. Since the final goal of this cleaning process is to concatenate the dataframes into one large dataframe, I will choose one preferred column order, and standardize each dataframe to that order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the preferred standard column order\n",
    "\n",
    "# Standardizing each dataframe with this preferred column order\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    happy.loc[year].df = happy.loc[year].df[['rank', 'country', 'region', 'happiness',\n",
    "                                             'gdp', 'social', 'life_expectancy', 'freedom',\n",
    "                                             'generosity', 'corruption', 'dystopia', 'year']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenating the DataFrames <a class=\"anchor\" id=\"concatenating\"></a>\n",
    "\n",
    "At long last, I can now concatenate the dataframes into one large dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the list of dataframes\n",
    "\n",
    "all_years = []\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    all_years.append(happy.loc[year].df)\n",
    "    \n",
    "# Reversing the order of the dataframes in the list, since the most recent data (2020) should be first\n",
    "    \n",
    "all_years.reverse()\n",
    "\n",
    "# Concatenating the list of dataframes into one large dataframe\n",
    "\n",
    "happy_all = pd.concat(all_years, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>happiness</th>\n",
       "      <th>gdp</th>\n",
       "      <th>social</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>freedom</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption</th>\n",
       "      <th>dystopia</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.842</td>\n",
       "      <td>1.446</td>\n",
       "      <td>1.106</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.481</td>\n",
       "      <td>3.253</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.620</td>\n",
       "      <td>1.502</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.485</td>\n",
       "      <td>2.868</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.570</td>\n",
       "      <td>1.566</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.413</td>\n",
       "      <td>2.839</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.482</td>\n",
       "      <td>1.172</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.170</td>\n",
       "      <td>2.967</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7.464</td>\n",
       "      <td>1.501</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.384</td>\n",
       "      <td>2.798</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank      country          region  happiness    gdp  social  \\\n",
       "0     1      Finland  Western Europe      7.842  1.446   1.106   \n",
       "1     2      Denmark  Western Europe      7.620  1.502   1.108   \n",
       "2     3  Switzerland  Western Europe      7.570  1.566   1.079   \n",
       "3     4      Iceland  Western Europe      7.554  1.482   1.172   \n",
       "4     5  Netherlands  Western Europe      7.464  1.501   1.079   \n",
       "\n",
       "   life_expectancy  freedom  generosity  corruption  dystopia  year  \n",
       "0            0.741    0.691       0.124       0.481     3.253  2021  \n",
       "1            0.763    0.686       0.208       0.485     2.868  2021  \n",
       "2            0.816    0.653       0.204       0.413     2.839  2021  \n",
       "3            0.772    0.698       0.293       0.170     2.967  2021  \n",
       "4            0.753    0.647       0.302       0.384     2.798  2021  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the first five rows of the dataframe\n",
    "\n",
    "happy_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>happiness</th>\n",
       "      <th>gdp</th>\n",
       "      <th>social</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>freedom</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption</th>\n",
       "      <th>dystopia</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>154</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.465038</td>\n",
       "      <td>0.222078</td>\n",
       "      <td>0.773701</td>\n",
       "      <td>0.428643</td>\n",
       "      <td>0.592007</td>\n",
       "      <td>0.226279</td>\n",
       "      <td>0.551911</td>\n",
       "      <td>0.670418</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>155</td>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>3.340090</td>\n",
       "      <td>0.286655</td>\n",
       "      <td>0.353862</td>\n",
       "      <td>0.319098</td>\n",
       "      <td>0.484496</td>\n",
       "      <td>0.182600</td>\n",
       "      <td>0.080100</td>\n",
       "      <td>1.633279</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>156</td>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and North Africa</td>\n",
       "      <td>3.006290</td>\n",
       "      <td>0.663199</td>\n",
       "      <td>0.474890</td>\n",
       "      <td>0.721932</td>\n",
       "      <td>0.156841</td>\n",
       "      <td>0.471790</td>\n",
       "      <td>0.189060</td>\n",
       "      <td>0.328579</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>157</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2.904535</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>0.415867</td>\n",
       "      <td>0.223961</td>\n",
       "      <td>0.118496</td>\n",
       "      <td>0.197269</td>\n",
       "      <td>0.100620</td>\n",
       "      <td>1.833019</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>158</td>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2.838959</td>\n",
       "      <td>0.208676</td>\n",
       "      <td>0.139945</td>\n",
       "      <td>0.284430</td>\n",
       "      <td>0.364528</td>\n",
       "      <td>0.166809</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>1.567258</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rank  country                        region  happiness       gdp  \\\n",
       "1079   154   Rwanda            Sub-Saharan Africa   3.465038  0.222078   \n",
       "1080   155    Benin            Sub-Saharan Africa   3.340090  0.286655   \n",
       "1081   156    Syria  Middle East and North Africa   3.006290  0.663199   \n",
       "1082   157  Burundi            Sub-Saharan Africa   2.904535  0.015303   \n",
       "1083   158     Togo            Sub-Saharan Africa   2.838959  0.208676   \n",
       "\n",
       "        social  life_expectancy   freedom  generosity  corruption  dystopia  \\\n",
       "1079  0.773701         0.428643  0.592007    0.226279    0.551911  0.670418   \n",
       "1080  0.353862         0.319098  0.484496    0.182600    0.080100  1.633279   \n",
       "1081  0.474890         0.721932  0.156841    0.471790    0.189060  0.328579   \n",
       "1082  0.415867         0.223961  0.118496    0.197269    0.100620  1.833019   \n",
       "1083  0.139945         0.284430  0.364528    0.166809    0.107313  1.567258   \n",
       "\n",
       "      year  \n",
       "1079  2015  \n",
       "1080  2015  \n",
       "1081  2015  \n",
       "1082  2015  \n",
       "1083  2015  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the last five rows of the dataframe\n",
    "\n",
    "happy_all.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1084, 12)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the shape of the dataframe\n",
    "\n",
    "happy_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1084 entries, 0 to 1083\n",
      "Data columns (total 12 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   rank             1084 non-null   int64  \n",
      " 1   country          1084 non-null   object \n",
      " 2   region           1071 non-null   object \n",
      " 3   happiness        1084 non-null   float64\n",
      " 4   gdp              1084 non-null   float64\n",
      " 5   social           1084 non-null   float64\n",
      " 6   life_expectancy  1084 non-null   float64\n",
      " 7   freedom          1084 non-null   float64\n",
      " 8   generosity       1084 non-null   float64\n",
      " 9   corruption       1084 non-null   float64\n",
      " 10  dystopia         1084 non-null   float64\n",
      " 11  year             1084 non-null   int64  \n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 101.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Viewing the columns, datatypes, and number of non-null values in the dataframe\n",
    "\n",
    "happy_all.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling the `region` column <a class=\"anchor\" id=\"filling\"></a>\n",
    "\n",
    "Some of the rows in the `region` column are now empty. This may be due to new countries being included in the happiness reports after 2015 (2015 is the year from which we got our region data), or this could be due to countries being entered into the datasets with slightly different names than in previous years. Let's go ahead and view some useful information about these `country` and `region` columns so that we can see if we can fill in any of these rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Somalia                    4\n",
       "Belize                     3\n",
       "Hong Kong SAR, China       1\n",
       "Northern Cyprus            1\n",
       "Trinidad & Tobago          1\n",
       "Puerto Rico                1\n",
       "North Macedonia            1\n",
       "Hong Kong S.A.R., China    1\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing which countries have a null `region` value\n",
    "\n",
    "happy_all[happy_all['region'].isnull()]['country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Google Maps, I can see where all of these countries are located. Let's see which regions our data is divided into so that we can fill in this information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sub-Saharan Africa                    267\n",
       "Latin America and Caribbean           147\n",
       "Western Europe                        146\n",
       "Middle East and North Africa          130\n",
       "Central and Eastern Europe            118\n",
       "Commonwealth of Independent States     84\n",
       "Southeast Asia                         62\n",
       "South Asia                             49\n",
       "East Asia                              40\n",
       "North America and ANZ                  28\n",
       "Name: region, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing which regions are included in the dataframe\n",
    "\n",
    "happy_all['region'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can now use the information that I gather from Google Maps to create a mapping dictionary and map these countries to their corresponding regions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new country-region mapping dictionary\n",
    "\n",
    "regions_null_mapping = {'Somalia': 'Sub-Saharan Africa',\n",
    "                        'Belize': 'Latin America and Caribbean',\n",
    "                        'Hong Kong SAR, China': 'East Asia',\n",
    "                        'Northern Cyprus': 'Western Europe',\n",
    "                        'Trinidad & Tobago': 'Latin America and Caribbean',\n",
    "                        'Hong Kong S.A.R., China': 'East Asia',\n",
    "                        'Puerto Rico': 'Latin America and Caribbean',\n",
    "                        'North Macedonia': 'Central and Eastern Europe'}\n",
    "\n",
    "# Merging the original country-region mapping dictionary + this new country-region mapping dictionary\n",
    "\n",
    "regions_mapping_full = merge_dictionaries(regions_mapping, regions_null_mapping)\n",
    "\n",
    "# Mapping the `region` column based on the comprehensive country-region mapping dictionary\n",
    "\n",
    "happy_all['region'] = happy_all['country'].map(regions_mapping_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1084 entries, 0 to 1083\n",
      "Data columns (total 12 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   rank             1084 non-null   int64  \n",
      " 1   country          1084 non-null   object \n",
      " 2   region           1084 non-null   object \n",
      " 3   happiness        1084 non-null   float64\n",
      " 4   gdp              1084 non-null   float64\n",
      " 5   social           1084 non-null   float64\n",
      " 6   life_expectancy  1084 non-null   float64\n",
      " 7   freedom          1084 non-null   float64\n",
      " 8   generosity       1084 non-null   float64\n",
      " 9   corruption       1084 non-null   float64\n",
      " 10  dystopia         1084 non-null   float64\n",
      " 11  year             1084 non-null   int64  \n",
      "dtypes: float64(8), int64(2), object(2)\n",
      "memory usage: 101.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Viewing the number of non-null values in the dataframe after mapping with the country-region mapping dictionary\n",
    "\n",
    "happy_all.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the Aggregate Data <a class=\"anchor\" id=\"aggregate\"></a>\n",
    "\n",
    "\\**How has Happiness changed in each region of the world since 2015?\\**\n",
    "\n",
    "To answer this question, I will group the data by the `region` column and calculate aggregate values. In this case, I will calculate the mean Happiness score for each region for each year. I will then display all of this aggregate data in one dataframe so that I can use it for visualization and analysis.\n",
    "\n",
    "### Calculating the Regional Mean Happiness Scores <a class=\"anchor\" id=\"calculating\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell is dedicated to creating a dataframe that contains the mean happiness score for each region for each year\n",
    "\n",
    "# Creating a list that will contain the mean happiness score of each region for each year\n",
    "\n",
    "regional_means = []\n",
    "\n",
    "# For each dataframe: \n",
    "\n",
    "# Grouping the dataframe by region\n",
    "\n",
    "# Calculating the mean happiness score of each region, and saving this information as a series object\n",
    "\n",
    "# Appending each of these series objects to the \"regional_means\" list\n",
    "\n",
    "for year in years:\n",
    "    \n",
    "    happy_grouped_regional = happy.loc[year].df.groupby('region')\n",
    "    \n",
    "    grouped_regional_mean = happy_grouped_regional['happiness'].mean().sort_values(ascending=False)\n",
    "    \n",
    "    regional_means.append(grouped_regional_mean)\n",
    "\n",
    "# Concatenating all of these series objects into one dataframe\n",
    "    \n",
    "regionals = pd.concat(regional_means, axis = 1, sort=False)\n",
    "\n",
    "# Naming the columns of this dataframe by their respective years\n",
    "\n",
    "regionals.columns = years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>North America and ANZ</th>\n",
       "      <td>7.279144</td>\n",
       "      <td>7.288750</td>\n",
       "      <td>7.226750</td>\n",
       "      <td>7.202500</td>\n",
       "      <td>7.176250</td>\n",
       "      <td>7.173498</td>\n",
       "      <td>7.128500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Europe</th>\n",
       "      <td>6.689729</td>\n",
       "      <td>6.685667</td>\n",
       "      <td>6.703714</td>\n",
       "      <td>6.810300</td>\n",
       "      <td>6.842190</td>\n",
       "      <td>6.899224</td>\n",
       "      <td>6.915000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and Caribbean</th>\n",
       "      <td>6.144683</td>\n",
       "      <td>6.065773</td>\n",
       "      <td>5.957905</td>\n",
       "      <td>5.937750</td>\n",
       "      <td>5.954429</td>\n",
       "      <td>5.981776</td>\n",
       "      <td>5.908000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Asia</th>\n",
       "      <td>5.626269</td>\n",
       "      <td>5.624167</td>\n",
       "      <td>5.681600</td>\n",
       "      <td>5.720400</td>\n",
       "      <td>5.688833</td>\n",
       "      <td>5.714858</td>\n",
       "      <td>5.810833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle East and North Africa</th>\n",
       "      <td>5.406915</td>\n",
       "      <td>5.386053</td>\n",
       "      <td>5.369684</td>\n",
       "      <td>5.294158</td>\n",
       "      <td>5.237000</td>\n",
       "      <td>5.227161</td>\n",
       "      <td>5.219706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central and Eastern Europe</th>\n",
       "      <td>5.365709</td>\n",
       "      <td>5.424882</td>\n",
       "      <td>5.521706</td>\n",
       "      <td>5.629706</td>\n",
       "      <td>5.776941</td>\n",
       "      <td>5.883814</td>\n",
       "      <td>6.039875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southeast Asia</th>\n",
       "      <td>5.317536</td>\n",
       "      <td>5.338889</td>\n",
       "      <td>5.444875</td>\n",
       "      <td>5.313444</td>\n",
       "      <td>5.273667</td>\n",
       "      <td>5.383367</td>\n",
       "      <td>5.407556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commonwealth of Independent States</th>\n",
       "      <td>5.286324</td>\n",
       "      <td>5.293917</td>\n",
       "      <td>5.251583</td>\n",
       "      <td>5.241917</td>\n",
       "      <td>5.256333</td>\n",
       "      <td>5.358342</td>\n",
       "      <td>5.467083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Asia</th>\n",
       "      <td>4.580709</td>\n",
       "      <td>4.563286</td>\n",
       "      <td>4.628429</td>\n",
       "      <td>4.603857</td>\n",
       "      <td>4.526857</td>\n",
       "      <td>4.475429</td>\n",
       "      <td>4.441714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Saharan Africa</th>\n",
       "      <td>4.202825</td>\n",
       "      <td>4.101189</td>\n",
       "      <td>4.084605</td>\n",
       "      <td>4.174316</td>\n",
       "      <td>4.290615</td>\n",
       "      <td>4.383499</td>\n",
       "      <td>4.494556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        2015      2016      2017      2018  \\\n",
       "North America and ANZ               7.279144  7.288750  7.226750  7.202500   \n",
       "Western Europe                      6.689729  6.685667  6.703714  6.810300   \n",
       "Latin America and Caribbean         6.144683  6.065773  5.957905  5.937750   \n",
       "East Asia                           5.626269  5.624167  5.681600  5.720400   \n",
       "Middle East and North Africa        5.406915  5.386053  5.369684  5.294158   \n",
       "Central and Eastern Europe          5.365709  5.424882  5.521706  5.629706   \n",
       "Southeast Asia                      5.317536  5.338889  5.444875  5.313444   \n",
       "Commonwealth of Independent States  5.286324  5.293917  5.251583  5.241917   \n",
       "South Asia                          4.580709  4.563286  4.628429  4.603857   \n",
       "Sub-Saharan Africa                  4.202825  4.101189  4.084605  4.174316   \n",
       "\n",
       "                                        2019      2020      2021  \n",
       "North America and ANZ               7.176250  7.173498  7.128500  \n",
       "Western Europe                      6.842190  6.899224  6.915000  \n",
       "Latin America and Caribbean         5.954429  5.981776  5.908000  \n",
       "East Asia                           5.688833  5.714858  5.810833  \n",
       "Middle East and North Africa        5.237000  5.227161  5.219706  \n",
       "Central and Eastern Europe          5.776941  5.883814  6.039875  \n",
       "Southeast Asia                      5.273667  5.383367  5.407556  \n",
       "Commonwealth of Independent States  5.256333  5.358342  5.467083  \n",
       "South Asia                          4.526857  4.475429  4.441714  \n",
       "Sub-Saharan Africa                  4.290615  4.383499  4.494556  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the dataframe\n",
    "\n",
    "regionals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the Regional Mean Happiness Scores <a class=\"anchor\" id=\"analyzing\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From viewing the dataframe I can already see some interesting patterns:\n",
    "\n",
    "* Regional mean happiness scores range from a low of 4.1 to a high of 7.3 (rounded) over the 6 years of study.\n",
    "* Two regions, \"North America and ANZ\" and \"Western Europe,\" are the only regions that have consistently had happiness scores higher than 6.0. Indeed, \"North America and ANZ\" is the only region that has always had a happiness score higher than 7.0.\n",
    "* The \"Latin America and Caribbean\" region is a particular edge case, in that it had a mean happiness score clearly higher than 6.0 during the 2015 and 2016 studies, but fell slightly below 6.0 (depending on rounding) after that, consitently having a happiness score between 5.94 and 5.98. However, if we round happiness scores to just the second significant digit (in this case the first decimal point), then the mean happiness score for the Latin America and Caribbean region only fell below 6.0 once in the 6 years of study.\n",
    "* Many regions of the world (5 regions) have consistently had mean happiness scores in the 5.0 - 5.99 range. Most of these regions are in Asia and/or Africa. These regions constitute half of the regions in The World Happiness Report.\n",
    "* 2 regions of the world, \"South Asia\" and \"Sub-Saharan Africa,\" have consistently had mean happiness scores between 4.0 and 4.99. The Sub-Saharan Africa region has had the lowest mean happiness score for every year of the study."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization <a class=\"anchor\" id=\"visualization\"></a>\n",
    "\n",
    "Visualization is key to understanding data. I will go ahead and chart the mean happiness score over time for each region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 619.2x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the mean happiness score of each year for each region\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "plt.figure(figsize=(8.6,6.7))\n",
    "\n",
    "year_ticks = [\"'15\", \"'16\", \"'17\", \"'18\", \"'19\", \"'20\", \"'21\"]\n",
    "\n",
    "for i in range(len(regionals)):\n",
    "    \n",
    "    plt.plot(regionals.iloc[i], linewidth=3)\n",
    "    \n",
    "plt.title('Happiness Over Time for Regions of the World', fontsize=15, y=1.01)\n",
    "\n",
    "plt.ylabel('Happiness Index', fontsize=12.5)\n",
    "    \n",
    "plt.xticks(years, labels = year_ticks, fontsize=12.5)\n",
    "\n",
    "plt.yticks([4,5,6,7], labels=[])\n",
    "\n",
    "plt.ylim((3,8))\n",
    "\n",
    "plt.legend(regionals.index, loc=(1.01,0.3), fontsize=11.5)\n",
    "\n",
    "plt.savefig(\"happiness_over_time_for_regions_of_the_world.png\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this graph a few interesting points can be seen:\n",
    "\n",
    "* Indeed, \"North America and ANZ\" and \"Western Europe\" stand out among all of the other regions as the regions with the highest mean happiness scores. Both regions appear to show minimal change in mean happiness over the course of the 6 years, but a discernible pattern is gleaned: Western Europe demonstrates a slight yet fairly consistent increase in mean happiness, while North America and ANZ demonstrates a slight yet fairly consistent decrease in mean happiness.\n",
    "* Many regions in the center of the graph seem to be cris-crossing one another and showing variable changes of direction. A lot of interesting information may be hidden in this central area of the graph. Indeed, the \"Central and Eastern Europe\" region in particular shows perhaps the greatest rise in mean happiness of all the regions over the course of the 6 years.\n",
    "* The two least happy regions, \"South Asia\" and \"Sub-Saharan Africa,\" are clearly separated from the rest of the regions on the graph as well. However, Sub-Saharan Africa is demonstrating an interesting phenomenon: it is showing a quick rise in mean happiness score, akin to the rise of the Central and Eastern European region, ever since its recent valley in 2017."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Highest Happiness Scores <a class=\"anchor\" id=\"highest\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 619.2x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the mean happiness score per year for the highest scoring regions\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8.6,6.7))\n",
    "\n",
    "for i in range(2):\n",
    "    \n",
    "    plt.plot(regionals.iloc[i], linewidth=4)\n",
    "    \n",
    "plt.title(\"Happiness over Time for World Regions with the Highest Happiness Scores\", fontsize=15, y=1.01)\n",
    "        \n",
    "plt.xticks(years, labels = year_ticks, fontsize=11)\n",
    "\n",
    "plt.yticks([])\n",
    "\n",
    "plt.ylabel('Happiness Index', fontsize=14)\n",
    "\n",
    "plt.text(2016.6, 7.15, 'North America and ANZ', fontsize=15)\n",
    "plt.text(2017, 6.84, 'Western Europe', fontsize=15.5)\n",
    "\n",
    "plt.text(2015.03, 7.25, round(regionals.iloc[0,0], 1), fontsize=13)\n",
    "plt.text(2015.03, 6.705, round(regionals.iloc[1,0], 1), fontsize=13)\n",
    "\n",
    "plt.text(2020.75, 7.15, round(regionals.iloc[0,6], 1), fontsize=13)\n",
    "plt.text(2020.75, 6.93, round(regionals.iloc[1,6], 1), fontsize=13)\n",
    "\n",
    "plt.savefig(\"happiness_over_time_for_world_regions_with_the_highest_happiness_scores.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Western Europe is experiencing a rise in happiness score, while North America and ANZ is experiencing a fall.\n",
    "* The mean happiness score of North America and ANZ has dropped by -0.2 points since 2015, while the mean happiness score of Western Europe has risen by +0.2 points over the same time period.\n",
    "* The happiness score of Western Europe is rising as fast as the happiness score of North America and ANZ is falling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Medium Happiness Scores <a class=\"anchor\" id=\"medium\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 619.2x727.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the mean happiness score per year for the medium scoring regions\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8.6,10.1))\n",
    "\n",
    "for i in range(2,8):\n",
    "    \n",
    "    plt.plot(regionals.iloc[i], linewidth=4)\n",
    "    \n",
    "plt.title(\"Happiness over Time for World Regions with the Medium Happiness Scores\", fontsize=15, y=1.01)\n",
    "        \n",
    "plt.xticks(years, labels = year_ticks, fontsize=11)\n",
    "\n",
    "plt.yticks([])\n",
    "\n",
    "plt.ylabel('Happiness Index', fontsize=14)\n",
    "\n",
    "plt.text(2016.9, 5.99, 'Latin America and Caribbean', fontsize=15)\n",
    "plt.text(2020.1, 5.7, 'East Asia', fontsize=15)\n",
    "plt.text(2018.45, 5.19, 'Middle East and North Africa', fontsize=14)\n",
    "plt.text(2017.1, 5.5, 'Central and Eastern Europe', fontsize=15)\n",
    "plt.text(2018, 5.34, 'Southeast Asia', fontsize=15)\n",
    "plt.text(2015, 5.215, 'Commonwealth of Independent States', fontsize=14)\n",
    "\n",
    "plt.text(2015.03, 6.155, round(regionals.iloc[2,0], 1), fontsize=13)\n",
    "plt.text(2015.03, 5.64, round(regionals.iloc[3,0], 1), fontsize=13)\n",
    "plt.text(2015.03, 5.42, round(regionals.iloc[4,0], 1), fontsize=13)\n",
    "# plt.text(2015.03, 5.35, round(regionals.iloc[5,0], 1), fontsize=13)\n",
    "# plt.text(2015.03, 5.3, round(regionals.iloc[6,0], 1), fontsize=13)\n",
    "plt.text(2015.03, 5.26, round(regionals.iloc[7,0], 1), fontsize=13)\n",
    "\n",
    "plt.text(2020.75, 5.88, round(regionals.iloc[2,6], 1), fontsize=13)\n",
    "plt.text(2020.75, 5.75, round(regionals.iloc[3,6], 1), fontsize=13)\n",
    "plt.text(2020.75, 5.24, round(regionals.iloc[4,6], 1), fontsize=13)\n",
    "plt.text(2020.75, 6.06, round(regionals.iloc[5,6], 1), fontsize=13)\n",
    "plt.text(2020.75, 5.37, round(regionals.iloc[6,6], 1), fontsize=13)\n",
    "plt.text(2020.75, 5.48, round(regionals.iloc[7,6], 1), fontsize=13)\n",
    "\n",
    "fig.savefig(\"happiness_over_time_for_world_regions_with_the_medium_happiness_scores.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Latin America and Caribbean has experienced a general downturn in happiness score, dropping -0.2 happiness points over the time period of the data.\n",
    "* Central and Eastern Europe has shown the largest change in happiness score so far. They began with a happiness score of 5.4 in 2015, and increased their score up to 6.0 over the time period of data collection. This is an absolutely massive change of +0.6 happiness points. This change gives Central and Eastern Europe a higher happiness score than Latin America and Caribbean, marking Central and Eastern Europe as the third happiest region in the world.\n",
    "* Southeast Asia has fluctuated more than any other region so far. Rather than demonstrating a fairly consistent trend, like many regions, Southeast Asia has increased/decreased happiness by +/- 0.1 points 3 times throughout the time period of data collection.\n",
    "* The Middle East and North Africa has shown the greatest drop in happiness so far, with a consistent drop over the time period of data collection, from 5.4 to the current 5.2, totalling a -0.2 change in happiness points. The Middle East and Africa is currently the least happy region of the medium Happiness score regions.\n",
    "* The Commonwealth of Independent States has increased happiness score by +0.2 points since 2015. They have surpassed The Middle East and North Africa region and the Southeast Asia region in terms of happiness score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Lowest Happiness Scores <a class=\"anchor\" id=\"lowest\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAGgCAYAAAAgmfRDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAACI+UlEQVR4nOzdd1hT1xsH8G9C2HtvEJAhG3GvCu69te5dZ1uttfrrsHW07olaZ9U66t7WPeqsCKKCoCB7yN6bkPP7A0mNCRAUCOP9PI9Pyz03N29Obm7enHU5jDEGQgghhJAPcGUdACGEEELqJ0oSCCGEECIRJQmEEEIIkYiSBEIIIYRIREkCIYQQQiSiJIEQQgghEtVIkjB+/Hj88MMPEssmTZqExYsX18TTSCUuLg729vbw8/Ors+dsSHx8fGBvb1/pv8ePH8Pe3h6JiYl1FldWVhYmT54MFxcXjBgxokaOOXjwYLFzr6CgAC4uLvDw8EBxcbFI2Zw5czB16tSPfj57e3ucO3euwvKqPguS3gsPDw8MGjQIFy5c+Oi4JPHx8UGPHj1q9Ji15f16LSgowOHDh4VlH/M63rx5gzt37gj/9vb2xvbt22sk1to8Zm3JzMzEyZMnKyyv7Hrg5+cHe3t7xMXF1WaIIhYvXoxJkybV2fNVV1ZWFn777Td4eXnB2dkZHTt2xIIFCxAdHS3r0D4KT9YB1DRjY2Pcv38fWlpasg6lXpoyZQo+//xz4d+dOnXCkiVL0LNnT+E2TU1N3L9/H7q6unUW18WLF/HkyRMcOXIEhoaGNXLMdu3a4datWyLbfH19oaqqiry8PDx9+hTt2rUTlvn7+2PatGk18twf68P3IiUlBTt37sTChQthZmYGDw+PGnmeKVOmYOzYsTVyrNp2//59aGhoAAD279+PEydOfFLss2fPxoABA9C1a9cairBhW7duHaKjozF8+HBZhyKVH374AQKBQNZhVGjGjBngcDhYvXo1TE1NkZiYiK1bt2L06NG4ePEidHR0ZB1itTS67gY5OTno6+tDXl5e1qHUS6qqqtDX1xf+AwA1NTWRbQoKCtDX1weXW3enR3Z2NvT19eHq6lqjSUJ0dDTS09OF2x48eIC2bdvC3d0d9+/fF24PDw9HRkYGOnbsWCPP/bE+fC8cHR2xdu1aKCoq4sqVKzX2PKqqqg3mYqWvrw9FRUUAQE2s/Ubrx4lqaPWhrq4OTU1NWYch0evXrxEQEIBffvkFbdq0gampKTw9PbF161bk5+fj4sWLsg6x2uo8SXj8+DHGjRsHDw8PODs7Y9CgQbh7966w3NvbG7t27cLEiRPh6uqKvn374tq1a8JyHx8fTJo0CZs2bULr1q3Rtm1brFixQth0/GF3w/jx47F+/XosXLgQLVu2RJcuXbB8+XLw+XzhMf38/PD555/D1dUV3bp1w/r161FUVCQsP336NPr06QNnZ2d4eXlhy5Ytwkw2NTUVc+fOFX7xTJo0CSEhIRW+/oKCAqxbtw7e3t7CpvVHjx4BAP7991/Y29sjNjZW5DH9+vXDxo0bAQBv377FV199hZYtW6JDhw6YP38+kpKShPuOHz8eS5YswdChQ9G6dWuxX9LS+LB50dvbG4cPH8aMGTOEdXTr1i1cu3YNPXv2hIeHB6ZPny7yZRwaGoqpU6fCzc0NXbp0wZIlS5CdnS3x+RYvXoxNmzYhISEB9vb2OH36NADg5s2bGDp0KNzc3NC1a1f4+PgI37fHjx/DxcUF27dvR5s2bTB+/Hix47Zq1Qo8Hg9Pnz4Vbnvw4AE6duyIjh07iiQJfn5+0NHRgYODgzD+6dOno3Xr1mjTpg2+++47kddnb2+PzZs3o0uXLujSpQtSUlJEnlsgEGDLli3o1KkTPDw8sHLlSpSWllbrfSjH5XLB4/HA4/3X8FdV/ebl5eGHH35AmzZt0KZNG6xevRrjx4+Hj48PAPFm+oSEBMyfPx/t27eHh4cHZs+eLXIeent7448//sDMmTPh5uaGbt26YevWrcLy6nwO5syZg3nz5gn/fvToEezt7XH+/HnhtqVLl2L27NkA/utuOH36NDZv3oz4+HhhtxhQ9iW3fft2dOrUCW5ubpg5cyZSU1MlPvf48eMRExODrVu3wtvbW7g9KSlJ+No6deqEHTt2iDzuxo0bGDhwIFxcXNC7d2/s3bv3k37N8vl87N69Gz179oSLiwsGDBiAv//+W/hcTk5OyM3NFe7v5eWFyZMnC/9+8eIFHB0dkZGRAQA4fvw4evXqBVdXVwwYMABnzpwR7ltaWorVq1ejc+fOcHZ2xoABA3D58mUAZefByZMn4evrWyPdBkVFRVi5cqWwqb1du3b43//+h4KCAgBl19IePXrg8OHD6NixIzw9PfHtt9+KnLv29vY4evQohg4dCldXVwwdOhRPnjwRlr/f3VB+Hbhx4wZ69+4Nd3d3jBw5UqS7ubi4GKtWrUKnTp3QsmVLjBs3Ds+ePROWV3XuVnb9/5CcnBwA4O7duyLJl6qqKs6ePYtBgwYJt7148QLjx4+Hu7s7OnXqhDVr1givb5V9T5TXwbx58zB+/Hh4enriyJEjAD7+PKgUqwHjxo1j33//vcSyiRMnskWLFjHGGEtISGAuLi5sw4YNLCYmhoWEhLAZM2awdu3asaKiIsYYY15eXszZ2Znt3LmThYeHsy1btjB7e3vm5+fHGGNsy5YtzMnJiY0fP56FhISwO3fusI4dO7KffvqJMcZYbGwss7OzY0+ePBHG5uTkxLZt28YiIiLYoUOHmL29PTt79ixjjLHg4GDm6urK9uzZw6KiotjDhw9Zv3792OLFixljjIWEhDAnJyd29epVFh8fz65du8ZcXV3ZmTNnGGOMzZ07l82YMYO9efOGhYeHs9mzZ7Nu3bpVWFczZsxg3t7e7O7du+zNmzds+fLlzNHRkT179owJBALm5eXFduzYIdw/ODiY2dnZsYiICJaXl8e6devGvv32W/b69WsWHBzM5syZw3r16iWsv3HjxjEHBwd2+fJlFhISwnJycip97+zs7IR1Ue7ff/9ldnZ27O3bt8L3xN3dnZ05c4ZFR0ezWbNmMQ8PDzZixAj24sUL9ujRI9a6dWu2Zs0axhhjiYmJrE2bNmzVqlUsPDycBQQEsNGjR7Px48dLjCE7O5utXbuWdenShSUnJ7OCggJ29epV1qJFC7Zr1y4WERHBLl26xFq3bs2WLVsmEuOECRNYVFQUe/XqlcRjjxw5UiQuOzs7Fhsby54/f87s7e1ZcnIyY4yxb7/9ls2bN48xVnYOtWzZki1YsIC9fv2aPXnyhA0YMIANGjSI8fl8Yb117NiRBQcHs+fPn4vV5datW5mnpye7fPkye/PmDVu0aBGzs7MTfhakfS+ys7PZihUrWIsWLVhQUJDU9Tt37lzWs2dP9vjxYxYcHMwmTJjA7O3t2ZYtWxhjZZ+j7t27M8YYy8nJYV26dGGTJ09mQUFBLDAwkI0bN4516dKFZWdnC88BV1dXdvToURYREcE2btwo8jmrzufg+PHjrE2bNqy0tJQxxtiGDRuYvb09++GHH4T7eHt7sxMnTojUS0FBgch5UlRUxLZs2cLs7OzYl19+ycLCwpivry/r0KGD8PP7oYyMDObl5cVWrVrF0tLShK/NycmJHTlyhMXExLCdO3cyOzs75uvryxhj7M6dO8zNzY2dOHGCRUdHsxs3brAuXbowHx+fCt9LLy8vtm3btgrLly9fztq2bcsuX77MIiIi2O+//87s7e3ZlStXWH5+PnNxcWE3btxgjDEWGRnJ7O3tmZubGysuLmaMMebj48PGjBnDGGPs8OHDrF27duzy5cssOjqanT17lnl6erLTp08zxhj7888/Wbdu3VhAQACLjY1lW7duZS1atGAxMTEsNzeXffPNN2zUqFEsOTlZeH6/78PrwfuePHki/EwxxtjSpUtZjx492JMnT1hsbCy7dOkSc3FxYfv27WOMMXbq1Cnm5OTEBgwYwAICAtiTJ09Yr1692JQpU4THtLOzYx4eHuzYsWPszZs37KeffmIuLi4sJiaGMcbYokWL2MSJE4Wx2dvbs0GDBrEnT56wly9fshEjRrAePXowgUDAGGPs66+/ZkOHDmVPnjxhERERzMfHh7m4uLCIiAjGWOXnblXXf0lmzJjB7OzsmJeXF/vxxx/ZuXPnWGpqqsg+MTExzM3Njf3www8sLCyMPXz4kHXs2JFt2LBBeIyKvifK68DOzo4dOHCAvXnzhiUnJ3/SeVCZGksSHB0dmbu7u9i/Fi1aCC+M0dHRbM+ePcI3jzHGHj16xOzs7FhCQgJjrOzDNXfuXJHjjxkzhs2fP58xVnZxc3V1Fan0EydOMCcnJ5aTkyMxSRg+fLjI8QYOHMh++eUXxhhjCxYsYF9++aVIuZ+fH7Ozs2NJSUns2rVrzNnZmQUGBgrL/f39WXx8PGOMsQEDBrBvv/2WFRYWMsYYS01NZf/++6/wAvi+sLAwZmdnx+7duyeyfcSIEcIYNm7cyPr37y8sW7VqFRs5ciRjrOzi2qFDB5EPclFREXN3d2cXLlwQvt7y/aUhbZLwzTffCMtv377N7Ozs2KNHj4Tbvv76azZ9+nTGWNlFf+jQoSLHLP+Cfvr0qcQ4tm3bxry8vIR/Dxs2TOQ5GSu7GDo6OrLs7GxhjHfv3q309W3YsIGNHj2aMcbYyZMnWc+ePRljjJWWlrI2bdoIP+xeXl7s+PHjjDHGVq9ezbp27Sq8IDPG2Js3b5idnR27ffs2Y6ys3tatWyfyXOV1KRAIWIcOHdjWrVuFZUVFRaxz585VJgnOzs7Cz46rqytzdHRko0aNYg8fPhR5TZXVb0xMDLOzsxN5TFpaGnN1dZWYJBw6dIi5u7uzjIwMkf3d3NzYoUOHhPXz9ddfizxnq1at2O7duxlj1fscJCcnM3t7e+Fnavjw4WzWrFnC9yY8PJw5ODgIP+Pvn6MfniflPxry8vKE25YvXy7yGfpQ9+7dhfVQ/to+PNc8PT3ZH3/8wRhj7PPPP2erVq0SKT937hxzdXWV+PrKj1lRkpCTk8McHR3Z0aNHRbaXf5kxxtjUqVOFCfHhw4fZlClTmKurK/P392eMlSW/e/bsYYwx1rlzZ+H7VG779u3C+iyvj/KEWCAQsLt37woTwO+//56NGzdOYqyM/Xc9cHNzE7u+u7q6iiQJZ8+eFf6gKzd27Fj2v//9jzFWliTY2dmJJPUPHz5kdnZ2LDw8nDFW9n6vXLlSWM7n85m3t7fw8/ZhkmBnZ8fu3Lkj3P/69evMzs6OpaWlsaioKGZnZ8dCQ0NFYpo0aZLwh2Vl525V139JSkpK2KFDh9iIESOYg4MDs7OzYy1atGBLliwRXlPWrVvHunXrJnItv3XrFjt06JBU3xOLFi1iHTt2FCn/1POgIjU2cLF79+745ptvxLYvWrRI+P8WFhYYPHgwDhw4gNevXyM6OlrYrPN+U2zr1q1FjuHm5ibSJWFtbS0yqM7d3R0lJSWIjIyEtra2WAzNmjUT+VtDQwMlJSUAgJCQEERHR4sMCGPvmonCw8PRuXNnuLm5YdiwYbC0tESnTp3Qt29fmJiYACgbBLVo0SJcu3YNrVu3RpcuXTB48GCJ/fmhoaEAgJYtW4ps9/T0FI62HjJkCH7//XeEhYXBxsYGf//9N2bOnAkACA4ORnp6Olq1aiXy+IKCAoSHhwv/NjMzE3vuT2VpaSn8f2VlZQBl72c5JSUlZGZmAiir05CQEImD7MLDw6UafBcWFoYhQ4aIbGvdujX4fD4iIiKE28zNzSs9Trt27bBv3z4UFxfjwYMH6NChA4CyJvy2bdvC19cXbdu2RXx8vLAsLCwMLi4uIuNabGxsoK2tjdDQUOGAt4qeOyMjA6mpqXB2dhZuU1BQgKOjY5Wve/78+ejWrRtKSkpw6tQpnDp1ChMnTkT79u2F+1RVv8nJyQDKPhfldHR0xD4H5cLCwmBtbS0y2FdHRwc2NjbCcxYQ/xypq6sLP0fV+Rzo6+vDyckJDx8+hKWlJYKDg3H06FEMHz4cSUlJuHv3Ltzc3KQeOGtgYAAVFRXh35qamiLdhdKwsrIS+VtDQwOFhYUAyuo7MDAQR48eFZYLBAIUFhYiPj6+ynPwQxEREeDz+WLXgfe7B728vHDo0CEAZd2QHTt2RHFxMXx9fWFlZYUXL15g1apVSE9PR1JSElavXo1169YJj8Xn81FaWori4mKMGTMG169fR5cuXeDs7IzOnTtj0KBBUFdXr1bce/bsEY5jKvfy5UvMnz9f+PegQYNw//59rFmzBlFRUXjz5g1iYmJErkmampqwt7cX/l1+noaGhsLa2lpYF+Xk5OTg7Owsci5+6P33r/x1lZSUIDg4GAAwcuRIkf2Li4uFXdSVnbtVXf8l4fF4GDt2LMaOHYvs7Gz4+vri/PnzOHr0KNTU1LBw4UKEhobCyclJ2D0BlL3nAITdTpV9TwCi1/naPA9qLElQU1MT+SIpp6SkJPz/sLAwjBkzBm5ubmjfvj369u0LPp8v/BIUBsUTDUsgEIDD4VRYXp5gVDTQTkFBQWxbeSIgLy+PwYMHY/r06WL76OvrQ0lJCYcOHUJgYCDu3r2Le/fu4ciRI1iwYAGmT5+O3r17o0OHDvjnn3/w8OFDbN++Hfv378fx48ehp6cncryKBl8JBALha7K0tISHhwcuXryIDh06ID09HX379hXG2rx5c5G+4HLvv9Hv13lN+bDOgYrrW15eHh07dsSPP/4oVibtYDlJr6H8fX4/lqpea/kHLSQkBI8ePcLy5cuFZR07dsTevXvh7+8PS0tLmJqaAvjvffqQQCAQSRwq2q/ch++zpPPwQ7q6usLP0aJFi1BUVIRvv/0W+vr6wuSwqvp9v69eGtK+3so+R9X5HABlF8SHDx/C2toaNjY2cHFxgZmZGXx9fXH37l2R8QJVef9C+2Fc0pJ0Lr9/jZg2bRoGDBggts/HDLKt6DwoLS0VntteXl5YtmwZEhIS8PjxY8ycORP5+fnw9fWFqakpLC0tYWVlhZycHADATz/9hDZt2ogdk8fjwdraGjdu3MCjR4/w4MEDXLp0CX/88Qd27tyJtm3bSh23mZkZjIyMRLZ9OBbnxx9/xI0bNzBkyBD07NkT8+fPx7Jly8Riel95//7770FV3wEfqujcLD9/jx49KnatKH9MVeduZdf/D127dg1RUVH44osvAJQlm927d0f37t2xYMEC/PPPP1i4cKHE62k5ab4nANFrX/nrrI3zoE4HLp4+fRrGxsbYs2cPpk6dis6dOwsH3b1fIS9fvhR53LNnz0R+hUVGRiI/P1/49/Pnz6GkpCTMQqujefPmCA8Ph6WlpfBfeno6Vq9ejby8PDx48ADbtm2Di4sL5syZg6NHj+Lzzz/HmTNnwOfzsXr1asTHx2PAgAFYuXIlLl26hPj4ePj6+oo9l62tLQCIDKQr/7t58+bCv4cMGYJr167h8uXL8PLyEo7ktbW1RVxcHLS0tISx6urqYuXKlZVm2XWtvE5NTEyEcXK5XPz22294+/atVMewsbGBv7+/yDZ/f3/Iy8uLtGBURVFREe7u7jh//jyys7NFpjx26NABMTExuH//vrAVoTz+wMBA4a9koGxufVZWFmxsbKp8Th0dHRgaGiIgIEC4TSAQCH/VVMd3330HU1NTLF68WDj4q6r6tbe3B4fDwfPnz4XHyczMrHCedvPmzRERESFsCQLKfplERkZK9Xqr+zkAgK5du8Lf3x93794Vvift27fH7du34evrW2GSUNkXhbSqe4zmzZsjKipK5BoRGhoqHExcXc2aNYO8vLzE87v8OmBiYgI7Ozv88ccfAAAHBwe0b98eT58+xfXr14X1o66uDkNDQ8TFxYnE9/DhQ+zduxdcLheHDx/GtWvX0KVLF/zvf//D5cuXYWZmJhy0VhN1CgC5ubk4deoUli1bhkWLFmHw4MGwsrJCbGysyPU9PT1d5DpQPoiwRYsWwm3vfwfw+Xy8fPlSqpa4D5Vfc9PS0kTqZ//+/bh582aV525l139Jyqc7vj+YvJy6urqwdczGxgbBwcEiAyCPHTuGoUOHSv098eGxP/U8qEidJgk6OjqIj4/HgwcPEB8fj3Pnzgk/aO8vbHPu3DkcO3YMkZGR2LhxIwIDAzFx4kRheU5ODv73v//hzZs3uH37NjZt2oQxY8YIm8GrY/r06Xjx4gVWrlyJ8PBw+Pr6YtGiRcjJyRFOpdy2bRv+/PNPxMbGIiAgAI8fP4abmxt4PB5evnyJJUuW4Pnz54iNjcWxY8cgLy8PJycnseeysLBAv3798Msvv+D+/fsIDw/HypUr8fLlS0yYMEG4X9++fREfH4/z589j8ODBwu0DBgyAtrY25s2bh8DAQISGhmLBggV4/vy58MSqD8aNG4fs7GwsXrwYr1+/RmBgIL755htERUVV2OT9oVmzZuHy5cvYvXs3oqKicPnyZWzZsgUjRoyodjNpu3btcPr0abi6ukJNTU243dzcHObm5rh69apIkjBu3DjhORYWFgY/Pz98++23wgu1NKZMmYI///wTZ8+eRUREBJYvX46EhIRqxQ2U/VpYunQpYmNjhTMTqqpfc3Nz9OzZE8uWLcOTJ0/w+vVrLFy4EAUFBRK/EAYOHAgdHR188803CA4OxsuXL/HNN99AQ0MD/fr1qzLG6n4OAMDJyQlaWlo4c+aM8FdM+/btcfnyZRgbG1d4MVRVVUVWVhYiIiKq3aXw/jGioqIkXsglmTVrFi5duoRdu3YhKioKd+7cwZIlS6CkpFRp61B0dDTu3r0r8q/8B83kyZOxadMmXLlyBVFRUdi1axeuXbsmMoOha9euOHbsGFq3bg0ulws3NzdwOByRJKE8vv379+PYsWOIiYnBhQsXsGrVKmHXQEZGBpYvX47bt28jPj4eN2/eRFxcHNzc3IT1kZSUhNjYWJFZX9WlqKgIFRUV3Lx5EzExMQgODsaCBQvw9u1bkes7YwzfffcdQkJC8OTJEyxduhS9evUS6bb5448/cPnyZYSHh2PJkiXIysrCqFGjqh2TpaUl+vbti59++gn//PMPYmJisHHjRhw9ehQ2NjZVnruVXf8lGTp0KExNTTFhwgT8/fffiIuLw8uXL7Fnzx6cOXNG2Go+duxYpKamYvny5QgPD8eDBw/g4+ODzz77TOrviQ996nlQkTpdTGnChAkIDw/H/PnzUVpaChsbGyxduhT/+9//EBgYKPzVMmTIEFy4cAErVqyAra0tdu/eLZJFmpmZwcLCAiNHjoSKigpGjRqFuXPnflRM9vb22LlzJzZv3owjR45AXV0dXl5e+O677wAAbdq0wW+//YY9e/Zg3bp1UFNTQ/fu3YXl69evx2+//YYZM2YgLy8Ptra22LZtm8SuFwBYvnw51q5di4ULFyI/Px8tWrTA3r17RfqX1dXV0a1bN/z777/o0qWLcLuSkhL27duHVatWYeLEieBwOHB3d8eBAwfqdOGjqujr62Pfvn1Yt24dRo4cCSUlJbRt2xabN2+WqskdADp37ozVq1cL3xsDAwNMmDABM2bMqHY87du3x+bNmyWugdChQwccP35cpLlNT08Pf/zxB9auXYthw4ZBWVkZ3t7eWLhwodTrb0yaNAmMMWzatAkZGRno1asXunfvXu3Yy+MfOnQo9u/fj379+sHJyanK+l2xYgWWLVuGGTNmgMfjYfTo0QgPD5cYv6KiIvbu3YtVq1Zh7NixkJOTQ/v27XH48GHhIkZVqe7ngMPhoGvXrjh16pSwebRdu3ZgjFXa1dCrVy+cPHkSAwcOxPr166WK7UOTJk3CihUrcP/+fZFpZRXp0qUL1qxZg127dmHLli3Q0dHB4MGDRfriJTl79izOnj0rsq1ly5b466+/8PXXXwtbfzIyMmBjY4MNGzagT58+wn29vLywa9cuYUsLj8dD69at8eLFC5H+6tGjR6O4uBh79+7F8uXLYWhoiNmzZwubvGfOnInCwkIsXboUqampMDY2xpdffikc8zN06FDcuHEDffv2xeHDh+Hq6ipVPX5IXl4emzZtwurVq9G/f3/o6OigS5cumDJlCm7cuCHcT05ODt26dRNOY+zbt6/welpu5MiR2LFjByIjI+Hq6oo///zzo9dPWbFiBdavX4/vv/8eOTk5sLGxgY+PjzDhr+zctbS0rPT6/yE1NTUcOXIEv//+OzZt2oS3b99CXl4ebm5u2L17t/BcNzQ0xO7du7Fu3ToMHjwYOjo6GD58uPB7TJrviQ996nlQEQ6rbuddLfP29sbw4cOFc6Q/5OPjg/Pnz+P69et1HBkhDUNRURHu3buHDh06CAf0lZSUoG3btliyZIlI6xQhden06dP48ccfK+16s7e3x5o1a0TWFCCy0+iWZSakqVNQUMAvv/yCLl26YPr06RAIBNi3bx/k5eVFWqYIIaQqjW5ZZkKaOg6Hg507dyI+Ph7Dhw/H8OHDkZCQgP379zeYpZgJIfVDvetuIIQQQkj9QC0JhBBCCJGIkgRCCCGESERJAiGEEEIkoiSBEEIIIRJRkkAIIYQQiShJIIQQQohEdbKYUkpKTq0cV1tbBRkZ+VXv2IRRHVWO6qdqVEdVozqqGtVR5WqrfvT1q3evmw816JYEHk/8FrFEFNVR5ah+qkZ1VDWqo6pRHVWuvtZPg04SCCGEEFJ7KEkghBBCiESUJBBCCCFEIkoSCCGEECIRJQmEEEIIkYiSBEIIIYRIREkCIYQQQiSiJIEQQgghElGSQAghhBCJKEkghBBCiESUJBBCCCFEIkoSSJPFGENOfjEYY7IOhRBC6qU6uQskIfWJQMDw6GUizt2PRGpWIVQUeWhmrI5mRhqwMlaHlbEGtNUVweFwZB0qIYTIFCUJpMlgjCEoMh0nbocjLiVXuD2/iI/gqAwER2UIt2moyKOZsQasjDXQzKgscdBQVZBF2IQQIjOUJJAmIToxB8dvv0FIdEbVOwPIzi/Bi/A0vAhPE27T0VCElZFGWauDsQasjNShoiRfWyETQojMUZJAGrWUzAKcuRuBf4OTPvlY6dlFSM9OgX9oinCbobaysLWhmbEGLA3VoahQP+8LTwgh1UVJAmmUcgtKcPFhFG49jQO/tOKBie2cjVDKFyAqMRspmYXVfp6kjAIkZRQIkxAOBzDRU4XVu/ENzYw1YKavBnkejREmhDQ8lCSQRqW4pBQ3/eNw8VE0Cor4Fe5nZ6aJEd7N0c7NDCkpOQDKEouot9mITMwp++/bbGTmFlfr+RkD4lPyEJ+Sh/uBbwEAclwOzA3UhF0UVsYaMNZTgRyXEgdCSP1GSQJpFMpnLJy5F4H07KIK9zPWVcGIrs3h1lxXbPaCmrI8nK114WytK9yWkVOEqMRsRL7NQVRiNqLe5iC3oKRasZUKGKIScxCVmIM777YpyHNhYaguHONgZawBA21lcGlGBSGkHqEkgTRoFc1Y+JCmmgIGd7JCJ1fjav2C11ZXhLa6Pjxs9YXPl5pViKjEHES+zUbU22xEJeagsLi0WnEXlwjwJi4Lb+KyhNuUFXnvxjb8lzzoaijRVExCiMxQkkAaLGlmLCgpyKFPWwv0bG1RIwMKORwO9LWUoa+ljNYOBgAAAWNISs9H5Nv/WhxiknJRwhdU69gFRXyERGeIvB51FXmRgZFWxhrQpKmYhJA6QkkCaXCkmbEgx+Wgq7spBnRsVuvrG3A5HBjrqsJYVxUdnI0BAPxSARJS84QtDpFvsxGfkodSQfVWd8yRMBVTW10RVsbvBka+a3FQpamYhJBaQEkCaTCknbHQysEAw7pYw1BHpQ6jE8WTKxtzYGGoji5uJgCAEn4pYpJzEfU2RzhA8m1qHqq7KHRGThEyclLw9L2pmAbvTcW0oqmYhJAaQkkCqfeqNWPBqzlsTDXrMDrpyfPkYGOiCRuT/+IrKOIjJilHZGBkcmZBtY+dnFGA5IwCPH5/KqauqnBQZDMjDZgb0FRMQkj1UJJA6q2amLFQ3ykr8mBvoQ17C23httyCEmHCEPluYGRGTsWvXxLGgPjUPMSn5uFBYCKAsi4YMwM1WL03vsGEpmISQipBSQKpd2p7xkJ9p6YsD2crXThb/TcVMzO3SJg0RH7CVMzoxBxEJ+YAzxIAAAq8sm6R92dUGOqoNLmpmIyxBpdgElIXKEkg9Yo0MxYUFeTQtwZnLDQEWmqKcLdVhLutHoCyL7W0rEKRhZ+ik3JQUFTNqZh8Ad7EZ+FN/PtTMeVgaaj+bnCkBlrx6vYy4efniyNH/kRw8EsUFxfByMgYn33mjfHjJ0FFRbVGnys5OQmrVq3AkiXLoaWlhbdvEzBixEAsX74KXl7dq32848ePYMuWDRgyZAQWLFhU7cd36tQKs2d/jTFjxlf7sYTUBkoSSL1Q32Ys1HccDgd6WsrQkzAV8/1uipikHBRXeypmKV7FZOJVTGbZhrNB8LDVw9gedtDRUKrhVyLq0aP7WLToG/TtOwDDho2CkpISQkNf49ChfQgI8Me2bbshJ1dziaGfny98fR/V2PGuXLkEKytrXL9+BXPnfg1FxerV144d+2BkZFxj8RDyqShJIDLVkGYs1HfvT8Vs72wEACgVCBCfUjYVM+rdOg5xKbnVnooZEJaKkOgMjOhqg888TGutO+LIkYNo3bodFi/+SbjN07M1LC2b4bvv5sHX9xHat+9UK8/9qSIiwhEa+hobN27Dt99+hdu3b6J3737VOoazs0stRUfIx6EkgchEY5mxUN/JcSVPxYxNzhNZMTJBiqmYhcWlOHgtFI+DkzCxjwOMdWu26R8AMjIyYGBgILa9TZt2+OKL2dDXNxRue/s2Adu3b8GzZ09RWFgIT89WmDNnHszNLQAAe/fuxNGjh3D9+j3hY8LCXmPy5LHYsmUHEhPf4rfflgIA+vfvjsmTp6Nv3wHvjv0W3377FQIC/KGmpo6hQ0dg4sSplcZ+5cpF6OrqoVWrNmjVqi0uXjwnliS8fBmE7ds3IzT0NXg8Hjw9W2Pu3HnC1oMPuxseP36Egwf34fXrVygt5cPCohkmT56Gzz7zrm7VEvJRKEkgdao6MxaGd7WBe3M9GlBWw+R5crA20YC1iYZwW2ExH9Hv7i9R3lWRnCF5KmZoXBZ+/uMJBnZsht5tLcCTq7lBo+3adcDRo4fw3Xfz0bNnb3h4eEJXVw88Hg8TJkwR7pecnITp0ydCX18fCxYsAmMM+/fvwezZ07Bv32Ho6elX+Vzt23fCxIlTceDAXqxf7wMrK2uUlpaN6di9ezvGjZuE0aPH48aNa9i9+3fY2NiiU6cuEo8lEAhw/fpV9OjRGxwOB7169cWyZT8iJiYaFhaWAIDCwkIsXPg1WrduiylTvkBOTja2b9+Cn3/+Hjt37hM7ZnBwEBYu/BqDBg3D5MnTkZ+fh8OH/8TSpT/i1KlL0NbWFnsMITWNkgRSJ6SesaCqgMGdG9+MhfpOSUF8KmZeYQn8X6fg5J1wsZkU/FIBTt+NwJNXyZjUxwFWxhofHvKjfPHFbGRnZ+HKlUt4+LCsBcDSshm6du2GUaPGQkOj7HmOHTuCoqIibNy4HVpaWgAAD49WGDlyEP766xC+/HJ+lc+lra0NU1MzAIC9fQvhwEUA6Nt3AKZOnQEAcHPzwJ07NxEQ4FdhkvDw4UOkpCQLWw4++6wrVFRUcfHiOcye/RUAIDIyHNnZWRgx4nM4O7sCADQ1tfD0qR8EAgG4H5zvkZER6NLFS2QApKGhEaZMGYfg4CB07Ni56gol5BNRkkBqHc1YaJhUleTRxc0E3m0t4XM0AE9eJYvtE5ucixV/+qFXawsM6mwFRflPe+8UFBTw/fc/Y9q0mXjw4B6ePHmMgAB/HDiwF5cuncf27XtgYmKK588D0LKlpzBBAAAtLS14erbGs2dPPykGAMIvcQDg8XgwMDBATk5OhfufPXsWzZpZwdDQSLhfhw6dcOXKJXzxxWzweDxYWjaDhoYmFi2aj27deqJ9+07w9GwNDw9Picfs128g+vUbiIKCAkRHRyImJgZPnz4BAJSUVO8W5oR8LEoSSK1JzSzA6XsR+Pdl5TMWPnM3wcCOVk1+xkJ9pa2uhFmDndEuLAUHr75GZq7oFxRjwBXfGDwNTcHE3vZo0Uznk5/TwMAQQ4YMx5Ahw8Hn83H16t9Yu/Y3/PHHLvz441Lk5GTD1tZO7HE6OjqIjIz45OdXUhKdlcDhcMGY5FEb+fn5uHnzJvLz89Gnj5dY+YMH9/DZZ15QUVHF1q27sH//Hly+fAmnT5+Ampo6ZsyYgyFDhos9rqCgAGvX/oabN68BACwsLGFraw8AFcZCSE2jJIHUOKlnLNjrY9hnNjRjoYHwsNWHvbk2Tt55gzvvFmN6X3JmAdYefYbOrsYY5d0cKtW86VRQUCD+978FWLVqA5ycnIXbeTwe+vUbiPv37yI6OhIAoKGhgfT0NLFjpKenQVOzbJArh8OBQCA6/TM/v/pLXlflzp2yBOHXX9dAXV2022XFip9x8eJZfPZZWfJgbW2DZctWoqSkBM+fB+DEib+wfv0q2Nk5iLxmANi4cQ18ff/FunWb4ebWEgoKCoiMjMC1a5dr/DUQUhFKEkiNoRkLjZ+KEg8TejugraMh9l9+hSQJgxvvvXiLF+FpGNfTDp724jMVKmJhYYH8/DycPHkUTk4rRMpKS0uRkBAPe3sHAICrqzvOnz+LzMxMYZdDZmYm/PyeYNCgoQAAVVVVFBUVIScnB+rq6gCAFy8CRI774TiAj3HlyiU4OTlJnHHQrVtPHD9+BMnJSYiICMeKFT/j4MHj0NbWRqtWbWBmZo4HD+4hKSlRLEl4+TIQbdu2R+vW7YTbHj9+CKCs9YaQukBJAvlkNGOh6bG30MbSKW1w/kEUrjyOgeCDb62svGJsOxMET3t9jOthB001xSqPqaGhiS++mA0fn43IyspEnz79YWBgiNTUFJw7dxopKcn47be1AICRI8fg778vYv782Zg0aRoYYzhwYC/k5eUxcuRoAEC7dh3h47MRq1Ytw7BhoxAW9hpnzpwUeU41tbLk4Z9/bqFNm3aorqSkRDx79hTz50seKNmzZ2/89ddB/P33BQwdOgIAww8/LMTYsRMhLy+P48ePQE1NHS1bthJ7rIODIx48uIvLly/C0NAI/v5P8NdfBwEARUWF1Y6VkI9BSQL5aDRjoWlTkJfD8K42aO1ggH2XQxCTJH4O+L9OQUhUBkZ5N0cnV+Mqk8NRo8bCzMwCp04dx+bN65CTkwMtLS3hAksmJqYAykb5b9u2G7//vgUrVvwMOTk5eHh4YunSlTAwKFtLwdKyGRYv/hEHDvyBb7/9Co6Ozli+fDVmzJgkfL5WrdqgTZv22LRpLQYMGIzRo6u3HPK1a5chEAjQu3dvieW2tvZo1swaly6dx8SJU7FunQ927PDB8uVLwOeXwNHRGZs2bRcZgFlu7tz5KCoqwubN6wEAzZpZ4ddf12DLlg0ICnqBPn36VytWQj4Gh9XBCJiUlIpHBX8KfX31Wjt2Y1FbdSTtjIU+bS3Qqx7PWKBzqGrS1FGpQICrvrE4dz8SJRUsA93CUhsT+zjAQEu5NsKUKTqPqkZ1VLnaqh99ffVPejy1JJBqoRkLRBI5Lhd921nC004f+y+/wuvYTLF9QqIzsGTPYwzubI0erc2oVYmQBqBBJwmCaq4/Tz4ezVgg0jDUUcHCMR649zwBx2+/EbsrZTFfgOO338A3JAmT+7aAuYGajCIlhEijQSYJviFJOHbrDbLyiqGroQhDbZWyfzrKMNRRgaG2MnQ1leiXSg0on7Fw6VE08iuZsWBrpomRNGOBoOxGU5+5m8LVRg+Hrr1GQFiq2D5RiTlYtv8J+rSzwIAOzSDPq5/dUYQ0dQ0uSSgo4mPX+WDhaOqUzEKkZBYiKDJdZD85Lgf6Wsow0lGBgXZZ8mD07r9a6oq1dhe7xoJmLJBPpa2uiLlDXeD/OgWHrr1Gdr7o0s6lAoaLD6Ph/zoFE3s7wM5cSzaBEkIq1OCShJz8YrHpVpKUChgS0/ORmJ4vVqbA45YlDtoqMNBRhpG2irAFQkNVoUl/2THG8DIyHcdpxgKpARwOB60cDOBgqY1jt8LwIDBRbJ+3aflYdfgpvFqaYvhnNlBWbHCXJUIarQb3aTTQVoF7cz08eyPehCmtYr4AcSl5iEvJEytTUpD7r+tC5L8qUFOu3gpyDU1jmbFA6h81ZXlM7eeIdo5GOHDlFVKzxOf5334aj2dhqZjQyx5uzfVkECUh5EMNcgqkgDE8C0tFQnoBIuMzkZRRgOSMAvBLJU+9qimqSrx3LQ5lyYPRu/830Faut79+pJlW05RnLNC0rKrVdB0VFZfizL0IXPeLrXDlwHaOhvi8uy00VBrGuUbnUdWojipXX6dANsgkodz7lSoQMKRnFyIpowBJGWXdDMkZBUhKz0dKZqFUXRSfQlNVAYbayjDQUXmXPCgLEwiFT7wz3qeo7MSjGQt04ZJGbdVRREI29l0OQbyEFj2grPVhdHdbtHM0rPddgHQeVY3qqHKUJNQCaSuVXypAWlYhEtPzhUlEUno+ktILkJ5diNquAJ3yGRjvJQ+GOsrQ11IGT652+/Ml1RHNWPgPXbiqVpt1xC8V4O9/o3HxYVSFiaqLtS4m9LKHrqaSxPL6gM6jqlEdVa6+Jgn1s428hvHkuGVf0BJ+CZfwS8taHD5IHpIy8sVuifux0rOLkJ5dJNbXz+VwoKupKOzCKG+BMNBRgZ6GErjcmv31RDMWSH3Dk+NiYEcreNob4MDlV3gTnyW2T2BEGn7c+xjDP7OBV0tTmplESB1qEklCZeR5cjDVV4OpvviiLoXFfGECkZiej+R3LRGJ6fnILSiRcLTqETD23xROiE7h5MmVTeEUGTz5kVM4qzNjYVBnK3SmGQukjpnqqWLxuJa4/TQeJ/8JR1Gx6CJMRcWlOHw9FI+DkzCpjwNM9FRlFCkhTUuT6G6oDXmFJcIWh6Tyboz0fCRl5IutMlfThFM4ywdRlv+/jgo0VORFfv1nFZVi1+kXNGOhAtQEWrW6rqO0rEL8efU1AiPSJJbz5Djo36EZ+razrPXuOmnReVQ1qqPK1dfuBkoSahhjDDn5Je+Shw+SiIx8FJfU7gwMZUU5GLxLHEoFDP6vUyrct7HOWKiO+ngO1TeyqCPGGP59mYS/boZV2Gpnpq+KSX1awNpEo05jk4TOo6pRHVWOkoRaIE2lrlq1HHFxsdi6dZfE8ujoKKxfvwovXwZCX98QM2fOQdeu3WojXDDGkJlbjKT0fCRm5CM5veDdYMp8pGQWVDrDoKZ5vpuxYNQIZyxUB124qibLOsrOL8ZfN8LwOFjy9FwOB+jRyhxDOlvLtBWMzqOqUR1Vrr4mCY16TIKfny8uXjwHd/eWEsvz8/Mxb95stGzZCvv3/4V//32IX375Afv2WcHKyrrG4+FwONBWV4S2uiIcLLVFygQChrTswv9aIN5rfUitwSmctmaaGOHVHM0b8YwF0nhoqChgxkAntHM0xJ9XXyMjR3TALWPAtSexeBqagol9HODUTEdGkRLSODXaJKGgoABr1vwKFxe3Cve5cuUSeDwe/ve/JeDxeDA3t8CTJ//i5csXtZIkVIb77l4T+lrKcLYSLeOXCpCaVfhu5sV/gyeTM/KRnl0k1RROmrFAGjK35npYYa6Fk/+E4/bTeLHy1KxCrD/6DJ1cjDGqW3OoKjXu1VEJqSuNNknYtWs7PDw8oaurhxcvnknc5+nTJ+jYsQt4vP+qYc2aTXUTYDXw5LgwerdI04eKS0qRnFmApPQCJL9bRKq8BSInrwRmhmro6m5CMxZIg6esyMP4nvZo28IQ+y+/knhflvuBb/EiIg3jetjB016fEmJCPlGjTBKCgl7g9u0b+PPPYzh69FCF+8XHx8HW1h7r1q3CvXu3oaurh6lTZ6Jjx851GO2nUZCXg5m+GswkTOEUMAZDAw3qBySNip25FpZOaY0LD6Nw+d8YlApE29Ky84qx/WwQPGz1MK6nPbTVFWUUKSENX6P7aVlcXIxVq5bjq68WQEOj8lHP+fn5+Ouvg1BXV8fatZvh7d0D33//LV69CqmjaGsXLTpDGit5nhyGdrHBTxNboZmR5IFZAWGp+HHPY/zzLB51MD6bkEap0SUJ+/bthpmZOby9u1e5r5ycHKytm2PGjDmws3PAuHGT0LZte5w/f7oOIiWEfCoLQ3X8MMETI72aQ4EnfjkrKOLjwJXXWPtXAJIyxLsnCCGVa3TdDTduXEVaWip69CjrMigpKYFAIECPHp1x/fo9kX319PRhamomss3c3BJRUZF1Fi8h5NPIcbno3dYCLe30cODKa4kLh72KycSSvb4Y3MkKPduY0/gcQqTU6JIEH5+d4PP/u2nRsWNH8OpVMH7+eYXYvk5OLnj8+JHItqioSBgbG9d6nISQmmWgrYJvP3fHvRdvcezWGxR8cPOyEr4AJ+6EwzckGZP7OsDC8NPmjxPSFDS6dNrIyBhmZubCf+rq6lBUVISZmTkAIC0tFUVFhQCAQYOGITY2Btu3b0F8fByOHz8CP7/HGDhwiCxfAiHkI3E4HHRxM8Gv09vC005f4j7RSTlYtt8Pp/4JRwm/dpdQJ6Sha3RJQlUGDeqNmzevAwCMjIywceM2BAT4Yfz4kTh//gx+/XUN7OwcZBwlIeRTaKkpYs5QF8wZ4gxNCUuOCxjDpUfRWPLHE7yOqfi+JoQ0dY1+WeamjuqoclQ/VWvodZRXWIITt9/g7vO3Fe7T1cMUI7raQFnx43pgG3od1QWqo8rV12WZm1xLAiGkaVFVksekPi2w8HN36GspSdznTkA8ftzzGM/CUus4OkLqN0oSCCFNQotmOlg2tS16t7GApCVEMnKKsOXUC+w4F4TsvOK6D5CQeoiSBEJIk6EoL4eR3s3x44RWElcpBQDfkGT8sPtfPAh8S4swkSaPkgRCSJNjZayBJZNaYWgXa/DkxC+DeYV87L0Ugo3HnyM1s0AGERJSP1CSQAhpknhyXPTv0AxLp7SGrZnkW6cHRabjp72+uP4kFgIBtSqQpoeSBEJIk2asq4pFY1tifE87KCnIiZUXlZTir5th+O2QP+JTcmUQISGyQ0kCIaTJ43I48GpphhXT2sLNRlfiPhEJ2fhl3xOcvReBEr6gjiMkRDYoSSCEkHd0NJTw1XBXzBjoBHUVebHyUgHD+QdRWLr/CcLjs2QQISF1i5IEQgh5D4fDQVtHQ6yY1hbtnYwk7pOQmoffDvrjyPVQFBbzJe5DSGNASQIhhEigrqKA6QMcMX+kG3Q1FMXKGYAb/nH4aY8vnr5KrvsACakDlCQQQkglXKx1sWxqW3TzNIOENZiQll2In3c/wqrDT+H3KhmlAhqvQBqPRneraEIIqWnKijyM7WGHto6G2Pd3CN6m5YvtExqbidDYTOhoKMK7pRm6uJlATVl8XAMhDQm1JBBCiJSam2ril8ltMLBjM8hxJbUrAOnZRTh5JxzfbnuA/ZdfIS6Zpk02BatWLcfcuV9UWD5//hx06tRK5N/du3fqLsCPRC0JhBBSDfI8LgZ3tkYrBwPsv/wKEQnZEvcr5gtw93kC7j5PgIOFFrq3Mod7cz1wK0guSMPl5+eLixfPwd29ZYX7REVFYunS30T2UVfXqIvwPgklCYQQ8hHM9NXw/XhPvIxMx90Xb+FfyeDFVzGZeBWTCT1NJXi3NENnN2OoKlFXRGNQUFCANWt+hYuLW4X75OXlIiUlGY6OztDV1avD6D4dJQmEEPKRuBwOXKx14d22GV68SsQt/3jcD3qLouJSifunZhXi+O03OHs/Ah2djdHN0wwmeqp1HDWpSbt2bYeHhyd0dfXw4sUziftERkZCQUERhoaSp9TWZzQmgRBCaoCxrirG9rTD+tkdMbqbLQy0lCvct7hEgNsB8fhxz2OsPxqAZ29SIaA7TjY4QUEvcPv2DcyZM6/S/aKiIqCuroaff/4egwb1wvTpE/Do0f26CfITUUsCIYTUIBUlHnq0Nkc3TzO8iEjDTb9YvIzKqHD/l1EZeBmVAQMtZXTzNENHF2OoKNGlub4rLi7GqlXL8dVXC6ChUfnYgujoKOTnF6BTpy6YMGEK7t69jUWLvsHvv/8BJyfnOor449CZSAghtYDL5cC9uR7cm+shPjUPN/3j8DDoLYpLJK+jkJxZgL9uhuH0vQh0cjZGt1ZmMNJRqeOoibT27dsNMzNzeHt3r3LfmTPnYsKEKVBXVwcA2Nra4fXrVzh37hQlCYQQ0tSZ6qliQi97DPvMGveev8Wtp3FIzSqUuG9RcSluPo3DzadxcLHWRfdWZnCy0gGXQ7Mi6pMbN64iLS0VPXp0BgCUlJRAIBCgR4/OuH79nsi+cnJywgShXLNmzfDmTVidxfuxKEkghJA6oqokj95tLdCztTmevUnFDb9YvIrJrHD/wIg0BEakwVBHBd09zdDB2QjKinTZrg98fHaCz//vvh3Hjh3Bq1fB+PnnFWL7/vjjd9DS0sG33y4WbgsNfY1mzazrJNZPQWcbIYTUMS6Xg5Z2+mhpp4+45Fzc8I/Do5eJFd6COik9H4evh+L03XB0cjFBN09TGGhTV4QsGRkZi/ytrq4ORUVFmJmZAwDS0lKhpqYGRUUldOzYBWvX/gZXV3e0aOGIa9cu48WL51i48HtZhF4tlCQQQogMmRmoYVIfBwzvaoO7zxNw62kc0rOLJO5bUFSK636xuOEXC1cbXXRvbQ5HS21wqCui3hk0qDe+//5n9O07AH369Ed+fh727t2BlJRkWFs3x4YNPjA1NZN1mFXiMFb7825SUnJq5bj6+uq1duzGguqoclQ/VaM6qlpN1lGpQICA0LKuiNC4rCr3N9FTRTdPM3RwMoKiglyNxFAb6DyqXG3Vj76+etU7VYJaEgghpB6R43LRysEArRwMEJ2Yg5v+cfg3OAn8UsldEQmpeTh49TVO3QlHFzcTeLc0hV4lazQQUh2UJBBCSD1laaSOKf1aYLiXDf55loDbT+OQmVsscd/8Ij6u+Mbg6pMYuDfXQ49W5rC30KKuCPJJKEkghJB6TkNFAQM6NEOfthZ4GpqCG35xeBMvuSuCMSAgLBUBYakw01dF91bmaOtoCEX5+tsVQeovShIIIaSB4Mlx0aaFIdq0METk22zc8IuDb0gSSgWSh5bFpeRh/+VXOHH7Dbq4m6BbSzPoaCjVcdSkIaMkgRBCGiArYw1MH+CIkeVdEQHxyMqT3BWRV8jH5X9jcPVxLFra6aF7K3PYmmlSVwSpEiUJhBDSgGmqKWJgJyv0bW+JJ6+SccMvFpFvJY+SFzAGv9cp8HudAgtDNXT3NEdbRwPI86grQlZKBaXwT36OwuQ82Kg0h6macdUPqkOUJBBCSCPAk+OivZMR2jsZITw+Czf84+D3KrnCroiYpFz88XcIjt9+g64eJvDyMIO2umIdR920FfALsfPFfoRlRgAAlHlKWNjqSxiq6Ms4sv9QkkAIIY2MjakmbEw1MdKrOe4ExOPOs3jk5JdI3De3oAQXH0bj8r8x8LTXR/dW5rAx0aCuiFqWU5yLbc/3IjYnXritgF+I0Iw3lCQQQgipfdrqihjSxRr9O1jCNyQZ1/1iEZOUK3HfUgGDb0gyfEOS0cxIHd1bmaG1gyHkedw6jrrxyyjMhM+z3UjKTxErM1IxlEFEFaMkgRBCGjl5nhw6uhijg7MR3sRn4bpfHJ6+ToGgggV3oxJzsOdiCI7fDkdXdxN4eZhCU426ImpCUl4yfJ7tQUZRpliZl1kn2GrXr5s+UZJACCFNBIfDga2ZFmzNtJCeXYjbAfH451kCcgskd0Vk5xXj/IMoXHoUjdYtDNCjlTmsjDXqOOrGIyYnDtue7UVuSZ5Y2XCnvuhq8JkMoqocJQmEENIE6WgoYdhnNhjQoRn+DU7CDb84xKVU3BXx78sk/PsyCTYmGujWygyt7A3Ak6OuCGmFZURgx4v9KCwtFCsbZjsAI5371st7W1CSQAghTZiCvBy6uJmgs6sxQmMzccMvDk/DUlDRrf/CE7IRfj4Yx9TewNvDFJ+5m0JDVaFug25gglJDsCfoIEoEfJHtXA4XYx2Go51xKxlFVjVKEgghhIDD4cDeQhv2FtpIzSzArYB43H2WgPwivsT9s3KLceZeJC48jELbFobo3soclkafdsfBxuhJYgD+DDkGARO9QRePI4cpzmPhpu8so8ikQ0kCIYQQEXpayhjp1RyDOlrhUXAibvrFIT5VvB8dAPilDA+CEvEgKBG2Zpro3socLe30IMelroi7cQ9xPPQcGESbZRTlFDDDZRLsdZrLKDLpUZJACCFEIkUFOXR1N8VnbiYIic7ADb84PH+Tigp6IhAWl4WwuCxoqyvCu6UpuriZQF2l6XVFMMZwJeoWLkZeFStTlVfBHLepsNQwl0Fk1UdJAiGEkEpxOBw4NtOBYzMdJGcW4JZ/HO69SEBBUanE/TNyinDqnwicfxCFdo5lXRH6+k2jK0LABDjz5hJuxd4TK9NS1MRc92kwVq1fayFUhpIEQgghUjPQUsbn3WwxuLMVHgYl4oZfHBLT8yXuW8IX4N6Lt7j34i3sLLTQwkIbztY6sDLSAJfb+FZ0LBWU4sirU/g30U+sTF9ZF1+6T4euso4MIvt4lCQQQgipNiUFHrxbmqGrhymCI9Nxwz8OL8LTKtw/NCYToTGZOHc/EmrK8nCy0oGzlQ6crXWh2QhmR5SUlmBf8F94nhIkVmaqZoy57tOgodDwWlMoSSCEEPLRuBwOnK114Wyti6T0fNz0j8P9wLcoLJbcFQGU3S/icXASHgcnAQAsDdXhbK0DF2td2JhqNLhBj4X8QuwK/BOvM96IlVlrNsMs18lQkVeWQWSfjpIEQgghNcJQRwVjethhSBdr3A98i5v+cUjOKKjycdFJOYhOysGlR9FQVuTBsZk2XKx14WylAx0NpTqI/OPlluRh+/M/EJ0dK1bmqGOP6S7joSDXcFtKKEkghBBSo5QVeejRyhzdPM0QFJEGv1cpCI5OR3p2UZWPLSjiw/91Cvxfl938yFRfFS5WunC21oGtmVa9uuFUZlEWfJ7tQWJekliZp4EbJjiOAo/bsL9mG3b0hBBC6i0uhwNXGz242uhBT08NAcGJCIxIQ1BEGsLislAqqGgy5X/iU/IQn5KHK74xUJSXQwvLssGPzta6MNCSXRN+cn4qtj7bjbTCDLGyTiZtMcp+CLic+pPQfCxKEgghhNQ6DocDcwM1mBuooW87SxQU8RESnYGgiDQERqQhTYpWhqKSUjx7k4pnb1IBlHVvuLwb/GhvoQVFebnafhkAgPjct/B5ths5xeL3uuhp6YWB1r3B4TSO2RuUJBBCCKlzyoo8tLTTR0s7fTDGkJiej8DwNARGpuN1TCb4pYIqj5GUno+k9Hzc8I+DPI8Le3MtOFvrwsVaB0Y6KrXyRR2RFYXtz/ehgC8+1mKwTV/0sOxa488pS5QkEEIIkSkOhwNjXVUY66qiZxsLFJWU4nVMprBrIkmKwY8lfAGCItMRFJmOozcBPU2lsoTBSgcOltpQVvz0r7vgtNfYFfgnSgSit9bmgIPRDkPR0aTtJz9HfUNJAiGEkHpFUV4Orja6cLXRBQAkZ+QjMCIdQRFpCInJQHFJ1a0MqVmFuBMQjzsB8ZDjcmBrplk2Y8JaF2b6qtVuZfBPeo4DwUdRykSndspx5DDJaTRaGrhW63gNBSUJhBBC6jUDbRV081RBN08zlPAFCIsrb2VIr/DGU+8rFTC8isnEq5hMnLgTDi01hXfdErpwbKYNVSX5Sh9/P/5fHH19RuxGTQpceXzhOhEtdOw+6fXVZ5QkEEIIaTDkeVzhfSRGeQPp2YUIikxHYHgagqPTK7yfxPsyc4tx/8Vb3H/xFhwOYGOiCZd3MyYsjdTBfa+V4Vr0bZwLvyx2DGWeMma7TYG1pmWNvr76hpIEQgghDZaOhhK6uJmgi5sJ+KUCRCRkI/DdjImYJPHZBx9iDHgTn4U38Vk4cy8S6irycLbSgZOVDmLl/HD3rfiNmjQU1DHXfRpM1Yxr4yXVK5QkEEIIaRR4clzYmWvBzlwLwz6zQVZuUVkrQ0QaXkamI6+QX+UxcvJL8OhlIvzyboJnECdWrqukg688pkNPWbc2XkK9Q0kCIYSQRklTTREdXYzR0cUYAgFDZGI2giLKkobIhGxIXMqJI4C89QvwdBPFywrUoZvjjZCwIjhbFUFbXbG2X4LMUZJACCGk0eNyObAx0YSNiSYGdbJCbkEJXr5rZQiKTEd2XjHA5UPBNgBymuJ3sxTkaqLotSeel+biecgrAICZvhpc3t2YqrmZJnhyDX+FxQ9RkkAIIaTJUVOWR1tHQ7R1NISAMYQlpODPsIPIFIgnCKVZuigO8wAEol+ZcSm5iEvJxeXHMVBUkIOjpbZwbQY9GS4ZXZMoSSCEENKk5RTn4GTcIWQKxG/UxM02RkGoC8AqbyUoKi5FQFgqAsLKlow21lWBs1XZ6o/2FlqQ59XNktE1jZIEQgghTVZqQTp8nu1GaoF4C0J749b4/LMhSOpYiMB3YxnC4jLBL636xlRv0/LxNi0f1/1iocDjwt6i7MZUrta6MNBWbjD3dqAkgRBCSJOUkJuIrc92I6s4R6ysm0UXDLHpBw6HA1N9NZjqq6F3WwsUFvPxKiZTeGOqlMzCKp+nmC8QTsv8C2HQ1ypfMloXDpZaUFKov1/F9TcyQgghpJZEZsXg9+d/II+fL1Y20Lo3elp6Sfy1r6TAg3tzPbg31wNjDMkZBcLBj6+iM1DMr3rJ6JTMQtx+Go/bT+PBk+PA1kwL7V1N4Nlct0buMVGT6lc0hBBCSC17lR6GnYEHUFxaLLKdAw5G2Q9GZ9P2Uh2Hw+HAUEcFhjoq6N7KHMUlpQiNyxROs3ybJp6AfIhfyhASnYGQ6Ayc01DET5NaQ0NF4aNeV22gJIEQQkiT8Sw5EPteHgH/gxs1cTlcTHT8HK0M3T/62ArycnC20oWzlS4+72aL1MwC4WJOwdEZKCqufMnotOwi+L9OgZeH6UfHUNMoSSCEENIkPEx4giOvTordqEmeK49pzuPgrNeiRp9PT0sZXT1M0dXDFPxSAd7EZSEwsuzGVLHJkpeMrm8LNEmVJDx58gStW7cW2y4QCLBv3z5MnTq1xgMjhBBCasrNmLs4/eai2HZlnhJmuk5Gcy2rWn1+nhwXDpbacLDUxoiuQEZOEYLeJQzBUekoFTB0cDaCs5VOrcZRXVIlCZMmTcLo0aOxYMECKCuXLRAREhKCH374AbGxsZQkEEIIqZcYY7gYcRVXom+JlanLq2GO+zSYq5vUeVza6oro7GqCzq5lz62vr46UFPFZFrIm1RqShw4dwr///osBAwbg7t27WLt2LUaMGAEbGxtcvix+C01CCCFE1gRMgOOhZyUmCDpK2vjGc5ZMEoSGRKqWBA8PD5w5cwaLFi3CjBkzICcnhy1btsDb27u24yOEEEKqrVRQij9DjsEv6ZlYmZGKAea6T4O2kladx9XQSJUkZGZmYsOGDbh69SqGDRuGsLAwLFmyBDk5ORg0aFBtx0gIIYRIrbi0GHuCDuFl2iuxMgt1M8xxmwo1BVUZRNbwSJUk9OrVC7q6ujhw4ABatWoFxhgOHjyIZcuW4cSJEzh06FBtx0kIIYRUqYBfgN+f70d4VqRYma2WNWa4ToIyT0kGkTVMUo1JGDNmDM6ePYtWrVoBKFtAYsKECbh48SLU1dVrNUBCCCFEGtnFOdj0dKfEBMFFzxFz3KZSglBNUrUkfP311wCApKQkREZGwt3dHbm5uTA2Nsbvv/9eqwESQgghVUkryMDWZ7uRXJAqVtbWyBNjHYZDjtsw78QoS1IlCfn5+Vi8eDGuXbsGLpeLq1ev4rfffkNGRga2bdsGXV3d2o6TEEIIkSgxLxk+z3YjsyhLrKyrWUcMsx0ALkeqhnPyAalqbdWqVcjIyMDNmzehqFi2GtTixYsBACtWrKi96AghhJBKxGTHYePT3yUmCP2semC47UBKED6BVC0Jt27dwq5du2Bq+t960paWlvjll18wYcKEWguOEEIIqUhoRjh2vtiPwtIisbIRtoPQ1byjDKJqXKRKEgoLCyEvLy+2vbi4GIwxCY8ghBBCas+LlJfY+/Iw+AK+yHYuh4vxLUaijVFLGUXWuEjVBtOtWzesX78e2dnZwm1RUVFYvnw5unbtWluxEUIIIWIev/XH7qCDYgkCj8vDFy4TKEGoQVIlCT/99BPk5eXRtm1bFBQUYNCgQejTpw+0tLTwww8/1HaMhBBCCADgdux9/BlyDAImENmuJKeIOW5T4aLnKJO4Vq1ajrlzv6hyv+zsLAwY0BN//32hDqL6dFJ1N6ipqcHHxwexsbEIDw8Hn8+HlZUVbGxsajs+QgghBIwx/B11A39HXhcrU5NXxRy3qbDQMJNBZICfny8uXjwHd/eqWzA2b16PjIz0OoiqZlSYJCQkJIhtk5OTg52dndg+JiZ0gwxCCCG1Q8AEOBV2AXfiHoiVaSlq4kv36TBSNZBBZEBBQQHWrPkVLi5uVe776NEDhIS8hJaWdh1EVjMqTBK8vb3B4XCkOkhISEiNBUQIIYSUKxWU4tCrE/BNfCpWZqCihy/dp0NHSXZfurt2bYeHhyd0dfXw4sWzCvfLz8/DunUr8dNPy7B06Y91F+AnqjBJuHbtmvD/7927h4MHD2Lx4sVwdnYGj8dDcHAwVq9ejVGjRtVJoIQQQpqWktIS7H15GIGpwWJl5mommOM+DeoKajKIrExQ0Avcvn0Df/55DEePVn4Po+3bt6Bt2/ZSdUnUJxUmCRYWFsL/37NnDzZt2gQ3t/+aUzp06IDly5dj9uzZGDNmTO1GSQghpEkp4Bdi54v9CMuMECuz0bTCLLdJUOYpyyCyMsXFxVi1ajm++moBNDQ0Kt03IMAfDx7cw8GDx+soupoj1cDF3Nxc8Pl8idtLSkpqPChCCCFNV25xHrY934uYnDixMiddB0xzHgcFOQUZRPaffft2w8zMHN7e3Svdr6ioEKtXr8C8eQuhpia7Vo+PJVWSMGDAAHz33Xf48ssv4eDgAMYYAgMD4ePjg88//7y2YySEENJEZBRmwufZHiTlJ4uVtTJ0x4QWo+rFjZpu3LiKtLRU9OjRGQBQUlICgUCAHj064/r1e8L9goNfIi4uFitWLEH5XQwKCwuxbt1KvHwZiIULv5dF+FKTKkn4/vvvoaamhjVr1iA9vWzqhp6eHsaMGYNZs2bVaoCEEEKahqT8FPgE7EZGUaZYWRfT9hhhN6je3IfBx2enSAv7sWNH8OpVMH7+WfR+Ro6OTjh69IzItjlzpmHkyDHo23dAncT6KaRKEng8Hr755ht88803wiRBR0enVgMjhBDSdMTmJGDbsz3IKckVK+vdrBv6W/WUesZdXTAyMhb5W11dHYqKijAzMwcApKWlQk1NDYqKSsJt5bhcOWhr60Bbu/5/j0qVJABAeHg4goKCwOfzxe7XMHz48BoPjBBCSNPwJjMSvz/fh8LSQrGyYc37w9uiiwyi+jSDBvXG99//3CBaCyojVZKwa9cubNiwAZqamlBVVRUp43A4lCQQQgj5KEGpIdgTdBAlH9yHgQMOxjoMR3uT1jKKrHq++GK2yN/37/tVuO+ZM3/Xdjg1Rqok4ejRo5g/fz5mzJhR2/EQQghpIvwSA3BAwn0YeBw5THYeC3d9ZxlFRspJlSRkZ2ejV69etR0LIYSQJuJu3CMcDz0LBtHuawU5BcxwmQgHHVsZRUbeJ9Uw0UGDBuHo0aNiYxEIIYSQ6mCM4UrULRwLPSOWIKjyVPCV+xeUINQjUrUkZGRk4Nq1a7hw4QJMTU0hLy8vUn748OFaCY4QQkjjwRjDmTeXcDP2rliZpoIG5rpPg4makQwiIxWRKkmwtrbGzJkzazsWQgghjVSpoBSHX53Eo7dPxMr0lHXxpft06CnX/ymBTY1UScLcuXNrOw5CCCGNVImAj42P9sD37TOxMhNVI8x1nwZNxcrvf0Bko8Ik4eTJk1IfhKZAEkIIkaSQX4RdgQfwOuONWJmVhiVmu02GiryKDCIj0qgwSdi+fbtUB6B1EgghhEiSVZSDXYEHEJUdI1bWQscO010mQFHGN2oilaswSbh161ZdxkEIIaQRCc+Mwt6gg8gqzhEr8zBwxSTHz8HjSr3oL5EReocIIYTUGMYY7sQ9wOk3F8UWSQKAjiZt8Ln90HpzoyZSOUoSCCGE1IhCfhGOvDoJ/+TnEst7WHTFIJs+9epGTaRylCQQQgj5ZIl5ydgddBCJeUliZTwuD9M8R8NF3UUGkZFPQUkCIYSQTxKQHIiDIcdQVFosVqarpI1pLuPhad0CKSni4xNI/SZ1kpCbmwt5eXkoKioiNDQUd+/ehbOzM9q1a1eb8RFCCKmnSgWlOBdxGTdjxFdQBABHXXtMchwNVZri2GBJNXLkzp076Ny5M/z9/REbG4uxY8fixIkTmDlzJo4ePVrbMRJCCKlnsopy4PNst8QEgQMO+ln1wCzXyZQgNHBSJQkbN27EjBkz0L59e5w8eRJ6enq4cuUK1q1bh71799Z2jIQQQuqR8MworH6yCWGZEWJlqjwVzHKbgr5WPWgGQyMgVXdDZGQkBg0aBA6Hg1u3bqF79+7gcDho0aIFkpOTaztGQggh9UBV0xst1E0xzXk8dOkeDI2GVEmCgYEBXr16haysLISFheGXX34BANy/fx+mpqa1GR8hhJB6oKrpjR2M22Ck3SDIy8lLLCcNk1RJwuTJk/Hll1+Cy+Wiffv28PT0xPbt27F9+3asWrWqtmMkhBAiQ1VNbxxlNwQdTFrLIDJS26RKEsaOHQsPDw8kJCSgU6dOAIAOHTrA29sbDg4OtRogIYQQ2ZFmeqOFupkMIiN1QeopkBYWFrCxsRFOgfTz84Ozs3NtxkYIIURGqpre6KTrgImOn9PshUaOpkASQggRIc30xpmukyhBaAJoCiQhhBAhmt5I3kdTIAkhhND0RiIRTYEkhJAmjqY3korQFEhCCGnCaHojqQxNgSSEkCaKpjeSqkg9BdLR0REFBQW4dOkSevXqBRUVFTRr1qwWQyOEEFIbaHojkZZUSUJaWhpmzpyJsLAwFBcXo02bNti4cSNCQ0Pxxx9/wNLSsrbjJIQQUgOyinKw7+VhibMXOOCgr1V39G7WjWYvEABSToFcvnw5TExM8O+//0JRUREAsHbtWtjb22PFihW1GiAhhJCaQdMbSXVJ1ZLw6NEjHD58GEpKSsJtampqWLBgAUaOHFlrwRFCCPl0NL2RfCypkgQul4uCggKx7SkpKcKWBUIIIfUPTW8kn0KqNqX+/ftjxYoVePXqFTgcDnJzc/HgwQP8/PPP6Nu3b23HSAgh5CMk5SVjrf9WiQkCj8vDWIcRGNtiOCUIpEJStSQsXLgQGzZswIgRI1BSUoIhQ4ZATk4OI0aMwMKFC2s7RkIIIdUUkByIQyHHUVhaJFZG0xuJtKRKEhQUFLB48WLMmzcPsbGxKC0thbm5OVRVVWs7PkIIIdVA0xtJTZJ6nYTs7GyEhoaCz+eDMYaMjAxhWfv27WslOEIIIdKj6Y2kpkmVJJw5cwZLly5FYWGhWBmHw0FISEiNB0YIIUR64ZlR2Bt0EFnFOWJlqjwVTHQaDSddexlERhoyqZKEHTt2YPjw4Zg3bx7U1NRqOyZCCCFSoumNpDZJlSQkJydj3LhxlCAQQkg9QtMbSW2TqmPK29sbt27dqu1YCCGESImmN5K6IFVLgo6ODjZu3IhLly7BwsIC8vKiJ92aNWtqJThCCCHiaHojqStSJQm5ubno379/bcdCCCGkEjS9kdQ1qZKElStX1nYchBBCKkHTG4ksVJgkbN68GV988QWUlZWxefPmCg/A4XDw1Vdf1UpwhBBCaHojkZ0KkwQ/Pz9MnjwZysrK8PPzq/AAHA6nVgIjhJCmjqY3ElmrMEk4ePCgxP8nhBBS+2h6I6kPpF6WOSwsDCdPnkR4eDi4XC4cHBwwYsQImJub12Z8hBDS5CTlJWNX0EEk5iWJlfG4PIyyG4IOJq1lEBlpaqQa4XLjxg0MHjwYwcHBsLW1RbNmzeDn54f+/fvD19e3tmMkhJAmIyA5EGv8fCQmCLpK2ljgOZsSBFJnpGpJWLduHebPn49p06aJbN+xYwd+++03nD17tjZiI4SQJoOmN5L6SKqWhMTERHh7e4tt79WrF6Kiomo6JkIIaVKyinLg82y3xASBAw76WfXATNdJlCCQOidVktCvXz/s3r0bJSUlItuPHTuGfv361UpghBDSFIRnRmH1k00S1z9Q5algltsU9LXqQesfEJmQqrshJycHt27dwr179+Do6Ag5OTmEhoYiISEBzs7OGDt2rHDfw4cP11qwhBDSWND0RtIQSJUk2NnZwc7OTmSbk5NTrQRECCGNXVXTGzuatMEIW5reSGRPqiRh7ty5tR0HIYQ0CVVNb/zcbgja0+wFUk9IvU7C0aNHcezYMeE6Cfb29hg3bhwGDBhQm/ERQkijQXdvJA2NVEnCtm3bsG/fPkycOBFfffUVSktLERgYiF9++QXZ2dkiYxIIIYSIoumNpKGSKkk4cuQIVq9ejW7dugm3de/eHY6Ojli5ciUlCYQQUoHs4hz8EUR3byQNk1RJQmlpKUxNTcW2W1tbIz8/v8aDIoSQxoDu3kgaOqlS1y+//BI//vgjXr16JdwWGxuLX3/9FbNnz4ZAIBD+I4SQpo4xhtux97EpYIfEBMFC3RSLWn9FCQKp9ziMMVbVTp06dUJmZiZKS0uhqKgILpeLgoIClD/0/dtFh4SEiD0+JUX8Q1IT9PXVa+3YjQXVUeWofqpGdVS19+uIpjdKRudR5WqrfvT11T/p8VJ1N2zYsOGTnoQQQpoCmt5IGhupkoQ2bdpUWJaYmAgjI6MaC4gQQhoimt5IGiOpkoTw8HCsWbMGYWFhwnEHjDEUFxcjMzNTYhcDIYQ0BaWCUhx8dgoXXt+QWE7TG0lDJtXAxZ9++glZWVmYMWMGMjIyMH36dPTr1w8FBQVYuXJlbcdICCH1UnZx2d0bJSUIdPdG0hhI1ZIQFBSEY8eOoUWLFjh79iysra0xduxYWFlZ4fjx4xg8eHAth0kIIfVLcn4qtgTsQkZRplgZTW8kjYVULQk8Hg/q6mUjJK2trYXdCx06dEBoaGjtRUcIIfVQYl4yNj39XWKCQNMbSWMiVZLg6emJvXv3oqCgAM7Ozrhx4wYEAgGeP38ORUXF2o6REELqjYTcRGx6Knn9g44mbfBNy9l0e2fSaEiVJCxevBgPHz7EkSNHMGjQIGRkZKBVq1b49ttvMWbMmNqOkRBC6oXYnARsCtiBnJJcke0cDgdj7IdhjMPwJrf+AWncpBqTYGNjg6tXr6KgoADKyso4deoUHj9+DG1tbbi7u9dyiIQQInvR2bHY+mwP8vkFItu5HC6+ajcFtsp2MoqMkNoj9a2iAUBZWRkAoKKiAi8vr1oJiBBC6puIrGhse7YXhaWFItvlOHKY6jwWHSw8aTVB0ihVmCQ4ODiILLdcGVongRDSWL3JjMT253tRVFossp3HkcN0lwlw1msho8gIqX0VJgn79u0T+ZsxhlmzZmHp0qUwNDSs9cAIIUTWXqe/wY4X+1AsKBHZLs/lYYbLJLTQpS4G0rhVmCS0b99ebBuXy4WnpyfMzc1rNShCCJG14LTX2BV4ACUCvsh2Ba48ZrpOhr1OcxlFRkjdqdaYBEIIaQoCU4OxJ/Ag+KxUZLuinAJmu01Fcy0rGUVGSN2iJIEQQt7zLCUIfwQdRukHCYIyTwlz3KbCStNSRpERUvcoSSCEkHf8k55hf/BRCJhAZLsKTxlfuk+HhQbdxZE0LRUmCSdPnhTbJhAIcPnyZejoiK4mNnz48JqPjBBC6tDjt/44GHIcDExku5q8Kr50nw4zdRMZRUaI7FSYJGzfvl1sm66uLo4ePSqyjcPhUJJACGnQHiY8wZFXJ8USBHUFNXzl/gVM1IxkFBkhslVhknDr1q26jIMQQmTiXvwjHH19Rmy7poI6vvKYASNVAxlERUj9QGMSCCFN1u3Y+zgZdl5su7aiFr7y+AIGKnoyiIqQ+oOSBEJIk3Q9+g7Ohv8ttl1XSRtfe8ygOzkSAkoSCCFN0OXIm7gYeVVsu76yLr72mAFtJa26D4qQeoiSBEJIk8EYw6XIa7gcdVOszFDFAF95TIeWoqYMIiOkfqIkgRDSJDDGcC78Mq7H3BErM1E1wpce06GhoF73gRFSj1GSQAhp9BhjOBV2Abfj7ouVmamZ4Ev36VBTUJVBZITUb5QkEEIaNQET4HjoOdyLfyRWZqFuhrnu06AqryKDyAip/yhJIIQ0WgImwF+vTuHh2ydiZVYaFpjjPhXKPGUZREZIw0BJAiGkURIwAQ6FnMDjRH+xMhtNK8x2mwwlnpIMIiOk4aAkgRDS6JQKSnEg+Cj8k5+LldlpN8dM10lQlFOQQWSENCyUJBBCGhW+gI99L4/gWUqQWFkLHTt84TIRCnLyMoiMkIaHkgRCSKNRIuBjb9BBBKaGiJU567bANOdxkKcEgRCpUZJACGkUiktLsCvwAELSQ8XK3PSdMcVpDHhcuuQRUh30iSGENHhFpcXY8WI/QjPeiJV5GrhhouPnkOPKySAyQho2ShIIIQ1aIb8Q25/vQ3hWpFhZG6OWGOcwghIEQj4SJQmEkAargF+Abc/+QGR2tFhZe+PWGOMwDFwOVwaREdI4UJJACGmQ8kvy4fNsD2Jy4sTKOpu2x0i7QZQgEPKJKEkghDQ4ucV58Hm2G3G5CWJlXmadMMx2ADgcjgwiI6RxoSSBENKgZBfnwCdgNxLyEsXKelh0xSCbPpQgEFJDKEkghDQYmUVZ2BKwG0n5yWJlvZt1Q3+rnpQgEFKDKEkghDQIGYWZ2BywEykFaWJl/a16oo9VdxlERUjjRkkCIaTeSytIx+aAXUgrTBcrG2zTFz0su9Z9UIQ0AZQkEELqteT8VGwJ2IWMokyxsmG2A+Bt3rnugyKkiaAkgRBSbyXmJWNLwC5kFWeLlY2yG4IuZu1lEBUhTQclCYSQeikhNxFbnu1CTnGuyHYOOBjjMAwdTNrIKDJCmg5KEggh9U5cTgJ8nu1GbkmeyHYOOBjfYiTaGnvKKDJCmhZKEggh9UpMdhx8nu1GPr9AZDuXw8VEx8/RytBdNoER0gRRkkAIqTcis6Kx7fleFPALRbZzOVxMcRoLDwMXGUVGSNNESQIhpF54kxmJ7c/3oqi0WGQ7jyOHaS7j4aLnKKPICGm6KEkghMhcaMYb/P58H4oFJSLb5bk8THeZCCddexlFRkjTRkkCIUSmQtJCsTNwP0oEfJHt8lx5zHSdBAcdWxlFRgihJIEQIjNBqSHYHfgn+KxUZLuinAJmuU6Brba1jCIjhACUJBBCZORZShD+CDqM0g8SBCU5JcxxnwprTUsZRUYIKUdJAiGkzvknPcf+4L8gYAKR7co8ZXzpPg2WGuYyiowQ8j5KEgghdco38Sn+DD4GBiayXVVeBV+6T4e5uqmMIiOEfIiSBEJInXmU8ASHX50USxDU5dXwpcd0mKoZyygyQogklCQQQurEvfh/cfT1abHtmgrq+MrjCxipGsogKkJIZShJIITUujuxD3Ai7JzYdi1FTXzt8QUMVPRlEBUhpCoNMkkISg3B+YgrAJfBy7QL2hu3knVIhJAK3Ij5B2feXBLbrqOkja89ZkBPWUcGURFCpNHgkoQCfgH2Bh0Srsx2KPs4YnPiMax5f8hx5WQcHSHkfVeibuJCxFWx7XrKuvja4wvoKGnLICpCiLS4sg6gunKK88SWbv0n7gF2Bh5A4Qc3hSGEyAZjDBcjrklMEAxU9DC/5UxKEAhpABpckqCvrAs7LRux7S/TXmG9/3akF2bIICpCSDnGGM6FX8blqBtiZUaqhpjnMQtaipoyiIwQUl0NLkngcDiY6jwONprNxMoS8hKxxs8H0dmxdR8YIQSMMZx6cwHXY+6IlZmqGWOexwxoKqrXfWCEkI/S4JIEAFBTUMWXHl+gs2UbsbKc4lxsfLoDAcmBMoiMkKZLwAQ4HnoWt2Pvi5WZq5viK48voK6gJoPICCEfq0EmCUDZLWTntp2E/lY9xcpKBCXYE3QQ16JvgzEm4dGEkJokYAL89eo07sY/EitrpmGBr9y/gJq8qgwiI4R8igabJABlXQ99rLpjsuNo8LjiEzXOhV/G4Vcnwf/gFrSEkJojYAIcCjmBh299xcpsNJthrvs0qMgryyAyQsinatBJQrlWRh742kPyL5VHb59g27O9yC/Jl0FkhDRupYJSHAg+iseJ/mJldlo2mO02Fco8JRlERgipCY0iSQAAa81mWNjqSxipGIiVhWaGY63/ViTnp8ogMkIaJ76Ajz9eHoFf0jOxMgdtW8xymwwlnmLdB0YIqTGNJkkAAD1lHSzwnAMHbVuxsuT8VKzz24o3mZEyiIyQxqVEwMeeoIN4liI+QNhJ1wEzXSdBQU5BBpERQmpSo0oSAEBFXhmz3aagk0lbsbI8fj58AnbBN/GpDCIjpHEoLi3BrhcHEJgaIlbmqueE6S4TIC8nL4PICCE1rdElCQAgx5XD5/ZDMbR5f3DAESnjs7I+1IsRV2nmAyHVVFRajB0v9iE4/bVYmYeBK6Y5j4O8hEHEhJCGqVEmCUDZzIduFl0w3WUCFLjiv2ouR93EvpdHUFJaIuHRhJAPFfILsf35XrzOeCNW1trQA5MdR9P9UwhpZBptklDOTd8J8z1nQVNBQ6zMP/k5NgfsQk5xrgwiI6ThKOAXYOuzvRLH9LQzaoUJjqMoQSCkEWr0SQIAWKib4bvWX8JczUSsLDI7Gmv9fPA2L0kGkRFS/+UW58EnYA8is6PFyjqZtMXYFsPB5TSJSwkhTU6T+WRrKWpiXstZcNFrIVaWVpiBdX7bEJIeKoPICKm/covzsPz2ZkTniN8P5TOzjvjcfiglCIQ0Yk3q063EU8QXLhPhbd5ZrKywtBDbn/+Be/H/yiAyQuqfnOJcbA7YichM8QShm3kXjLAdCA6HI+GRhJDGokklCQDA5XAxzHYAPrcfIvYLSMAEOPr6NE6FXYCACWQUISGyF54ZhVVPNiMhL1GsrJelN4Y070cJAiFNQJOdq9TZtD30lHSxJ+gQCksLRcpuxd5DSkEaJjmOphXjSJMiYALcjLmL8xFXJCbK/ax6oE+z7pQgENJENLmWhPe10LXDAs/Z0FXSFisLTA3Gpqe/I7MoSwaREVL3ckvysPPFfpwN/1tigjDIug/6WvWgBIGQJqRJJwkAYKJmhG9bzYWVhoVYWWxuAtY88UFsTrwMIiOk7kRkRWOV72YEpb0SK+Nxefjcfih6NvOSQWSEEFlq8kkCAGgoqOMrjxnwNHATK8sqzsYG/+14kfJSBpERUrsETIAbMf9g49PfkVGUKVaup6SDFd2+RWfTdnUfHCFE5ihJeEdBTh6TnEajd7NuYmXFghLsCvwTN2Pu0lLOpNHIK8nHzhcHcObNJYndC+76Lljc5mtY61jKIDpCSH3QZAcuSsLlcDHAuhcMlPVw+NVJlLJSYRkDw+k3F5Gcn4KRdoNpdTnSoEVmRWNv0GGJrQc8jhyG2PbHZ6YdaPwBIU0cJQkStDX2hI6SNnYH/ok8fr5I2f2Ex0gtSMc0l3FQ5inLKEJCPg5jDDdj7+Jc+GWJrQe6SjqY6jwWlhrmMoiOEFLfUHdDBWy1rfFtqzkwUNETK3uVEYZ1/tuRWpAug8gI+Th5JfnYGbi/ku4FZyxu/TUlCIQQIUoSKmGgoo9vPefCVstarCwxLwlr/XwQkSW+nj0h9U1kVjRW+m5CYGqIWJkcRw4jbAdhmvN4qMhT6xgh5D+UJFRBVV4Fc92noZ1xK7Gy3JI8bA7YCb+kZ3UfGCFSYIzhZsxdbKhg9oKukg4WeM5GV/OONP6AECKGxiRIgcflYZzDCBgq6+NcxGWRMr6Aj30vjyAlPxW9m3WjCy2pN/JL8vFnyHEEpgZLLHfTd8Y4hxHUekAIqVCjTxJWrVqOuLhYbN26S2L5zZvXsH//Hrx9mwBTU3NMnz4LnTp1EduPw+GgZzMv6Kno4s/goygR8EXKL0ZeQ3JBKsY4DIc8t9FXK6nnIrNi8MfLw0gvzBArk+PIYUjzfuhqRq0HhJDKNeruBj8/X1y8eK7C8ufPA7B8+RKMGDEa+/f/hf79B+GHHxYiNFR81blyLQ1cMa/lTKgrqImV+SY+hU/AbuQW59VI/IRUF2MMt2LvYePT3yUmCLpK2vjGcxa8zDtRgkAIqVKjTRIKCgqwZs2vcHERX0Wx3OXLF/HZZ94YOHAIzMzMMWLE52jZshVu3rxe6bGbaVhgoeeXMFE1EisLz4rEWv+tSMpL/uTXQEh15JfkY1fgnzgVdkFkjY9ybnpOWNz6azSTsAQ5IYRI0miThF27tsPDwxMeHp4V7jNs2ChMmjRNZBuHw0FxcXGVx9dV1sY3nrPhqGsvVpZakIa1/tsQmvGm+oET8hGismOw6slmvEgVXz5cjiOH4bYDMd1lAlTkVWQQHSGkoWqUSUJQ0Avcvn0Dc+bMq3Q/W1s7WFn9N70xIiIc/v5P4O7uIdXzKPOUMNNlErqYdhArK+AXwOfZHjxMeFKt2AmpDsYYbsfexwb/35EmoXtBh7oXCCGfoNGNsCsuLsaqVcvx1VcLoKGhIfXjMjLS8f33C+Hq6o7OnbtK/Tg5rhxG2Q+GoYo+ToadB8N/93YQMAEOvzqB5PwUDLTpDS6nUeZkREbyS/JxKOQEnktoPQAAVz0njG8xgloPCCEfrdElCfv27YaZmTm8vbtL/Zjk5CR8881cyMlxsWLFanC51f8y72reEXrKOvjj5WEUlYp2V1yPuYOUgjRMdBwFBTmFah+bkA9FZ8dib9Ahia0HXA4XQ2z6wsu8M7UeEEI+SaNLEm7cuIq0tFT06NEZAFBSUgKBQIAePTrj+vV7YvvHx8dh3rzZUFRUgo/PDmhqan30czvrtcA3LWfj9xf7kFmUJVL2LCUQGU8zMcN1IjQVpW/hIOR9jDHciXuAM28uSRycqK2ohanO42ClSYMTCSGfrtElCT4+O8Hn/7eGwbFjR/DqVTB+/nmF2L7Z2VmYP38OVFXVsGnTdmhpaX3y85upm+C7Vl9ix4v9iMmJEymLzonFWr+tmOU2GaZqxp/8XKRpyS8pwOFXJ/AsJUhiuYueI8a3GAlV6l4ghNSQRpckGBmJfvmqq6tDUVERZmZlN61JS0uFmpoaFBWVsGvXdmRlZeLXX9egtJSPtLRUAICiohLU1MTXQZCWpqIG5reciQPBR8Uu6BlFmVjvvw1TnMbCWa/FRz8HaVrKuhcOI61Q/KZiXA4Xg236wpu6FwghNazJjaQbNKi3cB2E27dvIC8vD5Mnj8WgQb2F/zZsWP3Jz6Mgp4CpzuPQw6KrWFlRaTF2vNiPO3EPPvl5SOPGGMOd2AdY779dYoKgraiFb1rOQjeLLpQgEEJqHIcxxqre7dOkpOTUynH19dVr7dg16WGCL/56fVri7Xk/M+uIYc37Q44rVyvP3VDqSFbqc/0U8AtwKOQknqUESix30WuB8S1G1Xr3Qn2uo/qC6qhqVEeVq6360ddX/6THN7ruhvqog0kb6CrpYHfQQRTwC0TK/ol7gNSCNExxGgMlnpKMIiT1TUx2HPYGHUJqBd0Lg2z6oJs5tR4QQmpXk+tukBV7neb41nMO9JR1xcpepr3Cev/tEtfaJ00LYwz/xD3Eev9tEhMEbUUtzG85C90tPqMEgRBS6yhJqENGqgZY6DkX1prNxMoS8hKxxs8H0dmxdR8YqRcK+AXYG3QIx0PPgi9heqOzrgMWt/ka1pqWMoiOENIUUZJQx9QUVPGVxxdobSi+9HNOcS42Pt2BgGTJfdCk8YrJicMq380IkDD+gMvhYkjzfpjhOglq8qoyiI4Q0lRRkiAD8lweJjp+jn5WPcTKSgQl2BN0ENeib6MOxpQSGWOM4W7cQ6z3k9y9oKWoifktZ6K7xWe0rDchpM7RwEUZ4XA46GvVAwbKejj46gT4Ar5I+bnwy0jOT8Xn9kPA49Lb1BgV8Atx5NVJPE1+IbHcSdcBExxHUesBIURm6NtHxloZeUBHWRs7XxxAbkmeSNmjt0+QVpCO6S7j6SY9jUxsTjz2BB1CakGaWBmXw8VA697oZtGFWg8IITJFV6B6wFqzGRa2mgsjFQOxstDMcKz134rk/FQZREZqWln3wiOs89sqMUHQUtTEPI+Z6GHZlRIEQojM0VWontBT1sUCzzlw0LYVK0vOT8U6v614kxkpg8hITSngF2LfyyM4FnpG4uwFJ10H/K/1PNhoNav74AghRAJKEuoRFXllzHabgo4mbcXK8vj58AnYBd/EpzKIjHyq2JwErH6yGf7Jz8XKyhdHmuk6CWoKNP6AEFJ/0JiEekaOK4fR9kNhqKKPM28ugeG/GQ58VooDwUeRnJ+CflY9aTGdBoAxhvsJj3Ey7LzY4FSgrHthstMYNNeykkF0hBBSOWpJqIc4HA66WXTBdJfxUODKi5VfjrqJfS+PoKS0RAbREWmVdy8cfX1aYoLgqGOP/7WeV2GC4Ofni2++mYvevb3g7d0BY8YMw86d25Cfnydx/4rs3bsTPXp0/qjXIBAI8Ndff2Hq1PHo3r0TevTogunTJ+L8+TPVnqL766+/YPz4kR8VR33wv/8tQKdOrXDr1g2J5U+e/ItRowbD27sDNm5cI3Gfv/++gE6dWiEzM7MWIyWk5lBLQj3mpu+M+Z6zsOP5fmQVZ4uU+Sc/R3phJma4ToS6wsff1prUjticBPwRdAjJBeIDTrkcLgZY9UJ3y4rXPnj06D4WLfoGffsOwLBho6CkpITQ0Nc4dGgfAgL8sW3bbsjJ1c5Nwd63c+c2nD59HGPHTsTMmXPA55fCz88X69evQlxcLGbP/qrWY6gPsrIy8ejRA1hZWePixbPw9u4uts/vv2+FoqIi1q3bAgMDQ4nHad++E3bs2PdJt6InpC5RklDPWaibYWGrudjxYj/ichNEyiKzo7HWzwez3KbAWFXyRYnUrZrqXjhy5CBat26HxYt/Em7z9GwNS8tm+O67efD1fYT27TvVePzvKy4uxokTR/Hll3MxZMho4fb27TuCy+Xi+PEjGD9+MtTVP+0ucw3BjRtXoaKiiqlTZ2DJkv8hMfEtjIyMRfbJyclGu3Yd0bJlqwqPo62tDW1t7doOl5AaQ90NDYC2UtlNfVz0WoiVpRVmYJ3fNoSkh8ogMvK+Qn4h9gf/VWH3QgsdOyxu/bVU4w8yMjLAJNxavE2bdvjii9nQ1y9LCiU1X+fk5KBTp1b4++8LIo+9cuUShg3rj27dOuK77+YhLq7y+4Tk5eWhuLhIYrfCwIFDMH36LJEYr127jGnTJqBbt47o1q0jZs6cgmfPxAfanjhxFMOG9Ye3d0fMnfsFoqOjhGWMMRw//hcmTBgFb+8O6NGjC+bNm43w8DfCfebO/QKrV/+Kb76Zi549P8PWrZsAAMHBQfj226/Qu3dXdO3aDqNHD8XZs6eEj/v77wvo168b/Px8MWnSGHh5tce4cSNw//4/ldZDed21bt0WHTp0hoqKCi5dOi8se/s2Afb29nj7NgFnzpxAp06t8PZtgsQ4Jb1f586dxrhxI+Ht3RFjxgzD+fNnhGV8Ph979+7E558PhZdXe/Tu7YXvv1+IpKTEKmMmpCZQktBAKPEU8YXLRHibi/ctF5YWYvvzP3Av/l8ZREYAID73LVb7bYFf0jOxMg44GGjdG7PdpkjdNdSuXQf4+v6L776bjxs3riItrazbgsfjYcKEKWjeXHyqbGUKCwvx++9bMG3aTPz00zLExMRg/vy54PPFk5ly2tracHBwhI+PD9atW4nHjx8hPz8fAGBuboGxYydCQ0MTAHD79g0sX74E7dt3xNq1m/H99z8jNzcHP//8P5SU/Dd2Jjo6CpcvX8S8ed/ihx9+RmxsDJYu/VFY/tdfh7Bjhw/69x+M9et9MH/+QkRFReLXX38Rie3vv8/DxMQUK1asRrduPZCYmIivvpoJZWUVLF++CitXroe5uSXWrVuJN2/ChI/Lz8/HypXLMHToCKxZsxGamlpYsuR7ZGdnVVgPMTFRCAkJRs+efaCgoAAvrx64dOk8BIKyBElXVw/Hjh2Drq4uunbthh079kFXV09inB86evQQ1q1bibZt22P16g3w8uqONWt+xfXrVwAAW7asx8mTxzBu3ERs2LAVX3wxG/7+vtiyZX2F8RJSk6i7oQHhcrgYZjsA+sp6OBF2DoL3fsUJmABHX59Gcn4KhjTvRwvx1BHGGB4m+OJE2DmUSGg90FTQwGSnMbDVtq7Wcb/4Yjays7Nw5colPHx4DwBgadkMXbt2w6hRY6GhoVHtOH/6aTlatWoDALCwaIaJEz/HjRtX0bt3vwoft2LFavz22884e/YUzp49BTk5OTg6OqNXr74YMGCwcFxEXFwchg4dgalTZwgfy+PJ44cfFiI2NhrW1s2F29es2Qg9PX0AQEpKMrZu3YS8vFyoqqohOTkJEydOxciRZd0bHh6eyMnJho/PRuTn50NFpWzlURUVVcybtxA8Xtkl7NGjB3BycsXPP68QbnNyckbfvt3w/PlTYVJVUlKC2bO/Fn5ha2vrYtKk0Xj61A9du3aTWAeXL1+CtrYO2rZtDwDo3bsfLlw4g8ePH6F9+45QUFCAu7s75OUVoKOjA2dnF+FjP4wzMjJCWCYQCHDw4D707TsAX345HwDQunVbJCTE48WL5+jRozcyMzMxZ87X6N9/kLA+YmKicf365QrfM0JqEiUJDVAXs/bQU9bB3qDDKCwtFCm7FXsPKQVpmOQ4Gko8RRlF2DQU8ovw1+tTElsPgLLuhYmOn3/UwFIFBQV8//3PmDZtJh48uIcnTx4jIMAfBw7sxaVL57F9+x6YmJhKfTw1NTVhggAA1tY2MDExRUjIS/Tu3U+sRUFOTg4cDgdGRsb466+/8PChPx4+vAc/P1+8fBmIwMDnuHnzGtav94GCggLGj58EoKyrIzo6CrGx0cJm/OLi/1oSDA2NhQkCABgbm7x7XFmSMG/etwDKultiYqIQHR2FBw/KkqSSkmIAZUmCmZmZ8IsXKBsn0b59RxQVFSEyMgJxcTEICQkWe34AcHL670vcwKBsldOCggKJ9cYYw7Vrl9Gp02fCfaytbWBoaISLF8+iffuOFda5pDjfFxMTjaysLHTs2EVk+88/rxD+/7JlKwEAqakpiI6OQlRUJF68eCb2mgipLZQkNFCOuvZY4Dkbv7/Yh/TCDJGywNRgbHr6O2a6TYY+Gv+gMlmIz32LvUGHkJSfIlbGAQf9rXuhZw0srWxgYIghQ4ZjyJDh4PP5uHr1b6xd+xv++GMXfvxxqdTH0dbWEdumpaWN1NQUvH2bgBEjBoqUbdmyQ2QAnq2tHWxt7TBx4lTk5eVi9+4dOHnyKK5fv4J+/QYiLS0Vq1Ytx7//PoS8vDyaNbOGsXH5wL7/xjQoKYkmrpx39VM+tiE6OgqrV6/AixfPoKSkhObN7aCiovpun4pfT2lpKbZu3YRz506Dzy+BiYkZ3N093j1OdEyFkpKShOeXPJ3z6VM/JCUl4sKFM7hw4YxIWVpaKjIy0iXWbUVxvq+8i6OygYyBgc+xbt0qhIeHQU1NDba29lBUVMT7dUpIbaIkoQEzUTPCwlZzsevFAURmx4iUxeYmYM0TH3yvOhdq0JJNgI0QYwwP3/riRGhF3QvqmOw0ttrdC+8LCgrE//63AKtWbYCTk7NwO4/HQ79+A3H//l1ER5ct0V2+oNb7AwgLCvLFjpmTkyO2LT09HdbWzaGnp489e/4UKbOwsMTx40dw5MhB3L0rOrBPVVUNX3+9ANeuXRbGsXTpj0hOTsaOHftgb+8AHo+HR4/u459/bkv9ugUCARYtmg8NDU38+edRNGtmDS6Xi9OnT8DX91Glj/3zzz9w/vxp/PjjUrRv3xHKysooLCzExYvnpH5+Sa5cuQRDQyP88MMvItuzs7Pw44+LcPnyRYwZM+Gjjl0+DTIzUzTJL2thyISVlQ2++24+XF3d8Ouva2BmZg4A2L59M968oYHKpG5QktDAaSio4yuPGTgYckzslsNZxdlYfG0ltBQ1oa+sC30VXegp60JfWe/df3WgxFOq4MjkQ4X8Ihx9fQZPkiQvje2gbYtJTqM/ed0KCwsL5Ofn4eTJo3ByWiFSVlpaioSEeNjbOwCA8Fd2amqK8Ffr8+cBYsfMzMzA69evhI97/foV3r6Nh4eHJ+Tl5eHg4Cj2GHNzS6SmpuDkyZPw9u4rUpaWlor8/DxYWdkAKEtsxowZL5LUPH5c9sUu7aJLmZkZiIuLxeLFk0TGMDx+/LDK4wQFBcLBwVFk/YLyx33sr+7CwkL8889tDBw4ROK0RgcHR1y8eO6jkwQLi2bQ0NDEw4f30blzV+H2PXt2IDk5CV9++Q1ycrIxcuQYYYIgEAjw5Mnjai9kRcjHoiShEVCQk8dkpzEwUNHHlaibImUMDBlFmcgoykRoZrjYY9Xl1aCvUp446PyXQKjoQpWnQks/v1N190JP9LT0qpEBoxoamvjii9nw8dmIrKxM9OnTHwYGhkhNTcG5c6eRkpKM335bCwBo2bIVFBQUsXnzekycOAVJSYnYv38vFBQURI6poKCAX375HjNnfgk+vwS//+6D5s3t4OUlebAeUDbDonPnrli6dCn8/ALQoUNnqKmpISoqAn/9dQi2tvbo3r0XAKBFC0f8/fcF2Ng0h7q6Bu7evS2cflhYWFjhc7xPR0cXhoZGOHHiL+jo6ILL5eLy5Yt4+PA+AKCoqOLjtGjhiEOH9uPUqWOwtm6OkJBg7N+/BxwOR+rn/9A//9xCfn4evLzEF04CgJ49e2PLlg14/jwA3bt3kbhPZXg8HsaPn4zff98CTU0teHq2xvPnAbh9+wZ++20tLC2bQUVFFfv374FAUIqioiKcPn0Cb96EgcPhgDFGn09S6yhJaCS4HC4GWPeCgbIeDr86iVIJdxmUJKckFzlZuYjIihYrU+YplbVACFsedIUJhKaCRpO4QDHG8OjtExwPPSuxe0FDQR2TncbATtumRp931KixMDOzwKlTx7F58zrk5ORAS0tLuMBS+aBFdXV1LFu2Ejt2+OC77+bDysoaP/20DN9//63I8YyMjDFy5Bhs2LAa+fl5aN++E+bPXwh5efFlv8txOBwsX74K166dx5kz53Dr1nUUFRXB0NAI3t49MH78ZOHjv//+Z6xfvwq//bYMiooKsLGxhY/PTnz77VcICgqEh4enVK/711/XYtOmtViyZDFUVVXRooUTNm3ajq+/noWgoBdiCxiVGzduEtLSUrFv324UFRXD3Nwc8+cvxPXrVxAUFCjVc3/o6tW/YWhoBEdHJ4nl3br1xLZtm3HhwtmPShIAYPTocVBUVMTx40dw/PgRmJmZ45dffhO2LPz662ps27YFixYtgJaWFtzcPLB8+Sr8+OMivHwZJDKTgpDawGF10G6VkiLeH1oT9PXVa+3YDVlYRgT2BB1Ebkn11vivDnmu/LsE4r/EobwrQ1tRE3Lc2l8yuCZUdg4V8otwLPRMhXfedNC2xUSnz6Gh0LgHh9LnrGpUR1WjOqpcbdWPvv6nXZ+oJaERstW2xrIO/8Oz5EBkIxPRaW+RWpCGlPw0sSmTH6tEUIKEvEQk5Imv/MblcKGnpCNMHt7vytBV1oE8t/6fdgm5idgTdAhJ+cliZRxw0M+qJ3o1q5nuBUIIqa/q/9WafBRFOQW0NfYUyU4ZY8gtyStLGMr/5ae9+zu1xloeBEyA5ILUspsbpYuWccCROJCyvEVC1ms7MMbw71s/HAs9ixKB+Fz0su6F0bDTbi7h0YQQ0rhQktCEcDgcqCuoQV1BDVaalmLlBfxCYQKRml+WOJQnE5lFFS9bWx1VDqRUUHtvHERZ60N5MlHbAymLSotx7PUZPE70l1hur90ck5xGN/ruBUIIKUdJAhFS5inBXN0U5uriK/kVl5YgrTAdKfmpoi0RBWlIL8wQWSL6U+QU5yKnuKKBlMrQf38GhrIu9FXKkolPHUiZkJuIvUGHkFhB90Jfq+7o3awbdS8QQpoUShKIVBTk5GGsaijxltSlglKkF2YKuy3eTyDSCtIkzgr4GAX8AsTkxCMmJ148Pq682AwMaQdSPnrrh2Ovz0jsXlBXUMNkxzGw16HuBUJI00NJAvlkcly5sgGKKrpoATuRMgETIKsoW6z1oaYHUhZXMpBSjiMHXSVt6KnoinRl6Crp4HjEafwTJfnumfbazTHRcTQ0Fal7gRDSNFGSQGoVl8OFtpIWtJW0YPvBWgJ1NZCylJX+N5BSChxw0MeqO/pQ9wIhpImjJIHITH0YSPkhdQU1THIcDQcd21o5PiGENCSUJJB6S5qBlGXdFqlIKUgXJhEfO5DSTssGk5zGUPcCIYS8Q0kCaZBqciAlBxz0adYNfay6U/cCIYS8h5IE0uhUZyAlV5HBTMEcZuomMoqWEELqL0oSSJPy4UBKWk+eEEIqRm2rhBBCCJGIkgRCCCGESERJAiGEEEIkoiSBEEIIIRJRkkAIIYQQiShJIIQQQohElCQQQgghRCJKEgghhBAiESUJhBBCCJGIkgRCCCGESERJAiGEEEIkoiSBEEIIIRJRkkAIIYQQiTiMMSbrIAghhBBS/1BLAiGEEEIkoiSBEEIIIRJRkkAIIYQQiShJIIQQQohElCQQQgghRCJKEgghhBAiUb1PEnx8fLB48WIAQElJCSZOnIjHjx8Ly7du3QovLy8MGjQIgwYNwuHDh2UVqsxUVUcREREYP348Bg4ciKlTpyIrK0tWocpMZXUUEhIiPH8GDRqEzp07o3///rIMt85VdQ69fPkSw4YNw8CBAzFjxgxkZ2fLKlSZqaqO/vnnHwwYMAADBgzAggULkJeXJ6tQZaa8jo4dO4b+/ftjwIAB+N///ofi4mIAZZ+1oUOHolevXvjhhx/A5/NlHHHdq6qOyn333Xc4ffq0jKL8T71PEhQVFaGkpCT8ogsICBApDwoKwoYNG3Du3DmcO3cOY8eOlVGkslNZHTHGMGvWLEyfPh3nz59HixYtsGvXLhlGKxuV1VGLFi2E58/Ro0ehqamJX375RXbBykBVn7Nff/0VX331Fc6fPw8rKyvs3btXRpHKTmV1lJ2djcWLF2Pjxo24cOECHBwcsHHjRhlGKxuKiopIS0vD3r17cfToUZw/fx4CgQBHjhwBACxcuBBLlizB1atXwRjD8ePHZRxx3auqjpKSkjBz5kxcvXpVxpGWqfdJgoODA1xcXHDy5ElMmzYNbm5uIuVBQUHYuXMnBgwYgGXLlqGoqEhGkcpOZXX08uVLqKiooEuXLgCAmTNnNslEqqrzqNzOnTvRunVrtGrVqo4jlK2q6kcgEAh/GRcUFEBJSUkWYcpUZXUUFRUFExMTNG/eHADg5eWFGzduyCpUmXFwcICbmxt+/vlnqKmpgcPhwM7ODgkJCYiPj0dhYSHc3d0BAEOHDsWVK1dkG7AMVFZHAHDhwgV069YNffr0kXGkZXiyDqAq5V9u5Q4cOCD8/7y8PLRo0QILFy6EpaUlFi9ejO3bt2P+/Pl1HaZMVVZHMTEx0NPTw/fff4+QkBBYW1vjp59+qusQZa6yOiqXk5OD48eP48KFC3UVVr1RVf0sXrwYU6ZMwW+//QZlZeUm+Quwsjpq1qwZEhMT8erVKzg4OODy5ctITU2t6xBlrkuXLiL1lJ6ejsOHD2PlypVITk6Gvr6+sExfXx9JSUmyCFOmKqsjAJg2bRoAwN/fXybxfajetyRURlVVFbt374aNjQ14PB6mTJmCf/75R9Zh1St8Ph++vr4YPXo0zpw5A3Nzc6xatUrWYdVL58+fR/fu3aGrqyvrUOqVwsJC/PDDD9i/fz/u37+PMWPGYNGiRbIOq17R0NDA6tWr8dNPP2HYsGEwMDCAvLy8rMOSqaSkJEycOBHDhg1D27ZtIRAIwOFwhOWMMZG/m6IP66g+atBJQkJCAk6ePCn8mzEGHq/eN47UKX19fVhaWsLFxQUA0L9/f7x48ULGUdVPN27cQN++fWUdRr0TGhoKRUVFuLq6AgBGjRoFX19fGUdVv5SWlsLIyAgnTpzAqVOn0KJFC5ibm8s6LJkJDw/H559/jiFDhmDOnDkAACMjI6SkpAj3SU1NhYGBgaxClDlJdVQfNegkQUlJCWvXrkVsbCwYYzh8+DB69Ogh67DqFQ8PD6Snp+PVq1cAgFu3bsHJyUnGUdU/jDG8fPkSHh4esg6l3rG0tERiYiIiIiIAADdv3hQmnaQMh8PBlClTkJSUBMYY9u/f32QTztzcXEydOhVff/01pkyZItxuamoKRUVFYTP6uXPnxLpwmoqK6qg+atA/u3V0dLBs2TLMmjULJSUlaNmyJSZPnizrsOoVJSUlbNu2DT/++CMKCgpgZGSENWvWyDqseic9PR3y8vJQVFSUdSj1jqamJlauXIl58+aBMQZdXV389ttvsg6rXuFyuVi2bBmmTZuG4uJitG/fHlOnTpV1WDJx8uRJpKamYt++fdi3bx8AwNvbG19//TXWrVuHH3/8Ebm5uXBycsKECRNkHK1sVFZH9Q3dKpoQQgghEjXo7gZCCCGE1B5KEgghhBAiESUJhBBCCJGIkgRCCCGESERJAiGEEEIkoiSBEEIIIRJRkkAIIf9vtw4EAAAAAAT5Wy8wQlEELEkAAFYrHIDb/eKSZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 619.2x482.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the mean happiness score per year for the lowest scoring regions\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8.6,6.7))\n",
    "\n",
    "for i in range(8,10):\n",
    "    \n",
    "    plt.plot(regionals.iloc[i], linewidth=4.5)\n",
    "    \n",
    "plt.title(\"Happiness over Time for World Regions with the Lowest Happiness Scores\", fontsize=15, y=1.01)\n",
    "    \n",
    "plt.xticks(years, labels = year_ticks, fontsize=11)\n",
    "\n",
    "plt.yticks([])\n",
    "\n",
    "plt.ylabel('Happiness Index', fontsize=14)\n",
    "\n",
    "plt.text(2018.35, 4.59, 'South Asia', fontsize=16)\n",
    "plt.text(2017.5,4.1, 'Sub-Saharan Africa', fontsize=16)\n",
    "\n",
    "plt.text(2015.06, 4.548, round(regionals.iloc[8,0], 1), fontsize=14)\n",
    "plt.text(2015.06, 4.14, round(regionals.iloc[9,0], 1), fontsize=14)\n",
    "\n",
    "plt.text(2020.72, 4.4, round(regionals.iloc[8,6], 1), fontsize=14)\n",
    "plt.text(2020.72, 4.51, round(regionals.iloc[9,6], 1), fontsize=14)\n",
    "\n",
    "plt.savefig(\"happiness_over_time_for_world_regions_with_the_lowest_happiness_scores.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Sub-Saharan Africa region has shown a strong steady rise in happiness since its dip in 2017. The Sub-Saharan Africa region has increased in happiness by +0.3 points since 2015, giving it a lead over the South Asia region.\n",
    "* South Asia continues to have a low happiness score, and is decreasing slightly after a peak in 2017. It has decreased by -0.2 happiness points in total.\n",
    "* The least happy region in the world has now become South Asia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing all Plots in one Figure <a class=\"anchor\" id=\"plots\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x2088 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting all three plots on the same figure and saving the figure to a .png file\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(9, 29))\n",
    "gs = gridspec.GridSpec(3, 1, height_ratios=[1,1.5,1])\n",
    "\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0])\n",
    "ax2 = fig.add_subplot(gs[1])\n",
    "ax3 = fig.add_subplot(gs[2])\n",
    "\n",
    "\n",
    "\n",
    "for i in range(2):\n",
    "    \n",
    "    ax1.plot(regionals.iloc[i], linewidth=4)\n",
    "    \n",
    "ax1.set_title(\"Happiness over Time for World Regions with the Highest Happiness Scores\", fontsize=15.5, y=1.01)\n",
    "\n",
    "ax1.set_xticklabels([])\n",
    "\n",
    "ax1.set_yticks([])\n",
    "\n",
    "ax1.text(2016.6, 7.15, 'North America and ANZ', fontsize=15)\n",
    "ax1.text(2017, 6.84, 'Western Europe', fontsize=15.5)\n",
    "\n",
    "ax1.text(2015.03, 7.25, round(regionals.iloc[0,0], 1), fontsize=13)\n",
    "ax1.text(2015.03, 6.705, round(regionals.iloc[1,0], 1), fontsize=13)\n",
    "\n",
    "ax1.text(2020.75, 7.15, round(regionals.iloc[0,6], 1), fontsize=13)\n",
    "ax1.text(2020.75, 6.93, round(regionals.iloc[1,6], 1), fontsize=13)\n",
    "\n",
    "\n",
    "for i in range(2,8):\n",
    "    \n",
    "    ax2.plot(regionals.iloc[i], linewidth=4)\n",
    "        \n",
    "ax2.set_title(\"Happiness over Time for World Regions with the Medium Happiness Scores\", fontsize=15.5, y=1.01)\n",
    "    \n",
    "ax2.set_xticklabels([])\n",
    "\n",
    "ax2.set_yticks([])\n",
    "\n",
    "ax2.text(2016.9, 5.99, 'Latin America and Caribbean', fontsize=15)\n",
    "ax2.text(2020.1, 5.7, 'East Asia', fontsize=15)\n",
    "ax2.text(2018.45, 5.19, 'Middle East and North Africa', fontsize=14)\n",
    "ax2.text(2017.1, 5.5, 'Central and Eastern Europe', fontsize=15)\n",
    "ax2.text(2018, 5.34, 'Southeast Asia', fontsize=15)\n",
    "ax2.text(2015, 5.215, 'Commonwealth of Independent States', fontsize=14)\n",
    "\n",
    "ax2.text(2015.03, 6.155, round(regionals.iloc[2,0], 1), fontsize=13)\n",
    "ax2.text(2015.03, 5.64, round(regionals.iloc[3,0], 1), fontsize=13)\n",
    "ax2.text(2015.03, 5.42, round(regionals.iloc[4,0], 1), fontsize=13)\n",
    "# ax2.text(2015.03, 5.35, round(regionals.iloc[5,0], 1), fontsize=13)\n",
    "# ax2.text(2015.03, 5.3, round(regionals.iloc[6,0], 1), fontsize=13)\n",
    "ax2.text(2015.03, 5.26, round(regionals.iloc[7,0], 1), fontsize=13)\n",
    "\n",
    "ax2.text(2020.75, 5.88, round(regionals.iloc[2,6], 1), fontsize=13)\n",
    "ax2.text(2020.75, 5.75, round(regionals.iloc[3,6], 1), fontsize=13)\n",
    "ax2.text(2020.75, 5.24, round(regionals.iloc[4,6], 1), fontsize=13)\n",
    "ax2.text(2020.75, 6.06, round(regionals.iloc[5,6], 1), fontsize=13)\n",
    "ax2.text(2020.75, 5.37, round(regionals.iloc[6,6], 1), fontsize=13)\n",
    "ax2.text(2020.75, 5.48, round(regionals.iloc[7,6], 1), fontsize=13)\n",
    "\n",
    "\n",
    "\n",
    "for i in range(8,10):\n",
    "    \n",
    "    ax3.plot(regionals.iloc[i], linewidth=4)\n",
    "        \n",
    "ax3.set_title(\"Happiness over Time for World Regions with the Lowest Happiness Scores\", fontsize=15.5, y=1.01)\n",
    "\n",
    "ax3.set_xticks(years)\n",
    "    \n",
    "ax3.set_xticklabels(year_ticks, fontsize=14)\n",
    "\n",
    "ax3.set_yticks([])\n",
    "\n",
    "ax3.set_ylabel('Happiness Score', fontsize=14)\n",
    "\n",
    "# ax3.set_xlabel('Year', fontsize=13)\n",
    "\n",
    "ax3.text(2018.35, 4.59, 'South Asia', fontsize=16)\n",
    "ax3.text(2017.5,4.1, 'Sub-Saharan Africa', fontsize=16)\n",
    "\n",
    "ax3.text(2015.06, 4.548, round(regionals.iloc[8,0], 1), fontsize=14)\n",
    "ax3.text(2015.06, 4.14, round(regionals.iloc[9,0], 1), fontsize=14)\n",
    "\n",
    "ax3.text(2020.72, 4.4, round(regionals.iloc[8,6], 1), fontsize=14)\n",
    "ax3.text(2020.72, 4.51, round(regionals.iloc[9,6], 1), fontsize=14)\n",
    "\n",
    "plt.savefig(\"happiness_over_time_for_world_regions_of_all_happiness_scores_3_plots.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion <a class=\"anchor\" id=\"conclusion\"></a>\n",
    "\n",
    "*How has Happiness changed in each region of the world since 2015?*\n",
    "\n",
    "Most regions in the world have had slight changes in happiness, with a mix of uptrends and downtrends. \n",
    "\n",
    "The happiest region in the world, North America and ANZ, has shown a slight downtrend, while the second happiest, Western Europe, has shown a slight uptrend, leading one to believe that Western Europe may, in the foreseeable future, become the happinest region of the world.\n",
    "\n",
    "Central and Eastern Europe has shown the greatest rise in regional happiness over the time period of the study, rising from the 6th happiest region in the world to the 3rd. \n",
    "\n",
    "Latin America and the Caribbean have shown a slight downtrend in happiness, similar to the downtrend seen in the North America and ANZ region. The Latin America and Caribbean region fell in happiness score from 3rd place to 4th place.\n",
    "\n",
    "Sub-Saharan Africa revealed a higher happiness score than South Asia in 2021, making South Asia the new least happy region in the world.\n",
    "\n",
    "The Commonwealth of Independent States, Southeast Asia, and the Middle East and North Africa are all hovering in the low-medium range of happiness score.\n",
    "\n",
    "East Asia's happiness score is nearest to the average regional happiness score. All things equal, this makes East Asia the seat of average happiness for the entire world.\n",
    "\n",
    "All in all, many regions in the world have maintained roughly the same happiness score since 2015, with a few regions rising quickly and still others trending slowly downward. The World Happiness Report is meant to be a tool to use in achieving the Sustainable Development Goals of the 21st Century. If we are to achieve those goals, we all will have to try a little harder to build and be part of communities that are more pleasant to live in.\n",
    "\n",
    "### Further Research <a class=\"anchor\" id=\"further\"></a>\n",
    "\n",
    "* What factors and countries weighed prominently in causing the uptrend of Western European happiness versus the downtrend of North America and ANZ happiness?\n",
    "* Import population information and use these values as weights in order to calculate the happiness score of the average person in each region.\n",
    "* What factors caused Eastern European happiness to rise faster than every other region?\n",
    "* What factors are causing the downtrend in the Middle East and North Africa region, the largest downtrend in the chart?\n",
    "* How will regional happiness change in the 2021 World Happiness Report?"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}