{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>GEO</th>\n",
       "      <th>INDIC_NRG</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1722.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Index, 2005=100</td>\n",
       "      <td>100.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Final Energy Consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1193.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Final Energy Consumption</td>\n",
       "      <td>Index, 2005=100</td>\n",
       "      <td>100.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Final Energy Consumption</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Distance to target - Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>239.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Distance to target - Primary energy consumption</td>\n",
       "      <td>Index, 2005=100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Distance to target - Primary energy consumption</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Distance to target - Final energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>107.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Distance to target - Final energy consumption</td>\n",
       "      <td>Index, 2005=100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Distance to target - Final energy consumption</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TIME                            GEO  \\\n",
       "0   2006  European Union (28 countries)   \n",
       "1   2006  European Union (28 countries)   \n",
       "2   2006  European Union (28 countries)   \n",
       "3   2006  European Union (28 countries)   \n",
       "4   2006  European Union (28 countries)   \n",
       "5   2006  European Union (28 countries)   \n",
       "6   2006  European Union (28 countries)   \n",
       "7   2006  European Union (28 countries)   \n",
       "8   2006  European Union (28 countries)   \n",
       "9   2006  European Union (28 countries)   \n",
       "10  2006  European Union (28 countries)   \n",
       "11  2006  European Union (28 countries)   \n",
       "\n",
       "                                          INDIC_NRG  \\\n",
       "0                        Primary energy consumption   \n",
       "1                        Primary energy consumption   \n",
       "2                        Primary energy consumption   \n",
       "3                          Final Energy Consumption   \n",
       "4                          Final Energy Consumption   \n",
       "5                          Final Energy Consumption   \n",
       "6   Distance to target - Primary energy consumption   \n",
       "7   Distance to target - Primary energy consumption   \n",
       "8   Distance to target - Primary energy consumption   \n",
       "9     Distance to target - Final energy consumption   \n",
       "10    Distance to target - Final energy consumption   \n",
       "11    Distance to target - Final energy consumption   \n",
       "\n",
       "                                      UNIT   Value  \n",
       "0   Million tonnes of oil equivalent (TOE)  1722.2  \n",
       "1                          Index, 2005=100   100.5  \n",
       "2                               Percentage     NaN  \n",
       "3   Million tonnes of oil equivalent (TOE)  1193.2  \n",
       "4                          Index, 2005=100   100.1  \n",
       "5                               Percentage     NaN  \n",
       "6   Million tonnes of oil equivalent (TOE)   239.2  \n",
       "7                          Index, 2005=100     NaN  \n",
       "8                               Percentage    16.1  \n",
       "9   Million tonnes of oil equivalent (TOE)   107.2  \n",
       "10                         Index, 2005=100     NaN  \n",
       "11                              Percentage     9.9  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see plots in Jupyter\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "#make pandas dataframe called \"energy\", comma as thousands separator, \":\" for na_values\n",
    "#specify dtype for the numeric columns (TIME and Value)\n",
    "filename = \"nrg_ind_334a_1_Data.csv\"\n",
    "energy = pd.read_csv(filename, encoding = \"ISO-8859-1\", thousands = \",\", na_values=\":\", \n",
    "                 dtype={\"TIME\": np.int, \"Value\": np.float})\n",
    "\n",
    "#see head (first rows of data frame)\n",
    "energy.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4920, 5)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get dimensions of data frame (rows, columns)\n",
    "energy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4920 entries, 0 to 4919\n",
      "Data columns (total 5 columns):\n",
      "TIME         4920 non-null int64\n",
      "GEO          4920 non-null object\n",
      "INDIC_NRG    4920 non-null object\n",
      "UNIT         4920 non-null object\n",
      "Value        1664 non-null float64\n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 192.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#list data type of each column, number of non-missing values, memory usage\n",
    "energy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Primary energy consumption', 'Final Energy Consumption',\n",
       "       'Distance to target - Primary energy consumption',\n",
       "       'Distance to target - Final energy consumption'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see which values in \"INDIC_NRG\" column exist\n",
    "energy.INDIC_NRG.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['European Union (28 countries)', 'Euro area (19 countries)',\n",
       "       'Belgium', 'Bulgaria', 'Czech Republic', 'Denmark',\n",
       "       'Germany (until 1990 former territory of the FRG)', 'Estonia',\n",
       "       'Ireland', 'Greece', 'Spain', 'France', 'Croatia', 'Italy',\n",
       "       'Cyprus', 'Latvia', 'Lithuania', 'Luxembourg', 'Hungary', 'Malta',\n",
       "       'Netherlands', 'Austria', 'Poland', 'Portugal', 'Romania',\n",
       "       'Slovenia', 'Slovakia', 'Finland', 'Sweden', 'United Kingdom',\n",
       "       'Iceland', 'Norway', 'Montenegro',\n",
       "       'Former Yugoslav Republic of Macedonia, the', 'Albania', 'Serbia',\n",
       "       'Turkey', 'Bosnia and Herzegovina',\n",
       "       'Kosovo (under United Nations Security Council Resolution 1244/99)',\n",
       "       'Moldova', 'Ukraine'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see which values in \"GEO\" column exist\n",
    "energy.GEO.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>GEO</th>\n",
       "      <th>INDIC_NRG</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1722.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>2007</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1693.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2008</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1692.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1476</th>\n",
       "      <td>2009</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1598.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>2010</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1656.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2460</th>\n",
       "      <td>2011</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1594.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2952</th>\n",
       "      <td>2012</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1585.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>2013</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1569.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>2014</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1508.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4428</th>\n",
       "      <td>2015</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1529.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TIME                            GEO                   INDIC_NRG  \\\n",
       "0     2006  European Union (28 countries)  Primary energy consumption   \n",
       "492   2007  European Union (28 countries)  Primary energy consumption   \n",
       "984   2008  European Union (28 countries)  Primary energy consumption   \n",
       "1476  2009  European Union (28 countries)  Primary energy consumption   \n",
       "1968  2010  European Union (28 countries)  Primary energy consumption   \n",
       "2460  2011  European Union (28 countries)  Primary energy consumption   \n",
       "2952  2012  European Union (28 countries)  Primary energy consumption   \n",
       "3444  2013  European Union (28 countries)  Primary energy consumption   \n",
       "3936  2014  European Union (28 countries)  Primary energy consumption   \n",
       "4428  2015  European Union (28 countries)  Primary energy consumption   \n",
       "\n",
       "                                        UNIT   Value  \n",
       "0     Million tonnes of oil equivalent (TOE)  1722.2  \n",
       "492   Million tonnes of oil equivalent (TOE)  1693.8  \n",
       "984   Million tonnes of oil equivalent (TOE)  1692.4  \n",
       "1476  Million tonnes of oil equivalent (TOE)  1598.2  \n",
       "1968  Million tonnes of oil equivalent (TOE)  1656.7  \n",
       "2460  Million tonnes of oil equivalent (TOE)  1594.2  \n",
       "2952  Million tonnes of oil equivalent (TOE)  1585.4  \n",
       "3444  Million tonnes of oil equivalent (TOE)  1569.9  \n",
       "3936  Million tonnes of oil equivalent (TOE)  1508.3  \n",
       "4428  Million tonnes of oil equivalent (TOE)  1529.6  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter dataframe by means of boolean indexing/ boolean selection: european union, Primary energy consumption, MTOE\n",
    "eu_crit1 = energy.GEO == \"European Union (28 countries)\"\n",
    "eu_crit2 = energy.INDIC_NRG == \"Primary energy consumption\"\n",
    "eu_crit3 = energy.UNIT == \"Million tonnes of oil equivalent (TOE)\"\n",
    "eu_crit = eu_crit1 & eu_crit2 & eu_crit3\n",
    "eu_energy = energy.loc[eu_crit]\n",
    "eu_energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ruixSNUENgM74PuijtRguBxAzm4B/4tnBRW+JXkSqkd5xFqk7Qv0WRmtl84Bi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\nyCMsWLCAtWvXctBBBzFw4EBuv/12WrZ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14nV1t9YdGTiEPg8ePJgpU6Zw5JFHMmHCBCZO\nnMiuXbs4+OCD+fnPf87tt99e6ulweno6CxcuZPz48eTm5tKlSxcefPDBMv2z9ujRg/nz53P77bdz\n7bXXsnv3bjp27Mj999/PtddeG7esr776Ks79P3t3Hh91fe1//HVmJskkIUDYlEUlxRWqtipiqyLu\ntK5XvFWrV5Ffrb21ar311ttaFe0mrV5tVW613ovUrVaxbrVYrUUUKmDdlVqVoqKoQMKWZZLMnN8f\nMwmTMAkz35mQCbyfj0ceM/P9fuY7ZyKi53s+n/Nxfv/73/P73/++w7m2eItdY2uCVU1xVjcmk+S2\nZLmxizsuTfWW13ZqAHUfhYnVwzoSLPp005TtkMHgsjDDyiMMK297jFBZhLMcurM1b/5EIpGM1eRo\nNMrw4cM7nJs3bx6HH344s2bNat9W6dprr+Xmm2/mm9/8JsOGDetw82fUqFGMGjWqkL+aXtW5ot/d\nMo1f//rXfPrpp+y4445ZXy+TpUuX8vzzz3PnnXdudq66urrDP4u+aEud4RcuXMigQYN48cUXOfro\no3n77bfZeeedueSSS7jwwgvbx+XSQb6zfN4rIptT4iwSQMgsuQ9w/1LqYnHeXd9MQ/8ELQmnJGRU\nRELa8isPW+P3GzJjh4oIO1RE+PxWmNZdETFq8pzWvaU11dOmTWPatGlbvM7w4cN5+umns/rMgw8+\nmCeeeKLbMaNHj94stjvuuKNPJMcATfFEqnKcrB6vSlWRc/0z0NxQmJtjsQajrLLjZye87cZMnDfq\nNh2viBjDyiMMjW5KpgdHw0SKtDq9tW/+5GPu3LkAzJw5k5kzZ3Y4lz67oq/LVNHPZ5nGRx99xPz5\n87fYIfuxxx7bbI35t7/9bcLhMNdffz177rlnwG/Uu7LtDL9y5UoaGho455xzmD59OnvvvTe///3v\nueiii6iqqmq/aZBLB/nO8nmviGzO+kpzm77AzGqAZcuWLaOmpqa3w9mMtqMSyU7nad0fN7TSkl0D\n6awNjYbbk+iemNYtm2uOO2uaWpNV5KY4q1KV5A0F+ofbVG9cfWj+nYGnP7uasuDbmRMCBkeTU7zb\nqtNDy8P0i4T67B7lkp22Cv6cOXP47W9/y/333w9suaJfXl7Oiy++2J4Q//Wvf2XixImMHz++wzKN\nG264gYsuuogbb7wRgOuuu45ly5YxadIkhg0bxttvv821115LbW0tS5YsYddddwUyL+fIZNKkSUQi\nkYyNtIrNj370I6644oqsmkS+9tpr7Lffflx++eXt3//oo49u7/iePl37+OOPZ+nSpbz77rtAcu/w\ntg7z6dsHPvvss5SUlNDY2HUzwXzeK7INC/wfQlWcRUQ62XrTuuMsWdVE2GBUZQk1/UuoKdC07u1Z\nS8JZk6oer27cNMV6XXOB7350Eq10Bg6P5zVdu3pEPK+kGSDBpj9fb6ZVp8vD1iGZHlYeYUgRV6cl\nd4Wq6Ge7TGOPPfbgwQcf5L777mP9+vUMGjSII444gmuuuaY9aZbM68jbtt866qijOow96qijePzx\nx2loaKCioqK9g/zll1/OzTffDCS3C/vyl7/M66+/3u3n5vNeEdmcEmcRkS3oblr3Rw2tbMyzYhl3\neG9jC+9tbGFep2ndo/uXUFWiWSKZtCac2timBl2rUsny2lgi7xsbQU2Y0sQTNwfPfA+c0nNbxDTG\nnfc3tvD+xpb2YwYMioYZ1l6hTibWVSWqTvdFhVrOAdkt0zjhhBM44YQTtnitTMs5Mpk3b15WsfVF\nndd9jxs3rv14ukQigbsTi8WoqKgAttxBvjv5vFdEOlLiLCKSo0zduj+qb+XDAk3rbsjQrXt7ntYd\nd6culSCnd7OujeVf+c9HSQgGloYZWBZmYGmIf6xt5sApTTx1a0WgLakipc6BpzRRGUlWhuticdY3\n9+xNAAfWNMVZ0xRn6drm9uPRsDG0rTIdTSbTQ8oj292fPZFCyLSO/MQTT+TKK6/kySefbO8MD8nu\n8GPGjKG6urrDNba0fWB38nmviGyixFlEJA/p07r37DSt+6P6ZJJX7NO6P21s5Y3aGBtbEjQnnNKQ\n0a8kxLhBZQzbinsJJ9xZG0vf6in5uCYWJ9GLGXLESCXHYQaWhRhYFqa6NExFxDr87ssjIda1NHDw\nVxuZP7si58/54hmNVAxwDhhaQU3/5DY8rQlnbXOculic2qY4dbEEtbE4zT38C2mKOx9sbOWDjR2b\nNw0qC29KqMvDDI1GGFCq6rRsPwrVGX7fffflzDPP5IorrgBobw42d+5c7rrrrvZx2W4fmKkzfLbv\nle3HihUruPbaa1m8eDGvvPIKzc3Nm816aOtJ0NmAAQPat44DeOqpp5g1axZ//etfWblyJTvttBNT\npkzh8ssv36wj/9/+9jcuu+wyFi5cSGlpKZMnT+a///u/GTFiRFZxP/PMM1xzzTUsXrwYSC4Tuf76\n6znssMNy/RXkRYmziEgBZZrW/XFDa6rRWM9M6x5dVZrc9iqHad1xd95a28yLnzayIrUHcazeaG4w\nSiucskpn0aeNjKqMsN/QcvYYWEq4QMmRu7OuOZUgp7pZr2pqpbYpnvfe2vkIGZuS49Iw1alKcr8s\npy2Prirh73VhJl/YwNqVYV790+bbwHRln2NiTL6wgWHRMLtUbep2GwkZQ6IRhkQjMCB5zN1paE1W\n4WtjyaS6LpZcw93Tv77a1Ge+lVadLgttqk63P0YjlIa3XjJdLDd/ZNtXqHXkALfffjsjR47kuuuu\nY82aNey+++7cc889nHHGGe1j8ukgvzW6z0vf8s477/DAAw8wfvx4DjzwQJ577rkux06fPp1jjz22\n/XUk0vHv0ltvvZWGhgauuuoqRo8ezauvvspVV13Fc889x/z589v/u/nWW29x2GGHsf/++3Pfffex\nYcMGvv/973PEEUfw8ssvE41Gu435wQcf5Ctf+QrTpk3ju9/9LmbGSy+9RENDQx6/iWDUVbuA1FVb\nRLqTPq37o4ZWVja0FLxb95BomJotTOtubE0wZ9l6VtS30rDWWPRglMVzoh2aWg0cHmfClCYmTGmi\nfIAzqjLCqZ/pTzSS/TZabd+3bXun1anK+Zqmwncpz0UIGFCarBy3TbOuLgvTrySU937rsXiCPyzf\nyLrmBHNvqmDhveW0Nnd9zUip88UzGpl8YQMDSkMcN7ofZeFgezO3Jpx1zW3JdKI9sY7Fe+e/8wNL\nQ+1NyIaWh9mhvLDV6Wxu/gA9cvNHRKQvSt+arKvO8G0V5zvvvJOzzjqry2utWrWqfXu1NnfffTdn\nnXUWzz33XPs6+q997Ws8/PDD/POf/2yvRC9ZsoQDDzyQm266iW9961tdfsa6dev4zGc+w7nnnst1\n110X6DtnoK7aIiLFbmtM616d2mqp87Tu0VWl7FAepinu3PnWOtY0xZl7UwUL7inPuB537cowT9xc\nyVO3VnDwV5OJ3W/eWsfZewzYLHl2d+pbvX17p7a9kNc0xYn14hxrA/qXhtqryNWp6db9S/NPkLtS\nFk4mv39eUc+XLm5g0tRGFs2JsqjTjYnqEXEOnNLEhFOSNyaGRcMcOaoycNIMyer04GiEwdFN/2l3\ndxrj3l6VTk73jrN2K1Sn1zYnWNvczD/WbapOl6aq00OjHbfKyvV7b37zp7zbmz8r6jcEuvkjSaro\ni2wb2pLmQuicNAPst99+QHI/9zaLFi3ikEMO6TB9e/z48QwePJiHHnqo28T5d7/7HbW1tR22bOtN\n+ttORKSXbM1p3dBAeRjAaGhx7v1+Fa9lMZU43mLMn13B2pVhTv/JBn73znoOHVFObSzRYS/kpl6q\narbpXxJqrx4PLEtOs+5fEiLcC82sysIhJu/cj/c2tLA0GqN8WiOTpjXS3GDEGoyyCqe0Ivn7GhZN\nbnm2S1VJjyTzZkZFxKiIhBhZuWkKeNyddWlV6bbHnv7n2JxwPqxP/hlPNyBVnR5aHk41I4swsCzz\nDY7G1kTBb/7I5npzOYeI9L5LLrmEqVOnUlVVxbHHHsuMGTPYZZddun3Ps88+C8Bee+3VfiwUClFW\ntvn/b5SVlbX3CujKwoULGTRoEC+++CJHH300b7/9NjvvvDOXXHIJF154YYBvlR8lziIiRaIsHGKX\nqlJ2qUo2hlrfHC/otO7GOIAz96aKrJLmdK/+qYyBw+N86eIG7nt3Q36B5KFfSWhTcpyqJA8oLb69\niENm1PQvpaZ/KXWxOO+ub6ahf4KWhFMSSiayY/qXUl3WO0tnwmYMioYZFA0zJu14Y2ui09rpBGub\ne74527rmBOuam3l73aZjJSEY0qEyHWFwWYjf/3MDa5rigW/+PLBsPWfsNkBJXjdU0RfZfpWVlfGN\nb3yDY445hsGDB/Pqq6/y4x//mC9+8Yu8/PLLGSvNAB9//DFXXnklkydP5rOf/Wz78d13350XXnih\nwzTx999/n5UrV1JSUpLxWm1WrlxJQ0MD55xzDtOnT29vonfRRRdRVVXV3ghva9Ea5wLSGmcR6SkJ\nd1Y3xfmwPvi0bndoXGf85NhBgbdL+t4TtZT3d3o656iIWPvU6oFlIapLwwwoC2s7pF6QSDVz69yM\nrKEXO7n98RcVgTqXTzyngS9d3MBJo6vYqzq3m0fbi2wr+m3CJd5e0R8cDauiL4EVsuMzwJo1a/iP\n//gPHnnkEVpbWzniiCP4xS9+wejRozuMW7BgAdOnT+ell16ipaWFvfbai+9+97uccsop3ca7dOlS\nLr30Ul555RVWr17NwIED2X///Zk+fTrjx48P9kvYirpa45zJa6+9xn777cfll1/O9OnTNzvf2NjI\nkUceyfLly1myZAkjR45sP/fss88yceJEvvWtb3H55ZezYcMGvv71r/Pss89SUlJCY2Njl5979NFH\n89RTT3HDDTd0mK59/PHHs3TpUt59993cvnSS1jiLiGzLQmbtjZbSp3Un94/Oblq3GSx+MBooaQZo\nbTYWPxhl0rld/0cuV+VhSzXpCqUqyMlkeWt2ZJbuhSx5E6O6LMxn0o43dahOJ5/X9XB1uu3mz4J7\nygO9f+G95Rw2tZEFKxsojxhhMyKW/I6RULISH7bUY9rr7WW7rbg7c5atV0W/F2gdeWE7PicSCY4/\n/ng++eQTbrvtNqLRKFdeeSVHHHEEr732GpWVlUAyITzqqKM4+OCDmTVrFqWlpcyaNYspU6bw6KOP\ncvzxx3cZw/r169lpp50488wzGTFiBKtXr+bGG2/kkEMOYdGiRXzuc5/L8zdSPPbee2/Gjh3LkiVL\nNjvX2trKV77yFd58803mz5/fIWkGOPTQQ7nxxhu5/PLLufnmmwGYMmUKX/7yl3n99de7/dzBgwcD\ncNRRR3U4ftRRR/H444/T0NBARUXuN1GD2j7+TRQR2cYEnda9aE732z5syeI5wRLnsrC1T61u3+qp\nLJRXMyzpXdFIiOGREMPT1k4n3FnfnOhQma4tYHW6UDd/Ks5t5LfvrM/6fSHokEi3JdYRM0KWbMwW\nso6vu0rC2x9DXb+OGIRCaUl9N+MLuTb+rbXNrKhvzXs5xz/WNquinwWtI+9o4sSJfPzxx0CyGtpd\n4jxmzBgOOuigLs8//PDDPP/88yxevLi9+rvvvvsyZswYfv3rX7dXL++//34AHnnkkfYE7Oijj2bJ\nkiXcfffd3SbOEyZMYMKECR2OHXvssQwZMoS77757m0qcIdlssvNNRHdn6tSpPPXUUzzxxBPss88+\nGd978cUXc/755/P2228zePBgRowYwdixY9s7b3dl3Lhx7Z+TLpFI4O7EYjElziIikpvO3brbpnWv\nrG9lVWpad1O9dVijGETdR2Fi9VBWmfl8acg2S44HloYp19TN7ULIrH2br3SxeKLDFlltSXWQXmS9\ncfMnASQS0NK+QKJ4lrkZdEiku0/mu0/c31gToyHPiv6kqY38bVWjEuct0DryzRWy4/Ojjz7Krrvu\n2mHK9M4778zEiRN59NFH2xPn5uZmSkpKKC/f9Gc+FApRVVVFIpF7Y5F+/foRjUY3q4D3da+88gpL\nly7dbB/ziy66iPvuu485c+YwceLEbq8RjUbZe++9AXjyySdZunQpt912W7fvOfHEE7nyyit58skn\n29/b9v4xY8ZQXV0d8BsFs239UxURkYzTup9cUc+yVYX5n/1YgxGtdIZEO+6DPLAsTHnYtptprZK9\nsnCIHStC7Fix6X87Eql9vtuT6abkY3031emtcfOnr3Gg1aE1XpikfvGc7tc0d6e1ObkvfPm5jdz6\nRi0VJSFKQkZJyChNPZaEoCS86XX7Y9g6jg3T/rynto/rLeoMn78tdXx+44032quV6caNG9deZQY4\n++yzuemmm/jOd77DZZddRklJCbNmzeLvf/87v/zlL7OKJZFIkEgk+OSTT/jZz36GmTFt2rT8v2QP\neeCBBwDaO1q3vR49ejQHHHAAl156KeFwmAkTJlBdXc0rr7zCT3/6U0aOHMkFF1zQfp1rr72Wm2++\nmW9+85sMGzaM559/vv3cqFGjGDVqFADvvfcet99+O1/4wheIRCIsWLCAGTNmcMkll3DIIYe0v2fe\nvHkcfvjhzJo1q73p17777suZZ57JFVdcAdDeHGzu3LncddddPfdL6oISZxGRbVxZOET/khBlFa1b\nHpyFaCXUVJUwccQ2knlIrwiZMaA0zIDSMKOrNh2PxROsjXWc7l0Xi9Pq0NxQmAQq1mCUVRZP1biY\nFKqiX9ecoK45z60AUsJGxgS7JJQ6niERTz+WPi59TG+sX9c68vxk2/G5rq6uw5ZIbQYNGkRtbW37\n67FjxzJv3jxOOukkbrjhBgAqKyv53e9+t8UKapuzzz6bu+++G4AddtiBP/7xj+y22275ftUe07lq\n3Pb6nHPO4Y477mDs2LHMnDmT2267jY0bN7Ljjjty0kkncc011zBo0KD2982dOxeAmTNnMnPmzA7X\nvOqqq9qbiJWWlrJgwQJuueUWGhoa2GOPPfjlL3/J17/+9azivf322xk5ciTXXXcda9asYffdd+ee\ne+7hjDPOCPorCEyJs4jIdqCiJERZpTNweDyvil31iDilFU5FyfZd7ZCeUxYOsUNFiB3SqtPuztMf\n1vNOfbwwn1GhpDmTYq3oxx0a405j3KGlcNcNwWYJdnpy3iEZ73As87i2Y5FuEnKtI8/P8OHD+Z//\n+Z/21xMnTuSwww5jv/3245ZbbmlP1jKtx207nu7tt99mypQpjB8/nm984xuUlJTw29/+ltNOO41H\nH310s6ZUmfzwhz/k4osv5sMPP2TmzJkcd9xxPPXUU+y33375fdkesqUu2tOmTcuqYj5v3rysPm/4\n8OE8/fTTWxw3adKkjLFFo1FmzJjBjBkzsvq8npRz4mxmEeBE4FjgIGA4UA6sBv4OPAPc7+6B+oOL\niEjhjelfyuu1MSZMaeKJm4P/H+2BU5raryeytZgZA8rClFW2FuTmz7YyTbvQtreKfgKIxZ1YkMX2\nW5CeYKcn2R/Xt2odeYFl6vg8aNAg6urqNhtbV1fXoWr6/e9/n379+vHggw8SDif/XjnqqKN4//33\nufTSS3n55Ze3+Pk1NTXU1NQwfvx4jjvuOPbZZx+uvPJKHnvssQJ8OykmWSfOZlYJXApcAFQDbwAv\nAU8CTaljNcB/AD82s3nAle6+oMAxi4hIjqrLwgyLhpkwpYmnbq0IvI/zhFOaGBZNNv4S2ZoKffPn\n+F36MaA0TNydhNP+mP6887G4J9dmx1PHElke63y97j6rB3K4rJUWqBKvij40J5zkTPXNfxeFWke+\nqrGVodvJVlVb0rnCPG7cuIxdud98880OU7jb9iduS5rb7L///u1Tt3NRUlLCvvvum1XCLX1PLv+2\nLUv9fA+Y4+5ruxpoZhOArwJ/NLPL3P1/uhorIiJbx57VZXza1MDBX21k/uzct2/44hmNlA9wVTmk\nVxT65s+QaPJ/gUoovnWiiQzJdE8k6enP1zbHoUDLOVTR716h1pH/+cN6Dt6xgqHR8HbdLCxTx+fj\njz+eWbNm8cILL3DAAQcA8MEHHzB//vwOU3533HFHXnrpJVpbWzt0wl6yZMlm+xFno6mpiSVLlrDn\nnnvm8Y2kWOWSOJ/l7k9mM9DdFwGLzOxqYJctjRcRkZ43uqqEv9eFmXxhA2tXhnk1h/V1+xwTY/KF\nDQyLhtmlqmTLbxDpAdvLzZ9Q2xZSWzGpr4vFeXj5hoJV9McOLCUSNloT0JpwWhNOizutCWhJvW71\nTecKtNV30SvkOvLltLB8wzoA+peEGFoeZmh5hGHRCEPLwwyKhvtcA7FCdXw++eSTGT9+PKeddhoz\nZswgGo1y1VVXMXLkSM4777z2cRdccAFf+cpXOPnkkzn//PPb1zj/+c9/7lBxztTx+Tvf+Q6hUIgJ\nEyYwdOhQPvjgA2bOnMmKFSuYPXt2T/+qpBdknThnmzR3ek8tULvFgSIi0uNCZhw5qpI/LN/I6T/Z\nwMDhcRbeW05rc9f/YxUpdb54RnIblAGlIY4cVbnNbQ0jfYdu/vScQlf0D9whtxsb7snkuT2pTkCr\ne4fXycQ7LfnO8Lql7b3tr3P+Gj2qp9aRr29JsL4lwbvrN3VPCxsMjoYZGo0wLJVUDy0P0y8SKtpt\nAwvV8TkUCvH4Rw8YggAAIABJREFU449zySWX8LWvfY3W1lYOP/xwHnjgAfr169fh+o8++igzZsxg\n6tSpxONxdtttN+68807OOuusbmMdP348t9xyC7NmzWLjxo2MGDGCAw88kFtvvbXDnsPbi08bW3mj\nNsbGlgTNCac0ZPQrCTFuUBnDtpElBbalzmrtA80q3L0hi3HlwG7u/mq+wfU1ZlYDLFu2bBk1NTW9\nHc5mPqpvob4lQVWp1iaKbM9i8QR/XlHPp01xGtcZi+ZEWTQn2qEKUj0izoFTmphwShPlA5xh0TBH\njqqkLLz9TgeU4hCLJ/jD8o2sa04w96aKnG/+HDe6n/4cd2HZ+mbmr2zgj7+oCFTRn3hOA1+6uIHD\nhldQUyQNBD01jT294r2pAp72PK0C3pKWuKe/7py4BymSN9UbVx86OO/vNf3Z1YGnxJeHrT2JHlYe\nYWhq6UJpuDiTaSlecXfeWtvMi582sqIhueVlrN5objBKK7z95s6oygj7DS1nj4GlxTALInAAuSTO\nceAL7r449ToEvAB81d3/njZuArDQ3be77EyJs4j0FQl33tvQwtK6GJ82Jbf4aW6wZBWjwtubBA2L\nhtmruoxdqkpUaZaioZs/PSPhztz3N/JJQ5zffr8q54r+6T/ZwA4VYSbv3G+7+PsinrECnj4VvePr\nD+tbWROLM+O46rzXkX/3sc07RueruiyUqk5vSqoHlhZvdVp6V2NrgjnL1rOivpWGtcmmdYs7/T08\ncHicCVOamDAl+ffwqMoIp36mf2+vyd8qiXMCOCgtcQ6T3E3vAHd/MW2cEmclziLSh9TF4ry7vpmG\n1gQtCackZFREQozpX6ru2VK0dPOnZ6ii33Pa1pHP+7/yvNaRH3thPZPObSxgZF0rCcGQ9Kne0WRC\nXb4dNyOTZNJ851vrWNMUZ+5NFSy4p/tO8eES5+CvJv+eGBwNc/YeA3ozeVbiXAyUOIuIiGx9uvlT\nWKro95zH39vAe58k+PExgwKvI//eE7X0H5j8//feaqrWryTEsGi4w5TvwWVhwiHdnNrWxd259+11\nfLChlXu/X8VrAWam7FQV4YzdBvTWtO3AH7ptrNQWERGR7VZ1WZgDhpb3dhjbjLJwiMk790tW9KMx\nyqc1Mmlaoyr6BVCIzvAVA5yDd6xgdFUJG1oS1MXi1MXaHuOs3wod0Ta2JNjYkmDZhk3NyEKkmpGl\nVaaHloepKtF0723JW2ubWVHfytybKnJKmgFe/VMZA4fH+dLFDfxjbXPR73DQmRJnEREREekgZEZN\n/1Jq+pduquj3V0U/X4XsDG9m9C8N0780zC5Vm8a1Jpy1sTh1zXFq05LqWLxny9MJYFVTnFWppRNt\nysKWnOqdtn566FZuRrY9dHzeWl78tJHGdcaCe4LdrFx4bzmTpjbyt1WN23zifIyZ7Zp6HgIcmGxm\n6bt8jylIZCIiIiLS61TRL5ytsS1gJGQMKY8wJC0hdHca495ela6LJZPqdc0JEj083TsWdz7Y2MoH\nG1s7HB9YGto01TuVVA8sCxVs5kI2HZ8XfdpYbB2fe13cnVirE0s4TfEEsVanKZ58vbqxlRUNrSya\n0/2a5u60NicbiZWf28iqxlaG9qEbF7mucc6Wa42z1jiLiIiIyOaKZR15wp11zYkOCXVdLE59Ly2e\njhgM6TTVe1g0QkVJbt+5D3d8zltrIpXoxhOpx+RPUzyRetz82Kbjiaz2Pi9UZ/iDhpUzaWTwRnkB\nbZXmYLvkcmF3fy9QRH2YEmcRERERyUYxd4aPxROsTU3xro3FWZt67K1mZJWR5N7TbftODy2PMCQa\nJpKhGVlf7vjs7rQkyJjQdp/wbjrWwzPyC7YX+dXPreHzI0s5YXTVlgcXVs83B9seE2ERERERkZ5Q\nzOvIy8IhdqgIsUNFx+neG1sS1HWqUK9vTtDT+XR9q1O/oYXlac3IjFQzsrTu3oOjYR5fvpE1TfGs\nOz7HW4z5sytYuzLM6T/ZwAPL1gfu+OyeSm4TTlNr11Xd7pLgXro3kbXmhsLcvGluMJp7ep1AgQWa\nVG5mw4GJwE6pQyuAZ9x9ZaECExERERHZHvSFdeRmRlVpmKrSMDv3K2k/3ppw1nVqRFYXi9PUw6VP\nB1Y3xVndFGfp2uYO5/Lt+PziqiZ26ley2fTmTElw5+nP27q2mRCFuE5pH9u+LKfE2cz6A78AzgTC\ndCx1t5rZPcDF7r6ucCGKiIiIiEgxioSMwdEIg6Md04rG1kSHRmRrYwnqmuM92ozMnbw7Ph82tZGn\nvB71CcssWukMHB7Pe41zaYVTlePa9d6WdbRmVg48DZwF3AOcAnwe2A/4F+C3JBPqp80s543pzGyU\nmd1sZovNLGZmm/1rZWajzcwz/KzNMHawmc02szoz22BmD5vZ6Azjdkmd25Aa+xszy3/ivoiIiIjI\ndqo8EmJEZQnjBkU5dHglJ4yu4qzdBnDy6CoOG17BPoPK2KlfhH4FTJ7MYPGD0bw6Pi9+MKqkuZOw\nQXnY6BdJ/mImTGnK63oHpt4/btC2ux3VpcCuwAR3f7HTuZeBh83sRuDPwHeAH+YYy67AqcASYDFw\nSDdjpwNPpL3u0N/ezELAY8AOwNeBJuAakkn93u5enxrXD/gLsBY4AygHfgY8ZmYHu3vP7yAvIiIi\nIrIdCJkxsCzMwLIw6W10m+PO2uaOjcjqYvGsOjx3tmhONK8YF8+JMuncxryuUWwiBqVhozRkmx7T\nn6c9lnQ+FzLCaVOqH39vAxOmNPHUrRWBblBESp0JpzQxqjLSp7aigtwS59OAGRmS5nbu/qKZ/Rz4\nKrknzvPdfUcAM/sB3SfO77r7892cPwk4CDjQ3ZekrvkK8C5wHnBjatx5wEjgUHf/MDVuBbAQOBF4\nKMfvICIiIiIiOSgNG8NSXbPbuDv1rb7ZVlnrumlG1lRveU0hBqj7KEysHsq2+i5JXSsJkTnR3cKx\nktRjIbux71ldxqdNDRz81Ubmz855kjFfPKOR8gHO/kW+pj+TXBLnz5BMKLdkAfCDXAMpcHX3BOCd\ntqQ5df33zWx+6tyNaePmtyXNqXF/NbNlqXNKnEVEREREtjIzo1+J0a8kxE4ZmpGlNyKri8VpjHvB\nOj7HGoyyysIsxjboNtEt2VLyG0r+LorF6KoS/l4XZvKFDaxdGebVHJqw7XNMjMkXNjCqMsLuA0t7\nMMqekUvi3AT0z2Jc/9TYnnSDmd0BbCA5ZfuyTttljQPeyPC+N4B/7TTu3i7Gjd1SEGZWDQxMOzRq\nS+8REREREZFgNjUj63j8Lx/W84/61sxvylFZWufokJFVdTfjNOewEbHiSnzzFTLjyFGV/GH5Rk7/\nyQYGDo+z8N5yWpu7/o6RUueLZ2zaK/vUz/QPtN1Xb8slcX6B5BTsR7cw7qzU2J4QA34F/AlYA+wD\nXA4sNLPPufuq1LhqYGmG99cCg9JeV5Nc35xp3B5ZxHMxcFV2oYuIiIiISE+oKg1RVqCOz2WVsOfA\nUg4YWk6kj22ZtDWUhUMcN7off15Rz5cubmDS1EYWzYmyaE60w+++ekScA6c0MeGUJsoHOKMqI5z6\nmf5EI32rm3abXBLnG0k2zfoQuMbd16efTG1VdSXJBl8nFC7ETVL7RP972qH5ZvYM8CJwAcmmYZCc\nFZFpfkWmP/nZjsvkF8DstNejgPlZvldERERERApgTP9SXq+NMWFKE0/cHHyBclvH5z0Glilp7kZZ\nOMTknfvx3oYWlkZjlE9rZNK0RmINRnODUVbh7Xs+j6qMsP/QcnYfWNonK81tsk6c3f1xM5tOMjmd\nZmbzgOUkE88aYBLJacs/dPfHCxxnd3G9ZmZvAuPTDteSrCZ3Vp0616Yuy3FdfXZd6hrAtjUNQ0RE\nRESkr6guCzMsGi5Ix+dh0TDVZfk1GdsehMyo6V9KTf9S6mJx3l3fzNrKOGGDfiVhqkpCjBtU1ue6\nZ3clp2/h7teY2QLgP4FjSW7fBMk1zfOB69z9qcKGmJXOFeY3yNyVeywdp3B3tZZ5LKoci4iIiIj0\nGYXq+LxXdd/aX7gYVJeFOWBoOWtjcYaUhxkS3TaS5XQ5TzB39z+7+2SSTcB2BIYDVe4+uTeSZjPb\nF9gLWJR2+DFgNzM7IG3cTsBEOq7Rfgw4zMxGpI2bAIxhy2u5RURERESkSIyuKmFYNNnxeZ9jYjm9\nt63j87BomF2qSrb8BtnumHt2rdbNLA58wd0X91gwZqemnk4BTmdTB+zl7v6CmV0HxEkmyXXAvsD3\ngEZgP3evTV0nBDwPDAYuI1kRv5pkY7C93X1jalwV8CrJRmNXkayg/wxYlfquOW2RZWY1wLJly5ZR\nU1OzxfFb20f1LdS3JKgq1dQTEREREdn2xOIJ/rB8I+uaE8y9qSKnjs8DSpNNr8rCfbN5VTHoAxXn\nwGtrc0mcE8BBPZw4dxXMbHefambTgG+SrAj3Az4G/ghc6e4fd7rWEOAGko3KIsBfgIvc/Z+dxtWQ\nbPJ1BNBKsgp9SVqH7lziV+IsIiIiItKLYvEEf15Rz6dNcRrXWVYdn4dFwxw5qlJJc56UOLN1Eue+\nTomziIiIiEjvS7gnOz7Xxfi0KQ5Ac4MR69TxeVg0zF7VZexSVUJIjX7zti0nzrl+o+yybBERERER\nkV6SqeNzQ/8ELQmnJGRUREKM6V+q7tmStVwT5wVZbrnk7q52dCIiIiIi0qvaOj6L5CPXxHk2sKIn\nAhEREREREREpRrkmzr/WGmcRERERERHZnqhtnIiIiIiIiEg3lDiLiIiIiIiIdCOXxHk2kPPexiIi\nIiIiIiJ9WdZrnN393J4MRERERERERKQYZZ04m9k/criuu/seAeIRERERERERKSq5dNXeFVgPPAE0\n9Ew4IiIiIiIiIsUll8T5f4EpwJeAh4C7gCfd3XsiMBEREREREZFikHVzMHc/D9gR+H/AAOBR4EMz\nu87MPtdD8YmIiIiIiIj0qpy2o3L3Zne/391PAkYCPwEOAf5mZq+Z2b/1RJAiIiIiIiIivSXwPs7u\nvtrdbwaOJJlA7wX8a6ECExERERERESkGuaxxbmdmIeAY4CzgJKAZ+D/gtsKFJiIiIiIiItL7ckqc\nzewAksny6cBA4I/AVOBRd28ueHQiIiIiIiIivSyXfZyXArsDC4GrgPvcfW1PBSYiIiIiIiJSDHKp\nOO8BbAR2AL4DfMfMuhrr7r5HnrGJiIiIiIiI9LpcEufZPRaFiIiIiIiISJHKOnF293N7MhARERER\nERGRYhR4OyoRERERERGR7UHWibOZTTOzcC4XN7PdzGxSzlGJiIiIiIiIFIlcKs7fBd4xsyvMrMvG\nX2YWNbOTzewB4BVgZL5BioiIiIiIiPSWXJqDjQPOA74NTDezNSQT41VAjOS+zjXAWCAOzAE+5+7/\nKGjEIiIiIiIiIltRLs3B4sCvgF+Z2SHA0cCBJBPlKFALvAj8EnjI3WsLH66IiIiIiIjI1pVLxbmd\nuz8HPFfgWERERERERESKjrpqi4iIiIiIiHRDibOIiIiIiIhIN5Q4i4iIiIiIiHRDibOIiIiIiIhI\nN5Q4i4iIiIiIiHQjUOJsZmeb2eAuzg0ys7PzC0tERERERESkOAStOM8CxnRxriZ1XkRERERERKTP\nC5o4Wzfn+gMtAa8rIiIiIiIiUlQi2Q40s0OAiWmHpprZUZ2GRYETgaUFiE1ERERERESk12WdOANH\nAlelnjvwjQxjmkkmzRflGZeIiIiIiIhIUch6qra7X+3uIXcPkZyqfVDb67SfqLt/3t2f7bmQRURE\nRERERLaeXCrO7VLJs4iIiIiIiMg2L1Di3MbMhgA7k1zb3IG7L8zn2iIiIiIiIiLFIFDibGYjgd8A\nkzKdJrkGOhw8LBEREREREZHiELTifCuwN3Ap8AYQK1hEIiIiIiIiIkUkaOJ8CHCBu99dyGBERERE\nREREik3QJl/rgdpCBiIiIiIiIiJSjIImzjcB5xcyEBEREREREZFiFHSqdimwr5m9CjwB1HU67+7+\n07wiExERERERESkCQRPnH6Y9/2yG8w4ocRYREREREZE+L1Di7O5Bp3iLiIiIiIiI9ClKgEVERERE\nRES6EThxNrNyM/uGmd1lZn80s11Tx//FzHYrXIgiIiIiIiIivSfQVG0zGwU8DewC/J3kOuf+qdNf\nAr4MnFeIAEVERERERER6U9CK841AHNgD2B+wtHN/AQ7LMy4RERERERGRohA0cT4KuMLdl5PsoJ3u\nI2Bkrhc0s1FmdrOZLTazmJl1vi5mdpSZ3W1my8ys0cz+YWY/NbN+ncaNNjPP8LM2wzUHm9lsM6sz\nsw1m9rCZjc41fhEREREREdk2Bd2OKgLUd3FuIBALcM1dgVOBJcBi4JAMY84HKoCrgeXAPqnnh5jZ\nRHfvnGxPJ7nPdJvW9JNmFgIeA3YAvg40AdcAT5vZ3u7e1XcUERERERGR7UTQxPlvwFnAHzOcOwV4\nPsA157v7jgBm9gMyJ87fdPdVaa+fMbNa4C7gi8CCTuPfdffuYjkJOAg40N2XpD77FeBdkmu0bwzw\nPURERERERGQbEjRx/iEwNzVF+l6S07UPNbPzgNOBw3O9oLsnshizKsPhF1OPI3L9TOAE4J22pDn1\nGe+b2fzUOSXOIiIiIiIi27lAa5zd/SmSleVxwD0km4PdAEwGprj7woJFuGWHph6XZjh3g5m1ptYv\n/9bMdul0fhzwRob3vQGM3dIHm1m1mdW0/QCjcopcREREREREil7QijPu/gjwSGr/5mHAGnd/q2CR\nZcHMdiS5Jnmuu7+edioG/Ar4E7CG5Froy4GFZva5tMp1NZkT7lpgUBYhXAxcFTB8ERERERER6QMC\nJ85t3P0d4J0CxJITMysHHgQSwNc6xbQS+Pe0Q/PN7BmS07ovINk0DJKV8s26d9Nxe63u/AKYnfZ6\nFDA/y/eKiIiIiIhIH5B14mxmX83lwu5+T+7hZB1LBPgdyenUE939wyziec3M3gTGpx2uJVl17qw6\ndW5L16wD6tLi2tJbREREREREpI/JpeJ8Vw5jneTa54KzZHZ6B8m9pI9191dzeTsdK8xvkLl791gy\nT+EWERERERGR7UwuiXNNj0WRm18Cp5FsQpb1tGgz2xfYC7g/7fBjwLlmdoC7v5AatxMwEbiscCGL\niIiIiIhIX5V14uzu7/VkIABmdmrq6bhOr5e7+wtm9l/At4CZwKdmdlDa21e4+4rU+64D4sAiklOp\n9wW+B3wI3JL2noeAJcB9ZnYZ0ARcnRr368J/QxEREREREelr8m4OVmD3d/F6NjCV5HZXAN9M/aS7\nmk1Nv95Mnf860A/4GHgYuNLd29cuu3vCzL5Mciut20n+Pv4CnOruG/P/OiIiIiIiItLXBU6czewb\nwDeA3YGyzufdPZzrNd292+5a7j4py+v8H/B/WY5dDfxbNmNFRERERERk+xMocTazacCNwP+S3CP5\n10AYOAlYB8wqVIAiIiIiIiIivSkU8H0XAT9KPQLc6u5fAz4DrAEaChCbiIiIiIiISK8LmjjvCiwA\nEqmfKgB33wDMAC4sSHQiIiIiIiIivSxo4rwBKHF3B1YCe6SdiwPD8g1MREREREREpBgEbQ72Esk9\nkf8EPAVcbmZ1QAvwU+DlwoQnIiIiIiIi0ruCVpx/ATSnnv8AqAXuAx4EKkjutSwiIiIiIiLS5wWq\nOLv7E2nPPzSz/YAxJJPmpe7eUqD4RERERERERHpV0O2odnX3d9pep9Y6v9PNW0RERERERET6pKBT\ntf9hZs+b2QVmNrSgEYmIiIiIiIgUkaCJ81nAauAG4EMz+4OZnWFm5YULTURERERERKT3BUqc3f0e\ndz8eGA5cAgwE7gY+NbM7zezYAsYoIiIiIiIi0muCVpwBcPc17n6Lux9MsjnYDOAI4A+FCE5ERERE\nRESkt+WVOLcxs0HAscAxwI5AQyGuKyIiIiIiItLbAifOZlZuZqeb2SPASuCXwHqS6593KFB8IiIi\nIiIiIr0q6HZUvwFOBvoBi4D/AO5z99UFjE1ERERERESk1wVKnIEJwHXAXe6+rIDxiIiIiIiIiBSV\nQImzu+9R6EBEREREREREilFBmoOJiIiIiIiIbKuyrjibWTNwsLsvMbMWwLsZ7u5elnd0IiIiIiIi\nIr0sl6naPwY+THveXeIsIiIiIiIisk3IOnF296vTnk/vkWhEREREREREiozWOIuIiIiIiIh0I+g+\nzrdtYYi7+/lBri0iIiIiIiJSTILu43wMm69xHgRUAeuAunyCEhERERERESkWQfdxHp3puJkdDNwK\nTA0ekoiIiIiIiEjxKOgaZ3dfAPw3cFMhrysiIiIiIiLSW3qiOdgHwD49cF0RERERERGRra6gibOZ\nDQAuBJYX8roiIiIiIiIivSVoV+232bw5WCkwnGQyflqecYmIiIiIiIgUhaBdtReweeLcBLwPPODu\nb+cVlYiIiIiIiEiRCNpVe2qB4xAREREREREpSj3RHExERERERERkmxF0jfNtOQx3dz8/yOeIiIiI\niIiI9Laga5yPBgYCA4BWYDUwJHW9dcDatLGd10KLiIiIiIiI9BlBp2qfA2wA/hWIuvsIIAp8BVgP\n/Ju716R+PlOYUEVERERERES2vqAV5+uBH7v7nLYD7p4AHjCzIcCNwAEFiE9ERERERESkVwWtOH8W\nWN7FuX8C4wJeV0RERERERKSoBE2cPwCmdnHu3NR5ERERERERkT4v6FTtHwKzzWwMMAf4FBgGTAH2\nB84uTHgiIiIiIiIivStQ4uzud5rZKuAqkkl0hGR37SXAl9z9T4ULUURERERERKT3BK044+5zgblm\nFgKGAqtSDcJEREREREREthlB1zina0u+rQDXEhERERERESkqgRNnM5tsZs8D9cCHwL6p4zPN7CsF\nik9ERERERESkVwVKnM3sJOAPwAbge52u8xEwLf/QRERERERERHpf0Irz1cAd7n40cGOnc68Ce+cV\nlYiIiIiIiEiRCJo47wncm3runc6tAwYHjkhERERERESkiARNnOuA4V2cGwN8HPC6IiIiIiIiIkUl\naOL8OHCFmY1MO+ZmNgC4BHg478hEREREREREikDQxPl7JLef+jvwKMnp2j8HlpLcnmp6IYITERER\nERER6W2BEmd3/xTYn2Sy3B94F6gEbgMOcve6gkUoIiIiIiIi0osiQd/o7uuBa1I/IiIiIiIiItuk\noFO1e4SZjTKzm81ssZnFzKxzx+62cYPNbLaZ1ZnZBjN72MxGZxi3S+rchtTY35jZZh2/zWxfM3va\nzBrM7BMz+6WZVRT+G4qIiIiIiEhfU1SJM7ArcCrwCbA40wAzCwGPAYcCXwe+CuwMPG1mlWnj+gF/\nAXYCzkiNPRR4LHWNtnGjgKeBOHAycBlwJjCrwN9NRERERERE+qDAU7V7yHx33xHAzH4AHJJhzEnA\nQcCB7r4kNfYVkuuszwNuTI07DxgJHOruH6bGrQAWAicCD6XG/SfQCpzs7vWpcTHgHjP7qbu/XPBv\nKSIiIiIiIn1GUVWc3T2RxbATgHfakubU+94H5qfOpY+b35Y0p8b9FViWYdwjbUlzyhygsdM4ERER\nERER2Q4VVeKcpXHAGxmOvwGMzWWcmZUDozuPc/dm4J1O19uMmVWbWU3bDzAqy+8gIiIiIiIifURf\nTJyrgbUZjtcCg3IcV01yP+psrpfJxSQr2G0/87cwXkRERERERPqYrNc4m9mfcriuu/uxAeLJKhQg\nU7dtyxTHFsZZluO68gtgdtrrUSh5FhERERER2abk0hyslMwJ5tZWS7JS3Fl16lybuizG1ZH8Tl2N\nyzTVu52716WuAYBZNrm2iIiIiIiI9CVZJ87uPqkH48jFG2Tutj0WWNppXKY1ymNJVYXdvcHMlnce\nZ2alJLfGeqAA8YqIiIiIiEgf1hfXOD8G7GZmB7QdMLOdgInAo53GHWZmI9LGTQDGZBh3oplVpB37\nF6C80zgRERERERHZDpl7drOvzeyLwCvuXp963i13XxgoILNTU0+nAKcD/5p6vdzdXzCzEPA8MBi4\nDGgCribZyGtvd9+Yuk4V8CqwBriKZCL8M2AV8IW2ra9SSferwBLg58AI4Hpgnru3xZJt7DXAsmXL\nllFTUxPg2/esj+pbqG9JUFUa7u1QRERERERkG7M2FmdIeZgh0VxWBG9VgdfW5vKNngMOAhannneV\ncbc17wqand3fxevZwFR3T5jZl4EbgNtJfoe/AKe2Jc0A7r7BzI4g2cDrPqCVZHX5kvT9ot39AzM7\nHLgReATYCNwL/FfA+EVERERERGQbkkvifDjwZtrzHuHuW7wL4O6rgX/LYtw/gROzGPcyMCmb+ERE\nRERERGT7kktzsGcyPRcRERERERHZluU1+dzMqoEJbNriaZG7ry1EYCIiIiIiIiLFIHDibGY/Bb5N\ncn/ntunVTWZ2vbtfUYjgRERERERERHpboO2ozOxSkh2t7yC5DdRuwKGp198zs0sKFJ+IiIiIiIhI\nrwpacT4fuN7d/zPt2LvAAjOrB/6dZNdrERERERERkT4tUMUZ2Bl4ootzT6TOi4iIiIiIiPR5QRPn\nT4D9uzh3QOq8iIiIiIiISJ8XdKr2PcB0M4sB95FMlHcATgeuAv67MOGJiIiIiIiI9K6gifOVQA3J\nBPn6tOMG3EsyeRYRERERERHp8wIlzu7eDJxmZj8k2VW7bR/nZ9z9zQLGJyIiIiIiItKrAu/jDODu\nrwOvFygWERERERERkaITtDmYiIiIiIiIyHZBibOIiIiIiIhIN5Q4i4iIiIiIiHRDibOIiIiIiIhI\nN7JOnM2s1sz2Sz3/PzOr6bmwRERERERERIpDLhXnSqAs9XwqMLTg0YiIiIiIiIgUmVy2o1oOnGdm\nbcnz580s2tVgd5+fT2AiIiIiIiIixSCXxPmHwP8C5wAOzOxinKXOh/MLTURERERERKT3ZZ04u/td\nZvY4sBsf6f29AAAgAElEQVTwV+B84M2eCkxERERERESkGORSccbda4FFZnY18Ad3/6hnwhIRERER\nEREpDjklzm3c/WoAMzNgD6AaqHX3twoYm4iIiIiIiEivC7yPs5mdD3wEvAE8B7xpZh+Z2f8rVHAi\nIiIiIiIivS1QxdnMzgH+B3gCuAv4GNgROBO4zcxi7n5XwaIUERERERER6SWBEmfgUmC2u5/b6fjd\nZjYL+C7JhFpERERERESkTws6VXs34N4uzt0L7B7wuiIiIiIiIiJFJWjivBYY08W5XYG6gNcVERER\nERERKSpBE+eHgJ+Y2cnpB83sX4AfAQ/mG5iIiIiIiIhIMQi6xvkyYB/gQTNrAlYBQ4EyYCHwX4UJ\nT0RERERERKR3Bd3HeZ2ZHQIcBxxKah9nYD7wuLt74UIUERERERER6T1BK864ewJ4NPUjIiIiIiIi\nsk0KusZZREREREREZLugxFlERERERESkG0qcRURERERERLqhxFlERERERESkG0qcRURERERERLoR\nKHE2s93NbHza66iZ/cjMHjKzfy9ceCIiIiIiIiK9K2jF+RZgStrrnwLfBYYDvzCzb+UbmIiIiIiI\niEgxCJo47wMsBDCzMHA2cJm7TwB+CJxfmPBEREREREREelfQxHkAUJd6vj8wEPht6vV8oCbPuERE\nRERERESKQtDE+SPgs6nnJwBvu/vK1Ov+QFO+gYmIiIiIiIgUg0jA990LzDCzI4EvAz9KO7cf8Ha+\ngYmIiIiIiIgUg6CJ85VAIzABmAH8PO3c54H784xLREREREREpCgESpzdPU7HKnP6uZPzikhERERE\nRESkiAStOANgZnsChwCDgTvc/RMz2wlY4+4NhQhQREREREREpDcFSpzNrBS4AzgNMMCBJ4FPgF8C\nfwe+V5gQRURERERERHpP0K7aPyXZFOxsYAeSyXObx4HJecYlIiIiIiIiUhSCTtU+A/i+u99tZuFO\n5/6J9nEWERERERGRbUTQivMg4K0uzoWBkoDXFRERERERESkqQRPnd4DDujh3CPBGwOuKiIiIiIiI\nFJWgU7V/BfzczGqB+1LHKszsNODbwLcKEZyIiIiIiIhIbwtUcXb3m4GbgeuAFanDzwB3Abe4++zC\nhLc5M5tnZt7Fz69SYyZ1cf7lDNfbxcweNrMNZlZnZr8xs8E9Fb+IiIiIiIj0LYH3cXb3/zSzW4Cj\ngaHAGuApd3+3UMF14ZtA/07HjgN+ADzW6fj5wKtpr+vTT5pZP+AvwFqSDc/KgZ8Bj5nZwe6eKGDc\nIiIiIiIi0gcFTpwB3H058OvChJL1Z77Z+ZiZXQ6sAuZ2OvWmuz/fzeXOA0YCh7r7h6lrrQAWAicC\nDxUkaBEREREREemz8kqczWwIsDMQ7XzO3Rfmc+0cYhhKct/ome7emuPbTwDmtyXNAO7+VzNbljqn\nxFlERERERGQ7FyhxNrORwG+ASZlOA05yW6qt4QyS3+PODOfmpJL71cAjwGXuXpt2fhxwb4b3vQGM\n3dIHm1k1MDDt0KhsgxYREREREZG+IWjF+VZgb+BSkklmrGAR5e7fgKXu/kLasXXA9cA8YCNwEPA9\nYIKZjXf3tnirSa5v7qwW2COLz74YuCpg3CIiIiIiItIHBE2cDwEucPe7CxlMrsxsT+AA4Pvpx939\nJeCltEPz/n97dx4vSVXfffzzZRMQhBEMBhAYUEnARzRuuKCAUaNG3DVukRgNMYlRo5i4gktCAjHG\nqAkqMYhbjDGKoLgCQQUxPgj6AC4wjAoKgszAAAZFzvPHOc3UNH3r9u3bM/femc/79arXvV11qupU\n9anq+tU5dSrJBcBnqDXUJ3aTj1r0mFl4O9DtQXx34Kwx55UkSZIkLQGTBs7XU2tlF9rzqIHvrAF8\nKeW0JNcBD2Bt4LyKWus8bBljbF8pZVVbBgDJuPG2JEmSJGmpmOg9zsA7qK96WjCpUepzgDNLKT8c\ndzbWrWGe6Vnm/YCL55dDSZIkSdLGYNIa562AA5J8C/gcnVrXppRSjplXzmb3cGBP4I3jJE7yeOr7\nn8/tjD4VOCbJrqWUH7d0DwL2AY6cbnYlSZIkSUtRShn1iO8sMyW3zpKklFLWa6/aSU4Ang3sUkpZ\nMzTtA8D3gfOBm6idg72qjXtwKeUXLd32wLeAn1E7+doGOJb6TugHl1Jm287hPC0HVqxYsYLly5fP\nY+vWjx/f+Etu/OWtbL/VhurwXJIkSdKmYvXNv2LnbTZn563n9dbj9WniZ2sn2qJSyqRNvKciydbA\n04BPDgfNzUXUoPqV1HdMXw68B3jjIGgGKKWsSXIotZOvjwK3UGuhXz7XoFmSJEmStHFatLcC+pRS\n/pd13588PP0YYKym4qWUy4DDppQ1SZIkSdJGZl6Bc5KdgT2otbrrKKWcPZ9lS5IkSZK0GEwUOCfZ\nDTgJOHjUZGrP1T5IK0mSJEla8iatcX438H+ozxBfCNw8tRxJkiRJkrSITBo4Pwz401LKh6aZGUmS\nJEmSFptJe8e+Hrh2mhmRJEmSJGkxmjRwfgdwxDQzIkmSJEnSYjRpU+2tgAOSfAv4HLBqaHppr4SS\nJEmSJGlJmzRwfnPn/3uNmF4Y8z3KkiRJkiQtZhMFzqWUSZt4S5IkSZK0pBgAS5IkSZLUY9Km2iTZ\nFjgceDiwjNrL9n8D7y+l/HwquZMkSZIkaYFNVOOcZFfgfOCdwP2AbYH7A/8MnJfkrlPLoSRJkiRJ\nC2jSptrHAdsDDy6l3KOUclAp5R7AgcCd2nRJkiRJkpa8SQPnxwB/VUo5tzuylPJ14LXAY+ebMUmS\nJEmSFoNJA+c7Aj+dYdpV1KbbkiRJkiQteZMGzt8GXjTDtD9q0yVJkiRJWvIm7VX7TcDJSc4DPgZc\nCdwVeAZwL+Cw6WRPkiRJkqSFNVHgXEo5Nclh1AD6LUCAApwHPKGU8tnpZVGSJEmSpIUz8XucSymf\nBj6d5I7AjsDqUsqNU8uZJEmSJEmLwKTvcX5fkuUApZQbSylXDILmJHsmed80MylJkiRJ0kKZtHOw\nw4G7zDBtZ+D5Ey5XkiRJkqRFZdLAGeozzaPsAdhkW5IkSZK0URj7GeckzwOe1z4W4B1Jrh9KtjVw\nX+Cr08meJEmSJEkLay41zpsBW7Yhnf+7w03ACcAfTDebkiRJkiQtjLFrnEsp7wfeD5DkDODFpZTv\nrK+MSZIkSZK0GEz6HudDpp0RSZIkSZIWo/l0DiZJkiRJ0kbPwFmSJEmSpB4GzpIkSZIk9TBwliRJ\nkiSph4GzJEmSJEk9JupVGyDJ9sBjgD2ArYcml1LKMfPJmCRJkiRJi8FEgXOShwEnA8tmSFIAA2dJ\nkiRJ0pI3aVPtfwQuBe4HbF1K2Wxo2Hx6WZQkSZIkaeFM2lT7N4GnllK+Oc3MSJIkSZK02Exa43wJ\nsP00MyJJkiRJ0mI0aeD8auA1SXabZmYkSZIkSVpsJm2q/XLgLsAlSf4fsGpoeimlPGZeOZMkSZIk\naRGYNHDeAvh+GwC2nE52JEmSJElaXCYKnEsph0w7I5IkSZIkLUaTPuMsSZIkSdImYeLAOcm9k/xH\nkiuT3JzkJ0n+Pcm9pplBSZIkSZIW0kRNtZMcCJwB3AB8ErgSuCtwGPCEJAeXUv5narmUJEmSJGmB\nTNo52DHAecCjSyk3DkYmeRnwBeBvgUfOP3uSJEmSJC2sSZtqPxA4ths0A7TPxwIPmm/GJEmSJEla\nDCYNnG9h5ldQbQn8asLlSpIkSZK0qEwaOH8ZODrJLt2RSe4KHAWcOc98SZIkSZK0KEz6jPORwFeA\ny5J8mbWdgx1E7TDsKdPJniRJkiRJC2uiGudSysXAvYF/AZYBDwF2BN4FHFBK+d7UcihJkiRJ0gKa\ntMaZUsoVwCummBdJkiRJkhadSZ9xliRJkiRpkzB24Jzk80n27fzfN3xufWU4ycFJyojh/KF0eyY5\nOcmaJKuSnJRkpxHLOyDJ6UluSnJVkn9Ksu36yr8kSZIkaWmZS1PtLYG0/7cCyvSzMydHAN/qfL7t\nndJJtgPOAFYDzwK2ob5f+tQkDy2l3NrS7Q6cDpwHPAnYFXgrsAvwzA2wDZIkSZKkRW7swLmUckjn\n/4PXS27m5qJSytdmmPYiYDfgoPYsNkkuB84GDgM+2dIdSX0n9ZNKKTe2dDcDH05yTCnl/NstWZIk\nSZK0SdlYn3F+AnDWIGgGKKWcA6xo07rpPjUImpuPAz8fSidJkiRJ2kSNXeOc5CFzWXAp5ey5Z2dO\nPp5kZ+Aa4FPAX5ZSrm3T9gc+MmKeC4H9AJJsA+zVxt2mlPKLJJcM0vVJsoz6Gq6B3ee4DZIkSZKk\nRW4uzzh/hfGea05Lt/lEOZrdddTnkM8EbgAOBF4NPCjJA0opN1PfLb16xLzXAvu2/5e1vM6U7s5j\n5OWlwFFzybwkSZIkaWmZS+B8yOxJ1r9SyjeBb3ZGnZnkAuAz1I7AThwkHTF7Rvw/W7o+bwfe3/m8\nO3DWmPNKkiRJkpaAuXQO9t/rMyPzUUo5Lcl1wAOogfMqao3ysGXU2mRamtKT7sIR44fXu6otB4Bk\n3HhbkiRJkrRUbEydgw2aiEPnWeYh+wEXA5RSbgJWDqdLshVw90E6SZIkSdKmbS6dg31+DsstpZTH\nTJCfiSR5PHAn4Nw26lTgmCS7llJ+3NI8CNiH+goqOumekeRlLZAGeDL1vc+nbJDMS5IkSZIWtbk8\n47wV43UOtl4l+QDwfeB84CZq52CvAs4DPtqSvRd4CfCpJEdRA+Fjga8DJ3cWdxzwPOCTSY4DdqV2\nPPbx9iy1JEmSJGkTN5dnnA9ej/mYi4uAZwOvBLYGLgfeA7yxlPILgFLKmiSHUjvv+ihwC7V2+eWl\nlFsHCyql/CjJIcA/Ul9pdQP1NVZ/teE2R5IkSZK0mKWUBa9E3mgkWQ6sWLFiBcuXL1/o7NzOj2/8\nJTf+8la232p9vSlMkiRJ0qZq9c2/YudtNmfnrefSsHmDmrg357k84/wQ4IJSyo3t/16llLMnzZQk\nSZIkSYvFXG4FfIX6PPHX2/8zVVUPere2WlOSJEmStOTNJXA+hPp88eB/SZIkSZI2enPpHOy/R/0v\nSZIkSdLGbLOFzoAkSZIkSYvZRN2dJdkMOAJ4OrA79bVQXaWUsuc88yZJkiRJ0oKbtJ/wvwdeBpwD\nfBn4xdRyJEmSJEnSIjJp4Pwc4PWllL+eZmYkSZIkSVps5vOM87lTy4UkSZIkSYvUpIHzScBTp5kR\nSZIkSZIWo0mbar8aeHeSU4EvAKuGE5RSTppPxiRJkiRJWgwmDZzvDzwO2KX9HVaotdKSJEmSJC1p\nkwbOxwPXAC8AvoO9akuSJEmSNlKTBs73BJ5aSjltmpmRJEmSJGmxmbRzsIuAHaeZEUmSJEmSFqNJ\nA+eXAn+V5IBpZkaSJEmSpMVm0qbaJwI7AecluZbb96pdSin7zidjkiRJkiQtBpMGzl+l9pwtSZIk\nSdJGbaLAuZRy+JTzIUmSJEnSojTpM86SJEmSJG0SDJwlSZIkSeph4CxJkiRJUg8DZ0mSJEmSehg4\nS5IkSZLUw8BZkiRJkqQeBs6SJEmSJPUwcJYkSZIkqYeBsyRJkiRJPQycJUmSJEnqYeAsSZIkSVIP\nA2dJkiRJknoYOEuSJEmS1MPAWZIkSZKkHgbOkiRJkiT1MHCWJEmSJKmHgbMkSZIkST0MnCVJkiRJ\n6mHgLEmSJElSDwNnSZIkSZJ6GDhLkiRJktTDwFmSJEmSpB4GzpIkSZIk9TBwliRJkiSph4GzJEmS\nJEk9DJwlSZIkSeph4CxJkiRJUg8DZ0mSJEmSehg4S5IkSZLUw8BZkiRJkqQeBs6SJEmSJPUwcJYk\nSZIkqceSC5yTPC3JJ5L8MMlNSS5McmSSLTtpDk5SRgznj1jenklOTrImyaokJyXZacNulSRJkiRp\nsdpioTMwgVcCK4FXAVcCDwHeBNwHeM5Q2iOAb3U+39idmGQ74AxgNfAsYBvgWODUJA8tpdy6HvIv\nSZIkSVpClmLg/IRSytWdz2cmCfCWJK8qpVzRmXZRKeVrPct6EbAbcNBgviSXA2cDhwGfnHLeJUmS\nJElLzJJrqj0UNA+c1/7uOsfFPQE4qxtsl1LOAVa0aZIkSZKkTdySC5xncBBwC3DJ0PiPJ/lVkquS\nvDfJnYem7w9cOGJ5FwL7zbbSJMuSLB8MwO6TZF6SJEmStHgtxaba60iyH/Ay4IRSyqo2+jrgrcCZ\nwA3AgcCrgQcleUAp5eaWbhn1+eZh1wL7jrH6lwJHTZ57SZIkSdJit6QD5yQ7U59DXkHtLAyAUso3\ngW92kp6Z5ALgM9ROwE7sTCujFj1mFt4OvL/zeXfgrDHnlSRJkiQtAUs2cE6yPXAasDVwaCllTV/6\nUsppSa4DHsDawHkVtdZ52DJqrXOvVsM9qOWm9lEmSZIkSdqYLMlnnJPcATgZWA48upRy+bizsm4N\n80zPMu8HXDyvTEqSJEmSNgpLLnBOsjnw79Sa48eVUr4z5nyPB+4EnNsZfSrwiCS7dtI9CNgHOGVq\nmZYkSZIkLVlLsan2u4AnAa8HNktyYGfapaWUq5N8APg+cD5wE7VzsFdRX1v10U769wIvAT6V5Chg\nG+BY4OvUGm1JkiRJ0iZuydU4A7/T/r4ZOGdoeHybdhHwdOCD1A7BXgC8Bzi4lPKLwYLac9GHAj+m\nBtQnAGcDv1tKuXW9b4kkSZIkadFbcjXOpZS9xkhzDHDMmMu7DDhsntmSJEmSJG2klmKNsyRJkiRJ\nG4yBsyRJkiRJPQycJUmSJEnqYeAsSZIkSVIPA2dJkiRJknoYOEuSJEmS1MPAWZIkSZKkHgbOkiRJ\nkiT1MHCWJEmSJKmHgbMkSZIkST0MnCVJkiRJ6mHgLEmSJElSDwNnSZIkSZJ6GDhLkiRJktTDwFmS\nJEmSpB4GzpIkSZIk9TBwliRJkiSph4GzJEmSJEk9DJwlSZIkSeph4CxJkiRJUg8DZ0mSJEmSehg4\nS5IkSZLUw8BZkiRJkqQeBs6SJEmSJPUwcJYkSZIkqYeBsyRJkiRJPQycJUmSJEnqYeAsSZIkSVIP\nA2dJkiRJknoYOEuSJEmS1MPAWZIkSZKkHgbOkiRJkiT1MHCWJEmSJKmHgbMkSZIkST0MnCVJkiRJ\n6mHgLEmSJElSDwNnSZIkSZJ6GDhLkiRJktTDwFmSJEmSpB4GzpIkSZIk9TBwliRJkiSph4GzJEmS\nJEk9DJwlSZIkSeph4CxJkiRJUg8DZ0mSJEmSehg4S5IkSZLUw8BZkiRJkqQeBs6SJEmSJPUwcJYk\nSZIkqYeBsyRJkiRJPQycJUmSJEnqYeAMJNkzyclJ1iRZleSkJDstdL4kSZIkSQtvkw+ck2wHnAHc\nDXgW8EfAQcCpSTb5/SNJkiRJm7otFjoDi8CLgN2Ag0opVwAkuRw4GzgM+OQC5k2SJEmStMCsUYUn\nAGcNgmaAUso5wIo2TZIkSZK0CbPGGfYHPjJi/IXAfn0zJlkG7NgZtSfAj370o6llbpquuumXrPnl\nrWyehc6JJEmSpI3NLbfCjdtszpo7LM4wc++9914OXF5K+eVc512cW7RhLQNWjxh/LbDvLPO+FDhq\neOQjHvGIKWRLkiRJkjRFK4C9gcvmOqOBc1VGjBunXvbtwPs7n7cG9gIuAW6Zf7amanfgLODhwOUL\nnBdpEpZhLXWWYS11lmEtdZZhwYTfvYEzrKLWOg9bRq11nlEpZVWbv+viKeVrqpLb7gNcXkqZ8x0W\naaFZhrXUWYa11FmGtdRZhjUfdg4287PM+7FIg2BJkiRJ0oZj4AynAo9IsutgRJIHAfsApyxYriRJ\nkiRJi4KBM7wX+DHwqSSPT/I0ai/bXwdOXtCcTddq4I2M7ghNWgosw1rqLMNa6izDWuosw5pYShnV\nL9amJclyakdfh1I79ToVeHkp5eoFzZgkSZIkacEZOEuSJEmS1MOm2pIkSZIk9TBwliRJkiSph4Gz\nJEmSJEk9DJwlSZIkSeph4LwEJHlakk8k+WGSm5JcmOTIJFsOpdszyclJ1iRZleSkJDuNWN4BSU5v\ny7oqyT8l2XZEui2T/GWS7yT535b2lCRbrM/t1cZlAcvv4UnOS3JDkitb2b3P+txWbZymWYaT3CvJ\ne5Kcn+SWJCt71jvWMSHNZiHK8LjrlMaxUOfhoflOS1KSvG6Km6YlxABoaXglsBJ4FXAl8BDgTcB9\ngOcAJNkOOIP6XrpnAdsAxwKnJnloKeXWlm534HTgPOBJwK7AW4FdgGcOVpgkwEeBhwFvAS4AdgIe\njTdcNDcLUX5fSH1H+zvbencAXgOcnuSAUsqP1usWa2MztTIM3A94DPA/7fOOo1Y4h+VJ49jgZXic\ndUpzsBBl+DZJntHWpU1ZKcVhkQ/AXUaMey1QgN3a55cDNw8+t3EPbmme1Bn3duAq4I6dcc9q6e7T\nGfc86jut7zPNbXHY9IYFKr9nAWcNrXPPlu6Ihd4nDktrmHIZ3qzz/wnAyhnWOdbyHBzGGRaoDM+6\nTgeHcYeFKMOdNHcCfkwN0AvwuoXeHw4LM1hzuASUUq4eMfq89nfX9vcJ1EDhis585wAr2jQ66T5V\nSrmxM+7jwM+H0v0xcEYp5fx5Zl+buAUqv1sB1w2tc/DZ857mZJpluIxfUzzuMSHNaiHK8JjrlMay\nQOfhgb8GvldK+dAc59NGxgvIpesgao3wJe3z/sCFI9JdCOwHkGQbYK/hdKWUX7TlDNJtCdwf+E6S\nf2jPiNyc5Mwk910P26JNz3orv82/AL+T5HlJdkiyB/DPwBXAf0xvM7QJm3MZnqNpL08atr7L8Djr\nlOZjvZfhJPcHXgT86STza+Ni4LwEJdkPeBlwQillVRu9jPpMx7BrgTt30mSMdDtRa+yeDzy8/X06\ntanKF5PcecT80lg2QPmllPJ+4AhqE6zVwA+ABwKHlFJ+NoXN0CZsHmV4Lqa9POk2G6gMj7NOaSIb\nogwn2Rw4HnhnKWVUQK5NjIHzEpNkZ+CT1GYnrxqaXEbNMuL/2dJt3v5uCfxuKeVTpZRPUZu5bId3\n3TShDVR+SfJE4B3APwKHAk+mdiby+SS7zT3nUjXPMjxX016etKHL8DjrlOZkA5bhPwPuChw94fza\nyNir9hKSZHvgNGBr4NBSyprO5FXUO23DllHvtA3SlJ50g7tp17Z0F5ZSrhwkKKVckeRi4F7z2Q5t\nmjZU+W09wr8H+FAp5S876/8StUfOI6l3qaU5mUIZnotpL0/a0GV4nHVKc7KhynALzt9M7c17iyTd\nnre3bp+vn+B5aS1h1jgvEUnuAJwMLAceXUq5fCjJTM9v7AdcDFBKuYkaOKyTLslWwN076X4OXMbo\nO3SbUU9W0tg2ZPmlvprq14BvdNO1H9fvAb8xj03RJmoaZXiOpr08beIWoAyPs05pbBu4DO8ObA+8\nmxqQDwaovXmvAvaY4zK1xBk4LwHtGYt/Bx4APK6U8p0RyU4FHpFk1858DwL2AU4ZSndYkm07455M\nfdddN93JwP5Dy7sbsC9r33snzWoByu8q6usoHjiUjztRy+8VSHMw5TI8rmkvT5uwhSjDY65TGssC\nlOFLgENGDADva/9fOXpWbaxSyqhHAbSYJDme2tHR64EvDk2+tJRydWu68i3gZ8BRrH3p+9XAgwdN\nSVrw+y1q8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1073917b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# style, color palette, figure size\n",
    "my_dpi=113\n",
    "plt.figure(figsize=(10, 6), dpi=my_dpi)\n",
    "\n",
    "#make plot\n",
    "plt.ylim(0, 2000)\n",
    "plt.fill_between(\"TIME\", \"Value\", data=eu_energy, color=\"skyblue\", alpha=0.2)\n",
    "plt.plot(\"TIME\", \"Value\", data=eu_energy, marker='o', markerfacecolor='blue', markersize=12, color='skyblue', \n",
    "         linewidth=4) \n",
    "sns.despine()\n",
    "\n",
    "#add values to the coordinates\n",
    "for index in eu_energy.index:\n",
    "    time = eu_energy[\"TIME\"].loc[index]\n",
    "    value = eu_energy[\"Value\"].loc[index]\n",
    "    plt.text(time, value + 50, str(value), ha='center', va='bottom')\n",
    "\n",
    "# Add titles, labels, text\n",
    "plt.title(\"Primary Energy Consumption - European Union (28 countries) 2006 - 2015\\n\", loc='center', \n",
    "\tfontsize=12, fontweight=0, color='black')\n",
    "plt.xlabel(\"\\nSource: Eurostat Database, http://ec.europa.eu/eurostat/data/database, Author: Bjoern Springer\", \n",
    "           fontsize=\"small\")\n",
    "plt.ylabel(\"million tonnes of oil equivalent (MTOE)\")\n",
    "\n",
    "# save as .png with less whitespace around plot\n",
    "plt.savefig(\"primary_energy_01.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>GEO</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>Value</th>\n",
       "      <th>Flag and Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>498300775.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>European Union (27 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>493987245.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>Euro area (19 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>331204579.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007</td>\n",
       "      <td>Euro area (18 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>327954596.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>10584534.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>7572673.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>10254233.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>5447084.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2007</td>\n",
       "      <td>Germany (until 1990 former territory of the FRG)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>82314906.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2007</td>\n",
       "      <td>Germany including former GDR</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>82314906.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>1342920.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2007</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>4340118.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TIME                                               GEO    AGE    SEX  \\\n",
       "0   2007                     European Union (28 countries)  Total  Total   \n",
       "1   2007                     European Union (27 countries)  Total  Total   \n",
       "2   2007                          Euro area (19 countries)  Total  Total   \n",
       "3   2007                          Euro area (18 countries)  Total  Total   \n",
       "4   2007                                           Belgium  Total  Total   \n",
       "5   2007                                          Bulgaria  Total  Total   \n",
       "6   2007                                    Czech Republic  Total  Total   \n",
       "7   2007                                           Denmark  Total  Total   \n",
       "8   2007  Germany (until 1990 former territory of the FRG)  Total  Total   \n",
       "9   2007                      Germany including former GDR  Total  Total   \n",
       "10  2007                                           Estonia  Total  Total   \n",
       "11  2007                                           Ireland  Total  Total   \n",
       "\n",
       "      UNIT        Value Flag and Footnotes  \n",
       "0   Number  498300775.0                NaN  \n",
       "1   Number  493987245.0                NaN  \n",
       "2   Number  331204579.0                NaN  \n",
       "3   Number  327954596.0                NaN  \n",
       "4   Number   10584534.0                NaN  \n",
       "5   Number    7572673.0                NaN  \n",
       "6   Number   10254233.0                NaN  \n",
       "7   Number    5447084.0                NaN  \n",
       "8   Number   82314906.0                NaN  \n",
       "9   Number   82314906.0                NaN  \n",
       "10  Number    1342920.0                NaN  \n",
       "11  Number    4340118.0                NaN  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make pandas dataframe called \"demo\", comma as thousands separator, \":\" for na_values\n",
    "#specify type for the numeric columns (TIME and Value)\n",
    "filename = \"demo_pjan_1_Data.csv\"\n",
    "demo = pd.read_csv(filename, encoding = \"ISO-8859-1\", thousands = \",\", na_values=\":\", \n",
    "                 dtype={\"TIME\": np.int, \"Value\": np.float})\n",
    "\n",
    "#see head\n",
    "demo.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(580, 7)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get dimensions of data frame\n",
    "demo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 580 entries, 0 to 579\n",
      "Data columns (total 7 columns):\n",
      "TIME                  580 non-null int64\n",
      "GEO                   580 non-null object\n",
      "AGE                   580 non-null object\n",
      "SEX                   580 non-null object\n",
      "UNIT                  580 non-null object\n",
      "Value                 542 non-null float64\n",
      "Flag and Footnotes    62 non-null object\n",
      "dtypes: float64(1), int64(1), object(5)\n",
      "memory usage: 31.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#list data type of each column, number of non-missing values, memry usage\n",
    "demo.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>GEO</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>Value</th>\n",
       "      <th>Flag and Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>498300775.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2008</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>500297033.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2009</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>502090235.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>2010</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>503170618.0</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2011</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>502964837.0</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2012</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>504041384.0</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2013</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>505143171.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2014</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>506973868.0</td>\n",
       "      <td>ep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>2015</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>508504320.0</td>\n",
       "      <td>bep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>2016</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>510278701.0</td>\n",
       "      <td>bep</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TIME                            GEO    AGE    SEX    UNIT        Value  \\\n",
       "0    2007  European Union (28 countries)  Total  Total  Number  498300775.0   \n",
       "58   2008  European Union (28 countries)  Total  Total  Number  500297033.0   \n",
       "116  2009  European Union (28 countries)  Total  Total  Number  502090235.0   \n",
       "174  2010  European Union (28 countries)  Total  Total  Number  503170618.0   \n",
       "232  2011  European Union (28 countries)  Total  Total  Number  502964837.0   \n",
       "290  2012  European Union (28 countries)  Total  Total  Number  504041384.0   \n",
       "348  2013  European Union (28 countries)  Total  Total  Number  505143171.0   \n",
       "406  2014  European Union (28 countries)  Total  Total  Number  506973868.0   \n",
       "464  2015  European Union (28 countries)  Total  Total  Number  508504320.0   \n",
       "522  2016  European Union (28 countries)  Total  Total  Number  510278701.0   \n",
       "\n",
       "    Flag and Footnotes  \n",
       "0                  NaN  \n",
       "58                 NaN  \n",
       "116                NaN  \n",
       "174                  b  \n",
       "232                  b  \n",
       "290                  b  \n",
       "348                NaN  \n",
       "406                 ep  \n",
       "464                bep  \n",
       "522                bep  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter dataframe by means of boolean indexing/ boolean selection: european union\n",
    "eu_demo_crit1 = demo.GEO == \"European Union (28 countries)\"\n",
    "eu_demo = demo.loc[eu_demo_crit1]\n",
    "eu_demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>498300775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2008</td>\n",
       "      <td>500297033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2009</td>\n",
       "      <td>502090235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>2010</td>\n",
       "      <td>503170618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2011</td>\n",
       "      <td>502964837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2012</td>\n",
       "      <td>504041384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2013</td>\n",
       "      <td>505143171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2014</td>\n",
       "      <td>506973868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>2015</td>\n",
       "      <td>508504320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>2016</td>\n",
       "      <td>510278701.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TIME        Value\n",
       "0    2007  498300775.0\n",
       "58   2008  500297033.0\n",
       "116  2009  502090235.0\n",
       "174  2010  503170618.0\n",
       "232  2011  502964837.0\n",
       "290  2012  504041384.0\n",
       "348  2013  505143171.0\n",
       "406  2014  506973868.0\n",
       "464  2015  508504320.0\n",
       "522  2016  510278701.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from eu_demo, only show columns: \"TIME\" and \"value\"\n",
    "eu_demo_tv = eu_demo[[\"TIME\", \"Value\"]]\n",
    "eu_demo_tv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>1722.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>2007</td>\n",
       "      <td>1693.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2008</td>\n",
       "      <td>1692.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1476</th>\n",
       "      <td>2009</td>\n",
       "      <td>1598.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>2010</td>\n",
       "      <td>1656.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2460</th>\n",
       "      <td>2011</td>\n",
       "      <td>1594.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2952</th>\n",
       "      <td>2012</td>\n",
       "      <td>1585.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>2013</td>\n",
       "      <td>1569.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>2014</td>\n",
       "      <td>1508.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4428</th>\n",
       "      <td>2015</td>\n",
       "      <td>1529.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TIME   Value\n",
       "0     2006  1722.2\n",
       "492   2007  1693.8\n",
       "984   2008  1692.4\n",
       "1476  2009  1598.2\n",
       "1968  2010  1656.7\n",
       "2460  2011  1594.2\n",
       "2952  2012  1585.4\n",
       "3444  2013  1569.9\n",
       "3936  2014  1508.3\n",
       "4428  2015  1529.6"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from eu_energy, only show columns: \"TIME\" and \"value\"\n",
    "eu_energy_tv = eu_energy[[\"TIME\", \"Value\"]]\n",
    "eu_energy_tv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>Value_energy</th>\n",
       "      <th>Value_demo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>1693.8</td>\n",
       "      <td>498300775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>1692.4</td>\n",
       "      <td>500297033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1598.2</td>\n",
       "      <td>502090235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>1656.7</td>\n",
       "      <td>503170618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1594.2</td>\n",
       "      <td>502964837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>1585.4</td>\n",
       "      <td>504041384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>1569.9</td>\n",
       "      <td>505143171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>1508.3</td>\n",
       "      <td>506973868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>1529.6</td>\n",
       "      <td>508504320.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TIME  Value_energy   Value_demo\n",
       "0  2007        1693.8  498300775.0\n",
       "1  2008        1692.4  500297033.0\n",
       "2  2009        1598.2  502090235.0\n",
       "3  2010        1656.7  503170618.0\n",
       "4  2011        1594.2  502964837.0\n",
       "5  2012        1585.4  504041384.0\n",
       "6  2013        1569.9  505143171.0\n",
       "7  2014        1508.3  506973868.0\n",
       "8  2015        1529.6  508504320.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge \"eu_energy_tv\" and \"eu_demo_tv\" along the \"TIME\" column, only shared \"TIME\" values (\"inner\") -> 2007 - 2015\n",
    "eu_pec_pc = pd.merge(eu_energy_tv, eu_demo_tv, on='TIME', how='inner', suffixes=('_energy', '_demo'))\n",
    "eu_pec_pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>Value_energy</th>\n",
       "      <th>Value_demo</th>\n",
       "      <th>pecpc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>1693.8</td>\n",
       "      <td>498300775.0</td>\n",
       "      <td>3.399152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>1692.4</td>\n",
       "      <td>500297033.0</td>\n",
       "      <td>3.382790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1598.2</td>\n",
       "      <td>502090235.0</td>\n",
       "      <td>3.183093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>1656.7</td>\n",
       "      <td>503170618.0</td>\n",
       "      <td>3.292521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1594.2</td>\n",
       "      <td>502964837.0</td>\n",
       "      <td>3.169605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>1585.4</td>\n",
       "      <td>504041384.0</td>\n",
       "      <td>3.145377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>1569.9</td>\n",
       "      <td>505143171.0</td>\n",
       "      <td>3.107832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>1508.3</td>\n",
       "      <td>506973868.0</td>\n",
       "      <td>2.975104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>1529.6</td>\n",
       "      <td>508504320.0</td>\n",
       "      <td>3.008037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TIME  Value_energy   Value_demo     pecpc\n",
       "0  2007        1693.8  498300775.0  3.399152\n",
       "1  2008        1692.4  500297033.0  3.382790\n",
       "2  2009        1598.2  502090235.0  3.183093\n",
       "3  2010        1656.7  503170618.0  3.292521\n",
       "4  2011        1594.2  502964837.0  3.169605\n",
       "5  2012        1585.4  504041384.0  3.145377\n",
       "6  2013        1569.9  505143171.0  3.107832\n",
       "7  2014        1508.3  506973868.0  2.975104\n",
       "8  2015        1529.6  508504320.0  3.008037"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add column \"pecpc\" showing primary energy consumption per capita\n",
    "eu_pec_pc[\"pecpc\"] = eu_pec_pc[\"Value_energy\"] / eu_pec_pc[\"Value_demo\"] * 1000000\n",
    "eu_pec_pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3/yvA32AfGvx/bQd87z34bf1N/AON5/DjLX8TX2o+tQO+E/z1qgr/24yvqfUH\n/LnyA6DQQjorC6r4R12P/81+F9/8YT6+JshZ+P17d5IByhX47f1T8+1Yn8cfi4fjzx/RmgGt3Wb/\nh792zgoC5F343/YyUmM+/vh/xHxnkDXAE865NpXYO+f+YWb/gw8A3w+W+Ql+Wx6OH/f+C/hz02eB\nx83sZXyJ6hb8OeBMfGeQs+MWHy2JnJtkdubgH+wvAQ4NORbKnXPRfj9wzi0zs9/ir/v/NrO/4e8Z\nvolv49qokybn3D/N7GH8g4lFZvYs/l70LPz5LNGDl8nBe6eU+gbH4+34+7tF+GM0Pll8p1k/w99j\n3mO+o9l1+GvcQfghkBpdV8x3xPXV4N/oefy75jtSBPiTcy7a0eB/AjcE22stvjPIo/C/2wrg28mW\n7Hdrrht0Oa1X+IsE4/w2k34tCbrdDz5P2PV+M989K+SzaTTtQj8D3xbkXfyFL3oyymnN94Z8Vz7+\nRnI7cV3fJzHvLJof5iN+HRLmKdG2xbdP+x98cFCFvylfhh8K5vPJbM+YNCXA7/EXngr8U8CTaRhW\n5qgE80WrhP+xHcfaF/FPCz/CB5C78cHPTYSPj/kt/Il6N/6m4EV8pyQtHish+2dozPpPxwdgG2KO\no3uBg0PmHxOkrcKX+N2B7wSiyb6K/66WfhfB9MnEDQlAzNBF+IDmlWBfbcAHDoUhyzkB/xBodzDv\n2pjPEh1Xo4G/BOtVg79Zv5qmwz8kPK5oYZilRMvB3wA9h7+R24p/+LRfgvmOx/fGuiHI5yfBsfBT\nwsdwbXIcJHl89sL3K7AI/xvbgw9Y5+JvfmLH5MzFBxOrg2NjBb5jqWFh2yq6D/AB2X34dk0V+Bvk\nY1pxLriShnEhG213fC2Bt/HnsnX433RRomWl6kXLQx01+U3gO2x5N9ifq/E3Ws1uuxby0OpzBf7h\nxXv4c9H7+JLiJkPn4AOYS/E3s7uC71iFP5edHZf2oOB42YJ/UPgv/DmkybFJM78dQs4LSeyHIfhz\n+2oazq9vBsdM2JjUTfIU89ms+P2WKD0++P81/vpUHRzbjwKHJjpWEuS/VccpvgR1B3FDCyVzPIYs\nayi+g7IPg2NyN77Tsh8Q87tPIk89gu30brAttuDPyxe2dZsF6f8T/7C2Gv/b/g1tuO8J28b44/tW\n/Hk1EnvctXQctnAMnYnvfG9LkO+P8A8tfhA9HvFB/03Btt6MP3e9jy89bjLGNf4h0jpixkpvYX+s\nbeFYaHK84R8cTsX3OlyFv1e6mwTj8Abb7/Ig39X4zg9nkGAoJvx97IdB2qTHqW7PK2Y/Nvdqso/x\n5+SHgn1YiX+IcF4Lx0KLy8fgt05QAAAgAElEQVQ/UJwdbLOdwbZYg/8NjuqMbdIZLwtWVqRbMrPD\n8TeNf3TOddST6m7NzF7AB1DFrqHNZeznNwP/DxjrnHsl/nNJneBJ6Tx8j9rTujY3qRNUUV4D3Ot8\npx37DDNbC+CcG9q1Odm3BSU/V+HH3pzftbmR9jCzY/EPPC5wzt3f1fmRjhU0ZVgDXO18u2CRbk1t\nfqW7uyx4v7NLc9EJzKzJeJ/mx1A9Ed/eOSzwLcaX/C5T4CsiIl3NObcY31Th8vj22JKWfo6vJXRL\nSwlFugO1+ZVuJwjofoDvCGEK8JRz7vWuzVWnuC9Y99fx1S4Px7e3rCBuSBQzOx5fNe8sfJXQH3Rq\nTkVERBL7GXA+vhOksi7Oi3Ssdfi2oLu6OiMiyVDwK91RL3yX7xXAk/jOLvYFDwHfw7dhLMJ33f8o\ncI1r2sHFKfgqghuAK13icRJFREQ6lXPuAzpnbGrpYs65zhplQSQl1OZXRERERERE0p7aYoiIiIiI\niEjaU/ArIiIiIiIiaU/Br4iIiIiIiKQ9Bb8iIiIiIiKS9hT8ioiIiIiISNpT8CsiIiIiIiJpT8Gv\niIiIiIiIpD0FvyIiIiIiIpL2FPyKiIiIiIhI2lPwKyIiIiIiImlPwa+IiIiIiIikPQW/IiIiIiIi\nkvYU/IqIiIiIiEjaU/ArIiIiIiIiaU/Br4iIiIiIiKQ9Bb8iIiIiIiKS9hT8ioiIiIiISNpT8Csi\nIiIiIiJpT8GviIiIiIiIpD0FvyIiIiIiIpL2FPyKiIiIiIhI2uvWwa+ZPWlmzsx+lUTaIWb2NzPb\naWbbzOw+M+vTGfkUERERERGR7q3bBr9m9g3gyCTT9gTmAQcAE4HvAycAfzezbruOIiIiIiIi0jmy\nujoDYcysCLgF+CnwQBKzfA/YHzjBObcuWEYZ8DJwFvDXDsqqiIiIiIiI7AW6a6nodcB7zrkHk0x/\nJvBiNPAFcM69AqwOPhMREREREZF9WLcr+TWzY/AluZ9rxWyHAbNDpi8HDm3h+3oBJTGTsoAC4B3n\n3J5W5EFERERERES6qW4V/JpZJvAHYKZzbnkrZu0FlIdM3wqMbmHeHwFXxU9cvXp1K75eRERERERE\nOoG1dcbuVu35YmAAMK0N87qQaclsmFuB4TGvE9vw3SIiIiIiItKNdZuSXzPrC1yD7+Qqy8xiqyLn\nBf/vcM7Vhcy+DV/6G68XvvQ3IefctmD+aD5am3URERERERHp5rpTye9goBC4Ax+MxgalVwR/H5hg\n3kRtew8FVqQ2myIiIiIiIrK36TYlv8Aq4KSQ6fOAu4H7gU8TzPt34AYzG+Sc+wTAzMYAI4CfdUBe\nRUREREREZC/SbYJf59wuYH789KAa8hrn3Pzg/8nAPcBJ0WnAncAlwBNmdhWQD9wELAb+1rE5FxER\nERERke6uO1V7bjPn3E7gi8AnwF+APwEvA2ckaCMsIiIiIiIi+xBzLqyT5H2XmQ0DVq9evZphw4Z1\ndXZERERERESkQdoMdSQiIiIiIiKScgp+RUREREREJO0p+BUREREREZG0p+BXRERERERE0p6CXxER\nEREREUl7Cn5FREREREQk7Sn4FRERERERkbSn4FdERERERETSnoJfERERERERSXsKfkVERERERCTt\nKfgVERERERGRtKfgV0RERERERNKegl8RERERERFJewp+RUREREREJO0p+BUREREREZG0p+BXRERE\nRERE0p6CXxEREREREUl7Cn5FREREREQk7Sn4FRERERERkbSn4FdERERERETSnoJfERERERERSXsK\nfkVERERERCTtKfgVERERERGRtKfgV0RERERERNKegl8RERERERFJewp+RUREREREJO0p+BURERER\nEZG0p+BXRERERERE0p6CXxEREREREUl7Cn5FREREREQk7Sn4FRERERERkbSn4FdERERERETSnoJf\nERERERERSXsKfkVERERERCTtKfgVERERERGRtKfgV0RERERERNKegl8RERERERFJewp+RURERERE\nJO0p+BUREREREZG0p+BXRERERERE0p6CXxEREREREUl7Cn5FREREREQk7Sn47QZeeuklTjnlFAYO\nHEhubi6DBw9mwoQJrFy5slXLOf300zEzrr322g7KqYiIiIiIyN4pq6szILBt2zYOP/xwpk6dSv/+\n/SkrK+OGG26gtLSU5cuXM2jQoBaX8fDDD/Pmm292Qm5FRERERET2Pir57QbOOussZsyYwbnnnsu4\nceP41re+xZw5cygvL+fxxx9vcf4dO3Zw2WWX8dvf/rYTcpte2lrq3pr5Fi5cyH/8x3/Qt29fiouL\nKS0t5bHHHuuoVRIRERERkRAKfrupvn37ApCV1XLh/BVXXMFBBx3Et771rY7OVtqJlrrfdtttPPPM\nM9x4440sW7aM0tJSPvnkk3bPt3TpUk455RScc9xzzz08/PDDDB06lHPOOYe///3vnbGKIiIiIiKC\nqj13K5FIhLq6Oj7++GMuv/xyBg8ezLnnntvsPK+//jp33nknS5Ys6aRcppezzjqLs846q9G0Y445\nhoMPPpjHH3+ciy66qF3zPfLIIwA88cQTFBQUAPAf//EfvPbaazz44IOcccYZqV4lEREREREJoZLf\nbmTcuHHk5OQwYsQIlixZwnPPPUfv3r0Tpo9EIlx44YVcfPHFHHbYYZ2Y0/TWmlL3luarqakhOzub\n/Pz8+mkZGRkUFhZSV1eXgtyKiIiIiEgyFPx2I3fddRevvvoqs2fPpqSkhFNOOYWPPvooYfqZM2fy\n6aefMm3atM7LZJqKRCLs2bOH1atXc9FFFyVV6p7MfBdccAGRSISf/OQnbNiwga1bt3LzzTfz7rvv\nJixVFhERERGR1FO1525k9OjRAIwZM4bTTz+d4cOHc9NNNzFz5swmaTdv3syVV17Jb3/7W2praykv\nL6//rKqqivLycoqKisjI0PONZIwbN46FCxcCMHLkyBZL3ZOd79BDD2X+/Pl85StfYcaMGQD06NGD\nhx9+mBNPPLED1kRERERERMKYc66r89CtmNkwYPXq1asZNmxYl+altLSUkpISnnrqqSafvfnmmxx1\n1FHNzr9mzRqGDh3aQblLLytXrqS8vJw1a9Zw8803s2HDBhYsWMCBBx7Yrvnef/99Tj75ZI466igu\nvPBCsrOzeeihh3jwwQeZO3cup5xySmesnoiIiIhIurA2z9hdgl8zOwG4CjgM6A1sAhYAVznnEo47\nY2bjgXkhH73lnDuyDfnoFsHv5s2bGTFiBJMmTeL2229v8vmuXbt4/fXXm0w/6aST+M53vsP5559P\naWkpeXl5nZHdtLJ9+3aGDx/OxIkTQ0vdWzPfueeey/Lly1m6dCmZmZn1aU899VQ2btyosZlFRERE\nRFqnzcFvd6r23AtYCtwBbAQGA78EXjWzw5xziced8aYCb8f8v7tDctkBJk2axMiRIznyyCMpKSlh\n1apVzJgxAzPjsssuA2DWrFlMmTKFefPmMX78eHr27Mn48eNDlzds2LCEn0nLiouLGTVqFKtWrWr3\nfEuXLuXoo49uFPgCfO5zn6uvBi0iIiIiIh2v2wS/zrkngCdip5nZ68C7wNeApsWfjb3jnHu1g7LX\noUpLS7nvvvu49dZbqaqq4oADDmDcuHFcccUVqrbcBTZv3syKFSuYNGlSu+cbMGAA//73v6mtrW3U\nC/Rrr73G/vvvn7I8i4iIiIhI87pNtecwZtYH2Axc6Jy7I0Ga8fhqzyc45xak4Du7RbVn6RyJSt3X\nrVvHa6+9xqhRo5qUuic7H/hxfr/xjW/w5S9/malTp9a3+b333nuZMWNGfcm+iIiIiIgkJS2qPQNg\nZpn4IZgOAK4HyoBHkph1jpn1xQfLTwD/7ZzbmsT39QJKYiYNbnWmZa/V1lL3ZOc799xzmTt3Ljfe\neCOTJ08mEokwatQo7r///laXLIuIiIiISNt1u5JfM1sAHBf8uwr4snPuvWbSHwV8C5gP7AJK8W2F\nPwQ+75yrbuH7puE72mpEJb8isi976aWXmD59OsuXL2fr1q3069eP448/nunTp9cPyxbm0Ucf5cEH\nH2TJkiVs3ryZYcOGMXnyZC677DKys7MbpX322Wf51a9+xZtvvklRURFf+9rXuOmmmyguLu7o1RMR\nEZG9197f23OUmY3Gl8QOA34C7Acc75z7qBXLOB34JzDFOTerhbRhJb8vKvgVkX3ZE088wbx58xg7\ndiz9+/enrKyMG264gXXr1rF8+XIGDRoUOl9paSlDhw7lq1/9KgMGDODll1/mmmuu4eyzz+bBBx+s\nT/fCCy9w8sknc9ZZZ/G9732Pjz/+mF/+8pccccQRPP/885i1+bomIiIi6S19gt9YZlYMrAZmO+cu\nbuW85cCDzrmLWjmf2vyKiIRYuXIlBx98MDNnzuSii8JPrZs2baJfv36Npl133XX86le/oqysrL6j\nt1NOOYX169ezdOlSMjIyAF9qHG0qcMYZZ3TsyoiIiMjeqs3Bb0Yqc5FqzrntwPvAyDbMbkD3jexb\nYWNlLfPW7Wbu2p3MWb2DuWt3Mm/dbjZW1nZ11kRkH9K3b1+ARj2Xx4sPfAGOPvpoAD75pGHEukWL\nFnHqqafWB74Ap512GgB//etfU5JfERERkVjdrsOrWEEHVocAD7Ryvi8DRcCijshXZ4g4x8ryGt7Y\nWElZhQ9yq3cbNRVGToEjt4dj0cZKBvfI4uh++YwuySFT1QRFJMUikQh1dXV8/PHHXH755QwePJhz\nzz23Vct46aWXyMrKYuTIhueYGRkZ5ObmNkqXk5ODmbF8+fKU5F1EREQkVrcJfs3sAXwHV28C5fjS\n3h/jS29vCdJMBu4BTnLOzQ+m3Y8vHX4TqMB3ePVz4A3gL525DqlSWVvHnNU7KNtdS0W5seixfBbP\nyaN8fWZ9mpKBEcacU8WYc6oo272TwT2y+PrwIvKyunVhflrYWFnL8q3V7NpTR02dIyfD6JmdwWG9\nc+mf321+UiIpMW7cOBYuXAjAyJEjee655+jdu3fS87/zzjvccsstfPe736VXr1710w866CAWLWr8\nfHLx4sU459i6tcWO+kVERERardu0+TWzi4ELgFFAHvAx8AJwnXNubZBmMk2D318C5wFDgvnKgMeA\n6c65nW3IR5e2+a2sreP+ldvZUhXhqd8VsPDP+UT2JC7Rzcx2HHdeJaddUkGfvEwuGF2sALgDJFMS\nD6gkXtLOypUrKS8vZ82aNdx8881s2LCBBQsWcOCBB7Y47+bNmxk7diw5OTm88sorFBYW1n92//33\nc8EFF3D99dfzve99j7KyMi644ALeeecdRo0axYoVKzpytURERGTvlZ4dXnWFrgx+I84x+/3tfLyz\nltmXF7L0mdyWZwoccWo1E67fyQGFWUwcVazAK4WalsTnNVsSn1/sVBIvaWn79u0MHz6ciRMnMnPm\nzGbT7ty5ky9+8Yts2LCBl19+mcGDGw+h7pzjl7/8JTNmzKCmpoaMjAwuvPBCFi1aRFFREc8//3xH\nroqIiIjsvRT8pkpXBr/vbKvmibU7efLWAl68t6DV85/47QpO/1EFXxlayCG9kg+cJTGVxIs0Vlpa\nSklJCU899VTCNNXV1Zx++um8/fbbLFiwgIMPPjhh2p07d7JmzRoGDRpESUkJffr04dJLL+Waa67p\niOyLiIjI3q/Nwa8aKHYjb2yspHK7sfDP+W2a/+XZ+YybXMmC9RVkZxiZBhkGmWYJ3hv+zjAjM8N3\n/63xNb2Ic8xZvYMtVZGkS+Ije4wX7y2gfH0mE67fyaOrd6gkXtLG5s2bWbFiBZMmTUqYJhKJMGHC\nBF577TWee+65ZgNfgMLCQo444ggA7rzzTqqqqpgyZUpK8y0iIiICCn67jY2VtZRV1LJoTvMli82p\nrTEWP5ZHwZRKHl29o815yQAfCFs0gG4aLIcF0tH0DQF143SxaZoNyjMgA2uUh/jvabzMxkF8qqws\nr6Fsdy1P/a6gVVXQAd5+JpeSgRFO/1EF75XXqCRe9jqTJk1i5MiRHHnkkZSUlLBq1SpmzJiBmXHZ\nZZcBMGvWLKZMmcK8efMYP348ABdddBF//etfueaaa6irq+PVV1+tX+aIESPqh0JasmQJ//rXvzjq\nqKOora3l2Wef5bbbbmPGjBkMHz6809dXRERE0p+C325i+dZqABbNyWvXchbPyWP8lMp2LaMOqKuD\nhmGS956q8UZ8YB1Xut1MUN4oiM+AD8prqGhnSfz4yZUs2VSp4Ff2OqWlpdx3333ceuutVFVVccAB\nBzBu3DiuuOIKhg4dmnC+aHXoK6+8kiuvvLLRZ/fccw+TJ08G/LBGTzzxBNdffz21tbUcccQRPPLI\nI5x99tkdtUoiIiKyj1Ob3zhd1eZ37tqdvFFWw7QT+rR7WdNe2kxujxRkSph/dz5Pz2z7xvzSJbsZ\nP6WS/zq4hH4aBklEREREpL3aXNVTPfF0EzV1jpqK1FTZrU7RciQ1JfHQULIvIiIiIiJdQ8FvN5GT\n4ceLTYXcFC1nX1e12xoNZ9QW2z7JpHo3/HtzFc+W7eL97dVU1dalKIfp5aWXXuKUU05h4MCB5Obm\nMnjwYCZMmMDKlSubna+srIyLL76YY489ltzc3GY7bFu2bBlf/epXGTBgAD179uTII4/kj3/8Y6pX\nRURERES6IQW/3UTP7AxyezhKBkbatZxegyKq8pwiqSyJr65zvL6pijmrd3Lr0q3cu7Kceet2s3pH\nDTURPawA2LZtG4cffji33XYbzzzzDDfeeCPLli2jtLSUTz75JOF8q1at4tFHH2W//fbj2GOPTZju\n008/5aSTTuLjjz/m9ttv569//SsnnHACU6dO5fbbb++IVRJJiY5+MDR58mTMLPR12mmndcQqiYiI\ndAm1+Y3TVW1+N1bWcve75SlrY/qVoYUU52RQ53x3VRHn/N8u5u+Y6f7lfGdX0b9d3N+ETE+UPmw6\nSS4zmN7VqnYb0zuhDXaGwaCCLA4szGZIz2z275FNVoaqrgOsXLmSgw8+mJkzZ3LRRReFpqmrqyMj\nwz/Hu/baa7nyyisJO6/dfffd/Nd//RcffPBBo96ETzrpJKqqqnjllVc6ZiX2IRsra1m+tZpde+qo\nqXPkZBg9szM4rHcu/dXmvc2eeOIJ5s2bx9ixY+nfvz9lZWXccMMNrFu3juXLlzNo0KDQ+ebPn8+E\nCRP4/Oc/T3l5OQsWLAj9bXzwwQds2rSp0bQ33niDiy66iN/97ndcfPHFHbJeIiIibaRxfvd2/fOz\nGFyQxZhzqnj2joI2DXeUleMYc3YV/fMy6ZXrq+tGY6jsth8jXcI5h8MHxBHncEFAHP07EgTK9X+3\nMrBuKVjfUhWBHnWUDIy0q+pzMiXxdQ7KdtdStruWl6kky2D/HtkMKfSvAQVZ++w4wX379gUgKyvx\nqSoa+LakpqYGgOLi4kbTi4uLqaioaGMOJeIcK8treGNjJWUVtQBU7zZqKnxTjtwejkUbKxncI4uj\n++UzuiRnnz2e2+qss87irLPOajTtmGOO4eCDD+bxxx9P+GDoxBNP5NNPPwX8g6EFCxaEphsxYgQj\nRoxoNO3BBx8kOzubCRMmpGANREREugcFv93I0f3zKavYyXHnVfLivQWtnn/sxEryi11aDKtjZvXD\nFmV1QeC+rTrC39buZMw5Ve0qiT/2nKpWz1Pr4MNde/hw1x5Y79uDH9AziwN7ZjOkMIf++ZkpHc+4\nu4lEItTV1fHxxx9z+eWXM3jwYM4999x2L/ecc87h17/+NZdccgm/+c1vKC4uZu7cuTz55JPMmjWr\n/RnfB1XW1jFn9Q7KdtdSUW4seiyfxXPyGj0wKhkYYcw5VYw5p4qy3TsZ3COLrw8vIi9LrW7aI5UP\nhuLt2bOHhx56iNNPP73+e6TBSy+9xPTp01m+fDlbt26lX79+HH/88UyfPp3Ro0cnnK+srIzf/OY3\nLF68mLfeeouamprQkvi1a9eG1jwrLi6mvLw8pesiIrKvUfDbjYwuyWFwjyxOu6SC8vWZvP1M8kHs\nEadWc9olFfTPy2RIYXYH5nLf0Cs3k/55me0uiT/27Cqcg/bEqjV1jg927OGDHXuACvIyjQN7ZtdX\nk+6bl9lsJ097m3HjxrFw4UIARo4cyXPPPUfv3r3bvdx+/frx8ssvc+aZZzJkyBDABw4zZ85k4sSJ\n7V7+vqayto77V25nS1WEp35XwMI/54f+TsrXZ/L0zB48e0cBx51XyWmXVHDfyu1cMLpYAXArddSD\noXhPPvkkmzdv5vzzz0/5stNBtH+CqVOnNqqGXlpa2mw19Gj/BJ///Oc59thjE5bER02bNo0vfelL\n9f8396BDRESSozNpN5JpxjnDi7h/5XYmXL+TkoERXp6dT21N4sAmK8cxdqK/oSzOyeDkwT3SulSw\nMx3cK5eNVRXtKokvKHaMLMqm1sH6ilqqU9C5VVXE8d72Gt7b7qvxFmQZQ4JS4SGF2ZTkZOzVwfBd\nd91FeXk5a9as4eabb+aUU05hwYIFHHjgge1a7qZNmzj77LPp27cvjz/+OMXFxTz11FNccskl5Ofn\nc8EFF6RoDdJfxDnmrN7BlqoIsy8vZGkSD+oie4wX7y2gfH0mE67fyaOrdzBxVLGqQLdCRz0Yinf/\n/fdTUlLCmWeemfJlp4OOroYeNWLECEpLS1OTaRERART8djv5WRlcMLqYR1fv4PQfVTB+ciWL5uSx\nKK4qYa9BEY49p4oxZ1eRX+zon5fJyYN7kJupkpRUGVqYzbvbMttdEj92QAEZZjjn2FZdx/qKPXxa\nWcunFbXsSUHPXhW1jhXlNawo98FwUXZGfanwkMJsinLaN1xTZ4tWGxwzZgynn346w4cP56abbmLm\nzJntWu5NN93EunXrePnll+nZsyfgO7vatm0bl112GZMmTWpzNdF9zcryGsp21/LU7wqSCnxjvf1M\nLiUDI5z+owreK69Ji2YanaWjHgzFKi8vZ+7cuXz7298mN1f7JlkdWQ1dRERSR8FvN5SXlcHEUcW8\nV17Dkk2V5H+nkvHfqaSmwqiOdiITjOXbPy+TQ3rlMqQwWyW+KZZhxsmDe/CPtbtSUhJvZvTOy6R3\nXiaH4TvX2loVYX1FLesratlYWUttCjpf37GnjmVbq1m2tRqAXrkZDOmZUx8Q98jee27AiouLGTVq\nFKtWrWr3spYuXcro0aPrA9+oz33uc9x5551s3LiRAQMGtPt79gVvbKykcrux8M/5bZr/5dn5jJ9c\nyZJNlQp+W6GjHgzFeuSRR6iurlaV5yR0dDX0H//4x0yePJnCwkK+9KUvceONN9Y32RARkbZR8NtN\nZZpxSK9cDumVy6Zg+JCde+rYtSdCxEFJbiYjinLqe3WWjpGbmcGXh/bkubLdKS+JzzCjb34WffOz\nOLyPr0q6udIHw59W1LKxqpa6FATD26rr2FZdxZtbfOdbfYN24Qf29K/8btzucvPmzaxYsYJJkya1\ne1kDBgzgtddeY8eOHRQVFdVPX7x4MXl5eR1SfTQdbayspayilkVzwtv4JqO2xlj0WB75UyrZVFlL\nPw2D1GqpfDAU6/7772f48OEcf/zxKV1uOuqoaui5ublceOGFnHrqqfTp04e3336b6667jrFjx/Lm\nm2/Sr1+/dn9Huujozsd27tzJtGnTWLx4MW+88QYVFRWsWbOGoUOHduBaiUhH0h3HXqBffhbj9/e7\naktVLZsqI5Qo6O00uZkZnHZgTz7cuYcVedVNSuJzCxw5KSiJzzRjv4Is9ivw+7q2zrExqB69vqKW\nzVURUjEq9+aqCJurIizZ5IPh/fIzfXvhntkM7pnVZVXnJ02axMiRIznyyCMpKSlh1apVzJgxAzPj\nsssuA2DWrFlMmTKFefPmMX78+Pp5H330UQCWL1/e6P+hQ4dyzDHHADB16lQefPBBTjvtNH7yk59Q\nVFTEU089xT333MOll15KTk5OJ67t3qPOOXbuqWNbdYRt1RHe3uJrFCyak9eu5S6ek8f4KZW8sqGS\nk/fvQUGW7dVt1TtbKh8MRa1du5YFCxZw5ZVXpmyZ6ayjqqEPHDiQ3//+9/X/n3jiiYwbN46jjz6a\n22+/nWnTprUz5+mjozsf27JlC7NmzeKYY45h3LhxPPnkkx25OiLSCRT8iiQhw4xhRTkMK8phW3WE\nD3bUUFFUx546R3aGUZCVkfKS+KwMY1CPbAb18L1376lzbAgC4U8ratlSHUnJ92yojLChspLFGysx\nYFCP6LBK2ezfI5vsjM4JSEpLS7nvvvu49dZbqaqq4oADDmDcuHFcccUVLT5lj69qGP3/29/+dv0w\nRl/4wheYP38+V199NT/84Q/ZvXs3w4cP55ZbbuGHP/xhR6zSXqPOObbXNAS40Vd5dR3lNb62Sayq\n3dau8a8Btn2SSfVueIdq3tlWTV6m0Scvkz65mf49L4s+eZkU52Ts8006OvrBUNQDDzyAc05VnpPU\nGdXQow4//HAOPfRQXnvttZQve2/W0Z2PDRkyhC1btgD+96HgV2Tvp+BXpJV65WZyTL+2tXVsj+wM\nY3DPbAb39MFwdaSuvlT404paymva33uWA9btrmXd7lpe2VBJpsH+PbLrg+FBBVlkdlAwfPHFF3Px\nxRc3m2by5MlMnjy5ab5DqquFOe6443j66afbkr29XqTOUV4TCarBR4PbCNtqImyvrqM1R09NRWqO\ngeoKI7eH33dVEVd/7MXKNOhdHxBn0ifXB8W98zI77cFMV+voB0NRDzzwAF/4whcYOXJkKrO/T+io\nauixnHOqHZGEVHY+pu0tkn4U/IrspXIzM4LhjXx13YpaHwxHA+KdKehKOuLgo117+GjXHhZ8CtkZ\nMLiHD4SH9Mxmv4Ksfb5UrjvZU+d8QBsNbmNKc3fU1KWk2jxQX82/vXKTWE7EwaaqCJuqmtZ0KMrJ\noG+uD4T75mX599xM8sTANG4AACAASURBVNOsCnVnPBgCePfdd1ubNQl0RDX0WG+99RYrVqzokHGd\n00FnjYEtIns/Bb8iaaIgK4PhRTkML/LB8K490ZLhPayvqKUiBV1J76mDNTv3sGbnHgByM40DejYM\nq9QvLzOtgo7uqCbifEBbE4kJdH2Qm4oHHsnI6+EoGRhpV9XnXoMi5PZoXz521NSxo6aO1cHxWJ+/\nTKNvUDrsq1Fn0TcvkyJVoZYU6Ohq6D/96U/JzMxkzJgx9OrVi7feeosbbriB/fffP2E13n1dZ42B\nLSJ7PwW/ImmqZ3YGI4tzGFmcgws6LVofUzJcFd+Qsw2qI45V22tYtd2PMZyfZQzp2VBNuneuguG2\nqIrUUV7dtA3utuoIu1MxHlYKjDmniqdntj16PfacqhTmprGqiKNsdy1lcVWos8w3W6ivQp2XRZ/c\nfasKtbRfR1dDP/TQQ/m///s//vjHP7Jr1y4GDBjAV77yFa6++moFdAl0xhjYIpIerDVVovYFZjYM\nWL169WqGDRvW1dlpQr09Syo45yivadxmuCYV4yrF6Zmd4YPhoJp0Rxy3G4OhwHbtqaOmzpGTYfTM\nzuCw3rn078ZD6FTWxga3dUE1Zf9/KkrpO0J2BhTlZLKrpo5t2+D6U3u3abijrBzHL5/eSkGRg24S\ncxbnZMR0uJVVHyAXdOOhwESkqe3btzN8+HAmTpyYVOdj1157LVdeeWWLTQQeeOABzj//fA11JNI9\ntPnuofveGYpIhzEzeuVm0ivXD81U53xV2mgg/GlFLamIv3btqWP5tmqWb/PD4xTnZNS3Fz6wMJvC\n7LYFwxHnWFlewxsbKymr8KV71buNmgojp8CR28OxaGMlg3tkcXS/fEaX5JDZySXQzjkqal1DgFsT\naVSam4qS946Qm2kUZmdQlJNBUXYmhTkZFGVnUJiTQW6Gb0u7ekcNL9ZVcNx5lbx4b0Grv2PsxEoK\nih0nDsxnv/xsttf49snbg863ttdEqOzk7bO9po7tNXWspnEV6vxoL9R5mfTO9W2L+6gKtUi31Rmd\nj4nI3qvNwa+ZnQ98EzgQiB/w0TnnEo8uLiLdSoZZUNqVxWd6+6FvNldF6kuGN1bWNhnupi2219Tx\n9pbq+rFi++Rm1pcKH1iYnVQpW2VtHXNW76Bsdy0V5caix/JZPCevUfvTkoERxpxTxZhzqijbvZPB\nPbL4+vAi8lJciuecY1dtXUPJbaMqynUdUpqeCnmZRlFORhDkZtYHu4XZGUmN8zy0MJt3t2Vy2iUV\nlK/P5O1ncpP+7iNOrea0Syron5fJ0MIcMszokZ3BoLga1NWRuiAgjfj3av++c0/qOu5KRmULVaij\nbYv75mXRu5tVod5ba0WItEdHdz4mInu3Nl39zOwq4CpgKbAMqE5lpkSka2WY0T8/i/75WRzRB2rr\nHJuqGtoLb6qMpCQA2VIdYUt1hH9v9u0/++dn1rcXPqBnNnlxgVhlbR33r9zOlqoIT/2ugIV/zg+t\ndlu+PpOnZ/bg2TsKOO68Sk67pIL7Vm7ngtHFrQ6AnXPs2FNXP+5to3FwayJ0Uh9TrVaQZaHBbVFO\n+4OzDDNOHtyDf6zdxYTrd1IyMMLLs/OprUm83Kwcx9iJfl8U52Rw8uAezZac5mZm0D8/o0mQFqnz\n+yM+KN5eE0lJbYVk1TbTC3VxToYPinMbeqHurCrUe0OtCJFU6YwxsJ988kl2795dP8byk08+Sb9+\n/ejXrx/jxo3rjNUUkRRqU5tfM/sQeNg597PUZ6lrqc2vSMv21Dk2Vja0F95SlZpgOJYBAwqy6nuS\nHlCQxZzVO/h4Zy2zLy9kaStLGydcv5MDCrOYOKq4yc1+nXPsqInvYKquvkflblpDmR5ZFhrcFmZn\nkNUJpY/VkTqeK9vNxqoIlduNRXPyWBRXCt9rUIRjz6lizNlV5Bc7+udlcvLgHkmVMLeGc47dta5R\nUFwe/N1dqpjnZ1nQprihs60+eZkU52SkpGO4prUi8pqtFZFf7DqsVoRIZ5g5cyb33Xcf77//fsLO\nxxIFv4l+c/FjYA8dOpQPP/ywSbpx48Yxf/78FK6NiLRCmy+abQ1+dwJfcc4939Yv7q4U/Iq0XnWk\njo2Vvs3w+oo9bKtOfXGoAQ548taCNrUzPfHbFZz+owpK++fTMyejUaC7vbqO7liAa/hOw8KC28Ls\nDDK7QfXaOuf4cOceVmyrZmNQClpTYVRXGLkFrn5M4P55vn35kMLsTm8r26gKddCmuLymjl2dXIU6\nkSyj0bBMsW2Mk32IkWytiKjMbFdfK6JPXmabakVI8lQFXUQkpTo9+H0EeMM5d0Nbv7i7UvAr0n5V\ntXV8GpQMr6+oZUdN+0NL56Byu3H9l9rXw3B+kaM71fLMAHrGdCoV28lUz+y9q1OlbdURPthRQ0Vt\nHXvqHNkZRkFWBiOKcujVDc9ZjapQV8dUpe7kKtTNKYn2Qh1TUtwnL5P8mEA14hyz39/eIbUipO2S\nqYIOqAq6iKTcSy+9xPTp01m+fDlbt26lX79+HH/88UyfPp3Ro5vvlunDDz/k0ksv5fnnnycrK4sz\nzzyTGTNm0KdPn/o0y5Yt47bbbmPx4sUsW7aMwYMHs3bt2g5eq0Y6vbfn3wAPmNke4ElgW3wC59wn\nbc2UiOzd8rIyGFqYw9DCHAAqahuPMbyrDQ1lzWDxY3ltCnwBamuMxY/lMX5KZZvmb49Mw5fWhvSg\n3CNr7wpwm9MrN5Nj+uV3dTaSlpnR0Os5hQ3T46tQl1dH6v/u7CrU5TV1lNfU8cGOxr1QF2RZfZvi\nPXW+U66nflfQqsAX4O1ncikZGOH0H1XwXnkNh/Rq3fwSrjt1zCci+55t27Zx+OGHM3XqVPr3709Z\nWRk33HADpaWlLF++nEGDBoXOt2vXLk466SRKSkqYPXs2lZWV/PznP+eMM85g4cKFZGT489OSJUv4\n/+zdeXzdZZn//9eVk6RpS1cWAStQVoX5OQoICIJV0UFFQcAFdbQ4Ki6D4IYDioAoCoqoMOPPcUZA\nBUVAEXBBWWTVIigjFESlRUB2mpbSpkmTXN8/zklIS9LmfM4nbZK+no/HeeScz5YrVKTv3Pd93Vde\neSUveclLAFi8ePE6+9kaVXTkd+DfXAd9QGaOvl/zD4Mjv9LIW9rVUx0ZXlYNw8Pd1ua0189Y5S+P\n9ZqxZQ/HXvGs39WVojkYNNxObakwqTlKWdOp9W/gFOrFA0aL1+cU6jJmRRx/5SJ22LLCO3ecPgIV\nblicgi5pNLrnnnt4/vOfz9lnn82HP/zhQa8588wz+Y//+A8WLFjAc5/7XAB++9vfsvfee/OTn/yE\ngw8+GIDe3t7+IPze976Xq666atyP/L6HIUKvJK3NlNYKU1or7DBtQn835b4g/EhHN52DhOEVy6Kh\n4AvQ/lCFzmUwYfLarx1MSxODhtsprU1MrBhwNwRDdaHu7k2WDphCvXjAFOqRHiwuY1bEvB+3MfGI\nDv7n7nbaKkFzU9AcQXMTVGpf+45VmqidC5qjOoLeHEOfb25a9VglGDezHVbXk8klC57iyRU9w56C\n3rMyuP68SSx+uMLbTl3KxQuecgq6xqSLL76Y888/n9tuu40nnniC2bNnM3fuXI455hhaWlrWeO9V\nV13FZz7zGW6//XamTp3Km970Jk4//XSmTZu2ynU33XQTJ510En/84x9ZuXIlL3jBCzj22GM55JBD\nRvJHGxc22WQTAJqbh45/l19+Ofvtt19/8AV46Utfyrbbbsvll1/eH377gu9YVCj8Zua5JdchaQMV\nEUxrrTCttcLzZ1TDcHtnLw8vX8kjtTC8srfaRKkMncujf63dYCY0xaDhdmpLExMMuBpC81qmUC/u\n6lllW6ayp1DPu6StoftvuaS6JOCJQbZuGglNQX+4HhiYBwbpyoBA/UwIH+T8KiH8mWeu+oy+e6rn\nRyp837O4yynoY4ANyEbGV77yFbbZZhtOP/10Nt98c26++WY++9nPcvvtt3P++ecPed91113HAQcc\nwBvf+EZOPPFEHnjgAY477jj+8pe/cM011/T/d/eOO+5g//33Z5999uGcc86htbWVc845h0MPPZTL\nL7+cAw88cF39qGNGT08Pvb29PPDAAxx//PHMmjWLN7/5zUNeP3/+fA4//PBnHd9ll1246667RrLU\ndaahf8MjYgawJzADWATMy8yxM+lb0qgTEcxsqzCzrcIuM6vdhK96cBmdk8r5S/mESUlTwCYTKoNO\nUy57Cx5t2CKCjVqqf7GeNXnVkY8VPb3ParRVZAr1aJgVUa/ehK5Mqr3w1v1EsiZWH5FeNXwXHe2+\n6eHlLF8S3HRBsbXvN/9gInPmdnDb4x2G35K5B/bIu/zyy9l00037P8+ZM4fM5DOf+Qynn376KqOJ\nA51yyinstNNOXHzxxf0jijNnzuTNb34zP/vZz/pD7UUXXQTAZZddxqRJ1V0fXv3qV/P73/+e888/\n3/A7iJe//OXcdNNNAGy//fZcffXVzJw5c8jr29vbmT792ctfZs6cyT333DNida5LhcNvRHwROAYY\n+P/OKyLijMw8oeHKJInqCM3MtgoPTe5m+hY9Da/5nTAZdp4xYUw1ZtL41FZpom1SE8+ZNPgU6sWr\njRQPNYV6Xc2KGE96ga7erL4pOXzfcsma1/iuycAp6D+/fynTWyu0NAWtlaC1qfaqBC1NwYTa19am\noKVp6H1rZQOydWVg8O2z6667AvDQQw8NGX7nzZvHe9/73lWm0h5wwAEAXHrppf2htquri5aWFiZO\nfOa/301NTUyZMoXe3tG4YeH697//+78sXryYhQsXcsYZZ7D//vtz4403stVWWw15z2D/X1KkR9Ro\nVSj8RsQngE8B3wK+DzwCbA68AzguIhZl5pmlVSlpg7bd1FbuXNTJnoeu4Mqziw9N7XHoiv7nSaPV\nKlOoB8hMnu6uNdyqBeP7n17Jiknl/KVkQknP2dCVNQX9T0921nVfXwjuC8qrBORBwnPrwOODHKvE\n+AjUw21AtvjhCleePZmrvjWpvwHZd+9ZYgOyBt1www00Nzez/fbbD3lNU1MTEyasOtOhtbWViGD+\n/Pn9x971rndx1lln8fGPf5xPfepTtLS0cM455/DnP/+Zb3zjGyP2M4xlfdsa7bnnnrz2ta9l2223\n5fTTT+fss88e9PoZM2bQ3v7sxqDt7e1rHDEeS4qO/B4JnJGZnxxw7F7gpohYBnwQMPxKKsWMCRU2\na6uw56EruOpbkwp3tN3zkBVs1vbsUCGNBRHBlJYKU1oq/VOoWx/v4M6eztJmRagx63MKeldvdRr5\nspI2qQ4YPDQ/6xirhOaW1UL1wGvXdaMzG5CtX3fddRdf+9rXeO9738uMGTOGvG7HHXdk3rx5qxy7\n5ZZbyEwWLVrUf2znnXfmN7/5DQcddBBnnlmNGZMnT+ZHP/oR++2338j8EOPItGnT2GGHHfjb3/42\n5DVDre296667xs0/46LhdyvgyiHOXQkcVfC5kjSo58+YwGMrlrPP2zu4/rxJdd+/9+EdTJyWrqPT\nuFL2rIidprXSWgl6shocenprX3PA1941f96Qjacp6Al09uSg3feLqgRrHXEecqR6iNC9ptFpG5Ct\nP0888QQHH3xw/0jjmnzkIx/hXe96F1/84hd53/vex4MPPsiHPvQhKpXKKlOh//rXv3LooYfykpe8\nhA984AO0tLTwwx/+kLe+9a1cfvnl7L///iP9Y41pTzzxBHfffTfvfOc7h7zmwAMP5LjjjuOhhx7q\n3wt43rx53HvvvXz5y19eV6WOqKLh91FgN+CqQc7tXjsvSaXZZkoLf26vcMBRy1n8cIU/1fEXmRe+\nppMDjlrOZm0Vtp6y5u0WpLGk7FkRL928/l8sDZSZ9MKzQ3OdIbq3jrA98PklDXoW1uoU9DXqSejo\nyWHv7T4cfSF4sBHn+5eutAHZerB06VJe+9rXsmLFCq655hqmTJmyxuvf+c53Mn/+fE466SSOP/54\nmpqa+MAHPkBraytTp07tv+74449no4024sc//jGVSnWGxf7778/999/PJz7xCW6//fYR/bnGkne+\n851sv/32vOhFL2L69On87W9/48wzzyQiOOaYYwA499xzOeKII7j22muZM2cOAO973/s466yzeOMb\n38jJJ59MR0cHxx57LHvssQcHHXRQ//OXL1/Oz3/+cwDuu+8+li9fzsUXXwxUR+h33nnndfsD16Fo\n+L0AOCkiOoELqYbd5wBvA04EvlpOeZJU1RTBq2ZN5mf3Pc3bTl3K9C16uPkHE+nuGvov+82tyd6H\nV9duTWtt4lWzJo/b/UW14RpNsyIiggpQ/Xvpuv93LTNJqDs01xXSh7iuqydpm5xOQV/HVvbCyt5k\nqMZlZTUge7yjm03dBmmtOjs7Oeigg1i4cCE33ngjs2bNWus9EcGXvvQlPv3pT7Nw4UK23HJLpk+f\nzsYbb8xHPvKR/uvuuOMOdt111/7g22e33Xbrnwatqr322ovvfve7fP3rX2fFihU873nP4+Uvfzmf\n/vSn2WabbYa8b8qUKVxzzTUcffTRvPWtb6W5uZkDDzyQM888c5VR+Mcee+xZWyb1fT7xxBM56aST\nRuLHKkUU6d4VEa3A94A3s+r/2wTwA+DdmdldSoXrWETMBhYsWLCA2bNnr+9ynuXJFd083tHDdNcs\nagPV2dPL1Q8u47EVPXQsCeZd0sa81bp2ztiyhz0OXcGeh6xg4rRks7YKr5o12W2MNC71ZvLL+5/m\n0eU9/PD4KXXPinjbqUt5zqQKB2y1kb8cakB7Zw8/vW8pv/nOxIamoP/LUcuYc0QHG09oIglW9ibd\nvVn9Oj4HhEfUaa+f0fAvI469op3ZU1rYe/NJbNpWsQHWEHp6ejjssMO46qqruPrqq9ljjz0KP+vb\n3/42//7v/87dd9/NtttuC1S3Tnr00Ue54447aG5+5hcR+++/PwsXLuTee+9t+GfQmFH4P1aFfoWV\nmV3AWyPiFGA/ntnn97rMHB87IEsalSZUmjhgq434+9KV3N3WycT3dDDnPR10LY/qOrlJ2T/1cLO2\nCi+YMYGtp7T4l3qNW86KGB3KnoL+uq2fPVU0a1O7Bwbilb0MeP/M+VVCc+9qxwY8Yzyv0y6zAdlC\nVrJw6RIANmppYtO2Cpu0VdhkYnPtfTOtlQ3736EPf/jDXHrppZxyyin09vbyu9/9rv/cdtttx6ab\nbjroVNvbbruNX//617z4xS+mu7ubq666im984xuceeaZ/cG37/lvectbOPjggznyyCP71/xeffXV\njvxq2Bqav5GZdwJ3llSLJA1LUwSzp7Yye2or7Z093PtUF8un9rKyN2lpCiY1N7Hd1Fa7OmuDMaHS\nxOu32YirH1zGa49ezpy5Hc6KWA9Gegp6RNAS0NJUXsjqzVUDcjUcDxKqnxWgBzlW+zxa8vRINSB7\nemUvT6/sZeHSlatcN621iU3aKmza1swmE6tfN26r0Fzin9do9stf/hKAE044gRNOOGGVc+eccw5z\n584d9L7W1lYuu+wyTj31VLq7u3nhC1/IRRddxCGHHLLKdW9+85u5/PLLOe2005g7dy49PT3ssMMO\nfO9731tjEydpoKLTnvcGZmbmFbXPM4CzgH+i2u35+MzsKbPQdcVpz5Kksao3szoror2Tx1ZU/zPs\nrIh1xynotaZnfaPPuVqoXn2ketAAvdqxWhAvYsWy4OR9N274ZzrphicKr8MOqrMCqqPE1UC8aVuF\nGW0Vt0+Silu3056B04BfAVfUPn8VOJBq8P0wsAz4XNGiJElS/ZwVsX45Bb3W9Cyg0hS0lfTMvune\ng41Ed+fg078fWd7NYnrXewOyBBZ19rCos4e/LHnmeFPAxrVQvOnE5v6v01qbxvSfvzTaFR35fQJ4\nV2b+PCImAE8AR2fmdyLiI8CHM3OnkmtdJxz5lSRJjbAx3/pXdgOydaU5YONaEO5bS7zJxApTW5rW\nuKexVIbHOrqZv6iTp1f20tWbtDYFG7U0scvMCWw2urqdr/OR30nA07X3ewITgUtrn28H1t7XXJIk\naRyyMd/6V1YDsj0OWUFz0L+F1kjrTni0o4dHO1ZdPdjaFGw6sTZ9uq259r6Zyc1hKFZDejK5Z3EX\nf3isgweXVzfr6VwWdC0PWiclEyYn8x7rYNbkZnbddCI7TW8d01P2i4bfvwN7A9cDhwJ3ZOai2rmN\neSYYS5IkbXCcgr7+ldGAbNK0ZJ/NJ7HNlBaWruxlcVcvizt7aO/sYXFXD0s6eym4JLkuXb3JP5Z1\n849l3UBn//GJlehfSzxwCvVEt2PSMHR093LJgqd4cFk3yxcH8348kVtWm6UyfYse9jx0BXseuoIH\nly1l1uRmDtt26pjd8qvotOePUV33+3/Ai4CjMvObtXOnA3tk5pwS61xnnPYsSZI09q2LBmS9mTzV\n1cvirlog7uylvauHpV2967Xr9UbNTbVQ/Mx2TBu3VZxWr34d3b18754lPLmih1+eNYmbLpi4xhkS\nlZZkn7dX+xNs3FbhXTtNW58BuPDQc6HwCxAR7wD2Am7NzPMGHP8WcGNmfq9oUeuT4VeSJGl86Ozp\n5Wf3Pc2Srl5+edakuhuQvX6bjQoFxu7eaihu7+phcWf11d5V3SJpfZraWt2juG87pk1q2zGVuX1W\nPcbQGtNxpSeTH/x1CQ8s7eYHx0/hjgK/GHrelGYO32Ha+poCve7Cb0S0AIcD8zLznqLfeLQy/EqS\nJI0fo6kB2creZElnTy0U9/ZPn17evf7GiQfbjmmTtgozR2g7puGsMQXGzRrT0eiu9k4uu28pv/j6\npEJLAvZ793Jee/RyDtpmypD7ko+wdTvyGxEdwOsy89qi33iQZ+4LnAjsAswEHgduBE5cW8iOiK2B\nbwCvBLqBy4GPZuaTBeow/EqSJI0jo30P7M6eXhZ3Dpg+3VUNxp3rosvWEAZux9Q3dbrR7Zievca0\nbY1rTCdOyzG/xnS0yEx6aluG/fBvT7HwkR6+8JqZhZvBHX/lInbYssI7d5w+AtWu1Trv9jwf2Kbo\nNx3CDOAO4FvAY1Q7Rh8H/C4idsnMhwa7KSI2Aq4FFlMdkZ4InA5cERH7ZOb6nV8iSZKk9Wq0NyCb\nUGniOZOaeM6kVf9q3tFdbbDVF4YX10aN18Xs6d6Ex1f08PiKHljc1X984HZMmwyYQr227ZiGu8Z0\n8cMVrjx7Mld9a1L/GtPv3rNkfa8xXSd6B+xbvXLAvtX9r57q8a7+/a6feT+cewb+KmXeJWte47sm\n3V3VX1xMPKKDxzu62XQMTVEvWumxwH9HxILMvK6MQjLzMuCygcci4lbgz8CbgP8c4tb3Ac8F9s3M\nf9TuexC4GXgjz2zBJEmSpA3cjAkVdt904vouY1gmNjcxsbmJLQZsVZyZLO/OAeuJq2uLl3T2sC5m\nT69pO6Zqx+nadky1EePJzUEvcMmCp3hyRc+w15j2rAyuP28Six+u8LZTl3LxgqfW5xpToPrPvjsH\nhMyeZGU+EzBXrhJGhwila7hnXQ70z7ukraH7b7mkjTlHdDB/USdznjv+w++3qG5pdE1ELAMehVV+\nmZCZuVOjxQFP1L52r+GaNwDX9wXf2jf/bUQsqJ0z/EqSJGlciAgmtwSTW5qYNbml/3hm8vTKXtpr\n2zH1jRIv6eqldx2Eqq7e5KHl3Ty0fNXtmNoqweTm4MnOatOxeporAfzpVxOYvkUPrz16OX9Z3LXW\nNaY9WQuYgwTPrrWF0jXc091b/RnHgxXLYpWp5kW0P1Sha3mwdD03catX0fB7E4xMB/eIqABNwPOA\nU4EHgYvWcMsuwA8GOT4f2HkY328GMHCy+qxhFytJkiSNAhHBlNYKU1orbLXRM6F4sO2YFnf18NQ6\n2o5pRU/S0Z10LAluuqDYiPvNP5jIy+d28KsHnubu9s5BwuwzgXWc5NMR1bW8nNHzruUx5n4hUCj8\nZubckusY6Dpgn9r7vwGvysxFa7h+BtX1vqtbBAxn9Ploqo22JEmSpHGlKYLpEypMn1BhmynPHO/p\nTZYMDMW1DtQjMZIXAbf8uK2hNaa3/Lg6zfYvS7rWfoPWqK/BWxnPaV1P22QV1fAE7YhopRpAn8jM\nnrVdPwz/RnUkdjbwceCqiHhZZt6/hnsG+xMc7p/E14HzBnyeBVw/zHslSZKkMafSFMysbWk00Eht\nx1TWGlM1rm1yMn2LnoamPs/YsofWScmUlrHVhKxw+I2IA4CTgN2oBs09gD9ExH8Bv8nMHxV57oBt\njeZFxC+ABVQbbP37ELe0Uw3fq5tBdfR3bd+vvfYMgDV2qZMkSZLGs5amYJOJzWyyWgffgdsxVdcT\nV9cWrxhGl6ay1ph2LoMJk9d+7XgUQHNT9c+nOYLmpqC5iWfeB7Vjgx9vqb1f3t3LDY90sOehK7jy\n7OL/MPc4dAUAu8xcL/v8FlYo/EbEQcCPgWuobkd0+oDTDwHvAQqF34Eyc0lE/BXYfg2XDbW2d2cc\nwZUkSZIatsbtmFYbJW7vXHU7prLWmHYuDyZMHr1rTFcJoIO8bxl4PGohdRjvW5qi1H2n71ncxZ6H\nruCqb00qvM/vnoesYNbk5jG1zREUH/k9GTg3M/8tIppZNfz+Cfhgw5UBEbEJ8ALg+2u47ArgixGx\nZd9ewBGxJ7Ad8Mky6pAkSZL0bP3bMU165ljfdkyLu3q47fEOOktaYzqhwec0BbTUAmWlKfrfrxI6\nBxtVrQXQSl8Y7T/+TJitxNiZQfr8GRN4bMVy9nl7B9efN2ntN6xm78M7mDgt2W2MbBk2UNHw+3zg\nE7X3q/+vcAnVbZDqEhHfp9rg6naqDay2Bz5ae/7XatfMBc4BXpGZv6nd+m3gKOCyiDgRmEg1jN8C\n/LTeOiRJkiQVN3A7poeXd7Oos7OUNaYTJsOMCU1sPql50AC6thHUMkdPx7JtprTw5/YKBxy1nMUP\nV/hTHdtPvfA1lBqWQQAAIABJREFUnRxw1HJmTW5mx+mtI1jlyCgaftuBLYY4tx3wSIFn/g54F9Xu\ny23AA1Q7P38hM+8b6qbMXBoRr6TauOpCqnsCXwF8NDPH1sZTkiRJ0jiy3dRW7lzUWdoa0/22mMyM\nCY2tH97QNUXwqlmT+dl9T/O2U5cyfYsebv7BRLq7hv7lQHNrsvfhHRxw1HI2bqtw2LZTqYzBXyZE\nZv3TByLif4F9gVdQDborqTa+WgDcCFyTmUeXWOc6ExGzgQULFixg9uzZ67ucZ3lyRTePd/Qw3X/p\nJUmSNAb8/O9L+fujvXzhNTMLrzE9/spFbP2cJl639ZS136Bh6ezp5eoHl/HYih46lgTzLmlj3iVt\nq4zQz9iyhz0OXcGeh6xg4rRk1uRmDtt2Km3N67XLc+HUXXTk9zjgJuDPwA1UpyZ/mWqTqSVUu0BL\nkiRJ2sCVtcb0BTPGVmfh0W5CpYkDttqIvy9dyd1tnUx8Twdz3tNB1/KoNheblP17As+a3Mxum05k\nx+mtY3LEt0+h8JuZj0XEbsAxwGuAe4HJwH8DZ2bmkvJKlCRJkjRWlbHGdLO2CltPaRnBKjdMTRHM\nntrK7KmttHf2cO9TXTw1uYcEprZWmNLSxC4zJ4y5rs5DKTTteTxz2rMkSZJUrs6eXn5239Ms6erl\nl2dNqmuN6bTWJl6/zUZMqKzXqbYbjKVdPUxuaWLLyaP2lw3rfNpz9btGbATsAmwJ/AO4KzOfbuSZ\nkiRJksaXCZVqgL36wWW89ujlzJnbMaw1ppu1VXjVrMkGX5WicPiNiBOAjwNTeCZ9PxURX8nMz5dR\nnCRJkqTxoZ41ppu1VXjBjAlsPaXFLYpUmkLhNyJOAY4H/gv4EfAo8BzgrcDJEdGamZ8trUpJkiRJ\nY95ga0yXT+1lZW/S0hRMam5iu6mtbmekEVF05Pe9wJcy89MDjv0FuCEingLeDxh+JUmSJA1qxoQK\nu286cX2XoQ1I0cnzU4Brhzh3DbBRwedKkiRJklS6ouH3emC/Ic7tVzsvSZIkSdKoUHTa8ynABRHR\nBlwCPAZsBhxWex0eEVv2XZyZDzVaqCRJkiRJRRUNvzfVvn6CasfnPrHa+T6uWJckSZIkrTdFw+97\ngCyzEEmSJEmSRkqh8JuZ55ZchyRJkiRJI6ZowytJkiRJksYMw68kSZIkadwz/EqSJEmSxj3DryRJ\nkiRp3Ks7/EZEU0RsFRFTRqIgSZIkSZLKVnTk915gvzILkSRJkiRppNQdfjOzF7gfaCm/HEmSJEmS\nyld05PdrwKec+ixJkiRJGguaC973QmAr4P6IuBF4BMgB5zMzj2y0OEmSJEmSylA0/O4PdNVe/1R7\nDZTPukOSJEmSpPWkUPjNzNllFyJJkiRJ0khxn19JkiRJ0rhXOPxGxOYR8aWIuDEi7o6InWvHPxQR\nu5dXoiRJkiRJjSkUfiNiF+BO4L3AImBHoK12egfgY6VUJ0mSJElSCYqO/H4VuAeYDRwCxIBzNwN7\nNViXJEmSJEmlKdrteR/g8MxcGhGV1c49CmzRWFmSJEmSJJWn6Mhv9xru3QxYWvC5kiRJkiSVrmj4\nvRn4UEQMnO7ct7fvu4HrGqpKkiRJkqQSFZ32fAJwA3Ar8COqwfdtEfEFYF9c8ytJkiRJGkUKjfxm\n5m3AfsAS4PNUG159HJgEzMnM+aVVKEmSJElSg4qO/JKZtwKvjIg2YCawODOXl1aZJEmSJEklKbrm\nt19mrgA6DL6SJEmSpNGqcPiNiP0i4tqIWAY8ERHLIuLqiNi3xPokSZIkSWpYofAbEW8FrgU2Br4E\nfLD2dRPg2oh4S2kVSpIkSZLUoKJrfk8GfpKZh612/JSI+DHwOapdoCVJkiRJWu+KTnveGvj2EOf+\nu3ZekiRJkqRRoWj4vRuYNcS559XOS5IkSZI0KhSd9vwx4NyIeDgzf953MCJeDxwPvLuM4iRJkiRJ\nKkPR8PttYCpweUR0Ao9TbXbVBiwB/ici+q7NzNyp0UIlSZIkSSqqaPi9CcgyC5EkSZIkaaQUCr+Z\nObfkOiRJkiRJGjFFG15JkiRJkjRmGH4lSZIkSeOe4VeSJEmSNO4ZfiVJkiRJ457hV5IkSZI07hl+\nJUmSJEnjXqGtjiKiGXgJsBewBTAReAL4M3B9Zj5cWoWSJEmSJDWorvAbEdsBHwHeAcwEeoHFwApg\nBtUQnBFxE/DfwAWZ2TvMZx9We+5uwCbAQuBc4GuZuXIN980Brh3k1P9l5ouG9YNJkiRJksa1YU97\njohvAncBuwIn1b5OyMxNMnNWZk4GNgcOAu4EvgrMj4iXDvNbfALoBI4FXgecD3yOagAejiOBlw54\n/esw75MkSZIkjXP1jPxOBV6cmXcNdUFmPgZcAVwREUcDRwA7Ab8dxvPfkJmPD/j8m4gI4PMRcWxm\n/mMt99+Vmb8bxveRJEmSJG1ghh1+M/Md9Ty4NlX5v+u4/vFBDv+h9nVLYG3hV5IkSZKkQY32bs/7\nAt3A34Zx7SUR0RMRj0bEtyNi5nC+QUTMiIjZfS9gViMFS5IkSZJGn3rW/P6o1vBq4LGPRMQmqx37\np4j4Aw2KiJ2BY4D/ycz2NVy6BDgD+DfgVcCZwFuoTpueMIxvdTSwYMDr+kbqliRJkiSNPpGZw7sw\nohfYKzNvqX2uAF3ASzLzDwOu2xO4OTMrhYuqBuqba89/aWYurfP+1wI/B47IzHPXcu0MYPqAQ7OA\n6xcsWMDs2bPrqntdeHJFN4939DB9QuF/vJIkSZI0qKVdPUxuaWLLyS3ru5ShRNEbC+3zW8Y3HvKB\nEVOAXwBtwCvrDb4AmfmLiFhCdS/ic9dybTvQP7Jc7bElSZIkSRpPGg2/papNU/4pMBt4WWY+2Mjj\ngOENa0uSJEmSxrVRE35r06h/SHW09lWZ+ecGnvV6qlszzSupPEmSJEnSGFZv+B1sJLWs0dX/BA4G\nTgCaImKvAefuzczHI2IucA7wisz8DUBEfA/4K3A7sBzYCziW6jZJF5ZUmyRJkiRpDKs3/N40yJrY\neSWtkz2g9vWU2mugIxh67e5dwNuBT1BdJ/wg1f2FT87MrjIKkyRJkiSNbfWE35NHrAogM7cZxjXn\nsloIzswvAl8ckaIkSZIkSePCsMNvZo5o+JUkSZIkaaQUbngV1bnOG9c+PpnD3TBYkiRJkqR1rKne\nGyJiTkT8HFgKPFp7LY2In0XEK8ouUJIkSZKkRtU18hsRnwE+BywBfg3cR3U/3a2BVwAHRMTJmfm5\nkuuUJEmSJKmwYYffiHgd1eD7NaqdlJesdn4acBJwYkTclpk/K7NQSZIkSZKKqmfk96PAhZn5scFO\n1sLwRyNiC+AYwPArSZIkSRoV6lnzuxtw/jCu+z6we7FyJEmSJEkqXz3ht41qk6u1WQpMKFaOJEmS\nJEnlqyf83gvsPYzr9gEWFCtHkiRJkqTy1RN+LwSOjYgXDXVBRLwY+CTww0YLkyRJkiSpLPU0vDoD\neBNwS0R8D/gp1a2OEphdO/cO4M7atZIkSZIkjQrDDr+Z2RERrwTOAt4FzF39Eqojvv+emR2lVShJ\nkiRJUoPqGfnt287oXRHxH8AcYBYQwAPAdZn5j9IrlCRJkiSpQcMOvxHxHeCUzFyYmQ8BF4xcWZIk\nSZIklaeehldzgU1HqA5JkiRJkkZMPeFXkiRJkqQxqd7wmyNShSRJkiRJI6iuhlfA8RHx+DCuy8w8\nskhBkiRJkiSVrd7wuyfQOYzrHCGWJEmSJI0a9YbfgzPzlhGpRJIkSZKkEWLDK0mSJEnSuGf4lSRJ\nkiSNe4ZfSZIkSdK4N+w1v5lpUJYkSZIkjUnDDrQR8bmImF7PwyPilRFxcP1lSZIkSZJUnnpGc/cB\n/h4R34mI10TERoNdFBE7RMQxEXErcDGwsoxCJUmSJEkqqp5pz6+KiAOAjwG/ADIiFgKPU937dzqw\nDTAVeBT4DnBGZraXXbQkSZIkSfWoa5/fzPwl8MuIeB7wauAlwBZAG3A38BPgRuC6zOwpuVZJkiRJ\nkgqpK/z2ycwHqI7sfqfcciRJkiRJKp8dnCVJkiRJ457hV5IkSZI07hl+JUmSJEnjnuFXkiRJkjTu\nGX4lSZIkSeNeofAbEZ+NiC2HOLdFRHy2sbIkSZIkSSpP0ZHfE4FZQ5zbsnZekiRJkqRRoWj4jTWc\n2xToLPhcSZIkSZJK1zzcCyPiDcAbBhw6LiIeX+2yNuAVwB9KqE2SJEmSpFIMO/wC2wKvrr1PYE+e\nPcLbCdwKHN94aZIkSZIklWPY4Tczvw58HSAiFgIHZ+b/jVRhkiRJkiSVpZ6R336ZObvsQiRJkiRJ\nGimFwi9ARASwG7AV1bW+q8jMCxqoS5IkSZKk0hQKvxGxM/ATYHsG7/ycgOFXkiRJkjQqFB35/SZQ\nAQ4D5uPWRpIkSZKkUaxo+N0V+NfMvLTMYiRJkiRJGglNBe97GOgtsxBJkiRJkkZK0fD7BeCTETGx\nzGIkSZIkSRoJRac9vwx4HrAwIm4A2lc7n5l5ZEOVSZIkSZJUkqLhd3+qHZ07gN0HOZ+FK5IkSZIk\nqWSFwm9mzi67EEmSJEmSRkrRNb+SJEmSJI0ZhcNvRGweEV+KiBsj4u6I2Ll2/EMRMdhU6LU977CI\n+ElE3B8RyyNifkR8MiJahnHv1hHx04hYGhHtEfHdiNi4yM8lSZIkSRp/CoXfiNgFuBN4L7AI2BFo\nq53eAfhYgcd+AugEjgVeB5wPfA44dy21bARcS7UB1+HA+4F9gSsiwpFtSZIkSVLhhldfBe4BDqDa\n9KprwLmbgdMKPPMNmfn4gM+/iYgAPh8Rx2bmP4a4733Ac4F9+66JiAdrdbwRuLRALZIkSZKkcaTo\nyOg+wJcycynP7uz8KLBFvQ9cLfj2+UPt65ZruPUNwPUDw3Fm/hZYUDsnSZIkSdrAFR357Wbo4LwZ\nsLTgc1e3b+17/W0N1+wC/GCQ4/OBndf2DSJiBjB9wKFZ9RQoSZIkSRr9io783gx8qDYtuU/fCPC7\ngesaqgqoNdA6BvifzGxfw6UzgMWDHF8EzBzGtzqa6ihx3+v6OkuVJEmSJI1yRUd+TwBuAG4FfkQ1\n+L4tIr5AdbR2r0aKiohNqK7VXUC1AdbarD71GiAGOTaYrwPnDfg8CwOwJEmSJI0rhUZ+M/M2YD9g\nCfB5qkHz48AkYE5mzi9aUERMAX5BtXv0AbV1xWvSTnX0d3UzqI7+rlFmtmfmwr4X8GC9NUuSJEmS\nRreiI79k5q3AKyOijer04sWZubyRYiJiAvBTYDbwsswcThAdam3vzjiCK0mSJEmi+Jrffpm5IjMf\nKiH4VoAfAi8BXpeZfx7mrVcAL4+I/o7QEbEnsB1weSM1SZIkSZLGh2GP/EbE8XU8NzPzi3XW8p/A\nwVTXEzdFxMB1w/dm5uMRMRc4B3hFZv6mdu7bwFHAZRFxIjAROB24heoosiRJkiRpA1fPtOfP13Ft\nAvWG3wNqX0+pvQY6Ajh30G+UuTQiXkm1cdWFVLdGugL4aGb21lmDJEmSJGkcGnb4zcyGp0iv5fnb\nDOOacxkkBNcaVb2x9KIkSZIkSePCiAZaSZIkSZJGg8LdnvtExGZUtyVaRWbe3+izJUmSJEkqQ6Hw\nW+vM/AXg/cC0IS6rFC1KkiRJkqQyFZ32/HHgSOBLQACfA04E7gEWAO8ppTpJkiRJkkpQNPy+GzgJ\nOKP2+fLM/DzwT8BfgR0bL02SJEmSpHIUDb/bAH/MzB6qWwtNA6htLfRfwNwyipMkSZIkqQxFw287\nMLn2/h/ACwecm1J7SZIkSZI0KhTt9jwP+GfgF8BPgRMjohVYCXwSuLmc8iRJkiRJalzR8HsasHXt\n/UnAbOBUqh2e5wEfaLgySZIkSZJKUij8ZuYtwC2194uBgyJiAjAhM58qsT5JkiRJkhpWaM1vRLwq\nImLgsczsNPhKkiRJkkajog2vfg08GBFfjogXl1mQJEmSJEllKxp+XwZcSnW/31sjYn5EHBcRW6/l\nPkmSJEmS1rlC4Tczb87MDwNbAG8E/g/4NLAgIm6IiPeXWKMkSZIkSQ0pOvILQGb2ZObPMvPtwHOA\nucA2wDcbL02SJEmSpHIU3epoFRHx/wHvAN4GPBdYUMZzJUmSJEkqQ+HwGxFbAW+vvXYBngR+BJyf\nmb8tpzxJkiRJkhpXKPxGxA3AS4FO4DLgOODKzOwusTZJkiRJkkpRdOS3A3gP8OPMfLrEeiRJkiRJ\nKl2h8JuZrym7EEmSJEmSRsqww29EbAk8lpndtfdrlJkPNVSZJEmSJEklqWfk9wGq63xvAR4Eci3X\nV4oWJUmSJElSmeoJv+8B7h3wfm3hV5IkSZKkUWHY4Tczzxvw/twRqUaSJEmSpBHQtL4LkCRJkiRp\npBXd5/dXa7vGjtCSJEmSpNGi6D6/rTx7ze/GwE7A48BfGilKkiRJkqQyFd3nd85gxyNiNvAT4LQG\napIkSZIkqVSlrvnNzIXAl4Avl/lcSZIkSZIaMRINrxYD247AcyVJkiRJKqTomt9niYgm4PnACcCf\ny3quJEmSJEmNKtrteSXPbnjVBASwFHhDg3VJkiRJklSaoiO/X+DZ4XcFcD/wi8xc3FBVkiRJkiSV\nqGi355NKrkOSJEmSpBEzEg2vJEmSJEkaVYqu+f1VHZdnZv5Lke8jSZIkSVIZiq75baHa2fk5wELg\n0dr72cAjwD2lVCdJkiRJUgmKht+zga8Ae2TmrX0HI2J34CLgG5n5kxLqkyRJkiSpYUXX/H4OOGFg\n8AWoff4s8PlGC5MkSZIkqSxFw+92wKIhzrUD2xZ8riRJkiRJpSsafu8BjomIVaZNR0QLcAzwl0YL\nkyRJkiSpLEXX/B4LXAbcFxGXA48BmwFvqH09sJzyJEmSJElqXKHwm5lXRsRuwKeBfwG2AB4GrgNO\nzcz55ZUoSZIkSVJjio78kpl3AoeXWIskSZIkSSOi6JrffhGxfUTsGRGTyihIkiRJkqSyFQ6/EfH+\niHiIavOrm4Hn145fFBEfLKk+SZIkSZIaVij8RsT7gG8CPwPeCsSA078D3tZ4aZIkSZIklaPoyO/H\ngTMy833AT1Y792dqo8CSJEmSJI0GRcPvNsAvhzi3DJhW8LmSJEmSJJWuaPh9DNhhiHO7APcXfK4k\nSZIkSaUrGn4vAU6KiF0HHMuI2Br4BHBhw5VJkiRJklSSouH3BGAhcAtwR+3Y+cBdwAPAF+p9YETM\nioizI+KWiOiMiBzmfdtERA7yWlxvDZIkSZKk8am5yE2Z+XRE7Ae8HXgN8CDwJHA68P3M7C7w2O2B\nw4DfUw3VL6vz/pOAKwd8LlKDJEmSJGkcKhR+AWoB97u1Vxmuz8zNASLiM9Qffu/NzN+VVIskSZIk\naRwpOu25dJnZu75rkCRJkiSNT6Mm/JbgzIjojoj2iPhhrfnWWkXEjIiY3fcCZo1wnZIkSZKkdazw\ntOdRpBP4/4FfUV13/ELg08DNEfGizHx8LfcfDZw4siVKkiRJktanMR9+M/Nh4IMDDl0fEdcBfwA+\nTLUR1pp8HThvwOdZwPVl1ihJkiRJWr/GfPgdTGbeERF3AS8ZxrXtQHvf54gYydIkSZIkSevBeFrz\nu7oAhrVXsCRJkiRpfBv2yG9E/JXhh8nMzJ2KldS4iPhn4AXAReurBkmSJEnS6FHPtOebGOGR1Ig4\nrPZ2l9U+35eZt0bEHOBa4IjMPLd2zVeAHmAe1enL/wwcB/wD+M+RrFeSJEmSNDYMO/xm5twRrKPP\n6iO1fZ/PA4b6/ncBHwLeD2wEPAL8FPhsZi4agRolSZIkSWPMqGp4lZlr7DaVmb+hupZ34LHvAN8Z\nwbIkSZIkSWNcPWt+3w78MjMX1d6vUWZe0FBlkiRJkiSVpJ6R3+8DewG31N6vSQKGX0mSJEnSqFBP\n+J0NPDzgvSRJkiRJY0I9Da/+Pth7SZIkSZJGu4YaXkXELsC+wAxgEXB9Zt5dRmGSJEmSJJWlUPiN\niBbgu8BbWLX7ckbEBVT34e0uoT5JkiRJkhrWVPC+zwGHAMcDzwNaal+PoxqITyqjOEmSJEmSylB0\n2vM7gJMz87QBx/4BnB4RzcCRwGcaLU6SJEmSpDIUHfl9DtUtjwZzS+28JEmSJEmjQtHwez+w/xDn\n9q+dlyRJkiRpVCg67fnbwBcjog24EHgE2Bx4G/Bh4NhyypMkSZIkqXGFwm9mnh4RGwNHA0cNOLUS\nOC0zv1pGcZIkSZIklaHwPr+Z+amIOA3Yi2f2+f1dZraXVZwkSZIkSWUoHH4BMnMR8POSapEkSZIk\naUQUbXglSZIkSdKYYfiVJEmSJI17hl9JkiRJ0rg37PAbEVMjIkayGEmSJEmSRkI9I7/twEsAIuKa\niHj+yJQkSZIkSVK56gm/XUBL7f0cYGrp1UiSJEmSNALq2eroHuD4iLio9vl1axr9zczvNlSZJEmS\nJEklqSf8fgo4H3gtkMBn13BtAoZfSZIkSdKoMOzwm5lXAptExHOAh4E3ALePVGGSJEmSJJWlnpFf\nADLz0Yg4AvhdZj45AjVJkiRJklSqusMvQGaeBxARM4A9gRnAImBeZi4urzxJkiRJkhpXKPwCRMQX\ngWOAVqBv/98VEXFGZp5QRnGSJEmSJJWhnq2O+kXEJ6g2wDoX2A/YAdi39vm4iPhoSfVJkiRJktSw\noiO/RwJnZOYnBxy7F7gpIpYBHwTObLQ4SZIkSZLKUGjkF9gKuHKIc1fWzkuSJEmSNCoUDb+PArsN\ncW732nlJkiRJkkaFotOeLwBOiohO4EKqYfc5wNuAE4GvllOeJEmSJEmNKxp+PwvMphpyzxhwPIAf\nUA3AkiRJkiSNCkX3+e0C3hoRp1Dt9ty3z+91mXlXifVJkiRJktSwwvv8AmTmncCdJdUiSZIkSdKI\nKNrwSpIkSZKkMcPwK0mSJEka9wy/kiRJkqRxz/ArSZIkSRr3DL+SJEmSpHGvUPiNiL0j4sABn2dG\nxPcj4vaIOC0iKuWVKEmSJElSY4qO/J4GvHjA5zOAA4F7gA8Dn26wLkmSJEmSSlM0/L4AuA0gIiYA\nhwEfy8y3AscD7yinPEmSJEmSGlc0/E4Cnq693xOYCFxa+3w7MKvBuiRJkiRJKk3R8Pt3YO/a+0OB\nOzJzUe3zxjwTjCVJkiRJWu+aC973beC0iDgMeBFw1IBzLwXubrQwSZIkSZLKUij8ZuZXI+IxqlOe\nz8rM8wacngZ8p4ziJEmSJEkqQ9GRXzLz+8D3Bzl+ZEMVSZIkSZJUssLhFyAiDgBeRnWd76mZ+UBE\n7AUszMxHyyhQkiRJkqRGFQq/ETEduIJq06ulwEZU1wE/QHX97xPA0SXVKEmSJElSQ4p2ez4DmM0z\no74x4Nyvgf0brEuSJEmSpNIUnfZ8EPCxzLw5Iiqrnfs7sFVjZUmSJEmSVJ6iI7+TgIfXcC7rfWBE\nzIqIsyPilojojIhhPyMiNo6I8yKiPSKWRsRPI2KbemuQJEmSJI1PRcPvfOCNQ5x7DfDHAs/cHjgM\neBS4Zbg3RUQT1fXH+wLvB95OdeT5moiYXKAOSZIkSdI4U3Ta81eACyKiB/hh7dh2EfFq4EiqIbZe\n12fm5gAR8Rmq64mH4yBgL2CPzPx97f7/A+4F3gd8rUAtkiRJkqRxpFD4zcwLI2IT4FSq3Z0BLgSe\nBj6amVcUeGZvkVqANwB/6wu+tWfdHxHX184ZfiVJkiRpA1d4n9/M/M+IOBd4KbAZ8CRwc2YuLam2\n4dqF6jTs1c0H3ry2myNiBjB9wKFZJdUlSZIkSRolCodfgMxcBlxVUi1FzQDuHuT4ImDmMO4/Gjix\n1IokSZIkSaNK4fAbEQHsRrW5VNvq5zPzggbqqqsUBu8uHYMcG8zXgfMGfJ4FXN9oUZIkSZKk0aNQ\n+I2InYGfUO3QPFjITGBdhd9FVEd/Vzejdm6NMrMdaO/7XM30kiRJkqTxpOjI7zeBCtWuzvOBztIq\nqt98Bu8MvTODT4eWJEmSJG1giobfXYF/zcxLyyymoCuAIyJi98y8FSAingfsB3xqvVYmSZIkSRoV\nmgre9zBQdGuiIUXEYRFxGNUOzv2fI2L32uc5EZERMXfAbZcCvwcurF17YO3YP4Bvl12jJEmSJGns\nKTry+wXgkxHx68zsKLGei4b4fB4wd7AbMrM3Il4HnAn8D9Wf6VrgsMx8usTaJEmSJEljVNHw+zLg\necDCiLiBAQ2jajIzj6z3oZm5xm5TmfkbBmmwlZlPAP9a7/eTJEmSJG0Yiobf/al2dO4Adh/k/GBb\nD0mSJEmStF4UCr+ZObvsQiRJkiRJGilFG15JkiRJkjRmFJ32TEQEsBuwFdC2+vnMvKCBuiRJkiRJ\nKk2h8BsROwM/AbZnkAZUVNf8Gn4lSZIkSaNC0ZHfbwIV4DBgPtBZWkWSJEmSJJWsaPjdFfjXzLy0\nzGIkSZIkSRoJRRtePQz0llmIJEmSJEkjpWj4/QLwyYiYWGYxkiRJkiSNhKLTnl8GPA9YGBE3AO2r\nnc/MPLKhyiRJkiRJKknR8Ls/1Y7OHcDug5zPwhVJkiRJklSyQuE3M2eXXYgkSZIkSSOl6JpfSZIk\nSZLGjKLTnomILYCPAvsBM4BFwHXA1zLzkXLKkyRJkiSpcYVGfiPi+cCfgKOBpcAtwNPAMcDtEbFj\naRVKkiRJktSgoiO/pwNPArtm5gN9ByNiFvCr2vmDGy9PkiRJkqTGFV3z+3LgswODL0BmPgicDMxp\nsC5JkiRJkkpTNPw2U93maDAraGAtsSRJkiRJZSsafn8PfCIiJgw8WPv8caprgCVJkiRJGhWKjtAe\nD1wN3BcRlwGPAJsDbwSmAq8opzxJkiRJkhpXKPxm5s0RsTfwGeBNPLPV0fXAKZn5p/JKlCRJkiSp\nMYXX5mbmH4FDS6xFkiRJkqQRUXSf32tqe/0Odm7HiLimsbIkSZIkSSpP0YZXc6iu7R3MVKpbIUmS\nJEmSNCozw7u8AAAgAElEQVQUDb8AOcTxfwKeauC5kiRJkiSVathrfiPiKOCo2scELo6IztUuawO2\nBC4ppzxJkiRJkhpXT8Or+4Gbau+3B+4AHl/tmk7gbuB/Gy9NkiRJkqRyDDv8ZuZPgZ8CRATA5zJz\n4QjVJUmSJElSaYru83tE2YVIkiRJkjRSGml4JUmSJEnSmGD4lSRJkiSNe4ZfSZIkSdK4Z/iVJEmS\npP/X3p2HW1KVhxp/v6aZx0aQhG6gG1AMeEXFAZQOCGJErgIRBxSEKIQ4oBcVExUEYxISiDEOOCBi\ny6jxGhkDGgRClEmiCBdRhqaRZoYeaGbBdf9Ya3dXV+999j59hr279vt7nnrO2VW1qlbVqumrVbVK\njWfwK0mSJElqPINfSZIkSVLjGfxKkiRJkhrP4FeSJEmS1HgGv5IkSZKkxjP4lSRJkiQ1nsGvJEmS\nJKnxDH4lSZIkSY1n8CtJkiRJajyDX0mSJElS4xn8SpIkSZIaz+BXkiRJktR4Br+SJEmSpMYz+JUk\nSZIkNZ7BryRJkiSp8Qx+JUmSJEmNZ/ArSZIkSWo8g19JkiRJUuMNVPAbEVtFxHkRsSQiFkbE6RHx\nvB7SHRoRqU137mTkW5IkSZI02Kb2OwMtEbEecDmwCDgQWBs4EbgwIl6bUvpDD5N5C/BQ5feCcc+o\nJEmSJGmVMzDBL3A4MB2YnVK6ByAi5gNXkYPaXmpxf5lSmj9xWZQkSZIkrYoG6bHnNwNXtgJfgJTS\n1cDcMkySJEmSpJUySMHvDsDNbfrfDGzf4zSuj4jnIuLuiDgpItbuliAipkXErFYHzBhFniVJkiRJ\nq4BBeux5Gvl937oFwHZd0t4HfBa4Fvg9sAfwUWBH4A1d0n4EOG5UOZUkSZIkrVIGKfgFSG36RddE\nKf0I+FGl16URcTfw1YjYPaV0xQjJvwh8p/J7BnBlD3mVJEmSJK0iBumx54Xk2t+6aaxcq83fLX9f\nOdJIKaWFKaU7Wx1gg1mSJEmS1DCDFPx2erd3e+CWlZheq8a4XW2yJEmSJGmIDFLweyGwW0Rs3uoR\nEa8GtgEuWInpHVj+XjsOeZMkSZIkrcIGKfj9JnAvcH5E7BMRBwDnANcB57VGiojjIyJFxMxKv0si\n4uMR8aaI+LOIOBH4F+DClNJ/T+pSSJIkSZIGzsA0eJVSWhIRe5AboPoe8Cy5NviolNIfuiT/DXA4\nMB1Ynfxt4M8BJ05cjiVJkiRJq4pIyVdiq8q3fufOnTuXWbNm9Ts7K3jkqWd56Mnn2GjN1fqdFUmS\nJEkNs+SZ51h39Slsvu7q/c5KJ12/BtTJID32LEmSJEnShDD4lSRJkiQ1nsGvJEmSJKnxDH4lSZIk\nSY1n8CtJkiRJajyDX0mSJElS4xn8SpIkSZIaz+BXkiRJktR4Br+SJEmSpMYz+JUkSZIkNZ7BryRJ\nkiSp8Qx+JUmSJEmNZ/ArSZIkSWo8g19JkiRJUuMZ/EqSJEmSGs/gV5IkSZLUeAa/kiRJkqTGM/iV\nJEmSJDWewa8kSZIkqfEMfiVJkiRJjWfwK0mSJElqPINfSZIkSVLjGfxKkiRJkhrP4FeSJEmS1HgG\nv5IkSZKkxjP4lSRJkiQ1nsGvJEmSJKnxDH4lSZIkSY1n8CtJkiRJajyDX0mSJElS4xn8SpIkSZIa\nz+BXkiRJktR4Br+SJEmSpMYz+JUkSZIkNZ7BryRJkiSp8Qx+JUmSJEmNZ/ArSZIkSWo8g19JkiRJ\nUuMZ/EqSJEmSGs/gV5IkSZLUeAa/kiRJkqTGM/iVJEmSJDWewa8kSZIkqfEMfiVJkiRJjWfwK0mS\nJElqPINfSZIkSVLjGfxKkiRJkhrP4FeSJEmS1HgGv5IkSZKkxjP4lSRJkiQ1nsGvJEmSJKnxBir4\njYitIuK8iFgSEQsj4vSIeF6PaXeMiMsi4omIeCAivhQR60x0niVJkiRJg29ggt+IWA+4HNgCOBD4\nS2A2cGFEjJjPiJgBXAY8B+wH/DXwbuDbE5lnSZIkSdKqYWq/M1BxODAdmJ1SugcgIuYDVwFvAc4d\nIe3RwLPAfimlx0vap4GzI+KElNINE5pzSZIkSdJAG5iaX+DNwJWtwBcgpXQ1MLcM65b2/FbgW/wA\neLKHtJIkSZKkhhukmt8dgHPa9L8Z2L5ToohYG5hZxlsqpfRMRNw+UtqSfhqwUaXXVgB33313T5me\nbAuffpaHn3yOewfptoUkSZKkRnguwfqrT+HpdVbvd1ba2nrrrWcB81NKvx9t2kEKfqcBi9r0XwBs\n1yVdjJB24y7z/QhwXL3nbrvt1iWZJEmSJGmSzQW2Bu4cbcJBCn4BUpt+0SVNa/jKpAX4IvCdyu+1\nyDXJt5PfIx4kM4ArgT8F5vc5L7I8Bo3lMTgsi8FieQwOy2KwWB6Dw7IYLKtCeaxUvgYp+F1IrsWt\nm0auwR0pXRoh7c1t+i+VUlpYplF1y0hp+iViaSw/P6U06jsdGl+Wx2CxPAaHZTFYLI/BYVkMFstj\ncFgWg6XJ5TFIb452erd3e0YIRlNKTwDz6mkjYg1g25HSSpIkSZKGwyAFvxcCu0XE5q0eEfFqYBvg\ngh7SviUi1qn02x9Yu4e0kiRJkqSGG6Tg95vAvcD5EbFPRBxAbv35OuC81kgRcXxEpIiYWUl7ErAm\ncG5E7BURhwAnAz9IKf1yshZgEiwCPkv7xr00+SyPwWJ5DA7LYrBYHoPDshgslsfgsCwGS2PLI1Jq\n105Uf0TELHIDVHuQG5u6EDgqpfRQZZzjya0zz0opzav0fynwr8CrgceA7wJ/U/v2ryRJkiRpCA1U\n8CtJkiRJ0kQYpMeeJUmSJEmaEAa/kiRJkqTGM/iVJEmSJDWewa8kSZIkqfEMfidJRBwQET+MiN9F\nxBMRcXNEHB0Rq9fG2yoizouIJRGxMCJOj4jntZnejhFxWZnWAxHxpdp3jimfhOrUvXOil3lQ9aMs\nyniHRsQvIuKxiLg/Ii4orZQPtT6Wx5ERcWtEPB0Rc8s8YyKXddCNZ1lExIsj4pSIuCEino2IeSPM\nt6eyHTb9KI+ImBERX4mI68q+YauY9K0seprnMOpTecyOiEsj4r6yb8yPiO9GxHYTuKgDr1/njVq6\niyNf2x4zjou2SurTvrF7tI81bpjARV1pU/udgSHycWAe8AngfuA1wN8CLwXeDRAR6wGXk7+pdSCw\nNnAicGFEvDal9Icy3gzgMuAXwH7A5sDngc2Ad1TmuUubfBwLvB748bgu3apl0ssiIg4jf8v6K2W+\nGwKfAi6LiB1TSndP6BIPtn6UxzHk79edWMbfDTgBWI/8KbVhNW5lAewE/Bnw8/J7o3YzHMX0htGk\nlwewLXBAGe86YNdxXaJVVz/Kous8h1g/ymMacBPwDeBBYAbwSeCaiNghpXTvOC7fqqQfZbFURLy9\nzEtZP8vjCODGyu/B/NxsSsluEjpg0zb9Pg0kYHr5fRTwdOt36bdLGWe/Sr8vAg8A61b6HVjGe+kI\neVgDeAT4Yb/Xx7CVBXAlcGVtnluV8Y7o9zoZpvIA1gKWAF+rzfMrwFPAJv1eJw0piymV/08F5nWY\nZ0/TG8auT+VRHe8YIPV7PQxC16ey6DrPYe36UR4d8rFdmd4H+71OhrEsgA2Ae8lBXQKO6ff66HfX\np2PV7iXtrv1e/l46H3ueJCmlh9r0/kX5u3n5+2ZygHRPJd3VwNwyjMp456eUqndUfgA8WRuvbh9g\nY+D00eW+WfpUFmsAi2vzbP0e6v2wD+WxA7mG95LaPC8B1gTeuBKL0QjjWRap9xrbXst26PSjPEZR\nbkOlT2XRyzyHUp+OVe08XP4+O4ZprNL6XBZ/D9yaUjprlOkaa4D2jYE11BfdA2A2+YB5e/m9A3Bz\nm/FuBrYHiIi1gZn18VJKz5TpbD/C/A4GFgAXjSXTDTXRZfE14I0RcXBEbBgRWwJfBe4B/m38FqMx\nJrI8Wgfzp2vTav3eYQz5bqJRl8Uojff0mm6iy0O960dZ1OepZSalPCJitYhYPSK2Bk4G5gPfX9np\nNdSEl0VEvAI4HPjgyqQfMpN1rPpBRDwXub2Vb0bExmOY1oQx+O2TiNge+D/AqSmlhaX3NPLz93UL\nyDW2rXGih/Hq85tGrvn9XgkGVExGWaSUvkN+F+LUMv5dwKuA16WUHhmHxWiMSSiP28kB8Ktr47R+\nD+TBuh/GUBajMd7Ta6xJKg/1oB9l0WGeYtLL47+AZ4A7yO9E7plSWjCG6TXKZJRFRKwGfB34Skqp\nXRCnYpL2jcXk9lXeB+wJfAF4O3BFRKy5EtObUDZ41QcRsQlwLvnxgk/UBrdrWTPa/N9tvLp3kB+9\nPaPHbA6FySqLiNgX+DLwr+THazckN0rw44jYtfroyTCbjPJIKS2JiDnARyPiRnKjD7PJJwdYVjM8\n1MZYFqM13tNrnEkuD42gH2XRZZ5DrQ/l8T5ywz+zgI8Bl5bz+O/GON1V3iSWxYeAPwKOX8n0Q2Gy\nyiOl9Evgl5VeV0TEr4D/ILe7MmdlpjtRrPmdZBGxPnAxudGdN6aUllQGLyTfjambRr4b0xon9TBe\n3cHA7eWZfjF5ZRERAZwCnJVS+uuU0uUppXOBvcmNNRw9DouzypvkfeNj5EbI/r2kOwf4TBl230ou\nQmOMQ1mMxnhPr3EmuTw0gn6URZd5DrV+lEdK6bcppWtTSt8lfz1jXbwhMWllUQK6z5FbMJ4aERtF\nRKsV4rXK76GPb/p93kgpXUyuEX7leExvPA39xjGZStX/eeS7hW9IKc2vjdLpWfvtgVsAUkpPkJsw\nX268iFiD/ImKW9rMd2tyU+fW+haTXBabAc8Hrq+OVw5EtwIvGsOiNMJk7xsppUUppTeT7xy/hOXL\n52djXJxV2niUxSiN9/QapQ/loQ76URY9zHNoDcK+kVJaDNxGPscMrUkuixnA+uRPTi2sdJBbNV4I\nbDnKaTbKIOwbrazQvoa5rwx+J0l5P+G75Dsgb0op/abNaBcCu0XE5pV0rwa2AS6ojfeWiFin0m9/\n8ne6quO1HFz+nrnyS9AcfSiLheTGlF5Vy8cG5M8kDPUjz/3cN1JKD6SUbiqB84fJNyMuH+MirbLG\nuSx6Nd7Ta4w+lYfa6EdZ9DjPoTQo+0aphfwT8vu/Q6kPZXE78Lo2HcBp5f/7RznNxhigfWMf8tON\n147H9MZTpDRwAXkjRcTXyQ0eHQtcWht8R0rpofKIwo3kb/Eex7KPTj8E7NJqcjwitijj/Rw4idx0\n+eeBK1JKB7SZ923A/Sml2ROxbKuafpRFRJwMvJ/cCMAl5APCx4BXkL+Ldt3ELO3g61N5HEgug1uB\nTck3iPYAXj/MrwaMc1msA7yppP0rcg37B8rvX6eUfl3G62l6w6gf5VHGbe0rbwXeCbyt/J6XUlru\nCZZh0ad9o+s8x28JVy19Ko8zyYHXDeTGgrYlfy91OvDKlNJtE7CoA69fx6k2+UjAsSmlvxuXBVtF\n9WnfOIP8BMQNwBPAzuRXAW4r0xushnbTAHxseBg68uOYqUN3aGW8WcD5wGPkg+uZtP9g9UuBK8jf\nL32I3JjSum3Ga320+i/7vQ4GpetHWQCrkxtUurFM7wFyELxzv9dHv7s+lceB5Md+niTXzJ8PvKTf\n66Lf3XiWBfmzU52mdXxt3J7Kdti6PpZHp/Hm9HudDFNZ9DrPYez6VB4fAq4jnzOeJN88/SYws9/r\nY9jKokM+EnBMv9dHv7s+7RufBG4CHiW3hD4X+Gdg/X6vj3adNb+SJEmSpMbznV9JkiRJUuMZ/EqS\nJEmSGs/gV5IkSZLUeAa/kiRJkqTGM/iVJEmSJDWewa8kSZIkqfEMfiVJkiRJjWfwK0mSJElqPINf\nSZIkSVLjGfxKkiRJkhrP4FeSJEmS1HgGv5IkSZKkxjP4lSRJkiQ1nsGvJEmSJKnxDH4lSZIkSY1n\n8CtJkiRJajyDX0mSJElS4xn8SpIkSZIaz+BXkiRJktR4Br+SJEmSpMYz+JUkSZIkNZ7BryRJkiSp\n8Qx+NdQiYuuI+H5E3B8Rj0XE3RHxw4hYo995G4uISBHxRFmmVvdwH/NzfERcOso0u0fEsz2MNy8i\nnoqIJRGxOCJ+ExHfiIgXjHJ+8yLioFGmuSIijhlNmskSEYdGxO1dxkkRsWut37gtU0SsUcpkq0q/\nb0TEZ8Zj+uosIg6MiMv6nY92etk226SZWbbXGW2GrbCd1YbfHhGHjnU+PaTtez7a7dODJiJ2Lvk8\nZyXSrnBeGEuZjVXl/NM6z94WEUfWxnksInaZ7LyNJCJWi4hjS36XRMQjEfGziHjdGKf77oj41Xjl\nU5oIBr8adhcD9wHbAesDuwA/AmIiZhYRq0/EdDt4Q0ppvUq3ycpOaJLzvTIOSymtD2wE7Esuvxsi\nYuf+Zmvo7QncnlK6CyAiAngL8MO+5qoHq8A2381+TNB6HsB1s9x2Zj4G3hHAI8CfR8Sm/c5MO6Pc\nxg9LKa1HvoZ4L/BPEbFXa2A5/1493nmEMe2LnwDeBexbzp0zgc8BT44lLymls1JKO67sNMYqsqn9\nmr9WESklO7uh7IDnAQnYvst47wd+CywGrgFmV4YdD1xaG/9S4Pjy/+7As8C7gTuAp0r/9YB/BuYC\nS4CbgV3LsKnAp4BbgUXAz4CdRrlsqTW9XoYDu+bDwdLfVwBfAP4deBQ4tvR/K/Crsi5+BexfSTOT\nfONgURl+A/mmwjuAZ8p6eKx0LwRmAP8BPFhft8Dm5JNwqqR5X4dlmQcc1Kb/pcDPKr//oZTBY8Cd\nwGeAKMMuAP4APFWGX1b6v7Msx2LgfuA7wMa19fSvwIUl3c3A3pXhOwKXAw8DC4GfAC+uDH8Z8NMy\n/YXAtcC08dgOgEOB24EPA3eX6X8TmFqG/6qs3ydK3ucAXwGeA54u/e4o484BzgTOKNvDHcChPeTh\nFOCYyu/XkIOD1u8Xl23mYeB3wAnA6rVt6vvkG1StdfC8DvNah7xP3QksAC4Btq2V1TG1NM8Cu1f2\n5Z8AJ5ayvrT0fwlwWVl/c4FjgNUq+UvA+0o5LQbOA55fmceHgV+T9/P5wBeBtUZYZyOWeymLU2tp\nbq+WB7BmycsWlWPdt8p28BDwb8BmnfYh8r6ZgJm18j+NHLR8q/TfjbzNLgZ+AxxRmca0UnaPlG3m\nFmA2+QbjU+T9rbVvv76U3w9KWS8BbmT548vikqfHS5rPttvOgNXJx64Hy7SOrq6flZ0PIxw/Bikf\nJc1HyMetJeTjT3U/6HZMe2cpqyXk/fL0yrARt6Mej0sblWV6T2u91IbPocP2TYfzAsv2w4PJx+Al\n5OP/9FreTy/zbLfc84Bjyfv6Y8DBPS7PPGrnH+DnwEdrZVI933Y8j5bhs8nnhQWlrD/GsnPV7rS/\npphHPm78hGXnotkj5PsC4MQRhrfW6UjHthXWWSmn6jH+CuDzwP8t5TIX+PPK8Cj5nk8+VvxLWYbj\nK+NsWdLfV7pTgPVr6/fDZb0/OdJy29mllAx+7Ya7A24CrisH+Bez4sXMgeWAvAv5ovR95BP3VmX4\n8XQPfhNwDvmkv07p/z3gv4Gty8H/BZQLFPLFzbVl2Gplng9TTtTkQHURsOUIyzUewe9ick3GFPKF\nWuuidZ+yLvYpv19d0pxNDq7WLPl+CeXCqMN62pJcO7UusAY5qFjAsuBvd+DZHspwHu2D38PJgVxr\nnR8MTC/r+5VlnR4y0nSAvYH/VZZnBnA18O3aeloC7FXWyXvKOplZhr+krMO1ynJ+HbiNZcHTVeSL\n19XIF8s7A+v2sh30sF4OBX4P/H0pk21K+veMtJ3QPkicU6Z1UFnON5TlfE1lnEXAuyq/p5AvMneo\n9DsJOKn8/3zyvnVEKf/pwPXAcWX4OuQLpZPJ+85U8ja4foflPZt8E2KzMr3Pkm9arTHCctWD32fJ\nF5prlPlvCDxQhq0J/EnJ09ElzcyyDq8E/qiMfz7w48o8DgC2JW93Lyrlf9wI5dZt/59D9+B3H+Dn\n5f8gH2tOLflbhxzAXNZp26d98PsMOTBarUxjFvlC87BSNjuT99+3VZbjIvKNviDf8JpV2TZvry3D\nesAhJY9Ty/9PAy+oresZtXTLbWfki/FbyzpfG/gaeds9dIzz6Xb8GJR8JPLNlup8f82yY07HY1op\n198De5Tf67LshmTX7ajH49KR5BtJawH/RN4fqsH7HEbYvmlzXqisqwuBTcg1sFcCp1XGuYS8PW5M\nvjFzEXBRbR+4G9ipLOs65HPUIkY+l86j7DtlG9iNHAju0u44S/fz6A7kc8q+pYxeRL7JcUhl+dtd\nU8wr62mHku4kYO4I+f54WbbPlGmu12GdjnRsa7fODmXF4Pdh4LVl/RxJvhm2Xhn+HvIx9mXkc+An\nyNtg6xpqrbJcf0venqeRb5p/u7Z+byRv81MY4eainV1KBr92Q94Bm5Jrm35Jvrh7mHzR3LrL+mPg\nH2pprgY+Wf4/nt6C3y0rw59f+u3QJj9RTnx/Wut/Ez3eiS7jpzKdRZXuP2vDuwW/p9WmeQpwdq3f\nOcA3yv9zyHeT/6RNflZYTx3yvQjYq7LuxhL87l2Wc3qHdF8Ezuo2nVqa/YF7auvpzNo4VwGf6pB+\nWsnTCyrpT6UEGeO5HZAvQh6lXPRWyuvLnbaDSp7aBb8/rfU7GzhlhPnvCtxa63cbJWAmX3xdVhv+\nVpbVNr+dfJd/ag/Lugkr7mdTyDdwZo+wXPXgd25t+LvINRJTKv2OAH5b/p9Z5vv6yvAXln6bd8jr\nUVSeSBhtudNb8HtqaxsEXkGu3V+zMrz11EurZnge3YPfell9Criq1u8E4EeV9XkN+cJ4Sm28Q6kF\nvx3Wx6+Aw2vruh4MLredlW3ssMrvdcnH9kPHMp82aerHj0HJR+ow39d0SL/0mEYOXp4APkDtJlsv\n21EvXdmWT67tK3tWho+4fTNy8PvK2n56U/l/8zL8hZXh25V+f1zZBz7T63JUpjOPfEN8Ectqpb/E\nirXxreC323n0K6x47v0Y8JPK8i93rKvk4+g2y9fpSZkgB54Xk29GPEO+ebBVbZ12PLa1W2e0D35P\nrvxeq0xjp/L7UuBva/m6i2XXUAdQzgmVcXYi3yhq3dBJVG7q2tl163znV0MtpfRQSumTKaWXke+i\nfpx8UffeMsoW5EeLqu4o/Xv1B/Ld0ZaZ5e+tbcbdhFwjcEFELGp15Fqg6aOYJ+THbzeqdHt1T7Kc\nebXf3dbF0eQ71BdGxH0R8eWIWK/TxCNi44g4rTQY8mhZzg3INwfGwwzyul9Y5vf+iLghIhaWeR3R\nbV4R8brSANQDEfEo+bHfepo7a7/nlnm3GmL5fmlI7dHKuK1p/AU5SPtZRNwZEZ8r7yuN13bwYErp\nucrvx8m1Iiuj43J2sNw7pxHxYvIyXVN6zQJeW1u+08g1t5D3k7kppa6NnpVpAdxYmdYCck3CaBrB\nmVf7vQVwZ0rpD5V+7fb/6rqZW/62toEDIuKaiHg4IhaTa+I7bXdjLveImAK8mWXrfha51vqByvTu\nINc2jeY4Nq/2u9vx4CTy44unAw9FxHciYjM6iIg1I+ILpVGo1vFgB7ofD+rvNs+gUh4ppcfJjx6P\naT49HD8GJR90mG9re+x4TEspPQG8CXgjMDci/ici3lUmNebtKCJeS37C6ltlfreSH+89opf0Pbiv\n8n/1WNfK39zK8Dtqw2DFbbxXR5Rz7NrkAHEv8pMP7XTbb2YBB9b2/+PIta8t9WuKlvryQ4fjfcpO\nTyntnVKaRq6R3oL8ekNVx2NbMa/d9DvlK6X0FPmJrFa+ppOD3aX5YvllmwVsWVsfPyEHvNV10ks+\nJMAGr6SlUkpPpJTmkB+faTXYcDfLLqxbtmbZwfkx8p31qs1XnHRKld/zyt92rRE/TD5p7VULXNdN\nKf1jzwvT3eMsn+96niGfYKtGXBflRsKHU0rbkN9Zeh35EaZ20wL4R2Ar8uNQG5JrRR9lWWNj7dKM\nxtuBa1NKT0TEa8jvEh0JbJpS2gj4RmVeK8wvcovfF5Dfe946pbQB+ZHDemNoM2u/Z5FrCyHf5X8a\neHlJ31p/AZBSujOl9N6U0nRyDczh5Lvxk7UdpDb9Oq33mbXf1eVspx4M7A+cVwkk7yLXZlSXb8OU\nG46BvJ/MiojVRlqAyrQg1+xUp7dOSqnVouxy+2pEPJ/8eGBVu21+q9JQV0t1/2+ZWfm/VcbzI7c+\n+z3ye8TTU0obAp+mc4N6vZR7fTlWY9kNA8j708KU0i3l913kGruNa9NcO6V0VbtpMj7Hg8dTSp9O\nKe1ADni2IAfE7aYFuXZrL3LgtWHZR2+m+/Ggvp3dQ6U8ImJdlg8ORz2fHo8fg5IPOsx3fi/HtJTS\nFSmlt5BrdU8AzoyIbehtO+qmFeT+R+QvLNwPvBzYr3JjpNv2vTLnhdb+OrPSb+vasJWd9nJSSreR\nH6n+3yPkZaRrirvIj/RW1/EGZT+qzCa1O3aPJd//Q35ipN5Y1czK/0uPbZV+Y11n95CvA4CljSJW\nb0jcBdxWWx8bpZTWSindM4750BAx+NXQiohpEXFCRLw4IlaPiKkR8Vbyhdp/l9HmAEdExKvK8EOB\nl5IfU4L8juLLI2KnMvyDtA9ql0opPUhuvOGrpWYwImLbiNi2nNC+BHw+Il5Y8rleRPxZRLS7IF1Z\n1wOHRP40x0yWBakjmQO8teRltYjYG/hz4Nsln+9oLQ+5tvVp8mOlkN+F2yoi1qxMb0PyxdQC8qNQ\nf0eu9aKSZrVy4dWziHhBRHyN/Bjixyvzeo5cA/JcRPwpucGQqvvJj4q1rFHytSCl9HhEbA38TZtZ\n7hcRe5byP4j8eOB3K/NdAiyMiA1ZdvHfyushEfHH5ecC8rtOz/ayHUT+5Ecq5bey6svc6vfCNuPu\nHPnzOVMj4vXkR5RPbzfRiHgJ+f2sayu99ydfdLecAbwiIg6PiLUjYkrkT4+9sQy/iPwo3hciYsOy\nzXevBkUAAAWnSURBVO0cESvUZJR96hzg6yXgJCI2ioj9Y9nTB9cD+0bEpmUa/9RppVRcRN4GPlX2\nle2Av6bUXFUcGxGbRcQG5ED3Jymle8m1G1PINfBPR8SOwAc7zazH/f96YM+ImFX2p78veWypr+fr\nyQ0cnRwRm5RpbhoR76yNc2CZ16bkVz+6OQfYKSLeU7aJV5GDm2+Vebw5Il5Ugpcl5H29ejzYrOwT\nLRuSaxEfBqZGxPvJNaEtD5EvcJdurx22szOAoyNim4hYm1we1eBw1POhy/FjUPJRcVRtvneUvI14\nTCvb8FsjYoPyxMgjZdBz9LAdRf6k3bw2+SEipgFvIzfG9dJKtx352PcXZdRu2/eozwtlX/wxeb/a\nqOTl88DFKaX7Rk49OuV4/CbyI+ztzGGE8yj5He13RsR+leuS7SNit3HO50cjYu/WPhj504CHsOza\np6XTsW28nAH8ZUTsGLnl6o+y/M23i4DVI3+Waf18uRTTI2L/ccyDhozBr4bZM+Q74v9OPvk+RG50\n6ciU0vcBUkpnky8EzyRfCHwAeFNKaV4ZfgX5JHoJ+dGe6eQGIrp5L/lC4r/IF4bnsewRns+QaxDO\njfxY2q3AX1H214iYHfm7gVt2mcePY/nv/D4WubYL4EPkxiEWkFtkPa1bhsvd/UPILeouJJ8ID0op\ntR5jfRl52VstTf6ijEuZx++A+yM/uvQCcoMwG5Mv/n5LbvRi6R3l8kjcV4FrS5rWxVE7p0b+VuGj\n5JPlVOBllRqJH5EvLq4uy3wkcFZtGn/HssfN/jOl9Bj5Yv4fIuKxMn49DeSL/aPI73x9mtySZevx\nsI+QHydbTL6o+89a2j2AX5TpX0V+j7Y1jxG3A3JjLLeT75yvrE8Dx5dlbgV0XyDf0FkUEb+tjPs9\n8kXdAnLDZh9IKf20NbBsX60L8f2Bc1u1E5G/ezqL3PIsAOWic3fyI7p3krepH1JqZMrjmnuQawFu\nI+9/J5EfZSYiLo6Ir1fydxi5YZ/LI6LVeu7bWFa7/QVyi8R3kC9MLyFf1HeUUlpMbtzr9eTt80fk\ngP9faqOeSb5ovJu87R1U0t9CPqb8oJThSdRuGETE1yPi4kqvbuV+FrnhmV+QH0O8l8pjg9RqIEtN\n+77kQOr6sm6uIa/7lmPKuriPfEw6e6T1UqZ7J3l7+BC5bM4gv//3b2WUbcjvEC4m1+I/ybJA6zLy\nupxbtrM9ycfRh8nHgN+RH638WWV+T5KPGWeVNMdR286KE8q0rynrZ15t/azMfLodPwYlHy3fJJ/X\nHiTfzN03pfRcD8e0KeSbM3eV7eRkckNL83rcjrYkv+PZziHkJ3tOSSndX+nmA18mB0BBl+17lOeF\nqoPI59rflG4R+SmbjiJiy3Jcm91l2qeW8R4nl+HV5P1iBd3Ooymlm8iNYB1J3h8fJJf5eH8S6lHy\n9jW3bAuXUm6K18Zre2wbR6eTt7OLyTc2ZpC3q6dhufPAduRyW1zy2rfPKWnVF2l8n5yQJE2CiDgd\nuKB1o2aC5zWHXCN9WI/j/5Lc+Mql5fdHyI3RjPeFU1+VWp47yQ3+jPQI+KSIXLN8IbkxnMaf3Ovb\n2bDno98i4lZy41Xt3kcdSuXJh2fJrTlf1+/89Kpfx7bIbRb8DvhEqXyQxp0fgpakVVBKacRai36J\n/F7hD1m+Bug+8veQNbHWBI4aksC33XY2tPkYBCmldq9LDLuXk5+qqDcYqCIi3kF++m0K8EnyO98X\nj5hIGgNrfiVJIxptze+wGLSaX0mDIyI+Sg7mTkopndjv/IzGZB7bIuKn5G9PB/D/gI+llK6eyHlq\nuBn8SpIkSZIazwavJEmSJEmNZ/ArSZIkSWo8g19JkiRJUuMZ/EqSJEmSGs/gV5IkSZLUeAa/kiRJ\nkqTGM/iVJEmSJDWewa8kSZIkqfEMfiVJkiRJjff/AfEwa1RzA3DrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0d722c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# style, color palette, figure size\n",
    "my_dpi=113\n",
    "plt.figure(figsize=(10, 6), dpi=my_dpi)\n",
    "\n",
    "#make plot\n",
    "plt.ylim(0, 4)\n",
    "plt.fill_between(\"TIME\", \"pecpc\", data=eu_pec_pc, color=\"skyblue\", alpha=0.2)\n",
    "plt.plot(\"TIME\", \"pecpc\", data=eu_pec_pc, marker='o', markerfacecolor='blue', markersize=12, color='skyblue', \n",
    "         linewidth=4) \n",
    "sns.despine()\n",
    "\n",
    "#add values to the coordinates\n",
    "for index in eu_pec_pc.index:\n",
    "    time = eu_pec_pc[\"TIME\"].loc[index]\n",
    "    pecpc = round(eu_pec_pc[\"pecpc\"].loc[index], 2)\n",
    "    plt.text(time, pecpc + 0.1, str(pecpc), ha='center', va='bottom')\n",
    "\n",
    "# Add titles, labels, text\n",
    "plt.title(\"Primary Energy Consumption per Capita - European Union (28 countries) 2007 - 2015\\n\", loc='center', \n",
    "\tfontsize=12, fontweight=0, color='black')\n",
    "plt.xlabel(\"\\nSource: Eurostat Database, http://ec.europa.eu/eurostat/data/database, Author: Bjoern Springer\", \n",
    "           fontsize=\"small\")\n",
    "plt.ylabel(\"tonnes of oil equivalent (TOE) per person\")\n",
    "\n",
    "# save as .png with less whitespace around plot\n",
    "plt.savefig(\"primary_energy_02.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>GEO</th>\n",
       "      <th>INDIC_NRG</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4500</th>\n",
       "      <td>2015</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>292.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4560</th>\n",
       "      <td>2015</td>\n",
       "      <td>France</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>239.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4776</th>\n",
       "      <td>2015</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4584</th>\n",
       "      <td>2015</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>149.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4860</th>\n",
       "      <td>2015</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>124.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4548</th>\n",
       "      <td>2015</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>117.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>2015</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4908</th>\n",
       "      <td>2015</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4668</th>\n",
       "      <td>2015</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4452</th>\n",
       "      <td>2015</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>45.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4764</th>\n",
       "      <td>2015</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476</th>\n",
       "      <td>2015</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>39.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4752</th>\n",
       "      <td>2015</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4680</th>\n",
       "      <td>2015</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4716</th>\n",
       "      <td>2015</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4800</th>\n",
       "      <td>2015</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4536</th>\n",
       "      <td>2015</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4644</th>\n",
       "      <td>2015</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4704</th>\n",
       "      <td>2015</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4464</th>\n",
       "      <td>2015</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4488</th>\n",
       "      <td>2015</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4740</th>\n",
       "      <td>2015</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848</th>\n",
       "      <td>2015</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4524</th>\n",
       "      <td>2015</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4572</th>\n",
       "      <td>2015</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4728</th>\n",
       "      <td>2015</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4512</th>\n",
       "      <td>2015</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4620</th>\n",
       "      <td>2015</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4788</th>\n",
       "      <td>2015</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4608</th>\n",
       "      <td>2015</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4632</th>\n",
       "      <td>2015</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4824</th>\n",
       "      <td>2015</td>\n",
       "      <td>Rep. of Macedonia</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4884</th>\n",
       "      <td>2015</td>\n",
       "      <td>Kosovo</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4596</th>\n",
       "      <td>2015</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4836</th>\n",
       "      <td>2015</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4812</th>\n",
       "      <td>2015</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4656</th>\n",
       "      <td>2015</td>\n",
       "      <td>Malta</td>\n",
       "      <td>Primary energy consumption</td>\n",
       "      <td>Million tonnes of oil equivalent (TOE)</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TIME                GEO                   INDIC_NRG  \\\n",
       "4500  2015            Germany  Primary energy consumption   \n",
       "4560  2015             France  Primary energy consumption   \n",
       "4776  2015     United Kingdom  Primary energy consumption   \n",
       "4584  2015              Italy  Primary energy consumption   \n",
       "4860  2015             Turkey  Primary energy consumption   \n",
       "4548  2015              Spain  Primary energy consumption   \n",
       "4692  2015             Poland  Primary energy consumption   \n",
       "4908  2015            Ukraine  Primary energy consumption   \n",
       "4668  2015        Netherlands  Primary energy consumption   \n",
       "4452  2015            Belgium  Primary energy consumption   \n",
       "4764  2015             Sweden  Primary energy consumption   \n",
       "4476  2015     Czech Republic  Primary energy consumption   \n",
       "4752  2015            Finland  Primary energy consumption   \n",
       "4680  2015            Austria  Primary energy consumption   \n",
       "4716  2015            Romania  Primary energy consumption   \n",
       "4800  2015             Norway  Primary energy consumption   \n",
       "4536  2015             Greece  Primary energy consumption   \n",
       "4644  2015            Hungary  Primary energy consumption   \n",
       "4704  2015           Portugal  Primary energy consumption   \n",
       "4464  2015           Bulgaria  Primary energy consumption   \n",
       "4488  2015            Denmark  Primary energy consumption   \n",
       "4740  2015           Slovakia  Primary energy consumption   \n",
       "4848  2015             Serbia  Primary energy consumption   \n",
       "4524  2015            Ireland  Primary energy consumption   \n",
       "4572  2015            Croatia  Primary energy consumption   \n",
       "4728  2015           Slovenia  Primary energy consumption   \n",
       "4512  2015            Estonia  Primary energy consumption   \n",
       "4620  2015          Lithuania  Primary energy consumption   \n",
       "4788  2015            Iceland  Primary energy consumption   \n",
       "4608  2015             Latvia  Primary energy consumption   \n",
       "4632  2015         Luxembourg  Primary energy consumption   \n",
       "4824  2015  Rep. of Macedonia  Primary energy consumption   \n",
       "4884  2015             Kosovo  Primary energy consumption   \n",
       "4596  2015             Cyprus  Primary energy consumption   \n",
       "4836  2015            Albania  Primary energy consumption   \n",
       "4812  2015         Montenegro  Primary energy consumption   \n",
       "4656  2015              Malta  Primary energy consumption   \n",
       "\n",
       "                                        UNIT  Value  \n",
       "4500  Million tonnes of oil equivalent (TOE)  292.9  \n",
       "4560  Million tonnes of oil equivalent (TOE)  239.4  \n",
       "4776  Million tonnes of oil equivalent (TOE)  183.0  \n",
       "4584  Million tonnes of oil equivalent (TOE)  149.6  \n",
       "4860  Million tonnes of oil equivalent (TOE)  124.9  \n",
       "4548  Million tonnes of oil equivalent (TOE)  117.1  \n",
       "4692  Million tonnes of oil equivalent (TOE)   90.0  \n",
       "4908  Million tonnes of oil equivalent (TOE)   85.5  \n",
       "4668  Million tonnes of oil equivalent (TOE)   64.3  \n",
       "4452  Million tonnes of oil equivalent (TOE)   45.7  \n",
       "4764  Million tonnes of oil equivalent (TOE)   43.7  \n",
       "4476  Million tonnes of oil equivalent (TOE)   39.9  \n",
       "4752  Million tonnes of oil equivalent (TOE)   32.0  \n",
       "4680  Million tonnes of oil equivalent (TOE)   31.3  \n",
       "4716  Million tonnes of oil equivalent (TOE)   31.3  \n",
       "4800  Million tonnes of oil equivalent (TOE)   27.5  \n",
       "4536  Million tonnes of oil equivalent (TOE)   23.7  \n",
       "4644  Million tonnes of oil equivalent (TOE)   22.3  \n",
       "4704  Million tonnes of oil equivalent (TOE)   21.7  \n",
       "4464  Million tonnes of oil equivalent (TOE)   17.9  \n",
       "4488  Million tonnes of oil equivalent (TOE)   16.5  \n",
       "4740  Million tonnes of oil equivalent (TOE)   15.4  \n",
       "4848  Million tonnes of oil equivalent (TOE)   14.1  \n",
       "4524  Million tonnes of oil equivalent (TOE)   14.0  \n",
       "4572  Million tonnes of oil equivalent (TOE)    8.0  \n",
       "4728  Million tonnes of oil equivalent (TOE)    6.5  \n",
       "4512  Million tonnes of oil equivalent (TOE)    6.2  \n",
       "4620  Million tonnes of oil equivalent (TOE)    5.8  \n",
       "4788  Million tonnes of oil equivalent (TOE)    5.8  \n",
       "4608  Million tonnes of oil equivalent (TOE)    4.3  \n",
       "4632  Million tonnes of oil equivalent (TOE)    4.1  \n",
       "4824  Million tonnes of oil equivalent (TOE)    2.6  \n",
       "4884  Million tonnes of oil equivalent (TOE)    2.5  \n",
       "4596  Million tonnes of oil equivalent (TOE)    2.2  \n",
       "4836  Million tonnes of oil equivalent (TOE)    2.1  \n",
       "4812  Million tonnes of oil equivalent (TOE)    1.0  \n",
       "4656  Million tonnes of oil equivalent (TOE)    0.8  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter \"energy\" dataframe by means of boolean indexing/ boolean selection: \n",
    "#single countries, Primary energy consumption > 0, MTOE, 2015\n",
    "countries_crit1 = energy.GEO != \"European Union (28 countries)\"\n",
    "countries_crit2 = energy.INDIC_NRG == \"Primary energy consumption\"\n",
    "countries_crit3 = energy.UNIT == \"Million tonnes of oil equivalent (TOE)\"\n",
    "countries_crit4 = energy.TIME == 2015\n",
    "countries_crit5 = energy.GEO != \"Euro area (19 countries)\"\n",
    "countries_crit6 = energy.Value > 0\n",
    "countries_crit = countries_crit1 & countries_crit2 & countries_crit3 & countries_crit4 & countries_crit5 & countries_crit6\n",
    "countries_energy = energy.loc[countries_crit]\n",
    "\n",
    "#replace long country names, sort by value (descending)\n",
    "countries_energy = countries_energy.replace('Germany (until 1990 former territory of the FRG)', 'Germany')\\\n",
    "    .replace('Former Yugoslav Republic of Macedonia, the', 'Rep. of Macedonia')\\\n",
    "    .replace('Kosovo (under United Nations Security Council Resolution 1244/99)', 'Kosovo')\\\n",
    "    .replace('Bosnia and Herzegovina', 'Bosnia & Herzeg.')\\\n",
    "    .sort_values(\"Value\", ascending=False)\n",
    "    \n",
    "countries_energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cvxF5G+eRgiflaXCdMk0Aliuk75fTLyzlvyeXZctS+u45/39VvJcB/LSirIfk\neReX0r9TWG5AIf2YnPbNUv5+Of2KBo5PbX+3b8M52Y/0RPKp4vsNfIoUVHgPWKWQPiRv4zVgnUJ6\nH9IP0idL59DsOu/7CqXzbTy5ZnNF3meBZ+ucC4+Wyn00c8/zUwvpXUk3vTOB1QvpAwrvx/6FdJFu\nzgP4fkX+IW0o6+3l9zav/885/bA659Xhpfxjcvo2DbyvPfKxv6GVz9KGpCd8AXy6MP/fwM2FfXql\ntHztPCiew5XHhhauU6W0y3LeI0vpZ+T0YRXrDeDMUvofc/q+jX4OSu9f5XmY5/82r/vkUvpPqf68\nT83pFwFdCumn5/QftTc/KdDwKvAC8KnSek6oOLf+RQqWdi/lXQro0cCxaeu+j8zpq7S27tK+r9ZK\nvh/lfOcW0obntC0KabvmtGPasP03qP5uGQJsXci7Oenz9QCwbCH986Tr4DzbBT6b086r2O585SQ1\nqwrgxkLap/I2r6lYxyXM/xleC+hW8V6Pz2VcrZXtiRQYfQfYpLSe/Sl9Jgv7GMD3CuldScGbAD7f\nyDFp5X26OR+HZSrm1Y7l8aROI2cDxxXmDwNmACsXzpkv19lO7ZhMqjO/Vv7HGyjztTnvHhWfj9/W\nOd++W2ddTwAv1Zn3h7zOfm05pp48eeq8qekF8OSptYm5Nx+TCl9C55CeNAXwHPPeDD6b0zeqs766\nP/zLX7rANjn9SUCleRfmeX0a2IdeOe+IOtvduc5y1+UfRcX9+3Re5vIGj9+Iwg+gqqnesdi2lN41\nl+X+QtoXct7hdbb9D2BqxXv5IqUfgXn+BGAWhZv4wrxJzH9zt2ou0zWlvKflvDs0cHxq59GnW8tb\nWOYkSjfhhXkn5nmHFNKG5LQTKvLfluf1yP+vkP//nwbKMZ72BSj2K6WvmdPfoXDjkOcdRymAw9yb\n6scrPhfrkX7gjqvIP6SRspJ+JAdwR0XePvkcubvivKr6nNZqNRxZte1S3g1z3ota+SxtSKqi/BHw\n6zxv2zzvu4V96tQABamm2HRSzZWupbw9SAGnlyvWO41C8DGnfyXP+02jn4PS+1d5Hub5U4DXK7a5\nFDCZFOAsBt6m5vd45VL+z+QyXlpKbzg/qcZBUArM5nnd8rGZUEj7Fymg17Xe/rVybNq67w0HKHJ5\nA/iogbx75rxXFdI6KkDR0vfLMRXb27NiPVdU5F/gAEVOvz1/TnqWPjtvUfg+a2U/9y+XvWp7wHa0\nfK17HHiuYh8fqsh7WJ53YCPHpJXyTwLerjNvToAi/z8OeCL/3ZX0fX1N6T1sb4Bihzz/lgbKfB7z\nBwxrn49604111nU76Xtpvs9SHaJ4AAAgAElEQVQx8Ku87Ffbckw9efLUeZM7ybTFycZ5gnRT+gLp\nC+yUmL/jow8i4nHartwZU22YwEciIurMW4N0gwCAUj8NPyI1L1mJeZtSrU61/6uT/kdSldx9SUEZ\nSNX8oe3NOzaKtnWS+VDxn4iYJWkKUBySdMv8uq6qh5BcDlhZ83dO9XBU95i9CemGqqoTr4nMff9r\nZZoi6Qbgm7Vt5Cqk+5MCV7e1sH8Lotau/Y6KeRPya9WwZg9VpL2UX1cEpkXEO5LGkIZGW5P0FGkC\n6Qds+Rxsr3rn+VORqrxWzVujYj13lcsUqWf0l6ne/0bVPb4R8Yyk5+usv+pzWjy+rVkpv7bY1CeX\n49X8Pu0n6XhSIGQa6f1aWD5NutG6O0rV+yNimqQHSFWXe8e8oxw9GRHlkYzacpwaJmkNoDfphmSe\nbUbER5LuJt0892Vu8wZIzWxeb0MZG81fu2ZtqdwvUcl00nGtuRr4JfCwpL+QgkQTo9Dcq54F2PfF\nzb8jom8D+Wqf2aqhR+8ifc91hj+RAoh7MPd7cxdSbbU/FTMqjeh0eC7LhqQb7qJ63+E1tfOrX53v\nxG7A2pKWiXn7C2ntu2FBrUQD17XsMuBiSVvlba9BBzTvyDqqGcWnooFOMgvezNteifk7hq0dl6qm\nY2bWBA5Q2OLkyojYu/VsQDt7Jo+I8rBVtR/9VcNZ1eYtVUuQ9F1SFfe3SVUqn2XuEJ4nkW4mqkyp\nkz4aeJnUn8I5ud39/nm94+os0yEqjgWkqv7Ftv+1G7pv56meTzDv6AX19rcH9Ye8q7fMRaQfnrUg\nzo6kGgEnN3hD/yrph+gapGqgjai1Ia7qaf/VUp6iescU5j2ue5JqYnyXuX01TJZ0ckT8scEytmSe\ncuTgU73yzXeeF9T7nE0hPelrr5aOby19XUldSzfmjR7femrBmWVazDXXpaQ+Tr5B6uvi6ooAT2dq\n5DjV8hXfqwU9Tm3RljIWtbWMjeavXbN+WKc8MO8wlSeSrl0Hk560Arwt6b+AkyLioxbW0959b0ik\nEQpeJwWBV4uIlkb+qLXjb2h0kE5S28+q60a963tHuJrUz8g+zA1Q7EN6av6XUt5L87yn8rwppJpS\n/XJ6ve/wmtr5tU8r+T7BvMN7d/Zn8gMaH4XqatLDn++RAhRTSX0NdYRawHutBvJ25Dlb6yS16vrc\n0jwzawJ3kmlLqo560txWx5Oq7G4eEXtHxLGRhtqar6fqono30vnm61Jg09w55Y6kG+kRHfg0fUHU\nflTtHxFqYSrXiKhX9mmkJ45VqjrSA/gbqQpqrWbJgXn9lSMpVLg7v27XYH6Yu99Vo0OsVsrTZhEx\nLSJ+SvoRtzHwE9I+XShpl0LW2ZB6o69YTUd0ptmalt6rdu8/LR/fWvr75VoDHaB247RSi7nmuo5U\nTfwC0vHuqKeMjWrkOBXzNcOiVsbadjZr4Xo1p2PRiJgVEWdFxGdInYoeSOpr5BekmhWNbKsz9/2e\n/DqglXy169vdLebqXLX9rLpuVF3fZ+fXBbq+5Zo1twBflbRa7jh2F+D2KHSGqzQqyz6kY/SZiDg4\nIo7L3+G3Nri52j7u3sp3Yrm2T2d7jdTUtFUR8S6pJtjewCBS/18tBeLa4nHSMeonqW5tlNxR7Tb5\n33vq5WuDXsD0vG9V86Bjhtw1sw7gAIVZx1qP1PnT06X0rRdgnRfl1wNp+813Z7s3v/bvoPU9DKwu\naZ2KeVtVLZBvUkcAn5e0A2k0lPER8UxV/gq1TgZ/oIqhYIsKo7DUquN+uSLbtvm1auz2NonksYg4\ni1SbAuCbhSxv5dd5ml/kHtgb+jG6gLYu93wuaT1SNeji/tduNBr9zqktO9/xzefGp+iA41vhRdIx\nbWi4zYiYQRrxZw1SzZ/bO6FMLXmC1Hndl8pBKqXhJDcl9YPRtB/eEfES6Qns5uWhHnN1+v6kJ5dP\nLaQitfuaFREvRMQI0mgAHzHvZ7Eq/8LY91pQ7JjcvG0+kjYk9TPwFnD9AmxrQdX9XDP3ZrSo8vqW\nbVqR1pI/kQIde5Fuupel1LyD1L8NwJiKG/JGv8M7+juxrK3X0ppHge51vlur1EYvWZYO/L2Rv6//\nh1T++YbYLvgeKZA1NloeUalV+TuqL3OHaC3rR6opuDg3szJbojhAYdaxXgA2kDTnCVEeRu7X7V1h\nRDxF6oNgP1J/FOMi4tkFLGeHiIiJpFEeDpG0Y3m+pGVzO9ZG/Zl0XTqptJ7vUOp/ouQicsehpCq4\nDffPkfsqOY90U31dHrZxHpLWkjSS1OlerZyzgWOLQY3cZ8SPSDcdLY03X5ek3pK+UDGr9oSxWA21\n1nfJfoXlu5J6XV8YNipuOxvK3NE8at7Irw0NF5fP7zuBbSV9o5aef2ieSrrRuKJ9RW5xu0F6erpJ\nC0MClw0ljVjz7YVdqykHSK4mdRJ6SGn2L0nVszv8OLXDFaT23T8tpR9JqiV0ZQc+oW3NVaQnpUMl\nfaY8U9JKeZjI2v87Vgw/uBKpiWwjVcI7e99Hkr4ftgAukDRPE4Tcz8a1pCZax9Z5gryw1AICxxUD\nNpI+T2oiNY8c4HkFGFi8LkvqBxzaxm2PItVu/G6ePmLeITwhfX9DaUhrSdsy/5DC9YwDHgOOlvSV\n8kxJn5D0xTaUu6xN19KCu/LrFg3mv5UUyPlmRNzXxm21ZijpM3i0pB+UZ+ZhgM8mBV9bCmI0qh/p\nWljVZxTAF0l9PDWzppmZFbgPCrOO9XtSL9f3S7qG1MHWrqRhQTdagPVexNwnZW3tHLPmCEnlzkRr\nRixA0OO7pB9lf5M0jlS7oAvppmk7UvXMnRpc10Wk/jYOlLQB6QfFuqQ+JsaQmrjMLi+UO04cB3yV\nVH20rcGB/0fq/2Iw8G9JfyP13t+N1JfC9qQAyLF5e/+SVBuD/pEcvFiKVCV2FdIIHvWOdWvWBO6T\n9CBpOL5XSVXLdye1jb+gkPcS4GfAr3MToJdyWWeQ+i7pbLcAf5S0K/A0qZp5f9L7VjxP/0naj30k\nTc9leysizmth3Yfl9Vwn6UrSzcP2pJo0t5OGhusM1wM7k57ottrPS+6orS2dtXW0n5OO+3mSvkZ6\nCrgFUPt7aPOKNsfJpH46Tpb0JdJ5/VlSDYTnyZ+rhSF3Hro36X1+SNJo0mf9E8D6pGvWecy9MRoN\nPJ87tHyB9FR59zzvbFrXqfseEbMl7QncCHwf+Lqkm0g3sn1J3z/LAkMjorM+MyvV6RAS4MOIODWX\n9f8k/Y4UnHlY0ihSR5X7AGNJn7uy35EC/PdKupZ0o7k7c2+gGxIR70m6nnSNngWMjog3SnmeyN8j\nO0q6i3RTvx7pwcANzH3fW9rO7BxQvxUYL2ksaQSNbqQaGgNIzRL3bLTspfW/IukJYGdJF5I6g/4w\nIloLSo8m9WnxVRr4foyI2aQmbB0uIl7OgecbgT9IOpTU+exHpGvX9qTg354RUdV5KMBPJNULKFyQ\ng1s1X82vN5QzStqI9HDigvI8M2seByjMOtZZpBvoQ0hPeF4mffGdQrppbK+ReT0f0s4n86SeyesZ\nT/3OKVsUEU9K2ox0o/wtUlXY6aSbtstpQ7v83LP9TqRhQr9NGsb0IVJ74UGkAMW0OotfQfohcmXF\nCAWtbpcUFLkc+EHeh11IP+ieInW++V/FIE5E/ErSv4GjSO/3bNLNx7CIuLEt2y95ltQZ3w7M7Wn+\nZdKT8tOiMBJLRLyYb0p/Q2raMg24hvRe1Pth15H+TqqtMZR0wzWNdENxXLF/iNyZ317AGaQg0HKk\nH9Z1AxQR8UiuffMr4Ouk4/Bc3tZpUT0KTEf4E+l41gJvi7SIeCkfp5NJ58supKfO55I6im36U8GI\neEvSNqSaUYNI5/ZrpKGaT2qlc8fOKM9t+Zr1c1IgZydSYPM50ntfDK79lHT+bUeqcj6VNHzysIiY\nQCsWxr5HGr3oy6QmgPuSaiMsT+rg8Trg3IjozL4nelGq9VbwHqnWU83RpO+GH5KunU+T+th5meoA\nxWmkGlM/IH2n/iv//S5tCFBkfyIFKLoyf/OOmtp1amdgM1LTiH1JN8ytBigAImJSDhj/nPR5/Aqp\n9sZk0shcC9pkYh/Sb419SIG192il1lwObNwE7CXpR514/WxIRNyXgwNHk767DibdkzxP+g4ZHhEv\ntLCKH7cw70bmjoAC6Tj9m7kjbFGaF7T/wY+ZdQItGv3smVlLJH2O1H73gogoV+X+WJA0gdS/Q8+I\nmC9IIek3pB+6W3fyj/GPPUkDSDfvJ+cO5JYoks4n3ZR8qupcM7OOlWth3QD8NCKGN7s8SyJJ25OG\n3t4jIhbmcMhNI6kvKaj1k4g4uzSvK/Akadjztga7zKwTuQ8Ks8XD0fn1wqaWYiGo6tk7V2H+Cqn/\njargRE/S08NJDk5YBxhKepp3ZLMLYmbWESJiHKlZ3vHNLstC9AtS06yqkdS+C6wDHLdQS2RmrXIT\nD7NFVL7p/iGpHfGBwM2d0FnVouiyvO/3karFfo7UtON9Sh3N5WrNA0hNS3qRjpfZAsnVob9Hapts\nZrak+BHwHUmrRsSUZhemM+VRbf4NXJE7FC4L4KCIeHThlszMWuMAhdmiqxep/e37wF+Bg5pbnIXm\nL6T2qN8hjXX/JqkPjqERUR5aciCp7fOrwAkRceXCLKgtuSKivX29mJktkvKoVUOaXY6FIXf0eWoL\n8xeFEY7MrIL7oDAzMzMzMzOzpnMfFGZmZmZmZmbWdA5QmJmZmZmZmVnTOUBhZmZmZmZmZk3nAIWZ\nmZmZmZmZNZ0DFGZmZmZmZmbWdA5QmJmZmZmZmVnTOUBhZmZmZmZmZk3nAIWZmZmZmZmZNZ0DFGZm\nZmZmZmbWdA5QmJmZmZmZmVnTOUBhZmZmZmZmZk3nAIWZmZmZmZmZNZ0DFGZmZmZmZmbWdA5QmJmZ\nmZmZmVnTOUBhZmZmZmZmZk3nAIWZmZmZmZmZNZ0DFGZmZmZmZmbWdA5QmJmZmZmZmVnTOUBhZmZm\nZmZmZk3nAIWZmZmZmZmZNZ0DFGZmZmZmZmbWdA5QmJmZmZmZmVnTOUBhZmZmZmZmZk3nAIWZmZmZ\nmZmZNZ0DFGZmZmZmZmbWdA5Q2CJL0lKS+khaqtllMTMzMzMzs87VrdkFMGvBWsDTTz/9dLPLYWZm\nZmZmZvNTR67MNSjMzMzMzMzMrOkUEc0ug1klSX2Ap5fuujRd5FiamZmZmZl9PLz/0fvNLkKjXIPC\nzMzMzMzMzJYsDlCYmZmZmZmZWdM5QGFmZmZmZma2mBg5ciS77747a6+9Nssttxwbb7wxZ555Jh99\n9NE8+caOHUv//v1ZZpllWHXVVTnkkEN4++2327WueiQNlHSPpOmSpkj6g6Se7d03BygWAknbShop\n6SVJH0p6U9Kdkn62IG+emZmZmZmZfbwMHz6cpZdemmHDhjF69Gj23XdfTjzxRAYPHjwnz4QJE9hp\np51YY401uPbaaznllFMYOXIkgwYNotgPZSPrqmfChAkANwMvAbsDxwN7AqMktatvCneS2ckk/Rw4\nDfgb8CfgOaAnsD3wfeCyiDiyeSVcdLmTTDMzMzMz+zhqqZPM1157jd69e8+T9utf/5rjjz+eyZMn\ns+aaazJw4EBefvllHnnkEbp0SfdSI0eOZK+99uKGG25g1113bXhd9QwcOJBbb731MeBzETEbQNKe\nwNXANyPixrbut+/6OpGkHUjBiWERsXNEXB4Rt0fEDRHxE6AvMGEBtyFJS3VEec3MzMzMzGzRVg4o\nAGy++eYAvPTSSwBMnDiRHXfccU5wAmCnnXYCYNSoUW1aVz0TJ04EGFMLTmQ359dBrexGJQcoOtfP\ngCnACVUzI+K1iBhZ+19SX0lXSXojt+H5u6Rti8tIGi9prKS9JT0GfAhsI2mApJC0q6TLJb0j6TVJ\nx+fldpX0iKT3JN0uqW9pvfvmdU/Ny94r6VulPIPzNrbITVbelfS8pCFSquIgaXVJH0k6rLy/ki6R\n9Ex7q/uYmZmZmZnZ/O644w66detG377pNq9Lly4svfTS8+Tp3r07knj00UfbtK56cvBjRin5QyCA\njdu0A7V1tmcha52kbsBXgLER0WoPI5LWAe4B1gd+COwBvA7cImnTUvbPAUOAU4BdgacK835Hakby\nbVLVmqGShgFDgV8B+wPrkpqbFPUBrgT2IbUbuovUdmiXiuL+D/AAKSp2NXBSXi8R8TJwHXBwaf9W\nAPYC/hh12hVJ6iWpT20C1qrKZ2ZmZmZmZsljjz3G2WefzUEHHUSvXr0A6NevX62Gwxz33nsvEcEb\nb7zRpnXV069fP4CtSslbAgJWavOO4D4oOo2k1YBXgNMj4heled0K/0ZEzJJ0MfB1YKOIeKeQbxLw\ncET8R04bD3w553uysM4BwDjgdxFxVE7rCkwmnRx9I+KFnP4D4A9An4h4tqLsXUjBq+uBmRHxrZw+\nGLgEODYizijkfxiYHBHfyP/vAIwFvhgR9+W0Q0nBk3UiorKukKQhpGDHPNwHhZmZmZmZfZy01AdF\n0dSpU9l6663p3r07d999Nz169ADg8ssv54ADDuDUU0/l4IMPZvLkyRxwwAE89thjbLDBBjz++OMN\nr6ue2jaAXwIXkh4wXwZ8BngyIjZqwy4DrkHRmSqbMUj6LPBRYXoiz/o6cCPwvqRuhSDGLaSARNET\nxeBEyS21PyJiFvAk8M9acCL7Z36dU0NB0kaS/lfSy8DMXLadgX4V2xhd+v9RYO3C/7cB/2LeWhQH\nATfVC05k5wDrFaavtJDXzMzMzMzsY2vatGnsvPPOTJ8+nZtvvnmegMJ+++3Hz3/+c4YMGULv3r35\nwhe+wLbbbsumm27K6quv3qZ11bPffvsBnEGq3f8acD9wB/Ag8HJ79skBis4zFZjOvDfukJpjfDFP\nxV5NVwV+wLzBi4+AI4CVS+t4pYXtvln6/8M6aQDLAEjqAYwBNgR+QRphpFa+ZRrYxoxivtyE4wJg\nH0nLS9oM+AIpqlZXRLwZEc/UJlLtDzMzMzMzMyuYMWMGu+22G8888wxjxoxhrbXmbR0vidNPP52p\nU6fy0EMP8eqrr3LOOefw5JNPss0227RpXfVIIiKOBVYBPg+sBvwI2IDUZUCbdWs9i7VHRMyUdAcw\nUFL3iPgwp08Has0eXi8s8jpwK3BW1epa+X9B9SfVpvhSRNxTS5S07AKs8xJSHxl7A5sDLwB/XZBC\nmpmZmZmZfdzNmjWLvffem3/84x/ceuutbLjhhnXz9ujRg0022QSACy+8kOnTp3PggQe2a131RMQ0\n4GEASQeTHl5f0uYV4QBFZzuTVDPh18BPW8n7N9KN/MO1YMZCtFx+nbPd3GnntrSzak5EvCHpauBw\nUgecZ5eGnzEzMzMzM7M2Ovzwwxk1ahRDhw5l9uzZ3HPPnGfMrL/++vTu3Zv777+fW265hc0224yZ\nM2cyduxYzj33XM466yzWW2+9Nq0LYMSIERx44IGMGzeOAQMGAHD//fezxRZbHEsaQKEbMBA4Cvhx\nRDzdnn1zgKITRcQteZjPUyRtAlxOGmHjE6SROAaS2uoAnAj8Axgn6b9IzRtWITWNmBURlUOVdpC7\ngWnA+ZJ+BfQitSN6kQVrBvR7UtWe2cBFC1hGMzMzMzOzj72bb74ZgBNOOIETTpj3NvGSSy5h8ODB\ndO/eneuvv55TTz2VmTNnsskmm3D11Vezxx57tHld9XTv3h3gW6ROMruRalHsFRHXtHffPIrHQpBH\n2DgK2JrUn8S7pI4lrwP+UBi1Yx3gZFKHmSsDU0gdjZwfEWNynvGkkTUGVmxjHLBtRNxZSB8LdIuI\nAYW0/qSgxNciYmxO+xownNQp5nOkWh/bAQMjYt2cZzCpqs6nImJyYX0jgC9HxHwD5Up6CXggIqqG\nK21RHmr0aY/iYWZmZmZmHyeNjuKxCKgcHKLdK3OAwjqLpC+Q+tsYFBHXtWN5ByjMzMzMzOxj5+Ma\noHATD+twktYA1icNOfMv5h2txMzMzMzMzGw+fixtneEHwHigB7BfRMxqbnHMzMzMzMxsUecmHrbI\nqjXxePrpp+nTp0+zi2NmZmZmZmbz6tAmHq5BYWZmZmZmZmZN5wCFmZmZmZmZmTWdAxRmZmZmZmZm\n1nTug8IWWbU+KHousxJdu3RtdnHMzMzMzOZ4/b0pzS6C2aLAfVCYmZmZmZmZ2ZLFAQozMzMzMzMz\nazoHKMzMzMzMzDrQyJEj2X333Vl77bVZbrnl2HjjjTnzzDP56KOP5uS54447GDhwIKuvvjpLL700\na621FnvvvTdPPPHEfOsbO3Ys/fv3Z5lllmHVVVflkEMO4e23325zuXbeeWckccoppyzQ/pl1Fgco\nFgOShkiKOlO3ZpfPzMzMzMzmGj58OEs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WrcrGjRszlCcmJmJmmc6uEBEpqQrDHhTJeL+4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17dccfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# style, color palette, figure size\n",
    "my_dpi=113\n",
    "plt.figure(figsize=(10, 10), dpi=my_dpi)\n",
    "\n",
    "#make plot\n",
    "ax = sns.barplot(y=countries_energy.GEO, x=countries_energy.Value, data = countries_energy, palette=\"cubehelix\")\n",
    "sns.despine()\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.xaxis.set_visible(False)\n",
    "\n",
    "#Attach a text label right of each bar displaying its length\n",
    "rects = ax.patches\n",
    "for rect in rects:\n",
    "    width = rect.get_width()\n",
    "    ax.text(width + 2, rect.get_y() + rect.get_height()/2, width, ha='left', va='center')\n",
    "\n",
    "# Add titles, labels, text\n",
    "plt.title(\"Europe's Biggest Energy Consumers 2015\\nPrimary Energy Consumption (Million Tonnes of Oil Equivalent (MTOE))\\n\", loc='left', \n",
    "\tfontsize=12, fontweight=0, color='black')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "plt.text(0, 38,\"\\nSource: Eurostat Database, http://ec.europa.eu/eurostat/data/database, Author: Bjoern Springer\", \n",
    "         fontsize=\"small\")\n",
    "\n",
    "# save as .png with less whitespace around plot\n",
    "plt.savefig(\"primary_energy_03.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME</th>\n",
       "      <th>GEO</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>Value</th>\n",
       "      <th>Flag and Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>2015</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>81197537.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>2015</td>\n",
       "      <td>Germany including former GDR</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>81197537.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>2015</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>77695904.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2015</td>\n",
       "      <td>France</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>66488186.0</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2015</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>64875165.0</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>2015</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>60795612.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>2015</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>46449565.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>2015</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>42759661.0</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>2015</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>38005614.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>2015</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>19870647.0</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>2015</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>16900726.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>2015</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>11237274.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2015</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>10858018.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>2015</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>10538275.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>2015</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>10374822.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>2015</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>9855571.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2015</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>9747355.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>2015</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>9593038.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>2015</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>9480868.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>2015</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>8576261.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2015</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>8237666.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2015</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>7202198.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>2015</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>7114393.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2015</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>5659715.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>2015</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>5471753.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>2015</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>5421349.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2015</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>5166493.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>2015</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>4628949.0</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>2015</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>4225316.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>2015</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>3729500.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>2015</td>\n",
       "      <td>Moldova</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>3555159.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>2015</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>3010598.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2015</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>2921262.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>2015</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>2892302.0</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>2015</td>\n",
       "      <td>Rep. of Macedonia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>2069172.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>2015</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>2062874.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>2015</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>1986096.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>2015</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>1314870.0</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>2015</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>847008.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>2015</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>622099.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>2015</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>562958.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>2015</td>\n",
       "      <td>Malta</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>429344.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2015</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>329100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2015</td>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>Number</td>\n",
       "      <td>37366.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TIME                           GEO    AGE    SEX    UNIT       Value  \\\n",
       "472  2015                       Germany  Total  Total  Number  81197537.0   \n",
       "473  2015  Germany including former GDR  Total  Total  Number  81197537.0   \n",
       "509  2015                        Turkey  Total  Total  Number  77695904.0   \n",
       "478  2015                        France  Total  Total  Number  66488186.0   \n",
       "497  2015                United Kingdom  Total  Total  Number  64875165.0   \n",
       "481  2015                         Italy  Total  Total  Number  60795612.0   \n",
       "477  2015                         Spain  Total  Total  Number  46449565.0   \n",
       "518  2015                       Ukraine  Total  Total  Number  42759661.0   \n",
       "490  2015                        Poland  Total  Total  Number  38005614.0   \n",
       "492  2015                       Romania  Total  Total  Number  19870647.0   \n",
       "488  2015                   Netherlands  Total  Total  Number  16900726.0   \n",
       "468  2015                       Belgium  Total  Total  Number  11237274.0   \n",
       "476  2015                        Greece  Total  Total  Number  10858018.0   \n",
       "470  2015                Czech Republic  Total  Total  Number  10538275.0   \n",
       "491  2015                      Portugal  Total  Total  Number  10374822.0   \n",
       "486  2015                       Hungary  Total  Total  Number   9855571.0   \n",
       "496  2015                        Sweden  Total  Total  Number   9747355.0   \n",
       "520  2015                    Azerbaijan  Total  Total  Number   9593038.0   \n",
       "511  2015                       Belarus  Total  Total  Number   9480868.0   \n",
       "489  2015                       Austria  Total  Total  Number   8576261.0   \n",
       "504  2015                   Switzerland  Total  Total  Number   8237666.0   \n",
       "469  2015                      Bulgaria  Total  Total  Number   7202198.0   \n",
       "508  2015                        Serbia  Total  Total  Number   7114393.0   \n",
       "471  2015                       Denmark  Total  Total  Number   5659715.0   \n",
       "495  2015                       Finland  Total  Total  Number   5471753.0   \n",
       "494  2015                      Slovakia  Total  Total  Number   5421349.0   \n",
       "503  2015                        Norway  Total  Total  Number   5166493.0   \n",
       "475  2015                       Ireland  Total  Total  Number   4628949.0   \n",
       "480  2015                       Croatia  Total  Total  Number   4225316.0   \n",
       "521  2015                       Georgia  Total  Total  Number   3729500.0   \n",
       "514  2015                       Moldova  Total  Total  Number   3555159.0   \n",
       "519  2015                       Armenia  Total  Total  Number   3010598.0   \n",
       "484  2015                     Lithuania  Total  Total  Number   2921262.0   \n",
       "507  2015                       Albania  Total  Total  Number   2892302.0   \n",
       "506  2015             Rep. of Macedonia  Total  Total  Number   2069172.0   \n",
       "493  2015                      Slovenia  Total  Total  Number   2062874.0   \n",
       "483  2015                        Latvia  Total  Total  Number   1986096.0   \n",
       "474  2015                       Estonia  Total  Total  Number   1314870.0   \n",
       "482  2015                        Cyprus  Total  Total  Number    847008.0   \n",
       "505  2015                    Montenegro  Total  Total  Number    622099.0   \n",
       "485  2015                    Luxembourg  Total  Total  Number    562958.0   \n",
       "487  2015                         Malta  Total  Total  Number    429344.0   \n",
       "501  2015                       Iceland  Total  Total  Number    329100.0   \n",
       "502  2015                 Liechtenstein  Total  Total  Number     37366.0   \n",
       "\n",
       "    Flag and Footnotes  \n",
       "472                NaN  \n",
       "473                NaN  \n",
       "509                NaN  \n",
       "478                  b  \n",
       "497                  e  \n",
       "481                NaN  \n",
       "477                NaN  \n",
       "518                  b  \n",
       "490                NaN  \n",
       "492                  e  \n",
       "488                NaN  \n",
       "468                NaN  \n",
       "476                NaN  \n",
       "470                NaN  \n",
       "491                NaN  \n",
       "486                NaN  \n",
       "496                NaN  \n",
       "520                NaN  \n",
       "511                NaN  \n",
       "489                NaN  \n",
       "504                NaN  \n",
       "469                NaN  \n",
       "508                NaN  \n",
       "471                NaN  \n",
       "495                NaN  \n",
       "494                NaN  \n",
       "503                NaN  \n",
       "475                  p  \n",
       "480                NaN  \n",
       "521                NaN  \n",
       "514                NaN  \n",
       "519                NaN  \n",
       "484                NaN  \n",
       "507                  e  \n",
       "506                NaN  \n",
       "493                NaN  \n",
       "483                NaN  \n",
       "474                  b  \n",
       "482                NaN  \n",
       "505                NaN  \n",
       "485                NaN  \n",
       "487                NaN  \n",
       "501                NaN  \n",
       "502                NaN  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter \"demo\" dataframe by means of boolean indexing/ boolean selection: single countries, 2015, Value > 0\n",
    "countries_demo_crit1 = ~ demo['GEO'].str.contains(\"Euro\", na=False)\n",
    "countries_demo_crit2 = demo.TIME == 2015\n",
    "countries_demo_crit3 = demo.Value > 0\n",
    "countries_demo_crit = countries_demo_crit1 & countries_demo_crit2 & countries_demo_crit3\n",
    "countries_demo = demo.loc[countries_demo_crit]\n",
    "\n",
    "#replace long country names (as used in countries_energy), sort by value (descending)\n",
    "countries_demo = countries_demo.replace('Germany (until 1990 former territory of the FRG)', 'Germany')\\\n",
    "    .replace('Former Yugoslav Republic of Macedonia, the', 'Rep. of Macedonia')\\\n",
    "    .replace('Kosovo (under United Nations Security Council Resolution 1244/99)', 'Kosovo')\\\n",
    "    .replace('Bosnia and Herzegovina', 'Bosnia & Herzeg.')\\\n",
    "    .sort_values(\"Value\", ascending=False)\n",
    "    \n",
    "countries_demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEO</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Germany</td>\n",
       "      <td>81197537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Germany including former GDR</td>\n",
       "      <td>81197537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>77695904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>France</td>\n",
       "      <td>66488186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64875165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>Italy</td>\n",
       "      <td>60795612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>Spain</td>\n",
       "      <td>46449565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>42759661.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>Poland</td>\n",
       "      <td>38005614.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Romania</td>\n",
       "      <td>19870647.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16900726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>11237274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>Greece</td>\n",
       "      <td>10858018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>10538275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>10374822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>9855571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>9747355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>9593038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>9480868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Austria</td>\n",
       "      <td>8576261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>8237666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>7202198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>7114393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>5659715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Finland</td>\n",
       "      <td>5471753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>5421349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Norway</td>\n",
       "      <td>5166493.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>4628949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4225316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>3729500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>3555159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>3010598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>2921262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2892302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>Rep. of Macedonia</td>\n",
       "      <td>2069172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>2062874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>1986096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>1314870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>847008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>622099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>562958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Malta</td>\n",
       "      <td>429344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>329100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>37366.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              GEO       Value\n",
       "472                       Germany  81197537.0\n",
       "473  Germany including former GDR  81197537.0\n",
       "509                        Turkey  77695904.0\n",
       "478                        France  66488186.0\n",
       "497                United Kingdom  64875165.0\n",
       "481                         Italy  60795612.0\n",
       "477                         Spain  46449565.0\n",
       "518                       Ukraine  42759661.0\n",
       "490                        Poland  38005614.0\n",
       "492                       Romania  19870647.0\n",
       "488                   Netherlands  16900726.0\n",
       "468                       Belgium  11237274.0\n",
       "476                        Greece  10858018.0\n",
       "470                Czech Republic  10538275.0\n",
       "491                      Portugal  10374822.0\n",
       "486                       Hungary   9855571.0\n",
       "496                        Sweden   9747355.0\n",
       "520                    Azerbaijan   9593038.0\n",
       "511                       Belarus   9480868.0\n",
       "489                       Austria   8576261.0\n",
       "504                   Switzerland   8237666.0\n",
       "469                      Bulgaria   7202198.0\n",
       "508                        Serbia   7114393.0\n",
       "471                       Denmark   5659715.0\n",
       "495                       Finland   5471753.0\n",
       "494                      Slovakia   5421349.0\n",
       "503                        Norway   5166493.0\n",
       "475                       Ireland   4628949.0\n",
       "480                       Croatia   4225316.0\n",
       "521                       Georgia   3729500.0\n",
       "514                       Moldova   3555159.0\n",
       "519                       Armenia   3010598.0\n",
       "484                     Lithuania   2921262.0\n",
       "507                       Albania   2892302.0\n",
       "506             Rep. of Macedonia   2069172.0\n",
       "493                      Slovenia   2062874.0\n",
       "483                        Latvia   1986096.0\n",
       "474                       Estonia   1314870.0\n",
       "482                        Cyprus    847008.0\n",
       "505                    Montenegro    622099.0\n",
       "485                    Luxembourg    562958.0\n",
       "487                         Malta    429344.0\n",
       "501                       Iceland    329100.0\n",
       "502                 Liechtenstein     37366.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from countries_demo, only show columns: \"GEO\" and \"value\"\n",
    "countries_demo_gv = countries_demo[[\"GEO\", \"Value\"]]\n",
    "countries_demo_gv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEO</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4500</th>\n",
       "      <td>Germany</td>\n",
       "      <td>292.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4560</th>\n",
       "      <td>France</td>\n",
       "      <td>239.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4776</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4584</th>\n",
       "      <td>Italy</td>\n",
       "      <td>149.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4860</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>124.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4548</th>\n",
       "      <td>Spain</td>\n",
       "      <td>117.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>Poland</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4908</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4668</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>64.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4452</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>45.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4764</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>39.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4752</th>\n",
       "      <td>Finland</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4680</th>\n",
       "      <td>Austria</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4716</th>\n",
       "      <td>Romania</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4800</th>\n",
       "      <td>Norway</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4536</th>\n",
       "      <td>Greece</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4644</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4704</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4464</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4488</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4740</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4524</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4572</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4728</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4512</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4620</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4788</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4608</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4632</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4824</th>\n",
       "      <td>Rep. of Macedonia</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4884</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4596</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4836</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4812</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4656</th>\n",
       "      <td>Malta</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    GEO  Value\n",
       "4500            Germany  292.9\n",
       "4560             France  239.4\n",
       "4776     United Kingdom  183.0\n",
       "4584              Italy  149.6\n",
       "4860             Turkey  124.9\n",
       "4548              Spain  117.1\n",
       "4692             Poland   90.0\n",
       "4908            Ukraine   85.5\n",
       "4668        Netherlands   64.3\n",
       "4452            Belgium   45.7\n",
       "4764             Sweden   43.7\n",
       "4476     Czech Republic   39.9\n",
       "4752            Finland   32.0\n",
       "4680            Austria   31.3\n",
       "4716            Romania   31.3\n",
       "4800             Norway   27.5\n",
       "4536             Greece   23.7\n",
       "4644            Hungary   22.3\n",
       "4704           Portugal   21.7\n",
       "4464           Bulgaria   17.9\n",
       "4488            Denmark   16.5\n",
       "4740           Slovakia   15.4\n",
       "4848             Serbia   14.1\n",
       "4524            Ireland   14.0\n",
       "4572            Croatia    8.0\n",
       "4728           Slovenia    6.5\n",
       "4512            Estonia    6.2\n",
       "4620          Lithuania    5.8\n",
       "4788            Iceland    5.8\n",
       "4608             Latvia    4.3\n",
       "4632         Luxembourg    4.1\n",
       "4824  Rep. of Macedonia    2.6\n",
       "4884             Kosovo    2.5\n",
       "4596             Cyprus    2.2\n",
       "4836            Albania    2.1\n",
       "4812         Montenegro    1.0\n",
       "4656              Malta    0.8"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from countries_energy, only show columns: \"GEO\" and \"value\"\n",
    "countries_energy_gv = countries_energy[[\"GEO\", \"Value\"]]\n",
    "countries_energy_gv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEO</th>\n",
       "      <th>Value_energy</th>\n",
       "      <th>Value_demo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>292.9</td>\n",
       "      <td>81197537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>239.4</td>\n",
       "      <td>66488186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64875165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Italy</td>\n",
       "      <td>149.6</td>\n",
       "      <td>60795612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>124.9</td>\n",
       "      <td>77695904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>117.1</td>\n",
       "      <td>46449565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Poland</td>\n",
       "      <td>90.0</td>\n",
       "      <td>38005614.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>85.5</td>\n",
       "      <td>42759661.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>64.3</td>\n",
       "      <td>16900726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>45.7</td>\n",
       "      <td>11237274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>43.7</td>\n",
       "      <td>9747355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>39.9</td>\n",
       "      <td>10538275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Finland</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5471753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Austria</td>\n",
       "      <td>31.3</td>\n",
       "      <td>8576261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Romania</td>\n",
       "      <td>31.3</td>\n",
       "      <td>19870647.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Norway</td>\n",
       "      <td>27.5</td>\n",
       "      <td>5166493.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Greece</td>\n",
       "      <td>23.7</td>\n",
       "      <td>10858018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>22.3</td>\n",
       "      <td>9855571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>21.7</td>\n",
       "      <td>10374822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>17.9</td>\n",
       "      <td>7202198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5659715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>15.4</td>\n",
       "      <td>5421349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>14.1</td>\n",
       "      <td>7114393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4628949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4225316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2062874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1314870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2921262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>5.8</td>\n",
       "      <td>329100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1986096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>4.1</td>\n",
       "      <td>562958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Rep. of Macedonia</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2069172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>2.2</td>\n",
       "      <td>847008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2892302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>1.0</td>\n",
       "      <td>622099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Malta</td>\n",
       "      <td>0.8</td>\n",
       "      <td>429344.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GEO  Value_energy  Value_demo\n",
       "0             Germany         292.9  81197537.0\n",
       "1              France         239.4  66488186.0\n",
       "2      United Kingdom         183.0  64875165.0\n",
       "3               Italy         149.6  60795612.0\n",
       "4              Turkey         124.9  77695904.0\n",
       "5               Spain         117.1  46449565.0\n",
       "6              Poland          90.0  38005614.0\n",
       "7             Ukraine          85.5  42759661.0\n",
       "8         Netherlands          64.3  16900726.0\n",
       "9             Belgium          45.7  11237274.0\n",
       "10             Sweden          43.7   9747355.0\n",
       "11     Czech Republic          39.9  10538275.0\n",
       "12            Finland          32.0   5471753.0\n",
       "13            Austria          31.3   8576261.0\n",
       "14            Romania          31.3  19870647.0\n",
       "15             Norway          27.5   5166493.0\n",
       "16             Greece          23.7  10858018.0\n",
       "17            Hungary          22.3   9855571.0\n",
       "18           Portugal          21.7  10374822.0\n",
       "19           Bulgaria          17.9   7202198.0\n",
       "20            Denmark          16.5   5659715.0\n",
       "21           Slovakia          15.4   5421349.0\n",
       "22             Serbia          14.1   7114393.0\n",
       "23            Ireland          14.0   4628949.0\n",
       "24            Croatia           8.0   4225316.0\n",
       "25           Slovenia           6.5   2062874.0\n",
       "26            Estonia           6.2   1314870.0\n",
       "27          Lithuania           5.8   2921262.0\n",
       "28            Iceland           5.8    329100.0\n",
       "29             Latvia           4.3   1986096.0\n",
       "30         Luxembourg           4.1    562958.0\n",
       "31  Rep. of Macedonia           2.6   2069172.0\n",
       "32             Cyprus           2.2    847008.0\n",
       "33            Albania           2.1   2892302.0\n",
       "34         Montenegro           1.0    622099.0\n",
       "35              Malta           0.8    429344.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge \"countries_energy_gv\" and \"countries_demo_gv\" along the \"GEO\" column, only shared \"GEO\" values (\"inner\")\n",
    "countries_pec_pc = pd.merge(countries_energy_gv, countries_demo_gv, on='GEO', how='inner', suffixes=('_energy', '_demo'))\n",
    "countries_pec_pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEO</th>\n",
       "      <th>Value_energy</th>\n",
       "      <th>Value_demo</th>\n",
       "      <th>pecpc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>5.8</td>\n",
       "      <td>329100.0</td>\n",
       "      <td>17.623823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>4.1</td>\n",
       "      <td>562958.0</td>\n",
       "      <td>7.282959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Finland</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5471753.0</td>\n",
       "      <td>5.848217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Norway</td>\n",
       "      <td>27.5</td>\n",
       "      <td>5166493.0</td>\n",
       "      <td>5.322760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1314870.0</td>\n",
       "      <td>4.715295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>43.7</td>\n",
       "      <td>9747355.0</td>\n",
       "      <td>4.483268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>45.7</td>\n",
       "      <td>11237274.0</td>\n",
       "      <td>4.066823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>64.3</td>\n",
       "      <td>16900726.0</td>\n",
       "      <td>3.804570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>39.9</td>\n",
       "      <td>10538275.0</td>\n",
       "      <td>3.786198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Austria</td>\n",
       "      <td>31.3</td>\n",
       "      <td>8576261.0</td>\n",
       "      <td>3.649609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>292.9</td>\n",
       "      <td>81197537.0</td>\n",
       "      <td>3.607252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>239.4</td>\n",
       "      <td>66488186.0</td>\n",
       "      <td>3.600640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2062874.0</td>\n",
       "      <td>3.150944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4628949.0</td>\n",
       "      <td>3.024445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5659715.0</td>\n",
       "      <td>2.915341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>15.4</td>\n",
       "      <td>5421349.0</td>\n",
       "      <td>2.840621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64875165.0</td>\n",
       "      <td>2.820802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>2.2</td>\n",
       "      <td>847008.0</td>\n",
       "      <td>2.597378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>117.1</td>\n",
       "      <td>46449565.0</td>\n",
       "      <td>2.521014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>17.9</td>\n",
       "      <td>7202198.0</td>\n",
       "      <td>2.485352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Italy</td>\n",
       "      <td>149.6</td>\n",
       "      <td>60795612.0</td>\n",
       "      <td>2.460704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Poland</td>\n",
       "      <td>90.0</td>\n",
       "      <td>38005614.0</td>\n",
       "      <td>2.368071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>22.3</td>\n",
       "      <td>9855571.0</td>\n",
       "      <td>2.262680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Greece</td>\n",
       "      <td>23.7</td>\n",
       "      <td>10858018.0</td>\n",
       "      <td>2.182719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1986096.0</td>\n",
       "      <td>2.165051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>21.7</td>\n",
       "      <td>10374822.0</td>\n",
       "      <td>2.091602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>85.5</td>\n",
       "      <td>42759661.0</td>\n",
       "      <td>1.999548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2921262.0</td>\n",
       "      <td>1.985443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>14.1</td>\n",
       "      <td>7114393.0</td>\n",
       "      <td>1.981898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4225316.0</td>\n",
       "      <td>1.893350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Malta</td>\n",
       "      <td>0.8</td>\n",
       "      <td>429344.0</td>\n",
       "      <td>1.863308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>124.9</td>\n",
       "      <td>77695904.0</td>\n",
       "      <td>1.607549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>1.0</td>\n",
       "      <td>622099.0</td>\n",
       "      <td>1.607461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Romania</td>\n",
       "      <td>31.3</td>\n",
       "      <td>19870647.0</td>\n",
       "      <td>1.575188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Rep. of Macedonia</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2069172.0</td>\n",
       "      <td>1.256541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2892302.0</td>\n",
       "      <td>0.726065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GEO  Value_energy  Value_demo      pecpc\n",
       "28            Iceland           5.8    329100.0  17.623823\n",
       "30         Luxembourg           4.1    562958.0   7.282959\n",
       "12            Finland          32.0   5471753.0   5.848217\n",
       "15             Norway          27.5   5166493.0   5.322760\n",
       "26            Estonia           6.2   1314870.0   4.715295\n",
       "10             Sweden          43.7   9747355.0   4.483268\n",
       "9             Belgium          45.7  11237274.0   4.066823\n",
       "8         Netherlands          64.3  16900726.0   3.804570\n",
       "11     Czech Republic          39.9  10538275.0   3.786198\n",
       "13            Austria          31.3   8576261.0   3.649609\n",
       "0             Germany         292.9  81197537.0   3.607252\n",
       "1              France         239.4  66488186.0   3.600640\n",
       "25           Slovenia           6.5   2062874.0   3.150944\n",
       "23            Ireland          14.0   4628949.0   3.024445\n",
       "20            Denmark          16.5   5659715.0   2.915341\n",
       "21           Slovakia          15.4   5421349.0   2.840621\n",
       "2      United Kingdom         183.0  64875165.0   2.820802\n",
       "32             Cyprus           2.2    847008.0   2.597378\n",
       "5               Spain         117.1  46449565.0   2.521014\n",
       "19           Bulgaria          17.9   7202198.0   2.485352\n",
       "3               Italy         149.6  60795612.0   2.460704\n",
       "6              Poland          90.0  38005614.0   2.368071\n",
       "17            Hungary          22.3   9855571.0   2.262680\n",
       "16             Greece          23.7  10858018.0   2.182719\n",
       "29             Latvia           4.3   1986096.0   2.165051\n",
       "18           Portugal          21.7  10374822.0   2.091602\n",
       "7             Ukraine          85.5  42759661.0   1.999548\n",
       "27          Lithuania           5.8   2921262.0   1.985443\n",
       "22             Serbia          14.1   7114393.0   1.981898\n",
       "24            Croatia           8.0   4225316.0   1.893350\n",
       "35              Malta           0.8    429344.0   1.863308\n",
       "4              Turkey         124.9  77695904.0   1.607549\n",
       "34         Montenegro           1.0    622099.0   1.607461\n",
       "14            Romania          31.3  19870647.0   1.575188\n",
       "31  Rep. of Macedonia           2.6   2069172.0   1.256541\n",
       "33            Albania           2.1   2892302.0   0.726065"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add column \"pecpc\" showing primary energy consumption per capita\n",
    "countries_pec_pc[\"pecpc\"] = countries_pec_pc[\"Value_energy\"] / countries_pec_pc[\"Value_demo\"] * 1000000\n",
    "countries_pec_pc = countries_pec_pc.sort_values(\"pecpc\", ascending=False)\n",
    "countries_pec_pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k/xpIVn/i2vZII/vQjgOJ69fFKaUXqiKklKYws2Bl4GxuD+BOckFeE8ssR1wP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Av7nJnFRV7A6+H108t4xmuB3KjXm2XEi9RYnTp1omfPnqSmpjJv3jy2bdtGRkZGifFHHHEEACkp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\nPpB0efYHeKpTf5c0PVMG65TYzlb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YeHfuqcL86UKhF/HxhSnEOvUA6c5nasq+G/Fu27vE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\nMzMzKzsOWJiZmZmZmZlZ2fn/RF6TDx/2p1gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a180496a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# style, color palette, figure size\n",
    "my_dpi=113\n",
    "plt.figure(figsize=(10, 10), dpi=my_dpi)\n",
    "\n",
    "#make plot\n",
    "ax = sns.barplot(y=countries_pec_pc.GEO, x=countries_pec_pc.pecpc, data = countries_pec_pc, palette=\"cubehelix\")\n",
    "sns.despine()\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.xaxis.set_visible(False)\n",
    "\n",
    "#Attach a text label right of each bar displaying its length\n",
    "rects = ax.patches\n",
    "for rect in rects:\n",
    "    width = rect.get_width()\n",
    "    ax.text(width + .2, rect.get_y() + rect.get_height()/2, round(width, 2), ha='left', va='center')\n",
    "\n",
    "# Add titles, labels, text\n",
    "plt.title(\"Europe's Biggest Energy Consumers 2015\\nPrimary Energy Consumption per Capita (Tonnes of Oil Equivalent (TOE) per Person)\\n\", loc='left', \n",
    "\tfontsize=12, fontweight=0, color='black')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "plt.text(0, 37,\"\\nSource: Eurostat Database, http://ec.europa.eu/eurostat/data/database, Author: Bjoern Springer\", \n",
    "         fontsize=\"small\")\n",
    "\n",
    "# save as .png with less whitespace around plot\n",
    "plt.savefig(\"primary_energy_04.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}