Data_and_epmirics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lesson: Pandas for Panel Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Display 6 columns for viewing purposes\n",
    "pd.set_option('display.max_columns', 6)\n",
    "\n",
    "# Reduce decimal points to 2\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "\n",
    "realwage = pd.read_csv('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/pandas_panel/realwage.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Time</th>\n",
       "      <th>Country</th>\n",
       "      <th>Series</th>\n",
       "      <th>Pay period</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
       "      <td>Annual</td>\n",
       "      <td>17,132.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
       "      <td>Annual</td>\n",
       "      <td>18,100.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
       "      <td>Annual</td>\n",
       "      <td>17,747.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
       "      <td>Annual</td>\n",
       "      <td>18,580.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>In 2015 constant prices at 2015 USD PPPs</td>\n",
       "      <td>Annual</td>\n",
       "      <td>18,755.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Time  Country                                    Series  \\\n",
       "0           0  2006-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs   \n",
       "1           1  2007-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs   \n",
       "2           2  2008-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs   \n",
       "3           3  2009-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs   \n",
       "4           4  2010-01-01  Ireland  In 2015 constant prices at 2015 USD PPPs   \n",
       "\n",
       "  Pay period     value  \n",
       "0     Annual 17,132.44  \n",
       "1     Annual 18,100.92  \n",
       "2     Annual 17,747.41  \n",
       "3     Annual 18,580.14  \n",
       "4     Annual 18,755.83  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.head()  # Show first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Australia</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</th>\n",
       "      <th colspan=\"2\" halign=\"left\">In 2015 constant prices at 2015 USD PPPs</th>\n",
       "      <th>In 2015 constant prices at 2015 USD exchange rates</th>\n",
       "      <th>...</th>\n",
       "      <th>In 2015 constant prices at 2015 USD PPPs</th>\n",
       "      <th colspan=\"2\" halign=\"left\">In 2015 constant prices at 2015 USD exchange rates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pay period</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Hourly</th>\n",
       "      <th>Annual</th>\n",
       "      <th>...</th>\n",
       "      <th>Hourly</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Hourly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>20,410.65</td>\n",
       "      <td>10.33</td>\n",
       "      <td>23,826.64</td>\n",
       "      <td>...</td>\n",
       "      <td>6.05</td>\n",
       "      <td>12,594.40</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>21,087.57</td>\n",
       "      <td>10.67</td>\n",
       "      <td>24,616.84</td>\n",
       "      <td>...</td>\n",
       "      <td>6.24</td>\n",
       "      <td>12,974.40</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>20,718.24</td>\n",
       "      <td>10.48</td>\n",
       "      <td>24,185.70</td>\n",
       "      <td>...</td>\n",
       "      <td>6.78</td>\n",
       "      <td>14,097.56</td>\n",
       "      <td>6.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>20,984.77</td>\n",
       "      <td>10.62</td>\n",
       "      <td>24,496.84</td>\n",
       "      <td>...</td>\n",
       "      <td>7.58</td>\n",
       "      <td>15,756.42</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>20,879.33</td>\n",
       "      <td>10.57</td>\n",
       "      <td>24,373.76</td>\n",
       "      <td>...</td>\n",
       "      <td>7.88</td>\n",
       "      <td>16,391.31</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country                                   Australia         \\\n",
       "Series     In 2015 constant prices at 2015 USD PPPs          \n",
       "Pay period                                   Annual Hourly   \n",
       "Time                                                         \n",
       "2006-01-01                                20,410.65  10.33   \n",
       "2007-01-01                                21,087.57  10.67   \n",
       "2008-01-01                                20,718.24  10.48   \n",
       "2009-01-01                                20,984.77  10.62   \n",
       "2010-01-01                                20,879.33  10.57   \n",
       "\n",
       "Country                                                        ...  \\\n",
       "Series     In 2015 constant prices at 2015 USD exchange rates  ...   \n",
       "Pay period                                             Annual  ...   \n",
       "Time                                                           ...   \n",
       "2006-01-01                                          23,826.64  ...   \n",
       "2007-01-01                                          24,616.84  ...   \n",
       "2008-01-01                                          24,185.70  ...   \n",
       "2009-01-01                                          24,496.84  ...   \n",
       "2010-01-01                                          24,373.76  ...   \n",
       "\n",
       "Country                               United States  \\\n",
       "Series     In 2015 constant prices at 2015 USD PPPs   \n",
       "Pay period                                   Hourly   \n",
       "Time                                                  \n",
       "2006-01-01                                     6.05   \n",
       "2007-01-01                                     6.24   \n",
       "2008-01-01                                     6.78   \n",
       "2009-01-01                                     7.58   \n",
       "2010-01-01                                     7.88   \n",
       "\n",
       "Country                                                               \n",
       "Series     In 2015 constant prices at 2015 USD exchange rates         \n",
       "Pay period                                             Annual Hourly  \n",
       "Time                                                                  \n",
       "2006-01-01                                          12,594.40   6.05  \n",
       "2007-01-01                                          12,974.40   6.24  \n",
       "2008-01-01                                          14,097.56   6.78  \n",
       "2009-01-01                                          15,756.42   7.58  \n",
       "2010-01-01                                          16,391.31   7.88  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage = realwage.pivot_table(values='value',\n",
    "                                index='Time',\n",
    "                                columns=['Country', 'Series', 'Pay period'])\n",
    "realwage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.index = pd.to_datetime(realwage.index)\n",
    "type(realwage.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.multi.MultiIndex"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(realwage.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList(['Country', 'Series', 'Pay period'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.columns.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>Series</th>\n",
       "      <th colspan=\"2\" halign=\"left\">In 2015 constant prices at 2015 USD PPPs</th>\n",
       "      <th colspan=\"2\" halign=\"left\">In 2015 constant prices at 2015 USD exchange rates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pay period</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Hourly</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Hourly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>12,594.40</td>\n",
       "      <td>6.05</td>\n",
       "      <td>12,594.40</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>12,974.40</td>\n",
       "      <td>6.24</td>\n",
       "      <td>12,974.40</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>14,097.56</td>\n",
       "      <td>6.78</td>\n",
       "      <td>14,097.56</td>\n",
       "      <td>6.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>15,756.42</td>\n",
       "      <td>7.58</td>\n",
       "      <td>15,756.42</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>16,391.31</td>\n",
       "      <td>7.88</td>\n",
       "      <td>16,391.31</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Series     In 2015 constant prices at 2015 USD PPPs         \\\n",
       "Pay period                                   Annual Hourly   \n",
       "Time                                                         \n",
       "2006-01-01                                12,594.40   6.05   \n",
       "2007-01-01                                12,974.40   6.24   \n",
       "2008-01-01                                14,097.56   6.78   \n",
       "2009-01-01                                15,756.42   7.58   \n",
       "2010-01-01                                16,391.31   7.88   \n",
       "\n",
       "Series     In 2015 constant prices at 2015 USD exchange rates         \n",
       "Pay period                                             Annual Hourly  \n",
       "Time                                                                  \n",
       "2006-01-01                                          12,594.40   6.05  \n",
       "2007-01-01                                          12,974.40   6.24  \n",
       "2008-01-01                                          14,097.56   6.78  \n",
       "2009-01-01                                          15,756.42   7.58  \n",
       "2010-01-01                                          16,391.31   7.88  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage['United States'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Australia</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>...</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th colspan=\"2\" halign=\"left\">United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Series</th>\n",
       "      <th>In 2015 constant prices at 2015 USD PPPs</th>\n",
       "      <th>In 2015 constant prices at 2015 USD exchange rates</th>\n",
       "      <th>In 2015 constant prices at 2015 USD PPPs</th>\n",
       "      <th>...</th>\n",
       "      <th>In 2015 constant prices at 2015 USD exchange rates</th>\n",
       "      <th>In 2015 constant prices at 2015 USD PPPs</th>\n",
       "      <th>In 2015 constant prices at 2015 USD exchange rates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th>Pay period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2006-01-01</th>\n",
       "      <th>Annual</th>\n",
       "      <td>20,410.65</td>\n",
       "      <td>23,826.64</td>\n",
       "      <td>21,042.28</td>\n",
       "      <td>...</td>\n",
       "      <td>20,376.32</td>\n",
       "      <td>12,594.40</td>\n",
       "      <td>12,594.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hourly</th>\n",
       "      <td>10.33</td>\n",
       "      <td>12.06</td>\n",
       "      <td>10.09</td>\n",
       "      <td>...</td>\n",
       "      <td>9.81</td>\n",
       "      <td>6.05</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2007-01-01</th>\n",
       "      <th>Annual</th>\n",
       "      <td>21,087.57</td>\n",
       "      <td>24,616.84</td>\n",
       "      <td>21,310.05</td>\n",
       "      <td>...</td>\n",
       "      <td>20,954.13</td>\n",
       "      <td>12,974.40</td>\n",
       "      <td>12,974.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hourly</th>\n",
       "      <td>10.67</td>\n",
       "      <td>12.46</td>\n",
       "      <td>10.22</td>\n",
       "      <td>...</td>\n",
       "      <td>10.07</td>\n",
       "      <td>6.24</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <th>Annual</th>\n",
       "      <td>20,718.24</td>\n",
       "      <td>24,185.70</td>\n",
       "      <td>21,416.96</td>\n",
       "      <td>...</td>\n",
       "      <td>20,902.87</td>\n",
       "      <td>14,097.56</td>\n",
       "      <td>14,097.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country                                              Australia  \\\n",
       "Series                In 2015 constant prices at 2015 USD PPPs   \n",
       "Time       Pay period                                            \n",
       "2006-01-01 Annual                                    20,410.65   \n",
       "           Hourly                                        10.33   \n",
       "2007-01-01 Annual                                    21,087.57   \n",
       "           Hourly                                        10.67   \n",
       "2008-01-01 Annual                                    20,718.24   \n",
       "\n",
       "Country                                                                   \\\n",
       "Series                In 2015 constant prices at 2015 USD exchange rates   \n",
       "Time       Pay period                                                      \n",
       "2006-01-01 Annual                                              23,826.64   \n",
       "           Hourly                                                  12.06   \n",
       "2007-01-01 Annual                                              24,616.84   \n",
       "           Hourly                                                  12.46   \n",
       "2008-01-01 Annual                                              24,185.70   \n",
       "\n",
       "Country                                                Belgium  ...  \\\n",
       "Series                In 2015 constant prices at 2015 USD PPPs  ...   \n",
       "Time       Pay period                                           ...   \n",
       "2006-01-01 Annual                                    21,042.28  ...   \n",
       "           Hourly                                        10.09  ...   \n",
       "2007-01-01 Annual                                    21,310.05  ...   \n",
       "           Hourly                                        10.22  ...   \n",
       "2008-01-01 Annual                                    21,416.96  ...   \n",
       "\n",
       "Country                                                   United Kingdom  \\\n",
       "Series                In 2015 constant prices at 2015 USD exchange rates   \n",
       "Time       Pay period                                                      \n",
       "2006-01-01 Annual                                              20,376.32   \n",
       "           Hourly                                                   9.81   \n",
       "2007-01-01 Annual                                              20,954.13   \n",
       "           Hourly                                                  10.07   \n",
       "2008-01-01 Annual                                              20,902.87   \n",
       "\n",
       "Country                                          United States  \\\n",
       "Series                In 2015 constant prices at 2015 USD PPPs   \n",
       "Time       Pay period                                            \n",
       "2006-01-01 Annual                                    12,594.40   \n",
       "           Hourly                                         6.05   \n",
       "2007-01-01 Annual                                    12,974.40   \n",
       "           Hourly                                         6.24   \n",
       "2008-01-01 Annual                                    14,097.56   \n",
       "\n",
       "Country                                                                   \n",
       "Series                In 2015 constant prices at 2015 USD exchange rates  \n",
       "Time       Pay period                                                     \n",
       "2006-01-01 Annual                                              12,594.40  \n",
       "           Hourly                                                   6.05  \n",
       "2007-01-01 Annual                                              12,974.40  \n",
       "           Hourly                                                   6.24  \n",
       "2008-01-01 Annual                                              14,097.56  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.stack().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Series</th>\n",
       "      <th colspan=\"2\" halign=\"left\">In 2015 constant prices at 2015 USD PPPs</th>\n",
       "      <th colspan=\"2\" halign=\"left\">In 2015 constant prices at 2015 USD exchange rates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Pay period</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Hourly</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Hourly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2006-01-01</th>\n",
       "      <th>Australia</th>\n",
       "      <td>20,410.65</td>\n",
       "      <td>10.33</td>\n",
       "      <td>23,826.64</td>\n",
       "      <td>12.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>21,042.28</td>\n",
       "      <td>10.09</td>\n",
       "      <td>20,228.74</td>\n",
       "      <td>9.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3,310.51</td>\n",
       "      <td>1.41</td>\n",
       "      <td>2,032.87</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>13,649.69</td>\n",
       "      <td>6.56</td>\n",
       "      <td>14,335.12</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>5,201.65</td>\n",
       "      <td>2.22</td>\n",
       "      <td>3,333.76</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Series               In 2015 constant prices at 2015 USD PPPs         \\\n",
       "Pay period                                             Annual Hourly   \n",
       "Time       Country                                                     \n",
       "2006-01-01 Australia                                20,410.65  10.33   \n",
       "           Belgium                                  21,042.28  10.09   \n",
       "           Brazil                                    3,310.51   1.41   \n",
       "           Canada                                   13,649.69   6.56   \n",
       "           Chile                                     5,201.65   2.22   \n",
       "\n",
       "Series               In 2015 constant prices at 2015 USD exchange rates         \n",
       "Pay period                                                       Annual Hourly  \n",
       "Time       Country                                                              \n",
       "2006-01-01 Australia                                          23,826.64  12.06  \n",
       "           Belgium                                            20,228.74   9.70  \n",
       "           Brazil                                              2,032.87   0.87  \n",
       "           Canada                                             14,335.12   6.89  \n",
       "           Chile                                               3,333.76   1.42  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.stack(level='Country').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th colspan=\"4\" halign=\"left\">2015-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</th>\n",
       "      <th colspan=\"2\" halign=\"left\">In 2015 constant prices at 2015 USD PPPs</th>\n",
       "      <th colspan=\"2\" halign=\"left\">In 2015 constant prices at 2015 USD exchange rates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pay period</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Hourly</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Hourly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>21,715.53</td>\n",
       "      <td>10.99</td>\n",
       "      <td>25,349.90</td>\n",
       "      <td>12.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>21,588.12</td>\n",
       "      <td>10.35</td>\n",
       "      <td>20,753.48</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4,628.63</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2,842.28</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>16,536.83</td>\n",
       "      <td>7.95</td>\n",
       "      <td>17,367.24</td>\n",
       "      <td>8.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>6,633.56</td>\n",
       "      <td>2.80</td>\n",
       "      <td>4,251.49</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Time                                     2015-01-01         \\\n",
       "Series     In 2015 constant prices at 2015 USD PPPs          \n",
       "Pay period                                   Annual Hourly   \n",
       "Country                                                      \n",
       "Australia                                 21,715.53  10.99   \n",
       "Belgium                                   21,588.12  10.35   \n",
       "Brazil                                     4,628.63   2.00   \n",
       "Canada                                    16,536.83   7.95   \n",
       "Chile                                      6,633.56   2.80   \n",
       "\n",
       "Time                                                                  \n",
       "Series     In 2015 constant prices at 2015 USD exchange rates         \n",
       "Pay period                                             Annual Hourly  \n",
       "Country                                                               \n",
       "Australia                                           25,349.90  12.83  \n",
       "Belgium                                             20,753.48   9.95  \n",
       "Brazil                                               2,842.28   1.21  \n",
       "Canada                                              17,367.24   8.35  \n",
       "Chile                                                4,251.49   1.81  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage['2015'].stack(level=(1, 2)).transpose().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>...</th>\n",
       "      <th>Turkey</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>12.06</td>\n",
       "      <td>9.70</td>\n",
       "      <td>0.87</td>\n",
       "      <td>...</td>\n",
       "      <td>2.27</td>\n",
       "      <td>9.81</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>12.46</td>\n",
       "      <td>9.82</td>\n",
       "      <td>0.92</td>\n",
       "      <td>...</td>\n",
       "      <td>2.26</td>\n",
       "      <td>10.07</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>12.24</td>\n",
       "      <td>9.87</td>\n",
       "      <td>0.96</td>\n",
       "      <td>...</td>\n",
       "      <td>2.22</td>\n",
       "      <td>10.04</td>\n",
       "      <td>6.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>12.40</td>\n",
       "      <td>10.21</td>\n",
       "      <td>1.03</td>\n",
       "      <td>...</td>\n",
       "      <td>2.28</td>\n",
       "      <td>10.15</td>\n",
       "      <td>7.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>12.34</td>\n",
       "      <td>10.05</td>\n",
       "      <td>1.08</td>\n",
       "      <td>...</td>\n",
       "      <td>2.30</td>\n",
       "      <td>9.96</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country     Australia  Belgium  Brazil  ...  Turkey  United Kingdom  \\\n",
       "Time                                    ...                           \n",
       "2006-01-01      12.06     9.70    0.87  ...    2.27            9.81   \n",
       "2007-01-01      12.46     9.82    0.92  ...    2.26           10.07   \n",
       "2008-01-01      12.24     9.87    0.96  ...    2.22           10.04   \n",
       "2009-01-01      12.40    10.21    1.03  ...    2.28           10.15   \n",
       "2010-01-01      12.34    10.05    1.08  ...    2.30            9.96   \n",
       "\n",
       "Country     United States  \n",
       "Time                       \n",
       "2006-01-01           6.05  \n",
       "2007-01-01           6.24  \n",
       "2008-01-01           6.78  \n",
       "2009-01-01           7.58  \n",
       "2010-01-01           7.88  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage_f = realwage.xs(('Hourly', 'In 2015 constant prices at 2015 USD exchange rates'),\n",
    "                         level=('Pay period', 'Series'), axis=1)\n",
    "realwage_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country (en)</th>\n",
       "      <th>Country (de)</th>\n",
       "      <th>Country (local)</th>\n",
       "      <th>...</th>\n",
       "      <th>Deathrate</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afganistan/Afqanestan</td>\n",
       "      <td>...</td>\n",
       "      <td>13.70</td>\n",
       "      <td>51.30</td>\n",
       "      <td>https://www.laenderdaten.info/Asien/Afghanista...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Ägypten</td>\n",
       "      <td>Misr</td>\n",
       "      <td>...</td>\n",
       "      <td>4.70</td>\n",
       "      <td>72.70</td>\n",
       "      <td>https://www.laenderdaten.info/Afrika/Aegypten/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Åland Islands</td>\n",
       "      <td>Ålandinseln</td>\n",
       "      <td>Åland</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>https://www.laenderdaten.info/Europa/Aland/ind...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albanien</td>\n",
       "      <td>Shqipëria</td>\n",
       "      <td>...</td>\n",
       "      <td>6.70</td>\n",
       "      <td>78.30</td>\n",
       "      <td>https://www.laenderdaten.info/Europa/Albanien/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Algerien</td>\n",
       "      <td>Al-Jaza’ir/Algérie</td>\n",
       "      <td>...</td>\n",
       "      <td>4.30</td>\n",
       "      <td>76.80</td>\n",
       "      <td>https://www.laenderdaten.info/Afrika/Algerien/...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country (en) Country (de)        Country (local)  ... Deathrate  \\\n",
       "0    Afghanistan  Afghanistan  Afganistan/Afqanestan  ...     13.70   \n",
       "1          Egypt      Ägypten                   Misr  ...      4.70   \n",
       "2  Åland Islands  Ålandinseln                  Åland  ...      0.00   \n",
       "3        Albania     Albanien              Shqipëria  ...      6.70   \n",
       "4        Algeria     Algerien     Al-Jaza’ir/Algérie  ...      4.30   \n",
       "\n",
       "  Life expectancy                                                Url  \n",
       "0           51.30  https://www.laenderdaten.info/Asien/Afghanista...  \n",
       "1           72.70  https://www.laenderdaten.info/Afrika/Aegypten/...  \n",
       "2            0.00  https://www.laenderdaten.info/Europa/Aland/ind...  \n",
       "3           78.30  https://www.laenderdaten.info/Europa/Albanien/...  \n",
       "4           76.80  https://www.laenderdaten.info/Afrika/Algerien/...  \n",
       "\n",
       "[5 rows x 17 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worlddata = pd.read_csv('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/pandas_panel/countries.csv', sep=';')\n",
    "worlddata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Afghanistan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Africa</td>\n",
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       "      <th>2</th>\n",
       "      <td>Åland Islands</td>\n",
       "      <td>Europe</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country Continent\n",
       "0    Afghanistan      Asia\n",
       "1          Egypt    Africa\n",
       "2  Åland Islands    Europe\n",
       "3        Albania    Europe\n",
       "4        Algeria    Africa"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worlddata = worlddata[['Country (en)', 'Continent']]\n",
    "worlddata = worlddata.rename(columns={'Country (en)': 'Country'})\n",
    "worlddata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Time</th>\n",
       "      <th>2006-01-01 00:00:00</th>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <th>2008-01-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2014-01-01 00:00:00</th>\n",
       "      <th>2015-01-01 00:00:00</th>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>12.06</td>\n",
       "      <td>12.46</td>\n",
       "      <td>12.24</td>\n",
       "      <td>...</td>\n",
       "      <td>12.67</td>\n",
       "      <td>12.83</td>\n",
       "      <td>12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>9.70</td>\n",
       "      <td>9.82</td>\n",
       "      <td>9.87</td>\n",
       "      <td>...</td>\n",
       "      <td>10.01</td>\n",
       "      <td>9.95</td>\n",
       "      <td>9.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.96</td>\n",
       "      <td>...</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>6.89</td>\n",
       "      <td>6.96</td>\n",
       "      <td>7.24</td>\n",
       "      <td>...</td>\n",
       "      <td>8.22</td>\n",
       "      <td>8.35</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Time       2006-01-01  2007-01-01  2008-01-01  ...  2014-01-01  2015-01-01  \\\n",
       "Country                                        ...                           \n",
       "Australia       12.06       12.46       12.24  ...       12.67       12.83   \n",
       "Belgium          9.70        9.82        9.87  ...       10.01        9.95   \n",
       "Brazil           0.87        0.92        0.96  ...        1.21        1.21   \n",
       "Canada           6.89        6.96        7.24  ...        8.22        8.35   \n",
       "Chile            1.42        1.45        1.44  ...        1.76        1.81   \n",
       "\n",
       "Time       2016-01-01  \n",
       "Country                \n",
       "Australia       12.98  \n",
       "Belgium          9.76  \n",
       "Brazil           1.24  \n",
       "Canada           8.48  \n",
       "Chile            1.91  \n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage_f.transpose().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2006-01-01 00:00:00</th>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <th>2008-01-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12.06</td>\n",
       "      <td>12.46</td>\n",
       "      <td>12.24</td>\n",
       "      <td>...</td>\n",
       "      <td>12.98</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9.70</td>\n",
       "      <td>9.82</td>\n",
       "      <td>9.87</td>\n",
       "      <td>...</td>\n",
       "      <td>9.76</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.96</td>\n",
       "      <td>...</td>\n",
       "      <td>1.24</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.89</td>\n",
       "      <td>6.96</td>\n",
       "      <td>7.24</td>\n",
       "      <td>...</td>\n",
       "      <td>8.48</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1.91</td>\n",
       "      <td>Chile</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2006-01-01 00:00:00  2007-01-01 00:00:00  2008-01-01 00:00:00  ...  \\\n",
       "17                 12.06                12.46                12.24  ...   \n",
       "23                  9.70                 9.82                 9.87  ...   \n",
       "32                  0.87                 0.92                 0.96  ...   \n",
       "100                 6.89                 6.96                 7.24  ...   \n",
       "38                  1.42                 1.45                 1.44  ...   \n",
       "\n",
       "     2016-01-01 00:00:00    Country      Continent  \n",
       "17                 12.98  Australia      Australia  \n",
       "23                  9.76    Belgium         Europe  \n",
       "32                  1.24     Brazil  South America  \n",
       "100                 8.48     Canada  North America  \n",
       "38                  1.91      Chile  South America  \n",
       "\n",
       "[5 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(realwage_f.transpose(), worlddata,\n",
    "                  how='left', left_index=True, right_on='Country')\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2006-01-01 00:00:00</th>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <th>2008-01-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>3.42</td>\n",
       "      <td>3.74</td>\n",
       "      <td>3.87</td>\n",
       "      <td>...</td>\n",
       "      <td>5.28</td>\n",
       "      <td>Korea</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0.55</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>1.50</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.71</td>\n",
       "      <td>...</td>\n",
       "      <td>2.08</td>\n",
       "      <td>Slovak Republic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2006-01-01 00:00:00  2007-01-01 00:00:00  2008-01-01 00:00:00  ...  \\\n",
       "247                 3.42                 3.74                 3.87  ...   \n",
       "247                 0.23                 0.45                 0.39  ...   \n",
       "247                 1.50                 1.64                 1.71  ...   \n",
       "\n",
       "     2016-01-01 00:00:00             Country  Continent  \n",
       "247                 5.28               Korea        NaN  \n",
       "247                 0.55  Russian Federation        NaN  \n",
       "247                 2.08     Slovak Republic        NaN  \n",
       "\n",
       "[3 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[merged['Continent'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17        NaN\n",
       "23        NaN\n",
       "32        NaN\n",
       "100       NaN\n",
       "38        NaN\n",
       "108       NaN\n",
       "41        NaN\n",
       "225       NaN\n",
       "53        NaN\n",
       "58        NaN\n",
       "45        NaN\n",
       "68        NaN\n",
       "233       NaN\n",
       "86        NaN\n",
       "88        NaN\n",
       "91        NaN\n",
       "247      Asia\n",
       "117       NaN\n",
       "122       NaN\n",
       "123       NaN\n",
       "138       NaN\n",
       "153       NaN\n",
       "151       NaN\n",
       "174       NaN\n",
       "175       NaN\n",
       "247    Europe\n",
       "247    Europe\n",
       "198       NaN\n",
       "200       NaN\n",
       "227       NaN\n",
       "241       NaN\n",
       "240       NaN\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_continents = {'Korea': 'Asia',\n",
    "                      'Russian Federation': 'Europe',\n",
    "                      'Slovak Republic': 'Europe'}\n",
    "\n",
    "merged['Country'].map(missing_continents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2006-01-01 00:00:00</th>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <th>2008-01-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>3.42</td>\n",
       "      <td>3.74</td>\n",
       "      <td>3.87</td>\n",
       "      <td>...</td>\n",
       "      <td>5.28</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2006-01-01 00:00:00  2007-01-01 00:00:00  2008-01-01 00:00:00  ...  \\\n",
       "247                 3.42                 3.74                 3.87  ...   \n",
       "\n",
       "     2016-01-01 00:00:00  Country  Continent  \n",
       "247                 5.28    Korea       Asia  \n",
       "\n",
       "[1 rows x 13 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['Continent'] = merged['Continent'].fillna(merged['Country'].map(missing_continents))\n",
    "\n",
    "# Check for whether continents were correctly mapped\n",
    "\n",
    "merged[merged['Country'] == 'Korea']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace = ['Central America', 'North America', 'South America']\n",
    "\n",
    "for country in replace:\n",
    "    merged['Continent'].replace(to_replace=country,\n",
    "                                value='America',\n",
    "                                inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>2006-01-01 00:00:00</th>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <th>2008-01-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2014-01-01 00:00:00</th>\n",
       "      <th>2015-01-01 00:00:00</th>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">America</th>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.96</td>\n",
       "      <td>...</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>6.89</td>\n",
       "      <td>6.96</td>\n",
       "      <td>7.24</td>\n",
       "      <td>...</td>\n",
       "      <td>8.22</td>\n",
       "      <td>8.35</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>1.01</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Costa Rica</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      2006-01-01  2007-01-01  2008-01-01  ...  2014-01-01  \\\n",
       "Continent Country                                         ...               \n",
       "America   Brazil            0.87        0.92        0.96  ...        1.21   \n",
       "          Canada            6.89        6.96        7.24  ...        8.22   \n",
       "          Chile             1.42        1.45        1.44  ...        1.76   \n",
       "          Colombia          1.01        1.02        1.01  ...        1.13   \n",
       "          Costa Rica         nan         nan         nan  ...        2.41   \n",
       "\n",
       "                      2015-01-01  2016-01-01  \n",
       "Continent Country                             \n",
       "America   Brazil            1.21        1.24  \n",
       "          Canada            8.35        8.48  \n",
       "          Chile             1.81        1.91  \n",
       "          Colombia          1.13        1.12  \n",
       "          Costa Rica        2.56        2.63  \n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merged.set_index(['Continent', 'Country']).sort_index()\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([2006-01-01 00:00:00, 2007-01-01 00:00:00, 2008-01-01 00:00:00,\n",
       "       2009-01-01 00:00:00, 2010-01-01 00:00:00, 2011-01-01 00:00:00,\n",
       "       2012-01-01 00:00:00, 2013-01-01 00:00:00, 2014-01-01 00:00:00,\n",
       "       2015-01-01 00:00:00, 2016-01-01 00:00:00],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2006-01-01', '2007-01-01', '2008-01-01', '2009-01-01',\n",
       "               '2010-01-01', '2011-01-01', '2012-01-01', '2013-01-01',\n",
       "               '2014-01-01', '2015-01-01', '2016-01-01'],\n",
       "              dtype='datetime64[ns]', name='Time', freq=None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.columns = pd.to_datetime(merged.columns)\n",
    "merged.columns = merged.columns.rename('Time')\n",
    "merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th colspan=\"3\" halign=\"left\">America</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Europe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Chile</th>\n",
       "      <th>...</th>\n",
       "      <th>Slovenia</th>\n",
       "      <th>Spain</th>\n",
       "      <th>United Kingdom</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>0.87</td>\n",
       "      <td>6.89</td>\n",
       "      <td>1.42</td>\n",
       "      <td>...</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.99</td>\n",
       "      <td>9.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>0.92</td>\n",
       "      <td>6.96</td>\n",
       "      <td>1.45</td>\n",
       "      <td>...</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.10</td>\n",
       "      <td>10.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>0.96</td>\n",
       "      <td>7.24</td>\n",
       "      <td>1.44</td>\n",
       "      <td>...</td>\n",
       "      <td>3.96</td>\n",
       "      <td>4.14</td>\n",
       "      <td>10.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>1.03</td>\n",
       "      <td>7.67</td>\n",
       "      <td>1.52</td>\n",
       "      <td>...</td>\n",
       "      <td>4.08</td>\n",
       "      <td>4.32</td>\n",
       "      <td>10.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>1.08</td>\n",
       "      <td>7.94</td>\n",
       "      <td>1.56</td>\n",
       "      <td>...</td>\n",
       "      <td>4.81</td>\n",
       "      <td>4.30</td>\n",
       "      <td>9.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Continent  America               ...   Europe                     \n",
       "Country     Brazil Canada Chile  ... Slovenia Spain United Kingdom\n",
       "Time                             ...                              \n",
       "2006-01-01    0.87   6.89  1.42  ...     3.92  3.99           9.81\n",
       "2007-01-01    0.92   6.96  1.45  ...     3.88  4.10          10.07\n",
       "2008-01-01    0.96   7.24  1.44  ...     3.96  4.14          10.04\n",
       "2009-01-01    1.03   7.67  1.52  ...     4.08  4.32          10.15\n",
       "2010-01-01    1.08   7.94  1.56  ...     4.81  4.30           9.96\n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merged.transpose()\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent  Country      \n",
       "America    Brazil          1.09\n",
       "           Canada          7.82\n",
       "           Chile           1.62\n",
       "           Colombia        1.07\n",
       "           Costa Rica      2.53\n",
       "           Mexico          0.53\n",
       "           United States   7.15\n",
       "Asia       Israel          5.95\n",
       "           Japan           6.18\n",
       "           Korea           4.22\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.mean().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.style.use('seaborn')\n",
    "\n",
    "merged.mean().sort_values(ascending=False).plot(kind='bar', title=\"Average real minimum wage 2006 - 2016\")\n",
    "\n",
    "#Set country labels\n",
    "country_labels = merged.mean().sort_values(ascending=False).index.get_level_values('Country').tolist()\n",
    "plt.xticks(range(0, len(country_labels)), country_labels)\n",
    "plt.xlabel('Country')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time\n",
       "2006-01-01   4.69\n",
       "2007-01-01   4.84\n",
       "2008-01-01   4.90\n",
       "2009-01-01   5.08\n",
       "2010-01-01   5.11\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.mean(axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+C8QYYyJMRVUdC5ZuI6lXbJdJFGDJwhhjgmr+knyqaxs4b2pOl0kUYMnCGGOCZn9FDe8t205qYhynH5sZ7nCCypKFMcYEyZuL86mt9zPzpBxiYzrvQwGPREjbSCKyHCj1Xm5V1Rubrfs2cAPgAL9S1XmhjMUYY0KpuLSaj1bsID0lnlPGDwh3OEEXsmQhIvEAqjr9EOvSgDuAiUA8sE5E3lRVJ1TxGGNMKM39dCv1DQ4XnjKEmOiu12njc5zQHJ9FZArwLJCPm5R+rKpLmq2PUdV6ERkBvKKq4wIUaYnEGNMp7Syq4PbfvU9WegIPfe8MoqM61VNkgxJMKLuhDgAPAE8CI4D5IiKqWg/gJYo7gV8Cf25LgUVF5aGKNaKkpydaXXisLppYXTTp6Lr469y1+P0OM6fmsK+4osP22xbp6cF5vEgo20q5wHOq6qhqLlAMfKUjT1Uf9pZNE5HTQxiLMcaExPaiCj5bt5vB/XtznKSHO5yQCWXL4iZgHHCHiGQCScAuABER4L+AS4E6oAbwhzAWY4wJiVc/3oIDXHzqUKIiYBKjIxXKlsVTQIqIfAL8Ezd53C0is1RVgZXAYmARsERVPwphLMYYE3Rbd5WxfONehmUlMX5Y33CHE1Iha1moai0wu8XiRc3W/xJ3vMIYYyLSqx9vAeCSacMiYmrUo9H1ru8yxpgOkLttP2u27mNUdmpETmbUXpYsjDGmnRzH4ZWPNgNwybShYY6mY1iyMMaYdlqbt4/c7aVMGNaXYVnJ4Q6nQ1iyMMaYdnBbFe5YxcXdpFUBliyMMaZdlm/cS15hOZNH9ovY+bSPhCULY4xpI7/j8OrCLfh8cPGpQ8IdToeyZGGMMW30+frd7Ciq5KQxGQzomxDucDqUJQtjjGmDBr+f1xduJTrKx6xTulerAixZGGNMmyxaXcjukipOnZBJekrPcIfT4SxZGGNMAHX1fuZ+upXYmChmnpQT7nDCwpKFMcYE8PHKnRSX1XD6sVmkJsaFO5ywsGRhjDGtqKlrYN6iPOJioznvxOxwhxM2liyMMaYV73+5ndLKWs48fiBJCT3CHU7YWLIwxpjDqKqp563F+fSKi+GcEwaHO5ywsmRhjDGHsWDpNiqr6zlnymB6xceGO5ywsmRhjDGHUFFVx4KlBST2imXG5IHhDifsLFkYY8whzP8sn6qaBs4/MZv4HqGcgToyWLIwxpgWSitqeO+L7aQmxnH6cVnhDqdTCGm6FJHlQKn3cquq3ths3b3AVd7Lt7xpVo0xJuzmLc6ntt7PlSflEBsTHe5wOoWQJQsRiQdQ1emHWDcUuAaYAjjAQhF5VVVXhSoeY4xpi+LSaj5asYO05HhOHT8g3OF0GqFsWUwAeonIAm8/P1bVJd66bcA5qtoAICKxQHUIYzHGmDZ5Y9FW6hscLjxlCDHR1lN/kM9xnJAULCLjgBOBJ4ERwHxAVLW+2TY+4H+ARFW9LUCRoQnUGGM8O4squP1375OZlsDD3z+D6ChfuEMKhqB8iFC2LHKBTarqALkiUgwMwG1VHOymehooB+5oS4FFReUhCjWypKcnWl14rC6aWF00OdK6+Osba/H7HWaelMO+4ooQRNbx0tODM5tfKJPFTcA44A4RyQSSgF3Q2KJ4HXhfVf87hDEYY0ybbC+q4LO1uxncrzeTJD3c4XQ6oUwWTwHPiMgnuF1INwF3i8gmIBo4DYgTkXO97X+kqotDGI8xxhzWawu34gAXTxtKlK9LdD8FVciSharWArNbLF7U7Of4UO3bGGPaY+uuMr7MLWJYZhLjh/UNdzidkg31G2O6vVcXbgHgkmlD8Vmr4pAsWRhjurXcbftZs2Ufo7JTGZXTJ9zhdFqWLIwx3ZbjOLzysduquHja0DBH07lZsjDGdFvr8krI3baf8cP6MjwrOdzhdGqWLIwx3ZLbqtgMuGMVpnWWLIwx3dKKjXvZuqucySP7Mbh/cG5c68osWRhjuh2/4/Dqwi34fHDRKUPCHU5EaPU+CxG5EbgTEKAKWAc8rKpzOiA2Y4wJiaXr97C9qJKTxmaQmZYQ7nAiwmFbFiLybeBe4LfA8cCpwP8CPxGRQA/9M8aYTqnB7+e1T7YSHeVjlrUq2qy1lsVtwOmqWtxs2QYR+Rh4E3gspJEZY0wILFpdyO59B5g+MZN+KT3DHU7EaG3Mwt8iUQCgqrtCGI8xxoRMXb2fuZ9uJSY6igtOygl3OBGl1WTRYVEYY0wH+HjlTorLajjjuCz6JNnj6dqjtW6oASLyH4dY7gMyQhSPMcaERE1dA/MW5REXG815J2aHO5yI01rL4lHcxNDyH9h4hTEmwrz/5XZKK2uZMXkgSQk9wh1OxDlsy0JVf9mRgRhjTKhU1dQzf0kBPeNiOGfK4HCHE5EOmyxEpCdwP/Ciqn4uIn8AbgGWA1er6o4OitEYY47Kv5Zuo6KqjounDSUhPjbc4USk1sYsHgTqgTwROQ+4BjjO+/cwcHHowzPGRAK/47B9TwX5heVkpiWQMyCR6KjO8YCIiqo63llaQO+escyYNDDc4USs1pLFVFUdByAiF+K2MDYCG0Xk5x0SnTGm09pXVs3avH2szythXd4+yg7UNa6L7xHNMYNSGDk4lVHZqQzq15uoqPBMKvT2ZwVU1TRw5RlD6BkXypmku7bWaq6h2c/TgX9v9tpGh0xYOY7DnpIqSqsb6BUDsTHR4Q6py6uqqWdDQQnrtpawNm8fhfsONK5L7t2DqWMyGJaVxI6iStbnl7BqczGrNru3aiXExyCDUxk5OIVR2alkpiV0yIx0pRU1vLtsGym9e3D6sVkh319X1lqyKBaRE4AEIAt4F0BEpgPb21K4iCwHSr2XW1X1xhbr03Hn5R6nqtXtC910J3X1DeQVlrNpeymbdrj/yr0z2egoH5lpCWRnJJKTkUh2RiKD0nvTI9YSyNGob/CzZWcZ6/L2sS6vhC07y/A7DgBxsdGMH9aX0Tl9GJNz6IN/SXkNGwpKWJ9fwob8Er7MLeLL3CIAknrFMjI7lZHZqYwanEq/1J4hSR5vLs6nts7PlWcMse/DUWotWdwLvAD0B+5Q1UoR+SlwN3B+oIJFJB5AVacfZv3ZuM+d6t/OmE03UFpZ6yWG/WzaUUp+YTn1DU7j+r5JcZwwqh99U3uRm7ePgj0VbNtTwSer3AcMRPl8ZKb1Ijsjkez+ieRkJDGof2/i7IBxWI7jsKv4QGPX0oaCEqpr3Q4Gnw+GDkhidE4fRuekMiwrmZjo1sckUhPjmDomg6lj3NuyivZXsSG/pDGBfL5+D5+v39O47ajs1MZuq77JR3/DXHFpNR+u2EFacjynjh9w1OV1dz7HcQJv5RGR4UCRqpa2YdspwLNAPm5S+rGqLmm2/kzgS2AZMLINLYu2B2oiit/vULC7nPV5+1i/tZgNeSXsKq5sXB8V5WNoVjKjcvo0/ktr8UyfhgY/2/ZUsGnbfjbv2M/m7aVs2VlKTW1Tb2qUDwb2T2RYVjLDB6YwbGAKQ7OSu3U/dkl5NStzi1ixsYgVuUUUlzb9GWamJTDxmHQmHtOPccPT6N0zeFcROY7Dzr2VrNpYxMpNe1m9aS9llbWN6wf0TWDc8DTGe/9Sj+Bu64fnrOCdJfl856pj+cbx3fpy2aA02Q6bLETk+haL/EAJsFhV9wUqWETGAScCTwIjgPmAqGp9i+3yaGOyKCoqD7TbbiE9PZFIrovq2nq27Cxzu5O2l7J5ZxlVNU1fi15xMQwfmMywrGRGZCUzZEAScT0O3SJorS78fodd+w6QX1hGXmE5+YXlFOyuoKauKYH4gIy+bgskp7/bhTW4f2JEJpC2fC9q6hrI3baftVvdrqXtRRWN63r3jGV0Tmpj6yEtueMesud3HHZ6Yx0bCkrYULD/K9+JAX17NbY8RmanBkxcdT4ft//2ffr36cn9/zYlbIPrnUF6emJQPnxrfxGnt3jtA/oBT4nIbFV9P0DZucAmVXWAXBEpBgYA2444WhNxHMdhX1kNG3fsZ/P2Mjbu2M+2PRU0P0fpn9qT445JY7h3xj+gby+igtB/HRXlIystgay0BE4a63ZD+P0Ou0sONCaP/MJy8neXs6v4AEvW7m6KqU8vsvv3JicjqbErq1d85CUQv98hf3e5lxz2sWlHaWN3XmxMFGNyUhk9pA+js/swqH/voNT7kYjy+RjYrzcD+/XmzOMHua3NPeWsz3e7rDZuK+X9L3fw/pc78AGD+vVuHPOQQSlfS+7/eEfxOw4XnjKkWyeKYGrtDu4bD7VcRAT4G26roTU3AeOAO0QkE0gC7Im1XVz9we6gZgPRJeU1jetjoqMaWwzDs5IZNjCZpF4dd3FdVJSPAX0TGNA3obEv3e9dWXUweeQVlpG/u+IrfeoA/VJ6fmUQfXD/xKB2zQTLnpIDrMtzr1jakF9CZbV7hu4DBvdPZPSQVMbk9GHEwOROexVZVJSPnIwkcjKSOHdKNvUNfvJ2lbM+fx8bCvazcXspBXsqWLB0G1E+H9kZiW7LIzuFhPhYPlq+nUH9ejN5ZL9wf5Quo11jFgeJyEpVnRBgmx7AM8Bg3PGGH+AmmE2qOrfZdnlYN1S7dKZuqIqqOrbsLGXj9lI27yhly64yauuaHliclNDDbTFkJTN8YDLZ/ROJjQnezVqhqgvHcSjaX9XYAskrLKdgd3njgfegtOR4BvdPJL5HND4f+PCBzx0fAZ+7zOfDe0mUt/7gtr5mPx9qubfmK+X4vP+0XF5WXc+y9bvZ22zcoW9SPGOGuF1Lo7JTSezAxBxKdfUNbNpRxob8EtYXlLB1ZxkN/q8ey+6+bDwTh6eFKcLOI1jdUO1OFiISDaxU1bHBCKAdLFl4wp0samobePnjzazduo9dxU3X2vuArPSExsQwPCuZ9JTQXBJ5UEfWheM47C2tbuy6OphIKqrqAr+5g/SKi2FUdqo79jCkD/1CXP+dRXVtPZu2l3pjHvsZnJHE9WeN6BafPZCQj1mIyLRDLE4BrgfeDsbOTWR6/t1cFq7aRVxsNKOyUxmelcyIgckMzUyiVxd+7o7P5yM9pSfpKT0buzccx6G0spb6ej+O99oBcGi8J8FxWlmO4613f8ZpufzrZRzcj7e5u9yBwVkpJMdFd8s++vgeMYwd2pexQ/sC4T+h6opaG7Fr+dTZg1dDvQc8HrKITKe2dMMeFq7axeD+vfnJdZM6bZ93R/H5fKT0jgt3GIAdIE1otTbA3fJqKNPNFZdW87f5G+gRG8Vts8Z0+0RhTHfSOR4LaTo9v9/hiTfWcqCmntkzjmFA34Rwh2SM6UCWLEybvLk4j9ztpUyWdHt0gjHdkCULE9CmHaW8/kkefZLi+Oa5I+0KE2O6odauhrpfVX8mIim4kx2dC9QBrwL/rqo2ktYNHKiu5/G5a3Ech1suGG2zjBnTTbXWsjj4ZNmHgAJgGDAa9y7sZ0Mcl+kEHMfhuQXK3tJqzj8pBxmcGu6QjDFh0paH3UxQ1euavf6ViKwNVUCm81i8tpAl63YzLDOJWSfnhDscY0wYtdayyBCRK4HtItL4HCgROR6wiYq6uD0lB/j7glzie0Rzy6wxAecuMMZ0ba0dAX4InAykAt8HEJHvAK/hToBkuqj6Bj+PzV1HTW0D150t9EvpuEdVG2M6p9ZuynuWr49NPA38WVX9h3iL6SJe/2QrW3eVMXVM/8Ynsxpjurd2PaBfVctCFYjpHDbkl/DW4nzSU+K59iwJdzjGmE6itUtnW86U9xVey8N0IRVVdTwxbx0+n49bZ42JyNnijDGh0drR4BvApcCLfH0OVwe7fLZLcRyHv83fQEl5DZdMG8qwzORwh2SM6URaG7P4poikAp+o6tMdGJMJg49X7mRZbhEyKIXzTswOdzjGmE4m0PWQtwF9OiIQEz67iiv5x7sbSYiP4ZaZo7vlfAjGmNa12imtqruABzooFhMGdfV+Hnt9LbX1fm6+YDR9kuLDHZIxphNqNVmIyNnA5cBA3MmPdgLzVfXlthQuIsuBUu/lVlW9sdm6W3BbLvXAr1V1XvvDN0fr5Y82U7CngmkTMm1ye2PMYbV2NdSvgBOA53CfB+UDMoB/E5Gpqvq91goWkXgAVZ1+iHUZuDf2TQZxXfiLAAAS2klEQVTigU9E5F+qWnOEn8McgdVbilmwdBsZfXpx9TdGhDscY0wn1lrL4kpgVMsb8ETkH8AaoNVkAUwAeonIAm8/P1bVJd66E4BPveRQIyKbgPHA0iP4DOYIlFbW8tS8dURH+bht1hjietisd8aYw2stWVTjdj8VtFieDbSlBXAAd7zjSWAEMF9ERFXrgSSauqcAyoGA12qmpye2Ybfdw9HUheM4/OW1JZQdqOPfZo1h8rjMIEbW8ex70cTqoonVRXC1lizuAxaKSC5uN5QDZALHADe0oexcYJOqOkCuiBQDA4BtQBnQ/DeZCOwPVKBNRu9KT088qrr41xfbWLZhD2OG9GHqqH4RXa9HWxddidVFE6uLJsFKmq3dZ/GuiAhul1Em7mW224HP2ji2cBMwDrhDRDJxWxO7vHWfA//pjWvEAaNwu7ZMiG3bU8GcDzaR2CuWm88fRZTNemeMaYNAz3M4GxgMvKWqmw8uFJFbVfXxAO99CnhGRD7BbZXcBNwtIptUda6I/BlYiJuEfqKq9tjzEKupa+CxuWupb3C46bxRJPeOC3dIxpgI0drVUL/FvVppPfATEfmeqj7nrf4W0GqyUNVaYHaLxYuarX8CeOJIgjZH5sX3N7FzbyUzJg1kwvC0cIdjjIkggaZVPUdV7wJOBe4Xkcu9ddZ3EWGW5xbxwfIdDExP4PLTh4U7HGNMhGktWfhwu49Q1Y3ABcCDIjL94HITGUrKa3j6rfXExkRx26wxxMbYZbLGmPZpLVnMAT4UkRMAVHUt7t3cLwJ2ahoh/I7Dk/PWUVldz1VnDCcrvXe4QzLGRKDDJgtV/SXwC9x7IA4u+xSYBPw15JGZoHj7swLW55cwcXga04/NCnc4xpgIFehBgu8dYtk24Dshi8gEzdZdZbz68RaSe/fgxvNG4rPLZI0xRyjQI8pNhKqqqeexuWvx+x1uuWA0ib16hDskY0wEs2TRRT3/bi57Sqo4Z8pgRufYlCTGmKNjyaIL+mzdbj5dXUh2RiIXTxsa7nCMMV2AJYsuZu/+Kp59R4mLjea2WWOIibZfsTHm6NmRpAtp8Pt5fN46qmrqmX3mCDL69Ap3SMaYLsKSRRcyb1E+m7aXcvzIfpwybkC4wzHGdCGWLLqIjdv3M/fTrfRNiuOb54hdJmuMCSpLFl3Ageo6Hp+7DoBbZo6hV3xsmCMyxnQ1liwinOM4PPuOUlxWzcyTcjhmUEq4QzLGdEGWLCLcp6sL+Xz9HoZnJTPz5Jxwh2OM6aIsWUSw3fsO8H//yqVnXDS3zhxNdJT9Oo0xoWFHlwhV3+Dnsblrqalr4PqzR5KW0jPcIRljujBLFhHq1YVbyCss5+SxGUwZ3T/c4RhjujhLFhFoZW4Rby8poF9KT2afeUy4wzHGdAOtPqL8aIlIP2AZcKaqbmi2/Drg+0Ap8IyqPhXKOLqS8gO1/OEfXxIV5ePWWWPoGRfSX6ExxgAhbFmISCzwGFDVYnka8GtgOnAacI2I5IQqjq7C73dYsq6Q3/7fl+wrq+aiU4cwNDMp3GEZY7qJUJ6WPgA8CvyoxfKhwApV3QcgIkuBE4G8QAWmpycGOcTOr77Bz4fLtjHnvY3s3FtJVJSPc6fmcN0FY4mOsru0oXt+Lw7H6qKJ1UVwhSRZiMgNQJGqviMiLZPFRmCMiPTHnbL1G0BuW8otKioPvFEXUVffwMJVu5i/JJ/ishqio3xMn5jJOSdmM2ZEv25VF61JT0+0uvBYXTSxumgSrKQZqpbFTYAjIjOAicCzIjJLVQtVtURE7gVeBrYDXwJ7QxRHxKmpbeDDFTt4+/MCSitqiY2JYsakgZwzZTB9kuLDHZ4xppsKSbJQ1WkHfxaRD4FvqWqh9zoGt9tpmrf/d4EfhyKOSHKgup73v9zOgqXbqKiqI65HNOdOGcxZJwwmOcGmRDXGhFeHXUojIrOB3qr6uIjU4l4lVQ38XlW7bcuioqqOfy3dxrvLtlNVU0+vuBhmnZzDjMmD6N3THghojOkcfI7jhDuGtnK6Uh9kaUUN7yzdxgdf7qCmroHePWM5+4RBnHHcwICXw1p/bBOriyZWF02sLpqkpycG5UoYu0i/g+0rq2b+ZwV8vHIndfV+knv34OJpQzltQiZxPaLDHZ4xxhySJYsOsmd/FW8tzufT1bto8Dv0TYrnvKnZnDIug9gYSxLGmM7NkkWI7dxbyZuL8/ls3W78jkP/1J6cPzWHE8f0JybanrZijIkMlixCpGB3OfMW57Nswx4cICs9gQum5nD8yH5E2c10xpgIY8kiyDbvLOXNRfms2ORe4JWdkcjMk3KYOCKNKJsX2xgToSxZBIkWlDBvUR5r80oAGmeuGzukDz5LEsaYCGfJ4ig4jsParft4Y1EeG7eXAjAqO5WZJ+Ugg1MsSRhjugxLFkfA7zis3LiXNxblkVfoXss9flhfLjgph+FZyWGOzhhjgs+SRTv4/Q5f6B7mLcpje1ElAJMknQum5pCdYU+4NMZ0XZYs2qi4tJo/zlnJzr2V+Hxw4pj+nH9iNlnpvcMdmjHGhJwlizY4UF3Pn15yE8VJYzOYeXIO/VN7hTssY4zpMJYsAqhv8PPIa6vZUVTJN44byOwzR9jAtTGm27FbiFvhOA7PLchlbV4JE4b15eoZliiMMd2TJYtWvO098G9w/97cduEYu/PaGNNtWbI4jKUb9jDnw82kJsZxz2UTiO9hPXbGmO7LksUhbNpRyhNvrCOuRzT3XDae1MS4cIdkjDFhZcmihT37q3jo5VX4/Q53XDSWwf3t/gljjLFk0UxldR0PzllJ+YE6rjlzBOOG9g13SMYY0ymEtCNeRPrhzrV9pqpuaLb8GuA+oAF4WlUfCWUcbVHf4Ocvr6xmV/EBzj5hEKcfNzDcIRljTKcRspaFiMQCjwFVh1j9ADADOBm4T0RSQxVHWziOwzPzN7ChYD+Tjknn8tOHhzMcY4zpdELZDfUA8Ciw8xDrVgHJQDzgA5wQxhHQG4vyWLSmkCEDkrh55mibd8IYY1oISTeUiNwAFKnqOyLyo0Nssga3e6oSeEVV97el3PT04A82f7hsG68t3Eq/1J788rappCbGB30foRCKuohUVhdNrC6aWF0El89xgn9SLyIf47YWHGAikAvMUtVCERkPvAhMASqA53ATxpwAxTpFReVBjVMLSvj9P1cQGxPNj6+bRFZaQlDLD5X09ESCXReRyuqiidVFE6uLJunpiUHpKglJy0JVpx38WUQ+BL6lqoXeolLccYwqVW0QkT1Ah49ZFO47wMOvrMZx4NsXj42YRGGMMeHQYbcli8hsoLeqPi4ijwGfiEgtsBl4pqPiACg/UMuf5qyksrqeG88dyeicPh25e2OMiTghTxaqOt37cUOzZY/iDn53uLr6Bh56ZTV7Sqo4f2o2p07IDEcYxhgTUbrVTXl+x+GpN9ezaXspJ4zqx8XThoY7JGOMiQjdKlm8tnALn6/fw/CByfzb+aPsElljjGmjbpMsFq7cybxF+fRL6cldl4wjNiY63CEZY0zE6BbJYl3ePp59R0mIj+E7V0wgsVePcIdkjDERpcsnix17K/nLq2vw+eCuS8eT0cfmzjbGmPbq0smitLKWB+espKqmnhvPG8Uxg1LCHZIxxkSkLpssauoa+PNLq9hbWs1Fpwxh6piMcIdkjDERq0smC7/j8OQb69i6q4yTxmYw8+SccIdkjDERrUsmi5c+2Myy3CJGDk7hhnNH4rNLZI0x5qh0uWTxwfIdvP15ARl9evHtS8YRE93lPqIxxnS4LnUkXbW5mOcWKIm9YvnOFRNIiI8Nd0jGGNMldJlkUbC7nEdeX0NMdBR3Xzqefik9wx2SMcZ0GV0iWZSU1/DgS6uoqW3g5gtGMywrOdwhGWNMlxLxyaK6tp4HX1pJSXkNl08fxvEj+4U7JGOM6XIiOln4/Q6Pvb6Wgt0VTJuQyTlTBoc7JGOM6ZIiOln8472NrNxczJghfbj2rGPsElljjAmRiE0W/1q6jfeWbScrPYHbLxxrl8gaY0wIReQRdnluES+8t5HkhB5857IJ9IrvsNlhjTGmW4q4ZJFXWMZjb6wlNjaKey4fT9/k+HCHZIwxXV5IT8lFpB+wDDhTVTd4yzKAF5ptNhH4oTcvd6uKS6t5cM4q6ur83HnJOHIykkIStzHGmK8KWbIQkVjgMaCq+XJVLQSme9tMBf4TeCJQeQeq6/jTSysprazl6m+M4Nhj0oMftDHGmEMKZTfUA8CjwM5DrRQRH/AQcLuqNgQq7Ld/W8qOokq+cdxAZkweGNxIjTHGtCokLQsRuQEoUtV3RORHh9lsJrBWVbUtZS7PLWLyqP7cddWxRNuVT6SnJ4Y7hE7D6qKJ1UUTq4vgClU31E2AIyIzcMcknhWRWV4X1EHXAg+2tcDpkwZyxWlD2bevMsihRp709ESKisrDHUanYHXRxOqiidVFk2AlzZAkC1WddvBnEfkQ+FaLRAEwCVjU1jLvmz3JfvnGGBMmHdafIyKzReRW7+d0oFxVnY7avzHGmCMX8rvZVHW69+OGZsuKcLunjDHGRAAbKTbGGBOQJQtjjDEBWbIwxhgTkCULY4wxAVmyMMYYE5AlC2OMMQH5HMdudTDGGNM6a1kYY4wJyJKFMcaYgCxZGGOMCciShTHGmIAsWRhjjAnIkoUxxpiALFkYY4wJKOSPKG+NiMQCTwM5QBzwa2Ad8AzgAGuAb6uqX0R+DpwP1APfUdXPRaQf8ASQCkQD16vq5o7+HMEQhLqYiDvneT2QC9ysqv6O/hzB0J668LYfDrymqmO912nA80BP3Dngb1TVAx37KYIjCHUx2Ht/DOADbm3rVMadzdHWRbNypgH/p6qDOiz4IAvC9yIBeAQYAvQA7lLVz1vbZ7hbFtcCxap6KnAu8DDwB+Cn3jIfcKGIHAecBkwBrgL+4r3/d7i/9GnAT4GRHRx/MB1tXfwc+JWqnoL75Tm/g+MPpjbVBYCIXAe8AKQ1e/9/AM972y4HbuvA2IPtaOvifuBhb16Z3wD/1XGhB93R1gUiMgi4D4jtwLhD4Wjr4vvAGm/bWwAJtMNwJ4s5wM+ava7HnW71I+/1fGAGcAqwQFUdVS0AYrzZ9k4GBorIu8A1wIcdFXgIHG1dLAf6iIgPSATqOizy4GtrXQCU4CbP5k4B3j7EtpHoaOviPuBN7+cYoDo0YXaIo6oLEYnHbX3fEdowO8TRfi/OBmpF5B2vnHcC7TCsyUJVK1S1XEQSgZdwWwe+ZtOtlgPJQBJQ2uytB5fnACWqOgMoAH7QUbEHWxDqYiPwZ2A90J8ITpztqAtUdZ6qVrYoonkdNW4biY62LlR1r6rWiYgADwC/7MDwgyoI34uHgQdUdUeHBR0iQaiLNCBVVc8G3sD9brQq3C2Lg83CD4C/q+rzQPN+9kRgP1Dm/dxyeTEw11v2BjA55AGH0FHWxYPAqao6EngW+H2HBB0ibayLw2leR4G27fSOsi4QkdOB14DrInW84qAjrQsRyQROBX4uIh/itsJfCHG4IXWU34t2HzvDmixEpD+wAPiBqj7tLV4uItO9n88FFgKfAmeLSJQ3YBelqnuBT4DzvG2nAWs7LPggC0Jd7MM9SII7qJvaYcEHWTvq4nA+pel7EWjbTu1o68JLFA8C56jqF6GMNdSOpi5UdaeqiqpO98Zv9qnqVaGOOVSC8DfS7mNnWK+GAn6Me1D7mYgc7H+7B/iziPTA7VJ5SVUbRGQhsBg3wX3b2/Y+4EkRuR2322F2h0YfXEdbFzcDL4hIPVCLO2gVqdpUF628/9fA30TkFmAv3eB70cr7/4R7tcvf3J4oVFUjdcD/aOuiKznauvgN7rFzMe745vWBdmiPKDfGGBNQ2McsjDHGdH6WLIwxxgRkycIYY0xAliyMMcYEZMnCGGNMQJYsjDkEEXlYRF5qsewsEdni3TVrTLdiycKYQ/shMElEZsFXntJ5k6qWhzUyY8LA7rMw5jBEZAbuY6BHAb/CvVv+XhGZgvuEz55AEe5jv/NF5Azcp7z2xH0uz3dU9Q0Rec57PRy4T1XfCsPHMeaoWMvCmMNQ1Xdxn8b5V+As4MciEoc7h8qVqnoc7sMbH/Pechdwg7f8dtwEc9BuVR1licJEqnA/7sOYzu4+3CcaX6SqVd4kU0OBed7jM3y4LQmAq4GZInI1cCLQu1k5n3VcyMYEn7UsjGmFqpbhPr0zz1sUDeSq6kRVnQgcB5zmzSPyKe6cAktxJxnyNSuqqsOCNiYELFkY0z7rgAwROcl7fSvwdyAdd4rKn+NOvHQhbmIxpkuwZGFMO6hqFXAF8KCIrMLterpFVffgJo21uE/87AkkiUjPwxZmTASxq6GMMcYEZC0LY4wxAVmyMMYYE5AlC2OMMQFZsjDGGBOQJQtjjDEBWbIwxhgTkCULY4wxAf0/bgjZa2+yjvYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged.mean(axis=1).plot()\n",
    "plt.title('Average real minimum wage 2006 - 2016')\n",
    "plt.ylabel('2015 USD')\n",
    "plt.xlabel('Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Continent</th>\n",
       "      <th>America</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Europe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>2.80</td>\n",
       "      <td>4.29</td>\n",
       "      <td>10.25</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.44</td>\n",
       "      <td>10.73</td>\n",
       "      <td>4.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>2.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>10.76</td>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>3.23</td>\n",
       "      <td>4.53</td>\n",
       "      <td>10.97</td>\n",
       "      <td>5.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.34</td>\n",
       "      <td>4.53</td>\n",
       "      <td>10.95</td>\n",
       "      <td>5.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Continent   America  Asia  Australia  Europe\n",
       "Time                                        \n",
       "2006-01-01     2.80  4.29      10.25    4.80\n",
       "2007-01-01     2.85  4.44      10.73    4.94\n",
       "2008-01-01     2.99  4.45      10.76    4.99\n",
       "2009-01-01     3.23  4.53      10.97    5.16\n",
       "2010-01-01     3.34  4.53      10.95    5.17"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.mean(level='Continent', axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged.mean(level='Continent', axis=1).plot()\n",
    "plt.title('Average real minimum wage')\n",
    "plt.ylabel('2015 USD')\n",
    "plt.xlabel('Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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INxun1wqW7NjzwTD+j+idEQoRrKsjULWXwN695qP1PNLeFl3O7vFQ9NnPk3vSyYPmy4I/HODvm19g+Z73cdmdXDztXD4xZj42W1LueppyEhYioWBDAy3vLqf5neUEqvYC4Mjy4szLw+Z0mj8OBzaXy3x0OrE5Xdicjrj5Tuh6HjfN5rIenQ5rnbjn3aY7zPW7reOkIMdD9ZbdhFpaCDc3E25pJtTcTLilhXBzE+GWFkLNzYQa6gns3pXws9rc7m7hEW2teL2x113zsrIGzUHS4Sbc3k6gai/BqqruwVBTvf8XE4cDd1ExLqVwl47CXVrKuIXzaGbwBP/25p08uv5JatrrGJM1iitnXsKozJKBLtZBkbAQPYoEArR+tJLmd5bRvn4dGAY2pxPvsfPIXriIjCNmDoodpTMrC/eo0bhHJV42EgwSbm21QqSZcHMLoZZmM2Sam2PPW1rw79xhtpj6YrPhyMwywyUvD1dBAc78Alz5BTjz83EWFODKyx805/APNkYkYob43r37tRTCTU37LW9PTydtQpkZCKNGRR9dhUX71bGnyAu1Lf31UXoVMSK8vv0/vLj1FSJGhJPHncCSyZ/GZR96vxNDr8QiZQzDoLOiguZ3l9HywftEOjoASJs8hewFC/EeO29Q3ELyUNldLux5ebjy8hIuaxgGkY6OfVorzdGWSlerJdzcTKixgcCe3T1vyGbDkZ2DqyAfZ16+GSTxoVKQjyPLO2S6Ig5FxO8nWFO9X7dRoLoKIxDovrDNhrOggIxZs7uHQukoHNnZQ6qeGjobeWz9U2xu3EKO28tlMz7L9PypA12sQyZhIQj66mh+9x2a31lOsKYaAGdePrmfPJnsBQtxlx7A1/Zhxmaz4cjIwJGRAQdwcDTS2UGwvp5QvY+gr55Qg4+Qr55gvY9QvY/O7dthy5ae38vl6t4iyc/fr5UyGEY/jWcYhnmSQiBAJOC3HgNE2tsJVFd1C4aQzweG0W19m9uNu6S0Wxi4R43CVVwy5I8bBSMhlu/+H0u3vkpHqIM5RbO4ZPpnyHIN3S9aIGExYkX8flpXfEjTO8vo2LgBMP+AvcfPJ3vBIjKmHzFoDgwOBfa0dDyjx/R6Dr8RiRBubo6GR6jeCpK4QGmvrup1+w6vt1trxJnXPVAc2dnm+xgGhMOxHbg/ENuhB4M9T7d29D29NpfteZkD4cjJJV1Njx5L6AoFZ17+sPv9CkfCvLf3Q17e9gYN/kbcDjeXTP8MC0bNG1Itot5IWIwgRiRCx+ZNNC9fRsuKDzH8nQCkT51G9sJFZB197KC4t/BwZLPbcebm4szNhV6Gv44EArEQ6WqlxAVKYM9u/Nu39fwGDgdb3G7Cfj9EIkksuA2b24Pd7cbmcePI9uJ0F5qv3e64R4/5mObBXVyKywoHR0ZG8soySEWMCB9UfcRLW1+jrrMel93JyeNO4NQJi4fEKbEHSsJiBAjU1ND87nKa311OqK4OAGdhIdmfOs3sZioqHuASCgC72219++6528swDMKtLYSsbq6gLy5Q6n04MAjbnT3syN2xHX5Prz2ebsvHz7M5ncPiW3EqRIwIH9Ws5p9bX6O6vRaHzcGJYxfwqQmfHDSD/yWThMUwFensoOXDD2hevoyOzZsAsHk8ZC9YRPaChaRPU8OuG2C4s9lsOK1Td+lhOO2iIi+1g+AMoOHOMAxW161j6ZZX2dNWhd1mZ+HoeXy67GTy0xKfPDFUSVgMI0YkQvvGDTS/s4zWlSui/crp048gZ8EisuYejT0tbYBLKcTQZBgG6+s1S7e8yo6WXdiwMa90LqeXnUJxRuFAFy/lUhoWSqmPgK4Tprdqra+Mm/cl4FogBNyltV6ayrIMZ4GqKprfWUbze+8Qqq8HwFVUTPbCRWTPX4CrYPj/IguRSpsaKnhxyytsadoOwNzics6ceCqlQ+zCusORsrBQSqUBaK0X9zCvFPgqcAyQBixTSr2mtfanqjzDTbi9jZYP3qf5neV0VlYA5kVLOSecSPb8RaRNmSJ9zUIcpsrGbSzd8gqbGisBKC+cyZkTTx2UQ4inWipbFnOADKXUq9b73Ka1fs+aNw9YboWDXylVAZQDH6SwPENWuKODUEMDocYGQg311G/eiO+9/5lXGNtsZMycRfaCRWQdNXfQnY8vxFC0vXknS7e8yvp6DcCMfMVZkz7FhOzBcbOkgZDKsGgH7gH+CEwFXlZKKa11CMgm1j0F0AIkPH2gqMibinIOGCMcJtDYSMBXb/7U+/BHn9cT8JmvI52d+62bPnYMxSd9kqLFJ+ApKBiA0g8ew+334nBIXcQcSl1sb9zF02uX8sHuVQDMLJ7GxbPOZnpRz6c7jySpDItNQIXW2gA2KaV8wChgJ9AMxP9LeoHGRBscSmd6RDo7rZZAQ/QxGPc81Nhgjn+zz5Wt8RxZXlxFRThz83Dm5ZmPuXmUlE+nI6cYm81Gc4RBMQbOQJEzgGKkLmIOti6q2mp4aetrrKgxQ2JSzgTOmngaKn8KMLT2PftK1heIVIbFVcBs4MtKqdGYrYm91rz3gR9axzU8wBHA2hSWJWm6rsTdNwjMx0bzsbEhOq5ST2xOJ87cPNxTploXasWFgfXoyM3F7ur5jlneIi+dQ/iXV4jBorbdx8vbXuf9qpUYGIz3juGsSacxI1/JMb99pDIsHgIeVUotAwzM8PiqUqpCa/2CUurXwNuAHfi21nr/vpZBINzeTvv6dbStWU273mCebdTHFbL2zEyc+QX77fydeWYouPLysWdlyS+iEAOovrOBf217g3f3fkjEiDA6s5SzJn2K8sKZ8rfZC5vRRzfIIGP0R1PQMAwCe/fStmYVbWtWmxe0WePnO7K8uEpKegiCri6i3H45wCzdDTFSFzFSFzG91UWTv5lXtv+b5bv/R8gIU5JRxJkTT+Wo4nLstuF5kWpRkTcp6ScX5QGRYICOjRvNgFi9mmBdbXSep2wimbPLySqfg2dCmVz1LMQQ1BJo5bXtb/Hf3e8QjIQoSMvnzImnckzJkTjsA39flqFgxIZF0OezwmEV7Rs3RK92tqenk3X0MWSWzyFzVjnOnOE3xosQI0V7sJ3Xd/yXN3ctIxAOkOvJ4YyyUzh+1DESEgdpxISFEQ7TUVlB22qzeyn+FpvuUaPJLC8nc/Yc0qdMlTubCTHEtQc7eGnra/x759t0hDrJdns5Z9LpLBw9D5ej5xNHRN+G9V4x1NJM+9o1ZkCsW0ukvR0wbzaTMaucLCsgXEVFA1xSIcShMAyD9lAHDZ2NNPgbaehsoq7Dx/+qV9AaaCPLlcl5U87khDHzcTvkgtXDMazCwjAM/Du2R1sPnVu3RK9jcOYX4J13PJnl5WSoI4b83biEGAn84UC3IGjobKDB32RNa6LB30ggvP+NmDJd6SyZdBqLxy4kzSmDZybDkA+LSGcHbevMU1vb1qwm3GRd22e3kz51Gpmz55BZXo579Bg5JU6IQSQUCdHob44Lg8a4IDBft4d6v14p05VBcXoheWk55HlyyUvLjT7OKZtKW2OoHz/N8DfkwsIwDILVVbStXk3bmlW0b9KxU1u9XrLnLySzfA4ZM2biyBza97wVYqiKGBGaAy09BEDseUugFYOeT933ONzkeXKZkD3OCoC4QEjLJc+T02e3UoYrnTbkNOJkGjJh0fDRx9S8/R5tq1cRrK2JTvdMKDPPXJo9h7QyObVViP7WGmijsmkrFY1b2dGyi/rORhr9TUSMni9eddoc5KblMiV3YlxroHvrIN2ZJj0Bg8yQCYv13/0BAPa0NPPU1tlzyJw127ynsRCi3zR0NlLRuJUKKyCq2qqj82zYyPFkM8E7rsfWQF5aLlmuzGF7AdxwNmTCYsz552KbOI30qdPk1FYh+olhGNS010aDobJxK77Ohuh8t93F9LypTM4tY0ruJMqyx8lZR8PUkNnrll1+qQxlIESKRYwIu1v3mi0HKxxagq3R+ZnODGYXzmBK7kSm5E5kXNYYubhthBgyYSGESL5gJMSO5l1UNG6homkrWxq30xmOjemZ68nh6OI5TMmdxJTciZRmFksX0gglYSHECNIZ6mRr0w4qmsxWw7bmHQQjsVNMi9MLmZs7m8lWy6EgLV8ONAtAwkKIYS3+TKWKxq3sat0TPUvJho3RWaXRVsPknInkeOROe6JnEhZCDCPRM5Uat1DRtK3bmUoOm4Oy7HFMzjFbDZNyyshwpQ9gacVQImEhxBAVMSLsaa1iS9N2tjRtZ1vrdmrbfNH5cqaSSCYJCyGGiI5QB1ubdrClaTtbm7azrXkHnWF/dH6WO1POVBIpI2EhxCBkGAa1HXXRVsPWpu3sbavuNjxGSUYxR+VMYJL1M3PCJHx1bQNYajGcSVgIMQgEwkF2tOxiqxUOW5q20RqM7fhddlf0OMOknAmU5Ywny9V97DM5pVWkkoSFEAOg0d8UbTFsadrOzpbdhI1wdH6eJ5eji+dEw2FM1ijpUhIDSsJCiBQLR8LsbtvbLRzq44bMsNvsjPOOsbqTypiYPZ68NBnzTAwuEhZCJFlbsJ2tccGwrXkHgUgwOj/LZR6I7gqH8d6xuOVWn2KQk7AQ4jCEIiHqOnxsadrB1qZtbGnaTlV7bAh9GzZGZZYwMe5AdFF6oVwVLYYcCQsh+mAYBm2hdnwd9dR1+KjrqDd/Os3XDZ2N3c5Q8jjcTM+bGg2HsuzxcuGbGBYkLMSIF4qEqO9sNAOhMy4QrHCIH1gvXo47m0k5EyhML6AsezyTciYwOqtUzkoSw5KEhRj2em4d+KIthH1bB11cdheF6fkUpk8yH9MKrNf55Kfly3EGMaJIWIhhIb51UNvho67TZ4VDfZ+tg1xPDpNyyqIhUJhuBkJBWgHZ7iw5tiCERcJCHLC2YDtVbTVUtVdT11FPOBKOfiM3MDD/i5timI9d/zeX2X++sc/6XVNj861pRtw8cwIBm5+9zbW9tg7cdheF6QUUdIWBtA6EOCQpDQulVDGwAjhVa70xbvpNwNVArTXpWq21TmVZxIExDINGfxNV7TVWMNRQ3WY+j79j2mDSW+ugML0Ar0taB8K0ZUsl99//azo7O+no6GD+/IVcddU1B/X78Z//vMnMmbOw2Ww88sgfufnmW5NStueff5Yzzzwb5yC+ZXTKSqaUcgG/Bzp6mD0XuExrvSJV7y/6Fo6E8XXWm4FghUJVWw3V7TXdBqcD8/TP/LQ8ZmZPpzSjmNLMYoozinDZndH55n82ov+3/gDNV9Z2bF2vrMdur22Yq8RNs8W96ra92LyxJQU0NXQvrxD7amlp4bvfvY0f/vBuxo0bTzgc5vbbb+X55//OuedecMDbeeaZJykru40JE8qSFhQAjz/+CJ/+9JlDNyyUUlcCNwIKc6e/HrhPa/3MAWz7HuAB4Fs9zDsa+JZSqhT4p9b6xwdS2KIiuTFLlwOti0AowJ6WGna37GV3cxW7mqvY3VzF3pYaQnF3SANw2B2MzipmTPYoxmSXMia7lLHZpYz2luB2Dt6hrYuKBm/Z+pv8jcTE18WyZa+zcOEC5s6dGZ32y1/+HJfLxb333suKFeb31rPOOovLL7+cW2+9Fbfbze7du6mpqeEnP/kJtbW1VFZu5ic/+R533303t9xyC08//TRLlixh3rx5aK2x2Wz87ne/w+v18vOf/5wPPvgAwzC44oorOP3007n00kuZPn06mzdvprW1lV/96le888471Nf7+NGP7uB3v/tdv9fTgeo1LJRSNwDXAj8A1mJ2O88Bvq2Uytda/76Pda8AarXWryilegqLp4DfAs3Ac0qps7TWSxMVtra2JdEiI0JRkXe/umgPdsR1HVVHu458nQ379eV7HG7GZI6iNLOY0oxiSjLN1kJhWv7+4w+FsL65D85v7z3VxUgldRGzb11s3bqTvLzi/epn+fLXqazcxm9/+xDhcJjrr78apcrp7AxSVDSKr3zlG7zwwnM89tgTfOMbtzF58lS+8Y3baGkJEAyGqa1tobm5hYULP8l1132d733vOyxd+gqZmVlUVm7lN7/5A36/n2uvvRKl5hAIhCgrm8Y113yV3//+t/z1r89y6aVXcN99v+W2276fkn+/ZH2B6KtlcS3wSa21L27aRqW4SONwAAAgAElEQVTUf4F/YnYx9eYqwFBKnQIcCfxJKXW21rpKKWUDfqm1bgJQSv0TOApIGBbDmWGYh23DkTBhI0LE+gkbYesxQsQw51VHdrNxz/a47qNqmgP7/5JluTKZnFtmdR2VRLuQcj050o8vRpSSklFs2rSx27Q9e3aj9QbmzDkSm82G0+lk5szZbNu2BYCpUxUAxcUlrFmzqs/tT5sWWzYQCFBdXYHWG7nxxmsACIVCVFXt7bZsSUkJPp+v5w0OQn2FRWSfoABAa71XKdXnRrXWJ3Q9V0q9BVynta6yJmUDa5VSRwBtwEnAwwdZ7n4VDAdZX6/ZUL8Zf9gft/OOEI6Eu+3YYzt683nXTj4S6b7D72mdQ5WflseMfLVfS2HfIayFGKkWLlzE448/zHnnXcCYMWMJhUL85je/YO7cY/j44xVcfPHnCYVCrF27mtNPPwt4p8cvVHa7nUikp7/V7stOmFDGUUcdwy23fJtIJMKjj/6RMWPGmEv2sF2bzR4922+w6jMskvlGSqlLgCyt9YNKqduANzH7Nt7QWr+UzPdKBjMgNrGyZhVr6zbsd9C3N3abPfrjsNlx2BzdXjvt7ug0h82O3ebAYe9ax2FNs++zTNx27HaKc3LxkktpZjElGcV45FaZQvQpMzOLb3/7e/z0p3cRiURob29n4cJPcMEFF1NdXcW1115JMBjkpJNOQanpvW5n1qxy7rrrTr75zW/3+X4LF57ARx+t4Mtf/iIdHe2ccMInycjo/cvbnDlHcvPNX+U3v/n9oG3123pLM6XUXuD+ntbBPNV1dCoL1gMj1f2xwUiIDT7Nypo1rKlbFw2IgrR85haXM6doFjkebw87c3Mnb7PZ+mWoB+mbjpG6iJG6iJG6iCkq8iYlffpqWTzAvm2rmL6OVwwpwUiIjfWbWFmzmtW166NX+uan5bFozPHMLS5nvHfsoE17IYToD72Ghdb6e/1ZkP4UioTYWL+ZlTWrWVW7LhoQeZ5cFo6Zx9HFcyQghBAiTl+nzqZjnjb7tNb6faXUvcCXgI+Az2mtd/dTGZMiPiBW162jIxQXEKPncVRxOWXZ4yQghBCiB311Q/0KCAHblFJnAJ/HvPJ6LnAfcF7qi3d4QpEQuqGCldWrWVW3jo6QeTF5nieX+aOOZW5xOWXZ4yUghBAigb7CYr7WejaAUuoczBbGZmCzUurOfindIQhHwmxsqOCjmtWsql1LuxUQuZ4c5o86JtqCkHsOCCHEgesrLMJxzxcD34x7PajO1QxHwuhoQKyjLdQOmAFxXOnRzC0xWxASEEIIcWj6CgufUmoekAmMAV4HUEotBnalvmh9C0fCbGqsNLuYatdGAyLHnc3isQuZWzyHiTkSEEKI5HviiUd55pknefrpF/B4PEnZ5ksvvUh2djaLFp2YlO0lW19h8f8wx3AqAb6stW5TSn0H+CpwZn8Ubl/hSJjNjVtYWbOKj2vX0hbsCggvJ45dyNziciblTJCAEEKk1Guv/YuTT/4Ub7zxKmecsSQp20zWdlKlr1NnVwMz9pn8FPCbrnGd+tODH/6F93aspDXYBkC228uJYxcwt3iOBIQQI8zT/67gg401vc53OGyEwwc3fMax04u56KQpCZdbufJDRo8ey7nnfobvf/8OzjhjCTfeeA1Tpkxj69ZK0tPTKS8/ivfff5fW1lbuvfc+MjIyuPvuH7Fr104ikQhf+tL1zJ17DJdeehHjxk3A5XIxfvwECgoKOPvs8/nlL+9mw4Z1BIMhrr76GhYs+AR33/0jamqqaWpq4vjjF/ClL11/UJ/vcPV16uxl+0yKAA1AfUpL1IvXK9/G687ihDELmFs8m8m5EyUghBD9bunS51my5FzGjy/D5XKxbt1aAGbMmMnXv34zN930FdLS0vjlL3/HXXfdyccfr8TnqyMnJ5dvfesOmpoaueGGa3jiiafp6OjgiiuuZtq06Tz0kHmt89tv/4empkb+8Ic/4fPV8fe/P82UKdOYOXM2t956O36/n/PPP2PwhAXwyX1e24Bi4CGl1CVa63+nrlj7+8HJN5MbKZSAEEJw0UlT+mwFpGq4j+bmZt59dzkNDfX87W9/pa2tlWef/SsA06aZY0p5vVmUlU20nmcTCPiprKxg9eqPWL/eDJZwOERTUyMA48eXdXuPHTu2M3NmOQAFBYVcc82XaWtrZcOGdaxc+SGZmZkEAsGkf7ZE+uqGurKn6coccvYx4PhUFarH9y2cLGO9CCEG1KuvvsRZZ53DDTd8DYDOzk4uvPBscnJy+7xea8KEMoqLi7nssqvw+zt57LGH8Xqzgf1HoS0rK+PNN98AoLW1lTvuuJX58xeRleXlm9/8Nrt27eSFF57DMIx+vUbsoL+mW/fKTk9BWYQQYlB78cXnOe20M6Kv09LSOPHEk9i1a0ef651zzvls376NG2+8huuuu4rS0lHY7T3vfhctOhGv18v111/NTTfdyIUXfo6jjz6W995bznXXXcU99/yYsWPHUVdXm9TPlkivo872RinlAFZprWelpki9Svmos0OFjKgZI3URI3URI3URk/JRZ5VSJ/QwORe4DPhXMt5cCCHE0NDXAe59R53tOhvqDeDBlJVICCHEoNPXAe59z4YSQggxQsl5qEIIIRKSsBBCCJGQhIUQQoiEeg0LpdQPrMdcpdQTSimfUqpKKXW/Usrbf0UUQojB5YknHuWcc07D7/f3uszjjz8avWJ7OOirZdE1suxvgB3AZMyBBfcCf0pxuYQQYtCKH3W2N5deegUzZvT35Wip09eps13maK0vjXv9faXUulQVSAghEnm2Yikf1azpdb7DbiMcObgLjo8qns35U85KuFxPo84+++wzvPzyUux2O+XlR3LDDV/jhz/8Lief/Clmzy7nJz+5i9bWFpqaGlmy5DzOO++CgyrbYNBXy6JUKXUxsEspFR0HSil1LNCZ8pIJIcQg1NOosy+99CJf+9rN/P73jzB69BhCoVB0+V27dnHKKZ/iF7/4LT/72S/561//PIClP3R9tSxuBRYCecA3gM8opb5uPb+oH8omhBA9On/KWX22Avp71NnbbruDJ598ggce+A0zZ87utk5BQQFPP/0X/vOfN8nIyOwWJENJXxfl/Yn9j008DPxaax1JaamEEGIQ6m3U2YyMLG6++Vt4PB5uuulG1qxZFV3nyScfZ9ascs477wJWrvyQd99dNlDFPywHcswiSmvdnKqCCCHEYPfii89z++3fj77uGnU2Pz+fL33pMnJz8ygqKmLGjFm89NKLACxceAL33PNjXn31ZXJycnA4HAQCAdxu90B9jEPS66izPdwprxur5dEnpVQxsAI4VWu9MW76EuAOIAQ8rLX+wwGUVUadtciImjFSFzFSFzFSFzEpH3UWOBn4DPA05l3y4hkkOH1WKeUCfg909DD9F8CxQBuwXCn1ota66uCKLoQQor/0dczicqVUHrBMa/3wIWz7HuAB4Fv7TD8CqNBaNwAopZYBnwCeOYT3EEII0Q8SHbO4Fvj8wW5UKXUFUKu1fkUptW9YZANNca9bgJwD2W5RkVw43kXqIkbqIkbqIkbqIrn6DAut9V7MFsLBugowlFKnAEcCf1JKnW11NTUD8f+KXqDxQDYqfZAm6Y+NkbqIkbqIkbqISVZo9hkWSqnTgAuBsZg3P9oDvKy1/ntf62mto3fZU0q9BVwXd0xiAzBVKZUPtAIncGiBJIQQop/0dVvV7wPzgCcwx4OyAaXA1Uqp+Vrrmw/mjZRSlwBZWusHlVI3Aa9gXkH+sNZ696F+ACGEEKnXV8viYuCIfS/AU0o9CawFDigstNaLracb46a9CLx4UCUVQogBtnLlh9xxx7coK5sYnZabm8ddd/10AEvVP/oKi07M7qcd+0yfAPQ+Lq8QQgxjRx99DN/73o8Huhj9rq+w+D/gbaXUJsxuKAMYDUwDrkh90YQQome1zzxFy4cf9Dp/u8NOOHxwoxJ5jzmWogs/e0jlufHGa/jGN25jwoQy/vGPv+Hz+TjjjCXccsv/Izs7h/nzF3Lsscfxi1/cjcPhwO12881vfgfDiHD77bdSUFBAbW0Nxx23gGuvvYHq6ip+9rMfEQj4cbs9fPObt1FSUnpIZUuWvq6zeF0ppTCPW4zGPL6wC/if1lpaFkKIEWnFig+58cZroq8XLFjU67L19T4eeugJXC4XV199Kbfe+h2mTlW8/fZb3Hffvdxww9epqtrDvff+hszMLL785S+i9Ub+/OfHuOCCi5k/fyEffvg+DzxwH3feeVd/fLxeJbrO4jRgPPCS1rqya6JS6hqt9YMpLZkQQvSi6MLP9tkKSOWpsz11Q73zTmxwwPgRlEaNGo3L5QKgrq6WqVMVAHPmzOWBB+4DYPLkaWRnm5eazZgxix07trFlSwWPP/4If/7zYwA4nQc1jF9K9HVb1Z8AX8HsdlqulPpC3OzrUl0wIYQYKtxuDz5fHQCbNkXP5cFmi+1iCwuLqKjYDMDHH69k3LjxAGzfvpXOzk7C4TDr16+lrGwS48eXcf31X+G++x7kG9+4jcWLT+7HT9OzvuLqTOAorXVIKfVr4FWllF9r/Qz7jxUlhBAjwr7dUACXXHIZ9977U4qLSygsLOpxvVtu+Ta/+MXPMAwDh8PBrbfeDoDL5eL222+hvr6exYtPZurUadxww9f4+c9/QiAQwO/v5GtfO6grFVKir1Fn12LeUjVsvZ4JvAZcAtyrtZ7bb6U0yaizFrk6NUbqIkbqImao1MXevXu4887bePDBR1P2Hskadbav26o+A7yllJoHoLVeh3k199PA5GS8uRBCiKGh17DQWn8P+C7mQH9d05YDRwOPpLxkQggxzI0aNTqlrYpkSjSQ4Bs9TNsJfD1lJRJCCDHo9NUNJYQQQgASFkIIIQ6AhIUQQoiEJCyEEEIkJGEhhBAiIQkLIYQQCUlYCCGESEjCQgghREISFkIIIRKSsBBCCJGQhIUQQoiEJCyEEEIkJGEhhBAiIQkLIYQQCUlYCCGESEjCQgghREISFkIIIRKSsBBCCJFQn7dVPRxKKQfwB0ABYeBKrXVl3PybgKuBWmvStVprnaryCCGEOHQpCwtgCYDWeqFSajFwL3BO3Py5wGVa6xUpLIMQQogkSFk3lNb6H8A11ssJQPU+ixwNfEsptUwp9a1UlUMIIcThsxmGkdI3UEo9BpwHXKC1fjVu+p3Ab4Fm4Dngfq310j42ldqCCiHE8GRLykZSHRYASqlS4H/ADK11m1LKBmRrrZus+V8GCrTWP+hjM0ZtbUvKyzoUFBV5kbowSV3ESF3ESF3EFBV5kxIWqTzAfSkwVmv9Y6AdiGAe6AbIBtYqpY4A2oCTgIdTVRYhhBCHJ5Wnzj4LHKWU+i/wCvB14Hyl1DVWi+I24E3gbWCd1vqlFJZFCCHEYUhZy0Jr3QZc1Mf8x4HHU/X+QgghkkcuyhNCCJGQhIUQQoiEJCyEEEIkJGEhhBAioVQO9yGEGIQMwyAcMQiGIgTDEUKhiPnceh3c73U4wfzYNrDBtLG5lE8uoCQ/Y6A/qkgiCQsxpLR3Btm0s4lNOxvROxvxB8O4nXYy0pxkeJzWo4t0j4OMNBcZHifp3eY5Sfc4SXM7sNmScq1SygRDETr8IToCITr94e7PAyE6/CE6A2Ha/SE6/SGw22lrDxzQzj6V1+Ku0LU8+cZmivPSKZ9UQPnkAtT4XFxOR+reVKSchIUY1JrbA2ze2Yje0cimnY3srGmNjvvisNvIznRT1xgkEIoc1HbtNpsVKPuHSzRUukLGmpYeF0ZpHgf2HsLGMMxv7B0Ba+du7ci7XncGYjv9Dn/YnGfN74yfHggRCh/6Ht1ht+F02nE57LicdtwuB5npruhrV9y82GsHLqfdXK/H+fu87jbNXLczEGL9tgZWV/pYt62e11fs4vUVu3C77BwxPo/yyQXMnlxAYU76IX82MTD6ZbiPJJHhPizDeSiDxlZ/NBj0zkb21LVF5zkddqaMyWbauFzUuFwmjclh7Ohcamtbot/C2/0h2jtDcc+D0Wnt1o45+rwztrw/GO6jVPuzAWlWkKS5HfiDsTAIRw7tbyrN7Yi2etI9TtK7XnucpLudpHscpFmP5nLWc7eTNI+DMaNyaW5sx+m04bAP/OHIUDjC5p2NrN7iY3Wlj72+9ui80YWZ0VbHlLE5OB3JLe9w/hs5WMka7kPCYggaTn8IdU0dZjDsMMOhpqEjOs/jcpjhMD4PNS6XiaOycTm771SSVRehcMTs0okPl26h01MQhejwB+kMhPG4Yzvt9P127uaOP83T8/N0jxOPu+eWysEY7L8XtY0drLGCY+P2hmhrMM3tYObEfMonFTBrUgF5Xs9hv9dgr4tDZRgGHf4wrZ1BWtuDtHYEaeswH1vinnf9tHcGefTOTw/usaGE2JdhGNQ0dpjBYLUefM2d0fnpHofZvz0ul2njc5lQ4k36N87eOB12stLtZKW7+uX9RqKi3HROmjuWk+aOJRAMo3c2srrSx+rKOlboWlZo8z5o40uyKJ9cQPmkQiaNzsZuH9zHlg5VJGLQ2hm3g7d2/q2d3V+3dQRp7QzR2h6grTN0wC1Xj9tBVlryfp+lZTEEDZVvTYZhsMfXbrUcGti0s5HG1kB0fmaa0+xSsloO44qzDnrHMFTqoj8M1bowDIPqhg5WV/pYU1mH3tkYPV6TmeZk1qQCyicVMHNSPtkZ7gPa5kDURTgSobktSGOrn8YWP01tgR6/7XcFQHtn6IDuu2ADMtKcZKW7uv1kdj3PcJGVFvc83UVmmivaCh/0o86KkSdiGOyqaUXvbGST1a3U2hGMzs/OdHPs9GLU+FymjctldGHmYXe9iKHPZrNRmp9BaX4Gnzp2HJ2BEBu2N7Cm0sfqLT7+t76a/62vxgZMHJ1N+STzIPmEUm+//P50hUBTm5/GloAZBq1+Glu7P29pCyTc+TvsNjLTXeRmeRhTlGXt+J1kpbutAHDijXveteMfDK0rCQtxyMKRCDuqW6NdSpt2NtLuD0Xn52d7mD+pJNp6KMlLH/Snq4qBl+Z2ctTUIo6aWoRhGOyuazODo9LH5l1NbNnTzD+WbSU7w8VsKzhmTswn8yC7XCIRg5b2AI2tARpa/TTFB0CLn8Y283lzW6DPU43dTju5WR5Kx+aQ6/WQm+UhJ8tNbqYn9k0/3YU33TUkTtnujXRDDUED2d1gGAarK338e+VuNu1qxB+InUVUlJuGGpcXbTkU5qSl/A9jqHa9pMJIqIv2zmD01Nw1W3w0tZndmnabjSljspk9uYDyyYVMHJ/Hlu31sRZA186/xR9tDTS3BYn0sf9zOe3kZrnJzYoFQJ71PDfLTY71PN0zuANAzoYawQZip2AYBqsqfbywbCvbqsz3HlWQYR6Mtn7ys9P6tUwwMnaQB2qk1UXEMNhZ3crqyjpWb/GxZXfzAR0DcDqsEPB6yM20wsDbPQDystyke5yDOgQOlByzEP2iKySeX7aV7VUt2IBjphdz9oIyxhZnDXTxxAhmt9mYUOplQqmXJQsn0tIeYN3WetZsqQe7jQy3o1vLoCsgMoZJCPQ3CQvRI8MwWFXh4/nlsZA4dnoxSxaWMbZIQkIMPt4MN8fPLOX4maUjrpXVHyQsRDeGYfBxRR0vLNvG9mozJOYdUcySBWWMkZAQYsSSsBCAFRKb63h++VZ2VLfGQmLhRMYUZg508YQQA0zCYoSTkBBCHAgJixHKMAw+2lzHC8u2sqPGDInjZpSwZEEZoyUkhBD7kLAYYSKGwUeb6nhxeSwkjp9RwlkSEkKIPkhYjBBdIfHC8q3srGnFZoPjZ5otiVEFEhJCiL5JWAxzZkjU8vyybeyqlZAQQhwaCYthKmIYrNS1vLB8K7tq27DZYP5Ms7tJQkIIcbAkLIaZnkOilCULyyjNzxjo4gkhhigJi2GiKySeX76V3RISQogkk7AY4iKGwQqrJdEVEgtmlbJkQRklEhJCiCRJWVgopRzAHwAFhIErtdaVcfOXAHcAIeBhrfUfUlWW4ShiGHy4sYYXl29jd10bdpuNhbNKOUtCQgiRAqlsWSwB0FovVEotBu4FzgFQSrmAXwDHAm3AcqXUi1rrqhSWZ1iIGAZvf7ybP7+8IRYSs62QyJOQEEKkRsrCQmv9D6XUUuvlBKA6bvYRQIXWugFAKbUM+ATwTF/bLCrypqKog54/GGZNRR3vr6/ig/XV1DV2YLfbOPnYcVx0yjRGF47sAf5G6u9FT6QuYqQukiulxyy01iGl1GPAecAFcbOygaa41y1ATqLtjaQhhxta/KyqrGN1hY/12+oJhCIAZHicnDpvPCcfNZrivAwwjBFVL/uSoahjpC5ipC5ikhWaKT/ArbW+XCl1C/A/pdQMrXUb0AzEfwIv0JjqsgxmEcNg294WVlXUsaqyjh3VrdF5owoymDOlkDmTC5gyNofSkhz5QxBC9KtUHuC+FBirtf4x0A5EMA90A2wApiql8oFW4ATgnlSVZbDq8IdYv62eVRU+Vm/x0WzdT9hhtzGzLI9yKyCK5ViEEGKApbJl8SzwiFLqv4AL+DpwvlIqS2v9oFLqJuAVwI55NtTuFJZl0Khp7GBVRR2rK+rYuKORcMS8a3B2hotFs0cxZ0oBM8rySffIWc1CiMEjlQe424CL+pj/IvBiqt5/sAhHIlTsamJVpY9VFXXs9bVH500o8TJnSgHlkwspG+XFLvcFFkIMUvL1NQVaO4Ks3eJjVaWPNZU+2v0hANxOO0dOKYwGRJ7XM8AlFUKIAyNhkQSGYbDH187qijpWVdSxeXcThtm7REG2h+NmljBnciHTx+fidjkGtrBCCHEIJCwOUTAUQe9sYFWF2b1U19QJgA2YPCaHOVMKmDO5kDFFmdike0kIMcRJWBwgwzBoaguwptLsXlq3tR5/0Dy5K93j4NjpxcyZUsCsSQVkZ7gHuLRCCJFcEhYWwzBoaQ/ia+6krqkTn/VT19SBr7kTX3MnHf5wdPmS/AzmTC5gzpRCpo7NwemwD2DphRAitUZMWEQMg6bWQPcAaLKCwXredZX0vtLcDgpz0ijMSWf6+FzKpxTKsN9CiBFl2IRFOBKhodnfrWVQ1xxrIfiaO6PXNOwrM83JqIJMCnLSKMhOozAnjYKc2GOGxynHHYQQI9qQCYtgKEx1fXs0AGJdRWYrob7FHz0DaV/ZmW4mlHopyI4LASsU8rPT5AI4IYRIYMjsJc+/ZWmP021ArtfD5DE53UKgq5VQkJ0mp6sKIcRhGjJhUT6lEG+6M651kE5BThr5Xo8cXBZCiBQbMmHxw+sXykirQggxQOQruRBCiIQkLIQQQiQkYSGEECIhCQshhBAJSVgIIYRISMJCCCFEQhIWQgghEpKwEEIIkZDN6G1AJSGEEMIiLQshhBAJSVgIIYRISMJCCCFEQhIWQgghEpKwEEIIkZCEhRBCiIQkLIQQQiQ0oDc/Ukq5gIeBMsAD3AWsBx4FDGAtcIPWOqKUuhM4EwgBX9dav6+UKgb+AOQBDuAyrXVlf3+OZEhCXRwJPGBN2wR8UWsd6e/PkQwHUxfW8lOAf2itZ1mvC4G/AOnAHuBKrXV7/36K5EhCXYy31ndi3oX4Gq217t9PkRyHWxdx2zkB+LPWely/FT7JkvB7kQncD0wE3MBXtNbv9/WeA92y+ALg01p/AjgduA+4F/iONc0GnKOUmgucCBwHfBb4rbX+zzD/0U8AvgNM7+fyJ9Ph1sWdwPe11oswf3nO7OfyJ9MB1QWAUupS4CmgMG79O4C/WMt+BFzbj2VPtsOtix8A92mtFwM/An7cf0VPusOtC5RS44D/A1z9WO5UONy6+Aaw1lr2S4BK9IYDHRbPALfHvQ4BRwP/sV6/DJwCLAJe1VobWusdgFMpVQQsBMYqpV4HPg+81V8FT4HDrYuPgHyllA3wAsF+K3nyHWhdADRghme8RcC/elh2KDrcuvg/4J/WcyfQmZpi9ovDqgulVBpm6/vLqS1mvzjc34vTgIBS6hVrO68kesMBDQutdavWukUp5QX+htk6sGmtu8YgaQFygGygKW7VrullQIPW+hRgB3BLf5U92ZJQF5uBXwMbgBKGcHAeRF2gtV6qtW7bZxPxdRRddig63LrQWtdprYNKKQXcA3yvH4ufVEn4vbgPuEdrvbvfCp0iSaiLQiBPa30a8CLm70afBrpl0dUsfBN4XGv9FyC+n90LNALN1vN9p/uAF6xpLwLHpLzAKXSYdfEr4BNa6+nAn4Cf90uhU+QA66I38XWUaNlB7zDrAqXUJ4F/AJcO1eMVXQ61LpRSo4FPAHcqpd7CbIU/leLiptRh/l4c9L5zQMNCKVUCvArcorV+2Jr8kVJqsfX8dOBtYDlwmlLKbh2ws2ut64BlwBnWsicA6/qt8EmWhLqox9xJgnlQN6/fCp9kB1EXvVlO7Pci0bKD2uHWhRUUvwI+rbX+MJVlTbXDqQut9R6ttdJaL7aO39RrrT+b6jKnShL+Rg563zmgZ0MBt2Hu1G5XSnX1v30N+LVSyo3ZpfI3rXVYKfU28C5mwN1gLft/wB+VUtdjdjtc0q+lT67DrYsvAk8ppUJAAPOg1VB1QHXRx/p3AY8ppb4E1DECfi/6WP+XmGe7PGb2RKG11kP1gP/h1sVwcrh18SPMfee7mMc3L0v0hjJEuRBCiIQG/JiFEEKIwU/CQgghREISFkIIIRKSsBBCCJGQhIUQQoiEJCyE6IFS6j6l1N/2mfYppdQW66pZIUYUCQshenYrcLRS6mzoNkrnVVrrlgEtmRADQK6zEKIXSqlTMIeBPgL4PubV8v9PKXUc5gif6UAt5rDf25VSJ2GO8pqOOS7P/2/vjlmjiKIwDL9LClmQdAFrEQ7plq1CGjs7SSpluzQKFgFhuzT+BksrC/0HilVai5AuoPBZ2ZoyzaHX0HQAAAEXSURBVLYWdwLbbIYlZjcr79PdC3O5U32cYTjnbZIvVfW5Wz8Bpkm+reF1pFuxspAWSHJK68b5EXgGnFTVA9oMlZdJxrTmjR+6R46Bo27/DS1grv1JsmtQaFOtu92HdN9NaR2ND5PMuiFTj4GvXfuMAa2SAJgAz6tqAuwBD+fOOVvdlaV/z8pCukGSK1r3zt/d1hbwK8koyQgYA0+7OSLfaTMFzmlDhgZzR81WdmnpDhgW0nJ+Ao+qar9bvwY+ATu0EZXvaIOXDmjBIv0XDAtpCUlmwAvgfVVd0D49vUpySQuNH7SOn0Ngu6qGCw+TNoh/Q0mSellZSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqddf6+WQBRO3cKMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged = merged.drop('Australia', level='Continent', axis=1)\n",
    "merged.mean(level='Continent', axis=1).plot()\n",
    "plt.title('Average real minimum wage')\n",
    "plt.ylabel('2015 USD')\n",
    "plt.xlabel('Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Continent</th>\n",
       "      <th>America</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>69.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.19</td>\n",
       "      <td>4.70</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.02</td>\n",
       "      <td>1.56</td>\n",
       "      <td>3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.52</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.03</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.44</td>\n",
       "      <td>5.48</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.96</td>\n",
       "      <td>5.95</td>\n",
       "      <td>9.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.48</td>\n",
       "      <td>6.65</td>\n",
       "      <td>12.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Continent  America  Asia  Europe\n",
       "count        69.00 44.00  200.00\n",
       "mean          3.19  4.70    5.15\n",
       "std           3.02  1.56    3.82\n",
       "min           0.52  2.22    0.23\n",
       "25%           1.03  3.37    2.02\n",
       "50%           1.44  5.48    3.54\n",
       "75%           6.96  5.95    9.70\n",
       "max           8.48  6.65   12.39"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.stack().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001CCD496CF28>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = merged.groupby(level='Continent', axis=1)\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent\n",
       "America     7\n",
       "Asia        4\n",
       "Europe     19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dVSkd3NPJ++bewPvm3jDiMYmsCvnNb36Xhx56gN/+9n9YvHjpW87Jy8vjkUf+yiuvvExGRiaBQGCYqydWNMF9G3Aj8Iiu62uAQ5EHwj32p4CXhBA/ueCcDcAu4DrgtZi1WFHG4WRnJSZMTA8H6rEoyigEoKq7JtbNUlLMcFUhMzKy+M///AZ2u52vfOV2Dh06cO6chx66nyVLLuK9772ZvXt3s33768lq/ltEE9yfAK7Rdf0NQAM+ruv6V4CTgBm4ArDrun5d+PhvAL8B7tN1/XXAB8QnAaUoUfAGfVR311KYkY/VZB3z+RlWJxmWDGp76uPQOiWVbNz4FN/5zvfPfR2pCpmbm8unPvURPJ4cCgoKWLRoCZs3bwTgbW+7nLvu+hHPP/8M2dnZmM1mfD4fNpstWU8DUFUh0ypPGY2p+HxfF/v41f7fj1gFcjRPnPwHVd013HX591K2vvtU/NlO9eerqkIqU9rJzkoASrKmj/saBc48AGp7GmLSJkWJNxXclbR3qsMI7tMzi8Z9jXynUUGypkftq6pMDiq4K2ktEApS2VlFriMHp8Ux7uvkh3vudSrvrkwSKrgrae1MezW+kI/iCfTaAXLsHkyaiRoV3JVJQgV3Ja1VtBjz2yeSbwcwm8zkOnKo72kgJEOxaJqixJUK7kpaO94aybePfX77hQqcefhCfpr7Wyd8LUWJt2jmuSvKpHWitZIMixO3beI1V/LD2/LV9tQzLaNgwtdTUsvevbv57ne/QXn5rHPf83hy+MEPfjLCWalLBXclbXV6u2jta2eWe2bU9dtHkn9uOmQ9KwsvmvD1lNSzatVqvve9HyW7GTGhgruStiJleosyC2NyvfMzZtRc93hrfvRvdO9+c8RjzppNBIPRj3+4Vl9MwS0fGP3AC9x++6f56le/ycyZ5Tz55N9pbW1lw4Yb+frXv4zbnc3atW/j4osv5ec/vxOz2YzNZuNrX/s2Uob4znfuIC8vj+bmJi69dB2f+cwXaGxs4Kc//SE+nxebzc7XvvZNpk2b2ID/UFRwV9LWma5q4HxtmIlyWhzYzXYa+9S+M+lqz57d3H77p899vW7d+mGPbWtr5U9/egCr1conPvFh7rjj28ybp/Paa1u55567+cIXvkRDQx133/0/ZGZm8fnPfxIhKnjwwfu4+eZ/Ye3at7F79y5++9t7+K//+kHMn4sK7kraquw8iwZMy4xNflzTNHLs2TT1txAMBTGbkrP/wFRQcMsHRu1lx6P8wFBpmTfeOF8IbHC1lunTi7FajVpFLS3NzJtnVAxdtmwlv/3tPQDMmTMft9uoeL5o0RKqqs5w+vRJ7r///3jwwfsAsFjiE4ZVcFfSUkiGqOquIT8jF7t56G3SxiPH4aGhr4mWgTY1qDpF2Gx2WltbmDmznOPHK8jPN37umnZ+smF+fgEnT55g7tx57N+/l7KyGQCcPVvJwMAAVquVo0cPs2HDjcyYUc5tt/0rS5cu4+zZM+zbtycu7VbBXUlL9b2NeIM+StwTm99+oRy7B4CmvmYV3NPQhWkZgA9+8CPcffdPKCycdi6wX+jrX/8WP//5T5FSYjabueMOY/M5q9XKd77zddra2nj729/BvHnz+cIXvsjPfvZjfD4fXu8AX/zif8bluajgrqSlyGBqiTu2A1U5DiO4N/Q2sTR/UUyvrSTXypWr2bTphSEfGyr3/vvf//ncv+fPX8Cvf/2HtzxeX19HTk4ud975y7d8v6SklLvvvmfiDR6FWsSkpKUzncZgaml2bHvuuY7zPXdFSWUquCtpqbKrCovJQmFGXkyvm21zo6GpGTPKqKZPL35L7z7RVHBX0s5AYICG3kamZRRgMsX2V9xsMuO2u1RwV1KeCu5K2qnqrkEiYza//UI5dg89/l56/X1xub6ixIIK7kraieTbY7Uy9UKRvLvqvSupTAV3Je2cKzuQMfFKkEOJTIdUwV1JZSq4K2lFSkllVxVZ1kyybJlxuUeOw1hx2NjbFJfrK0osqOCupJV2bwddvu64pWQAPHY1HVJJfSq4K2kl1sXChpJhcWIzWdWmHUpKU8FdSStnOmNb5ncomqaRbXfT0t+KHFxJSlFSiAruSlqp7KpCQ6PQGd+6L9l2N76Qny5fbKsSKkqsqOCupI1AKEB1dw35zjysZmtc75VtcwOo1IySslRwV9JGbU89/lAgrimZiGy7EdxbVHBXUpQK7kraqAzn26fHaX77YB67MR1SBXclVangrqSNyq6zABRlxj+4R3ruKi2jpCoV3JW0caazCofZjicceOMpy5qJSTOp4K6kLBXclbTQ7euhZaCNosxpaJoW9/uZNBNum0ulZZSUpYK7khbO1ZNJwGBqRLbdTa+/j/5Af8LuqSjRUsFdSQuRwdR4FQsbikdNh1RSmAruSlqo7IwMpiZu0+rz0yHbEnZPRYmWCu7KpBeSIc50VZPnyMFutifsvueCe5/quSupRwV3ZdKr723EF/IlZArkYNk2Y667SssoqUgFd2XSO5eSiWMlyKFk212AWsikpCYV3JVJ79xgaoJ77haThUxrBq0DKueupB4V3JVJ71RnJXaz7dzeponktrloH+gkGAom/N6KMhIV3JVJrdPbRXN/K9MzizBpif91dttchAjR4e1M+L0VZSSW0Q7Qdd0E3AssA7zAJ4UQJy84pgB4A1gqhBjQdV0DaoAT4UO2CyG+EdOWKwpwsqMSgJKs6Um5v9tm5N1bB9rJc+YmpQ2KMpRRgztwE+AQQqzVdX0N8DPgPZEHdV2/FvgxMDjhOQfYK4S4MZaNVZQLneqMBPeipNx/cHBXlFQSTXBfDzwLIITYoev66gseDwFXA3sGfW8VUKLr+stAP/BlIYQY6SY5ORlYLOaoGx5LBQWupNw3WdLp+Z7ZcxarycL86TMxm4b+/fF4MuJ2/+JQAVTDgKknJV7XVGhDIqnnO7xogrsbGJxQDOq6bhFCBACEEC8A6Lo++Jx64EdCiEd1XV8PPABcPNJN2tv7om50LBUUuGhunjpbpaXT8+3z91HVWUdJ1nS6u7xDHuPxZNDREb/fLZPf2PGpurUx6a9rOv1so6Ge78jBPpoRqC5g8BVMkcA+gt3AUwBCiNcxevHxL9WnTCmnOs8gkRQnKSUD4LJmoaGpEgRKyokmuG8DNgCEc+6Hojjnv4Avhc9ZBlQJIdQ28UpMneo4A0BJZnIGUwHMJjOZ1kza1Fx3JcVEk5Z5ArhG1/U3AA34uK7rXwFOCiGeHuacHwMP6Lp+PRAAPhaLxirKYMc7TmHClPDFSxfKtruo62kgEApgMUXzJ6Uo8Tfqb6IQIgR89oJvVwxxXPmgf7cD10+0cYoynD5/H1VdNUzPnIbNbE1qW9w2F7XU0z7QSUFGXlLboigRahGTMimJ9lNIJDNcpcluCi5bFoAqQ6CkFBXclUmpou04ADPcJUluCWSHN+1QwV1JJSq4K5NSRftJbCYb0xJcCXIokYVMbf1qIZOSOlRwVyadlv42WvpbKXUVJ6WezIXckdK/queupJDk/2UoyhidS8m4kp+SAciyZqKh0aZKECgpRAV3ZdKpaDPq0aXCYCqASTPhsmWphUxKSlHBXZlU/KEAR9uO47a58Nizk92cc9w2F12+bvxBf7KboiiACu7KGEgpaWjro761l96B5ASxE+2n8Aa9zM4uR9NSp6LFuUFVlZpRUoRaTqeMqm/Az1Ovn2F3RRPtPecLdM0vzebq1WWs1AswJSjQHmg5AsCc7PKE3C9akUHV1oF2pmUmfwaPoqjgroxo/8kW7numgs5eH067mQUzPNisZtq7vRyv6eR4TSfL5+bzyRsWkuGI70rRkAxxqPkoDrMjqcXChqLquiupRgV3ZVg7jjTwh01HMWkal100nUsWFGI2n8/ktXYNsGV3NftPtvC9P+/mq7ctJz/bGbf2VHXX0OnrYmHu/JSYAjnYueCuBlWVFJFafyFKythx1AjsdquZD149j7WLi94S2AHy3A5ueftcLl1YSHNHP3c/fIDuPl/c2nSgOTVTMqBy7krqUcFd+SfVTT383+YKbBYzt145l+l5mcMeazJpXLG8hEsWFNLQ1scvHj2IPxCKeZuklOxvOoRFM6fMFMjBMq0ZmDSTWsikpAwV3JW36PcGuPfJQ/gDITasmUlRbnRb1F2xvJhF5TlU1nfx2CunYt6uM13VNPW3MDu7HGuSq0AOJTLXXZUgUFKFCu7KWzz80kka2/q5eEEh80qjn0euaRrvvLiMXLed59+s5nBla0zbtbPB2KJ3Yd78mF43ltw2F93+HrzB+KWmFCVaKrgr55yq7eTVA3UUeBxcvqx4zOfbLGZuWFuOyaTxp03H6PeOthtjdPyhAHsa95NpzUjJlExEtsq7KylEBXcFgGAoxF+eEwBcs7oMs2l889aLcjNYu2ganb0+nt5WGZO2HWo5Sl+gHz1nbsrNkhnMpWbMKCkkdf9SlIR69UA91U09LJ2VS2lB1oSudcnCaWRn2nhhdw21Lb0TbtvO+nBKJlef8LXi6Xxdd9VzV5JPBXcFnz/Ixm2VWM2mcaVjLmS1mLhqZSmhkORvW45P6FrNfa0caa1gWkYB+c7cCbctntx2tSOTkjpUcFd4eV8tHT0+VukFZDpjMxNlbomb8iIXR860c+zs+HuyL1W/hkSyovCimLQrns4vZFI9dyX5VHCf4vq9Af6x/Sx2q4mLF8SuJooWXtUK8Pirp5BSjvkavf4+dtS/icuWxTzP7Ji1LV4yLBmYNbPquSspQQX3Ke7VA3X09PtZpRfitMe2GsX0vEzmlWZzqraLg6fGPjXytdod+EJ+lhcsTemB1AhN03DbXKrnrqSE1P+LUeImEAzxwpvVWM0mVs0viMs91i81eu9Pb6scU+/dG/SxteZ1bCYbi/MWxKVt8eC2uegL9NEfGEh2U5QpTgX3KWx3RRNt3V6Wzs6Nea89osDjZF5pNpX13VSMIff+UtWrdPt6WF64BLvZFpe2xcO50r9qOqSSZCq4T1FSSp7dWYWmweoY5tqHsmbRNAA27zgb1fHdvh5eqNqK0+JkVeGyeDYt5rLPlf5VwV1JLhXcp6jj1R1UNfUwv9SDJ8se13tNz8tkxrQsjpxp50xD16jHP3PmRbxBH5cWrcQ2iXrtAO7IXHfVc1eSTAX3KerlfbUArJifn5D7XbrQ6L0/v6t6xOOqu2t5rWY72TY3S/IWJqJpMRVJy7SohUxKkqngPgV19vrYI5rJy3ZQNsHVqNEqL3KR53bwZkUTnYO26hssGApy/7FHCBHiyrLLMJvMCWlbLGWrEgRKilDBfQp6/WAdwZBk+dz8hG0yrWkaK+fnEwxJXjlQN+Qxz5/dSm1PPYtydWa6U7dA2EjsZjs2k03l3JWkU8F9igmFJK/sr8NqNrGkPLHL+ReX52K3mti6r5ZA8K0betT1NPDMmS1kWjO4vGRtQtsVS5qm4ba7aO1vG9fCLUWJFRXcp5iDJ5tp6RxgwUwPdlti0x42q5kls/Lo6PGx93jzue+HZIgHKh4lKINcVXYZdkt8B3jjLdvmwhfy0+OfeNE0RRkvFdynmC3hAc2ls/OScv8V84wB3Bf31Jz73kvVr3G2qxo9Zy6zU3B/1LGK1JhpUXl3JYlUcJ9C+gb8bD9UR47LTkn+8PuixlOu28GsIhcnajqpauymqa+ZTaefw2lxckXpuqS0KdbcdmM6ZJvKuytJFJ9liUpK2nWsCV8gxKWzchM2kDqUFfMLqGzoZsueajqmvYI/FOCaGW/HaXEmrU2x5LaFS/+qGjNKEqngPoVsO1SPpsGSWcmtiz57upvsTBu7GndjdlQyJ7ucuZOg6mO0IguZWlTPXUkilZaZIpo6+jlV18WckmxcGcld9WkyaSyel4GppAIzVt5euj6pnyRiza3muispQAX3KWLX0UYAls2LT/XHserI3odmCWBq1Mm0ZiS7OTFlM1txWhxqrruSVCq4TxE7jjZiNmksmpWcWTKD1fSfpbJfYPHl0HW2hJqGoVesTmZum5u2gQ5CMjT6wYoSByq4TwE1TT3UtfQyu9gdt9K+0ZJS8nrLSwAscq4ENPYf60lqm+Ih2+YiKIN0ekcvlKYo8TDqX7qu6yadYxgzAAAgAElEQVTgXmAZ4AU+KYQ4ecExBcAbwFIhxICu607gAaAQ6AY+KoRoRkmKnceMlMzCmTlJbgkc7zlKs6+BUudMZnsKOZrZScXpXq5el4PTMflqyQznXAGx/jZyHJ4kt0aZiqLpud8EOIQQa4E7gJ8NflDX9WuB54Fpg779OeCQEOIy4C/At2PTXGWspJTsPNqIzWJiTnF2UtsSlAG2t23FhIlFrmVomsbccjvBIBw+nl6rOd2qrruSZNEE9/XAswBCiB3A6gseDwFXA21DnQM8E35cSYJTdV20dA4wtzQbqyWJWTi/j5N1O7G2tDHPMotMizEXfNYMOyYT7D/Wk1a1WFRddyXZoknAuoHOQV8HdV23CCECAEKIFwB0XR/unG5g1C5jTk4GFktyPpYXFLiSct9EePz1SgAuXjQdj8eYlRL5f7wF62rwH9hN4PABQg11LASMCu3bCWUeJFBajHf5UmaX5HKy2kdnr0Z5aezblqjnO1jIVginoIfuhP5+pfPv8lDU8x1eNMG9Cxh8RVMksEd5jgvoGO0m7e19UTQl9goKXDQ3dyfl3vEWDIV4dV8tTruZfJeNjo4+PJ4MOjri/Fq3NqO9/A+0YwcAkCYz/dPyqXZ047BkUtRrxdLVjU2cwCZO8B6Hkxddy3htpxNPVmy3/EvI8x2CDBkdldr2poT9fqXz7/JQ1PMdOdhHE9y3ATcCj+i6vgY4FOU5G4BdwHXAa1Gco8RYxdkOunp9LJ+bj9mUgEVCMgTbXkR75Vm0UAiZV4hcvBI5vYynzK/SJE1sCK6iA6MnbenowFF5FmflGd7VvIOGl48zUP4eHHMmZy33wcwmMy5rlsq5K0kTTXB/ArhG1/U3AA34uK7rXwFOCiGeHuac3wD36br+OuADPhiT1ipjsiuRs2T6e9GefBDt5DFkRiah1ethxhzQNOpkM02hdkplHm7Op0gCHg89Kzz0LZhPYNcRpjeeJfT7++B9G+DSFfFvc5y57S7qehrwhwJYTarSh5JYo/7GCSFCwGcv+HbFEMeVD/p3H3DLRBunjF8wFGLfiRYyHRZKC+JcAbKrA+3+X6O1tSCnlyHXXwOO80XAjsjTAMyTxUOeHnI68a1bzSM75/Luxtdw/H0TVNfCe68D8+SdHum2uailnvaBDgozErNXraJEqEVMaUpUddDT72d+mSe+dVs629Huu8cI7ItWIK+64S2BfUD6OClrcEknhXL4cXWHFWTJNP6v9Hp8nhzYuQ8eeAwCwfi1Pc7UdEglmVRwT1N7wmvG5pfGcQFNV4cR2DtakRddjFy5Fkxv/ZUS8ixBQswOFaEx8puMnu+n0+piy8JroXgaHBZw3yPg98fvOcRRtpoOqSSRCu5pKBSS7DnejNNuoawwKz438XnRHv4jWmcboWWXIJddAhd8QpBScliexiQ1ZsnRZ8EUZYVw20McabXSf81VUFYMFSfhgcchOPlqtERWqbYOqLruSuKp4J6GTtZ20tXrY15pNqZ4zJIJhdCeeACtoRY5dxEsvXBdm6GRNtrookTm4WD0MsOaBvPz/QRDGoebLfCut0NJERw9Do9vhkm2yEmV/lWSSQX3NLS7ogkAvSw+KRlt6zNoxw8ji0qRl17+Tz32iOPS2K+1PIpee8S83AAmTXKg1pgfz7Vvh/xc2LUPnn8lFs1PmExrBibNpDbtUJJCBfc0E5JGSsZuMzMjHimZUxVo27YgXdnIy68F09CzWUJSckJWY5MWimT0UzEdVpjpCdLSq1HbCdissOEqcGfBltfgwJEYPZH4M2kmXLYs1XNXkkIF9zRTWddFe7eXucXZmM0x/vH2dKE99SDSZEJe9k6wO4Y9tI5m+higVOZjHuOvmZ5vDKDuqwl/I8MJ77oSrBZ4eCPUNoz3GSScx5ZNj7+X/sBAspuiTDEquKeZyCwZfUaMUzIyZCxS6u1BrlwHeSOnWk6EUzIz5Njnd0cGViuaoD8yUSbXA1etN2bO3PcI9CanXMVYeezh/VT7W5PcEmWqUcE9jUgp2S2asFlMlBfFuKDSnjfQKo8jS2bCgotGPDQoQ5yUNTiklUI59jeZwQOrR+oHPTCrDFYvg/ZOePjpSTHAmm035vY3q+CuJJgK7mmkqrGHls4B5pRkY4llSqajDW3LRqTNjlxz5bADqBG1NDGAjzKZj2mUue3DiQys7q+9IIavWgql0+HYCXht57iunUiRnntzX0uSW6JMNSq4p5HdwpglMz+Ws2SkRNv0MJrfh1y9HjJGL2VwWtYBUBYa/5L7fxpYjdA0uOpt4HTA5hehum7c90gEj+q5K0mignuaiKRkrGYTs6e7Y3fhA7vOp2Nm66MeLqXktKzDJi3kj17Gf0SRgdX9tRc8kOE0AnwwZJQoGEjdDbZdNhcaGs39queuJJYK7mmitqWXxrZ+ZhW7Y7fjUl8v2pankRYr8tIrRk3HADTRTi/9FMvccadkIs4NrDbCwIUVCMqKYcUSaOuAx/6Rsvl3i8mMy5ZFc5/quSuJpYJ7mogsXJpfGrt9UrWXNqH19yGXXQyZ0Q3QVoZTMiUyb+L312B+foBASONw/RAHrF4G0wpg/xHYtX/C94uXbLubTl8X3qAv2U1RphAV3NPEHtGM2aQxpyRGwb3mDOzbifTkjjo7ZrDTsg6T1Ma0cGkk83L9Qw+sAphNcPV6sNvgqWehsTkm94y1SN5dTYdUEkkF9zRQ39pLbUsv5dNd2K0xqH8uQ2jPPoaGNNIxw6xCvVCn7KWVTqZJD1ZiU4d92IHVCFcWXLEW/AH46xMQGG0HyMTzhKtDqkFVJZFUcE8D5xYuxWqWzME9aPU1yFnzoXDoDTaGclYauZNYpGQGiwysvlk1zAGzZ8CCuVDXCM9ujem9Y+HcXHc1HVJJIBXc08Ae0YxJg7mxSMn4fWgv/wNpNiNXrBnTqVXS2NYvVimZiKKsELnOIMeboKN/mIPethqyXfDqdjhZGdP7T9S5ue5qxoySQCq4T3JNHf2cbexm5jQXDlsM9uncsRWtuxMWLot6EBWMVak1NOGSTrIYvubMeGgaLJnmR6Kxe7jeu9UK71hvHPzQU9A33LtA4mWfW8ik0jJK4qjgPsntjey4FItaMj1daNteRDqcyMWrxnRqA634CVA0jnID0ZiVEyTDGuJA3RDTIiMK82HVMujqhr9vSpnpkRaThSxrpuq5Kwmlgvskt0c0oWkwLwYpGW3rM8ZK1GWXgG30zTUGi1dKJsKkwaLCAP6gxt6aEQ5csRiKCuFQBew+EJe2jIfHnk27txOfmg6pJIgK7pNYW9cAp+q6KCvIIsNhndjFmuph/05kdg7MXTTm06tkAyapjatQWLQW5PuxmSVvVoFvuEkxJhO8421GHfgnnyXYlBq95RyH8bo0qUFVJUFUcJ/Ezu24NGPivWVty9NoUhrlfE1j+7Xol16aaCcPd8ymQA7FaoZFhX76/Rr7LixJMJgrCy67FHx++v74CASDcWtTtHLsRnBv7EvNufhK+lHBfRJ7s8JIycwvm2BK5lQF2qkKZFEplMwc8+k10niTKQrFr9cesajAj9Uk2XUW/CPF7HmzYN4sgpXVxg5OSZbriAT3piS3RJkqVHCfpM6lZAqzyJxISkaG0F7ciATkqnVR1Y+5UBVGvn16nPLtg9ktsLDAT69vlNw7wPpL0NxZ8OLrcPps3Ns2khyH6rkriaWC+yR1fhPsCQbUowfQGutg1nzILRjz6VJKqmQDNmnBQxz2bB3CkmlG7n17JQyMtCDVbsN+/ZXGvx98HHp6E9K+obisWVg0C429queuJIYK7pNUTFIywSDa1meQmsmYITMO7XTTQz9F0jPhKpDRsltg6TQ/AwGNXaN0yM0lRXDJcujqgYeehFBypkdqmobHkU1jXzMhGUpKG5SpRQX3Sai1M0YpmQO70NqaYd4icI3vTSLeUyCHs7DAj9MiefMsdI229/TyxTCjBI6fhheTl3/PtXvwhfx0eruS1gZl6lDBfRKK7Lg0oZSM34f26nNIswW5dPW4L1MlGwCYluDgbjXDymIf/pDGyydGOTiye1NWBrzwirFFXxJE8u4NalBVSQAV3Ceh3bFIyezeZpQZWLA0qq3zhhKUQWppxi2dZGIff1vGaV5egPyMIMcaNaraRznYYYdr325UuHzwCUjC/Pdzg6q9alBViT8V3CeZmKRkvANo27YgrTbk4pXjbks9rQQIJjwlE6FpsKbMWPH5XAUERktlF+TBFWvA64X7Hkl4/Znzc91Vz12JvxhUmlISKRYpGW37y2j9fYSWXwr28Rf5Sla+fbCCzBAL8v1UtFjZdlpyxdxRTpg/G1ra4OAxuO9R+NQHwZKYP4OccOnfhglMh5RS4qurw1t9Fn9TE/7WVqTfjwwG6MjOIuB0Yc3LxzFnDrbpxWhjXJCmpA8V3CeZXccmlpIJ9XTDzleQDueYdlgayvmSA7Hb2m88Vpf4qOkys+OMxrwCKB6tOWtWQnevMff94afhtvcaxWvizGq24rJm0TTGnnvI66X34AG69+ymXxwj2N095HE9F3xtysgka/ly3GvfhlNfoAL9FKOC+yTS2NZHZX0Xs4pc407J+F58Bs3nJXTxZWAdW3GwwfrkAM10UCizscSx5EA0rGZYP9PLsyecPHVI8rFLwTnSyxOpP7Ox39h/NTMD3nPtuBZwjVWOw0NVdw39gX6cFueIxw6cqaTj5ZfofnMn0mekn8wuFxmLFmObXowlJwdzdjYmqxVMZrLsGh11zfjb2vDV1uKtrqLrjW10vbENS34+uRtuIHvderQEfVJRkkv9lCeR7UeMmSmLynPHd4HOdnzbXkFmumDe4gm1pTpcciARq1KjMd0VYlmRjwMNNjYelty8fJTOuMUC110JTz8P2940Av6N18Q9wOc5cqnqrqGup5E5nvJ/elxKSe+hA7Rt2sjA6VMAmLM9ZKxcRYa+EEt+PtowbbR7MrCbndjLZsCy5UYKp7aG3iOH6Tt6hKa//Jm2f2yk4JZ/IWvVxcNeR0kPKrhPElJKdhxtxGLWmFs6vjSI9upzEAwYC5bME+ttV2G80SQz336h5dP9tPSZON1q4eUTkqvmjXKCww43Xg1PvwCv7TTqv9/4znGnaEIhSW9/kN7+EDIkMZs1sjLMOB2mc4E032m8Mdf11r8luEsp6T2wn9aNT+E9e8Zo3py5ZC1fgb181rgCsaZp2EvLsJeWkf229XTv2knPgf3U//ZeMpYspfBDH8ZWUDiu56qkPhXcJ4nK+m6a2vtZODNnfJtgtzQZi5Zy8oxSAxNglBxoxC6teBjfNMp4MGlwRbmXfwgTb1aZsFsk1442rOB0Gj32jS/A67uMjT4+cBNYR//T8PpCnKkZ4ExtP3VNPprbfISGmLHjsJsoLrQxu8xJfokxY6aux3hzlKEQPfv20rbpabzVxjZTTn0B7jXrsBaMvRzEcMxZLjxXXU3WilW0b3mevsOHOPvf32Hahz+Ke826mN1HSR0quE8SOyIpmZnj6ylrWzejSYn1knUEJziw1kYXfQwwUxagJajkQLTsFrh23gCbjzt4/bQJzRxg3YxRsi0ZTiPn/txWYxZNZzd8+GZjT9YLBIKSU2f7OXyih1NV/eeCuckEHreZrEwzDruGyaQRDEr6B0J0dAU5XT3A6eoBNFMQxyo43VpD9+5dtG58Gl9tDWgazoWLcF+6Fmt+flxeGwBLTg75N99K39EjdLz4Ag1//D19R49Q+K8fxTTGDVqU1KaC+yQQDIXYeawRp91C+XT32C9QV4V27AAyrxDzrLnQO7HdgFJhCuRIMm2Sd80b4PmTDl47AY0dsGERjDgG7bDDDVfDy2/AyTNw9+/glhthiY6UktpGL4eP91Jxuo8BrxHRPW4zpdOtFE+zkeMxYx4hndM/EKKqzseZyj7KK0KsPrGf+p49oGlkLFqCa80arLl5sX0hhqFpGpmLl2AvKaF100a63tiGt66Oktv/A4snNX+mytip4D4JHD3TTnefnxXz8kcMIEOSEu3FTcY/V66NySBapORAqgZ3AJddcr3ezytnnBxvNlG7XXLVfFg4bYSUutlsbLJdVIjcvhvtvkdomKHzvOsi6rzGzBanQ2PhXAflZTZysqP/88kwBVjdeoQrj+/F0ttL0AQHPLPYk7uca5eUcknOyDNn4sHiyaHwtg/R/sJzRprmB9+j5N+/hGNmecLbosTeqL+duq6bgHuBZYAX+KQQ4uSgxz8FfAYIAD8QQmzSdT0XOA4cDh/2hBDil7Fu/FSxYyKzZE4LtDMnkMUzoKh0wm0JhEsOZMsMnKT2x3iHBW5cHGT32QD7661sPKzx6knJshKYlQuFLjCHM1RSQpcXmro1qmw6bXMKuezsGxRVCW7TTnKydBHe1cvxzM7BFO0bpJTY6hvIOnyEjAqByecnZLFwVp/GCwsDlPuX0nnSxf17O6lo8vKB5dnYLYmdi66ZzeRcex3W/Hw6t75M9U9/RMntXyRj4di3WlRSSzRdj5sAhxBira7ra4CfAe8B0HW9CPgPYDXgAF7Xdf0FYCXwkBDi3+PT7KnD6wuy93gzniwbxXkZYztZhs732lesiUl76mghSCile+2DmU1wUZGf8pwAhxqsnGqz8OopjVeNWYY4rRIN8AYhGDoftM1aDqEl72T1wGnKTx9iYfUhZPUhBmbOYGB2Od6SEnz5eW9d3RoKYW1rx9rUjKOmBsfpSizhGvJBp5OexfPomzuHFkcXveZjZGu9fPSiEjad6OfNmgEaugN8Zk0uHmdi1w1omoZr9SWYXW7aNm+i5pd3M/1Tn8G16uKEtkOJrWiC+3rgWQAhxA5d1weXELwE2CaE8AJeXddPAhcBq4CVuq6/AjQB/yGEqI9t06eGfSea8fpDrJyfM/aUypH9aI21yHFuxDGUyZCSGYrbLnnbTB+rSnzUdZmp7zbT5TXR79dAgwyrxGWXeBwhpmUFKcgMYXSiy2jRi3HU1OA8XYnzbBXOs1XnrhuyWQnZ7Jh8Pky+t45lhGw2+mfOYGBmGb7C8/mgbGnMMGqlk1UOE7ctzuCFSi8Hm/zc+UoLt6/LZbp7ghuej0OGvgCTw0HrU09Q/7vfwKfBtVoF+MkqmuDuBjoHfR3Udd0ihAgM8Vg3kA1UAHuEEFt0Xf8Q8D/AzSPdJCcnA4slOSsdCwr+eVZEqthx7CAAay8qweOJPi8rAwF6Xn0GaTLhXHcZpqzzVRuzssZfwbG6uxGzNDHDno9FS+7K1Gg5nefTR04gxwXGEq6hKo1pDPlnsXAeAwvn4e3pwdLYjLmpBVN3N9qAF5Pfj8zMIJCTTcjlIpibQyg/l2BeLphMmMP3jXBIK5aAiXat69zP4v0X2Sk628/zJ/r45bY2vnbNdGbnj73uj8czxk93/3SBRbhzXZz58/00/OG3ZOdmkXfp+DZySYRU/tuNh7E832iCexcw+IqmcGAf6jEX0AHsBPrC33sC+P5oN2lv7xvtkLgoKHDR3Dx0rY5ka2zv4+DJFsoKs7Ag6egYw2v05muYWluQCy6iz+SAHi9gBPae8L/Hqlv20RLqZHooB/9AED8j7VCdGpxOG/39E5sd9BZmGxSXGP+Nxjv8HoBucyYtoU46u/sxa0aefXm+GVPIwXOnBvjRc3V8aX0eJdnR9+A9noyx/Y4Mx5VH3ntvpuWxR6j4yV0Uf+Hfybpo+cSvG2Op/LcbD0M935GCfTSjN9uADQDhnPuhQY/tAi7Tdd2h63o2sBBjEPWPwPvDx7wD2BNl+5VBXj1QB8CyOWOcIucdQHvteaTFilyyKmbtiaRkUqXkwGTmkZmEkLTx1l2ZLiq0smGug36/5J432mjqGWmT2Pixl5aS/76b0TSNunvvoffwodFPUlJKNMH9CWBA1/U3gJ8DX9Z1/Su6rr9bCNEA/Ap4DXgJ+JYQYgC4A/icrutbgc8CX4xL69NYIBhi26EGHDYz88s8YzpX27EVrbcHuXgFOCf4MX2Qs+eC+zhr2yjn5EljM/Em2fZPjy0usHL1LDvd3hD3bGujvT85n5DsZTPIe+/70YC6X/+KvmNHk9IOZXxGTcsIIUIYAXqwikGP/wH4wwXnVAJXxqKBU9WBky109fpYNb8Ai3kM0+M62+GNl5DODFi4LGbtCcoQ1TSSJR1kMf4a8IohVxofpxtpZ6gSbiuLbAwEJK9X+7hnWytfuiwPlz3xYxyOmeXkvee9tDz5OLX3/JKyr31DzYOfJFSB5xT1SiQlM3dsKRntpU1oAb8x9XECJX0v1EArPgJMlzkpV3JgMsomA5PUhuy5R6wtsbF6upXGniB/2NmOPygT2MLzHLNmk3f9jUivl9pf3o2vWe0kNRmo4J6CWjr7OXK6jeL8TPKzx7ByseYM2uG9yLxCmL0gpm06O0mnQKYqEyZyyKKVTgJy6LSLpmlcOdPOgjwLp9v8PLS/EymTE+Cd83U877iGYFcXtT+/i0B31+gnKUmlgnsKev1gPZIxDqTKENpzTxj/XL0+5nXJI7suTZNjy/8rw8uVWYSQtNAx7DGapnHdHAdFmSZ2Vfez5WRvAlv4VlkrVuK6dA3+piZqf/lzQt7xzbpSEkMF9xQTCkleO1iP3WpCnzGGQHpwD1pdFbJ8HhROj2mbesO7LhWkwK5L6SSSd2+S7SMeZzVrvG+BkyybxtNHujlYP5CI5g3Jvf5yMhYvwXumkvrf/hoZSM5sHmV0KrinmP0nW2jv9rJwZi62aBd1+bxoL21Cmi3IlWtj3iY1BTI+csMzZhoZPu8ekWUz8T7didkE9+3uoLbTH+/mDUnTNHLe+S7s5bPoPXSQxgfuS1qqSBmZCu4pZsvuagBWzo++prf2+ha0ni5YvAIyY79i7wxG5QgV3GPLRQYWaRq15x5RlGXm+rkOvEHJH3a10+cbaoVt/GlmM3nvvgnrtCK6Xn+N1qceT0o7lJGp4J5Cqhq7qajqoLzIFf1Aakcb7NiKzMgy5rXHWFAGOSsbyJIO3MRuzrwCJjRyyKKdLnwyuvSGnmdlTYmNlt4gf97dQShJvWaTzUb++27G7PHQtmkjHS+/lJR2KMNTwT2FbNldA8Cq+VEW+ZIS7dnH0IIBIx1jiX2xqWqa8BOgROapKZBxkCfdSIypptFaX2ZjtsfM0SYvm44lb/m9OTOTgptvxZSRQdNf76d7r1qInkpUcE8RnT1edhxtICfLzuziKHdbqjiEduIosqgUykfbDXp8KqUx374klJhdgqaaQmlsdl4nm6M+x6Rp3DDPiceh8fzxXvbV9sereaOyeHKMMgUWCw2//w39J44nrS3KW6ngniKe311NIChZvaAwutK+3gG05x5HmszIS6+I+dRHMDbCPi3rsEsr+Yxjez9lVPnSjSahdgzBHcBh0Xiv7sRqgvv3dlLflZwBVgBb0XTy3n0TMhSi9n9+gbe2NmltUc5TwT0F9A0EeHlvLZkOC0tnR1e3Rdu6Ga27E5asBHd85p430kYfAxTLXEwqJRMXNix4yKKBtmEXMw2nIMPMhrkOfEHJ73e20+tNXpVOx6zZ5Fx7HaG+Pmp/cRf+tujTTEp8qOCeArbur2XAF2SVHmUdmerTsOt1pNuDXLIybu06HUnJqEJhcVUoswkRGlPePSIywNrcG+TeV5uSNsAKkLl4CdmXv51Aezu1P/8Zwd7kLbhSVHBPOq8vyHO7qrBZTayYG8VAqt+H9vTfAIlcexWY47PHuZSSk7IaizSpkgNxViCNlNdYUzMR68tszPKYOVDbxz+O9cSyaWOWdfElZK1cha++jrp7fknIF8Na+sqYqOCeZC/tq6G7z8+q+YXYbaMvWtK2PoPW1gwLlsV8JepgTbTTSS8lMk+tSo2zgnEMqg5m0jRunOckx2niueM97K9L3gCrpmlkX/kOnPoC+k8cp+EPv0OGkjMff6pTwT2JBnwBntlRhd1q5uIFUfTaq07DzleQLjdyxaVxbdtxaSymmiFjs/eqMjw7VrJlBvW0Ehxj3j3CYdH44DK3McC6J7kDrJqmkXvd9djLZtCzbw9Nf31ArWJNAhXck+jFPTX09PtZvaAAh22U9MpAP9qTDwAg170jLnPaI6SUnJDVWKVFpWQSpFB6CBKifhx594hpLgvXhVew/n5n8lawAmgWC3k3vQ9rQSGdW1+i5bFHVYBPMBXck6S7z8fmHWdx2Mysnl846vHaM4+hdbbDklVQWBzXttXRQi/9lMo8zOpXJCGKw4PWkXUF47Ugz8qlxcYA6317kreCFcBkt5N/8y1YcnJpf3YzbRufSlpbpiL1l5skG7edod8bZN2SotFz7Qd3ox3eg8yfhrxoddzbdlxWATBTpWQSplBmY5FmKmX9hHu4l82wUe4xc6TRy+YkD7CaM7PIv/VfMGd7aH36Sdqe+UdS2zOVqOCeBA1tfby0r5acLDsr5o5SIKypHm3zI0irFbn+GjDFd3DTLwMcl9U4pY1CVbs9YcyYKJIeOumhnYmVFDBpGjfONVawPnu8hwN1ySsRDGBxuSm49QOYXW5aHnuU9i3PJ7U9U4UK7gkmpeRvL54gFJJcvrwY80jz2r0DaI/+H5rfb+TZXdlxb99JWYMPP7PkNLVwKcGKpVHi4Yysn/C1nFaNm+YbK1j/sqcjqQOsAJbsbApu/QCmrCya//ZXOraqQmPxpoJ7gu070cLBU63MnJbF/NIRgrUMoT39EFpbM3LRcpgxJyHtOyJPg4TZoaKE3E85r1jmgJx43j2iMNPMdXNSY4AVwJKTQ8EtHzAKjT3wF9qeeyap7Ul3Krgn0IAvwF9fOI7JpHH16rIRa8horzyHVnEQWViMXBH7DTiG0ia7qKeVadJDFo6E3FM5z4GNPFzU00q/jM0WdgvyrVwSHmD93c52fEnaZDvCmpdHwb/chjnLRcujD9Py+N/VLJo4UcE9gR5/9TRt3V4uXbeJFqMAABK0SURBVFhInnuE4HloD9przyOz3Mgr3gWmxPyYjsjTAMyRqteeLKWhPCSSU7ImZte8fIYNPc/CqVYff97dTjCU7ACfT8FtH8LiyaFt8yaaHrxfLXSKAxXcE6TibDtbdteQ67azZtEIwfPsKbSNDyGtNuRV14Mjyk07Jsgr/RyRlTikjRKpyvsmS7ksRJNwVJ6J2TVNmsb1cx3McJs5WO/l4QOdSe8tW7KzKbjtQ+fmwTf88fdqP9YYU8E9Afq9Af538zE0Da5fMxOrZZiXvf7/tXfmwXFUdx7/9Nya0WWdlgRYBlk/QGAZTIzBgE0MOSvAEjYkWUhINsfuJlu7W9lNNqkcpApCNhBCEsKSpEhINodTCWFDskACIebwgQELGxn7+Ta2bFmXdYw0mqvf/tGjWDaSLNkatd1+nyqVerp7en5veubbb17/3ve3F2vFD0Fr9FXvgJKZM+xq1TtIk6HRrjW57S5SQJjZehYH6aFH90/bcQM+xyK4OuZj9Z4Ev3exyMcI/liMyps/QKiujoF1a2n77n1kh4bcDsszmG9xntFa85Mnt9DVN8yl51VTUx4be8eug1i/+D6kkugl10DtmTMWY1ZneVVvI6D9NOj8+dUYJsdcXQ3A5mnsvQOEAxY3nXu4yMcfNg+43oP3RSJU3HQzkbPPYWhTK3vvuoN05/F57BiOxIh7nlnZ0sa6zR3UVcRYcuE4wtnZjvXT72ENDaIXL8tbVaXx2KL3MMQwDbqGEPlxmTRMnjpdTkgH2KL3YOvpHYuOhXzcfH6U0rDFkyrOY6+fBAIfDFJ+w40ULryE1IH9vHHnVxna/LqrMXkBI+55ZPu+Pn75520UhP1ct6Qev2+M7Jj2Nqyf3o81OIB9yRUwr2lGY8zqLC/rLfi0RaOdX1sDw+Tw42OOrmSIYXYyPWmRoykJ+/hAU5SyiMVT2wZ5tNV9gbd8PkqvXk7ptW8nOzTEvnvvpufxP7ge16mMEfc80XFoiO88spGsrXnP5fUURUNv3mn3dqz/+R4MDWIvXgbnNc94nBv0dvoZZJ6uJUp4xl/fMDYNuQtti52fmqRFYR/vb4pSXuDjmR2DrNjQ73oWDUBh8wIq3/9B/LFCun77G/Z/51tk+qfv3sPphBH3PNA/mOK+X28knkhz7SVnUj97jPqjG9Zh/fzBw2PsM9xjB0joJC/pzYR0gCZ75sb4DcemhCi1dhntdB93EY9jURjy8f6mAqqiPlbtHuKBNT2uT3QCCNfWUXXrhwnPqWfwtY3suf2LDL620e2wTjmMuE8z8USae1a00N4zxKLzqlhwtHdMNov19GP4HvslBALo5dfB2eJKrC/qTaRI02SfRYj8WQgbjo/zcxfcNXZr3oYnYkEfH7wgSsOsAKozxT3PddERdz8l0R+LUXHT+yhZejXZeJy2b99L+48fMqX7poAR92mkfzDFPSta2Nc5yEXzKljafNQYdn+vM76+5i/oohL0O94Ls+tciXWf7uA1vYMiXWAyZE5SKiim1i7jAF3s5sT9ZsYj5Le4QSIsqg3REc9yz7NdtLa7azYGTtGPorcsouqWDxGsqqJ/1fPs/vIX6F+31ozFTwIj7tNER2+Cr/3sFd44GGf+OeVcs/CMw/YCWjuzTn9wN9a+3eg5Deh3vQ9K3CmEkdRpnrLXYWmLxdlGk9d+EtNs12Npi+fsV8kcZ5WmyeCzLJbNCTteNBnNg2sPseLVPpIZ94dpQlXVVP3dhyi+4kqy8TjtP3iQfXd/ncFdu90O7aTGOlmugJ2dA64EUllZRGfniU3o2LS7h+//rpV4IsNlTdVccWHNYWHvO4T15G+xtraiAwH0xUugsQkm8JXJJ7FYiEcGnmOb3kuTfRYX2nNciWOmKCgIkUic2kWaW3w7Ub42FliNXOkb/6Z7YWGYePzEPWk6BrP83/ZhOodsKmN+PrywlPqyMRICXCDT20vvX/7M8I7tYFkULVpMxQ03Eqz0fu2BsbSqsrJoXCEx4n4C4p7J2jy+Zg+/W7ULy7K4ZuEZh8fY0ylY/QzW6mewMml0dR36sqtnxLZ3Il4JbGZ1spVyXcTy7Hx8Hu+1e0HcM2T5o7+FASvBdb4rmWONbV8xXeIOkLE1z7+R5KUDaSzg8voo7z63kOLIyVEsfXj3LuKrnmP4QDv4fBQvvpyyd76LUI1303mNuE+R4xX3vR1xfvz4Zna3D1AUDXL9krnUVsQcUV+/BmvVn7EGB9AFUcfV8Wxxrbc+wkZ7O8/qFgp1Addk5xPh5OiN5RMviDtADwM87d9AwArwt763Uma9OQNrOsV9hDf6Mjy1K0l3wibst3ibFPLWc2IE/e57/ZcURzjw4nr6164m090NlkW06UJKly4ldmEzVsBbE/KMuE+RqYp7XzzJY6t3s7KlDa2hqb6M5QvriCQHsV5ZDetXYw3G0YEgnDsffcHFEHRXRLXWrNWbeFlvJkKQ5ZlmipgZQzK38Yq4A+yyDvKifytRIvyNb+mbBD4f4g5ga82Gg2le2JsikdEUR3wsnRtjydwohSH3fvmVlkbp7R1Ca83w9m0MvPQiqf3OpC9/SSklV1xJyRVXeWbIxoj7FJmsuHf2Jnj65X2sfLWNdMamrDjM8ubZzB3Yh/Xay7BtE5Zto0NhaLzAKbARdt8TPa4TrLTXs4v9FOoIbwsuIDR8+qQ9ekncAZTVRot/J2GCXOu7lLnW4UynfIn7CMmMZm1bipaDKVJZCPrh0jOjLKmPckZJYML6BPlgRNxHk+7sZHDjBgZfb0UnnfciXD+XwuYFFC64iNAZE9dROJkx4j5FJhL3eCJNy7ZOXt7SSevObjQwO5hiackQc/r2YO3YgpVyPkB6Vjm68QKYKxB0XzyH9DCb9C5e0VtIk6HKLmGJfR6lBTFPid2x8Jq4g9ODf8m3DdvSNFpnsdA6lwqrJO/iPkIyo9nYkWZ9e4q+pPO1rYj5ubguwkW1BdSVBPDNgICOJe4j2Ok0ia2KoddbSe7dCzm/+EBZObEL51Mwbx4FDfMIlFecMmJvxH2KjH7Dkukse9oH2Lm/n9d2drNtTxezkv3UDnfSqHs4M9FBaLD3r8/VhcVw1tnouQJlxyh0nUc69SH6GMTWNocY4KDuYS8HsdGEdZBmu565uhoLy5NiNxFebW8vg7zo38ohKw5ANWU0hs/g3PRcItbMDAPaWrPjUIbNXRl2HMqQzmVNxkI+GspDzKsIcU55iOqiAKE8jNFPJO5HxJlMMrxrJ4kd2xneuROdPJzD7y8ppaChgUj9XEK1dYRqawmWV2DNUIGcqTDt4i4iPuABoBlIAh9TSm0ftf3jwCeBDHCHUuoPIlIB/AIoAPYDH1FKTXgWZkLcU+ksg8MZBhNp4ok0Xb0JEgNxDu5qY7Czi2RXDyWpfsrT/ZSn+piVHsDH4bB0OAKVs9FVNVA7B0rLXL9JmtFZvm8/is2Rb1+pjnG2XU29rj7C6dGrYjceXm6vRtNmdbPDaqfdOoS24EqrmQW+xhmPJZ3V7OzNsK0nw97+LAOpw59HC6gs9FNTFKSq0E9pgZ/SiJ/SAh+xkI9IwEdB0BrbWG8CJivuo9HZLKmOg6Ta2ki17SO5vw37qFmvVihEqKaWYGUVwfIyAmXlBMvKCMwqx19chD9WiBUKzXiPPx/ifiNwnVLqNhFZDHxeKXV9btts4CngEiACvJBbvhtYr5R6WET+E0gqpb410escr7gP79pJqr0dnc2gs1l0JuNUdMlm0dksdjKJnRhCbT3AYN8AYTtN2E4RzqYpsJMEx5kYYofCWCVlUDILXV4FVTXOpKOT8Cfc1uweOrNdWFjEiFCmC8fNhPGy2I3F6dLeJGnsKNRkagha7maJaK3pS2r29mc5EM/SNWTTOZQleYw5WH4f+CywsLAsaKoO89G3jD/R73jEfaxYs319pDs7SHd3k+7uItPdRaanZ8LKUFYggC9WiL+wEH8s5gh+JIwvGMIKBbGCIXyhEFYohC8YhEDunoTPh2XlGppbDtXUED7j2N5OUxX3yXwKrgCeBFBKrRWRS0ZtWwSsUkolgaSIbAfm557ztdw+T+SWJxT340FnMuz9xl3odPqY+45kBttYZAIh7FCIdKgMXVyMFY0RKi7CKizCKi6B0jL8BdFTZizuXN1ATWpyw0LRaATtd39q+UxxurQ3ApQXl0DS/fs9AFVFUDXqI6m1Jp606R3O0j9sMzBsM5DMkshoUhnNcMYmndVo7Uzo1kBZURjfBGUm/QUF+IZP/Ae/vyBKaPaRFhxa29jxQTL9fWT7+sj095Hp68NODGEnErm/ITLdXaTaTqzerS8W45z77p92vZmMuBcDfaMeZ0UkoJTKjLFtACg5av3IugmZ6Ao0EVW/WXE8TzMYDB6g0O0AZpjKyqJJ7zuZuwb9wOgj+nLCPta2IqD3qPUj6wwGg8EwQ0xG3FcB7wLIjbm/NmrbOuBKEYmISAlwHtA6+jnAO4Hnpy1ig8FgMByTqWTLzMe58f0RHOHerpR6LJct8wmcC8XXlFKPiEg18BOcXnsX8EGllDFiNhgMhhnipMlzNxgMBsP0cfJl6hsMBoPhhDHibjAYDB7EiLvBYDB4EG8ZHh8HImIB+4BtuVVrlFKfdzGkaedYFhJeRERaODzXYpdS6iNuxpMvRORS4L+UUstEpAF4GGcOUCvwKaWU+3XyppGj2nsx8HsOf3f/Wyn1K/eimz5EJAj8CKgHwsAdwOtM4fye9uIOnINjlfAetwPJIzcAEaXUZbl01m8C17scU94QkQiAUmqZy6HkFRH5LHArMJKJdi/wRaXUShF5EOccP+pWfNPNGO29GLhXKfVN96LKG7cA3UqpW0WkHGgBXmUK59cMy8BCoE5E/iIij4uIuB1QHjjCQgLH/8fLNANREfmTiDyTu6B5kR3AjaMeLwSezS0/AVwz4xHll7Ha+24ReU5EHhKRyU/fPPn5NfClUY8zTPH8nlbiLiJ/LyKto/+AduAupdTVOB44P3M3yrwwpoWEW8HMAEPAPcDbgX8Afu7F9iqlHgFGGytZSqmR3OZJ2X6cSozR3nXAfyilrgJ2Al9xJbA8oJSKK6UGches3wBfZIrn13Mf+IlQSj0EPDR6nYhEca6KKKVeEJE6ERn9JnqBiSwkvMhWnEl2GtgqIt1ADbDX3bDyzujx19PB9uNRpdRIGx8FvutmMNONiJyJ064HlFK/EJFvjNp8zPN7WvXcx+ErwL8CiEgz8IbHhB0mtpDwIh/Fua+AiNTi/HI54GpEM0OLiCzLLZ8Oth9/FJFFueXlwCtuBjOd5Gb5/wn4nFLqR7nVUzq/p1XPfRy+DvxMRN6N04O/zd1w8sKjwLUisprDFhJe5iHgYRF5ASez4KMe/6UywmeAH4pICNiM83Pey/wjcL+IpHCGVz/hcjzTyReAWcCXRGRk7P1fgO9M9vwa+wGDwWDwIGZYxmAwGDyIEXeDwWDwIEbcDQaDwYMYcTcYDAYPYsTdYDAYPIhJhTSc0ohIPbBSKVV/1HqtlLJyy58CPo6TBqpx/Eh+eozj3gYsU0rdNsE+K4Hbcw9v97qXjeHUwvTcDZ4m5yL4MeAypVQz8DbgjtyENYPBs5ieu8HrzMbpsUeBhFKqQ0RuAjqP3lFEbsXx8OgH9gDx3PrFwLeBCE5N4E+OZ5ksIkuBO3OvVwr8m1LqdyLyMFAONACfBZYC1+JYBvyvUuqr09VggwFMz93gfZ4AdgMHRORZEbkdx0p1/+idcjYF3wCuAi4j58WTmw24Avh0ruf/IPDLCV7vn3H88i/G+cVwx6ht3Uqp84CNwDtzx1sCnD9iU2wwTBdG3A2nOm8qVpArwKIBlFIppdQNwPnAr3BsUzeOYQN8ObBaKXUwZ1Uw4g7aCBxSSr2UO96vgQYRGc+R7xbggtyU8c8AhaO2vZj73wYkRGQVzpTyzymlhqfSaIPhWBhxN5zqHOLN1qdVufWIyIdEZLlSartS6oFcUZb7cIo+jEbjDN+MMOJFM9Z3xAL848TzPLAIx8TqzqOOmQDIXTwuxfHrLgfWiEjjuC00GI4DI+6GUxql1ACwTUTeO2r1J4Cnc8t+4C4RqYC/DrM04VS2Gc0LwGU5y2cfcPPISwDlIvKW3PPfB+xRSvUcHYuIlOH09L+MMxx0PWNcBETkIpyiC88ppf4dp3yaF4vEGFzEiLvBC9wC/JOIbBCRzcAFwKcAlFI/xqlqsyq3bQOOsB/h66+UOogzXv40ThGI/tz6JI7Q358r7vJpDgs/Rx2jJ3fcTTiufUU4FaFiR+3XAqwBWkVkPY64P3GC74HBcATGFdJgMBg8iOm5GwwGgwcx4m4wGAwexIi7wWAweBAj7gaDweBBjLgbDAaDBzHibjAYDB7EiLvBYDB4kP8Hc+N9LuvXizAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "continents = grouped.groups.keys()\n",
    "\n",
    "for continent in continents:\n",
    "    sns.kdeplot(grouped.get_group(continent)['2015'].unstack(), label=continent, shade=True)\n",
    "\n",
    "plt.title('Real minimum wages in 2015')\n",
    "plt.xlabel('US dollars')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 1\n",
    "\n",
    "![Ex1](img/pandas_panel1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>UNIT</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Percentage of total population</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Thousand persons</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <th colspan=\"3\" halign=\"left\">From 15 to 24 years</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">From 55 to 64 years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Females</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDIC_EM</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Active population</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Total employment (resident population concept - LFS)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEO</th>\n",
       "      <th>Austria</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Bulgaria</th>\n",
       "      <th>...</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>Turkey</th>\n",
       "      <th>United Kingdom</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>56.00</td>\n",
       "      <td>31.60</td>\n",
       "      <td>26.00</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>1,282.00</td>\n",
       "      <td>4,131.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>56.20</td>\n",
       "      <td>30.80</td>\n",
       "      <td>26.10</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>1,354.00</td>\n",
       "      <td>4,204.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>56.20</td>\n",
       "      <td>29.90</td>\n",
       "      <td>24.80</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>1,449.00</td>\n",
       "      <td>4,193.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>54.00</td>\n",
       "      <td>29.80</td>\n",
       "      <td>26.60</td>\n",
       "      <td>...</td>\n",
       "      <td>640.00</td>\n",
       "      <td>1,583.00</td>\n",
       "      <td>4,186.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>54.80</td>\n",
       "      <td>29.80</td>\n",
       "      <td>24.80</td>\n",
       "      <td>...</td>\n",
       "      <td>661.00</td>\n",
       "      <td>1,760.00</td>\n",
       "      <td>4,164.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1440 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "UNIT       Percentage of total population                   ...  \\\n",
       "AGE                   From 15 to 24 years                   ...   \n",
       "SEX                               Females                   ...   \n",
       "INDIC_EM                Active population                   ...   \n",
       "GEO                               Austria Belgium Bulgaria  ...   \n",
       "DATE                                                        ...   \n",
       "2007-01-01                          56.00   31.60    26.00  ...   \n",
       "2008-01-01                          56.20   30.80    26.10  ...   \n",
       "2009-01-01                          56.20   29.90    24.80  ...   \n",
       "2010-01-01                          54.00   29.80    26.60  ...   \n",
       "2011-01-01                          54.80   29.80    24.80  ...   \n",
       "\n",
       "UNIT                                           Thousand persons           \\\n",
       "AGE                                         From 55 to 64 years            \n",
       "SEX                                                       Total            \n",
       "INDIC_EM   Total employment (resident population concept - LFS)            \n",
       "GEO                                                 Switzerland   Turkey   \n",
       "DATE                                                                       \n",
       "2007-01-01                                                nan   1,282.00   \n",
       "2008-01-01                                                nan   1,354.00   \n",
       "2009-01-01                                                nan   1,449.00   \n",
       "2010-01-01                                             640.00   1,583.00   \n",
       "2011-01-01                                             661.00   1,760.00   \n",
       "\n",
       "UNIT                       \n",
       "AGE                        \n",
       "SEX                        \n",
       "INDIC_EM                   \n",
       "GEO        United Kingdom  \n",
       "DATE                       \n",
       "2007-01-01       4,131.00  \n",
       "2008-01-01       4,204.00  \n",
       "2009-01-01       4,193.00  \n",
       "2010-01-01       4,186.00  \n",
       "2011-01-01       4,164.00  \n",
       "\n",
       "[5 rows x 1440 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employ = pd.read_csv('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/pandas_panel/employ.csv')\n",
    "employ = employ.pivot_table(values='Value',\n",
    "                            index=['DATE'],\n",
    "                            columns=['UNIT','AGE', 'SEX', 'INDIC_EM', 'GEO'])\n",
    "employ.index = pd.to_datetime(employ.index) # ensure that dates are datetime format\n",
    "employ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNIT Index(['Percentage of total population', 'Thousand persons'], dtype='object', name='UNIT')\n",
      "AGE Index(['From 15 to 24 years', 'From 25 to 54 years', 'From 55 to 64 years'], dtype='object', name='AGE')\n",
      "SEX Index(['Females', 'Males', 'Total'], dtype='object', name='SEX')\n",
      "INDIC_EM Index(['Active population', 'Total employment (resident population concept - LFS)'], dtype='object', name='INDIC_EM')\n",
      "GEO Index(['Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus', 'Czech Republic',\n",
      "       'Denmark', 'Estonia', 'Euro area (17 countries)',\n",
      "       'Euro area (18 countries)', 'Euro area (19 countries)',\n",
      "       'European Union (15 countries)', 'European Union (27 countries)',\n",
      "       'European Union (28 countries)', 'Finland',\n",
      "       'Former Yugoslav Republic of Macedonia, the', 'France',\n",
      "       'France (metropolitan)',\n",
      "       'Germany (until 1990 former territory of the FRG)', 'Greece', 'Hungary',\n",
      "       'Iceland', 'Ireland', 'Italy', 'Latvia', 'Lithuania', 'Luxembourg',\n",
      "       'Malta', 'Netherlands', 'Norway', 'Poland', 'Portugal', 'Romania',\n",
      "       'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Turkey',\n",
      "       'United Kingdom'],\n",
      "      dtype='object', name='GEO')\n"
     ]
    }
   ],
   "source": [
    "for name in employ.columns.names:\n",
    "    print(name, employ.columns.get_level_values(name).unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 2\n",
    "\n",
    "![Ex1](img/pandas_panel2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "employ.columns = employ.columns.swaplevel(0,-1)\n",
    "employ = employ.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus', 'Czech Republic',\n",
       "       'Denmark', 'Estonia', 'Finland',\n",
       "       'Former Yugoslav Republic of Macedonia, the', 'France',\n",
       "       'France (metropolitan)',\n",
       "       'Germany (until 1990 former territory of the FRG)', 'Greece', 'Hungary',\n",
       "       'Iceland', 'Ireland', 'Italy', 'Latvia', 'Lithuania', 'Luxembourg',\n",
       "       'Malta', 'Netherlands', 'Norway', 'Poland', 'Portugal', 'Romania',\n",
       "       'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Turkey',\n",
       "       'United Kingdom'],\n",
       "      dtype='object', name='GEO')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_list = employ.columns.get_level_values('GEO').unique().tolist()\n",
    "countries = [x for x in geo_list if not x.startswith('Euro')]\n",
    "employ = employ[countries]\n",
    "employ.columns.get_level_values('GEO').unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>GEO</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Austria</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">United Kingdom</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <th colspan=\"3\" halign=\"left\">From 15 to 24 years</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"3\" halign=\"left\">From 55 to 64 years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "      <th>Total</th>\n",
       "      <th>...</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>56.00</td>\n",
       "      <td>62.90</td>\n",
       "      <td>59.40</td>\n",
       "      <td>...</td>\n",
       "      <td>49.90</td>\n",
       "      <td>68.90</td>\n",
       "      <td>59.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>56.20</td>\n",
       "      <td>62.90</td>\n",
       "      <td>59.50</td>\n",
       "      <td>...</td>\n",
       "      <td>50.20</td>\n",
       "      <td>69.80</td>\n",
       "      <td>59.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>56.20</td>\n",
       "      <td>62.90</td>\n",
       "      <td>59.50</td>\n",
       "      <td>...</td>\n",
       "      <td>50.60</td>\n",
       "      <td>70.30</td>\n",
       "      <td>60.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>54.00</td>\n",
       "      <td>62.60</td>\n",
       "      <td>58.30</td>\n",
       "      <td>...</td>\n",
       "      <td>51.10</td>\n",
       "      <td>69.20</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>54.80</td>\n",
       "      <td>63.60</td>\n",
       "      <td>59.20</td>\n",
       "      <td>...</td>\n",
       "      <td>51.30</td>\n",
       "      <td>68.40</td>\n",
       "      <td>59.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 306 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "GEO                    Austria              ...      United Kingdom        \\\n",
       "AGE        From 15 to 24 years              ... From 55 to 64 years         \n",
       "SEX                    Females Males Total  ...             Females Males   \n",
       "DATE                                        ...                             \n",
       "2007-01-01               56.00 62.90 59.40  ...               49.90 68.90   \n",
       "2008-01-01               56.20 62.90 59.50  ...               50.20 69.80   \n",
       "2009-01-01               56.20 62.90 59.50  ...               50.60 70.30   \n",
       "2010-01-01               54.00 62.60 58.30  ...               51.10 69.20   \n",
       "2011-01-01               54.80 63.60 59.20  ...               51.30 68.40   \n",
       "\n",
       "GEO               \n",
       "AGE               \n",
       "SEX        Total  \n",
       "DATE              \n",
       "2007-01-01 59.30  \n",
       "2008-01-01 59.80  \n",
       "2009-01-01 60.30  \n",
       "2010-01-01 60.00  \n",
       "2011-01-01 59.70  \n",
       "\n",
       "[5 rows x 306 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employ_f = employ.xs(('Percentage of total population', 'Active population'),\n",
    "                     level=('UNIT', 'INDIC_EM'),\n",
    "                     axis=1)\n",
    "employ_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "employ_f = employ_f.drop('Total', level='SEX', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = employ_f['2015'].unstack().reset_index()\n",
    "sns.boxplot(x=\"AGE\", y=0, hue=\"SEX\", data=box, palette=(\"husl\"), showfliers=False)\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation=35)\n",
    "plt.ylabel('Percentage of population (%)')\n",
    "plt.title('Employment in Europe (2015)')\n",
    "plt.legend(bbox_to_anchor=(1,0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lesson: Linear Regression in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "from linearmodels.iv import IV2SLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shortnam</th>\n",
       "      <th>euro1900</th>\n",
       "      <th>excolony</th>\n",
       "      <th>avexpr</th>\n",
       "      <th>logpgp95</th>\n",
       "      <th>cons1</th>\n",
       "      <th>cons90</th>\n",
       "      <th>democ00a</th>\n",
       "      <th>cons00a</th>\n",
       "      <th>extmort4</th>\n",
       "      <th>logem4</th>\n",
       "      <th>loghjypl</th>\n",
       "      <th>baseco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.699997</td>\n",
       "      <td>4.540098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.363636</td>\n",
       "      <td>7.770645</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>5.634789</td>\n",
       "      <td>-3.411248</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.181818</td>\n",
       "      <td>9.804219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARG</td>\n",
       "      <td>60.000004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.386364</td>\n",
       "      <td>9.133459</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.900002</td>\n",
       "      <td>4.232656</td>\n",
       "      <td>-0.872274</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARM</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.682482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  shortnam   euro1900  excolony    avexpr  logpgp95  cons1  cons90  democ00a  \\\n",
       "0      AFG   0.000000       1.0       NaN       NaN    1.0     2.0       1.0   \n",
       "1      AGO   8.000000       1.0  5.363636  7.770645    3.0     3.0       0.0   \n",
       "2      ARE   0.000000       1.0  7.181818  9.804219    NaN     NaN       NaN   \n",
       "3      ARG  60.000004       1.0  6.386364  9.133459    1.0     6.0       3.0   \n",
       "4      ARM   0.000000       0.0       NaN  7.682482    NaN     NaN       NaN   \n",
       "\n",
       "   cons00a    extmort4    logem4  loghjypl  baseco  \n",
       "0      1.0   93.699997  4.540098       NaN     NaN  \n",
       "1      1.0  280.000000  5.634789 -3.411248     1.0  \n",
       "2      NaN         NaN       NaN       NaN     NaN  \n",
       "3      3.0   68.900002  4.232656 -0.872274     1.0  \n",
       "4      NaN         NaN       NaN       NaN     NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_stata('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/ols/maketable1.dta')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bt25teD1q1Ch997vftW1QCA46ggHAGkvHlWVkZOjo0aMNr0+fPq3evXvbNigEB0dnAoA1llbYNTU1+t73vqerrrpKnTt31t///nf16tVL06ZNkyQ999xztg4S/sX+LQBYYymw77rrriav77jjDlsGg+Bh/xYArLEU2M27wePi4pSYmKiLL75YaWlptgwMgBla6vRPd3tQgA9ZCuxnnnlGBw4c0LXXXqtwOKy//e1v6tu3r8rKynTPPffQgAYEWEud/jk/u7bJz3D7HhA9S4EdDof16quv6sILL5QknTx5UvPnz1dubq6mTp1KYAM+056AtdLpz+17QPQsBfapU6cawlqSevfurVOnTiklJUURDkoDYKD2BKyVs7+5fQ+InqXAvvLKKzV79myNGzdOdXV1ev311zVkyBD95S9/UVJSkt1jBOCw9gSslU5/HugBRM9SYC9cuFAbNmzQxo0bFR8fr+HDh2vy5MnatWuXHn/8cbvHCMBh7QlYK53+3L4HRM9SYMfHx2vo0KGqqqpqCOz6M8UB+E+sA5bb94DoWQrsNWvWaOPGjfrmN7+p2tpaTZ8+Xb/4xS+UnZ1t9/iAdqEbOTYIWMB7LAX2pk2blJeXp5SUFEnSjBkzNGXKFAIbnkM3MgC/shTYoVBI8fFf/mjXrl2VnJxs26CAjqIbGc1RdYFfWArsgQMHavLkyRo7dqzi4+O1fft2paSk6Pe//70kaebMmbYOErCKbmRvcjM0qbrALywFdt++fdW3b19VVVWpqqpK119/vd3jAjrErm5kVmnRcTM0qbrALywFdqxW0NXV1Zo7d65OnDihTp06adGiRbrkkkti8ncDkn3NUqzSouNmaFJ1gV9YCuyRI0fq1KlTDQ/6OHfunNLS0pSRkaHFixcrMzPT0sV27typmpoabdiwQbt27dJTTz2lFStWdHz0iDlWki1jlRYdN0OTe8DhF5YCe9iwYfr2t7+tm2++WdIXwbt161ZNnTpVjzzyiDZs2GDpYgMGDFBtba3q6upUVlbWpJEN3sBKsmWs0qLjZmhyixr8wlJifvTRR1q2bFnD65EjR2r58uW64oorVFlZafliSUlJOnHihMaMGaOSkhKtWrWqzZ/v3j1J8fGdLf/9JkhPT3V7CG06e77qv17HYsxen3cks24dqpUvFejkmXL17pGk6dmDlZZsrfJg+tw7qvG80/XfT/Dyq6D+e0vBnbtT87YU2GlpadqwYYPGjx+vuro6vfbaa+rWrZs+/vhj1dXVWb7YunXrdMMNN2j27Nn697//rZ/85Cd67bXXlJiY2OLPl5SUW/67TZCenqri4tLIP+iiULMQCiUnRD1mE+Ztxe1jvtz6qSyvVHF55A+rps492q0RU+cdraDOWwru3O2Yd2sfACwF9rJly7R48WItXbpU8fHxuvbaa7VkyRJt27ZNs2fPtjyItLQ0denSRZLUrVs31dTUqLa21vKfh/1iWbqsf9M/e75KoeQE9sMNwtYI4D2WArt379763e9+p6KiItXW1mrQoEGKj4/X1KlT23Wx2267TfPnz9ett96q6upq3XvvvTzty2Niud/X+E2/npfe9Gmwax1NdoD3WArs/fv365577lEoFFJdXZ1Onz6tZ555RoMHD27XxZKTk7V8+fIODRTm8fqbfrSrSD8HPk12gPdYCuzFixfrySefbAjoffv2adGiRdq8ebOtg4PZvP6mH+0HCj+XjbkVCvAeS4FdXl7eZDX99a9/vV3d4Qim+jf5xnvYXhLtBwqvVxCiEdRbofxcNYH5LAV2t27d9Oc//7nhPuzt27crFArZOjCYr/5N36vdo9GuIr1eQUD7+blqAvNZCuxFixbp/vvv14MPPihJuuiii7R06VJbBwbYLdpVJGVj//Fz1QTmazOwp06dqri4OEnSBRdcoIyMDIXDYXXt2lULFizQc88958ggAS8KatnYz6iawMvaDOy7777bqXEAgOuomsDL2gzsq6++2qlxAIDrqJrAyzq5PQAAABAZj8uCkbj9BkDQENgwErffAAgaSuIwErffAAgaVtgwkkm331C+BxALBDaMZNLtN5TvAcQCgQ1jmLRSbfws8BOnmh7LSvkeQEcQ2DCGSSvVlp4FXs/L5XsA3kVgwxgmNZo1H1tSYrx6de/q+fI9AO8isGEMkxrNmo/1qwN6eLYaAMAMBDaMYVKjmdefBQ7APAQ2jGHSOc9efxY4APNwcAoAAAYgsAEAMACBDQCAAQhsAAAMQNMZPMmkU80AwAkENjzJpFPNAMAJlMThSSadagYATmCFDU8y6VQzL2htC4GtBcA/CGx4kkmnmnlBa1sIbC0A/kFgw5NMOtXMC1rbQmBrAfAPAhvwgda2EJzeWmj8HPD6M9QpwQOxQWADPtDaFoLTWwstPQecSgkQGwQ2mqBJyUytbSE4vbVACR6wD4GNJmhSio2globp7gfsQ2CjCVZIsRHU0jDPAQfsQ2CjCVZIsRGLDz4mbk/wHHDAPgQ2muD+59ho/sHnP+er9Ot1e9sVvGxPAGiMwEYT3P8cG41Lw6fPVqiktFIlpZXtCl62JwA0RmADNmhcGr576ZsqKa1s+J7V4GV7IlhM3AKBswhsBwS1Yxhf6Gjwsj0RLGyBIBIC2wFB7RjGFzoavGxPNOX3FShbIIiEwHYA/yEGm13B6/cAa87vK9DuKYk6oi8rMd1TE10cDbyIwHYAe5Gwg98DrDm/f/ANK9z0dTjcyk8iqAhsB3CYBOzg9wBrzu8ffM+WVbX5GiCwHcBhErCD3wOsOb834QXt3xPtR2ADhnIzwNzYP/d7E57fP5AgegQ2YCg3Ayxo++dO8PsHEkSvk9sDAGCeoO2fA15AYANot+b7q+y3AvajJB5B0O51BaxgvxVwHoEdAXt1wH9jvxVwHiXxCNirAwB4AYEdAXt1AAAvoCQeAXt1AAAvILAjYK8OAOAFBDZgM+40ABALBDZgM+40ABALNJ0BNuNOAwCx4OgKOy8vT1u2bJEkVVZWqrCwULt27VJaWpqTwwAcxVOYAMSCo4GdlZWlrKwsSdLChQuVnZ1NWMP3uNMAQCy4soe9f/9+HTp0SAsWLHDj8oCjuNMAQCzEhcPhsNMXnTlzpn784x9r+PDhbf5cTU2t4uM7OzQq//jP+SqteqlAJ8+Uq3ePJE3PHqy0ZLqSAcBkjq+wz507p6KioohhLUklJeUOjMg56empKi4ujfyDUVr58oGGruSPjp1VZWWNqys8p+btRUGdO/MOnqDO3Y55p6entvh1x7vE9+7dq+uuu87pywYKXckA4D+OB/bhw4eVkZHh9GUDhfPPAcB/HC+J33nnnU5fMnDoSgYA/+GkMx+iKxkA/IfAhqs4ZxsArAlMYBMM3sQ52wBgTWACO0jBYNKHEzraAcCawAR2kIJh7RuF2nfoM0lffDiprqnVryYOdnlULeOcbQCwJjCBHaRg+PDY2TZfe4kTHe0mVRycwO8DMFNgAjtYtzrFRXjtHU50tAdpO8QKfh+AmQIT2EG61emyi0LKP3T6y9f9Qi6Oxn1B2g6xgt8HYCbHTzqD/X46NlPDMnupf59UDcvspZ9+J9PtIbmKk9+a4vcBmCkwK+wgCVI1wYpgbYdExu8DMBOBDdvUNzedPV+lUHKCa81NfIBpit8HYCYCG7Zp3NxUj6AAgI5hDxu2obkJAGKHwIZtaG4CgNihJA7b1DczNd7DBgB0DIEN29Q3N6Wnp6q4uDTyHwAAtIrABgzCsaJAcBHYgEE4VhQILprOAIPQeQ8EF4ENGITOeyC4KIkDBuFYUSC4CGwHeOWITpiPY0WB4CKwHcARnQCAaLGH7QAahQAA0SKwHUCjEAAgWpTEHcARnQCAaBHYDuCITgBAtCiJAwBgAFbYgM/E+rxxzi8HvIHABnwm1ueNc3454A2UxAGfifVthNyWCHgDgQ34TKxvI+S2RMAbKIkDPhPr88Y5vxzwBgIb8JlYnzfO+eWAN1ASBwDAAAQ2AAAGILABADAAgQ0AgAEIbAAADEBgAwBgAAIbAAADcB82YIGfHoDhp7kAQUJgAxb46QEYfpoLECSUxAEL/PQADD/NBQgSAhuwwE8PwPDTXIAgoSQOWOCnB2D4aS5AkBDYgAV+egCGn+YCBAklcQAADEBgAwBgAAIbAAADENgAABiApjOgGU4CA+BFBDbQDCeBAfAiSuJAM5wEBsCLCGygGU4CA+BFlMSBZjgJDIAXEdhAM5wEBsCLHA/s1atX680331R1dbWmTJmiSZMmOT0EAACM42hg79mzR/n5+XrxxRdVUVGhtWvXOnl5AACM5Whgv/vuuxo0aJBmzJihsrIyPfDAA05eHgAAY8WFw+GwUxd76KGH9Mknn2jVqlU6fvy4pk+frq1btyouLq7Fn6+pqVV8fGenhgcAgGc5usIOhUIaOHCgEhISNHDgQCUmJurMmTPq2bNniz9fUlLu5PBsl56equLiUreH4bigzlsK7tyZd/AEde52zDs9PbXFrzt6H/bQoUP1zjvvKBwO6+TJk6qoqFAoFHJyCAAAGMnRFfZNN92kvXv3auLEiQqHw8rJyVHnzpS8AQCIxPHbumg0AwCg/TiaFAAAAxDYAAAYwNHbugAAQMewwgYAwAAENgAABiCwAQAwAIENAIABCGwAAAxAYAMAYADHTzoLqs8++0xZWVlau3atLrnkEreH45gJEyYoNfWLg+wzMjL06KOPujwiZ6xevVpvvvmmqqurNWXKFE2aNMntITkiLy9PW7ZskSRVVlaqsLBQu3btUlpamssjs1d1dbXmzp2rEydOqFOnTlq0aFEg/juvqqrSvHnzdOzYMaWkpCgnJ0f9+/d3e1i2Kygo0LJly5Sbm6ujR49q7ty5iouL06WXXqoFCxaoUyd71sIEtgOqq6uVk5OjCy64wO2hOKqyslKSlJub6/JInLVnzx7l5+frxRdfVEVFhdauXev2kByTlZWlrKwsSdLChQuVnZ3t+7CWpJ07d6qmpkYbNmzQrl279NRTT2nFihVuD8t2mzZtUlJSkjZt2qSioiItWrRIa9ascXtYtnr22Wf16quvqmvXrpKkRx99VLNmzdI111yjnJwc7dixQ7fccost16Yk7oAlS5bohz/8oXr16uX2UBx18OBBVVRU6Pbbb9e0adO0b98+t4fkiHfffVeDBg3SjBkz9Mtf/lLf+MY33B6S4/bv369Dhw5p8uTJbg/FEQMGDFBtba3q6upUVlam+PhgrIUOHTqkESNGSJIGDhyojz/+2OUR2a9fv35NPoy9//77uvrqqyVJI0aM0O7du227djD+X+WivLw89ejRQzfeeKP+8Ic/uD0cR11wwQW64447NGnSJB05ckQ/+9nPtHXrVt+/mZWUlOiTTz7RqlWrdPz4cU2fPl1bt25VXFyc20NzzOrVqzVjxgy3h+GYpKQknThxQmPGjFFJSYlWrVrl9pAccfnll+utt97SzTffrIKCAp08eVK1tbW+fgrj6NGjdfz48YbX4XC44b/t5ORklZba90xwVtg2e+mll7R7925NnTpVhYWFmjNnjoqLi90eliMGDBig8ePHKy4uTgMGDFAoFArE3EOhkG644QYlJCRo4MCBSkxM1JkzZ9welmPOnTunoqIiDR8+3O2hOGbdunW64YYbtG3bNr3yyiuaO3duw5aQn2VnZyslJUXTpk3TW2+9pa9+9au+DuuWNN6vPn/+vK1bQAS2zV544QU9//zzys3N1eWXX64lS5YoPT3d7WE5YvPmzXrsscckSSdPnlRZWVkg5j506FC98847CofDOnnypCoqKhQKhdwelmP27t2r6667zu1hOCotLa2hubJbt26qqalRbW2ty6Oy3/79+zV06FDl5ubq5ptv1kUXXeT2kBx3xRVXaM+ePZKkt99+W1dddZVt1/J3bRKumjhxoubNm6cpU6YoLi5Ov/3tb31fDpekm266SXv37tXEiRMVDoeVk5MTqFXH4cOHlZGR4fYwHHXbbbdp/vz5uvXWW1VdXa17771XSUlJbg/LdhdffLGWL1+utWvXKjU1VYsXL3Z7SI6bM2eOHn74YT3xxBMaOHCgRo8ebdu1eFoXAAAGoCQOAIABCGwAAAx2XY1yAAAC+klEQVRAYAMAYAACGwAAAxDYAAAYgMAGAMAABDYAAAbw/ykWQADV1NTokUce0UcffaTTp0/rsssuU0ZGhvr06aPbb79dknT33Xdr/PjxGjJkiHJycvTpp58qLi5Os2fP1nXXXae7775bl1xyiWbNmqVVq1apsLBQy5cv17XXXqtbbrlF+fn5Sk5O1rJly5SRkaFRo0bpa1/7mgoLC7V+/Xr17NnT5d8C4C+ssAEfys/PV5cuXbRx40Zt375dpaWl6tOnj/70pz9JksrKypSfn6+RI0dq8eLFys7OVl5enlauXKmcnByVlZXpkUceUV5enrZt26ZNmzZp4cKFkqQzZ85oyJAheu211zR27Fj95je/abjuiBEjtG3bNsIasAErbMCHhg0bplAopBdeeEFFRUU6cuSIunfvrqqqKh09elT5+fkaNWqUEhIStHv3bhUVFenpp5+W9MXq/NixY7r88ss1d+5c/epXv9Lq1asbzkNPTEzUhAkTJEnf//739cQTTzRcd/Dgwc5PFggIAhvwoR07dujpp5/WtGnTlJWVpZKSEoXDYY0fP15vvPGG8vPz9fOf/1ySVFdXpz/+8Y8NgXzq1KmGFXJRUZF69uypAwcONDzXu1OnTg2PE6yrq2tyTnpiYqKDswSChZI44EN//etfNWbMGGVnZystLU179uxRbW2txo0bpzfeeENHjx7V0KFDJUnDhw/X+vXrJUmHDh3SuHHjVFFRocLCQm3ZskV5eXnKy8vTwYMHJUkVFRV68803JX3xvPcRI0a4M0kgYFhhAz40adIk3XfffXr99dfVpUsXXXnllTp+/Li+8pWvqHv37hoyZEjDKvmhhx5STk6Oxo0bJ0l6/PHHlZiYqLlz52revHnq06ePHnjgAc2ZM0ebN2+WJG3dulVPPvmkevXqpSVLlrg2TyBIeFoXgHa57LLL9MEHH7g9DCBwKIkDAGAAVtgAABiAFTYAAAYgsAEAMACBDQCAAQhsAAAMQGADAGAAAhsAAAP8Px/jdRdmhxKHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "df1.plot(x='avexpr', y='logpgp95', kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dropping NA's is required to use numpy's polyfit\n",
    "df1_subset = df1.dropna(subset=['logpgp95', 'avexpr'])\n",
    "\n",
    "# Use only 'base sample' for plotting purposes\n",
    "df1_subset = df1_subset[df1_subset['baseco'] == 1]\n",
    "\n",
    "X = df1_subset['avexpr']\n",
    "y = df1_subset['logpgp95']\n",
    "labels = df1_subset['shortnam']\n",
    "\n",
    "# Replace markers with country labels\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X, y, marker='')\n",
    "\n",
    "for i, label in enumerate(labels):\n",
    "    ax.annotate(label, (X.iloc[i], y.iloc[i]))\n",
    "\n",
    "# Fit a linear trend line\n",
    "ax.plot(np.unique(X),\n",
    "         np.poly1d(np.polyfit(X, y, 1))(np.unique(X)),\n",
    "         color='black')\n",
    "\n",
    "ax.set_xlim([3.3,10.5])\n",
    "ax.set_ylim([4,10.5])\n",
    "ax.set_xlabel('Average Expropriation Risk 1985-95')\n",
    "ax.set_ylabel('Log GDP per capita, PPP, 1995')\n",
    "ax.set_title('Figure 2: OLS relationship between expropriation \\\n",
    "    risk and income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['const'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statsmodels.regression.linear_model.OLS"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1 = sm.OLS(endog=df1['logpgp95'], exog=df1[['const', 'avexpr']], \\\n",
    "    missing='drop')\n",
    "type(reg1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statsmodels.regression.linear_model.RegressionResultsWrapper"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = reg1.fit()\n",
    "type(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               logpgp95   R-squared:                       0.611\n",
      "Model:                            OLS   Adj. R-squared:                  0.608\n",
      "Method:                 Least Squares   F-statistic:                     171.4\n",
      "Date:                Sun, 01 Dec 2019   Prob (F-statistic):           4.16e-24\n",
      "Time:                        21:18:08   Log-Likelihood:                -119.71\n",
      "No. Observations:                 111   AIC:                             243.4\n",
      "Df Residuals:                     109   BIC:                             248.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          4.6261      0.301     15.391      0.000       4.030       5.222\n",
      "avexpr         0.5319      0.041     13.093      0.000       0.451       0.612\n",
      "==============================================================================\n",
      "Omnibus:                        9.251   Durbin-Watson:                   1.689\n",
      "Prob(Omnibus):                  0.010   Jarque-Bera (JB):                9.170\n",
      "Skew:                          -0.680   Prob(JB):                       0.0102\n",
      "Kurtosis:                       3.362   Cond. No.                         33.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.515625"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_expr = np.mean(df1_subset['avexpr'])\n",
    "mean_expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.3771"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_logpdp95 = 4.63 + 0.53 * 7.07\n",
    "predicted_logpdp95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.09156367])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.predict(exog=[1, mean_expr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop missing observations from whole sample\n",
    "\n",
    "df1_plot = df1.dropna(subset=['logpgp95', 'avexpr'])\n",
    "\n",
    "# Plot predicted values\n",
    "\n",
    "fix, ax = plt.subplots()\n",
    "ax.scatter(df1_plot['avexpr'], results.predict(), alpha=0.5,\n",
    "        label='predicted')\n",
    "\n",
    "# Plot observed values\n",
    "\n",
    "ax.scatter(df1_plot['avexpr'], df1_plot['logpgp95'], alpha=0.5,\n",
    "        label='observed')\n",
    "\n",
    "ax.legend()\n",
    "ax.set_title('OLS predicted values')\n",
    "ax.set_xlabel('avexpr')\n",
    "ax.set_ylabel('logpgp95')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_stata('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/ols/maketable2.dta')\n",
    "\n",
    "# Add constant term to dataset\n",
    "df2['const'] = 1\n",
    "\n",
    "# Create lists of variables to be used in each regression\n",
    "X1 = ['const', 'avexpr']\n",
    "X2 = ['const', 'avexpr', 'lat_abst']\n",
    "X3 = ['const', 'avexpr', 'lat_abst', 'asia', 'africa', 'other']\n",
    "\n",
    "# Estimate an OLS regression for each set of variables\n",
    "reg1 = sm.OLS(df2['logpgp95'], df2[X1], missing='drop').fit()\n",
    "reg2 = sm.OLS(df2['logpgp95'], df2[X2], missing='drop').fit()\n",
    "reg3 = sm.OLS(df2['logpgp95'], df2[X3], missing='drop').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Table 2 - OLS Regressions\n",
      "=========================================\n",
      "                 Model 1 Model 3 Model 4 \n",
      "-----------------------------------------\n",
      "const            4.63*** 4.87*** 5.85*** \n",
      "                 (0.30)  (0.33)  (0.34)  \n",
      "avexpr           0.53*** 0.46*** 0.39*** \n",
      "                 (0.04)  (0.06)  (0.05)  \n",
      "lat_abst                 0.87*   0.33    \n",
      "                         (0.49)  (0.45)  \n",
      "asia                             -0.15   \n",
      "                                 (0.15)  \n",
      "africa                           -0.92***\n",
      "                                 (0.17)  \n",
      "other                            0.30    \n",
      "                                 (0.37)  \n",
      "R-squared        0.61    0.62    0.72    \n",
      "No. observations 111     111     111     \n",
      "=========================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "info_dict={'R-squared' : lambda x: f\"{x.rsquared:.2f}\",\n",
    "           'No. observations' : lambda x: f\"{int(x.nobs):d}\"}\n",
    "\n",
    "results_table = summary_col(results=[reg1,reg2,reg3],\n",
    "                            float_format='%0.2f',\n",
    "                            stars = True,\n",
    "                            model_names=['Model 1',\n",
    "                                         'Model 3',\n",
    "                                         'Model 4'],\n",
    "                            info_dict=info_dict,\n",
    "                            regressor_order=['const',\n",
    "                                             'avexpr',\n",
    "                                             'lat_abst',\n",
    "                                             'asia',\n",
    "                                             'africa'])\n",
    "\n",
    "results_table.add_title('Table 2 - OLS Regressions')\n",
    "\n",
    "print(results_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dropping NA's is required to use numpy's polyfit\n",
    "df1_subset2 = df1.dropna(subset=['logem4', 'avexpr'])\n",
    "\n",
    "X = df1_subset2['logem4']\n",
    "y = df1_subset2['avexpr']\n",
    "labels = df1_subset2['shortnam']\n",
    "\n",
    "# Replace markers with country labels\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X, y, marker='')\n",
    "\n",
    "for i, label in enumerate(labels):\n",
    "    ax.annotate(label, (X.iloc[i], y.iloc[i]))\n",
    "\n",
    "# Fit a linear trend line\n",
    "ax.plot(np.unique(X),\n",
    "         np.poly1d(np.polyfit(X, y, 1))(np.unique(X)),\n",
    "         color='black')\n",
    "\n",
    "ax.set_xlim([1.8,8.4])\n",
    "ax.set_ylim([3.3,10.4])\n",
    "ax.set_xlabel('Log of Settler Mortality')\n",
    "ax.set_ylabel('Average Expropriation Risk 1985-95')\n",
    "ax.set_title('Figure 3: First-stage relationship between settler mortality \\\n",
    "    and expropriation risk')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 avexpr   R-squared:                       0.270\n",
      "Model:                            OLS   Adj. R-squared:                  0.258\n",
      "Method:                 Least Squares   F-statistic:                     22.95\n",
      "Date:                Sun, 01 Dec 2019   Prob (F-statistic):           1.08e-05\n",
      "Time:                        21:18:54   Log-Likelihood:                -104.83\n",
      "No. Observations:                  64   AIC:                             213.7\n",
      "Df Residuals:                      62   BIC:                             218.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          9.3414      0.611     15.296      0.000       8.121      10.562\n",
      "logem4        -0.6068      0.127     -4.790      0.000      -0.860      -0.354\n",
      "==============================================================================\n",
      "Omnibus:                        0.035   Durbin-Watson:                   2.003\n",
      "Prob(Omnibus):                  0.983   Jarque-Bera (JB):                0.172\n",
      "Skew:                           0.045   Prob(JB):                        0.918\n",
      "Kurtosis:                       2.763   Cond. No.                         19.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Import and select the data\n",
    "df4 = pd.read_stata('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/ols/maketable4.dta')\n",
    "df4 = df4[df4['baseco'] == 1]\n",
    "\n",
    "# Add a constant variable\n",
    "df4['const'] = 1\n",
    "\n",
    "# Fit the first stage regression and print summary\n",
    "results_fs = sm.OLS(df4['avexpr'],\n",
    "                    df4[['const', 'logem4']],\n",
    "                    missing='drop').fit()\n",
    "print(results_fs.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               logpgp95   R-squared:                       0.477\n",
      "Model:                            OLS   Adj. R-squared:                  0.469\n",
      "Method:                 Least Squares   F-statistic:                     56.60\n",
      "Date:                Sun, 01 Dec 2019   Prob (F-statistic):           2.66e-10\n",
      "Time:                        21:19:09   Log-Likelihood:                -72.268\n",
      "No. Observations:                  64   AIC:                             148.5\n",
      "Df Residuals:                      62   BIC:                             152.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "const                1.9097      0.823      2.320      0.024       0.264       3.555\n",
      "predicted_avexpr     0.9443      0.126      7.523      0.000       0.693       1.195\n",
      "==============================================================================\n",
      "Omnibus:                       10.547   Durbin-Watson:                   2.137\n",
      "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               11.010\n",
      "Skew:                          -0.790   Prob(JB):                      0.00407\n",
      "Kurtosis:                       4.277   Cond. No.                         58.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "df4['predicted_avexpr'] = results_fs.predict()\n",
    "\n",
    "results_ss = sm.OLS(df4['logpgp95'],\n",
    "                    df4[['const', 'predicted_avexpr']]).fit()\n",
    "print(results_ss.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          IV-2SLS Estimation Summary                          \n",
      "==============================================================================\n",
      "Dep. Variable:               logpgp95   R-squared:                      0.1870\n",
      "Estimator:                    IV-2SLS   Adj. R-squared:                 0.1739\n",
      "No. Observations:                  64   F-statistic:                    37.568\n",
      "Date:                Sun, Dec 01 2019   P-value (F-stat)                0.0000\n",
      "Time:                        21:19:20   Distribution:                  chi2(1)\n",
      "Cov. Estimator:            unadjusted                                         \n",
      "                                                                              \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "const          1.9097     1.0106     1.8897     0.0588     -0.0710      3.8903\n",
      "avexpr         0.9443     0.1541     6.1293     0.0000      0.6423      1.2462\n",
      "==============================================================================\n",
      "\n",
      "Endogenous: avexpr\n",
      "Instruments: logem4\n",
      "Unadjusted Covariance (Homoskedastic)\n",
      "Debiased: False\n"
     ]
    }
   ],
   "source": [
    "iv = IV2SLS(dependent=df4['logpgp95'],\n",
    "            exog=df4['const'],\n",
    "            endog=df4['avexpr'],\n",
    "            instruments=df4['logem4']).fit(cov_type='unadjusted')\n",
    "\n",
    "print(iv.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 1\n",
    "\n",
    "![Ex1](img/ols_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               logpgp95   R-squared:                       0.689\n",
      "Model:                            OLS   Adj. R-squared:                  0.679\n",
      "Method:                 Least Squares   F-statistic:                     74.05\n",
      "Date:                Mon, 02 Dec 2019   Prob (F-statistic):           1.07e-17\n",
      "Time:                        18:01:48   Log-Likelihood:                -62.031\n",
      "No. Observations:                  70   AIC:                             130.1\n",
      "Df Residuals:                      67   BIC:                             136.8\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          2.4782      0.547      4.530      0.000       1.386       3.570\n",
      "avexpr         0.8564      0.082     10.406      0.000       0.692       1.021\n",
      "resid         -0.4951      0.099     -5.017      0.000      -0.692      -0.298\n",
      "==============================================================================\n",
      "Omnibus:                       17.597   Durbin-Watson:                   2.086\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.194\n",
      "Skew:                          -1.054   Prob(JB):                     9.19e-06\n",
      "Kurtosis:                       4.873   Cond. No.                         53.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Load in data\n",
    "df4 = pd.read_stata('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/ols/maketable4.dta')\n",
    "\n",
    "# Add a constant term\n",
    "df4['const'] = 1\n",
    "\n",
    "# Estimate the first stage regression\n",
    "reg1 = sm.OLS(endog=df4['avexpr'],\n",
    "              exog=df4[['const', 'logem4']],\n",
    "              missing='drop').fit()\n",
    "\n",
    "# Retrieve the residuals\n",
    "df4['resid'] = reg1.resid\n",
    "\n",
    "# Estimate the second stage residuals\n",
    "reg2 = sm.OLS(endog=df4['logpgp95'],\n",
    "              exog=df4[['const', 'avexpr', 'resid']],\n",
    "              missing='drop').fit()\n",
    "\n",
    "print(reg2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 2\n",
    "\n",
    "![Ex2](img/ols_2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "β_0 = 4.6\n",
      "β_1 = 0.53\n"
     ]
    }
   ],
   "source": [
    "# Load in data\n",
    "df1 = pd.read_stata('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/ols/maketable1.dta')\n",
    "df1 = df1.dropna(subset=['logpgp95', 'avexpr'])\n",
    "\n",
    "# Add a constant term\n",
    "df1['const'] = 1\n",
    "\n",
    "# Define the X and y variables\n",
    "y = np.asarray(df1['logpgp95'])\n",
    "X = np.asarray(df1[['const', 'avexpr']])\n",
    "\n",
    "# Compute β_hat\n",
    "β_hat = np.linalg.solve(X.T @ X, X.T @ y)\n",
    "\n",
    "# Print out the results from the 2 x 1 vector β_hat\n",
    "print(f'β_0 = {β_hat[0]:.2}')\n",
    "print(f'β_1 = {β_hat[1]:.2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lesson: Maximum Likelihood Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import exp\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from scipy.special import factorial\n",
    "import pandas as pd\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.api import Poisson\n",
    "from scipy import stats\n",
    "from scipy.stats import norm\n",
    "from statsmodels.iolib.summary2 import summary_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "poisson_pmf = lambda y, μ: μ**y / factorial(y) * exp(-μ)\n",
    "y_values = range(0, 25)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "for μ in [1, 5, 10]:\n",
    "    distribution = []\n",
    "    for y_i in y_values:\n",
    "        distribution.append(poisson_pmf(y_i, μ))\n",
    "    ax.plot(y_values,\n",
    "            distribution,\n",
    "            label=f'$\\mu$={μ}',\n",
    "            alpha=0.5,\n",
    "            marker='o',\n",
    "            markersize=8)\n",
    "\n",
    "ax.grid()\n",
    "ax.set_xlabel('$y$', fontsize=14)\n",
    "ax.set_ylabel('$f(y \\mid \\mu)$', fontsize=14)\n",
    "ax.axis(xmin=0, ymin=0)\n",
    "ax.legend(fontsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>ccode</th>\n",
       "      <th>year</th>\n",
       "      <th>cyear</th>\n",
       "      <th>numbil</th>\n",
       "      <th>...</th>\n",
       "      <th>topint08</th>\n",
       "      <th>rintr</th>\n",
       "      <th>noyrs</th>\n",
       "      <th>roflaw</th>\n",
       "      <th>nrrents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>21990.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>4.988405</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>21991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>4.988405</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>21992.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>4.988405</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>21993.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>4.988405</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>21994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>4.988405</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  ccode    year    cyear  numbil  ...   topint08     rintr  \\\n",
       "0  United States    2.0  1990.0  21990.0     NaN  ...  39.799999  4.988405   \n",
       "1  United States    2.0  1991.0  21991.0     NaN  ...  39.799999  4.988405   \n",
       "2  United States    2.0  1992.0  21992.0     NaN  ...  39.799999  4.988405   \n",
       "3  United States    2.0  1993.0  21993.0     NaN  ...  39.799999  4.988405   \n",
       "4  United States    2.0  1994.0  21994.0     NaN  ...  39.799999  4.988405   \n",
       "\n",
       "   noyrs  roflaw  nrrents  \n",
       "0   20.0    1.61      NaN  \n",
       "1   20.0    1.61      NaN  \n",
       "2   20.0    1.61      NaN  \n",
       "3   20.0    1.61      NaN  \n",
       "4   20.0    1.61      NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = 10\n",
    "\n",
    "# Load in data and view\n",
    "df = pd.read_stata('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/mle/fp.dta')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbil0_2008 = df[(df['year'] == 2008) & (\n",
    "    df['country'] != 'United States')].loc[:, 'numbil0']\n",
    "\n",
    "plt.subplots(figsize=(12, 8))\n",
    "plt.hist(numbil0_2008, bins=30)\n",
    "plt.xlim(left=0)\n",
    "plt.grid()\n",
    "plt.xlabel('Number of billionaires in 2008')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_values = range(0, 20)\n",
    "\n",
    "# Define a parameter vector with estimates\n",
    "β = np.array([0.26, 0.18, 0.25, -0.1, -0.22])\n",
    "\n",
    "# Create some observations X\n",
    "datasets = [np.array([0, 1, 1, 1, 2]),\n",
    "            np.array([2, 3, 2, 4, 0]),\n",
    "            np.array([3, 4, 5, 3, 2]),\n",
    "            np.array([6, 5, 4, 4, 7])]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "for X in datasets:\n",
    "    μ = exp(X @ β)\n",
    "    distribution = []\n",
    "    for y_i in y_values:\n",
    "        distribution.append(poisson_pmf(y_i, μ))\n",
    "    ax.plot(y_values,\n",
    "            distribution,\n",
    "            label=f'$\\mu_i$={μ:.1}',\n",
    "            marker='o',\n",
    "            markersize=8,\n",
    "            alpha=0.5)\n",
    "\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "ax.set_xlabel('$y \\mid x_i$')\n",
    "ax.set_ylabel(r'$f(y \\mid x_i; \\beta )$')\n",
    "ax.axis(xmin=0, ymin=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_joint_poisson(μ=7, y_n=20):\n",
    "    yi_values = np.arange(0, y_n, 1)\n",
    "\n",
    "    # Create coordinate points of X and Y\n",
    "    X, Y = np.meshgrid(yi_values, yi_values)\n",
    "\n",
    "    # Multiply distributions together\n",
    "    Z = poisson_pmf(X, μ) * poisson_pmf(Y, μ)\n",
    "\n",
    "    fig = plt.figure(figsize=(12, 8))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    ax.plot_surface(X, Y, Z.T, cmap='terrain', alpha=0.6)\n",
    "    ax.scatter(X, Y, Z.T, color='black', alpha=0.5, linewidths=1)\n",
    "    ax.set(xlabel='$y_1$', ylabel='$y_2$')\n",
    "    ax.set_zlabel('$f(y_1, y_2)$', labelpad=10)\n",
    "    plt.show()\n",
    "\n",
    "plot_joint_poisson(μ=7, y_n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "β = np.linspace(1, 20)\n",
    "logL = -(β - 10) ** 2 - 10\n",
    "dlogL = -2 * β + 20\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(12, 8))\n",
    "\n",
    "ax1.plot(β, logL, lw=2)\n",
    "ax2.plot(β, dlogL, lw=2)\n",
    "\n",
    "ax1.set_ylabel(r'$log \\mathcal{L(\\beta)}$',\n",
    "               rotation=0,\n",
    "               labelpad=35,\n",
    "               fontsize=15)\n",
    "ax2.set_ylabel(r'$\\frac{dlog \\mathcal{L(\\beta)}}{d \\beta}$ ',\n",
    "               rotation=0,\n",
    "               labelpad=35,\n",
    "               fontsize=19)\n",
    "ax2.set_xlabel(r'$\\beta$', fontsize=15)\n",
    "ax1.grid(), ax2.grid()\n",
    "plt.axhline(c='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PoissonRegression:\n",
    "\n",
    "    def __init__(self, y, X, β):\n",
    "        self.X = X\n",
    "        self.n, self.k = X.shape\n",
    "        # Reshape y as a n_by_1 column vector\n",
    "        self.y = y.reshape(self.n,1)\n",
    "        # Reshape β as a k_by_1 column vector\n",
    "        self.β = β.reshape(self.k,1)\n",
    "\n",
    "    def μ(self):\n",
    "        return np.exp(self.X @ self.β)\n",
    "\n",
    "    def logL(self):\n",
    "        y = self.y\n",
    "        μ = self.μ()\n",
    "        return np.sum(y * np.log(μ) - μ - np.log(factorial(y)))\n",
    "\n",
    "    def G(self):\n",
    "        y = self.y\n",
    "        μ = self.μ()\n",
    "        return X.T @ (y - μ)\n",
    "\n",
    "    def H(self):\n",
    "        X = self.X\n",
    "        μ = self.μ()\n",
    "        return -(X.T @ (μ * X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def newton_raphson(model, tol=1e-3, max_iter=1000, display=True):\n",
    "\n",
    "    i = 0\n",
    "    error = 100  # Initial error value\n",
    "\n",
    "    # Print header of output\n",
    "    if display:\n",
    "        header = f'{\"Iteration_k\":<13}{\"Log-likelihood\":<16}{\"θ\":<60}'\n",
    "        print(header)\n",
    "        print(\"-\" * len(header))\n",
    "\n",
    "    # While loop runs while any value in error is greater\n",
    "    # than the tolerance until max iterations are reached\n",
    "    while np.any(error > tol) and i < max_iter:\n",
    "        H, G = model.H(), model.G()\n",
    "        β_new = model.β - (np.linalg.inv(H) @ G)\n",
    "        error = β_new - model.β\n",
    "        model.β = β_new\n",
    "\n",
    "        # Print iterations\n",
    "        if display:\n",
    "            β_list = [f'{t:.3}' for t in list(model.β.flatten())]\n",
    "            update = f'{i:<13}{model.logL():<16.8}{β_list}'\n",
    "            print(update)\n",
    "\n",
    "        i += 1\n",
    "\n",
    "    print(f'Number of iterations: {i}')\n",
    "    print(f'β_hat = {model.β.flatten()}')\n",
    "\n",
    "    # Return a flat array for β (instead of a k_by_1 column vector)\n",
    "    return model.β.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration_k  Log-likelihood  θ                                                           \n",
      "-----------------------------------------------------------------------------------------\n",
      "0            -4.3447622      ['-1.49', '0.265', '0.244']\n",
      "1            -3.5742413      ['-3.38', '0.528', '0.474']\n",
      "2            -3.3999526      ['-5.06', '0.782', '0.702']\n",
      "3            -3.3788646      ['-5.92', '0.909', '0.82']\n",
      "4            -3.3783559      ['-6.07', '0.933', '0.843']\n",
      "5            -3.3783555      ['-6.08', '0.933', '0.843']\n",
      "Number of iterations: 6\n",
      "β_hat = [-6.07848205  0.93340226  0.84329625]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[1, 2, 5],\n",
    "              [1, 1, 3],\n",
    "              [1, 4, 2],\n",
    "              [1, 5, 2],\n",
    "              [1, 3, 1]])\n",
    "\n",
    "y = np.array([1, 0, 1, 1, 0])\n",
    "\n",
    "# Take a guess at initial βs\n",
    "init_β = np.array([0.1, 0.1, 0.1])\n",
    "\n",
    "# Create an object with Poisson model values\n",
    "poi = PoissonRegression(y, X, β=init_β)\n",
    "\n",
    "# Use newton_raphson to find the MLE\n",
    "β_hat = newton_raphson(poi, display=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.95169228e-07],\n",
       "       [-1.00114805e-06],\n",
       "       [-7.73114562e-07]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poi.G()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MeiOH/nOIQ98cwt7dnpH/Gol/f3+twxJC1HOKqqpax9CohYSEqLGxsVqH0WhlZmbi6SmrgWhFxr/2JfyWQOTsSDo92onBLw++q75ef/11vLy8mDp16i237d27N4sWLTJd89DYVfdzPzsxm8jZkWQcz6DDyA70/3t/7Fzsqq3/hkZee7QjY68tRVEOqqra83b2kTMLQgjRSOScz2H33N20uLcFXR7tctf9vffee1Xedv/+/Xd9PFGZalQ5/P1hYj6PwdbRlmEfDKPdfe20DksI0YBIsiCEEI2AoczA1hlbsW5izdB3h1JsJTUJ6rvcpFwi50SSFpdGm7A2DHx9YKVpZUIIcbckWRBCiEZg37/2kZWQxcgFI2nq1ZTiTEkW6ivVqHJs5TH2/2s/VjZWDHl7CB1GdZDVoYQQNUKSBSGEaODORZ3j6I9H6f5Id/wHyAWv9VlBWgFRb0eRvD8Zvz5+DJ41mKZeTbUOSwjRgEmyIIQQDVhBWgFRb0XRPLA5oVNCtQ5H3CFVVUlYm8Dej/aiGlUGzBhA4PhAOZsghKhxkiwIIUQDZTQY2fbGNox6I0PfG4p1E2utQxJ3oCiriJ3/2Mn5HefxuceHsDlhuLR00TosIUQjIclCFSiK8iPQ6fKPbkCOqqohFrY7B+QDBkB/u0tTCSFEdfr9699JjU1l6LtDcW3lqnU44g4kbklk1/u7KC8up++0vnT7UzcUKzmbIISoPZIsVIGqqn+88v+KonwM5N5k8yGqqmbWfFRCCHFjl2IucWjRITqN7USHkR20DkfcppLcEnZ/sJszm87QvEtzwt4Kw72tu9ZhCSEaIUkWboNSMTl0IjBU61iEEOJGinXFbJu5DVd/V/r9Xz+twxG36fzO8+x8dycluSX0eq4XwU8EY2VtpXVYQohGSpKF2zMQSFNV9dQN2lVgk6IoKvClqqpfWdpIUZTJwGQAX19fMjPlRIRWcnNvdpJI1DQZ/+qnGlV2vrGTQl0hfd7qQ25hLhRW3k7GXluWxr+soIy4L+I4u+Esbu3cCHs7DLf2buiydbUfYAMnz3/tyNjXP5IsXKYoyhbAx0LTG6qq/nL5/x8Glt+km/6qql5SFMUL2KwoyglVVXdcv9HlJOIrgJCQEFXKnmtLxl9bMv7V6/B3h8k8lMmAvw+gQ++bTz+SsdfWteOfHJ1M1NtRFKYX0mtyL+59+l65IL2GyfNfOzL29YskC5epqjrsZu2KotgA44EeN+nj0uV/0xVF+RkIBSolC0IIURMyjmUQ/Wk0bYa0octDXbQOR1RBeXE50Z9Gc/S/R3H1d+WBxQ/g1c1L67CEEMJEkoWqGwacUFX1oqVGRVGaAlaqquZf/v/7gbdrM0AhRONVVlDG1hlbcWzuyOA3B8v6+/VA2uE0ImdHkpuUS/dHutPruV7Y2MvbshCibpFXpar7E9dNQVIUxRf4RlXVcMAb+PnyG7QN8IOqqhtqPUohRKOjqio739tJfko+Y74eg52LndYhiZswlBmI+yqOxJ8TcWrhRMSXEfj28NU6LCGEsEiShSpSVfVJC/ddAsIv/38iEFzLYQkhBCfXnOTMpjP0eq4XPsGWLr0SdUXG8QwiZ0eSkZBBt4e60fflvtg62modlhBC3JAkC0IIUY9lJ2az5597aBnakpAnK9WKFHWEUW/k0OJDHFp0CAcPBwa+N5Du4d21DksIIW5JkgUhhKin9KV6ts7Yiq2jLUPeHiKVfeuo7MRsts/aTuaJTDqGd6Tf9H7kl+VrHZYQQlSJVHkRQoh6au+8vejO6Ah7KwxHT0etwxHXUY0qccviWPXoKgrTChn+z+EMeXuIXFMiNDdjxgwWLFhQI32HhoZy9OjRGulbaEOSBSGEqIcStyZyfOVxgh8PplW/VlqHI66Tm5TLmqfXsP+T/fgP9OcPP/6BtkPbah2WaASKi4uZPXs2wcHBuLu707p1ax566CH27dsHQEZGBsuWLePZZ58F4Pjx4wwdOhRXV1c6dOjAzz//fNP+w8LCsLe3x8nJCScnJzp16mTWPn36dGbNmlUtj2XhwoX07NkTOzs7nnzyyWrpU9w+SRaEEKKeyb+Uz453duDV1Yuef+updTjiGqpR5eh/j7Ly4ZXknM1hyDtDGPbBMBw8HLQOTTQChYWFDB8+nIMHD7J8+XKys7M5evQow4cPZ9OmTQAsWbKE8PBwHBwc0Ov1PPDAA0RERKDT6fjqq6947LHHSEhIuOlxFi5cSEFBAQUFBZw8edKsbezYsWzfvp2UlJS7fjy+vr7MnDmTSZMm3XVf4s7JNQtCCFGPGPVGtr2xDVQY+t5QrG2lym9dUZBaQNTbUSRHJ9OqXysGzRxEU6+mWoclGpHXXnsNVVVZvXo1NjYVH/GcnJyYPHmyaZv169ebPnyfOHGCS5cu8fLLL6MoCkOHDqV///58++23vPPOO3cUg729PT169GDTpk088cQTd/V4xo8fD0BMTAwXL1oscyVqgZxZEEKIeiTmixjS4tMY+MZAXFq6aB2OoKLOxck1J/lp4k+kH0ln4BsDGfmvkZIoiFql0+n48ssvmTt3rilRsCQ+Pt40dUhV1Urtqqpy5MiRmx5rxowZeHp60r9/fyIjIyu1BwYGEhcXV+n+iIgI2rdvj5ubW6VbRETELR6h0IokC0IIUU9c3HeR2CWxdB7Xmfb3t9c6HAEUZRax8eWNRL0dhWdnT/6w4g8EjguUCtqi1m3btg0PDw8GDhxoui80NBQ3NzccHBzYsWMHADk5OTg7OwPQuXNnvLy8+PDDDykvL2fTpk1ERUVRVFR0w+N88MEHJCYmkpyczOTJkxkzZgxnzpwx28bZ2ZmcnJxK+65du5YzZ86Qk5NT6bZ27drqGAZRAyRZEEKIeqAoq4jts7bj3s6dfq/00zocAZzZdIafJv5EcnQyfV/pS8QXETj7OmsdlmikUlNT8fU1rwQeHR3NqVOnKCkpoVu3bgC4u7uTn1+xdK+trS2rV6/mt99+w8fHh48//piJEyfi5+d3w+P07t0bZ2dn7OzseOKJJ+jfvz/r1q0z2yY/Px83N7dqfoRCK5IsCCFEHacaVbbP2k5ZQRn3vX8fNvZyuZmWSnJK2PLaFra+vhVXf1cmLJ9A94e7S50LUSv0JXqL9/v7+5OUlITBYDC7PzY2llatWuHh4QFAUFCQ2QXMQUFBREVFkZWVxcaNG0lMTCQ0NLTK8SiKUmk60/HjxwkODq607ahRo2jdurVpJaVrb6NGjaryMUXtkmRBCCHquLhv40jen0y/6f3waO+hdTiN2vkd5/lp4k+cizxHr+d6MXbRWNxayzeoomaoqkpech6n1p9i1we7WPnISv4z6D8U64orbTts2DCcnZ2ZOnUqOp0OVVU5e/YsixcvNvvgHh4eTlRUlOnnw4cPU1JSQlFRER999BEpKSk3XKY0JyeHjRs3UlJSgl6v5/vvv2fHjh2MGDHCtE1paSkHDx5k+PDhlfZfv34958+fN62kdO1t/fr1lbbX6/WUlJRgMBgwGAym44raJV9PCSFEHZZ2OI0Dnx2g3bB2dB7XWetwGq2ygjL2fLyHhF8TaBbQjPDPwmnWsZnWYYkGxlBmION4BmlxaaQdrrhdSQxsHW3x6ubFPX+5x+K+jo6ObN68menTp9O5c2eMRiN+fn6MHj3aVFMB4PHHHyckJITi4mIcHBz49ttv+eabbygvL2fgwIFs3rwZO7urhQNHjRrFwIEDef311ykvL2fmzJmcOHECa2trOnfuzOrVq81qLaxZs4awsLBKU6LuxLvvvstbb71l+vm7775j9uzZzJkz5677FlWnWLoSXtSekJAQNTY2VuswGq3MzEw8PT21DqPRkvG/udL8UlY9sgoUGP/9eOycq6/yr4x91SVHJxM5J5KizCJCngrh3qfvvesla2X8tVVXxr8wvdCUFKQdTiPzRCZGvREA11aueAV54R3kjXeQNx7tPaptqtvrr7+Ol5cXU6dOrZb+rtW7d28WLVpkukbienVl7BsrRVEOqqp6WwV65MyCEELUQaqqsuOdHRSmFzLmmzHVmiiIqikvKmf/p/s59tMx3Nq48eCSB2nepbnWYYl6yqg3kpWQdTU5iEujIK0AAOsm1nh186L7o90rkoPu3jVayO+9996rsb73799fY30LbUiyIIQQddCJn09wdttZer/YG+/u3lqH0+ikxqYSOSeS/OR8gh4LouffemJjJ2+ZouqKs4tJj08nNS6V9Ph0Mo5moC+tmG/v5O2Ed7A3QUFBFWcNOnpIgUVRZ8krnxBC1DG60zr2fLQHvz5+BD0WpHU4jYqhzMCBfx8g/vt4nH2difgqghb3tNA6LFHHqUYV3WkdafEVZw3SD6eTm5QLgJWNFZ6dPQmcEGiaUiQF+0R9IsmCEELUIeXF5WydsZUmTk0Y8vYQWY6zFmUcyyBydiTZZ7MJnBBIn5f6YOtoq3VYog4qzSsl/Ui6aUpR+pF0yovKAXDwcMA7yJvO4zrjHexN88DmWDeRswai/pJkQQgh6pC9H+8l51wO4Z+F1+icZXGVodzAoUWHOLT4EI6ejoQvDMevz42LUonGRTWq5JzPMZtSlJ2YDYBipdCsYzM6ju6Id5A3PsE+OLVwkgreokGRZEEIIeqIM5vOcGL1CUKeCqFlaEutw2kUdKd1bJ+1nayELAIiAuj7Sl+5mLyRKy8qJ/1oullyUJpXCoCdix1e3b1oP6I93kHeeHX1krNPosGTZEEIIeqAvOQ8dry7A+8gb3o+e1ur2ok7oBpV4pbFEfNFDHYudtz/8f20GdxG67BELVNVlfxL+WYrFOlO61CNFcvKu7dzp+3QtqZrDVz9XWVqoGh0JFkQQgiNGcoNbJ2xFStrK4a+OxQrGyutQ2rQci/kEjk7krT4NNre15aBMwZi72avdViiFlwpenZ612mKzhWRFmeh6NmkeyrOGnTzws5FzjIJIcmCEEJo7MC/D5BxLIPh/xyOs6+z1uE0WKpR5eh/jxL9aTTWdtYM/cdQ2t/fXuaXN2A3Knqm1+vxaOOBXx+/q0XPOlRf0TMhGhJJFoQQQkNJe5I4/O1huvyhC22HttU6nAYr/1I+kW9FknIwBf8B/gx8YyBNm8vylQ1JpaJnh9MoSL1a9Kx51+Z0f6Q73sHe2Pja4NdRLmIXoiokWRBCCI0UZhSyfdZ2PDp40OflPlqH0yCpqsrJX06yd95eAAbPGkzAmAA5m9AA3KzoWVOvpngHe5sqIjcLaGZW9CwzM1OrsIWodyRZEEIIDahGle2ztqMv0XPf+/dJdeAaUJhRyI53dpC0Jwnfnr4Mnj0Y5xYyzas+Uo0qujO6q3UNri16Zn216JlXdy+8g7xx8nbSOGIhGg55dxJCCA3ELonl0oFLDJ41GPe27lqH06CoqsqZjWfY/c/dGEoN9Pu/fnR9qKvMR69HSvOvKXoWV7nomVd3Lzo92AmfYB88Az0l2RaiBslflxBC1LLU2FRivoih/Yj2BIwJ0DqcBqU4u5hd7+/i7LazeHf3JuytMFz9XbUOS9yEqqrkXsglLe7qtQY5Z3NQVRXFSsGjg0dF0bPu3ngHe+Ps6yzTyISoRZIsCCFELSrNK2XbG9twbuHMwNcHyoeeanQu8hw7/7GTsoIyQl8IJfjPwXI2oQ4qLyon41iG2YXIFoueda9YvlSKngmhLUkWhBCilqiqStTbURRlFfHA4gdo0rSJ1iE1CKX5pez5cA+n1p3Cs5Mnoz8fjUcHD63DElQ85wtSCkwXIafFpZF1Ksus6FmbIW3wCfaRomdC1FGSLAghRC059tMxzkWeo++0vjTv0lzrcBqEpL1J7HhnB0WZRdz7zL3cM+kes1VvRO26UvQsLS6NtPg0KXomRAMgyYIQQtSCrIQs9s3fh/8Af7o93E3rcOq98qJy9i3Yx/FVx3Fv5879H90vCZgGblT0DMClpYsUPROiAZBkQQghalh5UTlbZ2zF3s2ewbMHy3UKdynl9xQi50RSkFJA0J+D6PW3Xlg3kbMJNe12ip55d/fGwcNB44iFENVBkgUhhKhhuz/cTe6FXEZ/MRoHd/kAdaf0pXoOfHaAI8uP4NzSmTFfj8EnxEfrsBqsK0XPriQMYgIWAAAgAElEQVQGlYqeBd246JkQouGQZEEIIWrQqXWnSPg1gXufuRffHr5ah1NvpR9NJ3JWJDnnc+g6sSuhL4Ri6yCr5FSXK0XPTBWRpeiZEOIySRaEEKKG5F7IZdfcXfjc48O9T9+rdTj1kqHcwO9f/07skliaNm/K6H+PpmVoS63DqvfMip4dTiM9vnLRs87jOuPV3YvmXZpL0TMhGjH56xdCiBpgKDOw9fWtWNlYMfTdoVhZW2kdUr2TlZBF5JxIshKy6DS2E32n9aWJkyw3e7vMip5dXqGoUtGz8I6mC5GdW0rRMyHEVZIsCCFEDYheGE3miUxGzBshUzZuk9FgJG5pHAe/Ooidix0j5o2g9aDWWodVb9y06Jnz5aJn97c3LV8qRc+EEDcjyYIQQlSz8zvOE/9DPN3+1E0+5N6mnHM5RM6OJP1oOu2Gt2PAqwOwd7PXOqw660rRM1NicF3RM7c2brQJa2NaocitjZssXyqEuC2SLAghRDUqTC8k6q0oPDt50vvF3lqHU2+oRpUjK44QvTAaG3sb7nvvPtrf317rsOocQ5mBzBOZZhWRi7KKgKtFz0KeCsEn2EeKngkhqoUkC0IIUU1Uo8q2mdswlBu47/37ZO3/Ksq/lE/knEhSfk/Bf6A/g94YhKOno9Zh1QmFGVeLnqUfTifzRCaGcgMALn4utOzdEq/uXvgE+0jRMyFEjZBkQQghqsnv3/xOyu8pDHl7CK7+rlqHU+epqsqJn0+wb/4+UGDwrMEEjAlotBfXGvVGsk5lmaYTpcenk5+SD1wuetalOd0e7iZFz0SjN2PGDLy9vZk6depd9xUaGsp//vMfunbtWg2RNUySLAghRDVI+T2F37/5nYCIADqGd9Q6nDqvML2QqHeiuLj3Ir69fBk8azDOLZy1DqtWleSUVCQGl1coqlT0LNjblBxI0TPRWBQXFzN37lxWr17NhQsXcHFxITQ0lFdeeYU+ffqQkZHBsmXLOH36NAALFy5kyZIlxMfH8/DDD7NkyRKz/nQ6HX/5y1/YtGkTnp6evP/++zzyyCOm9unTpzNr1ixWrlx517Hf6lj1lSQLQghxl0pyStg2cxsufi70/3t/rcOp01RV5fT60+z5cA+GcgP9/96fLn/o0uCnz6hGlezEbNOUoqSYJIpTi4GrRc86j+9sWr5UVtASjVFhYSEjRozAzc2N5cuX06VLFwoKCvjhhx/YtGkTffr0YcmSJYSHh+PgUHFmzdfXl5kzZ7Jx40aKi4sr9fn888/TpEkT0tLSiI2NZfTo0QQHB5vOJIwdO5a//vWvpKSk0KJFi7uK/1bHqq8kWRBCiLugqiqRb0VSkl3Cg0sflGUob6JYV8zO93dybvs5vIO8CZsT1mCna92q6JlzB2eCJgZJ0TMhrvHaa6+hqiqrV6/Gxqbib8LJyYnJkyebtlm/fj2TJk0y/Tx+/HgAYmJiuHjxoll/hYWFrFy5kiNHjuDk5MSAAQMYO3Ys3377LXPnzgXA3t6eHj16sGnTJp544ok7jr0qx6qv5NVJCCHuwpEVR7iw8wL9/96fZgHNtA6nzjq77Sw739tJeWE5vV/sTdBjQQ3mbMIti561r1z0LCsrC09PT61DF6LO0Ol0fPnll2zdutWUKFgSHx9Pp06dqtRnQkIC1tbWBAQEmO4LDg4mKirKbLvAwEDi4uIq7R8REcGuXbss9j1gwADWrl1728eqjyRZuIaiKA8Bc4BAIFRV1Zhr2mYAfwEMwIuqqm60sH9bYAXgAfwO/FlV1bJaCF0IoYGM4xns/9d+Wg9uTZeHumgdTp1UmlfK7g93c3r9aTw7ezLk7SG4t3PXOqy7IkXPhKh+27Ztw8PDg4EDB5ruCw0NJSEhgdLSUjZu3MigQYPIycnB2blq1zcVFBTg6mp+9tLV1ZX8/Hyz+5ydnUlJSam0/7XJQHUdqz6SZMHcEWA88OW1dyqK0gX4E9AV8AW2KIoSoKqq4br9PwDmq6q6QlGUL6hILj6v+bCFELWtvKicba9vw7GZI2GzwxrtCj43k7QniR3v7KBYV0yPyT24Z9I9WNlYaR3Wbaly0bPLZw2k6JkQdyY1NRVfX1+z+6Kjo8nIyMDLy4tu3boB4O7uXuUP4E5OTuTl5Zndl5eXVynZyM/Px83N7S6ir/qx6iNJFq6hqupxwNKb/gPAClVVS4GziqKcBkKBvVc2UCp2Ggpcuex9KRVnKW6aLOh0ukpX7nft2pVevXpRXl7O999/X2mfkJAQQkJCKCoq4r///W+l9p49e9KtWzdyc3P5+eefK7X37duXTp06kZmZaTFrHjRoEO3atSM1NZUNGzZUar/vvvto1aoVSUlJbN26tVL7yJEj8fHxITExkR07dlRqj4iIwNPTk5MnT7J3795K7ePGjcPV1ZUjR44QExNTqX3ixIk4OjoSGxtLbGxspfZHH30UW1tbDhw4wNGjRyu1P/nkkwDs2bOHgwcPmv0h29jY8NhjjwEQFRXF2bNnzfZ1cHDgj3/8IwBbtmypND/SxcXFNH9yw4YNpKammrU3a9aMMWPGAPDrr7+SlZVl1u7j48PIkSMBWLVqVaUXHj8/P4YNGwbAjz/+WOlirrZt2zJ48GAAvvvuO/R6vVl7QEAA/fr1A6j0vIPae+6djTvLyr+upFxfjq3t5W9dFfD28sbJ2YmSkhKSk5Phyp/i5X99fX1p2rQphYWFXLp0CRRQrjQq4NfKD0dHR/Ly8q6O/TV9tG7dGgcHB3Jycszar/TRrn07mtg1QafTkZ6ebh68AgEdA7BpYkNGegYX911En6nHoYsDn038DIAuXbpgZW1Fampqpd8tQLfu3VAUheTkZHQ63dXYqLjItVu3ivYLFy6Qk5Nj1m5jY0PXbhUXyZ09e/bqc+PyNnZ2dnTpWnF24/Tp0+Tn51e8ll1ud3RwpHOXzgCcOHGC7Oxs7OzsTH04OzubTu0fOXKEktKSiqbLfbi6ulacXlcg9lAs5fpys8fWzLMZHTp0ACB6bzTFx4spSyrDyskKx2BHTiecxviVEYB9+/aZjeuV323rNq0xGo3s37/f/HcH+Pv708q/FeVl5Rw4cMB8fwXatGmDXys/iouKOfj7wUr9d+zYkRa+LcjPzyf20HWvGwp07twZbx9vdJk6Du04hD5Lj0FnQJ+pRy1VsXewx97RnibNm5DrkIvipmDdzJriJsWkXEohuHkwpSWlxPwWw/HjxyvF36NHD1xcXLiUcomTJ0/Ss2dPUl2vvj6Y3ncUyz9fu52VlRW2TWwrnrvXv1/dYH+z7ZRbt1elD9N9ltpv0M9tH8dCvLfqoyrHyM/Ox7bI1my7G/ZR1VhroI9K7dffXx2/vzrC39+fpKQkDAYD1tZXV/6KjY2lVatWeHh4ABAUFERCQgK9evW6ZZ8BAQHo9XpOnTpFx44Vq9TFxcVVuuD4+PHjpvf+a40aNYqdO3da7HvgwIGsX7/+to9VH0myUDUtgWve3bh4+b5rNQNyVFXV32QbABRFmQxMBnBzc6uUIefk5JCZmUl5ebnF7Dk7O5vMzEyKiopu2p6Xl3fT9qysLIvtOp0OFxeXm7Y7ODig0+kstmdlZWFjY3PT9itx3Ki9vLz8hu2ZmZk4OjretN3W1pacnJwbtkPFOF//YdvGxsas/fr9y8vLTe25ubk37d9Su62trand0u/HwcHhpu25ublm7SUlJWbtV547UPFNyfXJwvXt16ut515ubi56ez2GcgOKzeU3KxWMdkZUOxWjwYjeRm+6/8q/+iv/qRX/YrymcxVKy0qxsrKitLSU8vJys30BivKLMJYZKc4rpqzouhmCKuRn5WNrY0thbiGluaVmbQDZl7KxsbEh61QW5anlWDW3qjhOZsU2GWcysLKyIi87j5K8kqvHvyzNJg2AXF0uxQXFZn1bKVYk65NBheysbIqLzJ+bVlZWXCi8ACrkZOVU/O6v6b/UupTEzETT/mWlZahXNlCh1LaUUxdPoaoq2VnZlJaUUm519QN/mW0ZxxMqPuRmZ2Zj0BvM4ittUkppXMWY5GTmYDReM/gqlNmVUbCvgPLCcrLPZaPqVaycrDBipOBwAUknk9A11VU8/qzcSmNfal9KmmNaxfz/7OvagdMOp0l2SMZoNFYkUtdJcEzggv0FDAYDubmV9z+28xhn7M6g1+vNk3ADqOUqMUoMlEN5SfnVx24NRmsjBsVAsaEYfZ6e4nPFlJaWoqrmv9xjHKt0zGsd5ehNf75dNjY22NnZWbzVpQ+AddX1H0jFDRLUqiQvlvZVbtJmC+P+N65SHyEhITRt2pRnn32WV199FTc3Ny5cuMDnn39OYGCg6b1r8ODBbNy4kREjRgCg1+vR6/Xk5+dTVFTExYsXsbGxMV33MHr0aF599VXmz5/PkSNHWL16NevWrTP1V1paSkxMDPPnzzfdd8W333570zG7fvtbHau+Uq5/wWvoFEXZAvhYaHpDVdVfLm8TCUy/cs2CoiifAXtVVf3u8s+LgHWqqpoW5VUUpfnlbTpc/rnV5W263yyekJAQ1dK346J2ZGZmykWGGqqP459zPodVj67Cq5sXo/89ut5OOanusdeX6In+LJojy4/g2sqVsLfC8A7yvu1+TO9J6o3vq/I217Ub9UZ0p3SkH61YpSjzWCZ5KXmUlZZRpi+j1KWULOssLhRd4Ej6ES5kXLh65uo6tja2Zh82r9/u2p+vbzMaKhKtK/vf6BiKavl+vV6P0Wi0eExFUWjZsiVt27alTZs2FbfWFf/6+PhgpVydBnbtmFka06psU5Xfjen+G/xuqtpPlfqo4jFyc3NxcXG59TFu9Fgu31+lOO+0j2vuu5Pn/G1tc5O/mzs6xk1iLS4t5r437sOSM2fOMH36dHbv3o3RaMTPz4/Ro0fz7LPP4u/vD1S8doWEhHDq1CkcHByYM2cOb731llk/s2fPZs6cOUDFF5yTJk1i8+bNNGvWjLlz55rVPvjpp59Yvnw5q1atshjT7bjVseoCRVEOqqra87b2aWzJQlVYSBZmAKiq+v7lnzcCc1RVvX4aUgbgo6qqXlGUvpe3GXGzY0myoK36+GG1Ialv428oM7D6ydUUphcyYfkEmjZvqnVId6w6xz4tPo3I2ZHkXsil6x+7EjolFFsH7S/qvVL0LPVwKol7EkmJS6G4oJiSkhIK1AIull8kITeBVDWVTDIxmp2qgiZNmtChQwcCAgIq3by8vO74G/wff/yRvLw8nnnmmTva32g0kpSUREJCQqXbuXPnzM/6XMPOzo6OHTtafDyenp6N6oxEfXvtaUiqY+xff/11vLy8qqWCc+/evVm0aJHpmoiG7k6SBZmGVDVrgB8URZlHxQXOHYHoazdQVVVVFGU78AcqVkR6AviltgMVQtScfQv2kZWQxch/jazXiUJ1MZQZOPj1QeKWxtHUqymjPx9Ny14WZ1/WOKPBSOLBROK3xHM++jy64zpK00spLS2luKSYDDWDVFJJu/xfIYVAxRSJNm3bcE/APZU+QLdq1apGpqr88Y9/vKtpCVZWVrRu3ZrWrVszfPhws7bS0lISExMtJhKpqakcOXKEI0eOVOrTzc3NYhLRsWNHnJykQJyoW957771q68t0jZS4IUkWrqEoyjjgU6A58JuiKLGqqo5QVfWooij/BY4BeuD5KyshKYqyDnhaVdVLwKvACkVR3gUOAYs0eSBCiGp3LvIcR/97lKDHgvDv7691OJrLSshi+6zt6E7r6DS2E32n9aWJU5NaOXZJSQlb1m3h0MZDpMalUnyuGOssa6z0FVNsiik2JQVppJFBBp7engQEBDAkYIjZh+F27dphb29fK3HXBjs7OwIDAwkMDKzUlpeXx6lTpywmEjk5OURHRxMdHV1pP19fX7Mx69evH6GhoTLnX4hGQqYhaczf3199++23ze6T1ZBkNSRo2KshXXnu5efnm41/XX3uGXINZHyRQft72jN+6Xjij8bf9XMvISHBrK22n3vnzp0zG/uqPveMBiNfP/c1ui06rByscB3rin2AfY0+91RVxdPWk3P7znF4y2HyTuXhanRFQUFFRYeONNLIc8jDuYMz/l39Cehk/u349eufa2nLli3k5uYyYcIErUMBKsY3PT3dYhJx+vRpysoqlwtq3rw54eHhREREcP/991+d/19PyDQk7cjYa0umIQkhRDVTjSrZK7PBCIPeGoS1beP9NjX7bDaRsyPJ2Z2DfaA9ruGuWDlWf90EY5mRsotlpMSmkHE0A8MlA1xe+EuPnnzy0XnouGfkPXTo14HO3TsTEBCAt7d3vZh3f/HixTpVqElRFLy9vfH29jYriAUVqwZduHDBlDwcO3aMjRs3cvbsWZYuXcrSpUuxsbFh0KBBjBkzhoiICNMSukKIhkHOLGhMLnDWlnzDoa36MP4HPj/AoUWHuO+9+2h/f3utw6k2tzP2qlElfnk8Bz47gK2DLQNmDKDdsHbVEse1Rc8uHLhA/JZ40o6lkZuTi16vJ4cc0kgju0k2AQMDGP7QcAqLCnF3dzedqalvlixZQn5+Pi+88ILWodwRVVU5fvw4a9euZe3ataaVa67o1KkTERERRERE0L9//6u1VOqQ+vDa01DJ2GtLziwIIUQ1Sj6QTOziWDo90KlBJQq3Iy85j8g5kaQeSqX1oNYMmjkIBw+HO+7PUGYg80QmqXGppMenc3bfWdLPpZObm0t2fjapairppJNKKk38mjBi7AiejHiSsLAwHBwqjmtpGpOoPYqi0KVLF7p06cLf//53dDodGzZsYO3ataxfv56TJ09y8uRJPv74Y1xdXRk5ciQRERGMHDlSPiQKUQ9JsiCEEBYU64rZ/uZ23Nq40W96P63DqXWqqnJ81XH2L9iPYqUQ9lYYHcM73vY0n8KMQtLj0yuSg8PpZBzPIC8nr6KKdmEqpwtOmy5EziabPv36MC5iHBEREaaK1qJu8/Dw4JFHHuGRRx5Br9ezZ88e01mH48eP8+OPP/Ljjz9iZWVF3759TWcdunbtKr9fIeoBSRaEEOI6qlElck4kpXmljPp0VJ2oGVCbCtIK2PHODi7uu0jL3i0ZPGswTt63Xj7TqDeSdSqLtMNppMWlkR6fTn5KPga9gbyiPNIMaRxKOcS54nOkkUYxxbi4uDBy5Ehei3iNUaNGVemb5/p2Me316nv8N3Pl+oVBgwbxz3/+kzNnzvDbb7+xdu1aIiMj2b17N7t372bGjBm0bt3alDiEhYU1qFWphGhI5JoFjck1C9qSuZPaqqvjf/i7w+xbsI8BMwbQZUIXrcOpEZbGXlVVTq07xZ4P96AaVHpP7U3g+MAbfvt7pehZWnxFcpBxNAN9acUqSKqjSqZ1JnGpcexM2Em6Md1U9CwgIMD0IXHAgAF1ck57Taurz/2alJ+fz5YtW/j111/57bffSE9PN7U5OjoyfPhwIiIiCA8Px9fXt0ZjaYzjX1fI2GtLrlkQQoi7lHEsg+hPo2k7tC2B4yuvVd9QFeuK2fGPHZyPOo/PPT6EzQ7Dxe/qN+CqUSX7bDZpcWkVCcLhNHIv5AJgZW2FWwc3rLpacTL9JBsObeDYhWOmfW1sbAgLCyMiIoLRo0cTEBBQ649PaM/Z2Zlx48Yxbtw4jEYjMTExpulKhw4d4pdffuGXXypqmfbo0cOUUN57771YWVX/qltCiKqRMwsakzML2pJvOLRV18a/rKCMVY+uQjWqjP9+PHYudlqHVGOuHfvErYnsem8X5cXl9Hq+F90f7k55UTnpR9JNiUF6fDplhRXr7Tu4O+AV5IVDGweOZx1nc+xmNm7dSEFBgal/T09Ps3X4q7POwZUaHFfqQtQ3GzZsIDc311Q3Q0BycrJputKWLVvMasj4+PgwevRoIiIiGD58OE2b3n319Lr22tOYyNhrS84sCCHEHVJVlZ3v7SQ/JZ+x34xt0InCFaV5pez6YBenN5zGrY0b3R7uRu75XFY+vJLsxGxUVUWxUvDo4EH7ke3xCfbBq7sXp9JOsWDBAn785Eez4mtBQUGmb4NrssLv9QXn6pvU1NQ6VWehLmjZsiWTJ09m8uTJFBcXExkZydq1a/n1119JSkpi0aJFLFq0CBcXFyZPnswLL7yAv79UUheiNkiyIIQQwMk1Jzmz6QyhU0LxDvLWOpwaVV5czrHvjnFk0RGKMopwcHcg93wuMV/EYOdsh1d3L9oNb4d3kDde3bywdbTFaDSydu1anvnzM0RFRQFgbW3NqFGjGDNmDKNHj5YPb6JaODg4MGrUKEaNGsXChQuJj4/nt99+Y/Xq1URHR/PRRx8xf/58HnroIV5++WVCQ0O1DlmIBk2SBSFEo5edmM2ef+6hZWhLgh8P1jqcaqWqKgWpBabpRCm/p3Bh5wWKdcXY2NvQMrQlrfq1wjvYG+/u3ri1cUOxunpBc2FhIV//+2vmz5/P6dOngYrVfJ555hleeOEFWrdurdVDE42AoigEBQURFBTEjBkziImJYf78+fz3v/9lxYoVrFixggEDBjBt2jTGjh1bY2ezhGjMJFkQQjRq+lI9W2dsxbapLUPeGWL2Qbk+ulL07MoKRWmH0yjKLAIqljYtyS7BysaKrk905f537sfR09FiP8nJySxcuJAvv/yS7OxsANq0acNLL73EpEmTGvTyn6Lu6tmzJ99//z1z5841PT937drFrl27aNeuHVOnTuWpp57CyenWS/0KIapGkgUhRKO2d95edGd0hC8Mx7GZ5Q/OdVlRZpHprEHa4TQyj2diKDcA4NLSBd9evnh29iQ1NpWz287SokcLwt4Kw7qFtcVE4dChQ8yfP5/ly5ebrkfo27cv06ZN48EHH8TGRvu3jWbNmmkdwl1p1qxZo1wutjq1atWKDz74gDfffJP//Oc/LFiwgMTERF588UXefPNNnn32WV544QX8/Py0DlWIek9WQ9KYrIakLVmVQVtaj3/i1kS2vLqF4CeC6f1Cb83iqKorRc9MFZHj08m/VHGhrHUTazwDPU0XIXsHeePYzJG0w2lEzo4kNymXbg93I/T5UGzsbczG3mg0sm7dOubNm8f27dsBsLKyYsKECbz88sv07dtXs8fcUGn93G9oDAYDa9asYd68eezatQuoWLJ34sSJvPzyy/Tsab74i4y/dmTstXUnqyFJsqAxSRa0JS9a2tJy/PMv5bPykZW4tXFj7DdjsbKpe+u4l+SUVEwnulwROeNYBvqSim/7m3o1xTvI23Rr1qkZ1rZX52sbygzEfBnD4W8P4+TtxODZg/HtebXQVWZmJo6Ojixbtoz58+eTkJAAVKyF//TTT/Piiy/Spk2bWn28jYm89tSc6Oho5s+fz08//YTBUHGWbdCgQUybNo2IiAisra1l/DUkY68tSRbqIUkWtCUvWtrSavyNeiO/PvMr2WezmfDDBJx9nWs9huvdquhZs07NzJIDJ58bz8nOPJHJ9lnbyU7MpvO4zvR9uS+2jlenvaSkpPDhhx+ydOlSdDodAP7+/rz00kv85S9/qdaaCDXh119/BWDMmDEaR3Jnfv31V/Ly8nj00Ue1DqVBu3DhAp9++ilfffUVeXl5AHTo0IGpU6cSEREhF+drRN53tSV1FoQQogpivoghLT6NYXOHaZYolBWU3bDomb2bPd5B3nR6oBPe3b1p3qU5Nva3frk26o0c+s8hDn1zCHt3e0Z9MopW/VqZ2uPi4pg/fz4//PAD5eXlAISGhvLKK68wfvz4OnE9QlVkZWVpHcJdycrKkjoLtcDf358PP/yQWbNmsXjxYhYsWMDp06eZMmUKM2fO5K9//StTpkyhZcuWWocqRJ1WP94ZhBCimlzcd5HYJbEEjg+k3bB2tXJMVVXJS8q7eiFyXNrVomfK5aJnI9qbli91aeWCotzeqkzZidlEzo4k43gGHUZ1oP//9cfOxQ6j0ciGDRuYN28eW7duBSquR4iIiGDGjBn07dv3to8lRH3i7OzMSy+9xPPPP88vv/zCvHnz2LNnD3PnzuWjjz7iT3/6Ey+//DL33nuv1qEKUSdJsiCEaDSKsorYPms7Hu096PtKzV20qy/Rk3Esg9S41IqzBofTKcktAaCJUxO8unvRdlhbfIJ9aN61OU2aNrnjY6lGlcPfHybm8xhsHW0Z/s/htB3aluLiYr766ivmz5/PiRMnAHBycuIvf/kLL774Ii4uLjIVQDQqNjY2TJgwgQkTJrBhwwYWL17MypUr+e677/juu+8ICwtj2rRpjB49GiuruncNkxBakWRBCNEoqEaV7bO2U15YTsQXEdjYVc/L3/VFz9IPp5N5MhPVWHE9mFtrN1oPbo1Xdy98gn0qFT27G7lJuUTOiSQtLo02YW0Y+PpAcstyefPNN/n8889N03X8/Px46aWXePrpp3FzcwMq5g0L0Vj17NmTkSNHcu7cOT755BO++eYbIiMjiYyMpGPHjrz88ss8/vjjNG3aVOtQhdCcJAtCiEYh7ts4kvcnM2jmINzbud9xPzcremZjb4NXNy9CngwxLV9q72pfXQ/BRDWqHPvfMfZ/sh8rGyuGvD2EopZFPPfKc/zwww+UlVVc+9CzZ09eeeUVJkyY0KDW9ffx8dE6hLvi4+ODg4OD1mEIKgoNzps3jzlz5rBo0SL+9a9/cerUKZ577jnTdQ3PP/88vr6+t+5MiAZKVkPSmKyGpC1ZlUFbtTX+aYfTWPP0GtoNa8fQfwy9rTn6Nyt65uzrbDpj4B3kjUdHD6ysa3b6QkFqAVFvR5EcnYxfHz8MAwx8sugTtmzZAoCiKDz44INMmzaN/v373/CxynNfWzL+2rrR+Ov1elatWsXHH39MdHQ0ALa2tjz88MNMmzaN4ODg2g61wZHnvrZk6dR6SJIFbcmLlrZqY/xL80tZ9cgqUGDCDxNo4nTj6wOMBiO6Uzqz5MCs6FlnT9NFyN5B3hYrINcUVVVJWJvA3oXPxUsAACAASURBVI/2ohpVfCf4Mnf1XCKjIgFo2rQpkyZN4sUXX6RDhw637E+e+9qS8dfWrcZfVVX27t3LvHnz+PnnnzEajQBMmjSJ999/Hy8vr9oKtcGR5762ZOlUIYS4hqqq7HhnB4XphYxdPLZSolCSW2JatjQ1LpWMo9cUPWveFK8gL7r9qRte3b3w7OSJdRNrS4epcUVZRez8x07O7zhPs27N2G27m2emP4PBYKBZs2b83//9H5MnT8bd/c6nV9Unq1atAmD8+PEaR3JnVq1aRV5eHk8++aTWoYgbUBSFfv360a9fPxITE1mwYAFffPGF6aLod955h7/97W/1ZrlhIe6GPMuFEA3WiZ9PcHbbWXq/1Jvmgc3RndGZXYiccz4HuFr0rPODnU1Fz5p6N60TS4qe2XSGXXN3oS/RU96znOdWPUd6RjpWVlY899xzvPPOO3h4eGgdZq26UmCrvsrLy5M6C/VIu3bt+OSTT5gyZQovvfQSGzZs4MUXX+Trr79m4cKFDBo0SOsQhahRkiwIIRqktMNpbJ+9HUdPR5L3J3No0SHKCsyLngWMCcA7qOpFz2pTSU4Juz7YReLmRKx9rPk+5/v/Z+++46qq/ziOvw57uMOBe+QoF65ULNwb3DM1M1MrUyutNHc5KlMrrRw5+rlScYMTEFBBBQVXCqnkQBEBBRRkfn9/QOaewOFyP8/Howd4z3qfb1e8H853sHfhXgCaNGnC/PnzcXBw0DmlEMajSpUqbN++nW3btvHJJ59w4sQJmjZtyttvv82sWbNkELTIs3LXv45CCPECHlz07GrQVcI8w1BpClMzUxKiEqjU5uUWPctJF3wv4DvNl9sxtwnNH8pP7j+RTjolSpRg1qxZ9OvXL1fnFyKv0jSNTp060bp1a2bNmsXMmTNZvXo1W7duZdKkSYwaNQoLixdfN0WI3EiKBSGEwbl30bPIE5FcO36NOzf/W/Qs9U4qlgUsaTqxKdX7VH+pRc9yUvKtZPxm+xG6LZQbJjdYdHYR52PPY2ZmxqejPmXSpEkUKFBA75hCGD1ra2smTZrEO++8w2effcamTZv44osvWLJkCT///DNt2rTRO6IQWUaKBSFErvbvomf/DkKOPB5JdGg06WkZs5MUKleIck7/LXoWFRLF3ol7afJFE+oMrqNz+mcXfjgcn6k+XAu7xv5b+9l8aTPppNOiRQvmzZvH66+/rnfEXKN06dJ6R3gppUuXJjY2Vu8YIguUL1+ejRs3smvXLkaOHElISAht27alW7duzJkzh3LlyukdUYiXJlOn6kymTtWXTOGmr0e1f1pyGlEhURldio5lzFR0+/ptIGPRs6LVi1K8VnFK1C5BsZrF7lv0LO5yHBve3kCRykVwWeSS7WseZIWUhBQOzTvE8dXH+Sf2H/648AeRRFKmTBnmzJlD9+7ds6XLkbz39SXtr6/saP/k5GR+/PFHvv76a27fvo2VlRVfffUVn3/+OVZWWb84o6GS976+ZJ0FAyTFgr7kh5a+oqKisMHmiYue/Ts70dMWPUtLSWPr4K3EXYqj+5ru5CuRLydv5YVEBEewd/JezgWdwzPSk31J+zC1MOXzzz9n3Lhx2NraZtu15b2vL2l/fWVn+4eHh/P555+zZs0aIGM2pR9//BFnZ2cZa4S89/Um6ywIIXK1Bxc9uxR4iaSoJABMzU2xe82O6r2r3y0OnmfRs4BfArj+13Vaz2qd6wuFtOQ0An4LwO83P0KuhLAtfhtXuUqHDh346aefnmlRNWO2du1aAHr37q1zkhezdu1a4uLiGDJkiN5RRDYoVaoUq1evZtiwYXz88cecPHmSTp060aFDB3788UcqV66sd0QhnosUC0KIbPO0Rc8KVy5M+f7lKV67+EstenbxwEWOrzxO9V7VqdC8QlbeQpa7/td1tn+xndN+p/G74cdBDlKmYhm2/bQNZ2dnveMZhMTERL0jvJTExETu3LmjdwyRzZo2bUpQUBC//vorEydOZPv27Xh4eDBmzBi++uqrbH1yKERWkmJBCJElVLri5j83iTgW8dCiZ5qJhl01O6p2rnp3rEG+EvmIjo5+6cfRt6/fxnuyN69UfoVGnzTKilvJFmkpaQQuCmTXt7s4d/UcXmleRFtHM+mrSYwZM0b6NAuRB5mZmTFy5Eh69+7NuHHjWLZsGTNmzOB///sfc+bMoUePHtI1SeR6UiwIIV5I8u1kIk9G/rci8onIhxY9q+xcmRK1S2TbomcqXbF34l5S76TScmbLF34ykd1izsWweshqQv1COZl0Ej/8cO7uzOzZs2W2FCGMQPHixVm6dClDhw5l+PDhHD16lF69eslsZ8IgSLEghHiqu4uenfhvhqKYszEopdA0jcKVCuuy6FnQsiCuBF6h6eSmFCpfKNuv97xUusJzrie7p+8m4kYE+9iHVTUrtv28jdatW+sdTwiRwxo1asThw4dZsmQJ48aNw8vLi9q1azNy5EgmT54s66iIXEmKBSHEQ/5d9OzeWYruLnpma0GxWsWo27wuxWsXp1iNYrosenY16CpHFh7h1XavUsW5So5f/2muhV5jQZ8FXA2+ynl1nqO2Rxk7ZSwjR46UFV5fUoUKuXtcytNUqFCBmzdv6h1D6MTU1JShQ4fSvXt3Jk6cyIIFC5gzZw6rVq1i1qxZ9O/fX7omiVxFpk7VmUydqi+Zwu0Ri56diCQ65P5Fz4rVKnZ3bYNC5QuhmWTNP2Qv2v5JcUm49nHFzNKMbqu6YW5jniV5skJ6Wjorxq7g8LzD3E66zQEO0PDthnw/63tKliypd7y75L2vL2l/feWm9j969Cgff/wx/v7+ADRp0oT58+fj4OCgc7LskZva3hjJ1KlCiKd6lkXPag+sTbGaxSheszhWhXLXwFulFN5TvUmMSaTzss65qlA4tv8YC99eSNqlNC5xieuvX2fJb0twcnLSO5oQIpeqW7cu+/fvZ+XKlXzxxRccOHCAevXq8cEHH/DNN99QpEgRvSMKIyfFghB5XEJUwhMXPbOvZ/9Mi57lFn+t/4sLPhdo/Fljir5WVO84AMTHx/Ptu98SsSkCpRTBNsEM+nYQH374IWZm8mM2q61cuRKA/v3765zkxaxcuZL4+Hg+/PBDvaOIXMLExIR33nmHzp07M3XqVH7++Wd+/fVX1q5dy4wZMxg8eDCmprlzAgeR98m/YkLkIQ8uenbt+DXir8QDL7/oWW4QHRrNwbkHKftWWWr0raF3HAA2r9rMqg9WUeRWESKJxL6PPTt/2kmxYsX0jpZnpaam6h3hpaSmphr8PYjsUbBgQebMmcPgwYMZMWIEe/fuZdiwYSxevJhly5ZRo0bu+LknjIsUC0IYsDuxd4g8kTF96YOLntnY2VC8VvG7xcHLLHqWG6QkpOAx1gOrQlY0m9xM9wGASUlJTOgzgeubr1OAAkRUjGDCqgk0bNRQ11xCCMNXvXp1PD09Wb9+PaNHjyYwMJAGDRowb948Bg8erPvPP2FcpFjQWUxMDMuXL7/vterVq9OgQQNSUlJYtWrVQ8c4ODjg4OBAQkIC69ate2h7/fr1qVGjBrGxsWzatOmh7Y0bN6Zq1apERUXh5ub20HYnJycqVqxIREQEO3fufGh7y5YtKVOmDJcuXcLT0/Oh7e3ataNEiRKcP38eX1/fh7Y7OztjZ2dHSEjI3QFd9+ratSsFCxbk5MmTBAYGPrS9V69e2NjYEBwczKMGh/fr1w9zc3MCAgI4derUQ9vfffddAPz8/Dhy5Aj58+e/u83MzOxu1wYfHx/CwsLuO9ba2prevXsD4OHhweXLl+/bXqBAAbp16wbAzp07iYiIuG/7K6+8gouLCwDbtm0jOjr6vu0lSpSgXbt2AGzcuJG4uLj7tpcuXZpWrVqRfDuZeS3mkRyZsa4BGpiXMKd47eI4dXGieK3ibPbYTHhaOOEp4XAEOAJVqlTB0dER4KH3HeT8ey8+Pv6+9n/Se+/m5puY/2NOt6XduB5/Hc/N+r33Nv+5mQ2fbsAmwoY4LY5G4xqx+JvFmJjk7i5cQgjDoWkavXr1okOHDowaNYqlS5cyZMgQvLy8WLBggUyzKnKMFAtCGCALWwssy1hiWcMS8zLmmJc0x8TChLIVyvJq01cB8tRvnhKPJZJ4LJHqw6pjX9eeS5cu6ZZl+ZTleH/tjaWy5JzdOaZtnUajxrl35WghhGHLly8fS5YsoXnz5nzwwQesWbOGgIAA1q5dS926dfWOJ4yATJ2qM5k6VV8yhZu+nqX9Yy/GsrHfRuxes6Pjbx11G4Adez2Wae2nEXckjmiiuf3GbdbsWkOhQrlvMbhnYcjvfT8/P4C7T8kMjZ+fHzdv3qRDhw56RzFahvr+Dw0NpVevXhw7dgwLCwtmz57N8OHDDeqXQ4ba9nnFi0ydKs/MhRC5VlpyGp7jPDExN6HFtBa6FQoH1h5gbKWxxB6J5ZjpMZr91IztB7cbbKFg6BwdHQ22UICM/G+88YbeMYQBqlKlCgcPHuSjjz4iOTmZESNG0L17d27cuKF3NJGHSbFwD03TemqadkrTtHRN0+rf83prTdOOaJp2IvNri8ccP0XTtHBN04Iz/5NfGwnxEg7NO0RUSBTNpjTDtphtjl8/JSGFn3v9zMq3VxITH8PRskf5LeA3Ph75sUH9Jk8IkXdYWVnxyy+/sH79egoUKMCmTZuoU6cOBw8e1DuayKOkWLjfSaAb8ODIyCjARSlVExgIrHjCOeYqpRwy/9ueTTmFyPMu+F7g5JqT1Ohbg3JO5XL8+ucPnGdCtQmcXH+S4PRgzHqZsffkXurUqcPy5csfOUBc5AxDb//ly5ezZs0avWMIA9ejRw+CgoJo0KABFy5c4K233uKHH34gPT1d72gij5Fi4R5KqdNKqZBHvB6klLqS+cdTgJWmaZY5m04I43E78jbeU7yxq2pHwxE5OxVpalIqG7/YyE+tfuLipYvsttzN+7+/z8o/V943c5MQQuitYsWK7N+/n88++4zU1FQ+//xzXFxciIqK0juayENkNqTn1x0IUkolPWb7x5qmvQMEAqOVUg91JNQ0bSgwFKBkyZLyl1pHsbGxekcwao9q//S0dHzG+JCUmITT507ciMu5vrjRZ6JxHe7KpROXOKVOEVM5hhVLVvDaa6/dN81tfHzGQneG/HfXkN/7ht7+8fHxJCYmGmz+vMCQ3/+PMm7cOOrWrcuIESPYvn07NWvWZOHChblybE9ea3tjYHTFgqZpHkCJR2war5Ta8pRjqwPfAW0es8tvwDeAyvw6G3jvwZ2UUouARZAxG5LMCqAvaX99Pdj+RxYd4cbpGzT/ujkVHCrkSIa0lDT85vnhPt2dyzGX8cGHNu+14eeff8bW9uGxEv8+YTD0946h5jf09jf0/HlFXmv/fv364eTkRN++fTlw4ABdu3Zl6tSpjBs3DlPT3LUgZ15r+7zO6LohKaVaKaVqPOK/pxUKpYFNwDtKqXOPOfc1pVSaUiodWAzIdBdCPIcrR65w9PejVHGuQuUOlXPkmtF/R7PMZRnrv1rPoZhDuNu4M3PFTJYsWfLIQkEIIXKrMmXK4O3tzbhx40hPT2fixIm0bdv2oQVChXgeRvdk4UVomlYIcAfGKaUOPGE/e6XU1cw/diVjwLQQ4hncuXmHvRP2UqB0AZp80STbr5eels6xP46x/ZvthF4IxUf5UKh2IQ6uPUjVqlWfeGz16tWzPZ94PENv/+rVq3Pz5k29Y4g8yszMjBkzZtC0aVMGDBiAp6cntWvXZuXKlbRu3VrveMIAGd2ThSfRNK2rpmmXgcaAu6ZpuzI3fQy8Cky8Z1rUYpnH/H7PNKvfZ06vehxoDnya0/cghCFSSuE9xZs7N+/Q6ttWmNuYZ+v1bl64iesAV/4c/Sfe/3izTq2j40cdOXjw6YUCQIMGDWjQoEG2ZhSPZ+jt36BBA+rUqaN3DJHHtW3blmPHjtG8eXMiIyNp27Yt48ePJzU1Ve9owsDIk4V7KKU2kdHV6MHXpwHTHnPM+/d8PyD70gmRd51cc5KL+y/S5IsmvFLllWy7jkpXnFx7Eq8ZXpz++zQeSR5cz3+dFUtX0KNHj2c+T0pKCgDm5tlb1IhHM/T2T0lJuXsPQmQne3t79uzZw/Tp05k6dSozZszA19eX1atXU6ZMGb3jCQMhTxaEELq6fvo6h34+RLmm5Xi95+vZdp34K/G4feDG5i82s+vULlYkraBIgyIEBQc9V6EAsGrVKlatWpVNScXTGHr7r1q1CldXV71jCCNhamrKpEmT8PT0xN7env379+Pg4ICbm5ve0YSBkGJBCKGblIQUvL7ywuYVG5pNbpYtqyIrpTi96TRruq/B29WbVZdXsUPt4IPPPmD//v1UrFgxy68phBC5TbNmzTh27Bjt2rUjJiYGFxcXRo8eTXJyst7RRC4nxYIQQhdKKY78eIS48DiaT2uOZYGsX+fwduRtdo7ayY5xO/A87smiG4u4XuQ6W7duZfbs2VhYWGT5NYUQIrcqWrQo7u7ufPfdd5iamjJnzhzefPNNwsLC9I4mcjEpFoQQugh1C+Wi10XqDauHfR37LD23Uoq/d/zN+l7rObzlMMtCl7E+YT21m9QmODgYFxeXLL2eEEIYChMTE7744gv27dtH2bJlCQgIoE6dOmzYsEHvaCKXkmJBCJHjbv5zkwPfHaCYQzHqDMraWWESYxLx+NKD3WN3cyj0EHMvzuWkOsnYcWPZu3evDOoTQgigcePGBAUF0blzZ2JjY+nRowcff/wxd+7c0TuayGVkNiQhRI5KS07Dc5wnZlZmNBzbEM0k68YphHmFsX/mfiLDI9lyeQv74/djV9SOnSt20rZt2yy7joODQ5adSzw/Q29/BwcHbty4oXcMIShSpAibNm1i3rx5jBkzhl9++YUDBw6wdu1aqlSponc8kUtIsSCEyFEHfzxI9N/RtPupHdZ21llyzqS4JPx+8CPUPZQryVeYf3o+McTQvHlzVq1ahb191nZzMvQPq4bO0NvfwcGBqKgovWMIAYCmaYwcORJHR0d69+5NcHAw9erVY+HChbz99tt6xxO5gHRDEkLkmLC9YZxad4pa/WtRtknZLDnnJb9LuPZ25a+tf7E3bi/TTk7jpslNpk6dyp49e7K8UABISEggISEhy88rno2ht7+h5xd5U/369Tl69Ci9evXi1q1b9OvXj/fff1/eq0KKBSFEzoi/Go/v174Ufb0oDYa//Oq7KQkp7Juxjx0jdxBzK4Yf//mRtWfXUty+OJ6enkyaNAlTU9MsSP6wdevWsW7dumw5t3g6Q2//devWsWXLFr1jCPGQggUL8ueff7Jw4UKsrKxYsmQJDRo04PTp03pHEzqSYkEIke3S09LxGu9Felo6LWe0xNT85T7EXz16Fdc+rpzZdIZblW7x2YHPOBt7lnbt2nHs2DGaNWuWNcGFEMLIaJrG0KFDOXToEFWrVuWvv/7C0dGRffv26R1N6OSZiwVN0/pompasaZp5dgYSQuQ9RxYe4drxazhNcKJA6QIvfJ7UpFT85/izbeg2NBONi3UuMnrdaFJVKpMmTcLd3Z2iRYtmYXIhhDBOtWrVIjAwkC5dunDz5k1at24t06saqed5slAb+EsplZJdYZ6VpmlFnrDNIvPrKzmXSAjxOOGHwwleFkzVzlWp1KbSC58n8mQkG9/eyInVJ6jWvRq78+9m2uJpmJqasnjxYqZOnYqJiTwsFUKIrJIvXz5cXV358MMPSUpKomfPnsybN0/vWCKHPc+/rA7AsewKommaraZpX2qatlzTtIuapnV9xD6mmqaNBEo+YttnmqZdA+5omrYVsNU0bfS/xYMQIuclxiSyd+JeCpUvhOMYxxc6R1pyGgG/BrDlvS2k3kml+Q/Nmek7k2Url2FjY8OWLVt4//33szi5EEIIAFNTU3755RemT5+OUoqRI0fy5Zdfkp6ernc0kUOeZ+rU2sCsf/+gaZoNMAXoAxQBjgIjlFLH7tmnSOYx3YA0YDYZBcoApVS1B/bzAcYAc4BtwPvApgcyfA9sUUqdvPdFTdO+AHoBbwLJwHGgHrAF+Bb47DnuUwiRBVS6wnuKN0nxSXT4pQPm1s/fgzE6NBrvyd5E/x1N1U5VqdS/El16deHIkSPY2dnh7u7OG2+8kQ3pn6x+/fo5fk3xH0Nv//r168s6C8KgaJrGV199RalSpXj//ff5/vvvCQ8PZ+nSpVhYyO9k87pnKhY0TSsK2JP5ZEHTNGvACygIjAWiyPhAvlvTtGpKqRuaplkCe4B8wAjgJjAdKAAcfuASvwLXlVK7NE0rBTQCJj+QoQNQRinl+4hs44H6Sqm/NU0zI6NguK2UOqtpmommaZ2UUlufrUmEEFnh+KrjXPK7xFtfvUWRVx/bc/CR0tPSOfbHMY4sOoJVQSvazm1LcolkmrZuyvnz56lYsSI7d+6kcuXK2ZT+yWrUqKHLdUUGQ2//GjVqyDoLwiANHDiQ4sWL06NHD1atWsW1a9fYsGEDBQq8+Fg0kfs9azekf1fA+fepwVSgHPCWUmq1Umo30A8oCrTP3GcsUDlzn5VKKTfgY6D8PedB07TSZDwV2AWglApXShVSSv30QIbRgPsjsvUEDmcWCmWBxcA/gGfm9t1kPLEQQuSQyFORBMwPoELLClTrWu3pB9zj5j832TJoCwG/BlChRQV6rOvBNctrODo6cv78eerVq4efn59uhQJAbGwssbGxul3f2Bl6+8fGxhIXF6d3DCFeSLt27fDx8aFYsWJ4eHjg5OTE1atX9Y4lstGzFgu1gctKqejMMQBDgV+UUnd/NaKUigbigFKappmQURjMVUpF3nOesMyv94596ABowP7HXVzTtPxAc+DEIzZ3JOOJRu/M8w8Axiil0jK3Hwfe0jTtoXEOQoisl3wrGc9xntgWs8VpghOapj3TcSpdcWL1CTa8vYH48HhazmxJyxkt8dzvSfPmzYmKiqJdu3Z4e3tTvHjxbL6LJ9u0aRObNj3YS1LkFENv/02bNuHu/qjffQlhGOrVq4e/vz+VK1fm2LFjNG7cmDNnzugdS2ST5ykWjt3zfUEyuhjdpWmabebrEUAtwI6MsQf3KpX59fg9rzUD4nm4a9K9ypFRUNx84JpmwFtkdInaAFTNvOaCe3b795hXn3B+IUQWUErhO92XWxG3aDGjBZb5LZ/puLjwONw+cMN/jj+lG5Wmx7oeVGpdid9//53OnTuTkJDAu+++y9atW8mXL18234UQQoinqVixIgcOHKBhw4ZcuHCBJk2a4Ofnp3cskQ2epxvSv8WCXebXiAf2aZb5dT8Z4xsAIh/Yxwm4oZS6dM9rTQHPp0zJapX5NfWB1xuQMXA6SCmVqpQ6Cyznv6IE4N91ygs94fxCiCwQsiWE83vO02B4A4rXfPpv/5VSnN54mg19NhAdEk3TyU1pM7sN1kWsmTp1KkOGDCEtLY0JEyawdOlSzM1lmRchhMgtihYtipeXF87OzsTExNCyZUs2b96sdyyRxZ5aLGR2O6oGBGe+9O8H/Yr37GMGTAJ2KqXCyBjwDPf8Nl/TtILAJ9w/XqEKGdOgLn3MtW0zv72Y+fXBD/wtAB+l1L3zd3UADtzzZ5sHcgshssGN8zfwm+VHqYalqD2g9lP3vx15m31j97Fvxj6K1SxGj3U9qOpSlbS0NIYOHcqUKVMwMTHht99+45tvvnnm7kxCCCFyjo2NDZs2bWLIkCHcuXOH7t2789tvv+kdS2ShZ5kNqXrmfv9+yD9FxjSpP2maNgFIBz4FSgM9Mvc5RsaH8581TRuXefxXgC3/FR0A7YBooIemaYMBf2AJGUVMb+AMsEcpFalpWihQgfu7MDUHrDVNqwTEAh+RMeD5rXv2KUPGWIqQZ7hXIcQLSE1KxWOsB+a25rT4pgWayeM/2CulOLvjLAe+P0ByYjJNvmzC691fRzPRuH37Nr1798bd3R0rKyv+/PNPOnfunIN3IoQQ4nmZmZmxcOFCSpcuzeTJk/noo4+4fPky06ZNk1/05AHPUizUJqMrz1kApZTSNK0bMB/4Hxldg3YC7yilwjP3SdY0rScZYwfWkVFgfEPGE4R9AJqm1QdakTFbkR3wGhmDlT8F3IBvlFIX7smxiIyuTlsyj7cEHMkoPv4mY7pUd6CBUurcPcc5AiuUUgkIIbKF/2x/bpy/QYdfOmBdxPqx+yXGJLJvxj7+8f6HEg4lqDmiJhVqVwDg+vXrODs7c/jwYYoUKYKbmxuNGzfOqVt4Lrk1l7Ew9PZv3LixrLMg8hxN05g0aRKlSpVi2LBhzJgxg/DwcBYvXixdSA3cU4sFpdRyMsYB3PvaBcDlKccdAur8++fMlZfTgO2Z2wOBTs+R9RcyZj0qljnDUiMgXinlmNlVKvWB7kj/do/qBLz3HNcRQjyH8x7nOb3xNA7vOlC6YenH7+d5nv0z95OSkEKjTxpR8+2aRMdEA3Du3DnatWvH2bNnKV++PDt37qRq1ao5dQvPLTdnMwaG3v5Vq1aVdRZEnjV48GBKlChBr169+OOPP4iIiGD9+vXkz59f72jiBT3rAOfnomlaM03TvtI0rY2maR01TfsF+AH4WCl150XOmXncYOCbzOKgBRmrPqOUSn6wUMg0BpiSOa2rECKLxV+Jx3eaL8VrFqf+B49eVTcpLgmvCV54fOlBfvv8dFvZjVr9a93tqhQYGIijoyNnz57FwcEBPz+/XP9hMCoqSj7s6cjQ2z8qKoroaPlnSeRdHTt2ZO/evdjZ2bFr1y6aNWtGRMSD8+IIQ5EtxQIZg4r7k9FlaB1QA3BWSq17mZMqpf4GxgGNgUE8ZmA0gKZptYFlSqmgl7mmEOLR0lPT8fwqY+3DFtNbYGL28I+Tiwcusr7Xes7vOU+9YfXovKwzhSsWvrvdw8ODZs2aERkZSatWrfDxCIEqVwAAIABJREFU8cHe3v6h8+Q2bm5uuLm56R3DaBl6+7u5ubF79269YwiRrd544w38/f2pWLEiR48exdHRkdDQUL1jiReQLcWCUmq7Uup1pZS1UspWKdU0c5XnrDh3DBnTs9ZVSu18wn7HlFLXsuKaQoiHBfwWQOTJSJwmOpG/5P2Pl5NvJ+M7zZedo3ZiWcCSLn90od6QevcVFMuXL6d///7cvn2b/v374+7uToECBXL6NoQQQmSTV199FT8/P+rXr09YWBiOjo4EBgbqHUs8p+x6spCtlFJp964eLYTIWZcPXubYH8d4rftrVGxZ8b5tVwKvsKHPBkK2hlB7YG26reyGXTW7u9uVUkybNo1BgwaRlpbG2LFj+d///oeFhUVO34YQQohsVrx4cfbu3Uv79u2Jjo6mW7dubNv24Jq9IjczyGJBCKGfhOgE9k7aS5FKRWj82X+z0qTeScXvBz/cPnDDxMyETr93ouGIhphamP63T2oqH374IRMnTkTTNGbOnMnMmTNlaj0hhMjD8uXLx5YtWxg0aBCJiYl06dKFRYsW6R1LPKNnmTpVCCEAUOmKvRP3knI7BecFzphZZvwIuXb8Gt5TvIm9GEv1XtV5Y8QbmFvfP1VeQkICffv2ZevWrVhaWrJ69WqcnJz0uA0hhBA5zNzcnCVLllCkSBFmz57NsGHDCA8PZ8qUKfILo1xOigUhxDM79r9jhB8Ox2miE4UrFiYtOY0ji45w7H/HsC1mS8ffOlKqQamHjouOjsbFxQV/f38KFSrEtm3bePPNNw12RhspcvRl6O3v5ORETEyM3jGEyHGapjF27FgqV67MRx99xNdff014eDgLFizAzEw+kuZW8n9GCPFMrh2/RsCvAVRqU4mqnaoSFRKF9yRvYs7FULVzVRp/1hgL24fHHYSFhdG+fXtCQkIoU6YMO3fu5PXXX9fhDrJOxYoVn76TyDaG3v4VK1aUwfzCqA0bNgx7e3v69OnDkiVLuHr1KuvWrcPW1lbvaOIRZMyCEOKpkuKS8BrvRX77/DT5oglBS4LY/M5m7sTeod2P7Wg6sekjC4WgoCAcHR0JCQmhVq1a+Pv7G3yhABARESFzhuvI0Ns/IiKCa9dksj5h3Dp16oSnpyevvPIK27dvp3nz5kRGRuodSzyCFAtCiCdSSuE7zZfbkbdpMLwBO0buIHBBIBVbV6Tnup6UfbPsI4/bs2cPTk5ORERE0Lx5c3x9fSlV6uEuSoZo586d7Nz52JmbRTYz9PbfuXMnXl5eescQQneNGzfmwIEDlC9fnoCAgLsLdIrcRYoFIcQTnd54mvOe5ylRtwTeU7yJvxJPq+9a0WJaCywLWD7ymBUrVtChQwdu3bpF37592bFjBwULFszh5EIIIXK7qlWr4u/vT506dTh37hyOjo4EBAToHUvcQ4oFIcRjRf8dzb4Z+0i7k8aVgCuUblyanut6PrS2wr+UUnz77be88847pKamMnr0aFauXIml5aOLCiGEEKJEiRL4+PjQunVrrl+/TrNmzdi+fbvesUQmKRaEEI+UnJDM5nc2c/P8TawKW9H86+a0+aEN1kWsH7l/WloaI0aMYNy4cWiaxty5c/nhhx8wMZEfM0IIIZ4sf/78uLm5MWDAABISEujUqRNLly7VO5ZAZkMSQjzCrWu3WNNpDddPXqdyx8p0mN8B22KPn6UiOTmZt99+mw0bNmBhYcGKFSvo1atXDiYWQghh6CwsLPjjjz8oXbo0M2fOZPDgwVy9epXx48frHc2oSbEghLhLKUWoWyhe4724EXaDmv1q4rzQ+YkL5qSmptK3b182btxIwYIF2bx5M82aNcu50Dpo2bKl3hGMmqG3f8uWLWWdBSEeQ9M0ZsyYQalSpRgxYgQTJkzA1NSUsWPH6h3NaEmxIIQAICE6gX3T93He4zy3r9+mcsfKdPyt4xMLhbS0NAYOHHi3UPD09KRevXo5mFofZcqU0TuCUTP09i9TpgzW1o/uzieEyDB8+HAKFCjAwIEDGTduHDY2NowcOVLvWEZJigUhBOc9zrN/5n5SElKwLGCJ9SvWtP+xPSamjx9vkJ6ezrBhw1i9ejW2trbs2LHDKAoFgEuXLgGG/6HVUBl6+1+6dImYmBjs7Oz0jiJErjZgwAASExMZNmwYo0aNwsrKiqFDh+ody+jIyEMhjNid2Dt4fuWJx1gP8pfKT4VWFUi9k0qzyc3IVyLfY49TSjFq1CiWLFmClZUV7u7uNG7cOAeT68vT0xNPT0+9YxgtQ29/T09PfH199Y4hhEEYOnQoP/74IwAffPABK1as0DmR8ZEnC0IYqQv7LrBv2j7u3LxD/Q/rU6RyEXZ/tpvqvapTvln5xx6nlOLLL79k/vz5WFhYsGXLFpo2bZpzwYUQQhiVUaNGkZiYyLhx43j33XexsrKiZ8+eescyGlIsCGFkkm8l4z/Hn5CtIRR5tQjt57XHqrAVG/pu4JUqr9Dok0ZPPH7q1KnMmjULMzMzXF1dadOmTQ4lF0IIYazGjh1LYmIiX3/9NW+//TZWVla4uLjoHcsoSDckIYxIeEA4rn1cCXULxWGQA13/15UirxZh78S9pCWl0XJmS0wtTB97/HfffcfUqVMxMTFh1apV8oNaCCFEjpkyZQpjxowhNTWVHj16sGfPHr0jGQUpFoQwAimJKRz4/gDuH7pjamFKpyWdeGP4G5hamBK0LIgrgVdo8mUTCpUr9NhzzJs3j7Fjx6JpGsuWLZN1FIQQQuQoTdP4/vvvGT58OMnJyXTu3BkfHx+9Y+V50g3pHpqm9QSmAK8BbyilAjNfLw+cBkIydz2olPrgEccXAdYC5YF/gF5KqRvZHFuIJ7p2/Brek72JvRRLjb41eGP4G5hZZfzVvxp0lSMLj/Bq+1ep3LHyY8+xePHiu1PWLViwgHfeeSdHsudW7dq10zuCUTP09m/Xrh3R0dF6xxDCIGmaxs8//0xiYiJLly7F2dmZPXv20KjRk7vQihcnxcL9TgLdgIWP2HZOKeXwlOPHAp5KqW81TRub+ecvszijEM8kLTmNwAWBHF95nHwl8uG80JmS9Ure3X4n9g5e470oUKoAb41767HrKaxcuZJhw4YBMHfuXJm2DihRooTeEYyaobd/iRIlMDOTf36FeFEmJiYsWrSIxMRE1qxZQ7t27fDy8qJu3bp6R8uT5KfVPZRSp4EnLkL1FJ2BZpnf/wF4I8WC0EHUmSj2TtrLjfM3eK3bazT6pBHmNuZ3tyul8JnqQ2JMIl2Wd7lv271cXV0ZOHAgSilmzJjBJ598klO3kKudP38egIoVK+qcxDgZevufP39e1lkQ4iWZmpryxx9/cOfOHTZt2kSbNm3w9vamRo0aekfLc6RYeHYVNE0LAuKACUqpfY/Yp7hS6iqAUuqqpmnFHnUiTdOGAkMBSpYsSVRUVHZlFk8RGxurd4QslZ6azunVp/lr1V9YFbKi8deNsX/DntiEWEj4b7+/N/3NOa9zOHzkAHY88j24e/duBg4cSHp6OqNHj2bIkCFZ/l411PZ3d3cHoG/fvjoneXGG2vZg+O3v7u5OYmIi5cuX1zuK0TLk97+hy+q2nz9/PvHx8Xh4eNCiRQu2bdtGpUqVsvQaxs7oigVN0zyARz3DHq+U2vKYw64CZZVS0Zqm1QM2a5pWXSkV9yIZlFKLgEUADg4OSn67pK+80v43zt/Ad5IvUWeiqNqhKk0+b4JlAcuH9osKieKvpX9RsXlFGg9p/MgnaR4eHrz33nukpqYyevRoZs2a9TJP3J7IENs/f/78gGFmv5eh5jf09jf0/HmFtL9+srrtt27dirOzM15eXvTo0QNfX18qVKiQpdcwZkZXLCilWr3AMUlAUub3RzRNOwdUAQIf2PWapmn2mU8V7IHIlw4sxFOodMXxlccJ/C0Qi3wWtP6+NRVaPPqHZEpCCp7jPLEqbEWzyc0eWQD4+vrSqVMnkpKS+Oijj7K1UBBCCCFelrW1NVu3bqVdu3bs37+fli1b4uvrS+nSpfWOlifI1KnPQNO0opqmmWZ+XxGoDJx/xK5bgYGZ3w8EHvekQogsEXsxlq3vb+XQz4co82YZeqzt8dhCAWD/d/uJuxxHi2ktsCpk9dD2Q4cO0bFjRxITExk0aBDz5s2TQkEIIUSuZ2tri7u7Ow0aNCAsLIyWLVsSERGhd6w8QYqFe2ia1lXTtMtAY8Bd07RdmZucgOOaph0DXIEPlFIxmcf8rmla/cz9vgVaa5r2N9A6889CZDmVrji17hQb+m7gZthNmn/TnNbft8a6iPVjjwl1D+Vv97+p+35d7OvaP7Q9KCiIdu3acevWLfr27cvixYsxMZEfEUIIIQxDgQIF2LlzJ7Vr1yY0NJTWrVvLuNAsoCml9M5g1BwcHFRwcLDeMYxWVFSUwfVbvRVxC5+vfQg/HE4ZxzI4TXDCtpjtE4+JvRjLxn4bsXvNDucFzmgm9z8tOHXqFM2aNSMqKoquXbuydu1azM0fPUNSVjLE9of/BoQbYvZ/GWrbg+G3f1RUFNHR0VStWlXvKEbLkN//hi4n2j4yMpJmzZpx+vRp6tSpg5eXF4UKPX7RUWOiadoRpVT9p+/5H6MbsyCEoVJKEbotFL8f/AB4a/xbVOtS7andhNKS0/AY64GphSktprV4qFD4+++/adWqFVFRUbRv3541a9bkSKFgyORDhr4Mvf0NPb8QuV2xYsXw8PDAycmJoKAg2rdvz+7du+9OLiCej/QxEMIAJEQlsOuzXfh87YNdNTt6/NmD17q+9kzjCQ79fIjo0GiaTmn60BOIf/75526/zhYtWrBhwwYsLR+eQUncLyQkhJCQkKfvKLKFobd/SEgIZ8+e1TuGEHlayZIl8fT0pGzZshw8eBAXFxcSEhKefqB4iBQLQuRy53afY32v9YQfCqfx6MY4L3Amf8ln++3IBd8LnPzzJDXfrkm5t8rdty08PJwWLVpw6dIlmjRpwpYtW7C2fvyYB/Eff39//P399Y5htAy9/f39/QkICNA7hhB5Xrly5fDy8sLe3h4fHx+6du3KnTt39I5lcKRYECKXunPzDh7jPPD8ypOCZQvSfU13avat+VA3ose5de0W3lO8satmxxsfv3HftmvXrtGyZUvCwsKoX78+7u7u5MuXLztuQwghhNBNpUqV8PT0pGjRouzevZtevXqRkpKidyyDIsWCELnQBd8LrO+1nn/2/kODjxrQaUknCpV79sFZ6WnpeE3wIj01nZYzWmJqYXp3W3R0NK1btyYkJIRatWqxa9cuChYsmB23IYQQQujutddew8PDg8KFC7Nt2zb69etHamqq3rEMhgxwFiIXSb6VjN9sP0K3hfJKlVfoML8Dr1R55bnPc/T3o0QERdD8m+YULPtfIXDz5k3atGnDiRMnqFatGnv27KFIkSJZeQtCCCFErlOrVi12795Ny5YtWb9+PVZWVixfvlymCH8GUiwIkUuEHw7He4o3CVEJ1HmvDnWH1MXU3PTpBz7gSuAVgn4PoopLFSq3r3z39Vu3btGhQweOHj1697FssWLFsvIWhBBCiFyrfv36bN++nbZt27JixQqsra1ZsGCBLD76FFIsCKGzlIQUDs07xF/r/6JQuUJ0XtqZYjVe7EN84o1EvCZ4UbBsQZp83uTu6wkJCbi4uODv70/ZsmXx9PSkZMmSWXULRqdr1656RzBqht7+Xbt2JTo6Wu8YQhilJk2asHXrVjp27MiiRYuwtrZm7ty5UjA8gRQLQugoIjgC7ynexIfHU/PtmjQY3gAzyxf7a6nSFd6TvUmKS6L9z+0xt8lYKyEpKYlu3brh7e2Nvb09np6elCtX7ilnE08iYzz0ZejtX7BgQRlgKYSOWrRowcaNG+ncuTM//fQTNjY2TJ8+XQqGx5COWkLoIC05jYM/HWTbkG2odIXzQmcaf9b4hQsFgBNrTnDJ7xKNPml0d5xDSkoKvXv3ZteuXRQtWhRPT09effXVrLoNo3Xy5ElOnjypdwyjZejtf/LkSU6fPq13DCGMWvv27Vm7di2mpqbMnDmT6dOn6x0p15JiQYgcdv2v62zst5HjK45TrVs1evzZA/u69i99zsPzDlO+WXle7/k6AGlpaQwYMIAtW7ZQuHBh9uzZw2uvvZYVt2D0AgMDCQwM1DuG0TL09g8MDCQ4OFjvGEIYva5du7Jy5Uo0TWPixInMnj1b70i5knRDEiKHpKWkEbQ0iKAlQdjY2dB+XnvKNC7z0udNvp2M5zhPbF6xoemkpmiaRnp6OoMHD2bt2rXkz5+fXbt2Ubt27Sy4CyGEECLv6NOnD4mJibz33nuMGTMGa2trPvroI71j5SpSLAiRA2LOxuA92ZuokCgqd6yM4xhHLPNbvvR5lVLsm76P+KvxuCx2wbKAJUophg8fzh9//IGNjQ3bt2+nQYMGWXAXQgghRN4zaNAgEhMTGT58OMOHD8fa2ppBgwbpHSvXkGJBiGyk0hXHVhzjyIIjWOS3oM0PbSjfrHyWnT90Wyjndp+jwUcNKFG7BEopRo8ezYIFC7C0tGTbtm28+eabWXY9IYQQIi/66KOPuHPnDqNHj2bw4MFYWVnRt29fvWPlClIsCJFNYi/G4j3Zm2snrlGhRQXeHPcm1oWts+z8N8JucOC7A5RsUBKHdx0AmDhxInPnzsXc3JxNmzbRokWLLLueEEIIkZd99tlnJCQkMHHiRAYMGICVlZXBT9WcFaRYECKLqXTFqXWnODzvMKaWprSY3oJKbSpl6ZRsqUmpeI7zxNzGnOZfN0cz0fj555+ZPn06pqamrF27lvbt22fZ9cT9evXqpXcEo2bo7d+rVy+ioqL0jiGEeIQJEyaQmJjIjBkz6N27Nx4eHjg5OekdS1dSLAiRheKvxOM91ZurR65SxrEMThOdsC1qm+XXOTj3IDFnY2j/c3tsi9qyfft2Pv30UwCWL18uvwnJZjY2NnpHMGqG3v42NjYGfw9C5GXTpk0jLi6O+fPn061bNw4dOkSlSpX0jqUbmTpViCyglOLM5jO49nEl6nQUThOcaPdTu2wpFMK8wvjL9S9qDahFGccynDx5kj59+pCens7kyZPp379/ll9T3C84OFimvtSRobd/cHAwJ06c0DuGEOIxNE3jxx9/pH379kRHR+Ps7MzNmzf1jqUbKRaEeEm3r99m5yc78Z3mS9HXi9Ljzx5U61ItW1aCjL8Sj8/XPhR9vSgNPmpAZGQkzs7OxMfH06dPHyZPnpzl1xQPM/QPq4bO0Ns/ODjYoBeVE8IYmJqa8ueff1K9enXOnDlDr169SE1N1TuWLqRYEOIFKaU4u/Msrr1duRp4FcfPHen4a0fyl8yfLddLT03Ha4IXKGg5syUpaSl06dKFCxcu0LBhQ5YuXSpL1QshhBBZpECBAri5uVG0aFH27NnDqFGj9I6kCykWhHgBiTcS8fjSA68JXhQqV4jua7pTo3cNNJPs+7AeuDCQa8ev8db4t8hfMj/vv/8+/v7+lClThs2bN2NtnXUzLQkhhBACypcvz+bNm7GwsODXX39l/vz5ekfKcVIsCPGc/vH+B9ferlzcd5E3RrxBpyWdKFi2YLZe8/KhyxxbfoxqXapRqU0lpk+fzqpVq8iXLx9ubm6UKFEiW68vhBBCGCtHR0eWLl0KwKhRo9i5c6fOiXKWFAtCPKOk+CT2TtrL7jG7sbGzoeuKrjgMdMjWpwkAiTGJ7J24l0LlC+E4xpF169YxceJENE1j9erV1KpVK1uvL4QQQhi7fv36MWHCBNLT0+nduzenTp3SO1KOkalThXgGlw9exudrHxKiEqg7pC513quDqblptl9XpSv2TtpL8q1kOv7akaATQQwcOBCAH374ARcXl2zPIB7Wr18/vSMYNUNv/379+sk6C0IYoKlTpxISEsL69etxdnbm8OHDFC1aVO9Y2U6KBSGeICUhhYM/HeT0htMUrlCYNj+0oejrOfeD4fjK41w+eJm3vnqL25a36dSpE3fu3OH999+/u66CyHnm5uZ6RzBqht7+5ubmBn8PQhgjExMTli9fTlhYGIGBgXTt2hVPT08sLS31jpatpBuSEI9x9ehVXPu4cmbjGWoNqEW3Vd1ytFC4duIaAb8EUKFlBUq3Lk2nTp2IiIigefPm/PLLLzLzkY4CAgIICAjQO4bRMvT2DwgIICgoSO8YQogXYGNjw9atWyldujQHDhxgyJAhKKX0jpWtpFgQ4gGpSan4z/XHbZgbmqbhstiFRqMaYWqR/d2O/pUUn4TXeC9si9nSZFwT+vfvT3BwMJUrV8bV1RULC4scyyIedurUKaPqr5rbGHr7nzp1ijNnzugdQwjxguzt7dm2bRu2trasWLGCmTNn6h0pW0mxIMQ9Ik9FsrHfRk6sOsFr3V+j+5rulHDI2ZmGlFLsm76PWxG3aDGjBVNmTGHr1q0ULlwYNzc3ihQpkqN5hBBCCHE/BwcHVq1ahaZpjB8/ng0bNugdKdtIsSAEkJaSRsCvAWwZtIWUhBQ6/NKBN8e+iblNzvcrPrPpDOc9ztNgeAPcA9yZNWsWZmZmuLq6UqVKlRzPI4QQQoiHde7cme+++w6AAQMGEBgYqHOi7CEDnIXRi/47Gu/J3kSHRlO1U1Uaf9YYi3z6dPOJOReD3w9+lGpYiptlbzKszTAAfv31V1q0aKFLJiGEEEI82pgxYzh9+jTLli2jc+fOHD58mFKlSukdK0tJsSCMVnpaOqdXnyZkdQiWBSxpO6ct5ZzK6ZYn9U4qnuM8schnQflB5XFq60RqaiqfffYZQ4YM0S2XEEIIIR5N0zQWLFjAuXPn8PX1xcXFhX379mFra6t3tCyj5fUR3Lmdg4ODCg4O1juG0bl54Sbek725EnyFKu2r8OaXb2JVyErXTL7TfTmz6QxvzXyLriO6EhISgrOzM5s3b8bUNOcGV+ekqKgo7Ozs9I5hlKTt9SXtry9pf/3k1baPjo6mYcOGnDt3ji5durBhwwZMTHJfb39N044opeo/zzG57y6EyEYqXXFi9Qk29N1A7MVYGo1vRKuZrXQvFM7tPseZTWeoOaAmI74bQUhICDVr1mT16tV5tlAQQggh8opXXnkFNzc3ChYsyObNmxk/frzekbKMFAvCaMRficftAzf85/hTqmEpeq7rSdnmZfWORVx4HPum76NYzWL8ceoPPD09KVasGNu2bSN//vx6xxOP4Ofnh5+fn94xjJaht7+fnx+HDx/WO4YQIotVq1YNV1dXTE1N+fbbb1m+fLnekbKEFAsiz1NKcXrjaVz7uBJ1Joqmk5rSdk5bbOxs9I5GWkoaXl95gQZhFcJYuHghlpaWbNmyhXLl9Bs/IZ4sNDSU0NBQvWMYLUNv/9DQUM6dO6d3DCFENmjVqhXz588HYOjQoezbt0/nRC9PigWRp92OvM3OUTvZN2MfxWoUo+e6nlTtVDXXrH4c+FsgkacisWpjxZipYwBYvnw5jRo10jmZEEIIIV7EBx98wKhRo0hJSaFr164G/8sBmQ1J5ElKKc7uOIvfLD/SUtJo8kUTXu/xOppJ7igSAC75XeLY/45R5M0ivPfte6SnpzN58mT69OmjdzQhhBBCvITZs2cTGhrKjh07cHFxwc/Pj0KFCukd64XIkwWR5yTGJOLxpQd7J+2lUIVCdF/dneq9queqQiEhKgHvyd7YlLLhi01fEB8fT58+fZg8ebLe0YQQQgjxkkxNTfnzzz+pXr06p0+fpnfv3qSmpuod64VIsSDylDCvMFx7u3Jx30UajmxIp987UbBsQb1j3UelK7wmepF0K4lll5YRdjGMhg0bsnTp0lzTPUo8mZmZGWZm8mBWL4be/oaeXwjxbAoUKICbmxtFixZl9+7dfPLJJ3pHeiHy00rkCUlxSRyYdYCzO85iV80O54XOFK5YWO9YjxS8PJgrAVcItA3E66AXZcqUYfPmzVhbW+sdTTyj/v376x3BqBl6+/fv35+oqCi9YwghckD58uXZvHkzzZs355dffqFatWp8/PHHesd6LvJkQRi8S36XcO3tyvnd56k3tB5dlnfJtYVCxLEIAhcEEmEbwWLfxdja2uLm5kaJEiX0jiaEEEKIbODo6MjSpUsBGDVqFDt37tQ50fORYkEYrJSEFPbN2MeOkTuwyG9Blz+6UG9oPUzMcufbOikuCa/xXsSpOKb5TkPTNNasWUOtWrX0jiaek4+PDz4+PnrHMFqG3v4+Pj4cOHBA7xhCiBzUr18/JkyYQHp6Or179+bUqVN6R3pmufNTlRBPcfXoVVz7uHJm8xlqD6xNt5XdsKuWe5ePV0rh840P1y9cZ+7xuaSQwg8//ICLi4ve0cQLCAsLIywsTO8YRsvQ2z8sLIyLFy/qHUMIkcOmTp1Kz549iYuLw8XFhevXr+sd6ZlIsSAMSmpSKv5z/Nk2dBsmpiZ0+r0TDUc0xNTCVO9oT/SX61+E7gplw6UNhCeH8/777/Ppp5/qHUsIIYQQOcTExITly5dTv359wsLC6Nq1K0lJSXrHeiopFoTBiDwZyca3N3Ji9Qmq965Ot9XdKF6ruN6xnio6NJoDPxzg0NVD7I/df3eQk8x8JIQQQhgXGxsbtm7dSunSpTlw4ABDhgxBKaV3rCeSYuEemqb11DTtlKZp6Zqm1b/n9X6apgXf81+6pmkOjzh+iqZp4ffs1yFn7yBvSktO4/Avh9ny3hZSk1Lp+FtHmnzeBHNrc72jPVVKYgoeYz04E3YG1yhXXq38Kq6urlhYWOgdTQghhBA6sLe3Z+vWrdjY2LBixQq+/fZbvSM9kRQL9zsJdAN8731RKbVKKeWglHIABgD/KKWCH3OOuf/uq5Tans1587yokCg2vbOJ4GXBVHGpQo+1PSjVoJTesZ6Z3yw/TvmfYu31tVgXtsbNzY0iRYroHUu8JGtra5nqVkeG3v7W1tZYWVkKCm9aAAAgAElEQVTpHUMIoaM6deqwatUqNE3jq6++YsOGDXpHeixZZ+EeSqnTwNO6h/QF1uRIICOWnpZO8LJgji4+ilUhK9r92I6yb5bVO9Zz+XvH3xxYeoAdETuINItkl+suqlSponcskQV69+6tdwSjZujt37t3b1lnQQhBly5d+Pbbb/nyyy8ZMGAA5cuXp169enrHeogUC8+vN9D5Cds/1jTtHSAQGK2UuvHgDpqmDQWGApQsWVL+0XhA3IU4Dn9/mJiQGMo2L0udEXWwLGCZLe0UGxub5ecEiA+PZ8uYLRy+eJgjHGH297OpVauW/L9+QHa1v3g6aXt9SfvrS9pfP9L29xs0aBDBwcGsWbMGZ2dndu/ejb29vd6x7mN0xYKmaR7Ao1bAGq+U2vKUYxsCCUqpk4/Z5TfgG0Blfp0NvPfgTkqpRcAiAAcHB2Vnl3un/MxJKl1xYvUJAn4NwNzanHY/tKNiq4rZft2sbv+05DTcB7tzJvQMnsqTTz79RGY+egJDfP97eHgA0KpVK52TvBxDbHsw/Pb38PAgNjaW7t276x3FqBnq+z8vkLa/3/LlywkPD8fX15eBAweyb98+bG1t9Y51l9EVC0qpl/nXpQ9P6IKklLr27/eapi0G3F7iWkYl7nIc3lO9iQiKoJxTOZwmOGFdxDD7JHt/782RXUfwTPWkacemzJo1S+9IIotdvnxZ7whGzdDb//Lly8THx+sdQwiRS1hYWLBhwwYaNWpEUFAQAwYMwNXVFROT3DG0OHekMACappkAPYE/n7DPvc+NupIxYFo8gVKKvzb8xYa+G4gJjaHZlGa0md3GYAuFc17n2PH9Do4mHSV/zfysWbMGU9PcvQaEEEIIIfRlZ2eHm5sbBQsWZNOmTYwfP17vSHdJsXAPTdO6app2GWgMuGuatuuezU7AZaXU+QeO+f2eaVb/396dh1dVne0f/y5CmJR5ighaEEELaJRBQUU09C36Qy1awZZWrVVeylRKLYgoouAIKlAEHIA4YsJUBimKYRRBwhAxAQISJhmTQAaJwEmyfn8QeIMEkJBkZZ99f66Li2Tvfc6583hYnid7r7VfM8Z8Z4zZANwB6NqTc/jxwI/M7zOfr17+ijot6vD76N/TpHMTz95/4MjBI0x5ZAo7MnewrfY25s6dS+XKlV3HEhEREQ+45pprmD59OiEhIbzyyitMnVo61tPx3WVI52KtnQXMOsu+JcDNBWx/PN/Xfy62cEHEWsvW+Vv5euTX5GbncutTt3LtA9d6tkmAE/MtJnWfxN4f9rKk7BLmzp7LlVde6TqWiIiIeEjHjh0ZM2YMffr0oUePHrRs2dL5SopqFqRE/XToJ5a9uIydS3cSFh5Gh2EdqFK/iutYF+3zlz9n8+LNrGAFz456lrZt27qOJMWoShXvv2e9zOv193p+ESlevXr1Yvny5URFRdG1a1dWrVrl9N4sprTfYjrYhYeH27i4s93fLbgkxSTx1UtfEfgpQOverWnxhxaYMm7PJqSkpFz0qgy7vtnFmxFv8u2Rb6l6X1Vmzprp6bMkJako6i+Fo9q7pfq7pfq7o9r/MhkZGbRs2ZLvv/+enj17MmHChCJ5XmPMWmttq/Mf+X80Z0GK3bGMY8QMieHLQV9S+fLK3P/x/VzX/TrnjUJROJp2lHe6vcO+I/vY2WAnk6dMVqMgIiIiF6VKlSpER0dTrlw5Jk6cyKefnnV9nWKnZkGK1a6vdjGt6zS2f7mdVj1bcd/k+6jesLrrWEXCWsukP0/iwM4DLC27lKnTp1K9enD8bHJuCxYsYMGCBa5j+JbX679gwQJiYmJcxxCRUu6GG25g9OjRADzxxBNs3brVSQ7NWZBicfzIcVa+sZLE2YnUaFyDTmM6UatpcJ12XDx2MQn/TWAVqxj42kDatGnjOpKUkP3797uO4Gter//+/ft1nwUR+UV69uzJ4sWLmTZtGl27dmXlypUlPn9BZxakyO2J3cP0btPZMncL4Y+G0+WDLkHXKOz9di9zhswhKSeJRvc0on///q4jiYiISJAxxvDuu+9y1VVXERcXx4ABA0o8g5oFKTLZR7NZMXIFn/3tM0LKhXDvpHtp06cNIeWC66ZkgawA4x8YT8qRFLbV38aUyCmapyAiIiLFomrVqqfmL0yYMIHo6OgSfX01C1IkDmw4wIw/ziAhKoHmf2jOA588QN0WdV3HKnLWWiY9OomD2w6yNGQpH077kBo1ariOJSIiIkHsxhtv5I033gDg8ccf5/vvvy+x19acBbkoOcdzWPP2GjZ8uIFL615K54mdqdeqnutYxearyV+xYeYG1rGOf7z2D26++Yz79IkP1KxZ03UEX/N6/WvWrEloaKjrGCLiMb169WLJkiVMnz6drl278vXXX5fI/AXdZ8ExL99nIWVzCkueW8KhbYe4pss1tP1HW0Ireet/gBey3nPy1mRG3DiC7T9uh84we85sXX50kbTetjuqvVuqv1uqvzuq/cVJT0/nxhtvJCkpid69ezNu3LgLerzusyAlau27azmafpS7xt5F+yHtPdcoXIic4zmMuWcMaT+mkXh5IpHvR6pREBERkRKVf/7CW2+9xfTp04v9NdUsSKG1H9KeB6MfpEG7Bq6jFLvJPSaTnJjM8pDlRE6L1DwFn5s7dy5z5851HcO3vF7/uXPn8vnnn7uOISIe1bJlS15//XUA/vrXv7Jt27ZifT01C1JoFWtUpHyV8q5jFLtVn65i/Ufr+Y7v6PVKL9q2bes6kjiWmppKamqq6xi+5fX6p6amcujQIdcxRMTDevfuzf33309GRgZdu3bl2LFjxfZaahZEzuHw7sN88r+fcCDnALXuruVkfWMRERGR/IwxTJo0iYYNG7Ju3TqefPLJYnstNQsiZ5Gbk8vrd7/Ojxk/svGyjUz5YAplyuifjIiIiLhXrVo1oqOjCQ0NZdy4ccyYMaNYXkeffETO4v2+75Mcn8yKMiuYNH2S55drFBERkeDSqlUrRo0aBcBjjz1GUlJSkb+GmgWRAqydu5bYd2JJJJHHX3mcdu3auY4kpUhYWBhhYWGuY/iW1+sfFhZGnTp1XMcQkSDRt29funTpQkZGBt26dSvy+Qu6z4JjXr7PQjAoaL3njAMZDGk6hIPpB8n6bRaz58/W5UfFROttu6Pau6X6u6X6u6PaF4+0tDRuuOEGduzYQb9+/RgzZkyBx+k+CyIXyeZaXrv7NX5K/4n4uvFM+UjzFERERKR0q1atGlFRUYSGhjJ27FhmzpxZZM+tT0Ei+Xw48EOS1yWz2qzmnRnv6LcfUqCZM2cW6UAsF8br9Z85cybz5s1zHUNEgkybNm0YOXIkcGL+wvbt24vkedUsiOTZELOBlaNXsoMd/PGlP3LLLbe4jiSlVEZGBhkZGa5j+JbX65+RkUFmZqbrGCIShPr168fvfvc70tPT6datG8ePH7/o51SzIAIcOXyEtx98m4ycDMp1LMfAgQNdRxIRERG5IMYYJk+ezJVXXklsbGyRfJ5RsyC+Z63l5btfJnA4wHd1vmPKVM1TEBEREW+qXr06UVFRlC1bljFjxvCf//znop5Pn4jE9z4Z+gnJq5JZa9YyfsZ4zVMQERERT7vpppt47bXXAPjLX/7Cjh07Cv1cahbE17au3sqyV5axhz08MPwBbr31VteRxAPq169P/fr1XcfwLa/Xv379+tSrV891DBEJcv379+fee+8lLS3touYvlC3iXCKecfTIUSL/HMlP2T/BHfDU4KdcRxKP6Nixo+sIvub1+nfs2JGUlBTXMUQkyBljmDJlCjfccAOrV6/mqacK9zlHzYJjhw4dIjIy8rRtzZo1o3Xr1gQCAT7++OMzHhMeHk54eDhZWVlER0efsb9Vq1Y0b96c9PR0Zs2adcb+tm3b0rRpU1JSUgpcvq99+/Y0atSI/fv3s2DBgjP2R0RE0KBBA3bv3k1MTMwZ+zt16kRYWBhJSUksW7bsjP2dO3emVq1aJCYmsnLlyjP2d+nShapVqxIfH8+aNWvO2N+1a1cqVapEXFwcBd3Qrnv37oSGhhIbG0tCQsIZ+x999FEABt46EA5BfK14Po/6XPMUREREJKjUqFGDqKgobrvtNt58881CPYc+HYkvRb0SxbG4Y2xgA6NnjKZ27dquI4mHREVFERUV5TqGb3m9/lFRUQX+IkdEpDjcfPPNvPrqq4V+vLHWFmEcuVDh4eG2oN+OS/HZsnYLo24eRUp2Ctl3ZzPnszmuI/lWSkqKJyeUnzwbePIslRd5tfbg/fpHRkaSmZlJ3759XUfxLS+//71OtXfDWst9993H3Llz11prW13IY3VmQXzl+NHjvP7/XieQHWBXk11E/E+E60giIiIixcoYc8Zl77+U5iyIrwy/bzgcgIQaCTzc+2GMMa4jiYiIiBS7GjVqFOpxOrMgvjF9zHQOfHGAzWYzI2eOpEqVKq4jiYiIiJRqOrMgvrAtfhv//dd/ySSTjs925PbbbwcgLS3NcTLxooYNG7qO4Gter3/Dhg019oiIZ6hZkKAXOB7gld++ggkYjt5ylKeHPg3A7bffrrXOpVBONpvihtfrr7FHRLxEzYIEveEPDqfM3jLEV4tn1sxZhISEuI4kIiIi4gmasyBBbdbbs9g3Zx/b2c6ImSOoU6fOqX0fffQR06ZNc5hOvOqjjz7io48+ch3Dt7xef409IuIlOrMgQWvHlh3M7jeb4xzn1iG3cscdd5y2Pzs7m+zsbEfpxMv0vnHL6/XX2CMiXqIzCxKUAoEAL3R8gdDjofx4048MeX6I60giIiIinqMzCxKUhv9xOKG7Q9lcdTPRs6M1T0FERESkENQsSNCZ+8Fc9k7fyz728dyM56hbt67rSCIiIiKepGZBgsrBvQeZ9r8nJg62GdiGOyPuPOuxTZo00VrnUihNmjRxHcHXvF5/jT0i4iVqFiSoDL9rOBWOVmDP9XuY8tKUcx7brl07rXUuhdKuXTvXEXzN6/XX2CMiXqJmQYLG+0Pf5/iG42wM3Ujk9EjNUxARERG5SFoNSYLC9g3bWfHKCg5wgG4ju9G4cePzPiYyMpKpU6eWQDoJNpGRkURGRrqO4Vter7/GHhHxEjUL4nk5gRzG3juW44HjHG13lN59e7uOJCIiIhIUdBmSeN67Pd4la2cWqyusZv7H8ylTRj2wiIiISFHQp6p8jDEjjTGbjTEbjDGzjDHV8u0bbIz53hiTaIz57Vke39AY840xZqsxJsoYU67k0vvTt/O+ZcOHG9jIRv4++u/86le/ch1JREREJGioWTjdQqC5tfY6YAswGMAY82vgIaAZ0AkYb4wpaPbsq8Cb1tqrgcPAX0sktU8dST7CB499QHJOMpUiKtGjRw/XkURERESCii5Dysda+0W+b1cBv8/7+j7gU2vtMWC7MeZ7oA2w8uTBxhgD3An8MW/T+8AwYEIxx/Ylm2t556F3SEtOY9Wlq/h6ytec+E/wyzVr1kxrnUuhNGvWzHUEX/N6/TX2iIiXqFk4u8eAqLyvL+dE83DSD3nb8qsJpFlrs89xDADGmB5AD4B69eppve1CWDVhFVuXbmUFK/jXi/+iYsWKF1zHhg0bkp6ervo7lJ6e7jpCoTRs2BDA0+8dr9YevF9/jT3uefn973Wqvff4rlkwxnwJhBWwa4i1dnbeMUOAbODjkw8r4Hj786f+Bcec2GjtO8A7AOHh4bZWrVq/ILmctG/9Ppa+vpTEnESuuusq+vbte8FnFQACgQCBQADV3y0v1j8QCAAQGhrqOMnF8WLtwfv119hTOqj+7qj23uK7ZsFa2/Fc+40xjwCdgQhr7ckP+z8ADfIdVh/Y+7OHpgDVjDFl884uFHSMXKRjGcf44NEP+CH9BzZU3cC3731bqEYB4OOPPyYzM5O+ffsWcUoJdh9/fOL3CI8++qjbID7l9fpr7BERL9EE53yMMZ2AQcC91tqsfLvmAA8ZY8obYxoCVwOr8z82r7FYzP/Nc3gEmF38qf3DWsu8f81je8J2YojhzXFvUq9ePdexRERERIKWmoXTjQMqAwuNMXHGmIkA1toEIBrYCCwAeltrcwCMMfONMSc/sQ4CBuRNgK4JTCrpHyCYJUQnsGrqKr7O+Zp297Wje/furiOJiIiIBDXfXYZ0LtbaxufY9yLwYgHb7873dRInVkmSIpa6JZU5g+cQnxnP3hp7Wfj2wkJffiQiIiIiv4yaBSn1AlkB5vSbw5ZdW1jCEiLHR1K3bl3XsURERESCnpoFKfW+eu0r4r+OZ2HOQu558B66detWJM8bHh7O4cOHi+S5xF/Cw8NdR/A1r9dfY4+IeImaBSnVts7fyorJK1iWuYxA7QBvvfVWkT13eHi41jmXQvH6h1Wv83r9NfaIiJeoWZBSK31XOgufW8j6vetZy1pmvD2D2rVrF9nzZ2VlkZWVdf4DRX7m5PumUqVKjpP4k9frr7FHRLxEqyFJqZRzPIcvB39J4tZEPs/5nD/88Q906dKlSF8jOjqa2bO1uq1cuOjoaKKjo13H8C2v119jj4h4iZoFKZVWj1vNxmUbmZ0+m8phlfn3v//tOpKIiIiI7+gyJCl1di7bSezkWL7c/yU72cmcd+ZQo0YN17FEREREfEdnFqRUOXLwCEuGLSFhfwIrslfwyCOPcM8997iOJSIiIuJLahak1LC5lkXPLGLn9p1EpUYRdnkYo0ePdh1LRERExLd0GZKUGuveW8f2FduJ+iGKdNL59L1PqVatWrG9XqtWrbTWuRRKq1atXEfwNa/XX2OPiHiJmgUpFfat28fad9fyzeFv2Ji9kSeeeIJOnToV62s2b95ca51LoTRv3tx1BF/zev019oiIl+gyJHHuaNpRFj2ziH1H9jFt3zSuuOIKRo0aVeyvm56eTkZGRrG/jgSf9PR00tPTXcfwLa/XX2OPiHiJmgVxylrLkueXkLonlYlbJxIgwOTJk6lSpUqxv/asWbP47LPPiv11JPjMmjWLWbNmuY7hW16vv8YeEfESNQviVPyn8exctpPP0z5nf/Z+evXqRUREhOtYIiIiIoKaBXEoeVMy34z5hn2h+5i/az6NGjXi1VdfdR1LRERERPKoWRAnAlkBFj29iOMhxxkZOxKAKVOmcOmllzpOJiIiIiInaTUkKXHWWpa/vJz0H9L55NAnZOVm8fe//5327du7jiYiIiIi+ahZkBK39bOtfP/f79lVexcr1qzg6quv5qWXXirxHG3bttVa51Iobdu2dR3B17xef409IuIlahakRKXtTOOrV74ipEEIL89+mTJlyhAZGUmlSpVKPEvTpk211rkUStOmTV1H8DWv119jj4h4ieYsSInJOZ5DzOAYypQrw9jvxpJrc/nnP/9Ju3btnORJSUkhNTXVyWuLt6WkpOjDnkNer7/GHhHxEjULUmJWjV5F6pZUNtXZxIZtG7j22mt54YUXnOWZN28eX3zxhbPXF++aN28e8+bNcx3Dt7xef409IuIlahakROxYsoOE6AQubXspI6NHEhISwvvvv0+FChVcRxMRERGRs9CcBSl2P+7/kaUvLKX61dUZ8t8hWGsZNGgQrVu3dh1NRERERM5BZxakWOXm5BIzJIbc7FxWVFjB1m1badGiBUOHDnUdTURERETOQ82CFKu176zlwLcHqNq5KmMix1C2bFkiIyMpX76862giIiIich66DEmKzZ7YPcRNjqNhp4b0mNADgCFDhnDjjTc6TnZC+/btOXTokOsY4kG6gaBbXq+/xh4R8RI1C1Isfjr0E4ufWUzVK6sy88BMduzYQXh4OEOGDHEd7ZRGjRpRpUoV1zHEgxo1auQ6gq95vf4ae0TES3QZkhQ5m2tZMmwJxzKPUaFzBSa+N5HQ0FA++OADQkNDXcc7Zf/+/Rw4cMB1DPGg/fv3s3//ftcxfMvr9dfYIyJeomZBitx3n3zH7q93c33P6+kztA8Aw4YNo0WLFo6TnW7BggUsWrTIdQzxoAULFrBgwQLXMXzL6/XX2CMiXqJmQYrUwYSDrP73ahre2ZCJSyeye/duWrduzcCBA11HExEREZELpDkLUmSO/3icmMExVKpdiaNtjjLpwUmUL1+eyMhIypbVW01ERETEa/QJToqEtZblLy3nx/0/cufoO2n/4InVSoYPH86vf/1rx+lEREREpDB0GZIUicTZiWz7Yhut/taKsVFj2bt3LzfddBMDBgxwHU1ERERECklnFuSiHU46zNcjv+byNpeT2yKXcY+Po0yZMrz99tuEhIS4jndWERERWutcCiUiIsJ1BF/zev019oiIl6hZkIuSfSybL5/6ktBLQmk/rD0d7+lIbm4u/fv35/rrr3cd75waNGhAxYoVXccQD2rQoIHrCL7m9fpr7BERL9FlSHJRVr6+ksNJh+nwfAc+nvkxq1evpl69erzwwguuo53X7t272bNnj+sY4kG7d+9m9+7drmP4ltfrr7FHRLxEzYIUWtKXSWyauYnrH76e8leVZ/DgwQCMHj2aypUrO053fjExMSxbtsx1DPGgmJgYYmJiXMfwLa/XX2OPiHiJLkOSQsncm8myEcuo07wOrXu15i9//QtpaWn89re/5fe//73reCIiIiJSBHRmQS5YbnYuMU/HgIWIlyJYvmI5H3zwAeXLl2fcuHEYY1xHFBEREZEioDMLcsFiJ8RyMP4gES9HUL5Wef7W8W8ADB48mMaNGztOJyIiIiJFRWcW5IL8sOoHvn3/W669/1qu+s1VvPnmm2zatInGjRszaNAg1/FEREREpAjpzIL8YlmpWSweupjqjarTdkBbdu7ceWrVo/Hjx1OhQgXHCS9Mp06dSE1NdR1DPKhTp06uI/ia1+uvsUdEvETNgvwiNtey+NnFBI4E6DyxM2UrlKVfv35kZWXRrVs3fvOb37iOeMHCwsIoW1b/BOTChYWFuY7ga16vv8YeEfESXYYkv8i3H3zLntV7aPtkW6o3qs6cOXOYM2cOlStX5o033nAdr1CSkpLYsWOH6xjiQUlJSSQlJbmO4Vter7/GHhHxEv1qQ87rwIYDxI6PpdFvGnHN767hyJEj9O3bF4Dhw4dTr149xwkLZ9myZWRmZtKqVSvXUcRjTq6R36hRI8dJ/Mnr9dfYIyJeojML+RhjRhpjNhtjNhhjZhljquVt/40xZq0x5ru8v+88y+OHGWP2GGPi8v7cXbI/QdE7lnGMRUMWcWnYpbQf0h5jDCNGjGDXrl2Eh4fTu3dv1xFFREREpJioWTjdQqC5tfY6YAswOG97CnCPtbYF8Ajw4Tme401rbXjen/nFG7d4WWtZNmIZRw4eIeKlCMpdWo6NGzcyatQojDFMnDhR192KiIiIBDE1C/lYa7+w1mbnfbsKqJ+3fb21dm/e9gSggjGmvIuMJWnTzE1sX7Sd1r1bU6d5Hay19OrVi+zsbJ544gluuukm1xFFREREpBjp18Jn9xgQVcD2B4D11tpjZ3lcH2PMw8Aa4J/W2sM/P8AY0wPoAVCvXj1SUlKKKHLRSUtKY/mry6kdXpt6nU5kjIqKYunSpdSqVYsnn3yyVOa+EJmZmfz000+e/zm8LD093XWEQsnMzATw9HvHq7UH79dfY497Xn7/e51q7z3GWus6Q4kyxnwJFLTu3hBr7ey8Y4YArYD7bb4CGWOaAXOA/7HWbivguety4pIlCwwHLrPWPnauPOHh4TYuLq6wP06xCPwU4D8P/4djGcd4YOoDVKxRkcOHD9O0aVOSk5OJjIzkkUcecR3zoqWkpJCamkrTpk1dR/GtlJQUatWq5TrGBTv5Ic+L2U/yau3B+/XX2OOel9//Xqfau2WMWWutvaDVFXx3ZsFa2/Fc+40xjwCdgYifNQr1gVnAwwU1CnnPfSDf8e8C84okdAlb+fpK0nakcfdbd1OxRkUAnn76aZKTk7ntttt4+OGHHScsGhqspLD03nHL6/X3en4R8RfNWcjHGNMJGATca63Nyre9GvAZMNhau+Icj78s37ddgPjiylpctn2xjc3/2Uz4X8K5vM3lAKxevZq3336bsmXLMmHCBIwxjlMWjcTERL7//nvXMcSDEhMTSUxMdB3Dt7xef409IuIlahZONw6oDCzMW/p0Yt72PkBj4Nl8y6LWATDGvGeMOXk657W85VU3AHcA/yjpH+BiZOzJYNmIZdS9ri4t/7clANnZ2fTs2RNrLQMGDKBZs2aOUxadlStXEhsb6zqGeNDKlStZuXKl6xi+5fX6a+wRES/x3WVI52KtbXyW7SOAEWfZ93i+r/9cTNGKXU4gh5jBMZQJKUPESxGUCTnRR44fP57169dzxRVXMHToUMcpRURERKQk6cyCABA7Ppbkjcm0H9qeS8MuBWDfvn0888wzAIwdO5ZLLrnEZUQRERERKWFqFoTdX+9mw4cbaNa1GQ3vaHhq+4ABA8jMzOSee+7hvvvuc5hQRERERFxQs+BzR5KPsHjoYmpeXZOb+998avvChQv59NNPqVixImPHjnWYUERERERc0ZwFH7O5lsVDF5N9NJuIlyMIKRcCwNGjR+nduzcAzz77LL/61a8cpiw+Xbp0ITU11XUM8aAuXbq4juBrXq+/xh4R8RI1Cz4WFxnH3ti93P7c7VT7VbVT20eOHMnWrVu59tpr+ec//+kwYfGqWrUqgUDAdQzxoKpVq7qO4Gter7/GHhHxEl2G5FP74/azZuIaGt/VmCadm5zavm3bNl588UXgxEpI5cqVcxWx2MXHx7Np0ybXMcSD4uPjiY/33G1UgobX66+xR0S8RM2CDx3LOMaiIYuocnkVbht826mbrFlr6dOnD8eOHeNPf/oTHTp0cBu0mK1Zs4a4uDjXMcSD1qxZw5o1a1zH8C2v119jj4h4iZoFn7HWsvSFpWSlZnHnS3cSWin01L4ZM2awYMECqlWrxqhRoxymFBEREZHSQM2Cz2yctm0Re7AAAAosSURBVJEdS3ZwU7+bqH1t7VPbMzMz6d+/PwAvvfQSdevWdRVRREREREoJNQs+krollVVvruKK266g+R+an7Zv2LBh7Nmzh9atW9OjRw9HCUVERESkNFGz4BOBrAAxg2OoUK0CHZ7rcGqeAsCGDRsYM2YMZcqUYcKECYSEhDhMKiIiIiKlhZZO9YkVI1eQvjudzhM7U6FahVPbc3Nz6dmzJzk5OfTp04eWLVs6TFmyunbtSkpKiusY4kFdu3Z1HcHXvF5/jT0i4iVqFnxg6/ytbJm7hZY9WnLZjZedtm/y5MmsXLmSsLAwRowY4SihG5UqVaJSpUquY4gH6X3jltfrr7FHRLxElyEFufRd6Xz1yldcduNl3PDXG07bl5KSwqBBgwB44403PH+jowsVFxfHd9995zqGeFBcXJyWvnTI6/XX2CMiXqJmIYjlHM8h5ukYypQtw50j7qRMyOn/uQcNGsShQ4eIiIjgoYcecpTSnbi4OE/f2Enc8fqHVa/zev019oiIl6hZCGKrx60mZXMKHYZ14JI6l5y2b8WKFUyePJly5crx1ltvnTbhWUREREQE1CwErZ3LdvLdJ9/R/A/NubL9laftCwQC9OzZE4CBAwfStGlTFxFFREREpJRTsxCEjhw8wtLnl1KraS1u6nvTGfvHjBlDfHw8jRo14umnn3aQUERERES8QM1CkLG5lkXPLCInkEPEyxGElDv9ngm7d+9m2LBhAIwbN46KFSs6SCkiIiIiXqClU4PMuvfWsW/dPu544Q6qXnHm6kb9+/fnyJEj3H///dx1110OEpYe3bt311rnUijdu3d3HcHXvF5/jT0i4iVqFoLIvnX7WPfeOpp0bsLVd199xv758+czc+ZMLrnkEkaPHu0gYekSGhpKaGio6xjiQXrfuOX1+mvsEREv0WVIQeJo2lEWPbOIKvWrcMvAW87Yn5WVRZ8+fQB4/vnnadCgQUlHLHViY2NZv3696xjiQbGxscTGxrqO4Vter7/GHhHxEjULQcBay5Lnl3D08FE6vtKR0Epn/sbq5ZdfZvv27bRo0YJ+/fo5SFn6JCQksHnzZtcxxIMSEhJISEhwHcO3vF5/jT0i4iVqFoJA/Kfx7Fq+i5v/cTM1m9Q8Y39iYiKvvvoqABMmTNDpbxERERH5RdQseFzypmS+GfMNV95+Jb9+8Ndn7LfW0qtXLwKBAI899hi33HLmJUoiIiIiIgVRs+BhgawAi55eRKWalejwXIcC78I8depUFi1aRI0aNU6dXRARERER+SXULHiUtZblLy8nY08Gd4y4g/JVyp9xTHp6OgMGDADgtddeo1atWiUdU0REREQ8zFhrXWfwNWNMJpDoOoeP1QK04Lk7qr87qr1bqr9bqr87qr1bTa21lS/kAbrPgnuJ1tpWrkP4lTFmjervjurvjmrvlurvlurvjmrvljFmzYU+RpchiYiIiIhIgdQsiIiIiIhIgdQsuPeO6wA+p/q7pfq7o9q7pfq7pfq7o9q7dcH11wRnEREREREpkM4siIiIiIhIgdQsiIiIiIhIgdQsOGSMqWaMmW6M2WyM2WSMaes6k18YY5oaY+Ly/ckwxvR3ncsvjDH/MMYkGGPijTFTjTEVXGfyE2PM3/Nqn6D3ffEzxkw2xhw0xsTn21bDGLPQGLM17+/qLjMGq7PU/sG8936uMUZLeBajs9R/ZN7nng3GmFnGmGouMwazs9R/eF7t44wxXxhj6p3vedQsuDUGWGCtvQa4HtjkOI9vWGsTrbXh1tpwoCWQBcxyHMsXjDGXA/2AVtba5kAI8JDbVP5hjGkOPAG04cS409kYc7XbVEEvEuj0s21PATHW2quBmLzvpehFcmbt44H7gWUlnsZ/Ijmz/guB5tba64AtwOCSDuUjkZxZ/5HW2uvyPv/MA4ae70nULDhijKkCtAcmAVhrj1tr09ym8q0IYJu1dqfrID5SFqhojCkLVAL2Os7jJ9cCq6y1WdbabGAp0MVxpqBmrV0GHPrZ5vuA9/O+fh/4XYmG8omCam+t3WStTXQUyVfOUv8v8sYegFVA/RIP5hNnqX9Gvm8vAc670pGaBXcaAcnAFGPMemPMe8aYS1yH8qmHgKmuQ/iFtXYPMArYBewD0q21X7hN5SvxQHtjTE1jTCXgbqCB40x+VNdauw8g7+86jvOIuPAY8F/XIfzGGPOiMWY30B2dWSjVygI3AhOstTcAR9Bp6BJnjCkH3AtMc53FL/Kuzb4PaAjUAy4xxvzJbSr/sNZuAl7lxKUAC4BvgexzPkhEpIgZY4ZwYuz52HUWv7HWDrHWNuBE7fuc73g1C+78APxgrf0m7/vpnGgepGTdBayz1h5wHcRHOgLbrbXJ1toAMBNo5ziTr1hrJ1lrb7TWtufEKeqtrjP50AFjzGUAeX8fdJxHpMQYYx4BOgPdrW745dInwAPnO0jNgiPW2v3AbmNM07xNEcBGh5H86g/oEqSStgu42RhTyRhjOPHe1+T+EmSMqZP39xWcmOipfwMlbw7wSN7XjwCzHWYRKTHGmE7AIOBea22W6zx+87MFLe4FNp/3MWro3DHGhAPvAeWAJOAv1trDblP5R9712ruBRtbadNd5/MQY8zzQjROnoNcDj1trj7lN5R/GmOVATSAADLDWxjiOFNSMMVOBDkAt4ADwHPAfIBq4ghMN9IPW2p9PgpaLdJbaHwL+DdQG0oA4a+1vXWUMZmep/2CgPJCad9gqa21PJwGD3FnqfzfQFMgFdgI98+YSnv151CyIiIiIiEhBdBmSiIiIiIgUSM2CiIiIiIgUSM2CiIiIiIgUSM2CiIiIiIgUSM2CiIiIiIgUSM2CiIiIiIgUSM2CiIiIiIgUSM2CiIh4hjGmhTFmvjEmNe/PrJN3pBYRkaKnZkFERDzBGHM5sBiwQHfgb0B74B8uc4mIBLOyrgOIiIj8Qv2ADOB+a+0xAGPMY0Blp6lERIKYziyIiIhX/D9gVr5GoTpwKxDrNJWISBBTsyAiIqWeMeYS4Fog1hhT2RhzG7AA+AGIchpORCSI6TIkERHxgus48Quu9cBOoDpwDOhgrT3qMpiISDDTmQUREfGCcOBHIAn4PdAT2A58ZowJcxlMRCSYqVkQEREvuAH41lobsNYusta+DdwL1ABudxtNRCR4qVkQEREvCOfEJUj5nbz86EAJZxER8Q01CyIiUqoZY0KA5sDBn+3qDhwCvirxUCIiPqEJziIiUtpdA1QEBhpjUoFNnFgydQjwN2tttstwIiLBTM2CiIiUduFAAJgFvMKJOzhvArpba2e4DCYiEuzULIiISGkXDmyy1j7sOoiIiN9ozoKIiJR2N3Dm5GYRESkBahZERKS0ux6Icx1CRMSPjLXWdQYRERERESmFdGZBREREREQKpGZBREREREQKpGZBREREREQKpGZBREREREQKpGZBREREREQKpGZBREREREQKpGZBREREREQK9P8B0VX4r+90eXYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logL = lambda x: -(x - 10) ** 2 - 10\n",
    "\n",
    "def find_tangent(β, a=0.01):\n",
    "    y1 = logL(β)\n",
    "    y2 = logL(β+a)\n",
    "    x = np.array([[β, 1], [β+a, 1]])\n",
    "    m, c = np.linalg.lstsq(x, np.array([y1, y2]), rcond=None)[0]\n",
    "    return m, c\n",
    "\n",
    "β = np.linspace(2, 18)\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax.plot(β, logL(β), lw=2, c='black')\n",
    "\n",
    "for β in [7, 8.5, 9.5, 10]:\n",
    "    β_line = np.linspace(β-2, β+2)\n",
    "    m, c = find_tangent(β)\n",
    "    y = m * β_line + c\n",
    "    ax.plot(β_line, y, '-', c='purple', alpha=0.8)\n",
    "    ax.text(β+2.05, y[-1], f'$G({β}) = {abs(m):.0f}$', fontsize=12)\n",
    "    ax.vlines(β, -24, logL(β), linestyles='--', alpha=0.5)\n",
    "    ax.hlines(logL(β), 6, β, linestyles='--', alpha=0.5)\n",
    "\n",
    "ax.set(ylim=(-24, -4), xlim=(6, 13))\n",
    "ax.set_xlabel(r'$\\beta$', fontsize=15)\n",
    "ax.set_ylabel(r'$log \\mathcal{L(\\beta)}$',\n",
    "               rotation=0,\n",
    "               labelpad=25,\n",
    "               fontsize=15)\n",
    "ax.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.675671\n",
      "         Iterations 7\n",
      "                          Poisson Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                    5\n",
      "Model:                        Poisson   Df Residuals:                        2\n",
      "Method:                           MLE   Df Model:                            2\n",
      "Date:                Fri, 06 Dec 2019   Pseudo R-squ.:                  0.2546\n",
      "Time:                        21:36:56   Log-Likelihood:                -3.3784\n",
      "converged:                       True   LL-Null:                       -4.5325\n",
      "                                        LLR p-value:                    0.3153\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -6.0785      5.279     -1.151      0.250     -16.425       4.268\n",
      "x1             0.9334      0.829      1.126      0.260      -0.691       2.558\n",
      "x2             0.8433      0.798      1.057      0.291      -0.720       2.407\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[1, 2, 5],\n",
    "              [1, 1, 3],\n",
    "              [1, 4, 2],\n",
    "              [1, 5, 2],\n",
    "              [1, 3, 1]])\n",
    "\n",
    "y = np.array([1, 0, 1, 1, 0])\n",
    "\n",
    "stats_poisson = Poisson(y, X).fit()\n",
    "print(stats_poisson.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep only year 2008\n",
    "df = df[df['year'] == 2008]\n",
    "\n",
    "# Add a constant\n",
    "df['const'] = 1\n",
    "\n",
    "# Variable sets\n",
    "reg1 = ['const', 'lngdppc', 'lnpop', 'gattwto08']\n",
    "reg2 = ['const', 'lngdppc', 'lnpop',\n",
    "        'gattwto08', 'lnmcap08', 'rintr', 'topint08']\n",
    "reg3 = ['const', 'lngdppc', 'lnpop', 'gattwto08', 'lnmcap08',\n",
    "        'rintr', 'topint08', 'nrrents', 'roflaw']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 2.226090\n",
      "         Iterations 9\n",
      "                          Poisson Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                numbil0   No. Observations:                  197\n",
      "Model:                        Poisson   Df Residuals:                      193\n",
      "Method:                           MLE   Df Model:                            3\n",
      "Date:                Fri, 06 Dec 2019   Pseudo R-squ.:                  0.8574\n",
      "Time:                        21:36:56   Log-Likelihood:                -438.54\n",
      "converged:                       True   LL-Null:                       -3074.7\n",
      "                                        LLR p-value:                     0.000\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        -29.0495      2.578    -11.268      0.000     -34.103     -23.997\n",
      "lngdppc        1.0839      0.138      7.834      0.000       0.813       1.355\n",
      "lnpop          1.1714      0.097     12.024      0.000       0.980       1.362\n",
      "gattwto08      0.0060      0.007      0.868      0.386      -0.008       0.019\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "# Specify model\n",
    "poisson_reg = sm.Poisson(df[['numbil0']], df[reg1],\n",
    "                         missing='drop').fit(cov_type='HC0')\n",
    "print(poisson_reg.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table 1 - Explaining the Number of Billionaires                         in 2008\n",
      "=================================================\n",
      "                  Model 1    Model 2    Model 3  \n",
      "-------------------------------------------------\n",
      "const            -29.050*** -19.444*** -20.858***\n",
      "                 (2.578)    (4.820)    (4.255)   \n",
      "lngdppc          1.084***   0.717***   0.737***  \n",
      "                 (0.138)    (0.244)    (0.233)   \n",
      "lnpop            1.171***   0.806***   0.929***  \n",
      "                 (0.097)    (0.213)    (0.195)   \n",
      "gattwto08        0.006      0.007      0.004     \n",
      "                 (0.007)    (0.006)    (0.006)   \n",
      "lnmcap08                    0.399**    0.286*    \n",
      "                            (0.172)    (0.167)   \n",
      "rintr                       -0.010     -0.009    \n",
      "                            (0.010)    (0.010)   \n",
      "topint08                    -0.051***  -0.058*** \n",
      "                            (0.011)    (0.012)   \n",
      "nrrents                                -0.005    \n",
      "                                       (0.010)   \n",
      "roflaw                                 0.203     \n",
      "                                       (0.372)   \n",
      "Pseudo R-squared 0.86       0.90       0.90      \n",
      "No. observations 197        131        131       \n",
      "=================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "regs = [reg1, reg2, reg3]\n",
    "reg_names = ['Model 1', 'Model 2', 'Model 3']\n",
    "info_dict = {'Pseudo R-squared': lambda x: f\"{x.prsquared:.2f}\",\n",
    "             'No. observations': lambda x: f\"{int(x.nobs):d}\"}\n",
    "regressor_order = ['const',\n",
    "                   'lngdppc',\n",
    "                   'lnpop',\n",
    "                   'gattwto08',\n",
    "                   'lnmcap08',\n",
    "                   'rintr',\n",
    "                   'topint08',\n",
    "                   'nrrents',\n",
    "                   'roflaw']\n",
    "results = []\n",
    "\n",
    "for reg in regs:\n",
    "    result = sm.Poisson(df[['numbil0']], df[reg],\n",
    "                        missing='drop').fit(cov_type='HC0',\n",
    "                                            maxiter=100, disp=0)\n",
    "    results.append(result)\n",
    "\n",
    "results_table = summary_col(results=results,\n",
    "                            float_format='%0.3f',\n",
    "                            stars=True,\n",
    "                            model_names=reg_names,\n",
    "                            info_dict=info_dict,\n",
    "                            regressor_order=regressor_order)\n",
    "results_table.add_title('Table 1 - Explaining the Number of Billionaires \\\n",
    "                        in 2008')\n",
    "print(results_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = ['const', 'lngdppc', 'lnpop', 'gattwto08', 'lnmcap08', 'rintr',\n",
    "        'topint08', 'nrrents', 'roflaw', 'numbil0', 'country']\n",
    "results_df = df[data].dropna()\n",
    "\n",
    "# Use last model (model 3)\n",
    "results_df['prediction'] = results[-1].predict()\n",
    "\n",
    "# Calculate difference\n",
    "results_df['difference'] = results_df['numbil0'] - results_df['prediction']\n",
    "\n",
    "# Sort in descending order\n",
    "results_df.sort_values('difference', ascending=False, inplace=True)\n",
    "\n",
    "# Plot the first 15 data points\n",
    "results_df[:15].plot('country', 'difference', kind='bar',\n",
    "                    figsize=(12,8), legend=False)\n",
    "plt.ylabel('Number of billionaires above predicted level')\n",
    "plt.xlabel('Country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 1\n",
    "\n",
    "![Ex1](img/mle_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ProbitRegression:\n",
    "\n",
    "    def __init__(self, y, X, β):\n",
    "        self.X, self.y, self.β = X, y, β\n",
    "        self.n, self.k = X.shape\n",
    "\n",
    "    def μ(self):\n",
    "        return norm.cdf(self.X @ self.β.T)\n",
    "\n",
    "    def ϕ(self):\n",
    "        return norm.pdf(self.X @ self.β.T)\n",
    "\n",
    "    def logL(self):\n",
    "        μ = self.μ()\n",
    "        return np.sum(y * np.log(μ) + (1 - y) * np.log(1 - μ))\n",
    "\n",
    "    def G(self):\n",
    "        μ = self.μ()\n",
    "        ϕ = self.ϕ()\n",
    "        return np.sum((X.T * y * ϕ / μ - X.T * (1 - y) * ϕ / (1 - μ)),\n",
    "                     axis=1)\n",
    "\n",
    "    def H(self):\n",
    "        X = self.X\n",
    "        β = self.β\n",
    "        μ = self.μ()\n",
    "        ϕ = self.ϕ()\n",
    "        a = (ϕ + (X @ β.T) * μ) / μ**2\n",
    "        b = (ϕ - (X @ β.T) * (1 - μ)) / (1 - μ)**2\n",
    "        return -(ϕ * (y * a + (1 - y) * b) * X.T) @ X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise 2\n",
    "\n",
    "![Ex2](img/mle_2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration_k  Log-likelihood  θ                                                           \n",
      "-----------------------------------------------------------------------------------------\n",
      "0            -2.3796884      ['-1.34', '0.775', '-0.157']\n",
      "1            -2.3687526      ['-1.53', '0.775', '-0.0981']\n",
      "2            -2.3687294      ['-1.55', '0.778', '-0.0971']\n",
      "3            -2.3687294      ['-1.55', '0.778', '-0.0971']\n",
      "Number of iterations: 4\n",
      "β_hat = [-1.54625858  0.77778952 -0.09709757]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-1.54625858,  0.77778952, -0.09709757])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[1, 2, 4],\n",
    "              [1, 1, 1],\n",
    "              [1, 4, 3],\n",
    "              [1, 5, 6],\n",
    "              [1, 3, 5]])\n",
    "\n",
    "y = np.array([1, 0, 1, 1, 0])\n",
    "\n",
    "# Take a guess at initial βs\n",
    "β = np.array([0.1, 0.1, 0.1])\n",
    "\n",
    "# Create instance of Probit regression class\n",
    "prob = ProbitRegression(y, X, β)\n",
    "\n",
    "# Run Newton-Raphson algorithm\n",
    "newton_raphson(prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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