Data_and_empirics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lesson: Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.465168\n",
       "1   -1.328659\n",
       "2   -2.199089\n",
       "3   -1.014124\n",
       "Name: daily returns, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Two important data types defined by pandas are Series and DataFrame\n",
    "\n",
    "s = pd.Series(np.random.randn(4), name='daily returns')\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.000000\n",
       "mean    -1.019176\n",
       "std      1.109246\n",
       "min     -2.199089\n",
       "25%     -1.546266\n",
       "50%     -1.171391\n",
       "75%     -0.644301\n",
       "max      0.465168\n",
       "Name: daily returns, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pandas Series are built on top of NumPy arrays, and support many similar operations\n",
    "\n",
    "#But there are more than NP methods\n",
    "\n",
    "s.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMZN    0.465168\n",
       "AAPL   -1.328659\n",
       "MSFT   -2.199089\n",
       "GOOG   -1.014124\n",
       "Name: daily returns, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index = ['AMZN', 'AAPL', 'MSFT', 'GOOG']\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46516817998713056"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series are like fast and efficient Python dictionaries. \n",
    "s['AMZN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'AAPL' in s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing test_pwt.csv\n"
     ]
    }
   ],
   "source": [
    "%%file test_pwt.csv\n",
    "\"country\",\"country isocode\",\"year\",\"POP\",\"XRAT\",\"tcgdp\",\"cc\",\"cg\"\n",
    "\"Argentina\",\"ARG\",\"2000\",\"37335.653\",\"0.9995\",\"295072.21869\",\"75.716805379\",\"5.5788042896\"\n",
    "\"Australia\",\"AUS\",\"2000\",\"19053.186\",\"1.72483\",\"541804.6521\",\"67.759025993\",\"6.7200975332\"\n",
    "\"India\",\"IND\",\"2000\",\"1006300.297\",\"44.9416\",\"1728144.3748\",\"64.575551328\",\"14.072205773\"\n",
    "\"Israel\",\"ISR\",\"2000\",\"6114.57\",\"4.07733\",\"129253.89423\",\"64.436450847\",\"10.266688415\"\n",
    "\"Malawi\",\"MWI\",\"2000\",\"11801.505\",\"59.543808333\",\"5026.2217836\",\"74.707624181\",\"11.658954494\"\n",
    "\"South Africa\",\"ZAF\",\"2000\",\"45064.098\",\"6.93983\",\"227242.36949\",\"72.718710427\",\"5.7265463933\"\n",
    "\"United States\",\"USA\",\"2000\",\"282171.957\",\"1\",\"9898700\",\"72.347054303\",\"6.0324539789\"\n",
    "\"Uruguay\",\"URY\",\"2000\",\"3219.793\",\"12.099591667\",\"25255.961693\",\"78.978740282\",\"5.108067988\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>country isocode</th>\n",
       "      <th>year</th>\n",
       "      <th>POP</th>\n",
       "      <th>XRAT</th>\n",
       "      <th>tcgdp</th>\n",
       "      <th>cc</th>\n",
       "      <th>cg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>2000</td>\n",
       "      <td>37335.653</td>\n",
       "      <td>0.999500</td>\n",
       "      <td>2.950722e+05</td>\n",
       "      <td>75.716805</td>\n",
       "      <td>5.578804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>2000</td>\n",
       "      <td>19053.186</td>\n",
       "      <td>1.724830</td>\n",
       "      <td>5.418047e+05</td>\n",
       "      <td>67.759026</td>\n",
       "      <td>6.720098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2000</td>\n",
       "      <td>1006300.297</td>\n",
       "      <td>44.941600</td>\n",
       "      <td>1.728144e+06</td>\n",
       "      <td>64.575551</td>\n",
       "      <td>14.072206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Israel</td>\n",
       "      <td>ISR</td>\n",
       "      <td>2000</td>\n",
       "      <td>6114.570</td>\n",
       "      <td>4.077330</td>\n",
       "      <td>1.292539e+05</td>\n",
       "      <td>64.436451</td>\n",
       "      <td>10.266688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>MWI</td>\n",
       "      <td>2000</td>\n",
       "      <td>11801.505</td>\n",
       "      <td>59.543808</td>\n",
       "      <td>5.026222e+03</td>\n",
       "      <td>74.707624</td>\n",
       "      <td>11.658954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>2000</td>\n",
       "      <td>45064.098</td>\n",
       "      <td>6.939830</td>\n",
       "      <td>2.272424e+05</td>\n",
       "      <td>72.718710</td>\n",
       "      <td>5.726546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>2000</td>\n",
       "      <td>282171.957</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.898700e+06</td>\n",
       "      <td>72.347054</td>\n",
       "      <td>6.032454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>URY</td>\n",
       "      <td>2000</td>\n",
       "      <td>3219.793</td>\n",
       "      <td>12.099592</td>\n",
       "      <td>2.525596e+04</td>\n",
       "      <td>78.978740</td>\n",
       "      <td>5.108068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country country isocode  year          POP       XRAT         tcgdp  \\\n",
       "0      Argentina             ARG  2000    37335.653   0.999500  2.950722e+05   \n",
       "1      Australia             AUS  2000    19053.186   1.724830  5.418047e+05   \n",
       "2          India             IND  2000  1006300.297  44.941600  1.728144e+06   \n",
       "3         Israel             ISR  2000     6114.570   4.077330  1.292539e+05   \n",
       "4         Malawi             MWI  2000    11801.505  59.543808  5.026222e+03   \n",
       "5   South Africa             ZAF  2000    45064.098   6.939830  2.272424e+05   \n",
       "6  United States             USA  2000   282171.957   1.000000  9.898700e+06   \n",
       "7        Uruguay             URY  2000     3219.793  12.099592  2.525596e+04   \n",
       "\n",
       "          cc         cg  \n",
       "0  75.716805   5.578804  \n",
       "1  67.759026   6.720098  \n",
       "2  64.575551  14.072206  \n",
       "3  64.436451  10.266688  \n",
       "4  74.707624  11.658954  \n",
       "5  72.718710   5.726546  \n",
       "6  72.347054   6.032454  \n",
       "7  78.978740   5.108068  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/pandas/data/test_pwt.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>country isocode</th>\n",
       "      <th>year</th>\n",
       "      <th>POP</th>\n",
       "      <th>XRAT</th>\n",
       "      <th>tcgdp</th>\n",
       "      <th>cc</th>\n",
       "      <th>cg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2000</td>\n",
       "      <td>1006300.297</td>\n",
       "      <td>44.941600</td>\n",
       "      <td>1.728144e+06</td>\n",
       "      <td>64.575551</td>\n",
       "      <td>14.072206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Israel</td>\n",
       "      <td>ISR</td>\n",
       "      <td>2000</td>\n",
       "      <td>6114.570</td>\n",
       "      <td>4.077330</td>\n",
       "      <td>1.292539e+05</td>\n",
       "      <td>64.436451</td>\n",
       "      <td>10.266688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>MWI</td>\n",
       "      <td>2000</td>\n",
       "      <td>11801.505</td>\n",
       "      <td>59.543808</td>\n",
       "      <td>5.026222e+03</td>\n",
       "      <td>74.707624</td>\n",
       "      <td>11.658954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country country isocode  year          POP       XRAT         tcgdp  \\\n",
       "2   India             IND  2000  1006300.297  44.941600  1.728144e+06   \n",
       "3  Israel             ISR  2000     6114.570   4.077330  1.292539e+05   \n",
       "4  Malawi             MWI  2000    11801.505  59.543808  5.026222e+03   \n",
       "\n",
       "          cc         cg  \n",
       "2  64.575551  14.072206  \n",
       "3  64.436451  10.266688  \n",
       "4  74.707624  11.658954  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>tcgdp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.950722e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>5.418047e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>1.728144e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Israel</td>\n",
       "      <td>1.292539e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>5.026222e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>2.272424e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>United States</td>\n",
       "      <td>9.898700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2.525596e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country         tcgdp\n",
       "0      Argentina  2.950722e+05\n",
       "1      Australia  5.418047e+05\n",
       "2          India  1.728144e+06\n",
       "3         Israel  1.292539e+05\n",
       "4         Malawi  5.026222e+03\n",
       "5   South Africa  2.272424e+05\n",
       "6  United States  9.898700e+06\n",
       "7        Uruguay  2.525596e+04"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['country', 'tcgdp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>country isocode</th>\n",
       "      <th>year</th>\n",
       "      <th>POP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2000</td>\n",
       "      <td>1006300.297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Israel</td>\n",
       "      <td>ISR</td>\n",
       "      <td>2000</td>\n",
       "      <td>6114.570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>MWI</td>\n",
       "      <td>2000</td>\n",
       "      <td>11801.505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country country isocode  year          POP\n",
       "2   India             IND  2000  1006300.297\n",
       "3  Israel             ISR  2000     6114.570\n",
       "4  Malawi             MWI  2000    11801.505"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:5, 0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>country</th>\n",
       "      <th>tcgdp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>1.728144e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Israel</td>\n",
       "      <td>1.292539e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>5.026222e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country         tcgdp\n",
       "2   India  1.728144e+06\n",
       "3  Israel  1.292539e+05\n",
       "4  Malawi  5.026222e+03"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.index[2:5], ['country', 'tcgdp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>POP</th>\n",
       "      <th>tcgdp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>37335.653</td>\n",
       "      <td>2.950722e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>19053.186</td>\n",
       "      <td>5.418047e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>1006300.297</td>\n",
       "      <td>1.728144e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Israel</td>\n",
       "      <td>6114.570</td>\n",
       "      <td>1.292539e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>11801.505</td>\n",
       "      <td>5.026222e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>45064.098</td>\n",
       "      <td>2.272424e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>United States</td>\n",
       "      <td>282171.957</td>\n",
       "      <td>9.898700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3219.793</td>\n",
       "      <td>2.525596e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country          POP         tcgdp\n",
       "0      Argentina    37335.653  2.950722e+05\n",
       "1      Australia    19053.186  5.418047e+05\n",
       "2          India  1006300.297  1.728144e+06\n",
       "3         Israel     6114.570  1.292539e+05\n",
       "4         Malawi    11801.505  5.026222e+03\n",
       "5   South Africa    45064.098  2.272424e+05\n",
       "6  United States   282171.957  9.898700e+06\n",
       "7        Uruguay     3219.793  2.525596e+04"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['country', 'POP', 'tcgdp']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POP</th>\n",
       "      <th>tcgdp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>37335.653</td>\n",
       "      <td>2.950722e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>19053.186</td>\n",
       "      <td>5.418047e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1006300.297</td>\n",
       "      <td>1.728144e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>6114.570</td>\n",
       "      <td>1.292539e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>11801.505</td>\n",
       "      <td>5.026222e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>45064.098</td>\n",
       "      <td>2.272424e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>282171.957</td>\n",
       "      <td>9.898700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3219.793</td>\n",
       "      <td>2.525596e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       POP         tcgdp\n",
       "country                                 \n",
       "Argentina        37335.653  2.950722e+05\n",
       "Australia        19053.186  5.418047e+05\n",
       "India          1006300.297  1.728144e+06\n",
       "Israel            6114.570  1.292539e+05\n",
       "Malawi           11801.505  5.026222e+03\n",
       "South Africa     45064.098  2.272424e+05\n",
       "United States   282171.957  9.898700e+06\n",
       "Uruguay           3219.793  2.525596e+04"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Changing the index\n",
    "\n",
    "df = df.set_index('country')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>population</th>\n",
       "      <th>total GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>37335.653</td>\n",
       "      <td>2.950722e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>19053.186</td>\n",
       "      <td>5.418047e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1006300.297</td>\n",
       "      <td>1.728144e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>6114.570</td>\n",
       "      <td>1.292539e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>11801.505</td>\n",
       "      <td>5.026222e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>45064.098</td>\n",
       "      <td>2.272424e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>282171.957</td>\n",
       "      <td>9.898700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3219.793</td>\n",
       "      <td>2.525596e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                population     total GDP\n",
       "country                                 \n",
       "Argentina        37335.653  2.950722e+05\n",
       "Australia        19053.186  5.418047e+05\n",
       "India          1006300.297  1.728144e+06\n",
       "Israel            6114.570  1.292539e+05\n",
       "Malawi           11801.505  5.026222e+03\n",
       "South Africa     45064.098  2.272424e+05\n",
       "United States   282171.957  9.898700e+06\n",
       "Uruguay           3219.793  2.525596e+04"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#renaming columns\n",
    "\n",
    "df.columns = 'population', 'total GDP'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>total GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>3.733565e+07</td>\n",
       "      <td>2.950722e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.905319e+07</td>\n",
       "      <td>5.418047e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.006300e+09</td>\n",
       "      <td>1.728144e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>6.114570e+06</td>\n",
       "      <td>1.292539e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>1.180150e+07</td>\n",
       "      <td>5.026222e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>4.506410e+07</td>\n",
       "      <td>2.272424e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2.821720e+08</td>\n",
       "      <td>9.898700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.219793e+06</td>\n",
       "      <td>2.525596e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 population     total GDP\n",
       "country                                  \n",
       "Argentina      3.733565e+07  2.950722e+05\n",
       "Australia      1.905319e+07  5.418047e+05\n",
       "India          1.006300e+09  1.728144e+06\n",
       "Israel         6.114570e+06  1.292539e+05\n",
       "Malawi         1.180150e+07  5.026222e+03\n",
       "South Africa   4.506410e+07  2.272424e+05\n",
       "United States  2.821720e+08  9.898700e+06\n",
       "Uruguay        3.219793e+06  2.525596e+04"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#OPerations over data\n",
    "\n",
    "df['population'] = df['population'] * 1e3\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>population</th>\n",
       "      <th>total GDP</th>\n",
       "      <th>GDP percap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>3.733565e+07</td>\n",
       "      <td>2.950722e+05</td>\n",
       "      <td>7903.229085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.905319e+07</td>\n",
       "      <td>5.418047e+05</td>\n",
       "      <td>28436.433261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.006300e+09</td>\n",
       "      <td>1.728144e+06</td>\n",
       "      <td>1717.324719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>6.114570e+06</td>\n",
       "      <td>1.292539e+05</td>\n",
       "      <td>21138.672749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>1.180150e+07</td>\n",
       "      <td>5.026222e+03</td>\n",
       "      <td>425.896679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>4.506410e+07</td>\n",
       "      <td>2.272424e+05</td>\n",
       "      <td>5042.647686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2.821720e+08</td>\n",
       "      <td>9.898700e+06</td>\n",
       "      <td>35080.381854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.219793e+06</td>\n",
       "      <td>2.525596e+04</td>\n",
       "      <td>7843.970620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 population     total GDP    GDP percap\n",
       "country                                                \n",
       "Argentina      3.733565e+07  2.950722e+05   7903.229085\n",
       "Australia      1.905319e+07  5.418047e+05  28436.433261\n",
       "India          1.006300e+09  1.728144e+06   1717.324719\n",
       "Israel         6.114570e+06  1.292539e+05  21138.672749\n",
       "Malawi         1.180150e+07  5.026222e+03    425.896679\n",
       "South Africa   4.506410e+07  2.272424e+05   5042.647686\n",
       "United States  2.821720e+08  9.898700e+06  35080.381854\n",
       "Uruguay        3.219793e+06  2.525596e+04   7843.970620"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GDP percap'] = df['total GDP'] * 1e6 / df['population']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Easy plotting methods\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df['GDP percap'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.sort_values(by='GDP percap', ascending=False)\n",
    "df['GDP percap'].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important database for economists is [FRED](https://research.stlouisfed.org/fred2/) — a vast collection of time series data maintained by the St. Louis Fed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's take a look at requests library\n",
    "\n",
    "import requests\n",
    "\n",
    "r = requests.get('http://research.stlouisfed.org/fred2/series/UNRATE/downloaddata/UNRATE.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DATE,VALUE\\r'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'http://research.stlouisfed.org/fred2/series/UNRATE/downloaddata/UNRATE.csv'\n",
    "source = requests.get(url).content.decode().split(\"\\n\")\n",
    "source[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1948-02-01,3.8\\r'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(url, index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>VALUE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948-01-01</th>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-02-01</th>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-03-01</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-04-01</th>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-05-01</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VALUE\n",
       "DATE             \n",
       "1948-01-01    3.4\n",
       "1948-02-01    3.8\n",
       "1948-03-01    4.0\n",
       "1948-04-01    3.9\n",
       "1948-05-01    3.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>851.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VALUE\n",
       "count  851.0\n",
       "mean     5.8\n",
       "std      1.6\n",
       "min      2.5\n",
       "25%      4.6\n",
       "50%      5.6\n",
       "75%      6.8\n",
       "max     10.8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('precision', 1)\n",
    "data.describe()  # Your output might differ slightly"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Unemployment rate plot\n",
    "\n",
    "data['2006':'2012'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the data collected by the World Bank [here](https://data.worldbank.org/indicator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# == Get data and read into file gd.xls == #\n",
    "wb_data_query = \"http://api.worldbank.org/v2/en/indicator/gc.dod.totl.gd.zs?downloadformat=excel\"\n",
    "r = requests.get(wb_data_query)\n",
    "with open('gd.xls', 'wb') as output:\n",
    "    output.write(r.content)\n",
    "\n",
    "# == Parse data into a DataFrame == #\n",
    "govt_debt = pd.read_excel('gd.xls', sheet_name='Data', skiprows=3, index_col=1)\n",
    "\n",
    "# == Take desired values and plot == #\n",
    "govt_debt = govt_debt.transpose()\n",
    "govt_debt = govt_debt[['AUS', 'USA']]\n",
    "govt_debt = govt_debt[38:]\n",
    "govt_debt.plot(lw=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 1\n",
    "\n",
    "Write a program to calculate the percentage price change over 2013 for the following shares\n",
    "\n",
    "Plot teh result as bar graph like:\n",
    "![Graph](img/pnds_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20e1e5d36d8>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EX1\n",
    "\n",
    "#The ticker list\n",
    "ticker_list = {'INTC': 'Intel',\n",
    "               'MSFT': 'Microsoft',\n",
    "               'IBM': 'IBM',\n",
    "               'BHP': 'BHP',\n",
    "               'TM': 'Toyota',\n",
    "               'AAPL': 'Apple',\n",
    "               'AMZN': 'Amazon',\n",
    "               'BA': 'Boeing',\n",
    "               'QCOM': 'Qualcomm',\n",
    "               'KO': 'Coca-Cola',\n",
    "               'GOOG': 'Google',\n",
    "               'SNE': 'Sony',\n",
    "               'PTR': 'PetroChina'}\n",
    "\n",
    "#Loading the data\n",
    "ticker = pd.read_csv('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/pandas/data/ticker_data.csv')\n",
    "ticker.set_index('Date', inplace=True)\n",
    "\n",
    "#Renaming the columns\n",
    "ticker = ticker.rename(columns = ticker_list)\n",
    "\n",
    "#Calculating the delta\n",
    "delta = pd.Series((ticker.iloc[-1] - ticker.iloc[0]) / ticker.iloc[0] )\n",
    "\n",
    "#Plotting\n",
    "delta = delta.sort_values(ascending=True)\n",
    "delta.plot(kind='bar', color = 'blue')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lesson: Pandas for Panel Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.head()  # Show first 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is currently in long format, which is difficult to analyse when there are several dimensions to the data\n",
    "\n",
    "We will use **pivot**_table to create a wide format panel, with a **MultiIndex** to handle higher dimensional data\n",
    "\n",
    "**pivot_table** arguments should specify the data (values), the index, and the columns we want in our resulting dataframe\n",
    "\n",
    "By passing a list in columns, we can create a **MultiIndex** in our column axis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>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": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.index = pd.to_datetime(realwage.index)\n",
    "type(realwage.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns contain multiple levels of indexing, known as a **MultiIndex**, with levels being ordered hierarchically (Country > Series > Pay period)\n",
    "\n",
    "A **MultiIndex** is the simplest and most flexible way to manage panel data in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.multi.MultiIndex"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(realwage.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList(['Country', 'Series', 'Pay period'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.columns.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage['United States'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stacking and unstacking levels of the **MultiIndex** will be used throughout this lecture to reshape our dataframe into a format we need\n",
    "\n",
    "**.stack()** rotates the lowest level of the column MultiIndex to the row index (**.unstack()** works in the opposite direction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<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                                      ...                          \\\n",
       "Series                                       ...                           \n",
       "Time       Pay period                        ...                           \n",
       "2006-01-01 Annual                            ...                           \n",
       "           Hourly                            ...                           \n",
       "2007-01-01 Annual                            ...                           \n",
       "           Hourly                            ...                           \n",
       "2008-01-01 Annual                            ...                           \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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.stack().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realwage.stack(level='Country').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
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       "\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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting one year and stacking the two lower levels of the MultiIndex creates a cross-section of our panel data\n",
    "\n",
    "realwage['2015'].stack(level=(1, 2)).transpose().head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the xs method to select values at lower levels in the multiindex, while keeping the higher levels (countries in this case)\n",
    "\n",
    "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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 (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)  \\\n",
       "0    Afghanistan  Afghanistan  Afganistan/Afqanestan   \n",
       "1          Egypt      Ägypten                   Misr   \n",
       "2  Åland Islands  Ålandinseln                  Åland   \n",
       "3        Albania     Albanien              Shqipëria   \n",
       "4        Algeria     Algerien     Al-Jaza’ir/Algérie   \n",
       "\n",
       "                         ...                         Deathrate  \\\n",
       "0                        ...                             13.70   \n",
       "1                        ...                              4.70   \n",
       "2                        ...                              0.00   \n",
       "3                        ...                              6.70   \n",
       "4                        ...                              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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, to merging dataframes\n",
    "\n",
    "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Åland Islands</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting add renaming columns\n",
    "\n",
    "worlddata = worlddata[['Country (en)', 'Continent']]\n",
    "worlddata = worlddata.rename(columns={'Country (en)': 'Country'})\n",
    "worlddata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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  \\\n",
       "Country                                           ...                   \n",
       "Australia       12.06       12.46       12.24     ...           12.67   \n",
       "Belgium          9.70        9.82        9.87     ...           10.01   \n",
       "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",
       "\n",
       "Time       2015-01-01  2016-01-01  \n",
       "Country                            \n",
       "Australia       12.83       12.98  \n",
       "Belgium          9.95        9.76  \n",
       "Brazil           1.21        1.24  \n",
       "Canada           8.35        8.48  \n",
       "Chile            1.81        1.91  \n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We need to transpose so that rows correspond to sountry names in both dataframes\n",
    "\n",
    "realwage_f.transpose().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use either left, right, inner, or outer join to merge our datasets:\n",
    "\n",
    "* left join includes only countries from the left dataset\n",
    "* right join includes only countries from the right dataset\n",
    "* outer join includes countries that are in either the left and right datasets\n",
    "* inner join includes only countries common to both the left and right datasets\n",
    "\n",
    "By default, merge will use an inner join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need to specify where the country name is located in each dataframe, which will be the key that is used to merge the dataframes ‘on’\n",
    "\n",
    "Our ‘left’ dataframe (realwage_f.transpose()) contains countries in the index, so we set left_index=True\n",
    "\n",
    "Our ‘right’ dataframe (worlddata) contains countries in the ‘Country’ column, so we set right_on='Country'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 17,
     "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": 18,
   "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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       "    }\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>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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[merged['Continent'].isnull()] #Checking for NaNs in the COntent column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have three missing values!\n",
    "\n",
    "One option to deal with NaN values is to create a dictionary containing these countries and their respective continents\n",
    "\n",
    ".map() will match countries in merged['Country'] with their continent from the dictionary\n",
    "\n",
    "Notice how countries not in our dictionary are mapped with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "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": "markdown",
   "metadata": {},
   "source": [
    "We don’t want to overwrite the entire series with this mapping\n",
    "\n",
    ".fillna() only fills in NaN values in merged['Continent'] with the mapping, while leaving other values in the column unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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": 20,
     "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": "markdown",
   "metadata": {},
   "source": [
    "We will also combine the Americas into a single continent - this will make our visualization nicer later on\n",
    "\n",
    "To do this, we will use .replace() and loop through a list of the continent values we want to replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all the data we want in a single DataFrame, we will reshape it back into panel form with a MultiIndex\n",
    "\n",
    "We should also ensure to sort the index using .sort_index() so that we can efficiently filter our dataframe later on\n",
    "\n",
    "By default, levels will be sorted top-down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>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     ...      \\\n",
       "Continent Country                                            ...       \n",
       "America   Brazil            0.87        0.92        0.96     ...       \n",
       "          Canada            6.89        6.96        7.24     ...       \n",
       "          Chile             1.42        1.45        1.44     ...       \n",
       "          Colombia          1.01        1.02        1.01     ...       \n",
       "          Costa Rica         nan         nan         nan     ...       \n",
       "\n",
       "                      2014-01-01  2015-01-01  2016-01-01  \n",
       "Continent Country                                         \n",
       "America   Brazil            1.21        1.21        1.24  \n",
       "          Canada            8.22        8.35        8.48  \n",
       "          Chile             1.76        1.81        1.91  \n",
       "          Colombia          1.13        1.13        1.12  \n",
       "          Costa Rica        2.41        2.56        2.63  \n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merged.set_index(['Continent', 'Country']).sort_index()\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# While merging, we lost our DatetimeIndex, as we merged columns that were not in datetime format\n",
    "\n",
    "merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now that we have set the merged columns as the index, we can recreate a DatetimeIndex using .to_datetime()\n",
    "\n",
    "merged.columns = pd.to_datetime(merged.columns)\n",
    "merged.columns = merged.columns.rename('Time')\n",
    "merged.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DatetimeIndex tends to work more smoothly in the row axis, so we will go ahead and transpose merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merged.transpose()\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping and summarizing data\n",
    "\n",
    "merged.mean().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using this series, we can plot the average real minimum wage over the past decade for each country in our data set\n",
    "\n",
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "Passing in axis=1 to .mean() will aggregate over columns (giving the average minimum wage for all countries over time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.mean(axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We can plot this time series as a line graph\n",
    "\n",
    "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": 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>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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also specify a level of the MultiIndex (in the column axis) to aggregate over\n",
    "\n",
    "merged.mean(level='Continent', axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We can plot the average minimum wages in each continent as a time series\n",
    "\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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We will drop Australia as a continent for plotting purposes\n",
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    ".describe() is useful for quickly retrieving a number of common summary statistics"
   ]
  },
  {
   "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 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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.stack().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using groupby generally follows a ‘split-apply-combine’ process:\n",
    "\n",
    "* split: data is grouped based on one or more keys\n",
    "* apply: a function is called on each group independently\n",
    "* combine: the results of the function calls are combined into a new data structure\n",
    "\n",
    "The groupby method achieves the first step of this process, creating a new DataFrameGroupBy object with data split into groups\n",
    "\n",
    "Let’s split merged by continent again, this time using the groupby function, and name the resulting object grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x000001721AAE7438>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = merged.groupby(level='Continent', axis=1)\n",
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling an aggregation method on the object applies the function to each group, the results of which are combined in a new data structure\n",
    "\n",
    "For example, we can return the number of countries in our dataset for each continent using .size()\n",
    "\n",
    "In this case, our new data structure is a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent\n",
       "America     7\n",
       "Asia        4\n",
       "Europe     19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling .get_group() to return just the countries in a single group, we can create a kernel density estimate of the distribution of real minimum wages in 2016 for each continent\n",
    "\n",
    "grouped.groups.keys() will return the keys from the groupby object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
    "![Ex1](img/pnds2_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "emp = pd.read_csv('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/pandas_panel/employ.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>GEO</th>\n",
       "      <th>AGE</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>INDIC_EM</th>\n",
       "      <th>Value</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>From 15 to 24 years</td>\n",
       "      <td>Thousand persons</td>\n",
       "      <td>Total</td>\n",
       "      <td>Active population</td>\n",
       "      <td>26839.0</td>\n",
       "      <td>2007-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>From 15 to 24 years</td>\n",
       "      <td>Thousand persons</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total employment (resident population concept ...</td>\n",
       "      <td>22669.0</td>\n",
       "      <td>2007-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>From 15 to 24 years</td>\n",
       "      <td>Thousand persons</td>\n",
       "      <td>Males</td>\n",
       "      <td>Active population</td>\n",
       "      <td>14665.0</td>\n",
       "      <td>2007-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>From 15 to 24 years</td>\n",
       "      <td>Thousand persons</td>\n",
       "      <td>Males</td>\n",
       "      <td>Total employment (resident population concept ...</td>\n",
       "      <td>12430.0</td>\n",
       "      <td>2007-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>European Union (28 countries)</td>\n",
       "      <td>From 15 to 24 years</td>\n",
       "      <td>Thousand persons</td>\n",
       "      <td>Females</td>\n",
       "      <td>Active population</td>\n",
       "      <td>12173.0</td>\n",
       "      <td>2007-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                            GEO                  AGE  \\\n",
       "0           0  European Union (28 countries)  From 15 to 24 years   \n",
       "1           1  European Union (28 countries)  From 15 to 24 years   \n",
       "2           2  European Union (28 countries)  From 15 to 24 years   \n",
       "3           3  European Union (28 countries)  From 15 to 24 years   \n",
       "4           4  European Union (28 countries)  From 15 to 24 years   \n",
       "\n",
       "               UNIT      SEX  \\\n",
       "0  Thousand persons    Total   \n",
       "1  Thousand persons    Total   \n",
       "2  Thousand persons    Males   \n",
       "3  Thousand persons    Males   \n",
       "4  Thousand persons  Females   \n",
       "\n",
       "                                            INDIC_EM    Value        DATE  \n",
       "0                                  Active population  26839.0  2007-01-01  \n",
       "1  Total employment (resident population concept ...  22669.0  2007-01-01  \n",
       "2                                  Active population  14665.0  2007-01-01  \n",
       "3  Total employment (resident population concept ...  12430.0  2007-01-01  \n",
       "4                                  Active population  12173.0  2007-01-01  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.head()  # Show first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>UNIT</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Percentage of total population</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Thousand persons</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <th colspan=\"10\" halign=\"left\">From 15 to 24 years</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">From 55 to 64 years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Females</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDIC_EM</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Active population</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" 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>Croatia</th>\n",
       "      <th>Cyprus</th>\n",
       "      <th>Czech Republic</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Estonia</th>\n",
       "      <th>Euro area (17 countries)</th>\n",
       "      <th>Euro area (18 countries)</th>\n",
       "      <th>...</th>\n",
       "      <th>Poland</th>\n",
       "      <th>Portugal</th>\n",
       "      <th>Romania</th>\n",
       "      <th>Slovakia</th>\n",
       "      <th>Slovenia</th>\n",
       "      <th>Spain</th>\n",
       "      <th>Sweden</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>56.0</td>\n",
       "      <td>31.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>39.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>69.1</td>\n",
       "      <td>32.1</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1273.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2135.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1282.0</td>\n",
       "      <td>4131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>56.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>26.1</td>\n",
       "      <td>29.9</td>\n",
       "      <td>40.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>71.5</td>\n",
       "      <td>37.1</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1432.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2229.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>4204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>56.2</td>\n",
       "      <td>29.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>26.1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>34.1</td>\n",
       "      <td>40.4</td>\n",
       "      <td>40.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2185.0</td>\n",
       "      <td>846.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1449.0</td>\n",
       "      <td>4193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>54.0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>26.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>40.2</td>\n",
       "      <td>25.3</td>\n",
       "      <td>67.4</td>\n",
       "      <td>34.3</td>\n",
       "      <td>39.2</td>\n",
       "      <td>39.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2190.0</td>\n",
       "      <td>841.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>4186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>54.8</td>\n",
       "      <td>29.8</td>\n",
       "      <td>24.8</td>\n",
       "      <td>26.9</td>\n",
       "      <td>36.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>67.1</td>\n",
       "      <td>36.5</td>\n",
       "      <td>39.2</td>\n",
       "      <td>39.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2274.0</td>\n",
       "      <td>849.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>4164.0</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 Croatia Cyprus   \n",
       "DATE                                                                        \n",
       "2007-01-01                           56.0    31.6     26.0    31.5   39.7   \n",
       "2008-01-01                           56.2    30.8     26.1    29.9   40.5   \n",
       "2009-01-01                           56.2    29.9     24.8    30.0   38.8   \n",
       "2010-01-01                           54.0    29.8     26.6    30.7   40.2   \n",
       "2011-01-01                           54.8    29.8     24.8    26.9   36.6   \n",
       "\n",
       "UNIT                                                                \\\n",
       "AGE                                                                  \n",
       "SEX                                                                  \n",
       "INDIC_EM                                                             \n",
       "GEO        Czech Republic Denmark Estonia Euro area (17 countries)   \n",
       "DATE                                                                 \n",
       "2007-01-01           26.9    69.1    32.1                     40.9   \n",
       "2008-01-01           26.1    71.5    37.1                     40.9   \n",
       "2009-01-01           26.1    70.0    34.1                     40.4   \n",
       "2010-01-01           25.3    67.4    34.3                     39.2   \n",
       "2011-01-01           24.1    67.1    36.5                     39.2   \n",
       "\n",
       "UNIT                                     ...        \\\n",
       "AGE                                      ...         \n",
       "SEX                                      ...         \n",
       "INDIC_EM                                 ...         \n",
       "GEO        Euro area (18 countries)      ...         \n",
       "DATE                                     ...         \n",
       "2007-01-01                     40.8      ...         \n",
       "2008-01-01                     40.9      ...         \n",
       "2009-01-01                     40.4      ...         \n",
       "2010-01-01                     39.2      ...         \n",
       "2011-01-01                     39.1      ...         \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                                                      Poland Portugal   \n",
       "DATE                                                                       \n",
       "2007-01-01                                             1273.0      627.0   \n",
       "2008-01-01                                             1432.0      635.0   \n",
       "2009-01-01                                             1545.0      631.0   \n",
       "2010-01-01                                             1704.0      637.0   \n",
       "2011-01-01                                             1919.0      623.0   \n",
       "\n",
       "UNIT                                                                     \\\n",
       "AGE                                                                       \n",
       "SEX                                                                       \n",
       "INDIC_EM                                                                  \n",
       "GEO        Romania Slovakia Slovenia   Spain Sweden Switzerland  Turkey   \n",
       "DATE                                                                      \n",
       "2007-01-01   963.0    204.0     81.0  2135.0  854.0         NaN  1282.0   \n",
       "2008-01-01  1036.0    235.0     82.0  2229.0  855.0         NaN  1354.0   \n",
       "2009-01-01  1052.0    249.0     89.0  2185.0  846.0         NaN  1449.0   \n",
       "2010-01-01  1098.0    266.0     93.0  2190.0  841.0       640.0  1583.0   \n",
       "2011-01-01  1077.0    284.0     88.0  2274.0  849.0       661.0  1760.0   \n",
       "\n",
       "UNIT                       \n",
       "AGE                        \n",
       "SEX                        \n",
       "INDIC_EM                   \n",
       "GEO        United Kingdom  \n",
       "DATE                       \n",
       "2007-01-01         4131.0  \n",
       "2008-01-01         4204.0  \n",
       "2009-01-01         4193.0  \n",
       "2010-01-01         4186.0  \n",
       "2011-01-01         4164.0  \n",
       "\n",
       "[5 rows x 1440 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp = emp.pivot_table(values='Value',\n",
    "                                index='DATE',\n",
    "                                columns=['UNIT', 'AGE', 'SEX', 'INDIC_EM', 'GEO'])\n",
    "\n",
    "emp.index = pd.to_datetime(emp.index) \n",
    "emp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "#Exploring the variables\n",
    "\n",
    "for name in emp.columns.names:\n",
    "    print(name, emp.columns.get_level_values(name).unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Ex1](img/pnds2_2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#So, first we will need to temove areas from GEO\n",
    "\n",
    "emp.columns = emp.columns.swaplevel(0,-1)\n",
    "emp = emp.sort_index(axis=1)\n",
    "\n",
    "geo_list = emp.columns.get_level_values('GEO').unique().tolist()\n",
    "countries = [x for x in geo_list if not x.startswith('Euro')]\n",
    "emp = emp[countries]\n",
    "emp.columns.get_level_values('GEO').unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>GEO</th>\n",
       "      <th colspan=\"9\" halign=\"left\">Austria</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>...</th>\n",
       "      <th>Turkey</th>\n",
       "      <th colspan=\"9\" 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 colspan=\"3\" halign=\"left\">From 25 to 54 years</th>\n",
       "      <th colspan=\"3\" halign=\"left\">From 55 to 64 years</th>\n",
       "      <th>From 15 to 24 years</th>\n",
       "      <th>...</th>\n",
       "      <th>From 55 to 64 years</th>\n",
       "      <th colspan=\"3\" halign=\"left\">From 15 to 24 years</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "      <th>Total</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "      <th>Total</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "      <th>Total</th>\n",
       "      <th>Females</th>\n",
       "      <th>...</th>\n",
       "      <th>Total</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "      <th>Total</th>\n",
       "      <th>Females</th>\n",
       "      <th>Males</th>\n",
       "      <th>Total</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>56.0</td>\n",
       "      <td>62.9</td>\n",
       "      <td>59.4</td>\n",
       "      <td>80.5</td>\n",
       "      <td>92.5</td>\n",
       "      <td>86.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>47.6</td>\n",
       "      <td>37.2</td>\n",
       "      <td>31.6</td>\n",
       "      <td>...</td>\n",
       "      <td>28.1</td>\n",
       "      <td>58.6</td>\n",
       "      <td>64.2</td>\n",
       "      <td>61.4</td>\n",
       "      <td>77.5</td>\n",
       "      <td>91.6</td>\n",
       "      <td>84.5</td>\n",
       "      <td>49.9</td>\n",
       "      <td>68.9</td>\n",
       "      <td>59.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>56.2</td>\n",
       "      <td>62.9</td>\n",
       "      <td>59.5</td>\n",
       "      <td>80.9</td>\n",
       "      <td>92.1</td>\n",
       "      <td>86.5</td>\n",
       "      <td>30.1</td>\n",
       "      <td>49.9</td>\n",
       "      <td>39.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>...</td>\n",
       "      <td>28.7</td>\n",
       "      <td>58.2</td>\n",
       "      <td>64.3</td>\n",
       "      <td>61.2</td>\n",
       "      <td>78.2</td>\n",
       "      <td>91.6</td>\n",
       "      <td>84.8</td>\n",
       "      <td>50.2</td>\n",
       "      <td>69.8</td>\n",
       "      <td>59.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>56.2</td>\n",
       "      <td>62.9</td>\n",
       "      <td>59.5</td>\n",
       "      <td>82.1</td>\n",
       "      <td>91.9</td>\n",
       "      <td>87.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>50.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>29.9</td>\n",
       "      <td>...</td>\n",
       "      <td>29.9</td>\n",
       "      <td>57.1</td>\n",
       "      <td>61.3</td>\n",
       "      <td>59.2</td>\n",
       "      <td>78.6</td>\n",
       "      <td>91.7</td>\n",
       "      <td>85.0</td>\n",
       "      <td>50.6</td>\n",
       "      <td>70.3</td>\n",
       "      <td>60.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>54.0</td>\n",
       "      <td>62.6</td>\n",
       "      <td>58.3</td>\n",
       "      <td>82.4</td>\n",
       "      <td>91.9</td>\n",
       "      <td>87.1</td>\n",
       "      <td>33.6</td>\n",
       "      <td>51.4</td>\n",
       "      <td>42.2</td>\n",
       "      <td>29.8</td>\n",
       "      <td>...</td>\n",
       "      <td>31.1</td>\n",
       "      <td>55.9</td>\n",
       "      <td>60.9</td>\n",
       "      <td>58.4</td>\n",
       "      <td>78.6</td>\n",
       "      <td>91.4</td>\n",
       "      <td>84.9</td>\n",
       "      <td>51.1</td>\n",
       "      <td>69.2</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>54.8</td>\n",
       "      <td>63.6</td>\n",
       "      <td>59.2</td>\n",
       "      <td>83.2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>50.4</td>\n",
       "      <td>41.4</td>\n",
       "      <td>29.8</td>\n",
       "      <td>...</td>\n",
       "      <td>32.8</td>\n",
       "      <td>55.7</td>\n",
       "      <td>60.7</td>\n",
       "      <td>58.2</td>\n",
       "      <td>79.0</td>\n",
       "      <td>91.7</td>\n",
       "      <td>85.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>68.4</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 306 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "GEO                    Austria                                              \\\n",
       "AGE        From 15 to 24 years             From 25 to 54 years               \n",
       "SEX                    Females Males Total             Females Males Total   \n",
       "DATE                                                                         \n",
       "2007-01-01                56.0  62.9  59.4                80.5  92.5  86.5   \n",
       "2008-01-01                56.2  62.9  59.5                80.9  92.1  86.5   \n",
       "2009-01-01                56.2  62.9  59.5                82.1  91.9  87.0   \n",
       "2010-01-01                54.0  62.6  58.3                82.4  91.9  87.1   \n",
       "2011-01-01                54.8  63.6  59.2                83.2  92.0  87.6   \n",
       "\n",
       "GEO                                                    Belgium  ...   \\\n",
       "AGE        From 55 to 64 years             From 15 to 24 years  ...    \n",
       "SEX                    Females Males Total             Females  ...    \n",
       "DATE                                                            ...    \n",
       "2007-01-01                27.5  47.6  37.2                31.6  ...    \n",
       "2008-01-01                30.1  49.9  39.7                30.8  ...    \n",
       "2009-01-01                31.1  50.5  40.5                29.9  ...    \n",
       "2010-01-01                33.6  51.4  42.2                29.8  ...    \n",
       "2011-01-01                33.0  50.4  41.4                29.8  ...    \n",
       "\n",
       "GEO                     Turkey      United Kingdom              \\\n",
       "AGE        From 55 to 64 years From 15 to 24 years               \n",
       "SEX                      Total             Females Males Total   \n",
       "DATE                                                             \n",
       "2007-01-01                28.1                58.6  64.2  61.4   \n",
       "2008-01-01                28.7                58.2  64.3  61.2   \n",
       "2009-01-01                29.9                57.1  61.3  59.2   \n",
       "2010-01-01                31.1                55.9  60.9  58.4   \n",
       "2011-01-01                32.8                55.7  60.7  58.2   \n",
       "\n",
       "GEO                                                                         \n",
       "AGE        From 25 to 54 years             From 55 to 64 years              \n",
       "SEX                    Females Males Total             Females Males Total  \n",
       "DATE                                                                        \n",
       "2007-01-01                77.5  91.6  84.5                49.9  68.9  59.3  \n",
       "2008-01-01                78.2  91.6  84.8                50.2  69.8  59.8  \n",
       "2009-01-01                78.6  91.7  85.0                50.6  70.3  60.3  \n",
       "2010-01-01                78.6  91.4  84.9                51.1  69.2  60.0  \n",
       "2011-01-01                79.0  91.7  85.3                51.3  68.4  59.7  \n",
       "\n",
       "[5 rows x 306 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_active = emp.xs(('Percentage of total population', 'Active population'),\n",
    "                     level=('UNIT', 'INDIC_EM'),\n",
    "                     axis=1)\n",
    "emp_active.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "emp_active = emp_active.drop('Total', level='SEX', axis=1)\n",
    "\n",
    "box = emp_active['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": 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": [
    "#Using statsmodels library\n",
    "\n",
    "#First, getting the data:\n",
    "import pandas as pd\n",
    "\n",
    "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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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Looking for some obvious relationship between GDP per capita and\n",
    "#protecton against expropriation index\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "df1.plot(x='avexpr', y='logpgp95', kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observing a positive realtionship. Choosing a linear model to describe this relationship seems reasonable\n",
    "\n",
    "Writing model as: \n",
    "logpgp95_i = β_0 + β_1 * avexpr_i + u_i\n",
    "\n",
    "* β_0 - the intercept of the linear trend line on y-axis\n",
    "* β_1 - the slope of the linear trend line, repr. the __marginal effect__ of protection against risk on log GDP per capita\n",
    "* u_i - a random error term\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 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",
    "plt.scatter(X, y, marker='')\n",
    "\n",
    "for i, label in enumerate(labels):\n",
    "    plt.annotate(label, (X.iloc[i], y.iloc[i]))\n",
    "\n",
    "# Fit a linear trend line\n",
    "plt.plot(np.unique(X),\n",
    "         np.poly1d(np.polyfit(X, y, 1))(np.unique(X)),\n",
    "         color='black')\n",
    "\n",
    "plt.xlim([3.3,10.5])\n",
    "plt.ylim([4,10.5])\n",
    "plt.xlabel('Average Expropriation Risk 1985-95')\n",
    "plt.ylabel('Log GDP per capita, PPP, 1995')\n",
    "plt.title('Figure 2: OLS relationship between expropriation risk and income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statsmodels.regression.linear_model.OLS"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['const'] = 1\n",
    "\n",
    "#Using OLS model for estimating the parameters\n",
    "import statsmodels.api as sm\n",
    "\n",
    "reg1 = sm.OLS(endog=df1['logpgp95'], exog=df1[['const', 'avexpr']], missing='drop')\n",
    "type(reg1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statsmodels.regression.linear_model.RegressionResultsWrapper"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We've \"built\" the model, now getting the estimations\n",
    "\n",
    "results = reg1.fit()\n",
    "type(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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:                Tue, 01 Jan 2019   Prob (F-statistic):           4.16e-24\n",
      "Time:                        18:05:22   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": "markdown",
   "metadata": {},
   "source": [
    "So, the results are:\n",
    "* β_est_0 = 4.63\n",
    "* β_est_1 = 0.53\n",
    "* β_est_1 > 0, so instit quality has a positive effect on economic outcomes. \n",
    "* R-squared = 0.611 indicates that 61% of variation in log GDP per capita is explained b protection against expropriation.\n",
    "\n",
    "Now, this model can be used for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.515625"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_expr = np.mean(df1_subset['avexpr'])\n",
    "mean_expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.3771"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_logpdp95 = 4.63 + 0.53 * 7.07\n",
    "predicted_logpdp95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.09156367])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#An easier and more productive way to obtin the prediction:\n",
    "\n",
    "results.predict(exog=[1, mean_expr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparing the predicted values to the real ones\n",
    "\n",
    "# Drop missing observations from whole sample\n",
    "\n",
    "df1_plot = df1.dropna(subset=['logpgp95', 'avexpr'])\n",
    "\n",
    "# Plot predicted values\n",
    "\n",
    "plt.scatter(df1_plot['avexpr'], results.predict(), alpha=0.5, label='predicted')\n",
    "\n",
    "# Plot observed values\n",
    "\n",
    "plt.scatter(df1_plot['avexpr'], df1_plot['logpgp95'], alpha=0.5, label='observed')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('OLS predicted values')\n",
    "plt.xlabel('avexpr')\n",
    "plt.ylabel('logpgp95')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extending to bivariate regression to prevent omitted variable bias. \n",
    "#Now building a multivariate regression model\n",
    "\n",
    "#Loading more data\n",
    "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "#Results to one table\n",
    "\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "\n",
    "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uning two-stage least squares (2SLS) regression to deal with endogeneity issues\n",
    "\n",
    "# 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",
    "plt.scatter(X, y, marker='')\n",
    "\n",
    "for i, label in enumerate(labels):\n",
    "    plt.annotate(label, (X.iloc[i], y.iloc[i]))\n",
    "\n",
    "# Fit a linear trend line\n",
    "plt.plot(np.unique(X),\n",
    "         np.poly1d(np.polyfit(X, y, 1))(np.unique(X)),\n",
    "         color='black')\n",
    "\n",
    "plt.xlim([1.8,8.4])\n",
    "plt.ylim([3.3,10.4])\n",
    "plt.xlabel('Log of Settler Mortality')\n",
    "plt.ylabel('Average Expropriation Risk 1985-95')\n",
    "plt.title('Figure 3: First-stage relationship between settler mortality 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:               logpgp95   R-squared:                       0.477\n",
      "Model:                            OLS   Adj. R-squared:                  0.469\n",
      "Method:                 Least Squares   F-statistic:                     56.60\n",
      "Date:                Tue, 01 Jan 2019   Prob (F-statistic):           2.66e-10\n",
      "Time:                        18:07:37   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": [
    "# 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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from linearmodels.iv import IV2SLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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:                Tue, Jan 01 2019   P-value (F-stat)                0.0000\n",
      "Time:                        18:09:39   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": [
    "#### Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Ex1](img/linreg_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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:                Tue, 01 Jan 2019   Prob (F-statistic):           1.07e-17\n",
      "Time:                        18:21:33   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",
      "regr          -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": [
    "#Ex1\n",
    "\n",
    "# Importing the data\n",
    "df4 = pd.read_stata('https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/ols/maketable4.dta')\n",
    "\n",
    "# Add a constant variable\n",
    "df4['const'] = 1\n",
    "\n",
    "# First stage of the regression\n",
    "regression_1 = sm.OLS(df4['avexpr'],\n",
    "                    df4[['const', 'logem4']],\n",
    "                    missing='drop').fit()\n",
    "\n",
    "# Adding the results to the data\n",
    "df4['regr'] = regression_1.resid\n",
    "\n",
    "# Second stage of the regression\n",
    "regression_2 = sm.OLS(df4['logpgp95'],\n",
    "                    df4[['const', 'avexpr','regr']],\n",
    "                    missing='drop').fit()\n",
    "\n",
    "print(regression_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the output shows, the p-value is less than 0.05, so we reject the null-hypothesis and conclude that __avexpr__ is endogenous."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Ex2](img/linreg_2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-b57c2b7fa9b6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Importing the data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_stata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'https://github.com/QuantEcon/QuantEcon.lectures.code/raw/master/ols/maketable1.dta'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'logpgp95'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'avexpr'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# Add a constant variable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "# Importing the 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 variable\n",
    "df1['const'] = 1\n",
    "\n",
    "# Defining matrices\n",
    "y = np.asarray(df1['logpgp95'])\n",
    "X = np.asarray(df1[['const', 'avexpr']])\n",
    "\n",
    "y = np.array(df1['logpgp95'])\n",
    "x = np.array(df1[['const', 'avexpr']])\n",
    "\n",
    "# Getting the minimizarion done\n",
    "β = np.linalg.solve(x.T @ x, x.T @ y)\n",
    "\n",
    "# The results\n",
    "for i,el in enumerate(β):\n",
    "    print('β_' + str(i) + ' = ' + str(el))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lesson: Maximum Likelihood Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Maximum Likelihood estimates for counting billionaires in countries\n",
    "\n",
    "#Choosinhg Poisson distribution, as the number predicted is integer\n",
    "\n",
    "from numpy import exp\n",
    "from scipy.special import factorial\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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  \\\n",
       "0  United States    2.0  1990.0  21990.0     NaN   ...     39.799999   \n",
       "1  United States    2.0  1991.0  21991.0     NaN   ...     39.799999   \n",
       "2  United States    2.0  1992.0  21992.0     NaN   ...     39.799999   \n",
       "3  United States    2.0  1993.0  21993.0     NaN   ...     39.799999   \n",
       "4  United States    2.0  1994.0  21994.0     NaN   ...     39.799999   \n",
       "\n",
       "      rintr  noyrs  roflaw  nrrents  \n",
       "0  4.988405   20.0    1.61      NaN  \n",
       "1  4.988405   20.0    1.61      NaN  \n",
       "2  4.988405   20.0    1.61      NaN  \n",
       "3  4.988405   20.0    1.61      NaN  \n",
       "4  4.988405   20.0    1.61      NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data\n",
    "\n",
    "import pandas as pd\n",
    "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution per country\n",
    "\n",
    "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(xmin=0)\n",
    "plt.grid()\n",
    "plt.xlabel('Number of billionaires in 2008')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization that y_i depends on x_i\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "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]).T\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization of a 2D PMF with independent variables\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Taking a look at  Newton-Rapson algorithm\n",
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "![Pic1](img/maxlik_1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PoissonRegression:\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 np.exp(self.X @ self.β.T)\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",
    "        μ = self.μ()\n",
    "        return ((self.y - μ) @ self.X).reshape(self.k, 1)\n",
    "\n",
    "    def H(self):\n",
    "        X = self.X\n",
    "        μ = self.μ()\n",
    "        return -(μ * X.T @ X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The implementation of method itself\n",
    "\n",
    "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).T\n",
    "        error = β_new - model.β\n",
    "        model.β = β_new.flatten()\n",
    "\n",
    "        # Print iterations\n",
    "        if display:\n",
    "            β_list = [f'{t:.3}' for t in list(model.β)]\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}')\n",
    "\n",
    "    return model.β"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# Test\n",
    "\n",
    "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.95169229e-07],\n",
       "       [-1.00114805e-06],\n",
       "       [-7.73114567e-07]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As expected, gradient vector is close to 0 near the calculated value\n",
    "\n",
    "poi.G()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of the iterative process\n",
    "\n",
    "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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:                Sat, 05 Jan 2019   Pseudo R-squ.:                  0.2546\n",
      "Time:                        23:26:05   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": [
    "# Comparing test tresults with that of Poisson regression model from the library\n",
    "\n",
    "from statsmodels.api import Poisson\n",
    "from scipy import stats\n",
    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)\n",
    "\n",
    "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Back to the data - creating the datasets\n",
    "\n",
    "# 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": 15,
   "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:                Sat, 05 Jan 2019   Pseudo R-squ.:                  0.8574\n",
      "Time:                        23:28:19   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": [
    "import statsmodels.api as sm\n",
    "\n",
    "# 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": 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:                Sat, 05 Jan 2019   Pseudo R-squ.:                  0.8574\n",
      "Time:                        23:29:23   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": [
    "# In case we'd needed to increste the maximum iteration level, we'd use\n",
    "\n",
    "poisson_reg = sm.Poisson(df[['numbil0']], df[reg1],\n",
    "                         missing='drop').fit(cov_type='HC0', maxiter=100)\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": [
    "# Comparing differentr models and results - using different data to predict the values\n",
    "\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "\n",
    "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', 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 in 2008')\n",
    "print(results_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Difference between predicted and actual\n",
    "\n",
    "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', 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": [
    "#### Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Ex1](img/maxlik_2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ex1\n",
    "\n",
    "from scipy.stats import norm\n",
    "\n",
    "class Probit:\n",
    "    \n",
    "    \"\"\"\n",
    "    This class implements a probit regression model\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, y, X, β):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "        self.β = β\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)), 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": [
    "![Ex1](img/maxlik_3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ex2\n",
    "\n",
    "# Data input\n",
    "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",
    "β = np.array([0.1, 0.1, 0.1])\n",
    "\n",
    "# Probit\n",
    "prob = Probit(y, X, β)\n",
    "\n",
    "# Newton-Raphson\n",
    "newton_raphson(prob)"
   ]
  }
 ],
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