exercises/week-2-exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Week 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise:\n",
    "Use and experience methods from previous lessons with given data. Try to search for other pandas methods to analyze the data such as `groupby()` and `read_csv()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can find covid related data under project's data folder \n",
    "base_url = \"../data/covid/\"\n",
    "infected_dataset_url = base_url + \"time_series_covid19_confirmed_global.csv\"\n",
    "recovered_dataset_url = base_url + \"time_series_covid19_recovered_global.csv\"\n",
    "deaths_dataset_url = base_url + \"time_series_covid19_deaths_global.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_url = \"../data/covid/\"\n",
    "infected_dataset_url = base_url + \"time_series_covid19_confirmed_global.csv\"\n",
    "recovered_dataset_url = base_url + \"time_series_covid19_recovered_global.csv\"\n",
    "deaths_dataset_url = base_url + \"time_series_covid19_deaths_global.csv\"\n",
    "countries_dataset_url = base_url + \"UID_ISO_FIPS_LookUp_Table.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "infected = pd.read_csv(infected_dataset_url)\n",
    "recovered = pd.read_csv(recovered_dataset_url)\n",
    "deaths = pd.read_csv(deaths_dataset_url)\n",
    "countries = pd.read_csv(countries_dataset_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19551</td>\n",
       "      <td>20342</td>\n",
       "      <td>20917</td>\n",
       "      <td>21459</td>\n",
       "      <td>22142</td>\n",
       "      <td>22890</td>\n",
       "      <td>23546</td>\n",
       "      <td>24102</td>\n",
       "      <td>24766</td>\n",
       "      <td>25527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1232</td>\n",
       "      <td>1246</td>\n",
       "      <td>1263</td>\n",
       "      <td>1299</td>\n",
       "      <td>1341</td>\n",
       "      <td>1385</td>\n",
       "      <td>1416</td>\n",
       "      <td>1464</td>\n",
       "      <td>1521</td>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10050</td>\n",
       "      <td>10154</td>\n",
       "      <td>10265</td>\n",
       "      <td>10382</td>\n",
       "      <td>10484</td>\n",
       "      <td>10589</td>\n",
       "      <td>10698</td>\n",
       "      <td>10810</td>\n",
       "      <td>10919</td>\n",
       "      <td>11031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>96</td>\n",
       "      <td>113</td>\n",
       "      <td>118</td>\n",
       "      <td>130</td>\n",
       "      <td>138</td>\n",
       "      <td>140</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  6/6/20  6/7/20  6/8/20  6/9/20  6/10/20  \\\n",
       "0        0        0        0  ...   19551   20342   20917   21459    22142   \n",
       "1        0        0        0  ...    1232    1246    1263    1299     1341   \n",
       "2        0        0        0  ...   10050   10154   10265   10382    10484   \n",
       "3        0        0        0  ...     852     852     852     852      852   \n",
       "4        0        0        0  ...      88      91      92      96      113   \n",
       "\n",
       "   6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "0    22890    23546    24102    24766    25527  \n",
       "1     1385     1416     1464     1521     1590  \n",
       "2    10589    10698    10810    10919    11031  \n",
       "3      852      853      853      853      853  \n",
       "4      118      130      138      140      142  \n",
       "\n",
       "[5 rows x 150 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 266 entries, 0 to 265\n",
      "Columns: 150 entries, Province/State to 6/15/20\n",
      "dtypes: float64(2), int64(146), object(2)\n",
      "memory usage: 311.8+ KB\n"
     ]
    }
   ],
   "source": [
    "infected.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 150)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.148043</td>\n",
       "      <td>22.538640</td>\n",
       "      <td>2.086466</td>\n",
       "      <td>2.458647</td>\n",
       "      <td>3.537594</td>\n",
       "      <td>5.390977</td>\n",
       "      <td>7.962406</td>\n",
       "      <td>11.003759</td>\n",
       "      <td>20.969925</td>\n",
       "      <td>23.180451</td>\n",
       "      <td>...</td>\n",
       "      <td>2.594981e+04</td>\n",
       "      <td>2.637496e+04</td>\n",
       "      <td>2.676449e+04</td>\n",
       "      <td>2.722817e+04</td>\n",
       "      <td>2.773058e+04</td>\n",
       "      <td>2.825084e+04</td>\n",
       "      <td>2.873782e+04</td>\n",
       "      <td>2.924391e+04</td>\n",
       "      <td>2.974596e+04</td>\n",
       "      <td>3.020474e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.909990</td>\n",
       "      <td>70.466256</td>\n",
       "      <td>27.279200</td>\n",
       "      <td>27.377862</td>\n",
       "      <td>34.083035</td>\n",
       "      <td>47.434934</td>\n",
       "      <td>66.289178</td>\n",
       "      <td>89.313757</td>\n",
       "      <td>219.187744</td>\n",
       "      <td>220.524977</td>\n",
       "      <td>...</td>\n",
       "      <td>1.333309e+05</td>\n",
       "      <td>1.349130e+05</td>\n",
       "      <td>1.363934e+05</td>\n",
       "      <td>1.382464e+05</td>\n",
       "      <td>1.402889e+05</td>\n",
       "      <td>1.424252e+05</td>\n",
       "      <td>1.445871e+05</td>\n",
       "      <td>1.467117e+05</td>\n",
       "      <td>1.484660e+05</td>\n",
       "      <td>1.502466e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-51.796300</td>\n",
       "      <td>-135.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.907750</td>\n",
       "      <td>-18.093125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.572500e+02</td>\n",
       "      <td>1.572500e+02</td>\n",
       "      <td>1.572500e+02</td>\n",
       "      <td>1.580000e+02</td>\n",
       "      <td>1.625000e+02</td>\n",
       "      <td>1.650000e+02</td>\n",
       "      <td>1.710000e+02</td>\n",
       "      <td>1.722500e+02</td>\n",
       "      <td>1.760000e+02</td>\n",
       "      <td>1.760000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.488100</td>\n",
       "      <td>20.972650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.535000e+02</td>\n",
       "      <td>9.710000e+02</td>\n",
       "      <td>9.820000e+02</td>\n",
       "      <td>9.825000e+02</td>\n",
       "      <td>9.825000e+02</td>\n",
       "      <td>9.830000e+02</td>\n",
       "      <td>1.005000e+03</td>\n",
       "      <td>1.005000e+03</td>\n",
       "      <td>1.005000e+03</td>\n",
       "      <td>1.005000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.143200</td>\n",
       "      <td>77.191525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.094500e+03</td>\n",
       "      <td>7.117250e+03</td>\n",
       "      <td>7.427000e+03</td>\n",
       "      <td>7.706750e+03</td>\n",
       "      <td>8.005750e+03</td>\n",
       "      <td>8.194000e+03</td>\n",
       "      <td>8.334500e+03</td>\n",
       "      <td>8.452500e+03</td>\n",
       "      <td>8.586500e+03</td>\n",
       "      <td>8.608750e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>71.706900</td>\n",
       "      <td>178.065000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>761.000000</td>\n",
       "      <td>1058.000000</td>\n",
       "      <td>1423.000000</td>\n",
       "      <td>3554.000000</td>\n",
       "      <td>3554.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.926538e+06</td>\n",
       "      <td>1.944309e+06</td>\n",
       "      <td>1.961781e+06</td>\n",
       "      <td>1.979868e+06</td>\n",
       "      <td>2.000702e+06</td>\n",
       "      <td>2.023590e+06</td>\n",
       "      <td>2.048986e+06</td>\n",
       "      <td>2.074526e+06</td>\n",
       "      <td>2.094058e+06</td>\n",
       "      <td>2.114026e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Lat        Long     1/22/20     1/23/20     1/24/20     1/25/20  \\\n",
       "count  266.000000  266.000000  266.000000  266.000000  266.000000  266.000000   \n",
       "mean    21.148043   22.538640    2.086466    2.458647    3.537594    5.390977   \n",
       "std     24.909990   70.466256   27.279200   27.377862   34.083035   47.434934   \n",
       "min    -51.796300 -135.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      6.907750  -18.093125    0.000000    0.000000    0.000000    0.000000   \n",
       "50%     23.488100   20.972650    0.000000    0.000000    0.000000    0.000000   \n",
       "75%     41.143200   77.191525    0.000000    0.000000    0.000000    0.000000   \n",
       "max     71.706900  178.065000  444.000000  444.000000  549.000000  761.000000   \n",
       "\n",
       "           1/26/20      1/27/20      1/28/20      1/29/20  ...        6/6/20  \\\n",
       "count   266.000000   266.000000   266.000000   266.000000  ...  2.660000e+02   \n",
       "mean      7.962406    11.003759    20.969925    23.180451  ...  2.594981e+04   \n",
       "std      66.289178    89.313757   219.187744   220.524977  ...  1.333309e+05   \n",
       "min       0.000000     0.000000     0.000000     0.000000  ...  0.000000e+00   \n",
       "25%       0.000000     0.000000     0.000000     0.000000  ...  1.572500e+02   \n",
       "50%       0.000000     0.000000     0.000000     0.000000  ...  9.535000e+02   \n",
       "75%       0.000000     0.000000     0.000000     0.000000  ...  7.094500e+03   \n",
       "max    1058.000000  1423.000000  3554.000000  3554.000000  ...  1.926538e+06   \n",
       "\n",
       "             6/7/20        6/8/20        6/9/20       6/10/20       6/11/20  \\\n",
       "count  2.660000e+02  2.660000e+02  2.660000e+02  2.660000e+02  2.660000e+02   \n",
       "mean   2.637496e+04  2.676449e+04  2.722817e+04  2.773058e+04  2.825084e+04   \n",
       "std    1.349130e+05  1.363934e+05  1.382464e+05  1.402889e+05  1.424252e+05   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    1.572500e+02  1.572500e+02  1.580000e+02  1.625000e+02  1.650000e+02   \n",
       "50%    9.710000e+02  9.820000e+02  9.825000e+02  9.825000e+02  9.830000e+02   \n",
       "75%    7.117250e+03  7.427000e+03  7.706750e+03  8.005750e+03  8.194000e+03   \n",
       "max    1.944309e+06  1.961781e+06  1.979868e+06  2.000702e+06  2.023590e+06   \n",
       "\n",
       "            6/12/20       6/13/20       6/14/20       6/15/20  \n",
       "count  2.660000e+02  2.660000e+02  2.660000e+02  2.660000e+02  \n",
       "mean   2.873782e+04  2.924391e+04  2.974596e+04  3.020474e+04  \n",
       "std    1.445871e+05  1.467117e+05  1.484660e+05  1.502466e+05  \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "25%    1.710000e+02  1.722500e+02  1.760000e+02  1.760000e+02  \n",
       "50%    1.005000e+03  1.005000e+03  1.005000e+03  1.005000e+03  \n",
       "75%    8.334500e+03  8.452500e+03  8.586500e+03  8.608750e+03  \n",
       "max    2.048986e+06  2.074526e+06  2.094058e+06  2.114026e+06  \n",
       "\n",
       "[8 rows x 148 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Province/State\n",
       "Australian Capital Territory    1\n",
       "Qinghai                         1\n",
       "Reunion                         1\n",
       "New Caledonia                   1\n",
       "Mayotte                         1\n",
       "                               ..\n",
       "Guizhou                         1\n",
       "Guangxi                         1\n",
       "Guangdong                       1\n",
       "Gansu                           1\n",
       "Saint Pierre and Miquelon       1\n",
       "Name: count, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected['Province/State'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Anhui</td>\n",
       "      <td>China</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "      <td>991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>40.1824</td>\n",
       "      <td>116.4142</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>594</td>\n",
       "      <td>594</td>\n",
       "      <td>594</td>\n",
       "      <td>594</td>\n",
       "      <td>594</td>\n",
       "      <td>595</td>\n",
       "      <td>601</td>\n",
       "      <td>637</td>\n",
       "      <td>673</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Chongqing</td>\n",
       "      <td>China</td>\n",
       "      <td>30.0572</td>\n",
       "      <td>107.8740</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>57</td>\n",
       "      <td>75</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>580</td>\n",
       "      <td>582</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Fujian</td>\n",
       "      <td>China</td>\n",
       "      <td>26.0789</td>\n",
       "      <td>117.9874</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>359</td>\n",
       "      <td>359</td>\n",
       "      <td>359</td>\n",
       "      <td>359</td>\n",
       "      <td>360</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>362</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Gansu</td>\n",
       "      <td>China</td>\n",
       "      <td>37.8099</td>\n",
       "      <td>101.0583</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>23.3417</td>\n",
       "      <td>113.4244</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>53</td>\n",
       "      <td>78</td>\n",
       "      <td>111</td>\n",
       "      <td>151</td>\n",
       "      <td>...</td>\n",
       "      <td>1602</td>\n",
       "      <td>1602</td>\n",
       "      <td>1604</td>\n",
       "      <td>1604</td>\n",
       "      <td>1607</td>\n",
       "      <td>1607</td>\n",
       "      <td>1608</td>\n",
       "      <td>1625</td>\n",
       "      <td>1625</td>\n",
       "      <td>1628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Guangxi</td>\n",
       "      <td>China</td>\n",
       "      <td>23.8298</td>\n",
       "      <td>108.7881</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Guizhou</td>\n",
       "      <td>China</td>\n",
       "      <td>26.8154</td>\n",
       "      <td>106.8748</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Hainan</td>\n",
       "      <td>China</td>\n",
       "      <td>19.1959</td>\n",
       "      <td>109.7453</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>171</td>\n",
       "      <td>171</td>\n",
       "      <td>171</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Hebei</td>\n",
       "      <td>China</td>\n",
       "      <td>39.5490</td>\n",
       "      <td>116.1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>331</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Heilongjiang</td>\n",
       "      <td>China</td>\n",
       "      <td>47.8620</td>\n",
       "      <td>127.7615</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>33.8820</td>\n",
       "      <td>113.6140</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>83</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>China</td>\n",
       "      <td>22.3000</td>\n",
       "      <td>114.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1105</td>\n",
       "      <td>1106</td>\n",
       "      <td>1107</td>\n",
       "      <td>1107</td>\n",
       "      <td>1107</td>\n",
       "      <td>1107</td>\n",
       "      <td>1108</td>\n",
       "      <td>1109</td>\n",
       "      <td>1109</td>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>444</td>\n",
       "      <td>444</td>\n",
       "      <td>549</td>\n",
       "      <td>761</td>\n",
       "      <td>1058</td>\n",
       "      <td>1423</td>\n",
       "      <td>...</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "      <td>68135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>27.6104</td>\n",
       "      <td>111.7088</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "      <td>1019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Inner Mongolia</td>\n",
       "      <td>China</td>\n",
       "      <td>44.0935</td>\n",
       "      <td>113.9448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>237</td>\n",
       "      <td>237</td>\n",
       "      <td>237</td>\n",
       "      <td>237</td>\n",
       "      <td>237</td>\n",
       "      <td>237</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>China</td>\n",
       "      <td>32.9711</td>\n",
       "      <td>119.4550</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>33</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Jiangxi</td>\n",
       "      <td>China</td>\n",
       "      <td>27.6140</td>\n",
       "      <td>115.7221</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Jilin</td>\n",
       "      <td>China</td>\n",
       "      <td>43.6661</td>\n",
       "      <td>126.1923</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Liaoning</td>\n",
       "      <td>China</td>\n",
       "      <td>41.2956</td>\n",
       "      <td>122.6085</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Macau</td>\n",
       "      <td>China</td>\n",
       "      <td>22.1667</td>\n",
       "      <td>113.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Ningxia</td>\n",
       "      <td>China</td>\n",
       "      <td>37.2692</td>\n",
       "      <td>106.1655</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Qinghai</td>\n",
       "      <td>China</td>\n",
       "      <td>35.7452</td>\n",
       "      <td>95.9956</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Shaanxi</td>\n",
       "      <td>China</td>\n",
       "      <td>35.1917</td>\n",
       "      <td>108.8701</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>311</td>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Shandong</td>\n",
       "      <td>China</td>\n",
       "      <td>36.3427</td>\n",
       "      <td>118.1498</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>31.2020</td>\n",
       "      <td>121.4491</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>...</td>\n",
       "      <td>677</td>\n",
       "      <td>678</td>\n",
       "      <td>678</td>\n",
       "      <td>678</td>\n",
       "      <td>684</td>\n",
       "      <td>689</td>\n",
       "      <td>690</td>\n",
       "      <td>691</td>\n",
       "      <td>692</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Shanxi</td>\n",
       "      <td>China</td>\n",
       "      <td>37.5777</td>\n",
       "      <td>112.2922</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Sichuan</td>\n",
       "      <td>China</td>\n",
       "      <td>30.6171</td>\n",
       "      <td>102.7103</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>578</td>\n",
       "      <td>581</td>\n",
       "      <td>582</td>\n",
       "      <td>582</td>\n",
       "      <td>582</td>\n",
       "      <td>582</td>\n",
       "      <td>583</td>\n",
       "      <td>583</td>\n",
       "      <td>587</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>China</td>\n",
       "      <td>39.3054</td>\n",
       "      <td>117.3230</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>194</td>\n",
       "      <td>195</td>\n",
       "      <td>195</td>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Tibet</td>\n",
       "      <td>China</td>\n",
       "      <td>31.6927</td>\n",
       "      <td>88.0924</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Xinjiang</td>\n",
       "      <td>China</td>\n",
       "      <td>41.1129</td>\n",
       "      <td>85.2401</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Yunnan</td>\n",
       "      <td>China</td>\n",
       "      <td>24.9740</td>\n",
       "      <td>101.4870</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>29.1832</td>\n",
       "      <td>120.0934</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "      <td>62</td>\n",
       "      <td>104</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State Country/Region      Lat      Long  1/22/20  1/23/20  \\\n",
       "49           Anhui          China  31.8257  117.2264        1        9   \n",
       "50         Beijing          China  40.1824  116.4142       14       22   \n",
       "51       Chongqing          China  30.0572  107.8740        6        9   \n",
       "52          Fujian          China  26.0789  117.9874        1        5   \n",
       "53           Gansu          China  37.8099  101.0583        0        2   \n",
       "54       Guangdong          China  23.3417  113.4244       26       32   \n",
       "55         Guangxi          China  23.8298  108.7881        2        5   \n",
       "56         Guizhou          China  26.8154  106.8748        1        3   \n",
       "57          Hainan          China  19.1959  109.7453        4        5   \n",
       "58           Hebei          China  39.5490  116.1306        1        1   \n",
       "59    Heilongjiang          China  47.8620  127.7615        0        2   \n",
       "60           Henan          China  33.8820  113.6140        5        5   \n",
       "61       Hong Kong          China  22.3000  114.2000        0        2   \n",
       "62           Hubei          China  30.9756  112.2707      444      444   \n",
       "63           Hunan          China  27.6104  111.7088        4        9   \n",
       "64  Inner Mongolia          China  44.0935  113.9448        0        0   \n",
       "65         Jiangsu          China  32.9711  119.4550        1        5   \n",
       "66         Jiangxi          China  27.6140  115.7221        2        7   \n",
       "67           Jilin          China  43.6661  126.1923        0        1   \n",
       "68        Liaoning          China  41.2956  122.6085        2        3   \n",
       "69           Macau          China  22.1667  113.5500        1        2   \n",
       "70         Ningxia          China  37.2692  106.1655        1        1   \n",
       "71         Qinghai          China  35.7452   95.9956        0        0   \n",
       "72         Shaanxi          China  35.1917  108.8701        0        3   \n",
       "73        Shandong          China  36.3427  118.1498        2        6   \n",
       "74        Shanghai          China  31.2020  121.4491        9       16   \n",
       "75          Shanxi          China  37.5777  112.2922        1        1   \n",
       "76         Sichuan          China  30.6171  102.7103        5        8   \n",
       "77         Tianjin          China  39.3054  117.3230        4        4   \n",
       "78           Tibet          China  31.6927   88.0924        0        0   \n",
       "79        Xinjiang          China  41.1129   85.2401        0        2   \n",
       "80          Yunnan          China  24.9740  101.4870        1        2   \n",
       "81        Zhejiang          China  29.1832  120.0934       10       27   \n",
       "\n",
       "    1/24/20  1/25/20  1/26/20  1/27/20  ...  6/6/20  6/7/20  6/8/20  6/9/20  \\\n",
       "49       15       39       60       70  ...     991     991     991     991   \n",
       "50       36       41       68       80  ...     594     594     594     594   \n",
       "51       27       57       75      110  ...     579     579     579     579   \n",
       "52       10       18       35       59  ...     359     359     359     359   \n",
       "53        2        4        7       14  ...     139     139     139     139   \n",
       "54       53       78      111      151  ...    1602    1602    1604    1604   \n",
       "55       23       23       36       46  ...     254     254     254     254   \n",
       "56        3        4        5        7  ...     147     147     147     147   \n",
       "57        8       19       22       33  ...     170     170     170     170   \n",
       "58        2        8       13       18  ...     328     328     328     328   \n",
       "59        4        9       15       21  ...     947     947     947     947   \n",
       "60        9       32       83      128  ...    1276    1276    1276    1276   \n",
       "61        2        5        8        8  ...    1105    1106    1107    1107   \n",
       "62      549      761     1058     1423  ...   68135   68135   68135   68135   \n",
       "63       24       43       69      100  ...    1019    1019    1019    1019   \n",
       "64        1        7        7       11  ...     235     235     235     237   \n",
       "65        9       18       33       47  ...     653     653     653     653   \n",
       "66       18       18       36       72  ...     932     932     932     932   \n",
       "67        3        4        4        6  ...     155     155     155     155   \n",
       "68        4       17       21       27  ...     149     149     149     149   \n",
       "69        2        2        5        6  ...      45      45      45      45   \n",
       "70        2        3        4        7  ...      75      75      75      75   \n",
       "71        0        1        1        6  ...      18      18      18      18   \n",
       "72        5       15       22       35  ...     311     311     311     311   \n",
       "73       15       27       46       75  ...     792     792     792     792   \n",
       "74       20       33       40       53  ...     677     678     678     678   \n",
       "75        1        6        9       13  ...     198     198     198     198   \n",
       "76       15       28       44       69  ...     578     581     582     582   \n",
       "77        8       10       14       23  ...     193     193     193     194   \n",
       "78        0        0        0        0  ...       1       1       1       1   \n",
       "79        2        3        4        5  ...      76      76      76      76   \n",
       "80        5       11       16       26  ...     185     185     185     185   \n",
       "81       43       62      104      128  ...    1268    1268    1268    1268   \n",
       "\n",
       "    6/10/20  6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "49      991      991      991      991      991      991  \n",
       "50      594      595      601      637      673      700  \n",
       "51      579      579      579      580      582      582  \n",
       "52      360      361      361      361      362      362  \n",
       "53      139      139      139      139      139      139  \n",
       "54     1607     1607     1608     1625     1625     1628  \n",
       "55      254      254      254      254      254      254  \n",
       "56      147      147      147      147      147      147  \n",
       "57      170      170      171      171      171      171  \n",
       "58      328      328      328      328      331      335  \n",
       "59      947      947      947      947      947      947  \n",
       "60     1276     1276     1276     1276     1276     1276  \n",
       "61     1107     1107     1108     1109     1109     1112  \n",
       "62    68135    68135    68135    68135    68135    68135  \n",
       "63     1019     1019     1019     1019     1019     1019  \n",
       "64      237      237      237      237      237      238  \n",
       "65      653      653      653      653      653      653  \n",
       "66      932      932      932      932      932      932  \n",
       "67      155      155      155      155      155      155  \n",
       "68      149      149      149      151      151      152  \n",
       "69       45       45       45       45       45       45  \n",
       "70       75       75       75       75       75       75  \n",
       "71       18       18       18       18       18       18  \n",
       "72      311      311      311      311      313      313  \n",
       "73      792      792      792      792      792      792  \n",
       "74      684      689      690      691      692      695  \n",
       "75      198      198      198      198      198      198  \n",
       "76      582      582      583      583      587      588  \n",
       "77      195      195      196      196      196      196  \n",
       "78        1        1        1        1        1        1  \n",
       "79       76       76       76       76       76       76  \n",
       "80      185      185      185      185      185      185  \n",
       "81     1268     1268     1268     1268     1268     1268  \n",
       "\n",
       "[33 rows x 150 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected[infected['Country/Region'] == 'China']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>1875</td>\n",
       "      <td>2171</td>\n",
       "      <td>2651</td>\n",
       "      <td>3013</td>\n",
       "      <td>3326</td>\n",
       "      <td>3928</td>\n",
       "      <td>4201</td>\n",
       "      <td>4725</td>\n",
       "      <td>5164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>925</td>\n",
       "      <td>938</td>\n",
       "      <td>945</td>\n",
       "      <td>960</td>\n",
       "      <td>980</td>\n",
       "      <td>1001</td>\n",
       "      <td>1034</td>\n",
       "      <td>1039</td>\n",
       "      <td>1044</td>\n",
       "      <td>1055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6631</td>\n",
       "      <td>6717</td>\n",
       "      <td>6799</td>\n",
       "      <td>6951</td>\n",
       "      <td>7074</td>\n",
       "      <td>7255</td>\n",
       "      <td>7322</td>\n",
       "      <td>7420</td>\n",
       "      <td>7606</td>\n",
       "      <td>7735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>741</td>\n",
       "      <td>744</td>\n",
       "      <td>751</td>\n",
       "      <td>757</td>\n",
       "      <td>759</td>\n",
       "      <td>780</td>\n",
       "      <td>781</td>\n",
       "      <td>781</td>\n",
       "      <td>781</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  6/6/20  6/7/20  6/8/20  6/9/20  6/10/20  \\\n",
       "0        0        0        0  ...    1830    1875    2171    2651     3013   \n",
       "1        0        0        0  ...     925     938     945     960      980   \n",
       "2        0        0        0  ...    6631    6717    6799    6951     7074   \n",
       "3        0        0        0  ...     741     744     751     757      759   \n",
       "4        0        0        0  ...      24      24      38      38       40   \n",
       "\n",
       "   6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "0     3326     3928     4201     4725     5164  \n",
       "1     1001     1034     1039     1044     1055  \n",
       "2     7255     7322     7420     7606     7735  \n",
       "3      780      781      781      781      789  \n",
       "4       41       42       61       61       64  \n",
       "\n",
       "[5 rows x 150 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>327</td>\n",
       "      <td>357</td>\n",
       "      <td>369</td>\n",
       "      <td>384</td>\n",
       "      <td>405</td>\n",
       "      <td>426</td>\n",
       "      <td>446</td>\n",
       "      <td>451</td>\n",
       "      <td>471</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>698</td>\n",
       "      <td>707</td>\n",
       "      <td>715</td>\n",
       "      <td>724</td>\n",
       "      <td>732</td>\n",
       "      <td>741</td>\n",
       "      <td>751</td>\n",
       "      <td>760</td>\n",
       "      <td>767</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  6/6/20  6/7/20  6/8/20  6/9/20  6/10/20  \\\n",
       "0        0        0        0  ...     327     357     369     384      405   \n",
       "1        0        0        0  ...      34      34      34      34       34   \n",
       "2        0        0        0  ...     698     707     715     724      732   \n",
       "3        0        0        0  ...      51      51      51      51       51   \n",
       "4        0        0        0  ...       4       4       4       4        4   \n",
       "\n",
       "   6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "0      426      446      451      471      478  \n",
       "1       35       36       36       36       36  \n",
       "2      741      751      760      767      777  \n",
       "3       51       51       51       51       51  \n",
       "4        5        5        6        6        6  \n",
       "\n",
       "[5 rows x 150 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 253 entries, 0 to 252\n",
      "Columns: 150 entries, Province/State to 6/15/20\n",
      "dtypes: float64(2), int64(146), object(2)\n",
      "memory usage: 296.6+ KB\n"
     ]
    }
   ],
   "source": [
    "recovered.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected.columns == recovered.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(infected.columns == recovered.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_columns(df1, df2):\n",
    "    return all(df1.columns == df2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(compare_columns(recovered, infected))\n",
    "print(compare_columns(infected, deaths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 150)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_infected = infected.groupby('Country/Region').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19551</td>\n",
       "      <td>20342</td>\n",
       "      <td>20917</td>\n",
       "      <td>21459</td>\n",
       "      <td>22142</td>\n",
       "      <td>22890</td>\n",
       "      <td>23546</td>\n",
       "      <td>24102</td>\n",
       "      <td>24766</td>\n",
       "      <td>25527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1232</td>\n",
       "      <td>1246</td>\n",
       "      <td>1263</td>\n",
       "      <td>1299</td>\n",
       "      <td>1341</td>\n",
       "      <td>1385</td>\n",
       "      <td>1416</td>\n",
       "      <td>1464</td>\n",
       "      <td>1521</td>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10050</td>\n",
       "      <td>10154</td>\n",
       "      <td>10265</td>\n",
       "      <td>10382</td>\n",
       "      <td>10484</td>\n",
       "      <td>10589</td>\n",
       "      <td>10698</td>\n",
       "      <td>10810</td>\n",
       "      <td>10919</td>\n",
       "      <td>11031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>96</td>\n",
       "      <td>113</td>\n",
       "      <td>118</td>\n",
       "      <td>130</td>\n",
       "      <td>138</td>\n",
       "      <td>140</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Province/State      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "Country/Region                                                               \n",
       "Afghanistan                 0  33.0000  65.0000        0        0        0   \n",
       "Albania                     0  41.1533  20.1683        0        0        0   \n",
       "Algeria                     0  28.0339   1.6596        0        0        0   \n",
       "Andorra                     0  42.5063   1.5218        0        0        0   \n",
       "Angola                      0 -11.2027  17.8739        0        0        0   \n",
       "\n",
       "                1/25/20  1/26/20  1/27/20  1/28/20  ...  6/6/20  6/7/20  \\\n",
       "Country/Region                                      ...                   \n",
       "Afghanistan           0        0        0        0  ...   19551   20342   \n",
       "Albania               0        0        0        0  ...    1232    1246   \n",
       "Algeria               0        0        0        0  ...   10050   10154   \n",
       "Andorra               0        0        0        0  ...     852     852   \n",
       "Angola                0        0        0        0  ...      88      91   \n",
       "\n",
       "                6/8/20  6/9/20  6/10/20  6/11/20  6/12/20  6/13/20  6/14/20  \\\n",
       "Country/Region                                                                \n",
       "Afghanistan      20917   21459    22142    22890    23546    24102    24766   \n",
       "Albania           1263    1299     1341     1385     1416     1464     1521   \n",
       "Algeria          10265   10382    10484    10589    10698    10810    10919   \n",
       "Andorra            852     852      852      852      853      853      853   \n",
       "Angola              92      96      113      118      130      138      140   \n",
       "\n",
       "                6/15/20  \n",
       "Country/Region           \n",
       "Afghanistan       25527  \n",
       "Albania            1590  \n",
       "Algeria           11031  \n",
       "Andorra             853  \n",
       "Angola              142  \n",
       "\n",
       "[5 rows x 149 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_infected.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 188 entries, Afghanistan to Zimbabwe\n",
      "Columns: 149 entries, Province/State to 6/15/20\n",
      "dtypes: float64(2), int64(146), object(1)\n",
      "memory usage: 220.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_infected.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>Province/State</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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>Australia</th>\n",
       "      <td>Australian Capital TerritoryNew South WalesNor...</td>\n",
       "      <td>-255.9695</td>\n",
       "      <td>1129.8623</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>7259</td>\n",
       "      <td>7265</td>\n",
       "      <td>7267</td>\n",
       "      <td>7274</td>\n",
       "      <td>7285</td>\n",
       "      <td>7289</td>\n",
       "      <td>7294</td>\n",
       "      <td>7320</td>\n",
       "      <td>7335</td>\n",
       "      <td>7347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>AlbertaBritish ColumbiaGrand PrincessManitobaN...</td>\n",
       "      <td>671.7607</td>\n",
       "      <td>-1237.6289</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>96475</td>\n",
       "      <td>97178</td>\n",
       "      <td>97779</td>\n",
       "      <td>98241</td>\n",
       "      <td>98720</td>\n",
       "      <td>99159</td>\n",
       "      <td>99595</td>\n",
       "      <td>100043</td>\n",
       "      <td>100404</td>\n",
       "      <td>100763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>AnhuiBeijingChongqingFujianGansuGuangdongGuang...</td>\n",
       "      <td>1083.3367</td>\n",
       "      <td>3684.4197</td>\n",
       "      <td>548</td>\n",
       "      <td>643</td>\n",
       "      <td>920</td>\n",
       "      <td>1406</td>\n",
       "      <td>2075</td>\n",
       "      <td>2877</td>\n",
       "      <td>5509</td>\n",
       "      <td>...</td>\n",
       "      <td>84186</td>\n",
       "      <td>84191</td>\n",
       "      <td>84195</td>\n",
       "      <td>84198</td>\n",
       "      <td>84209</td>\n",
       "      <td>84216</td>\n",
       "      <td>84228</td>\n",
       "      <td>84286</td>\n",
       "      <td>84335</td>\n",
       "      <td>84378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>Faroe IslandsGreenland</td>\n",
       "      <td>189.8634</td>\n",
       "      <td>-40.0143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12124</td>\n",
       "      <td>12148</td>\n",
       "      <td>12162</td>\n",
       "      <td>12201</td>\n",
       "      <td>12216</td>\n",
       "      <td>12235</td>\n",
       "      <td>12299</td>\n",
       "      <td>12339</td>\n",
       "      <td>12393</td>\n",
       "      <td>12417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>French GuianaFrench PolynesiaGuadeloupeMayotte...</td>\n",
       "      <td>91.3624</td>\n",
       "      <td>59.7192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>190759</td>\n",
       "      <td>191102</td>\n",
       "      <td>191313</td>\n",
       "      <td>191523</td>\n",
       "      <td>192068</td>\n",
       "      <td>192493</td>\n",
       "      <td>193220</td>\n",
       "      <td>193746</td>\n",
       "      <td>194153</td>\n",
       "      <td>194305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>ArubaCuracaoSint MaartenBonaire, Sint Eustatiu...</td>\n",
       "      <td>107.0417</td>\n",
       "      <td>-265.0278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>47541</td>\n",
       "      <td>47780</td>\n",
       "      <td>47945</td>\n",
       "      <td>48109</td>\n",
       "      <td>48294</td>\n",
       "      <td>48458</td>\n",
       "      <td>48668</td>\n",
       "      <td>48847</td>\n",
       "      <td>48990</td>\n",
       "      <td>49155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>BermudaCayman IslandsChannel IslandsGibraltarI...</td>\n",
       "      <td>270.0299</td>\n",
       "      <td>-482.9247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>286294</td>\n",
       "      <td>287621</td>\n",
       "      <td>288834</td>\n",
       "      <td>290581</td>\n",
       "      <td>291588</td>\n",
       "      <td>292860</td>\n",
       "      <td>294402</td>\n",
       "      <td>295828</td>\n",
       "      <td>297342</td>\n",
       "      <td>298315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Province/State        Lat  \\\n",
       "Country/Region                                                                 \n",
       "Australia       Australian Capital TerritoryNew South WalesNor...  -255.9695   \n",
       "Canada          AlbertaBritish ColumbiaGrand PrincessManitobaN...   671.7607   \n",
       "China           AnhuiBeijingChongqingFujianGansuGuangdongGuang...  1083.3367   \n",
       "Denmark                                    Faroe IslandsGreenland   189.8634   \n",
       "France          French GuianaFrench PolynesiaGuadeloupeMayotte...    91.3624   \n",
       "Netherlands     ArubaCuracaoSint MaartenBonaire, Sint Eustatiu...   107.0417   \n",
       "United Kingdom  BermudaCayman IslandsChannel IslandsGibraltarI...   270.0299   \n",
       "\n",
       "                     Long  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  \\\n",
       "Country/Region                                                           \n",
       "Australia       1129.8623        0        0        0        0        4   \n",
       "Canada         -1237.6289        0        0        0        0        1   \n",
       "China           3684.4197      548      643      920     1406     2075   \n",
       "Denmark          -40.0143        0        0        0        0        0   \n",
       "France            59.7192        0        0        2        3        3   \n",
       "Netherlands     -265.0278        0        0        0        0        0   \n",
       "United Kingdom  -482.9247        0        0        0        0        0   \n",
       "\n",
       "                1/27/20  1/28/20  ...  6/6/20  6/7/20  6/8/20  6/9/20  \\\n",
       "Country/Region                    ...                                   \n",
       "Australia             5        5  ...    7259    7265    7267    7274   \n",
       "Canada                1        2  ...   96475   97178   97779   98241   \n",
       "China              2877     5509  ...   84186   84191   84195   84198   \n",
       "Denmark               0        0  ...   12124   12148   12162   12201   \n",
       "France                3        4  ...  190759  191102  191313  191523   \n",
       "Netherlands           0        0  ...   47541   47780   47945   48109   \n",
       "United Kingdom        0        0  ...  286294  287621  288834  290581   \n",
       "\n",
       "                6/10/20  6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "Country/Region                                                        \n",
       "Australia          7285     7289     7294     7320     7335     7347  \n",
       "Canada            98720    99159    99595   100043   100404   100763  \n",
       "China             84209    84216    84228    84286    84335    84378  \n",
       "Denmark           12216    12235    12299    12339    12393    12417  \n",
       "France           192068   192493   193220   193746   194153   194305  \n",
       "Netherlands       48294    48458    48668    48847    48990    49155  \n",
       "United Kingdom   291588   292860   294402   295828   297342   298315  \n",
       "\n",
       "[7 rows x 149 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_infected[df_infected['Province/State'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19551</td>\n",
       "      <td>20342</td>\n",
       "      <td>20917</td>\n",
       "      <td>21459</td>\n",
       "      <td>22142</td>\n",
       "      <td>22890</td>\n",
       "      <td>23546</td>\n",
       "      <td>24102</td>\n",
       "      <td>24766</td>\n",
       "      <td>25527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1232</td>\n",
       "      <td>1246</td>\n",
       "      <td>1263</td>\n",
       "      <td>1299</td>\n",
       "      <td>1341</td>\n",
       "      <td>1385</td>\n",
       "      <td>1416</td>\n",
       "      <td>1464</td>\n",
       "      <td>1521</td>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10050</td>\n",
       "      <td>10154</td>\n",
       "      <td>10265</td>\n",
       "      <td>10382</td>\n",
       "      <td>10484</td>\n",
       "      <td>10589</td>\n",
       "      <td>10698</td>\n",
       "      <td>10810</td>\n",
       "      <td>10919</td>\n",
       "      <td>11031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>96</td>\n",
       "      <td>113</td>\n",
       "      <td>118</td>\n",
       "      <td>130</td>\n",
       "      <td>138</td>\n",
       "      <td>140</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  6/6/20  6/7/20  6/8/20  6/9/20  6/10/20  \\\n",
       "0        0        0        0  ...   19551   20342   20917   21459    22142   \n",
       "1        0        0        0  ...    1232    1246    1263    1299     1341   \n",
       "2        0        0        0  ...   10050   10154   10265   10382    10484   \n",
       "3        0        0        0  ...     852     852     852     852      852   \n",
       "4        0        0        0  ...      88      91      92      96      113   \n",
       "\n",
       "   6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "0    22890    23546    24102    24766    25527  \n",
       "1     1385     1416     1464     1521     1590  \n",
       "2    10589    10698    10810    10919    11031  \n",
       "3      852      853      853      853      853  \n",
       "4      118      130      138      140      142  \n",
       "\n",
       "[5 rows x 150 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "548"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected[infected['Country/Region'] == 'China']['1/22/20'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recovered = recovered.groupby('Country/Region').sum()\n",
    "df_deaths = deaths.groupby('Country/Region').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Province/State    AnhuiBeijingChongqingFujianGansuGuangdongGuang...\n",
       "Lat                                                       1083.3367\n",
       "Long                                                      3684.4197\n",
       "1/22/20                                                         548\n",
       "1/23/20                                                         643\n",
       "                                        ...                        \n",
       "6/11/20                                                       84216\n",
       "6/12/20                                                       84228\n",
       "6/13/20                                                       84286\n",
       "6/14/20                                                       84335\n",
       "6/15/20                                                       84378\n",
       "Name: China, Length: 149, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_infected.loc['China']"
   ]
  },
  {
   "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>China</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Province/State</th>\n",
       "      <td>AnhuiBeijingChongqingFujianGansuGuangdongGuang...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lat</th>\n",
       "      <td>1083.3367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Long</th>\n",
       "      <td>3684.4197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/22/20</th>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/23/20</th>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/11/20</th>\n",
       "      <td>84216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/12/20</th>\n",
       "      <td>84228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/13/20</th>\n",
       "      <td>84286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/14/20</th>\n",
       "      <td>84335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6/15/20</th>\n",
       "      <td>84378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            China\n",
       "Province/State  AnhuiBeijingChongqingFujianGansuGuangdongGuang...\n",
       "Lat                                                     1083.3367\n",
       "Long                                                    3684.4197\n",
       "1/22/20                                                       548\n",
       "1/23/20                                                       643\n",
       "...                                                           ...\n",
       "6/11/20                                                     84216\n",
       "6/12/20                                                     84228\n",
       "6/13/20                                                     84286\n",
       "6/14/20                                                     84335\n",
       "6/15/20                                                     84378\n",
       "\n",
       "[149 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_infected.loc['China'].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_infected.loc['China'][2:].plot()\n",
    "df_deaths.loc['China'][2:].plot()\n",
    "df_recovered.loc['China'][2:].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_infected.drop(columns=['Lat', 'Long', 'Province/State'], inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19551</td>\n",
       "      <td>20342</td>\n",
       "      <td>20917</td>\n",
       "      <td>21459</td>\n",
       "      <td>22142</td>\n",
       "      <td>22890</td>\n",
       "      <td>23546</td>\n",
       "      <td>24102</td>\n",
       "      <td>24766</td>\n",
       "      <td>25527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1232</td>\n",
       "      <td>1246</td>\n",
       "      <td>1263</td>\n",
       "      <td>1299</td>\n",
       "      <td>1341</td>\n",
       "      <td>1385</td>\n",
       "      <td>1416</td>\n",
       "      <td>1464</td>\n",
       "      <td>1521</td>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10050</td>\n",
       "      <td>10154</td>\n",
       "      <td>10265</td>\n",
       "      <td>10382</td>\n",
       "      <td>10484</td>\n",
       "      <td>10589</td>\n",
       "      <td>10698</td>\n",
       "      <td>10810</td>\n",
       "      <td>10919</td>\n",
       "      <td>11031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>852</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>96</td>\n",
       "      <td>113</td>\n",
       "      <td>118</td>\n",
       "      <td>130</td>\n",
       "      <td>138</td>\n",
       "      <td>140</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 146 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  6/6/20  6/7/20  6/8/20  \\\n",
       "Country/Region                             ...                           \n",
       "Afghanistan           0        0        0  ...   19551   20342   20917   \n",
       "Albania               0        0        0  ...    1232    1246    1263   \n",
       "Algeria               0        0        0  ...   10050   10154   10265   \n",
       "Andorra               0        0        0  ...     852     852     852   \n",
       "Angola                0        0        0  ...      88      91      92   \n",
       "\n",
       "                6/9/20  6/10/20  6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "Country/Region                                                                \n",
       "Afghanistan      21459    22142    22890    23546    24102    24766    25527  \n",
       "Albania           1299     1341     1385     1416     1464     1521     1590  \n",
       "Algeria          10382    10484    10589    10698    10810    10919    11031  \n",
       "Andorra            852      852      852      853      853      853      853  \n",
       "Angola              96      113      118      130      138      140      142  \n",
       "\n",
       "[5 rows x 146 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_infected.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recovered = df_recovered.drop(columns=['Lat', 'Long', 'Province/State'])\n",
    "df_deaths = df_deaths.drop(columns=['Lat', 'Long', 'Province/State'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1830</td>\n",
       "      <td>1875</td>\n",
       "      <td>2171</td>\n",
       "      <td>2651</td>\n",
       "      <td>3013</td>\n",
       "      <td>3326</td>\n",
       "      <td>3928</td>\n",
       "      <td>4201</td>\n",
       "      <td>4725</td>\n",
       "      <td>5164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>925</td>\n",
       "      <td>938</td>\n",
       "      <td>945</td>\n",
       "      <td>960</td>\n",
       "      <td>980</td>\n",
       "      <td>1001</td>\n",
       "      <td>1034</td>\n",
       "      <td>1039</td>\n",
       "      <td>1044</td>\n",
       "      <td>1055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6631</td>\n",
       "      <td>6717</td>\n",
       "      <td>6799</td>\n",
       "      <td>6951</td>\n",
       "      <td>7074</td>\n",
       "      <td>7255</td>\n",
       "      <td>7322</td>\n",
       "      <td>7420</td>\n",
       "      <td>7606</td>\n",
       "      <td>7735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>741</td>\n",
       "      <td>744</td>\n",
       "      <td>751</td>\n",
       "      <td>757</td>\n",
       "      <td>759</td>\n",
       "      <td>780</td>\n",
       "      <td>781</td>\n",
       "      <td>781</td>\n",
       "      <td>781</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 146 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  6/6/20  6/7/20  6/8/20  \\\n",
       "Country/Region                             ...                           \n",
       "Afghanistan           0        0        0  ...    1830    1875    2171   \n",
       "Albania               0        0        0  ...     925     938     945   \n",
       "Algeria               0        0        0  ...    6631    6717    6799   \n",
       "Andorra               0        0        0  ...     741     744     751   \n",
       "Angola                0        0        0  ...      24      24      38   \n",
       "\n",
       "                6/9/20  6/10/20  6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "Country/Region                                                                \n",
       "Afghanistan       2651     3013     3326     3928     4201     4725     5164  \n",
       "Albania            960      980     1001     1034     1039     1044     1055  \n",
       "Algeria           6951     7074     7255     7322     7420     7606     7735  \n",
       "Andorra            757      759      780      781      781      781      789  \n",
       "Angola              38       40       41       42       61       61       64  \n",
       "\n",
       "[5 rows x 146 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recovered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>6/6/20</th>\n",
       "      <th>6/7/20</th>\n",
       "      <th>6/8/20</th>\n",
       "      <th>6/9/20</th>\n",
       "      <th>6/10/20</th>\n",
       "      <th>6/11/20</th>\n",
       "      <th>6/12/20</th>\n",
       "      <th>6/13/20</th>\n",
       "      <th>6/14/20</th>\n",
       "      <th>6/15/20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>327</td>\n",
       "      <td>357</td>\n",
       "      <td>369</td>\n",
       "      <td>384</td>\n",
       "      <td>405</td>\n",
       "      <td>426</td>\n",
       "      <td>446</td>\n",
       "      <td>451</td>\n",
       "      <td>471</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>698</td>\n",
       "      <td>707</td>\n",
       "      <td>715</td>\n",
       "      <td>724</td>\n",
       "      <td>732</td>\n",
       "      <td>741</td>\n",
       "      <td>751</td>\n",
       "      <td>760</td>\n",
       "      <td>767</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 146 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  6/6/20  6/7/20  6/8/20  \\\n",
       "Country/Region                             ...                           \n",
       "Afghanistan           0        0        0  ...     327     357     369   \n",
       "Albania               0        0        0  ...      34      34      34   \n",
       "Algeria               0        0        0  ...     698     707     715   \n",
       "Andorra               0        0        0  ...      51      51      51   \n",
       "Angola                0        0        0  ...       4       4       4   \n",
       "\n",
       "                6/9/20  6/10/20  6/11/20  6/12/20  6/13/20  6/14/20  6/15/20  \n",
       "Country/Region                                                                \n",
       "Afghanistan        384      405      426      446      451      471      478  \n",
       "Albania             34       34       35       36       36       36       36  \n",
       "Algeria            724      732      741      751      760      767      777  \n",
       "Andorra             51       51       51       51       51       51       51  \n",
       "Angola               4        4        5        5        6        6        6  \n",
       "\n",
       "[5 rows x 146 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_deaths.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_frame(country:str):\n",
    "    \"\"\"Take country name as parameter, return infected, recovered, deaths values as timeseries table.\n",
    "    pd.DataFrame()\n",
    "    \"\"\"\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_frame(country:str):\n",
    "    \"\"\"Take country name as parameter, return infected, recovered, deaths values as timeseries table.\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    df = pd.DataFrame({\n",
    "        'infected' : df_infected.loc[country],\n",
    "        'recovered': df_recovered.loc[country],\n",
    "        'deaths': df_deaths.loc[country]\n",
    "    })\n",
    "    df.index = pd.to_datetime(df.index, format='%m/%d/%y')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_us = make_frame('US')\n",
    "df_turkey = make_frame('Turkey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>infected</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-11</th>\n",
       "      <td>174023</td>\n",
       "      <td>147860</td>\n",
       "      <td>4763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12</th>\n",
       "      <td>175218</td>\n",
       "      <td>149102</td>\n",
       "      <td>4778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-13</th>\n",
       "      <td>176677</td>\n",
       "      <td>150087</td>\n",
       "      <td>4792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-14</th>\n",
       "      <td>178239</td>\n",
       "      <td>151417</td>\n",
       "      <td>4807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>179831</td>\n",
       "      <td>152364</td>\n",
       "      <td>4825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            infected  recovered  deaths\n",
       "2020-01-22         0          0       0\n",
       "2020-01-23         0          0       0\n",
       "2020-01-24         0          0       0\n",
       "2020-01-25         0          0       0\n",
       "2020-01-26         0          0       0\n",
       "...              ...        ...     ...\n",
       "2020-06-11    174023     147860    4763\n",
       "2020-06-12    175218     149102    4778\n",
       "2020-06-13    176677     150087    4792\n",
       "2020-06-14    178239     151417    4807\n",
       "2020-06-15    179831     152364    4825\n",
       "\n",
       "[146 rows x 3 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_turkey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_us.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_us['new_infected'] = df_us['infected'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>infected</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "      <th>new_infected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-11</th>\n",
       "      <td>2023590</td>\n",
       "      <td>540292</td>\n",
       "      <td>113823</td>\n",
       "      <td>22888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12</th>\n",
       "      <td>2048986</td>\n",
       "      <td>547386</td>\n",
       "      <td>114669</td>\n",
       "      <td>25396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-13</th>\n",
       "      <td>2074526</td>\n",
       "      <td>556606</td>\n",
       "      <td>115436</td>\n",
       "      <td>25540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-14</th>\n",
       "      <td>2094058</td>\n",
       "      <td>561816</td>\n",
       "      <td>115732</td>\n",
       "      <td>19532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>2114026</td>\n",
       "      <td>576334</td>\n",
       "      <td>116127</td>\n",
       "      <td>19968.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            infected  recovered  deaths  new_infected\n",
       "2020-01-22         1          0       0           NaN\n",
       "2020-01-23         1          0       0           0.0\n",
       "2020-01-24         2          0       0           1.0\n",
       "2020-01-25         2          0       0           0.0\n",
       "2020-01-26         5          0       0           3.0\n",
       "...              ...        ...     ...           ...\n",
       "2020-06-11   2023590     540292  113823       22888.0\n",
       "2020-06-12   2048986     547386  114669       25396.0\n",
       "2020-06-13   2074526     556606  115436       25540.0\n",
       "2020-06-14   2094058     561816  115732       19532.0\n",
       "2020-06-15   2114026     576334  116127       19968.0\n",
       "\n",
       "[146 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_us['new_infected'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+sAAAIqCAYAAABcw9vsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACycklEQVR4nOzde1zUdfY/8NdcmOE6IHcUBBQVEUXFG5pmZaJpq5uVbaVW2sXVdtXdLu6WtfXbb22XrXYz3a7abdU02/KCkqaW4gUUBRRvoCAw3GFguMwwM78/5qLkjcvMfObyej4ePHaFz8wcMITzOe9zjshgMBhARERERERERA5DLHQARERERERERNQek3UiIiIiIiIiB8NknYiIiIiIiMjBMFknIiIiIiIicjBM1omIiIiIiIgcDJN1IiIiIiIiIgfDZJ2IiIiIiIjIwTBZJyIiIiIiInIwUqEDEJJer0dpaSn8/PwgEomEDoeIiIiIiIhcnMFgQENDA3r27Amx+Pr1c7dO1ktLSxEVFSV0GERERERERORmiouLERkZed2Pu3Wy7ufnB8D4RVIoFAJHQ0RERERERK5OpVIhKirKko9ej1sn6+aj7wqFgsk6ERERERER2c3NWrE5YI6IiIiIiIjIwTBZJyIiIiIiInIwTNaJiIiIiIiIHAyTdSIiIiIiIiIHw2SdiIiIiIiIyMEwWSciIiIiIiJyMEzWiYiIiIiIiBwMk3UiIiIiIiIiB8NknYiIiIiIiMjBMFknIiIiIiIicjBM1omIiIiIiIgcDJN1IiIiIiIiIgfDZJ2IiIiIiIjIwTBZJyIiIiIiInIwTNaJiIiIiIiIHAyTdSIiIiIiIiIHw2SdiIiIiIiIyMEwWSciIiIiIiJyMJ1K1letWoUhQ4ZAoVBAoVAgJSUF27dvv+o6g8GAqVOnQiQS4bvvvmv3saKiIkybNg3e3t4IDQ3FM888g7a2tnbX7NmzB8OHD4dcLkdcXBzWrFlz1WusXLkSMTEx8PT0xOjRo3H48OHOfCrtaNr0XX4sERERERERkbV1KlmPjIzE66+/jqysLGRmZuL222/HjBkzkJeX1+66d999FyKR6KrH63Q6TJs2DRqNBgcOHMDatWuxZs0arFixwnJNYWEhpk2bhttuuw3Z2dlYsmQJFixYgB07dliuWb9+PZYtW4aXXnoJR48eRVJSElJTU1FRUdHZzx8AcLKsvkuPIyIiIiIiIrIFkcFgMHTnCQIDA/Hmm29i/vz5AIDs7GxMnz4dmZmZiIiIwObNmzFz5kwAwPbt2zF9+nSUlpYiLCwMALB69Wo899xzqKyshEwmw3PPPYetW7ciNzfX8hoPPPAA6urqkJaWBgAYPXo0Ro4ciffffx8AoNfrERUVhaeffhrPP/98h2NXqVTw9/fHu9uO4Y9Th3bny0BERERERER0U+Y8tL6+HgqF4rrXdblnXafTYd26dVCr1UhJSQEANDU14cEHH8TKlSsRHh5+1WMyMjIwePBgS6IOAKmpqVCpVJbqfEZGBiZNmtTucampqcjIyAAAaDQaZGVltbtGLBZj0qRJlmuup7W1FSqVqt0bABwtquv8F4CIiIiIiIjIRjqdrOfk5MDX1xdyuRxPPfUUNm/ejISEBADA0qVLMXbsWMyYMeOaj1Uqle0SdQCWPyuVyhteo1Kp0NzcjKqqKuh0umteY36O63nttdfg7+9veYuKigIAHLtYC72+WwcMiIiIiIiIiKxG2tkHDBgwANnZ2aivr8fGjRsxb9487N27F+fOncPu3btx7NgxW8RpFcuXL8eyZcssf1apVIiKikJdcxsKqhoRF+onYHRERERERERERp1O1mUyGeLi4gAAycnJOHLkCN577z14eXnh/PnzCAgIaHf9rFmzMH78eOzZswfh4eFXTW0vLy8HAMux+fDwcMv7rrxGoVDAy8sLEokEEonkmtdc6+j9leRyOeRy+TU/duRCLZN1IiIiIiIicgjd3rOu1+vR2tqK559/HidOnEB2drblDQDeeecdfPbZZwCAlJQU5OTktJvanp6eDoVCYTlKn5KSgl27drV7jfT0dEtfvEwmQ3Jycrtr9Ho9du3aZbmmK45cqOnyY4mIiIiIiIisqVOV9eXLl2Pq1Kno3bs3Ghoa8PXXX2PPnj3YsWMHwsPDr1nZ7t27N2JjYwEAkydPRkJCAubMmYM33ngDSqUSL7zwAhYtWmSpeD/11FN4//338eyzz+Kxxx7D7t27sWHDBmzdutXynMuWLcO8efMwYsQIjBo1Cu+++y7UajUeffTRLn8hMi/UdvmxRERERERERNbUqWS9oqICc+fORVlZGfz9/TFkyBDs2LEDd955Z4ceL5FIsGXLFixcuBApKSnw8fHBvHnz8Morr1iuiY2NxdatW7F06VK89957iIyMxMcff4zU1FTLNbNnz0ZlZSVWrFgBpVKJoUOHIi0t7aqhcx0lFgFFNU0oV7UgTOHZpecgIiIiIiIispZu71l3Zub9dpNe346ztTqsfHA4pg2JEDosIiIiIiIiclE237PuSpJ7BwBg3zoRERERERE5BibrAIb17gEAyLzIZJ2IiIiIiIiEx2QdwDBTZf1kqQqNrW3CBkNERERERERuj8k6gHB/L0T28ILeABwr4lR4IiIiIiIiEhaTdZORMYEAgCNc4UZEREREREQCY7JuMiLG2Ld+pJB960RERERERCQsJusm5sr6seJaaHV6gaMhIiIiIiIid8Zk3SQuxBcB3h5o0eqRV6oSOhwiIiIiIiJyY0zWTcRiEUZEm1a4cd86ERERERERCYjJ+hVGWIbMMVknIiIiIiIi4TBZv8LIGHNlvRYGg0HgaIiIiIiIiMhdMVm/QmIvf8ikYlSrNSisUgsdDhEREREREbkpJutXkEslGBoZAMBYXSciIiIiIiISApP1X7HsW2ffOhEREREREQmEyfqvmPetZ15kZZ2IiIiIiIiEwWT9V4b37gGRCCisUqOyoVXocIiIiIiIiMgNMVn/FX9vDwwI8wPAfetEREREREQkDCbr1zDSsm+dR+GJiIiIiIjI/pisX4N5yFzmRVbWiYiIiIiIyP6YrF+DubKeV6qCurVN4GiIiIiIiIjI3TBZv4aeAV7oFeAFnd6A7OI6ocMhIiIiIiIiN8Nk/Tq4b52IiIiIiIiEwmT9OkaY961zyBwRERERERHZGZP16xhpqqwfLapFm04vcDRERERERETkTpisX0f/UD/4eUrRpNHhVFmD0OEQERERERGRG2Gyfh1isQgjotm3TkRERERERPbHZP0GLH3r3LdOREREREREdsRk/QbM+9YPF9bCYDAIHA0RERERERG5CybrNzAk0h8yiRhVja24WN0kdDhERERERETkJpis34CnhwRDIv0BsG+diIiIiIiI7IfJ+k1w3zoRERERERHZG5P1mzDvWz/CIXNERERERERkJ0zWbyLZtL6toFKN6sZWgaMhIiIiIiIid8Bk/SYCvGXoH+YLAMi8yKPwREREREREZHtM1jvgct86j8ITERERERGR7TFZ7wBL3zqHzBEREREREZEdMFnvgBHRxsp6bkk9mjU6gaMhIiIiIiIiV8dkvQMie3ghXOGJNr0B2cV1QodDRERERERELo7JegeIRCKMMB2FZ986ERERERER2RqT9Q4aFWs8Cn+YyToRERERERHZGJP1DjL3rR+9WIs2nV7gaIiIiIiIiMiVMVnvoAHhfvCTS6HW6JCvbBA6HCIiIiIiInJhnUrWV61ahSFDhkChUEChUCAlJQXbt28HANTU1ODpp5/GgAED4OXlhd69e+MPf/gD6uvr2z1HUVERpk2bBm9vb4SGhuKZZ55BW1tbu2v27NmD4cOHQy6XIy4uDmvWrLkqlpUrVyImJgaenp4YPXo0Dh8+3MlPvXMkYhGGR7NvnYiIiIiIiGyvU8l6ZGQkXn/9dWRlZSEzMxO33347ZsyYgby8PJSWlqK0tBRvvfUWcnNzsWbNGqSlpWH+/PmWx+t0OkybNg0ajQYHDhzA2rVrsWbNGqxYscJyTWFhIaZNm4bbbrsN2dnZWLJkCRYsWIAdO3ZYrlm/fj2WLVuGl156CUePHkVSUhJSU1NRUVFhhS/J9Vn2rV/kvnUiIiIiIiKyHZHBYDB05wkCAwPx5ptvtkvKzb755hs8/PDDUKvVkEql2L59O6ZPn47S0lKEhYUBAFavXo3nnnsOlZWVkMlkeO6557B161bk5uZanueBBx5AXV0d0tLSAACjR4/GyJEj8f777wMA9Ho9oqKi8PTTT+P555/vcOwqlQr+/v6or6+HQqG46fUHC6rxwIcHEaaQ4+DyOyASiTr8WkREREREREQdzUO73LOu0+mwbt06qNVqpKSkXPMa84tLpVIAQEZGBgYPHmxJ1AEgNTUVKpUKeXl5lmsmTZrU7nlSU1ORkZEBANBoNMjKymp3jVgsxqRJkyzXXE9raytUKlW7t85IigyAh0SEclUrLtU2d+qxRERERERERB3V6WQ9JycHvr6+kMvleOqpp7B582YkJCRcdV1VVRVeffVVPPHEE5b3KZXKdok6AMuflUrlDa9RqVRobm5GVVUVdDrdNa8xP8f1vPbaa/D397e8RUVFdfwTB+AlkyCxlz8A4Aj71omIiIiIiMhGOp2sDxgwANnZ2Th06BAWLlyIefPm4eTJk+2uUalUmDZtGhISEvDyyy9bK9ZuW758Oerr6y1vxcXFnX6OkTHGFW5HLrBvnYiIiIiIiGxD2tkHyGQyxMXFAQCSk5Nx5MgRvPfee/jPf/4DAGhoaMCUKVPg5+eHzZs3w8PDw/LY8PDwq6a2l5eXWz5m/l/z+668RqFQwMvLCxKJBBKJ5JrXmJ/jeuRyOeRyeWc/5XZGRPfAh+BEeCIiIiIiIrKdbu9Z1+v1aG1tBWCsqE+ePBkymQzff/89PD09212bkpKCnJycdlPb09PToVAoLEfpU1JSsGvXrnaPS09Pt/TFy2QyJCcnt7tGr9dj165d1+2dt6YRpsr62YpG1Ko1Nn89IiIiIiIicj+dStaXL1+Offv24cKFC8jJycHy5cuxZ88ePPTQQ5ZEXa1W45NPPoFKpYJSqYRSqYROpwMATJ48GQkJCZgzZw6OHz+OHTt24IUXXsCiRYssFe+nnnoKBQUFePbZZ5Gfn48PPvgAGzZswNKlSy1xLFu2DB999BHWrl2LU6dOYeHChVCr1Xj00Uet+KW5tkAfGeJCfQEAmVzhRkRERERERDbQqWPwFRUVmDt3LsrKyuDv748hQ4Zgx44duPPOO7Fnzx4cOnQIACzH5M0KCwsRExMDiUSCLVu2YOHChUhJSYGPjw/mzZuHV155xXJtbGwstm7diqVLl+K9995DZGQkPv74Y6SmplqumT17NiorK7FixQoolUoMHToUaWlpVw2ds5WRMT1wrqIRmRdqcGeCfV6TiIiIiIiI3Ee396w7s87uWTfblHUJf/rmOIb3DsC3vx9nwwiJiIiIiIjIldh8z7o7M0+EzympR4tWJ3A0RERERERE5GqYrHdBVKAXQv3k0OoMOF5cJ3Q4RERERERE5GKYrHeBSCSyVNc5ZI6IiIiIiIisjcl6F42I6QEAOMJ960RERERERGRlTNa7yFxZz7pYC53ebWf0ERERERERkQ0wWe+i+HA/+MgkaGhpw5nyBqHDISIiIiIiIhfCZL2LpBIxhkcbj8Jn8ig8ERERERERWRGT9W4YEW08Cn/kAofMERERERERkfUwWe+GkbGsrBMREREREZH1MVnvhqFRAZCKRSitb8Gl2iahwyEiIiIiIiIXwWS9G7xlUgzq5Q8AyORReCIiIiIiIrISJuvdNDKa+9aJiIiIiIjIupisd9MI0751VtaJiIiIiIjIWpisd9OIGGNl/XR5A+qbtAJHQ0RERERERK6AyXo3BfvK0SfYBwCQVcSj8ERERERERNR9TNatwFxd5751IiIiIiIisgYm61ZwuW+dlXUiIiIiIiLqPibrVjDSlKwfL65Hi1YncDRERERERETk7JisW0FMkDeCfWXQ6PTILakXOhwiIiIiIiJyckzWrUAkElmq6+xbJyIiIiIiou5ism4lIyzJOvvWiYiIiIiIqHuYrFvJSNNE+MwLNdDrDQJHQ0RERERERM6MybqVJEQo4C2TQNXShrMVjUKHQ0RERERERE6MybqVSCViDOsdAIBH4YmIiIiIiKh7mKxb0Yho7lsnIiIiIiKi7mOybkWcCE9ERERERETWwGTdiob2DoBELEJJXTNK65qFDoeIiIiIiIicFJN1K/KVS5EQoQAAZF5kdZ2IiIiIiIi6hsm6lY24YoUbERERERERUVcwWbcy9q0TERERERFRdzFZtzJzZT1fqYKqRStwNEREREREROSMmKxbWaifJ2KCvGEwAFnsWyciIiIiIqIuYLJuAyNiuG+diIiIiIiIuo7Jug2MNB2FZ986ERERERERdQWTdRswV9aPF9ehtU0ncDRERERERETkbJis20CfYB8E+sjQ2qZHbolK6HCIiIiIiIjIyTBZtwGRSIQR0dy3TkRERERERF3DZN1GuG+diIiIiIiIuorJuo2Y961nXayBXm8QOBoiIiIiIiJyJkzWbWRQT394eohR26RFQVWj0OEQERERERGRE2GybiMyqRjDorjCjYiIiIiIiDqPyboNXd63ziFzRERERERE1HGdStZXrVqFIUOGQKFQQKFQICUlBdu3b7d8vKWlBYsWLUJQUBB8fX0xa9YslJeXt3uOoqIiTJs2Dd7e3ggNDcUzzzyDtra2dtfs2bMHw4cPh1wuR1xcHNasWXNVLCtXrkRMTAw8PT0xevRoHD58uDOfil2Y961nsrJOREREREREndCpZD0yMhKvv/46srKykJmZidtvvx0zZsxAXl4eAGDp0qX44Ycf8M0332Dv3r0oLS3FPffcY3m8TqfDtGnToNFocODAAaxduxZr1qzBihUrLNcUFhZi2rRpuO2225CdnY0lS5ZgwYIF2LFjh+Wa9evXY9myZXjppZdw9OhRJCUlITU1FRUVFd39eljVsN4BEIuAopomlKtahA6HiIiIiIiInITIYDB0a1R5YGAg3nzzTdx7770ICQnB119/jXvvvRcAkJ+fj4EDByIjIwNjxozB9u3bMX36dJSWliIsLAwAsHr1ajz33HOorKyETCbDc889h61btyI3N9fyGg888ADq6uqQlpYGABg9ejRGjhyJ999/HwCg1+sRFRWFp59+Gs8//3yHY1epVPD390d9fT0UCkV3vgzXNe1fPyOvVIX3HxyG6UN62uQ1iIiIiIiIyDl0NA/tcs+6TqfDunXroFarkZKSgqysLGi1WkyaNMlyTXx8PHr37o2MjAwAQEZGBgYPHmxJ1AEgNTUVKpXKUp3PyMho9xzma8zPodFokJWV1e4asViMSZMmWa65ntbWVqhUqnZvtjaSR+GJiIiIiIiokzqdrOfk5MDX1xdyuRxPPfUUNm/ejISEBCiVSshkMgQEBLS7PiwsDEqlEgCgVCrbJermj5s/dqNrVCoVmpubUVVVBZ1Od81rzM9xPa+99hr8/f0tb1FRUZ399DttBIfMERERERERUSd1OlkfMGAAsrOzcejQISxcuBDz5s3DyZMnbRGb1S1fvhz19fWWt+LiYpu/5ohoY2X9VJkKDS1am78eEREREREROT9pZx8gk8kQFxcHAEhOTsaRI0fw3nvvYfbs2dBoNKirq2tXXS8vL0d4eDgAIDw8/Kqp7eZp8Vde8+sJ8uXl5VAoFPDy8oJEIoFEIrnmNebnuB65XA65XN7ZT7lbwv09ERXoheKaZhwrqsOE/iF2fX0iIiIiIiJyPt3es67X69Ha2ork5GR4eHhg165dlo+dPn0aRUVFSElJAQCkpKQgJyen3dT29PR0KBQKJCQkWK658jnM15ifQyaTITk5ud01er0eu3btslzjaEZGm/vWeRSeiIiIiIiIbq5TlfXly5dj6tSp6N27NxoaGvD1119jz5492LFjB/z9/TF//nwsW7YMgYGBUCgUePrpp5GSkoIxY8YAACZPnoyEhATMmTMHb7zxBpRKJV544QUsWrTIUvF+6qmn8P777+PZZ5/FY489ht27d2PDhg3YunWrJY5ly5Zh3rx5GDFiBEaNGoV3330XarUajz76qBW/NNYzIiYQ3x4rwREOmSMiIiIiIqIO6FSyXlFRgblz56KsrAz+/v4YMmQIduzYgTvvvBMA8M4770AsFmPWrFlobW1FamoqPvjgA8vjJRIJtmzZgoULFyIlJQU+Pj6YN28eXnnlFcs1sbGx2Lp1K5YuXYr33nsPkZGR+Pjjj5Gammq5Zvbs2aisrMSKFSugVCoxdOhQpKWlXTV0zlGMijUOmTtWXAutTg8PSbcPNBAREREREZEL6/aedWdmjz3rAGAwGDD81XTUNmnx3aJxGBoVYLPXIiIiIiIiIsdl8z3r1HEikQjJ7FsnIiIiIiKiDmKybicjTfvWDxcyWSciIiIiIqIbY7JuJyNiTJX1i7Vw484DIiIiIiIi6gAm63aS2EsBuVSMGrUGBVVqocMhIiIiIiIiB8Zk3U7kUgmSTIPl2LdOREREREREN8Jk3Y7Mfevct05EREREREQ3wmTdjix966ysExERERER0Q0wWbej4b17QCQCLlQ3oaKhRehwiIiIiIiIyEExWbcjfy8PDAjzAwBk8Sg8ERERERERXQeTdTsbFWs8Cs++dSIiIiIiIroeJut2dnnfOvvWiYiIiIiI6NqYrNuZeSJ8XqkK6tY2gaMhIiIiIiIiR8Rk3c4i/L3QK8ALOr0B2cV1QodDREREREREDojJugDM1fXDhTwKT0RERERERFdjsi4Ac9/6wYJqgSMhIiIiIiIiR8RkXQAT+oUAADIv1qJGrRE4GiIiIiIiInI0TNYF0DvIGwkRCuj0Bvx4slzocIiIiIiIiMjBMFkXyNTEcADA9twygSMhIiIiIiIiR8NkXSBTBxuT9V/OVUHVohU4GiIiIiIiInIkTNYFEhfqh74hPtDqDPgpv0LocIiIiIiIiMiBMFkX0NTECADA9hylwJEQERERERGRI2GyLqAppr71PWcq0KRpEzgaIiIiIiIichRM1gU0qKcCUYFeaNHqsfd0pdDhEBERERERkYNgsi4gkUiEKYOM1fW0PB6FJyIiIiIiIiMm6wKbYupb332qAq1tOoGjISIiIiIiIkfAZF1gw6ICEKaQo6G1DfvPVQkdDhERERERETkAJusCE4tFSDUfhc/lUXgiIiIiIiJisu4QzFPh00+Wo02nFzgaIiIiIiIiEhqTdQcwKiYQgT4y1DZpcaiwRuhwiIiIiIiISGBM1h2AVCLG5IQwAMD23DKBoyEiIiIiIiKhMVl3EKmmo/A78sqh1xsEjoaIiIiIiIiExGTdQYzrGww/TykqG1pxtKhW6HCIiIiIiIhIQEzWHYRMKsakgeaj8JwKT0RERERE5M6YrDuQK1e4GQw8Ck9EREREROSumKw7kFv7h8DLQ4KSumbklqiEDoeIiIiIiIgEwmTdgXjJJLgtPgQAp8ITERERERG5MybrDmZKYgQAHoUnIiIiIiJyZ0zWHcxtA0Igk4hRUKXG2YpGocMhIiIiIiIiATBZdzB+nh4Y3y8YALA9h1PhiYiIiIiI3BGTdQc0JdE4FZ5960RERERERO6JyboDujMhDBKxCPnKBlyoUgsdDhEREREREdkZk3UHFOAtQ0qfIABAWh6PwhMREREREbmbTiXrr732GkaOHAk/Pz+EhoZi5syZOH36dLtrlEol5syZg/DwcPj4+GD48OHYtGlTu2tqamrw0EMPQaFQICAgAPPnz0djY/thaidOnMD48ePh6emJqKgovPHGG1fF88033yA+Ph6enp4YPHgwtm3b1plPx6FdPgrPZJ2IiIiIiMjddCpZ37t3LxYtWoSDBw8iPT0dWq0WkydPhlp9+aj23Llzcfr0aXz//ffIycnBPffcg/vvvx/Hjh2zXPPQQw8hLy8P6enp2LJlC/bt24cnnnjC8nGVSoXJkycjOjoaWVlZePPNN/Hyyy/jww8/tFxz4MAB/O53v8P8+fNx7NgxzJw5EzNnzkRubm53vh4OY/KgMIhEwPHiOpTWNQsdDhEREREREdmRyNCNZd6VlZUIDQ3F3r17MWHCBACAr68vVq1ahTlz5liuCwoKwj/+8Q8sWLAAp06dQkJCAo4cOYIRI0YAANLS0nDXXXfh0qVL6NmzJ1atWoW//vWvUCqVkMlkAIDnn38e3333HfLz8wEAs2fPhlqtxpYtWyyvM2bMGAwdOhSrV6/uUPwqlQr+/v6or6+HQqHo6pfBZu5bfQBHLtRixfQEPHZLrNDhEBERERERUTd1NA/tVs96fX09ACAwMNDyvrFjx2L9+vWoqamBXq/HunXr0NLSgokTJwIAMjIyEBAQYEnUAWDSpEkQi8U4dOiQ5ZoJEyZYEnUASE1NxenTp1FbW2u5ZtKkSe3iSU1NRUZGRnc+JYcyJTECAPvWiYiIiIiI3E2Xk3W9Xo8lS5Zg3LhxSExMtLx/w4YN0Gq1CAoKglwux5NPPonNmzcjLi4OgLGnPTQ0tN1zSaVSBAYGQqlUWq4JCwtrd435zze7xvzxa2ltbYVKpWr35sjMfetHLtSgsqFV4GiIiIiIiIjIXrqcrC9atAi5ublYt25du/e/+OKLqKurw48//ojMzEwsW7YM999/P3JycrodbHe99tpr8Pf3t7xFRUUJHdIN9QrwQlKkPwwGYOdJVteJiIiIiIjcRZeS9cWLF2PLli346aefEBkZaXn/+fPn8f777+PTTz/FHXfcgaSkJLz00ksYMWIEVq5cCQAIDw9HRUVFu+dra2tDTU0NwsPDLdeUl5e3u8b855tdY/74tSxfvhz19fWWt+Li4q58+naVaqqup3EqPHXRoYJqlNVzSCERERERkTPpVLJuMBiwePFibN68Gbt370ZsbPuhZ01NTcYnFbd/WolEAr1eDwBISUlBXV0dsrKyLB/fvXs39Ho9Ro8ebblm37590Gq1lmvS09MxYMAA9OjRw3LNrl272r1Oeno6UlJSrhu/XC6HQqFo9+boppr61jPOV6O+SXuTq4na23qiDLM/PIjb3tqDVXvOQ6vTCx0SERERERF1QKeS9UWLFuHLL7/E119/DT8/PyiVSiiVSjQ3G6t28fHxiIuLw5NPPonDhw/j/PnzePvtt5Geno6ZM2cCAAYOHIgpU6bg8ccfx+HDh7F//34sXrwYDzzwAHr27AkAePDBByGTyTB//nzk5eVh/fr1eO+997Bs2TJLLH/84x+RlpaGt99+G/n5+Xj55ZeRmZmJxYsXW+lL4xhig30QH+6HNr0B6afKb/4AIhODwYDVe88DAFq0evwjLR/T//ULsi7WCBwZERERERHdTKeS9VWrVqG+vh4TJ05ERESE5W39+vUAAA8PD2zbtg0hISG4++67MWTIEHz++edYu3Yt7rrrLsvzfPXVV4iPj8cdd9yBu+66C7fccku7Her+/v7YuXMnCgsLkZycjD/96U9YsWJFu13sY8eOxddff40PP/wQSUlJ2LhxI7777rt2w+5cReog81H4MoEjIWdy5EItckrqIZeK8cqMQQj0keF0eQNmrcrA8m9PoK5JI3SIRERERER0Hd3as+7sHH3Pulm+UoUp7/4MmVSMoy/eCV+5VOiQyAk88Xkmdp4sx+9G9cZr9wxGrVqD17afwobMSwCAIB8ZXpyegBlDe0IkEgkcLRERERGRe7DLnnWyjwFhfogN9oGmTY+f8itu/gByexer1Za2ifm3xAAAevjI8Ma9SdjwZAriQn1RrdZgyfpszPnkMAqr1AJGS0REREREv8Zk3QmIRCLLznVOhaeO+Gz/BRgMwG0DQhAX6tfuY6NiA7HtD+PxTOoAyKVi/HKuCqnv7sN7P55Fa5tOoIiJiIiIiOhKTNadxBRT3/pPpyvQomVCRddX36zFhkzjWsL5t/S55jUyqRiLbovDzqUTMKF/CDRterzz4xlMfe9nZJyvtme4RE7vWFEtVC3c1kFERETWxWTdSQyJ9EevAC80aXTYd6ZS6HDIga07XIQmjQ7x4X4YFxd0w2ujg3yw9tGR+NfvhiHYV46CSjV+99FB/GnDcdSoOYCO6GYOF9bgtx8cwGOfHYEbj4AhIiIiG2Cy7iREItEVU+F5FJ6uTavTY82BCwCAx26J7dDgOJFIhN8k9cSuP92Kh8f0hkgEbDp6Cbe/vQcbjhQzASG6gcOFxpMomRdrsec0b6QSERGR9TBZdyLmvvX0U+XQtOkFjoYc0bacMpTVtyDYV44ZQ3t26rH+Xh74fzMH49uFYxEf7oe6Ji2e3XQCs/9zEGfLG2wUMZFzO1V2+XvjnR/P8OYWERERWQ2TdSeSHN0Dwb5yNLS0IaOAfcXUnsFgwCe/FAIA5qZEQy6VdOl5hvXugR+evgV/uSseXh4SHL5Qg7v+9TPe2nGa8xKIfuWUUmX5/ycu1WM3N3YQERGRlTBZdyISsQipg8IAAGm5ZQJHQ44m82ItTlyqh0wqxkOje3fruTwkYjwxoS/Sl03ApIGh0OoMeP+nc0h9dx9nJhCZNGt0uGBaezhreCQA4N0fz7K6TkRERFbBZN3JTE2MAADszCuHTs9fCOmyj38uAADMGt4LQb5yqzxnZA9vfDR3BFY/nIxwhScuVjdh7qeH8fR/j6GiocUqr0HkrM6UN0BvAIJ9ZfjLXfHwlkmQU1KPXadYXSciIqLuY7LuZEb3CYS/lweq1RocLqwROhxyEBer1dh5shwA8Ni4WKs+t0gkwpTEcPz4p1vx6LgYiEXAD8dLccfbe/HlwYvQ86YRualTZcYj8AMjFAjylWNuSgwA4N1d7F0nIiKi7mOy7mQ8JGLcmWA8Cr8jj1Phyeiz/RdgMAC39g9BvzA/m7yGr1yKl+4ehO8X34Ihkf5oaGnDC9/lYtbqA5akhcid5CuNw+Xiw43fc09M6AMfmQS5JSqkm26eEREREXUVk3UnNDXx8go3VjWpvlmLDZnFAIAF461bVb+WxF7+2Pz7cXj57gT4yqU4VlSH6f/+Bf+37RSaNG02f30iR3Hyiso6AAT6yDBvbAwA9q4TERFR9zFZd0Lj4oLhI5NAqWpB9qU6ocMhga0/UoQmjQ4DwvxwS1ywXV5TIhbhkXGx+HHZrbhrcDh0egM+3FeAO/+5D7tOsaJIrs9gMCDflKzHhyss7398vLG6frJMhR15/F4gIiKirmOy7oQ8PSS4faB5KjyPwrszrU6PNfsvAADm3xILkUhk19cP9/fEBw8l49NHRqBXgBdK6poxf20mnvoiC2X1zXaNhcieSutboGppg1QsQlyor+X9PXxkeGRcDADgvV1nefqJiIiIuozJupO68ig8j1q6r+25SpTWtyDYV4bfDO0pWBy3x4chfdkEPHlrH0jFIqTlKTHp7b349JdCbi0gl2SuqseF+kImbf+j9PHxfeArl+JUmQo7T/KGKhEREXUNk3UnNXFACORSMYpqmix9k+ReDAYDPjGta5szJgaeHhJB4/GWSbF86kBs+cMtGN47AGqNDq9sOYknv8hkwk4u59Sv+tWvFOAtw6Om6vq7P7K6TkRERF3DZN1JecukuLV/CAAehXdXWRdrcfxSPWRSMR4a01vocCziwxXY+NRY/N9vB0MuFePHUxV4Y0e+0GERWdWpX02C/7UFt/SBn1yKfGUD0ri5g4iIiLqAyboTmzr48lF4cj8f/1wIALhnWC8E+8oFjqY9sViEB0f3xlv3JQEA/rO3AN8evSRwVETWY66sx1+jsg4A/t4eePQW43aG91hdJyIioi5gsu7Ebo8Pg4dEhLMVjThX0Sh0OGRHRdVNll7Yx26x/bq2rro7qScW3xYHAHj+2xwcK6oVOCKi7mvW6HChSg0AGBhx7co6YBz66OcpxenyBmzLLbNXeEREROQimKw7MX8vD4wzrepK4y+CbuWzA4XQG4AJ/UPQP+z6yYIjWHZnf9yZEAZNmx5PfpEFZX2L0CERdcuZ8gboDUCQjwwhNzjV4u/lgcfGsbpOREREXcNk3clNGWQ8Cr+dR+HdhqpFiw1HigEACxy4qm4mFovwzuyhGBDmh4qGVjzxRSZatDqhwyLqsnzl5eFyN1uX+Jipun62ohFbc3hTlYiIiDqOybqTuzMhDGIRkFeqQnFNk9DhkB2sP1wMtUaH/mG+GN8vWOhwOsRXLsXH80agh7cHTlyqx7MbT3DlIDmtU2U3Hi53JX8vDyy4pQ8A4951bkYgIiKijmKy7uSCfOUYHRsEgIPm3EGbTo81By4AMPbD3qyq50iiAr2x6uFkSMUifH+8FB/sOS90SERdcqO1bdfy6C0xUHhKca6iEVtOlNoyNCIiInIhTNZdwJRE81F4HrF0dWl5SpTUNSPIR4YZQ3sJHU6njekThL/NGAQAeGvnaaSfLBc4IqLOMRgMV0yC79i8CIWnBx4fb6yu/4vVdSIiIuogJusuINXUt360qA7lKg7vclUGgwEfmda1PTwmGp4eEoEj6pqHRkdjzphoGAzAknXHcNq0r5rIGZTVt0DV0gapWIS4UN8OP+6RcTHw9/LA+Uo1q+tERETUIUzWXUC4vyeG9w4AAOzI41F4V3W0qBbHi+sgk4rx8JhoocPplhV3JyClTxDUGh0WfH4ENWqN0CERdYi5qt43xBdyacdvmPl5euDx8abJ8KyuExERUQcwWXcRUxMjAADbc5isu6pPfjFW1X87tBdC/K6/LsoZeEjE+OCh4egd6I3immb8/qssaHV6ocMiuql800mQG+1Xv555Y2MQ4O2Bgko1vj9eYu3QiIiIyMUwWXcR5r71Q4XVqG5sFTgasrbimibLAMHHnGBdW0f08JHh43kj4CuX4mBBDf72Q57QIRHd1ElLv3rHhstdya9d7/o5tPEGFREREd0Ak3UXERXojUE9FdAbgB9PcWiXq/ls/wXoDcD4fsEY0IF1Uc6if5gf3p09FCIR8OXBInxx8KLQIRHdUH4nJ8H/2ryxMejh7YHCKjX+l83edSIiIro+JusuZKplKjyPwrsSVYsWGzKLAQALTFU5VzIpIQzPpA4AAPzt+zwcOF8lcERE19ai1aGwSg0AGNjFm2a+cimemNAXAPDv3WdZXSciIqLrYrLuQsxH4fefq0J9s1bgaMhaNhwpRmNrG/qF+mJCv2Chw7GJhbf2xcyhPdGmN+D3Xx1FUXWT0CERXeVMeQP0BiDIR9atuRFzU6IR6CPDheomfMfqOhEREV0Hk3UXEhfqh7hQX2h1BuzO51F4V9Cm0+Oz/RcAAPNviYVIJBI2IBsRiUR4fdYQJEX6o65JiwWfH0FDC284kWPJLzMOl4uP8OvW96KPXIonJhhPybC6TkRERNfDZN3FmI/Cp/EovEvYkVeOkrpmBPnIMHNYL6HDsSlPDwn+M2cEQv3kOFPeiKXrs6HneityIObhcgPDu9avfqW5KdEI8pHhYnUTvj3GyfBERER0NSbrLsZ8FH7vmUo0adoEjoa66+NfCgAAD42JhqdHx3c6O6twf098OHcEZFIxfjxVgbd2nhY6JCKLfGXXJ8H/mrdMiidvvVxd5+pCIiIi+jUm6y4mIUKBqEAvtGj12HO6UuhwqBuyLtbiWFEdZBIx5oyJFjocuxkaFYA3Zg0BAHyw5zz+l82qIwnPYDDgVFnXd6xfy8NjohHsK0NxTTO+PXrJKs9JREREroPJuosRiUSYmhgBgEfhnd2nvxQCAGYO69mtYVbOaOawXnjqVuPE7Gc3nsDx4jphAyK3p1S1oL5ZC6lYhLhQX6s8p7dMavnv/N+7z0HTxuo6ERERXcZk3QWZj8Lvzq9Aa5tO4GioK4prmrA9twwA8NgtsQJHI4xnUgfgjvhQtLbp8cQXmShXtQgdErmxU6Z+9b4hvpBLrdeS8tDoaAT7ynGpltV1IiIiao/JugsaGhmAMIUcja1t+OUsd1Y7ozUHLkBvAMb3C0a8FYZZOSOJWIR3HxiKfqG+KFe14okvstCi5c0nEsapKybBW5OXTIKnLL3rrK4TERHRZUzWXZBYLMKUQcbq+nYehXc6DS1arD9SDMB9q+pmfp4e+HjeCAR4e+B4cR2Wf5sDg4ET4sn+zJX1gVYYLvdrD4+JRoifHCV1zdiYxeo6ERERGTFZd1FTTH3rP54q55RhJ7P+SDEaW9sQF+qLW/uFCB2O4KKDfPDBg8MhEYuw+VgJ/rOvQOiQyA3lK02V9XDrVtYB49rChabe9ZU/sbpORERERkzWXdSo2EAE+chQ16TFoYIaocOhDmrT6fHZ/gsAgPm3xEIsFgkbkIMYGxeMl+5OAAD8Iy0fu06VCxwRuZMWrQ4FlY0AbFNZB4AHR/dGqKm6viGz2CavQURERM6FybqLkohFuDMhDAAsg8rI8e08WY6SumYE+sjw22G9hA7HocwZE40HR/eGwQD8cV02zpY3CB0SuYmz5Y3QG4BAHxlCbbSZwdNDgt9PvFxd53BQIiIiYrLuwsxT4XfklUOnZ5+vM/j4Z+MR74dH94anh/UmTrsCkUiEl+8ehFGxgWhsbcOCzzNRq9YIHRa5AXO/eny4H0Qi2512eWBUb4Qp5Cirb8GGTPauExERubtOJeuvvfYaRo4cCT8/P4SGhmLmzJk4ffr0VddlZGTg9ttvh4+PDxQKBSZMmIDm5mbLx2tqavDQQw9BoVAgICAA8+fPR2NjY7vnOHHiBMaPHw9PT09ERUXhjTfeuOp1vvnmG8THx8PT0xODBw/Gtm3bOvPpuLyxfYPh5ylFVWMrjhbVCh0O3cTRolocLaqDTCLGwynRQofjkGRSMVY/nIzIHl64WN2ERV8f5UwGsrlTStsNl7uSsboeBwD4gNV1IiIit9epZH3v3r1YtGgRDh48iPT0dGi1WkyePBlqtdpyTUZGBqZMmYLJkyfj8OHDOHLkCBYvXgyx+PJLPfTQQ8jLy0N6ejq2bNmCffv24YknnrB8XKVSYfLkyYiOjkZWVhbefPNNvPzyy/jwww8t1xw4cAC/+93vMH/+fBw7dgwzZ87EzJkzkZub252vh0uRScWYNNB0FD6HU+Ed3Se/FAIAfjO0J0L9PAWOxnEF+sjw8bwR8JZJcOB8Nf7flpNCh0Qu7srKuq3NHhmFcIUnyupbLFshiIiIyD2JDN3Yg1RZWYnQ0FDs3bsXEyZMAACMGTMGd955J1599dVrPubUqVNISEjAkSNHMGLECABAWloa7rrrLly6dAk9e/bEqlWr8Ne//hVKpRIymQwA8Pzzz+O7775Dfn4+AGD27NlQq9XYsmWL5bnHjBmDoUOHYvXq1R2KX6VSwd/fH/X19VAoXHOX9Y48JZ78Igu9Arzwy3O32fQIJ3XdpdomTHjjJ+gNwPY/jrd5Bc8VmP/bBoD/++1gPDi6t8ARkSsyGAwY9mo66pq02PL0LUjs5W/z1/wi4wJe/F8ewhRy7H3mNrbEEBERuZiO5qHd6lmvr68HAAQGBgIAKioqcOjQIYSGhmLs2LEICwvDrbfeil9++cXymIyMDAQEBFgSdQCYNGkSxGIxDh06ZLlmwoQJlkQdAFJTU3H69GnU1tZarpk0aVK7eFJTU5GRkXHdeFtbW6FSqdq9ubpb+4fAWyZBSV0zckrqhQ6HrmPtgQvQG4Bb4oKZqHdQ6qBw/HlyfwDAiv/l4lBBtcARkStSqlpQ16SFRCxCXKivXV7z/pFR6OnviXJVK9YdLrLLaxIREZHj6XKyrtfrsWTJEowbNw6JiYkAgIIC43Csl19+GY8//jjS0tIwfPhw3HHHHTh79iwAQKlUIjQ0tN1zSaVSBAYGQqlUWq4JCwtrd435zze7xvzxa3nttdfg7+9veYuKiurqp+80PD0kuG2A8eu9PZdH4R1RQ4sW6w4bj7vOvyVW4Gicy6Lb4jB9SATa9AYs/OooimuahA6JXEx+mXHrQN8QH7tVuOVSCX5/m6l3fc95tGjZu05EROSOupysL1q0CLm5uVi3bp3lfXq9cdDTk08+iUcffRTDhg3DO++8gwEDBuDTTz/tfrTdtHz5ctTX11veiovdox8w1TQVPi1XiW50PZCNbMi8hIbWNvQN8cGt/UOEDsepiEQivHlvEgb38keNWoPHP89EY2ub0GGRCzlp6Ve374mX+0ZEoqe/JyoaWvFfVteJiIjcUpeS9cWLF2PLli346aefEBkZaXl/REQEACAhIaHd9QMHDkRRkfGXjfDwcFRUVLT7eFtbG2pqahAeHm65pry8vN015j/f7Brzx69FLpdDoVC0e3MHt8eHQiYVo7BKjdPcTe1QdHoDPttvHCw3/5Y+EIs5U6CzvGQSfDg3GcG+cuQrG7BsfTb0XFVIVpKvNP6bae/2FLlUgkW3s7pORETkzjqVrBsMBixevBibN2/G7t27ERvb/shuTEwMevbsedU6tzNnziA62riKKiUlBXV1dcjKyrJ8fPfu3dDr9Rg9erTlmn379kGr1VquSU9Px4ABA9CjRw/LNbt27Wr3Ounp6UhJSenMp+QWfOVSTOgXDMBYXSfHsTNPiUu1zejh7YF7hvcSOhynFeHvhQ/nJkMmEWPnyXK88+MZoUMiF2GZBB9h+0nwv3ZfchR6BXihsqEVXx1idZ2IiMjddCpZX7RoEb788kt8/fXX8PPzg1KphFKptOxQF4lEeOaZZ/Cvf/0LGzduxLlz5/Diiy8iPz8f8+fPB2Cssk+ZMgWPP/44Dh8+jP3792Px4sV44IEH0LNnTwDAgw8+CJlMhvnz5yMvLw/r16/He++9h2XLllli+eMf/4i0tDS8/fbbyM/Px8svv4zMzEwsXrzYWl8blzIl0Xjqgcm6Y/nYtK7t4THRnPjcTcN798D/3TMYAPDv3efww/FSgSMiZ9ei1aGgshEAkCDA4EeZVIzFpur6qj3n0axhdZ2IiMiddCpZX7VqFerr6zFx4kRERERY3tavX2+5ZsmSJVi+fDmWLl2KpKQk7Nq1C+np6ejbt6/lmq+++grx8fG44447cNddd+GWW25pt0Pd398fO3fuRGFhIZKTk/GnP/0JK1asaLeLfezYsfj666/x4YcfIikpCRs3bsR3331nGXZH7U0aGAqpWIR8ZQMKq9RCh0MAjhXVIutiLWQSMeakRAsdjku4NzkSj483nvj58zfHkXOJGxCo686WN0JvAHp4eyDUTy5IDLOGRyKyhxeqGlvx1aGLgsRAREREwujWnnVn5w571q8055ND+PlsFZ6dMgC/nxgndDhub/HXR7HlRBlmDY/E2/cnCR2Oy9DpDZi/9gj2nK5ESp8g/PeJMUKHRE5qQ2Yxnt14AmP7BuHrx4X772jd4SI8/20Ogn1l+PnZ2+El4ykcIiIiZ2aXPevkXKaYpsLv4FF4wZXUNVtW6XFdm3VJxCIsnzoQAHD8Uh2HzVGXmde22XsS/K/NSo5EVKAXqho1+PIgq+tERETugsm6G7kzIQwiEXD8Uj1K6pqFDsetrT1wATq9AWP7BiGhp+uf6rA3405sMZo0OhRWs+2DusY8XG6gAMPlruQhEePp2/oBAFbvPY8mDdcTEhERuQMm624k1M8TI6MDAXDQnJAaW9vwX9Nk5wXjWVW3BalEbFm1lVvCvnXqPIPBgHylOVkX/obab4f3Qu9Ab1SrNfgig9V1IiIid8Bk3c2Yj8JvPcFJ2ULZcKQYDa1t6BPig4n9Q4UOx2Ul9vQHAOSVqgSOhJxRuaoVtU1aSMQixIX6Ch2Osbpumgz/n30FULeyuk7uw43HKxGRm2Oy7mamDYmAWAQcLarjVHgB6PQGfHbAuK7tsXGxEItFAkfkuhJ7sbJOXXfKVFXvE+zjMGsVfzusF6KDvFGj1uBzVtfJTeSV1mPYq+l4buMJJu1E5HaYrLuZMIUnJvQPAQBsyrokcDTuJ/2kEsU1zQjw9sCs4ZFCh+PSBpkq67kl9fwFjzrtcr+68EfgzaQSMZ6+3di7/uG+86yuk8vT6Q34y7c5qGvSYn1mMf69+5zQIRER2RWTdTd0b7IxSdx09BJ0nJRtVx//bKyqPzw6muuXbKx/mB88JCKoWtpwqZYDFalzLJPgBR4u92szh/ZEbLAPapu0WJtxQehwiGxq3ZEiHL9UD5nE+OvqP9PPYFtOmcBRERHZD5N1NzRpYBgUnlKU1bcg43y10OG4jeziOmRerIWHRIS5KdFCh+PyZFIxBoQbEy0ehafOcsTKOmCurht71z/cV4BGVtfJRVU3tuKNtNMAgOV3xePRcTEAgGUbspFzif+mE5F7YLLuhjw9JPjN0J4AgI1ZxQJH4z4++cVYVb87qSdCFZ4CR+MezEPmcpisUye0aHUoMM30GCjwjvVr+U1ST/QJ9kFdkxZrD1wQOhwim/hHWj7qm7VIiFBgzpho/PWugZjQPwQtWj0e/zwTFaoWoUMkIrI5Jutu6t7kKABAWp4SqhatwNG4vpK6ZsvRvfm3cF2bvST2MvWtcyI8dcK5ikbo9AYEeHsgTCEXOpyrSCVi/OEOc+96ARr4bzi5mKyLNdiQaZyr8+rMREglYkglYrz/4DD0DfGBUtWCxz/PRItWJ3CkRES2xWTdTSVF+iMu1BctWj22nWD/l619fuACdHoDUvoEWQafke2Zk/U8DpmjTrAcgQ9XQCRyzI0Ndyf1RJ8QH9Q3a/H7r47i458LsPdMJZT1LfxvnZxam06PF77LAwDcPyISydE9LB9TeHrgk3kjEeDtgeOX6vEMJ8QTkYuTCh0ACUMkEuHe5Ei8vj0fG7Mu4YFRvYUOyWU1tGjx9eEiAMCC8ayq21N8uB8kYhGq1RooVS2I8PcSOiRyAqccdLjclSRiEZZM6o8//PcYfj5bhZ/PVlk+pvCUon+YH/qH+2FAmJ/x/4f5IsjX8U4JEP3aFwcv4lSZCv5eHnhuSvxVH48J9sGqh5Ix55ND+OF4KfqH+uJp00kTIiJXw2Tdjf12WC+8kZaPzIu1uFClRkywj9AhuZy6Jg0eW3MEDS1t6BPsg9sGhAodklvx9JCgX6gv8pUNyC1RMVmnDslXOuZwuV+7e0gE/ORSHL9UhzPlDTitbMCF6iaoWtqQebEWmRdr210f7CszJe7GtwHhvugX5geFp4dAnwFRexWqFvxz5xkAwLNTBlz3BlNK3yC8MiMRf9mcg7fTzyAu1BdTB0fYM1QiIrtgsu7GwhSeGN8vBHvPVGLT0Uv40+QBQofkUpT1LZj76SGcKW+Ev5cH/jl7KMRixzxS68oG9fQ3Jev1uDMhTOhwyMEZDIZ2x+AdmUgkwm3xobgt/vJNwNY2HQoq1Zbk/Ux5I86UN6CopglVjRpUNVbjwK+2gET4e5qSdz/0C/XFgHA/xIX6wlvGXxHIvv5v2yk0tLYhKdIfD4y88Ym/B0f3xpnyBqw5cAFLN2QjKtDb0vpEROQq+JPYzd2bHGlM1rMuYemk/kwmraSgshFzPjmMkrpmhCnk+GL+aPQPc9wjta4ssZcCm44CeaWcCE83V9HQitomLcQioF+Yr9DhdJpcKsHACMVVpwLUrW04V2FM3M+UN+B0eSPOljegrL7F8rb3TKXlepEIiOrhbanAm6vxfUJ8IJdK7P1pkRvIOF+N77JLIRIZh8pJOvD7yAvTBqKgSo19ZyqxYG0mvl88jttWiMilMFl3c3cmhMHPU4rS+hZkFFRjXFyw0CE5vZxL9Xjks8OoVmsQG+yDzx8bhahAb6HDcluWifAlnAhPN3fSVFXvE+ILTw/XSUp95FIkRQUgKSqg3fvrm7U4W365Am+sxjegWq1BUU0Timqa8OOpcsv1ErEIscE+6B9mTOBnDY/kv2/UbVqdHiv+lwsAeGh0bwyJDOjQ46QSMf79u2G454P9OF+pxuNfZGH9E2Nc6nuXiNwbk3U35+khwW+SeuKrQ0XYmHWJyXo3HThfhSc+z0JjaxsSeymw5tFRCOZQJ0ENjFBAJAKUqhZUNrQixI9/H3R9+abhco7er24t/l4eGBETiBExge3eX9XYaqzCKxtwpqIRZ5QNOF3egIYWY4X+XEUjtuUocbiwBl8/Pkag6MlVfPpLIc5WNCLIR4ZnJl89VO5G/L2ME+JnfrAfx4vr8OzGE3jvgaEOu8mBiKgzmKwT7k2OxFeHirA9twyvzBgEPw4b6pK0XCX+8N9j0Oj0SOkThA/nJvNr6QB85VLEBvugoFKNvNJ6TOSQP7oBc796fLh7t60E+8oR7CvH2L6Xb+AaDAYoVS04U96IrAs1+NfuczheXAe93sAWKuqy0rpmvLfrLADg+anx8Pfu/M/NmGAffPDQcMz95DC+P16K/mG+WHw7J8QTkfPjnnXC0KgA9A3xMe5cz+HO9a5Yd7gIv/8qCxqdHlMGheOzR0cyUXcgiabd9nmlPApPN2aeBJ/gJpX1zhCJRIjw98Kt/UPwhzv6QS4VQ63R4WJNk9ChkRN7dctJNGl0GBHdA7OGR3b5ecb2DcbfZgwCALy18wy28/cZInIBTNbJtHM9CgCwMeuSwNE4F4PBgA/2nMPz3+ZAbwAeGBmFlQ8NZ7+cg0nsZUy8cks4ZI6ur0Wrw/lKNQDH3rHuCKQSMeJNNzQ4vJG6au+ZSmzPVUIiFuHVmYndPqHx0OhoPDI2BgCwbMNx/ptPRE6PyToBMO5cF4uAIxeMO9fp5vR6A/6+9RTeSDsNAPj9xL547Z7BHZpgS/ZlrqznMqmgGzhX0Qid3oAAbw+Ec6L0TSVYknWeWKHOa9Hq8JJpqNwjY2OsNifihWkDMaF/CJq1Ojz+eSYqVC1WeV4iIiEwWScAQLi/cec6AGw6yur6zWh1ejyz8QQ+/qUQgPGXg2enxHOgjYMaZErWi2uaUd+kFTgaclRX9qvze/nmBvVksk5d9+G+AlyobkKonxxLJlmvv9w8Ib5PiA/K6lvwxBdZaNHqrPb8RET2xGSdLO5NNvaKfXu0BHq9QeBoHFeLVoenvsjCpqOXIBGL8PZ9SVgwvo/QYdEN+Ht7ICrQCwCP7NL15SvdaxJ8d5mT9ZOl9TAY+DODOq64pgkrfzoHAHhheoLVZ7yYJ8T7e3kgu7gOz206wf9GicgpMVknC/PO9ZK6ZhwsqBY6HIdU36zF3E8OY1d+BeRSMf7zcDJmJXd9IA7ZD4/C082Yh8sNDGey3hHx4QqIRUBVowYVDa1Ch0NO5OXv89DapsfYvkG4e0iETV4jNtgHqx4aDqlYhP9ll+KDPedt8jpERLbEZJ0sPD0kuDupJwAOmruWioYWPPDhQRy+UAM/Tym+mD8akxLChA6LOiixlylZL+GRXbqawWDAKTfbsd5dXjIJ+ob4AuCJFeq49JPl2JVfAQ+JCK/MSLRpy8nYuMsT4t/ccRppuZwQT0TOhck6tWM+Cr8ttwwNLeztNbtYrca9qzJwqkyFED851j+RglGxgUKHRZ1wOVlnUkFXq2xoRY1aA7EI6BfmK3Q4TsPSt86bYNQBzRodXv4+DwCwYHwfxIXa/nvtygnxS9dzQjwRORcm69TOsKgA9DHtXN+eoxQ6HIdwslSFWasyUFTThN6B3tj4VAoSerLy5mzMSUVBlZo3ougqJ03D5fqE+HL1YieYhzdyyBx1xMqfzqGkrhm9Arzw9O1xdnvdF6YNxPh+wZcnxDdwQjwROQcm69SOcee6sbrOo/DA4cIazP4wA1WNrYgP98PGp1IQHeQjdFjUBcG+ckT4G9dxmY87E5mZh8vFh3O/emdYKutlrFbSjRVUNuLDfQUAgBenJ8BbJrXba0slYrz/4PDLE+I/54R4InIOTNbpKvcMi4RYBBy+UOPWO9d/PFmOOZ8cQkNLG0bFBGL9kykI5e5lp2auAvIYJP2aeW0b+9U7x3zKqLimGfXNPLFC12YwGPDS93nQ6PSYOCAEqYPsP+/l1xPin+eEeCJyAkzW6Srh/p64xbRz/Vs33bm+MesSnvwyC61tekwaGIrP54+Cv5d1V8uQ/SX2MiYWnAhPv5ZvGS7HynpnBHjL0CvAuBbxJI/C03Vsy1Hi57NVkEnF+NtvBtl0qNyNXDkh/jtOiCciJ8Bkna7JfBR+kxvuXP9oXwH+/M1x6PQG3DO8F1Y9nMweVhdhXt/GYVh0pdY2Hc5XNgJgZb0rLEfheROMrqGxtQ2vbjkJAFh4a1/BW8nGxgXj5d9cOSGe83mIyHExWadrmuyGO9cNBgP+kZaPv287BQB4fHws3ro3CR4Sfpu4CvNE+LMVDWjWsF+RjM5VNKJNb4C/lwfC2erSaeb2ElbW6Vr+tesslKoW9A70xsKJfYUOBwDw8JhozEuJBgAsXZ/NG01EAqpr0nCGxA0wC6Frcred6206PZZ/m4NVpiNxz02Jx1/uGgixWJijemQbYQo5gn1l0BuAfCUTCzIyDxyMD/cT7HiuM7tcWef3FLV3prwBn/5SCAD4228GOdQptRenJ1yeEL+WE+KJhLDvTCVG/v1HDH55B6b/+2f8dXMONhwpRr5SBZ2bney9HibrdF3mo/Dbc5VobG0TOBrbadHqsOjro1h3pBhiEfD6PYOxcGJf/tLugkQi0eUhc0wsyCSfw+W6ZZBpFsS5ykZWR8jCYDDghe9y0aY3YHJCGG6LDxU6pHakEjHe/91w9An2QWl9C578ghPiieypurEVf/rmOLQ6A7Q6A3JLVPjqUBGe3XQCU979GYkv7cB9qw/g1S0n8f3xUlysVrvlUEj77c0gpzMsKgB9gn1QUKXGtpwy3D8iSuiQrK6hRYsnPs9CRkE1ZBIx/vW7oZiSGCF0WGRDib0U2HumEnmcCE8mp5TmZJ3D5boiXOGJQB8ZatQanFY2ICkqQOiQyAF8l12Cw4U18PQQY8XdCUKHc03+3h745JGRmLlyP44V1WH5tzn45/1JvFlPZGMGgwHLv81BZUMr+oX6YvWcZOSXNeDEpTpkF9cht6Qeao0ORy7U4siFWsvjArw9MLiXP5IiAzAk0h9DowJcflMTk3W6LpFIhFnJkXhzx2lszLrkcsl6VWMrHvnsMHJLVPCVS/Hh3GSM7RssdFhkY4mWyjqTdTL+wnDKMgmelfWuMJ5YUeDns1XIK1UxWSfUN2vx9635AICnb++HyB7eAkd0feYJ8XM+PYzNx0oQF+qLRbfFCR0WkUtbf6QYO0+Ww0MiwrsPDEXfEF/0DfHFtCHGgplOb0BBZSOOX6rHiUt1OH6pHqdKVahr0uLns1X4+WyV5bnCFZ4YEumPpChjAj+kVwD8vV1ngxOTdbqhe4b3wls7T+NwYQ0uVqsFn+JqLcU1TZj76WEUVqkR5CPD2sdGWYaPkWsz/z2fVjZA06aHTMpuIHdW2dCKGrUGYhHQP4yV9a5KsCTrvAlGwDvpZ1DV2Io+IT54fHwfocO5KfOE+Be/y8WbO04jLtQXqYPChQ6LyCUVVqnxtx+MGyL+PHmApT3xShKxCP3C/NAvzM/Slqtp0+O0sgHZl+pworgOJy7V42xFA5SqFihPtmDnyXLL42OCvE3JewCSIv0xqKc/vGSOMzOjM5is0w1F+Hvhlrhg/Hy2CpuOlmDZnf2FDqnbTisbMPfTQyhXtaJXgBe+mD8KfUJ8hQ6L7CSyhxcUnlKoWtpwpryBN2nc3CmlsaoeG+zjUMOvnI35ly0OmaPcknp8nnEBAPDqjESnuSE6Z0w0zpY34POMi1i6PhsbnxqLhJ48bUNkTVqdHkvWZ6NZq0NKn6BO3cyTScUYHOmPwZH+wBjjNgd1axvySlWW6vvx4joU1TThQrXx7X/ZpQBMyX+oL4aaEvghkf4YEO7nFBufmKzTTd2bHGlM1rMuYckd/Zx6QnrWxRo8tiYT9c1a9A/zxeePjUa4v2v3ulB7IpEIib38ceB8NfJK65msu7lTpuFy8TwC3y3mifDmCb4SJ/45QV2n1xuHyukNwPQhERgX51ytZSumJ6CgUo1fzlVhwdoj+N/iWxDiJxc6LCKX8e9dZ3G8uA4KTynevj+p2zmFj1yKUbGBGBUbaHlfrVqDEyX1OFFsSuAv1aGyoRX5ygbkKxuw7kgxAEAuFSOhpwJP3x6H2+PDuhWHLTFZp5tKHRQOP7lp53phtdP2df90ugILv8xCi1aPYb0D8NkjIxHgLRM6LBKAOVnPLVFh9kihoyEhmSfBJzBZ75bYIB94yyRo0uhQUNmIfmwpcEsbMouRXVwHH5kEL0xzzKFyNyKViLHyweH47Qf7UVClxpNfZOLrx8fw1A2RFWReqMH7P50DAPz9t4PRM8DLJq/Tw0eGW/uH4Nb+IQCMs2mUqhYcLzb2v58w9cGrWtpwrKgOr2455dDJuuPX/klwnh4STHfynetHLtTg8bWZaNHqcWv/EHy1YDQTdTdmrgJyyBxduWOduk4sFlkG9J0s41F4d1Sr1uAfacahckvv7O+0p9b8vT3w8bwRUHhKcdQ0Id4d10URWVNDixZLN2RDbwDuGdYLd5vyCnsQiUSI8PfClMRwPDslHl8uGI3sFZOxc+kESMUiFFapUVTdZLd4OovJOnWIZed6jvPtXFe3tuFPG46jTW/AlEHh+GjuCHjLeKjEnZmPvp8qU6FNpxc4GhJKa5sO5ysbAXASvDWYTyewb909vbEjH7VNWgwI88O8sTFCh9MtfUJ88cFDyZCIRdh8rAQf/1wodEhETu3l70+iuKYZkT288LcZg4QOB2KxCP3D/JAc3QMAsPdMhcARXV+nkvXXXnsNI0eOhJ+fH0JDQzFz5kycPn36mtcaDAZMnToVIpEI3333XbuPFRUVYdq0afD29kZoaCieeeYZtLW1TwD37NmD4cOHQy6XIy4uDmvWrLnqNVauXImYmBh4enpi9OjROHz4cGc+HeqE4b2NO9ebtTpsyykTOpxO+b9tp1BU04ReAV54874hTjPshmwnNsgHPjIJWrR6FFSphQ6HBHKuohFtegMUnlJEOGkV0JGYT6xwIrz7OVZUa+kDfXVmolMMbbqZW/oF4yXTfvh3fzyDGrVG4IiInNPWE2XYdPQSxCLgndlD4efpOGvVbh1gPCq/53SlwJFcX6f+Nd27dy8WLVqEgwcPIj09HVqtFpMnT4ZaffUvu++++y5EoquHBuh0OkybNg0ajQYHDhzA2rVrsWbNGqxYscJyTWFhIaZNm4bbbrsN2dnZWLJkCRYsWIAdO3ZYrlm/fj2WLVuGl156CUePHkVSUhJSU1NRUeG4d0acmXnnOuBcR+H3nqnEV4eKAABv3jfEof6BIOGIxSLL9OqcS0ws3FX+FfvVr/XzijrnyonwPDbsPnR6A178Xy4MBuO61ysHPTm7OWOiMainAmqNDh/9XCB0OEROp6y+GX/ZnAMA+P3EOIyMcax/Hyb2DwUAHDhfjdY2ncDRXFunkvW0tDQ88sgjGDRoEJKSkrBmzRoUFRUhKyur3XXZ2dl4++238emnn171HDt37sTJkyfx5ZdfYujQoZg6dSpeffVVrFy5EhqN8a7l6tWrERsbi7fffhsDBw7E4sWLce+99+Kdd96xPM8///lPPP7443j00UeRkJCA1atXw9vb+5qvSdZxz/BeEImAw4U1Dt3bYVbfpMWzG48DAB4ZG+O0g/HINgb1Yt+6uzNPgucReOvoH+4LqViEuiYtSutbhA6H7OSrQxeRW6KCwlOK5VMHCh2OVYlEIiydZFxZu/bABVQ3tgocEZHz0OsN+PM3x1HfrMWQSH/8cVI/oUO6ysAIP4T6ydGs1eFIYa3Q4VxTt84p1dcbf8kNDLx8l6SpqQkPPvggVq5cifDw8Ksek5GRgcGDByMs7PLUvdTUVKhUKuTl5VmumTRpUrvHpaamIiMjAwCg0WiQlZXV7hqxWIxJkyZZrrmW1tZWqFSqdm/Ucead6wCw6ajjV9df/iEP5apW9An2wXNT4oUOhxxMorkKWMJ/B9xVvtJcWedwOWuQSyWIC/UFAOSV8CaYO6hsaMWbO4ztkM+kDnDJNWd3DAzFkEh/NGl0+HAfq+tEHfXJL4XYf64aXh4SvDt7qEO2x4hEIsvUeEftW+/yV02v12PJkiUYN24cEhMTLe9funQpxo4dixkzZlzzcUqlsl2iDsDyZ6VSecNrVCoVmpubUVVVBZ1Od81rzM9xLa+99hr8/f0tb1FRUR3/hAnA5UFzm45egl7vuMcc03LLsPlYCcQi4K37k+Al49oVas88ZC6vtN6h/1sm2zAYDJd3rIezsm4tVx6FJ9f32vZTaGhpw+Be/nhwdLTQ4diESCTCElNF8POMi6hidZ3opk6Wqiw38l6cnoA+Ib4CR3R9jt633uVkfdGiRcjNzcW6dess7/v++++xe/duvPvuu9aIzeqWL1+O+vp6y1txcbHQITmdyQnGneuXaptxqLBG6HCuqaqxFX/ZnAsAWDixL4b37iFwROSI+ob4QC4VQ63R4UI1h8y5m8rGVlSrNRCLgP7cCW41l4fMMVl3dYcLa/Dt0RKIRMahchKx6859uG1AKJKiAtCs1eE/e88LHQ6RQ2vR6rBk/TFodHpMGhiG341y7OLo+LgQiEXA2YpGlNQ1Cx3OVbqUrC9evBhbtmzBTz/9hMjISMv7d+/ejfPnzyMgIABSqRRSqXE91qxZszBx4kQAQHh4OMrLy9s9n/nP5mPz17tGoVDAy8sLwcHBkEgk17zmWkfvzeRyORQKRbs36hwvmQTTkyIAOOagOYPBgL98m4MatQbx4X74wx2O1x9DjkEqEVt6lXOZWLgd83C5mGAfnryxInOyfpKzIFyaVqfHi98Zb4o/MLI3hkYFCBuQjV1ZXf/i4EVUNHAmA9H1vL49H2fKGxHsK8c/Zg12+AGu/t4eGGYq7O11wOp6p5J1g8GAxYsXY/Pmzdi9ezdiY2Pbffz555/HiRMnkJ2dbXkDgHfeeQefffYZACAlJQU5OTntpranp6dDoVAgISHBcs2uXbvaPXd6ejpSUlIAADKZDMnJye2u0ev12LVrl+Uash3LzvXcMqgdbOf65mMl2HmyHB4SEd6ZPRRyKX8Jp+tLNA2ZY3+t++FwOdtIMCXrpfUtqOWqK5e19sAFnC5vQA9vDzybOkDocOxiYv8QDOsdgBatHqv3sHed6Fr2nqnEmgMXABi3MAX5Oscci4kO3LfeqWR90aJF+PLLL/H111/Dz88PSqUSSqUSzc3GIwPh4eFITExs9wYAvXv3tiT2kydPRkJCAubMmYPjx49jx44deOGFF7Bo0SLI5ca/0KeeegoFBQV49tlnkZ+fjw8++AAbNmzA0qVLLbEsW7YMH330EdauXYtTp05h4cKFUKvVePTRR63yhaHrG967B2KDfdCkcayd66V1zXjpe+OQwiWT+vOXcLop85A5ToR3P5bhcuE8Am9Nfp4eiA7yBsCj8K5KWd+Cd9LPAACenxqPHj4ygSOyjysnw3916CIqVKyuE12pRq3Bn78xbmGalxKN2waEChxRx5n71vefq4amTS9wNO11KllftWoV6uvrMXHiRERERFje1q9f3+HnkEgk2LJlCyQSCVJSUvDwww9j7ty5eOWVVyzXxMbGYuvWrUhPT0dSUhLefvttfPzxx0hNTbVcM3v2bLz11ltYsWIFhg4diuzsbKSlpV01dI6sTyQSWarrjnIU3mAw4LlNJ9DQ0oZhvQPw5IQ+QodETsA8ZC63hHuh3Q0r67ZzuW+dN8Fc0f/behJqjQ7DewfgvmTH7kW1tvH9gpEc3QOtbXp8sIe960RmBoMBz286gcqGVsSF+mL5Xc61xjGxpz+CfGRobG3D0SLHWuEm7czFXfll9lqPiY6OxrZt2274uIkTJ+LYsWM3vGbx4sVYvHhxp2Oi7vvtsF54a+dpHDLtXO9tqqQI5ctDRfj5bBU8PcR4+74kSB1wPQQ5nn5hvvCQiFDfrMWl2mZEBQr73zHZh6ZNj3MVjQCAeCbrVjeopz+25ShZWXdB+89VYcuJMohNQ+XELjxU7lrM1fWHPzmErw8X4alb+yLc31PosIgEt/5IsaUN9b0HhsLTw7naUMViESb0D8HmYyXYc7oSY/oECR2SBTMa6pKeAY6zc/1ClRr/t/UUAOC5KfEOvR6CHItcKrFMAmcV0H2cq2hEm94AhacUPfmLttUlsLLuklrbdHjxf8ahcnNTYixr+tzNuLggjIoJhKZNj1V7zgkdDpHgCqvU+NsPJwEAf548wGn/bZg4wNy37lhD5pisU5eZj8J/e0y4nes6vQF//uY4mrU6pPQJwryUGEHiIOdl6VsvYRXQXeQrTfvVIxQOP6XWGZmPwRdUqdGkcawhpNR1H/9ciIJKNYJ95Vg2ub/Q4QhGJBJhyZ3GyfD/PVyMUgdc9URkL1qdHkvWZ6NZq8OYPoFYMN5521DH9wuBSGRskyt3oJkUTNapy8w714trmnH4gjA71z/+uQCZF2vhK5fizfuGuN2RPOo+80R4DplzH5Z+dQ6Xs4lQP0+E+MlhMACnTCvyyLmV1jXj37vPAgD+Oi0eCk8PgSMS1ti+wRgdGwiNTo8PWF0nN/bvXWdxvLgOCk8p/nn/UEic+PfwQB8ZhkQGAHCs6jqTdeoyL5kE04YIt3P9THkD3t5pnEi7YnoCInuw35g6b5BlyFw9h8y5CcskePar2wz3rbuWdYeL0KLVY0R0D8wc2kvocBzC0juNpwvWHylGCavr5IayLtbg/Z+MN6v+/tvB6BngJXBE3XereYWbA+1bZ7JO3WI+Cr8tx74717U6PZZtyIZGp8ft8aG4b0Sk3V6bXMvAcAXEIqCqUYOKhlahwyE7MFfWOVzOdi5PhGd7ibPT6w3YdLQEADB3bAxbR0zG9AlCSp8gaHUGrPyJ1XVyLw0tWixZnw29wTh0+u6knkKHZBXmvvWfz1aiTecYK9yYrFO3JEf3QEyQN5o0OmzPVdrtdf+9+xxyS1QI8PbA6/cM5i8P1GVeMgn6hRqPQ+eWsAro6iobWlHVqIFIBAwI4zF4WzEPGGKy7vwOFlSjpK4Zfp5STE7getwrmavr32QWo7imSeBoiOzn5e9PorimGb0CvPC3GYOEDsdqkiIDEODtAVVLG7KL64QOBwCTdeqm9jvXi+3ymicu1VnuYr86IxGhCk5zpu4ZZO5b55A5l2euqscG+cBL5lyrZZyJubJ+WtkArYNUJ6hrzG1udyf1dLp1TLY2KjYQt8QFs7pObmXriTJsOnoJYhHwzuyhLjXDQiIWYXw/x5oKz2Sduu23wyMhEgEHC2psfme5RavDsg3HodMbMH1IhMscuyFhWSbCs7/W5ZknwbNf3baienjDTy6FRnd5pz05n8bWNsupOfONeWpvqWky/MasS6yuk8srq2/GXzbnAAAWTuyLUbGBAkdkfea+9T0O0rfOZJ26rVeAF8b1tc/O9bd3nsa5ikaE+Mnx6oxEm74WuY/EK4bMkWszTyeP5yR4mxKLRRjIvnWnty2nDM1aHfoE+2BYVIDQ4Tik5OhAjO8XjDa9wTIxn8gV6U3rkuubtRgS6Y8lk1xzheOE/sacJqekHlWNws8yYrJOVmG+477pqO12rh8qqMbHvxQCAP4xazB6+Mhs8jrkfhJMSUVZfYtD/MNMtmNZ28bKus0lRJgnwjNZd1bmI/CzkiM5G+YGzL3rm46W4GK1WuBoiGzj0/2F2H+uGl4eErw7eyg8JK6ZRob6eVpaufY5wFF41/wqk92lDgqHrw13rqtb2/DnjcdhMACzR0Th9ngOuSHr8ZVL0SfYBwCrgK5M06bH+Urjkez4CFbWbe3yRHieWHFGF6vVOFxYA5EIuGc417XdyPDePTBxQAh0egP+vZu96+R6Tpaq8EbaaQDAC9MHok+Ir8AR2ZZ5Krwj9K0zWSer8JJJMN2GO9f/vu2UZerkC9MHWv35iQbxKLzLO1/ZCK3OAD9PKXq5wD5YR2eeCH+yTAWDwTYnrsh2zOvabokLRoQ/v19uxnwk+Nujl1BYxeo6uY4WrQ5L1h+DRqfHpIFheHBUb6FDsrlb+4cCMFbWdTY6MdxRTNbJamy1c33P6Qp8fagIAPDmfUPg50JTJ8lxJLIK6PIsR+DDFTzSawf9wnwhk4jR0NKG4ppmocOhTtDrDdhkuvHOwXIdMzQqALfHh0JvAP69i73r5Dr+kZaPM+WNCPaV4x+z3GNd8vDeAfDzlKK2SYscgYs4TNbJaq7cuZ5mpZ3r9U1aPLfpBADgkbExGGsaZEdkbZeHzPEYvKvKVxqHyw3kEXi78JCI0T/ceFSSN8Gcy6HCGuNudbkUqYPChQ7HaSyZZJwM/112iaXlhsiZ7TtTic/2XwAAvHnvEAT5yoUNyE6kEjFuiTPmHHtOVwgaC5N1shqRSIRZw807161zFP6l73NRrmpFn2AfPDcl3irPSXQt5v7aopom1DdpBY6GbMFcWY/ncDm7GRRhvAnGWRDOxfwzfHpSBHerd8KQyABMGsjqOrmGGrUGf/rmOABgbko0bosPFTgi+3KUvnUm62RV9yQbd65nFFR3e9/o9pwyfJddCrEIeOv+JHjJ+AsD2U6AtwyRPYx9mXllrAK6IvPaNk6Ct59Bvdhe4mzUrW3YnlsGgEfgu8Lcu/798VKcq2gQOBqirjEYDHh+0wlUNrQiLtQXy6e637yoCaZ969nFdahVawSLg8k6WVWvAC+M7RsEAPjWNJymKyobWvHX73IBAAsn9sXw3j2sEh/RjSSaBmLl8Si8y6lsaEVVYytEIqB/mGtPsXUkg7hr3elsyylDk0aH2GAf/uztgsRe/picEAa9AXhvFyfDk3PakFmMnSfL4SER4d3ZQ92yYBbh74X4cD8YDMDP56oEi4PJOlmd+U78xqPFXdq5bjAY8NfNOahRaxAf7oc/3NHP2iESXVOiqQqYyyqgy8lXGpPF2CAfeMukAkfjPuLDFRCJgIqGVlQ2tAodDnXAxisGy7nDIClbMFfXt5woxZlyVtfJuRRWqfG3H04CAP40eYBlpo87utVUXReyb53JOlndlTvXj3Rh5/q3R0ssd/P+ef9QyKXudzePhMH1ba4r33QEnvvV7ctHLkVssA8AHoV3BkXVTThk2q3+22Hcrd5VCT0VmDIoHAYD8B5718mJaHV6LFmfjSaNDmP6BOLx8X2EDklQt5r61vedqepSAdIamKyT1XnLpJg2uGs710vrmvHy93kAjHemE3qyt5Tsx3wMvqBKbdX1gyS8K9e2kX2Z963zKLzj23TU+DP7lrhg9AzgbvXu+KNpMvy2nDKcVrK6Ts7h37vP4XhxHfw8pXj7/qGQiN37dM2I6ED4yCSoamzFyTJhfoYxWSebuHeE8Sj81k7sXDcYDHh24wk0tLZhaFQAnpzg3nfzyP5C/OQIU8hhMFxO7sg1nFKaK+tM1u3N3Ld+ksm6Q9PrDfj2GHerW8vACAWmDY4wVdfPCB0O0U1lXazB+7uNJ0H+/tvB6MUbdpBJxRhrWuEm1FR4JutkEyOieyC6kzvXvzx4Eb+cq4Knhxhv358EqYT/eZL9DeZReJejadNbpjJzx7r9XR4yx+8pR3b4Qg2Ka5rhK5dicgJ3q1vDHyf1g0gEbMtR8mYVObSGFi2WrM+G3mBsgflNUk+hQ3IYQvetMxsimxCJRLjXtHPdfKzuRi5UqfF/2/IBAM9NiUffEE5rJmGYj+zm8hcrl1FQ1QitzgA/TykrBQIwf09dqG5CQ4tW4Gjoeiy71YdEuOXkZ1voH+ZnaQtkdZ0c2d9+OInimmb0CvDC32YMEjoch2JO1o8W1aG+2f4/w5isk82Yd64fOF+NS7XX37mu0xvw52+Oo1mrQ0qfIMxLibFfkES/ksjKusu5sl+d063tL9BHhgh/TwCXd92TY1G3tmFbDner28If7zBW13fklfN0CTmkIxdqsDHrEsQi4J3ZQ6Hw9BA6JIcSFeiNviE+0OkN2C/ACjcm62QzHd25/vHPBci8WAtfuRRv3jcEYjcfZkHCMq9vO1vRiBatTuBoyBo4CV54PArv2LbnKtGk0SEmyBvJ0dytbk39wvxw9xDjkeJ3f+RkeHI8u04Zj3f/JqknRsUGChyNY5o4IBQAsPe0/fvWmayTTc0yHYXfmHUJBsPVKw9OKxvw9k7j0bAV0xMQ2cPbrvER/Vq4whNBPjLo9Abkc4KvSzBPcI3nJHjBJHAivEPbmFUMgLvVbeUPd/SDWASknyxHziXesCLHcuC8sVo8wXTcm65mPgq/90zlNfMZW2KyTjY1JTEcPjIJimqacORCbbuPadr0WLYhGxqdHrfHh+K+ETx6R8ITiUTct+5izDddOFxOOJcr60zWHU1xTRMOFph2qw/nz2FbiAv1xYyhxr317/7I3nVyHPVNWuSYftcZZ5p6TlcbFRsITw8xlKoWnC63byGHyTrZlLdMimlDzDvXi9t97P2fziGvVIUAbw+8fs9g3s0nh5HII7suo6qxFZUNrRCJgAHhTNaFYk7Wz5Y3oLWN7SWOxNymNrZvEAcw2tDTt8dBLAJ25VfgeHGd0OEQAQAyCqphMAB9Q3wQpvAUOhyH5ekhQUofY2vvHjsfhWeyTjZ3b3IUAGDriTI0aYw7148X12HlT+cAAK/OSEQo/4EgB3J5yByrgM7O3K8eE+QDb5lU4GjcV68AL/h7eaBNb8DZ8kahwyETvd6AjUcvH4En2+kT4ouZw1hdJ8diPgLPqvrNCdW3zmSdbG5kTA/0DvSG2rRzvUWrw5++OQ6d3oDpQyJwN3c5koNJNPXXnlY2QNOmFzga6o5Tln51VtWFJBKJOGTOAR25Yrd66iDuVre1P9zeDxKxCD+drsSxotqbP4DIxg6crwYAy0Bouj5z33rmxRo0trbZ7XWZrJPNiUQiyx37jVmX8NaO0zhX0YgQPzlenZEocHREV4sK9IKfpxQanR5nKzhkzpmdUprWtkVwuJzQEkx/ByfZt+4wzLvVpw2O4MkTO4gJ9sE9luo6J8OTsMpVLThX0QiRCBjTh8n6zcQE+yAmyBtanQEH7LjCjck62cU9w40/nA6cr8Yn+wsBAK/fMxg9fGRChkV0TSKRyFJdz+NReKdm3uvNyrrwBvXikDlHom5tw1bzbnUOeLWbp2/vB6lYhL1nKpF1kdV1Eo75CHxiT38EePP38Y4wV9f3nLHfUXgm62QXkT28LUdsDAbg/hGRuGNgmMBREV2fed96Lo/sOi2tTo9zFeZJ8KysC22Q6QbYqTIV9Hr7rr6hq6WZdqtHB3ljBHer203vIG/LWlv2rpOQ9p8zHYGPY1W9o67sW7fXCjcm62Q3948wDprrFeCFF6cnCBwN0Y0lcn2b0ztf2QitzgA/uRSRPTjlWmh9gn0gl4qh1uhwoVotdDhuz3wE/t7h3K1ub4tvj4NULMLPZ6uQeaFG6HDIDRkMBmRY+tU5XK6jxvQJgkwqRkldM85X2mdYKpN1spsZQ3vivQeGYt0TY+Dn6SF0OEQ3ZK4CnixTQccqoFMyT4KPj/BjMuIApBIx4iN4FN4RXKptQkaB8Rf135ra1Mh+ogK9cZ+pgPEOq+skgIvVTSipa4aHRISRMTxZ01FeMglGxwYCsN8KNybrZDcikQgzhvZCVKC30KEQ3VRssA+8ZRK0aPUosNPdU7Kuy5PgeQTeUVyeCM9kXUhX7laP7MGfyUJYfHscPCQi7D9XjUOmGydE9rLf1K8+rHcPDpfsJHPf+l479a0zWSciugaJWGSZXs2+ded0Ssl+dUfD9W3CMxgMl4/Ac7e6YHoFeFnaA1ldJ3s7YOpXH8cj8J1m7ls/VFCDJo3tV7gxWSciuo7LfeusAjojS2U9gpPgHYWlvaRUZbfhPNTekQu1KKppgo9MgimJ3K0upEW3xUEmEeNgQY2lf5jI1vR6g2US/DgOl+u0viE+6BXgBY1Oj4N2OBXDZJ2I6Do4ZM55VTW2orKhFSIRMCCMybqjiA/3g0QsQrVag3JVq9DhuKWNWcUAgGlDuFtdaD0DvDB75OXqOm9gkT3kKxtQ26SFt0yCIZEBQofjdEQiESYOMB2Ft0PfOpN1IqLrMK9vO1nKVVPOxjxcLjrQGz5yJiSOwtNDgr4hPgB4FF4ITZo2bD1h2q2eHCVwNAQAv7+tL2RSMQ4XsrpO9mGuqo+KDYRMylSwK+y5b51/Q0RE1xEX4gu5VIyG1jYU1TQJHQ51Qr7SeASe/eqOx3wUnkPm7C8tVwm1Rofegd6cAO0gIvy98OCo3gBYXSf72H/OdASe/epdNjYuGB4SES5WN+FClW1XkTJZJyK6jitXTXHInHM5yUnwDotD5oSz6ahxsNws7lZ3KAsn9oVcKsaRC7X4xZRIEdmCVqfH4cIaAMBY9qt3ma9cihHR5hVuFTZ9rU4l66+99hpGjhwJPz8/hIaGYubMmTh9+rTl4zU1NXj66acxYMAAeHl5oXfv3vjDH/6A+vr2P5CLioowbdo0eHt7IzQ0FM888wza2tpP09uzZw+GDx8OuVyOuLg4rFmz5qp4Vq5ciZiYGHh6emL06NE4fPhwZz4dIqKbSjQlFhwy51zMx+AHcricw0ng+jZBXKptwgHTMet7uFvdoYQpPPHgaFN1PZ3VdbKdE5fqoNbo0MPbAwN5M7tbLH3rNj4K36lkfe/evVi0aBEOHjyI9PR0aLVaTJ48GWq1sfxfWlqK0tJSvPXWW8jNzcWaNWuQlpaG+fPnW55Dp9Nh2rRp0Gg0OHDgANauXYs1a9ZgxYoVlmsKCwsxbdo03HbbbcjOzsaSJUuwYMEC7Nixw3LN+vXrsWzZMrz00ks4evQokpKSkJqaiooK297dICL3wiFzzker0+NcRSMAHoN3RIMijN9Tl2qbUd+kFTga97H5aAkMBiClTxCiArlb3dEsvNVYXT9aVId9Z1ldJ9vYb1rZltI3CGIxT9d0x62mZD2joBotWp3NXqdTyXpaWhoeeeQRDBo0CElJSVizZg2KioqQlZUFAEhMTMSmTZtw9913o2/fvrj99tvx97//HT/88IOlcr5z506cPHkSX375JYYOHYqpU6fi1VdfxcqVK6HRaAAAq1evRmxsLN5++20MHDgQixcvxr333ot33nnHEss///lPPP7443j00UeRkJCA1atXw9vbG59++qm1vjZEREg09dfmltaz2uEkCirV0Oj08JVLEdnDS+hw6Ff8vT0sfy95ZbwJZg8GgwEbj3K3uiMLVXhizphoAKyuk+2Y+9XHsl+92waE+SFc4YkW7eXWAlvoVs+6+Xh7YGDgDa9RKBSQSo3TeDMyMjB48GCEhYVZrklNTYVKpUJeXp7lmkmTJrV7ntTUVGRkZAAANBoNsrKy2l0jFosxadIkyzXX0traCpVK1e6NiOhG+of7QioWoa5Ji5K6ZqHDoQ4wD5eLD/djX66DMvetn+RReLvIvFiLi9XG3epTB3O3uqN68ta+8PQQI7u4zi5Tpsm9NGt0OFZUBwAYF8dkvbtEItHlqfA2XOHW5WRdr9djyZIlGDduHBITE695TVVVFV599VU88cQTlvcplcp2iToAy5+VSuUNr1GpVGhubkZVVRV0Ot01rzE/x7W89tpr8Pf3t7xFRXFtCRHdmFwqQX/Tnm72rV9fk6YNf/shD+uPFAm+5s48XI5H4B0XJ8Lb18ZMY1X9rsHcre7IQvzkmJsSA4DVdbK+IxdqoNHpEeHviZggtsJYw+W+ddu1YXc5WV+0aBFyc3Oxbt26a35cpVJh2rRpSEhIwMsvv9zVl7Gq5cuXo76+3vJWXFwsdEhE5ATM+9Y5vfr6vsi4iM/2X8Bzm3LwwEcHUWjjVSY3Yh4uF8/hcg6LE+Htp1mjw9Yc4271WTwC7/CemNAHXh4SnLhUj935nMNE1mMeMDm2bzBPnVnJ2LhgSMQinK9Uo9hGK367lKwvXrwYW7ZswU8//YTIyKv/4W9oaMCUKVPg5+eHzZs3w8PDw/Kx8PBwlJeXt7ve/Ofw8PAbXqNQKODl5YXg4GBIJJJrXmN+jmuRy+VQKBTt3oiIboZD5m7MYDDgmyxj5U4kAg4X1mDKu/uweu95tOn0do/nFNe2OTxzZf18pdqmg3kI2JGnRGNrG6ICvTAq5vpti+QYgn3lmDvW2Lv+7o9nWV0nqzlw3rRfnSvbrMbfywPJvXsAsN1U+E4l6waDAYsXL8bmzZuxe/duxMbGXnWNSqXC5MmTIZPJ8P3338PT07Pdx1NSUpCTk9Nuant6ejoUCgUSEhIs1+zatavd49LT05GSkgIAkMlkSE5ObneNXq/Hrl27LNcQEVnLIMuQOR7ZvZbs4jqcq2iEp4cY2/4wHuP7BaO1TY/Xt+fjtx8csGtfcnVjKyoaWgEYe9bJMYUp5AjykUGnNyBf2SB0OC5tY9bl3eqc/uwcnpzQFz4yCXJK6vHjKVbXqfvqm7TIMRUc2K9uXeap8LbqW+9Usr5o0SJ8+eWX+Prrr+Hn5welUgmlUonmZuPQJXOirlar8cknn0ClUlmu0emMd84nT56MhIQEzJkzB8ePH8eOHTvwwgsvYNGiRZDL5QCAp556CgUFBXj22WeRn5+PDz74ABs2bMDSpUstsSxbtgwfffQR1q5di1OnTmHhwoVQq9V49NFHrfW1ISICYNzVLRYBlQ2tqFC1CB2Ow9lg7odNjMDACAU+f2wU3rx3CBSeUuSU1OM37/+Ct3eeRmub7Suo5sQvOsgbPnL25joqkUh0xb51nlixlZK6Zuw3VdNmDecReGcR6CPDvLExAIDnN53A8eI6QeMh55dRUA2DAegb4oMwhefNH0AdZh4yd+B8FTRt1j9N2KlkfdWqVaivr8fEiRMRERFheVu/fj0A4OjRozh06BBycnIQFxfX7hpzf7hEIsGWLVsgkUiQkpKChx9+GHPnzsUrr7xieZ3Y2Fhs3boV6enpSEpKwttvv42PP/4Yqamplmtmz56Nt956CytWrMDQoUORnZ2NtLS0q4bOERF1l7dMir4hvgCMK9zosmaNDluOlwIA7h1hTAZEIhHuGxGFH/90K6YMCkeb3oB/7z6Haf/6BVkXa20aj/kI/EAegXd4l5N1nlixlc1HL8FgAMb0CeRudSfz1MS+SOylQLVagwc+PIifTrPCTl2XcZ4r22wlIUKBYF85mjQ6ZF6w/gq3TpUdbtY3M3HixA711kRHR2Pbtm03fa5jx47d8JrFixdj8eLFN309IqLuSuzlj7MVjcgtUeH2eN4UNNuRp0RDaxsie3hhTGz7PrhQP0+snpOM7TllePF/eThX0Yh7Vx/AI2Nj8OfJA2xS+T7F4XJOw9xe4mzr24prmhCm8IRM2q3ttzZnMBgsR+DvTeb2G2ej8PTAuidSsPDLLPx8tgoL1mbitXsG4/4R/LukzttvGi7HfnXrE4uNK9w2Hb2EvWcqMdbKbQaO/ZOGiMhBmKdXc8hce99kGU9N3Zt8/X7YqYMj8OOyCbg3ORIGA/DZ/gtIfXcffj5r/f4u8451rm1zfObvqXylCjqB1/11VMb5akx48yfcs2o/6pu1QodzQ1kXa3GhugneMgmmJnK3ujPylUvx6SMjcc/wXtDpDXh24wn8axeHzlHnlKtacK6iESIRMKYPk3VbsGXfOpN1IqIOGNyLe6F/7VJtk2UVzM36YQO8ZXjrviR8/tgo9ArwwqXaZsz55DCe+eY46pusk/RodXqcLW8EwGPwziA2yAfeMglatHoUVDYKHU6HfLjvPAwGILdEhUc+O4zG1jahQ7quTUeNVfWpiRGc3+DEPCRivH1fEhbd1hcA8M/0M/jL5lxBNm2QczJPgU/s6Y8Ab5nA0bim8XHBEIuA0+UNKKtvtupzM1knIuoAc39tSV0zatQagaNxDJuySmAwAGP7BnW4H3ZC/xDsXDoBj4yNgUgEfJN1CZPe2Yu03LJux1NYpYZGp4evXIrIHl7dfj6yLbFYZDkB4Qw3wQoqG/HT6UqIRIDCU4pjRXV4bM0RNGscb/WccZaE8XvqXu5Wd3oikQjPpMbj1ZmJEIuA/x4uwlNfZjnkf3vkeA6cM+9XZ1XdVnr4yJAUFQAA2Gvl6jqTdSKiDvDz9EBssA8ATq8GAL3egI1HjUfg7xvRuWTARy7Fy78ZhG+eTEHfEB9UNrTiqS+PYuGXWaho6Pq0ffNwuQHhflxR5SQGOdFE+M8zLgIAbh8Qii8XjIafXIrDhTV44otMh9sVv/Pk5VkSo2O5W91VzBkTjVUPJ0MuFePHUxX43UcHefOYbshgMFhOwFm7l5ram9g/FID1960zWSci6qDLfeuOXwW0tUOFNSiuaYafXIopgyK69BwjYgKx9Q/jsfi2OEjEImzPVeLOf+7DxqxLXerJNA+XG8jhck5jkJNMhFe1aPFNpvHm1KPjYjEkMgBrHhsJb5kEP5+twqKvjtpkZU9Xcbe660odFI6vHx+NAG8PZBfXYdaqAyiqbhI6LHJQF6ubUFLXDA+JCCNjeggdjksz963/crYKWiu2qTBZJyLqoERT3zrXt10eLDc9qSe8ZJIuP4+nhwR/Th2A7xePw6CeCtQ3a/Hnb45j7qeHUVzTuV9AzZX1eParOw3zRPi8UpVDD83amHkJao0OcaG+lmnKydGB+HjeCMilYuzKr8CS9cccoo+4tK4Zv5zjbnVXlhwdiI1PjUWvAC8UVqlxz6oDHH5K17Tf1K8+rHcPeMs4u8KWhvTyR6CPDA2tbThWVGe152WyTkTUQYnmxMLNfylqaNFie44SQOePwF/PoJ7++N+icXhuSjxkUjF+PluF1Hf3Yc3+Qug7OCmck+CdT78wX0jFItQ3a1FSZ92hPNai1xuwNuMCAJhmLVyuVI/tG4z/zEmGTCLGthwlntl4QvDJ9puPGWdJjIoNRO8g7lZ3VXGhvtj8+7FIiFCgqrEVs/+TYfXjt+T8zP3q47hf3ebEYhHG9zN+nfecrrDe81rtmYiIXJz5yO6F6iaoWhx7bZMtbT1RhmatDn1DfDDMNFDFGqQSMRZO7IvtfxyPUTGBaNLo8PIPJ3H/fzJwruLG08Jr1BqUq1oBGHvWyTnIpRL0CzP+fTnqUfifTlfgYnUTFJ5S3DO811UfnzggFO8/OAxSsQibj5Xgr5tzOnyDydra71ZnVd3VhSo8sf7JMbglLhhqjQ7z1xzBJtPfP5Feb0BGgblfncPl7GGi6Si8NW+cMVknIuqgHj4y9AowThnPc+O+9W9MvwzeNyKqXZXRWvqG+GLdE2Pw6oxB8JFJkHmxFne99zNW/nTuun1g+aYj8NFB3vDlmiqn4uh962sOXAAAPDCq93WPkU4eFI53HxgKsQhYd6QYr2w5Kcix/qNFdSisUsPLQ4K7BndtlgQ5Fz9PD3z6yEjMHNoTbXoD/vTNcaz86ZxDt5WQfeQrG1Cj1sBbJkFSZIDQ4biF8f2MyXpeqapbA3OvxGSdiKgTEns5z/RqWzhf2Yisi7WQiEW4Z9jVVUZrEYtFmJMSg53LbsWt/UOg0enx5o7TmPH+/mv2Zp609Kuzqu5szMn6SQf8njpb3oCfz1ZBLDJO4r6R6UN64s17kwAYE/zX0/LtnjCZq+pTB4fzppUbkUnF+Of9Q/HUrcZd7G/uOI0X/5creEsGCcu8X31UbCBkUqZ89hDsK8eQSGPL5L4zVVZ5Tv7NERF1grlv3V2H+ZiTgVv7hyBU4Wnz1+sV4IU1j47EO7OTEODtgZNlKsxYuR//SMtvty4rX2meBM9+dWdz5ZA5R2Ouqt+ZEIaowJv3f89KjsTff5sIAPjP3gK8t+usLcNrp0Wrw5bjpQB4BN4dicUiPD81Hi/fnQCRCPjyYBEWfpnlcGsFyX72mwZNsl/dvm7tb6yuW6tvnck6EVEnXJ4I73iJha3p9AZ8e9R0BN6OyYBIJMJvh0Xix2W3YtqQCOj0Bqzacx53vfczjlyoAcBJ8M7MvGqvrL7FoXZG1zdp8e3REgDAI2NjO/y4h0ZHY8X0BADAuz+exeq9520S36/tyDPuVu8V4IUxsexPdVePjIvFBw8Oh0wqxs6T5Xjo40OodaDvK7IPrU6Pw4XGn48pffnvgT2Z+9Z/PltlldMtTNaJiDphkOkY/PnKRjRp2gSOxr72na1EuaoVPbw9cMfAMLu/frCvHCsfHI7/zElGqJ8cBVVq3Lc6Ay9+l4uzpgF0CaysOx0/Tw/EmKaWO1J7yfrMIjRrdYgP98OYPoGdeuxjt8Ti2SkDAACvb8/HZ/sLbRFiO5bd6sncre7upg6OwJfzR0PhKUXWxVrMWn2g06swybmduFQHtUaHHt4e/LloZ0mRAVB4SlHfrEV2cV23n4/JOhFRJ4T6eSLUTw6D4XI1111szDQmAzOG9hK0/y11UDjSl92K2SOiAABfHLwITZsePjIJInt4CRYXdZ2jHYXX6Q1Ye+AiAODRcTFdGqT4+4lx+MPtcQCAv/1wEv89XGTVGK9UVn/lbnXbzZIg5zEqNhCbFo5FT39PFFQad7E70s0wsq39ppVtKX2DePPOzqQSMcb3t95UeCbrRESdZDkK70YT4euaNEg/WQ7AervVu8PfywP/uHcIvlowGlGBxgQ9sZc/fylxUgkONhH+x1PlKKlrRg9vD8wY2vXkd+md/fHEhD4AgL9szsHmY7ZZq/XtUdNu9ZhARAf52OQ1yPn0C/PDt78fh/hwP1Q2tGL2fw7il7PWGXpFjs3crz6W/eqCMPet77VC3zqTdSKiTko0JRbuNGTuf9ml0Oj0GNRTYamCOoJxccHYsWQC/v7bRPzfPYOFDoe66PL6Nsf4njIfW//dqN7w9JB0+XlEIhGWT43H3JRoGAzAnzYcx9YTZdYKE4Bxt/qmo9ytTtcW7u+JDU+lIKVPEBpb2/DIZ4dtdtOIHEOzRodjRXUAgLHsVxfERFOyfqKkHtWNrd16LibrRESdNMgNh8xtyCwGYN/Bch3lLZPiodHR6BviK3Qo1EXmG0CFVWqoW4WdBXGqTIWDBTWQiEV4+Cbr2jpCJBLh5bsHYfaIKOgNwB/XHcOPplMq1nCsuA4Flabd6kO4W52upvD0wJrHRuLuJOMu9qXrj2PVnvPcxe6iMi/WQKPTI8LfE7HBPGkjhFCFJwZGKGAwGAfNdQeTdSKiThpsStbPlje4xVqck6Uq5JWqIJOIu3UkmOh6QvzkllkQ+Uphb4KtNa1rmzIoHD0DrDMDQSwW4f/uGYwZQ43J0u+/Oop9VuhlBK7YrZ7I3ep0fXKpBO/NHorHxxs3G/wjLR8vf5/HXewuyNyvPrZvcJfmbZB1mKfCd7dvnck6EVEnRfh7ItBHhja9AWfKG4QOx+a+yTJW1SclhKKHj0zgaMhVDXKAvvUatQabjxnXtT06Lsaqzy0Ri/D2fUmYmhgOjU6PJ77IxMGC6m49Z4tWhx+4W506SCwW4a/TEvDidOMu9rUZF7Hoq6NucdPZnRw4b9qvHscj8EIy963vO1MJfTduijFZJyLqJJFIZEksXH3InKZNj/9lG5OB+5KjBI6GXJllyJyA31PrjhShtU2PxF4KJEf3sPrzSyVivPfAMNweH4oWrR6PrTmCrIu1XX6+nSfL0dBi2q3eh7+YU8fMvyUW//7dMMgkYqTlKTHnk0Ooa+IudldQ36RFjmmeDofLCSs5ugd85VJUqzXI7cY8FibrRERdYJkI7yADsWxld345atQahPrJMb4ff/CT7VjWt5UJ8z2l1enxRYZpXdvYWJsdH5VJxfjgoeG4JS4YTRodHvnscJeHVZqPwN8zvBc3IVCnTB/SE2sfGwU/TymOXKjFvaszUFLXLHRY1E0HC6thMAB9QnwQ7u8pdDhuzUMitpxu2HO660fhmawTEXVBojmxcPGJ8N9kmpOBSEgl/JFBtmM+rXJG2QitTm/319+ZV46y+hYE+8owPcm2g9o8PST4cG4yRsUEoqGlDQ9/cginlZ1rqVHWt+CXs8ZfAGcN5xF46ryUvkHY+NRYhCs8ca6iEfd8sB+nylz7tJirO2Ba2TaOVXWHMHFAKIDu9a3zNy8ioi5I7GVMLE4pGwRJLOyhoqEFe0w/YBxhtzq5tqge3vCTS6HR6XGuotHur7/mgHFd24OjekMu7fq6to7ylknx6aMjMTQqAHVNWjz08UGcr+z45735WAn0BmBkTA/EcOIzddGAcD98+/ux6B/mi3JVK+5fnWFJ+Mj57D9vnIPBfnXHYO5bP1ZU2+VWEybrRERd0DvQG36eUmjahEks7GHz0RLo9AYM7x3AtWhkc2KxCAMFGjKXW1KPIxdqIbXSuraO8pVLsfbRUUiIUKCqUYOHPjqEouqmmz7OYDBgo2nwIwfLUXf1DPDCN0+NxejYQDS0tmHeZ4fxv+wSocOiTipXteBcRSNEInCGhYPoGeCF/mG+0BuAX7p4E4zJOhFRF7QfMud6R+ENBgO+MfXD3jeCg+XIPi5PhLfv99Rn+y8AAKYNiUCowr59nv7eHvhywWj0D/OFUtWC3310EKU36R3OLq7D+Uo1PD3EuGswd6tT9/l7eWDtY6MwbXAEtDoD/rguG18duih0WNQJ5inwg3oqEODNzS2Owlxd72rfOpN1IqIuMvetu2Kynl1ch3MVjfD0EGP6ECYDZB+WIXN2rKxXNrRa1p89MjbGbq97pUAfGb5cMBqxwT4oqWvGgx8dRIWq5brXX96tHgE/Tw97hUkuztNDgn//bphlbeHffjiJgk60ZpCwDpj2q7Nf3bFc2bduMHR+hRuTdSKiLro8Ed71BvJsMA2Wu4vJANmRubJ+qlTVrb20nfHfw0XQ6PQYGhWAYb2tv66to0L9PPHVgtGI7OGFC9VNeOjjQ6hubL3quhatDt+bbi5wsBxZm1gsworpCZjQPwSaNj2Wf5tjt+9F6jqDwYADpn71sXFM1h3JiJge8JZJUNnQipNdGODIZJ2IqIvMQ+ZOlqqgc6FfZpo1OmwxJQP3crAc2VFcqC9kUjEaWttQXHvz3u3u0rTp8eVB07o2UzVRSD0DvPDfx8cgwt8TZysa8fAnh1HfpG13Tbppt3pPf0+k9GVfKlmfSCTC32cmwlsmwaHCGqzPLBY6JLqJi9VNKKlrhodEhJExwt10pKvJpRKMNf1b3ZWp8EzWiYi6KDbYF14eEjRrdSiscp2jgjvylGhobUNkDy+MiWUyQPbjIRFjQJgfAPschd+eW4aKhlaE+skxNdEx2j2iAr3x1YLRCPaV41SZCnM/O4yGlssJ+6ajl9cpSrhbnWwkKtAbf548AADwf9tOofwGbRkkvP2mfvVhvXvAWyYVOBr6te70rTNZJyLqIolYhATLkDnXOQr/zRVTpsVMBsjO7DlkzjxY7uEx0ZBJHedXoj4hvvhqwWj08PbA8eI6PLbmCJo0bShXtWCfqTIzi1PgycbmjY1BUlQAGlrasOJ/uUKHQzdgOQLP0zYO6db+xr71oxdroWrR3uTq9hznJxMRkRNKdLGJ8Jdqmyw/9NkPS0IYZKf1bceKapFdXAeZRIzfjept09fqigHhfvhi/mgoPKU4cqEWC9Zm4r+Hi6A3ACOieyCWu9XJxiRiEf4xazCkYhF25JUjLbdM6JDoGvR6AzIs+9XZr+6Iegd5o0+wD9r0Bhzo5Ao3JutERN0wyDJkzjWS9U1ZJTAYjHfnowK9hQ6H3FCCnSbCrzlwAQBwd1JPhPjJbfpaXZXYyx9rHxsFH5kEB85X490fzwLgbnWyn/hwBRZO7AsAePF/eahv7lxVkGwvX9mAGrUG3jIJkiIDhA6HruPWAcaj8J3tW2eyTkTUDeb1bXkl9ptebSt6vQEbjxqPwN/HwXIkkIERfhCJjCvVKhps0ydbrmrB1hPGKqEjDJa7kWG9e+DTR0bC08P4K5tcKsZdXKdIdrTotjj0CfFBZUMrXt9+Suhw6FfM+9VHxQY6VDsPtXdl33pnVrjxb5SIqBv6hdl3erUtHSqsQXFNM/zkUkwZxGSAhOEtk6KP6Yi3rarrXx28iDa9ASNjelhWMDqy0X2C8PHckQj0keHRcbFQcJ0i2ZGnhwSv3zMEAPDfw8WWI9fkGNiv7hzG9AmCXCpGWX0LzlZ0fCgxk3Uiom7wkIgxMNw4vdrZh8yZB8tNT4qAl0wicDTkzgaZTqyctEGy3tqmw1eHigAAj4yNtfrz28ot/YKR9cIkPD81XuhQyA2Nig3EQ6ONsx3+sjkHLVqdwBERAGh1ehwqMCfr7Fd3ZJ4eEozpY7yhsud0RYcfx2SdiKibXKFvvaFFi+05SgDAfSOiBI6G3J0tJ8JvOV6GarUGEf6emDwozOrPb0siEbczkHCemxqPMIUchVVq/GvXWaHDIQAnLtVBrdGhh7cHEiIUQodDNzGxC33rTNaJiLrJ3LfuzBPht54oQ7NWh74hPhgWFSB0OOTmBtloyJzBYMBnBwoBAHNSouEh4a9BRB2l8PTAqzMSAQD/2Vdgk5Mv1Dn7zxmr6il9g7hq1QmY+9aPFNZC3drWocfwpxQRUTcl9rq8aqozQ0McyTdZlwAYq+qs3pHQzJX1i9VNnd5JeyNZF2uRW6KCXPr/27v3sKjL/P/jr2E4HwYEOSkHT1kiikmmaFmaCxW2nmo72aqt688W2ktsyyzLdrf9ursdt7T1u+43qXUts3Qz3TVdD5gKmRgqHigNwVTwQIAgMMjM7w9kitSEBGYGno/rmiv5fO657/fHInjPfd/v20X3DXK849oAR5fYN0x39gtTncWqJ1fs0fk6i71D6tC2XTgGLIEl8E6he2cfRQV6y1xn0Y78kia9h2QdAK5S71A/uboYVFJp1omy1qle3ZoOn6pQdsE3MroYNP76rvYOB1AnH3d18feUJB1owdm7xReOaxs7oKsCfdxbrF+gI3nup31l8nTVnq/LbEcgou1Vmev0eWGpJGkYxeWcgsFgsM2ubz3UtKXwJOsAcJU83Yy6JrShyJzzLYV//8Ks+i29gxVi8rRzNEC9lj5v/Xhpldbm1tdlmOzgx7UBjizEz1NPJ/eRJL24Lk+FZ5z7JBRntbOgROY6i8L9PdX9wgkacHwN+9Y/+fJ0k9qTrANAC4i9sGw318n28NVZrFqx68IS+HjOVofjiOny7faSlrAkq0B1FquG9AhUHwoxAVflZzdEKqFHkKprLXpq5V6n3QLmzBr2qw/t2Znta04koWeQ3I0uOl7atJWYJOsA0AIazmre52Qz61u+PKXi8hp18nbTbX2cqzI22reWrAhfXVund3bUH9c2ZZjzHNcGOCqDwaB54/vJw9VFWw+d1ge7jtk7pA5n++H6mdlhvVgC70y83V11Y/fAJrdvVrI+b948DRo0SH5+fgoJCdHYsWOVl5fXqE11dbVSUlIUFBQkX19fTZgwQcXFxY3aFBYWKjk5Wd7e3goJCdHjjz+u8+cbV8TbvHmzBg4cKA8PD/Xq1Uvp6ekXxbNgwQJ169ZNnp6eGjx4sHbs2NGcxwGAFtNQZM7Zjm97f2f9rPqYAV3l7srnt3AcDcn6oZMVqjl/dWc6f5hzTN+cq1XXAC+N4kMpoEV06+yjtJ/0liT9fvV+nTpbY+eIOo6yc7W2bXecr+58GvatN0WzfjPLyMhQSkqKsrKytH79etXW1ioxMVGVlZW2Nmlpafroo4+0fPlyZWRk6Pjx4xo/frztfl1dnZKTk2U2m7V9+3a99dZbSk9P17PPPmtrk5+fr+TkZI0YMUI5OTmaMWOGpk6dqo8//tjWZtmyZZo5c6bmzp2rXbt2KS4uTklJSTp5sumHzANAS+kTbpLBIBWX1+jkWecoMld6zqz1++s/TL3nBpbAw7F0DfCSv5ebzlus+rK44kf3Y7VatXjbEUnSpKHRMnK8EdBipt7UXX27mFRWVavfrd5v73A6jKz8M7JYpR7BPgrzp9aMs7n12mD5eRqb1LZZyfratWs1efJk9e3bV3FxcUpPT1dhYaGys7MlSWVlZfq///s/vfzyyxo5cqTi4+O1ePFibd++XVlZWZKkdevWaf/+/VqyZIkGDBigO+64Q7///e+1YMECmc1mSdLChQvVvXt3vfTSS+rTp49SU1N1991365VXXrHF8vLLL+uXv/ylpkyZopiYGC1cuFDe3t568803m/NIANAivN1d1TPYV5K075hz7Fv/MOe4zHUWxYSbbOdaA47CYDC0yFL4rK9KdLDorLzcjLr3Bo5rA1qSq9FFf5rQX0YXgz7afVwbDhRf+U24atsvHNk2jFl1p9QrxFefPDGySW2vas1jWVn9D8/AwPp199nZ2aqtrdWoUaNsba677jpFRUUpMzNTkpSZmal+/fopNPTbZWhJSUkqLy/Xvn37bG2+20dDm4Y+zGazsrOzG7VxcXHRqFGjbG0upaamRuXl5Y1eANBSbEXmnGTf+vLso5KYVYfj6tsCRebSt+dLksYP7Cp/b7cWiQvAt2K7+mvqTfW1IOb8K1cVNeev8A5crW2H64vLsV/dORkMBrkam5aG/+hk3WKxaMaMGRo2bJhiY2MlSUVFRXJ3d1dAQECjtqGhoSoqKrK1+W6i3nC/4d4PtSkvL1dVVZVOnz6turq6S7Zp6ONS5s2bJ39/f9srMjKy+Q8OAJfRUGTOGfat7z9ertxj5XI3umjsAM5Wh2Pqe5XHtx0tOWfb6jF5aLeWCgvA98wY1VtRgd46UVatF9YetHc47VpxebUOnayQwSAN6UGy3t796GQ9JSVFubm5evfdd1synlY1e/ZslZWV2V5Hjx61d0gA2pGGxCLXCZbBN8yqj4oJUScfdztHA1xaw8z6gRPlqrM0/2iof2QVyGKVbr6ms64J9Wvp8ABc4OVu1Lzx/SRJb2cVKLugxM4RtV+ZF2bV+3YxKcCbn9/t3Y9K1lNTU7V69Wpt2rRJERHfLp8MCwuT2WxWaWlpo/bFxcUKCwuztfl+dfiGr6/UxmQyycvLS507d5bRaLxkm4Y+LsXDw0Mmk6nRCwBaSsO50MdKq/RNpdnO0Vye+bxFH+YclyTdE88KIziuHsG+8nRz0TlznY6cqbzyG77jnPm83r1wXBuz6kDrG9ars+6Jj5DVKs36YO9Vn+KAS9vGfvUOpVnJutVqVWpqqlauXKmNGzeqe/fGZ5XGx8fLzc1NGzZssF3Ly8tTYWGhEhISJEkJCQnau3dvo6rt69evl8lkUkxMjK3Nd/toaNPQh7u7u+Lj4xu1sVgs2rBhg60NALQ1fy83RQd5S7q6PbatbePBYpVUmhXi56Gbr+GHPRyX0cWg68J+3L71FbuOqbz6vKKDvDXi2pDWCA/A9zyd3Eedfd116GSF3th02N7htDtWq1XbL8ysD+3Fz++OoFnJekpKipYsWaKlS5fKz89PRUVFKioqUlVVlSTJ399fv/jFLzRz5kxt2rRJ2dnZmjJlihISEjRkyBBJUmJiomJiYvTQQw9p9+7d+vjjjzVnzhylpKTIw8NDkjR9+nR99dVXeuKJJ3Tw4EG98cYbeu+995SWlmaLZebMmVq0aJHeeustHThwQI888ogqKys1ZcqUlvq7AYBmc4Z968svnK0+fmBEkwucAPbyYyrCW61WpW8/IkmalNBNLhzXBrSJAG93PffTvpKkNzYf0hfFZ+0cUftScOacjpVWyc1o0KBunewdDtpAs35L++tf/6qysjLdeuutCg8Pt72WLVtma/PKK69o9OjRmjBhgoYPH66wsDCtWLHCdt9oNGr16tUyGo1KSEjQxIkT9fOf/1y/+93vbG26d++uNWvWaP369YqLi9NLL72kv//970pKSrK1uffee/Xiiy/q2Wef1YABA5STk6O1a9deVHQOANpS7IV965vzTjrkEsCTZ6u1+YtTkqgCD+fQUAtifzNm1rcdOqNDJyvk427U3fx3DrSp5H7hGtUnRLV1Vs36YM+PqjeBS2uYVb8+spO83V3tHA3agsFqtXbY76Dy8nL5+/urrKyM/esAWsS+42Ua/fpWWa1SXGSA3nhwoLoGeNk7LJv/zTisef85qIFRAVrxq2H2Dge4ot1HSzVmwTYF+rgre84oGQxXniX/Rfpn2nDwpCYP7Wab5QPQdk6UVeknL29RRc15/fanfTWJuhEtImXpLq3Zc0IzRl2jGaN62zscXIWm5qGsfwSAFtS3i7/enDRI/l5u2n20VKNf+0RbLsxk25vVatXy7Pol8PfcQGE5OIdrw/xkdDGopNKsovLqK7Y/crpSG/Pq6+L8PCG6tcMDcAnh/l6adfu1kqQ/rz2oY6VVdo7I+VksVlsl+GHsV+8wSNYBoIWNuC5Eqx+9Sf26+uubc7WatHiH/vLfL2Wx81LAnKOlOnSyQp5uLhrdP9yusQBN5elmVK9gX0nSviYci/h2ZoGsVunWa4PV48L7ALS9BwdH64boTqo012nOyr3qwIt5W8TBorMqqTTL292ouIgAe4eDNkKyDgCtIDLQW8unJ+iBwVGyWqVX/vuFHn7rM7se6dYwq35HbLj8PN3sFgfQXN8WmfvhZL2i5ryW7zwqSZoyrPsPtgXQulxcDPrjhH5yN7poU94pfbTnhL1DcmrbD9cf2TaoW6DcXUnhOgr+TQNAK/F0M+p/xvXTS/fEydPNRZvzTmn061u1+2hpm8dSZa7TRw1nq1NwC04mpokV4T/I/lpna86rR7CPbmaZKGB3vUL8lDqylyTpt6v22fUDa2e33bYEPsjOkaAtkawDQCubEB+hlb8apm5B3jpWWqV7Fmbqn58WtOmSwI/3FelszXlFdPLSkO78oIdzaagI/0Mz6xbLt8e1TRnKcW2Ao5h+S0/1DvXVmUqznl9zwN7hOKXaOos+/erC+eo9+SCyIyFZB4A20CfcpFWP3qSkvqEy11n09MpcPfbeblWZ2+Z4t+XZ9UuD746PIImB02mYWT9WWqXSc5eemcv48pTyT1fKz8NV4weyegRwFO6uLvrjhP4yGKQPdn2tT750jKKrzmTP16WqNNcpwNtNMeGcYNWRkKwDQBsxebpp4cR4PXXndTK6GLTi82Mau2CbvjpV0arjfv3NOdvyuQkkMXBC/l5uigysPwLxcuetp287Ikn62aBI+Xhw/jDgSAZGddKkhG6SpNkr9uqc+bx9A3Iy2w7V/wxP6BHEB+4dDMk6ALQhg8GgacN76p9TB6uzr4fyis/qp/O3aW1u6xXe+SD7mKxWaWjPIEUGerfaOEBr6ht++aXwh05WKOOLUzIYZEsIADiW3yRdq64BXvr6myq9vO4Le4fjVBqKyw2lFkeHQ7IOAHYwpEeQ/v3rm3Rjt0BV1JzX9CW79Ic1+1VbZ2nRcSwWq97fVb8EnsJycGY/VGTu7cwjkqTbrgtVVBAfSAGOyNfDVc+Pi5Ukvbkt3y7FVp1RlblOuwpKJUnDelJzpqMhWQcAOwkxeeqfvxysacN7SJIWfZKvBxd9qpPl1S02xqf5JTpaUiU/D1fd3pez1eG8Lnd8W1lVrd6/cCzhw8O6tXVYAJphxLUhGjOgiyxWadYHe1r8A+r2aGdBicx1FoX7e6p7Zx97h4M2RrIOAHbkZnTRU3f20cKJA+Xr4aodR0p052tblXWh6uvVaigsNzouXF7uxhbpE7CHhorwh09VNCrMuHznUZ0z16l3qK8SmHUCHN6zo2PUydtNB4vO6m9bvrJ3OA6vYb/60J6dZTCwX72jIVkHAAdwe2y4VqUO03VhfjpdUaMH//6p/jfj8FUd71ZRc17/2VskSbo7PrKlQgXsItTkoSAfd1ms0sGi+tn1OotVb2cWSJImD+3OL7KAEwjy9dAzo2MkSX/Z8GWrF1l1dpkN+9X5MLJDIlkHAAfRI9hXK381TOOv76o6i1Xz/nNQ/+8f2Sqvrv1R/a3Zc1xVtXXqEeyjgVEBLRss0MYMBoNt3/r+E/XJ+saDJ1VYck7+Xm4ad31Xe4YHoBnGXd9VN1/TWebzFs1esVcWy4//YLo9K6uq1d5j9XU6hlFcrkMiWQcAB+LlbtRLP4vTH8bFyt3oonX7i/XT17fqwIlLH1f1Q97bWb+P92c3RDLjiHahYSl8w7719O35kqT7boxkmwfgRAwGg/5nXD95uRn1aX6Jlu08au+QHFLWV2dksUo9gn0U5u9p73BgByTrAOBgDAaDHhwcrfcfSVDXAC8dOXNO497YZiui1RSHT1Uou+AbGV0MGs+MI9qJ7xaZ+6L4rLYdOiMXg/TQkGg7RwaguSIDvfVYYm9J0v/8+4CKW7C4anux/VD9EvhhPZlV76hI1gHAQfWPCNDqR2/SLb2DVV1r0W+W79bsFXtVXVt3xfc2JPa39A5WiIlP49E+NCTrB0+U6/8+qZ9VT+obpohOHNcGOKMpw7orLsJfZ6vPa+6H++wdjsPZdrihuBz71TsqknUAcGCdfNy1ePIgzfxJbxkM0js7CnXPwkwdLTl32ffUWaxasas+Wb8nnrPV0X50C/KRj7tRNecteu/CSQeTh3azb1AAfjSji0F/nNBfri4Grd1XpLW5J+wdksM4WV6tQycrZDCIky46MJJ1AHBwLi4G/fq2a/TWlBvVydtNe4+VafTrW7Xp4MlLtt/y5SkVl9eok7ebbusT2sbRAq3HxcWgPuH1s+tWqxQTbtKN3QPtHBWAq9En3KTpt/SUJD374T6VVf24oqrtzfYLs+p9u5gU4O1u52hgLyTrAOAkhvcO1upf36y4yACVVdVqSvpnenldnuq+V0X3/QuF5cYM6Cp3V/43j/alYSm8JE0e1o3iiUA7kDqyl3p09tHJszX6438O2Dsch7CN/eoQyToAOJWuAV567/8N0c8T6gtqvbbxkCYv3qEzFTWSpNJzZq3fXyxJuucGlsCj/enbtb4ifKCPu34a18XO0QBoCZ5uRs0b30+S9M6Oo1q8LV9ZX51R/ulKVZmvXKelvbFarbaZ9aEc2dahudo7AABA83i4GvW7MbGKj+6kJz/Yq0++PK3Rr2/VggcHau/XZTLXWRQTbrIdcwW0J6P7h2tHfolu7xsmTzeOawPai8E9gvTA4Cgt/bRQv/1of6N7Jk9Xhfl7KtRU/wozeSrU/8I/TR4KM3kqyNdDRpf2sdKmsOScjpVWyc1o0KBunewdDuyIZB0AnNSYAV3r9/otydZXpyp17/9m2va1MauO9srb3VUv3hNn7zAAtIKn7+wjH3ej9h4rU3F5jYrKqlVVW6fy6vMqr67QF8UVl32v0cWgED8PhZg8FXYhgf82ob+Q5Pt7ytfD8dOfbYfqZ9Wvj+wkb3fHjxeth3/7AODEeof6aVXqTXri/d36994inTpbIzejQWMGcLY6AMC5+Hi46unkGNvXVqtVZ2vOq7isWkXl1Soqq9bJs/VJfFF5tYovXDtdUaM6i1Unyqp1oqxau39gDF8PV4WaPC49Q+/vpT7hfvJwte+qnW2H6/erD+1FFfiOjmQdAJycr4erFjwwUG9uO6IXP87T/TdGKdCHyrEAAOdmMBhk8nSTydNN14T6Xbbd+TqLTleYbQl9cUMi/52Evri8RhU15+tfp87r8KnKS/YV6OOun90QqQcHRyky0Lu1Hu2yLBarMi/sVx/GfvUOz2C1Wq1XbtY+lZeXy9/fX2VlZTKZTFd+AwA4uDqLVS4GUSEbAIDvqag5X5/IN8zUl1frZPm3M/WFJedUUmmWJBkM0q29g/VQQrRu6R3SZvvh9x8v152vfSIvN6N2z03kVJd2qql5KDPrANCOtJfiOgAAtDRfD1f5BvuqZ7DvJe+fr7No48GT+kdWgT758rQ25Z3SprxTiujkpQcHR+tnN0QoyNejVWPcfmEJ/I3dA0nUQbIOAAAAAK5GFyX2DVNi3zDln67UP7MKtDz7a339TZX+tPagXln/he7sF6aHEqI1MKpTq6xi225bAs9+dbAMnmXwAAAAAC6purZOH+0+riVZBdr9dZntep9wkx4aEq0xA7rIp4UqzNfWWTTgt+tUaa7T6kdvUmxXjmBtr5qah5Ksk6wDAAAAuILdR0u1JKtAq3YfV815iyTJz8NVE+IjNHFIlHqFXL4IXlNkF5Rowl8zFeDtpl1zfiIXtra1W+xZBwAAAIAWEhcZoLjIAD2d3EfvZ3+tJVkFOnLmnNK3H1H69iMa0iNQDw3ppsS+oXIzNn+/+fYL56sn9AgiUYckknUAAAAAaLIAb3dNvbmHHh7WXdsOn9Y/Mgv03wPFyvqqRFlflSjEz0P33Ril+2+MVLi/V5P7/fZ8dY5sQz2SdQAAAABoJhcXg26+Jlg3XxOs46VVemdHod7ZcVQnz9botQ1fasGmQ/pJn1BNHBKtYb2CfrAgXZW5TrsKSiVJw3pSXA71SNYBAAAA4Cp0CfDSY4nX6tGR12jd/iL9I7NAn+aXaO2+Iq3dV6QenX304JBo3T0wQv7ebhe9f2dBicx1FoX7e6p7Zx87PAEcEck6AAAAALQAd1cXje7fRaP7d9EXxWe1JKtAK3Yd01enK/X71fv1wscHNSauqyYOiVa/iG+rvW9r2K/e84dn4NGxkKwDAAAAQAvrHeqn342J1RO3X6d/fX5MS7IKdLDorJbtPKplO48qLjJADw2J1uj+4cq8sF99WE/2q+NbHN3G0W0AAAAAWpnVatXOgm+0JKtA/957QrV19WlYgLebyqtqZbFKWbNvU5i/p50jRWvj6DYAAAAAcBAGg0GDugVqULdAPTM6Rss+O6qlnxbqWGmVJKlHsA+JOhohWQcAAACANtTZ10MpI3pp+i09tTnvpNbtK9ZdcV3sHRYcDMk6AAAAANiB0cWg2/qE6rY+ofYOBQ7Ixd4BAAAAAACAxkjWAQAAAABwMCTrAAAAAAA4mGYn61u2bNFdd92lLl26yGAw6F//+lej+xUVFUpNTVVERIS8vLwUExOjhQsXNmpTXV2tlJQUBQUFydfXVxMmTFBxcXGjNoWFhUpOTpa3t7dCQkL0+OOP6/z5843abN68WQMHDpSHh4d69eql9PT05j4OAAAAAAAOp9nJemVlpeLi4rRgwYJL3p85c6bWrl2rJUuW6MCBA5oxY4ZSU1O1atUqW5u0tDR99NFHWr58uTIyMnT8+HGNHz/edr+urk7Jyckym83avn273nrrLaWnp+vZZ5+1tcnPz1dycrJGjBihnJwczZgxQ1OnTtXHH3/c3EcCAAAAAMChGKxWq/VHv9lg0MqVKzV27FjbtdjYWN1777165plnbNfi4+N1xx136Pnnn1dZWZmCg4O1dOlS3X333ZKkgwcPqk+fPsrMzNSQIUP0n//8R6NHj9bx48cVGlpfGXHhwoWaNWuWTp06JXd3d82aNUtr1qxRbm6ubZz77rtPpaWlWrt2bZPib+ph9AAAAAAAtISm5qEtvmd96NChWrVqlY4dOyar1apNmzbpiy++UGJioiQpOztbtbW1GjVqlO091113naKiopSZmSlJyszMVL9+/WyJuiQlJSWpvLxc+/bts7X5bh8NbRr6uJSamhqVl5c3egEAAAAA4GhaPFl//fXXFRMTo4iICLm7u+v222/XggULNHz4cElSUVGR3N3dFRAQ0Oh9oaGhKioqsrX5bqLecL/h3g+1KS8vV1VV1SVjmzdvnvz9/W2vyMjIq35eAAAAAABaWqsk61lZWVq1apWys7P10ksvKSUlRf/9739beqhmmz17tsrKymyvo0eP2jskAAAAAAAu4tqSnVVVVempp57SypUrlZycLEnq37+/cnJy9OKLL2rUqFEKCwuT2WxWaWlpo9n14uJihYWFSZLCwsK0Y8eORn03VIv/bpvvV5AvLi6WyWSSl5fXJePz8PCQh4dHizwrAAAAAACtpUVn1mtra1VbWysXl8bdGo1GWSwWSfXF5tzc3LRhwwbb/by8PBUWFiohIUGSlJCQoL179+rkyZO2NuvXr5fJZFJMTIytzXf7aGjT0AcAAAAAAM6q2TPrFRUVOnTokO3r/Px85eTkKDAwUFFRUbrlllv0+OOPy8vLS9HR0crIyNDbb7+tl19+WZLk7++vX/ziF5o5c6YCAwNlMpn06KOPKiEhQUOGDJEkJSYmKiYmRg899JD+/Oc/q6ioSHPmzFFKSoptZnz69OmaP3++nnjiCT388MPauHGj3nvvPa1Zs6Yl/l4AAAAAALCbZh/dtnnzZo0YMeKi65MmTVJ6erqKioo0e/ZsrVu3TiUlJYqOjta0adOUlpYmg8EgSaqurtZjjz2md955RzU1NUpKStIbb7xhW+IuSQUFBXrkkUe0efNm+fj4aNKkSfrjH/8oV1fXRrGkpaVp//79ioiI0DPPPKPJkyc3+Vk4ug0AAAAA0Jaamode1Tnrzo5kHQAAAADQlux2zjoAAAAAALg6JOsAAAAAADgYknUAAAAAABwMyToAAAAAAA6m2Ue3tScNtfXKy8vtHAkAAAAAoCNoyD+vVOu9QyfrZ86ckSRFRkbaORIAAAAAQEdy5swZ+fv7X/Z+h07WAwMDJUmFhYU/+JfUWgYNGqTPPvuszcdlbMZmbMZmbMZmbMZmbMZmbMZmbPuMXVZWpqioKFs+ejkdOll3canfsu/v72+Xc9aNRqPdzndnbMZmbMZmbMZmbMZmbMZmbMZmbPuMLX2bj172fhvFgUtISUlhbMZmbMZmbMZmbMZmbMZmbMZm7A42dlMYrFfa1d6OlZeXy9/fX2VlZXb9RAUAAAAA0DE0NQ/t0DPrHh4emjt3rjw8POwdCgAAAACgA2hqHtqhZ9YBAAAAAHBEHXpmHQAAAAAAR0SyjlazYMECdevWTZ6enho8eLB27NhxURur1ao77rhDBoNB//rXv9o+SMAJbdmyRXfddZe6dOlyye+dFStWKDExUUFBQTIYDMrJybFLnICzutL3WEVFhVJTUxURESEvLy/FxMRo4cKF9gkWcELz5s3ToEGD5Ofnp5CQEI0dO1Z5eXmN2tx6660yGAyNXtOnT7dTxIB9kKyjVSxbtkwzZ87U3LlztWvXLsXFxSkpKUknT55s1O7VV1+VwWCwU5SAc6qsrFRcXJwWLFhw2fs33XST/vSnP7VxZED7cKXvsZkzZ2rt2rVasmSJDhw4oBkzZig1NVWrVq1q40gB55SRkaGUlBRlZWVp/fr1qq2tVWJioiorKxu1++Uvf6kTJ07YXn/+85/tFDFgH+xZR6sYPHiwBg0apPnz50uSLBaLIiMj9eijj+rJJ5+UJOXk5Gj06NHauXOnwsPDtXLlSo0dO9aOUQPOx2AwXPZ758iRI+revbs+//xzDRgwoM1jA9qDS32PxcbG6t5779UzzzxjuxYfH6877rhDzz//vB2iBJzbqVOnFBISooyMDA0fPlxS/cz6gAED9Oqrr9o3OMCOmFlHizObzcrOztaoUaNs11xcXDRq1ChlZmZKks6dO6cHHnhACxYsUFhYmL1CBQCg2YYOHapVq1bp2LFjslqt2rRpk7744gslJibaOzTAKZWVlUmSAgMDG13/5z//qc6dOys2NlazZ8/WuXPn7BEeYDeu9g4A7c/p06dVV1en0NDQRtdDQ0N18OBBSVJaWpqGDh2qMWPG2CNEAAB+tNdff13Tpk1TRESEXF1d5eLiokWLFtlmBAE0ncVi0YwZMzRs2DDFxsbarj/wwAOKjo5Wly5dtGfPHs2aNUt5eXlasWKFHaMF2hbJOtrcqlWrtHHjRn3++ef2DgUAgGZ7/fXXlZWVpVWrVik6OlpbtmxRSkqKunTp0mhVGYArS0lJUW5urrZu3dro+rRp02x/7tevn8LDw3Xbbbfp8OHD6tmzZ1uHCdgFyTpaXOfOnWU0GlVcXNzoenFxscLCwrRx40YdPnxYAQEBje5PmDBBN998szZv3tx2wQIA0AxVVVV66qmntHLlSiUnJ0uS+vfvr5ycHL344osk60AzpKamavXq1dqyZYsiIiJ+sO3gwYMlSYcOHSJZR4fBnnW0OHd3d8XHx2vDhg22axaLRRs2bFBCQoKefPJJ7dmzRzk5ObaXJL3yyitavHixnaIGAODKamtrVVtbKxeXxr9CGY1GWSwWO0UFOBer1arU1FStXLlSGzduVPfu3a/4nobfF8PDw1s5OsBxMLOOVjFz5kxNmjRJN9xwg2688Ua9+uqrqqys1JQpUxQaGnrJonJRUVFN+p810NFVVFTo0KFDtq/z8/OVk5OjwMBARUVFqaSkRIWFhTp+/Lgk2c6uDQsLo6Aj0ARX+h675ZZb9Pjjj8vLy0vR0dHKyMjQ22+/rZdfftmOUQPOIyUlRUuXLtWHH34oPz8/FRUVSZL8/f3l5eWlw4cPa+nSpbrzzjsVFBSkPXv2KC0tTcOHD1f//v3tHD3Qdji6Da1m/vz5euGFF1RUVKQBAwbotddesy1h+r4fOn4KQGObN2/WiBEjLro+adIkpaenKz09XVOmTLno/ty5c/Xcc8+1QYSAc7vS91hRUZFmz56tdevWqaSkRNHR0Zo2bZrS0tJkMBjsEDHgXC73fbJ48WJNnjxZR48e1cSJE5Wbm6vKykpFRkZq3LhxmjNnjkwmUxtHC9gPyToAAAAAAA6GPesAAAAAADgYknUAAAAAABwMyToAAAAAAA6GZB0AAAAAAAdDsg4AAAAAgIMhWQcAAAAAwMGQrAMAAAAA4GBI1gEAAAAAcDAk6wAAAAAAOBiSdQAAAAAAHAzJOgAAAAAADoZkHQAAAAAAB0OyDgAAAACAgyFZBwAAAADAwZCsAwAAAADgYEjWAQAAAABwMCTrAAAAAAA4GJJ1AAAAAAAcDMk6AAAAAAAOhmQdAAAAAAAHQ7IOAAAAAICDIVkHAAAAAMDBkKwDAAAAAOBgSNYBAAAAAHAw7SpZnzx5sgwGg6ZPn37RvZSUFBkMBk2ePLntAwMAAAAAoBnaVbIuSZGRkXr33XdVVVVlu1ZdXa2lS5cqKirKjpEBAAAAANA07S5ZHzhwoCIjI7VixQrbtRUrVigqKkrXX3+97dratWt10003KSAgQEFBQRo9erQOHz5suz9y5EilpqY26vvUqVNyd3fXhg0bWv9BAAAAAAAdVrtL1iXp4Ycf1uLFi21fv/nmm5oyZUqjNpWVlZo5c6Z27typDRs2yMXFRePGjZPFYpEkTZ06VUuXLlVNTY3tPUuWLFHXrl01cuTItnkQAAAAAECH1C6T9YkTJ2rr1q0qKChQQUGBtm3bpokTJzZqM2HCBI0fP169evXSgAED9Oabb2rv3r3av3+/JGn8+PGSpA8//ND2nvT0dNu+eAAAAAAAWku7TNaDg4OVnJys9PR0LV68WMnJyercuXOjNl9++aXuv/9+9ejRQyaTSd26dZMkFRYWSpI8PT310EMP6c0335Qk7dq1S7m5uRSoAwAAAAC0Old7B9BaHn74Ydue8wULFlx0/6677lJ0dLQWLVqkLl26yGKxKDY2Vmaz2dZm6tSpGjBggL7++mstXrxYI0eOVHR0dJs9AwAAAACgY2q3yfrtt98us9ksg8GgpKSkRvfOnDmjvLw8LVq0SDfffLMkaevWrRf10a9fP91www1atGiRli5dqvnz57dJ7AAAAACAjq3dJutGo1EHDhyw/fm7OnXqpKCgIP3tb39TeHi4CgsL9eSTT16yn6lTpyo1NVU+Pj4aN25cq8cNAAAAAEC73LPewGQyyWQyXXTdxcVF7777rrKzsxUbG6u0tDS98MILl+zj/vvvl6urq+6//355enq2dsgAAAAAAMhgtVqt9g7CkR05ckQ9e/bUZ599poEDB9o7HAAAAABAB0Cyfhm1tbU6c+aMfvOb3yg/P1/btm2zd0gAAAAAgA6iXS+Dvxrbtm1TeHi4PvvsMy1cuNDe4QAAAAAAOhBm1gEAAAAAcDDMrAMAAAAA4GBI1gEAAAAAcDBOnazPmzdPgwYNkp+fn0JCQjR27Fjl5eU1alNdXa2UlBQFBQXJ19dXEyZMUHFxse3+7t27df/99ysyMlJeXl7q06eP/vKXv1w01ubNmzVw4EB5eHioV69eSk9Pb+3HAwAAAAB0UE6drGdkZCglJUVZWVlav369amtrlZiYqMrKSlubtLQ0ffTRR1q+fLkyMjJ0/PhxjR8/3nY/OztbISEhWrJkifbt26enn35as2fP1vz5821t8vPzlZycrBEjRignJ0czZszQ1KlT9fHHH7fp8wIAAAAAOoZ2VWDu1KlTCgkJUUZGhoYPH66ysjIFBwdr6dKluvvuuyVJBw8eVJ8+fZSZmakhQ4Zcsp+UlBQdOHBAGzdulCTNmjVLa9asUW5urq3Nfffdp9LSUq1du7b1HwwAAAAA0KE49cz695WVlUmSAgMDJdXPmtfW1mrUqFG2Ntddd52ioqKUmZn5g/009CFJmZmZjfqQpKSkpB/sAwAAAACAH8vV3gG0FIvFohkzZmjYsGGKjY2VJBUVFcnd3V0BAQGN2oaGhqqoqOiS/Wzfvl3Lli3TmjVrbNeKiooUGhp6UR/l5eWqqqqSl5dXyz4MAAAAAKBDazfJekpKinJzc7V169Yf3Udubq7GjBmjuXPnKjExsQWjAwAAAACg6drFMvjU1FStXr1amzZtUkREhO16WFiYzGazSktLG7UvLi5WWFhYo2v79+/XbbfdpmnTpmnOnDmN7oWFhTWqIN/Qh8lkYlYdAAAAANDinDpZt1qtSk1N1cqVK7Vx40Z179690f34+Hi5ublpw4YNtmt5eXkqLCxUQkKC7dq+ffs0YsQITZo0SX/4wx8uGichIaFRH5K0fv36Rn0AAAAAANBSnLoa/K9+9SstXbpUH374oa699lrbdX9/f9uM9yOPPKJ///vfSk9Pl8lk0qOPPiqpfm+6VL/0feTIkUpKStILL7xg68NoNCo4OFhS/dFtsbGxSklJ0cMPP6yNGzfq17/+tdasWaOkpKS2elwAAAAAQAfh1Mm6wWC45PXFixdr8uTJkqTq6mo99thjeuedd1RTU6OkpCS98cYbtmXwzz33nH77299e1Ed0dLSOHDli+3rz5s1KS0vT/v37FRERoWeeecY2BgAAAAAALcmpk3UAAAAAANojp96zDgAAAABAe0SyDgAAAACAgyFZBwAAAADAwZCsAwAAAADgYEjWAQAAAABwMCTrAAAAAAA4GJJ1AAAAAAAcDMk6AAAAAAAOhmQdAAAAAAAHQ7IOAAAAAICDIVkHAAAAAMDBkKwDAAAAAOBg/j9BOLKrUUX8zQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_us[(df_us.index.year==2020) & (df_us.index.month==5)]['new_infected'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_us['new_infected_avg'] = df_us['new_infected'].rolling(window=7).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_us['new_infected_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38928341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>AL</td>\n",
       "      <td>ALB</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2877800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43851043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>AD</td>\n",
       "      <td>AND</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>77265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>AO</td>\n",
       "      <td>AGO</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>Angola</td>\n",
       "      <td>32866268.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UID iso2 iso3  code3  FIPS Admin2 Province_State Country_Region       Lat  \\\n",
       "0    4   AF  AFG    4.0   NaN    NaN            NaN    Afghanistan  33.93911   \n",
       "1    8   AL  ALB    8.0   NaN    NaN            NaN        Albania  41.15330   \n",
       "2   12   DZ  DZA   12.0   NaN    NaN            NaN        Algeria  28.03390   \n",
       "3   20   AD  AND   20.0   NaN    NaN            NaN        Andorra  42.50630   \n",
       "4   24   AO  AGO   24.0   NaN    NaN            NaN         Angola -11.20270   \n",
       "\n",
       "       Long_ Combined_Key  Population  \n",
       "0  67.709953  Afghanistan  38928341.0  \n",
       "1  20.168300      Albania   2877800.0  \n",
       "2   1.659600      Algeria  43851043.0  \n",
       "3   1.521800      Andorra     77265.0  \n",
       "4  17.873900       Angola  32866268.0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4021, 12)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>840</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>US</td>\n",
       "      <td>329466283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>16</td>\n",
       "      <td>AS</td>\n",
       "      <td>ASM</td>\n",
       "      <td>16.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>US</td>\n",
       "      <td>-14.271000</td>\n",
       "      <td>-170.132000</td>\n",
       "      <td>American Samoa, US</td>\n",
       "      <td>55641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>316</td>\n",
       "      <td>GU</td>\n",
       "      <td>GUM</td>\n",
       "      <td>316.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Guam</td>\n",
       "      <td>US</td>\n",
       "      <td>13.444300</td>\n",
       "      <td>144.793700</td>\n",
       "      <td>Guam, US</td>\n",
       "      <td>164229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>580</td>\n",
       "      <td>MP</td>\n",
       "      <td>MNP</td>\n",
       "      <td>580.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>15.097900</td>\n",
       "      <td>145.673900</td>\n",
       "      <td>Northern Mariana Islands, US</td>\n",
       "      <td>55144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>850</td>\n",
       "      <td>VI</td>\n",
       "      <td>VIR</td>\n",
       "      <td>850.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>18.335800</td>\n",
       "      <td>-64.896300</td>\n",
       "      <td>Virgin Islands, US</td>\n",
       "      <td>107268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4016</th>\n",
       "      <td>84056037</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56037.0</td>\n",
       "      <td>Sweetwater</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>41.659439</td>\n",
       "      <td>-108.882788</td>\n",
       "      <td>Sweetwater, Wyoming, US</td>\n",
       "      <td>42343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4017</th>\n",
       "      <td>84056039</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56039.0</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.935225</td>\n",
       "      <td>-110.589080</td>\n",
       "      <td>Teton, Wyoming, US</td>\n",
       "      <td>23464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4018</th>\n",
       "      <td>84056041</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56041.0</td>\n",
       "      <td>Uinta</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>41.287818</td>\n",
       "      <td>-110.547578</td>\n",
       "      <td>Uinta, Wyoming, US</td>\n",
       "      <td>20226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>84056043</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56043.0</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.904516</td>\n",
       "      <td>-107.680187</td>\n",
       "      <td>Washakie, Wyoming, US</td>\n",
       "      <td>7805.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>84056045</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56045.0</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.839612</td>\n",
       "      <td>-104.567488</td>\n",
       "      <td>Weston, Wyoming, US</td>\n",
       "      <td>6927.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3324 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           UID iso2 iso3  code3     FIPS      Admin2  \\\n",
       "697        840   US  USA  840.0      NaN         NaN   \n",
       "698         16   AS  ASM   16.0     60.0         NaN   \n",
       "699        316   GU  GUM  316.0     66.0         NaN   \n",
       "700        580   MP  MNP  580.0     69.0         NaN   \n",
       "701        850   VI  VIR  850.0     78.0         NaN   \n",
       "...        ...  ...  ...    ...      ...         ...   \n",
       "4016  84056037   US  USA  840.0  56037.0  Sweetwater   \n",
       "4017  84056039   US  USA  840.0  56039.0       Teton   \n",
       "4018  84056041   US  USA  840.0  56041.0       Uinta   \n",
       "4019  84056043   US  USA  840.0  56043.0    Washakie   \n",
       "4020  84056045   US  USA  840.0  56045.0      Weston   \n",
       "\n",
       "                Province_State Country_Region        Lat       Long_  \\\n",
       "697                        NaN             US  40.000000 -100.000000   \n",
       "698             American Samoa             US -14.271000 -170.132000   \n",
       "699                       Guam             US  13.444300  144.793700   \n",
       "700   Northern Mariana Islands             US  15.097900  145.673900   \n",
       "701             Virgin Islands             US  18.335800  -64.896300   \n",
       "...                        ...            ...        ...         ...   \n",
       "4016                   Wyoming             US  41.659439 -108.882788   \n",
       "4017                   Wyoming             US  43.935225 -110.589080   \n",
       "4018                   Wyoming             US  41.287818 -110.547578   \n",
       "4019                   Wyoming             US  43.904516 -107.680187   \n",
       "4020                   Wyoming             US  43.839612 -104.567488   \n",
       "\n",
       "                      Combined_Key   Population  \n",
       "697                             US  329466283.0  \n",
       "698             American Samoa, US      55641.0  \n",
       "699                       Guam, US     164229.0  \n",
       "700   Northern Mariana Islands, US      55144.0  \n",
       "701             Virgin Islands, US     107268.0  \n",
       "...                            ...          ...  \n",
       "4016       Sweetwater, Wyoming, US      42343.0  \n",
       "4017            Teton, Wyoming, US      23464.0  \n",
       "4018            Uinta, Wyoming, US      20226.0  \n",
       "4019         Washakie, Wyoming, US       7805.0  \n",
       "4020           Weston, Wyoming, US       6927.0  \n",
       "\n",
       "[3324 rows x 12 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[countries['Country_Region'] == 'US']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>840</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>US</td>\n",
       "      <td>329466283.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     UID iso2 iso3  code3  FIPS Admin2 Province_State Country_Region   Lat  \\\n",
       "697  840   US  USA  840.0   NaN    NaN            NaN             US  40.0   \n",
       "\n",
       "     Long_ Combined_Key   Population  \n",
       "697 -100.0           US  329466283.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[(countries['Country_Region'] == 'US') & (countries['Province_State'].isna())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "697    329466283.0\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[(countries['Country_Region'] == 'US') & (countries['Province_State'].isna())]['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_us = countries[(countries['Country_Region'] == 'US') & (countries['Province_State'].isna())]['Population'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "329466283.0\n"
     ]
    }
   ],
   "source": [
    "print(pop_us)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>infected</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "      <th>new_infected</th>\n",
       "      <th>new_infected_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            infected  recovered  deaths  new_infected  new_infected_avg\n",
       "2020-01-22         1          0       0           NaN               NaN\n",
       "2020-01-23         1          0       0           0.0               NaN\n",
       "2020-01-24         2          0       0           1.0               NaN\n",
       "2020-01-25         2          0       0           0.0               NaN\n",
       "2020-01-26         5          0       0           3.0               NaN\n",
       "2020-01-27         5          0       0           0.0               NaN\n",
       "2020-01-28         5          0       0           0.0               NaN\n",
       "2020-01-29         5          0       0           0.0          0.571429"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_us.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_us['pop_infected'] = df_us['infected'] * 100 / pop_us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>infected</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "      <th>new_infected</th>\n",
       "      <th>new_infected_avg</th>\n",
       "      <th>pop_infected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.035212e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.035212e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.070424e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.070424e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.517606e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-11</th>\n",
       "      <td>2023590</td>\n",
       "      <td>540292</td>\n",
       "      <td>113823</td>\n",
       "      <td>22888.0</td>\n",
       "      <td>20721.000000</td>\n",
       "      <td>6.142025e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12</th>\n",
       "      <td>2048986</td>\n",
       "      <td>547386</td>\n",
       "      <td>114669</td>\n",
       "      <td>25396.0</td>\n",
       "      <td>20733.142857</td>\n",
       "      <td>6.219107e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-13</th>\n",
       "      <td>2074526</td>\n",
       "      <td>556606</td>\n",
       "      <td>115436</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>21141.142857</td>\n",
       "      <td>6.296626e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-14</th>\n",
       "      <td>2094058</td>\n",
       "      <td>561816</td>\n",
       "      <td>115732</td>\n",
       "      <td>19532.0</td>\n",
       "      <td>21392.714286</td>\n",
       "      <td>6.355910e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>2114026</td>\n",
       "      <td>576334</td>\n",
       "      <td>116127</td>\n",
       "      <td>19968.0</td>\n",
       "      <td>21749.285714</td>\n",
       "      <td>6.416517e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            infected  recovered  deaths  new_infected  new_infected_avg  \\\n",
       "2020-01-22         1          0       0           NaN               NaN   \n",
       "2020-01-23         1          0       0           0.0               NaN   \n",
       "2020-01-24         2          0       0           1.0               NaN   \n",
       "2020-01-25         2          0       0           0.0               NaN   \n",
       "2020-01-26         5          0       0           3.0               NaN   \n",
       "...              ...        ...     ...           ...               ...   \n",
       "2020-06-11   2023590     540292  113823       22888.0      20721.000000   \n",
       "2020-06-12   2048986     547386  114669       25396.0      20733.142857   \n",
       "2020-06-13   2074526     556606  115436       25540.0      21141.142857   \n",
       "2020-06-14   2094058     561816  115732       19532.0      21392.714286   \n",
       "2020-06-15   2114026     576334  116127       19968.0      21749.285714   \n",
       "\n",
       "            pop_infected  \n",
       "2020-01-22  3.035212e-07  \n",
       "2020-01-23  3.035212e-07  \n",
       "2020-01-24  6.070424e-07  \n",
       "2020-01-25  6.070424e-07  \n",
       "2020-01-26  1.517606e-06  \n",
       "...                  ...  \n",
       "2020-06-11  6.142025e-01  \n",
       "2020-06-12  6.219107e-01  \n",
       "2020-06-13  6.296626e-01  \n",
       "2020-06-14  6.355910e-01  \n",
       "2020-06-15  6.416517e-01  \n",
       "\n",
       "[146 rows x 6 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9UAAAIJCAYAAACrwR7/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABYy0lEQVR4nO3deVxU9eLG8WdmgEHZXBBQRHHfFQVF28yy6GZ73cxMTbPVzKLbTVvUbKH1F/deLcssyyVtzzRtwWy5mQvuC6i54AaIyi7bzPn94Y0uVy0GgTMDn/frxSvnzDlnnkPA8HDO+X4thmEYAgAAAAAALrOaHQAAAAAAAE9FqQYAAAAAoIoo1QAAAAAAVBGlGgAAAACAKqJUAwAAAABQRZRqAAAAAACqiFINAAAAAEAVeZkdoDKcTqcOHz6sgIAAWSwWs+MAAAAAAOo4wzCUl5enFi1ayGo9+/lojyjVhw8fVkREhNkxAAAAAAD1zIEDB9SyZcuzPu8RpTogIEDSqYMJDAw0OQ0AAAAAoK7Lzc1VREREeR89G48o1b9d8h0YGEipBgAAAADUmj+7BZmBygAAAAAAqCJKNQAAAAAAVUSpBgAAAACgiijVAAAAAABUEaUaAAAAAIAqolQDAAAAAFBFlGoAAAAAAKqIUg0AAAAAQBVRqgEAAAAAqCJKNQAAAAAAVUSpBgAAAACgiijVAAAAAABUEaUaAAAAAIAqolQDAAAAAFBFlGoAAAAAAKqIUg0AAAAAQBVRqgEAAAAA+B/H84srtR6lGgAAAACA/3A6DS1Ynaar/vVTpdb3quE8AAAAAAB4hK2HcvTEZ1u18UC2nMVlldqGM9UAAAAAgHotr6hUT32xTddM/0kbD2TL3+6lR6/oVKltOVMNAAAAAKiXDMPQks1H9PSS7crMO3UP9VU9m+vJq7qqgUp0fyX2QakGAAAAANQ7e47ma/Ln2/TT7ixJUmTThpp2bXdd1LGZJCk3t6RS+6FUAwAAAADqjaJSh177brdmfr9HJQ6nfLysGndxe909sK18vW0u749SDQAAAACoF75LzdSUz7cp7XihJOmijs007Zpuigz2q/I+KdUAAAAAgDrtSM5JTftiu5ZtTZckhQX6avLVXfWX7mGyWCzntG9KNQAAAACgzvp6W7oe/mCT8orLZLNaNPq8SD14WUf526unDlOqAQAAAAB1jsNp6NVvdmr6d7slSVERjZRwQw91aR5Yra9DqQYAAAAA1CnZhSV6YOFG/bDzqCRp9PmReuzKLvK2Wav9tSjVAAAAAIA6Y9vhHN0zL1kHjp+Ur7dVL9zYU9dGhdfY61GqAQAAAAB1wqcbDmrix1tUXOZUqyYN9caI6Gq/3Pt/UaoBAAAAAB6tpMyp577coTk/75MkXdypmf4xtLeCGnrX+GtTqgEAAAAAHiszt0jjFqzX2n0nJEkPXNpBD17aQVbruU2VVVmUagAAAACAR0ref1z3zluvzLxiBdi99OrQKA3uGlqrGSjVAAAAAACPYhiG5v6yX9O+2K4yp6GOof56Y0SM2gT71XoWSjUAAAAAwGMUlTr02Kdb9Mn6Q5KkIT2b68Ube8rPbk69pVQDAAAAADzC7sw8jX9/o3YcyZXVIk36SxeNvbCNLJbauX/6TCjVAAAAAAC3ZhiG5v2yX88s3aHiMqea+Plo+rDeOq99sNnRKNUAAAAAAPd1NK9Yj368WStSMiVJF3Vsppdv6qmQQF+Tk51CqQYAAAAAuKXvUjL1yEeblJVfIh8vqyb9pbNGDYistemyKoNSDQAAAABwK4UlZXp+WYreW7VfktQ5LECJt0Spc1igyclOR6kGAAAAALiNf+/O0sRPNuvA8ZOSpDHnt9Hfr+gkX2+bycnOjFINAAAAADBdTmGpnlm6XR8mH5QktQjyVcKNPTWwYzOTk/0xa1U2mjFjhiIjI+Xr66vY2FitWbPmD9fPzs7WuHHj1Lx5c9ntdnXs2FFffvlllQIDAAAAAOqWZVuO6NL/+14fJh+UxSKNHNBaX8cPdPtCLVXhTPWiRYsUHx+vmTNnKjY2VomJiYqLi1NqaqpCQkJOW7+kpESXXXaZQkJC9NFHHyk8PFz79+9Xo0aNqiM/AAAAAMBDZeQWafLnW/XVtgxJUrtmfnrhxp6KiWxicrLKsxiGYbiyQWxsrPr27avp06dLkpxOpyIiIjR+/HhNnDjxtPVnzpypl156SSkpKfL29q7UaxQXF6u4uLj8cW5uriIiIpSTk6PAQPe7MR0AAAAAUHkFxWV644c9mvXDHp0sdcjLatG9F7fTuEHt3ebe6dzcXAUFBf1pD3Xp8u+SkhIlJydr8ODBv+/AatXgwYO1atWqM26zePFiDRgwQOPGjVNoaKi6d++u5557Tg6H46yvk5CQoKCgoPKPiIgIV2ICAAAAANxQmcOp+av3a+BLK/XPpF06WepQn1aN9MX4C/Tw5e47GNkfceny76ysLDkcDoWGhlZYHhoaqpSUlDNus2fPHq1YsULDhw/Xl19+qd27d+u+++5TaWmppkyZcsZtJk2apPj4+PLHv52pBgAAAAB4HsMwlLQjU88vT9HuzHxJUmTThnr0is66onuYLBb3mXfaVTU++rfT6VRISIjefPNN2Ww2RUdH69ChQ3rppZfOWqrtdrvsdntNRwMAAAAA1LDNB7P17NIdWr33uCSpcUNvTbi0g26NbS0fryqNne1WXCrVwcHBstlsysjIqLA8IyNDYWFhZ9ymefPm8vb2ls32+2n8Ll26KD09XSUlJfLx8alCbAAAAACAOztwvFAvfZWqxZsOS5LsXlaNuaCN7r24nQJ9Kzfelidw6c8CPj4+io6OVlJSUvkyp9OppKQkDRgw4IzbnH/++dq9e7ecTmf5sp07d6p58+YUagAAAACoY3IKS/Xs0u269JXvtXjTYVks0g19wrXibxfr0Ss616lCLVXh8u/4+HiNGjVKMTEx6tevnxITE1VQUKDRo0dLkkaOHKnw8HAlJCRIku69915Nnz5dEyZM0Pjx47Vr1y4999xzeuCBB6r3SAAAAAAApikuc2juqv3614rdyjlZKkm6oH2wJv6ls7qHB5mcrua4XKqHDh2qo0ePavLkyUpPT1dUVJSWL19ePnhZWlqarNbfT4BHREToq6++0kMPPaSePXsqPDxcEyZM0KOPPlp9RwEAAAAAMIXTaeiLzYf10lepOnjipCSpU2iAJl3ZWQM7NvPoQcgqw+V5qs1Q2fnBAAAAAAC155c9x5Tw5Q5tOpgjSQoNtOvhyzrpxuiWslk9u0xXtofW+OjfAAAAAIC6ZXdmnp5flqJvd2RKkvx8bLpnYDvdcWEbNfSpXzWzfh0tAAAAAKDKCorL9MLyFM1fnSaH05DNatGwfhGacGlHNQuon9MiU6oBAAAAAH9qQ9oJPbRoo/YdK5QkXdY1VI9e0VntQ/xNTmYuSjUAAAAA4KzKHE7N+O5X/XPFLjmchpoH+eqlm3rpgg7BZkdzC5RqAAAAAMAZ7T9WoAcXbdSGtGxJ0tW9WuiZa7srqGHdmmv6XFCqAQAAAAAVGIahD9cd1NQvtqmwxKEAXy89c113XRsVbnY0t0OpBgAAAACUKywp06RPtujzjYclSbFtmuiVm3upZeOGJidzT5RqAAAAAIAkaV9Wge6Zl6yU9DzZrBb97fJOuuuith4/53RNolQDAAAAAJS0I0MPLtqovKIyBfvbNePW3opt29TsWG6PUg0AAAAA9ZjDaegf3+7UP1fsliRFt26s14b3UWigr8nJPAOlGgAAAADqqezCEk1YuFHf7zwqSRo1oLUeH9JVPl5Wk5N5Dko1AAAAANRD36VkatInW5SeWyRfb6sSbuih63u3NDuWx6FUAwAAAEA9kl1YomlLtuuT9YckSW2C/TTj1j7q2iLQ5GSeiVINAAAAAPXE19vS9fhnW3U0r1gWi3TH+W308OWd1MDHZnY0j0WpBgAAAIA67nhBiaYs3qYvNp2ae7pdMz+9eFMvRbdubHIyz0epBgAAAIA6yjAMfbbxkJ5ZskPHCkpktUh3XdRODw7uIF9vzk5XB0o1AAAAANRBuzPz9eRnW7VqzzFJUsdQf710Uy/1imhkbrA6hlINAAAAAHVIUalD01fs1hs//KpShyG7l1UPXNpBd17YlqmyagClGgAAAADqiO9SMzXl821KO14oSRrUqZmmXdtdEU0ampys7qJUAwAAAICHy8ov1tTF27Rk8xFJUvMgX025uqviuoXJYrGYnK5uo1QDAAAAgIcyDEOfbjikaUu2K7uwVDarRaPPi9SDl3WUv526Vxv4LAMAAACABzp4olCPf7pV3+88Kknq2jxQL97UU93Dg0xOVr9QqgEAAADAgzidhuat3q8XlqWooMQhHy+rJlzaQXdd1FbeNgYiq22UagAAAADwEMn7j+vZpTu0Pi1bktQ3srGev7Gn2jXzNzdYPUapBgAAAAA3tysjTy9+lapvtmdIkvx8bJr4l84aHttaVisDkZmJUg0AAAAAbupw9kklfrtTHyUflNOQrBbp5pgIPTi4o8KCfM2OB1GqAQAAAMDt5BaVasaK3Zrz8z4VlzklSXHdQvVIXCe1DwkwOR3+G6UaAAAAANyEYRj6cku6pn6xTUfziiVJ/SKb6NG/dFZ068Ymp8OZUKoBAAAAwA0cOF6oJz/fqpWpp6bIahPspyeGdNElnUNksXDftLuiVAMAAACAiUodTs3+aa8Sv92polKnfGxW3XtxO917cTv5etvMjoc/QakGAAAAAJNsSDuhSZ9sUUp6niSpf9smevb6HkyR5UEo1QAAAABQy5xOQ2/+uEcvfZUqh9NQ44beemJIV93QJ5xLvT0MpRoAAAAAatGJghI9/OEmrUjJlCRd3auFnrqmm5r4+ZicDFVBqQYAAACAWrI+7YTun79eh3OK5ONl1dSru2lYvwjOTnswSjUAAAAA1DDDMDT7p716flmKypyGIps21IzhfdStRZDZ0XCOKNUAAAAAUINyCkv1yEeb9PX2DEnSkB7N9fyNPRTg621yMlQHSjUAAAAA1JDNB7N13/z1OnjipHxsVj15VRfd1r81l3vXIZRqAAAAAKhmhmHovVX79ezSHSpxOBXRpIFeuzVaPVpyuXddQ6kGAAAAgGqUW1SqSR9v0dItRyRJcd1C9eJNvRTUgMu96yJKNQAAAABUk62HcjRuwXrtP1YoL6tFk67sojHnR3K5dx1GqQYAAACAc2QYhub9sl9PL92hkjKnwhs10PRbe6t3q8ZmR0MNo1QDAAAAwDnIyi/Wox9tVlJKpiRpcJcQvfzXXmrU0MfkZKgNlGoAAAAAqKLvUjL1yEeblJVfIh8vqx69ojOXe9czlGoAAAAAcNHJEoee+3KH5v6yX5LUKTRA/xgWpc5hgSYnQ22jVAMAAACAC7YdztGEhRu1OzNfkjTm/Db6+xWd5OttMzkZzECpBgAAAIBKyC4sUeK3uzTvl/0qcxoKCbDr5b/20kUdm5kdDSaiVAMAAADAHyh1ODXvl/1K/HaXck6WSpL+0j1Mz17fQ038GIysvqNUAwAAAMAZGIah71Iz9czSHdpztECS1DksQE9e1VXntw82OR3cBaUaAAAAAP7Hzow8Pb1ku37clSVJaurno4cv76ShfSNkszKyN35HqQYAAACA/8g5WarEb3fqvVX75XAa8rFZNfqCSI0b1F6Bvt5mx4MbolQDAAAAqPccTkMfrjugF79K1fGCEknSZV1D9cSQLmrd1M/kdHBnlGoAAAAA9Vry/uOauni7thzKkSS1a+anKVd3Y1RvVAqlGgAAAEC9lJFbpBeWpeiTDYckSQF2L00Y3EGjzouUt81qcjp4Cko1AAAAgHqluMyhd/69T/9K2qWCEocsFunm6Aj9La6TmgXYzY4HD1OlP7/MmDFDkZGR8vX1VWxsrNasWXPWdefMmSOLxVLhw9fXt8qBAQAAAKCqvkvJ1BWJP+r5ZSkqKHGod6tG+uy+8/XCTT0p1KgSl89UL1q0SPHx8Zo5c6ZiY2OVmJiouLg4paamKiQk5IzbBAYGKjU1tfyxxcIQ9AAAAABqz96sAj29ZLtWpGRKkpoF2DXxis66vne4rEyRhXPgcqn+v//7P915550aPXq0JGnmzJlaunSp3n77bU2cOPGM21gsFoWFhZ1bUgAAAABwUXGZQ69996teW7lbpQ5D3jaLxpzfRvdf0l4BTJGFauBSqS4pKVFycrImTZpUvsxqtWrw4MFatWrVWbfLz89X69at5XQ61adPHz333HPq1q3bWdcvLi5WcXFx+ePc3FxXYgIAAACAkvef0MSPN2tXZr4kaWDHZpp8dVe1a+ZvcjLUJS7dU52VlSWHw6HQ0NAKy0NDQ5Wenn7GbTp16qS3335bn3/+uebNmyen06nzzjtPBw8ePOvrJCQkKCgoqPwjIiLClZgAAAAA6rGC4jJNXbxNN838Wbsy8xXs76Ppt/bWnNF9KdSodjU++veAAQM0YMCA8sfnnXeeunTpojfeeENPP/30GbeZNGmS4uPjyx/n5uZSrAEAAAD8qZWpmXr80606lH1SknRTdEs9fmUXNfbzMTkZ6iqXSnVwcLBsNpsyMjIqLM/IyKj0PdPe3t7q3bu3du/efdZ17Ha77HZG3gMAAABQOTmFpXpqyTZ9sv7UnNMtGzdQwg09dGGHZiYnQ13n0uXfPj4+io6OVlJSUvkyp9OppKSkCmej/4jD4dCWLVvUvHlz15ICAAAAwBmsTM3U5Ynf65P1h2S1SGMvaKOvH7qIQo1a4fLl3/Hx8Ro1apRiYmLUr18/JSYmqqCgoHw08JEjRyo8PFwJCQmSpGnTpql///5q3769srOz9dJLL2n//v0aO3Zs9R4JAAAAgHolr6hUzy7doYVrD0iS2gb76aW/9lJ068YmJ0N94nKpHjp0qI4eParJkycrPT1dUVFRWr58efngZWlpabJafz8BfuLECd15551KT09X48aNFR0drZ9//lldu3atvqMAAAAAUK/8vDtLj3y0WYeyT8pikUaf10aPxHVSAx+b2dFQz1gMwzDMDvFncnNzFRQUpJycHAUGBpodBwAAAIBJikoden5Ziub8vE+SFNGkgV6+qZdi2zY1NxjqnMr20Bof/RsAAAAAqsOvR/M1bv56paTnSZJu699Kk/7SRX52ag3Mw1cfAAAAALf32YZDeuzTLSoscSjY30ev3BylgR0ZiAzmo1QDAAAAcFsnSxyaunibFq07NRjZgLZN9Y9bohQS6GtyMuAUSjUAAAAAt7Q7M0/j5m9QakaeLBbpgUs66IFLO8hmtZgdDShHqQYAAADgdj7bcEiTPtmik6UOBfvb9c9bonRe+2CzYwGnoVQDAAAAcBulDqee+3KH3vn3PknS+e2b6tWhUQoJ4HJvuCdKNQAAAAC3kJlXpPvnb9CafcclSeMvaa8HB3fkcm+4NUo1AAAAANOtTzuhe+clKyO3WP52L71ycy/FdQszOxbwpyjVAAAAAExjGIYWrEnT1MXbVOow1D7EX2+MiFa7Zv5mRwMqhVINAAAAwBQnSxyasnirPlh3UJJ0RbcwvXxzL/nbqSnwHHy1AgAAAKh1qel5un/Beu3KzJfVIv0trpPuHdhOFgv3T8OzUKoBAAAA1BrDMDR/dZqeXrJdxWVONQuwK3FolM5nuix4KEo1AAAAgFqRU1iqiZ9s1rKt6ZKkizs108t/7aVgf7vJyYCqo1QDAAAAqHHr9h3XhIUbdSj7pLxtFv09rrPuuKCNrEyXBQ9HqQYAAABQY4pKHfpn0i698cMeOZyGWjdtqH8N662eLRuZHQ2oFpRqAAAAADXi591ZeuzTLdp3rFCSdG1UCz1zXXcF+HqbnAyoPpRqAAAAANXqREGJnvtyhz5MPjVVVmigXdOu7a64bmEmJwOqH6UaAAAAQLUwDEOLNx3WtC+261hBiSwW6bbY1nrkik4K5Ow06ihKNQAAAIBzlp5TpMc+3aIVKZmSpA4h/nr+xh6Kbt3E5GRAzaJUAwAAAKgywzD06YZDmrp4m3KLyuRjs+r+S9rrnoHt5ONlNTseUOMo1QAAAACqJDOvSI99slXf7siQJPVqGaSX/9pLHUIDTE4G1B5KNQAAAACX/Hbv9JTF25RdWCpvm0UPDu6ouy9qKy8bZ6dRv1CqAQAAAFRaek6Rpi7epuXb0iVJ3VoE6pWbe6lzWKDJyQBzUKoBAAAA/KniMofe+nGvZny3W4UlDnlZLRp/SQfdN6idvDk7jXqMUg0AAADgD61IydC0L7Zr37FCSVJ068aadm03dWsRZHIywHyUagAAAABntDerQE8v2V4+TVZIgF2Truys66LCZbFYTE4HuAdKNQAAAIAKnE5Dr3//q/7x7S6VOJzytlk05oI2Gn9JB/nbqRDAf+M7AgAAAEC5nMJSPfTBxvKz0wM7NtPkq7uqXTN/k5MB7olSDQAAAECStP1wru6Zl6y044Xy8bLqmeu666/RLbnUG/gDlGoAAAAA+jj5oB77dIuKy5xq2biBZt4Wre7hDEQG/BlKNQAAAFCPFZc59PSS7Zr3S5qkU5d7/+OWKDVq6GNyMsAzUKoBAACAeupIzkndO2+9Nh7IlsUiPXBJB024tIOsVi73BiqLUg0AAADUQz/vztL49zfoWEGJAn299I9bemtQ5xCzYwEeh1INAAAA1COGYeiNH/boxeUpchpS1+aBmnlbtFo1bWh2NMAjUaoBAACAeiKvqFR/+3CTvtqWIUm6sU9LPXt9d/l620xOBnguSjUAAABQD+zMyNM9c5O1J6tAPjarplzTVbf2a8V0WcA5olQDAAAAddzyrUf00KJNOlnqUIsgX712W7SiIhqZHQuoEyjVAAAAQB312/3Tzy9LkSSd376p/nlLbzX1t5ucDKg7KNUAAABAHVTqcOrJz7Zq4doDkqRRA1rryau6ystmNTkZULdQqgEAAIA6JudkqcbNX6+fdmfJapGevKqrRp/fxuxYQJ1EqQYAAADqkAPHCzVmzlrtysxXQx+b/jWsty7tEmp2LKDOolQDAAAAdcT6tBO66711ysovUWigXbNH9VX38CCzYwF1GqUaAAAA8HDFZQ7NWLFbr638VWVOQ12bB+rt2/sqLMjX7GhAnUepBgAAADzYxgPZ+vtHm7QzI1+SNKRHc714U0/52flVH6gNfKcBAAAAHqio1KFXv9mpWT/ukdOQgv19NO3a7rqyR3OzowH1CqUaAAAA8DBr9x3X3z/arL1ZBZKk63uHa/JVXdXYz8fkZED9Q6kGAAAAPERRqUMvLk/VOz/vlWFIoYF2PXd9D0b3BkxEqQYAAAA8wIa0E3r4w03ac/TU2emhMRF6bEgXBTXwNjkZUL9RqgEAAAA3Vlzm0D+Tdun1lb/K+Z+z08/f2FODOoWYHQ2AKNUAAACA29p+OFfxH2xUSnqeJOm6qBZ66pruCmrI2WnAXVCqAQAAADdzssShmd//qtdW7lapw1ATPx89e113/YWRvQG3Q6kGAAAA3IRhGFq2NV3PLt2hQ9knJUmXdw3Vczf0ULC/3eR0AM6EUg0AAAC4gZT0XE1dvE2/7DkuSWoR5KvHh3TVlT3CZLFYTE4H4Gwo1QAAAICJsgtL9Oo3OzX3l/1yGpLdy6p7BrbTPQPbqYGPzex4AP4EpRoAAAAwgdNp6KPkg0pYtkMnCkslSX/pHqbHruyiiCYNTU4HoLKsVdloxowZioyMlK+vr2JjY7VmzZpKbbdw4UJZLBZdd911VXlZAAAAoE5ISc/VzW+s0t8/3qwThaXqGOqv+WNj9fpt0RRqwMO4fKZ60aJFio+P18yZMxUbG6vExETFxcUpNTVVISFnnytv3759+tvf/qYLL7zwnAIDAAAAnqqguEz/SNql2T/tlcNpqKGPTQ8O7qDR57eRt61K57sAmMxiGIbhygaxsbHq27evpk+fLklyOp2KiIjQ+PHjNXHixDNu43A4dNFFF2nMmDH68ccflZ2drc8+++ysr1FcXKzi4uLyx7m5uYqIiFBOTo4CAwNdiQsAAACYzjAMfbUtXU99sV1HcookSVd0C9Pkq7uqRaMGJqcDcCa5ubkKCgr60x7q0p/DSkpKlJycrMGDB/++A6tVgwcP1qpVq8663bRp0xQSEqI77rijUq+TkJCgoKCg8o+IiAhXYgIAAABu42SJQw8u2qh75q3XkZwiRTRpoHdu76uZI6Ip1EAd4NLl31lZWXI4HAoNDa2wPDQ0VCkpKWfc5qefftLs2bO1cePGSr/OpEmTFB8fX/74tzPVAAAAgCc5cLxQd81N1o4jubJZLbp3YDuNG9SeUb2BOqRGR//Oy8vTiBEjNGvWLAUHB1d6O7vdLrudye0BAADguX7cdVTj39+g7MJSBfv7aPqtfdS/bVOzYwGoZi6V6uDgYNlsNmVkZFRYnpGRobCwsNPW//XXX7Vv3z5dffXV5cucTuepF/byUmpqqtq1a1eV3AAAAIBbMgxDb/ywRy8uT5HTkHq1DNLMEdFqHsSl3kBd5NI91T4+PoqOjlZSUlL5MqfTqaSkJA0YMOC09Tt37qwtW7Zo48aN5R/XXHONBg0apI0bN3JJNwAAAOqUwpIy3f/+Bj2/7FSh/mt0Sy26ewCFGqjDXL78Oz4+XqNGjVJMTIz69eunxMREFRQUaPTo0ZKkkSNHKjw8XAkJCfL19VX37t0rbN+oUSNJOm05AAAA4MkOZZ/UHXPWKiU9T15Wi6Zc0023xbaSxWIxOxqAGuRyqR46dKiOHj2qyZMnKz09XVFRUVq+fHn54GVpaWmyWpljDwAAAPXHpgPZuuPddcrKL1awv12v39ZHfSObmB0LQC1weZ5qM1R2fjAAAACgti3bckQPfbBRRaVOdQ4L0Nu392WqLKAOqGwPrdHRvwEAAIC6yjAMzfx+j15Yfmpq2UGdmulft/aRv51fsYH6hO94AAAAwEUlZU49+dlWLVp3QJJ0+3mRemJIF3nZuA0SqG8o1QAAAIALcgpLde/8ZP386zFZLdLkq7rq9vPbmB0LgEko1QAAAEAl7T9WoNFz1mrP0QL5+dg0/dY+GtQ5xOxYAExEqQYAAAAqYd2+47prbrKOF5SoRZCvZt/eV12aM4guUN9RqgEAAIA/8fnGQ3rkw80qcTjVIzxIs0fFKCTQ1+xYANwApRoAAAA4C8Mw9I+kXUr8dpckKa5bqF4dGqWGPvwaDeAUfhoAAAAAZ1BU6tDEjzfrs42HJUl3D2yrR+M6y2q1mJwMgDuhVAMAAAD/IyO3SPfOS9b6tGx5WS16+rruGtavldmxALghSjUAAADwX37Zc0z3L9igrPxiBfh66fXh0bqgQ7DZsQC4KUo1AAAAoFP3T8/+aa8SlqXI4TTUOSxAb4yIVuumfmZHA+DGKNUAAACo9wqKy/T3jzdr6eYjkqTre4fruet7qIGPzeRkANwdpRoAAAD12q9H83X33GTtzsyXl9WiyVd31Yj+rWWxMCAZgD9HqQYAAEC99cWmw5r0yRblF5cpNNCu14b3UXTrJmbHAuBBKNUAAACod4pKHXpm6XbN+yVNktSvTRNNv7W3QgJ8TU4GwNNQqgEAAFCv7M0q0Lj567X9SK4kadygdnpocEd52awmJwPgiSjVAAAAqDf++3LvJn4+enVolAZ2bGZ2LAAejFINAACAOq+o1KGnl2zX/NW/X+79z1t6KyyIy70BnBtKNQAAAOq07Ydz9dCijUrNyJPFIo27uL0eHNyBy70BVAtKNQAAAOokh9PQrB/36JWvU1XqMBTs76NXbuZybwDVi1INAACAOufA8UI9/OEmrdl7XJJ0WddQPX9DDzX1t5ucDEBdQ6kGAABAnWEYhj5ef0hTF29TfnGZ/HxsmnJ1N/01pqUsFovZ8QDUQZRqAAAA1Am5RaV69KPNWrY1XZIU07qx/u/mKLVq2tDkZADqMko1AAAAPN6ujDzdPTdZe7IK5G2z6KHLOurui9rJZuXsNICaRakGAACAR1u25Yj+9uEmFZQ41CLIV6/fFq1eEY3MjgWgnqBUAwAAwCM5nIZe/jpVr6/8VZI0oG1TTb+1N4ORAahVlGoAAAB4nBMFJXpg4Qb9uCtLknTnhW306BWdmXsaQK2jVAMAAMCjpKTnauy763TwxEk18LbphZt66ppeLcyOBaCeolQDAADAY6zec0xj31unvKIytWrSUG+MiFaX5oFmxwJQj1GqAQAA4BGWb03XAws3qKTMqb6RjTVrZIwaNfQxOxaAeo5SDQAAALc3f/V+PfnZVjkN6bKuofrXsN7y9baZHQsAKNUAAABwX4Zh6J9Ju/XqtzslScP6Rejpa7szIBkAt0GpBgAAgFtyOA1N/nyr5q9OkyQ9cEl7PXRZR1ksFpOTAcDvKNUAAABwO2UOpyYs3KilW47IYpGmXdNNIwZEmh0LAE5DqQYAAIBbcToN/f2jzVq65Yh8bFYl3hKlK3s0NzsWAJwRpRoAAABuwzAMTf1imz7ZcEg2q0XTb+2ty7uFmR0LAM6KER4AAADgNl76KlXvrdovi0V65a+9KNQA3B6lGgAAAG5hxne79drKXyVJz1zXXdf1Djc5EQD8OUo1AAAATPfeqn166atUSdJjV3bW8NjWJicCgMqhVAMAAMBUHyUf1OTPt0k6NW3WXRe1MzkRAFQepRoAAACm+XTDQf39o02SpNHnR+qhyzqanAgAXMPo3wAAADDF2z/t1bQl2yVJQ2Mi9OSQrrJYLCanAgDXUKoBAABQqwzD0P99s1P/WrFbkjTm/DZ6YkgXWa0UagCeh1INAACAWuNwGnry861asDpNkvRIXCfdd3E7zlAD8FiUagAAANSK4jKH4hdt0tItR2SxnJo2i1G+AXg6SjUAAABqXEFxme6Zl6wfd2XJ22ZR4tDeGtKzudmxAOCcUaoBAABQozJyizT23XXacihHDX1semNEtC7s0MzsWABQLSjVAAAAqDHbDufojjnrlJ5bpCZ+Pnr79r6KimhkdiwAqDaUagAAANSIb7dn6IGFG1RY4lC7Zn565/Z+atW0odmxAKBaUaoBAABQrQzD0Oyf9urZL3fIMKQL2gdrxvA+CmrgbXY0AKh2lGoAAABUm1KHU1MWbyufMmtYv1aadm03edusJicDgJpBqQYAAEC1OFFQovHvb9BPu7NksUiPX9lFd1zQhjmoAdRplGoAAACcs+T9JzR+wXodzilSQx+b/nFLb13WNdTsWABQ4yjVAAAAqDLDMPT2v/cp4csdKnMaahPsp9eG91GX5oFmRwOAWlGlm1tmzJihyMhI+fr6KjY2VmvWrDnrup988oliYmLUqFEj+fn5KSoqSnPnzq1yYAAAALiH3KJS3TtvvZ5esl1lTkNDejbX4vvPp1ADqFdcPlO9aNEixcfHa+bMmYqNjVViYqLi4uKUmpqqkJCQ09Zv0qSJHn/8cXXu3Fk+Pj5asmSJRo8erZCQEMXFxVXLQQAAAKB2bT2Uo/vmr1fa8UJ52yx68qquGtG/NfdPA6h3LIZhGK5sEBsbq759+2r69OmSJKfTqYiICI0fP14TJ06s1D769OmjIUOG6Omnn67U+rm5uQoKClJOTo4CA/nLJwAAgFkMw9B7q/br2S93qKTMqZaNG2jGrX3UK6KR2dEAoFpVtoe6dPl3SUmJkpOTNXjw4N93YLVq8ODBWrVq1Z9ubxiGkpKSlJqaqosuuuis6xUXFys3N7fCBwAAAMyVkVukkW+v0ZTF21RS5tTgLiFaOv5CCjWAes2ly7+zsrLkcDgUGlpxJMfQ0FClpKScdbucnByFh4eruLhYNptNr732mi677LKzrp+QkKCnnnrKlWgAAACoQUs3H9Hjn21RdmGp7F5WPXZlF43o31pWK5d7A6jfamX074CAAG3cuFH5+flKSkpSfHy82rZtq4svvviM60+aNEnx8fHlj3NzcxUREVEbUQEAAPBfck6Wauribfp0wyFJUo/wIL06NErtQ/xNTgYA7sGlUh0cHCybzaaMjIwKyzMyMhQWFnbW7axWq9q3by9JioqK0o4dO5SQkHDWUm2322W3212JBgAAgGr2y55jil+0UYdzimS1SOMGtdcDl3aQt61KE8gAQJ3k0k9EHx8fRUdHKykpqXyZ0+lUUlKSBgwYUOn9OJ1OFRcXu/LSAAAAqEXzftmv4W+t1uGcIrVu2lAf3nOeHr68E4UaAP6Hy5d/x8fHa9SoUYqJiVG/fv2UmJiogoICjR49WpI0cuRIhYeHKyEhQdKp+6NjYmLUrl07FRcX68svv9TcuXP1+uuvV++RAAAA4Jw5nYYSlu3QrB/3SpKui2qhZ6/vIT97rdw1CAAex+WfjkOHDtXRo0c1efJkpaenKyoqSsuXLy8fvCwtLU1W6+9/wSwoKNB9992ngwcPqkGDBurcubPmzZunoUOHVt9RAAAA4JydLHHowUUb9NW2U7f6xV/WUeMvac/c0wDwB1yep9oMzFMNAABQszLzijT23XXafDBHPjarXvprT10bFW52LAAwTWV7KNfxAAAA1HOp6XkaM2etDmWfVOOG3npzZIz6RjYxOxYAeARKNQAAQD22Zu9xjZmzVvnFZWoT7Kd3bu+ryGA/s2MBgMegVAMAANRTyfuP6/Z31qiwxKF+bZrojdui1djPx+xYAOBRKNUAAAD10Ia0Exr19loVljh0QftgvTUqRr7eNrNjAYDHYaJBAACAembzwWyNfHuN8ovL1L9tE80aSaEGgKqiVAMAANQjWw/laMTsNcorKlPfyMaaPaqvGvhQqAGgqijVAAAA9URKeq5GzF6tnJOl6tOqkd4Z3U9+du4GBIBzQakGAACoB3Zl5Gn4rNU6UViqXhGNNGdMP/lTqAHgnFGqAQAA6riDJwo1/K3VOlZQoh7hQXpvTD8F+nqbHQsA6gRKNQAAQB2WXVii299Zq8y8YnUM9dfcO/opqAGFGgCqC6UaAACgjioqdejO99Zpd2a+mgf56t0x/dSoIfNQA0B1olQDAADUQQ6noYcWbdTafScU4OulOaP7qXlQA7NjAUCdQ6kGAACoYwzD0LQvtmnZ1nT52KyaNTJGncICzI4FAHUSpRoAAKCOeeOHPXp31X5J0v8N7aX+bZuanAgA6i5KNQAAQB3y2YZDen5ZiiTpyau66qqeLUxOBAB1G6UaAACgjliZmqlHPtokSbrzwja644I2JicCgLqPUg0AAFAHrN5zTPfMS1apw9A1vVpo0l+6mB0JAOoFSjUAAICH23wwW3e8u05FpU5d0jlEr9zcS1arxexYAFAvUKoBAAA82M6MPI16e43yi8vUv20TvTa8j7xt/IoHALWFn7gAAAAeav+xAt321mqdKCxVr4hGemtUX/l628yOBQD1CqUaAADAAx3JOanhb61WZl6xOoUG6N3RfeVv9zI7FgDUO5RqAAAAD3Msv1i3vbVaB0+cVGTThpo7tp8aNfQxOxYA1EuUagAAAA+SdqxQf31jlX49WqDmQb6aNzZWIQG+ZscCgHqLa4QAAAA8xKYD2brj3bXKyi9RiyBfzR0bq5aNG5odCwDqNUo1AACAB/hme4bGv79eRaVOdW0eqHdG91VoIGeoAcBslGoAAAA39+7P+/TUF9vkNKSBHZtpxvA+DEoGAG6Cn8YAAABuyuk0lLBsh2b9uFeSNKxfhJ6+tru8mIcaANwGpRoAAMANFZaU6eEPNmnZ1nRJ0iNxnXTfxe1ksVhMTgYA+G+UagAAADezL6tAd89NVmpGnrxtFr381166Nirc7FgAgDOgVAMAALiRFSkZmrBwo/KKytQswK7XhvdR38gmZscCAJwFpRoAAMANOJ2G/rlilxK/3SVJim7dWK8N78MI3wDg5ijVAAAAJss5WaqHFm3UipRMSdLIAa31xJCu8vFiQDIAcHeUagAAABPtzMjTne+t0/5jhbJ7WfXs9T10U3RLs2MBACqJUg0AAGCS73ce1f3z1yuvuEzhjRrojRHR6h4eZHYsAIALKNUAAAAmmLtqn6Z+sV0Op6F+kU00c0S0mvj5mB0LAOAiSjUAAEAtcjgNPb1ku+b8vE+SdGOflnruhu6ye9nMDQYAqBJKNQAAQC3JLy7T+AXr9V3qUUnSI3GddN/F7WSxWExOBgCoKko1AABALTiUfVJ3zFmrlPQ82b2senVolK7s0dzsWACAc0SpBgAAqGFr9h7XffOTlZVfomYBdr01Mka9IhqZHQsAUA0o1QAAADXEMAzNX52mqYu3qcxpqEvzQL01KkbhjRqYHQ0AUE0o1QAAADWguMyhqYu36f01ByRJV/Vsrhdv6qmGPvz6BQB1CT/VAQAAqllmbpHunb9eyftPyGKR/h7XWfcMbMuAZABQB1GqAQAAqtHGA9m6e+46ZeQWK8DXS/8a1lsXdwoxOxYAoIZQqgEAAKrJF5sO6+EPN6mkzKn2If6aNTJGbYL9zI4FAKhBlGoAAIBzZBiG3vxhjxKWpUiSBncJ1atDeynA19vkZACAmkapBgAAOAcOp6Gnvtim91btlyTdfl6knryqq2xW7p8GgPqAUg0AAFBFJ0scGv/+Bn27I0MWi/T4lV009sK2ZscCANQiSjUAAEAVZOUX645312nTgWz5eFmVODRKV/ZobnYsAEAto1QDAAC4aG9WgUa9vUZpxwvVqKG33hoZo5jIJmbHAgCYgFINAADggl+P5uuWN3/R0bxitWrSUHNG91XbZv5mxwIAmIRSDQAAUEl7swo07D+FunNYgOaNjVWwv93sWAAAE1GqAQAAKmH/sVOFOjOvWJ1CAzR/bKyaUqgBoN6zmh0AAADA3aUdK9SwN39Rem6ROoT4a/6dFGoAwCmUagAAgD9w4Hihhs36RYdzitSumZ/m38kl3wCA31GqAQAAzuJQ9kkNm/WLDmWfVNtgP71/Z3+FBPiaHQsA4EaqVKpnzJihyMhI+fr6KjY2VmvWrDnrurNmzdKFF16oxo0bq3Hjxho8ePAfrg8AAOAODmWf1LA3f9HBEycV2bShFtzZXyGBFGoAQEUul+pFixYpPj5eU6ZM0fr169WrVy/FxcUpMzPzjOuvXLlSw4YN03fffadVq1YpIiJCl19+uQ4dOnTO4QEAAGpC2rFC3TxzldKOF6pVk1OFOiyIQg0AOJ3FMAzDlQ1iY2PVt29fTZ8+XZLkdDoVERGh8ePHa+LEiX+6vcPhUOPGjTV9+nSNHDnyjOsUFxeruLi4/HFubq4iIiKUk5OjwMBAV+ICAAC4ZM/RfN06a7XSc4vUJthPC+6MVfOgBmbHAgDUstzcXAUFBf1pD3XpTHVJSYmSk5M1ePDg33dgtWrw4MFatWpVpfZRWFio0tJSNWnS5KzrJCQkKCgoqPwjIiLClZgAAABVsjMjTze/8fso34vu6k+hBgD8IZdKdVZWlhwOh0JDQyssDw0NVXp6eqX28eijj6pFixYVivn/mjRpknJycso/Dhw44EpMAAAAl20/nKtb3vxFWfnF6tI8UAvv4h5qAMCf86rNF3v++ee1cOFCrVy5Ur6+Z3+TstvtstuZqgIAANSOzQezNWL2GuWcLFWP8CDNvaOfGjX0MTsWAMADuFSqg4ODZbPZlJGRUWF5RkaGwsLC/nDbl19+Wc8//7y+/fZb9ezZ0/WkAAAANWDdvuMa/c5a5RWXqU+rRpozpp8Cfb3NjgUA8BAuXf7t4+Oj6OhoJSUllS9zOp1KSkrSgAEDzrrdiy++qKefflrLly9XTExM1dMCAABUo2+2Z2j4W6uVV1ym2DZN9N4dsRRqAIBLXL78Oz4+XqNGjVJMTIz69eunxMREFRQUaPTo0ZKkkSNHKjw8XAkJCZKkF154QZMnT9aCBQsUGRlZfu+1v7+//P39q/FQAAAAKm/hmjQ99ukWOQ3p0s4hmn5rHzXwsZkdCwDgYVwu1UOHDtXRo0c1efJkpaenKyoqSsuXLy8fvCwtLU1W6+8nwF9//XWVlJTopptuqrCfKVOmaOrUqeeWHgAAwEWGYWj6it165ZudkqSbY1rquet7yMvm0gV8AABIqsI81Wao7PxgAAAAf8ThNDR18TbN/WW/JOn+Qe318OUdZbFYTE4GAHA3le2htTr6NwAAgFmKSh16aNFGLduaLotFmnp1N406L9LsWAAAD0epBgAAdd6JghLdPS9Za/Yel4/NqleHRmlIz+ZmxwIA1AGUagAAUKftzszXHe+u1f5jhfK3e+nNkdE6r12w2bEAAHUEpRoAANRZP+w8qnEL1iuvqEwRTRpo9qi+6hgaYHYsAEAdQqkGAAB10rs/79O0JdvlcBrqG9lYM2+LVlN/u9mxAAB1DKUaAADUKaUOp6Z9sb18hO8b+7TUczd0l92LOagBANWPUg0AAOqM7MIS3b9gg37anSWLRXr0is66+6K2TJkFAKgxlGoAAFAnbD2Uo3vnJ+vA8ZNq6GNT4tAoXd4tzOxYAIA6jlINAAA83kfJB/X4p1tUXOZUqyYNNfO2aHVtEWh2LABAPUCpBgAAHqukzKlpS7Zp3i9pkqRBnZopcWhvBTX0NjkZAKC+oFQDAACPdCTnpO6bv14b0rJlsUgTLu2gBy7pIKuV+6cBALWHUg0AADzO6j3HNG7BemXllyjQ10v/uKW3BnUOMTsWAKAeolQDAACP8vnGQ/rbh5tU6jDUpXmg3rgtWq2aNjQ7FgCgnqJUAwAAj2AYht74YY+eX5YiSbqyR5he+WuUGvgw/zQAwDyUagAA4PYcTkPTvtimd1ftlyTdcUEbPX5lF+6fBgCYjlINAADcWlGpQxMWbtBX2zJksUiPX9lFYy9sa3YsAAAkUaoBAIAbO15QorHvrtX6tGz52Kx6dWiUhvRsbnYsAADKUaoBAIBbOniiUCNnr9GerAIF+npp1sgYxbZtanYsAAAqoFQDAAC3szszT7e9tUbpuUVqEeSrd8f0U4fQALNjAQBwGko1AABwK5sPZmvU22t0orBU7UP8NfeOfmoe1MDsWAAAnBGlGgAAuI1Vvx7T2HfXqqDEoV4tg/TO6H5q4udjdiwAAM6KUg0AANzCN9szNG7BepWUOTWgbVPNGhUjfzu/qgAA3BvvVAAAwHQfJx/U3z/eLIfT0GVdQ/WvYb3l620zOxYAAH+KUg0AAEw175f9euKzrZKkG/u01As39pCXzWpyKgAAKodSDQAATPPB2gPlhXr0+ZF6ckhXWa0Wk1MBAFB5lGoAAGCKzzce0qOfbJYk3XFBGz0xpIssFgo1AMCzcG0VAACodcu3HlH8B5tkGNLw2FYUagCAx6JUAwCAWvVdSqbGv79BDqehG/u01NPXdqdQAwA8FqUaAADUmp92ZenueckqdRi6qmdzvXhTT+6hBgB4NEo1AACoFWv2Hted761TSZlTl3cN1atDo2SjUAMAPBylGgAA1LgtB3M0Zs5anSx1aGDHZvrXrb3lzbRZAIA6gHczAABQo3Zn5mvUO2uUX1ym/m2b6I0R0bJ72cyOBQBAtaBUAwCAGnMo+6RGzF6t4wUl6tkySG+N6itfbwo1AKDuoFQDAIAakZVfrBFvrdaRnCK1a+anOaP7yd/uZXYsAACqFaUaAABUu9yiUo16e432ZBUovFEDzb0jVk38fMyOBQBAtaNUAwCAalVU6tDYd9dp2+FcNfXz0dw7+qlFowZmxwIAoEZQqgEAQLUpdTg1bv56rdl7XAF2L707pp/aNvM3OxYAADWGUg0AAKpFSZlT4xdsUFJKpuxeVs2+va+6hweZHQsAgBrFaCEAAOCcFZc5dP+CDfpme4Z8bFbNvC1a/do0MTsWAAA1jlINAADOSVGpQ/fNX68VKZny8bLqzRHRurhTiNmxAACoFZRqAABQZUWlDt01N1k/7Dwqu5dVb42K0YUdmpkdCwCAWkOpBgAAVXKyxKE731unn3ZnqYG3TbNHxei89sFmxwIAoFZRqgEAgMsKS8p0x5x1WrXnmBr62PTO7X0V27ap2bEAAKh1lGoAAOCSvKJS3TFnndbsOy5/u5fmjO6rmEgGJQMA1E+UagAAUGnZhSUa+fYabT6Yc2oe6jv6qU+rxmbHAgDANJRqAABQKUfzijVi9mqlpOepcUNvzb0jlnmoAQD1HqUaAAD8qSM5JzV81mrtySpQSIBd88bGqmNogNmxAAAwHaUaAAD8of3HCjT8rdU6eOKkwhs10PyxsYoM9jM7FgAAboFSDQAAzmp3Zp6Gv7VaGbnFahPsp3ljYxXeqIHZsQAAcBuUagAAcEZbD+Vo1NtrdKygRJ1CAzR3bD+FBPiaHQsAALdCqQYAAKf5+dcs3fVesvKLy9QjPEjvjemnxn4+ZscCAMDtUKoBAEAFy7em64H3N6jE4dSAtk315shoBfh6mx0LAAC3RKkGAADlFq5J02OfbpHTkK7oFqbEW6Lk620zOxYAAG6LUg0AAGQYhl7//le9uDxVknRL3wg9e30P2awWk5MBAODerFXZaMaMGYqMjJSvr69iY2O1Zs2as667bds23XjjjYqMjJTFYlFiYmJVswIAgBrgdBp6dumO8kJ938XtlHADhRoAgMpwuVQvWrRI8fHxmjJlitavX69evXopLi5OmZmZZ1y/sLBQbdu21fPPP6+wsLBzDgwAAKpPcZlD8R9s1Fs/7ZUkPTGki/5+RWdZLBRqAAAqw2IYhuHKBrGxserbt6+mT58uSXI6nYqIiND48eM1ceLEP9w2MjJSDz74oB588EGXQubm5iooKEg5OTkKDAx0aVsAAHBm2YUlumtustbsPS6b1aIXbuypm6Jbmh0LAAC3UNke6tI91SUlJUpOTtakSZPKl1mtVg0ePFirVq2qetr/UVxcrOLi4vLHubm51bZvAAAg7T9WoNHvrNWerAIF2L302m19dGGHZmbHAgDA47h0+XdWVpYcDodCQ0MrLA8NDVV6enq1hUpISFBQUFD5R0RERLXtGwCA+i55/wld/9rP2pNVoBZBvvro3vMo1AAAVFGVBiqraZMmTVJOTk75x4EDB8yOBABAnbB08xENm/WLjheUqHt4oD4bd746hQWYHQsAAI/l0uXfwcHBstlsysjIqLA8IyOjWgchs9vtstvt1bY/AAAgvfH9r0pYliJJGtwlRP8c1lsNfZhdEwCAc+HSmWofHx9FR0crKSmpfJnT6VRSUpIGDBhQ7eEAAED1mL5iV3mhvv28SL0xIoZCDQBANXD53TQ+Pl6jRo1STEyM+vXrp8TERBUUFGj06NGSpJEjRyo8PFwJCQmSTg1utn379vJ/Hzp0SBs3bpS/v7/at29fjYcCAADO5M0fftXLX++UJP39ik6672LefwEAqC4ul+qhQ4fq6NGjmjx5stLT0xUVFaXly5eXD16WlpYmq/X3E+CHDx9W7969yx+//PLLevnllzVw4ECtXLny3I8AAACc1Tv/3qvnvjx1hvrhyzpSqAEAqGYuz1NtBuapBgDAdfNX79fjn26VJI2/pL0evryTyYkAAPAcle2hbjn6NwAAODcfrDtQXqjvHthW8Zd1NDkRAAB1E6UaAIA65rMNh/Tox5slSaPPj9TEKzrLYrGYnAoAgLqJUg0AQB3yxabDiv9gowxDuq1/K02+qiuFGgCAGkSpBgCgjvh84yFNWLhBTkMaGhOhadd0p1ADAFDDKNUAANQBn244qIcWbSwv1Ak39JDVSqEGAKCmuTylFgAAcC8fJx/U3z7aJMOQhvWL0LPXUagBAKgtnKkGAMCDfbDuQHmhHh7bikINAEAt40w1AAAeatHaNE38ZIsMQxrRv7WmXduNe6gBAKhllGoAADzQ+2vSNOmTLZKk28+L1JSrGeUbAAAzUKoBAPAwb/24R88s3SHp1DzUTJsFAIB5KNUAAHgIwzD06re79M+kXZKkuy9qq4l/6UyhBgDARJRqAAA8gNNp6Oml2/XOv/dJkh6J66T7Lm5HoQYAwGSUagAA3JzDaWjix5v1YfJBSdLUq7vq9vPbmJwKAABIlGoAANxaSZlTDy7aoC+3pMtqkV68qZduim5pdiwAAPAflGoAANxUQXGZ7pu/Xt/vPCpvm0X/GtZbV3RvbnYsAADwXyjVAAC4oX1ZBbp7brJSM/Lk623VmyNidFHHZmbHAgAA/4NSDQCAm1mZmqkH3t+g3KIyBfvb9caIaEW3bmx2LAAAcAaUagAA3IRhGHpt5a96+etUGYbUu1UjvT48WmFBvmZHAwAAZ0GpBgDADeQXl+mRDzdp2dZ0SdKwfhGaek032b1sJicDAAB/hFINAIDJ9mYV6K731mlXZr68bRY9dU133RrbyuxYAACgEijVAACYaEVKhiYs3Ki8ojKFBNj1+m3cPw0AgCehVAMAYAKn09CM73br/77dKcOQols31uvD+ygkkPunAQDwJJRqAABqWX5xmeIXbdTX2zMkSbf1b6XJV3WTj5fV5GQAAMBVlGoAAGrRr0fzdffcZO3OzJePzaqnr+umoX25fxoAAE9FqQYAoJZ8uz1DDy3aqLziMoUF+ur12/qodyvunwYAwJNRqgEAqGH5xWV67ssdWrA6TZLUN7KxZgzvo5AA7p8GAMDTUaoBAKhBP+3K0qMfb9ah7JOSpNvPi9RjV3bh/mkAAOoISjUAADUgr6hUz32ZovfXnDo7HdGkgV64safOaxdscjIAAFCdKNUAAFSzH3cd1cSPt5SfnR45oLUevaKz/Oy87QIAUNfw7g4AQDXJzCvS88tS9Mn6Q5JOnZ1+8cZeGtCuqcnJAABATaFUAwBwjkodTr378z4lfrtL+cVlkjg7DQBAfcE7PQAA5+Dn3VmasnibdmXmS5J6tgzSU9d0Y6osAADqCUo1AABVcDj7pJ79coeWbj4iSWri56O/x3XSzTERslotJqcDAAC1hVINAIALSh1Ovf3TXiV+u0snSx2yWqQR/Vsr/rJOCmrobXY8AABQyyjVAABU0tp9x/XEp1uVmpEnSYpp3VjTru2uri0CTU4GAADMQqkGAOBPHMsv1vPLUvRh8kFJUuOG3pp0ZRfd1Kcll3oDAFDPUaoBADgLp9PQB+sO6PnlKcouLJUkDesXob/HdVZjPx+T0wEAAHdAqQYA4AxW/XpMz365XVsP5UqSujQP1DPXdVd0a0b1BgAAv6NUAwDwX/YczVfCshR9sz1DkhRg99KEwR10+3mR8rJZTU4HAADcDaUaAABJJwpK9I+kXZr3y36VOQ3ZrBYNj22lCZd2UFN/u9nxAACAm6JUAwDqteIyh+au2q9/Ju1SblGZJOmSziF67MrOah8SYHI6AADg7ijVAIB6yTAMLd+arueXp2j/sUJJUuewAD0xpKsu6BBscjoAAOApKNUAgHpn04FsPbN0u9buOyFJahZg198u76iboiNkY4osAADgAko1AKDeOJR9Ui8tT9FnGw9Lkny9rbrrwra6e2A7+dl5SwQAAK7jNwgAQJ1WUubUd6mZ+mT9Qa1IyVSpw5Ak3dAnXI/EdVLzoAYmJwQAAJ6MUg0AqHMMw9CWQzn6ZP0hLd50WMcLSsqfi23TRI8P6aKeLRuZFxAAANQZlGoAQJ1gGIZSM/L09bYMLdl8WDsz8sufaxZg1/W9w3VDn3B1Dgs0MSUAAKhrKNUAAI/ldBpan3ZCX21L19fbM8pH8ZYku5dVl3cL0419wnVB+2B52awmJgUAAHUVpRoA4FGKyxz6+ddj+npbur7Znqms/OLy53y8rLqoQ7Au7xamK7qHKdDX28SkAACgPqBUAwDcXl5RqVamHtVX29K1MvWo8ovLyp8L8PXSpZ1DFNctTBd1bMYo3gAAoFbxmwcAwO2UOZzafiRXq/cc179/zdLPu4+pxOEsfz4kwK7Lu4Xq8q5h6t+2qXy8uLQbAACYg1INADBdSZlTWw/naPWe41q995jW7TtR4Wy0JLUN9tPl3cJ0ebdQRbVsJKvVYlJaAACA31GqAQC1qqjUodT0PG09nKOth3K19VCOUtPzKpyJlk5d1t03soli2zTRJZ1D1D7EXxYLRRoAALgXSjUAoEaUOpzaf6xQuzPztCsjX7sy87UzI0+7M/NV5jROW79RQ2/1i2yi2LZNFdumibo0D5SNs9EAAMDNUaoBAOekuMyhvVkF5cX5txK971iBSh2nl2dJatzQW93Dg9Q9PEg9woPUvUWQIpo04Ew0AADwOFUq1TNmzNBLL72k9PR09erVS//617/Ur1+/s67/4Ycf6sknn9S+ffvUoUMHvfDCC7ryyiurHBoAULNKypzKLy5TflGZcotKdaygRMfyi3Usv0RZBf/5b36x0o4Vat+xAp3hxLMkqaGPTR1C/NU+JEAdQv3Vvpm/urQIVIsgXwo0AACoE1wu1YsWLVJ8fLxmzpyp2NhYJSYmKi4uTqmpqQoJCTlt/Z9//lnDhg1TQkKCrrrqKi1YsEDXXXed1q9fr+7du1fLQcB8hnGW36jPuK6L+66hHK7u+9T+Xdm3i1lcDVOD+3Ylu6f+/3T58+3i+k7DkMMw5DQMOZ069W/nqceO8v/qfx4bchr/tew/j8ucTpU5DJU5nSpxGCpzOFXqqPjvUoehUsep9So8djpVUnZq21JHxX//7zZFpQ7lF5cpr6hMxWXOPz/I/xLo66UOoQH/KdCnPjqEBlCeAQBAnWcxXPytNTY2Vn379tX06dMlSU6nUxERERo/frwmTpx42vpDhw5VQUGBlixZUr6sf//+ioqK0syZM8/4GsXFxSouLi5/nJubq4iICLX920ey2Rv+eUgKxFn27+IGAOq9hj42+du91MTPR8H+dgX7+6ipv11N/X0U7GdXeOMG6hDir2YBdsozAACoU3JzcxUUFKScnBwFBgaedT2XzlSXlJQoOTlZkyZNKl9mtVo1ePBgrVq16ozbrFq1SvHx8RWWxcXF6bPPPjvr6yQkJOipp546bXlpmVMOm2tnTwDATFaLZLVYZLVaZLNYZLNaZLFItv88tlotslpU/m+b1XJq/f+sc+rfFnl7WeVjs8jLapW3l1XeVou8bVZ52Szy+c9/vW3W/3z89tx/tvnf5VaLfLysp/b1X9vZva0K8PWSv91LAXZv+dlt8rIx/zMAAMAfcalUZ2VlyeFwKDQ0tMLy0NBQpaSknHGb9PT0M66fnp5+1teZNGlShSL+25nqrx+6SAF/8BeC/+bK+RJXTq5YXNiza/t1gYsng9whsytnsGrq/92pfdfMAfJ5q/q+a2K/NZm3sqtb/lOMOXsLAABQt7nl6N92u112u/205S0aNVBgYAMTEgEAAAAAcDqXrusLDg6WzWZTRkZGheUZGRkKCws74zZhYWEurQ8AAAAAgKdwqVT7+PgoOjpaSUlJ5cucTqeSkpI0YMCAM24zYMCACutL0jfffHPW9QEAAAAA8BQuX/4dHx+vUaNGKSYmRv369VNiYqIKCgo0evRoSdLIkSMVHh6uhIQESdKECRM0cOBAvfLKKxoyZIgWLlyodevW6c0336zeIwEAAAAAoJa5XKqHDh2qo0ePavLkyUpPT1dUVJSWL19ePhhZWlqarNbfT4Cfd955WrBggZ544gk99thj6tChgz777DPmqAYAAAAAeDyX56k2Q2XnBwMAAAAAoDpUtocyASkAAAAAAFVEqQYAAAAAoIoo1QAAAAAAVBGlGgAAAACAKqJUAwAAAABQRZRqAAAAAACqiFINAAAAAEAVUaoBAAAAAKgiSjUAAAAAAFVEqQYAAAAAoIoo1QAAAAAAVBGlGgAAAACAKvIyO0BlGIYhScrNzTU5CQAAAACgPvitf/7WR8/GI0r1sWPHJEkREREmJwEAAAAA1CfHjh1TUFDQWZ/3iFLdpEkTSVJaWtofHkxN6tu3r9auXWvKa+fm5ioiIkIHDhxQYGBgpbczM3NVeVpmT8sruW/mP/o6d9fMf4TMtaM+ZK7qe0B1qg+fZ3fgaZlrI291f/172udYInNt8bTM7vDeUBWe9nnOyclRq1atyvvo2XhEqbZaT936HRQUZNoXjc1mM/0LNjAw0KUM7pDZVZ6W2dPySu6f+Uxf5+6e+UzIXDvqU2ZX3wOqU336PJvJ0zLXZt7q+vr3tM+xROba4omZJXPfG6rCUz/Pv/XRsz5fSzk83rhx48yO4DIy1zxPyyuRubaQuXaQuXaQuXZ4WmZPyyuRubaQGWdTVz/PFuPP7rp2A7m5uQoKClJOTo5H/mXjXNX340f9wNc5cGZ8b6A+4+sfODO+N2pHZT/PHnGm2m63a8qUKbLb7WZHMUV9P37UD3ydA2fG9wbqM77+gTPje6N2VPbz7BFnqgEAAAAAcEcecaYaAAAAAAB3RKkGAAAAAKCKKNUAAAAAAFQRpRoAAAAAgCqiVHsoi8Wizz77zOwYAAAAAFCvUapNdPvtt8tisZz2sXv3brOjAdXit6/xe+6557Tnxo0bJ4vFottvv732gwFuZNWqVbLZbBoyZIjZUYAax/sC8Oduv/12XXfddWbHgAso1Sa74oordOTIkQofbdq0MTsWUG0iIiK0cOFCnTx5snxZUVGRFixYoFatWp3TvktLS881HmC62bNna/z48frhhx90+PDhc9qXw+GQ0+mspmRAzajJ9wUAMAOl2mR2u11hYWEVPmw2mz7//HP16dNHvr6+atu2rZ566imVlZVV2PbIkSP6y1/+ogYNGqht27b66KOPTDoK4Oz69OmjiIgIffLJJ+XLPvnkE7Vq1Uq9e/cuX7Z8+XJdcMEFatSokZo2baqrrrpKv/76a/nz+/btk8Vi0aJFizRw4ED5+vpq/vz5tXosQHXLz8/XokWLdO+992rIkCGaM2dO+XMrV66UxWLR0qVL1bNnT/n6+qp///7aunVr+Tpz5sxRo0aNtHjxYnXt2lV2u11paWkmHAlQedX1vnDJJZfo/vvvr7Dvo0ePysfHR0lJSTV/IEAtiIyMVGJiYoVlUVFRmjp1avlji8Wit956S9dff70aNmyoDh06aPHixbUbtJ6jVLuhH3/8USNHjtSECRO0fft2vfHGG5ozZ46effbZCus9+eSTuvHGG7Vp0yYNHz5ct9xyi3bs2GFSauDsxowZo3feeaf88dtvv63Ro0dXWKegoEDx8fFat26dkpKSZLVadf3115921m3ixImaMGGCduzYobi4uFrJD9SUDz74QJ07d1anTp1022236e2335ZhGBXWeeSRR/TKK69o7dq1atasma6++uoKV2kUFhbqhRde0FtvvaVt27YpJCSktg8DcFl1vC+MHTtWCxYsUHFxcfk28+bNU3h4uC655JLaORDATTz11FO6+eabtXnzZl155ZUaPny4jh8/bnas+sOAaUaNGmXYbDbDz8+v/OOmm24yLr30UuO5556rsO7cuXON5s2blz+WZNxzzz0V1omNjTXuvffeWskOVMaoUaOMa6+91sjMzDTsdruxb98+Y9++fYavr69x9OhR49prrzVGjRp1xm2PHj1qSDK2bNliGIZh7N2715BkJCYm1uIRADXrvPPOK/+aLi0tNYKDg43vvvvOMAzD+O677wxJxsKFC8vXP3bsmNGgQQNj0aJFhmEYxjvvvGNIMjZu3Fjr2YGqqM73hZMnTxqNGzcu/34wDMPo2bOnMXXq1No4FKDG/PZ9YhiG0bp1a+PVV1+t8HyvXr2MKVOmlD+WZDzxxBPlj/Pz8w1JxrJly2ohLQzDMLzMq/OQpEGDBun1118vf+zn56eePXvq3//+d4Uz0w6HQ0VFRSosLFTDhg0lSQMGDKiwrwEDBmjjxo21khtwRbNmzcovbTUMQ0OGDFFwcHCFdXbt2qXJkydr9erVysrKKj8TkZaWpu7du5evFxMTU6vZgZqSmpqqNWvW6NNPP5UkeXl5aejQoZo9e7Yuvvji8vX++2d9kyZN1KlTpwpXJfn4+Khnz561lhuoDtXxvuDr66sRI0bo7bff1s0336z169dr69atXPaKeum/3wf8/PwUGBiozMxMExPVL5Rqk/n5+al9+/YVluXn5+upp57SDTfccNr6vr6+tRUNqFZjxowpv/dtxowZpz1/9dVXq3Xr1po1a5ZatGghp9Op7t27q6SkpMJ6fn5+tZIXqGmzZ89WWVmZWrRoUb7MMAzZ7XZNnz690vtp0KCBLBZLTUQEalR1vC+MHTtWUVFROnjwoN555x1dcsklat26da0dA1DTrFbrabcFnWmgVm9v7wqPLRYLA1fWIkq1G+rTp49SU1NPK9v/65dfftHIkSMrPP7vAT4Ad3LFFVeopKREFovltHuhjx07ptTUVM2aNUsXXnihJOmnn34yIyZQK8rKyvTee+/plVde0eWXX17hueuuu07vv/++OnfuLOnUz/bfRkQ+ceKEdu7cqS5dutR6ZqC6Vcf7Qo8ePRQTE6NZs2ZpwYIFLv1BCvAEzZo105EjR8of5+bmau/evSYmwplQqt3Q5MmTddVVV6lVq1a66aabZLVatWnTJm3dulXPPPNM+XoffvihYmJidMEFF2j+/Plas2aNZs+ebWJy4OxsNlv5Jas2m63Cc40bN1bTpk315ptvqnnz5kpLS9PEiRPNiAnUiiVLlujEiRO64447FBQUVOG5G2+8UbNnz9ZLL70kSZo2bZqaNm2q0NBQPf744woODmb+UtQJ1fW+MHbsWN1///3y8/PT9ddfX+O5gdp0ySWXaM6cObr66qvVqFEjTZ48+bTvF5iP0b/dUFxcnJYsWaKvv/5affv2Vf/+/fXqq6+edjnTU089pYULF6pnz55677339P7776tr164mpQb+XGBgoAIDA09bbrVatXDhQiUnJ6t79+566KGHygsFUBfNnj1bgwcPPq1QS6dK9bp167R582ZJ0vPPP68JEyYoOjpa6enp+uKLL+Tj41PbkYEaUR3vC8OGDZOXl5eGDRvGbXKoE5xOp7y8Tp37nDRpkgYOHKirrrpKQ4YM0XXXXad27dqZnBD/y2L870X6AADAdCtXrtSgQYN04sQJNWrUyOw4gNvat2+f2rVrp7Vr16pPnz5mxwHO2RVXXKH27dtzO4MH4Uw1AAAAPE5paanS09P1xBNPqH///hRqeLwTJ05oyZIlWrlypQYPHmx2HLiAe6oBAADgcf79739r0KBB6tixoz766COz4wDnbMyYMVq7dq0efvhhXXvttWbHgQu4/BsAAAAAgCri8m8AAAAAAKqIUg0AAAAAQBVRqmtBQkKC+vbtq4CAAIWEhOi6665TampqhXWKioo0btw4NW3aVP7+/rrxxhuVkZFR/vymTZs0bNgwRUREqEGDBurSpYv+8Y9/nPZaK1euVJ8+fWS329W+fXvNmTOnpg8PAAAAAOotSnUt+P777zVu3Dj98ssv+uabb1RaWqrLL79cBQUF5es89NBD+uKLL/Thhx/q+++/1+HDh3XDDTeUP5+cnKyQkBDNmzdP27Zt0+OPP65JkyZVGGp/7969GjJkiAYNGqSNGzfqwQcf1NixY/XVV1/V6vECAAAAQH3BQGUmOHr0qEJCQvT999/roosuUk5Ojpo1a6YFCxbopptukiSlpKSoS5cuWrVqlfr373/G/YwbN047duzQihUrJEmPPvqoli5dqq1bt5avc8sttyg7O1vLly+v+QMDAAAAgHqGM9UmyMnJkSQ1adJE0qmz0KWlpRXmo+vcubNatWqlVatW/eF+ftuHJK1ateq0Oe3i4uL+cB8AAAAAgKpjnupa5nQ69eCDD+r8889X9+7dJUnp6eny8fFRo0aNKqwbGhqq9PT0M+7n559/1qJFi7R06dLyZenp6QoNDT1tH7m5uTp58qQaNGhQvQcDAAAAAPUcpbqWjRs3Tlu3btVPP/1U5X1s3bpV1157raZMmaLLL7+8GtMBAAAAAFzB5d+16P7779eSJUv03XffqWXLluXLw8LCVFJSouzs7ArrZ2RkKCwsrMKy7du369JLL9Vdd92lJ554osJzYWFhFUYM/20fgYGBnKUGAAAAgBpAqa4FhmHo/vvv16effqoVK1aoTZs2FZ6Pjo6Wt7e3kpKSypelpqYqLS1NAwYMKF+2bds2DRo0SKNGjdKzzz572usMGDCgwj4k6ZtvvqmwDwAAAABA9WH071pw3333acGCBfr888/VqVOn8uVBQUHlZ5Dvvfdeffnll5ozZ44CAwM1fvx4SafunZZOXfJ9ySWXKC4uTi+99FL5Pmw2m5o1aybp1JRa3bt317hx4zRmzBitWLFCDzzwgJYuXaq4uLjaOlwAAAAAqDco1bXAYrGccfk777yj22+/XZJUVFSkhx9+WO+//76Ki4sVFxen1157rfzy76lTp+qpp546bR+tW7fWvn37yh+vXLlSDz30kLZv366WLVvqySefLH8NAAAAAED1olQDAAAAAFBF3FMNAAAAAEAVUaoBAAAAAKgiSjUAAAAAAFVEqQYAAAAAoIoo1QAAAAAAVBGlGgAAAACAKqJUAwAAAABQRZRqAAAAAACqiFINAAAAAEAVUaoBAAAAAKgiSjUAAAAAAFX0/+bLm1CUKNfEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_us['pop_infected'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The basic reproduction number\n",
    "df_us['Rt'] = df_us['new_infected'].rolling(8).apply(lambda x: x[4:].sum() / x[:4].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>infected</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "      <th>new_infected</th>\n",
       "      <th>new_infected_avg</th>\n",
       "      <th>pop_infected</th>\n",
       "      <th>Rt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.035212e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.035212e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.070424e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.070424e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.517606e-06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-11</th>\n",
       "      <td>2023590</td>\n",
       "      <td>540292</td>\n",
       "      <td>113823</td>\n",
       "      <td>22888.0</td>\n",
       "      <td>20721.000000</td>\n",
       "      <td>6.142025e-01</td>\n",
       "      <td>0.910637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12</th>\n",
       "      <td>2048986</td>\n",
       "      <td>547386</td>\n",
       "      <td>114669</td>\n",
       "      <td>25396.0</td>\n",
       "      <td>20733.142857</td>\n",
       "      <td>6.219107e-01</td>\n",
       "      <td>1.047659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-13</th>\n",
       "      <td>2074526</td>\n",
       "      <td>556606</td>\n",
       "      <td>115436</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>21141.142857</td>\n",
       "      <td>6.296626e-01</td>\n",
       "      <td>1.245271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-14</th>\n",
       "      <td>2094058</td>\n",
       "      <td>561816</td>\n",
       "      <td>115732</td>\n",
       "      <td>19532.0</td>\n",
       "      <td>21392.714286</td>\n",
       "      <td>6.355910e-01</td>\n",
       "      <td>1.258778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>2114026</td>\n",
       "      <td>576334</td>\n",
       "      <td>116127</td>\n",
       "      <td>19968.0</td>\n",
       "      <td>21749.285714</td>\n",
       "      <td>6.416517e-01</td>\n",
       "      <td>1.140702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            infected  recovered  deaths  new_infected  new_infected_avg  \\\n",
       "2020-01-22         1          0       0           NaN               NaN   \n",
       "2020-01-23         1          0       0           0.0               NaN   \n",
       "2020-01-24         2          0       0           1.0               NaN   \n",
       "2020-01-25         2          0       0           0.0               NaN   \n",
       "2020-01-26         5          0       0           3.0               NaN   \n",
       "...              ...        ...     ...           ...               ...   \n",
       "2020-06-11   2023590     540292  113823       22888.0      20721.000000   \n",
       "2020-06-12   2048986     547386  114669       25396.0      20733.142857   \n",
       "2020-06-13   2074526     556606  115436       25540.0      21141.142857   \n",
       "2020-06-14   2094058     561816  115732       19532.0      21392.714286   \n",
       "2020-06-15   2114026     576334  116127       19968.0      21749.285714   \n",
       "\n",
       "            pop_infected        Rt  \n",
       "2020-01-22  3.035212e-07       NaN  \n",
       "2020-01-23  3.035212e-07       NaN  \n",
       "2020-01-24  6.070424e-07       NaN  \n",
       "2020-01-25  6.070424e-07       NaN  \n",
       "2020-01-26  1.517606e-06       NaN  \n",
       "...                  ...       ...  \n",
       "2020-06-11  6.142025e-01  0.910637  \n",
       "2020-06-12  6.219107e-01  1.047659  \n",
       "2020-06-13  6.296626e-01  1.245271  \n",
       "2020-06-14  6.355910e-01  1.258778  \n",
       "2020-06-15  6.416517e-01  1.140702  \n",
       "\n",
       "[146 rows x 7 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_us['Rt'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# na ve inf değerler için neler yapılabilabilir:\n",
    "# log değerler alınabilir\n",
    "# virgülden sonra basamak sayısını azaltmak\n",
    "# eksik verileri tamamlamak\n",
    "# eksik veriyi doldurmak için pandas metodlarına bakmak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5546/3574983804.py:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  df_us['Rt'].replace(np.inf, np.nan).fillna(method='bfill')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2020-01-22    0.000000\n",
       "2020-01-23    0.000000\n",
       "2020-01-24    0.000000\n",
       "2020-01-25    0.000000\n",
       "2020-01-26    0.000000\n",
       "                ...   \n",
       "2020-06-11    0.910637\n",
       "2020-06-12    1.047659\n",
       "2020-06-13    1.245271\n",
       "2020-06-14    1.258778\n",
       "2020-06-15    1.140702\n",
       "Name: Rt, Length: 146, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_us['Rt'].replace(np.inf, np.nan).fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "rt_array = df_us[df_us.index < '2020-06-01']['Rt'].replace(np.inf, np.nan).bfill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-22    0.000000\n",
      "2020-01-23    0.000000\n",
      "2020-01-24    0.000000\n",
      "2020-01-25    0.000000\n",
      "2020-01-26    0.000000\n",
      "                ...   \n",
      "2020-04-26    1.224152\n",
      "2020-04-27    1.035872\n",
      "2020-04-28    0.866481\n",
      "2020-04-29    0.775719\n",
      "2020-04-30    0.794414\n",
      "Name: Rt, Length: 100, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(rt_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9EAAAIJCAYAAACiKr6FAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABVG0lEQVR4nO3de3zU5Zn///ecJ+eQQBIOCYIHUBEVREjV1gOVWuuqYIuuXQ+1261Fq9it1W9bW926dHtY7QFt61L5uYpQrNaqW63FglVBAUUFFU9gkJBwTCanOc/vj8l8JpEACUnmc0/m9Xw88kgyM5ncGVvyeee67ut2JBKJhAAAAAAAwCE57V4AAAAAAADZghANAAAAAEAvEaIBAAAAAOglQjQAAAAAAL1EiAYAAAAAoJcI0QAAAAAA9BIhGgAAAACAXnLbvYBPisfjqq+vV1FRkRwOh93LAQAAAAAMcYlEQi0tLRo1apSczoPXmo0L0fX19aqurrZ7GQAAAACAHLNt2zaNGTPmoI8xLkQXFRVJSi6+uLjY5tUAAAAAAIa6QCCg6upqK48ejHEhOtXCXVxcTIgGAAAAAGRMb7YUM1gMAAAAAIBeIkQDAAAAANBLhGgAAAAAAHqJEA0AAAAAQC8RogEAAAAA6CVCNAAAAAAAvUSIBgAAAACglwjRAAAAAAD0EiEaAAAAAIBeIkQDAAAAANBLhGgAAAAAAHqJEA0AAAAAQC8RogEAAAAA6CVCNAAAAAAAvUSIBgAAAACglwjRAAAAAAD0EiEaAAAAAIBeIkQDAIzxwnu7dcZPntOL7++2eykAAAA9IkQDAIzx7FsN2ra3Q39/Z6fdSwEAAOgRIRoAYIxAMCpJCsfiNq8EAACgZ4RoAIAxAh0RSVI4SogGAABmIkQDAIzRnArRVKIBAIChCNEAAGMEglSiAQCA2QjRAABjBDo690QTogEAgKEI0QAAY9DODQAATEeIBgAYIRyNqyMSkyRFCNEAAMBQhGgAgBFaOvdDS7RzAwAAcxGiAQBGSJ0RLRGiAQCAuQjRAAAjpPZDS1KIEA0AAAxFiAYAGCHQJUQzWAwAAJiKEA0AMEKgy55oBosBAABTEaIBAEbo2s7NnmgAAGAqQjQAwAiBDgaLAQAA8xGiAQBGCHDEFQAAyAKEaACAEbq2c0diCRtXAgAAcGCEaACAET45nTuRIEgDAADzEKIBAEYIBKPdPueYKwAAYCJCNADACF3buSX2RQMAADMRogEARmghRAMAgCxAiAYAGKHrdG6J4WIAAMBMhGgAgO0SiQTt3AAAICsQogEAtgtG4lbl2etK/moKx2J2LgkAAKBHhGgAgO1Srdwup0Ol+R5JUohKNAAAMBAhGgBgu1Qrd7HfLa+7sxJNiAYAAAZy270AAAACqRCd55HL6ZDEYDEAAGAmKtEAANul2rlL8jzpPdFUogEAgIEI0QAA26XbuT3pdm4GiwEAAAMRogEAtgt0RCVJxXluKtEAAMBohGgAgO1Se6JL8rpWotkTDQAAzEOIBgDYrms7t4dKNAAAMBghGgBgu9RgseKulWhCNAAAMBAhGgBgu/Se6K4hmsFiAADAPIRoAIDtrEq03y1fqp07RiUaAACYhxANALCdtSe6SyU6wmAxAABgIEI0AMB2qUp0SV56sFiIPdEAAMBAhGgAgO2sPdF+BosBAACzEaIBALaKxxNdpnO7CdEAAMBohGgAgK1aw1ElOrc/F/s98rpSe6IJ0QAAwDyEaACArQKdQ8V8bqf8HheVaAAAYDRCNADAVl0nc0uyKtEccQUAAExEiAYA2Co9VMwtSVSiAQCA0QjRAABbdT3eSkqHaI64AgAAJiJEAwBsdaB2bgaLAQAAExGiAQC2Sg0WK/YnQ7SHdm4AAGAwQjQAwFaBYHJPdAmDxQAAQBYgRAMAbGVVovOSg8V8VKIBAIDB+hSif/jDH8rhcHR7mzhxonV/MBjUvHnzVF5ersLCQs2ZM0eNjY0DvmgAwNDxyXZupnMDAACT9bkSffzxx2vHjh3W2wsvvGDdN3/+fD3xxBNavny5Vq1apfr6es2ePXtAFwwAGFoONJ2bwWIAAMBE7j5/gdutqqqq/W5vbm7WokWLtGTJEp199tmSpPvvv1/HHnus1qxZoxkzZvR/tQCAIeeT07k9Lo64AgAA5upzJfq9997TqFGjNH78eF1++eWqq6uTJK1fv16RSEQzZ860Hjtx4kTV1NRo9erVB3y+UCikQCDQ7Q0AkDsCHcnBYlY7N4PFAACAwfoUoqdPn67Fixfr6aef1r333qstW7bojDPOUEtLixoaGuT1elVaWtrtayorK9XQ0HDA51ywYIFKSkqst+rq6sP6QQAA2elA7dzsiQYAACbqUzv3eeedZ308efJkTZ8+XWPHjtUf/vAH5eXlHdYCbr31Vt10003W54FAgCANADmk+QDTudkTDQAATNSvI65KS0t1zDHH6P3331dVVZXC4bCampq6PaaxsbHHPdQpPp9PxcXF3d4AALkhEourPRyTlG7nTu2JphINAABM1K8Q3draqg8++EAjR47U1KlT5fF4tGLFCuv+zZs3q66uTrW1tf1eKABg6GkJRq2Pi/zJSnSqnTsaTygeT9iyLgAAgAPpUzv3v//7v+uCCy7Q2LFjVV9frx/84AdyuVy67LLLVFJSomuuuUY33XSTysrKVFxcrOuvv161tbVM5gYA9Ch1RnShzy13ZwU6FaKl5HAxv9Nly9oAAAB60qcQ/fHHH+uyyy7Tnj17NGLECJ1++ulas2aNRowYIUm666675HQ6NWfOHIVCIc2aNUv33HPPoCwcAJD9rP3Q/vSvo9R0bil5zJXfQ4gGAADm6FOIXrp06UHv9/v9WrhwoRYuXNivRQEAckNqMnfqjGhJ8rgc1scMFwMAAKbp155oAAD6wzojukuIdjgc6bOiGS4GAAAMQ4gGANgm3c7t6XY7Z0UDAABTEaIBALZJtXOX5B0gRNPODQAADEOIBgDYJjWduziv+4gO2rkBAICpCNEAANscsp2bSjQAADAMIRoAYJtAMDlY7JPt3KkJ3VSiAQCAaQjRAADbpNu5P1mJTp4NTYgGAACmIUQDAGyTbuf+xJ5opnMDAABDEaIBALZJTef+ZCXa1zlYLMKeaAAAYBhCNADANoGOA+yJdnfuiSZEAwAAwxCiAQC2SCQSB94T3VmJDtHODQAADEOIBgDYIhSNW5Vm9kQDAIBsQYgGANgiVYV2OqRC3ydDNNO5AQCAmQjRAABbNHdp5XY4HN3u8zJYDAAAGIoQDQCwhTWZ2+/Z7z5varAYlWgAAGAYQjQAwBYHmswtpSvRTOcGAACmIUQDAGyRbud273cfg8UAAICpCNEAAFscvJ2bSjQAADATIRoAYIvUdO6e2rk9LirRAADATIRoAIAtuk7n/iTauQEAgKkI0QAAW6QGixX7e9gTzWAxAABgKEI0AMAWqT3RPbVz+6hEAwAAQxGiAQC2sAaLHaSdO0IlGgAAGIYQDQCwhbUnuofp3KnBYiEq0QAAwDCEaACALaw90QwWAwAAWYQQDQCwRXpPNIPFAABA9iBEAwAyLh5PWOdE99TOTSUaAACYihANAMi4tnBU8UTyYwaLAQCAbEKIBgBkXCCY3A/tdTvl97j2u99q56YSDQAADEOIBgBkXHP7gVu5Jdq5AQCAuQjRAICMO9hQMalLiKadGwAAGIYQDQDIOGuoWA/7oSXauQEAgLkI0QCAjGs+yGRuSfJwxBUAADAUIRoAkHGpwWIlB6hE+9gTDQAADEWIBgBkXLqd++B7ouMJKUo1GgAAGIQQDQDIuEO1c6dCtERLNwAAMAshGgCQcanp3IcaLCZJkWgiI2sCAADoDUI0ACDjAh0H3xPtcjrkcCQ/DsVimVoWAADAIRGiAQAZFzhEO7fD4eCYKwAAYCRCNAAg49Lt3D0PFpPS+6IJ0QAAwCSEaABAxqUq0Qdq55bSx1xFYuyJBgAA5iBEAwAy7lDTuSXJQzs3AAAwECEaAJBR0VhcbeHksLADTeeWurRzM1gMAAAYhBANAMiolmDU+rjYf5A90Z2V6BCVaAAAYBBCNAAgo1JDxQq8LrldB/41xGAxAABgIkI0ACCjrP3QB2nlltIhmsFiAADAJIRoAEBGBTqS7dwHm8wtMVgMAACYiRANAMgo64zog0zmltJHXDFYDAAAmIQQDQDIqHQ794GHiknpwWJUogEAgEkI0QCAjAr0cU80IRoAAJiEEA0AyKjetnOnz4lmsBgAADAHIRoAkFG9nc7NYDEAAGAiQjQAIKN6O52bdm4AAGAiQjQAIKPS7dy9HCzGdG4AAGAQQjQAIKN6286dOuIqwp5oAABgEEI0ACCjUtO5D9XOzZ5oAABgIkI0ACCjAsHknujeTucOEaIBAIBBCNEAgIxKt3MfYk80g8UAAICBCNEAgIwJRmJWKD7kdG5rsBghGgAAmIMQDQDImNRkbqdDKvD2rhIdoRINAAAMQogGAGRMaqhYkd8jp9Nx0MdSiQYAACYiRAMAMqa5o3Oo2CH2Q0vsiQYAAGYiRAMAMibVzn2o/dASIRoAAJipXyH6xz/+sRwOh2688UbrtmAwqHnz5qm8vFyFhYWaM2eOGhsb+7tOAMAQkGrnPtTxVhLt3AAAwEyHHaLXrl2r3/72t5o8eXK32+fPn68nnnhCy5cv16pVq1RfX6/Zs2f3e6EAgOzXlxDtoRINAAAMdFghurW1VZdffrnuu+8+DRs2zLq9ublZixYt0n//93/r7LPP1tSpU3X//ffrpZde0po1a3p8rlAopEAg0O0NADA0BYLJPdG9auemEg0AAAx0WCF63rx5Ov/88zVz5sxut69fv16RSKTb7RMnTlRNTY1Wr17d43MtWLBAJSUl1lt1dfXhLAkAkAWsSjSDxQAAQJbqc4heunSpXn31VS1YsGC/+xoaGuT1elVaWtrt9srKSjU0NPT4fLfeequam5utt23btvV1SQCALNHch3ZuHyEaAAAY6NClgC62bdumG264Qc8++6z8fv+ALMDn88nn8w3IcwEAzGZN587v/XTuCO3cAADAIH2qRK9fv147d+7UlClT5Ha75Xa7tWrVKv3yl7+U2+1WZWWlwuGwmpqaun1dY2OjqqqqBnLdAIAsFEidE92bwWIuKtEAAMA8fapEn3POOXrzzTe73Xb11Vdr4sSJ+s53vqPq6mp5PB6tWLFCc+bMkSRt3rxZdXV1qq2tHbhVAwCyUvNh7IkOUYkGAAAG6VOILioq0qRJk7rdVlBQoPLycuv2a665RjfddJPKyspUXFys66+/XrW1tZoxY8bArRoAkJWsdu6+TOeOxpVIJORwOAZ1bQAAAL3RpxDdG3fddZecTqfmzJmjUCikWbNm6Z577hnobwMAyEJ9OSc6VYmWpEgsIa+bEA0AAOzX7xC9cuXKbp/7/X4tXLhQCxcu7O9TAwCGkEQiYZ0TXdyLSrSvW4iOdwvVAAAAduGKBACQEW3hmGLxhKTetXOnBotJDBcDAADmIEQDADIi1crtdTm7VZkPxOV0yOVMtnCHGS4GAAAMQYgGAGRE18ncvR0S5uWYKwAAYBhCNAAgI6yhYr1o5U5J7YOmEg0AAExBiAYAZIQ1VKwXk7lTPFSiAQCAYQjRAICMaD6MSnRq7zQhGgAAmIIQDQDIiFQ7d28mc6fQzg0AAExDiAYAZEQg2FmJ9rt7/TUMFgMAAKYhRAMAMuJw2rmpRAMAANMQogEAGRHoSA4W60s7t8fVeU40lWgAAGAIQjQAICPS7dyHUYkmRAMAAEMQogEAGZFu5+7Dnmi3SxIhGgAAmIMQDQDIiMOazu1iTzQAADBL78sBAICc8/aOgJa+UqdIPNHv5/p4X4ekvrVzp86JjhCiAQCAIQjRAIAD+vlfN+tvb+8csOdzOKTKYn+vH89gMQAAYBpCNADggJraky3YF5w4SkdXFPb7+SZUFamqpPchOjVYLESIBgAAhiBEAwAOKBiNSZJmnzxaZ02syPj3Zzo3AAAwDYPFAAAHFIwkw6vPY8+vC68rOZ2bPdEAAMAUhGgAwAEFI8lKtN/jsuX7e9zsiQYAAGYhRAMADii1F9nvtidE+zjiCgAAGIYQDQA4oHQl2qZ2bvZEAwAAwxCiAQAHFOrcE21XOzchGgAAmIYQDQDoUSyesNqobQvRtHMDAADDEKIBAD0KdR5vJdnXzu2hEg0AAAxDiAYA9Ch1vJVk32AxKtEAAMA0hGgAQI9SQ8W8LqecTocta2BPNAAAMA0hGgDQo1SI9rnt+1WR+t4RKtEAAMAQhGgAQI9S7dw+m4aKSZLHRSUaAACYhRANAOhRMGrvGdFSup07RIgGAACGIEQDAHqUaue263gricFiAADAPIRoAECPQpHUGdH2V6Jp5wYAAKYgRAMAemRVom063kpKh2gGiwEAAFMQogEAPUrviTagnZtKNAAAMAQhGgDQoyDt3AAAAPshRAMAemSdE21nJdrNYDEAAGAWQjQAoEdWJdrOPdGu1J7ohOLxhG3rAAAASCFEAwB6lD7iyv52bkmKxKlGAwAA+xGiAQA9CkVTe6Ltq0R7XOlfU+yLBgAAJiBEAwB6ZEQlmhANAAAMQ4gGAPQoFLX/nGin0yGPyyGJ4WIAAMAMhGgAQI/SR1zZF6KlLsPFogwWAwAA9iNEAwB6ZEI7tyR5rGOuYrauAwAAQCJEAwAOwIRzoqV0JTrEnmgAAGAAQjQAoEfGtHOnKtGEaAAAYABCNACgR8HOwWI+t72/KgjRAADAJIRoAECPjKlEpwaLxRgsBgAA7EeIBgD0KJQaLGZKJZrBYgAAwACEaABAj9LTuc2oRNPODQAATECIBgD0KBg1pJ3bzXRuAABgDkI0AKBHppwTnQrR7IkGAAAmIEQDAPaTSCSMaef20M4NAAAMQogGAOwnEkso3ln49bvNaOcORxksBgAA7EeIBgDsJ9glsPpsbuf2pSrRMSrRAADAfoRoAMB+Uq3cDofkM+WIK9q5AQCAAQjRAID9hCLJwOpzO+VwOGxdS/qcaAaLAQAA+xGiAQD7CUXNGComMVgMAACYhRANANhPsLMSbfdQMYl2bgAAYBZCNABgP6acES1JXmuwGNO5AQCA/ey/OgIAGMeqRBvQzk0lGgAAmIQQDQDYT6oS7TMgRKemg0cYLAYAAAxAiAYA7Cd1TrTf5uOtJAaLAQAAs9h/dQQAME6qnduESnSqnTtEiAYAAAboU4i+9957NXnyZBUXF6u4uFi1tbX6y1/+Yt0fDAY1b948lZeXq7CwUHPmzFFjY+OALxoAMLiswWIGVKLTg8UI0QAAwH59ujoaM2aMfvzjH2v9+vVat26dzj77bF144YXatGmTJGn+/Pl64okntHz5cq1atUr19fWaPXv2oCwcADB40tO5zalER6hEAwAAA7j78uALLrig2+d33nmn7r33Xq1Zs0ZjxozRokWLtGTJEp199tmSpPvvv1/HHnus1qxZoxkzZgzcqgEAgyrVOm3CEVceKtEAAMAgh311FIvFtHTpUrW1tam2tlbr169XJBLRzJkzrcdMnDhRNTU1Wr169QGfJxQKKRAIdHsDANjLpEq0jyOuAACAQfocot98800VFhbK5/Pp61//uh577DEdd9xxamhokNfrVWlpabfHV1ZWqqGh4YDPt2DBApWUlFhv1dXVff4hAAADy6QQzTnRAADAJH0O0RMmTNCGDRv08ssv69prr9WVV16pt95667AXcOutt6q5udl627Zt22E/FwBgYKSmcxsxWMxNOzcAADBHn/ZES5LX69VRRx0lSZo6darWrl2rX/ziF5o7d67C4bCampq6VaMbGxtVVVV1wOfz+Xzy+Xx9XzkAYNCkKtFGHHHFOdEAAMAg/S4xxONxhUIhTZ06VR6PRytWrLDu27x5s+rq6lRbW9vfbwMAyKCgNVjM/hDNYDEAAGCSPlWib731Vp133nmqqalRS0uLlixZopUrV+qZZ55RSUmJrrnmGt10000qKytTcXGxrr/+etXW1jKZGwCyTHpPtEHt3FSiAQCAAfoUonfu3KkrrrhCO3bsUElJiSZPnqxnnnlGn/3sZyVJd911l5xOp+bMmaNQKKRZs2bpnnvuGZSFAwAGj3XEldv+SjTTuQEAgEn6FKIXLVp00Pv9fr8WLlyohQsX9mtRAAB7GTmdm3ZuAABgAPv79AAAxgkZ1M6d2hMdiycUiydsXg0AAMh19l8dAQCMYx1xZVAlWpIiVKMBAIDNCNEAgP0Eo+ZUolNHXEnpvdoAAAB2sf/qCABgHOucaAMGi3lcDutjhosBAAC7EaIBAPsxqZ3b4XBYLd20cwMAALsRogEA+0lXos34NZFq6aYSDQAA7GbG1REAwBiJRCJ9TrQBlWiJY64AAIA5CNEAgG66Du8yYbCYRCUaAACYw4yrIwCAMVKt3JJ5lWimcwMAALsRogEA3aSGirmcDnlcZvyaYLAYAAAwhRlXRwAAY6Qq0X5DhopJssI87dwAAMBu5lwhAQCMEIx2hmhDWrmlLoPFCNEAAMBmhGgAQDcmnRGd4nMxnRsAAJiBEA0A6MY6I9qQydwSe6IBAIA5zLlCAgAYIb0n2pxKtMflkMR0bgAAYD9CNACgm3Q7tzm/ItgTDQAATGHOFRIAwAghIweLJddCiAYAAHYjRAMAugkZOFjMy2AxAABgCEI0AKCb9BFX5vyKsAaLUYkGAAA2M+cKCQBgBBMHi3k7B4tRiQYAAHYjRAMAukkNFvOZ1M7NYDEAAGAIQjQAoBurEm1gOzdHXAEAALuZc4UEADBC0MjBYp3TuWnnBgAANiNEAwC6SQ0W87nN+RXhcSf3RDNYDAAA2M2cKyQAgBHS7dwmVaI54goAAJiBEA0A6MY6J9qgSrSPwWIAAMAQ5lwhAQCMYGQlmhANAAAMQYgGAHST2hNtZIimnRsAANiMEA0A6CY9nducXxEeF5VoAABgBnOukAAARki1c/tMqkQzWAwAABiCEA0A6MbaE+02KESzJxoAABiCEA0A6MbEdm5CNAAAMIU5V0gAACOEDBwsljriKkI7NwAAsBkhGgDQjXVOtEEhmsFiAADAFIRoAEA36SOuzPkVwRFXAADAFOZcIQEAbBeLJxSJJSQZNlissxIdohINAABsRogGAFhSk7kls9q5veyJBgAAhiBEAwAsXUN0apiXCbzsiQYAAIYw5woJAGC7YGdI9bqdcjodNq8mLVWJjiekKNVoAABgI0I0AMCSqkT7DapCS+kQLTFcDAAA2MusqyQAgK1SIdpn0H5oKd3OLdHSDQAA7EWIBgBYgtYZ0Wb9enC7nEp1l1OJBgAAdjLrKgkAYKuQ1c5tViVakjwMFwMAAAYgRAMALMFoZ4g2rJ1bSu+LJkQDAAA7EaIBABZT27ml9JFbtHMDAAA7mXeVBACwjTWd28RKNO3cAADAAIRoAIAlVYn2GbgnOtXOHaESDQAAbESIBgBY0pVo8349pAaLhahEAwAAG5l3lQQAsA2DxQAAAA6OEA0AsJg8WIwQDQAATGDeVRIAwDahqLnnRKcGi0ViCZtXAgAAchkhGgBgCVmVaANDtHXEVczmlQAAgFxGiAYAWEweLMYRVwAAwATmXSUBAGxj9DnR7IkGAAAGIEQDACzWOdEGh2iOuAIAAHYiRAMALNYRV27zfj0wWAwAAJjAvKskAIBtTG7n9tDODQAADECIBgBYrHZugyvRTOcGAAB2Mu8qCQBgG5Mr0T4q0QAAwACEaACAJTW0y8QQnRosxp5oAABgJ0I0AMBi8jnRHhfTuQEAgP3Mu0oCANjG5HZuzokGAAAmIEQDACypwWJ+t4Eh2hosRogGAAD26VOIXrBggaZNm6aioiJVVFTooosu0ubNm7s9JhgMat68eSovL1dhYaHmzJmjxsbGAV00AGDgJRKJ9DnRBrZzpyvRTOcGAAD26dNV0qpVqzRv3jytWbNGzz77rCKRiM4991y1tbVZj5k/f76eeOIJLV++XKtWrVJ9fb1mz5494AsHAAyscCyuROfMLp/B7dwMFgMAAHZy9+XBTz/9dLfPFy9erIqKCq1fv16f/vSn1dzcrEWLFmnJkiU6++yzJUn333+/jj32WK1Zs0YzZswYuJUDAAZUqpVbMrQS7WJPNAAAsF+/rpKam5slSWVlZZKk9evXKxKJaObMmdZjJk6cqJqaGq1evbrH5wiFQgoEAt3eAACZF+ocKuZwpAOrSRgsBgAATHDYV0nxeFw33nijTjvtNE2aNEmS1NDQIK/Xq9LS0m6PraysVENDQ4/Ps2DBApWUlFhv1dXVh7skAEA/WGdEu11yOBw2r2Z/qWAfYrAYAACw0WGH6Hnz5mnjxo1aunRpvxZw6623qrm52Xrbtm1bv54PAHB4TD4jWqISDQAAzNCnPdEp1113nZ588kk9//zzGjNmjHV7VVWVwuGwmpqaulWjGxsbVVVV1eNz+Xw++Xy+w1kGAGAAWcdbGThUTOo6WIwQDQAA7NOnckMikdB1112nxx57TM8995zGjRvX7f6pU6fK4/FoxYoV1m2bN29WXV2damtrB2bFAIBBkT7eyswQ7WGwGAAAMECfKtHz5s3TkiVL9Pjjj6uoqMja51xSUqK8vDyVlJTommuu0U033aSysjIVFxfr+uuvV21tLZO5AcBwqXZun9vMdm4f7dwAAMAAfQrR9957ryTpzDPP7Hb7/fffr6uuukqSdNddd8npdGrOnDkKhUKaNWuW7rnnngFZLABg8GRLO3eYdm4AAGCjPoXoRCJxyMf4/X4tXLhQCxcuPOxFAQAyz/jBYp3t3BEq0QAAwEZmXikBADIuHaLNrER73BxxBQAA7EeIBgBIkoKdFV5T90R7uwwW601nFAAAwGAw80oJAJBxIcMr0d4u4T4SI0QDAAB7EKIBAJK6tHO7zQzRXSvkDBcDAAB2IUQDACR1nc5t5q+G1DnREsPFAACAfcy8UgIAZJzpg8VcTodcTockKtEAAMA+hGgAgCQpGE2GaJ+hIVrqPlwMAADADoRoAIAk89u5pfRwsRAhGgAA2MTcKyUAQEaZPlhMSodoKtEAAMAuhGgAgKSulWiDQ3RnO3eEPdEAAMAmhGgAgCQpFE0NFjP3V4NViSZEAwAAm5h7pQQAyKhQFlWiaecGAAB2IUQDACSlp3NnRSWaEA0AAGxi7pUSACCjsmqwGO3cAADAJoRoAICk9GAxk8+J9rgckqhEAwAA+xCiAQCSulSijW7nTgZ8QjQAALCLuVdKAICMSodocyvR1mAx2rkBAIBNCNEAAElSMGr+dG4fg8UAAIDNCNEAAMXjCSuYpoKqiVKDxSJUogEAgE3MvVICAGRMqEtl1+RKdGqwWIhKNAAAsAkhGgBg7YeWJH8WVKJp5wYAAHYx90oJAJAxwWgyRLudDrld5v5q8Lo6p3PTzg0AAGxi7pUSACBjUmdEm9zKLXXZE00lGgAA2IQQDQDIijOiJcnbuSeaSjQAALCL2VdLAICMSIVonzs7KtHsiQYAAHYhRAMAurRzm/1rgRANAADsZvbVEgAgI1KDxYzfE9059CxEOzcAALAJIRoAoFAkS0J0Z7s5g8UAAIBdCNEAAIWi2dHO7WGwGAAAsJnZV0sAgIywpnMzWAwAAOCgCNEAgKw5J9pHiAYAADYjRAMA0kdcGd7ObVWiaecGAAA2MftqCQCQEdlSifa4qEQDAAB7EaIBAOkjrkzfE+2iEg0AAOxFiAYApAeLZUs7N5VoAABgE7OvlgAAGZFq5/aZXokmRAMAAJsRogEACmVJJTo1nTtCOzcAALCJ2VdLAICMsPZEM1gMAADgoAjRAIAu07nN/rXAEVcAAMBuZl8tAQAyIj1YzOxKdGo6dySWUDyesHk1AAAgFxGiAQBWiM6WwWIS1WgAAGAPQjQAIOvauSWGiwEAAHuYfbUEAMiIrBks5uxSiWa4GAAAsAEhGgCgkFWJNjtEO50OeVwOSbRzAwAAexCiAQBdBouZ/2vByzFXAADARuZfLQEABl2oM5D6DR8sJqX3RbMnGgAA2IEQDQDImiOuJMnTWYkOUYkGAAA2IEQDQI6LxuKKdp65nBXt3G7auQEAgH3Mv1oCAAyqYJcwmg2VaEI0AACwEyEaAHJcqpVbknxu838tWIPF2BMNAABsYP7VEgBgUKVCtM/tlMPhsHk1h+ZjsBgAALARIRoAclwwS86ITvFwxBUAALARIRoAclw2nREtpfdEM50bAADYITuumAAAgyYUTbVzZ0clmsFiAADAToRoAMhx6Xbu7PiVwGAxAABgp+y4YgIADJp0O3d2VKI9qcFiVKIBAIANCNEAkOOsSnSWtHP7qEQDAAAbEaIBIMdZR1xlSzs3e6IBAICNsuOKCQAwaILR7GrnJkQDAAA7EaIBIMdl2znR6cFiCZtXAgAAchEhGgBynDVYzJ0dvxI8VKIBAICNsuOKCQAwaEJZNp07XYmO2bwSAACQiwjRAJDjgtEsOyeaSjQAALBRn6+Ynn/+eV1wwQUaNWqUHA6H/vSnP3W7P5FI6LbbbtPIkSOVl5enmTNn6r333huo9QIABli2VaJ9hGgAAGCjPofotrY2nXjiiVq4cGGP9//kJz/RL3/5S/3mN7/Ryy+/rIKCAs2aNUvBYLDfiwUADLysGyzWGaIjDBYDAAA2cPf1C8477zydd955Pd6XSCR0991363vf+54uvPBCSdIDDzygyspK/elPf9Kll16639eEQiGFQiHr80Ag0NclAQD6IXXElS9bBot17okOUYkGAAA2GNArpi1btqihoUEzZ860bispKdH06dO1evXqHr9mwYIFKikpsd6qq6sHckkAgEMIZlk7d3qwGCEaAABk3oCG6IaGBklSZWVlt9srKyut+z7p1ltvVXNzs/W2bdu2gVwSAOAQsrWdOxxlOjcAAMi8PrdzDzSfzyefz2f3MgAgZ6Ur0dnRzt2b6dyRWFzL1m7Tlt1t+vasCVnzBwIAAGC+AQ3RVVVVkqTGxkaNHDnSur2xsVEnnXTSQH4rAMAAsY64cmdH0Ey1c/c0WCyRSOhvb+/Ugr+8rQ93tUmSxo8o0OXTx2Z0jQAAYOga0LLDuHHjVFVVpRUrVli3BQIBvfzyy6qtrR3IbwUAGCCpI658WV6J3ri9WZfdt0b/+sA6fbirTQ5H8vbVH+zJ9BIBAMAQ1udKdGtrq95//33r8y1btmjDhg0qKytTTU2NbrzxRv3oRz/S0UcfrXHjxun73/++Ro0apYsuumgg1w0AGCBZN1jM3X2wWH1Th372zGY9+tp26/5rTh+nqTXD9NUH1mnNh3uVSCTkSKVqAACAfuhziF63bp3OOuss6/ObbrpJknTllVdq8eLFuvnmm9XW1qavfe1rampq0umnn66nn35afr9/4FYNABgw1mCxLGvnbg1F9bNnNuu+f3xoHXd14Umj9O1ZEzRmWL5C0Zh8bqd2t4b0wa5WHVVRZOeyAQDAENHnEH3mmWcqkdh/H1qKw+HQHXfcoTvuuKNfCwMAZEbqnOhsGyy2qyWkX/892Rk17Yhh+u75x+mk6lLrcT63S1PHDtNLH+zR6g/2EKIBAMCAyI4rJgDAoMm2du5CX/rvv0eU5+s3X56qP/xbbbcAnVI7vlyStPpD9kUDAICBYfsRVwAA+yQSCaudO1sGi1WX5et75x8rv8elL51SbVWme1J7ZLn0rLTmw72KxxNyOtkXDQAA+ocQDQA5LNRlwnW2VKIl6atnjO/V4yaPKVWex6W9bWG9u7NFE6uKB3llAABgqMuOsgMAYFCEIl1CdJYMFusLr9upU44YJomjrgAAwMAgRANADksNFXM6JI9raLY6z+jcF72GfdEAAGAAEKIBIId1HSo2VM9Rrj0yGaJf3pLcFw0AANAfhGgAyGGpPdHZtB+6r04YXaICr0tN7RG93RCwezkAACDLEaIBIIdZleiDTLjOdh6XU9PGlUliXzQAAOi/oXvVBAA4pNTxVkO5Ei2lz4tmXzQAAOgvQjQA5LBUJdo31EN0l33RMfZFAwCAfiBEA0AOSw8WG9q/Do4fVaIin1stwajeqmdfNAAAOHxD+6oJAHBQwdRgsSF4RnRXLqdDp6b2RX+42+bVAACAbEaIBoAcliuVaCnd0s1wMQAA0B9D/6oJAHBAodSe6CFeiZakGZ3DxdZu3adoLG7zagAAQLYiRANADktP5x76vw6OG1mskjyPWkNRvbm92e7lAACALDX0r5oAAAeUbuce+pVop9Oh6da+aFq6AQDA4SFEA0AOC0ZzJ0RL7IsGAAD9R4gGgByWauf25UA7t5QO0eu27lOEfdEAAOAw5MZVEwCgR1Y7dw4MFpOkYyqKNCzfo45ITG983GT3cgAAQBYiRANADksPFsuNEO10Oqwp3bR0AwCAw0GIBoAclt4TnTu/Dqx90QwXyykf7GrVzpag3csAAAwBbrsXAACwTyiHpnOn1I5P74sORWM5cUZ2rnvj4yZdfM9LcjkcuuSUMbr2M0equizf7mUBALJU7pQeAAD7yaVzolOOqijU8EKfQtG4NtQ12b0cZMADqz9SLJ5QOBbXkpfrdObPVuqmP2zQ+ztb7V4aACAL5c5VEwBgP6Fobg0WkySHw6EZ4zkvOlc0d0T05Bv1kqQfXHCcPn3MCMXiCT366nZ99q5VmvfQq9pU32zzKgEA2YQQDQA5LNcGi6Wk9kWvIUQPeX/esF3BSFxHVxTqqk8doQe+cqr+fN1pOve4SiUS0lNv7tD5v3xB1yxeq1fr9tm9XABAFmBPNADksNQRV7lyTnRKakL3q3VNCkZiOfdHhFyRSCS05JVtkqTLTq2Rw+GQJE0eU6rfXXGK3mkI6J6/f6An36jXind2asU7OzVjfJnmTqvWucdVqcDHZRIAYH+5ddUEAOgmPZ07t0Lk+OEFqijyKRyNU30cwt74uFlv7wjI63Zq9pTR+90/sapYv7zsZK341pn60ilj5HY6tObDvZq/7HVNu/Nvmr9sg55/d5di8YQNqwcAmIoQDQA5zGrnzqE90VJyX7TV0s150UPWw6/USZI+P6lKpfneAz5u3PAC/eSSE7Xq5rN048yjNbY8X+3hmB57bbuu+P0rql2wQnc+9Zbeqg9kaukAAIPRpwQAOSwYyb1zolNqx5fr8Q31DBcbolpDUf359eRAsUtPrenV14wuzdONM4/RDeccrVfrmvTYax/ryTd2aGdLSPf9Y4vu+8cWTawq0sUnj9alp9aoJM8zmD8CAMBQuXfVBACwhHJ0sJiUHi62YVuTOsIxm1eDgfbE6/VqD8c0fniBpo8r69PXOhwOTR07TD+66AS98v9m6rf/MlWfO75KXpdT7zS0aMFf3tHM/16lpzc2DNLqAQAmoxINADkqdW6ulJshuqYsX6NK/KpvDmr9R/t0+tHDe3xcNBZXWygmn8c5ZF+npa/U6a6/vavjRhbrU0cO16eOKtexVcVyOh12L+2wpVq5Lz212hoodji8bqdmHV+lWcdXqbk9oqfe3KH/+ceH+nB3m77+4Hqdf8JI/fCfjteIIt9ALR0AYDhCNADkqNQZ0ZLkc+deY5LD4dCMI8v16Kvb9aOn3lJlsV9toahaQ1G1haNqC8XUFooqFE3+ocHldOj4UcWaOnaYThlbplOOGKbKYr/NP0X/vfDebn33TxsViyfUGNilv2/eJUkqK/Cqdny5PnVUuU47crjGluf3K4xm0sbtzXrj42Z5XA7NmTJmwJ63JN+jf55eo9lTRuuXK97Tb5//UE+9uUMvvL9bt33hOM2eMjprXiMAwOEjRANAjkoNFZNysxItSZ8+eoQefXW73mlo0TsNLQd9bCye0BsfJ8PZ/S9ulSSNGZanU8YO09QjynTK2GE6prJIriyq3m7Z3aZ5S15VLJ7QFyaP1EnVpXrx/d16ecte7W0L66k3d+ipN3dISu4Xrj2yXFd96ghNGl1i88oPbunaZBX63OOrVF448BViv8elmz83UZ8/YaRufuQNvbUjoG8tf11/fr1e/zn7BI0uzRvw7wkAMIcjkUgYdW5DIBBQSUmJmpubVVxcbPdyAGDIqm/q0Kd+/Jw8Lofeu/Pzdi/HFrF4Qn989WN1hGMq8LlV6HOpwOdWvtetQp9bBT5X53u3draEtG7rXq3/aJ/Wbd2ndxoC+uTJR2UFXv3y0pMP2BpukkAwoosXvqgPdrXp5JpSPfyvM6w/pkRicb2+rUkvvr9HL32wW6/W7VMklvxhi/xurbjpM6owtArfHo5q+p0r1BKK6qGvTtdpRw3uf4tILK7fPf+hfrHiPYWjcRV4XfrOeRP15eljs7odHgByTV9yKCEaAHLUh7tadfbPV6nI59abt8+yezlZpyUY0YZtTVq3dZ/WfbRXr9U1qT0c04gin/42/zMqyTd3cnMsntA1/99ardy8S1XFfv35utMOGoo7wjGt3bpXP/7LO3prR0DnTarSvV+emsEV997yddv07UfeUE1Zvlb++5kZC7Lv72zVLX98Q+s+Sp47Pu2IYfqvOZM1fkRhRr4/AKB/+pJDc28THABAUrqd25ejrdz9VeT36IyjR2j+Z4/RQ1+doVe//1mNH1GgXS0h/cdTb9m9vIP6ydPvaOXmXfJ7nLrvilMOWVXO87r06WNG6GdfPFEup0N/2digv24yczJ1aqDY3GnVGa0EH1VRqD/8W63uuPB4FXhdWrt1ny789Yt6p4GzpQFgqCFEA0COCkZz94zoweD3uPTTSybL4ZAeWf+xVm7eafeSevTH9R/rt89/KEn66SUn6oQxvd/ffNyoYn3t0+MlSbc9vkktwcigrPFwbW5o0at1TXI7HfriKQM3UKy3nE6Hrqg9Qs/M/7ROrilVSyiqq+9fq4bmYMbXAgAYPFw5AUCOCkZSIZpK9ECZOrZMV39qnCTp/z36pnEh89W6fbr10TclSdeffZQuOHFUn5/jhnOO1tjyfDUEgvrpM5sHeon9kqpCn3NshSqK7NuzPWZYvu6/apqOHFGgHc1BXb14rVpDUdvWAwAYWIRoAMhRoUjqjGh+FQykf591jGrK8lXfHNSP//KO3cux7Gju0NceWK9wLK5zj6vU/JnHHNbz+D0u/efFJ0iS/nfNR1rfuQfYbsFITI+9tl2SdNmpNTavRirN92rx1adqeKFPb+8I6NoH1ysSix/6CwEAxuPKCQBylFWJdlOJHkj5Xrd+PCcZMh96uU4vfbDb5hUlB4N97YH12t0a0sSqIt0196R+7Rc+7ajhumTqGCUS0q2PvqFw1P5w+JeNO9TcEdHo0jydcfQIu5cjSaouS1ak8zwu/eO93fruY2/KsHmuAIDDQIgGgByV3hNNiB5onzpyuC6fnqyG3vLHN9Uetq+VN5FI6OY/vqE3tzerrMCr+644RQU+d7+f97ufP1blBV6929iq3z3/wQCstH8efmWbpORAMZPO6j5hTIkWXn6ynA7pD+s+1q+ee9/uJQEA+okQDQA5inbuwXXLeRM1qsSvur3t+tkz72b8+wcjMa3bule3Pb5JT7xeL7fToXsvn6LqsvwBef5hBV7ddsFxkqRfPve+PtzVOiDPezg+2NWqV7bsldMhWwaKHcrZEyv1HxdNkiT997Pv6pH1H9u8IgBAf/T/T9EAgKyUaufmiKvBUeT3aMGcybry96/o/pe26PzJVZo6tmxQvlc8ntCWPW3aUNekDduSb2/vCCgaT7cO33HhJE0fXz6g3/efThylP766Xc+/u0u3PvqmHv7XGRk9ViplaedAsbMnVmhkSV7Gv39vXD59rD7e16F7V36gW/74hkaW+HXaUcPtXhYA4DAQogEgRwU797GyJ3rwfOaYEfri1DFavv5jffuRN/R/3zyjV+3zu1pC2ri9WaFoXJFYXOHO95FYXOFYIvlxNK62cEyb6pv1+rYmBYL7t4wPL/TppOpSff6EKs2eMvAVWofDoTsvmqRz73peL2/Zq+Xrt2nutMwO9QpFY/rjq8mBYpdm+Hv31bfPnaDt+zr059fr9fX/Xa/l19ZqYlWx3csCAPQRIRoAclT6iCvauQfT984/Tqve3aUPd7XpFyve03c+N7HHx8XiCT3/7i4tXVunFW/v7FZF7g2f26lJo0t0cnWpTqop1UnVpRpdmieHY3Arw9Vl+frWucfoR0+9rTufeltnTczc8VI7W4J6cPVH2tsWVlWxX2dOMGOg2IE4nQ799IuT1RgI6uUte3X1/Wv16Dc+ZWz1HADQM0I0AOSooLUnmkr0YCrJ9+jOi0/Qvz6wTr97/kOdN6lKk8eUWvdv29uu5eu2afn6j7WjOWjdfnRFoUryPPK4nPK4nfK6HPK6ncnPO998bqeOHFGgk6qHaeLIInlc9vxB5KpPHaHHN9Trze3NuuOJt/Trf54yKN8nFk9ow7Ymrdy8U3/fvFMbtwes++ZOq5bbpp+/L3xul373L6dozm9e0vs7W3X1/Wv1w386XlPHDrPtvx8AoG8I0QCQo6hEZ85nj6vUP504Sn9+vV43P/KGHrn2U1q5eaeWrd2mF97frdSpR6X5Hs0+eYzmTqvWhKoiexfdB26XUwtmn6ALF76oJ9/YodlTGnX2xEpJUjQW1972sHa3hLW7NaTdrSHtaQ1rb3tYeR6XSvM9KsnzqDTfq9I8j0rzPSrN86rI75bT6dCe1pCef2+XVm7epVXv7lJTe6Tb9z5hdIlmHV+pf/vMkXb86IelJN+jxVdP08X3vKR3Glp06e/WqMjn1mlHDdeZE0bozAkVqirJTDUfANB3hGgAyFGhKOdEZ9IP/+l4vfj+br3T0KKp//GsQl3OVj79qOGaO61a5x5fKV+W/veYNLpE15w+Tr97/kPNX/a6Kot92t0a1r72sA7naGSHQyr2exQIRrp9fbHfrTOOGaGzJlToM8eM0Igi38D9EBk0Zli+Hv7XGVr49/e16t1d2tsW1tObGvT0pgZJ0sSqIn1mQvLnpEoNAGYhRANAjkq1c/uoRGdEWYFXt194vK5b8ppC0biqiv364ilj9KVTqgfs2Cm7zZ95jJ7e2KC6ve1q7khXjJ2O5M8/vNCn4YU+lRd6NSzfq1A0pqb2SPKtI6Lm9rCaOiJqD8eUSMh6juNGFuvMCSN01sQKnVxdmhVt271xVEWh7pp7kuLxhN7c3qyVm3dp5bs7tWFbk95paNE7DS367aoPVehz6+SaUk0aXaLjRxXr+FElGluWb8skdAAAIRoAcla6nTs7K5/Z6AuTR8nvdsnlcuiMo4YPmTCYkud1adm/zdC6rfs0LN+r4UXJ4Dws3ytXHwJfKBpTc0dEze0RleR5VFE8tFubnU6HTqwu1YnVpbph5tHa2xbWPzpb2J9/d5f2tIX1j/d26x/v7ba+ptDn1nEji3XcqGIrXI8bXiCPyymnQ4M+UA4AchkhGgMqGovrvn9s0bqtew/52HyfW/96xrhuA3YAZI4VorO0fThbzTyu0u4lDKqRJXm64MT+TZv2uV2qKHJlbMq3acoKvLrwpNG68KTRiscT2lQf0Bvbm7SpPqBN25v1dkOLWkNRvbJ1r145wO9bt9Mhp9Mht9MhV5f3LqdD+V638r0uFXjdyve5lO91Kd/rVoHXpXxf8n2qfdzhkBxyKJXJHQ6HHJ23u11OleR5NCzfo2H5XpV2vs/3ugjxAIY0QjQGzO7WkK5f8ppWf7in11/zzMYG3XHh8br0VLPP9gSGItq5AfM5nQ6dMKZEJ4wpsW6LxOL6YFerNm0PaGN9szbVB/RWfUCtofRZ4dF4QoonFO7xWUODumavy6mS/HS4nlBVpGlHlOnUcWWqHOJdBQByAyEaA+K1un36xkOvakdzUPlel24452gNy/ce9Gv++laj/vZ2o2559E29Vtek2y88nrZSIIOCUdq5gWzkcTk1sapYE6uKNWfqGElSPJ5QaziqWCyhWCKhWDyhaDzR5fO4ovGEorGEOiIxtYWiag8n3yc/j6k9HLXeh2Odg+8SUkJSIpHofJ/+PBKLq7kjuad9X3tY+9ojCkfjCsfi2tUS0q6WZFh/ectePbD6I0nS2PJ8nXpEmaaNK9P0cWWqKcunag0g6xCi0S+JREJLXqnT7X9+S+FYXOOHF+i3/zJVR1ce+miWS6aO0b2rPtDP/rpZy9Zt09sNAd375akaXdq/NkAAvcM50cDQ4XQ6VOz32LqGRCIZ0Pe1R7SvLaym9oh2t4b0+sdNemXLXr29I6CP9rTroz3tWr7+Y0lSRZFPp44r06TRJSrNSx53VpznUbE/9bFbRX5Pn/bUA8BgcyQSh3PwxOAJBAIqKSlRc3OziouL7V4ODiIYien7f9po/SKcdXylfvbFE1XUx1/iz7+7SzcsfU372iMalu/Rry6botOPHj4YSwbQxdk/W6kPd7dp2ddmaPr4cruXA2CICwQjWv/RPr2yZa/Wbtmr1z9uUiTWu8vQIp9bxXkeDS/yqcJ686uiuPvH5QVea2BfoktFPp5IV+aj8YTCsbhag1G1BCNqCUXVEoymPw9G1RpKvrmdDvk9Lvk9LuV5XPJ7nMrzurrdltxT7lKBL73XvMDnltfNVhkgm/Qlh1KJxmHZtrdd1z60Xhu3B+R0SN+eNVFf/8z4w2rJ+vQxI/TE9afr2gdf1Zvbm3XF71/Wv8+aoGs/cyQtXsAgYjo3gEwq9nt01oQKnTWhQlLy36AN25JV6q272xQIRpJT2TsiCnRE1dwRUUfnv1MtoahaQlFtb+o46PdwOJLt7rF4MkDbyeNyWAPbCv1uVRb7NWZYnkaX5mn0sDyNKkm+ryr2D7lJ/cht8c7/73UdSDjUEKLRZ8+/u0vfXPqamtojKivw6peXntzvyvGYYfla/vVa3fb4Rv1h3cf6ydOb9fq2psOqbAPonWCUdm4A9vF7XJoxvlwzDtIJE47GFQhGFOhIniW+qyWknS0h7QoEtTP1cUtIO1uC2t0aViyeULjz37aD8bqdKvK5Veh3q8jvVqEv2TZe5Et+XuT3qMDnVjyRUEc4po5ITMFIl/fhmIKRuDoiyT3k7eGY2sMxtYai1vePxBLWHwXULL3b2NrjWlxOh6qK/RpdmqfKEr9GFPo0oqjLW+fnZQV9OyoOmZVIJDscHHJY0/GHomAkpsZAUPVNQe1o7tCO5s73TUHr433tkQN+fSpPOx0OjSj0adzwAo0bUaDxwws0bniBjhheoOph+cZ3chCiIUmqb+rQhm1NOlRz/9s7Alq48n0lEtLkMSUDuofZ73HpJ5ecqJNrhukHj2/SM5sa9V7ji/rmOUdbR20cSEWxT6eMHZZ1f+na3RrS2i17ZfMfyw/bqFK/Tq4ZZvcycJjSlWizf1EByF1et1PDC30aXug75GNj8YT2toUVjsX3O9rL7XRanw92uInE4p2hOjmorS0UVSAY0Y7moLbv69D2pg5t39eh+uYO1Td1KBJLJG87RJXd6ZDKC5Mt68V+T2fY7wz/3d4n3+JxKRyLKxyNKxSNdb5PDn4LReI93pe6PRSNdXtMvPMCsetgOSn5sVLXMI5kMHI5kq+x05H8A4HD4ZCr8z6/x6WKIp8qS/yqLPKpqsSvimK/qor9GlHkO+T1XqaEo3E1BoJqDATVEAiqoTn5R5rWUKSz7T/ZGdHa2frfEoyoNRTdb3vCfv87dDnldDjkcztV4EtuAShIHTnncydv87qV703+gWd4obfbtoVCn3vAr3Xj8YRaQlE1tYe1uzWsPa2hLu9D2t3W/baDBeTeSGWNWCKRfG0Dwf1O9nE5HaoelmeF6pqyfOutuizfiD/+E6JzWCQW14q3G/XwK9v0/Hu7Dhmgu7p0WrV++E+DM037slNrdOzIYl374Hp9uLtNNy7b0Kuvu/jk0frPi09Qntf+/2P1xqrOveBN/fzHyG7/PL1GP7jgOPk4azjrhKhEAxhCXE6HRhQdOmwPNo/LqZK85BnahxKPJ7SrNaSPO8P1zkBQu1pD2t0S1q7WkDXlfE9bSPGEuk09H2ocDqm8wKfKYp9K8z0q8qUHy3X9o0FxXvJjv8clR7evT3+W+ighKRSJKRSNK3iA96FITLvbwmpsTgfmPW09Hw7XV9HOPfkD9V/M73EmQ3WRTxXFyS6FPK9bzs4/Ujgdydch9bHTmTzjPRpLqKk9oqb2sJo6ktP0m9uT3R1N7eE+F3P8HqdGleRpZKlfVcV5GlXqV1WJ37pteKFPzs7/Hl3/4JLKGgklFI9LO5o7tGV3m7bsbtOHu9u0tfPj9nBMW/e0a+uedmnzrv2+f0WRr1uori7LV3mhV6V5HpXmJ98X5w3uQEIGi+WgD3e1atm6bfrj+o+1uzX9j8QJo0uUf4gA6nY5NPvkMdaRGoNpT2tI//X0O/poT/tBH5dISOvr9ikWT2hiVZF++y9TNba8YNDXd7ji8YTuWfm+fv7su0okksd9VGXhuZnxRELrPtqnREI6sbpU914+RaOYrJ41IrG4jv7uXyRJr992rkry2TYBAKaKxuLa2x7WrpaQ9raF1RKMKtCRHILWEowokKqOdhmM5nRIPrdLXrcz+eZyyudJvve6ndZ9Pnf6dp/HlfzcenPJ43LK6ZCVTB1KBrNUPHE4kp/H4wkljydP7kePJxKdHydvi8cTag/HtLMl1K3KuzOQ/DxqWFue1+VUZYlPVcV+VXZWypOBPtn+X9h1G0DnxwVet+SQtSc/Odgu3u3zSCzZ9t3eObyuPRxTWziqtlD6iLm2cEyBzu0LqbeWLufAD4Y8j0vlhV6VF/o0otCr8gKfhhel3vs0vNCr4YXJinhJnmfQuj8TiYR2toT04a5koN66p03b9rarbm+76va09/p1cDiScxhK8z1WuPa4nOkhg/G4orEuQwfjCXW0tepvt5zXqxxKiM4RwUhMf9m4Q0tf2aaXt+y1bh9e6NMXTxmjuadU64jh5gbPQ3n5wz2at+Q17W4Nqcjv1t1zT9I5x1bavaz9BIIR3bTsdf3t7UZJ0mWnVusHF2Tv+dh/37xTNy7doOaOiMoLvPrVP5+sTx3JZPVs0BKM6IQf/lWS9M5/fC5r/zcIAMh+8XhCe9vDamgOamdLUIGOZAt8SzDauSc+/QeC1O2haHJLklXdPECiSf6RIPnHAb8n+YcBv8cpv/UHA5eGFXhVVexXVYlPVcV5qirxa1j+4AXFw9EejqZnArSErK6FUCRu/fEikUieCx9PJMNovPMPGC6nI1mhzfdoWL5HJXmpj5PvS/I8WXEdkEgk5wzUpUL13nZt29uuj/d1aG/nsXrNHcnW+sMRD7Vr291fIkQjuYd56St1euy17QoEk/+DcjqkMydUaO60ap09scKY/Sf91dAc1DceWq9X65okSd8852jdeM7Rxgx22NzQon/733XauqddXrdT/3Hh8Zo7rcbuZfVb3Z52ff3B9XprR3JS+3c+N1Ff+/ThTWpH/4Wjcf3t7UY9987Ogw7XCUfjenpTgxwO6cP//Dz/vQAAwJAQicXV3BFJt7C3J1vYY/GEnPvNSpBcTqfcToc62lt1/tQj7Q3RCxcu1E9/+lM1NDToxBNP1K9+9Sudeuqph/w6QnT/tYai+vOGei1bW6fXP262bh9dmqe506p1ydQxQ7btNhyN60dPvaUHVn8kSTpzwgjdPfckleZ7bV3Xn1+v13ceeUMdkZhGl+bp3i9P0eQxpbauaSAFIzF997GN+uOryTPDP39ClX5yyYkq9DF2IVM+2NWqZWuT2zT6sperosinV747cxBXBgAAYL6+5NBBCdHLli3TFVdcod/85jeaPn267r77bi1fvlybN29WRUXFQb+WEH14EomEXq1r0rK1dXryjR1qDydbXDwuhz57XKXmTqvRGUcNN6YqO9geffVj3fromwpF46ouy9NvvjxVx48qyfg6IrG4FvzfO/r9i1skSacfNVy/vOxklRXYG+oHQyKR0IMv1+mOJzYpEkvoyBEF+u2/nKKjKgrtXtqQ1RGO6f/e3KFla7fpla3pbRoVRT5dfPJoVfZir/2M8eU6bhT/1gIAgNxme4iePn26pk2bpl//+teSpHg8rurqal1//fW65ZZbDvq1hOi+2dsW1mOvbdeytXXdzh88ckSBLp1Wo4unjO7VsRBD0ab6Zn39wfXatrdDPrdT/3nxCRkZiJaysyWo6x56zQo33zjzSH3r3AlD/ozH9R/t0zceWq/GQEgFXpd+9sUTdd4JI+1e1pCycXuzlq3dpj9t2K6WLts0zp5YobnTanTWhBFyD5FtGgAAAJlga4gOh8PKz8/XI488oosuusi6/corr1RTU5Mef/zxbo8PhUIKhdKD3wOBgKqrq3Xkvz8ily9/IJc2JEVicWssvd/j1PknjNKlp1Zn5ZnJg6GpPawbl23Qys7x+F63U5l6VVKT/gp9bv3siyfqc5OqMvSd7berJaTrlrxqDbHzuQl0AyUhddvrPGZYni6dVq1LplarqiT7prwDAACYoC8hesA3LO7evVuxWEyVld0nI1dWVuqdd97Z7/ELFizQ7bffvt/t4WhcTteBh+IgbdLoYs2dVqMLTxqlYj/H1HRVmu/V76+cpl+seE+/eu69gw5aGgwTKot0z5en6MgRudXSPKLIpwe/Ol3/9Zd3tOjFLdZ5xBgYHpdD5x5fpcum1ehTR5bnzDYNAAAAEwx4Jbq+vl6jR4/WSy+9pNraWuv2m2++WatWrdLLL7/c7fEHqkS/tXWHimjnPiSvy6mKLDxj2A7N7RG1hCIZ+34Oh0Mji/05H3D2tYXVFh7csw1zTUmeR0X8wQwAAGDA2FqJHj58uFwulxobG7vd3tjYqKqq/dtZfT6ffL799+yOHpav4mLauTFwSvI9KskneGTasAKvhg3BQWoAAADITQO+UdHr9Wrq1KlasWKFdVs8HteKFSu6VaYBAAAAAMg2g3KI60033aQrr7xSp5xyik499VTdfffdamtr09VXXz0Y3w4AAAAAgIwYlBA9d+5c7dq1S7fddpsaGhp00kkn6emnn95v2BgAAAAAANlkUM6J7g/OiQYAAAAAZFJfciiHtwIAAAAA0EuEaAAAAAAAeokQDQAAAABALxGiAQAAAADoJUI0AAAAAAC9RIgGAAAAAKCXCNEAAAAAAPQSIRoAAAAAgF4iRAMAAAAA0EuEaAAAAAAAeokQDQAAAABALxGiAQAAAADoJUI0AAAAAAC95LZ7AZ+USCQkSYFAwOaVAAAAAAByQSp/pvLowRgXovfs2SNJqq6utnklAAAAAIBcsmfPHpWUlBz0McaF6LKyMklSXV3dIRdvomnTpmnt2rV2L6PPAoGAqqurtW3bNhUXF/f7+bL1dRhovA5JvA5D/zXo7b8hQ/116C1ehyReB16DlIF6HQb6eibT+N9DEq8Dr0FKJl+H5uZm1dTUWHn0YIwL0U5ncpt2SUlJVv7j53K5snLdKcXFxQOy/mx/HQYKr0MSr0PuvAaH+jckV16HQ+F1SOJ14DVIGejXYaCuZzKN/z0k8TrwGqTY8Tqk8uhBH5OBdeSUefPm2b0EI/A6JPE6JPE68Bqk8Dok8Tok8TrwGqTwOiTxOiTxOvAapJj6OjgSvdk5nUGBQEAlJSVqbm7mry8ZxOsOoD/4NwSACfi3CMDh6su/H8ZVon0+n37wgx/I5/PZvZScwusOoD/4NwSACfi3CMDh6su/H8ZVogEAAAAAMJVxlWgAAAAAAExFiAYAAAAAoJcI0QAAAAAA9BIhGgAAAACAXiJE44AcDof+9Kc/2b0MAAAAADAGIXqIu+qqq+RwOPZ7e//99+1eGgDDpf79+PrXv77fffPmzZPD4dBVV12V+YUByFmrV6+Wy+XS+eefb/dSAOQwQnQO+NznPqcdO3Z0exs3bpzdywKQBaqrq7V06VJ1dHRYtwWDQS1ZskQ1NTX9eu5IJNLf5QHIMYsWLdL111+v559/XvX19f16rlgspng8PkArA5BLCNE5wOfzqaqqqtuby+XS448/rilTpsjv92v8+PG6/fbbFY1Gu33tjh07dN555ykvL0/jx4/XI488YtNPAcAOU6ZMUXV1tR599FHrtkcffVQ1NTU6+eSTrduefvppnX766SotLVV5ebm+8IUv6IMPPrDu37p1qxwOh5YtW6bPfOYz8vv9euihhzL6swDIbq2trVq2bJmuvfZanX/++Vq8eLF138qVK+VwOPTUU09p8uTJ8vv9mjFjhjZu3Gg9ZvHixSotLdWf//xnHXfccfL5fKqrq7PhJwGQ7QjROeof//iHrrjiCt1www1666239Nvf/laLFy/WnXfe2e1x3//+9zVnzhy9/vrruvzyy3XppZfq7bfftmnVAOzwla98Rffff7/1+e9//3tdffXV3R7T1tamm266SevWrdOKFSvkdDp18cUX71flueWWW3TDDTfo7bff1qxZszKyfgBDwx/+8AdNnDhREyZM0Je//GX9/ve/VyKR6PaYb3/72/r5z3+utWvXasSIEbrgggu6db20t7frv/7rv/Q///M/2rRpkyoqKjL9YwAYAhyJT/7rgyHlqquu0oMPPii/32/ddt5552nfvn0655xzdOutt1q3P/jgg7r55put9qjUXsh7773XesyMGTM0ZcoU3XPPPZn7IQDY4qqrrlJTU5Puu+8+VVdXa/PmzZKkiRMnatu2bfrqV7+q0tLSbtWglN27d2vEiBF68803NWnSJG3dulXjxo3T3XffrRtuuCHDPwmAoeC0007Tl770Jd1www2KRqMaOXKkli9frjPPPFMrV67UWWedpaVLl2ru3LmSpL1792rMmDFavHixvvSlL2nx4sW6+uqrtWHDBp144ok2/zQAspnb7gVg8J111lndgnBBQYEmT56sF198sVvlORaLKRgMqr29Xfn5+ZKk2trabs9VW1urDRs2ZGTdAMwwYsQIq3UykUjo/PPP1/Dhw7s95r333tNtt92ml19+Wbt377Yq0HV1dZo0aZL1uFNOOSWjawcwNGzevFmvvPKKHnvsMUmS2+3W3LlztWjRIp155pnW47pet5SVlWnChAndOui8Xq8mT56csXUDGJoI0TmgoKBARx11VLfbWltbdfvtt2v27Nn7Pb5r1RoApGRL93XXXSdJWrhw4X73X3DBBRo7dqzuu+8+jRo1SvF4XJMmTVI4HO72uIKCgoysF8DQsmjRIkWjUY0aNcq6LZFIyOfz6de//nWvnycvL08Oh2MwlggghxCic9SUKVO0efPm/cL1J61Zs0ZXXHFFt8+7DhMCkBs+97nPKRwOy+Fw7LeXec+ePdq8ebPuu+8+nXHGGZKkF154wY5lAhiCotGoHnjgAf385z/Xueee2+2+iy66SA8//LAmTpwoKXmdkjo5YN++fXr33Xd17LHHZnzNAIY2QnSOuu222/SFL3xBNTU1uuSSS+R0OvX6669r48aN+tGPfmQ9bvny5TrllFN0+umn66GHHtIrr7yiRYsW2bhyAHZwuVxWS6TL5ep237Bhw1ReXq7f/e53GjlypOrq6nTLLbfYsUwAQ9CTTz6pffv26ZprrlFJSUm3++bMmaNFixbppz/9qSTpjjvuUHl5uSorK/Xd735Xw4cP10UXXWTDqgEMZUznzlGzZs3Sk08+qb/+9a+aNm2aZsyYobvuuktjx47t9rjbb79dS5cu1eTJk/XAAw/o4Ycf1nHHHWfTqgHYqbi4WMXFxfvd7nQ6tXTpUq1fv16TJk3S/PnzrQtaAOivRYsWaebMmfsFaCkZotetW6c33nhDkvTjH/9YN9xwg6ZOnaqGhgY98cQT8nq9mV4ygCGO6dwAAADIaqnp3Pv27VNpaandywEwxFGJBgAAAACglwjRAAAAAAD0Eu3cAAAAAAD0EpVoAAAAAAB6iRANAAAAAEAvEaKHkAULFmjatGkqKipSRUWFLrroIm3evLnbY4LBoObNm6fy8nIVFhZqzpw5amxstO5//fXXddlll6m6ulp5eXk69thj9Ytf/GK/77Vy5UpNmTJFPp9PRx11lBYvXjzYPx4AAAAA2I4QPYSsWrVK8+bN05o1a/Tss88qEono3HPPVVtbm/WY+fPn64knntDy5cu1atUq1dfXa/bs2db969evV0VFhR588EFt2rRJ3/3ud3Xrrbfq17/+tfWYLVu26Pzzz9dZZ52lDRs26MYbb9RXv/pVPfPMMxn9eQEAAAAg0xgsNoTt2rVLFRUVWrVqlT796U+rublZI0aM0JIlS3TJJZdIkt555x0de+yxWr16tWbMmNHj88ybN09vv/22nnvuOUnSd77zHT311FPauHGj9ZhLL71UTU1Nevrppwf/BwMAAAAAm1CJHsKam5slSWVlZZKSVeZIJKKZM2daj5k4caJqamq0evXqgz5P6jkkafXq1d2eQ5JmzZp10OcAAAAAgKHAbfcCMDji8bhuvPFGnXbaaZo0aZIkqaGhQV6vV6Wlpd0eW1lZqYaGhh6f56WXXtKyZcv01FNPWbc1NDSosrJyv+cIBALq6OhQXl7ewP4wAAAAAGAIQvQQNW/ePG3cuFEvvPDCYT/Hxo0bdeGFF+oHP/iBzj333AFcHQAAAABkJ9q5h6DrrrtOTz75pP7+979rzJgx1u1VVVUKh8Nqamrq9vjGxkZVVVV1u+2tt97SOeeco6997Wv63ve+1+2+qqqqbhO9U89RXFxMFRoAAADAkEaIHkISiYSuu+46PfbYY3ruuec0bty4bvdPnTpVHo9HK1assG7bvHmz6urqVFtba922adMmnXXWWbryyit155137vd9amtruz2HJD377LPdngMAAAAAhiKmcw8h3/jGN7RkyRI9/vjjmjBhgnV7SUmJVSG+9tpr9X//939avHixiouLdf3110tK7n2Wki3cZ599tmbNmqWf/vSn1nO4XC6NGDFCUvKIq0mTJmnevHn6yle+oueee07f/OY39dRTT2nWrFmZ+nEBAAAAIOMI0UOIw+Ho8fb7779fV111lSQpGAzqW9/6lh5++GGFQiHNmjVL99xzj9XO/cMf/lC33377fs8xduxYbd261fp85cqVmj9/vt566y2NGTNG3//+963vAQAAAABDFSEaAAAAAIBeYk80AAAAAAC9RIgGAAAAAKCXCNEAAAAAAPQSIRoAAAAAgF4iRAMAAAAA0EuEaAAAAAAAeokQDQAAAABALxGiAQAAAADoJUI0AAAAAAC9RIgGAAAAAKCXCNEAAAAAAPTS/w8hDvcaj1RprQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rt_array.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rt_array.plot()\n",
    "plt.ylim([0,6])\n",
    "plt.axhline(1, color='red', linestyle='dotted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>infected</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "      <th>new_infected</th>\n",
       "      <th>new_infected_avg</th>\n",
       "      <th>pop_infected</th>\n",
       "      <th>Rt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.035212e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.035212e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.070424e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.070424e-07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.517606e-06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            infected  recovered  deaths  new_infected  new_infected_avg  \\\n",
       "2020-01-22         1          0       0           NaN               NaN   \n",
       "2020-01-23         1          0       0           0.0               NaN   \n",
       "2020-01-24         2          0       0           1.0               NaN   \n",
       "2020-01-25         2          0       0           0.0               NaN   \n",
       "2020-01-26         5          0       0           3.0               NaN   \n",
       "\n",
       "            pop_infected  Rt  \n",
       "2020-01-22  3.035212e-07 NaN  \n",
       "2020-01-23  3.035212e-07 NaN  \n",
       "2020-01-24  6.070424e-07 NaN  \n",
       "2020-01-25  6.070424e-07 NaN  \n",
       "2020-01-26  1.517606e-06 NaN  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_us.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_us['new_infected'].diff().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_us['new_infected'].diff().rolling(7).mean().plot()\n",
    "plt.axhline(0, color='red', linestyle='dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TODOs:\n",
    "\n",
    "- See the spread of pandemic in different countries.\n",
    "- Plot Rt graphs in different countries side by side or in the same graph.\n",
    "- Check correlation between deaths and recoveries.\n",
    "- Compare recoveries between countries.\n",
    "- Are there any outliers (anomalies)?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}