act_aprendizaje no supervisado.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering\n",
    "\n",
    "# Análisis Exploratorio de Datos\n",
    "\n",
    "#### Contexto\n",
    "\n",
    "#### Objetivo\n",
    "\n",
    "Encontrar cómo se agrupan los países de acuerdo a el porcentaje de población que vive en hogares que tienen una instalación de lavado de manos con agua y jabón en el hogar respecto a la cantidad de muertes y contagios por Covid-19 durante el año 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Librerías a utlizar\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>dateRep</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>countriesAndTerritories</th>\n",
       "      <th>countryterritoryCode</th>\n",
       "      <th>popData2019</th>\n",
       "      <th>continentExp</th>\n",
       "      <th>Cumulative_number_for_14_days_of_COVID-19_cases_per_100000</th>\n",
       "      <th>percentage_handwashing_facility_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>746</td>\n",
       "      <td>6</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>9.013779</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>298</td>\n",
       "      <td>9</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.052776</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>113</td>\n",
       "      <td>11</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.868768</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>7.134266</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>202</td>\n",
       "      <td>16</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.968658</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     dateRep  day  month  year  cases  deaths  \\\n",
       "0           0  2020-12-14   14     12  2020    746       6   \n",
       "1           1  2020-12-13   13     12  2020    298       9   \n",
       "2           2  2020-12-12   12     12  2020    113      11   \n",
       "3           3  2020-11-12   11     12  2020     63      10   \n",
       "4           4  2020-10-12   10     12  2020    202      16   \n",
       "\n",
       "  countriesAndTerritories countryterritoryCode  popData2019 continentExp  \\\n",
       "0             Afghanistan                  AFG   38041757.0         Asia   \n",
       "1             Afghanistan                  AFG   38041757.0         Asia   \n",
       "2             Afghanistan                  AFG   38041757.0         Asia   \n",
       "3             Afghanistan                  AFG   38041757.0         Asia   \n",
       "4             Afghanistan                  AFG   38041757.0         Asia   \n",
       "\n",
       "   Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  \\\n",
       "0                                           9.013779            \n",
       "1                                           7.052776            \n",
       "2                                           6.868768            \n",
       "3                                           7.134266            \n",
       "4                                           6.968658            \n",
       "\n",
       "   percentage_handwashing_facility_2020  \n",
       "0                                    38  \n",
       "1                                    38  \n",
       "2                                    38  \n",
       "3                                    38  \n",
       "4                                    38  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('clustering_dataset.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15302 entries, 0 to 15301\n",
      "Data columns (total 13 columns):\n",
      " #   Column                                                      Non-Null Count  Dtype  \n",
      "---  ------                                                      --------------  -----  \n",
      " 0   Unnamed: 0                                                  15302 non-null  int64  \n",
      " 1   dateRep                                                     15302 non-null  object \n",
      " 2   day                                                         15302 non-null  int64  \n",
      " 3   month                                                       15302 non-null  int64  \n",
      " 4   year                                                        15302 non-null  int64  \n",
      " 5   cases                                                       15302 non-null  int64  \n",
      " 6   deaths                                                      15302 non-null  int64  \n",
      " 7   countriesAndTerritories                                     15302 non-null  object \n",
      " 8   countryterritoryCode                                        15302 non-null  object \n",
      " 9   popData2019                                                 15302 non-null  float64\n",
      " 10  continentExp                                                15302 non-null  object \n",
      " 11  Cumulative_number_for_14_days_of_COVID-19_cases_per_100000  15302 non-null  float64\n",
      " 12  percentage_handwashing_facility_2020                        15302 non-null  int64  \n",
      "dtypes: float64(2), int64(7), object(4)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Elimino la columna del índice y año\n",
    "data=data.drop(['Unnamed: 0', 'year','countryterritoryCode'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dateRep', 'day', 'month', 'cases', 'deaths', 'countriesAndTerritories',\n",
       "       'popData2019', 'continentExp',\n",
       "       'Cumulative_number_for_14_days_of_COVID-19_cases_per_100000',\n",
       "       'percentage_handwashing_facility_2020'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = ['date', 'day', 'month','cases', 'deaths', 'country', 'population', 'continent', 'comulative_days','handwashing_facilities']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15302 entries, 0 to 15301\n",
      "Data columns (total 10 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   date                    15302 non-null  object \n",
      " 1   day                     15302 non-null  int64  \n",
      " 2   month                   15302 non-null  int64  \n",
      " 3   cases                   15302 non-null  int64  \n",
      " 4   deaths                  15302 non-null  int64  \n",
      " 5   country                 15302 non-null  object \n",
      " 6   population              15302 non-null  float64\n",
      " 7   continent               15302 non-null  object \n",
      " 8   comulative_days         15302 non-null  float64\n",
      " 9   handwashing_facilities  15302 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(3)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIqLSEFV+eLWISlSmT5+O0NBQnD9/HiqVCgEBAZxMS0REZGSkHqbRB1GJCgD06NEDPXr00GcsRERERDpEJypERERk3ORYUZHhaBYRERHJBSsqREREMsGKChEREZEBsaJCREQkE1Ivd68PTFSIiIhkQoZ5Cod+iIiIyHixokJERCQXrKgQERERGQ4rKkRERDIhyLD8IMMuERERmSZB0M+jNPbv349evXqhe/fu2Lx5c7HjkZGR8Pf3R58+fTB27FikpaWV+HpMVIiIiKhMPH78GMuWLcOWLVsQEhKCbdu24datWzptFixYgEmTJmHfvn1wd3fHd999V+JrcuiHiIhIJvS1jkpaWtpzKx92dnaws7PTbp8+fRpeXl5wcHAAAPj4+CA0NBQTJkzQtlGr1cjMzAQAZGdnw97evsT3ZqJCREREJdqwYQNWrlxZbP+ECRMwceJE7XZ8fDycnZ212y4uLrhy5YrOcz788EOMHDkSn3/+OaysrLB9+/YS35uJChERkUzoa8G34cOHw8/Pr9j+Z6spQFG15Nmqjkaj0dnOycnBnDlzsH79ejRu3Bjr1q3DzJkzsWbNmhe+NxMVIiIimdBXovLPIZ4XqVixIi5cuKDdTkhIgIuLi3b7xo0bsLCwQOPGjQEAgwYNwldffVXia3IyLREREZWJdu3a4cyZM0hKSkJ2djYOHz6MDh06aI9Xr14djx49wp07dwAAR44cQaNGjUp8TYNWVJyrsYCjbwn3C6QOQfbMLGS49KORUcjxhiVGxszMTOoQSB8k/qfj6uqKqVOnIiAgAPn5+RgwYAAaN26M0aNHY9KkSWjUqBGCg4MxZcoUaDQalC9fHp9//nmJr8nMgYiIiMqMr68vfH19dfatXbtW+3PHjh3RsWNH0a/HRIWIiEgm5FiM5BwVIiIiMlqsqBAREcmEoJBfSYWJChERkUxw6IeIiIjIgFhRISIikglWVIiIiIgMiBUVIiIimZBjRYWJChERkVzIMFHh0A8REREZLVZUiIiIZEKOQz+sqBAREZHRYkWFiIhIJgQZllSYqBAREcmEIMNxEhl2iYiIiOSCFRUiIiKZkOHIDysqREREZLxYUSEiIpIJVlSIiIiIDIgVFSIiIpmQY0WFiQoREZFMCAr5ZSoc+iEiIiKjxYoKERGRTMhx6IcVFSIiIjJarKgQERHJhQwrKkxUiIiIZIJDP0REREQGxIoKERGRTLCiQkRERGRArKgQERHJhBwXfGOiQkREJBMc+iEiIiIyIFZUiIiIZIIVFSIiIiIDYkWFiIhILlhRISIiIjIcVlSIiIhkQo5zVJioAKhWxwotuzlCoRKQ/CgPJ0MSkZ+r0WlTv40t6nnZoTBfg5SEPJw+kIS8bLVEEcvLG++6ICE2DxGhKVKH8sr57fJpbNn1DfLz81C9Wi2MGzEL1lY2Om1iYm/j+83LkJWdAYWgwJjhM+BRw1OnzRcrZ8HRoQLeHfqBIcM3Wg2resCvuTdUSiUeJMdj46mDyMnPE9VGEAQMadMdtSu6AQCuxt7GrgtHAQCV7MtjaLuesDAzh0ajwZ7fjuNa3F2D98/YNahSE32advjr3CZgy9nQYuf/qWFteyIuJRFHos4bOErjJMdExeSHfiytFXjdrwKO/BiPXV89QHpyAVp1c9RpU8ndEo1ft8ehdY8Q8r84xN7IRvu+5SWKWD7KVzLDkBmVUbdVOalDeSWlpiXjf98vwLT3FmB58Fa4OlfG5p2rdNrk5uZg/tKp6NvzLXzx6XoM6DMCy9d8ptNm76HNiLpxxZChG7VyFlYY/tobWH1sNz7ZswaJ6Snwa9FJdBsvj4ZwtXfCvL3fImjvd6hT0Q3NqxclhkPa+uDUzSuYv+97bDx1EGO8+0Ehx0+W/6CchRWGtu2Bb0+EIGjfd3iSkYI+TTsUa+dq54SJXQeiafU6EkRJhmTyiUqVWlZIfJCLtKQCAEBURDo8muh+cJavbI4Ht3OQlVYIAPjzWhbc6lpDoTR4uLLSvIs9Lp9Iw/XzGVKH8kq6EhkBD/d6qORaDQDQvZMfTp49DI3m72rg5cgIuDpXQfPG7QAALZu2x/vjgrTHI6Mv4vc/zqK7d1/DBm/E6lepiZjEh4hPTwYA/HL9d7SpWV90G4UgwEJlDpVCCTOlEkqFAgWFBX8dU8DawhIAYGlmjvzCQkN165XhWakGYp48QkJ6CgDg5I1LaOVev1i7DnWb4cytP/B7zA0DR2jcBEHQy0NKooZ+8vLycOfOHXh6emL//v24du0aRo8eDScnJ33Hp3c29ipkpP79xyIzrQDmlgqYWQja4Z+E2Fw08LJDOXslMlILUbt5OShVAiyslMjO4B+al/XzpkQAgHtDa4kjeTUlJsWjgpOLdru8ozOyszORnZOlHf55+Pg+HOyd8L/vgxFz/yZsrG0xdOB4AEBScgLWbfkKc95fip+P75WkD8bI0cYWSVlp2u3kzDRYmVvC0sxcO/xQUpvTt/5Aixr1sGjgBCgVClx7cBdXYm8BAH48G4b3fd5C1/qtYGtpg29/CYFaozvMbOocbWyRkpmu3U7JSoeVuYXO+QeAHeePAChKbEjeRFVUpk+fjv379+Py5ctYsWIFypUrh1mzZuk7NoMoShSL/6HQPDP95HFMLn4/loIub7mgT2AlQAPkZBVCXcg/MCQdjUaN512LqFD8/c+6oKAAv/9xBt069sGiT75Hz64D8PmyacjPz8OXqz/F8CGT4OhQwYBRGz8FhOf9SdBJKEpq07tJe6TnZGH6tuWYuf1r2FhYoWuD1lAplRjdsR/W//oTPtzxNZaEbsLb7XrC0dpWj7159QgQnndqoVbz760YgkI/DymJevvY2FhMnz4dhw8fxoABA/Dee+8hMTFR37EZREZqAaxt/y4s2dgqkZtViIL8v/9RmJkLePhnDvaueoh93zxETFQWACCXk2lJQhXKV0Ryyt//DpOSE2FjYwtLCyvtPieHCqhSqQZqezQAALRq9jrUajXuxNxAfEIcNmxdjmmfDMfh4yE4HXEUq9YFG7wfxiYpMw321n8P/zpY2yIzNxt5Bfmi2jSrXhenbl5GoVqNnPxcnLn9B+pWrI4qDs4wV5nhj7+qK3cT4hCXkgB358qG69wrIDkrHfbPTAi3f3puC/NLeBY9JQj6eUhJVKJSWFiIpKQkhIeHw9vbGwkJCcjNzdV3bAbx4FY2XKpZwM6pKFnxbG2LmOgsnTbWdkq8MbIizCyK/ms17WiPO1cyDR4r0bOaNGiNm3ci8fDxfQDA4eN70Krp6zptmjb2QnxiHG7/GQ0AuHb9EgQBcK9eG98s3YMln23Aks82oLt3P7Rr3RnjRsijUvpfXIu7i5rOVeBiWzSpvkPdZrh876boNveePELLGvUAFM1JaVKtNu4mPEB8ejKszC1Q07kKAKCCrQMq2VfAvaTHhuraKyEq7k/UqFAZzrYOAIDXazfRJndkmkTNURk1ahQGDhyIzp07o06dOvDx8cHkyZP1HZtB5GSqcWJ3IjoPcYFSCaQlFeCXXYmoUNkc7ftVQMj/4pCaWIDLJ1PRZ2wlQBDwOCYHZw4kSR06mTh7O0eMHzkbS7+ei4LCfLg6V8GEdz/C7btRWLV+IZZ8tgGO9uUxY8JCfPvDUuTmZUOlMse09z6HuZmF1OEbrfScLGz49SeM6eQHlUKJhPQUrDu5H9XLV8Sw13ph/r7vX9gGKJo7MbhNd3zmNwZqtRrRD2MQdvUsCtVqrDq6C4PadIOZUolCtRqbzhxC4l+TRqlIRm4WNp05hFEd+kKlUCIxPQUbTx+Em5Mr3vLqgYUHN0gdonGT4UVkgkZT+plchYWFUCpLf8nLdx/9WernUOkk3C+QOgTZ6zXaQeoQZO/rm2ulDkH2zFRcRssQVg6dbtD3O3c4Xi+v26a7y7830hNRv6nHjx/H119/jeTkZJ1LH48cOaK3wIiIiKh0pJ5Pog+iEpUFCxZgzpw5qFWrluTXUxMREdHzyfEzWlSiYmtrC29vbz2HQkRERKSrxETl/PmieyfUqlUL8+fPR5cuXaB6ZlyzVatW+o2OiIiIRJNhQaXkRGX58uXanx8+fIjr169rtwVBwMaNG/UXGREREZm8EhOVH374AQBw8+ZN1K5dW+fYpUuX9BYUERERlZ7JVVR+++03qNVqzJ07FwsWLNBe8VNQUIBPP/0UYWFhBgmSiIiITFOJicrp06cRERGB+Ph4fPXVV38/SaXCoEGD9B4cERERiSf1fXn0ocREZeLEiQCAkJAQ9OvXzxDxEBER0csytaGfp5o2bYr58+cjKysLGo0GarUasbGx2Lx5s77jIyIiIhMmqkj0/vvvw87ODlFRUahXrx7i4uKKTa4lIiIiaQmCoJeHlERVVPLz8zFp0iQUFBSgfv36GDhwIPz9/fUdGxEREZk4URUVKysr5OXloUaNGoiMjISlpaW+4yIiIqJSEgT9PKQkqqLSp08fBAYGYsmSJRg0aBBOnjwJV1dXfcdGREREpSB1UqEPohKVoUOHol+/fihXrhx++OEH/PHHH3jttdf0HRsRERGZOFFDP3l5edi0aRNmzJiBcuXK4fr16zr3/CEiIiLpyXHoR1SiMm/ePGRlZeHatWtQKpW4d+8eZs+ere/YiIiIyMSJSlQiIyPx/vvvQ6VSwcrKCosWLUJ0dLS+YyMiIqJSkGNFRdT4jSAIyMvL015LnZycLPl11URERPQPpraE/lMBAQEYMWIEEhISsGDBAoSHh+O9997Td2xERERk4kpMVEJCQrQ/v/HGG9BoNCgsLMSIESM4mZaIiMjIyHG0o8Rs49y5cwCAe/fu4d69e+jYsSMUCgV+/fVX1KpVizcqJCIiIr0qMVEJDg4GAAwbNgx79+6Fk5MTACA1NZVDP0REREZGhgUVcdNu4uPj4eDgoN22srJCQkKCvmIiIiIiAiByMq23tzdGjBiB7t27Q6PR4NChQ+jZs6e+YyMiIqJSkGNFRVSiMmvWLISFhSEiIgKCIGDkyJHo0qWLvmMjIiKiUjDZRAUAfHx84OPjo89YiIiIiHTwGmMiIiKZkGNFRYZr2BEREZFU9u/fj169eqF79+7YvHlzseN37tzBsGHD0KdPH4waNQqpqaklvh4TFSIiIrmQ+GY/jx8/xrJly7BlyxaEhIRg27ZtuHXrlva4RqPBuHHjMHr0aOzbtw/16tXDmjVrSnxNJipEREQyISj08xDr9OnT8PLygoODA6ytreHj44PQ0FDt8cjISFhbW6NDhw4AgMDAQLz99tslvibnqBAREVGJ0tLSkJaWVmy/nZ0d7OzstNvx8fFwdnbWbru4uODKlSva7Xv37qFChQqYPXs2oqKiULNmTXz00UclvjcrKkRERDKhr5GfDRs2oEuXLsUeGzZs0Hl/tVqtc78hjUajs11QUICIiAgMGTIEe/bsQbVq1bBw4cIS+8SKChEREZVo+PDh8PPzK7b/2WoKAFSsWBEXLlzQbickJMDFxUW77ezsjOrVq6NRo0YAgN69e2PSpEklvjcrKkRERDKhr4qKnZ0dqlatWuzxz0SlXbt2OHPmDJKSkpCdnY3Dhw9r56MAQLNmzZCUlITo6GgAwNGjR9GgQYMS+8SKChERkUxIvY6Kq6srpk6dioCAAOTn52PAgAFo3LgxRo8ejUmTJqFRo0b4+uuvMXfuXGRnZ6NixYpYvHhxia/JRIWIiIjKjK+vL3x9fXX2rV27VvtzkyZNsHPnTtGvx0SFiIhIJqSuqOiDQROVpp1sDPl2JunnjanoNdpB6jBk7+DaFKlDkDdvIPLBHamjkLWm1eugoLBQ6jCI/hUrKjLDJEX/mKToH5MU/WOSIlMyLKnwqh8iIiIyWqyoEBERyYQMCypMVIiIiOSiNPfleVXIsEtEREQkF6yoEBERyYQch35YUSEiIiKjxYoKERGRTMixosJEhYiISCYEGWYqHPohIiIio8WKChERkVzIr6DCigoREREZL1ZUiIiIZEKGU1SYqBAREcmFHBMVDv0QERGR0WJFhYiISCZ4rx8iIiIiA2JFhYiISCa44BsRERGRAbGiQkREJBMyLKgwUSEiIpINGSYqHPohIiIio8WKChERkUzIceiHFRUiIiIyWqyoEBERyYQcKypMVIiIiGRCjokKh36IiIjIaLGiQkREJBOCQn4lFVZUiIiIyGixokJERCQTcpyjwkSFiIhILmSYqHDoh4iIiIwWKypEREQyIcehH1ZUiIiIyGixokJERCQTcqyoMFEhIiKSCUGGmQqHfoiIiMhosaJCREQkE4IMyw8y7BIRERHJhUlWVH7/4zS27l2Ngvw8VKvqgTFDZ8Haykanzb0Ht7Fh2zJkZWdCoVBg1FvTUbO6JwDg519249ipA8jLz4W7W12MGfohzMzMpeiKUfvt8mls2fUN8vPzUL1aLYwbUfw8x8TexveblyErOwMKQYExw2fAo4anTpsvVs6Co0MFvDv0A0OGLytvvOuChNg8RISmSB3KK6NtrUYY690f5ioVbsfHIvjABmTl5bxUuwX+45CYkYJlYT8CADwr1cCkboNgZW4BhSBg85lQHL56ziD9MhYNq3igX/OOUCmVeJCcgB9OH0ROfp6oNoIgYHCbbqjj6gYAuPrgNnZdOAYAqFPRDQNadoZCUCAzNxvbzx/Bg+R4g/dPKjKcomJ6FZW09GSs3vg5poyZj6Wf/QjXCpWxNWSVTpvcvBwsXP4+end/G8Fz1sGv1zv4et08AEDE778g7PguzJ78JRZ/9APy8nJx6Og2Kbpi1FLTkvG/7xdg2nsLsDx4K1ydK2Pzzn+c59wczF86FX17voUvPl2PAX1GYPmaz3Ta7D20GVE3rhgydFkpX8kMQ2ZURt1W5aQO5ZXiYF0Os3u/g7m7VuGtbz5CXHIixnXu/1Lt3vLyQeNqtXX2LfAPxPcn9mHEt/MwbetyTOw6EFUdXfTaJ2NSzsIKAa/1wprje/BpyFokpqfAr7m36DZeNRvC1a485u37DkH7vkdtVzc0r14XlmYWGOvdH7suHMP8/d9jy9kwjO7YFyqF0vCdpDJjconKlajzqFmjHiq5VAMAdO3gh1MRP0Oj0fzd5loEXJwro1nDtgCAFo3bY/K7RYnKyXOheKPLYJSzsfur0jIN7dv0MHxHjNyVyAh4uNdDJdei89y9kx9Onj2sc54vR0bA1bkKmjduBwBo2bQ93h8XpD0eGX0Rv/9xFt29+xo2eBlp3sUel0+k4fr5DKlDeaW0cm+AqId/Ivavb+J7Lh5HtwZtSt2umVsdtPFoiL2//6LdZ65U4fuTB3DhzygAQEJ6MlKyMuBs56jPLhmV+pXdEfPkIeLTkwEAJ67/jtY164tuIwgCLFRmUCmUMFMqoVIokV9YCBc7R+Tk5+L6oxgAwOO0JOTk56GmcxUD9k5igp4eEhI99HPz5k2kpqbqfNC0atVKL0HpU1LyY5R/5puLk4MzsnMykZ2TpR2WeBR/Hw525bHmh2DExN6CtVU5vNV/fNGxx/eRWj0ZC1e8j+TUJ/D0aIwhfx2jvyUmxaOC09/nubyjM7Kzdc/zw8f34WDvhP99H4yY+zdhY22LoQOLzmVScgLWbfkKc95fip+P75WkD3Lw86ZEAIB7Q2uJI3m1uNo5Ij4tWbudkJaMcpbWsDa31BnWKamdlbkFJncfjA+2foW+zTpo2+QVFuCny79qt/s0ex3W5haIfHBHz70yHo42dkjOTNduJ2elwcrcEpZm5trhn5LanLn9B1rU8MTCNydAqRBwLe5P/BF7C5Zm5jBXmaFe5RqIivsT1ctXRGWHCrCztikWg1wpZDj0IypR+eyzz3Ds2DFUq1ZNu08QBGzcuFFvgemLWq3B89JDheLv4lJhYQEuXT2DuVOXo5Z7A1y4fBKLv56O5fN3oqCwAFejz+P9wIUwNzPHqg3zsX3vGgQMnGzAXhg/jUaNfzvPBQUF+P2PM/h0+grU9miA87+fxOfLpmHVF7vw5epPMXzIJDg6VDBg1ERFBEGh86XsKbVGLaqdIAj4tN9oLP95O55kpL7wfYa27YEBrbtg2o9fIa8g/78H/ooQBAEaPO/8akS16d2kPdJzsjBj+3KYKc0wrlN/dK3fGuHXIvDN0V3o27wj/Ft0xs3H9xH9MAaFhepir0OvDlGJyqlTpxAaGgpLS0t9x6N3FZxccfvPa9rtpJRE2FjbwtLCSrvPwb4CKlesjlruDQAALZu8jrWbFiE+MQ6ODhXQqmlHbVWgfWsf7D64zrCdeAVUKF8RN+88c56TE2Fjo3uenRwqoEqlGqjtUXSeWzV7HavWLcSdmBuIT4jDhq3LAQApqUlQq9XIz8/DuBGzDNsRMhmjOvRB+zpNAQA25pa4nfBAe6yCrQPSsjOLTfZ8nPYE9au4F2tXo0IlVHZ0xsRuAwEATjZ2UAgKmKvMsOinjTBTqjDHdwRqVKiEwPUL8Sj1if47aESSMtPgXqGydtvB2haZudk6yVpJbZq61cG2iJ9RqFajUJ2Ls7evoln1ujhyLQI5Bfn4v7At2ud91m+MdvjIFBQK8kvKRM1RqVat2nO/NbyKGtVrjZt3I/Ew/j4A4MjJELRo8rpOm6YNvJDw5CHuxEQDAKJuXoIAwLlCJbRp5o2zvx1FXl4uNBoNLlw+iZrV6xm4F8avSYPWuHknEg8fF53nw8f3oFXTf5znxl6IT4zD7T+LzvO165cgCIB79dr4ZukeLPlsA5Z8tgHdvfuhXevOTFJIr777a3LriG/nYcz6YDSoXFM7wbVf8444eeNSsedE3Ln23HaRD+7Af8VM7evtvfgLjkadx6KfiqrQH/cdBWsLSwRuML0kBQCi4u7C3bkyXGyL5uV0qNsMl+/fFN3mftJjtPjr6kCFoEDjarVwNzEOGgATu74Jt/IVAQAtangiv7DApK76kSNRFRV7e3u88cYbaNasGczN/74MNzg4WG+B6Yu9nSPGBszGV2vmoqCwAK4VqmDcO3NxJyYaazctRPCc9XCwL4/3A4OxbutS5ObmQKUyw5SxC2BuZoFuHf2QkZmGOcGjoFYXooZbHYzynyF1t4yOvZ0jxo+cjaVfz0VBYT5cnatgwrsf4fbdKKxavxBLPtsAR/vymDFhIb79YSly87KhUplj2nufw9zMQurwycSlZKXj8wPrMN8/ECqlCg+SEzB/33cAgLqVquPDN4ZjxLfzSmz3Ig2q1ESnei1x78kjrBr+oXb/qqO7EHEnUq/9MhbpOVnYeOonjPH2g1KhQEJ6Ctb/egBu5StiWLueWLB/3QvbAMCO8+EY3KY7Pu03GmqNGtEPY3D46lkARQnn0LY9oVIqkJqViW+O7ZKyqwZXAPlVVASNiFLJnj17nrvfz8+vVG/229GEUrWn0jOzkOFMKiNzcG2K1CHI3oHai6QOQfYaVvWQOgST8M0zyaghxKcl6eV1Xeyc9PK6Yoga+vHz80ODBg2QmZmJ1NRUeHp6ljpJISIiIiotUYlKSEgIxo8fj9jYWMTFxWHChAnYuXOnvmMjIiKiUtBoNHp5SEnUHJV169Zhx44dcHQsmtQUGBiIgIAADBgwQK/BERERkWkTlaio1WptkgIATk5OEOR4QwEiIqJXmNTVD30QlajUrVsXCxYs0FZQdu7cCU9Pz395FhERERmSWoaJiqg5KvPnz4eZmRlmz56NWbNmQaVS4ZNPPtF3bERERGTiRFVULC0tMWMG1wohIiIyZhoZrqNSYqLi5+eHPXv2wNPTU2dOikajgSAIiIqK0nuAREREZLpKTFSeLvQWHR1d7FheXl6xfURERCQdk52jMmjQIJ1ttVoNf39/vQRERERE9FSJFZWAgABEREQAgM5VPiqVCp07d9ZvZERERFQqJnd58saNRXf6nD9/PubOnWuQgIiIiOjlmFyi8tT06dPx888/IzMzEwBQWFiI2NhYTJ48Wa/BERERkWkTlah88MEHSE1Nxb1799CyZUucO3cOzZs313dsREREVAomO5n2+vXr2LhxI7p164Z3330XP/74Ix48eKDv2IiIiMjEiUpUypcvD0EQ4O7ujuvXr6NatWrIz8/Xd2xERERUCiZ79+TatWsjKCgIQ4YMwbRp0xAfHy954ERERKTLZId+Pv30U/Ts2RO1atXCpEmTEB8fj6VLl+o7NiIiIjJxJVZUzp8/X2zb1tYWPj4+SE1N1WtgREREVDpyHO0oMVFZvnz5C48JgqBdZ4WIiIhIH0pMVH744QdDxUFERET/kQYmVlF5atiwYTp3T36KFRUiIiLjIcfJtKISlYkTJ2p/LigowJEjR2BnZ6e3oIiIiIgAkYlK69atdbbbtWuHN998k0voExERGRGTm0z7VFxcnPZnjUaDW7duISUlRV8xEREREQEQmagMHTpU+7MgCHBycuLdlImIiIyMMcxR2b9/P1atWoWCggIMHz4cb7/99nPbHT9+HPPmzcPRo0dLfD1Ricq/vQgRERHR48ePsWzZMuzevRvm5uYYPHgw2rRpg1q1aum0S0xMxKJFi0S9pqiVaePi4jB+/Hg0b94crVu3xrRp05CUlFT6HhAREZHeaDRqvTzS0tIQGxtb7JGWlqbz/qdPn4aXlxccHBxgbW0NHx8fhIaGFotz7ty5mDBhgqg+iUpUpk2bhnbt2uHEiRMIDw9Hw4YNMXPmTFFvQERERIahr5sSbtiwAV26dCn22LBhg877x8fHw9nZWbvt4uKCx48f67TZuHEj6tevjyZNmojqk6ihn4yMDJ15Ku+88w52794t6g2IiIjo1TZ8+HD4+fkV2//PpUrUarXOumsajUZn+8aNGzh8+DDWr1+PR48eiXpvUYlKs2bNsHfvXvTt2xdA0QSY+vXri3oDIiIiMgx9Taa1s7MTtX5axYoVceHCBe12QkICXFxctNuhoaFISEiAv78/8vPzER8fj7feegtbtmx54WsKGhEXXbdr1w5JSUmwsLCAQqFAdnb23y8gCIiKivrX4AHgt6MJotrRyzOzKL6CMJWtg2tTpA5B9g7UFjfJjl5ew6oeUodgEr4Z/qFB3+/yvRt6ed0mbnVEtXv8+DGGDBmCnTt3wsrKCoMHD0ZQUBAaN25crG1sbCwCAgLK5qqf06dPiwqQiIiIpCP1vX5cXV0xdepUBAQEID8/HwMGDEDjxo0xevRoTJo0CY0aNSr1a4pKVLKzs7Fy5UqcOXMGhYWF8PLywuTJk2FtbV3qNyQiIiL9MIZ1VHx9feHr66uzb+3atcXaVa1aVdTyJ6Ku+pk3bx6ys7Px+eefY9GiRcjPz8cnn3wiMmQiIiKilyOqohIZGYl9+/Zptz/++GP06tVLb0ERERFR6cnxXj+iKioajUZnUZe0tDQolUq9BUVEREQEiKyovPPOO3jzzTfRuXNnaDQaHD16FGPGjNF3bERERFQKcqyoiEpUfH198fDhQ6xatQoajQazZs2Cv79/qd/sm5jik2mobCkEXp6sb5G170gdguz1vsmVr/XuJlAjkL/LcmMMk2nLmqhE5aOPPkJubi5WrFgBtVqNvXv34t69e5gzZ46+4yMiIj1gkkKvClGJyuXLl3VuKtS5c2f07t1bb0ERERFR6clx6EfUZNqqVasiJiZGu52YmAhXV1e9BUVEREQEiKyoFBQUoG/fvmjZsiVUKhV+++03ODs7IyAgAEDRnRCJiIhIWnKsqIhKVMaPH6+zPXLkSL0EQ0RERPQsUYlK69at9R0HERER/Ucme9UPERERGT8N1FKHUOZETaYlIiIikgIrKkRERDIhx6EfVlSIiIjIaLGiQkREJBMme3kyERERGT85Jioc+iEiIiKjxYoKERGRTHAyLREREZEBsaJCREQkE3Kco8JEhYiISCY49ENERERkQKyoEBERyYQGrKgQERERGQwrKkRERDIhx8m0rKgQERGR0WJFhYiISCbkeNUPExUiIiKZ4NAPERERkQGxokJERCQTao1a6hDKHCsqREREZLRYUSEiIpIJOc5RYaJCREQkE3JMVDj0Q0REREaLFRUiIiKZkOM6KqyoEBERkdFiRYWIiEgm5Hj3ZCYqREREMsGhHyIiIiIDYkWFiIhIJnh5MhEREZEBsaJCREQkE6yoEBERERmQyVRUGlX1QP8WnaBSKhGbFI8Np35CTn6eqDaCIOAtLx/UqegGAPgj9hZ2nj+q89zXajdGM7e6WHlkh8H6ZGwaVvWAX3NvqJRKPEiOx8ZTB4ud4xe1EQQBQ9p0R+2/zvHV2NvYdaHoHFeyL4+h7XrCwswcGo0Ge347jmtxdw3eP2PRtlYjjPXuD3OVCrfjYxF8YAOy8nJeqt0C/3FIzEjBsrAfAQCelWpgUrdBsDK3gEIQsPlMKA5fPWeQfr3q3njXBQmxeYgITZE6lFfOjUtXEb5jPwoKCuBarTL6jnoLllZWOm0unzqPU4fCIQgCzMzN0XPoAFRxd8O2Fd8hKT5B2y454Qlq1K2Ft6aONXQ3jAKv+nlFlbOwxjvte2PVsV34aPdqJGakoH+LTqLbtPVoiIr2Tvg0ZC3mhXyLuq5uaFHDEwBgbW6JoW17YHCbbhAEg3fNaJSzsMLw197A6mO78cmeNUhMT4FfsXP84jZeHg3hau+EeXu/RdDe71CnohuaVy86x0Pa+uDUzSuYv+97bDx1EGO8+0FhoifbwbocZvd+B3N3rcJb33yEuOREjOvc/6XaveXlg8bVauvsW+AfiO9P7MOIb+dh2tblmNh1IKo6uui1T6+68pXMMGRGZdRtVU7qUF5JmWnpCPl2MwZNHIVJiz6Co3MFhG/fp9Mm8eFjHN4WgmHTxmNc0Ifo0McH25Z/CwAYNHEUxgV9iHFBH6LPiCGwtLbCGwEDpeiKUdBoNHp5SMkkEpUGVdzxZ+JDxKclAwCOR19EG48GotsoBAXMVWYwUyihUiqhVCqRX1gAAGjlXg8pWRnY8Y8Ki6mpX6UmYhIfIj696Pz9cv13tKlZX3QbhSDAQmUOlUIJM6USSoUCBX+dY4WggLWFJQDA0swc+YWFhuqW0Wnl3gBRD/9EbHI8AGDPxePo1qBNqds1c6uDNh4Nsff3X7T7zJUqfH/yAC78GQUASEhPRkpWBpztHPXZpVde8y72uHwiDdfPZ0gdyivp9tVoVK7phvIVixLiVp3b48qZCzofjkqVCn1HDoGtgz0AoLK7GzJS01BQUKBtU1BQgD1rN6HnW/6wL8/fWTkRNfSTkpKCa9euoV27dli9ejUiIyMxbdo0uLm56Tu+MuFoY4fkzDTtdnJmGqzNLWFpZq4dmiipzalbV9CihicWD5oIpUKByAd3ceX+LQBFH7YA0K5WIwP2yPg42tgiKUv3/FkVO8cvbnP61h9oUaMeFg2cAKVCgWsP7uJKbNE5/vFsGN73eQtd67eCraUNvv0lRJblTTFc7Ry1yTQAJKQlo5ylNazNLXWGdUpqZ2VugcndB+ODrV+hb7MO2jZ5hQX46fKv2u0+zV6HtbkFIh/c0XOvXm0/b0oEALg3tJY4kldTalIy7J3+TizsnByQm52D3Jwc7fCPo3N5ODqXB1BUMQjbsht1mzWCSvX3R9jFX87A1sEO9Vo2MWwHjIzU1Q99EFVR+eCDDxAVFYXTp08jNDQUnTt3xpw5c/QdW5lRCMJzFxV+9sOupDa+TV9Hek4WPtj6FWZsWwkbCyt0a9Bab/G+ihQQ8LwTqHOOS2jTu0l7pOdkYfq25Zi5/WvYWFiha4PWUCmVGN2xH9b/+hM+3PE1loRuwtvtesLR2laPvTFegqB47h8itUYtqp0gCPi032gs/3k7nmSkvvB9hrbtgZEd+mDm9pXIK8j/74ETvUDR72nxoVyFovjHU15uLrZ//T2S4hPRZ+QQnWNnw46hQ58e+gqTJCSqopKamopRo0YhKCgIfn5+6NevHzZu3Kjv2MrMk4xUuDtX1m47WNsiMzdb5w9wSW2aV6+LH88eRqFajWx1Ls78VWH5OTLCoP0wZkmZaajxL+e4pDbNqtfF1nNF57hQnYszt/9A8+qeuPnoHsxVZvjjr+rK3YQ4xKUkwN25MpJjrhuugxIa1aEP2tdpCgCwMbfE7YQH2mMVbB2Qlp1ZbNLy47QnqF/FvVi7GhUqobKjMyZ2KxrDd7Kx0w5tLvppI8yUKszxHYEaFSohcP1CPEp9ov8Okkmzd3JC7O0Y7XZ6ciqsbKxhbmGh0y7lSRK2LFsN58oV8c6HE2Fmbq499jDmPtRqNWp41jJY3MZKDfW/N3rFiKqoqNVqXL16FeHh4ejUqROioqJQ+ArNE7gWdxc1navA5a+x9o6ezXHp3g3Rbe49eYSW7vUAAEpBgSZudXAnIc6APTB+2vNnW3T+OtRthsv3bopuc+/JI7SsUXSOFYICTarVxt2EB4hPT4aVuQVqOlcBUPSBW8m+Au4lPTZU1yT33V+TW0d8Ow9j1gejQeWa2gmu/Zp3xMkbl4o9J+LOtee2i3xwB/4rZmpfb+/FX3A06jwW/VT0xePjvqNgbWGJwA1MUsgwPBp5Ivb2n3jyqGg+1fmjv6JuM92h9NzsHKwPXo56LZrgzfEjdJIUAPgz+hbc69WBYKKT7J8lx8m0oioq06dPx+LFizFixAhUq1YNAwcOxKxZs/QdW5lJz8nCul8PILBTf6gUSiSkJ+O7E/tRvXxFDH/tDczb990L2wDAtohwvOXVHfP8xkKjUSPq4Z8I/eOMxL0yLuk5Wdjw608Y08nvr/OXgnUni87xsNd6Yf6+71/YBgB2nD+CwW264zO/MVCr1Yh+GIOwq2dRqFZj1dFdGNSmG8yUShSq1dh05hAS01Ok7bBEUrLS8fmBdZjvHwiVUoUHyQmYv+87AEDdStXx4RvDMeLbeSW2e5EGVWqiU72WuPfkEVYN/1C7f9XRXYi4E6nXfpHpKmdni37vvo1tK79DYUEhnFwqwG/MMDy4ew/7vt+CcUEf4lz4CaQkJiH64hVEX7yife7wmRNhXc4GTx4nwKGCk4S9IH0SNCJSpW+//RZ9+/aFs7Pzf3qz0es+/0/Pp39nqpftGhInl+pf75szpQ5B9moE8vfYEAZ7dTfo+/1w+qBeXndYu156eV0xRA395OTkYNiwYRgzZgwOHTqE/HxOriMiIiL9E5WoTJgwAaGhoRgzZgzOnTuHvn37Yt68eYiKitJ3fERERCSSHOeoiF7wLSsrC7Gxsbh//z4UCgXs7e2xYMECLF26VJ/xERERkUhyTFRETaadNm0azpw5g44dO2LcuHFo2bIlACAvLw/t27fHBx98oNcgiYiIyDSJSlS8vLwwb948WFvrrrxobm6On376SS+BERERUenIcdVuUYlKp06dsH37dmRmZkKj0UCtViM2NhaLFy/+z1cCEREREb2IqDkqU6dORVRUFPbt24fs7GyEhYU9d3ljIiIiko4c56iIyjbi4+OxaNEidO7cGd27d8emTZtw7do1fcdGREREJk5UomJvX3RrbXd3d0RHR8PRkbfQJiIiMjZqjUYvDymJnkw7adIkzJw5EyNHjkRkZCQsLS31HRsRERGVguZ5t6h/xYlKVKZOnYp79+6hSpUq+L//+z+cP38e7733nr5jIyIiIhNXYqISEhKis33x4kUAgIODA86cOYN+/frpKy4iIiIqJaknvupDiYnKuXPnAAD37t1DTEwMOnbsCKVSiV9//RW1atViokJERER6VWKiEhwcDAAYNmwY9u3bByenottop6amcuiHiIjIyEg98VUfRM1RiY+Ph4ODg3bbysoKCQkJ+oqJiIiIXoJGo5Y6hDInKlHx9vbGiBEj0L17d2g0Ghw6dAg9e/bUd2xERERk4kQlKrNmzUJYWBgiIiIgCAJGjhyJLl26AAASEhK4jD4REZERMNmhHwDw8fGBj49Psf1jxozBnj17yjQoIiIiIqAUicqLyPFSKCIioleRHD+T/3OiIghCWcRBRERE/5Ech354C2QiIiIyWkxUiIiIZEKj0ejlURr79+9Hr1690L17d2zevLnY8fDwcPTt2xd9+vTB+PHjkZqaWuLr/edERY7jYURERFR6jx8/xrJly7BlyxaEhIRg27ZtuHXrlvZ4RkYGPv30U6xZswb79u1D3bp1sWLFihJfU3SiEhsbi+PHj6OwsBD379/X7p87d+5LdIWIiIjKmlqj1stDrNOnT8PLywsODg6wtraGj48PQkNDtcfz8/PxySefwNXVFQBQt25dPHz4sMTXFDWZ9uDBg1i1ahWys7Oxbds2DB48GDNmzEDfvn3RsmVL0R0gIiIi/dHXKEdaWhrS0tKK7bezs4OdnZ12Oz4+XmdtNRcXF1y5ckW77ejoiG7dugEAcnJysGbNGgwbNqzE9xZVUVm7di1+/PFHlCtXDuXLl8eePXuwZs0aMU8lIiKiV9yGDRvQpUuXYo8NGzbotFOr1TpXA2s0mudeHZyeno4xY8bA09MTfn5+Jb63qIqKQqFAuXLltNsuLi5QKDgPl4iIyJiooZ+KyvDhw5+bUDxbTQGAihUr4sKFC9rthIQEuLi46LSJj4/HqFGj4OXlhdmzZ//re4tKVGrXro1NmzahoKAAUVFR2LJlCzw9PcU8lYiIiF5x/xzieZF27dphxYoVSEpKgpWVFQ4fPoygoCDt8cLCQgQGBqJnz54YP368qPcWlah8/PHHWLVqFSwsLDB79mx4eXlh5syZot6AiIiIDEPqK3FdXV0xdepUBAQEID8/HwMGDEDjxo0xevRoTJo0CY8ePcK1a9dQWFiIsLAwAEDDhg2xYMGCF76mqERlx44deOedd/DBBx+UTU+IiIhIlnx9feHr66uzb+3atQCARo0aITo6ulSvJypRefToEd58803UrFkTffr0Qbdu3WBlZVWqNyIiIiL9krqiog+iZsTOnDkTR48eRWBgIC5duoR+/fphxowZ+o6NiIiISkGt0ejlISXRl+5oNBrk5+cjPz8fgiDAzMxMn3ERERERiRv6mT9/Pn7++WfUq1cPffr0wdy5c2FhYaHv2IiIiKgU5Dj0IypRqV69Ovbs2QMnJyd9x0NERESkVWKism3bNgwaNAipqanYsmVLseMTJkzQW2BERERUOlLPJ9GHEhMVOZaQiIiI5EqOn9slJiqDBw8GAFSpUqXY0rmbN2/WX1RERERE+JdEZf369cjIyMDWrVvx4MED7f7CwkLs378fb7/9dqneTI6ZnrHh1Vj617Cqh9QhyF6NLnekDkH2/vymptQhmAYvw76dHD9nS7w8uUaNGs/db25ujoULF+ojHiIiIiKtEisq3t7e8Pb2Rs+ePeHhofstMicnR6+BERERUeno6+7JUhJ1eXJMTAymTJmC7OxsaDQaqNVqZGdn4+zZs/qOj4iIiESS49CPqEQlODgYQUFBWLduHQIDAxEeHo7s7Gx9x0ZEREQmTlSiYmtrCy8vL1y8eBHp6emYPn06evXqpe/YiIiIqBQ0GrXUIZQ5Uff6sbS0xN27d+Hh4YGIiAjk5eUhPz9f37ERERGRiROVqEyZMgVffvklOnXqhDNnzuC1115Dly5d9B0bERERlYIc754sauindevWaN26NQBg165dSE1Nhb29vV4DIyIiIioxURk2bBgEQXjh8Y0bN5Z5QERERPRyTO6qn4kTJxoqDiIiIvqPpB6m0YcSE5Wnwz3nz583SDBEREREzxI1R2X58uXanwsKCnD9+nW0bNkSrVq10ltgREREVDomN/Tz1A8//KCzff/+fQQHB+slICIiIqKnRCUq/1StWjXcucO7mxIRERkTjane62fWrFk627dv30adOnX0EhARERG9HJObTPvU00m1ACAIAnr06IG2bdvqLSgiIiIiQGSi4ufnh4yMDKSlpWn3JSYmonLlynoLjIiIiErHZCfTLlq0CNu3b4eDgwOAohMhCAKOHDmiz9iIiIjIxIlKVI4cOYITJ07AxsZG3/EQERHRSzLZOSp169ZFXl4eExUiIiIjZrJDP3379kX37t1Rp04dKJVK7X7e64eIiIj0SVSismzZMsyZM4eTZ4mIiIyYyVZUbG1t0a9fPz2HQkRERKRLVKJSv359TJw4ER06dICZmZl2P5MXIiIi46HWqKUOocyJSlSys7NRrlw5XLx4UWc/ExUiIiLSJ1GJSnBwMPLz83H37l0UFhaidu3aUKle6jZBREREpCcmO0fl6tWrmDRpEhwcHKBWq5GYmIivv/4aTZo00Xd8REREJJLaVG9KOH/+fCxbtkybmFy6dAlBQUHYuXOnXoMjIiIi0yYqUcnKytKpnjRt2hS5ubl6C4qIiIhKT45DPwoxjezt7REeHq7dDg8P1973h4iIiEhfRFVUgoKCMHbsWMyZM0e7b+vWrXoLioiIiErPZCsqJ06cgJWVFY4dO4YNGzbAyckJERER+o6NiIiISkGt0ejlISVRicr27dvx448/wtraGp6enti9ezc2bdqk79iIiIjIxIka+snPz9dZkfbZn4mIiMg4yHHoR1Si0rVrVwwfPhw9e/aEIAgICwtDly5d9B0bERERmThRicr06dMRGhqK8+fPQ6VSISAgAF27dtV3bERERFQKUs8n0QfR6+D36NEDPXr00GcsRERE9B/IcehH1GRaIiIiIinwzoJEREQyoTHVe/3IQaOqteDf0hsqpQqxSfFY/+sB5OTniWoT2Kk/XOyctO0q2NrjxqN7WBm+AzbmlhjS1geVHSrATKnCT5dP4eztq4buntFrUKUm+jTtAJVSiQfJCdhyNrTY+X9qWNueiEtJxJGo8waO8tXQsIoH+jXvqD2XP5w+WOxcvqiNIAgY3KYb6ri6AQCuPriNXReOAQDqVHTDgJadoRAUyMzNxvbzR/AgOd7g/TM2Ny5dRfiO/SgoKIBrtcroO+otWFpZ6bS5fOo8Th0KhyAIMDM3R8+hA1DF3Q3bVnyHpPgEbbvkhCeoUbcW3po61tDdkIU33nVBQmweIkJTpA6FDMgkEpVyltYY8XpvLPxpA+LTkuHfshP8W3bG5jOhotp8c2y3tl2NCpUwrrO/9rkjOvjiYUoivv1lLxytbfGp32hcfxiD5Kx0g/fTWJWzsMLQtj3wf2FbkJCegr7NOqBP0w7Yfj5cp52rnRMGtu6KGhUqIS4lUaJojVs5CysEvNYLSw5tQnx6Mvyae8OvuTd+PHdYVBuvmg3halce8/Z9BwECZvQahubV6+Ja3J8Y690fa47vwfVHMXC1c8K4zv6Yv+97FKgLJeyxtDLT0hHy7WaMmjsV5Su64PC2vQjfvg+9hw/Stkl8+BiHt4UgcN4M2DrY48blSGxb/i3eXzYPgyaO0rZ7cCcG21Z+hzcCBkrRlVda+Upm6D7MGZU8LJEQmyR1OEZNjpNpTWKOSoPK7vgz8SHi05IBAMejL6KNR4NSt1EqFBj5ui+2nvsZyZnpsDG3RP3K7tj/+0kAQHJWOj7fvx6ZudkG6NWrw7NSDcQ8eYSE9BQAwMkbl9DKvX6xdh3qNsOZW3/g95gbBo7w1VG/sjtinjxEfHrR7+mJ67+jdc36otsIggALlRlUCiXMlEqoFErkFxbCxc4ROfm5uP4oBgDwOC0JOfl5qOlcxYC9Mz63r0ajck03lK/oAgBo1bk9rpy5oDNhUalSoe/IIbB1sAcAVHZ3Q0ZqGgoKCrRtCgoKsGftJvR8yx/25R0N2wkZaN7FHpdPpOH6+QypQyEJiKqoXLlyBb/99hvefvttBAYG4tq1a1i8eDE6dOig7/jKhFM5OyRlpmm3kzPTYG1uCUszc23JXEyb1+s0RUp2Bn6PuQ4AcLFzQmp2Bro1bINGVT2gUqoQ9sdZPE5jxv8sRxtbpGT+XWFKyUqHlbmFzrkFgB3njwAoSmzo+Rxt7JD8zLlMzkqD1T9+T0tqc+b2H2hRwxML35wApULAtbg/8UfsLViamcNcZYZ6lWsgKu5PVC9fEZUdKsDO2sbgfTQmqUnJsHf6O7Gwc3JAbnYOcnNytMM/js7l4ehcHkDRFRdhW3ajbrNGUKn+/vN68ZczsHWwQ72WTUCl9/Omogqre0NriSMxfhqNWuoQypyoisr8+fNRu3ZthIWFwdLSEnv27MFXX32l79jKjAABeE457NkSmZg2XRu0xk+XftVuKxUKONsWfRNd+NNGrDm2B4PadEP18hXLuAevNgHCc6d3qdXyK1HqmyAIz50sp/O7XEKb3k3aIz0nCzO2L8eHO/4HG3NLdK3fGjn5efjm6C70bNQOc31HwsujEaIfxqCwUH5/9EqjqHIiFNuvUBT/05mXm4vtX3+PpPhE9Bk5ROfY2bBj6NCHyzuQ/mk0Gr08pCQqUVGr1Wjfvj2OHz+O7t27o1KlSigsfHXGrZMy02BvbavddrC2RWZuNvIK8kW3qebkCqVCgeuP7mnbpPw1D+XXG1cAAPHpybj1+D7cnSvrtT+vmuSsdNhb/f3N3P7puS3ML+FZ9DxJmWlwsPr33+UXtWnqVgenb11BoVqNnPxcnL19FXUqukEAkFOQj/8L24L5+7/Htoif4WrnpB0+MlX2Tk5IT0nVbqcnp8LKxhrmFhY67VKeJOHboP+DQqHAOx9OhJXN39/8H8bch1qtRg3PWgaLm0hORCUqVlZW+P7773H27Fl06tQJGzduhI3Nq1MSjnxwBx4uleFiV1TC9fZsjkv/mAfxb23qVnRDdFyMznMSM1IRk/gQ7Wo3BgDYWdrAw6Uq/kx8qM/uvHKi4v5EjQqV4WzrAAB4vXYT/BF7S9qgXlFRcXfh7lwZLrZFv6cd6jbD5fs3Rbe5n/QYLWp4AgAUggKNq9XC3cQ4aABM7Pom3P6qBrao4Yn8wgKTv+rHo5EnYm//iSePis7D+aO/om6zRjptcrNzsD54Oeq1aII3x4+Ambm5zvE/o2/BvV4dCELxygxRWZPj3ZNFzVFZsmQJduzYgZUrV8Le3h6PHz/G0qVL9R1bmUnPycK6kwcwrrM/VAol4tOS8f2JfahevhKGt38D8/Z++8I2T7nYOyExI6XYa399ZCfebtsD3p7NoRAE7L90konKP2TkZmHTmUMY1aEvVAolEtNTsPH0Qbg5ueItrx5YeHCD1CG+MtJzsrDx1E8Y4+0HpUKBhPQUrP/1ANzKV8Swdj2xYP+6F7YBgB3nwzG4TXd82m801Bo1oh/G4PDVswCA707sw9C2PaFSKpCalYlvju2SsqtGoZydLfq9+za2rfwOhQWFcHKpAL8xw/Dg7j3s+34LxgV9iHPhJ5CSmIToi1cQffGK9rnDZ06EdTkbPHmcAIcKTiW8CxGVRNCIHHy6cOECbt68CX9/f1y+fBmtWrUq9Zu9+/2CUj+HSsfyH9/mqOwVvELDnq8q77rNpQ5B9v78pqbUIZiED9cbdshv6OqP9fK6m8bO08vriiFq6GfDhg346quvsH79emRmZuLjjz/Gd999p+/YiIiIqBTkOPQjKlHZs2cPvvvuO1hZWcHR0RE7d+7Erl0sCxMREZF+iZqjolAoYP7MkIKFhQWUSqXegiIiIqLSk/pSYn0Qlai0bt0aixYtQnZ2NsLDw7Ft2zZ4eXnpOzYiIiIycaKGfmbMmIHq1aujbt26CAkJQceOHTFz5kx9x0ZERESloNHT/6RUYkUlISEBzs7OePToETp06KCzZH58fDwqV+bCZkRERMZC6omv+lBiojJ37lysXr0aQ4cO1VmsSKPRQBAEHDlyRO8BEhERkekqMVFZvXo1AGD37t1wcHDQORYbG6u3oIiIiKj05DiZtsQ5Kg8fPkRcXByGDh2q/TkuLg7379/Hu+++a6gYiYiIyESVWFFZvnw5zp07h/j4eLz99tt/P0mlgre3t75jIyIiolIwuTkqwcHBAIA1a9ZgzJgxBgmIiIiI6KkSE5Vt27Zh0KBByMvLw8qVK4sdnzBhgt4CIyIiotKR4xyVEhMVOXaYiIhIrjQatdQhlLkSE5XBgwcDYOWEiIiIpFFiouLp6amzfspTT9dRiYqK0ltgREREVDomN5k2OjraUHEQERERFSNqMu3zJtICHBIiIiIyJlLfl0cfSlzwjZNpiYiIXh1qjUYvj9LYv38/evXqhe7du2Pz5s3FjkdFRaF///7w8fHBnDlzUFBQUOLrlZioPJ1MGxgYiHr16mHChAl46623ULFiRbz33nulCpyIiIjk7fHjx1i2bBm2bNmCkJAQbNu2Dbdu3dJpM336dHz88ccICwuDRqPB9u3bS3zNEhOVpz766CMcPnxYu33u3Dl88sknL9EFIiIi0heNRqOXh1inT5+Gl5cXHBwcYG1tDR8fH4SGhmqPP3jwADk5OWjatCkAoH///jrHn6fEOSpPXb16Ffv37wcAODk54YsvvoCvr6/owImIiOjVlZaWhrS0tGL77ezsYGdnp92Oj4+Hs7OzdtvFxQVXrlx54XFnZ2c8fvy4xPcWlaio1WrEx8fDxcUFAPDkyRMoFKKKMURERGQg+ppbumHDhheuUD9x4kTttlqt1lnW5OlyJmKPP4+oRCUwMBB+fn5o0aIFAODy5cuYPXu2mKcSERGRgehrHZXhw4fDz8+v2P5nqykAULFiRVy4cEG7nZCQoC1yPD2ekJCg3U5MTNQ5/jyiEhVfX1+0bt0aly5dgkqlwty5c//1hYmIiEge/jnE8yLt2rXDihUrkJSUBCsrKxw+fBhBQUHa41WqVIGFhQV+++03tGjRAnv37kWHDh1KfE1RiUpSUhIOHTqEzMxMaDQaREZGIjY2FosXLxbzdCIiIjIAqZcVcXV1xdSpUxEQEID8/HwMGDAAjRs3xujRozFp0iQ0atQIS5Yswdy5c5GRkYEGDRogICCgxNcUlahMmTIFlSpVwqVLl9C1a1ccP34cjRo1KpNOERERkXz4+voWu+Bm7dq12p89PT2xc+dO0a8nakZsfHw8Fi1ahM6dO6N79+7YtGkTrl27JvpNiIiISP+kvjxZH0QlKvb29gAAd3d3REdHw9HRUa9BEREREQEih368vLwwadIkzJw5EyNHjkRkZCQsLS31HRsRERGVgsndPfngwYPo1asXhg8fjoyMDFSpUgVLly7FhQsXuIQ+ERGRkTG5mxIuW7YMBQUFGDVqFNzc3AAADRs2xDvvvANXV1eDBEhERESmq8SKSsuWLdGoUSNoNBrUq1dPu//pSnJRUVF6D5CIiIjEUWvUUodQ5kqsqAQHByMqKgqdOnVCVFSU9hEdHa1NUp5dYY6IiIioLIm66mfVqlUvPDZmzJgyC4aIiIhenhwvTxZ11U9JpO4AERERFZHjZ/J/vgXyv931kIiIiOhl/eeKChERERkHOa6j8p8rKkRERET6wjkqREREMiHHz2RBI6JXcXFxuk8SBFhYWMDJyQkXLlxAy5Yt9RYgERERmS5RFZX33nsPN2/eRJ06daDRaHDz5k04OztDqVRi3rx5+o6RiIiITJSoOSqurq7YunUrdu/ejT179mDXrl1o2LAhfvjhByxdulTfMRIREZGJEpWoPHjwAA0bNtRu161bF/fu3UOlSpWgVstvuV4iIiIyDqKGfqpVq4YlS5agb9++UKvVOHDgAKpXr47ff/8dCgUvHCIiIiL9EDWZNiMjAytXrsTp06ehVCrRtm1bjB8/HkePHkXNmjV1qi1EREREZUVUokJEREQkBVFDP7t378aiRYuQlpYGoOg6bUEQtHdQJiIiItIHURWVrl274n//+x/q1KljiJiIiIiIAIi86sfFxYVJChERERmcqIrKggUL8PjxY7z22muwsLDQ7u/Xr58+YyMiIiITJ2qOSkZGBmxsbHDp0iWd/aaQqGRkZKBcuXJSh0FERGSSeNXPPxw7dgwXLlzA+PHjMWDAACQlJWHmzJno37+/1KHJRl5eHu7cuQNPT0/s378f165dw+jRo+Hk5CR1aLJz8+ZNpKam6tyorFWrVhJGJC8pKSm4du0a2rVrh9WrVyMyMhLTpk2Dm5ub1KHJxpUrV/Dbb7/h7bffRmBgIK5du4bFixejQ4cOUodGBlJiRWXs2LFYvXo1OnfuDEEQih0/cuSI3gKTysqVK7FgwQIcPHgQjRs3xscff4xhw4YxUSlD06dPR9WqVZGbm4sVK1agb9++mDVrFlavXi11aLLy2Wef4dixY6hWrZp2nyAI2Lhxo4RRycsHH3yAdu3aAQBCQ0MxfPhwzJkzBz/88IPEkcnH/PnzMWnSJISFhcHS0hJ79uzBhAkTmKiYkBITlaCgIAAwuX90np6eWLFiBfr06QMbGxvk5+dLHZKsxMbG4quvvsIXX3yBAQMGYMyYMfD395c6LNk5deoUQkNDYWlpKXUospWamopRo0YhKCgIfn5+6NevHxPBMqZWq9G+fXt88MEH6N69OypVqoTCwkKpwyIDKvGqHxcXF+3/37p1C+fPn9d5yFGFChUQFBSEq1ev4vXXX8fChQtRuXJlqcOSlcLCQiQlJSE8PBze3t5ISEhAbm6u1GHJTrVq1cCRXf1Sq9W4evUqwsPD0alTJ0RFRfFDtIxZWVnh+++/x9mzZ9GpUyds3LgRNjY2UodFBiRqMu3kyZORkJAADw8PnSEgOU6mXbp0KcLDwzF8+HBYW1ujWrVqmDBhgtRhycqoUaMwcOBAdO7cGXXq1IGPjw8mT54sdViyY29vjzfeeAPNmjWDubm5dn9wcLCEUcnL9OnTsXjxYowYMQLVqlXDwIEDMWvWLKnDkpUlS5Zgx44dWLlyJezt7fH48WMsXbpU6rDIgERNpu3RowdCQ0MNEY9R2LdvH27fvo3AwECEhYXJMiEzJoWFhVAqlVKHITt79ux57n4/Pz8DRyJf3377Lfr27QtnZ2epQ5G1Cxcu4ObNm/D398fly5c5IdzEiKqouLm5IS4uziSGQJYsWYJHjx4hMjISo0ePxq5duxAdHY0PP/xQ6tBk4/jx4/j666+RnJysMzQhx8nZUvLz88ONGzcQERGBgoICtGnTBvXq1ZM6LFnJycnBsGHD4ObmBj8/P3Tt2hVmZmZShyUrGzZsQHh4OOLj49GjRw98/PHHGDBgAEaNGiV1aGQgJVZUhg0bBkEQkJSUhIcPH8LT0xNKpVJ7rx85Thrr168f9uzZAz8/P4SEhKCgoAB9+vTBwYMHpQ5NNrp164Y5c+agVq1aOkOJVapUkTAq+QkJCcHKlSvRtWtXqNVqHDlyBOPGjcOAAQOkDk12Lly4gAMHDiAiIgJeXl548803mRSWkX79+mH79u0YOHAgQkJCkJmZiTfffJN/k01IiRWViRMnGioOo6FQFM0vfvoBmpeXp91HZcPW1hbe3t5ShyF769atw44dO+Do6AgACAwMREBAABOVMpaVlYXY2Fjcv38fCoUC9vb2WLBgAZo1a4YPPvhA6vBeeQqFQmeOlYWFBYeKTUyJiUrr1q0BvHiBLjnq0aMHpkyZgtTUVKxfvx579+5F7969pQ5LFp5eKVarVi3Mnz8fXbp0gUr1968gx53Lllqt1iYpAODk5PTc9ZDo5U2bNg1nzpxBx44dMW7cOLRs2RJA0d/Mp5fU0n/TunVrLFq0CNnZ2QgPD8e2bdvg5eUldVhkQKIm006ePBlVq1aFj48Ppk2bhr59++LKlSuyXaDr5MmTOH36NNRqNdq2bctv/2Vk2LBhLzwm16FEKU2bNg2Ojo7aCsrOnTuRkpKCL774QuLI5GPnzp3o1asXrK2tix1LSEjgJNsyoFarsX37du3fZC8vLwwePFjnSw7Jm6hExd/fH7t27cIXX3wBe3t77QJdu3btMkSMBvVs9Wjfvn2Iiori8u5l7ObNm6hdu7bOvkuXLqFp06bSBCRTOTk5WL58Oc6dOweNRoM2bdrgvffe472rytCTJ0+wf/9+ZGZmQqPRQK1WIzY2FosXL5Y6tFfe00QvLi7uucdN4eIOKiIqJX12ga4VK1bIeoGup8u75+Xl4euvv0afPn24vHsZ+e2336BWqzF37lwsWLBAe8VPQUEBPv30U4SFhUkcobxYWlpixowZUocha1OnTkWlSpVw6dIldO3aFcePH0ejRo2kDksW5s6di9WrV2Po0KE6Q5ZPL+bgVYKmQ1SiYkoLdD27vLu/vz+Xdy9Dp0+fRkREBOLj4/HVV19p96tUKgwaNEjCyOTFz88Pe/bsgaen53P/wEdFRUkYnbzEx8dj48aNWLRoEbp37453330Xw4cPlzosWXj65XD37t1wcHDQORYbGytBRCQVUYmKr68vfH19tdsHDx6U7f1vTKl6ZGhPryILCQnhInp69HSht+jo6GLH8vLyDB2OrNnb2wMA3N3dER0djSZNmkgckXw8fPgQGo0GY8aMwdq1a7UV2MLCQowePdqkFiE1daISlaNHj+LLL79EVlaWdhw2OzsbZ8+e1Xd8BmdK1SOpNG3aFPPnz9f5fYqNjcXmzZulDk1WBg0ahG3btmm31Wo1/P39sX//fgmjkhcvLy9MmjQJM2fOxMiRIxEZGcmbQJaRp/Or4uPj8fbbb2v3q1QqXuBgYkRNpu3WrRuCgoKwbt06BAYGIjw8HNnZ2fj4448NEaOkCgsLkZ+fzz8+Zah///7w9vbGsWPH4Ofnh59//hkeHh749NNPpQ5NFgICAhAREVFsv0qlQufOnbF8+XIJopKve/fuwc3NDZGRkTh//jx69uwJV1dXqcOSjTVr1mDMmDFSh0ESElVRsbW1hZeXFy5evIj09HRMnz4dvXr10ndskjCl6pFU8vPzMWnSJBQUFKB+/foYOHAg5wGVoaeXec+fPx9z586VOBp5CgkJ0dm+ePEiAMDBwQFnzpzh0GYZ2LZtGwYNGoS8vDysXLmy2HHeLNZ0iEpULC0tcffuXXh4eGiXiJbrHJXg4ODnVo+o7FhZWSEvLw81atRAZGSkdpEsKlvTp0/Hzz//jMzMTABF1cHY2FgOZZaBc+fOASiqpsTExKBjx45QKpX49ddfUatWLSYqZUBEsZ9MhKhEZerUqfjyyy/xxRdfYM2aNdi2bZtsl+E2peqRVPr06YPAwEAsWbIEgwYNwsmTJ1kq14MPPvgAqampuHfvHlq2bIlz586hefPmUoclC8HBwQCKFjHct2+fdp2l1NRUvPfee1KGJhuDBw8GwMoJiUxUrKystJeT7tq1C6mpqdrZ7nJjStUjqQwdOhT9+vVDuXLl8MMPP+CPP/7Aa6+9JnVYsnP9+nUcPnwYCxYsgL+/P6ZMmYIpU6ZIHZasxMfH61w6a2VlhYSEBOkCkpF/Xl7/FC+zNz2iEpVly5YhJiYGXl5e8Pb2lvWHytSpUxEUFIRvvvkGa9euxaZNmzBkyBCpw5KVvLw8bNq0CXfu3MHHH3+M69evo2PHjlKHJTvly5eHIAhwd3fH9evX0a9fPybdZczb2xsjRoxA9+7dodFocOjQIfTs2VPqsGTheZfXk2kSlah89913yM3NxdmzZ3Hy5EkEBwejZs2aWLt2rb7jM7ioqCgkJSXB3NwcX375JUaNGoXq1atLHZaszJs3D05OTrh27RqUSiXu3buH2bNnY8mSJVKHJiu1a9dGUFAQhgwZgmnTpiE+Pp7j/mVs1qxZCAsLQ0REBARBwMiRI9GlSxcAvNfPf/V0Mu3zJtICHBIyJaISlaSkJERERCAiIgIXLlyAvb09atWqpe/YJLF9+3bs2LEDAFC1alWEhIRg4MCB2vFS+u8iIyOxZ88enDhxAlZWVli0aJHOgoJUNj799FP8/vvvqFWrFiZNmoTTp09j6dKlUoclOz4+PvDx8Sm2f8yYMdrF96j0mFTTU6ISlXbt2qFChQoICAjADz/8INv5KUDRpbNmZmba7Wd/prIhCALy8vK048/JycnPHYuml3P+/Pli27a2tvDx8UFqaqpEUZkeftD+N0+/HAYGBuKXX35Bly5dkJSUhKNHj3I5AxMjKlE5dOgQzp49i3PnziEgIAC1atVCmzZtMHDgQH3HZ3Bdu3bF8OHD0bNnTwiCgLCwMG0pl8pGQEAARowYgYSEBCxYsADh4eG8UqIMlbSgmyAI2nVWSL+YfJeNjz76CGq1Wvt3+Ny5c7hy5QrmzZsncWRkKKJWpn0qOjoap0+fxtatWwEAhw8f1ltgUgoNDcX58+ehUqnQqlUrdO3aVeqQZOHZRbKeLqhXWFgIALCzs+PaEyQrT28OSf+Nr69vsds+PG8fyZfodVQuXrwId3d3eHt745tvvkHNmjX1HZtkevTogR49ekgdhuw8u0jWvXv30LFjRygUCi6SpSfDhg177rd6VlToVaJWqxEfHw8XFxcAwJMnT6BQKCSOigxJVKLSs2dPBAUFoVy5cvqOh2Ts2UWy9u7dy0Wy9Ozp3aoBoKCgAEeOHIGdnZ2EEZkWzlEpG4GBgfDz80OLFi0AAJcvX8bs2bMljooMqcShnxd9I3uK38zoZfj4+ODQoUPab0V5eXnw9fVFWFiYxJHJ35tvvqm9qo3KRmxsLG7duoXXX38dcXFxqFatGgDgwoULvD1EGXn8+DEuXboElUqFRo0aaasrZBpKrKg8/Ua2fft2WFpaol+/flCpVDhw4AByc3MNEiDJDxfJMoy4uDjtzxqNBrdu3UJKSop0AcnQwYMHsWrVKmRnZ2Pbtm0YPHgwZsyYgb59+zJJKSNJSUk4dOgQMjMzodFoEBkZidjYWCxevFjq0MhARE2m9ff3x65du3T29e/fH7t379ZbYCRvzy6S1bZtW15ZpQedO3fW/iwIApycnDBhwgSuAlyG/Pz88MMPP2Do0KEICQlBfHw8RowYgZ9++knq0GQjICAAlSpVwqVLl9C1a1ccP34cjRo1wsKFC6UOjQxE1ByV3Nxc3L17F+7u7gCK7iFSUFCg18BI3l60SBaVnaNHj0odguwpFAqduXsuLi6c6FnG4uPjsXHjRixatAjdu3fHu+++i+HDh0sdFhmQqETlww8/xLBhw+Dq6gqNRoMnT55whUsiIxcXF4f58+fj7NmzUKlU6NChA2bPnq2dxEz/Xe3atbFp0yYUFBQgKioKW7Zsgaenp9RhycrTBUbd3d0RHR2NJk2aSBwRGZrodVTy8vJw48YNCIKAunXrQqUSleMQkUTeeust9OrVC/369YNarcbu3btx6tQpWd6jSypZWVlYtWoVTp8+DbVaDS8vL7z33nu8QrIMLVu2DHfv3sXMmTMxcuRItGnTBtHR0di+fbvUoZGBiEpUHjx4gE2bNiE1NVXnkrunl5sSkfHp06cP9u3b96/76OVt2LABvXv3Rvny5aUORXYOHjyIXr16ISkpCRkZGXBzc8PVq1dx4cIF9OzZE66urlKHSAYiajB1ypQpAICWLVuidevW2gcRGa9mzZph79692u3jx4+jfv36EkYkP48ePcKbb76Jd999F/v27UN2drbUIcnGsmXLUFBQgFGjRsHNzQ0A0LBhQ7zzzjtMUkyMqIoKl4ImevW0a9cOSUlJsLCwgEKh0PkQFQQBUVFREkYnLxcuXMDBgwdx6tQpNGnShJfOloFZs2YhJCQEGo1GZz2vp9v8/TUdoiaatGjRAkePHkX79u1hbm6u75iIqAycPn1a6hBMgkajQX5+PvLz8yEIAu+4XkaCg4MRHByMcePGYdWqVc9tk5CQAGdnZwNHRoYmqqLSvn17JCYm6j6RGS2RUcvOzsbKlStx5swZFBYWwsvLC5MnT4a1tbXUocnG/Pnz8fPPP6NevXro06cPunTpAgsLC6nDMhms9psGURWVX3/9Vd9xEFEZmzdvHqysrPD5558DKFph+pNPPsEXX3whcWTyUb16dezZs4eXfEuE91MyDaISlaSkJOzbt0+7hLFareYSxkRGLjIyUucKn48//hi9evWSMCL52LZtGwYNGoTU1FRs2bKl2PEJEyZIEJXpKeledCQfoq/6iYqK0s5qDwsL4+qLREZOo9EgLS1Nu52WlgalUilhRPLBb/JEhiOqosIljIlePe+88w7efPNNdO7cGRqNBkePHsWYMWOkDksWBg8eDACoUqUK/Pz8dI5t3rxZipCIZEtUovK8JYz5jYLIuPn6+uLhw4dYtWoVNBoNZs2aBX9/f6nDkoX169cjIyMDW7duxYMHD7T7CwsLsX//frz99tsSRmc6+DlkGkQlKl5eXpg0aZJ2CePIyEheOUBk5D766CPk5uZixYoVUKvV2Lt3L+7du4c5c+ZIHdorr0aNGrh69Wqx/ebm5ryrbxmLi4vT2RYEARYWFnBycsLcuXMliooMSdTlyXl5edi6dSvs7OyQnJwMQRCQm5uLsWPHGiJGInoJPXr0QGhoqHZbrVajd+/eOHjwoIRRycvt27fh4eGhsy8nJweWlpYSRSQ/fn5+uHnzJurUqQONRoObN2/C2dkZSqUS8+bNQ7t27aQOkfRMVEVlypQpSEhIgIeHB2dZE70iqlatipiYGFSvXh0AkJiYyKXHy1hMTAymTJmC7Oxs7RWR2dnZOHv2rNShyYarqyuCgoLQsGFDAMD169excuVKzJ49GxMmTGCiYgJEJSp37tzR+WZGRMavoKAAffv2RcuWLaFSqfDbb7/B2dkZAQEBAICNGzdKHOGrLzg4GEFBQVi3bh0CAwMRHh7O+/2UsQcPHmiTFACoW7cu7t27h0qVKkGtVksYGRmKqETFzc0NcXFxqFy5sr7jIaIyMn78eJ3tkSNHShSJfNna2sLLywsXL15Eeno6pk+fzrVqyli1atWwZMkS9O3bF2q1GgcOHED16tXx+++/c5kME1FiojJs2DAIgoCkpCT4+vrC09NTZx0GfiMjMl68w7n+WVpa4u7du/Dw8EBERAS8vLyQn58vdViysnjxYqxcuRIffPABlEol2rZti88//xxHjx7FZ599JnV4ZAAlTqaNiIgo8cn8Q0hEpiwiIgKbN2/GF198gSFDhuDevXvw9/fHhx9+KHVoRLIh6qofIiL6d6mpqdp1p6hs7N69G4sWLdKusqzRaHhTXBPDRIWIqJSeDou/CIfFy07Xrl3xv//9D3Xq1JE6FJKIqMm0RET0t4kTJ0odgslwcXFhkmLiWFEhInpJ58+ff+7+Vq1aGTgS+VqwYAEeP36M1157DRYWFtr9/fr1ky4oMihWVIiIXtLy5cu1PxcUFOD69eto2bIlE5UylJGRARsbG1y6dElnPxMV08GKChFRGbl//z6Cg4Pxv//9T+pQiGSDFRUiojJSrVo13LlzR+owZGHs2LFYvXo1Onfu/NyJy0eOHJEgKpICKypERC9p1qxZOtu3b99GxYoVdYaE6OXEx8fDxcUFDx48eO7xKlWqGDgikgoTFSKil7Rnzx7tz4IgwNHREW3btoW5ubmEUclLfn4+Tp8+jeTkZJ39nKNiOpioEBH9BxkZGdrFyJ7ifdHKzvjx45GQkAAPDw+dIaDg4GAJoyJD4hwVIqKXtGjRImzfvh0ODg4A/l41lfMnys6dO3cQGhoqdRgkISYqREQv6ciRIzhx4gRsbGykDkW23NzcEBcXxyqVCWOiQkT0kurWrYu8vDwmKnrw9DYFSUlJ8PX1haenJ5RKpbZqxdsUmA4mKkREL6lv377o3r076tSpA6VSqd3PD9H/jrcpoKc4mZaI6CW98cYbGD16dLFhidatW0sUkfzk5eXhzp078PT0xP79+3Ht2jWMHj0aTk5OUodGBsKKChHRS7K1teVlsno2ffp0VK1aFXl5eVixYgX69u2LWbNmYfXq1VKHRgbCRIWI6CXVr18fEydORIcOHWBmZqbdz+Sl7MTGxuKrr77CF198gQEDBmDMmDHw9/eXOiwyICYqREQvKTs7G+XKlcPFixd19jNRKTuFhYVISkpCeHg4VqxYgYSEBOTm5kodFhkQExUiopcUHByM/Px83L17F4WFhahduzZUKv5ZLUujRo3CwIED0blzZ9SpUwc+Pj6YPHmy1GGRAXEyLRHRS7p69SomTZoEBwcHqNVqJCYm4uuvv0aTJk2kDk22CgsLkZ+fD0tLS6lDIQNhokJE9JIGDx6MWbNmaROTS5cuYf78+di5c6fEkcnH0aNH8eWXXyIrKwsajQZqtRrZ2dk4e/as1KGRgbBGSUT0krKysnSqJ02bNuX8iTIWHByMoKAgrFu3DoGBgQgPD0d2drbUYZEBKaQOgIjoVWVvb4/w8HDtdnh4uPa+P1Q2bG1t4eXlhSZNmiA9PR3Tp09nNcXEsKJCRPSSgoKCMHbsWMyZM0e7b+vWrRJGJD+Wlpa4e/cuPDw8EBERAS8vL+Tn50sdFhkQKypERC/pxIkTsLKywrFjx7BhwwY4OTkhIiJC6rBkZerUqfjyyy/RqVMnnDlzBq+99hq6du0qdVhkQJxMS0T0knr37o0dO3bAysoKQNG6KgMHDsT+/fsljkw+rl69ioYNG2q3U1NTYW9vL2FEZGgc+iEiekn5+fk6K9I++zOVjWXLliEmJgZeXl7w9vbGa6+9JnVIZGCsqBARvaQvvvgCly5dQs+ePSEIAsLCwtC8eXNMmTJF6tBkJTc3F2fPnsXJkydx7Ngx1KxZE2vXrpU6LDIQJipERP9BaGgozp8/D5VKhVatWnH+RBlLSkpCREQEIiIicOHCBahUKrRp0wYzZ86UOjQyECYqRERktDw9PVGhQgUEBARg0KBBnJ9igpioEBGR0bp79y7Onj2Lc+fO4e7du6hVqxbatGmDgQMHSh0aGQgTFSIiMnrR0dE4ffq0dp2aw4cPSxwRGQoTFSIiMlpTp07FxYsX4e7uDm9vb3To0AE1a9aUOiwyICYqRERktA4fPox27dqhXLlyUodCEmGiQkRERmfYsGEQBOGFxzdu3GjAaEhKXPCNiIiMzsSJEwEA27dvh6WlJfr16weVSoUDBw7wDtUmhhUVIiIyWv7+/ti1a5fOvv79+2P37t0SRUSGxpsSEhGR0crNzcXdu3e129evX0dBQYGEEZGhceiHiIiM1ocffohhw4bB1dUVGo0GT548wdKlS6UOiwyIQz9ERGTU8vLycOPGDQiCgLp160Kl4ndsU8L/2kREZLQePHiATZs2ITU1Fc9+rw4ODpYwKjIkJipERGS0pkyZgpYtW6Jly5YlXq5M8sVEhYiIjFZBQQHvlGzieNUPEREZrRYtWuDo0aPIy8uTOhSSCCfTEhGR0Wrfvj0SExN19gmCgKioKIkiIkNjokJERERGi3NUiIjIaCUlJWHfvn3IzMyERqOBWq1GbGwsFi9eLHVoZCCco0JEREZrypQpiIqKwr59+5CdnY2wsDAoFPzoMiX8r01EREYrPj4eixYtQufOndG9e3ds2rQJ165dkzosMiAmKkREZLTs7e0BAO7u7oiOjoajoyM4tdK0cI4KEREZLS8vL0yaNAkzZ87EyJEjERkZCWtra6nDIgPiVT9ERGS08vLysHXrVtjZ2SE5ORmCICA3Nxdjx46VOjQyEFZUiIjIaE2ZMgUJCQnw8PDgEvomiokKEREZrTt37iA0NFTqMEhCnExLRERGy83NDXFxcVKHQRLiHBUiIjI6w4YNgyAISEpKwsOHD+Hp6QmlUqk9vnHjRgmjI0Pi0A8RERmdiRMnSh0CGQlWVIiIiMhocY4KERERGS0mKkRERGS0mKgQERGR0WKiQkREREaLiQoREREZrf8HyjN74GazxLMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dcorr = data.loc[:,['cases', 'deaths','population','comulative_days','handwashing_facilities']]\n",
    "f, ax = plt.subplots(figsize=(10, 8))\n",
    "corr = dcorr.corr()\n",
    "sns.heatmap(corr, cmap=sns.diverging_palette(150, 275, as_cmap=True), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "totals_country = data.groupby(['country', 'population', 'continent','handwashing_facilities']).agg(\n",
    "                                  {'deaths':'sum', \n",
    "                                   'cases':'sum'\n",
    "                                  }).reset_index()"
   ]
  },
  {
   "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>country</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38</td>\n",
       "      <td>1971</td>\n",
       "      <td>49273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>43053054.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>85</td>\n",
       "      <td>2596</td>\n",
       "      <td>92102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>31825299.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>27</td>\n",
       "      <td>369</td>\n",
       "      <td>16180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>2957728.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>95</td>\n",
       "      <td>2503</td>\n",
       "      <td>148682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>163046173.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>58</td>\n",
       "      <td>7047</td>\n",
       "      <td>490485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country   population continent  handwashing_facilities  deaths   cases\n",
       "0  Afghanistan   38041757.0      Asia                      38    1971   49273\n",
       "1      Algeria   43053054.0    Africa                      85    2596   92102\n",
       "2       Angola   31825299.0    Africa                      27     369   16180\n",
       "3      Armenia    2957728.0    Europe                      95    2503  148682\n",
       "4   Bangladesh  163046173.0      Asia                      58    7047  490485"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.804176e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38</td>\n",
       "      <td>1971</td>\n",
       "      <td>49273</td>\n",
       "      <td>1295.234602</td>\n",
       "      <td>51.811487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4.305305e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>85</td>\n",
       "      <td>2596</td>\n",
       "      <td>92102</td>\n",
       "      <td>2139.267519</td>\n",
       "      <td>60.297697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>3.182530e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>27</td>\n",
       "      <td>369</td>\n",
       "      <td>16180</td>\n",
       "      <td>508.400565</td>\n",
       "      <td>11.594549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>2.957728e+06</td>\n",
       "      <td>Europe</td>\n",
       "      <td>95</td>\n",
       "      <td>2503</td>\n",
       "      <td>148682</td>\n",
       "      <td>50268.990252</td>\n",
       "      <td>846.257668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>1.630462e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>58</td>\n",
       "      <td>7047</td>\n",
       "      <td>490485</td>\n",
       "      <td>3008.258280</td>\n",
       "      <td>43.220886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Belize</td>\n",
       "      <td>3.903510e+05</td>\n",
       "      <td>America</td>\n",
       "      <td>90</td>\n",
       "      <td>195</td>\n",
       "      <td>9291</td>\n",
       "      <td>23801.655433</td>\n",
       "      <td>499.550405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Benin</td>\n",
       "      <td>1.180115e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>3084</td>\n",
       "      <td>261.330441</td>\n",
       "      <td>3.728450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>7.630940e+05</td>\n",
       "      <td>Asia</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>435</td>\n",
       "      <td>570.047727</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>1.153058e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>724</td>\n",
       "      <td>62.789572</td>\n",
       "      <td>0.086726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>1.648654e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>359</td>\n",
       "      <td>21.775337</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>2.587639e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>36</td>\n",
       "      <td>442</td>\n",
       "      <td>25071</td>\n",
       "      <td>968.875601</td>\n",
       "      <td>17.081210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chad</td>\n",
       "      <td>1.594688e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>25</td>\n",
       "      <td>102</td>\n",
       "      <td>1763</td>\n",
       "      <td>110.554527</td>\n",
       "      <td>6.396235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>5.033944e+07</td>\n",
       "      <td>America</td>\n",
       "      <td>68</td>\n",
       "      <td>39051</td>\n",
       "      <td>1425539</td>\n",
       "      <td>28318.529468</td>\n",
       "      <td>775.753518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>1.133348e+07</td>\n",
       "      <td>America</td>\n",
       "      <td>92</td>\n",
       "      <td>135</td>\n",
       "      <td>9356</td>\n",
       "      <td>825.518437</td>\n",
       "      <td>11.911606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>1.737366e+07</td>\n",
       "      <td>America</td>\n",
       "      <td>87</td>\n",
       "      <td>13875</td>\n",
       "      <td>202110</td>\n",
       "      <td>11633.129398</td>\n",
       "      <td>798.622881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>1.003881e+08</td>\n",
       "      <td>Africa</td>\n",
       "      <td>90</td>\n",
       "      <td>6920</td>\n",
       "      <td>121575</td>\n",
       "      <td>1211.050205</td>\n",
       "      <td>68.932490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Eswatini</td>\n",
       "      <td>1.148133e+06</td>\n",
       "      <td>Africa</td>\n",
       "      <td>24</td>\n",
       "      <td>127</td>\n",
       "      <td>6762</td>\n",
       "      <td>5889.561575</td>\n",
       "      <td>110.614363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>1.120787e+08</td>\n",
       "      <td>Africa</td>\n",
       "      <td>8</td>\n",
       "      <td>1806</td>\n",
       "      <td>116757</td>\n",
       "      <td>1041.740954</td>\n",
       "      <td>16.113673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>2.347696e+06</td>\n",
       "      <td>Africa</td>\n",
       "      <td>18</td>\n",
       "      <td>122</td>\n",
       "      <td>3779</td>\n",
       "      <td>1609.663261</td>\n",
       "      <td>51.965842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>3.996762e+06</td>\n",
       "      <td>Europe</td>\n",
       "      <td>92</td>\n",
       "      <td>1839</td>\n",
       "      <td>191063</td>\n",
       "      <td>47804.447700</td>\n",
       "      <td>460.122469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>3.041786e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42</td>\n",
       "      <td>324</td>\n",
       "      <td>52946</td>\n",
       "      <td>1740.622236</td>\n",
       "      <td>10.651638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>1.277125e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>13415</td>\n",
       "      <td>1050.406515</td>\n",
       "      <td>6.185771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>1.126308e+07</td>\n",
       "      <td>America</td>\n",
       "      <td>22</td>\n",
       "      <td>234</td>\n",
       "      <td>9550</td>\n",
       "      <td>847.903136</td>\n",
       "      <td>20.775846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>India</td>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>68</td>\n",
       "      <td>143355</td>\n",
       "      <td>9884100</td>\n",
       "      <td>7233.585744</td>\n",
       "      <td>104.913010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>2.706256e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>94</td>\n",
       "      <td>18819</td>\n",
       "      <td>617820</td>\n",
       "      <td>2282.932861</td>\n",
       "      <td>69.538884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>3.930979e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>97</td>\n",
       "      <td>12579</td>\n",
       "      <td>574634</td>\n",
       "      <td>14618.089148</td>\n",
       "      <td>319.996630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>5.257397e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>27</td>\n",
       "      <td>1587</td>\n",
       "      <td>91867</td>\n",
       "      <td>1747.385736</td>\n",
       "      <td>30.186042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>6.415851e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>100</td>\n",
       "      <td>1312</td>\n",
       "      <td>77590</td>\n",
       "      <td>12093.485338</td>\n",
       "      <td>204.493527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>2.125267e+06</td>\n",
       "      <td>Africa</td>\n",
       "      <td>6</td>\n",
       "      <td>44</td>\n",
       "      <td>2248</td>\n",
       "      <td>1057.749450</td>\n",
       "      <td>20.703281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>2.696931e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>27</td>\n",
       "      <td>258</td>\n",
       "      <td>17584</td>\n",
       "      <td>652.000463</td>\n",
       "      <td>9.566431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>1.862875e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>8</td>\n",
       "      <td>185</td>\n",
       "      <td>6050</td>\n",
       "      <td>324.766843</td>\n",
       "      <td>9.930887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>5.309570e+05</td>\n",
       "      <td>Asia</td>\n",
       "      <td>96</td>\n",
       "      <td>48</td>\n",
       "      <td>13355</td>\n",
       "      <td>25152.695981</td>\n",
       "      <td>90.402801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Mali</td>\n",
       "      <td>1.965802e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>17</td>\n",
       "      <td>189</td>\n",
       "      <td>5767</td>\n",
       "      <td>293.366225</td>\n",
       "      <td>9.614395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>3.225166e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>901</td>\n",
       "      <td>279.365465</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>6.221820e+05</td>\n",
       "      <td>Europe</td>\n",
       "      <td>99</td>\n",
       "      <td>581</td>\n",
       "      <td>41341</td>\n",
       "      <td>66445.188064</td>\n",
       "      <td>933.810364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>5.404542e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>75</td>\n",
       "      <td>2267</td>\n",
       "      <td>108322</td>\n",
       "      <td>2004.277069</td>\n",
       "      <td>41.946199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>2.860872e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>62</td>\n",
       "      <td>1698</td>\n",
       "      <td>248423</td>\n",
       "      <td>8683.472851</td>\n",
       "      <td>59.352543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Niger</td>\n",
       "      <td>2.331072e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>2184</td>\n",
       "      <td>93.690804</td>\n",
       "      <td>3.217404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2.009636e+08</td>\n",
       "      <td>Africa</td>\n",
       "      <td>33</td>\n",
       "      <td>1197</td>\n",
       "      <td>73175</td>\n",
       "      <td>364.120661</td>\n",
       "      <td>5.956302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Oman</td>\n",
       "      <td>4.974992e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>97</td>\n",
       "      <td>1471</td>\n",
       "      <td>126240</td>\n",
       "      <td>25374.915176</td>\n",
       "      <td>295.678867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2.165653e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>80</td>\n",
       "      <td>8832</td>\n",
       "      <td>440787</td>\n",
       "      <td>2035.353611</td>\n",
       "      <td>40.782153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>7.044639e+06</td>\n",
       "      <td>America</td>\n",
       "      <td>80</td>\n",
       "      <td>1952</td>\n",
       "      <td>93555</td>\n",
       "      <td>13280.311454</td>\n",
       "      <td>277.090139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>1.081166e+08</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82</td>\n",
       "      <td>8733</td>\n",
       "      <td>449400</td>\n",
       "      <td>4156.622651</td>\n",
       "      <td>80.773889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>1.262694e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>6609</td>\n",
       "      <td>523.404803</td>\n",
       "      <td>4.434963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>1.629636e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>22</td>\n",
       "      <td>349</td>\n",
       "      <td>17019</td>\n",
       "      <td>1044.343517</td>\n",
       "      <td>21.415823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>1.544291e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>25</td>\n",
       "      <td>121</td>\n",
       "      <td>4576</td>\n",
       "      <td>296.317286</td>\n",
       "      <td>7.835313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>4.281324e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>13</td>\n",
       "      <td>1346</td>\n",
       "      <td>21383</td>\n",
       "      <td>499.448337</td>\n",
       "      <td>31.438875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>5.813630e+05</td>\n",
       "      <td>America</td>\n",
       "      <td>72</td>\n",
       "      <td>117</td>\n",
       "      <td>5343</td>\n",
       "      <td>9190.471358</td>\n",
       "      <td>201.251198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>9.321023e+06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "      <td>11975</td>\n",
       "      <td>1284.730228</td>\n",
       "      <td>6.973483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>6.962558e+07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>4237</td>\n",
       "      <td>60.854070</td>\n",
       "      <td>0.861752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Togo</td>\n",
       "      <td>8.082359e+06</td>\n",
       "      <td>Africa</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "      <td>3228</td>\n",
       "      <td>399.388347</td>\n",
       "      <td>8.165933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>1.169472e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>84</td>\n",
       "      <td>3893</td>\n",
       "      <td>111307</td>\n",
       "      <td>9517.713163</td>\n",
       "      <td>332.885239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>4.426959e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>23</td>\n",
       "      <td>221</td>\n",
       "      <td>27487</td>\n",
       "      <td>620.900303</td>\n",
       "      <td>4.992141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1.786103e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>18</td>\n",
       "      <td>367</td>\n",
       "      <td>18239</td>\n",
       "      <td>1021.161485</td>\n",
       "      <td>20.547523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1.464547e+07</td>\n",
       "      <td>Africa</td>\n",
       "      <td>42</td>\n",
       "      <td>306</td>\n",
       "      <td>11238</td>\n",
       "      <td>767.336091</td>\n",
       "      <td>20.893828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country    population continent  handwashing_facilities  deaths  \\\n",
       "0   Afghanistan  3.804176e+07      Asia                      38    1971   \n",
       "1       Algeria  4.305305e+07    Africa                      85    2596   \n",
       "2        Angola  3.182530e+07    Africa                      27     369   \n",
       "3       Armenia  2.957728e+06    Europe                      95    2503   \n",
       "4    Bangladesh  1.630462e+08      Asia                      58    7047   \n",
       "5        Belize  3.903510e+05   America                      90     195   \n",
       "6         Benin  1.180115e+07    Africa                      12      44   \n",
       "7        Bhutan  7.630940e+05      Asia                      92       0   \n",
       "8       Burundi  1.153058e+07    Africa                       6       1   \n",
       "9      Cambodia  1.648654e+07      Asia                      74       0   \n",
       "10     Cameroon  2.587639e+07    Africa                      36     442   \n",
       "11         Chad  1.594688e+07    Africa                      25     102   \n",
       "12     Colombia  5.033944e+07   America                      68   39051   \n",
       "13         Cuba  1.133348e+07   America                      92     135   \n",
       "14      Ecuador  1.737366e+07   America                      87   13875   \n",
       "15        Egypt  1.003881e+08    Africa                      90    6920   \n",
       "16     Eswatini  1.148133e+06    Africa                      24     127   \n",
       "17     Ethiopia  1.120787e+08    Africa                       8    1806   \n",
       "18       Gambia  2.347696e+06    Africa                      18     122   \n",
       "19      Georgia  3.996762e+06    Europe                      92    1839   \n",
       "20        Ghana  3.041786e+07    Africa                      42     324   \n",
       "21       Guinea  1.277125e+07    Africa                      20      79   \n",
       "22        Haiti  1.126308e+07   America                      22     234   \n",
       "23        India  1.366418e+09      Asia                      68  143355   \n",
       "24    Indonesia  2.706256e+08      Asia                      94   18819   \n",
       "25         Iraq  3.930979e+07      Asia                      97   12579   \n",
       "26        Kenya  5.257397e+07    Africa                      27    1587   \n",
       "27   Kyrgyzstan  6.415851e+06      Asia                     100    1312   \n",
       "28      Lesotho  2.125267e+06    Africa                       6      44   \n",
       "29   Madagascar  2.696931e+07    Africa                      27     258   \n",
       "30       Malawi  1.862875e+07    Africa                       8     185   \n",
       "31     Maldives  5.309570e+05      Asia                      96      48   \n",
       "32         Mali  1.965802e+07    Africa                      17     189   \n",
       "33     Mongolia  3.225166e+06      Asia                      86       0   \n",
       "34   Montenegro  6.221820e+05    Europe                      99     581   \n",
       "35      Myanmar  5.404542e+07      Asia                      75    2267   \n",
       "36        Nepal  2.860872e+07      Asia                      62    1698   \n",
       "37        Niger  2.331072e+07    Africa                      23      75   \n",
       "38      Nigeria  2.009636e+08    Africa                      33    1197   \n",
       "39         Oman  4.974992e+06      Asia                      97    1471   \n",
       "40     Pakistan  2.165653e+08      Asia                      80    8832   \n",
       "41     Paraguay  7.044639e+06   America                      80    1952   \n",
       "42  Philippines  1.081166e+08      Asia                      82    8733   \n",
       "43       Rwanda  1.262694e+07    Africa                       5      56   \n",
       "44      Senegal  1.629636e+07    Africa                      22     349   \n",
       "45      Somalia  1.544291e+07    Africa                      25     121   \n",
       "46        Sudan  4.281324e+07    Africa                      13    1346   \n",
       "47     Suriname  5.813630e+05   America                      72     117   \n",
       "48   Tajikistan  9.321023e+06      Asia                      73      65   \n",
       "49     Thailand  6.962558e+07      Asia                      85      60   \n",
       "50         Togo  8.082359e+06    Africa                      17      66   \n",
       "51      Tunisia  1.169472e+07    Africa                      84    3893   \n",
       "52       Uganda  4.426959e+07    Africa                      23     221   \n",
       "53       Zambia  1.786103e+07    Africa                      18     367   \n",
       "54     Zimbabwe  1.464547e+07    Africa                      42     306   \n",
       "\n",
       "      cases  total_cases_per_million  total_deaths_per_million  \n",
       "0     49273              1295.234602                 51.811487  \n",
       "1     92102              2139.267519                 60.297697  \n",
       "2     16180               508.400565                 11.594549  \n",
       "3    148682             50268.990252                846.257668  \n",
       "4    490485              3008.258280                 43.220886  \n",
       "5      9291             23801.655433                499.550405  \n",
       "6      3084               261.330441                  3.728450  \n",
       "7       435               570.047727                  0.000000  \n",
       "8       724                62.789572                  0.086726  \n",
       "9       359                21.775337                  0.000000  \n",
       "10    25071               968.875601                 17.081210  \n",
       "11     1763               110.554527                  6.396235  \n",
       "12  1425539             28318.529468                775.753518  \n",
       "13     9356               825.518437                 11.911606  \n",
       "14   202110             11633.129398                798.622881  \n",
       "15   121575              1211.050205                 68.932490  \n",
       "16     6762              5889.561575                110.614363  \n",
       "17   116757              1041.740954                 16.113673  \n",
       "18     3779              1609.663261                 51.965842  \n",
       "19   191063             47804.447700                460.122469  \n",
       "20    52946              1740.622236                 10.651638  \n",
       "21    13415              1050.406515                  6.185771  \n",
       "22     9550               847.903136                 20.775846  \n",
       "23  9884100              7233.585744                104.913010  \n",
       "24   617820              2282.932861                 69.538884  \n",
       "25   574634             14618.089148                319.996630  \n",
       "26    91867              1747.385736                 30.186042  \n",
       "27    77590             12093.485338                204.493527  \n",
       "28     2248              1057.749450                 20.703281  \n",
       "29    17584               652.000463                  9.566431  \n",
       "30     6050               324.766843                  9.930887  \n",
       "31    13355             25152.695981                 90.402801  \n",
       "32     5767               293.366225                  9.614395  \n",
       "33      901               279.365465                  0.000000  \n",
       "34    41341             66445.188064                933.810364  \n",
       "35   108322              2004.277069                 41.946199  \n",
       "36   248423              8683.472851                 59.352543  \n",
       "37     2184                93.690804                  3.217404  \n",
       "38    73175               364.120661                  5.956302  \n",
       "39   126240             25374.915176                295.678867  \n",
       "40   440787              2035.353611                 40.782153  \n",
       "41    93555             13280.311454                277.090139  \n",
       "42   449400              4156.622651                 80.773889  \n",
       "43     6609               523.404803                  4.434963  \n",
       "44    17019              1044.343517                 21.415823  \n",
       "45     4576               296.317286                  7.835313  \n",
       "46    21383               499.448337                 31.438875  \n",
       "47     5343              9190.471358                201.251198  \n",
       "48    11975              1284.730228                  6.973483  \n",
       "49     4237                60.854070                  0.861752  \n",
       "50     3228               399.388347                  8.165933  \n",
       "51   111307              9517.713163                332.885239  \n",
       "52    27487               620.900303                  4.992141  \n",
       "53    18239              1021.161485                 20.547523  \n",
       "54    11238               767.336091                 20.893828  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals_country[\"total_cases_per_million\"] = totals_country[\"cases\"]/ (totals_country[\"population\"]/1000000)\n",
    "totals_country[\"total_deaths_per_million\"] = totals_country[\"deaths\"]/ (totals_country[\"population\"]/1000000)\n",
    "totals_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 55 entries, 0 to 54\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   country                   55 non-null     object \n",
      " 1   population                55 non-null     float64\n",
      " 2   continent                 55 non-null     object \n",
      " 3   handwashing_facilities    55 non-null     int64  \n",
      " 4   deaths                    55 non-null     int64  \n",
      " 5   cases                     55 non-null     int64  \n",
      " 6   total_cases_per_million   55 non-null     float64\n",
      " 7   total_deaths_per_million  55 non-null     float64\n",
      "dtypes: float64(3), int64(3), object(2)\n",
      "memory usage: 3.6+ KB\n"
     ]
    }
   ],
   "source": [
    "totals_country.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.234855e+07</td>\n",
       "      <td>53.400000</td>\n",
       "      <td>5307.836364</td>\n",
       "      <td>2.928779e+05</td>\n",
       "      <td>7243.439951</td>\n",
       "      <td>129.842277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.878255e+08</td>\n",
       "      <td>33.354771</td>\n",
       "      <td>19974.300710</td>\n",
       "      <td>1.337746e+06</td>\n",
       "      <td>13652.007241</td>\n",
       "      <td>231.441579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.903510e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.590000e+02</td>\n",
       "      <td>21.775337</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.563499e+06</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>109.500000</td>\n",
       "      <td>5.908500e+03</td>\n",
       "      <td>515.902684</td>\n",
       "      <td>8.000623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.648654e+07</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>1.823900e+04</td>\n",
       "      <td>1211.050205</td>\n",
       "      <td>21.415823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.293315e+07</td>\n",
       "      <td>85.500000</td>\n",
       "      <td>1961.500000</td>\n",
       "      <td>1.140320e+05</td>\n",
       "      <td>7958.529297</td>\n",
       "      <td>97.657906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>143355.000000</td>\n",
       "      <td>9.884100e+06</td>\n",
       "      <td>66445.188064</td>\n",
       "      <td>933.810364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         population  handwashing_facilities         deaths         cases  \\\n",
       "count  5.500000e+01               55.000000      55.000000  5.500000e+01   \n",
       "mean   6.234855e+07               53.400000    5307.836364  2.928779e+05   \n",
       "std    1.878255e+08               33.354771   19974.300710  1.337746e+06   \n",
       "min    3.903510e+05                5.000000       0.000000  3.590000e+02   \n",
       "25%    7.563499e+06               22.500000     109.500000  5.908500e+03   \n",
       "50%    1.648654e+07               58.000000     349.000000  1.823900e+04   \n",
       "75%    4.293315e+07               85.500000    1961.500000  1.140320e+05   \n",
       "max    1.366418e+09              100.000000  143355.000000  9.884100e+06   \n",
       "\n",
       "       total_cases_per_million  total_deaths_per_million  \n",
       "count                55.000000                 55.000000  \n",
       "mean               7243.439951                129.842277  \n",
       "std               13652.007241                231.441579  \n",
       "min                  21.775337                  0.000000  \n",
       "25%                 515.902684                  8.000623  \n",
       "50%                1211.050205                 21.415823  \n",
       "75%                7958.529297                 97.657906  \n",
       "max               66445.188064                933.810364  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals_country.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de distribución para cada variable categórica\n",
    "\n",
    "sns.set()\n",
    "fig, ax= plt.subplots(nrows=1, ncols=3, figsize=(20, 5))\n",
    "\n",
    "sns.barplot(ax=ax[0], data = totals_country, y= \"country\", x=\"total_cases_per_million\", palette=\"winter\")\n",
    "\n",
    "sns.barplot(ax=ax[1], data = totals_country, y= \"country\", x=\"total_deaths_per_million\", palette=\"winter\")\n",
    "\n",
    "sns.barplot(ax=ax[2], data = totals_country, y= \"country\", x=\"handwashing_facilities\", palette=\"winter\")\n",
    "\n",
    "fig.set_size_inches(20,20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dcorr = totals_country.loc[:,['cases', 'deaths','population','handwashing_facilities', 'total_cases_per_million', 'total_deaths_per_million']]\n",
    "f, ax = plt.subplots(figsize=(10, 8))\n",
    "corr = dcorr.corr()\n",
    "sns.heatmap(corr, cmap=sns.diverging_palette(150, 275, as_cmap=True), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Método del codo para determinar el número de clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'population', 'continent', 'handwashing_facilities',\n",
       "       'deaths', 'cases', 'total_cases_per_million',\n",
       "       'total_deaths_per_million'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals_country.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_modelo = totals_country.drop(['country', 'continent'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Preparamos una lista donde vamos a ir agregando las distancias medias\n",
    "Sum_of_squared_distances = []\n",
    "# Fiteammos un modelo para cada numero de cluster que queremos testear\n",
    "K = np.arange(2,14)\n",
    "for k in K:\n",
    "    # Definimos y entrenamos el modelo\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km = km.fit(data_modelo)\n",
    "    # Calculamos la distancia media y agregamos a la lista\n",
    "    distancia_total = km.inertia_\n",
    "    distancia_media = np.divide(distancia_total,data_modelo.shape[0])\n",
    "    Sum_of_squared_distances.append(distancia_media)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "plt.plot(K, Sum_of_squared_distances, lw=3)\n",
    "plt.scatter(K, Sum_of_squared_distances,s=55,c='r')\n",
    "plt.xlabel('Cantidad de Clusters K')\n",
    "plt.ylabel('Inercia media')\n",
    "plt.title('Método del codo para el Dataset')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aplicación algoritmo K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</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>1</th>\n",
       "      <td>1.335621e+07</td>\n",
       "      <td>51.2</td>\n",
       "      <td>1202.4</td>\n",
       "      <td>53581.2</td>\n",
       "      <td>8541.6</td>\n",
       "      <td>144.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>68.0</td>\n",
       "      <td>143355.0</td>\n",
       "      <td>9884100.0</td>\n",
       "      <td>7233.6</td>\n",
       "      <td>104.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.128002e+08</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8973.8</td>\n",
       "      <td>405566.8</td>\n",
       "      <td>1922.7</td>\n",
       "      <td>39.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.773037e+07</td>\n",
       "      <td>55.6</td>\n",
       "      <td>6458.7</td>\n",
       "      <td>245866.9</td>\n",
       "      <td>4180.0</td>\n",
       "      <td>111.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           population  handwashing_facilities    deaths      cases  \\\n",
       "cluster                                                              \n",
       "1        1.335621e+07                    51.2    1202.4    53581.2   \n",
       "2        1.366418e+09                    68.0  143355.0  9884100.0   \n",
       "3        2.128002e+08                    66.2    8973.8   405566.8   \n",
       "4        6.773037e+07                    55.6    6458.7   245866.9   \n",
       "\n",
       "         total_cases_per_million  total_deaths_per_million  \n",
       "cluster                                                     \n",
       "1                         8541.6                     144.1  \n",
       "2                         7233.6                     104.9  \n",
       "3                         1922.7                      39.9  \n",
       "4                         4180.0                     111.1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "## Number of clusters, in this case 4 clusters\n",
    "kmeans = KMeans(n_clusters = 4, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(data_modelo)\n",
    "y_kmeans1=y_kmeans+1\n",
    "cluster = pd.DataFrame(y_kmeans1)\n",
    "# Adding cluster to the Dataset\n",
    "data_modelo['cluster'] = cluster\n",
    "#Mean of clusters\n",
    "kmeans_mean_cluster = pd.DataFrame(round(data_modelo.groupby('cluster').mean(),1))\n",
    "\n",
    "## Identificación de los valores promedio (centroides) \n",
    "kmeans_mean_cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Concateno los datasets"
   ]
  },
  {
   "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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>population</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1971</td>\n",
       "      <td>49273</td>\n",
       "      <td>1295.234602</td>\n",
       "      <td>51.811487</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43053054.0</td>\n",
       "      <td>85</td>\n",
       "      <td>2596</td>\n",
       "      <td>92102</td>\n",
       "      <td>2139.267519</td>\n",
       "      <td>60.297697</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>31825299.0</td>\n",
       "      <td>27</td>\n",
       "      <td>369</td>\n",
       "      <td>16180</td>\n",
       "      <td>508.400565</td>\n",
       "      <td>11.594549</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2957728.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2503</td>\n",
       "      <td>148682</td>\n",
       "      <td>50268.990252</td>\n",
       "      <td>846.257668</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asia</td>\n",
       "      <td>163046173.0</td>\n",
       "      <td>58</td>\n",
       "      <td>7047</td>\n",
       "      <td>490485</td>\n",
       "      <td>3008.258280</td>\n",
       "      <td>43.220886</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Belize</td>\n",
       "      <td>America</td>\n",
       "      <td>390351.0</td>\n",
       "      <td>90</td>\n",
       "      <td>195</td>\n",
       "      <td>9291</td>\n",
       "      <td>23801.655433</td>\n",
       "      <td>499.550405</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11801151.0</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>3084</td>\n",
       "      <td>261.330441</td>\n",
       "      <td>3.728450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>763094.0</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>435</td>\n",
       "      <td>570.047727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11530577.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>724</td>\n",
       "      <td>62.789572</td>\n",
       "      <td>0.086726</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>16486542.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>359</td>\n",
       "      <td>21.775337</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent   population  handwashing_facilities  deaths   cases  \\\n",
       "0  Afghanistan      Asia   38041757.0                      38    1971   49273   \n",
       "1      Algeria    Africa   43053054.0                      85    2596   92102   \n",
       "2       Angola    Africa   31825299.0                      27     369   16180   \n",
       "3      Armenia    Europe    2957728.0                      95    2503  148682   \n",
       "4   Bangladesh      Asia  163046173.0                      58    7047  490485   \n",
       "5       Belize   America     390351.0                      90     195    9291   \n",
       "6        Benin    Africa   11801151.0                      12      44    3084   \n",
       "7       Bhutan      Asia     763094.0                      92       0     435   \n",
       "8      Burundi    Africa   11530577.0                       6       1     724   \n",
       "9     Cambodia      Asia   16486542.0                      74       0     359   \n",
       "\n",
       "   total_cases_per_million  total_deaths_per_million  cluster  \n",
       "0              1295.234602                 51.811487        1  \n",
       "1              2139.267519                 60.297697        4  \n",
       "2               508.400565                 11.594549        1  \n",
       "3             50268.990252                846.257668        1  \n",
       "4              3008.258280                 43.220886        3  \n",
       "5             23801.655433                499.550405        1  \n",
       "6               261.330441                  3.728450        1  \n",
       "7               570.047727                  0.000000        1  \n",
       "8                62.789572                  0.086726        1  \n",
       "9                21.775337                  0.000000        1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clusters=pd.concat([totals_country\n",
    "                         [['country', 'continent']],data_modelo],axis=1)\n",
    "data_clusters.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Se muestran los resultados de cada cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Países incluídos en el grupo 1\n",
      "['Afghanistan', 'Angola', 'Armenia', 'Belize', 'Benin', 'Bhutan', 'Burundi', 'Cambodia', 'Cameroon', 'Chad', 'Cuba', 'Ecuador', 'Eswatini', 'Gambia', 'Georgia', 'Ghana', 'Guinea', 'Haiti', 'Iraq', 'Kyrgyzstan', 'Lesotho', 'Madagascar', 'Malawi', 'Maldives', 'Mali', 'Mongolia', 'Montenegro', 'Nepal', 'Niger', 'Oman', 'Paraguay', 'Rwanda', 'Senegal', 'Somalia', 'Suriname', 'Tajikistan', 'Togo', 'Tunisia', 'Zambia', 'Zimbabwe']\n",
      "Países incluídos en el grupo 2\n",
      "['India']\n",
      "Países incluídos en el grupo 3\n",
      "['Bangladesh', 'Indonesia', 'Nigeria', 'Pakistan']\n",
      "Países incluídos en el grupo 4\n",
      "['Algeria', 'Colombia', 'Egypt', 'Ethiopia', 'Kenya', 'Myanmar', 'Philippines', 'Sudan', 'Thailand', 'Uganda']\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,5):\n",
    "    print('Países incluídos en el grupo {}'.format(str(i)))\n",
    "    print(list(data_clusters[data_clusters.cluster==i]['country'].values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster Jerárquico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_jerarquico = totals_country.drop(['country', 'continent'],axis=1)"
   ]
  },
  {
   "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>population</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38041757.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1971</td>\n",
       "      <td>49273</td>\n",
       "      <td>1295.234602</td>\n",
       "      <td>51.811487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43053054.0</td>\n",
       "      <td>85</td>\n",
       "      <td>2596</td>\n",
       "      <td>92102</td>\n",
       "      <td>2139.267519</td>\n",
       "      <td>60.297697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31825299.0</td>\n",
       "      <td>27</td>\n",
       "      <td>369</td>\n",
       "      <td>16180</td>\n",
       "      <td>508.400565</td>\n",
       "      <td>11.594549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2957728.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2503</td>\n",
       "      <td>148682</td>\n",
       "      <td>50268.990252</td>\n",
       "      <td>846.257668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>163046173.0</td>\n",
       "      <td>58</td>\n",
       "      <td>7047</td>\n",
       "      <td>490485</td>\n",
       "      <td>3008.258280</td>\n",
       "      <td>43.220886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    population  handwashing_facilities  deaths   cases  \\\n",
       "0   38041757.0                      38    1971   49273   \n",
       "1   43053054.0                      85    2596   92102   \n",
       "2   31825299.0                      27     369   16180   \n",
       "3    2957728.0                      95    2503  148682   \n",
       "4  163046173.0                      58    7047  490485   \n",
       "\n",
       "   total_cases_per_million  total_deaths_per_million  \n",
       "0              1295.234602                 51.811487  \n",
       "1              2139.267519                 60.297697  \n",
       "2               508.400565                 11.594549  \n",
       "3             50268.990252                846.257668  \n",
       "4              3008.258280                 43.220886  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jerarquico.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-18T15:23:05.046127Z",
     "start_time": "2022-02-18T15:23:00.799101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "\n",
    "# creando el Dendograma \n",
    "dendrogram = sch.dendrogram(sch.linkage(data_jerarquico, method  = \"ward\"))\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('handwashing_facilities')\n",
    "plt.ylabel('distances')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definición de clusters y evaluación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-18T15:23:12.191819Z",
     "start_time": "2022-02-18T15:23:12.161820Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering \n",
    "\n",
    "# creando los grupos\n",
    "hc = AgglomerativeClustering(n_clusters=4, affinity = 'euclidean', linkage = 'ward')\n",
    "y_hc = hc.fit_predict(data_jerarquico)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Concateno los datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>population</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>deaths</th>\n",
       "      <th>cases</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38041757.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1971</td>\n",
       "      <td>49273</td>\n",
       "      <td>1295.234602</td>\n",
       "      <td>51.811487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43053054.0</td>\n",
       "      <td>85</td>\n",
       "      <td>2596</td>\n",
       "      <td>92102</td>\n",
       "      <td>2139.267519</td>\n",
       "      <td>60.297697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>31825299.0</td>\n",
       "      <td>27</td>\n",
       "      <td>369</td>\n",
       "      <td>16180</td>\n",
       "      <td>508.400565</td>\n",
       "      <td>11.594549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2957728.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2503</td>\n",
       "      <td>148682</td>\n",
       "      <td>50268.990252</td>\n",
       "      <td>846.257668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asia</td>\n",
       "      <td>163046173.0</td>\n",
       "      <td>58</td>\n",
       "      <td>7047</td>\n",
       "      <td>490485</td>\n",
       "      <td>3008.258280</td>\n",
       "      <td>43.220886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Belize</td>\n",
       "      <td>America</td>\n",
       "      <td>390351.0</td>\n",
       "      <td>90</td>\n",
       "      <td>195</td>\n",
       "      <td>9291</td>\n",
       "      <td>23801.655433</td>\n",
       "      <td>499.550405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11801151.0</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>3084</td>\n",
       "      <td>261.330441</td>\n",
       "      <td>3.728450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>763094.0</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>435</td>\n",
       "      <td>570.047727</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Africa</td>\n",
       "      <td>11530577.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>724</td>\n",
       "      <td>62.789572</td>\n",
       "      <td>0.086726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>16486542.0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>359</td>\n",
       "      <td>21.775337</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent   population  handwashing_facilities  deaths   cases  \\\n",
       "0  Afghanistan      Asia   38041757.0                      38    1971   49273   \n",
       "1      Algeria    Africa   43053054.0                      85    2596   92102   \n",
       "2       Angola    Africa   31825299.0                      27     369   16180   \n",
       "3      Armenia    Europe    2957728.0                      95    2503  148682   \n",
       "4   Bangladesh      Asia  163046173.0                      58    7047  490485   \n",
       "5       Belize   America     390351.0                      90     195    9291   \n",
       "6        Benin    Africa   11801151.0                      12      44    3084   \n",
       "7       Bhutan      Asia     763094.0                      92       0     435   \n",
       "8      Burundi    Africa   11530577.0                       6       1     724   \n",
       "9     Cambodia      Asia   16486542.0                      74       0     359   \n",
       "\n",
       "   total_cases_per_million  total_deaths_per_million  \n",
       "0              1295.234602                 51.811487  \n",
       "1              2139.267519                 60.297697  \n",
       "2               508.400565                 11.594549  \n",
       "3             50268.990252                846.257668  \n",
       "4              3008.258280                 43.220886  \n",
       "5             23801.655433                499.550405  \n",
       "6               261.330441                  3.728450  \n",
       "7               570.047727                  0.000000  \n",
       "8                62.789572                  0.086726  \n",
       "9                21.775337                  0.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_hc=pd.concat([totals_country[['country', 'continent']],data_jerarquico],axis=1)\n",
    "cluster_hc.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "heirarchy=cluster_hc.copy()\n",
    "heirarchy['cluster'] = y_hc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Name of countries in cluster 0**\n",
      "['Afghanistan', 'Algeria', 'Angola', 'Armenia', 'Belize', 'Benin', 'Bhutan', 'Burundi', 'Cambodia', 'Cameroon', 'Chad', 'Colombia', 'Cuba', 'Ecuador', 'Eswatini', 'Gambia', 'Georgia', 'Ghana', 'Guinea', 'Haiti', 'Iraq', 'Kenya', 'Kyrgyzstan', 'Lesotho', 'Madagascar', 'Malawi', 'Maldives', 'Mali', 'Mongolia', 'Montenegro', 'Myanmar', 'Nepal', 'Niger', 'Oman', 'Paraguay', 'Rwanda', 'Senegal', 'Somalia', 'Sudan', 'Suriname', 'Tajikistan', 'Thailand', 'Togo', 'Tunisia', 'Uganda', 'Zambia', 'Zimbabwe']\n",
      "**Name of countries in cluster 1**\n",
      "['Egypt', 'Ethiopia', 'Philippines']\n",
      "**Name of countries in cluster 2**\n",
      "['Bangladesh', 'Indonesia', 'Nigeria', 'Pakistan']\n",
      "**Name of countries in cluster 3**\n",
      "['India']\n"
     ]
    }
   ],
   "source": [
    "# Aca se anota el numero de clústers si se desea poner de forma manual \n",
    "for i in range(4):\n",
    "    print('**Name of countries in cluster {}**'.format(str(i)))\n",
    "    print(list(heirarchy[heirarchy.cluster==i]['country'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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