df-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limpeza de dados da coluna BMI(IMC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como primeiro passo, é necessário que o conjunto de dados seja carregado. Após isso, podemos visualizar a tabela e perceber dados com valor NaN na coluna bmi(imc). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male  67.0             0              1          Yes   \n",
       "1  51676  Female  61.0             0              0          Yes   \n",
       "2  31112    Male  80.0             0              1          Yes   \n",
       "3  60182  Female  49.0             0              0          Yes   \n",
       "4   1665  Female  79.0             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "df = pd.read_csv('./data/stroke-data.csv')\n",
    " \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para prosseguir, é necessário descobrir quantos itens não estão preenchidos na tabela(estão como Not a Number)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5110 non-null   int64  \n",
      " 1   gender             5110 non-null   object \n",
      " 2   age                5110 non-null   float64\n",
      " 3   hypertension       5110 non-null   int64  \n",
      " 4   heart_disease      5110 non-null   int64  \n",
      " 5   ever_married       5110 non-null   object \n",
      " 6   work_type          5110 non-null   object \n",
      " 7   Residence_type     5110 non-null   object \n",
      " 8   avg_glucose_level  5110 non-null   float64\n",
      " 9   bmi                4909 non-null   float64\n",
      " 10  smoking_status     5110 non-null   object \n",
      " 11  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 479.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fica claro que a única coluna com dados faltantes é a bmi, com 201 NaN. Para escolher entre as técnicas de substituição disponíveis, faz-se necessário um melhor entendimento dos valores nulos correspondentes na relação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>201</td>\n",
       "      <td>0.039335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ever_married</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residence_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Total   Percent\n",
       "bmi                  201  0.039335\n",
       "id                     0  0.000000\n",
       "gender                 0  0.000000\n",
       "age                    0  0.000000\n",
       "hypertension           0  0.000000\n",
       "heart_disease          0  0.000000\n",
       "ever_married           0  0.000000\n",
       "work_type              0  0.000000\n",
       "Residence_type         0  0.000000\n",
       "avg_glucose_level      0  0.000000\n",
       "smoking_status         0  0.000000\n",
       "stroke                 0  0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def draw_missing_data_table(df):\n",
    "    total = df.isnull().sum().sort_values(ascending=False)\n",
    "    percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
    "    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "    return missing_data\n",
    "draw_missing_data_table(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De cara, fica perceptível que o valor de dados nulos não chega nos 4%. Portanto, é possível utilizar o valor médio dos dados presentes na coluna bmi. No entanto, é necessário se atentar a outros detalhes antes da decisão."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ernan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "c:\\Users\\ernan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='Density'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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15e+ffKhYQ4mkr0fN8jrrq3R2dEaJ5ILXpQAAygThDL6TzjhdSMyXRjjL1XhkgNEzAMDaIJzBdy5MzSntnNpiUa9LWVFHrpuUeWcAgLVCOIPvDCXmJPm7GSCvvjqshpow4QwAsGYIZ/CdwcmkTFJrCYycmZl2dcR1iKYAAMAaIZzBdwYnk2qqjSgcLI1vz12dcR0dmFQ6wzZOAIDrVxo//VBRhibnSuKSZt6uzpiSCxmdHpn2uhQAQBkgnMFXUumMRqbn1Bb3/yXNvF2dcUnSoT7mnQEArh/hDL4yPDWnjCuNZoC8be11CgWMpgAAwJognMFXhiZznZqx0gln0VBQm1trdZS1zgAAa4BwBl8ZnEwqYFJLXcTrUq7Kjo44C9ECANYE4Qy+MpiYU3NdVKES6dTM29kR0/nxWbZxAgBct9L6CYiyNzSZLImdAZba2RGTJB0bZPQMAHB9CGfwjYV0RqPT82oroflmeTty4ewwi9ECAK4T4Qy+MTI1LyeV5MjZuoZqxaIhmgIAANeNcAbfGEokJZXGtk1LmZm2d8QIZwCA60Y4g28MJ+ZKZk/N5ezoiOnIwKScYxsnAMC1I5zBN4YSc2qoCZfMnppL7eyIaTKZ0sBk0utSAAAlrDR/CqIsDSfmSrIZIG9He7YpgPXOAADXg3AGX0hnnC5MzZXsJU1J2tmR3WOTeWcAgOtBOIMv9I7NKJVxJdmpmVdfE1ZnfRXhDABwXUJeFwBIUs/QlKTSbAZ4YN/Zi7djVSE9dWLkkmN5992xoZhlAQBKFCNn8IV8OCvlOWeS1BGv0nBiTukMHZsAgGtDOIMvnBieUl00pOpI0OtSrkt7vEppl50/BwDAtSCcwRd6hqZK8pLmUh312ZE/ltMAAFwrwhk855wrm3DWWhdVwKTBCcIZAODaEM7gueGpOU0mUyXdqZkXCgbUUhdl5AwAcM0IZ/BcKXdqLqejvkqDhDMAwDUinMFzJ8qkUzOvI16lsZkFJRfSXpcCAChBhDN4rmco26kZryqPZffa49mQyegZAOBaEM7guZ7hKW1prZWZeV3KmuiI07EJALh2hDN4rmdoSlva6rwuY8001IQVDQUYOQMAXBPCGTw1mVzQ4OSctpZRODMztcerNDDBQrQAgKtHOIOn8s0AW1vLJ5xJ2Uubg5NJOcc2TgCAq0M4g6fyy2iU08iZJLXXV2l2Ia3JZMrrUgAAJYZwBk/1DE8pEgxoQ1ON16WsqYtNAewUAAC4SoQzeOrE0JQ2ttQoFCyvb8UOltMAAFyj8vqJiJLTMzRVdpc0Jak6ElS8KsRyGgCAq0Y4g2eSC2mdHZ0pu2aAvPZ4lYYIZwCAq1TQcGZmbzOzo2bWY2YfXebxqJl9Lff4PjPbuOTxDWY2ZWa/Vcg64Y3TI9PKOJXVGmeLtcerNJSYU4aOTQDAVShYODOzoKTPSnq7pN2S3mtmu5ec9n5JY865rZI+LemTSx7/lKS/L1SN8Fa5dmrmtcejSmWcRqfnvS4FAFBCCjlytldSj3PupHNuXtJXJd2z5Jx7JH0pd/tBSW+23B4+Zvbzkk5JOljAGuGhE0PTMpO2lOllzfxG7lzaBABcjUKGs3WSzi2635s7tuw5zrmUpAlJzWZWJ+n/kfT7BawPHusZnlJ3Y7WqwkGvSymItnhUkjSYYKcAAMDq+bUh4OOSPu2cm7rSSWb2ATPbb2b7h4eHi1MZ1kzP0FTZNgNIUjQUVGNNmOU0AABXpZDh7Lyk9Yvud+eOLXuOmYUk1UsakXSHpD8ys9OS/qOk3zOzDy/9BM65zznn9jjn9rS2tq75HwCFk844nRwuz2U0FmuLVWlokpEzAMDqhQr42s9I2mZmm5QNYfdKum/JOQ9Jep+kpyS9W9JjLrsZ4RvyJ5jZxyVNOec+U8BaUWTnx2Y1l8qU7XyzvPZ4lXqGp5TO0LEJAFidgoUz51wqN9r1iKSgpC865w6a2Sck7XfOPSTpC5LuN7MeSaPKBjhUgJ7hhKTy7dTMa49Hlc44jUwxegYAWJ1CjpzJOfewpIeXHPvYottJSb+0wmt8vCDFwVPlvoxGXlt+GyeaAgAAq+TXhgCUuZ6hKbXURdRQE/G6lIJqi0VlYjkNAMDqEc7giZ6hqbKfbyZJ4WBATbUROjYBAKtGOEPROefKdsPz5bTFq7isCQBYNcIZim54ak6TyVTFhLP2eFQjU3OaS6W9LgUAUAIK2hAA5D2w7+zF2yeGs80AZ0dnLjlertpjVco46dSFae3siHtdDgDA5xg5Q9EN5y7x5feeLHf5bZyODV5xwwsAACQRzuCBocScoqGA4lWVMXDbWhdVwKRjAwmvSwEAlADCGYpuOJFUaywqM/O6lKIIBQNqro3q2CDhDACwMsIZim44MafWuqjXZRRVWzyq40Nc1gQArIxwhqJKLqQ1mUypLVZZ4aw9XqXTI9NKLtCxCQC4MsIZiirfDNBaIc0Aee3xKjn3yrZVAABcDuEMRTV0sVOzskbO8n/e40PMOwMAXBnhDEU1nEgqaKbG2vLeU3OplrqowkHT0QFGzgAAV0Y4Q1ENJebUEosoGKiMTs28YMC0uaVOx+nYBACsgHCGohpKzFXM4rNLbWuv0zEuawIAVkA4Q9HMpzIam56/uGJ+pdnRHtO50VlNz6W8LgUA4GOEMxTN8NScnLJ7TVaibe0xSXRsAgCujHCGohmaTEqqvE7NvO3tdZLETgEAgCsinKFohhJzCpqpucJ2B8i7oblWkVCAcAYAuCLCGYpmaDKp5rrK69TMCwZMW1vrdGyQy5oAgMsjnKFoBhNzaotX5nyzvO3tLKcBALgywhmKYiGd69Ss0Plmeds7YuqbSGoyueB1KQAAnyKcoSiGE7lOzUofOWvLdmwe59ImAOAyCGcoiqFEZXdq5m1vz4czLm0CAJZHOENRDE3OKWBSc11l7am5VHdjtarDQR0lnAEALoNwhqIYSsypuS6qUKCyv+UCAdO29jouawIALivkdQGoDIOTSXXUV/Z8swf2nZUkhQKmA73jF+8vdt8dG4pdFgDAZyp7GANFkVxIa3R6vmI3PF+qPV6lRDKlmXn22AQAvBrhDAV36sJ0rlOzspsB8vIhdXByzuNKAAB+RDhDweW3K2LkLCsfUvMdrAAALEY4Q8H1DE0pYFJLhXdq5tVXhxUNBTQ4STgDALwa4QwFd2wwoabaqEJBvt0kyczUFotyWRMAsCx+WqLgjg9NVfzis0u1x6s0xMgZAGAZhDMU1FwqrTMjM2qjGeAS7fEqTc+nNTVHxyYA4FKrCmdm9jdm9rNmRpjDVTl1YVrpjFM7zQCXyIdV5p0BAJZabdj6U0n3STpuZn9oZjsKWBPKSH4lfEbOLpUPq1zaBAAstapw5pz7R+fcr0i6XdJpSf9oZv9sZr9uZuFCFojSdvxipybhbLFYVUjV4SBNAQCAV1n1ZUoza5b0LyX9a0nPS/oTZcPa9wpSGcrC8cGEbmiuVZhOzUuYmdriUQ2y1hkAYIlV7a1pZt+UtEPS/ZLe4Zzrzz30NTPbX6jiUPqOD01pa1ud12X4UnusSi+dn5BzTmbmdTkAAJ9Y7XDGXzjndjvn/ns+mJlZVJKcc3sKVh1K2nwqo9MXprW9nXC2nPZ4VLMLaSWSdGwCAF6x2nD2B8sce2otC0H5OT0yrVTGaVtbzOtSfKktnttjk0ubAIBFrnhZ08w6JK2TVG1mt0nKX3uJS6opcG0ocflOza1tdXqxd8LjavynPZ7v2JwjwAIALlppztlblW0C6Jb0qUXHE5J+r0A1oUwcG0zIjHB2OXXRkGojQdY6AwBc4orhzDn3JUlfMrN3Oee+UaSaUCaODSa0oalGVeGg16X4Vlu8inAGALjESpc1f9U592VJG83sI0sfd859apmnAZKkowMJ7ezgct2VtMer9PzZMTo2AQAXrdQQUJv7WCcptsz/wLJm59M6PTKtHR1xr0vxtfZ4VHOpjCZmF7wuBQDgEytd1vzz3MffL045KBfHhxLKOGkXI2dX1Jbbxmlwck4NNRGPqwEA+MFqNz7/IzOLm1nYzB41s2Ez+9VCF4fSdWQgIUnaQTi7onY2QAcALLHadc5+2jk3KennlN1bc6uk3y5UUSh9R/oTqgoHdENz7conV7CaSEixqpCGWOsMAJCz2nCWv/z5s5L+2jnHugi4oqODk9reHlMwwCT3lbTHqtgAHQBw0WrD2XfM7Iik10p61MxaJfGrPi7r6EBCO9q5pLkaHfXZ5TTSGed1KQAAH1hVOHPOfVTSj0na45xbkDQt6Z5CFobSNZyY04Wpee3spFNzNTrrq5TKOF2YYvQMALDyDgGL7VR2vbPFz/nLNa4HZeBorhmANc5Wp7O+WpLUP8FgNABgleHMzO6XtEXSC5LSucNOhDMs48jApCQ6NVerNRZVMGAamJj1uhQAgA+sduRsj6TdzjkmxWBFRwYSaqmLqqUu6nUpJSEYMLXHo4ycAQAkrb4h4GVJHYUsBOWDbZuuXme8Wn3js+L3HwDAasNZi6RDZvaImT2U/7+QhaE0pTNOxwYJZ1ers6FK0/NpDSdoCgCASrfay5ofL2QRKB+nR6Y1l8ow3+wq5ZsCDvZPqi1e5XE1AAAvrXYpje8ruzNAOHf7GUnPFbAulKhXOjVZRuNqdNZnA9mhvkmPKwEAeG21e2v+G0kPSvrz3KF1kr61iue9zcyOmlmPmX10mcejZva13OP7zGxj7vheM3sh9/8BM/uF1f6B4K0j/ZMKmLStvc7rUkpKVTioxpqwDvcTzgCg0q12ztmHJN0laVKSnHPHJbVd6QlmFpT0WUlvl7Rb0nvNbPeS094vacw5t1XSpyV9Mnf8ZWUXvL1V0tsk/fmS9dXgU0cGEtrYUquqcNDrUkpOZ321DhHOAKDirTaczTnn5vN3ckFppbayvZJ6nHMnc8/9ql69q8A9kr6Uu/2gpDebmTnnZpxzqdzxqlV8LvjEUZoBrllnfZVOXZjWzHxq5ZMBAGVrteHs+2b2e5Kqzewtkv5a0rdXeM46SecW3e/NHVv2nFwYm5DULElmdoeZHZT0kqQPLgpr8KnpuZTOjMww3+waddZXy7ns6CMAoHKtNpx9VNKwskHp30p6WNJ/LlRRkuSc2+ecu1HS6yT9rpm9qoXNzD5gZvvNbP/w8HAhy8EqHBvMhgo6Na9NZwNNAQCAVS6l4ZzLmNm3JH3LObfaFHRe0vpF97tzx5Y7pzd3qbRe0siSz33YzKYkvUbS/iWPfU7S5yRpz549XPr0WH7EZxcjZ9ekoTqseFWIeWcAUOGuOHJmWR83swuSjko6ambDZvaxVbz2M5K2mdkmM4tIulfS0oVrH5L0vtztd0t6zDnncs8J5Wq4QdlN10+v+k8FTxwdSKgmElR3Y7XXpZQkM9PurjgdmwBQ4Va6rPmbynZpvs451+Sca5J0h6S7zOw3r/TE3ByxD0t6RNJhSV93zh00s0+Y2Ttzp31BUrOZ9Uj6iLKXTyXpbkkHzOwFSd+U9BvOuQtX/8dDMR0ZmNT29pgCAfO6lJK1qzOuI/0JpTMMBANApVrpsua/kPSWxcHIOXfSzH5V0j8ou/zFZTnnHlZ2ftriYx9bdDsp6ZeWed79ku5fsXr4wgP7zso5pwPnJvSadXE9sO+s1yWVrN2dcc0upHV6ZFpbWlkrDgAq0UojZ+HlRqxy887ChSkJpSiRTGl2Ia12th66Lru7svP1aAoAgMq1Ujibv8bHUGEGJpOSpA7C2XXZ1hZTOGg0BQBABVvpsuYtZrbcTwlTdnFYQJI0MEE4WwuRUEBb22I0BQBABbtiOHPOsQcPVmVwMql4VUg1UXbZul67OmN68jj9LwBQqVa7CC1wRX0Ts+qoZ9RsLezujGsoMafhxJzXpQAAPEA4w3VbSGc0nJhTVz3rm62FfFMAlzYBoDIRznDdBieTyjipq4FwthZ2d+Y6NglnAFCRCGe4bn3j2WYAwtnaaKiJaF1DNctpAECFIpzhuvVNzKoqHFBjDUvfrZVdnXFGzgCgQhHOcN36x2fVWV8tM7ZtWiu7u+I6OTylmfmU16UAAIqMcIbrks44DUwm1UWn5pq6aV29Mo6mAACoRIQzXJeTw1NaSDt1Mt9sTd20rl6S9FLvhMeVAACKjXCG63IwN2mdZTTWVns8qpa6qF46z8gZAFQawhmuy8vnJxQKmFpjUa9LKStmppvWxfXyeUbOAKDSEM5wXQ72TaqjvkrBAM0Aa+2mdfU6PpSgKQAAKgzhDNfMOaeDfRPq5JJmQbyGpgAAqEiEM1yz3rFZTSZT6mqgU7MQbuqmKQAAKhHhDNeMZoDC6ohXqaUuQlMAAFQYwhmu2aG+CQVMao8zclYIZqbXrKunKQAAKgzhDNfsYN+ktrTWKRLi26hQbs41BczOp70uBQBQJPxUxTU72DepG7viXpdR1vJNAeyzCQCVg3CGa3Jhak4Dk0m9JreSPQoj3xTApU0AqByEM1yTfDPAbkbOCirbFBDVgd5xr0sBABQJ4QzX5GBfdiTnxk5GzgrJzHTr+nq9yHIaAFAxQl4XgNJ0sG9S3Y3Vqq8Je11KWXlg39llj58YmtIXnzylqnBQ992xochVAQCKiZEzXJNDNAMUTXdjjZyk8+OzXpcCACgCwhmu2tRcSqcuTOvGLi5pFkN3Y3aR394xwhkAVALCGa5afq9HRs6KoyYSUlNtRL1jM16XAgAoAsIZrtrB3LIOLKNRPN2N1YycAUCFIJzhqh3sm1RLXURtsajXpVSM7sYaTcwuKJFc8LoUAECBEc5w1Q72TWp3V73MzOtSKsZ65p0BQMUgnOGqzKcyOj6UYL5ZkXXWVytgYt4ZAFQAwhmuyrHBhBbSTrs7CWfFFAkF1B6vYuQMACoAi9DispZbEPXZM6OSpFPD05ddMBWF0d1YrZfPT8o5xyVlAChjjJzhqvSNJxUJBtRUF/G6lIrT3Vij2YW0Tl2Y9roUAEABEc5wVfonZtVRX6UAIzdFt6GpRpL03NlxbwsBABQU4QyrlnFO/RNJdTVUeV1KRWqNRVUVDui5s2NelwIAKCDCGVZtbHpec6mMOuurvS6lIgXMtL6xRs+dIZwBQDkjnGHV+iaSkqQuwpln1jfV6NhgQlNzKa9LAQAUCOEMq9Y/PquASW1xdgbwyoamGmWcdODcuNelAAAKhHCGVeufSKotVqVwkG8br6xvzDUFcGkTAMoWP2Wxan0Ts+qspxnAS9WRoLa11dEUAABljHCGVUkkF5RIptTZwHwzr92+oVHPnxuXc87rUgAABUA4w6r0X2wGYOTMa7ff0KDxmQWdZDFaAChLhDOsSv94dk9HltHw3u0bGiUx7wwAyhXhDKvSN5FUY01Y1ZGg16VUvC2tdYpXhZh3BgBlinCGVemfmGXUzCcCAdOejU16+tSo16UAAAqAcIYVzaXSGpmaVyfbNvnG6zY26cTwtC5MzXldCgBgjRHOsKKBiaSc2BnAT/ZuapIk7T/N6BkAlBvCGVaU37aJNc7846Z19aoKB7SPS5sAUHYIZ1hR//isaiJB1VeHvS4FOZFQQLetb9QzjJwBQNkhnGFF/RNJddVXy8y8LgWL7N3UpEN9k5pMLnhdCgBgDRHOcEXpjNPAZJJLmj60d1OTMk56lvXOAKCsEM5wRcOJOaUzjm2bfOi2DQ0KBYwlNQCgzBDOcEV9E/mdARg585uaSEivWVevZwhnAFBWQl4XAH/rH59VOGhqjUW9LgU5D+w7e/F2rCqkf+4Z0Zf++bTCwVd+17rvjg1elAYAWAOMnOGK+iaS6ohXKUAzgC9taqlV2jmdGZnxuhQAwBohnOGynHMamEiqg8VnfWtTc60CJp0YnvK6FADAGiGc4bImkynNLqTVwXwz34qGg1rfWEM4A4AyQjjDZQ1OZncGaI8z38zPtrTV6fzYrGbn016XAgBYAwUNZ2b2NjM7amY9ZvbRZR6PmtnXco/vM7ONueNvMbNnzeyl3Mc3FbJOLC8fzjpijJz52dbWOjlxaRMAykXBwpmZBSV9VtLbJe2W9F4z273ktPdLGnPObZX0aUmfzB2/IOkdzrmbJL1P0v2FqhOXNzCRVKwqpJooTb1+tr6pRpFQgHAGAGWikCNneyX1OOdOOufmJX1V0j1LzrlH0pdytx+U9GYzM+fc8865vtzxg5KqzYxra0U2mMh2asLfggHTpuZawhkAlIlChrN1ks4tut+bO7bsOc65lKQJSc1LznmXpOecc3NLP4GZfcDM9pvZ/uHh4TUrHNltm4Ym59ROOCsJW9rqdGFqXuMz816XAgC4Tr5uCDCzG5W91Plvl3vcOfc559we59ye1tbW4hZX5s6MTCuVcYSzErG1tU4S884AoBwUMpydl7R+0f3u3LFlzzGzkKR6SSO5+92Svinp15xzJwpYJ5ZxbDAhiU7NUtEej6ouGtLxIcIZAJS6QoazZyRtM7NNZhaRdK+kh5ac85CyE/4l6d2SHnPOOTNrkPR3kj7qnPthAWvEZRwZSMgktdGpWRLMTNva6tQzNKWMc16XAwC4DgULZ7k5ZB+W9Iikw5K+7pw7aGafMLN35k77gqRmM+uR9BFJ+eU2Pixpq6SPmdkLuf/bClUrXu3YYEJNtRFFQr6+8o1FdnTENDOf1rlRtnICgFJW0DUSnHMPS3p4ybGPLbqdlPRLyzzvDyT9QSFrw5UdGUgw36zEbGuLKWDS0YGE16UAAK4DwyJ4leRCWqcvTBPOSkx1JKgNTbU6Okg4A4BSRjjDq2TnLYk9NUvQjo6Y+ieSGphIel0KAOAaEc7wKhc7NWN0apaaHR0xSdLjR4c8rgQAcK0IZ3iVY4NTCgdNzXWEs1LTHouqoTqsx44QzgCgVBHO8Co9Q1Pa2FyrYMC8LgVXycy0oyOmH/Zc0Fwq7XU5AIBrQDjDq5wcntLWtjqvy8A1yi+p8dSJEa9LAQBcA8IZLjGfyujM6Iy2tBLOStWW1jrVRoJ65OCg16UAAK4B4QyXOD0yrXTGMXJWwsLBgH5iZ5u+d2hA6Qy7BQBAqSGc4RI9ub0ZCWel7W03dujC1LyeOzvmdSkAgKtEOMMl8uFsc2utx5XgevzkzjZFggF99+UBr0sBAFwlwhku0TM0pXUN1aqJFHRnLxRYXTSkN2xr0XdfHpBjI3QAKCmEM1ziBJ2aZeOtr+nQ+fFZHeyb9LoUAMBVIJzhokzG6cTwFJ2aZeKndrUrGDAubQJAiSGc4aLz47NKLmQYOSsTTbUR3bGpSQ+/3M+lTQAoIYQzXNQzTKdmufm5m7t0cnhah/sTXpcCAFglwhkuOsEyGmXnba/pUDBg+vaLfV6XAgBYJcIZLuoZmlJTbURNtRGvS8EaaaqN6O6tLfr2gT4ubQJAiSCc4aKeoSltpRmg7Lzjli71js3qhXPjXpcCAFgFwhkuOjE8pS1c0iw7P31juyLBgL59oN/rUgAAq0A4gyRpZGpOYzML2sLOAGUnXhXWT+xo1Xde7GOvTQAoAYQzSGJPzXL3jlu6NJSY09OnRr0uBQCwAsIZJLGMRrl786421UaC+ubzvV6XAgBYAeEMkrIjZ9XhoLrqq70uBQVQEwnp7Td16uGXBjQ7n/a6HADAFRDOICkbzra01SoQMK9LQYG86/ZuTc2l9A+H2M4JAPyMcAZJ0snhaZbRKHN3bGrSuoZqPfgslzYBwM9CXhcAbzyw7+zF23OptM6Pz2rXXPyS4ygvgYDpF29fp88+3qOBiaQ66qu8LgkAsAxGzqALiXlJUlss6nElKLRfvL1bGSd964XzXpcCALgMwhk0lEhKkloJZ2VvU0utXntDo77xbC/bOQGAT3FZExpOzClgUnMde2qWiytdnu5urNbfnhnTy+cndVN3fRGrAgCsBiNn0FBiTk21UYUCfDtUgpvXNSgUMH3jORoDAMCP+GkMDSfmmG9WQaojQe3qjOuhA32aT2W8LgcAsAThrMKlM04j03PMN6swt21o0Oj0vP7p6JDXpQAAliCcVbiR6TllHM0AlWZbW0wtdVH9zXN0bQKA3xDOKtxwYk4Sy2hUmmDA9PO3dunRI4Mam573uhwAwCKEswqXD2etdYSzSvOLt3drIe300IE+r0sBACxCOKtwQ4k51VeHFQ0HvS4FRba7K66b1tXrK0+fZc0zAPARwlmFG07QDFDJ3rt3g44MJPTCuXGvSwEA5BDOKphzjnBW4d55a5dqIkF95Wn2VAUAvyCcVbCJ2QXNpzM0A1SwumhI99zapW8f6NdkcsHrcgAAIpxVNJoBIGUvbc4upPW3L9AYAAB+QDirYEP5cMbIWUW7aV29buyK64F9NAYAgB8QzirYcGJO1eGg6qIhr0uBh8xM992xQYf7J/XsmTGvywGAisdP5Qo2lGsGMDOvS4EHHtj3ShPAfCqjqnBAv//tQ3rv3g2XnHffHRuWPhUAUECMnFWw4USSZgBIkqKhoF67oVEH+yY0OUtjAAB4iXBWoWbmUpqeTzPfDBfdublZzkn7To16XQoAVDTCWYUanqIZAJdqrotqe3tMT58eVSqd8bocAKhYhLMKNXRxw/MqjyuBn7x+S7Om51J66fyE16UAQMUinFWo4cScQgFTQ03Y61LgI1vb6tRaF9WTPRdYVgMAPEI4q1BDiaRaY1EF6NTEIgEzvWFbi/onkuoZnvK6HACoSISzCsWemricW9c3KFYV0hPHLnhdCgBUJMJZBZqdT2t8ZoFwhmWFggHdtaVFPcNTOj8+63U5AFBxCGcV6MTwlJxoBsDl7d3UpGgooCeOD3tdCgBUHMJZBTo+lJAkFqDFZVWFg9q7qUkv9U7o9IVpr8sBgIpCOKtAxwanFDRTSx3hDJd399YWBQOmzzze43UpAFBRCGcV6PhgQs11EQUDdGri8mJVYd2xqUnffP48o2cAUESEswp0fGhKbXHmm2Flb9zeqhCjZwBQVISzCjM7n9bZ0Rm1M98MqxCrCutX7riB0TMAKCLCWYU5MTwl58TIGVbtgz++WaGA6X89dtzrUgCgIhDOKgydmrhabfEqve/HNuqbz5/X0YGE1+UAQNkraDgzs7eZ2VEz6zGzjy7zeNTMvpZ7fJ+Zbcwdbzazx81sysw+U8gaK82xwSmFg3Rq4ur8ux/forpISH/8D0e9LgUAyl7BwpmZBSV9VtLbJe2W9F4z273ktPdLGnPObZX0aUmfzB1PSvp/Jf1WoeqrVMcHE9rUUkunJq5KY21EH3jjZn3v0KCePTPmdTkAUNYKOXK2V1KPc+6kc25e0lcl3bPknHskfSl3+0FJbzYzc85NO+eeVDakYQ0dG5zStvaY12WgBP2ruzeppS6iP/ruETnnvC4HAMpWIcPZOknnFt3vzR1b9hznXErShKTmAtZU0Wbn0zo3NqNtbXVel4ISVBsN6cM/uVX7To3qB8fZFB0ACqWkGwLM7ANmtt/M9g8PswfgSvKdmtsZOcM1eu8dG9TdWK0/+u4RZTKMngFAIRQynJ2XtH7R/e7csWXPMbOQpHpJI6v9BM65zznn9jjn9rS2tl5nueXv2GC20257OyNnuDbRUFAfect2Heyb1MMv93tdDgCUpVABX/sZSdvMbJOyIexeSfctOechSe+T9JSkd0t6zDGZpWCOD2U7NW9ortXTp5jUjdV5YN/ZS+5nnFN7PKr/8rcHNTa9oGDAdN8dGzyqDgDKT8FGznJzyD4s6RFJhyV93Tl30Mw+YWbvzJ32BUnNZtYj6SOSLi63YWanJX1K0r80s95lOj1xlY4OJLS5pU7hYElfzYbHAmb66d0dGpmep3MTAAqgkCNncs49LOnhJcc+tuh2UtIvXea5GwtZWyU63D+pOzY1eV0GysDOjpg2NNXosSODum1Dg9flAEBZYQilQoxNz6t/IqldnXGvS0EZMDO99cYOTSZTeurEqqeJAgBWgXBWIQ73T0oS4QxrZlNLrba31+n7x4Y1MbvgdTkAUDYIZxXiEOEMBfDTuzs0u5DWX/zgpNelAEDZIJxViMP9CbXGomplw3Osoa6Gat3cXa8vPHlKQwk29ACAtUA4qxCH+ycZNUNBvGVXuxbSGX3msR6vSwGAskA4qwDzqYyODyW0q5OdAbD2muui+uXXrdcD+87q7MiM1+UAQMkjnFWAE8NTWkg77WbkDAXyH968TcGA6dP/eMzrUgCg5BHOKgCdmii09niVfv2uTfrWC+cvfr8BAK5NQRehhT8c7p9UJBTQ5pZar0tBmXpg31m11kUVDQX0m197Qb/2+o2vOoctngBgdRg5qwCH+xPa0R5TiG2bUEDVkaDu3tqiIwMJDUzSuQkA14qf1mXOOadD/ZM0A6Ao7tzcrEgwoCeODXtdCgCULMJZmRtKzGl0ep75ZiiKmkhIr9vYqAO94xqfmfe6HAAoSYSzMvdS74Qk6caueo8rQaW4a2uLJOnJngseVwIApYlwVuZeODeuYMB00zrCGYqjoSaiW7ob9MzpUc3MpbwuBwBKDuGszB3oHdeO9piqI0GvS0EFecP2Vi2knZ46NeJ1KQBQcghnZSyTcXrh3LhuWd/gdSmoMB3xKu1oj+mpEyOaT2W8LgcASgrhrIydGplWIpnSbYQzeOCN21s1M5/Ws2dGvS4FAEoK4ayMHTg3LkmMnMETG5trtKGpRk/2XFA647wuBwBKBuGsjL1wbly1kaC2ttV5XQoqkJnpjdtaNTazoJfOT3hdDgCUDLZvKkMP7DsrSXrsyJDa4lX62jPnPK4IlWpnZ0ytsah+cGxYzjmZmdclAYDvMXJWphbSGfWPJ7W+scbrUlDBAmZ647YWDUwm9cRx1j0DgNUgnJWpgYmk0s6pu7Ha61JQ4W7pblCsKqTP/eCk16UAQEkgnJWpc2MzkqT1TYycwVuhYEA/tqVFT/Zc0MvMPQOAFRHOylTv2KziVSHVV4e9LgXQ3o1Nqo0E9RdPMHoGACshnJUh55xOXZjWBkbN4BPVkaDeu3eDvvNiv3pzo7oAgOURzsrQ6PS8JmYXtLmVJTTgH//q7k0ySX/B3DMAuCLCWRk6eWFakrS5pdbjSoBXdDVU6123d+srz5zT0GTS63IAwLcIZ2XoxPCUYtGQWmNRr0sBLvEbP7lF6YyjcxMAroBwVmacczo1PK1NrbUs+AnfuaG5Vvfc2qUv7zujC1NzXpcDAL5EOCszJ4anlZhLaUsL883gTx/6ya2aT2Xo3ASAyyCclZmnTo5Ikja3Mt8M/rSltU7vuKVL9z91RkMJ5p4BwFKEszLz1IkLqq8Oq6k24nUpwGX95k9t10I6o//xyDGvSwEA3yGclZFMxulHJ0e1uYX5ZvC3jS21+vW7Nunrz55j1wAAWIJwVkaODSU0Oj3P+mYoCR9+01Y11UT0X79zSM45r8sBAN8gnJWRRw8PSZK2thHO4H/xqrA+8tPbte/UqP7+5QGvywEA3yCclZFHDg7o1vUN7KeJkvGePet1Y1dc//lbL7MwLQDkEM7KxPnxWb3YO6G33tjhdSnAqoWCAf3JvbdqZj6lj3z9gDIZLm8CQMjrArA2/uFg9rLQW29s149OjnpcDfBqD+w7e9nH3nZjp771wnl9/smT+sAbtxSxKgDwH0bOysQjBwe0vb2OZgCUpNdtbNSNXXH90XeP6tHDg16XAwCeIpyVgZGpOT19apRLmihZZqZfvK1bu7vi+ndffk6PHxnyuiQA8AzhrAw8enhIGSfCGUpadSSo+//VHdrREdO/vf9Zffflfq9LAgBPEM7KwHcPDmhdQ7Vu7Ip7XQpwXeprwvry++/Qrs6YPvjl5/Tbf31Ak8kFr8sCgKIinJW4gYmkvn9sWD93Sye7AqAs1NeE9fUPvl4f+skt+sZzvXrrp3+gv3zqtGbmU16XBgBFQbdmifvK02eVcU6/svcGr0sB1kw0FNRvv3WnfmpXuz7+7UP62N8e1Ke+d0y/vGe93nlLlw6cG1/xl5H77thQpGoBYG0RzkrYQjqjrz5zVm/c1qoNzTVelwOsuds2NOpbv/Fj2n9mTJ9/4qS++OQpfe4HJ9VSF9XN3fW6ubtebbEqr8sEgDVFOCthjx4e1ODknP7g5xk1Q3m40lpoP769Ta+7oUkv903qxd5xPX5kSI8dGVJnfZVu6W7QTd31aqyJFLFaACgMwlkJu/9HZ9RVX6U37WzzuhSgKGqiIe3d1KS9m5o0Obugl85P6MXecX334IC+m1vr7+6trdrSWut1qQBwzQhnJerk8JR+2DOi//SW7QoGaARA5YlXh3XX1hbdtbVFo9Pzev7smPadGtUXf3hKHfEqRcNBveOWTkVDQa9LBYCrQrdmifpfjx5XNBTQe/au97oUwHNNtRG9eVe7fuetO/Su29fJyem3/vqA7v7k4/rMY8c1Nj3vdYkAsGqMnJWgF3vH9a0X+vShn9zCZGhgkVAwoNfe0KTbNzRqQ3ONPv/EKf3xPxzTZx7v0S/c1q133tKlvZuaGG0G4GuEsxLjnNMf/N1hNddG9MEfZ4NoYDlmpjdsa9UbtrXq2GBCX3jilL75fK++8vRZNddGdOfmZu3uimtbW53qq8OqjYbknDQ1l9LUXErTSz7OzKcVDQVUGw2ppS6irW0xbW+vU6wq7PUfFUAZIpyVmO8dGtTTp0b1X3/+NfxgAFZhe3tMn3z3zfov79ytfzo6rO++PKAXzo3r715a3fZQZlJ1OKi5VEbpjLvksd2dcf3Ejla9eVe7bt/QwELQANYE4ayETM2l9N8ePqwtrbW693XMNQOuRk0kpJ+5qVM/c1OnJGkyuaA/+6cTSi5kNJ9Ky8wUCQUUDQVUFQoqEs7ejgQDMjM555TKOE3OLmgoMaf+iaR6hqb0Z98/oT/9pxNqro1ozw2Nuv2Gxsv+4sTCuABWg3BWIpxz+u2/PqCzozP6q399p8JBejmA6xGvCqu7cfWLN5uZwkFTc11UzXVR7eqM600725RcSOtg36SePTOqRw4N6tEjQ7p1fYPu2tqi9jhzQgFcPcJZifiLJ07q718e0O/9zE69fkuz1+UAvnelBW3XUlU4qNfe0KjX3tCooURST50Y0XNnx7T/zJi2t9fprq0t2tpaxyVPAKtGOCsBjx8Z0h/+/RH9zE0d+jdv2Ox1OQAuoy1WpXtuXaef2tWup0+P6kcnRvR/fnhaHfEq3bW1Re967TrWXQOwInPOrXxWCdizZ4/bv3+/12Wsua8/c06/+82XtKM9pq9/8PV66IU+r0sCsEqpdEYHeif0w54LGphMqqUuqve9/gb96p03qLGWraaASmZmzzrn9iz3GCNnPrWQzuh//uMxffbxE3rDthb96a/crrooXy6glGTXXWvU7RsadGJ4WieGp/Q/vndMn/2nHr3j5i793C1d+rEtzcwhBXAJftr70BPHh/X73z6knqEp/fKebv1/v3AT/3gDJczMtLWtTh97x24dG0zoi0+e0nde7NdfP9urxpqwfmxLi+7YnF08d3NrrWoi/NMMVLKC/gtgZm+T9CeSgpI+75z7wyWPRyX9paTXShqR9B7n3OncY78r6f2S0pL+vXPukULW6rXkQlqPHBzQA/vOat+pUd3QXKMvvG+P3rSzjYnEQBnZ3h7TH77rZn38nTfq+8eG9cjLA3rq5Mgl66511VepuS6q+uqw6qvDileHFa8OKV4VVqwqlP0/GlZdVUiNNRE11obVWBPhlzigTBRszpmZBSUdk/QWSb2SnpH0XufcoUXn/Iakm51zHzSzeyX9gnPuPWa2W9JXJO2V1CXpHyVtd86lL/f5Sm3OWSqd0ckL03rm9KiePH5Bjx8dUnIho8aasO7c3Kw7N3OpA6gUzjmNzSyob3xWw1NzupCY0/R8SsmFjGbn05pdyP6/dBHcpWLRkBprI2qsjaipJpz7mL0frw6rNhJUTSSomkjo0o/RoGojIVWHgwqwtRVQFF7NOdsrqcc5dzJXxFcl3SPp0KJz7pH08dztByV9xrLDRPdI+qpzbk7SKTPryb3eUwWs95o455RxUsY5pTNOM/NpzcynLv6DOpVMaTCRVP9EUoMT2Y99E7M6Njil+VRGUva35Bu76nVLd4M2t9YqwEgZUFHMTE21ETWt0CSwkM4ouZDWXCr7MbmQ0exCWtNzKc3Mp3L//mTv5wPezFxa8+nMqmupDgcvBraacCj7MRfkaiNBVec+1uRvR4OqDgdVGw2pOpINeTWRoKKhgAIBU8BMAdPFf9fy/1Zmcov6pjNOmYyUXnQ8eyz3uMvezjgpFDSFAwGFg6ZQMLtAcChoCgezx8K5+9njAQXNlMpkd3ZIZZxSafeq+wvpjObTGS2kMlpYdD+VdgoGpGAgoFDAFAzYKx+DpmCujkgwcMnnDQcDCody9QQCvgq7zjnlx2Nc/v7FxySn7N99/u8h//eT/bvK/v2kMhkt5G+nnRYy2fNS6Yycsl/nYMAUDCy6baZA7u8ufyy06PbicwNmck7Zr0Emo4VU9nMspLJ1zC+qy+W/J4KmUOCV74VQ4JXvhXAwoHDg0u+TUNCfX5/FChnO1kk6t+h+r6Q7LneOcy5lZhOSmnPHf7TkuesKV+rKJmYXdOd/e1SZ3Dd32rmLt1crVhVSZ32VOuqr9b7XZ/f2u7m7QZtbavWVp8+t/AIAKlo498M/dpXPy4e6+VQ2eMynMpfeXnwsldFcLqzMpTKaSqY0MjWvhXT2/sKix8uj17+wAqaLU1PyMSD/+7fp4o0rPp6/nw9Qr9yWdDFsuVeO6ZXgVSYLMhRMwKRgwLJ/19n/FDDTG7e36M//xbKDWkVR0rNOzewDkj6QuztlZke9rGc1Xva6gOJpkXTB6yKwIr5O/sfXqDTwdSoNq/o6HZH0uV8reC03XO6BQoaz85IWbwDZnTu23Dm9ZhaSVK9sY8Bqnivn3OckfW4Na8YaMbP9l7uWDv/g6+R/fI1KA1+n0lAqX6dCzjh/RtI2M9tkZhFJ90p6aMk5D0l6X+72uyU95rIdCg9JutfMoma2SdI2SU8XsFYAAABfKNjIWW4O2YclPaLsUhpfdM4dNLNPSNrvnHtI0hck3Z+b8D+qbIBT7ryvK9s8kJL0oSt1agIAAJSLstm+Cf5iZh/IXXaGj/F18j++RqWBr1NpKJWvE+EMAADAR1jlFAAAwEcIZ7guZrbezB43s0NmdtDM/kPueJOZfc/Mjuc+NnpdK7I7d5jZ82b2ndz9TWa2z8x6zOxrueYdeMjMGszsQTM7YmaHzez1vJ/8xcx+M/fv3ctm9hUzq+K95A9m9kUzGzKzlxcdW/b9Y1n/K/c1e9HMbveu8ksRznC9UpL+k3Nut6Q7JX0ot/3WRyU96pzbJunR3H147z9IOrzo/iclfdo5t1XSmLL72cJbfyLpu865nZJuUfbrxfvJJ8xsnaR/L2mPc+41yja83SveS37xfyW9bcmxy71/3q7sahDblF0z9X8XqcYVEc5wXZxz/c6553K3E8r+IFmn7BZcX8qd9iVJP+9JgbjIzLol/aykz+fum6Q3Kbt1msTXyXNmVi/pjcp2sss5N++cGxfvJ78JSarOrc9ZI6lfvJd8wTn3A2VXf1jscu+feyT9pcv6kaQGM+ssSqErIJxhzZjZRkm3Sdonqd051597aEBSu1d14aL/Kel3JOU3WmyWNO6cS+Xue75NGrRJ0rCk/5O7/Px5M6sV7yffcM6dl/THks4qG8omJD0r3kt+drn3z3LbTPri60Y4w5owszpJ35D0H51zk4sfyy0sTFuwh8zs5yQNOeee9boWXFFI0u2S/rdz7jZJ01pyCZP3k7dy85XuUTZId0mq1asvo8GnSuX9QzjDdTOzsLLB7K+cc3+TOzyYHx7OfRzyqj5Iku6S9E4zOy3pq8pegvkTZYfx84tRL7tNGoqqV1Kvc25f7v6DyoY13k/+8VOSTjnnhp1zC5L+Rtn3F+8l/7rc+2dVW0V6gXCG65Kbt/QFSYedc59a9NDirbneJ+lvi10bXuGc+13nXLdzbqOyk5cfc879iqTHld06TeLr5Dnn3ICkc2a2I3fozcrulML7yT/OSrrTzGpy//7lv0a8l/zrcu+fhyT9Wq5r805JE4suf3qKRWhxXczsbklPSHpJr8xl+j1l5519XdIGSWck/bJzbukkTXjAzH5C0m85537OzDYrO5LWJOl5Sb/qnJvzsLyKZ2a3Ktu0EZF0UtKvK/uLNO8nnzCz35f0HmW71Z+X9K+VnavEe8ljZvYVST8hqUXSoKT/IulbWub9kwvXn1H2svSMpF93zu33oOxXIZwBAAD4CJc1AQAAfIRwBgAA4COEMwAAAB8hnAEAAPgI4QwAAMBHCGcAKpqZbTSzl6/xuV1m9uDKZwLA6oVWPgUAsBznXJ9eWXgUANYEI2cAIIXM7K/M7LCZPZhb/f20mf13M3vBzPab2e1m9oiZnTCzD0rXN+oGAJdDOAMAaYekP3XO7ZI0Kek3csfPOuduVXYXjP+r7CjZnZJ+34MaAVQILmsCgHTOOffD3O0vS/r3udsP5T6+JKnOOZeQlDCzOTNrKHKNACoEI2cAIC3dxy5/P783YmbR7fx9frkFUBCEMwCQNpjZ63O375P0pJfFAKhshDMAkI5K+pCZHZbUKOl/e1wPgApmzi0dzQcAAIBXGDkDAADwEcIZAACAjxDOAAAAfIRwBgAA4COEMwAAAB8hnAEAAPgI4QwAAMBHCGcAAAA+8v8D05Lc3psPFUkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,6))\n",
    "sns.boxplot(df.bmi)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10,8))\n",
    "sns.distplot(df.bmi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nas imagens acima, percebe-se que não há muitos valores discrepantes, dessa forma, eles não terão impacto significatvo na média. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Substituição dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após decidir qual método de substituição utilizar, precisamos saber qual o valor médio da coluna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>4909.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36517.829354</td>\n",
       "      <td>43.226614</td>\n",
       "      <td>0.097456</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>106.147677</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0.048728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21161.721625</td>\n",
       "      <td>22.612647</td>\n",
       "      <td>0.296607</td>\n",
       "      <td>0.226063</td>\n",
       "      <td>45.283560</td>\n",
       "      <td>7.854067</td>\n",
       "      <td>0.215320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17741.250000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.245000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36932.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.885000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54682.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.090000</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72940.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          age  hypertension  heart_disease  \\\n",
       "count   5110.000000  5110.000000   5110.000000    5110.000000   \n",
       "mean   36517.829354    43.226614      0.097456       0.054012   \n",
       "std    21161.721625    22.612647      0.296607       0.226063   \n",
       "min       67.000000     0.080000      0.000000       0.000000   \n",
       "25%    17741.250000    25.000000      0.000000       0.000000   \n",
       "50%    36932.000000    45.000000      0.000000       0.000000   \n",
       "75%    54682.000000    61.000000      0.000000       0.000000   \n",
       "max    72940.000000    82.000000      1.000000       1.000000   \n",
       "\n",
       "       avg_glucose_level          bmi       stroke  \n",
       "count        5110.000000  4909.000000  5110.000000  \n",
       "mean          106.147677    28.893237     0.048728  \n",
       "std            45.283560     7.854067     0.215320  \n",
       "min            55.120000    10.300000     0.000000  \n",
       "25%            77.245000    23.500000     0.000000  \n",
       "50%            91.885000    28.100000     0.000000  \n",
       "75%           114.090000    33.100000     0.000000  \n",
       "max           271.740000    97.600000     1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df.fillna(np.mean(df['bmi']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com a substituição feita, visualizamos que não há mais valores ausentes. Com isso, exportamos nosso DataFrame para o formato CSV novamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5110 non-null   int64  \n",
      " 1   gender             5110 non-null   object \n",
      " 2   age                5110 non-null   float64\n",
      " 3   hypertension       5110 non-null   int64  \n",
      " 4   heart_disease      5110 non-null   int64  \n",
      " 5   ever_married       5110 non-null   object \n",
      " 6   work_type          5110 non-null   object \n",
      " 7   Residence_type     5110 non-null   object \n",
      " 8   avg_glucose_level  5110 non-null   float64\n",
      " 9   bmi                5110 non-null   float64\n",
      " 10  smoking_status     5110 non-null   object \n",
      " 11  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 479.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n",
    "data.to_csv('./data/newstroke-data.csv', index = False)"
   ]
  }
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