notebooks/eda-tempo[python].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Exploratória de Dados\n",
    "\n",
    "**Objetivo**: Limpeza e tratamento de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregando os dados\n",
    "df = pd.read_csv('../datasets/tempo.csv', sep=';', encoding='iso-8859-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## exploração"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Aparencia</th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>Umidade</th>\n",
       "      <th>Vento</th>\n",
       "      <th>Jogar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sol</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sol</td>\n",
       "      <td>80</td>\n",
       "      <td>90.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nublado</td>\n",
       "      <td>83</td>\n",
       "      <td>86.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chuva</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chuva</td>\n",
       "      <td>68</td>\n",
       "      <td>80.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chuva</td>\n",
       "      <td>65</td>\n",
       "      <td>70.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>menos</td>\n",
       "      <td>64</td>\n",
       "      <td>65.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sol</td>\n",
       "      <td>1220</td>\n",
       "      <td>95.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sol</td>\n",
       "      <td>69</td>\n",
       "      <td>70.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>chuva</td>\n",
       "      <td>75</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sol</td>\n",
       "      <td>75</td>\n",
       "      <td>70.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nublado</td>\n",
       "      <td>72</td>\n",
       "      <td>90.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>nublado</td>\n",
       "      <td>81</td>\n",
       "      <td>75.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>chuva</td>\n",
       "      <td>71</td>\n",
       "      <td>91.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aparencia  Temperatura  Umidade       Vento Jogar\n",
       "0        sol           85     85.0       FALSO   nao\n",
       "1        sol           80     90.0  VERDADEIRO   nao\n",
       "2    nublado           83     86.0       FALSO   sim\n",
       "3      chuva           70      NaN       FALSO   sim\n",
       "4      chuva           68     80.0       FALSO   sim\n",
       "5      chuva           65     70.0  VERDADEIRO   nao\n",
       "6      menos           64     65.0  VERDADEIRO   sim\n",
       "7        sol         1220     95.0       FALSO   nao\n",
       "8        sol           69     70.0       FALSO   sim\n",
       "9      chuva           75    200.0         NaN   sim\n",
       "10       sol           75     70.0  VERDADEIRO   sim\n",
       "11   nublado           72     90.0  VERDADEIRO   sim\n",
       "12   nublado           81     75.0       FALSO   sim\n",
       "13     chuva           71     91.0  VERDADEIRO   nao"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conhecendo os dados\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 5)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conhecendo o shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14 entries, 0 to 13\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Aparencia    14 non-null     object \n",
      " 1   Temperatura  14 non-null     int64  \n",
      " 2   Umidade      13 non-null     float64\n",
      " 3   Vento        13 non-null     object \n",
      " 4   Jogar        14 non-null     object \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 692.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# conhecendo o dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aparencia       object\n",
       "Temperatura      int64\n",
       "Umidade        float64\n",
       "Vento           object\n",
       "Jogar           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conhecendo tipos de dados\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aparencia      0\n",
       "Temperatura    0\n",
       "Umidade        1\n",
       "Vento          1\n",
       "Jogar          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conhecendo os dados faltantes\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>Umidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>155.571429</td>\n",
       "      <td>89.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>306.433759</td>\n",
       "      <td>34.540203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>69.250000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73.500000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>80.750000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1220.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperatura     Umidade\n",
       "count    14.000000   13.000000\n",
       "mean    155.571429   89.769231\n",
       "std     306.433759   34.540203\n",
       "min      64.000000   65.000000\n",
       "25%      69.250000   70.000000\n",
       "50%      73.500000   85.000000\n",
       "75%      80.750000   90.000000\n",
       "max    1220.000000  200.000000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# analise estatistica\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## atributos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### categoricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Contagem de valores únicos: \n",
      "Aparencia\n",
      "sol        5\n",
      "chuva      5\n",
      "nublado    3\n",
      "menos      1\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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g9Tpw4IA++eQTt1B9rvOPu/P3mbz7/PKWVZT9oyDF2U4oGEHlT87Ly0tt2rTR559/rp9//vmiB1PeQblv3758fffu3et2RuOPOnHihBYsWKBnn31WTz/9tKs9Kysr3/e6hIaGuv7wnev8m1Tz6vvggw9c/zO5kkJDQ7VmzZp87YXVOWzYMN15550Fzqt27doXXFa3bt00YMAAzZgxQ6NHj9bbb7+t22+/XcHBwa4+77zzjqpVq6Y5c+a43XSalZVVpHXZt29fvva9e/e6rUOe829qvZR19Pf3V3JyspKTk3XgwAHXGYekpCR9//33hdaYFzKzsrLcbhQvyZsR857UOP9m8nMFBwerTJkyRXq/iruueQq6MXv//v2qUaOGJGnLli365ptvNGPGDPXs2dPV54cffih0nudvu0s5Nguyfft2ff3115LkdgbrXAsXLnQ7m3mh4zvvc2n27Nny8fHRggUL3P6DMW/evCKtl/T7doiNjdXo0aMLnCYvWBZVUfaPghRnO6FgXPr5Cxg2bJiMMXr44Ydd32dwrrNnz+qTTz6RJLVu3VrS73/gzrV27Vqlp6erTZs2l7z88/8HksfhcMgYk+9JpDfffDPfnf4JCQlavHix2x+g3Nxcvf/++279EhMT5e3trZ07d6px48YFvi6nVq1a6ZdfftH8+fPd2v/73/+6DdeuXVs1a9bUN998U2id5cuXv+CygoODdfvtt2vmzJlasGCB9u/fn++yj8PhUNmyZd0+sPfv31+kp37atGmjrVu3asOGDW7tM2fOlMPhUKtWrS44fXHXsVKlSurVq5e6deumbdu26bfffit0GXlnfDZv3uzWnrc/l4RatWqpevXqmjZtWqEBz9/fX02bNtWHH37otp/n5ubqnXfeUURERL5LENKlrWued9991214xYoV+umnn9SyZUtJ//vjfP5x9cYbb1x03nku5dgsSN73pUyZMkVLlixxe3322Wfy8fHJd0avsOOmTJkyatGihasub29vtzMxp06d0ttvv13kdbv11lu1ZcsWVa9evcB98lKDSlH2j4KUxHbC7zij8hcQHx+viRMnqm/fvoqLi1OfPn107bXX6uzZs9q4caMmT56s+vXrKykpSbVr19Yjjzyi1157TWXKlFH79u21e/dujRgxQpGRkerfv/8lL7969ery8/PTu+++q7p16yogIEDh4eEKDw9XixYt9OKLL6pChQqKjo7W0qVLNXXqVF111VVu8xg+fLg++eQTtWnTRsOHD5efn58mTZqkkydPSvrfJZ3o6GiNGjVKw4cP148//qhbbrlFwcHBOnDggNasWeP63+zlcv/992vChAm6//77NXr0aNWsWVOfffaZFi5cmK/vG2+8ofbt2ysxMVG9evVS1apVdfToUaWnp2vDhg35QlhBevfurTlz5ujxxx9XRESE2rZt6zY+7/HMvn37qnPnzsrIyNBzzz2nKlWqaMeOHRecd//+/TVz5kx17NhRo0aNUlRUlD799FO9/vrr6tOnT4F/eIu7jk2bNtWtt96q2NhYBQcHKz09XW+//bbi4+Mv+J0eHTp0UEhIiB588EGNGjVK3t7emjFjhjIyMi5a26X4z3/+o6SkJDVr1kz9+/fX1VdfrT179mjhwoWu4DBmzBjdfPPNatWqlQYNGqSyZcvq9ddf15YtWzRr1izXH6birmuedevW6aGHHlKXLl2UkZGh4cOHq2rVqurbt68kqU6dOqpevbqefvppGWMUEhKiTz75RKmpqUVe38DAwCIfm+fLzs7WzJkzVbduXT300EMF9klKStL8+fN16NAh1xmJ0NBQ9enTR3v27FGtWrX02WefacqUKerTp4/rrEzHjh318ssvq3v37nrkkUd05MgRvfTSS5f0fT2jRo1Samqqmjdvrn79+ql27do6ffq0du/erc8++0yTJk265Ms4Rdk/zlcS2wn/z3P38aKkbdq0yfTs2dNcffXVpmzZssbf399cd9115plnnjEHDx509cvJyTHjxo0ztWrVMj4+PqZChQrmvvvucz1SnCchIcFce+21+ZbTs2dPExUV5dY2a9YsU6dOHePj42MkmWeffdYYY8zPP/9s7rrrLhMcHGzKly9vbrnlFrNly5Z8T6AYY8yyZctM06ZNjdPpNJUrVzaDBw8248aNM5LcHhU0xph58+aZVq1amcDAQON0Ok1UVJTp3Lmz+eKLL9zq9Pf3z1d/YesVFRVlOnbsWOB7e668dQoICDDly5c3d911l1mxYkWBTz598803pmvXriYsLMz4+PiYypUrm9atW5tJkyZddDnG/L6tIiMjjSQzfPjwAvuMHTvWREdHG6fTaerWrWumTJlS4NMyBb3nP/30k+nevbsJDQ01Pj4+pnbt2ubFF190eyIp7+mIF198scDlF2Udn376adO4cWMTHBxsnE6nueaaa0z//v3N4cOHL/oerFmzxjRv3tz4+/ubqlWrmmeffda8+eabBT71U9D2S0hIMAkJCa7hgp76McaYlStXmvbt25ugoCDjdDpN9erVTf/+/d36LFu2zLRu3dr4+/sbPz8/06xZM/PJJ5+49SnuuuY9NbJo0SLTo0cPc9VVVxk/Pz/ToUMHs2PHDre+W7duNTfffLMpX768CQ4ONl26dDF79uxxO/aM+d9TPwU9bnwpx+a55s2bZySZV155pdA+KSkpRpIZP368MeZ/x1xaWppp3LixcTqdpkqVKubvf/97vifGpk2bZmrXru1678aMGWOmTp1a5O1tjDGHDh0y/fr1M9WqVTM+Pj4mJCTExMXFmeHDh5tff/3VGHPh/fr899GYi+8fBT31U9TthAtzGHPet+gAFmnXrp12796t7du3e7oU4LKaMWOGHnjgAa1du/ayX8K80lq2bKnDhw9ry5Ytni4Ff0Jc+oE1BgwYoOuuu06RkZE6evSo3n33XaWmppbq3w8BgNKOoAJr5OTk6JlnntH+/fvlcDhUr149vf3227rvvvs8XRoAwEO49AMAAKzF48kAAMBaBBUAAGAtggoAALDWn/pm2tzcXO3du1fly5cv8KuUAQCAfYwx+uWXXxQeHp7vN9rO96cOKnv37lVkZKSnywAAAMWQkZFx0W8K/lMHlbzfEcnIyFBgYKCHqwEAAEWRmZmpyMjIi/7mmfQnDyp5l3sCAwMJKgAA/MkU5bYNbqYFAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGt5NKiMHDlSDofD7VW5cmVPlgQAACzi8d/6ufbaa/XFF1+4hr28vDxYDQAAsInHg4q3tzdnUQAAQIE8fo/Kjh07FB4ermrVqumee+7Rjz/+6OmSAACAJTx6RqVp06aaOXOmatWqpQMHDuj5559X8+bN9d133yk0NDRf/6ysLGVlZbmGMzMzr2S5AADgCnMYY4yni8hz8uRJVa9eXUOGDNGAAQPyjR85cqSSk5PztZ84cUKBgYFFXk7c4Jl/qE6UnPUv3u/pEvAXw/FtD45vFCYzM1NBQUFF+vvt8Us/5/L391dMTIx27NhR4Phhw4bpxIkTrldGRsYVrhAAAFxJHr+Z9lxZWVlKT0/XTTfdVOB4p9Mpp9N5hasCAACe4tEzKoMGDdLSpUu1a9curV69Wp07d1ZmZqZ69uzpybIAAIAlPHpG5eeff1a3bt10+PBhVaxYUc2aNdOqVasUFRXlybIAAIAlPBpUZs+e7cnFAwAAy1l1My0AAMC5CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsJY1QWXMmDFyOBx66qmnPF0KAACwhBVBZe3atZo8ebJiY2M9XQoAALCIx4PKr7/+qnvvvVdTpkxRcHCwp8sBAAAW8XhQeeyxx9SxY0e1bdv2on2zsrKUmZnp9gIAAH9d3p5c+OzZs7VhwwatXbu2SP3HjBmj5OTky1wVAACwhcfOqGRkZOjJJ5/UO++8I19f3yJNM2zYMJ04ccL1ysjIuMxVAgAAT/LYGZX169fr4MGDiouLc7Xl5OToq6++0r///W9lZWXJy8vLbRqn0ymn03mlSwUAAB7isaDSpk0bffvtt25tDzzwgOrUqaOhQ4fmCykAAKD08VhQKV++vOrXr+/W5u/vr9DQ0HztAACgdPL4Uz8AAACF8ehTP+dLS0vzdAkAAMAinFEBAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLU8GlQmTpyo2NhYBQYGKjAwUPHx8fr88889WRIAALCIR4NKRESExo4dq3Xr1mndunVq3bq1OnXqpO+++86TZQEAAEt4e3LhSUlJbsOjR4/WxIkTtWrVKl177bUeqgoAANjCo0HlXDk5OXr//fd18uRJxcfHe7ocAABgAY8HlW+//Vbx8fE6ffq0AgIC9NFHH6levXoF9s3KylJWVpZrODMz80qVCQAAPMDjQaV27dratGmTjh8/rrlz56pnz55aunRpgWFlzJgxSk5O9kCV+DOLGzzT0yXg/61/8X5PlwDgT8bjjyeXLVtWNWrUUOPGjTVmzBg1aNBAr776aoF9hw0bphMnTrheGRkZV7haAABwJXn8jMr5jDFul3fO5XQ65XQ6r3BFAADAUzwaVP7+97+rffv2ioyM1C+//KLZs2crLS1NKSkpniwLAABYwqNB5cCBA+rRo4f27dunoKAgxcbGKiUlRTfffLMnywIAAJbwaFCZOnWqJxcPAAAs5/GbaQEAAApDUAEAANYiqAAAAGsVK6i0bt1ax48fz9eemZmp1q1b/9GaAAAAJBUzqKSlpenMmTP52k+fPq1ly5b94aIAAACkS3zqZ/Pmza5/b926Vfv373cN5+TkKCUlRVWrVi256gAAQKl2SUGlYcOGcjgccjgcBV7i8fPz02uvvVZixQEAgNLtkoLKrl27ZIzRNddcozVr1qhixYqucWXLllVYWJi8vLxKvEgAAFA6XVJQiYqKkiTl5uZelmIAAADOVexvpt2+fbvS0tJ08ODBfMHlmWee+cOFAQAAFCuoTJkyRX369FGFChVUuXJlORwO1ziHw0FQAQAAJaJYQeX555/X6NGjNXTo0JKuBwAAwKVY36Ny7NgxdenSpaRrAQAAcFOsoNKlSxctWrSopGsBAABwU6xLPzVq1NCIESO0atUqxcTEyMfHx218v379SqQ4AABQuhUrqEyePFkBAQFaunSpli5d6jbO4XAQVAAAQIkoVlDZtWtXSdcBAACQT7HuUQEAALgSinVGpXfv3hccP23atGIVAwAAcK5iBZVjx465DZ89e1ZbtmzR8ePHC/yxQgAAgOIoVlD56KOP8rXl5uaqb9++uuaaa/5wUQAAAFIJ3qNSpkwZ9e/fXxMmTCipWQIAgFKuRG+m3blzp7Kzs0tylgAAoBQr1qWfAQMGuA0bY7Rv3z59+umn6tmzZ4kUBgAAUKygsnHjRrfhMmXKqGLFiho/fvxFnwgCAAAoqmIFlSVLlpR0HQAAAPkUK6jkOXTokLZt2yaHw6FatWqpYsWKJVUXAABA8W6mPXnypHr37q0qVaqoRYsWuummmxQeHq4HH3xQv/32W0nXCAAASqliBZUBAwZo6dKl+uSTT3T8+HEdP35cH3/8sZYuXaqBAweWdI0AAKCUKtaln7lz5+qDDz5Qy5YtXW0dOnSQn5+funbtqokTJ5ZUfQAAoBQr1hmV3377TZUqVcrXHhYWxqUfAABQYooVVOLj4/Xss8/q9OnTrrZTp04pOTlZ8fHxJVYcAAAo3Yp16eeVV15R+/btFRERoQYNGsjhcGjTpk1yOp1atGhRSdcIAABKqWIFlZiYGO3YsUPvvPOOvv/+exljdM899+jee++Vn59fSdcIAABKqWIFlTFjxqhSpUp6+OGH3dqnTZumQ4cOaejQoSVSHAAAKN2KdY/KG2+8oTp16uRrv/baazVp0qQ/XBQAAIBUzKCyf/9+ValSJV97xYoVtW/fvj9cFAAAgFTMoBIZGanly5fna1++fLnCw8P/cFEAAABSMe9Reeihh/TUU0/p7Nmzat26tSTpyy+/1JAhQ/hmWgAAUGKKFVSGDBmio0ePqm/fvjpz5owkydfXV0OHDtWwYcNKtEAAAFB6FSuoOBwOjRs3TiNGjFB6err8/PxUs2ZNOZ3Okq4PAACUYsUKKnkCAgLUpEmTkqoFAADATbFupgUAALgSCCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWh4NKmPGjFGTJk1Uvnx5hYWF6fbbb9e2bds8WRIAALCIR4PK0qVL9dhjj2nVqlVKTU1Vdna22rVrp5MnT3qyLAAAYAlvTy48JSXFbXj69OkKCwvT+vXr1aJFCw9VBQAAbOHRoHK+EydOSJJCQkIKHJ+VlaWsrCzXcGZm5hWpCwAAeIY1N9MaYzRgwADdeOONql+/foF9xowZo6CgINcrMjLyClcJAACuJGuCyuOPP67Nmzdr1qxZhfYZNmyYTpw44XplZGRcwQoBAMCVZsWlnyeeeELz58/XV199pYiIiEL7OZ1OOZ3OK1gZAADwJI8GFWOMnnjiCX300UdKS0tTtWrVPFkOAACwjEeDymOPPab//ve/+vjjj1W+fHnt379fkhQUFCQ/Pz9PlgYAACzg0XtUJk6cqBMnTqhly5aqUqWK6zVnzhxPlgUAACzh8Us/AAAAhbHmqR8AAIDzEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoeDSpfffWVkpKSFB4eLofDoXnz5nmyHAAAYBmPBpWTJ0+qQYMG+ve//+3JMgAAgKW8Pbnw9u3bq3379p4sAQAAWMyjQeVSZWVlKSsryzWcmZnpwWoAAMDl9qcKKmPGjFFycrKnywAAWCJu8ExPl4D/t/7F+y/LfP9UT/0MGzZMJ06ccL0yMjI8XRIAALiM/lRnVJxOp5xOp6fLAAAAV8if6owKAAAoXTx6RuXXX3/VDz/84BretWuXNm3apJCQEF199dUerAwAANjAo0Fl3bp1atWqlWt4wIABkqSePXtqxowZHqoKAADYwqNBpWXLljLGeLIEAABgMe5RAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1PB5UXn/9dVWrVk2+vr6Ki4vTsmXLPF0SAACwhEeDypw5c/TUU09p+PDh2rhxo2666Sa1b99ee/bs8WRZAADAEh4NKi+//LIefPBBPfTQQ6pbt65eeeUVRUZGauLEiZ4sCwAAWMJjQeXMmTNav3692rVr59berl07rVixwkNVAQAAm3h7asGHDx9WTk6OKlWq5NZeqVIl7d+/v8BpsrKylJWV5Ro+ceKEJCkzM/OSlp2TdeoSq8XlcqnbrjjY3vZge5cubO/S5VK2d15fY8xF+3osqORxOBxuw8aYfG15xowZo+Tk5HztkZGRl6U2XH5Br/3N0yXgCmJ7ly5s79KlONv7l19+UVBQ0AX7eCyoVKhQQV5eXvnOnhw8eDDfWZY8w4YN04ABA1zDubm5Onr0qEJDQwsNN39FmZmZioyMVEZGhgIDAz1dDi4ztnfpwvYuXUrr9jbG6JdfflF4ePhF+3osqJQtW1ZxcXFKTU3VHXfc4WpPTU1Vp06dCpzG6XTK6XS6tV111VWXs0yrBQYGlqodu7Rje5cubO/SpTRu74udScnj0Us/AwYMUI8ePdS4cWPFx8dr8uTJ2rNnj/72N04XAgAADweVu+++W0eOHNGoUaO0b98+1a9fX5999pmioqI8WRYAALCEx2+m7du3r/r27evpMv5UnE6nnn322XyXwfDXxPYuXdjepQvb++IcpijPBgEAAHiAx3/rBwAAoDAEFQAAYC2CCgAAsBZB5S9m9+7dcjgc2rRpk6dLwUWkpaXJ4XDo+PHjhfaZMWNGiXxXUHR0tF555ZU/PB9cOo5J4I8hqAAAAGsRVAAAgLUIKpb64IMPFBMTIz8/P4WGhqpt27Y6efKkcnNzNWrUKEVERMjpdKphw4ZKSUnxdLmlUsuWLdWvXz8NGTJEISEhqly5skaOHCmp4NP9x48fl8PhUFpamtt8li9frgYNGsjX11dNmzbVt99+W+gyd+7cqU6dOqlSpUoKCAhQkyZN9MUXX7j1OXjwoJKSkuTn56dq1arp3XffzTefPXv2qFOnTgoICFBgYKC6du2qAwcOFPu9wO+/PTZu3DjVqFFDTqdTV199tUaPHu0a/+OPP6pVq1YqV66cGjRooJUrV7rGjRw5Ug0bNnSb3yuvvKLo6GhJ0sKFC+Xr65vvMmG/fv2UkJAgSTpy5Ii6deumiIgIlStXTjExMZo1a9ZlWdfSqmXLlnriiSf01FNPKTg4WJUqVdLkyZN18uRJPfDAAypfvryqV6+uzz//3DXN1q1b1aFDBwUEBKhSpUrq0aOHDh8+7DbPwj5H8lzseP3mm2/UqlUrlS9fXoGBgYqLi9O6desu+/txpRBULLRv3z5169ZNvXv3Vnp6utLS0nTnnXfKGKNXX31V48eP10svvaTNmzcrMTFRt912m3bs2OHpskult956S/7+/lq9erVeeOEFjRo1SqmpqZc0j8GDB+ull17S2rVrFRYWpttuu01nz54tsO+vv/6qDh066IsvvtDGjRuVmJiopKQk7dmzx9WnV69e2r17txYvXqwPPvhAr7/+ug4ePOgab4zR7bffrqNHj2rp0qVKTU3Vzp07dffddxfvTYCk3380ddy4cRoxYoS2bt2q//73v24/sDp8+HANGjRImzZtUq1atdStWzdlZ2cXad5t27bVVVddpblz57racnJy9N577+nee++VJJ0+fVpxcXFasGCBtmzZokceeUQ9evTQ6tWrS3ZFS7m33npLFSpU0Jo1a/TEE0+oT58+6tKli5o3b64NGzYoMTFRPXr00G+//aZ9+/YpISFBDRs21Lp165SSkqIDBw6oa9eu+eZZ2OdIUY7Xe++9VxEREVq7dq3Wr1+vp59+Wj4+Plf0fbmsDKyzfv16I8ns3r0737jw8HAzevRot7YmTZqYvn37GmOM2bVrl5FkNm7ceCVKLdUSEhLMjTfe6NbWpEkTM3To0AK3w7Fjx4wks2TJEmOMMUuWLDGSzOzZs119jhw5Yvz8/MycOXOMMcZMnz7dBAUFXbCOevXqmddee80YY8y2bduMJLNq1SrX+PT0dCPJTJgwwRhjzKJFi4yXl5fZs2ePq893331nJJk1a9Zc6tsAY0xmZqZxOp1mypQp+cbl7Qtvvvmmqy3v/U5PTzfGGPPss8+aBg0auE03YcIEExUV5Rru16+fad26tWt44cKFpmzZsubo0aOF1tWhQwczcODAYq4Vznf+MZ+dnW38/f1Njx49XG379u0zkszKlSvNiBEjTLt27dzmkZGRYSSZbdu2FThPY/73OWJM0Y7X8uXLmxkzZpTsylqEMyoWatCggdq0aaOYmBh16dJFU6ZM0bFjx5SZmam9e/fqhhtucOt/ww03KD093UPVlm6xsbFuw1WqVHE7e1EU8fHxrn+HhISodu3ahW7PkydPasiQIapXr56uuuoqBQQE6Pvvv3edUUlPT5e3t7caN27smqZOnTpuTw6lp6crMjJSkZGRrra8+bEfFU96erqysrLUpk2bQvucu69UqVJFki5pX7n33nuVlpamvXv3SpLeffdddejQQcHBwZJ+P8MyevRoxcbGKjQ0VAEBAVq0aJHb2Tb8ceduRy8vL4WGhiomJsbVlncW7eDBg1q/fr2WLFmigIAA16tOnTqSfr+MW9A8JffPkaIcrwMGDNBDDz2ktm3bauzYsW7z/isgqFjIy8tLqamp+vzzz1WvXj299tprql27tnbt2iVJcjgcbv2NMfnacGWcf3rV4XAoNzdXZcr8fmiZc36horDLOQUpbHsOHjxYc+fO1ejRo7Vs2TJt2rRJMTExOnPmjNvyLrQ/FLa/sB8Vn5+f30X7nLuv5L3Pubm5kqQyZcq47StS/v3l+uuvV/Xq1TV79mydOnVKH330ke677z7X+PHjx2vChAkaMmSIFi9erE2bNikxMdG1b6BkFHTMF7Ztc3NzlZSUpE2bNrm9duzYoRYtWlxwnnn7RlGO15EjR+q7775Tx44dtXjxYtWrV08fffRRyaywBQgqlnI4HLrhhhuUnJysjRs3qmzZsvryyy8VHh6ur7/+2q3vihUrVLduXQ9VioJUrFhR0u/3G+Up7Hs0Vq1a5fr3sWPHtH37dtf/us63bNky9erVS3fccYdiYmJUuXJl7d692zW+bt26ys7OdruRbtu2bW43YdarV0979uxRRkaGq23r1q06ceIE+1Ex1axZU35+fvryyy+LNX3FihW1f/9+t7BS0P7SvXt3vfvuu/rkk09UpkwZdezY0TVu2bJl6tSpk+677z41aNBA11xzDfeueVijRo303XffKTo6WjVq1HB7+fv7F2keRT1ea9Wqpf79+2vRokW68847NX369BJfH08hqFho9erV+uc//6l169Zpz549+vDDD3Xo0CHVrVtXgwcP1rhx4zRnzhxt27ZNTz/9tDZt2qQnn3zS02XjHH5+fmrWrJnGjh2rrVu36quvvtI//vGPAvuOGjVKX375pbZs2aJevXqpQoUKuv322wvsW6NGDX344YfatGmTvvnmG3Xv3t31Py9Jql27tm655RY9/PDDWr16tdavX6+HHnrI7X/8bdu2VWxsrO69915t2LBBa9as0f3336+EhAS3S0YoOl9fXw0dOlRDhgzRzJkztXPnTq1atUpTp04t0vQtW7bUoUOH9MILL2jnzp36z3/+4/bkSJ68bTZ69Gh17txZvr6+rnE1atRQamqqVqxYofT0dD366KPav39/ia0jLt1jjz2mo0ePqlu3blqzZo1+/PFHLVq0SL1791ZOTk6R5nGx4/XUqVN6/PHHlZaWpp9++knLly/X2rVr/1L/6SCoWCgwMFBfffWVOnTooFq1aukf//iHxo8fr/bt26tfv34aOHCgBg4cqJiYGKWkpGj+/PmqWbOmp8vGeaZNm6azZ8+qcePGevLJJ/X8888X2G/s2LF68sknFRcXp3379mn+/PkqW7ZsgX0nTJig4OBgNW/eXElJSUpMTFSjRo3c+kyfPl2RkZFKSEjQnXfeqUceeURhYWGu8Q6HQ/PmzVNwcLBatGihtm3b6pprrtGcOXNKbuVLoREjRmjgwIF65plnVLduXd19991Fvgelbt26ev311/Wf//xHDRo00Jo1azRo0KB8/WrWrKkmTZpo8+bNrqd9zl1+o0aNlJiYqJYtW6py5cqFBl5cGeHh4Vq+fLlycnKUmJio+vXr68knn1RQUJDr8vDFXOx49fLy0pEjR3T//ferVq1a6tq1q9q3b6/k5OTLuWpXlMOcf2EUAADAEpxRAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACoNSZMWOG2w81ArAXQQVAoVasWCEvLy/dcsstni6lRN19993avn27p8sAUAR8My2AQj300EMKCAjQm2++qa1bt+rqq6++rMs7c+ZMoT8fAKB04owKgAKdPHlS7733nvr06aNbb71VM2bMcI1LS0uTw+HQp59+qgYNGsjX11dNmzbVt99+6+pz5MgRdevWTRERESpXrpxiYmI0a9Yst2W0bNlSjz/+uAYMGKAKFSro5ptvlvT7r8N26NBBAQEBqlSpknr06KHDhw+7TdevXz8NGTJEISEhqly5skaOHOk27+PHj+uRRx5RpUqV5Ovrq/r162vBggWS8l/62blzpzp16qRKlSopICBATZo00RdffFFC7ySAP4KgAqBAc+bMUe3atVW7dm3dd999mj59us4/ATt48GC99NJLWrt2rcLCwnTbbbfp7NmzkqTTp08rLi5OCxYs0JYtW/TII4+oR48eWr16tds83nrrLXl7e2v58uV64403tG/fPiUkJKhhw4Zat26dUlJSdODAAXXt2jXfdP7+/lq9erVeeOEFjRo1SqmpqZKk3NxctW/fXitWrNA777yjrVu3auzYsfLy8ipwXX/99Vd16NBBX3zxhTZu3KjExEQlJSVpz549JfV2AiguAwAFaN68uXnllVeMMcacPXvWVKhQwaSmphpjjFmyZImRZGbPnu3qf+TIEePn52fmzJlT6Dw7dOhgBg4c6BpOSEgwDRs2dOszYsQI065dO7e2jIwMI8ls27bNNd2NN97o1qdJkyZm6NChxhhjFi5caMqUKePqf77p06eboKCgC62+qVevnnnttdcu2AfA5ccZFQD5bNu2TWvWrNE999wjSfL29tbdd9+tadOmufWLj493/TskJES1a9dWenq6JCknJ0ejR49WbGysQkNDFRAQoEWLFuU7S9G4cWO34fXr12vJkiUKCAhwverUqSPp90s0eWJjY92mq1Klig4ePChJ2rRpkyIiIlSrVq0ire/Jkyc1ZMgQ1atXT1dddZUCAgL0/fffc0YFsIC3pwsAYJ+pU6cqOztbVatWdbUZY+Tj46Njx45dcFqHwyFJGj9+vCZMmKBXXnlFMTEx8vf311NPPaUzZ8649ff393cbzs3NVVJSksaNG5dv3lWqVHH928fHJ99yc3NzJUl+fn5FWMv/GTx4sBYuXKiXXnpJNWrUkJ+fnzp37pyvVgBXHkEFgJvs7GzNnDlT48ePV7t27dzG3XXXXXr33XdVv359SdKqVatcTwIdO3ZM27dvd539WLZsmTp16qT77rtP0u8BZMeOHapbt+4Fl9+oUSPNnTtX0dHR8vYu3kdUbGysfv75Z23fvr1IZ1WWLVumXr166Y477pD0+z0ru3fvLtayAZQsLv0AcLNgwQIdO3ZMDz74oOrXr+/26ty5s6ZOnerqO2rUKH355ZfasmWLevXqpQoVKuj222+XJNWoUUOpqalasWKF0tPT9eijj2r//v0XXf5jjz2mo0ePqlu3blqzZo1+/PFHLVq0SL1791ZOTk6R1iEhIUEtWrTQXXfdpdTUVO3atUuff/65UlJSCuxfo0YNffjhh9q0aZO++eYbde/e3XV2BoBnEVQAuJk6daratm2roKCgfOPuuusubdq0SRs2bJAkjR07Vk8++aTi4uK0b98+zZ8/3/U9KCNGjFCjRo2UmJioli1bqnLlyq4QcyHh4eFavny5cnJylJiYqPr16+vJJ59UUFCQypQp+kfW3Llz1aRJE3Xr1k316tXTkCFDCg06EyZMUHBwsJo3b66kpCQlJiaqUaNGRV4WgMuHL3wDcMnS0tLUqlUrHTt2jK+iB3BZcUYFAABYi6ACAACsxaUfAABgLc6oAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABr/R/kAtfjKI9otgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Contagem de valores únicos: \n",
      "Vento\n",
      "FALSO         7\n",
      "VERDADEIRO    6\n",
      "NaN           1\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Contagem de valores únicos: \n",
      "Jogar\n",
      "sim    9\n",
      "nao    5\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# conhecendo os valores unicos\n",
    "for col in df.select_dtypes(include=['object']).columns:\n",
    "    print(f'\\nContagem de valores únicos: \\n{df[col].value_counts(dropna=False)}')\n",
    "    sns.countplot(data=df, x=col).set_title(f'Contagem de valores únicos para {col}');\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numericos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resumo estatístico para Temperatura:\n",
      "count      14.000000\n",
      "mean      155.571429\n",
      "std       306.433759\n",
      "min        64.000000\n",
      "25%        69.250000\n",
      "50%        73.500000\n",
      "75%        80.750000\n",
      "max      1220.000000\n",
      "Name: Temperatura, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resumo estatístico para Umidade:\n",
      "count     13.000000\n",
      "mean      89.769231\n",
      "std       34.540203\n",
      "min       65.000000\n",
      "25%       70.000000\n",
      "50%       85.000000\n",
      "75%       90.000000\n",
      "max      200.000000\n",
      "Name: Umidade, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# visualizando os dados\n",
    "for col in df.select_dtypes(include=np.number).columns:\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    sns.boxplot(x=df[col], ax=ax[0]).set_title(f'Boxplot de {col}')\n",
    "    sns.histplot(x=df[col],kde=True, ax=ax[1]).set_title(f'Histograma de {col}')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    print(f'Resumo estatístico para {col}:\\n{df[col].describe()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tratamento \n",
    "\n",
    "**Regras de negócio:**\n",
    "- Aparencia: sol, chuva, nublado\n",
    "- Temperatura: -130 a 130\n",
    "- Umidade: 0 a 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe copia\n",
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### aparencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores únicos antes do tratamento: ['sol' 'nublado' 'chuva' 'menos']\n",
      "Valores faltantes antes do tratamento: 0\n"
     ]
    }
   ],
   "source": [
    "print(f'Valores únicos antes do tratamento: {df2[\"Aparencia\"].unique()}')\n",
    "print(f'Valores faltantes antes do tratamento: {df2.Aparencia.isna().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores únicos depois do tratamento: ['sol' 'nublado' 'chuva']\n"
     ]
    }
   ],
   "source": [
    "# substituindo valores fora de dominio pela moda\n",
    "df2['Aparencia'] = df2['Aparencia'].replace('menos', df2['Aparencia'].mode()[0])\n",
    "print(f'Valores únicos depois do tratamento: {df2[\"Aparencia\"].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizando\n",
    "sns.countplot(x='Aparencia', data=df2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### temperatura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores faltantes depois do tratamento: 0\n",
      "Valores fora do domínio antes do tratamento: 1 \n"
     ]
    }
   ],
   "source": [
    "print(f'Valores faltantes depois do tratamento: {df2.Temperatura.isna().sum()}')\n",
    "print(f'Valores fora do domínio antes do tratamento: {df2.query('Temperatura <= -130 | Temperatura >= 130')['Temperatura'].count()} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores fora do domínio depois do tratamento: 0 \n"
     ]
    }
   ],
   "source": [
    "# substituindo valores fora de dominio pela mediana\n",
    "df2.Temperatura = df2.Temperatura.astype(float)\n",
    "\n",
    "df2.loc[(df2['Temperatura'] <= -130) | (df2['Temperatura'] >= 130), 'Temperatura']= df2['Temperatura'].median()\n",
    "\n",
    "print(f'Valores fora do domínio depois do tratamento: {df2.query('Temperatura <= -130 | Temperatura >= 130')['Temperatura'].count()} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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teapvNBQX5KX/GRpHgQ50EgkhPvrVsHgN7xYiD7NJh4/U6M0fDun7/UWqb2xydTwA6BA4Ex0AAABwA6tWrdKdd96pc889Vw0NDXrooYc0fvx47dy5Uz4+Psfdd8+ePfL3929ZDgsLa++46CQcDY36dHueDhVXS5IGJwRpRLcQmc1M9QB0JhazWUOTgpUc6adVewuVXlSlDYeOaE9+hUb3ClO3UB+meAHQpVGiAwAAAG7gs88+a7W8cOFChYeHa+PGjRo1atRx9w0PD1dgYGA7pkNndKS6Th9tydGR6npZzCZd3CdCvSL8XB0LQDsK8LLq8v7ROlhYqZV7C1VR26B/b81VYoi3xiSHK4ALCAPoopjOBQAAAHBDZWVlkqTg4OATbjtw4EBFRUVp3LhxWrFiRXtHQydwqLhK76zP0pHqevnaLLpmcCwFOtCFdAvz1Y3DEnRuYpDMJimjuFpvrD2kHw4Wq4EpXgB0QZyJDgAAALgZwzA0Y8YMXXDBBerbt6/T7aKiorRgwQINHjxYDodDb7zxhsaNG6eVK1c6PXvd4XDI4XC0LJeXl5/x/Oi4DMPQlsNl+mZfoQxDivS369J+UfKxcegIdDVWD7NGdA9VSqS/VuwtUFZJjdaml2hXXoXGJIcpMeT4U4kBQGfCSAgAAABwM3fddZe2bt2q77777rjbJScnKzk5uWV5+PDhysrK0ty5c52W6HPmzNHs2bPPaF64h4amJq3cU6gdOUc/OEmJ8tOFvcNlMfMHzEBXFuTjqasGxGhfQaW+2Veospp6fbA5R93DfDS6V5j87EzxAqDzYzQEAAAAuJG7775bH374oVasWKHY2NhT3n/YsGHat2+f0/tnzZqlsrKylltWVtbPiQs3UV3XoKWbsrUjp1wmSSN7hurilAgKdACSJJPJpF4RfrppWKIGxgfKZJIOFFbpjbWHtD6jhCleAHR6nIkOAAAAuAHDMHT33Xfr/fff18qVK5WUlHRaj5OWlqaoqCin99tsNtlsttONCTdUWOHQh1tyVOlokKfFrEl9I5XANA0A2uBpMWtUzzD1ifLXit0Fyimr1eoDxdqRU65RPUOVFOojk8nk6pgAcMZRogMAAABu4M4779Q///lPffDBB/Lz81NeXp4kKSAgQF5eXpKOnkWenZ2t119/XZI0b948JSYmKjU1VXV1dVq8eLGWLFmiJUuWuOx1oGM5UFipz7bnqaHJUKC3VZf3i1aQj6erYwHo4EJ9bbpmcKz25Ffou31FKqup10dbcxUf7K3RvcIUzM8RAJ0MJToAAADgBl566SVJ0pgxY1qtX7hwoW6++WZJUm5urjIzM1vuq6ur08yZM5WdnS0vLy+lpqbq448/1qRJk85WbHRQhmEoLatU3+4rkiTFB3trUt9I2aweLk4GwF2YTCb1jvRXt1Bfrc8oUVpmqTJLqvXmD4fUPzZQ53ULls3CzxQAnQMlOgAAAOAGDMM44TaLFi1qtXz//ffr/vvvb6dEcFdNTYZW7i3UtuwySdI5MQEa0ytMZjNTMAA4dZ4Ws87vEarUaH99s69I6UVVSssq1e68Co3oEaLUKH+meAHg9ijRAQAAAKCLcDQ06tNteTpUUi3p6AVEB8YFUnAB+NkCvT11ef9oZRRX6Zu9hTpSXa+vdhVo2+Eyje4VpuhAL1dHBIDTRokOAAAAAF1AeU29PtySo+KqOlnMJl3SN1Ldw3xdHQtAJ5MY4qO487y15XCpfjhYooIKh97beFi9I/10fo9Q+dqoogC4H35yAQAAAEAnl1dWq4+25qi6rlE+nh66rH+0Ivztro4FoJPyMJs0KD5IyRF+Wn2gWDtzy7U7r0IHCit1bmKwBsYFyuJhdnVMADhplOgAAAAA0IntK6jQFzvy1dBkKNT36HQLfnarq2MB6AJ8bBZd3CdC58QGaNWeQuWV12r1gWJtyy7T+d1D1SvCl+mkALgFSnQAAAAA6IQMw9DGzCP6fn+xJCkxxFsT+0bJ08LZnwDOrkh/u64dEqvdeRVafaBYFbUN+mxHntKybBrVk/nSAXR8lOgAAAAA0Mk0NRlasadA23PKJUn9YwM0qmeYzGbO+ATgGiaTSSlR/uoR7qu0zFJtOFSi/PKj86X3CPPV+T1CXB0RAJyiRAcAAACATqS+sUmfbMtVRnG1JGl0rzANiAt0bSgA+A+rh1lDk4KVGu2vtQeLtSOnXPsLK3WwqFLdfD1ktnPBYwAdD3/HBwAAAACdRJWjQf/aeFgZxdXyMJt0ab8oCnQAHZKPzaJxKRG6/rx4JYR4q8mQ9ld4KPo3/9BHeypV19Dk6ogA0IISHQAAAAA6gSPVdXpv42EVVDhkt5p19aAYdQ/jjE4AHVuor01XDojRlQOi5W9tkoeXnxZuqdC4Z1bq/bTDamwyXB0RACjRAQAAAMDd5ZbV6N0NWSqrqZe/3aJrh8QpKoAL9QFwHwkhProoskHFnz6rILtZWSU1uu+dLZr83LdavjNfhkGZDsB1KNEBAAAAwI0dKKzUkk3Zqq1vUrifTdcOiVOQt6erYwHAKTOZpMqtX+qFSWG6/5Jk+dst2p1XoWmvb9A189foh4PFro4IoIuiRAcAAAAAN7XlcKk+3pqrxiZDiSHeunpQrHxsFlfHAoCfxW4x644xPfTt/Rfq9jHdZbeatfHQEV23YK1uXrhOO3LKXB0RQBdDiQ4AAAAAbsYwDH23v0gr9xTKkJQa7a/L+kXL08IhHoDOI8Dbqgcu6a1vfj9WNwyLl8Vs0so9hZr83He6+600pRdVuToigC6CERYAAAAAuJHGJkNf7MzXxkNHJEnDugVrXO9wmc0mFycDgPYR7m/XY1eeo+UzRuvy/tGSpI+25OiiZ1bp9+9t0aFiynQA7YsSHQAAAGhH3bp1U3HxsXO4lpaWqlu3bi5IBHdW19CkD7fkaHdehUwm6aKUcJ2XFCKTiQIdQOeXGOqj5/5noD6+5wKNTQ5TY5Oh9zYe1oVPr9LM97YogzPTAbQTSnQAAACgHWVkZKixsfGY9Q6HQ9nZ2S5IBHdVU9eopWmHlVlSLYvZpMv7Rys1OsDVsQDgrEuNDtDCXw/V0jtGaHSvo2X6vzYe1rhnVul371KmAzjzuOIMAAAA0A4+/PDDlv///PPPFRDw37KzsbFRX331lRITE12QDO6ovLZey9KydaS6XnaLWVcMiFFkgN3VsQDApQbFB+m1W4YqLfOInv1qn1buKdSSTYf1ftphXTkwRndf2FNJoT6ujgmgE6BEBwAAANrBlVdeKUkymUyaOnVqq/usVqsSExP19NNPuyAZ3E1xpUPLNueo0tEgX5tFVw2MUbCPp6tjAUCHMTA+SIt+PVSbs0r17PK9WrGnUEs3ZWtZWrauGBCjuy7soe5hvq6OCcCNUaIDAAAA7aCpqUmSlJSUpPXr1ys0NNTFieCOcstq9MHmHDkamhTs7akrB0bLz251dSwA6JAGxAVq4a+HaktWqZ77ap++2l2g99OytWxztib0idT0Md01IC7Q1TEBuCFKdAAAAKAdpaenuzoC3FR6UZU+2ZarhiZDkf52XT4gWl5WD1fHAoAOr39coF65+VxtPVyq577ar+W78vXZjjx9tiNP5yUFa/qY7hrTK4yLMgM4aZToAAAAQDv76quv9NVXX6mgoKDlDPVmr776qotSoSPblVuuL3flyzCkhBBvTT4nSlYPs6tjAYBb6RcbqP+bOkR78yu04JuDWpaWrR/SS/RDeol6R/rpt6O76dJ+0fx8BXBC/JQAAAAA2tHs2bM1fvx4ffXVVyoqKtKRI0da3YCf2pR5RF/sPFqg947002UUPADws/SK8NPcX/TXtw+M1bQLkuTj6aHdeRW6750tGvPUSr36Xbqq6xpcHRNAB8aZ6AAAAEA7mj9/vhYtWqQbb7zR1VHQwRmGoe8PFGvjoaMfrgyMC9TInqFMNwAAZ0hUgJf+eGkf3X1hTy3+4ZAWfp+u7NIa/fnfO/Xc1/t0/dB43TAsQdGBXq6OCqCD4XQGAAAAoB3V1dVpxIgRro6BDq6pydDyXQUtBfr53UMo0AGgnQR4W3Xn2B767oEL9fhVfZUQ4q3S6nq9uPKARj65Qne+uUnr0ktkGIarowLoICjRAQAAgHY0bdo0/fOf/3R1DHRgDY1N+nhbrnbmlssk6aKUcA1JDKZAB4B2Zrd66FfnJejr343R/BsGaVi3YDU2Gfp4W66ufXmNJj/3nd7dkKXa+kZXRwXgYkznAgAAALSj2tpaLViwQMuXL1e/fv1ktVpb3f/MM8+4KBk6AkdDoz7ckqOc0lp5mE2a2DdS3cN8XR0LALoUD7NJl/SN0iV9o7Qrt1yvr8nQ+2nZ2plbrvv/tVVzPtml/2GqF6BLo0QHAAAA2tHWrVs1YMAASdL27dtb3ceZxl1bdV2Dlm3OUWGFQ54Wsy7vF62YIMoZAHCllCh/zZnSTw9c0lvvrM/S62sOKbu0Ri+uPKCXvzmo8X0i9D9D43VBj1CZzfweB7oKSnQAAACgHa1YscLVEdABVdTW6/20bB2prpeX1UNXDYxRmJ/N1bEAAP8R6O2p347urlsvSNLyXQV6bXWG1hws1qfb8/Tp9jzFBnnpuiFx+sWQOEUG2F0dF0A7o0QHAAAAgLPoSHWd3k/LVkVtg3xtFk0ZFKMgb09XxwIAtMHiYdYlfSN1Sd9I7c4r11s/ZOr9tGwdPlKjp7/cq/9dvldjk8P1y6HxGpscJosHlx8EOiNKdAAAAKAdjR079rjTtnz99ddnMQ1crbDCoWWbs1Vd16hAb6uuGhgjf7v1xDsCAFyud6S/Zl/RV7MmpeiTbbl6e32W1qWX6KvdBfpqd4HC/Wz6xZBYXTckXvEh3q6OC+AMokQHAAAA2lHzfOjN6uvrtXnzZm3fvl1Tp051TSi4RG5ZjT7YnCNHQ5PCfG26cmC0vD05JAMAd2O3emjKoFhNGRSrA4WVemd9lpZsPKyCCodeWHFAL6w4oGHdgnXVwBhNPCeKD0uBToARGwAAANCO/vd//7fN9Y8++qgqKyvPchq4SmZJtT7akqOGJkNRAXZd0T9aNquHq2MBAH6m7mG+enBSimaOT9byXfl6a12mvttfpLUHS7T2YIn+9MEOXZwSoSsHxmh0rzB5WpjuBXBHJ12iOxwOORyOluXy8vJ2CXSqDh065OoI6GL4ngPgSvwMQlfRFb7Xb7jhBg0dOlRz5851dRS0s/0Flfpse54aDUPxwd66tF+UrMyZCwCdiqfFrEnnRGnSOVHKLq3RB5uz9f6mbO0rqNTH23L18bZcBXpbdWm/KF01MFaD4gOPO90bgI7lpEv0OXPmaPbs2e2Z5bQ8/vjjro4AAMBZw+89oPNYs2aN7Ha7q2Ogne3KLdeXu/JlGFKPMF9N6Bshi5kCHQA6s5hAL90xpoduH91dO3LKtSwtWx9syVFhhUOL12Zq8dpMxQd768oB0ZrcL1q9Inwp1IEO7qRL9FmzZmnGjBkty+Xl5YqLi2uXUKfioYceUkJCgqtjoAs5dOgQJRYAl+H3HrqKzvT7dsqUKa2WDcNQbm6uNmzYoD/96U8uSoWzYXNWqVbtLZQk9Yny17je4TKbKUkAoKswmUzqGxOgvjEBmjUpRasPFOn9tGx9tj1PmSXVeu7r/Xru6/3qHuajSedEaWLfKKVE+VGoAx3QSZfoNptNNputPbOcloSEBPXq1cvVMQAAOCv4vQe4n4CAgFbLZrNZycnJ+vOf/6zx48e7KBXak2EYWp9xRGsOFkuSBsQFalTPUEoRAOjCPMwmjewZppE9w/TYlQ36cme+PtqSo2/2FulAYZX+/vV+/f3r/UoK9dHEvpGadE6UUqP9+d0BdBBcWBQAAABoRwsXLnR1BJxFhmHou/1F2pRZKkkalhSsoUnBlCAAgBbenhZdMSBGVwyIUUVtvb7eXaCPt+Zq5d5CpRdV6cWVB/TiygOKD/bWxHMidUlqpPrHBvLXTIALUaIDAAAAZ8HGjRu1a9cumUwm9enTRwMHDnR1JJxhTYahFbsLtD2nXJI0qmeoBsYHuTgVAKAj87NbWwr1SkeDvt5doE+35WrFngJlllTr5VUH9fKqgwr1tWlc73Bd1CdCF/QIlZenh6ujA10KJToAAADQjgoKCvTLX/5SK1euVGBgoAzDUFlZmcaOHau3335bYWFhJ/U4c+bM0dKlS7V79255eXlpxIgReuKJJ5ScnHzc/VatWqUZM2Zox44dio6O1v3336/p06efiZeGH2lqMvTFrnztyauQSdK4lHClRgeccD8AAJr52iy6vH+0Lu8freq6Bq3YXahPtudq1Z5CFVU69M6GLL2zIUs2i1kX9AjVRX0iNK53uML9uVA50N64LDwAAADQju6++26Vl5drx44dKikp0ZEjR7R9+3aVl5frnnvuOenHWbVqle68806tXbtWX375pRoaGjR+/HhVVVU53Sc9PV2TJk3SyJEjlZaWpgcffFD33HOPlixZciZeGv6jscnQp9vztCevQmaTNLFvJAU6AOBn8fa0aHK/KL1w/SBt+tPFeuPWoZo6PEExgV5yNDTpq90FmrV0m4b+9Std8fx3eu6rfdp2uExNTYarowOdEmeiAwAAAO3os88+0/Lly5WSktKyrk+fPnrhhRdO6cKin332WavlhQsXKjw8XBs3btSoUaPa3Gf+/PmKj4/XvHnzJEkpKSnasGGD5s6dq6uvvvrUXwyO0dDYpE+25ym9qEoeJpMmnROpbmG+ro4FAOhEPC3mlouSPnq5oT35FVq+M19f7irQlqxSbTlcpi2Hy/TMl3sV4uOpUb3CNLpXmEb2DFWIr83V8YFOgRIdAAAAaEdNTU2yWq3HrLdarWpqajrtxy0rK5MkBQcHO91mzZo1xxT1EyZM0CuvvKL6+vo2czkcDjkcjpbl8vLy087Y2dU3NunfW3OVWVItD7NJl/WLUkKIj6tjAQA6MZPJpN6R/uod6a+7LuypgvJafb27QF/tLtDq/UUqrqrT+2nZej8tWyaT1C8mQKN7hWl0cpj6xwbK4sGkFMDpoEQHAAAA2tGFF16o//f//p/eeustRUdHS5Kys7N13333ady4caf1mIZhaMaMGbrgggvUt29fp9vl5eUpIiKi1bqIiAg1NDSoqKhIUVFRx+wzZ84czZ49+7RydSV1DU36cEuOsktrZPUw6fL+0YoN8nZ1LABAFxPub9cvh8brl0PjVdfQpE2ZR7RyT6FW7S3UrtzylrPUn/t6v/ztFl3QM1Qjuofqgh6hSgjxlslkcvVLANwCJToAAADQjp5//nldccUVSkxMVFxcnEwmkzIzM3XOOedo8eLFp/WYd911l7Zu3arvvvvuhNv+9ODYMIw21zebNWuWZsyY0bJcXl6uuLi408rZWTnqG7Vsc47yymvl6WHWlQOjFRXg5epYAIAuztNi1rBuIRrWLUR/mNhb+eW1+mbv0UL9231FKqup1yfb8vTJtjxJUkygl87vEaLzexwt1sP8mPoFcIYSHQAAAGhHcXFx2rRpk7788kvt3r1bhmGoT58+uuiii07r8e6++259+OGH+uabbxQbG3vcbSMjI5WXl9dqXUFBgSwWi0JCQtrcx2azyWbjINqZmrpGvb85W4UVDtktZl05MEYR/nZXxwIA4BgR/nb9YkicfjEkTo1NhjZnler7/UX6bn+R0jKPKLu0Ru9uOKx3NxyWJPWO9Dt6lnrPEA1NCpGvjdoQaMa/BgAAAKAdfP3117rrrru0du1a+fv76+KLL9bFF18s6eh85qmpqZo/f75Gjhx5Uo9nGIbuvvtuvf/++1q5cqWSkpJOuM/w4cP10UcftVr3xRdfaMiQIW3Oh47jq3I06P20bBVX1cnL6qGrBsZw1h4AwC14mE0anBCkwQlBumdcT1XXNWhdeolWHyjWd/uKtDO3XLvzKrQ7r0Kvfp8ui9mkAXGBOr9HqM7vEaoBcYHytDCfOrouSnQAAACgHcybN0+33Xab/P39j7kvICBAv/3tb/XMM8+cdIl+55136p///Kc++OAD+fn5tZxhHhAQIC+vo1OJzJo1S9nZ2Xr99dclSdOnT9fzzz+vGTNm6LbbbtOaNWv0yiuv6K233jpDr7LrqKit19K0bJVW18vH5qEpA2MV7OPp6lgAAJwWb0+LxiSHa0xyuCSpuNKhNQeL9f3+In2/v1iZJdXacOiINhw6ome/2icvq4eGJAZpRPdQjegeor4xAfIwM586ug5KdAAAAKAdbNmyRU888YTT+8ePH6+5c+ee9OO99NJLkqQxY8a0Wr9w4ULdfPPNkqTc3FxlZma23JeUlKRPPvlE9913n1544QVFR0frueee09VXX33yLwQqr6nXkk2HVV7bID+7RVMGxijQmwIdANB5hPjadGm/aF3a7+hF0LNKqlumfllzoFjFVXX6dl+Rvt1XJEnys1s0rFuIRnQP0YjuoeoV4ctFStGpUaIDAAAA7SA/P/+4U6ZYLBYVFhae9OM1XxD0eBYtWnTMutGjR2vTpk0n/Txo7Uh1nZZuylalo0EBXlZNGRQjfztT4QAAOre4YG/9cmi8fjk0XoZhaG9+pVYfKNLqA8Vae7BYFbUN+nJnvr7cmS9JCvX1/E+pfvRM9YQQb0p1dCqU6AAAAEA7iImJ0bZt29SjR48279+6dauioqLOciqciuJKh5amZau6rlFB3lZNGRTLRdYAAF2OyWRScqSfkiP99Ovzk9TYZGhHTpm+31+s1QeKtD6jREWVdfr31lz9e2uuJCkm0EvDux89U3149xBFBXi5+FUAPw8jQAAAAKAdTJo0SQ8//LAmTpwou93e6r6amho98sgjuvTSS12UDidSWOHQ+2nZqqlvVKivp64aGCNvTw6fAADwMJvULzZQ/WIDdfuY7qpraNLmrNKWM9XTMo8ou7RG/9p4WP/aeFiS1C3U5z+leqiGdQtWiC8X5oZ7YRQIAAAAtIM//vGPWrp0qXr16qW77rpLycnJMplM2rVrl1544QU1NjbqoYcecnVMtCGvvFbL0rLlaGhSuJ9NVw2Mkd3q4epYAAB0SJ4Ws4YmBWtoUrDuvUiqqWvUhkMlWn2gWKv3F2lbdpkOFlXpYFGV3vzh6LVbekf6tUz9cl63YBe/AuDEKNEBAACAdhAREaHVq1fr9ttv16xZs1rmNDeZTJowYYJefPFFRUREuDglfiq7tEYfbs5RXWOTogLsumJAtGwWCnQAAE6Wl6eHRvYM08ieYZKkspp6rUsv0eoDRy9SujuvouX26vfp8jCb1C3QosBRNym/1qSIxiZZPcwufhVAa5ToAAAAQDtJSEjQJ598oiNHjmj//v0yDEM9e/ZUUFCQq6OhDVkl1fpwS44amgzFBnrpsv7R8rRwEA8AwM8R4GXVxX0idHGfoycPFFU6tPZgsVYfKNaaA8VKL6rSvpJ6BQy/Vt8VSGsKDyoywK64IC/FBnsr0t8uDzMXKYVrUaIDAAAA7SwoKEjnnnuuq2PgODKKqvTvbblqbDKUEOytS/tFycJZcAAAnHGhvjZd2i9al/aLliTllNbora83as4rSxU24ELVNB79y7Ds0hopvURWD5OiA70UF+StuCAvhfnZZDJRquPsokQHAAAA0KXtL6jUp9tz1WQcvfDZxHMiZTFToAMAcDZEB3ppbKK3Zn7yv7pp0kj5xvbU4ZIaZR2p1uEjNaqpb9Sh4modKq6WJHl7eigh2FsJIT5KCPHmuiU4KyjRAQAAAHRZe/Iq9PnOPBmG1DPcVxNSI/mTcQAAXMRkkoK8PRXk7alzYgNkGIaKq+qUVVKtrCM1OnykWtV1jdqVV6FdeRUySYrwtysx5GipHuHPWepoH5ToAAAAALqknTnlWr4rX4aklEg/XZQSITMFOgAAHYbJZFKor02hvjYNjA9SY5OhnNIaZRRX6VBxtYqr6pRXXqu88lqtTS+Rl9VD8SHeSgrxUWKIt2ycpY4zhBIdAAAAQJez9XCpVuwplCT1jfH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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resumo estatístico para temperatura:\n",
      "count    14.000000\n",
      "mean     73.678571\n",
      "std       6.556286\n",
      "min      64.000000\n",
      "25%      69.250000\n",
      "50%      72.750000\n",
      "75%      78.750000\n",
      "max      85.000000\n",
      "Name: Temperatura, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# visualizando\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "sns.boxplot(x=df2['Temperatura'], ax=ax[0]).set_title('Boxplot de temperatura')\n",
    "sns.histplot(x=df2['Temperatura'],kde=True, ax=ax[1]).set_title('Histograma de temperatura')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(f'Resumo estatístico para temperatura:\\n{df2[\"Temperatura\"].describe()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### umidade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores faltantes antes do tratamento: 1\n",
      "Valores fora do domínio antes do tratamento: 1\n"
     ]
    }
   ],
   "source": [
    "print(f'Valores faltantes antes do tratamento: {df2.Umidade.isna().sum()}')\n",
    "print(f'Valores fora do domínio antes do tratamento: {df2.query(\"Umidade < 0 | Umidade > 100\").Umidade.count()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores faltantes depois do tratamento: 0\n",
      "Valores fora do domínio depois do tratamento: 0\n"
     ]
    }
   ],
   "source": [
    "# substituindo valores faltantes e valores fora de dominio pela mediana\n",
    "df2.Umidade = df2.Umidade.fillna(df2.Umidade.median())\n",
    "df2.loc[(df2['Umidade'] < 0) | (df2['Umidade'] > 100), 'Umidade'] = df2['Umidade'].median()\n",
    "\n",
    "print(f'Valores faltantes depois do tratamento: {df2.Umidade.isna().sum()}')\n",
    "print(f'Valores fora do domínio depois do tratamento: {df2.query(\"Umidade < 0 | Umidade > 100\").Umidade.count()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7VDaGlb3/X/7yl/HUU0/FBRdcELvvvns0bdo08vLyYsCAARU+O2yJ8wfUHoJ0IGt23nnneOyxx+Ltt9+OPfbYI5o3bx7/+te/IkmSjDB98eLFsWrVqvJn3UVELF++PI466qjo1KlTfPTRRzF48OD4xz/+UeEYq1atik8//TQjzF37hTXfDni/qXnz5rFw4cIK7R9++GFEREYtG6N58+aVfmHOt9vW7v+Pf/xj7LnnnpXua10fFr65bsGCBZWuX7BgQer2m0Pz5s1Tz/83rX2e+eWXX57R/sknn0SzZs0y9rklzh8AABsnPz8/hg4dGkOHDo0vvvginnzyyRg+fHgccMABMX/+/GjYsOE6t107V1y4cGFst912Ges+/PDD8rnd2n5rg89vWrRoUaU3wlT2fT4TJkyIPn36xOjRozPa1/UF9pvq6aefjg8//DAmT55cfhd6RJQ/N359Nnbu+/e//z3atWu30XVWl88OL774YoX2b7/XJUuWxIMPPhgXXnhhnHvuueXtZWVl8dlnn1XY55Y4f0Dt4Z/VgKyZOXNmRPzfl3P27ds3li1bFvfdd19Gv9tuu618/VpDhgyJefPmxT333BM333xz3H///fH73/++0uPccccdGctrv1zm218i9E19+/aNN998M15++eUKteTl5cW+++4bERGFhYUREeu9m2StfffdN95444145ZVXKq1prR/+8IfRrFmzePPNN6NHjx6Vvtbe2VGZHXbYIdq1axd/+9vfKnw51McffxyTJk2KH/3oRxtU86Zae47Wdf6/KS8vr/xcrvXQQw9VmMxvqfMHAMCma9asWfz0pz+Nk046KT777LP44IMPImLdc+f99tsvIv4TcH/TtGnTYtasWeWfA37wgx9EYWFh3HXXXRn9XnjhhdRHDn5bZXPPV199NZ5//vkN3seGHiciKhzrT3/60wZtv++++8ZTTz2V8Q8Hq1evrvD+DzjggMjPz4/33ntvnXPfNHvuuWc0bty4wn4j/vOFnW+88cYW+eywdOnSCl9I+u15fl5eXiRJUuGc/vnPf65wR/2WOn9A7eGOdGCLeP3112PVqlUR8Z8/sbvnnnviiSeeiMMOO6z8UR1HH3103HjjjTFo0KD44IMP4r/+67/iueeei8svvzwGDBhQPnn785//HBMmTIhbbrklunXrFt26dYuTTz45zjnnnPjhD3+Y8VzzevXqxbXXXhvLli2L3XffPaZOnRqXXnpp9O/fP/VZj2eccUbcdtttcdBBB8XFF18c7dq1i4ceeihuuummOOGEE6JTp04REdGkSZNo165d/OMf/4i+ffvG1ltvHS1atFjnMxpPP/30GDduXBx00EFx6aWXRnFxcdxxxx3x1ltvZfRr3Lhx/PGPf4xBgwbFZ599Fj/96U+jZcuW8fHHH8crr7wSH3/8cYU7aL7tmmuuicMPPzz69u0bxx9/fLRq1SreeeeduOKKK6JevXpxwQUXpA/ad9SvX7/o1atXnH322bF8+fLo0aNH/POf/4zbb7+9Qt+DDz44xo8fHzvuuGPsvPPO8dJLL8XVV19d4Y6kLXn+AADYcIccckjstNNO0aNHj9hmm21i7ty5MWrUqGjXrl3ssMMOERHxX//1XxERcf3118egQYOioKAgOnfuHJ07d47f/OY38cc//jHq1KkT/fv3jw8++CAuuOCCaNOmTZxxxhkR8Z9HhgwdOjRGjhwZW221VRx22GHx73//O0aMGBElJSUb/AiOgw8+OC655JK48MILo3fv3jF79uy4+OKLo0OHDuWfWTaHvfbaK7baaqsYMmRIXHjhhVFQUBB33HFHhZtC1uX888+P+++/P/bbb7/43e9+Fw0bNowbb7yxwnf9tG/fPi6++OI477zz4v33348DDzwwttpqq/joo4/ixRdfjEaNGsWIESPWeZwmTZrEiBEj4swzz4w1a9bEEUccEVtttVW89tprcfnll0e7du3W+SjNzeXoo4+O3//+93H00UfHZZddFjvssEM8/PDD8dhjj2X0a9q0afTq1Suuvvrq8s9dU6ZMiZtvvjnjL1kjttz5A2qR7H7XKVDTrf0G+G++ioqKkl133TW57rrrkq+//jqj/6effpoMGTIkKSkpSfLz85N27dolw4YNK+/36quvJg0aNEgGDRqUsd3XX3+ddO/ePWnfvn3y+eefJ0mSJIMGDUoaNWqUvPrqq0mfPn2SBg0aJFtvvXVywgknJMuWLcvYvl27dhX2OXfu3OSXv/xl0rx586SgoCDp3LlzcvXVV2d8M32SJMmTTz6Z7LbbbklhYWESERX2821vvvlmsv/++yf169dPtt566+S4445L/vGPfyQRkUyaNCmj75QpU5KDDjoo2XrrrZOCgoJk2223TQ466KDkb3/7W+oxvllbv379kmbNmiX5+flJSUlJcuSRRybvvPNOhb4RkZx00kkZbXPmzEkiIrn66qsz2idNmpREREYdgwYNStq1a5fR74svvkiOPfbYpFmzZknDhg2T/fffP3nrrbeSiEguvPDC8n6ff/55ctxxxyUtW7ZMGjZsmOy9997Js88+m/Tu3Tvp3bt31s4fAEBtsHbOPm3atErXH3TQQRXmed+eP1977bXJXnvtlbRo0SKpV69e0rZt2+S4445LPvjgg4zthg0blrRu3TqpU6dOxvxt9erVyZVXXpl06tQpKSgoSFq0aJEceeSRyfz58zO2X7NmTXLppZcm2223XVKvXr1k5513Th588MFkl112SQ477LDyfpXNV9cqKytLzjrrrGTbbbdN6tevn3z/+99P7rvvvgrz2Y2ZC6/rPE6dOjXp2bNn0rBhw2SbbbZJBg8enLz88stJRCS33HJLpef7m/75z38me+65Z1JYWJi0atUq+e1vf5uMHTs2iYhkzpw5GX3vu+++ZN99902aNm2aFBYWJu3atUt++tOfJk8++eR6j5MkSXL33Xcne++9d9KkSZMkPz8/adu2bXLCCSckixYtyui3Oc5LZfP8f//738nAgQOTxo0bJ02aNEkGDhyYTJ06tcK5Wttvq622Spo0aZIceOCByeuvv17pZ7otef6Ami8vSb71N/8ANcQxxxwTf//732PZsmXZLgUAAKgic+bMiR133DEuvPDCGD58eLbLAaCG8mgXAAAAICe88sorceedd8Zee+0VTZs2jdmzZ8dVV10VTZs2jeOOOy7b5QFQgwnSAQAAgJzQqFGjmD59etx8883xxRdfRFFRUfTp0ycuu+yyKC4uznZ5ANRgHu0CAAAAAAApNuwrrQEAAAAAoJYSpAMAAAAAQApBOgAAAAAApNjkLxtds2ZNfPjhh9GkSZPIy8vbnDUBAEBWJUkSS5cujdatW0edOrX73hPzfgAAaqqNmfdvcpD+4YcfRps2bTZ1cwAAqPbmz58f2223XbbLyCrzfgAAaroNmfdvcpDepEmT8oM0bdp0U3cDAADVTmlpabRp06Z8zlubmfcDAFBTbcy8f5OD9LV/1tm0aVMTagAAaiSPMjHvBwCg5tuQeX/tfuAjAAAAAACshyAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAgx4wcOTLy8vLi9NNPT+03ZcqU6N69e9SvXz86duwYY8aM2TIFAgBADSNIBwCAHDJt2rQYO3Zs7Lzzzqn95syZEwMGDIh99tknZsyYEcOHD49TTz01Jk6cuIUqBQCAmkOQDgAAOWLZsmXxq1/9Kv73f/83ttpqq9S+Y8aMibZt28aoUaOiS5cuMXjw4Dj22GPjmmuu2ULVAgBAzSFIBwCAHHHSSSfFQQcdFD/60Y/W2/f555+Pfv36ZbQdcMABMX369Fi5cmVVlQgAADVSfrYLAAAA1u+vf/1rvPzyyzFt2rQN6r9o0aIoLi7OaCsuLo5Vq1bFJ598EiUlJZVuV1ZWFmVlZeXLpaWlm170ZjBv3rz45JNPsloD2dOiRYto27ZttssAtiA/92uvsrKyKCwszHYZZEGu/L4XpAMAQDU3f/78OO200+Lxxx+P+vXrb/B2eXl5GctJklTa/k0jR46MESNGbFqhm9m8efNixx27xFdffZntUsiSBg0axltvzcqJD9fAd+fnfi2Xlxfx/+cq1C658vtekA4AANXcSy+9FIsXL47u3buXt61evTqeeeaZuOGGG6KsrCzq1q2bsU2rVq1i0aJFGW2LFy+O/Pz8aN68+TqPNWzYsBg6dGj5cmlpabRp02YzvZON88knn8RXX30ZPzj2wmha0j4rNZA9pQs/iH+NGxGffPJJtf9gDWwefu7XXgtfez5ev39s7PrLc2KbDjtmuxy2oFz6fS9IBwCAaq5v377x2muvZbT9+te/jh133DHOOeecCiF6RETPnj3jgQceyGh7/PHHo0ePHlFQULDOYxUWFla7P6tuWtI+tm7bOdtlALCF+Llf+5Qu/CAiIhq3bGvsqbYE6QAAUM01adIkdtppp4y2Ro0aRfPmzcvbhw0bFgsWLIjbbrstIiKGDBkSN9xwQwwdOjSOP/74eP755+Pmm2+OO++8c4vXDwAAua5OtgsAAAC+u4ULF8a8efPKlzt06BAPP/xwTJ48OXbddde45JJL4g9/+EMMHDgwi1UCAEBuckc6AADkoMmTJ2csjx8/vkKf3r17x8svv7xlCgIAgBrMHekAAAAAAJBCkA4AAAAAACkE6QAAAAAAkEKQDgAAAAAAKQTpAAAAAACQQpAOAAAAAAApBOkAAAAAAJBCkA4AAAAAACkE6QAAAAAAkEKQDgAAAAAAKQTpAAAAAACQQpAOAAAAAAApBOkAAAAAAJBCkA4AAAAAACnys10AbC4fffRRLFmyJNtlALCZFRUVRXFxcbbLAAAAoBYTpFMjfPTRR3HkUUfHyhVl2S4FgM2soF5hTLj9NmE6AAAAWSNIp0ZYsmRJrFxRFl917B1r6hdluxygFqvz1RfRYM4z8VWHXrGmQbNsl5Pz6ny9JOL9KbFkyRJBOgAAAFkjSKdGWVO/KNY0apHtMgBiTYNmfh4BAABADeHLRgEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAgB4wePTp23nnnaNq0aTRt2jR69uwZjzzyyDr7T548OfLy8iq83nrrrS1YNQAA1Az52S4AAABYv+222y6uuOKK2H777SMi4tZbb41DDz00ZsyYEd26dVvndrNnz46mTZuWL2+zzTZVXisAANQ0gnQAAMgBhxxySMbyZZddFqNHj44XXnghNUhv2bJlNGvWrIqrAwCAms2jXQAAIMesXr06/vrXv8by5cujZ8+eqX132223KCkpib59+8akSZPWu++ysrIoLS3NeAEAQG0nSAcAgBzx2muvRePGjaOwsDCGDBkS9957b3Tt2rXSviUlJTF27NiYOHFi3HPPPdG5c+fo27dvPPPMM6nHGDlyZBQVFZW/2rRpUxVvBQAAcopHuwAAQI7o3LlzzJw5M7744ouYOHFiDBo0KKZMmVJpmN65c+fo3Llz+XLPnj1j/vz5cc0110SvXr3WeYxhw4bF0KFDy5dLS0uF6QAA1HqCdAAAyBH16tUr/7LRHj16xLRp0+L666+PP/3pTxu0/Z577hkTJkxI7VNYWBiFhYXfuVYAAKhJPNoFAAByVJIkUVZWtsH9Z8yYESUlJVVYEQAA1EzuSAcAgBwwfPjw6N+/f7Rp0yaWLl0af/3rX2Py5Mnx6KOPRsR/HsmyYMGCuO222yIiYtSoUdG+ffvo1q1brFixIiZMmBATJ06MiRMnZvNtAABAThKkAwBADvjoo4/iqKOOioULF0ZRUVHsvPPO8eijj8b+++8fERELFy6MefPmlfdfsWJFnHXWWbFgwYJo0KBBdOvWLR566KEYMGBAtt4CAADkLEE6AADkgJtvvjl1/fjx4zOWzz777Dj77LOrsCIAAKg9PCMdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBAAAAACCFIB0AAAAAAFII0gEAAAAAIIUgHQAAAAAAUgjSAQAAAAAghSAdAAAAAABSCNIBACAHjB49Onbeeedo2rRpNG3aNHr27BmPPPJI6jZTpkyJ7t27R/369aNjx44xZsyYLVQtAADULIJ0AADIAdttt11cccUVMX369Jg+fXrst99+ceihh8Ybb7xRaf85c+bEgAEDYp999okZM2bE8OHD49RTT42JEydu4coBACD35We7AAAAYP0OOeSQjOXLLrssRo8eHS+88EJ069atQv8xY8ZE27ZtY9SoURER0aVLl5g+fXpcc801MXDgwC1RMgAA1BjuSAcAgByzevXq+Otf/xrLly+Pnj17Vtrn+eefj379+mW0HXDAATF9+vRYuXLlOvddVlYWpaWlGS8AAKjtBOkAAJAjXnvttWjcuHEUFhbGkCFD4t57742uXbtW2nfRokVRXFyc0VZcXByrVq2KTz75ZJ3HGDlyZBQVFZW/2rRps1nfAwAA5CJBOgAA5IjOnTvHzJkz44UXXogTTjghBg0aFG+++eY6++fl5WUsJ0lSafs3DRs2LJYsWVL+mj9//uYpHgAAcphnpAMAQI6oV69ebL/99hER0aNHj5g2bVpcf/318ac//alC31atWsWiRYsy2hYvXhz5+fnRvHnzdR6jsLAwCgsLN2/hAACQ49yRDgAAOSpJkigrK6t0Xc+ePeOJJ57IaHv88cejR48eUVBQsCXKAwCAGkOQDgAAOWD48OHx7LPPxgcffBCvvfZanHfeeTF58uT41a9+FRH/eSTL0UcfXd5/yJAhMXfu3Bg6dGjMmjUrxo0bFzfffHOcddZZ2XoLAACQs3L20S5ff/11zJs3L9q2bRv169fPdjkAAGxG5noVffTRR3HUUUfFwoULo6ioKHbeeed49NFHY//994+IiIULF8a8efPK+3fo0CEefvjhOOOMM+LGG2+M1q1bxx/+8IcYOHBgtt4CAADkrJwN0ufNmxe/+c1vYuzYsdGpU6dslwMAwGZkrlfRzTffnLp+/PjxFdp69+4dL7/8chVVBAAAtYdHuwAAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAA5ICRI0fG7rvvHk2aNImWLVvGj3/845g9e3bqNpMnT468vLwKr7feemsLVQ0AADWDIB0AAHLAlClT4qSTTooXXnghnnjiiVi1alX069cvli9fvt5tZ8+eHQsXLix/7bDDDlugYgAAqDnys10AAACwfo8++mjG8i233BItW7aMl156KXr16pW6bcuWLaNZs2ZVWB0AANRs7kgHAIActGTJkoiI2Hrrrdfbd7fddouSkpLo27dvTJo0qapLAwCAGscd6QAAkGOSJImhQ4fG3nvvHTvttNM6+5WUlMTYsWOje/fuUVZWFrfffnv07ds3Jk+evM672MvKyqKsrKx8ubS0dLPXDwAAuUaQDgAAOebkk0+OV199NZ577rnUfp07d47OnTuXL/fs2TPmz58f11xzzTqD9JEjR8aIESM2a70AAJDrPNoFAAByyCmnnBL3339/TJo0KbbbbruN3n7PPfeMd955Z53rhw0bFkuWLCl/zZ8//7uUCwAANYI70gEAIAckSRKnnHJK3HvvvTF58uTo0KHDJu1nxowZUVJSss71hYWFUVhYuKllAgBAjSRIBwCAHHDSSSfFX/7yl/jHP/4RTZo0iUWLFkVERFFRUTRo0CAi/nM3+YIFC+K2226LiIhRo0ZF+/bto1u3brFixYqYMGFCTJw4MSZOnJi19wEAALlIkA4AADlg9OjRERHRp0+fjPZbbrkljjnmmIiIWLhwYcybN6983YoVK+Kss86KBQsWRIMGDaJbt27x0EMPxYABA7ZU2QAAUCMI0gEAIAckSbLePuPHj89YPvvss+Pss8+uoooAAKD28GWjAAAAAACQQpAOAAAAAAApBOkAAAAAAJBCkA4AAAAAACkE6QAAAAAAkEKQDgAAAAAAKQTpAAAAAACQQpAOAAAAAAApBOkAAAAAAJBCkA4AAAAAACkE6QAAAAAAkEKQDgAAAAAAKQTpAAAAAACQQpAOAAAAAAApBOkAAFCFOnbsGJ9++mmF9i+++CI6duyYhYoAAICNJUgHAIAq9MEHH8Tq1asrtJeVlcWCBQuyUBEAALCx8rNdAAAA1ET3339/+f9/7LHHoqioqHx59erV8dRTT0X79u2zUBkAALCxBOkAAFAFfvzjH0dERF5eXgwaNChjXUFBQbRv3z6uvfbaLFQGAABsLEE6AABUgTVr1kRERIcOHWLatGnRokWLLFcEAABsKkE6AABUoTlz5mS7BAAA4DsSpAMAQBV76qmn4qmnnorFixeX36m+1rhx47JUFQAAsKEE6QAAUIVGjBgRF198cfTo0SNKSkoiLy8v2yUBAAAbSZAOAABVaMyYMTF+/Pg46qijsl0KAACwiepkuwAAAKjJVqxYEXvttVe2ywAAAL4DQToAAFShwYMHx1/+8pdslwEAAHwHHu0CAABV6Ouvv46xY8fGk08+GTvvvHMUFBRkrL/uuuuyVBkAALChBOkAAFCFXn311dh1110jIuL111/PWOeLRwEAIDcI0gEAoApNmjQp2yUAAADfkWekAwAAAABACnekAwBAFdp3331TH+Hy9NNPb8FqAACATSFIBwCAKrT2+ehrrVy5MmbOnBmvv/56DBo0KDtFAQAAG0WQDgAAVej3v/99pe0XXXRRLFu2bAtXAwAAbIoNDtLLysqirKysfLm0tLRKCtpYc+fOzXYJVAP+OwCo2fycr31qw5gfeeSRsccee8Q111yT7VIAAID12OAgfeTIkTFixIiqrGWTXHbZZdkuAQCoYn7fUxM9//zzUb9+/WyXAQAAbIANDtKHDRsWQ4cOLV8uLS2NNm3aVElRG+O8886Ldu3aZbsMsmzu3LlCFoAazO/72qcm/W7/yU9+krGcJEksXLgwpk+fHhdccEGWqgIAADbGBgfphYWFUVhYWJW1bJJ27dpFp06dsl0GAFCF/L4nlxUVFWUs16lTJzp37hwXX3xx9OvXL0tVAQAAG8OXjQIAQBW65ZZbsl0CAADwHQnSAQBgC3jppZdi1qxZkZeXF127do3ddtst2yUBAAAbqE62CwAAgJps8eLFsd9++8Xuu+8ep556apx88snRvXv36Nu3b3z88ccbvJ+RI0fG7rvvHk2aNImWLVvGj3/845g9e/Z6t5syZUp079496tevHx07dowxY8Z8l7cDAAC1kiAdAACq0CmnnBKlpaXxxhtvxGeffRaff/55vP7661FaWhqnnnrqBu9nypQpcdJJJ8ULL7wQTzzxRKxatSr69esXy5cvX+c2c+bMiQEDBsQ+++wTM2bMiOHDh8epp54aEydO3BxvDQAAag2PdgEAgCr06KOPxpNPPhldunQpb+vatWvceOONG/Vlo48++mjG8i233BItW7aMl156KXr16lXpNmPGjIm2bdvGqFGjIiKiS5cuMX369Ljmmmti4MCBG/9mAACglnJHOgAAVKE1a9ZEQUFBhfaCgoJYs2bNJu93yZIlERGx9dZbr7PP888/XyGsP+CAA2L69OmxcuXKSrcpKyuL0tLSjBcAANR2gnQAAKhC++23X5x22mnx4YcflrctWLAgzjjjjOjbt+8m7TNJkhg6dGjsvffesdNOO62z36JFi6K4uDijrbi4OFatWhWffPJJpduMHDkyioqKyl9t2rTZpBoBAKAmEaQDAEAVuuGGG2Lp0qXRvn37+N73vhfbb799dOjQIZYuXRp//OMfN2mfJ598crz66qtx5513rrdvXl5exnKSJJW2rzVs2LBYsmRJ+Wv+/PmbVCMAANQknpEOAABVqE2bNvHyyy/HE088EW+99VYkSRJdu3aNH/3oR5u0v1NOOSXuv//+eOaZZ2K77bZL7duqVatYtGhRRtvixYsjPz8/mjdvXuk2hYWFUVhYuEm1AQBATeWOdAAAqAJPP/10dO3atfwZ4/vvv3+ccsopceqpp8buu+8e3bp1i2effXaD95ckSZx88slxzz33xNNPPx0dOnRY7zY9e/aMJ554IqPt8ccfjx49elT63HYAAKBygnQAAKgCo0aNiuOPPz6aNm1aYV1RUVH8z//8T1x33XUbvL+TTjopJkyYEH/5y1+iSZMmsWjRoli0aFF89dVX5X2GDRsWRx99dPnykCFDYu7cuTF06NCYNWtWjBs3Lm6++eY466yzvtubAwCAWkaQDgAAVeCVV16JAw88cJ3r+/XrFy+99NIG72/06NGxZMmS6NOnT5SUlJS/7rrrrvI+CxcujHnz5pUvd+jQIR5++OGYPHly7LrrrnHJJZfEH/7whxg4cOCmvSkAAKilPCMdAACqwEcffZT6+JT8/Pz4+OOPN3h/a78kNM348eMrtPXu3TtefvnlDT4OAABQkTvSAQCgCmy77bbx2muvrXP9q6++GiUlJVuwIgAAYFMJ0gEAoAoMGDAgfve738XXX39dYd1XX30VF154YRx88MFZqAwAANhYHu0CAABV4Pzzz4977rknOnXqFCeffHJ07tw58vLyYtasWXHjjTfG6tWr47zzzst2mQAAwAYQpAMAQBUoLi6OqVOnxgknnBDDhg0rf8Z5Xl5eHHDAAXHTTTdFcXFxlqsEAAA2hCAdAACqSLt27eLhhx+Ozz//PN59991IkiR22GGH2GqrrbJdGgAAsBEE6QAAUMW22mqr2H333bNdBgAAsIl82SgAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAQA545pln4pBDDonWrVtHXl5e3Hfffan9J0+eHHl5eRVeb7311pYpGAAAapD8bBcAAACs3/Lly2OXXXaJX//61zFw4MAN3m727NnRtGnT8uVtttmmKsoDAIAaTZAOAAA5oH///tG/f/+N3q5ly5bRrFmzzV8QAADUIh7tAgAANdhuu+0WJSUl0bdv35g0aVK2ywEAgJzkjnQAAKiBSkpKYuzYsdG9e/coKyuL22+/Pfr27RuTJ0+OXr16rXO7srKyKCsrK18uLS3dEuUCAEC1JkgHAIAaqHPnztG5c+fy5Z49e8b8+fPjmmuuSQ3SR44cGSNGjNgSJQIAQM7waBcAAKgl9txzz3jnnXdS+wwbNiyWLFlS/po/f/4Wqg4AAKovd6QDAEAtMWPGjCgpKUntU1hYGIWFhVuoIgAAyA2CdAAAyAHLli2Ld999t3x5zpw5MXPmzNh6662jbdu2MWzYsFiwYEHcdtttERExatSoaN++fXTr1i1WrFgREyZMiIkTJ8bEiROz9RYAACBnCdIBACAHTJ8+Pfbdd9/y5aFDh0ZExKBBg2L8+PGxcOHCmDdvXvn6FStWxFlnnRULFiyIBg0aRLdu3eKhhx6KAQMGbPHaAQAg1wnSAQAgB/Tp0yeSJFnn+vHjx2csn3322XH22WdXcVUAAFA7+LJRAAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFDkbpLdt2zbGjh0bbdu2zXYpAABsZuZ6AABAdZKf7QI2Vf369aNTp07ZLgMAgCpgrgcAAFQnOXtHOgAAAAAAbAmCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAMgBzzzzTBxyyCHRunXryMvLi/vuu2+920yZMiW6d+8e9evXj44dO8aYMWOqvlAAAKiBBOkAAJADli9fHrvsskvccMMNG9R/zpw5MWDAgNhnn31ixowZMXz48Dj11FNj4sSJVVwpAADUPPnZLgAAAFi//v37R//+/Te4/5gxY6Jt27YxatSoiIjo0qVLTJ8+Pa655poYOHBgFVUJAAA1kzvSAQCgBnr++eejX79+GW0HHHBATJ8+PVauXJmlqgAAIDe5Ix0AAGqgRYsWRXFxcUZbcXFxrFq1Kj755JMoKSmpdLuysrIoKysrXy4tLa3SOmF9Zs2ale0SyIKysrIoLCzMdhlsYa53oDoTpAMAQA2Vl5eXsZwkSaXt3zRy5MgYMWJEldYFG+KrJZ9GRF4ceeSR2S6FbMjLi/j/P7OofVaWrch2CQAVCNIBAKAGatWqVSxatCijbfHixZGfnx/Nmzdf53bDhg2LoUOHli+XlpZGmzZtqqxOWJeVXy6NiCR2/eU5sU2HHbNdDlvQwteej9fvH2vsa6G1Y79q1apslwJQgSAdAABqoJ49e8YDDzyQ0fb4449Hjx49oqCgYJ3bFRYWepwC1Urjlm1j67ads10GW1Dpwg8iwtjXRmvHHqA68mWjAACQA5YtWxYzZ86MmTNnRkTEnDlzYubMmTFv3ryI+M+d5EcffXR5/yFDhsTcuXNj6NChMWvWrBg3blzcfPPNcdZZZ2WjfAAAyGnuSAcAgBwwffr02HfffcuX1z5+ZdCgQTF+/PhYuHBheageEdGhQ4d4+OGH44wzzogbb7wxWrduHX/4wx9i4MCBW7x2AADIdYJ0AADIAX369Cn/stDKjB8/vkJb79694+WXX67CqgAAoHbwaBcAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAyCE33XRTdOjQIerXrx/du3ePZ599dp19J0+eHHl5eRVeb7311hasGAAAcp8gHQAAcsRdd90Vp59+epx33nkxY8aM2GeffaJ///4xb9681O1mz54dCxcuLH/tsMMOW6hiAACoGQTpAACQI6677ro47rjjYvDgwdGlS5cYNWpUtGnTJkaPHp26XcuWLaNVq1blr7p1626higEAoGYQpAMAQA5YsWJFvPTSS9GvX7+M9n79+sXUqVNTt91tt92ipKQk+vbtG5MmTUrtW1ZWFqWlpRkvAACo7QTpAACQAz755JNYvXp1FBcXZ7QXFxfHokWLKt2mpKQkxo4dGxMnTox77rknOnfuHH379o1nnnlmnccZOXJkFBUVlb/atGmzWd8HAADkovxsFwAAAGy4vLy8jOUkSSq0rdW5c+fo3Llz+XLPnj1j/vz5cc0110SvXr0q3WbYsGExdOjQ8uXS0lJhOgAAtZ470gEAIAe0aNEi6tatW+Hu88WLF1e4Sz3NnnvuGe+888461xcWFkbTpk0zXgAAUNsJ0gEAIAfUq1cvunfvHk888URG+xNPPBF77bXXBu9nxowZUVJSsrnLAwCAGs2jXQAAIEcMHTo0jjrqqOjRo0f07Nkzxo4dG/PmzYshQ4ZExH8ey7JgwYK47bbbIiJi1KhR0b59++jWrVusWLEiJkyYEBMnToyJEydm820AAEDOEaQDAECOOOKII+LTTz+Niy++OBYuXBg77bRTPPzww9GuXbuIiFi4cGHMmzevvP+KFSvirLPOigULFkSDBg2iW7du8dBDD8WAAQOy9RYAACAnCdIBACCHnHjiiXHiiSdWum78+PEZy2effXacffbZW6AqAACo2QTp1Ch1vl6S7RKAWq7OV19k/C/fjZ/rAAAAVAeCdGqEoqKiKKhXGPH+lGyXAhAREQ3mPJPtEmqMgnqFUVRUlO0yAAAAqMUE6dQIxcXFMeH222LJEncuAtQ0RUVFUVxcnO0yAAAAqMUE6dQYxcXFghYAAAAAYLOrk+0CAAAAAACgOhOkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAEAOuemmm6JDhw5Rv3796N69ezz77LOp/adMmRLdu3eP+vXrR8eOHWPMmDFbqFIAAKg5BOkAAJAj7rrrrjj99NPjvPPOixkzZsQ+++wT/fv3j3nz5lXaf86cOTFgwIDYZ599YsaMGTF8+PA49dRTY+LEiVu4cgAAyG2CdAAAyBHXXXddHHfccTF48ODo0qVLjBo1Ktq0aROjR4+utP+YMWOibdu2MWrUqOjSpUsMHjw4jj322Ljmmmu2cOUAAJDbBOkAAJADVqxYES+99FL069cvo71fv34xderUSrd5/vnnK/Q/4IADYvr06bFy5coqqxUAAGqa/E3dMEmSiIgoLS3dbMUAAEB1sHaOu3bOWx188sknsXr16iguLs5oLy4ujkWLFlW6zaJFiyrtv2rVqvjkk0+ipKSkwjZlZWVRVlZWvrxkyZKIyM68f9myZRER8dnc2bGq7Kstfnyyq3Th3IiIWLLgnSjIz8tyNWxJxr72Mva1l7GvvUoX/ecRhcuWLcvKfHNj5v2bHKQvXbo0IiLatGmzqbsAAIBqbenSpVFUVJTtMjLk5WV+uEySpELb+vpX1r7WyJEjY8SIERXasznvf2nCFVk7Ntn32t9GZbsEssTY117GvvYy9rVX7969s3r8DZn3b3KQ3rp165g/f340adIkdeJeVUpLS6NNmzYxf/78aNq06RY/PhvGOOUG45QbjFNuME7VnzHKDdkepyRJYunSpdG6destfux1adGiRdStW7fC3eeLFy+ucNf5Wq1ataq0f35+fjRv3rzSbYYNGxZDhw4tX16zZk189tln0bx58y0+78/2fwdsGOOUG4xTbjBOucE45QbjlBuyPU4bM+/f5CC9Tp06sd12223q5ptN06ZNXQw5wDjlBuOUG4xTbjBO1Z8xyg3ZHKfqdid6vXr1onv37vHEE0/EYYcdVt7+xBNPxKGHHlrpNj179owHHnggo+3xxx+PHj16REFBQaXbFBYWRmFhYUZbs2bNvlvx35HrNTcYp9xgnHKDccoNxik3GKfckAvzfl82CgAAOWLo0KHx5z//OcaNGxezZs2KM844I+bNmxdDhgyJiP/cTX700UeX9x8yZEjMnTs3hg4dGrNmzYpx48bFzTffHGeddVa23gIAAOSkTb4jHQAA2LKOOOKI+PTTT+Piiy+OhQsXxk477RQPP/xwtGvXLiIiFi5cGPPmzSvv36FDh3j44YfjjDPOiBtvvDFat24df/jDH2LgwIHZegsAAJCTcjZILywsjAsvvLDCn51SvRin3GCccoNxyg3GqfozRrnBOK3biSeeGCeeeGKl68aPH1+hrXfv3vHyyy9XcVVVw38HucE45QbjlBuMU24wTrnBOOWGXBqnvCRJkmwXAQAAAAAA1ZVnpAMAAAAAQApBOgAAAAAApBCkAwAAAABAimofpC9YsCCOPPLIaN68eTRs2DB23XXXeOmll8rXH3PMMZGXl5fx2nPPPbNYce3Tvn37CmOQl5cXJ510UkREJEkSF110UbRu3ToaNGgQffr0iTfeeCPLVdc+6xsn11L1sGrVqjj//POjQ4cO0aBBg+jYsWNcfPHFsWbNmvI+rqns25Bxck1l39KlS+P000+Pdu3aRYMGDWKvvfaKadOmla93LVUP6xsn11LtYd5f/Zn35wbz/urPnD83mPPnDvP+3FAT5v352S4gzeeffx4//OEPY999941HHnkkWrZsGe+99140a9Yso9+BBx4Yt9xyS/lyvXr1tnCltdu0adNi9erV5cuvv/567L///vGzn/0sIiKuuuqquO6662L8+PHRqVOnuPTSS2P//feP2bNnR5MmTbJVdq2zvnGKcC1VB1deeWWMGTMmbr311ujWrVtMnz49fv3rX0dRUVGcdtppEeGaqg42ZJwiXFPZNnjw4Hj99dfj9ttvj9atW8eECRPiRz/6Ubz55pux7bbbupaqifWNU4RrqTYw788N5v25wby/+jPnzw3m/LnDvD831Ih5f1KNnXPOOcnee++d2mfQoEHJoYceumUKYoOcdtppyfe+971kzZo1yZo1a5JWrVolV1xxRfn6r7/+OikqKkrGjBmTxSr55jgliWupujjooIOSY489NqPtJz/5SXLkkUcmSZK4pqqJ9Y1Tkrimsu3LL79M6tatmzz44IMZ7bvsskty3nnnuZaqifWNU5K4lmoL8/7cZN6fG8z7qx9z/txgzp8bzPtzQ02Z91frR7vcf//90aNHj/jZz34WLVu2jN122y3+93//t0K/yZMnR8uWLaNTp05x/PHHx+LFi7NQLRERK1asiAkTJsSxxx4beXl5MWfOnFi0aFH069evvE9hYWH07t07pk6dmsVKa7dvj9NarqXs23vvveOpp56Kt99+OyIiXnnllXjuuediwIABERGuqWpifeO0lmsqe1atWhWrV6+O+vXrZ7Q3aNAgnnvuOddSNbG+cVrLtVTzmffnHvP+3GDeXz2Z8+cGc/7cYN6fG2rKvL9aP9rl/fffj9GjR8fQoUNj+PDh8eKLL8app54ahYWFcfTRR0dERP/+/eNnP/tZtGvXLubMmRMXXHBB7LfffvHSSy9FYWFhlt9B7XPffffFF198Ecccc0xERCxatCgiIoqLizP6FRcXx9y5c7d0efx/3x6nCNdSdXHOOefEkiVLYscdd4y6devG6tWr47LLLotf/OIXEeGaqi7WN04Rrqlsa9KkSfTs2TMuueSS6NKlSxQXF8edd94Z//rXv2KHHXZwLVUT6xunCNdSbWHen3vM+3ODeX/1ZM6fG8z5c4N5f26oMfP+bN8Sn6agoCDp2bNnRtspp5yS7Lnnnuvc5sMPP0wKCgqSiRMnVnV5VKJfv37JwQcfXL78z3/+M4mI5MMPP8zoN3jw4OSAAw7Y0uXx/317nCrjWsqOO++8M9luu+2SO++8M3n11VeT2267Ldl6662T8ePHJ0nimqou1jdOlXFNbXnvvvtu0qtXryQikrp16ya777578qtf/Srp0qWLa6kaSRunyriWaibz/txj3p8bzPurJ3P+3GDOnzvM+3NDTZj3V+tHu5SUlETXrl0z2rp06RLz5s1L3aZdu3bxzjvvVHV5fMvcuXPjySefjMGDB5e3tWrVKiL+71/U11q8eHGFfw1ky6hsnCrjWsqO3/72t3HuuefGz3/+8/iv//qvOOqoo+KMM86IkSNHRoRrqrpY3zhVxjW15X3ve9+LKVOmxLJly2L+/Pnx4osvxsqVK6NDhw6upWokbZwq41qqmcz7c4t5f24w76++zPlzgzl/7jDvzw01Yd5frYP0H/7whzF79uyMtrfffjvatWu3zm0+/fTTmD9/fpSUlFR1eXzLLbfcEi1btoyDDjqovG3tD60nnniivG3FihUxZcqU2GuvvbJRZq1X2ThVxrWUHV9++WXUqZP5o7lu3bqxZs2aiHBNVRfrG6fKuKayp1GjRlFSUhKff/55PPbYY3HooYe6lqqhysapMq6lmsm8P7eY9+cG8/7qy5w/N5jz5x7z/tyQ0/P+bN8Sn+bFF19M8vPzk8suuyx55513kjvuuCNp2LBhMmHChCRJkmTp0qXJmWeemUydOjWZM2dOMmnSpKRnz57Jtttum5SWlma5+tpl9erVSdu2bZNzzjmnwrorrrgiKSoqSu65557ktddeS37xi18kJSUlxigL1jVOrqXqY9CgQcm2226bPPjgg8mcOXOSe+65J2nRokVy9tlnl/dxTWXf+sbJNVU9PProo8kjjzySvP/++8njjz+e7LLLLskee+yRrFixIkkS11J1kTZOrqXaw7w/d5j35wbz/urNnD83mPPnDvP+3FAT5v3VOkhPkiR54IEHkp122ikpLCxMdtxxx2Ts2LHl67788sukX79+yTbbbJMUFBQkbdu2TQYNGpTMmzcvixXXTo899lgSEcns2bMrrFuzZk1y4YUXJq1atUoKCwuTXr16Ja+99loWqmRd4+Raqj5KS0uT0047LWnbtm1Sv379pGPHjsl5552XlJWVlfdxTWXf+sbJNVU93HXXXUnHjh2TevXqJa1atUpOOumk5Isvvihf71qqHtLGybVUu5j35wbz/txg3l+9mfPnBnP+3GHenxtqwrw/L0mSJLv3xAMAAAAAQPVVrZ+RDgAAAAAA2SZIBwAAAACAFIJ0AAAAAABIIUgHAAAAAIAUgnQAAAAAAEghSAcAAAAAgBSCdAAAAAAASCFIBwAAAACAFIJ0gGomLy8v7rvvvnWu/+CDDyIvLy9mzpz5nY5zzDHHxI9//OPvtA8AAGDjmfMD5B5BOsAm6NOnT5x++ukV2u+7777Iy8v7TvteuHBh9O/f/zvtAwAA+G7M+QH4pvxsFwBAplatWmW7BAAAoAqZ8wPkHnekA1SRiy66KHbdddcYN25ctG3bNho3bhwnnHBCrF69Oq666qpo1apVtGzZMi677LKM7b79Z54vvvhi7LbbblG/fv3o0aNHzJgxI6P/6tWr47jjjosOHTpEgwYNonPnznH99ddX6DN06NBo1qxZNG/ePM4+++xIkiSjT5IkcdVVV0XHjh2jQYMGscsuu8Tf//73zXtSAACgBjHnB6g93JEOUIXee++9eOSRR+LRRx+N9957L37605/GnDlzolOnTjFlypSYOnVqHHvssdG3b9/Yc889K2y/fPnyOPjgg2O//faLCRMmxJw5c+K0007L6LNmzZrYbrvt4u67744WLVrE1KlT4ze/+U2UlJTE4YcfHhER1157bYwbNy5uvvnm6Nq1a1x77bVx7733xn777Ve+n/PPPz/uueeeGD16dOywww7xzDPPxJFHHhnbbLNN9O7du2pPFAAA5ChzfoDaQZAOUIXWrFkT48aNiyZNmkTXrl1j3333jdmzZ8fDDz8cderUic6dO8eVV14ZkydPrnRSfccdd8Tq1atj3Lhx0bBhw+jWrVv8+9//jhNOOKG8T0FBQYwYMaJ8uUOHDjF16tS4++67yyfVo0aNimHDhsXAgQMjImLMmDHx2GOPlW+zfPnyuO666+Lpp5+Onj17RkREx44d47nnnos//elPJtUAALAO5vwAtYMgHaAKtW/fPpo0aVK+XFxcHHXr1o06depktC1evLjS7WfNmhW77LJLNGzYsLxt7aT3m8aMGRN//vOfY+7cufHVV1/FihUrYtddd42IiCVLlsTChQsztsvPz48ePXqU/6nnm2++GV9//XXsv//+GftdsWJF7Lbbbhv/xgEAoJYw5weoHQTpAJugadOmsWTJkgrtX3zxRTRt2rR8uaCgIGN9Xl5epW1r1qyp9DjffqZhZe6+++4444wz4tprr42ePXtGkyZN4uqrr45//etfG/JWIiLKj//QQw/Ftttum7GusLBwg/cDAAA1hTk/AN8kSAfYBDvuuGM88sgjFdqnTZsWnTt33mzH6dq1a9x+++3x1VdfRYMGDSIi4oUXXsjo8+yzz8Zee+0VJ554Ynnbe++9V/7/i4qKoqSkJF544YXo1atXRESsWrUqXnrppfj+979ffpzCwsKYN2+eP+kEAIAw5wcgU531dwHg20488cR477334qSTTopXXnkl3n777bjxxhvj5ptvjt/+9reb7Ti//OUvo06dOnHcccfFm2++GQ8//HBcc801GX223377mD59ejz22GPx9ttvxwUXXBDTpk3L6HPaaafFFVdcEffee2+89dZbceKJJ8YXX3xRvr5JkyZx1llnxRlnnBG33nprvPfeezFjxoy48cYb49Zbb91s7wcAAHKFOT8A3+SOdIBN0L59+3j22WfjvPPOi379+sXXX38dnTp1ivHjx8fPfvazzXacxo0bxwMPPBBDhgyJ3XbbLbp27RpXXnll+RcIRUQMGTIkZs6cGUcccUTk5eXFL37xizjxxBMz7p4588wzY+HChXHMMcdEnTp14thjj43DDjss409VL7nkkmjZsmWMHDky3n///WjWrFl8//vfj+HDh2+29wMAALnCnB+Ab8pLNuRhXAAAAAAAUEt5tAsAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAEAKQToAAAAAAKQQpAMAAAAAQApBOgAAAAAApBCkAwAAAABACkE6AAAAAACkEKQDAAAAAECK/wd5O6KRMS2yBQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resumo estatístico para umidade:\n",
      "count    14.000000\n",
      "mean     81.214286\n",
      "std       9.552918\n",
      "min      65.000000\n",
      "25%      71.250000\n",
      "50%      85.000000\n",
      "75%      89.000000\n",
      "max      95.000000\n",
      "Name: Umidade, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# visualizando\n",
    "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
    "sns.boxplot(x='Umidade', data=df2, ax=ax[0]).set_title('Boxplot de Umidade')\n",
    "sns.histplot(x='Umidade', data=df2, ax=ax[1]).set_title('Histograma de Umidade')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(f'Resumo estatístico para umidade:\\n{df2.Umidade.describe()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Resumo de Análise e Tratamento de Dados**\n",
    "\n",
    "- **Objetivo:** Limpeza de dados para atender às regras de negócio:\n",
    "  - **Aparência:** Valores válidos: \"sol\", \"chuva\", \"nublado\".\n",
    "  - **Temperatura:** Entre -130 e 130.\n",
    "  - **Umidade:** Entre 0 e 100.\n",
    "\n",
    "- **Dados iniciais:**\n",
    "  - **Shape:** (14, 5)\n",
    "  - **Valores faltantes:** `Umidade (1)` e `Vento (1)`.\n",
    "  - **Fora do domínio:** `Temperatura (1)` e `Umidade (1)`.\n",
    "\n",
    "- **Tratamentos realizados:**\n",
    "  1. Substituído `menos` em `Aparência` pela moda (`sol`).\n",
    "  2. Valores fora do domínio em `Temperatura` e `Umidade` substituídos pela mediana.\n",
    "  3. Valores ausentes em `Umidade` preenchidos com a mediana.\n",
    "\n",
    "- **Dados finais:**\n",
    "  - Sem valores ausentes ou fora do domínio.\n",
    "  - **Temperatura:** Média: 73.68, Máx: 85.\n",
    "  - **Umidade:** Média: 81.21, Máx: 95.\n",
    "\n",
    "- **Visualizações:** Gráficos exploratórios (boxplot, histograma e contagem de valores categóricos)."
   ]
  }
 ],
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