aula_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "##cientista de dados de uma empresa imobiliaria \n",
    " \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/alura-cursos/pandas-conhecendo-a-biblioteca/main/base-de-dados/aluguel.csv'\n",
    "\n",
    "dados = pd.read_csv(url, sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Jardim Botânico</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conjunto Comercial/Sala</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>4020.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>800.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Higienópolis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>800.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32955</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Centro</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>800.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32956</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Jacarepaguá</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32957</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>São Francisco Xavier</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32958</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Leblon</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32959</th>\n",
       "      <td>Conjunto Comercial/Sala</td>\n",
       "      <td>Centro</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>4206.0</td>\n",
       "      <td>1109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32960 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Tipo                Bairro  Quartos  Vagas  Suites  \\\n",
       "0                    Quitinete            Copacabana        1      0       0   \n",
       "1                         Casa       Jardim Botânico        2      0       1   \n",
       "2      Conjunto Comercial/Sala       Barra da Tijuca        0      4       0   \n",
       "3                  Apartamento                Centro        1      0       0   \n",
       "4                  Apartamento          Higienópolis        1      0       0   \n",
       "...                        ...                   ...      ...    ...     ...   \n",
       "32955                Quitinete                Centro        0      0       0   \n",
       "32956              Apartamento           Jacarepaguá        3      1       2   \n",
       "32957              Apartamento  São Francisco Xavier        2      1       0   \n",
       "32958              Apartamento                Leblon        2      0       0   \n",
       "32959  Conjunto Comercial/Sala                Centro        0      0       0   \n",
       "\n",
       "       Area   Valor  Condominio    IPTU  \n",
       "0        40  1700.0       500.0    60.0  \n",
       "1       100  7000.0         NaN     NaN  \n",
       "2       150  5200.0      4020.0  1111.0  \n",
       "3        15   800.0       390.0    20.0  \n",
       "4        48   800.0       230.0     NaN  \n",
       "...     ...     ...         ...     ...  \n",
       "32955    27   800.0       350.0    25.0  \n",
       "32956    78  1800.0       800.0    40.0  \n",
       "32957    48  1400.0       509.0    37.0  \n",
       "32958    70  3000.0       760.0     NaN  \n",
       "32959   250  6500.0      4206.0  1109.0  \n",
       "\n",
       "[32960 rows x 9 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Jardim Botânico</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conjunto Comercial/Sala</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>4020.0</td>\n",
       "      <td>1111.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>800.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Higienópolis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>800.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Tipo           Bairro  Quartos  Vagas  Suites  Area  \\\n",
       "0                Quitinete       Copacabana        1      0       0    40   \n",
       "1                     Casa  Jardim Botânico        2      0       1   100   \n",
       "2  Conjunto Comercial/Sala  Barra da Tijuca        0      4       0   150   \n",
       "3              Apartamento           Centro        1      0       0    15   \n",
       "4              Apartamento     Higienópolis        1      0       0    48   \n",
       "\n",
       "    Valor  Condominio    IPTU  \n",
       "0  1700.0       500.0    60.0  \n",
       "1  7000.0         NaN     NaN  \n",
       "2  5200.0      4020.0  1111.0  \n",
       "3   800.0       390.0    20.0  \n",
       "4   800.0       230.0     NaN  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32955</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Centro</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>800.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32956</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Jacarepaguá</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32957</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>São Francisco Xavier</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32958</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Leblon</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32959</th>\n",
       "      <td>Conjunto Comercial/Sala</td>\n",
       "      <td>Centro</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>4206.0</td>\n",
       "      <td>1109.0</td>\n",
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      "text/plain": [
       "                          Tipo                Bairro  Quartos  Vagas  Suites  \\\n",
       "32955                Quitinete                Centro        0      0       0   \n",
       "32956              Apartamento           Jacarepaguá        3      1       2   \n",
       "32957              Apartamento  São Francisco Xavier        2      1       0   \n",
       "32958              Apartamento                Leblon        2      0       0   \n",
       "32959  Conjunto Comercial/Sala                Centro        0      0       0   \n",
       "\n",
       "       Area   Valor  Condominio    IPTU  \n",
       "32955    27   800.0       350.0    25.0  \n",
       "32956    78  1800.0       800.0    40.0  \n",
       "32957    48  1400.0       509.0    37.0  \n",
       "32958    70  3000.0       760.0     NaN  \n",
       "32959   250  6500.0      4206.0  1109.0  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32960, 9)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Tipo', 'Bairro', 'Quartos', 'Vagas', 'Suites', 'Area', 'Valor',\n",
       "       'Condominio', 'IPTU'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32960 entries, 0 to 32959\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Tipo        32960 non-null  object \n",
      " 1   Bairro      32960 non-null  object \n",
      " 2   Quartos     32960 non-null  int64  \n",
      " 3   Vagas       32960 non-null  int64  \n",
      " 4   Suites      32960 non-null  int64  \n",
      " 5   Area        32960 non-null  int64  \n",
      " 6   Valor       32943 non-null  float64\n",
      " 7   Condominio  28867 non-null  float64\n",
      " 8   IPTU        22723 non-null  float64\n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "dados.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      Quitinete\n",
       "1                           Casa\n",
       "2        Conjunto Comercial/Sala\n",
       "3                    Apartamento\n",
       "4                    Apartamento\n",
       "                  ...           \n",
       "32955                  Quitinete\n",
       "32956                Apartamento\n",
       "32957                Apartamento\n",
       "32958                Apartamento\n",
       "32959    Conjunto Comercial/Sala\n",
       "Name: Tipo, Length: 32960, dtype: object"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados[\"Tipo\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32955</th>\n",
       "      <td>0</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32956</th>\n",
       "      <td>3</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32957</th>\n",
       "      <td>2</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32958</th>\n",
       "      <td>2</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32959</th>\n",
       "      <td>0</td>\n",
       "      <td>6500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32960 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quartos   Valor\n",
       "0            1  1700.0\n",
       "1            2  7000.0\n",
       "2            0  5200.0\n",
       "3            1   800.0\n",
       "4            1   800.0\n",
       "...        ...     ...\n",
       "32955        0   800.0\n",
       "32956        3  1800.0\n",
       "32957        2  1400.0\n",
       "32958        2  3000.0\n",
       "32959        0  6500.0\n",
       "\n",
       "[32960 rows x 2 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados[[\"Quartos\", \"Valor\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18 entries, 0 to 17\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Nome      18 non-null     object \n",
      " 1   Idade     18 non-null     int64  \n",
      " 2   Notas     12 non-null     float64\n",
      " 3   Aprovado  18 non-null     bool   \n",
      "dtypes: bool(1), float64(1), int64(1), object(1)\n",
      "memory usage: 578.0+ bytes\n"
     ]
    },
    {
     "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>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32960.000000</td>\n",
       "      <td>32960.000000</td>\n",
       "      <td>32960.000000</td>\n",
       "      <td>32960.000000</td>\n",
       "      <td>3.294300e+04</td>\n",
       "      <td>2.886700e+04</td>\n",
       "      <td>2.272300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.771390</td>\n",
       "      <td>1.749059</td>\n",
       "      <td>0.665777</td>\n",
       "      <td>231.901547</td>\n",
       "      <td>1.295266e+04</td>\n",
       "      <td>2.388062e+03</td>\n",
       "      <td>2.364410e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.717869</td>\n",
       "      <td>20.380402</td>\n",
       "      <td>1.176525</td>\n",
       "      <td>1135.254152</td>\n",
       "      <td>6.675220e+05</td>\n",
       "      <td>3.918496e+04</td>\n",
       "      <td>1.795641e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.500000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.500000e+03</td>\n",
       "      <td>5.900000e+02</td>\n",
       "      <td>1.100000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>2.800000e+03</td>\n",
       "      <td>9.900000e+02</td>\n",
       "      <td>2.500000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>1.800000e+03</td>\n",
       "      <td>6.970000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1966.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>90000.000000</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>6.552570e+06</td>\n",
       "      <td>2.705307e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quartos         Vagas        Suites          Area         Valor  \\\n",
       "count  32960.000000  32960.000000  32960.000000  32960.000000  3.294300e+04   \n",
       "mean       1.771390      1.749059      0.665777    231.901547  1.295266e+04   \n",
       "std        1.717869     20.380402      1.176525   1135.254152  6.675220e+05   \n",
       "min        0.000000      0.000000      0.000000      0.000000  7.500000e+01   \n",
       "25%        0.000000      0.000000      0.000000     55.000000  1.500000e+03   \n",
       "50%        2.000000      1.000000      0.000000     87.000000  2.800000e+03   \n",
       "75%        3.000000      2.000000      1.000000    170.000000  6.500000e+03   \n",
       "max      100.000000   1966.000000     70.000000  90000.000000  1.200000e+08   \n",
       "\n",
       "         Condominio          IPTU  \n",
       "count  2.886700e+04  2.272300e+04  \n",
       "mean   2.388062e+03  2.364410e+03  \n",
       "std    3.918496e+04  1.795641e+05  \n",
       "min    1.000000e+00  1.000000e+00  \n",
       "25%    5.900000e+02  1.100000e+02  \n",
       "50%    9.900000e+02  2.500000e+02  \n",
       "75%    1.800000e+03  6.970000e+02  \n",
       "max    6.552570e+06  2.705307e+07  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url2 = \"https://raw.githubusercontent.com/alura-cursos/pandas-conhecendo-a-biblioteca/main/desafios/alunos.csv\"\n",
    "\n",
    "dados2 = pd.read_csv(url2, sep=',') \n",
    "\n",
    "dados2\n",
    "\n",
    "dados2.columns\n",
    "\n",
    "dados2.info()\n",
    "\n",
    "# conferindo os tipos de dados de cada coluna\n",
    "dados2.dtypes\n",
    "\n",
    "dados.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Jardim Botânico</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conjunto Comercial/Sala</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>4020.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>800.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Higienópolis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>800.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Tipo           Bairro  Quartos  Vagas  Suites  Area  \\\n",
       "0                Quitinete       Copacabana        1      0       0    40   \n",
       "1                     Casa  Jardim Botânico        2      0       1   100   \n",
       "2  Conjunto Comercial/Sala  Barra da Tijuca        0      4       0   150   \n",
       "3              Apartamento           Centro        1      0       0    15   \n",
       "4              Apartamento     Higienópolis        1      0       0    48   \n",
       "\n",
       "    Valor  Condominio    IPTU  \n",
       "0  1700.0       500.0    60.0  \n",
       "1  7000.0         NaN     NaN  \n",
       "2  5200.0      4020.0  1111.0  \n",
       "3   800.0       390.0    20.0  \n",
       "4   800.0       230.0     NaN  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## data wrangling and EDA\n",
    "\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7490594660194174"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados[\"Vagas\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tipo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apartamento</th>\n",
       "      <td>2.481569</td>\n",
       "      <td>1.232388</td>\n",
       "      <td>0.875998</td>\n",
       "      <td>119.790498</td>\n",
       "      <td>4744.614648</td>\n",
       "      <td>1722.653697</td>\n",
       "      <td>686.170413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Box/Garagem</th>\n",
       "      <td>0.060976</td>\n",
       "      <td>1.304878</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.524390</td>\n",
       "      <td>1899.756098</td>\n",
       "      <td>305.450000</td>\n",
       "      <td>125.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa</th>\n",
       "      <td>3.133402</td>\n",
       "      <td>1.795243</td>\n",
       "      <td>1.258532</td>\n",
       "      <td>250.578077</td>\n",
       "      <td>6793.454922</td>\n",
       "      <td>931.280952</td>\n",
       "      <td>1582.506394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa Comercial</th>\n",
       "      <td>1.562264</td>\n",
       "      <td>2.203774</td>\n",
       "      <td>0.260377</td>\n",
       "      <td>372.241509</td>\n",
       "      <td>14478.264151</td>\n",
       "      <td>1935.709677</td>\n",
       "      <td>1707.386740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa de Condomínio</th>\n",
       "      <td>4.007028</td>\n",
       "      <td>3.101406</td>\n",
       "      <td>2.864458</td>\n",
       "      <td>456.161647</td>\n",
       "      <td>11952.061245</td>\n",
       "      <td>1342.025527</td>\n",
       "      <td>3252.517730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa de Vila</th>\n",
       "      <td>1.927711</td>\n",
       "      <td>0.389558</td>\n",
       "      <td>0.228916</td>\n",
       "      <td>78.469880</td>\n",
       "      <td>1574.369478</td>\n",
       "      <td>138.768421</td>\n",
       "      <td>183.878788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chácara</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4900.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conjunto Comercial/Sala</th>\n",
       "      <td>0.040939</td>\n",
       "      <td>2.516801</td>\n",
       "      <td>0.010858</td>\n",
       "      <td>214.472487</td>\n",
       "      <td>14715.045521</td>\n",
       "      <td>4512.218100</td>\n",
       "      <td>1399.596571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flat</th>\n",
       "      <td>1.415966</td>\n",
       "      <td>0.962185</td>\n",
       "      <td>0.754202</td>\n",
       "      <td>65.483193</td>\n",
       "      <td>4546.189076</td>\n",
       "      <td>2027.720554</td>\n",
       "      <td>493.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galpão/Depósito/Armazém</th>\n",
       "      <td>0.043339</td>\n",
       "      <td>5.587480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2514.810594</td>\n",
       "      <td>53407.062600</td>\n",
       "      <td>10560.308824</td>\n",
       "      <td>5050.465241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotel</th>\n",
       "      <td>25.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>885.500000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indústria</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loft</th>\n",
       "      <td>1.117647</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.372549</td>\n",
       "      <td>58.372549</td>\n",
       "      <td>2557.882353</td>\n",
       "      <td>869.239130</td>\n",
       "      <td>238.243243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loja Shopping/ Ct Comercial</th>\n",
       "      <td>0.044534</td>\n",
       "      <td>6.874494</td>\n",
       "      <td>0.008097</td>\n",
       "      <td>88.109312</td>\n",
       "      <td>8126.178862</td>\n",
       "      <td>1790.109005</td>\n",
       "      <td>1052.415459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loja/Salão</th>\n",
       "      <td>0.010519</td>\n",
       "      <td>1.217391</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>228.852034</td>\n",
       "      <td>13769.646770</td>\n",
       "      <td>2605.601954</td>\n",
       "      <td>2670.331409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loteamento/Condomínio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2069.000000</td>\n",
       "      <td>22000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4885.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pousada/Chalé</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>862.000000</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prédio Inteiro</th>\n",
       "      <td>0.335548</td>\n",
       "      <td>11.199336</td>\n",
       "      <td>0.019934</td>\n",
       "      <td>1773.837209</td>\n",
       "      <td>498637.239203</td>\n",
       "      <td>55066.750000</td>\n",
       "      <td>165480.022472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quitinete</th>\n",
       "      <td>0.834928</td>\n",
       "      <td>0.017943</td>\n",
       "      <td>0.037081</td>\n",
       "      <td>28.954545</td>\n",
       "      <td>1246.840909</td>\n",
       "      <td>427.341969</td>\n",
       "      <td>75.790741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Studio</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>103.250000</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>151.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sítio</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>15217.000000</td>\n",
       "      <td>12499.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5848.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terreno Padrão</th>\n",
       "      <td>0.042857</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5044.814286</td>\n",
       "      <td>32567.714286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5138.170732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Quartos      Vagas     Suites          Area  \\\n",
       "Tipo                                                                         \n",
       "Apartamento                   2.481569   1.232388   0.875998    119.790498   \n",
       "Box/Garagem                   0.060976   1.304878   0.000000     17.524390   \n",
       "Casa                          3.133402   1.795243   1.258532    250.578077   \n",
       "Casa Comercial                1.562264   2.203774   0.260377    372.241509   \n",
       "Casa de Condomínio            4.007028   3.101406   2.864458    456.161647   \n",
       "Casa de Vila                  1.927711   0.389558   0.228916     78.469880   \n",
       "Chácara                       3.000000   0.000000   0.000000      0.000000   \n",
       "Conjunto Comercial/Sala       0.040939   2.516801   0.010858    214.472487   \n",
       "Flat                          1.415966   0.962185   0.754202     65.483193   \n",
       "Galpão/Depósito/Armazém       0.043339   5.587480   0.000000   2514.810594   \n",
       "Hotel                        25.500000   2.500000  12.000000    885.500000   \n",
       "Indústria                     0.000000   0.000000   0.000000     22.000000   \n",
       "Loft                          1.117647   0.666667   0.372549     58.372549   \n",
       "Loja Shopping/ Ct Comercial   0.044534   6.874494   0.008097     88.109312   \n",
       "Loja/Salão                    0.010519   1.217391   0.043478    228.852034   \n",
       "Loteamento/Condomínio         0.000000   0.000000   0.000000   2069.000000   \n",
       "Pousada/Chalé                23.000000   0.000000  23.000000    862.000000   \n",
       "Prédio Inteiro                0.335548  11.199336   0.019934   1773.837209   \n",
       "Quitinete                     0.834928   0.017943   0.037081     28.954545   \n",
       "Studio                        0.500000   0.250000   0.000000    103.250000   \n",
       "Sítio                         5.100000   1.000000   3.800000  15217.000000   \n",
       "Terreno Padrão                0.042857   0.057143   0.000000   5044.814286   \n",
       "\n",
       "                                     Valor    Condominio           IPTU  \n",
       "Tipo                                                                     \n",
       "Apartamento                    4744.614648   1722.653697     686.170413  \n",
       "Box/Garagem                    1899.756098    305.450000     125.846154  \n",
       "Casa                           6793.454922    931.280952    1582.506394  \n",
       "Casa Comercial                14478.264151   1935.709677    1707.386740  \n",
       "Casa de Condomínio            11952.061245   1342.025527    3252.517730  \n",
       "Casa de Vila                   1574.369478    138.768421     183.878788  \n",
       "Chácara                        4900.000000           NaN            NaN  \n",
       "Conjunto Comercial/Sala       14715.045521   4512.218100    1399.596571  \n",
       "Flat                           4546.189076   2027.720554     493.166667  \n",
       "Galpão/Depósito/Armazém       53407.062600  10560.308824    5050.465241  \n",
       "Hotel                         25000.000000           NaN    6202.000000  \n",
       "Indústria                    120000.000000           NaN            NaN  \n",
       "Loft                           2557.882353    869.239130     238.243243  \n",
       "Loja Shopping/ Ct Comercial    8126.178862   1790.109005    1052.415459  \n",
       "Loja/Salão                    13769.646770   2605.601954    2670.331409  \n",
       "Loteamento/Condomínio         22000.000000           NaN    4885.000000  \n",
       "Pousada/Chalé                  1480.000000    295.000000            NaN  \n",
       "Prédio Inteiro               498637.239203  55066.750000  165480.022472  \n",
       "Quitinete                      1246.840909    427.341969      75.790741  \n",
       "Studio                         2400.000000    549.000000     151.250000  \n",
       "Sítio                         12499.000000           NaN    5848.800000  \n",
       "Terreno Padrão                32567.714286      1.000000    5138.170732  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.groupby(\"Tipo\").mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento                      4744.614648\n",
       "Box/Garagem                      1899.756098\n",
       "Casa                             6793.454922\n",
       "Casa Comercial                  14478.264151\n",
       "Casa de Condomínio              11952.061245\n",
       "Casa de Vila                     1574.369478\n",
       "Chácara                          4900.000000\n",
       "Conjunto Comercial/Sala         14715.045521\n",
       "Flat                             4546.189076\n",
       "Galpão/Depósito/Armazém         53407.062600\n",
       "Hotel                           25000.000000\n",
       "Indústria                      120000.000000\n",
       "Loft                             2557.882353\n",
       "Loja Shopping/ Ct Comercial      8126.178862\n",
       "Loja/Salão                      13769.646770\n",
       "Loteamento/Condomínio           22000.000000\n",
       "Pousada/Chalé                    1480.000000\n",
       "Prédio Inteiro                 498637.239203\n",
       "Quitinete                        1246.840909\n",
       "Studio                           2400.000000\n",
       "Sítio                           12499.000000\n",
       "Terreno Padrão                  32567.714286\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.groupby(\"Tipo\")[\"Valor\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_preco_tipo = dados.groupby(\"Tipo\")[[\"Valor\"]].mean().sort_values(by=\"Valor\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Tipo'>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_preco_tipo.plot(kind='barh', figsize=(14, 10), color ='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Quitinete', 'Casa', 'Conjunto Comercial/Sala', 'Apartamento',\n",
       "       'Casa de Condomínio', 'Prédio Inteiro', 'Flat', 'Loja/Salão',\n",
       "       'Galpão/Depósito/Armazém', 'Casa Comercial', 'Casa de Vila',\n",
       "       'Terreno Padrão', 'Box/Garagem', 'Loft',\n",
       "       'Loja Shopping/ Ct Comercial', 'Chácara', 'Loteamento/Condomínio',\n",
       "       'Sítio', 'Pousada/Chalé', 'Studio', 'Hotel', 'Indústria'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Tipo.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "imoveis_comerciais = ['Conjunto Comercial/Sala', \n",
    "                      'Prédio Inteiro', 'Loja/Salão', \n",
    "                      'Galpão/Depósito/Armazém', \n",
    "                      'Casa Comercial', 'Terreno Padrão',\n",
    "                      'Loja Shopping/ Ct Comercial',\n",
    "                      'Box/Garagem', 'Chácara',\n",
    "                      'Loteamento/Condomínio', 'Sítio',\n",
    "                      'Pousada/Chalé', 'Hotel', 'Indústria']                                         \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dados.query(\"@imoveis_comerciais not in Tipo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Jardim Botânico</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>800.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Higienópolis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>800.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Vista Alegre</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo           Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0    Quitinete       Copacabana        1      0       0    40  1700.0   \n",
       "1         Casa  Jardim Botânico        2      0       1   100  7000.0   \n",
       "3  Apartamento           Centro        1      0       0    15   800.0   \n",
       "4  Apartamento     Higienópolis        1      0       0    48   800.0   \n",
       "5  Apartamento     Vista Alegre        3      1       0    70  1200.0   \n",
       "\n",
       "   Condominio  IPTU  \n",
       "0       500.0  60.0  \n",
       "1         NaN   NaN  \n",
       "3       390.0  20.0  \n",
       "4       230.0   NaN  \n",
       "5         NaN   NaN  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Quitinete', 'Casa', 'Apartamento', 'Casa de Condomínio', 'Flat',\n",
       "       'Casa de Vila', 'Loft', 'Studio'], dtype=object)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Tipo.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Tipo'>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"Tipo\")[\"Valor\"].mean()\n",
    "\n",
    "df_preco_tipo = df.groupby(\"Tipo\")[[\"Valor\"]].mean().sort_values(by=\"Valor\", ascending=True)\n",
    "\n",
    "df_preco_tipo.plot(kind='barh', figsize=(14, 10), color ='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Quitinete', 'Casa', 'Apartamento', 'Casa de Condomínio', 'Flat',\n",
       "       'Casa de Vila', 'Loft', 'Studio'], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Tipo.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento           0.845139\n",
       "Casa de Condomínio    0.043096\n",
       "Casa                  0.041842\n",
       "Quitinete             0.036173\n",
       "Flat                  0.020596\n",
       "Casa de Vila          0.010774\n",
       "Loft                  0.002207\n",
       "Studio                0.000173\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Tipo.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>proportion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tipo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Studio</th>\n",
       "      <td>0.000173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quitinete</th>\n",
       "      <td>0.036173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loft</th>\n",
       "      <td>0.002207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flat</th>\n",
       "      <td>0.020596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa de Vila</th>\n",
       "      <td>0.010774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa de Condomínio</th>\n",
       "      <td>0.043096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa</th>\n",
       "      <td>0.041842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apartamento</th>\n",
       "      <td>0.845139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    proportion\n",
       "Tipo                          \n",
       "Studio                0.000173\n",
       "Quitinete             0.036173\n",
       "Loft                  0.002207\n",
       "Flat                  0.020596\n",
       "Casa de Vila          0.010774\n",
       "Casa de Condomínio    0.043096\n",
       "Casa                  0.041842\n",
       "Apartamento           0.845139"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Tipo.value_counts(normalize = True).to_frame().sort_values(by=\"Tipo\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Tipos', ylabel='Percentual'>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ce+65+bd/+7d88YtfzMYbb5zrr7++XgMCAAAAUL/WqhAaOnRo7ddbb711nnnmmSxatCidOnWq3WkMAAAAgMZprQqh1dloo43q66UAAAAAWIfWqhDaa6+9PnYm0D333LPWgQAAAABYt9aqEOrfv3+dx++//34ee+yxPPHEExk+fHh95AIAAABgHVmrQujiiy9e7fjZZ5+dd9999zMFAgAAAGDdqqzPF/vud7+biRMn1udLAgAAAFDP6rUQmjp1alq3bl2fLwkAAABAPVurW8YOOuigOo9ramry+uuv55FHHsmZZ55ZL8EAAAAAWDfWqhDq0KFDnV3GKisrs+222+bcc8/NvvvuW2/hAAAAAKh/a1UIXXXVVfUcAwAAAICGslZrCPXu3TtvvPHGKuNvvfVWevfu/ZlDAQAAALDurFUhNHv27FRXV68yvnz58rz66qufORQAAAAA686numXslltuqf36jjvuSMeOHWsfV1dXZ9KkSenVq1e9hQMAAACg/n2qQujAAw9MklRUVGT48OF1zrVo0SK9evXKRRddVG/hAAAAAKh/n6oQWrlyZZJkyy23zMMPP5zOnTuvk1AAAAAArDtrtcvYrFmz6jsHAAAAAA1krQqhJJk0aVImTZqU+fPn184c+sjEiRM/czAAAAAA1o21KoTOOeecnHvuuRk4cGC6d++eioqK+s4FAAAAwDqyVoXQhAkTctVVV+Xwww+v7zwAAAAArGOVa/OkFStWZPfdd6/vLAAAAAA0gLUqhI4++uhce+219Z0FAAAAgAawVreMLVu2LJdffnnuvvvu7LTTTmnRokWd82PHjq2XcAAAAADUv7UqhGbOnJn+/fsnSZ544ok65ywwDQAAANC4rVUhNHny5PrOAQAAAEADWas1hD7ywgsv5I477sh7772XJKmpqamXUAAAAACsO2tVCL3xxhvZZ599ss0222T//ffP66+/niQ56qijcvLJJ9drQAAAAADq11oVQieddFJatGiRqqqqtG3btnb8kEMOye23315v4QAAAACof2u1htCdd96ZO+64I5tvvnmd8T59+uTll1+ul2AAAAAArBtrNUNoyZIldWYGfWTRokVp1arVZw4FAAAAwLqzVoXQF7/4xVxzzTW1jysqKrJy5cpceOGF2WuvveotHAAAAAD1b61uGbvwwguzzz775JFHHsmKFSvyH//xH3nyySezaNGi3H///fWdEQAAAIB6tFYzhHbcccc899xz+cIXvpBvfOMbWbJkSQ466KA8+uij2Wqrreo7IwAAAAD1aK1mCCVJx44d85Of/KQ+swAAAADQANZqhtDvf//7/OlPf1pl/E9/+lOuvvrqzxwKAAAAgHVnrQqhMWPGpHPnzquMd+nSJeeff/5nDgUAAADAurNWhVBVVVW23HLLVca32GKLVFVVfeZQAAAAAKw7a1UIdenSJTNnzlxlfMaMGdl4440/cygAAAAA1p21KoQOPfTQjBw5MpMnT051dXWqq6tzzz335MQTT8y3v/3t+s4IAAAAQD1aq13GzjvvvMyePTv77LNPmjf/8CVWrlyZYcOGWUMIAAAAoJH71IVQTU1N5s6dm6uuuio//elP89hjj6VNmzbp169ftthii3WREQAAAIB6tFaF0NZbb50nn3wyffr0SZ8+fdZFLgAAAADWkU+9hlBlZWX69OmTN954Y13kAQAAAGAdW6tFpS+44IKccsopeeKJJ+o7DwAAAADr2FotKj1s2LAsXbo0O++8c1q2bJk2bdrUOb9o0aJ6CQcAAABA/VurQmjcuHH1HAMAAACAhrJWhdDw4cPrOwcAAAAADWSt1hBKkhdffDFnnHFGDj300MyfPz9J8v/+3//Lk08+WW/hAAAAAKh/a1UI3XvvvenXr1/+/ve/5+abb867776bJJkxY0ZGjx5drwEBAAAAqF9rVQiddtpp+elPf5q77rorLVu2rB3fe++98+CDD9ZbOAAAAADq31oVQo8//ni++c1vrjLepUuXLFy48DOHAgAAAGDdWatCaMMNN8zrr7++yvijjz6azTbb7DOHAgAAAGDdWatC6Nvf/nZOPfXUzJ07NxUVFVm5cmXuv//+/PjHP86wYcPqOyMAAAAA9WitCqHzzz8/22+/fXr27Jl33303ffv2zZe+9KXsvvvuOeOMM+o7IwAAAAD1qPmnuXjlypX5xS9+kVtuuSUrVqzI4Ycfnm9961t5991387nPfS59+vRZVzkBAAAAqCefqhD62c9+lrPPPjtDhgxJmzZtcu2116ampiYTJ05cV/kAAAAAqGef6paxa665Jr/+9a9zxx135M9//nNuvfXW/OEPf8jKlSs/U4jx48enV69ead26dXbdddc89NBDn+h51113XSoqKnLggQd+pvcHAAAAKJNPVQhVVVVl//33r308ZMiQVFRU5LXXXlvrANdff31GjRqV0aNHZ/r06dl5550zdOjQzJ8//2OfN3v27Pz4xz/OF7/4xbV+bwAAAIAy+lSF0AcffJDWrVvXGWvRokXef//9tQ4wduzYHHPMMRkxYkT69u2bCRMmpG3bth97G1p1dXUOO+ywnHPOOendu/davzcAAABAGX2qNYRqampyxBFHpFWrVrVjy5Ytyw9+8INssMEGtWM333zzJ3q9FStWZNq0aTn99NNrxyorKzNkyJBMnTp1jc8799xz06VLlxx11FH529/+9mm+BQAAAIDS+1SF0PDhw1cZ++53v7vWb75w4cJUV1ena9eudca7du2aZ555ZrXPue+++3LllVfmscce+0TvsXz58ixfvrz28eLFi9c6LwAAAMD64FMVQr///e/XVY5P5J133snhhx+eK664Ip07d/5EzxkzZkzOOeecdZwMAAAAoOn4VIVQfevcuXOaNWuWefPm1RmfN29eunXrtsr1L774YmbPnp2vf/3rtWMf7XDWvHnzPPvss9lqq63qPOf000/PqFGjah8vXrw4PXr0qM9vAwAAAKBJKbQQatmyZQYMGJBJkybVbh2/cuXKTJo0Kccff/wq12+33XZ5/PHH64ydccYZeeedd3LJJZestuhp1apVnTWPAAAAAMqu0EIoSUaNGpXhw4dn4MCBGTRoUMaNG5clS5ZkxIgRSZJhw4Zls802y5gxY9K6devsuOOOdZ6/4YYbJskq4wAAAACsXuGF0CGHHJIFCxbkrLPOyty5c9O/f//cfvvttQtNV1VVpbKysuCUAAAAAOuPwguhJDn++ONXe4tYkkyZMuVjn3vVVVfVfyAAAACA9ZipNwAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASqZRFELjx49Pr1690rp16+y666556KGH1njtzTffnIEDB2bDDTfMBhtskP79++c///M/GzAtAAAAQNNWeCF0/fXXZ9SoURk9enSmT5+enXfeOUOHDs38+fNXe/1GG22Un/zkJ5k6dWpmzpyZESNGZMSIEbnjjjsaODkAAABA01R4ITR27Ngcc8wxGTFiRPr27ZsJEyakbdu2mThx4mqv//KXv5xvfvOb2X777bPVVlvlxBNPzE477ZT77ruvgZMDAAAANE2FFkIrVqzItGnTMmTIkNqxysrKDBkyJFOnTv2Xz6+pqcmkSZPy7LPP5ktf+tJqr1m+fHkWL15c5wAAAAAos0ILoYULF6a6ujpdu3atM961a9fMnTt3jc97++23065du7Rs2TJf+9rX8qtf/Spf+cpXVnvtmDFj0rFjx9qjR48e9fo9AAAAADQ1hd8ytjbat2+fxx57LA8//HB+9rOfZdSoUZkyZcpqrz399NPz9ttv1x5z5sxp2LAAAAAAjUzzIt+8c+fOadasWebNm1dnfN68eenWrdsan1dZWZmtt946SdK/f/88/fTTGTNmTL785S+vcm2rVq3SqlWres0NAAAA0JQVOkOoZcuWGTBgQCZNmlQ7tnLlykyaNCmDBw/+xK+zcuXKLF++fF1EBAAAAFjvFDpDKElGjRqV4cOHZ+DAgRk0aFDGjRuXJUuWZMSIEUmSYcOGZbPNNsuYMWOSfLgm0MCBA7PVVltl+fLlue222/Kf//mf+c1vflPktwEAAADQZBReCB1yyCFZsGBBzjrrrMydOzf9+/fP7bffXrvQdFVVVSor/3ci05IlS3LcccfllVdeSZs2bbLddtvlv/7rv3LIIYcU9S0AAAAANCkVNTU1NUWHaEiLFy9Ox44d8/bbb6dDhw5FxwEAAIAmafr06RkwYEDyvSSbFp2mYK8luTyZNm1adtlll8JifJrOo0nuMgYAAADA2lMIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACXTKAqh8ePHp1evXmndunV23XXXPPTQQ2u89oorrsgXv/jFdOrUKZ06dcqQIUM+9noAAAAA6iq8ELr++uszatSojB49OtOnT8/OO++coUOHZv78+au9fsqUKTn00EMzefLkTJ06NT169Mi+++6bV199tYGTAwAAADRNhRdCY8eOzTHHHJMRI0akb9++mTBhQtq2bZuJEyeu9vo//OEPOe6449K/f/9st912+d3vfpeVK1dm0qRJDZwcAAAAoGkqtBBasWJFpk2bliFDhtSOVVZWZsiQIZk6deoneo2lS5fm/fffz0YbbbTa88uXL8/ixYvrHAAAAABlVmghtHDhwlRXV6dr1651xrt27Zq5c+d+otc49dRTs+mmm9Yplf7RmDFj0rFjx9qjR48enzk3AAAAQFNW+C1jn8UFF1yQ6667Lv/93/+d1q1br/aa008/PW+//XbtMWfOnAZOCQAAANC4NC/yzTt37pxmzZpl3rx5dcbnzZuXbt26fexzf/nLX+aCCy7I3XffnZ122mmN17Vq1SqtWrWql7wAAAAA64NCZwi1bNkyAwYMqLMg9EcLRA8ePHiNz7vwwgtz3nnn5fbbb8/AgQMbIioAAADAeqPQGUJJMmrUqAwfPjwDBw7MoEGDMm7cuCxZsiQjRoxIkgwbNiybbbZZxowZkyT5+c9/nrPOOivXXnttevXqVbvWULt27dKuXbvCvg8AAACApqLwQuiQQw7JggULctZZZ2Xu3Lnp379/br/99tqFpquqqlJZ+b8TmX7zm99kxYoV+T//5//UeZ3Ro0fn7LPPbsjoAAAAAE1S4YVQkhx//PE5/vjjV3tuypQpdR7Pnj173QcCAAAAWI816V3GAAAAAPj0FEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAyRReCI0fPz69evVK69ats+uuu+ahhx5a47VPPvlkvvWtb6VXr16pqKjIuHHjGi4oAAAAwHqi0ELo+uuvz6hRozJ69OhMnz49O++8c4YOHZr58+ev9vqlS5emd+/eueCCC9KtW7cGTgsAAACwfii0EBo7dmyOOeaYjBgxIn379s2ECRPStm3bTJw4cbXXf/7zn88vfvGLfPvb306rVq0aOC0AAADA+qGwQmjFihWZNm1ahgwZ8r9hKiszZMiQTJ06td7eZ/ny5Vm8eHGdAwAAAKDMCiuEFi5cmOrq6nTt2rXOeNeuXTN37tx6e58xY8akY8eOtUePHj3q7bUBAAAAmqLCF5Ve104//fS8/fbbtcecOXOKjgQAAABQqOZFvXHnzp3TrFmzzJs3r874vHnz6nXB6FatWllvCAAAAOAfFDZDqGXLlhkwYEAmTZpUO7Zy5cpMmjQpgwcPLioWAAAAwHqvsBlCSTJq1KgMHz48AwcOzKBBgzJu3LgsWbIkI0aMSJIMGzYsm222WcaMGZPkw4Won3rqqdqvX3311Tz22GNp165dtt5668K+DwAAAICmpNBC6JBDDsmCBQty1llnZe7cuenfv39uv/322oWmq6qqUln5v5OYXnvttXzuc5+rffzLX/4yv/zlL7PnnntmypQpDR0fAAAAoEkqtBBKkuOPPz7HH3/8as/9c8nTq1ev1NTUNEAqAAAAgPXXer/LGAAAAAB1KYQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGQUQgAAAAAloxACAAAAKBmFEAAAAEDJKIQAAAAASkYhBAAAAFAyCiEAAACAklEIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMkohAAAAABKRiEEAAAAUDIKIQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAyTQvOgAAAFCsqqqqLFy4sOgYjUbnzp3Ts2fPomMArFMKIQAAKLGqqqpsu922WfbesqKjNBqt27TOs888qxT6/1MY1qUwZH2hEAIAgBJbuHDhh2XQQUk6F52mEViYLLt5WRYuXOiH/igMV0dhyPpCIQQAAHxYBm1adAgaG4XhP1EYsh5RCAEAAPDxFIaw3rHLGAAAAEDJmCEEjZTF++qyeB8AAED9UQhBI2TxvlVZvA8AAKD+KISgEbJ43z+xeB8AAEC9UghBY2bxPgAAANYBi0oDAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUjEIIAAAAoGSaFx0AAKg/VVVVWbhwYdExGo3OnTunZ8+eRccAAGh0FEIAsJ6oqqrKttttm2XvLSs6SqPRuk3rPPvMs0ohAIB/ohACgPXEwoULPyyDDkrSueg0jcDCZNnNy7Jw4UKFEADAP1EIAcD6pnOSTYsOAQBAY2ZRaQAAAICSUQgBAAAAlIxCCAAAAKBkFEIAAAAAJWNRaQCAkqiqqsrChQuLjtEodO7c2e5zAJSaQggAoASqqqqy7XbbZtl7y4qO0ii0btM6zz7zrFIIgNJSCAEAlMDChQs/LIMOStK56DQFW5gsu3lZFi5cqBACoLQUQgAAZdI5yaZFhwAAimZRaQAAAICSUQgBAAAAlIxCCAAAAKBkGkUhNH78+PTq1SutW7fOrrvumoceeuhjr//Tn/6U7bbbLq1bt06/fv1y2223NVBSAAAAgKav8EWlr7/++owaNSoTJkzIrrvumnHjxmXo0KF59tln06VLl1Wuf+CBB3LooYdmzJgx+bd/+7dce+21OfDAAzN9+vTsuOOOBXwHAA2rqqoqCxcuLDpGo9G5c2e7BAEAwKdUeCE0duzYHHPMMRkxYkSSZMKECfnLX/6SiRMn5rTTTlvl+ksuuST77bdfTjnllCTJeeedl7vuuiuXXXZZJkyY0KDZPys/1NXlhzr416qqqrLtdtt+uHU0SZLWbVrn2Wee9ecHAAB8CoUWQitWrMi0adNy+umn145VVlZmyJAhmTp16mqfM3Xq1IwaNarO2NChQ/PnP/95tdcvX748y5cvr3389ttvJ0kWL178GdN/NnPmzMmAgQOyfNnyf31xSbRq3SrTHpmWHj16FB2lcO++++6HX7yeZEWhURqHNz78n3fffbfwf3eLNnv27A/LoN2TdCg6TSOwOFn2wLLMnj07G264YdFpCufPjn/iz446fD7+gc9GHT4b/8Tnow6fj3/i81GHz8c/aCSfjY/eu6am5l9fXFOgV199tSZJzQMPPFBn/JRTTqkZNGjQap/TokWLmmuvvbbO2Pjx42u6dOmy2utHjx5dk8ThcDgcDofD4XA4HA6HoxTHnDlz/mUnU/gtY+va6aefXmdG0cqVK7No0aJsvPHGqaioKDBZ47B48eL06NEjc+bMSYcOphtQl88Ha+Kzwcfx+eDj+HywJj4bfByfD9bEZ6OumpqavPPOO9l0003/5bWFFkKdO3dOs2bNMm/evDrj8+bNS7du3Vb7nG7dun2q61u1apVWrVrVGXNbwao6dOjgXx7WyOeDNfHZ4OP4fPBxfD5YE58NPo7PB2vis/G/Onbs+ImuK3Tb+ZYtW2bAgAGZNGlS7djKlSszadKkDB48eLXPGTx4cJ3rk+Suu+5a4/UAAAAA1FX4LWOjRo3K8OHDM3DgwAwaNCjjxo3LkiVLancdGzZsWDbbbLOMGTMmSXLiiSdmzz33zEUXXZSvfe1rue666/LII4/k8ssvL/LbAAAAAGgyCi+EDjnkkCxYsCBnnXVW5s6dm/79++f2229P165dk3y4xXJl5f9OZNp9991z7bXX5owzzsj//b//N3369Mmf//zn7LjjjkV9C01aq1atMnr06FVuq4PE54M189ng4/h88HF8PlgTnw0+js8Ha+KzsfYqamo+yV5kAAAAAKwvCl1DCAAAAICGpxACAAAAKBmFEAAAAEDJKIQAAAAASqbwXcYAAIDyWrBgQZ599tkkybbbbptNNtmk4EQA5aAQKrFp06bl6aefTpL07ds3u+yyS8GJaCw++OCDTJkyJS+++GK+853vpH379nnttdfSoUOHtGvXruh4ADQxr7zySm655ZZUVVVlxYoVdc6NHTu2oFQUbcmSJTnhhBPyn//5n6murk6SNGvWLMOGDcuvfvWrtG3btuCEQGP24osvZty4cXV+pj3xxBOz1VZbFZys6VAIldD8+fPz7W9/O1OmTMmGG26YJHnrrbey11575brrrvNbmZJ7+eWXs99++6WqqirLly/PV77ylbRv3z4///nPs3z58kyYMKHoiBSod+/eefjhh7PxxhvXGX/rrbeyyy675KWXXiooGUU78sgjc8kll6R9+/Z1xj/6gW/ixIkFJaNokyZNygEHHJDevXvnmWeeyY477pjZs2enpqbGL6NKbtSoUbn33ntzyy23ZI899kiS3HfffRk5cmROPvnk/OY3vyk4IY3BjTfemBtuuGG1hfL06dMLSkXR7rjjjhxwwAHp379/7Z8f999/f3bYYYfceuut+cpXvlJwwqbBGkIldMIJJ+Sdd97Jk08+mUWLFmXRokV54oknsnjx4owcObLoeBTsxBNPzMCBA/Pmm2+mTZs2tePf/OY3M2nSpAKT0RjMnj279re4/2j58uV59dVXC0hEY3H11VfnvffeW2X8vffeyzXXXFNAIhqL008/PT/+8Y/z+OOPp3Xr1rnpppsyZ86c7Lnnnjn44IOLjkeBbrrpplx55ZX56le/mg4dOqRDhw7Zf//9c8UVV+TGG28sOh6NwKWXXpoRI0aka9euefTRRzNo0KBsvPHGeemll/LVr3616HgU6LTTTstJJ52Uv//97xk7dmzGjh2bv//97/nRj36UU089teh4TYYZQiV0++235+677872229fO9a3b9+MHz8+++67b4HJaAz+9re/5YEHHkjLli3rjPfq1csP/CV2yy231H59xx13pGPHjrWPq6urM2nSpPTq1auAZBRt8eLFqampSU1NTd555520bt269lx1dXVuu+22dOnSpcCEFO3pp5/OH//4xyRJ8+bN895776Vdu3Y599xz841vfCPHHntswQkpytKlS9O1a9dVxrt06ZKlS5cWkIjG5te//nUuv/zyHHroobnqqqvyH//xH+ndu3fOOuusLFq0qOh4FOjpp5/ODTfcsMr4kUcemXHjxjV8oCZKIVRCK1euTIsWLVYZb9GiRVauXFlAIhqTlStXrnYGyCuvvLLKrSCUx4EHHlj79fDhw+uca9GiRXr16pWLLrqogVPRGGy44YapqKhIRUVFttlmm1XOV1RU5JxzzikgGY3FBhtsUHubR/fu3fPiiy9mhx12SJIsXLiwyGgUbPDgwRk9enSuueaa2jL5vffeyznnnJPBgwcXnI7GoKqqKrvvvnuSpE2bNnnnnXeSJIcffnh22223XHbZZUXGo0CbbLJJHnvssfTp06fO+GOPPeYXUZ+CQqiE9t5775x44on54x//mE033TRJ8uqrr+akk07KPvvsU3A6irbvvvtm3Lhxufzyy5N8+MPcu+++m9GjR2f//fcvOB1FmDlzZt5///00a9YsW265ZR5++OF07ty56Fg0EpMnT05NTU323nvv3HTTTdloo41qz7Vs2TJbbLFF7d81lNNuu+2W++67L9tvv33233//nHzyyXn88cdz8803Z7fddis6HgW65JJLMnTo0Gy++ebZeeedkyQzZsxI69atc8cddxScjsagW7duWbRoUbbYYov07NkzDz74YHbeeefMmjUrNTU1RcejQMccc0y+973v5aWXXqotDe+///78/Oc/z6hRowpO13RU1Pg3qXTmzJmTAw44IE8++WR69OhRO7bjjjvmlltuyeabb15wQor0yiuvZOjQoampqcnzzz+fgQMH5vnnn0/nzp3z17/+VeNeQs2aNcvcuXOzySabrHFRaXj55ZfTo0ePVFZanpC6Xnrppbz77rvZaaedsmTJkpx88sl54IEH0qdPn4wdOzZbbLFF0REp0NKlS/OHP/whzzzzTJJk++23z2GHHVZnHUPK6+ijj06PHj0yevTojB8/Pqecckr22GOPPPLIIznooINy5ZVXFh2RgtTU1GTcuHG56KKL8tprryVJNt1005xyyikZOXJkKioqCk7YNCiESqqmpiZ33313nb98hwwZUnAqGosPPvgg119/fWbMmJF33303u+yyi/84K7GNN944t912W3bdddc65RCsztKlS1e7E8xOO+1UUCIAmqqVK1dm5cqVad78wxtbrrvuutpC+fvf//4qa15STh/dSmh5i09PIQTU8de//jW777577V+8H/nggw/ywAMP5Etf+lJBySjK9773vVxzzTXp3r17qqqqsvnmm6dZs2arvda28+W1YMGCjBgxIv/v//2/1Z5f3dpkQPnccsst+epXv5oWLVrU2bBgdQ444IAGSgU0NXvvvXduvvnmbLjhhnXGFy9enAMPPDD33HNPMcGaGIVQSVx66aX53ve+l9atW+fSSy/92GttPV9uzZo1y+uvv77KrWFvvPFGunTp4oe6krr99tvzwgsvZOTIkTn33HPX+BuYE088sYGT0VgcdthhefnllzNu3Lh8+ctfzn//939n3rx5+elPf5qLLrooX/va14qOSAPq1KnTJ56ub6egcqmsrMzcuXPTpUuXj73FtKKiwn9zkNtvvz3t2rXLF77whSTJ+PHjc8UVV9TukNypU6eCE1KUf/yz5B/Nnz8/m222Wd5///2CkjUtCqGS2HLLLfPII49k4403zpZbbrnG6yoqKvyGv+QqKyszb968VW4Jeu655zJw4MAsXry4oGQ0BiNGjMill15qSi6r6N69e/7nf/4ngwYNSocOHfLII49km222yS233JILL7ww9913X9ERaUBXX331J772n3cuBPhIv3798vOf/zz7779/Hn/88QwcODAnn3xyJk+enO222y6///3vi45IA5s5c2aSpH///rnnnnvqbGZRXV2d22+/Pb/97W8ze/bsghI2LXYZK4lZs2at9mv4yEEHHZTkw1LwiCOOSKtWrWrPVVdXZ+bMmbUr+FNe//gfXq+88kqSWIieJMmSJUtqf0vXqVOnLFiwINtss0369euX6dOnF5yOhqbkAerDrFmz0rdv3yTJTTfdlK9//es5//zzM336dLvfllT//v1TUVGRioqK7L333qucb9OmTX71q18VkKxpUggBSZKOHTsm+XDB8fbt29dZQLply5bZbbfdcswxxxQVj0Zi5cqVtbcAvfvuu0k+XMDv5JNPzk9+8hM7TJXYtttum2effTa9evXKzjvvnN/+9rfp1atXJkyYkO7duxcdjwa2ePHidOjQofbrj/PRdZTTpEmTMmnSpMyfPz8rV66sc27ixIkFpaKxaNmyZZYuXZokufvuuzNs2LAkyUYbbWTWeknNmjUrNTU16d27dx566KE6dzW0bNkyXbp0WeNal6xKIVQSo0aN+sTXjh07dh0mobH6aOZHr1698uMf/zgbbLBBwYlojH7yk5/kyiuvzAUXXJA99tgjSXLffffl7LPPzrJly/Kzn/2s4IQU5cQTT8zrr7+eJBk9enT222+//OEPf0jLli1z1VVXFRuOBtepU6fa9eg23HDD1a4nVFNTY52YkjvnnHNy7rnnZuDAgenevbttolnFF77whYwaNSp77LFHHnrooVx//fVJPlzKwAzlctpiiy2SZJUCmbVjDaGS2Guvveo8nj59ej744INsu+22ST78Q7VZs2YZMGCAFdnJBx98kClTpuTFF1/Md77znbRv3z6vvfZaOnTokHbt2hUdjwJtuummmTBhwio7v/zP//xPjjvuuLz66qsFJaOxWbp0aZ555pn07NkznTt3LjoODezee+/NHnvskebNm2fKlCkf+4P+nnvu2YDJaEy6d++eCy+8MIcffnjRUWikqqqqctxxx2XOnDkZOXJkjjrqqCTJSSedlOrq6n+5WQ7rt+effz6TJ09e7QzDs846q6BUTYtCqITGjh2bKVOm5Oqrr65dmf/NN9/MiBEj8sUvfjEnn3xywQkp0ssvv5z99tsvVVVVWb58eZ577rn07t07J554YpYvX54JEyYUHZECtW7dOjNnzsw222xTZ/zZZ59N//7989577xWUDGhsLrvssnz3u99dZUtg+MjGG2+chx56KFtttVXRUYAm5oorrsixxx6bzp07p1u3bnV+8VBRUWH9wk9IIVRCm222We68887ssMMOdcafeOKJ7LvvvnnttdcKSkZjcOCBB6Z9+/a58sors/HGG2fGjBnp3bt3pkyZkmOOOSbPP/980REp0K677ppdd911ld/InXDCCXnooYfy97//vaBkFMHtyHycjh075v3338+BBx6Yo48+erWLf1Jup556atq1a5czzzyz6Cg0AcuWLcuKFSvqjFmDrLy22GKLHHfccTn11FOLjtKkWUOohBYvXpwFCxasMr5gwYK88847BSSiMfnb3/6WBx54IC1btqwz3qtXL7cDkQsvvDBf+9rXcvfdd2fw4MFJkqlTp2bOnDm57bbbCk5HQ3v00Uc/0XXWBSmnuXPn5k9/+lN+//vf5ytf+Up69uyZI488MkcccUR69OhRdDwagWXLluXyyy/P3XffnZ122iktWrSoc16RzJIlS3LqqafmhhtuyBtvvLHKeWuQldebb76Zgw8+uOgYTZ5CqIS++c1vZsSIEbnooosyaNCgJMnf//73nHLKKbVbj1NeK1euXO1frq+88krat29fQCIakz333DPPPfdcxo8fn2eeeSZJctBBB+V73/tefvrTn+aLX/xiwQlpSJMnT85LL72UXr162WGOVbRp0ybDhg3LsGHD8tJLL+Wqq67KlVdemXPOOSdDhgzJUUcdlQMPPHCVEoDymDlzZvr375/kw5nq/0iRTJL8x3/8RyZPnpzf/OY3OfzwwzN+/Pi8+uqr+e1vf5sLLrig6HgU6OCDD86dd96ZH/zgB0VHadLcMlZCS5cuzY9//ONMnDgx77//fpKkefPmOeqoo/KLX/zC7lIld8ghh6Rjx465/PLL0759+8ycOTObbLJJvvGNb6Rnz561u5HBP5oxY0Z22WUXv6kroWbNmtXuJpV8+GfIpZdemq5duxacjMaopqYmd999d6666qr8+c9/zgYbbJD58+cXHYsG8tZbb1lTik+lZ8+eueaaa/LlL385HTp0yPTp07P11lvnP//zP/PHP/7R7OQSGzNmTMaOHZuvfe1r6dev3yq/XBg5cmRByZoWhVCJLVmyJC+++GKSZKuttlIEkeTDmUBDhw5NTU1Nnn/++QwcODDPP/98OnfunL/+9a+1P/TBP1IIlVdlZWXmzp1b+2dD+/bta9ceg9WZPHlyrrzyytx8881p1apV3nzzzaIj0UDOO++8tGnTJj/+8Y+LjkIT0a5duzz11FPp2bNnNt9889x8880ZNGhQZs2alX79+uXdd98tOiIF2XLLLdd4rqKiIi+99FIDpmm63DJWYhtssEF22mmnomPQyGy++eaZMWNGrrvuusycOTPvvvtujjrqqBx22GFp06ZN0fEAaILmzJmT3//+97nqqqtSVVWVL33pS7niiivyrW99q+hoNKDvf//7+fd///e8+uqrufjii/PNb37zY28Nu/nmmxswHY1R7969M2vWrPTs2TPbbbddbrjhhgwaNCi33nqr2WYlN2vWrKIjrBcUQiW01157fexfvvfcc08DpqExat68eb773e8WHQNoAioqKlb5O8XaHyTJihUrcvPNN2fixIm555570r179wwfPjxHHnmkGWQl1aVLl0yaNCmnnHJKkviBnn9pxIgRmTFjRvbcc8+cdtpp+frXv57LLrssK1asyMUXX1x0PBqBFStWZNasWdlqq63SvLl649Nyy1gJnXTSSXUev//++3nsscfyxBNPZPjw4bnkkksKSkZj8fzzz2fy5MmZP39+Vq5cWefcWWedVVAqivSvFpx/6623cu+997plrIQqKyvz1a9+Na1atUqS3Hrrrdl7771XuQ3Zb/rLZ6ONNsrSpUvzb//2bznqqKMydOhQi48Dn8nLL7+cadOmpU+fPunXr1/RcSjQ0qVLc8IJJ+Tqq69Okjz33HPp3bt3TjjhhGy22WY57bTTCk7YNKjQSmhNbfrZZ5/tPlxyxRVX5Nhjj03nzp3TrVu3Or/pr6ioUAiVVMeOHf/l+WHDhjVQGhqT4cOH13lsdiEfOeOMM3L44Ydnk002KToK0MTcc889Of744/Pggw+mQ4cOteNbbLFFNtxww+y+++6ZMGGC3U1L7PTTT8+MGTMyZcqU7LfffrXjQ4YMydlnn60Q+oTMEKLWCy+8kEGDBmXRokVFR6FAW2yxRY477riceuqpRUcBANZjb7zxRs4666w1zkr236TldcABB2SvvfZa5c6Gj1x66aWZPHly/vu//7uBk9FYbLHFFrn++uuz22671dnQ4oUXXsguu+ySxYsXFx2xSTBDiFpTp05N69ati45Bwd58880cfPDBRccAANZzhx9+eF544YUcddRR6dq1q/XHqDVjxoz8/Oc/X+P5fffdN7/85S8bMBGNzYIFC1a7+/GSJUv8WfIpKIRK6J/XAqmpqcnrr7+eRx55JGeeeWZBqWgsDj744Nx55535wQ9+UHQUAGA99re//S333Xdfdt5556Kj0MjMmzcvLVq0WOP55s2bZ8GCBQ2YiMZm4MCB+ctf/pITTjghyf9uaPG73/0ugwcPLjJak6IQKqEOHTrUaU0rKyuz7bbb5txzz82+++5bYDIag6233jpnnnlmHnzwwfTr12+Vv4xHjhxZUDIAYH2y3Xbb5b333is6Bo3QZpttlieeeCJbb731as/PnDkz3bt3b+BUNCbnn39+vvrVr+app57KBx98kEsuuSRPPfVUHnjggdx7771Fx2syrCEE1LHllluu8VxFRUVeeumlBkwDwPrC1sD8s4cffjinnXZazjrrrOy4446r/BLqHxcTplxOOOGETJkyJQ8//PAqS1q89957GTRoUPbaa69ceumlBSWkMXjxxRdzwQUXZMaMGXn33Xezyy675NRTT7UD3aegECqh3r175+GHH87GG29cZ/ytt97KLrvs4gd+AKDe2BqYNXn++efzne98J9OnT68zXlNTk4qKilRXVxeUjKLNmzcvu+yyS5o1a5bjjz8+2267bZLkmWeeyfjx41NdXZ3p06ena9euBSeFps2vZ0po9uzZq/0Ldvny5Xn11VcLSAQArK9sDcyaHHbYYWnRokWuvfZai0pTR9euXfPAAw/k2GOPzemnn56P5jBUVFRk6NChGT9+vDKIJMn8+fNXu0vhTjvtVFCipkUhVCK33HJL7dd33HFHOnbsWPu4uro6kyZNSq9evQpIRtFGjRqV8847LxtssEFGjRr1sdeOHTu2gVIBsD7485//XLs18D/+wL/DDjvkxRdfLDAZRXviiSfy6KOP1s7+gH+0xRZb5Lbbbsubb76ZF154ITU1NenTp086depUdDQagWnTpmX48OF5+umn8883PZlh+MkphErkwAMPTPLhvyDDhw+vc65Fixbp1atXLrroogKSUbRHH30077//fu3XAFBfbA3MmgwcODBz5sxRCPGxOnXqlM9//vNFx6CROfLII7PNNtvkyiuvNMPwM7CGUAltueWWefjhh9O5c+eiowAA67kvfelLOfjgg3PCCSekffv2mTlzZrbccsuccMIJef7553P77bcXHZGC/OlPf8rZZ5+dU045ZbU7m7rlA1iT9u3b59FHH13jTnR8MmYIlcjUqVPzxhtvZNasWbVj11xzTUaPHp0lS5bkwAMPzK9+9au0atWqwJQU7cgjj8wll1yS9u3b1xlfsmRJTjjhhEycOLGgZAA0RbYGZk0OOeSQJB/+t8dHKioqLCoN/Ev77LNPZsyYoRD6jMwQKpH99tsve+21V0499dQkyeOPP55ddtklRxxxRLbffvv84he/yPe///2cffbZxQalUM2aNcvrr7++yvT+hQsXplu3bvnggw8KSgZAU2VrYFbn5Zdf/tjzW2yxRQMlAZqahQsXZvjw4Rk0aFB23HHHVWYYHnDAAQUla1oUQiXSvXv33HrrrRk4cGCS5Cc/+Unuvffe3HfffUk+nLY7evToPPXUU0XGpCCLFy9OTU1NOnXqlOeffz6bbLJJ7bnq6urceuutOe200/Laa68VmBIAACi7W2+9NYcffngWL168yjkzDD85t4yVyJtvvllne8Z77703X/3qV2sff/7zn8+cOXOKiEYjsOGGG6aioiIVFRXZZpttVjlfUVGRc845p4BkADQ1q/sP9DXp0KHDOkxCY/fiiy9m3Lhxefrpp5Mkffv2zYknnpitttqq4GRAY3bCCSfku9/9bs4888w6P+Py6SiESqRr166ZNWtWevTokRUrVmT69Ol1fsB/5513VplqR3lMnjw5NTU12XvvvXPTTTdlo402qj3XsmXLbLHFFtl0000LTAhAU/HRLxk+Cb/FLa877rgjBxxwQPr375899tgjSXL//fdnhx12yK233pqvfOUrBScEGqs33ngjJ510kjLoM1IIlcj++++f0047LT//+c/z5z//OW3bts0Xv/jF2vMzZ87025gS23PPPZMks2bNSs+ePW3dCMBamzx5cu3Xs2fPzmmnnZYjjjgigwcPTvLhRhdXX311xowZU1REGoHTTjstJ510Ui644IJVxk899VSFELBGBx10UCZPnuzn18/IGkIlsnDhwhx00EG577770q5du1x99dX55je/WXt+n332yW677Zaf/exnBaakCDNnzsyOO+6YysrKzJw582OvtQUsAJ/GPvvsk6OPPjqHHnponfFrr702l19+eaZMmVJMMArXunXrPP744+nTp0+d8eeeey477bRTli1bVlAyoLH72c9+lnHjxuVrX/ta+vXrt8qdLiNHjiwoWdOiECqht99+O+3atUuzZs3qjC9atCjt2rVLy5YtC0pGUSorKzN37tx06dIllZWVtVu+/jMLtAHwabVt2zYzZsxY7Q/9/fv3z9KlSwtKRtF69OiRsWPH5uCDD64zfsMNN+THP/5xqqqqCkoGNHZbbrnlGs9VVFTkpZdeasA0TZdbxkqoY8eOqx3/xzVjKJdZs2bV7io2a9asgtMAsD7p0aNHrrjiilx44YV1xn/3u9+lR48eBaWiMTjmmGPyve99Ly+99FJ23333JB+uIfTzn/88o0aNKjgd0Jj5maV+mCEEAMA6c9ttt+Vb3/pWtt566+y6665JkoceeijPP/98brrppuy///4FJ6QoNTU1GTduXC666KK89tprSZJNN900p5xySkaOHGk9Q4B1TCEE1HHNNdd87Plhw4Y1UBIA1hevvPJKfvOb39RuLb799tvnBz/4gRlC1HrnnXeSJO3bty84CdBUvPLKK7nllltSVVWVFStW1Dk3duzYglI1LQohoI5OnTrVefz+++9n6dKladmyZdq2bZtFixYVlAwAACCZNGlSDjjggPTu3TvPPPNMdtxxx8yePTs1NTXZZZddcs899xQdsUmwhhBQx5tvvrnK2PPPP59jjz02p5xySgGJAID1xec+97lPfCvY9OnT13EaoKk6/fTT8+Mf/zjnnHNO2rdvn5tuuildunTJYYcdlv3226/oeE2GGULAJ/LII4/ku9/9bp555pmiowAATdQ555xT+/WyZcvy61//On379s3gwYOTJA8++GCefPLJHHfccRkzZkxRMYFGrn379nnsscey1VZbpVOnTrnvvvuyww47ZMaMGfnGN76R2bNnFx2xSTBDCPhEmjdvXrvgIwDA2hg9enTt10cffXRGjhyZ8847b5Vr5syZ09DRgCZkgw02qF03qHv37nnxxRezww47JEkWLlxYZLQmRSEE1HHLLbfUeVxTU5PXX389l112WfbYY4+CUgEA65s//elPeeSRR1YZ/+53v5uBAwdm4sSJBaQCmoLddtst9913X7bffvvsv//+Ofnkk/P444/n5ptvzm677VZ0vCZDIQTUceCBB9Z5XFFRkU022SR77713LrroomJCAQDrnTZt2uT+++9Pnz596ozff//9ad26dUGpgKZg7Nixeffdd5N8eCvqu+++m+uvvz59+vSxw9inoBAC6li5cmWSZMGCBWnZsmU6duxYcCIAmrobb7wxN9xww2q3BrZwcHn96Ec/yrHHHpvp06dn0KBBSZK///3vmThxYs4888yC0wGNWe/evWu/3mCDDTJhwoQC0zRdlUUHABqPt956Kz/84Q/TuXPndOvWLRtttFG6deuW008/PUuXLi06HgBN0KWXXpoRI0aka9euefTRRzNo0KBsvPHGeemll/LVr3616HgU6LTTTsvVV1+dadOmZeTIkRk5cmSmT5+e3//+9znttNOKjgc0Yr17984bb7yxyvhbb71Vpyzi49llDEiSLFq0KIMHD86rr76aww47LNtvv32S5Kmnnsq1116b7bbbLvfdd19mzpyZBx98MCNHjiw4MQBNwXbbbZfRo0fn0EMPTfv27TNjxoz07t07Z511VhYtWpTLLrus6IgANDGVlZWZO3duunTpUmd83rx56dmzZ5YvX15QsqbFLWNAkuTcc89Ny5Yt8+KLL6Zr166rnNt3331z+OGH584778yll15aUEoAmpqqqqrsvvvuST5cM+add95Jkhx++OHZbbfdFEJkxYoVmT9/fu1t6x/p2bNnQYmAxuofN8C544476ixvUV1dnUmTJqVXr14FJGuaFEJAkuTPf/5zfvvb365SBiVJt27dcuGFF2b//ffP6NGjM3z48AISAtAUdevWLYsWLcoWW2yRnj175sEHH8zOO++cWbNmxUT1cnv++edz5JFH5oEHHqgzXlNTk4qKilRXVxeUDGis/nEDnH/+maRFixbp1auXjXA+BYUQkCR5/fXXs8MOO6zx/I477pjKysqMHj26AVMB0NTtvffeueWWW/K5z30uI0aMyEknnZQbb7wxjzzySA466KCi41GgI444Is2bN8//9//9f+nevXsqKiqKjgQ0ch/NJNxyyy3zyCOPZOONNy44UdOmEAKSJJ07d87s2bOz+eabr/b8rFmzVrlHFwD+lcsvv7z2P+B/+MMfZuONN84DDzyQAw44IN///vcLTkeRHnvssUybNi3bbbdd0VGAJuT9999P7969s2jRIoXQZ2RRaSBJcuSRR+bFF1/MXXfdlZYtW9Y5t3z58gwdOjS9e/fOxIkTC0oIAKxPPv/5z+fiiy/OF77whaKjAE3MJptskgceeCB9+vQpOkqTphACkiSvvPJKBg4cmFatWuWHP/xhtttuu9TU1OTpp5/Or3/96yxfvjwPP/ywBR4B+FRuv/32tGvXrvaH/vHjx+eKK65I3759M378+HTq1KnghBTlnnvuyRlnnJHzzz8//fr1S4sWLeqc79ChQ0HJgMbupJNOSqtWrXLBBRcUHaVJUwgBtWbNmpXjjjsud955Z+1CnxUVFfnKV76Syy67LFtvvXXBCQFoavr165ef//zn2X///fP4449n4MCBOfnkkzN58uRst912+f3vf190RApSWVmZJKusHWRRaeBfOeGEE3LNNdekT58+GTBgQDbYYIM658eOHVtQsqZFIQSs4s0338zzzz+fJNl6662z0UYbFZwIgKaqXbt2eeKJJ9KrV6+cffbZeeKJJ3LjjTdm+vTp2X///TN37tyiI1KQe++992PP77nnng2UBGhq9tprrzWeq6ioyD333NOAaZoui0oDq+jUqVMGDRpUdAwA1gMtW7bM0qVLkyR33313hg0bliTZaKONsnjx4iKjUTCFD7C2Jk+eXHSE9YJCCACAdeYLX/hCRo0alT322CMPPfRQrr/++iTJc889t8adLSmPt956K1deeWWefvrpJMkOO+yQI488Mh07diw4GcD6zy1jAACsM1VVVTnuuOMyZ86cjBw5MkcddVSSDxcEra6uzqWXXlpwQoryyCOPZOjQoWnTpk3tzOSHH3447733Xu68887ssssuBScEGrNHHnkkN9xwQ6qqqrJixYo6526++eaCUjUtCiEAAKDBffGLX8zWW2+dK664Is2bf3jjwgcffJCjjz46L730Uv76178WnBBorK677roMGzYsQ4cOzZ133pl99903zz33XObNm5dvfvObNiz4hBRCAAA0iGXLlq3yW1xbi5dXmzZt8uijj2a77barM/7UU09l4MCBtWtPAfyznXbaKd///vfzwx/+MO3bt8+MGTOy5ZZb5vvf/366d++ec845p+iITUJl0QEAAFh/LVmyJMcff3y6dOmSDTbYIJ06dapzUF4dOnRIVVXVKuNz5sxJ+/btC0gENBUvvvhivva1ryX5cPOCJUuWpKKiIieddFIuv/zygtM1HQohAADWmf/4j//IPffck9/85jdp1apVfve73+Wcc87JpptummuuuaboeBTokEMOyVFHHZXrr78+c+bMyZw5c3Ldddfl6KOPzqGHHlp0PKAR69SpU955550kyWabbZYnnngiyYcL1Ztd+MnZZQwAgHXm1ltvzTXXXJMvf/nLGTFiRO26MVtssUX+8Ic/5LDDDis6IgX55S9/mYqKigwbNiwffPBBkqRFixY59thjc8EFFxScDmjMvvSlL+Wuu+5Kv379cvDBB+fEE0/MPffck7vuuiv77LNP0fGaDGsIAQCwzrRr1y5PPfVUevbsmc033zw333xzBg0alFmzZqVfv3559913i45IwZYuXZoXX3wxSbLVVlulbdu2BScCGrtFixZl2bJl2XTTTbNy5cpceOGFeeCBB9KnT5+cccYZbkn+hMwQAgBgnendu3dmzZqVnj17ZrvttssNN9yQQYMG5dZbb82GG25YdDwKUF1dnSeffDJ9+vRJmzZt0rZt2/Tr1y9J8t5772XmzJnZcccdU1lpdQtg9TbaaKParysrK3PaaacVmKbpMkMIAIB15uKLL06zZs0ycuTI3H333fn617+empqavP/++xk7dmxOPPHEoiPSwK666qpcdtll+fvf/55mzZrVOffBBx9kt912y49+9KN897vfLSgh0BRUV1fnv//7v/P0008nSfr27ZtvfOMbad7cvJdPSiEEAECDefnllzNt2rRsvfXW2WmnnYqOQwG++MUv5oc//GG+/e1vr/b8DTfckMsuuyx//etfGzgZ0FQ8+eSTOeCAAzJ37txsu+22SZLnnnsum2yySW699dbsuOOOBSdsGhRCAABAg+nSpUseeuih9OrVa7XnZ82alUGDBmXBggUNGwxoMgYPHpxNNtkkV199de16QW+++WaOOOKILFiwIA888EDBCZsGN+YCAFDv7rnnnvTt2zeLFy9e5dzbb7+dHXbYIX/7298KSEbRlixZstrPxUfeeecd20YDH+uxxx7LmDFj6iwe3alTp/zsZz/Lo48+WmCypkUhBABAvRs3blyOOeaYdOjQYZVzHTt2zPe///2MHTu2gGQUrU+fPh/72/v77rsvffr0acBEQFOzzTbbZN68eauMz58/P1tvvXUBiZomhRAAAPVuxowZ2W+//dZ4ft999820adMaMBGNxXe+852cccYZmTlz5irnZsyYkbPOOivf+c53CkgGNBVjxozJyJEjc+ONN+aVV17JK6+8khtvvDE/+tGP8vOf/zyLFy+uPVgzawgBAFDvWrdunSeeeGKNv6l94YUX0q9fv7z33nsNnIyivf/++9l3331z3333ZciQIdluu+2SJM8880zuvvvu7LHHHrnrrrvSokWLgpMCjVVl5f/ObamoqEiSfFRt/OPjioqKVFdXN3zAJsJ+bAAA1LvNNtvsYwuhmTNnpnv37g2cisagRYsWufPOO3PxxRfn2muvzV//+tfU1NRkm222yc9+9rP86Ec/UgYBH2vy5MlrPDdz5ky7WH5CZggBAFDvTjjhhEyZMiUPP/xwWrduXefce++9l0GDBmWvvfbKpZdeWlBCANYX77zzTv74xz/md7/7XaZNm2ZW0CekEAIAoN7Nmzcvu+yyS5o1a5bjjz8+2267bZIPbwsaP358qqurM3369HTt2rXgpAA0VX/9619z5ZVX5qabbsqmm26agw46KN/61rfy+c9/vuhoTYJCCACAdeLll1/OsccemzvuuKPO2g5Dhw7N+PHjs+WWWxacEICmZu7cubnqqqty5ZVXZvHixfn3f//3TJgwITNmzEjfvn2LjtekKIQAAFin3nzzzbzwwgupqalJnz590qlTp6IjAdAEff3rX89f//rXfO1rX8thhx2W/fbbL82aNUuLFi0UQmtBIQQAAAA0es2bN8/IkSNz7LHHpk+fPrXjCqG1U/mvLwEAAFg3VqxYkWeffTYffPBB0VGARu6+++7LO++8kwEDBmTXXXfNZZddloULFxYdq8lSCAEAAA1u6dKlOeqoo9K2bdvssMMOqaqqSvLhDnUXXHBBwemAxmi33XbLFVdckddffz3f//73c91112XTTTfNypUrc9ddd+Wdd94pOmKTohACAAAa3Omnn54ZM2ZkypQpad26de34kCFDcv311xeYDGjsNthggxx55JG577778vjjj+fkk0/OBRdckC5duuSAAw4oOl6ToRACAAAa3J///Odcdtll+cIXvpCKiora8R122CEvvvhigcmApmTbbbfNhRdemFdeeSV//OMfi47TpCiEAACABrdgwYJ06dJllfElS5bUKYgAPolmzZrlwAMPzC233FJ0lCZDIQQAADS4gQMH5i9/+Uvt449KoN/97ncZPHhwUbEASqN50QEAAIDyOf/88/PVr341Tz31VD744INccskleeqpp/LAAw/k3nvvLToewHrPDCEAAKDBfeELX8hjjz2WDz74IP369cudd96ZLl26ZOrUqRkwYEDR8QDWexU1NTU1RYcAAAAAoOG4ZQwAAGgQixcv/sTXdujQYR0mAcAMIQAAoEFUVlZ+4h3Eqqur13EagHIzQwgAAGgQkydPrv169uzZOe2003LEEUfU7io2derUXH311RkzZkxREQFKwwwhAACgwe2zzz45+uijc+ihh9YZv/baa3P55ZdnypQpxQQDKAmFEAAA0ODatm2bGTNmpE+fPnXGn3vuufTv3z9Lly4tKBlAOdh2HgAAaHA9evTIFVdcscr47373u/9fe/cbmmXZ9wH8Oza9KW22jLRsQsPhPwqJ0NIICTIdioVEFCkKmYbD1aLe6Qgi6I3TVHyREBlhikYYrAQxMCuTXOgCs4XDIYREk4zSNN39Ip6BhA883Had93Odnw9csOs89uJ7vLy+/I7jTGNjYwGJAMrFhBAAAFBxXV1dWbRoUSZMmJAZM2YkSQ4fPpze3t7s3r07LS0tBScEqG4KIQAAoBCnT5/Oli1bcvz48STJ5MmTs3LlShNCABWgEAIAAAAoGXcIAQAAAJSMQggAAACgZBRCAAAAACWjEAIAAAAoGYUQAAAAQMnUFR0AAAAop127dmXnzp3p7+/PxYsXr1rr7u4uKBVAOZgQAgAAKu7NN9/MsmXLMmbMmHzzzTeZPn16Ro8enZMnT2bevHlFxwOoejWDg4ODRYcAAADKZdKkSeno6MhTTz2Vm266KUePHk1TU1PWrl2bgYGBbNq0qeiIAFXNhBAAAFBx/f39mTlzZpLkhhtuyK+//pokWbx4cbZv315kNIBSUAgBAAAVN3bs2AwMDCRJxo8fn0OHDiVJ+vr64hADwD9PIQQAAFTcww8/nD179iRJli1blhdffDGPPPJInnzyyTz++OMFpwOofu4QAgAAKu7KlSu5cuVK6ur+evHx+++/ny+++CLNzc1ZsWJFhg8fXnBCgOqmEAIAAAAoGUfGAACAivvkk09y8ODBoe+bN2/OtGnT8vTTT+fs2bMFJgMoB4UQAABQcS+//HLOnTuXJOnp6Ul7e3taWlrS19eX9vb2gtMBVL+6ogMAAADl09fXlylTpiRJdu/enQULFuT1119Pd3d3WlpaCk4HUP1MCAEAABU3fPjw/P7770mSffv2Zc6cOUmSW265ZWhyCIB/jgkhAACg4h588MG0t7dn1qxZOXz4cHbs2JEk+f7773PnnXcWnA6g+pkQAgAAKm7Tpk2pq6vLrl27smXLlowbNy5J8vHHH2fu3LkFpwOofl47DwAAAFAyjowBAACFunDhQi5evHjVs/r6+oLSAJSDI2MAAEDF/fbbb2ltbc1tt92WESNGpKGh4aoPAP8shRAAAFBxr7zySvbv358tW7bkX//6V7Zu3ZpXX301d9xxR7Zt21Z0PICq5w4hAACg4saPH59t27Zl9uzZqa+vT3d3dyZMmJB3330327dvT1dXV9ERAaqaCSEAAKDiBgYG0tTUlOSv+4IGBgaS/PU6+gMHDhQZDaAUFEIAAEDFNTU1pa+vL0kyadKk7Ny5M0ny0Ucf5eabby4wGUA5ODIGAABUXGdnZ2pra7N69ers27cvCxYsyODgYC5dupR169alra2t6IgAVU0hBAAAFO7UqVM5cuRIJkyYkHvuuafoOABVTyEEAAAAUDLuEAIAACpm//79mTJlSs6dO/e3tV9++SVTp07NZ599VkAygHJRCAEAABWzfv36LF++PPX19X9bGzVqVFasWJF169YVkAygXBRCAABAxRw9ejRz58695vqcOXNy5MiRCiYCKCeFEAAAUDFnzpzJsGHDrrleV1eXn376qYKJAMpJIQQAAFTMuHHj8u23315z/dixY7n99tsrmAignBRCAABAxbS0tGTNmjW5cOHC39bOnz+fjo6OzJ8/v4BkAOXitfMAAEDFnDlzJvfee29qa2vT2tqaiRMnJkm+++67bN68OZcvX053d3fGjBlTcFKA6qYQAgAAKurUqVN5/vnns3fv3vzPz5Gampo8+uij2bx5c+66666CEwJUP4UQAABQiLNnz+aHH37I4OBgmpub09DQUHQkgNJQCAEAAACUjEulAQAAAEpGIQQAAABQMgohAAAAgJJRCAEA/AeWLl2axx57rOgYAAD/J3VFBwAA+G9VU1Pzv653dHRkw4YN8Y4OAOD/G4UQAMA1/Pjjj0N/79ixI2vXrs2JEyeGno0cOTIjR44sIhoAwH/EkTEAgGsYO3bs0GfUqFGpqam56tnIkSP/dmRs9uzZaW1tTWtra0aNGpVbb701a9asuWqK6OzZs1myZEkaGhpy4403Zt68eent7R1aP3XqVBYsWJCGhoaMGDEiU6dOTVdXVyW3DgBUOYUQAMB19s4776Suri6HDx/Ohg0bsm7dumzdunVofenSpfn666+zZ8+efPnllxkcHExLS0suXbqUJFm1alX++OOPHDhwID09PXnjjTdMIgEA15UjYwAA11ljY2M6OztTU1OTiRMnpqenJ52dnVm+fHl6e3uzZ8+efP7555k5c2aS5L333ktjY2M+/PDDPPHEE+nv78+iRYty9913J0mampqK3A4AUIVMCAEAXGf333//VRdSP/DAA+nt7c3ly5dz/Pjx1NXVZcaMGUPro0ePzsSJE3P8+PEkyerVq/Paa69l1qxZ6ejoyLFjxyq+BwCguimEAAD+yzz77LM5efJkFi9enJ6entx3333ZuHFj0bEAgCqiEAIAuM6++uqrq74fOnQozc3Nqa2tzeTJk/Pnn39e9T8///xzTpw4kSlTpgw9a2xszMqVK/PBBx/kpZdeyltvvVWx/ABA9VMIAQBcZ/39/Wlvb8+JEyeyffv2bNy4MW1tbUmS5ubmLFy4MMuXL8/Bgwdz9OjRPPPMMxk3blwWLlyYJHnhhReyd+/e9PX1pbu7O59++mkmT55c5JYAgCrjUmkAgOtsyZIlOX/+fKZPn57a2tq0tbXlueeeG1p/++2309bWlvnz5+fixYt56KGH0tXVlWHDhiVJLl++nFWrVuX06dOpr6/P3Llz09nZWdR2AIAqVDM4ODhYdAgAgGoxe/bsTJs2LevXry86CgDANTkyBgAAAFAyCiEAAACAknFkDAAAAKBkTAgBAAAAlIxCCAAAAKBkFEIAAAAAJaMQAgAAACgZhRAAAABAySiEAAAAAEpGIQQAAABQMgohAAAAgJJRCAEAAACUzL8BSAbfxDZg+u4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_percentual_tipo = df['Tipo'].value_counts(normalize=True).to_frame().sort_values('Tipo', ascending=False)\n",
    "\n",
    "df_percentual_tipo.plot(kind='bar', figsize=(14, 10), color ='green', edgecolor='black',\n",
    "                        xlabel = 'Tipos', ylabel = 'Percentual')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>proportion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tipo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apartamento</th>\n",
       "      <td>0.845139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa</th>\n",
       "      <td>0.041842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa de Condomínio</th>\n",
       "      <td>0.043096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa de Vila</th>\n",
       "      <td>0.010774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flat</th>\n",
       "      <td>0.020596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loft</th>\n",
       "      <td>0.002207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quitinete</th>\n",
       "      <td>0.036173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Studio</th>\n",
       "      <td>0.000173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    proportion\n",
       "Tipo                          \n",
       "Apartamento           0.845139\n",
       "Casa                  0.041842\n",
       "Casa de Condomínio    0.043096\n",
       "Casa de Vila          0.010774\n",
       "Flat                  0.020596\n",
       "Loft                  0.002207\n",
       "Quitinete             0.036173\n",
       "Studio                0.000173"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tipo'].value_counts(normalize=True).to_frame().sort_values('Tipo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4837523257323353"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quartos'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['Bairro'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bairro'].nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bairro</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Joá</th>\n",
       "      <td>16773.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cidade Jardim</th>\n",
       "      <td>12000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arpoador</th>\n",
       "      <td>10896.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paquetá</th>\n",
       "      <td>10550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ipanema</th>\n",
       "      <td>9196.116863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honório Gurgel</th>\n",
       "      <td>655.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senador Camará</th>\n",
       "      <td>600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paciência</th>\n",
       "      <td>580.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vila Militar</th>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ricardo de Albuquerque</th>\n",
       "      <td>340.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Valor\n",
       "Bairro                              \n",
       "Joá                     16773.478261\n",
       "Cidade Jardim           12000.000000\n",
       "Arpoador                10896.062500\n",
       "Paquetá                 10550.000000\n",
       "Ipanema                  9196.116863\n",
       "...                              ...\n",
       "Honório Gurgel            655.555556\n",
       "Senador Camará            600.000000\n",
       "Paciência                 580.000000\n",
       "Vila Militar              500.000000\n",
       "Ricardo de Albuquerque    340.000000\n",
       "\n",
       "[154 rows x 1 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Bairro')[['Valor']].mean().sort_values('Valor', ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bairros = df.groupby('Bairro')[['Valor']].mean().sort_values('Valor', ascending=False).head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bairros.plot(kind='barh', figsize=(14,10), color='blue'); \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}