Analise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projeto Pandas\n",
    "Nós fomos contratados como cientistas de dados de uma empresa imobiliária. Nossa principal função é dar suporte as demandas do time de Machine Learning e do time de Desenvolvimento dessa empresa.\n",
    "\n",
    "Para atendermos essas demandas, foi disponibilizado um board no Trello, com as etapas e tarefas do projeto que devemos realizar. Também foi disponibilizada uma base de dados, que utilizaremos para desenvolver o projeto."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "po_EJXY_WTZ6"
   },
   "source": [
    "# Conhecendo a base de dados\n",
    "\n",
    "A base de dados que vamos utilizar para desenvolver o projeto é uma base com dados de diferentes tipos de imóveis do Rio de Janeiro, como apartamento, casas, comércios, dentre outros.\n",
    "\n",
    "Nessa base, nós vamos encontrar os valores dos aluguéis de cada imóvel, condomínio, IPTU e também suas características, como: quantidade de quartos, suítes, vagas de garagem, etc.\n",
    "\n",
    "Link para acessar a base de dados: aluguel.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yg7As1VwWnTw"
   },
   "source": [
    "## Importando os dados\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O primeiro passo para começarmos o projeto é importar a base de dados e verificar algumas características gerais, como:\n",
    "\n",
    "- quantidade de linhas e colunas;\n",
    "\n",
    "- quais as colunas da base de dados;\n",
    "\n",
    "- explorar as diferentes colunas existentes;\n",
    "\n",
    "- verificar os tipos dos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "A0m9bw60V9Uj"
   },
   "outputs": [],
   "source": [
    "# Importando as bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "-8GmBuLzWlnS"
   },
   "outputs": [],
   "source": [
    "# Importando os dados de um link\n",
    "url = 'https://raw.githubusercontent.com/alura-cursos/pandas-conhecendo-a-biblioteca/main/base-de-dados/aluguel.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Af6xejuuWli2"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando os primeiros dados \n",
    "dados = pd.read_csv(url, sep=';')\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>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",
       "</div>"
      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando os ultimos dados \n",
    "dados = pd.read_csv(url, sep=';')\n",
    "dados.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rwL6VcT9W6W-"
   },
   "source": [
    "## Características gerais da base de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "1EjEmItCWleQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A quantidade de linhas é 32960 com 9 colunas.\n"
     ]
    }
   ],
   "source": [
    "# Qual a quantidade de linhas da base de dados?\n",
    "print(f'A quantidade de linhas é {dados.shape[0]} com {dados.shape[1]} colunas.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "KaWMKbLmWlZj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Tipo', 'Bairro', 'Quartos', 'Vagas', 'Suites', 'Area', 'Valor',\n",
       "       'Condominio', 'IPTU'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando as colunas\n",
    "dados.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "cfLu1_AcW1Y1"
   },
   "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": [
    "# Quais os tipos dos dados\n",
    "dados.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "55WX-hlnW6wm"
   },
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a coluna tipo de imoveis \n",
    "dados['Tipo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Quartos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conjunto Comercial/Sala</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32955</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32956</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32957</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32958</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32959</th>\n",
       "      <td>Conjunto Comercial/Sala</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32960 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Tipo  Quartos\n",
       "0                    Quitinete        1\n",
       "1                         Casa        2\n",
       "2      Conjunto Comercial/Sala        0\n",
       "3                  Apartamento        1\n",
       "4                  Apartamento        1\n",
       "...                        ...      ...\n",
       "32955                Quitinete        0\n",
       "32956              Apartamento        3\n",
       "32957              Apartamento        2\n",
       "32958              Apartamento        2\n",
       "32959  Conjunto Comercial/Sala        0\n",
       "\n",
       "[32960 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a coluna tipo de imoveis \n",
    "dados[['Tipo','Quartos']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "V3RRXTIYW7KV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento                    19532\n",
       "Conjunto Comercial/Sala         6815\n",
       "Loja/Salão                      1426\n",
       "Casa de Condomínio               996\n",
       "Casa                             967\n",
       "Quitinete                        836\n",
       "Galpão/Depósito/Armazém          623\n",
       "Flat                             476\n",
       "Prédio Inteiro                   301\n",
       "Casa Comercial                   265\n",
       "Casa de Vila                     249\n",
       "Loja Shopping/ Ct Comercial      247\n",
       "Box/Garagem                       82\n",
       "Terreno Padrão                    70\n",
       "Loft                              51\n",
       "Sítio                             10\n",
       "Loteamento/Condomínio              5\n",
       "Studio                             4\n",
       "Hotel                              2\n",
       "Chácara                            1\n",
       "Pousada/Chalé                      1\n",
       "Indústria                          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a quantidade por tipo de imoveis \n",
    "dados['Tipo'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cwVMOIMpWWfM"
   },
   "source": [
    "# Análise exploratória de dados\n",
    "\n",
    "Algumas perguntas que podemos fazer nesse momento:\n",
    "\n",
    "- Quais os valores médios de aluguel por tipo de imóvel?\n",
    "\n",
    "- Qual o percentual de cada tipo de imóvel na nossa base de dados?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rnsDqFNDYV9-"
   },
   "source": [
    "## Qual o valor médio de aluguel por tipo de imóvel?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "_Ud_6L8WTqWY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Prédio Inteiro                 498637.239203\n",
       "Indústria                      120000.000000\n",
       "Galpão/Depósito/Armazém         53407.062600\n",
       "Terreno Padrão                  32567.714286\n",
       "Hotel                           25000.000000\n",
       "Loteamento/Condomínio           22000.000000\n",
       "Conjunto Comercial/Sala         14715.045521\n",
       "Casa Comercial                  14478.264151\n",
       "Loja/Salão                      13769.646770\n",
       "Sítio                           12499.000000\n",
       "Casa de Condomínio              11952.061245\n",
       "Loja Shopping/ Ct Comercial      8126.178862\n",
       "Casa                             6793.454922\n",
       "Chácara                          4900.000000\n",
       "Apartamento                      4744.614648\n",
       "Flat                             4546.189076\n",
       "Loft                             2557.882353\n",
       "Studio                           2400.000000\n",
       "Box/Garagem                      1899.756098\n",
       "Casa de Vila                     1574.369478\n",
       "Pousada/Chalé                    1480.000000\n",
       "Quitinete                        1246.840909\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Qual o valor médio de aluguel por tipo de imovel\n",
    "dados.groupby('Tipo')['Valor'].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "7No8Z_W_YXJ1"
   },
   "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>Tipo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Quitinete</th>\n",
       "      <td>1246.840909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pousada/Chalé</th>\n",
       "      <td>1480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa de Vila</th>\n",
       "      <td>1574.369478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Box/Garagem</th>\n",
       "      <td>1899.756098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Studio</th>\n",
       "      <td>2400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loft</th>\n",
       "      <td>2557.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flat</th>\n",
       "      <td>4546.189076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apartamento</th>\n",
       "      <td>4744.614648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chácara</th>\n",
       "      <td>4900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casa</th>\n",
       "      <td>6793.454922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Valor\n",
       "Tipo                      \n",
       "Quitinete      1246.840909\n",
       "Pousada/Chalé  1480.000000\n",
       "Casa de Vila   1574.369478\n",
       "Box/Garagem    1899.756098\n",
       "Studio         2400.000000\n",
       "Loft           2557.882353\n",
       "Flat           4546.189076\n",
       "Apartamento    4744.614648\n",
       "Chácara        4900.000000\n",
       "Casa           6793.454922"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deixar como um dataframe\n",
    "media_imoveis = dados.groupby('Tipo')[['Valor']].mean().sort_values(by='Valor')\n",
    "media_imoveis.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "xwNUoofAYXFU"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando um grafico \n",
    "media_imoveis.plot(kind='barh', figsize=(14,10), color='purple');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c2rdhKbgZMpT"
   },
   "source": [
    "## Removendo os imóveis comerciais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "rDVKB-xtZUU9"
   },
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando os valores unicos da coluna\n",
    "dados.Tipo.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "GK9p8CEK4CtU"
   },
   "outputs": [],
   "source": [
    "# Criando uma lista com os imoveis comerciais\n",
    "imoveis_comerciais = ['Conjunto Comercial/Sala', 'Prédio Inteiro', 'Loja/Salão',\n",
    "       'Galpão/Depósito/Armazém', 'Casa Comercial', 'Terreno Padrão', 'Box/Garagem', \n",
    "       'Loja Shopping/ Ct Comercial', 'Chácara', 'Loteamento/Condomínio',\n",
    "       'Sítio', 'Pousada/Chalé', 'Hotel', 'Indústria']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "JELgjo5dZIwe"
   },
   "outputs": [],
   "source": [
    "# Retornando as linhas de imoveis comerciais, usando a função query e um @ para retornar todas as linhas\n",
    "df_comercial =dados.query('@imoveis_comerciais in Tipo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "v7dZPR5c4Oo2"
   },
   "outputs": [],
   "source": [
    "# Retornando as linhas de imoveis residenciais, usando a função query\n",
    "df_residenciais = dados.query('@imoveis_comerciais not in Tipo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "DrCXtYhi4RcD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Quitinete', 'Casa', 'Apartamento', 'Casa de Condomínio', 'Flat',\n",
       "       'Casa de Vila', 'Loft', 'Studio'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os valores unicos\n",
    "df_residenciais['Tipo'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "PsTmoPPh5VGa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Retornando a media a partir dos dados de imoveis residenciais\n",
    "media_imoveis = df_residenciais.groupby('Tipo')[['Valor']].mean().sort_values(by='Valor')\n",
    "\n",
    "# Plotando um grafico \n",
    "media_imoveis.plot(kind='barh', figsize=(14,10), color='purple');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ueADvdISZUSe"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-wW1PcKCXP_8"
   },
   "source": [
    "## Qual o percentual de cada tipo de imóvel na nossa base de dados?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "LFn-BeGNXa2k"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento           19532\n",
       "Casa de Condomínio      996\n",
       "Casa                    967\n",
       "Quitinete               836\n",
       "Flat                    476\n",
       "Casa de Vila            249\n",
       "Loft                     51\n",
       "Studio                    4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando a quantidade por tipo\n",
    "df_residenciais['Tipo'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "qpjKJkjmXa0A"
   },
   "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>Percentuais</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": [
       "                    Percentuais\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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando o percentual por tipo\n",
    "df_percentual_residencial = df_residenciais['Tipo'].value_counts(normalize=True).to_frame().sort_values('Tipo')\n",
    "df_percentual_residencial.rename(columns={'proportion': 'Percentuais'}, inplace=True)\n",
    "df_percentual_residencial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "XMWQmHhbXaxG"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gerando um grafico\n",
    "df_percentual_residencial.plot(kind='bar', figsize=(14,8), color='green', xlabel='Tipo', ylabel='Percentual');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2TJSa7Nr59qr"
   },
   "source": [
    "### **Selecionando apenas os imóveis do tipo apartamento**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "HZ7AhVhl6kXH"
   },
   "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>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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Cachambi</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</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",
       "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",
       "6   Apartamento      Cachambi        2      0       0    50  1300.0   \n",
       "10  Apartamento        Centro        1      0       0    36  1200.0   \n",
       "\n",
       "    Condominio  IPTU  \n",
       "3        390.0  20.0  \n",
       "4        230.0   NaN  \n",
       "5          NaN   NaN  \n",
       "6        301.0  17.0  \n",
       "10         NaN   NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_apartamentos = df_residenciais.query('Tipo == \"Apartamento\"')\n",
    "df_apartamentos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O time de ML chegou com algumas demandas de última hora para resolvermos nesse momento da análise exploratória. Essas demandas são:\n",
    "\n",
    "1) Calcular a média de quartos por apartamento;\n",
    "\n",
    "2) Conferir quantos bairros únicos existem na nossa base de dados;\n",
    "\n",
    "3) Analisar quais bairros possuem a média de valor de aluguel mais elevadas;\n",
    "\n",
    "4) Criar um gráfico de barras horizontais que apresente os 5 bairros com as médias de valores de aluguel mais elevadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2.4837523257323353)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular a média de quarto por apartamento\n",
    "df_residenciais['Quartos'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantos bairros unicos existem na base \n",
    "df_residenciais['Bairro'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quais os bairros possuem a média do valor do aluguel mais elevado\n",
    "media_bairros = df_residenciais.groupby('Bairro')[['Valor']].mean().sort_values('Valor', ascending=False)\n",
    "media_bairros.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Bairros', ylabel='Valor Médio'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gerar um grafico com os 5 bairros mais elevados\n",
    "media_bairros.head(5).plot(kind='bar', figsize=(10,6), color='purple', xlabel='Bairros', ylabel='Valor Médio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qicz5ljuWYIG"
   },
   "source": [
    "# Tratando e filtrando os dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sx6e737KaaWV"
   },
   "source": [
    "## Lidando com dados nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "XYFrVbLlWX0j"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo             0\n",
       "Bairro           0\n",
       "Quartos          0\n",
       "Vagas            0\n",
       "Suites           0\n",
       "Area             0\n",
       "Valor            7\n",
       "Condominio     745\n",
       "IPTU          5472\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando a quantidade de valores nulos\n",
    "df_apartamentos.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "Mw2m96vCaZvE"
   },
   "outputs": [],
   "source": [
    "# Tratando os valores nulos com valor 0\n",
    "df_apartamentos = df_apartamentos.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "vtgPgHGnWYTZ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo          19532\n",
       "Bairro        19532\n",
       "Quartos       19532\n",
       "Vagas         19532\n",
       "Suites        19532\n",
       "Area          19532\n",
       "Valor         19532\n",
       "Condominio    19532\n",
       "IPTU          19532\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantidade dos valores por coluna\n",
    "df_apartamentos.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B_OC_jS8cL0n"
   },
   "source": [
    "## Removendo registros\n",
    "\n",
    "Alguns registros existentes na base de dados não fazem muito sentido, por exemplo:\n",
    "\n",
    "- apartamentos que possuem valor de aluguel igual a 0;\n",
    "\n",
    "- apartamentos com o valor do condomínio igual a 0.\n",
    "\n",
    "Esses registros são inconsistentes, por isso devemos removê-los da nossa base de dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "H955G70zcKwX"
   },
   "outputs": [],
   "source": [
    "# Localizando os valores diferentes de 0 no aluguel e condominio, salvando no dataframe\n",
    "df_apartamentos = df_apartamentos.loc[(df_apartamentos['Valor'] != 0) & (df_apartamentos['Condominio'] != 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "R28sDOakx0dX"
   },
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Tipo, Bairro, Quartos, Vagas, Suites, Area, Valor, Condominio, IPTU]\n",
       "Index: []"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando as alterações\n",
    "df_apartamentos.query('Valor == 0 | Condominio == 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "p4UIflVTcKtN"
   },
   "outputs": [],
   "source": [
    "# removendo a coluna Tipo\n",
    "df_apartamentos.drop('Tipo', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "-hydAdP2e4m4"
   },
   "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>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>3</th>\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>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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cachambi</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Grajaú</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lins de Vasconcelos</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Bairro  Quartos  Vagas  Suites  Area   Valor  Condominio  \\\n",
       "3                Centro        1      0       0    15   800.0       390.0   \n",
       "4          Higienópolis        1      0       0    48   800.0       230.0   \n",
       "6              Cachambi        2      0       0    50  1300.0       301.0   \n",
       "11               Grajaú        2      1       0    70  1500.0       642.0   \n",
       "12  Lins de Vasconcelos        3      1       1    90  1500.0       455.0   \n",
       "\n",
       "    IPTU  \n",
       "3   20.0  \n",
       "4    0.0  \n",
       "6   17.0  \n",
       "11  74.0  \n",
       "12  14.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando \n",
    "df_apartamentos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "khQPzyksg5Hx"
   },
   "source": [
    "## Filtros\n",
    "\n",
    "Nesse momento, o time de ML deseja avaliar apenas imóveis em cenários específicos, como:\n",
    "\n",
    "1. Apartamentos que possuem 1 quarto e aluguel menor que R$ 1200;\n",
    "\n",
    "2. Apartamentos que possuem pelo menos 2 quartos, aluguel menor que R$ 3000 e área maior que 70 m².\n",
    "\n",
    "Com base nisso, precisamos aplicar os filtros necessários para obter essas informações específicas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ruP5z4iUy0HA"
   },
   "source": [
    "### **1. Apartamentos que possuem `1 quarto` e `aluguel menor que 1200`**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "qTimHdpn-UjL"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>3</th>\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>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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Tijuca</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>750.0</td>\n",
       "      <td>772.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>750.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32596</th>\n",
       "      <td>Grajaú</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32617</th>\n",
       "      <td>Pechincha</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>900.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32627</th>\n",
       "      <td>Glória</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32714</th>\n",
       "      <td>Botafogo</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32950</th>\n",
       "      <td>Tijuca</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>499 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Bairro  Quartos  Vagas  Suites  Area   Valor  Condominio   IPTU\n",
       "3            Centro        1      0       0    15   800.0       390.0   20.0\n",
       "4      Higienópolis        1      0       0    48   800.0       230.0    0.0\n",
       "28           Tijuca        1      0       0    48   750.0       772.0   25.0\n",
       "54           Centro        1      0       0    27   750.0       371.0   19.0\n",
       "94           Centro        1      1       0    37  1100.0       540.0  900.0\n",
       "...             ...      ...    ...     ...   ...     ...         ...    ...\n",
       "32596        Grajaú        1      0       1    37  1000.0       200.0    0.0\n",
       "32617     Pechincha        1      0       1    69   900.0       640.0  126.0\n",
       "32627        Glória        1      0       0    46  1100.0       453.0   32.0\n",
       "32714      Botafogo        1      0       0    21  1000.0       282.0   25.0\n",
       "32950        Tijuca        1      0       0    28  1000.0       360.0   25.0\n",
       "\n",
       "[499 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtrando com loc\n",
    "df_apartamentos.loc[(df_apartamentos['Quartos'] == 1) & (df_apartamentos['Valor'] < 1200)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cFeUf691y0oy"
   },
   "source": [
    "### **2. `Apartamentos` que possuem pelo menos `2 quartos`, `aluguel menor que 3000` e `area maior que 70`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "nt0alj_leMeZ"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
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       "      <th>IPTU</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lins de Vasconcelos</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Tijuca</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>138.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tijuca</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Botafogo</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Recreio dos Bandeirantes</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32934</th>\n",
       "      <td>Jacarepaguá</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32940</th>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1561.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32941</th>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32944</th>\n",
       "      <td>Recreio dos Bandeirantes</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32956</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "<p>4459 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "12          Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "21                       Tijuca        2      1       0   110  1900.0   \n",
       "24                       Tijuca        2      1       0    78  2000.0   \n",
       "32                     Botafogo        2      1       0    76  2500.0   \n",
       "36     Recreio dos Bandeirantes        3      1       1   105  2300.0   \n",
       "...                         ...      ...    ...     ...   ...     ...   \n",
       "32934               Jacarepaguá        2      1       1    78  2200.0   \n",
       "32940           Barra da Tijuca        2      1       1    85  2250.0   \n",
       "32941           Barra da Tijuca        3      2       0    98  2300.0   \n",
       "32944  Recreio dos Bandeirantes        3      2       1    88  1550.0   \n",
       "32956               Jacarepaguá        3      1       2    78  1800.0   \n",
       "\n",
       "       Condominio   IPTU  \n",
       "12          455.0   14.0  \n",
       "21          700.0  138.0  \n",
       "24          700.0    0.0  \n",
       "32          740.0    0.0  \n",
       "36          648.0    0.0  \n",
       "...           ...    ...  \n",
       "32934       790.0  180.0  \n",
       "32940      1561.0  197.0  \n",
       "32941       887.0  177.0  \n",
       "32944       790.0    0.0  \n",
       "32956       800.0   40.0  \n",
       "\n",
       "[4459 rows x 8 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtrando com loc\n",
    "df_apartamentos.loc[(df_apartamentos['Quartos'] >= 2) & (df_apartamentos['Valor'] < 3000) & (df_apartamentos['Area'] > 70)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X44FZQVd8Bdy"
   },
   "source": [
    "## Salvando os dados\n",
    "\n",
    "Para finalizar, precisamos salvar nosso dataframe no formato csv, após todas as análises e tratamentos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "KrvHtvZm9BXQ"
   },
   "outputs": [],
   "source": [
    "# Salvando \n",
    "df_apartamentos.to_csv('dados_apartamentos.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "3MkTUyhj-G4L"
   },
   "outputs": [
    {
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       "      <td>Cachambi</td>\n",
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       "      <td>0</td>\n",
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       "      <td>50</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>18776</th>\n",
       "      <td>Méier</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>900.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18777</th>\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>18778</th>\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>18779</th>\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>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18780 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Bairro  Quartos  Vagas  Suites  Area   Valor  Condominio  \\\n",
       "0                    Centro        1      0       0    15   800.0       390.0   \n",
       "1              Higienópolis        1      0       0    48   800.0       230.0   \n",
       "2                  Cachambi        2      0       0    50  1300.0       301.0   \n",
       "3                    Grajaú        2      1       0    70  1500.0       642.0   \n",
       "4       Lins de Vasconcelos        3      1       1    90  1500.0       455.0   \n",
       "...                     ...      ...    ...     ...   ...     ...         ...   \n",
       "18775        Vila Valqueire        2      0       0    52  1000.0       550.0   \n",
       "18776                 Méier        2      0       0    70   900.0       490.0   \n",
       "18777           Jacarepaguá        3      1       2    78  1800.0       800.0   \n",
       "18778  São Francisco Xavier        2      1       0    48  1400.0       509.0   \n",
       "18779                Leblon        2      0       0    70  3000.0       760.0   \n",
       "\n",
       "       IPTU  \n",
       "0      20.0  \n",
       "1       0.0  \n",
       "2      17.0  \n",
       "3      74.0  \n",
       "4      14.0  \n",
       "...     ...  \n",
       "18775   0.0  \n",
       "18776  48.0  \n",
       "18777  40.0  \n",
       "18778  37.0  \n",
       "18779   0.0  \n",
       "\n",
       "[18780 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os valores salvos\n",
    "df = pd.read_csv('dados_apartamentos.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Durante o desenvolvimento do nosso projeto, nós aplicamos alguns filtros na nossa base de dados, salvando os DataFrames resultantes em diferentes variáveis, sendo elas: df_1 e df_2.\n",
    "\n",
    "Com base nisso, salve esses DataFrames em 2 arquivos csv distintos para que o time de ML possa ter acesso a esses dados de forma mais prática."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "PuNhu13f96o0"
   },
   "outputs": [],
   "source": [
    "# Salvando nas variaveis\n",
    "df_1 = df_apartamentos.loc[(df_apartamentos['Quartos'] == 1) & (df_apartamentos['Valor'] < 1200)]\n",
    "df_2 = df_apartamentos.loc[(df_apartamentos['Quartos'] >= 2) & (df_apartamentos['Valor'] < 3000) & (df_apartamentos['Area'] > 70)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "JlTd_yEP-F6B"
   },
   "outputs": [],
   "source": [
    "# Salvando os dataframes em arquivos csv\n",
    "df_1.to_csv('df_apartamentos1.csv', sep=',', index=False)\n",
    "df_2.to_csv('df_apartamentos2.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e6eJTZfKWXEs"
   },
   "source": [
    "# Manipulando os dados\n",
    "\n",
    "O time de desenvolvedores do site da empresa solicitou a criação de duas novas colunas numéricas na nossa base de dados. No site dos imóveis, eles gostariam de apresentar informações mais sumarizadas em relação a valores. Para isso, essas colunas serão importantes:\n",
    "\n",
    "valor_por_mes: essa coluna deve conter os gastos mensais de cada imóvel, incluindo aluguel e condomínio;\n",
    "\n",
    "valor_por_ano: essa coluna deve conter os gastos anuais por imóvel, ou seja, IPTU mais 12 meses de aluguel e condomínio.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3i_NaXfjadEI"
   },
   "source": [
    "## Criando colunas numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando os dados novamente\n",
    "dados = pd.read_csv(url, sep=';')\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "rBejF2_badiG"
   },
   "outputs": [],
   "source": [
    "# Adicionando coluna mensal\n",
    "dados['Valor_por_mes'] = dados['Valor'] + dados['Condominio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coluna anual\n",
    "dados['Valor_por_ano'] = (dados['Valor_por_mes'] * 12) + dados['IPTU']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "66yfztw3ad2P"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Valor_por_mes</th>\n",
       "      <th>Valor_por_ano</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",
       "      <td>2200.0</td>\n",
       "      <td>26460.0</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",
       "      <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",
       "      <td>9220.0</td>\n",
       "      <td>111751.0</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",
       "      <td>1190.0</td>\n",
       "      <td>14300.0</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",
       "      <td>1030.0</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  Valor_por_mes  Valor_por_ano  \n",
       "0  1700.0       500.0    60.0         2200.0        26460.0  \n",
       "1  7000.0         NaN     NaN            NaN            NaN  \n",
       "2  5200.0      4020.0  1111.0         9220.0       111751.0  \n",
       "3   800.0       390.0    20.0         1190.0        14300.0  \n",
       "4   800.0       230.0     NaN         1030.0            NaN  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TZtb57m2bjzR"
   },
   "source": [
    "## Criando colunas categóricas\n",
    "\n",
    "O time de desenvolvimento solicitou a criação de mais duas colunas. No entanto, dessa vez elas são categóricas:\n",
    "\n",
    "Descrição: essa coluna deve possuir uma sumarização das principais informações dos imóveis que serão apresentadas no site: tipo de imóvel, bairro, quantidade de quartos e vagas de garagem;\n",
    "\n",
    "Possui_suite: essa deve ser uma coluna que informe apenas se o imóvel possui ou não suítes, sem se importar com a quantidade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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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       "</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",
       "      <th>Valor_por_mes</th>\n",
       "      <th>Valor_por_ano</th>\n",
       "      <th>Descricao</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",
       "      <td>2200.0</td>\n",
       "      <td>26460.0</td>\n",
       "      <td>Quitinete em Copacabana com 1 quarto(s)  e 0 v...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Casa em Jardim Botânico com 2 quarto(s)  e 0 v...</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",
       "      <td>9220.0</td>\n",
       "      <td>111751.0</td>\n",
       "      <td>Conjunto Comercial/Sala em Barra da Tijuca com...</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",
       "      <td>1190.0</td>\n",
       "      <td>14300.0</td>\n",
       "      <td>Apartamento em Centro com 1 quarto(s)  e 0 vag...</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",
       "      <td>1030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartamento em Higienópolis com 1 quarto(s)  e...</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  Valor_por_mes  Valor_por_ano  \\\n",
       "0  1700.0       500.0    60.0         2200.0        26460.0   \n",
       "1  7000.0         NaN     NaN            NaN            NaN   \n",
       "2  5200.0      4020.0  1111.0         9220.0       111751.0   \n",
       "3   800.0       390.0    20.0         1190.0        14300.0   \n",
       "4   800.0       230.0     NaN         1030.0            NaN   \n",
       "\n",
       "                                           Descricao  \n",
       "0  Quitinete em Copacabana com 1 quarto(s)  e 0 v...  \n",
       "1  Casa em Jardim Botânico com 2 quarto(s)  e 0 v...  \n",
       "2  Conjunto Comercial/Sala em Barra da Tijuca com...  \n",
       "3  Apartamento em Centro com 1 quarto(s)  e 0 vag...  \n",
       "4  Apartamento em Higienópolis com 1 quarto(s)  e...  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adicionando a descrição\n",
    "dados['Descricao'] = dados['Tipo'] + ' em ' + dados['Bairro'] + ' com ' + \\\n",
    "                                        dados['Quartos'].astype(str) + ' quarto(s) ' + \\\n",
    "                                        ' e ' + dados['Vagas'].astype(str) + ' vaga(s) de garagem.'\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "tb4crkqWJO3u"
   },
   "outputs": [],
   "source": [
    "# Adicionando a coluna de suites\n",
    "dados['Possui suite'] = dados['Suites'].apply(lambda x: 'Sim' if x > 0 else 'Não')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "gmfpWCBENjGx"
   },
   "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",
       "      <th>Valor_por_mes</th>\n",
       "      <th>Valor_por_ano</th>\n",
       "      <th>Descricao</th>\n",
       "      <th>Possui suite</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",
       "      <td>2200.0</td>\n",
       "      <td>26460.0</td>\n",
       "      <td>Quitinete em Copacabana com 1 quarto(s)  e 0 v...</td>\n",
       "      <td>Não</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Casa em Jardim Botânico com 2 quarto(s)  e 0 v...</td>\n",
       "      <td>Sim</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",
       "      <td>9220.0</td>\n",
       "      <td>111751.0</td>\n",
       "      <td>Conjunto Comercial/Sala em Barra da Tijuca com...</td>\n",
       "      <td>Não</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",
       "      <td>1190.0</td>\n",
       "      <td>14300.0</td>\n",
       "      <td>Apartamento em Centro com 1 quarto(s)  e 0 vag...</td>\n",
       "      <td>Não</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",
       "      <td>1030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartamento em Higienópolis com 1 quarto(s)  e...</td>\n",
       "      <td>Não</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  Valor_por_mes  Valor_por_ano  \\\n",
       "0  1700.0       500.0    60.0         2200.0        26460.0   \n",
       "1  7000.0         NaN     NaN            NaN            NaN   \n",
       "2  5200.0      4020.0  1111.0         9220.0       111751.0   \n",
       "3   800.0       390.0    20.0         1190.0        14300.0   \n",
       "4   800.0       230.0     NaN         1030.0            NaN   \n",
       "\n",
       "                                           Descricao Possui suite  \n",
       "0  Quitinete em Copacabana com 1 quarto(s)  e 0 v...          Não  \n",
       "1  Casa em Jardim Botânico com 2 quarto(s)  e 0 v...          Sim  \n",
       "2  Conjunto Comercial/Sala em Barra da Tijuca com...          Não  \n",
       "3  Apartamento em Centro com 1 quarto(s)  e 0 vag...          Não  \n",
       "4  Apartamento em Higienópolis com 1 quarto(s)  e...          Não  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z-27rFc7NSwq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QSLfaWHlbkgD"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oAYh5rVAbkZp"
   },
   "outputs": [],
   "source": []
  }
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