Relatório de Análise VII.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "35f60b0c-0dc0-4391-bf41-5cc5b1ec3198",
   "metadata": {},
   "source": [
    "# Relatório de Análise VII"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4673a7fa-68a4-4cd9-8bc4-ff2575161f9a",
   "metadata": {},
   "source": [
    "Neste sétimo relatório, vamos obter estatísticas descritivas de nossa base de dados. Esta tarefa será uma preparação para identificarmos os dados discrepantes de nossa base para uma posterior remoção."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee95b971-39a7-4a4e-98e5-c6c687765bb4",
   "metadata": {},
   "source": [
    "## Estatísticas Descritivas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa6d2e84-1bcf-475d-ab92-b95a5d07ab04",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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 Bruto</th>\n",
       "      <th>Tipo Agregado</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>2260.0</td>\n",
       "      <td>Apartamento</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>1210.0</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>0.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "      <td>1618.0</td>\n",
       "      <td>Apartamento</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",
       "2  Apartamento           Centro        1      0       0    15   800.0   \n",
       "3  Apartamento     Higienópolis        1      0       0    48   800.0   \n",
       "4  Apartamento         Cachambi        2      0       0    50  1300.0   \n",
       "\n",
       "   Condominio  IPTU  Valor Bruto Tipo Agregado  \n",
       "0       500.0  60.0       2260.0   Apartamento  \n",
       "1         0.0   0.0       7000.0          Casa  \n",
       "2       390.0  20.0       1210.0   Apartamento  \n",
       "3       230.0   0.0       1030.0   Apartamento  \n",
       "4       301.0  17.0       1618.0   Apartamento  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importa o pandas com o apelido pd\n",
    "import pandas as pd\n",
    "\n",
    "# Importa o módulo os\n",
    "import os\n",
    "\n",
    "# Muda para o diretório com os dados\n",
    "os.chdir('dados')\n",
    "\n",
    "# Importa o CSV com os dados de imóveis residenciais\n",
    "imoveis = pd.read_csv('imoveis-residenciais.csv', sep=';')\n",
    "\n",
    "# Exibe as cinco primeiras linhas do dataframe\n",
    "imoveis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b4ac7827-53da-4d47-b328-a2e363e83113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5046.17"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtém o valor médio dos aluguéis\n",
    "imoveis['Valor'].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f94e29a-77da-4ac0-9fe1-8d9eaab4b8dd",
   "metadata": {},
   "source": [
    "Para o conjunto de dados disponível, a média dos valores dos aluguéis é de **R$ 5.046,17**. Desejamos, porém, obter o valor médio do aluguel para cada um dos tipos de imóveis. Para obter esta informação, precisamos fazer agrupamentos no dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba4ae839-25ea-4500-ab74-1d555530d054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento            4805.30\n",
       "Casa                   6793.45\n",
       "Casa de Condomínio    11952.06\n",
       "Casa de Vila           1574.37\n",
       "Quitinete              1246.84\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imoveis.groupby('Tipo')['Valor'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ceb78df-2202-4350-b6f7-21b540c72a2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             Copacabana\n",
       "1        Jardim Botânico\n",
       "2                 Centro\n",
       "3           Higienópolis\n",
       "4               Cachambi\n",
       "              ...       \n",
       "13855          Bancários\n",
       "15277         Cavalcanti\n",
       "15280       Rio da Prata\n",
       "15412      Cidade Jardim\n",
       "16428        Coelho Neto\n",
       "Name: Bairro, Length: 152, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtém uma série com os bairros em que os imóveis estão localizados\n",
    "imoveis['Bairro'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f549868-04c9-46c4-9cdf-65777ccddfb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Barra da Tijuca',\n",
       " 'Copacabana',\n",
       " 'Ipanema',\n",
       " 'Botafogo',\n",
       " 'Flamengo',\n",
       " 'Leblon',\n",
       " 'Tijuca']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lista com bairros de interesse\n",
    "bairros = ['Barra da Tijuca', 'Copacabana', 'Ipanema', 'Botafogo', 'Flamengo', 'Leblon', 'Tijuca']\n",
    "bairros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd98b2cc-d0c5-4265-922e-b359f1a07ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Copacabana\n",
       "5     Barra da Tijuca\n",
       "15             Tijuca\n",
       "19            Ipanema\n",
       "23           Botafogo\n",
       "27             Leblon\n",
       "78           Flamengo\n",
       "Name: Bairro, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtro para selecionar somente os imóveis que estão na lista com os bairros de interesse\n",
    "filtro = imoveis['Bairro'].isin(bairros)\n",
    "\n",
    "# Filtra o dataframe\n",
    "imoveis = imoveis[filtro]\n",
    "\n",
    "# Exibe os bairros distintos da coluna `Bairro`\n",
    "imoveis['Bairro'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18569d6f-8435-4e39-bfeb-124dea9c8fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bairro\n",
       "Barra da Tijuca    7069.55\n",
       "Botafogo           8791.83\n",
       "Copacabana         4126.68\n",
       "Flamengo           4113.53\n",
       "Ipanema            9352.00\n",
       "Leblon             8746.34\n",
       "Tijuca             2043.52\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtém o valor médio do aluguel para cada bairro\n",
    "imoveis.groupby('Bairro')['Valor'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ff26946-bb21-446c-b77f-c2f62bf4b995",
   "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>count</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bairro</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>Barra da Tijuca</th>\n",
       "      <td>3863</td>\n",
       "      <td>800.0</td>\n",
       "      <td>7069.552938</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botafogo</th>\n",
       "      <td>873</td>\n",
       "      <td>700.0</td>\n",
       "      <td>8791.828179</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copacabana</th>\n",
       "      <td>2644</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4126.677005</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flamengo</th>\n",
       "      <td>714</td>\n",
       "      <td>800.0</td>\n",
       "      <td>4113.526611</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ipanema</th>\n",
       "      <td>1764</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>9352.001134</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leblon</th>\n",
       "      <td>1258</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8746.344992</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tijuca</th>\n",
       "      <td>1100</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2043.520000</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count     min         mean  median        max\n",
       "Bairro                                                        \n",
       "Barra da Tijuca   3863   800.0  7069.552938  4500.0   600000.0\n",
       "Botafogo           873   700.0  8791.828179  3000.0  4500000.0\n",
       "Copacabana        2644   100.0  4126.677005  3000.0    35000.0\n",
       "Flamengo           714   800.0  4113.526611  2900.0    35000.0\n",
       "Ipanema           1764  1200.0  9352.001134  7000.0    90000.0\n",
       "Leblon            1258   100.0  8746.344992  7000.0   100000.0\n",
       "Tijuca            1100   750.0  2043.520000  1800.0    45000.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtém um conjunto de medidas acerca da variável aluguel\n",
    "imoveis.groupby('Bairro')['Valor'].agg(['count', 'min', 'mean', 'median', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad153c93-8c90-471e-a24e-938ab8817e26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>Bairro</th>\n",
       "      <th></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>Barra da Tijuca</th>\n",
       "      <td>3863.0</td>\n",
       "      <td>7069.55</td>\n",
       "      <td>11874.15</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botafogo</th>\n",
       "      <td>873.0</td>\n",
       "      <td>8791.83</td>\n",
       "      <td>152202.41</td>\n",
       "      <td>700.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4350.0</td>\n",
       "      <td>4500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copacabana</th>\n",
       "      <td>2644.0</td>\n",
       "      <td>4126.68</td>\n",
       "      <td>3611.41</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flamengo</th>\n",
       "      <td>714.0</td>\n",
       "      <td>4113.53</td>\n",
       "      <td>3839.13</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ipanema</th>\n",
       "      <td>1764.0</td>\n",
       "      <td>9352.00</td>\n",
       "      <td>8219.72</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leblon</th>\n",
       "      <td>1258.0</td>\n",
       "      <td>8746.34</td>\n",
       "      <td>7004.04</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tijuca</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>2043.52</td>\n",
       "      <td>1664.34</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count     mean        std     min     25%     50%      75%  \\\n",
       "Bairro                                                                         \n",
       "Barra da Tijuca  3863.0  7069.55   11874.15   800.0  2500.0  4500.0   8500.0   \n",
       "Botafogo          873.0  8791.83  152202.41   700.0  2200.0  3000.0   4350.0   \n",
       "Copacabana       2644.0  4126.68    3611.41   100.0  2000.0  3000.0   4800.0   \n",
       "Flamengo          714.0  4113.53    3839.13   800.0  1900.0  2900.0   4975.0   \n",
       "Ipanema          1764.0  9352.00    8219.72  1200.0  4500.0  7000.0  11000.0   \n",
       "Leblon           1258.0  8746.34    7004.04   100.0  4500.0  7000.0  10500.0   \n",
       "Tijuca           1100.0  2043.52    1664.34   750.0  1500.0  1800.0   2300.0   \n",
       "\n",
       "                       max  \n",
       "Bairro                      \n",
       "Barra da Tijuca   600000.0  \n",
       "Botafogo         4500000.0  \n",
       "Copacabana         35000.0  \n",
       "Flamengo           35000.0  \n",
       "Ipanema            90000.0  \n",
       "Leblon            100000.0  \n",
       "Tijuca             45000.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtém um resumo mais completo acerca dos valores da variável `Valor`\n",
    "imoveis.groupby('Bairro')['Valor'].describe().round(2)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "12c7c22f-bd0b-4389-8e43-5aa0be441513",
   "metadata": {},
   "source": [
    "Observações:\n",
    "\n",
    "Com a análise do resumo acima, notamos que existe pelo menos um imóvel localizado na Barra da Tijuca cujo valor do aluguel é de R$ 600.000,00 e que há pelo menos um imóvel localizado no Botafogo cujo aluguel é de R$ 4.500.000,00.\n",
    "\n",
    "Estes valores podem estar incorretos e muito provavelmente representam os valores de venda dos respectivos imóveis.\n",
    "\n",
    "Portanto, vamos analisar mais de perto o caso destes imóveis no próximo relatório."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ba82680-eff6-4459-97ad-fddea9d48880",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporta o dataframe para análises posteriores\n",
    "imoveis.to_csv('imoveis-residenciais.csv', sep=';', index=False)"
   ]
  }
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