Removendo_Outliers.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relatório de Análise VIII"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identificando e Removendo Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize = (14,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "      <th>Valor m2</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>42.50</td>\n",
       "      <td>Casa</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>70.00</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>53.33</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>16.67</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>26.00</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22348</th>\n",
       "      <td>Apartamento</td>\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",
       "      <td>12.86</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22349</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",
       "      <td>29.63</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22350</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",
       "      <td>23.08</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22351</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",
       "      <td>29.17</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22352</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>0.0</td>\n",
       "      <td>42.86</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22353 rows × 11 columns</p>\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      Apartamento                Centro        1      0       0    15   \n",
       "3      Apartamento          Higienópolis        1      0       0    48   \n",
       "4      Apartamento              Cachambi        2      0       0    50   \n",
       "...            ...                   ...      ...    ...     ...   ...   \n",
       "22348  Apartamento                 Méier        2      0       0    70   \n",
       "22349    Quitinete                Centro        0      0       0    27   \n",
       "22350  Apartamento           Jacarepaguá        3      1       2    78   \n",
       "22351  Apartamento  São Francisco Xavier        2      1       0    48   \n",
       "22352  Apartamento                Leblon        2      0       0    70   \n",
       "\n",
       "        Valor  Condominio  IPTU  Valor m2 Tipo Agregado  \n",
       "0      1700.0       500.0  60.0     42.50          Casa  \n",
       "1      7000.0         0.0   0.0     70.00          Casa  \n",
       "2       800.0       390.0  20.0     53.33   Apartamento  \n",
       "3       800.0       230.0   0.0     16.67   Apartamento  \n",
       "4      1300.0       301.0  17.0     26.00   Apartamento  \n",
       "...       ...         ...   ...       ...           ...  \n",
       "22348   900.0       490.0  48.0     12.86   Apartamento  \n",
       "22349   800.0       350.0  25.0     29.63          Casa  \n",
       "22350  1800.0       800.0  40.0     23.08   Apartamento  \n",
       "22351  1400.0       509.0  37.0     29.17   Apartamento  \n",
       "22352  3000.0       760.0   0.0     42.86   Apartamento  \n",
       "\n",
       "[22353 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('dados/alugueis_residencial_tratado_melhorado.csv', sep = ';')\n",
    "dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box-plot"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"Extras\\box_plot.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Box-plot possui a seguinte configuração: temos uma mediana, em que dividimos os dados em 50%, para a direita e esquerda, igualmente.\n",
    "#Q1, que se refere ao primeiro quartil e parte em 25% e 75%\n",
    "#Q3 parte os dados em 75% e 25%\n",
    "#A diferente entre Q3 e Q1 gera o intervalo interquartirico, isto é, as estatísticas que geraremos para realizar o corte de outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#criar Box-plot\n",
    "#está invertido e só conseguimos perceber os dados discrepantes.\n",
    "dataset.boxplot(['Valor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "      <th>Valor m2</th>\n",
       "      <th>Tipo Agregado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7819</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>9230.77</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10895</th>\n",
       "      <td>Casa de Condomínio</td>\n",
       "      <td>Freguesia (Jacarepaguá)</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>163</td>\n",
       "      <td>800000.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4907.98</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Freguesia (Jacarepaguá)</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>550000.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3666.67</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14181</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Recreio dos Bandeirantes</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>7485.03</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15895</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Tipo                    Bairro  Quartos  Vagas  Suites  \\\n",
       "7819          Apartamento           Barra da Tijuca        1      1       0   \n",
       "10895  Casa de Condomínio   Freguesia (Jacarepaguá)        4      2       3   \n",
       "12975         Apartamento   Freguesia (Jacarepaguá)        2      2       1   \n",
       "14181         Apartamento  Recreio dos Bandeirantes        3      2       1   \n",
       "15895         Apartamento                  Botafogo        4      1       1   \n",
       "\n",
       "       Area      Valor  Condominio   IPTU  Valor m2 Tipo Agregado  \n",
       "7819     65   600000.0       980.0  120.0   9230.77   Apartamento  \n",
       "10895   163   800000.0       900.0    0.0   4907.98          Casa  \n",
       "12975   150   550000.0       850.0  150.0   3666.67   Apartamento  \n",
       "14181   167  1250000.0      1186.0  320.0   7485.03   Apartamento  \n",
       "15895   300  4500000.0      1100.0    0.0  15000.00   Apartamento  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizar melhor os dados discrepantes\n",
    "dataset[dataset['Valor'] >= 500000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1700.0\n",
       "1        7000.0\n",
       "2         800.0\n",
       "3         800.0\n",
       "4        1300.0\n",
       "          ...  \n",
       "22348     900.0\n",
       "22349     800.0\n",
       "22350    1800.0\n",
       "22351    1400.0\n",
       "22352    3000.0\n",
       "Name: Valor, Length: 22353, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Criar uma series com apenas a coluna valor para facilitar a análise\n",
    "valor = dataset['Valor']\n",
    "valor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1600.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calcular o Q1 (primeiro quartil)\n",
    "Q1 = valor.quantile(.25)\n",
    "Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5500.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calcular o Q3 (terceiro quartil)\n",
    "Q3 = valor.quantile(.75)\n",
    "Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3900.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calcular o intervalo inter quartil\n",
    "IIQ = Q3 - Q1\n",
    "IIQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4250.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calcular o limite inferior\n",
    "limite_inferior = Q1 - (1.5 * IIQ)\n",
    "limite_inferior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11350.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calcular limite superior\n",
    "limite_superior = Q3 + (1.5 * IIQ)\n",
    "limite_superior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "      <th>Valor m2</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>42.50</td>\n",
       "      <td>Casa</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>70.00</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>53.33</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>16.67</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>26.00</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22348</th>\n",
       "      <td>Apartamento</td>\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",
       "      <td>12.86</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22349</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",
       "      <td>29.63</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22350</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",
       "      <td>23.08</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22351</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",
       "      <td>29.17</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22352</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>0.0</td>\n",
       "      <td>42.86</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20324 rows × 11 columns</p>\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      Apartamento                Centro        1      0       0    15   \n",
       "3      Apartamento          Higienópolis        1      0       0    48   \n",
       "4      Apartamento              Cachambi        2      0       0    50   \n",
       "...            ...                   ...      ...    ...     ...   ...   \n",
       "22348  Apartamento                 Méier        2      0       0    70   \n",
       "22349    Quitinete                Centro        0      0       0    27   \n",
       "22350  Apartamento           Jacarepaguá        3      1       2    78   \n",
       "22351  Apartamento  São Francisco Xavier        2      1       0    48   \n",
       "22352  Apartamento                Leblon        2      0       0    70   \n",
       "\n",
       "        Valor  Condominio  IPTU  Valor m2 Tipo Agregado  \n",
       "0      1700.0       500.0  60.0     42.50          Casa  \n",
       "1      7000.0         0.0   0.0     70.00          Casa  \n",
       "2       800.0       390.0  20.0     53.33   Apartamento  \n",
       "3       800.0       230.0   0.0     16.67   Apartamento  \n",
       "4      1300.0       301.0  17.0     26.00   Apartamento  \n",
       "...       ...         ...   ...       ...           ...  \n",
       "22348   900.0       490.0  48.0     12.86   Apartamento  \n",
       "22349   800.0       350.0  25.0     29.63          Casa  \n",
       "22350  1800.0       800.0  40.0     23.08   Apartamento  \n",
       "22351  1400.0       509.0  37.0     29.17   Apartamento  \n",
       "22352  3000.0       760.0   0.0     42.86   Apartamento  \n",
       "\n",
       "[20324 rows x 11 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecionar os dados que estão dentro dos limites\n",
    "dataset_new = dataset[(valor >= limite_inferior) & (valor <= limite_superior)]\n",
    "dataset_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#O box-plot indica que existem dados discrepantes de 10000 de condomínio, mas em nosso banco de dados realmente existem imóveis de alto padrão que apresentam esse valor\n",
    "dataset_new.boxplot(['Valor'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograma\n",
    "#### distribuição de frequências dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'Valor'}>]], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Para dataset é apenas uma barra\n",
    "#Mas em dataset_new existe uma distribuição de barras que nos permite algumas análises.\n",
    "#Seriam necessários alguns tratamentos antes de utilizar essas informações em uma regressão, por exemplo.\n",
    "dataset.hist(['Valor'])\n",
    "dataset_new.hist(['Valor'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identificando e Removendo Outliers por Grupos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Valor'}, xlabel='Tipo'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mostra o Box-plot por tipo\n",
    "# Ap e Casa de COndominio estão com os dados mais discrepantes\n",
    "dataset.boxplot(['Valor'], by= 'Tipo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.generic.SeriesGroupBy"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cria um grupo dos tipos com apenas a variavel valor\n",
    "grupo_tipo = dataset.groupby('Tipo')['Valor']\n",
    "type(grupo_tipo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Apartamento': [2, 3, 4, 7, 8, 9, 11, 14, 15, 16, 18, 19, 21, 22, 23, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, ...], 'Casa': [1, 24, 56, 59, 98, 102, 148, 164, 184, 244, 256, 259, 270, 293, 317, 323, 341, 346, 364, 385, 392, 406, 431, 441, 482, 483, 489, 519, 551, 552, 577, 582, 583, 629, 632, 641, 646, 647, 671, 687, 711, 732, 736, 772, 783, 793, 870, 889, 955, 1000, 1048, 1056, 1141, 1146, 1148, 1180, 1201, 1274, 1281, 1342, 1361, 1376, 1399, 1443, 1457, 1461, 1472, 1477, 1504, 1507, 1520, 1619, 1638, 1691, 1697, 1701, 1707, 1720, 1746, 1754, 1800, 1851, 1876, 1917, 1947, 1976, 1985, 2003, 2063, 2083, 2100, 2119, 2146, 2152, 2153, 2178, 2216, 2247, 2250, 2257, ...], 'Casa de Condomínio': [5, 6, 13, 17, 44, 60, 170, 172, 187, 212, 227, 252, 265, 271, 286, 298, 315, 343, 398, 408, 447, 452, 456, 510, 565, 620, 633, 668, 674, 684, 718, 735, 794, 821, 823, 877, 899, 903, 952, 976, 979, 997, 1004, 1010, 1022, 1026, 1047, 1103, 1117, 1135, 1152, 1171, 1181, 1205, 1245, 1252, 1254, 1264, 1271, 1334, 1338, 1346, 1367, 1383, 1435, 1469, 1470, 1499, 1528, 1564, 1593, 1615, 1634, 1649, 1681, 1750, 1759, 1765, 1794, 1802, 1808, 1842, 1901, 1922, 1925, 1941, 1981, 2075, 2077, 2115, 2197, 2214, 2246, 2271, 2294, 2296, 2377, 2383, 2408, 2422, ...], 'Casa de Vila': [83, 217, 225, 310, 339, 708, 836, 858, 936, 1030, 1379, 1389, 1479, 1524, 1586, 1674, 1705, 1740, 1807, 2132, 2297, 2315, 2497, 2588, 2603, 2662, 2698, 2789, 2907, 2940, 3046, 3053, 3087, 3095, 3385, 3513, 3611, 3795, 3917, 3950, 3953, 3958, 4195, 4248, 4255, 4267, 4444, 4552, 4946, 5277, 5297, 5302, 5325, 5425, 5547, 5673, 5737, 5869, 5897, 6063, 6102, 6149, 6163, 6186, 6204, 6359, 6394, 6458, 6508, 6563, 6590, 6922, 6964, 7112, 7131, 7210, 7268, 7323, 7479, 7889, 7905, 7976, 8035, 8041, 8169, 8207, 8341, 8640, 8666, 8796, 9456, 9461, 9546, 9706, 9854, 9859, 9955, 9978, 10026, 10112, ...], 'Flat': [12, 20, 143, 144, 197, 272, 458, 535, 576, 734, 784, 798, 878, 915, 917, 1032, 1053, 1085, 1111, 1132, 1214, 1230, 1290, 1380, 1391, 1433, 1438, 1442, 1471, 1523, 1635, 1668, 1703, 1727, 1790, 1824, 1839, 1847, 1903, 1971, 2120, 2259, 2279, 2332, 2370, 2389, 2518, 2519, 2560, 2581, 2595, 2625, 2702, 2712, 2730, 2743, 2777, 2803, 2822, 2849, 2895, 2913, 2970, 3062, 3106, 3133, 3138, 3154, 3162, 3172, 3188, 3210, 3237, 3265, 3303, 3356, 3381, 3390, 3481, 3655, 3688, 3694, 3710, 3801, 3833, 3928, 3959, 3977, 4040, 4042, 4089, 4209, 4350, 4422, 4447, 4453, 4464, 4473, 4650, 4674, ...], 'Loft': [238, 581, 887, 2037, 2212, 2888, 3253, 3282, 3525, 4033, 4811, 4867, 5133, 5411, 6043, 6352, 6637, 6695, 7161, 7705, 7720, 7822, 8412, 8415, 8560, 9709, 11505, 11596, 11714, 11799, 12022, 12481, 13331, 13390, 14306, 14317, 14621, 14811, 15139, 15798, 15961, 17381, 18309, 18314, 18490, 18590, 19956, 20147, 20637, 20659, 22022], 'Quitinete': [0, 10, 30, 73, 80, 88, 103, 122, 150, 178, 195, 211, 228, 254, 308, 321, 334, 351, 362, 432, 433, 434, 468, 494, 541, 644, 661, 691, 822, 885, 935, 937, 942, 946, 957, 962, 988, 1019, 1035, 1064, 1091, 1179, 1193, 1195, 1207, 1216, 1220, 1237, 1242, 1286, 1300, 1360, 1377, 1387, 1421, 1432, 1436, 1515, 1529, 1543, 1576, 1628, 1645, 1647, 1667, 1733, 1734, 1743, 1770, 1791, 1810, 1865, 1880, 1894, 1952, 2057, 2130, 2143, 2170, 2187, 2194, 2201, 2205, 2274, 2285, 2287, 2306, 2320, 2376, 2413, 2433, 2434, 2435, 2445, 2496, 2508, 2513, 2546, 2561, 2687, ...]}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ddicionario com a chave (tipo) e os valores (indices que o tipo aparece)\n",
    "grupo_tipo.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Todos estão em formato diferente (Series)\n",
    "Q1_ = grupo_tipo.quantile(.25)\n",
    "Q3_ = grupo_tipo.quantile(.75)\n",
    "IIQ_ = Q3_ - Q1_\n",
    "limite_inf = Q1_ - 1.5 * IIQ\n",
    "limite_sup = Q3_ + 1.5 * IIQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento           1700.0\n",
       "Casa                  1100.0\n",
       "Casa de Condomínio    4000.0\n",
       "Casa de Vila           750.0\n",
       "Flat                  2500.0\n",
       "Loft                  1750.0\n",
       "Quitinete              900.0\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Formato Series\n",
    "Q1_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento            5000.0\n",
       "Casa                   9800.0\n",
       "Casa de Condomínio    15250.0\n",
       "Casa de Vila           1800.0\n",
       "Flat                   6000.0\n",
       "Loft                   3000.0\n",
       "Quitinete              1500.0\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q3_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento          -4150.0\n",
       "Casa                 -4750.0\n",
       "Casa de Condomínio   -1850.0\n",
       "Casa de Vila         -5100.0\n",
       "Flat                 -3350.0\n",
       "Loft                 -4100.0\n",
       "Quitinete            -4950.0\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limite_inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento           10850.0\n",
       "Casa                  15650.0\n",
       "Casa de Condomínio    21100.0\n",
       "Casa de Vila           7650.0\n",
       "Flat                  11850.0\n",
       "Loft                   8850.0\n",
       "Quitinete              7350.0\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limite_sup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10850.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Acessando limite superior de um Apartamento\n",
    "limite_sup['Apartamento']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apartamento\n",
      "Casa\n",
      "Casa de Condomínio\n",
      "Casa de Vila\n",
      "Flat\n",
      "Loft\n",
      "Quitinete\n"
     ]
    }
   ],
   "source": [
    "#Pegando as chaves do dicionario de grupo\n",
    "for tipo in grupo_tipo.groups.keys():\n",
    "    print(tipo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seleção em grupo com mais de um tipo de variavel (tipo, valor)\n",
    "dataset_new_ = pd.DataFrame()\n",
    "for tipo in grupo_tipo.groups.keys():\n",
    "    eh_tipo = dataset['Tipo'] == tipo\n",
    "    eh_dentro_limite = (dataset['Valor'] >= limite_inf[tipo]) & (dataset['Valor'] <= limite_sup[tipo]) #coloca o [tipo] para pegar o valor para cada tipo por vez já que se trata de uma Series\n",
    "    selecao = eh_tipo & eh_dentro_limite\n",
    "    dataset_selecao = dataset[selecao]\n",
    "    dataset_new_ = pd.concat([dataset_new_, dataset_selecao])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'Apartamento'}>,\n",
       "        <AxesSubplot: title={'center': 'Casa'}>,\n",
       "        <AxesSubplot: title={'center': 'Casa de Condomínio'}>],\n",
       "       [<AxesSubplot: title={'center': 'Casa de Vila'}>,\n",
       "        <AxesSubplot: title={'center': 'Flat'}>,\n",
       "        <AxesSubplot: title={'center': 'Loft'}>],\n",
       "       [<AxesSubplot: title={'center': 'Quitinete'}>, <AxesSubplot: >,\n",
       "        <AxesSubplot: >]], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for tipo in grupo_tipo.groups.keys():\n",
    "dataset_new_.hist(by='Tipo')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Valor'}, xlabel='[Tipo]'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_new_.boxplot(['Valor'], by = ['Tipo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exportação\n",
    "dataset_new_.to_csv('dados/alugueis_residencial_tratado_melhorado_sem_outliers.csv', sep = ';', index= False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ambiente",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15 (main, Nov 24 2022, 14:39:17) [MSC v.1916 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "078be856d3baac0a1b5fca3d500da5e3f0197fb170b2c6c6bc3b09ecf224b0d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}