EstudoDataCleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reatório Análise I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>21.43</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo               Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0  Apartamento               Centro        1      0       0    15   800.0   \n",
       "1  Apartamento         Higienópolis        1      0       0    48   800.0   \n",
       "2  Apartamento             Cachambi        2      0       0    50  1300.0   \n",
       "3  Apartamento               Grajaú        2      1       0    70  1500.0   \n",
       "4  Apartamento  Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "\n",
       "   Condominio  IPTU  Valor m2 Tipo Agregado  \n",
       "0       390.0  20.0     53.33   Apartamento  \n",
       "1       230.0   0.0     16.67   Apartamento  \n",
       "2       301.0  17.0     26.00   Apartamento  \n",
       "3       642.0  74.0     21.43   Apartamento  \n",
       "4       455.0  14.0     16.67   Apartamento  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('dados/aluguel.csv', sep=';')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19831 entries, 0 to 19830\n",
      "Data columns (total 11 columns):\n",
      "Tipo             19831 non-null object\n",
      "Bairro           19831 non-null object\n",
      "Quartos          19831 non-null int64\n",
      "Vagas            19831 non-null int64\n",
      "Suites           19831 non-null int64\n",
      "Area             19831 non-null int64\n",
      "Valor            19831 non-null float64\n",
      "Condominio       19831 non-null float64\n",
      "IPTU             19831 non-null float64\n",
      "Valor m2         19831 non-null float64\n",
      "Tipo Agregado    19831 non-null object\n",
      "dtypes: float64(4), int64(4), object(3)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemplo importação json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "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",
       "\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",
       "      <td>0</td>\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</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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</td>\n",
       "      <td>4020.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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</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       500.0    60.0  \n",
       "1   7000         NaN     NaN  \n",
       "2   5200      4020.0  1111.0  \n",
       "3    800       390.0    20.0  \n",
       "4    800       230.0     NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_json = pd.read_json('dados/aluguel.json')\n",
    "exemplo_json.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemplo importação txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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",
       "      <td>0</td>\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</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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</td>\n",
       "      <td>4020.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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</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       500.0    60.0  \n",
       "1   7000         NaN     NaN  \n",
       "2   5200      4020.0  1111.0  \n",
       "3    800       390.0    20.0  \n",
       "4    800       230.0     NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_txt = pd.read_table('dados/aluguel.txt')\n",
    "exemplo_txt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemplo importação .xlsx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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</td>\n",
       "      <td>4020.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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</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       500.0    60.0  \n",
       "1   7000         NaN     NaN  \n",
       "2   5200      4020.0  1111.0  \n",
       "3    800       390.0    20.0  \n",
       "4    800       230.0     NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_xlsx = pd.read_excel('dados/aluguel.xlsx')\n",
    "exemplo_xlsx.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemplo importação HTML\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FAIXA ETÁRIA</th>\n",
       "      <th>UNAFISCO SAÚDE SOFT II</th>\n",
       "      <th>SOFT PARTICIPATIVO</th>\n",
       "      <th>UNIQUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>00 A 18</td>\n",
       "      <td>R$218,99</td>\n",
       "      <td>R$192,60</td>\n",
       "      <td>R$334,48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>19 A 23</td>\n",
       "      <td>R$263,63</td>\n",
       "      <td>R$207,67</td>\n",
       "      <td>R$380,06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>24 A 28</td>\n",
       "      <td>R$322,44</td>\n",
       "      <td>R$224,26</td>\n",
       "      <td>R$464,39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>29 A 33</td>\n",
       "      <td>R$402,05</td>\n",
       "      <td>R$258,63</td>\n",
       "      <td>R$566,68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>34 A 38</td>\n",
       "      <td>R$455,17</td>\n",
       "      <td>R$306,37</td>\n",
       "      <td>R$664,61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>39 A 43</td>\n",
       "      <td>R$489,29</td>\n",
       "      <td>R$417,93</td>\n",
       "      <td>R$742,72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>44 A 48</td>\n",
       "      <td>R$582,65</td>\n",
       "      <td>R$495,24</td>\n",
       "      <td>R$869,47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>49 A 53</td>\n",
       "      <td>R$718,86</td>\n",
       "      <td>R$562,64</td>\n",
       "      <td>R$1.046,29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>54 A 58</td>\n",
       "      <td>R$908,54</td>\n",
       "      <td>R$721,00</td>\n",
       "      <td>R$1.355,74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>59 OU MAIS</td>\n",
       "      <td>R$1.244,50</td>\n",
       "      <td>R$1.151,62</td>\n",
       "      <td>R$2.003,53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FAIXA ETÁRIA UNAFISCO SAÚDE SOFT II SOFT PARTICIPATIVO      UNIQUE\n",
       "0      00 A 18               R$218,99           R$192,60    R$334,48\n",
       "1      19 A 23               R$263,63           R$207,67    R$380,06\n",
       "2      24 A 28               R$322,44           R$224,26    R$464,39\n",
       "3      29 A 33               R$402,05           R$258,63    R$566,68\n",
       "4      34 A 38               R$455,17           R$306,37    R$664,61\n",
       "5      39 A 43               R$489,29           R$417,93    R$742,72\n",
       "6      44 A 48               R$582,65           R$495,24    R$869,47\n",
       "7      49 A 53               R$718,86           R$562,64  R$1.046,29\n",
       "8      54 A 58               R$908,54           R$721,00  R$1.355,74\n",
       "9   59 OU MAIS             R$1.244,50         R$1.151,62  R$2.003,53"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_html = pd.read_html('dados/dados_html_1.html')\n",
    "exemplo_html[0]"
   ]
  },
  {
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Reserve balances required</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Reserve balances maintained 4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Reserve balance requirements 1</th>\n",
       "      <th>Top of penalty- free band 2</th>\n",
       "      <th>Bottom of penalty- free band 3</th>\n",
       "      <th>Total</th>\n",
       "      <th>Balances maintained to satisfy reserve balance requirements 5</th>\n",
       "      <th>Balances maintained that exceed the top of the penalty- free band 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Month 7</td>\n",
       "      <td>Month 7</td>\n",
       "      <td>Month 7</td>\n",
       "      <td>Month 7</td>\n",
       "      <td>Month 7</td>\n",
       "      <td>Month 7</td>\n",
       "      <td>Month 7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Aug. 2019</td>\n",
       "      <td>134639</td>\n",
       "      <td>148114</td>\n",
       "      <td>121167</td>\n",
       "      <td>1520876</td>\n",
       "      <td>147555</td>\n",
       "      <td>1373321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Sept. 2019</td>\n",
       "      <td>134707</td>\n",
       "      <td>148188</td>\n",
       "      <td>121227</td>\n",
       "      <td>1439771</td>\n",
       "      <td>147498</td>\n",
       "      <td>1292273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Oct. 2019</td>\n",
       "      <td>134998</td>\n",
       "      <td>148509</td>\n",
       "      <td>121489</td>\n",
       "      <td>1481513</td>\n",
       "      <td>148050</td>\n",
       "      <td>1333462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Nov. 2019</td>\n",
       "      <td>140705</td>\n",
       "      <td>154787</td>\n",
       "      <td>126625</td>\n",
       "      <td>1529341</td>\n",
       "      <td>154256</td>\n",
       "      <td>1375085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Dec. 2019</td>\n",
       "      <td>138984</td>\n",
       "      <td>152893</td>\n",
       "      <td>125076</td>\n",
       "      <td>1630090</td>\n",
       "      <td>152562</td>\n",
       "      <td>1477528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Jan. 2020</td>\n",
       "      <td>144816</td>\n",
       "      <td>159309</td>\n",
       "      <td>130326</td>\n",
       "      <td>1645384</td>\n",
       "      <td>158765</td>\n",
       "      <td>1486619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Feb. 2020</td>\n",
       "      <td>138370</td>\n",
       "      <td>152218</td>\n",
       "      <td>124524</td>\n",
       "      <td>1656989</td>\n",
       "      <td>151612</td>\n",
       "      <td>1505377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Mar. 2020</td>\n",
       "      <td>113139</td>\n",
       "      <td>124461</td>\n",
       "      <td>101817</td>\n",
       "      <td>2045148</td>\n",
       "      <td>124147</td>\n",
       "      <td>1921000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Apr. 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2953586</td>\n",
       "      <td>0</td>\n",
       "      <td>2953586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>May 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3217635</td>\n",
       "      <td>0</td>\n",
       "      <td>3217635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>June 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3043556</td>\n",
       "      <td>0</td>\n",
       "      <td>3043556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>July 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2718529</td>\n",
       "      <td>0</td>\n",
       "      <td>2718529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Aug. 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2799719</td>\n",
       "      <td>0</td>\n",
       "      <td>2799719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Two weeks ending 8</td>\n",
       "      <td>Two weeks ending 8</td>\n",
       "      <td>Two weeks ending 8</td>\n",
       "      <td>Two weeks ending 8</td>\n",
       "      <td>Two weeks ending 8</td>\n",
       "      <td>Two weeks ending 8</td>\n",
       "      <td>Two weeks ending 8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>July 15, 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2795785</td>\n",
       "      <td>0</td>\n",
       "      <td>2795785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>July 29, 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2623526</td>\n",
       "      <td>0</td>\n",
       "      <td>2623526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Aug. 12, 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2751124</td>\n",
       "      <td>0</td>\n",
       "      <td>2751124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Aug. 26, 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2821740</td>\n",
       "      <td>0</td>\n",
       "      <td>2821740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Sept. 9, 2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2854690</td>\n",
       "      <td>0</td>\n",
       "      <td>2854690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date      Reserve balances required  \\\n",
       "                  Date Reserve balance requirements 1   \n",
       "0              Month 7                        Month 7   \n",
       "1            Aug. 2019                         134639   \n",
       "2           Sept. 2019                         134707   \n",
       "3            Oct. 2019                         134998   \n",
       "4            Nov. 2019                         140705   \n",
       "5            Dec. 2019                         138984   \n",
       "6            Jan. 2020                         144816   \n",
       "7            Feb. 2020                         138370   \n",
       "8            Mar. 2020                         113139   \n",
       "9            Apr. 2020                              0   \n",
       "10            May 2020                              0   \n",
       "11           June 2020                              0   \n",
       "12           July 2020                              0   \n",
       "13           Aug. 2020                              0   \n",
       "14                 NaN                            NaN   \n",
       "15  Two weeks ending 8             Two weeks ending 8   \n",
       "16       July 15, 2020                              0   \n",
       "17       July 29, 2020                              0   \n",
       "18       Aug. 12, 2020                              0   \n",
       "19       Aug. 26, 2020                              0   \n",
       "20       Sept. 9, 2020                              0   \n",
       "\n",
       "                                                               \\\n",
       "   Top of penalty- free band 2 Bottom of penalty- free band 3   \n",
       "0                      Month 7                        Month 7   \n",
       "1                       148114                         121167   \n",
       "2                       148188                         121227   \n",
       "3                       148509                         121489   \n",
       "4                       154787                         126625   \n",
       "5                       152893                         125076   \n",
       "6                       159309                         130326   \n",
       "7                       152218                         124524   \n",
       "8                       124461                         101817   \n",
       "9                            0                              0   \n",
       "10                           0                              0   \n",
       "11                           0                              0   \n",
       "12                           0                              0   \n",
       "13                           0                              0   \n",
       "14                         NaN                            NaN   \n",
       "15          Two weeks ending 8             Two weeks ending 8   \n",
       "16                           0                              0   \n",
       "17                           0                              0   \n",
       "18                           0                              0   \n",
       "19                           0                              0   \n",
       "20                           0                              0   \n",
       "\n",
       "   Reserve balances maintained 4  \\\n",
       "                           Total   \n",
       "0                        Month 7   \n",
       "1                        1520876   \n",
       "2                        1439771   \n",
       "3                        1481513   \n",
       "4                        1529341   \n",
       "5                        1630090   \n",
       "6                        1645384   \n",
       "7                        1656989   \n",
       "8                        2045148   \n",
       "9                        2953586   \n",
       "10                       3217635   \n",
       "11                       3043556   \n",
       "12                       2718529   \n",
       "13                       2799719   \n",
       "14                           NaN   \n",
       "15            Two weeks ending 8   \n",
       "16                       2795785   \n",
       "17                       2623526   \n",
       "18                       2751124   \n",
       "19                       2821740   \n",
       "20                       2854690   \n",
       "\n",
       "                                                                  \\\n",
       "   Balances maintained to satisfy reserve balance requirements 5   \n",
       "0                                             Month 7              \n",
       "1                                              147555              \n",
       "2                                              147498              \n",
       "3                                              148050              \n",
       "4                                              154256              \n",
       "5                                              152562              \n",
       "6                                              158765              \n",
       "7                                              151612              \n",
       "8                                              124147              \n",
       "9                                                   0              \n",
       "10                                                  0              \n",
       "11                                                  0              \n",
       "12                                                  0              \n",
       "13                                                  0              \n",
       "14                                                NaN              \n",
       "15                                 Two weeks ending 8              \n",
       "16                                                  0              \n",
       "17                                                  0              \n",
       "18                                                  0              \n",
       "19                                                  0              \n",
       "20                                                  0              \n",
       "\n",
       "                                                                        \n",
       "   Balances maintained that exceed the top of the penalty- free band 6  \n",
       "0                                             Month 7                   \n",
       "1                                             1373321                   \n",
       "2                                             1292273                   \n",
       "3                                             1333462                   \n",
       "4                                             1375085                   \n",
       "5                                             1477528                   \n",
       "6                                             1486619                   \n",
       "7                                             1505377                   \n",
       "8                                             1921000                   \n",
       "9                                             2953586                   \n",
       "10                                            3217635                   \n",
       "11                                            3043556                   \n",
       "12                                            2718529                   \n",
       "13                                            2799719                   \n",
       "14                                                NaN                   \n",
       "15                                 Two weeks ending 8                   \n",
       "16                                            2795785                   \n",
       "17                                            2623526                   \n",
       "18                                            2751124                   \n",
       "19                                            2821740                   \n",
       "20                                            2854690                   "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_html2 = pd.read_html('https://www.federalreserve.gov/releases/h3/current/default.htm')\n",
    "\n",
    "exemplo_html2[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Informações Gerais sobre a Base de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo              object\n",
       "Bairro            object\n",
       "Quartos            int64\n",
       "Vagas              int64\n",
       "Suites             int64\n",
       "Area               int64\n",
       "Valor            float64\n",
       "Condominio       float64\n",
       "IPTU             float64\n",
       "Valor m2         float64\n",
       "Tipo Agregado     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#object = String\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Tipos de Dados</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Tipo</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bairro</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Quartos</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vagas</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Suites</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Tipos de Dados\n",
       "Tipo            object\n",
       "Bairro          object\n",
       "Quartos          int64\n",
       "Vagas            int64\n",
       "Suites           int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipos_de_dados = pd.DataFrame(df.dtypes, columns=['Tipos de Dados'])\n",
    "\n",
    "tipos_de_dados.head()"
   ]
  },
  {
   "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>Variáveis</th>\n",
       "      <th>Tipos de Dados</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Tipo</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bairro</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Quartos</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vagas</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Suites</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Variáveis Tipos de Dados\n",
       "Tipo              object\n",
       "Bairro            object\n",
       "Quartos            int64\n",
       "Vagas              int64\n",
       "Suites             int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipos_de_dados.columns.name = 'Variáveis'\n",
    "\n",
    "tipos_de_dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A base da dados apredenta 19831 registros (imóveis) e 11 variáveis\n"
     ]
    }
   ],
   "source": [
    "print(f'A base da dados apredenta {df.shape[0]} registros (imóveis) e {df.shape[1]} variáveis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reatório Análise II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tipos de imóveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Apartamento\n",
       "1        Apartamento\n",
       "2        Apartamento\n",
       "3        Apartamento\n",
       "4        Apartamento\n",
       "            ...     \n",
       "19826      Quitinete\n",
       "19827      Quitinete\n",
       "19828      Quitinete\n",
       "19829      Quitinete\n",
       "19830      Quitinete\n",
       "Name: Tipo, Length: 19831, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tipo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipo_de_imovel = df['Tipo']\n",
    "\n",
    "type(tipo_de_imovel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "tipo_de_imovel.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Apartamento\n",
       "16923                  Casa\n",
       "17821    Casa de Condomínio\n",
       "18785          Casa de Vila\n",
       "19018             Quitinete\n",
       "Name: Tipo, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipo_de_imovel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Organizando a Visualização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16923</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17821</td>\n",
       "      <td>Casa de Condomínio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18785</td>\n",
       "      <td>Casa de Vila</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19018</td>\n",
       "      <td>Quitinete</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Tipo\n",
       "0             Apartamento\n",
       "16923                Casa\n",
       "17821  Casa de Condomínio\n",
       "18785        Casa de Vila\n",
       "19018           Quitinete"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipo_de_imovel = pd.DataFrame(tipo_de_imovel)\n",
    "\n",
    "tipo_de_imovel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([0, 16923, 17821, 18785, 19018], dtype='int64')\n",
      "(5, 1)\n"
     ]
    }
   ],
   "source": [
    "print(tipo_de_imovel.index)\n",
    "print(tipo_de_imovel.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Casa de Condomínio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Casa de Vila</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Quitinete</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Tipo\n",
       "0         Apartamento\n",
       "1                Casa\n",
       "2  Casa de Condomínio\n",
       "3        Casa de Vila\n",
       "4           Quitinete"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipo_de_imovel.index = range(tipo_de_imovel.shape[0])\n",
    "tipo_de_imovel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Id</th>\n",
       "      <th>Tipo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Casa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Casa de Condomínio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Casa de Vila</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Quitinete</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Id                Tipo\n",
       "0          Apartamento\n",
       "1                 Casa\n",
       "2   Casa de Condomínio\n",
       "3         Casa de Vila\n",
       "4            Quitinete"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipo_de_imovel.columns.name = 'Id'\n",
    "\n",
    "tipo_de_imovel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando Tipos de Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linha0    1\n",
       "Linha1    2\n",
       "Linha2    3\n",
       "Linha3    4\n",
       "Linha4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [1,2,3,4,5]\n",
    "index = ['Linha' + str(i) for i in range(5)]\n",
    "\n",
    "s = pd.Series(data = data, index=index)\n",
    "\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linha0     1\n",
       "Linha1     4\n",
       "Linha2     9\n",
       "Linha3    16\n",
       "Linha4    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = s ** 2 \n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Coluna0</th>\n",
       "      <th>Coluna1</th>\n",
       "      <th>Coluna2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Linha0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Coluna0  Coluna1  Coluna2\n",
       "Linha0        1        2        3\n",
       "Linha1        2        3        4\n",
       "Linha2        3        4        5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[1,2,3],\n",
    "        [2,3,4],\n",
    "        [3,4,5]]\n",
    "\n",
    "index = ['Linha' + str(i) for i in range(3)]\n",
    "columns = ['Coluna' + str(i) for i in range(3)]\n",
    "\n",
    "\n",
    "df1 = pd.DataFrame(data=data, index=index, columns=columns)\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Coluna0</th>\n",
       "      <th>Coluna1</th>\n",
       "      <th>Coluna2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Linha0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha1</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha2</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Coluna0  Coluna1  Coluna2\n",
       "Linha0        5       10       15\n",
       "Linha1       10       15       20\n",
       "Linha2       15       20       25"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1 * 5\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Coluna0</th>\n",
       "      <th>Coluna1</th>\n",
       "      <th>Coluna2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Linha0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha1</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha2</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Coluna0  Coluna1  Coluna2\n",
       "Linha0        1        2        3\n",
       "Linha1        2        3        4\n",
       "Linha2        3        4        5\n",
       "Linha0        5       10       15\n",
       "Linha1       10       15       20\n",
       "Linha2       15       20       25"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.concat([df1,df2])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Coluna0</th>\n",
       "      <th>Coluna1</th>\n",
       "      <th>Coluna2</th>\n",
       "      <th>Coluna0</th>\n",
       "      <th>Coluna1</th>\n",
       "      <th>Coluna2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Linha0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Linha2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Coluna0  Coluna1  Coluna2  Coluna0  Coluna1  Coluna2\n",
       "Linha0        1        2        3        5       10       15\n",
       "Linha1        2        3        4       10       15       20\n",
       "Linha2        3        4        5       15       20       25"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.concat([df1,df2], axis = 1)\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relatório de Análise III"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imóveis Residênciais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>21.43</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo               Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0  Apartamento               Centro        1      0       0    15   800.0   \n",
       "1  Apartamento         Higienópolis        1      0       0    48   800.0   \n",
       "2  Apartamento             Cachambi        2      0       0    50  1300.0   \n",
       "3  Apartamento               Grajaú        2      1       0    70  1500.0   \n",
       "4  Apartamento  Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "\n",
       "   Condominio  IPTU  Valor m2 Tipo Agregado  \n",
       "0       390.0  20.0     53.33   Apartamento  \n",
       "1       230.0   0.0     16.67   Apartamento  \n",
       "2       301.0  17.0     26.00   Apartamento  \n",
       "3       642.0  74.0     21.43   Apartamento  \n",
       "4       455.0  14.0     16.67   Apartamento  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Apartamento', 'Casa', 'Casa de Condomínio', 'Casa de Vila', 'Quitinete']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df['Tipo'].drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "16923    False\n",
       "17821     True\n",
       "18785     True\n",
       "19018     True\n",
       "Name: Tipo, dtype: bool"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtrando o Dataframe\n",
    "#Ordenação: sort_index(), sort_values()\n",
    "#Exportar = pd.to_csv()\n",
    "\n",
    "residencial_menos_casa = ['Apartamento','Casa de Condomínio', 'Casa de Vila', 'Quitinete']\n",
    "\n",
    "selecao = df['Tipo'].isin(residencial_menos_casa_menos_casa)\n",
    "\n",
    "selecao\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relatório de Análise III\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequência de imóveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>21.43</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Freguesia (Jacarepaguá)</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>950.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.59</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>25.37</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>17.27</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Olaria</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.71</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo                   Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0  Apartamento                   Centro        1      0       0    15   800.0   \n",
       "1  Apartamento             Higienópolis        1      0       0    48   800.0   \n",
       "2  Apartamento                 Cachambi        2      0       0    50  1300.0   \n",
       "3  Apartamento                   Grajaú        2      1       0    70  1500.0   \n",
       "4  Apartamento      Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "5  Apartamento               Copacabana        1      0       1    40  2000.0   \n",
       "6  Apartamento  Freguesia (Jacarepaguá)        3      0       0    54   950.0   \n",
       "7  Apartamento          Barra da Tijuca        2      1       1    67  1700.0   \n",
       "8  Apartamento                   Tijuca        2      1       0   110  1900.0   \n",
       "9  Apartamento                   Olaria        3      1       0    68  1000.0   \n",
       "\n",
       "   Condominio   IPTU  Valor m2 Tipo Agregado  \n",
       "0       390.0   20.0     53.33   Apartamento  \n",
       "1       230.0    0.0     16.67   Apartamento  \n",
       "2       301.0   17.0     26.00   Apartamento  \n",
       "3       642.0   74.0     21.43   Apartamento  \n",
       "4       455.0   14.0     16.67   Apartamento  \n",
       "5       561.0   50.0     50.00   Apartamento  \n",
       "6       300.0   28.0     17.59   Apartamento  \n",
       "7       589.0  147.0     25.37   Apartamento  \n",
       "8       700.0  138.0     17.27   Apartamento  \n",
       "9       670.0    0.0     14.71   Apartamento  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados = pd.read_csv('dados/aluguel.csv', sep=';')\n",
    "\n",
    "dados.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16923"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleção de imóveis classificados como Apartamento\n",
    "\n",
    "selecao = dados['Tipo'] == 'Apartamento'\n",
    "n1 = dados[selecao].shape[0]\n",
    "n1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2095"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleção de imóveis classificados como 'Casa', 'Casa de Condomínio' e 'Casa de Vila'\n",
    "# OR (I) / AND (&)\n",
    "\n",
    "selecao = (dados['Tipo'] == 'Casa') | (dados['Tipo'] == 'Casa de Condomínio') | (dados['Tipo'] == 'Casa de Vila')\n",
    "n2 = dados[selecao].shape[0]\n",
    "n2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8362"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleção de imóveis com área entre 60 e 100 metros quadrados.\n",
    "\n",
    "selecao = (dados['Area'] >=60) & (dados['Area'] <=100)\n",
    "n3 = dados[selecao].shape[0]\n",
    "n3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleção de imóveis com 4 quartos ou mais e aluguel menor que R$ 2.000,00\n",
    "\n",
    "selecao = (dados['Quartos'] >=4) & (dados['Valor'] <=2000)\n",
    "n4 = dados[selecao].shape[0]\n",
    "n4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insight de frequência de imóveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seleção de imóveis classificados como Apartamento: 16923\n",
      "Seleção de imóveis classificados como Casa, Casa de Condomínio e Casa de Vila: 2095\n",
      " Seleção de imóveis com área entre 60 e 100 metros quadrados: 8362\n",
      "Seleção de imóveis com 4 quartos ou mais e aluguel menor que R$ 2.000,00: 55\n"
     ]
    }
   ],
   "source": [
    "print(f'Seleção de imóveis classificados como Apartamento: {n1}')\n",
    "print(f'Seleção de imóveis classificados como Casa, Casa de Condomínio e Casa de Vila: {n2}')\n",
    "print(f' Seleção de imóveis com área entre 60 e 100 metros quadrados: {n3}')\n",
    "print(f'Seleção de imóveis com 4 quartos ou mais e aluguel menor que R$ 2.000,00: {n4}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tratando dados faltantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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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 m2</th>\n",
       "      <th>Tipo Agregado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>21.43</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Freguesia (Jacarepaguá)</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>950.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.59</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>25.37</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>17.27</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Olaria</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.71</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo                   Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0  Apartamento                   Centro        1      0       0    15   800.0   \n",
       "1  Apartamento             Higienópolis        1      0       0    48   800.0   \n",
       "2  Apartamento                 Cachambi        2      0       0    50  1300.0   \n",
       "3  Apartamento                   Grajaú        2      1       0    70  1500.0   \n",
       "4  Apartamento      Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "5  Apartamento               Copacabana        1      0       1    40  2000.0   \n",
       "6  Apartamento  Freguesia (Jacarepaguá)        3      0       0    54   950.0   \n",
       "7  Apartamento          Barra da Tijuca        2      1       1    67  1700.0   \n",
       "8  Apartamento                   Tijuca        2      1       0   110  1900.0   \n",
       "9  Apartamento                   Olaria        3      1       0    68  1000.0   \n",
       "\n",
       "   Condominio   IPTU  Valor m2 Tipo Agregado  \n",
       "0       390.0   20.0     53.33   Apartamento  \n",
       "1       230.0    0.0     16.67   Apartamento  \n",
       "2       301.0   17.0     26.00   Apartamento  \n",
       "3       642.0   74.0     21.43   Apartamento  \n",
       "4       455.0   14.0     16.67   Apartamento  \n",
       "5       561.0   50.0     50.00   Apartamento  \n",
       "6       300.0   28.0     17.59   Apartamento  \n",
       "7       589.0  147.0     25.37   Apartamento  \n",
       "8       700.0  138.0     17.27   Apartamento  \n",
       "9       670.0    0.0     14.71   Apartamento  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dados/aluguel.csv', sep=';')\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "        Tipo  Bairro  Quartos  Vagas  Suites   Area  Valor  Condominio   IPTU  \\\n",
       "0      False   False    False  False   False  False  False       False  False   \n",
       "1      False   False    False  False   False  False  False       False  False   \n",
       "2      False   False    False  False   False  False  False       False  False   \n",
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       "19830  False   False    False  False   False  False  False       False  False   \n",
       "\n",
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       "2         False          False  \n",
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       "\n",
       "[19831 rows x 11 columns]"
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     "execution_count": 67,
     "metadata": {},
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   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
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       "      <td>19828</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19829</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19830</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19831 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tipo  Bairro  Quartos  Vagas  Suites  Area  Valor  Condominio  IPTU  \\\n",
       "0      True    True     True   True    True  True   True        True  True   \n",
       "1      True    True     True   True    True  True   True        True  True   \n",
       "2      True    True     True   True    True  True   True        True  True   \n",
       "3      True    True     True   True    True  True   True        True  True   \n",
       "4      True    True     True   True    True  True   True        True  True   \n",
       "...     ...     ...      ...    ...     ...   ...    ...         ...   ...   \n",
       "19826  True    True     True   True    True  True   True        True  True   \n",
       "19827  True    True     True   True    True  True   True        True  True   \n",
       "19828  True    True     True   True    True  True   True        True  True   \n",
       "19829  True    True     True   True    True  True   True        True  True   \n",
       "19830  True    True     True   True    True  True   True        True  True   \n",
       "\n",
       "       Valor m2  Tipo Agregado  \n",
       "0          True           True  \n",
       "1          True           True  \n",
       "2          True           True  \n",
       "3          True           True  \n",
       "4          True           True  \n",
       "...         ...            ...  \n",
       "19826      True           True  \n",
       "19827      True           True  \n",
       "19828      True           True  \n",
       "19829      True           True  \n",
       "19830      True           True  \n",
       "\n",
       "[19831 rows x 11 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19831 entries, 0 to 19830\n",
      "Data columns (total 11 columns):\n",
      "Tipo             19831 non-null object\n",
      "Bairro           19831 non-null object\n",
      "Quartos          19831 non-null int64\n",
      "Vagas            19831 non-null int64\n",
      "Suites           19831 non-null int64\n",
      "Area             19831 non-null int64\n",
      "Valor            19831 non-null float64\n",
      "Condominio       19831 non-null float64\n",
      "IPTU             19831 non-null float64\n",
      "Valor m2         19831 non-null float64\n",
      "Tipo Agregado    19831 non-null object\n",
      "dtypes: float64(4), int64(4), object(3)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Tipo, Bairro, Quartos, Vagas, Suites, Area, Valor, Condominio, IPTU, Valor m2, Tipo Agregado]\n",
       "Index: []"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Não há variáveis nulos\n",
    "\n",
    "df[df['Valor'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Simulando exclusão de valores nulos em 'Valor'\n",
    "\n",
    "df.dropna(subset=['Valor'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Atribuindo valor 0 a valores nulos\n",
    "\n",
    "df.fillna(0, inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando Variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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",
       "      <th>Valor Bruto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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",
       "      <td>1030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "      <td>1618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>21.43</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>2216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1969.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19826</td>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Glória</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>400.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>517.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19827</td>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>900.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.13</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19828</td>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.83</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19829</td>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>68.18</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19830</td>\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>Apartamento</td>\n",
       "      <td>1175.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19831 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Tipo               Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0      Apartamento               Centro        1      0       0    15   800.0   \n",
       "1      Apartamento         Higienópolis        1      0       0    48   800.0   \n",
       "2      Apartamento             Cachambi        2      0       0    50  1300.0   \n",
       "3      Apartamento               Grajaú        2      1       0    70  1500.0   \n",
       "4      Apartamento  Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "...            ...                  ...      ...    ...     ...   ...     ...   \n",
       "19826    Quitinete               Glória        1      0       0    10   400.0   \n",
       "19827    Quitinete             Flamengo        1      0       0    23   900.0   \n",
       "19828    Quitinete               Centro        1      0       0    24  1100.0   \n",
       "19829    Quitinete           Copacabana        1      0       0    22  1500.0   \n",
       "19830    Quitinete               Centro        0      0       0    27   800.0   \n",
       "\n",
       "       Condominio   IPTU  Valor m2 Tipo Agregado  Valor Bruto  \n",
       "0           390.0   20.0     53.33   Apartamento       1210.0  \n",
       "1           230.0    0.0     16.67   Apartamento       1030.0  \n",
       "2           301.0   17.0     26.00   Apartamento       1618.0  \n",
       "3           642.0   74.0     21.43   Apartamento       2216.0  \n",
       "4           455.0   14.0     16.67   Apartamento       1969.0  \n",
       "...           ...    ...       ...           ...          ...  \n",
       "19826       107.0   10.0     40.00   Apartamento        517.0  \n",
       "19827       605.0    0.0     39.13   Apartamento       1505.0  \n",
       "19828       323.0    0.0     45.83   Apartamento       1423.0  \n",
       "19829       286.0  200.0     68.18   Apartamento       1986.0  \n",
       "19830       350.0   25.0     29.63   Apartamento       1175.0  \n",
       "\n",
       "[19831 rows x 12 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Valor Bruto'] = df['Valor'] + df['Condominio'] + df['IPTU']\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excluindo Variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>Condominio</th>\n",
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       "      <th>Valor m2</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>15</td>\n",
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       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>53.33</td>\n",
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       "      <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",
       "      <td>1030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "      <td>1618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>21.43</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>2216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo               Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0  Apartamento               Centro        1      0       0    15   800.0   \n",
       "1  Apartamento         Higienópolis        1      0       0    48   800.0   \n",
       "2  Apartamento             Cachambi        2      0       0    50  1300.0   \n",
       "3  Apartamento               Grajaú        2      1       0    70  1500.0   \n",
       "4  Apartamento  Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "\n",
       "   Condominio  IPTU  Valor m2 Tipo Agregado  Valor Bruto  \n",
       "0       390.0  20.0     53.33   Apartamento       1210.0  \n",
       "1       230.0   0.0     16.67   Apartamento       1030.0  \n",
       "2       301.0  17.0     26.00   Apartamento       1618.0  \n",
       "3       642.0  74.0     21.43   Apartamento       2216.0  \n",
       "4       455.0  14.0     16.67   Apartamento       1969.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_aux = pd.DataFrame(df)\n",
    "\n",
    "dados_aux.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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",
       "        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>Valor Bruto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>1210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>1030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>1618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>21.43</td>\n",
       "      <td>2216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>16.67</td>\n",
       "      <td>1969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo               Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0  Apartamento               Centro        1      0       0    15   800.0   \n",
       "1  Apartamento         Higienópolis        1      0       0    48   800.0   \n",
       "2  Apartamento             Cachambi        2      0       0    50  1300.0   \n",
       "3  Apartamento               Grajaú        2      1       0    70  1500.0   \n",
       "4  Apartamento  Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "\n",
       "   Condominio  IPTU  Valor m2  Valor Bruto  \n",
       "0       390.0  20.0     53.33       1210.0  \n",
       "1       230.0   0.0     16.67       1030.0  \n",
       "2       301.0  17.0     26.00       1618.0  \n",
       "3       642.0  74.0     21.43       2216.0  \n",
       "4       455.0  14.0     16.67       1969.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del dados_aux['Tipo Agregado']\n",
    "\n",
    "dados_aux.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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",
       "        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>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>Valor Bruto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</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>1210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Higienópolis</td>\n",
       "      <td>1</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>1030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Cachambi</td>\n",
       "      <td>2</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>1618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Grajaú</td>\n",
       "      <td>2</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",
       "      <td>21.43</td>\n",
       "      <td>2216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Lins de Vasconcelos</td>\n",
       "      <td>3</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",
       "      <td>16.67</td>\n",
       "      <td>1969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo               Bairro  Quartos  Suites  Area   Valor  \\\n",
       "0  Apartamento               Centro        1       0    15   800.0   \n",
       "1  Apartamento         Higienópolis        1       0    48   800.0   \n",
       "2  Apartamento             Cachambi        2       0    50  1300.0   \n",
       "3  Apartamento               Grajaú        2       0    70  1500.0   \n",
       "4  Apartamento  Lins de Vasconcelos        3       1    90  1500.0   \n",
       "\n",
       "   Condominio  IPTU  Valor m2  Valor Bruto  \n",
       "0       390.0  20.0     53.33       1210.0  \n",
       "1       230.0   0.0     16.67       1030.0  \n",
       "2       301.0  17.0     26.00       1618.0  \n",
       "3       642.0  74.0     21.43       2216.0  \n",
       "4       455.0  14.0     16.67       1969.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_aux.drop(['Vagas'], axis= 1,inplace= True)\n",
    "\n",
    "dados_aux.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando agrupamentos (Groupby)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
       "        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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>21.43</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>16.67</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Freguesia (Jacarepaguá)</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>950.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.59</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>25.37</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Apartamento</td>\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",
       "      <td>17.27</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Olaria</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.71</td>\n",
       "      <td>Apartamento</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo                   Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0  Apartamento                   Centro        1      0       0    15   800.0   \n",
       "1  Apartamento             Higienópolis        1      0       0    48   800.0   \n",
       "2  Apartamento                 Cachambi        2      0       0    50  1300.0   \n",
       "3  Apartamento                   Grajaú        2      1       0    70  1500.0   \n",
       "4  Apartamento      Lins de Vasconcelos        3      1       1    90  1500.0   \n",
       "5  Apartamento               Copacabana        1      0       1    40  2000.0   \n",
       "6  Apartamento  Freguesia (Jacarepaguá)        3      0       0    54   950.0   \n",
       "7  Apartamento          Barra da Tijuca        2      1       1    67  1700.0   \n",
       "8  Apartamento                   Tijuca        2      1       0   110  1900.0   \n",
       "9  Apartamento                   Olaria        3      1       0    68  1000.0   \n",
       "\n",
       "   Condominio   IPTU  Valor m2 Tipo Agregado  \n",
       "0       390.0   20.0     53.33   Apartamento  \n",
       "1       230.0    0.0     16.67   Apartamento  \n",
       "2       301.0   17.0     26.00   Apartamento  \n",
       "3       642.0   74.0     21.43   Apartamento  \n",
       "4       455.0   14.0     16.67   Apartamento  \n",
       "5       561.0   50.0     50.00   Apartamento  \n",
       "6       300.0   28.0     17.59   Apartamento  \n",
       "7       589.0  147.0     25.37   Apartamento  \n",
       "8       700.0  138.0     17.27   Apartamento  \n",
       "9       670.0    0.0     14.71   Apartamento  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dados/aluguel.csv', sep=';')\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3465.402299430185"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Valor'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "bairros = df['Bairro'].unique()\n",
    "selecao = df['Bairro'].isin(bairros)\n",
    "df = df[selecao]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     Centro\n",
       "1               Higienópolis\n",
       "2                   Cachambi\n",
       "3                     Grajaú\n",
       "4        Lins de Vasconcelos\n",
       "                ...         \n",
       "17150        Parque Anchieta\n",
       "17192         Senador Camará\n",
       "18233             Cachamorra\n",
       "18510           Rio da Prata\n",
       "18849           Vila Militar\n",
       "Name: Bairro, Length: 151, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bairro'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "grupo_bairro = df.groupby('Bairro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Abolição': Int64Index([ 2470,  5370,  5902, 10205, 11004, 12465, 14575, 15606, 16585,\n",
       "             17191, 17605, 17625, 17704, 18798, 18884],\n",
       "            dtype='int64'),\n",
       " 'Alto da Boa Vista': Int64Index([9321, 9714, 10264, 17013, 18419, 18932], dtype='int64'),\n",
       " 'Anchieta': Int64Index([6877, 17561, 17659, 18814], dtype='int64'),\n",
       " 'Andaraí': Int64Index([  289,   340,   390,   471,   609,   653,   660,   759,   908,\n",
       "              1079,  1084,  1287,  1468,  1558,  2080,  2428,  2492,  2710,\n",
       "              2778,  2897,  2989,  3272,  3529,  3700,  3780,  3785,  3843,\n",
       "              4281,  4441,  4623,  4823,  4990,  5047,  5339,  5535,  5727,\n",
       "              6095,  6096,  6229,  6533,  7118,  7194,  7230,  7266,  7529,\n",
       "              7691,  7828,  7950,  7965,  8402,  8462,  8465,  8907,  9145,\n",
       "              9884, 10267, 10381, 10385, 10681, 10779, 10803, 10809, 11028,\n",
       "             11312, 11369, 12356, 12432, 12803, 12879, 13576, 13767, 13786,\n",
       "             13879, 14119, 14296, 14543, 14646, 14670, 14756, 15249, 15313,\n",
       "             15600, 15688, 15919, 16178, 16309, 16347, 16379, 16537, 16636,\n",
       "             16706, 17419, 17598, 17738, 17811, 18319, 18956],\n",
       "            dtype='int64'),\n",
       " 'Anil': Int64Index([  184,   463,  1249,  1272,  1366,  1488,  1649,  2846,  2992,\n",
       "              3373,  3599,  3712,  4064,  4369,  4739,  4754,  4895,  4923,\n",
       "              5102,  5310,  5347,  5438,  5449,  5747,  6261,  6310,  7462,\n",
       "              7692,  7873,  7998,  8195,  8237,  9256,  9262,  9660, 10392,\n",
       "             10906, 11147, 11264, 11534, 12865, 12924, 13795, 14101, 14356,\n",
       "             15078, 15158, 15338, 16392, 16804, 16845, 16951, 17019, 17057,\n",
       "             17181, 17224, 17269, 17461, 17642, 17875, 18028, 18057, 18238,\n",
       "             18417, 18519, 18682, 18747, 18826, 19495, 19634, 19699],\n",
       "            dtype='int64'),\n",
       " 'Arpoador': Int64Index([2154, 3494, 7384, 11488, 12829, 14322, 14483], dtype='int64'),\n",
       " 'Bancários': Int64Index([10749, 11457, 12350, 17763], dtype='int64'),\n",
       " 'Bangu': Int64Index([  982,  3065,  5686,  8906, 14095, 15889, 16994, 17011, 17076,\n",
       "             17329, 17333, 17369, 17399, 17506, 17663, 18290, 18330, 18422,\n",
       "             18800, 18823],\n",
       "            dtype='int64'),\n",
       " 'Barra da Tijuca': Int64Index([    7,    13,    19,    21,    24,    31,    44,    47,    51,\n",
       "                56,\n",
       "             ...\n",
       "             18776, 18777, 18778, 18779, 18780, 18781, 18783, 18784, 19392,\n",
       "             19795],\n",
       "            dtype='int64', length=3383),\n",
       " 'Barra de Guaratiba': Int64Index([2847, 15112, 16188, 18270], dtype='int64'),\n",
       " 'Benfica': Int64Index([8712, 14164, 14878, 16927, 18838], dtype='int64'),\n",
       " 'Bento Ribeiro': Int64Index([ 2287,  3998,  5754,  7890, 10149, 10673, 12294, 16575, 16782,\n",
       "             17016, 17464, 17487, 17680, 17714, 17754, 17819, 18813, 18843,\n",
       "             18879, 18921, 18942, 19004, 19426],\n",
       "            dtype='int64'),\n",
       " 'Bonsucesso': Int64Index([   50,   274,   543,   978,  1111,  1149,  1598,  1723,  2068,\n",
       "              2076,  2371,  2506,  2529,  3196,  3294,  3364,  3813,  3975,\n",
       "              4206,  4345,  5307,  5862,  5973,  6163,  6674,  7773,  8088,\n",
       "              8854,  9180,  9552,  9567,  9579, 11582, 11606, 11926, 12409,\n",
       "             12529, 12771, 13104, 13154, 13202, 13505, 13779, 14022, 14648,\n",
       "             14707, 14722, 16233, 16934, 17196, 17311, 17439, 17522, 17603,\n",
       "             17734, 17773, 18792, 18877, 19489],\n",
       "            dtype='int64'),\n",
       " 'Botafogo': Int64Index([   14,    35,    66,    67,    85,    91,    96,   102,   147,\n",
       "               151,\n",
       "             ...\n",
       "             19739, 19743, 19768, 19796, 19800, 19803, 19809, 19812, 19816,\n",
       "             19817],\n",
       "            dtype='int64', length=849),\n",
       " 'Braz de Pina': Int64Index([ 3316,  4611,  5264,  5586,  7196,  9480, 10987, 12204, 12340,\n",
       "             15238, 16966, 17202, 17410, 17443, 17532, 17650, 18987, 19164,\n",
       "             19355, 19696],\n",
       "            dtype='int64'),\n",
       " 'Cachambi': Int64Index([    2,    42,   188,   190,   400,   481,  1058,  1286,  1428,\n",
       "              1500,\n",
       "             ...\n",
       "             16628, 16644, 16759, 17324, 18328, 18567, 18802, 18872, 18895,\n",
       "             18979],\n",
       "            dtype='int64', length=132),\n",
       " 'Cachamorra': Int64Index([18233], dtype='int64'),\n",
       " 'Cacuia': Int64Index([10179, 14432, 14574], dtype='int64'),\n",
       " 'Caju': Int64Index([1830], dtype='int64'),\n",
       " 'Camorim': Int64Index([  420,   477,   511,   772,   877,  1463,  1508,  1509,  1621,\n",
       "              1685,  2378,  3207,  4229,  4471,  4566,  4840,  5134,  6287,\n",
       "              7689,  7712,  8053,  8937,  9833,  9967, 10435, 10658, 10661,\n",
       "             11513, 11683, 11983, 12578, 13454, 14003, 14204, 14295, 14719,\n",
       "             15379, 15710, 16191, 16214, 17185, 17270, 18485, 18667],\n",
       "            dtype='int64'),\n",
       " 'Campinho': Int64Index([  856,  5045,  5721,  5785,  6664,  7292, 10177, 10269, 11274,\n",
       "             14057, 15847, 16904, 17500, 18751, 18854],\n",
       "            dtype='int64'),\n",
       " 'Campo Grande': Int64Index([  104,   134,   240,   406,   506,   518,   701,   795,   819,\n",
       "               848,\n",
       "             ...\n",
       "             18657, 18740, 18793, 18853, 18862, 18926, 18968, 18977, 18986,\n",
       "             18994],\n",
       "            dtype='int64', length=449),\n",
       " 'Cascadura': Int64Index([  426,   677,  1242,  1481,  3434,  4492,  4859,  5114,  6627,\n",
       "              7962,  9114,  9163,  9486,  9847, 11556, 12481, 12980, 15195,\n",
       "             15595, 15726, 16872, 17349, 17554, 17748, 18871, 18901, 18945,\n",
       "             18946, 18960, 18966, 18988],\n",
       "            dtype='int64'),\n",
       " 'Catete': Int64Index([   71,    74,   145,   226,   521,   897,  1070,  1294,  1375,\n",
       "              1462,\n",
       "             ...\n",
       "             19579, 19584, 19700, 19717, 19734, 19786, 19799, 19801, 19802,\n",
       "             19811],\n",
       "            dtype='int64', length=159),\n",
       " 'Catumbi': Int64Index([1024, 2441, 3477, 6501, 6794, 12945, 16290, 18961], dtype='int64'),\n",
       " 'Cavalcanti': Int64Index([11852, 18970], dtype='int64'),\n",
       " 'Centro': Int64Index([    0,    27,    48,   135,   234,   447,   508,   636,   730,\n",
       "               850,\n",
       "             ...\n",
       "             19772, 19775, 19779, 19782, 19790, 19792, 19804, 19806, 19828,\n",
       "             19830],\n",
       "            dtype='int64', length=342),\n",
       " 'Cidade Nova': Int64Index([1738, 1761, 2117, 4854, 10109, 12512, 16723], dtype='int64'),\n",
       " 'Cocotá': Int64Index([6961, 14400, 17780], dtype='int64'),\n",
       " 'Coelho Neto': Int64Index([12745], dtype='int64'),\n",
       " 'Colégio': Int64Index([16988, 18811, 18937, 18973], dtype='int64'),\n",
       " 'Copacabana': Int64Index([    5,    15,    20,    69,    78,    83,    97,   107,   108,\n",
       "               109,\n",
       "             ...\n",
       "             19777, 19784, 19787, 19788, 19794, 19797, 19805, 19823, 19825,\n",
       "             19829],\n",
       "            dtype='int64', length=2445),\n",
       " 'Cordovil': Int64Index([1771, 4961, 5986, 10998, 14025, 15512, 17451, 17577, 19405], dtype='int64'),\n",
       " 'Cosme Velho': Int64Index([   49,   810,  1190,  1223,  2100,  3239,  3605,  5081,  5773,\n",
       "              6147,  7288,  7551,  7627,  8489,  8542,  9913, 11620, 11651,\n",
       "             11653, 12080, 12538, 12930, 14030, 15117, 16443, 17359, 17507,\n",
       "             17630, 17661, 18063],\n",
       "            dtype='int64'),\n",
       " 'Cosmos': Int64Index([ 6518, 11570, 12078, 12320, 12684, 12902, 16512, 17472, 17610,\n",
       "             17617, 17641, 17795],\n",
       "            dtype='int64'),\n",
       " 'Curicica': Int64Index([  324,   475,   920,  1135,  1295,  1710,  1743,  2001,  2593,\n",
       "              2790,  2907,  3063,  3172,  3688,  4849,  5574,  5951,  6209,\n",
       "              6388,  6448,  6744,  7007,  7606,  7747,  7848,  7994,  8207,\n",
       "              8351,  8446,  8652,  9191,  9253,  9354,  9709,  9781,  9791,\n",
       "              9823, 10569, 10847, 11092, 11505, 11853, 11938, 12394, 12410,\n",
       "             12870, 12907, 12910, 13362, 13644, 13946, 14450, 15727, 16047,\n",
       "             16066, 16122, 16135, 16675, 16869, 17008, 17373, 17388, 17427,\n",
       "             17440, 17465, 17552, 17582, 17674, 17791, 17798, 18333, 18911,\n",
       "             18997, 19059, 19517, 19683],\n",
       "            dtype='int64'),\n",
       " 'Del Castilho': Int64Index([ 2077,  2223,  2767,  2884,  2947,  3561,  5312,  6289,  6379,\n",
       "              6582,  6957,  7104,  8546,  8663,  8924, 10772, 12184, 12452,\n",
       "             14367, 14835, 15605, 18848, 18930],\n",
       "            dtype='int64'),\n",
       " 'Encantado': Int64Index([  831,  1546,  4830,  6246,  7932, 10239, 11897, 14951, 16828,\n",
       "             17226, 17623, 17658, 17675, 18805, 18963],\n",
       "            dtype='int64'),\n",
       " 'Engenho Novo': Int64Index([   99,   175,   181,   419,   776,  1011,  1085,  1469,  1529,\n",
       "              2083,\n",
       "             ...\n",
       "             16685, 16935, 17135, 17421, 17481, 17813, 18834, 18898, 18993,\n",
       "             19780],\n",
       "            dtype='int64', length=103),\n",
       " 'Engenho da Rainha': Int64Index([7197, 7759, 8036, 11342, 11928, 12817, 13978], dtype='int64'),\n",
       " 'Engenho de Dentro': Int64Index([  106,   576,   880,  1170,  1594,  1712,  1726,  1920,  1981,\n",
       "              2169,  2290,  2313,  2447,  3054,  3182,  3322,  3344,  3552,\n",
       "              3684,  3848,  4246,  4306,  4564,  4588,  4689,  4959,  6273,\n",
       "              6337,  6645,  6983,  7090,  7264,  7468,  8106,  8676,  9203,\n",
       "              9463,  9796, 10027, 10988, 12031, 12891, 12899, 13039, 13088,\n",
       "             13110, 13523, 14031, 14581, 14582, 14635, 14740, 14991, 15176,\n",
       "             15335, 15384, 15776, 15783, 15945, 16299, 16413, 17006, 17318,\n",
       "             17770, 18824, 18832, 18864, 18894, 18897, 18929, 18933, 18941,\n",
       "             18951, 19157],\n",
       "            dtype='int64'),\n",
       " 'Estácio': Int64Index([ 2178,  2712,  3671,  3944,  4088,  4437,  5777,  6279, 10356,\n",
       "             11016, 11068, 11792, 12197, 12418, 13419, 13757, 14105, 14475,\n",
       "             15655, 16397, 19099, 19208, 19223],\n",
       "            dtype='int64'),\n",
       " 'Flamengo': Int64Index([  105,   165,   215,   239,   256,   264,   268,   276,   319,\n",
       "               323,\n",
       "             ...\n",
       "             19774, 19778, 19789, 19793, 19798, 19813, 19814, 19815, 19821,\n",
       "             19827],\n",
       "            dtype='int64', length=654),\n",
       " 'Freguesia (Ilha do Governador)': Int64Index([3798, 5828, 13459, 14150, 18904], dtype='int64'),\n",
       " 'Freguesia (Jacarepaguá)': Int64Index([    6,    63,    80,    93,   103,   119,   122,   141,   155,\n",
       "               204,\n",
       "             ...\n",
       "             18705, 18734, 18742, 18744, 18782, 18809, 18815, 18928, 18950,\n",
       "             18999],\n",
       "            dtype='int64', length=454),\n",
       " 'Fátima': Int64Index([577, 14959, 19174, 19240, 19586, 19763], dtype='int64'),\n",
       " 'Gamboa': Int64Index([2726, 10300, 15094], dtype='int64'),\n",
       " 'Gardênia Azul': Int64Index([5178, 8528, 19036, 19112, 19209, 19350, 19486, 19681, 19818], dtype='int64'),\n",
       " 'Glória': Int64Index([  302,   417,   634,   735,   765,  1279,  1404,  1440,  1557,\n",
       "              1623,\n",
       "             ...\n",
       "             19624, 19629, 19642, 19685, 19687, 19727, 19760, 19773, 19822,\n",
       "             19826],\n",
       "            dtype='int64', length=106),\n",
       " 'Grajaú': Int64Index([    3,   236,   307,   309,   357,   436,   743,  1020,  1103,\n",
       "              1193,\n",
       "             ...\n",
       "             16931, 17025, 17374, 17614, 17672, 17708, 17792, 19219, 19457,\n",
       "             19534],\n",
       "            dtype='int64', length=144),\n",
       " 'Guadalupe': Int64Index([4142, 9459, 17220, 18882, 18885, 19431], dtype='int64'),\n",
       " 'Guaratiba': Int64Index([ 6290,  7878,  9453, 17099, 17102, 17165, 17230, 17232, 17271,\n",
       "             17389, 17877, 17881, 17989, 18016, 18077, 18089, 18115, 18281,\n",
       "             18285, 18305, 18339, 18515, 18541, 18544, 18580, 18585, 18620,\n",
       "             18722, 18723, 18861, 18925],\n",
       "            dtype='int64'),\n",
       " 'Gávea': Int64Index([   46,    82,   162,   170,   185,   224,   348,   395,   410,\n",
       "               662,\n",
       "             ...\n",
       "             17935, 17972, 17990, 18066, 18403, 18495, 18507, 18597, 18766,\n",
       "             19066],\n",
       "            dtype='int64', length=204),\n",
       " 'Higienópolis': Int64Index([    1,   581,   736,  1627,  1693,  2601,  6128,  7006,  8691,\n",
       "             11001, 11769, 13122, 13167, 14982, 17098, 19706],\n",
       "            dtype='int64'),\n",
       " 'Honório Gurgel': Int64Index([7018, 7370, 10357, 14531, 16528, 17696, 18949], dtype='int64'),\n",
       " 'Humaitá': Int64Index([   25,    59,   159,   171,   218,   280,   322,   427,   640,\n",
       "               642,\n",
       "             ...\n",
       "             17004, 17067, 17217, 17486, 17536, 17702, 18637, 18690, 19686,\n",
       "             19688],\n",
       "            dtype='int64', length=157),\n",
       " 'Icaraí': Int64Index([2793, 3871, 8426, 10391, 11047, 12767, 14800, 17958], dtype='int64'),\n",
       " 'Ilha do Governador': Int64Index([  157,  2453,  3053,  3916,  4128,  4513,  5408,  5428,  5676,\n",
       "              8419,  8463,  9926, 10274, 10521, 13614, 13738, 14018, 14178,\n",
       "             14369, 16390, 16765, 17283, 17514, 18030, 18498],\n",
       "            dtype='int64'),\n",
       " 'Inhaúma': Int64Index([301, 2639, 3971, 6986, 9972, 17267, 17969], dtype='int64'),\n",
       " 'Inhoaíba': Int64Index([586, 14346, 17139, 17227, 17299, 17799], dtype='int64'),\n",
       " 'Ipanema': Int64Index([   11,    28,    29,    32,    39,    40,    55,    64,   121,\n",
       "               133,\n",
       "             ...\n",
       "             19536, 19561, 19592, 19603, 19630, 19652, 19654, 19755, 19765,\n",
       "             19791],\n",
       "            dtype='int64', length=1227),\n",
       " 'Irajá': Int64Index([  221,   919,   962,  1154,  1686,  1730,  1805,  1835,  2070,\n",
       "              3758,  3862,  4644,  4850,  5095,  5253,  5284,  5416,  5460,\n",
       "              5894,  6069,  6850,  7004,  7362,  7420,  7594,  7661,  7899,\n",
       "              8321,  8943,  9195,  9761,  9960, 10137, 10166, 10327, 10770,\n",
       "             10956, 11029, 11192, 11383, 11491, 11515, 12209, 12816, 13387,\n",
       "             13755, 14015, 14134, 14345, 14565, 14789, 15399, 15409, 16294,\n",
       "             16356, 16878, 16889, 17103, 17235, 17238, 17341, 17391, 17406,\n",
       "             17446, 17640, 17709, 17750, 17802, 18396, 18791, 18819, 18857,\n",
       "             18908, 18916, 18923, 18984, 19255, 19712, 19819],\n",
       "            dtype='int64'),\n",
       " 'Itanhangá': Int64Index([   57,   361,   444,  1004,  1917,  2703,  3346,  3371,  5737,\n",
       "              5789,  7433,  7658,  8480,  8501,  8786, 13453, 14038, 15237,\n",
       "             16837, 16945, 17048, 17170, 17281, 17477, 17485, 17574, 17648,\n",
       "             17725, 17878, 17908, 17921, 17934, 17941, 17988, 17993, 18029,\n",
       "             18068, 18093, 18094, 18138, 18167, 18196, 18222, 18252, 18314,\n",
       "             18372, 18428, 18470, 18481, 18500, 18509, 18518, 18531, 18573,\n",
       "             18666, 18700, 18717, 18733, 18745, 18759, 18774],\n",
       "            dtype='int64'),\n",
       " 'Jabour': Int64Index([629, 1641, 9821], dtype='int64'),\n",
       " 'Jacarepaguá': Int64Index([   38,    54,   149,   217,   271,   383,   397,   428,   520,\n",
       "               539,\n",
       "             ...\n",
       "             18088, 18188, 18227, 18295, 18499, 18704, 18789, 18893, 19150,\n",
       "             19383],\n",
       "            dtype='int64', length=244),\n",
       " 'Jacaré': Int64Index([6849, 11685, 17796], dtype='int64'),\n",
       " 'Jardim América': Int64Index([2878, 17017, 17035, 17174, 17436, 17667, 17711], dtype='int64'),\n",
       " 'Jardim Botânico': Int64Index([   41,   241,   260,   335,   371,   374,   490,   517,   622,\n",
       "               905,\n",
       "             ...\n",
       "             17609, 17668, 17671, 17695, 17740, 17744, 17755, 17842, 18803,\n",
       "             18833],\n",
       "            dtype='int64', length=221),\n",
       " 'Jardim Carioca': Int64Index([72, 1850, 9289, 12338, 12441, 13433, 16928, 16967, 17184], dtype='int64'),\n",
       " 'Jardim Guanabara': Int64Index([  620,   771,  1066,  1152,  1430,  1906,  2138,  2220,  2412,\n",
       "              3699,  3940,  4434,  4549,  4650,  4663,  5469,  5620,  5786,\n",
       "              6031,  6855,  6878,  7034,  7115,  7755,  8093,  8125,  8294,\n",
       "              8315,  8372,  8557,  8834,  9254,  9387,  9528, 10256, 10548,\n",
       "             11140, 11145, 11233, 11379, 11512, 11525, 11993, 12191, 13048,\n",
       "             13573, 13642, 13773, 14306, 14791, 14962, 15189, 15271, 16053,\n",
       "             16132, 16183, 16435, 16489, 16674, 16786, 17555, 17606, 17620,\n",
       "             17742],\n",
       "            dtype='int64'),\n",
       " 'Jardim Oceânico': Int64Index([   18,   154,   684,  1010,  1374,  1585,  4696,  4772,  6084,\n",
       "              8333,  9361,  9370,  9782, 10332, 15242, 15285],\n",
       "            dtype='int64'),\n",
       " 'Jardim Sulacap': Int64Index([2526, 3735, 3847, 5506, 9911, 12280, 17148, 17379, 18786], dtype='int64'),\n",
       " 'Joá': Int64Index([17010, 17233, 17518, 17626, 17676, 17727, 17846, 18047, 18098,\n",
       "             18122, 18194, 18340, 18359, 18552, 18559, 18575, 18628, 18720],\n",
       "            dtype='int64'),\n",
       " 'Lagoa': Int64Index([   79,    94,   111,   173,   228,   238,   315,   316,   345,\n",
       "               386,\n",
       "             ...\n",
       "             16777, 16832, 16882, 16891, 16962, 17093, 17221, 17342, 17600,\n",
       "             17812],\n",
       "            dtype='int64', length=374),\n",
       " 'Lapa': Int64Index([  986,  1381,  1794,  2479,  2592,  2888,  3307,  3748,  4141,\n",
       "              4493,  4943,  6556,  6918,  7124,  7243,  7595, 10795, 13153,\n",
       "             14110, 14488, 15252, 19128, 19385, 19422, 19628, 19721, 19781],\n",
       "            dtype='int64'),\n",
       " 'Laranjeiras': Int64Index([   53,   117,   126,   131,   205,   214,   286,   312,   353,\n",
       "               488,\n",
       "             ...\n",
       "             19176, 19256, 19297, 19446, 19464, 19480, 19713, 19738, 19766,\n",
       "             19783],\n",
       "            dtype='int64', length=415),\n",
       " 'Largo do Machado': Int64Index([3400, 6376, 8783, 19231, 19470], dtype='int64'),\n",
       " 'Leblon': Int64Index([   17,    43,    68,    73,   101,   125,   128,   166,   261,\n",
       "               262,\n",
       "             ...\n",
       "             19282, 19337, 19437, 19506, 19562, 19677, 19732, 19776, 19810,\n",
       "             19824],\n",
       "            dtype='int64', length=890),\n",
       " 'Leme': Int64Index([  216,   298,   363,   448,   567,   695,   815,   893,   960,\n",
       "              1233,\n",
       "             ...\n",
       "             19121, 19182, 19279, 19304, 19505, 19529, 19535, 19620, 19621,\n",
       "             19750],\n",
       "            dtype='int64', length=217),\n",
       " 'Lins de Vasconcelos': Int64Index([    4,   403,  1764,  1831,  2260,  2561,  2587,  3232,  3282,\n",
       "              3418,  3436,  3566,  3717,  5158,  5382,  5558,  5792,  5840,\n",
       "              5917,  6561,  6890,  7162,  7611,  9156,  9323, 10447, 10966,\n",
       "             10971, 11082, 11474, 12786, 13279, 13526, 13625, 13951, 14338,\n",
       "             14996, 15608, 16095, 16340, 16929, 17177, 17254, 17300, 17404,\n",
       "             18873, 19008],\n",
       "            dtype='int64'),\n",
       " 'Madureira': Int64Index([  197,  1646,  2153,  2388,  2732,  3170,  3677,  4647,  4778,\n",
       "              5174,  5379,  6422,  7290,  7768,  8241,  8305,  8334,  8387,\n",
       "              9418,  9573, 10214, 10343, 10677, 10706, 11143, 11881, 12569,\n",
       "             12737, 12777, 13155, 13330, 13512, 13676, 15810, 15839, 17176,\n",
       "             17190, 17653, 17721, 17753, 17778, 17943, 18602, 18867, 18876,\n",
       "             18906, 19003],\n",
       "            dtype='int64'),\n",
       " 'Magalhães Bastos': Int64Index([13487, 18905], dtype='int64'),\n",
       " 'Maracanã': Int64Index([   36,   115,   146,   160,   193,   265,   484,   619,   633,\n",
       "               665,\n",
       "             ...\n",
       "             17282, 17407, 17814, 18255, 18818, 18874, 18902, 18914, 18935,\n",
       "             19010],\n",
       "            dtype='int64', length=129),\n",
       " 'Marechal Hermes': Int64Index([308, 2326, 3386, 5204, 6291, 12576, 17159, 17677, 18808], dtype='int64'),\n",
       " 'Maria da Graça': Int64Index([ 3908,  5442, 13024, 13369, 14177, 15266, 17060, 17179, 17434,\n",
       "             17539, 17647, 18116],\n",
       "            dtype='int64'),\n",
       " 'Moneró': Int64Index([354, 2575, 3608, 9411, 11614, 13150, 16972, 17409, 17611], dtype='int64'),\n",
       " 'Méier': Int64Index([   33,    92,   235,   320,   332,   455,   482,   592,   704,\n",
       "               822,\n",
       "             ...\n",
       "             18797, 18799, 18804, 18836, 18886, 18890, 18976, 18982, 18989,\n",
       "             19504],\n",
       "            dtype='int64', length=255),\n",
       " 'Olaria': Int64Index([    9,    65,   158,  2728,  3556,  4100,  4175,  5127,  5594,\n",
       "              6959,  7086,  7227,  7348,  8683,  9199,  9555, 10022, 10117,\n",
       "             10319, 11617, 13413, 14449, 14465, 15772, 16895, 17127, 17480,\n",
       "             17684, 17781, 18880, 18978, 19011],\n",
       "            dtype='int64'),\n",
       " 'Oswaldo Cruz': Int64Index([ 2565,  3823,  3863,  7648,  8718, 15028, 16975, 17087, 17297,\n",
       "             17387, 17657, 17691, 18787, 18817, 18821, 18865, 18896, 18910],\n",
       "            dtype='int64'),\n",
       " 'Paciência': Int64Index([2024, 4875, 15116, 15495, 17470, 17547, 17746, 17771], dtype='int64'),\n",
       " 'Padre Miguel': Int64Index([1855, 7830, 13316, 17345, 18835, 18850, 18975, 19807], dtype='int64'),\n",
       " 'Paquetá': Int64Index([17072, 18868], dtype='int64'),\n",
       " 'Parada de Lucas': Int64Index([3695, 5536, 16857, 17294], dtype='int64'),\n",
       " 'Parque Anchieta': Int64Index([17150, 17197, 17239, 17587], dtype='int64'),\n",
       " 'Pavuna': Int64Index([ 1349,  2485,  6534,  7338,  7854, 10238, 14225, 14395, 17186,\n",
       "             17201, 17275, 17411, 17520, 17546, 17581, 18829, 18842, 18944,\n",
       "             19017],\n",
       "            dtype='int64'),\n",
       " 'Pechincha': Int64Index([  212,   305,   317,   341,   392,   430,   585,   731,   787,\n",
       "               798,\n",
       "             ...\n",
       "             18336, 18383, 18439, 18529, 18591, 18592, 18845, 18866, 18974,\n",
       "             19476],\n",
       "            dtype='int64', length=202),\n",
       " 'Pedra de Guaratiba': Int64Index([10586, 10962, 17062, 17071, 17100, 17104, 17117, 17178, 17259,\n",
       "             17290, 17501, 17549, 17705, 17918, 17992, 18139, 18514, 18610,\n",
       "             19249],\n",
       "            dtype='int64'),\n",
       " 'Penha': Int64Index([  259,  2141,  2831,  2962,  3431,  3723,  3973,  4231,  4570,\n",
       "              4787,  6089,  6540,  6725,  6838,  7020,  7366,  7649,  7986,\n",
       "              8933,  9265,  9391, 12577, 13194, 13867, 13961, 14474, 15657,\n",
       "             15712, 15969, 17129],\n",
       "            dtype='int64'),\n",
       " 'Penha Circular': Int64Index([4342, 4580, 6013, 6245, 7533, 9124, 9770, 13113, 14777, 15377,\n",
       "             16981, 17033],\n",
       "            dtype='int64'),\n",
       " 'Península': Int64Index([ 2073,  2131,  3425,  3621,  4851,  5597,  5909,  5974,  7125,\n",
       "              7222,  7414,  7511,  8874,  9368,  9575, 10892, 11423, 11624,\n",
       "             11907, 12223, 12469, 13236, 13769, 13804, 13958, 14303, 14313,\n",
       "             14550, 14984, 15604, 16355],\n",
       "            dtype='int64'),\n",
       " 'Piedade': Int64Index([ 2444,  4983,  5901,  6027,  7801,  8570,  8598, 10251, 10645,\n",
       "             11062, 11318, 13844, 14609, 15211, 15273, 16492, 16957, 17043,\n",
       "             17214, 17376, 17426, 18421, 18785, 18917, 18996, 19262],\n",
       "            dtype='int64'),\n",
       " 'Pilares': Int64Index([  129,   885,  1538,  3672,  3705,  4218,  5966,  9376,  9431,\n",
       "              9442, 11076, 12448, 13762, 16688, 16993, 17361, 17385, 17701,\n",
       "             18812, 18851],\n",
       "            dtype='int64'),\n",
       " 'Pitangueiras': Int64Index([5519, 11216, 17175, 17583], dtype='int64'),\n",
       " 'Portuguesa': Int64Index([4037, 4542, 4800, 5625, 8282, 10643, 10865, 13989, 15018, 15392,\n",
       "             18220],\n",
       "            dtype='int64'),\n",
       " 'Praia da Bandeira': Int64Index([433, 4996], dtype='int64'),\n",
       " 'Praça Seca': Int64Index([   76,   200,   310,   621,   793,   926,  1446,  1683,  1786,\n",
       "              1870,  1889,  1913,  1955,  2482,  2826,  3039,  3176,  3355,\n",
       "              3358,  3603,  3720,  3764,  4575,  4769,  4989,  5031,  5301,\n",
       "              5362,  5432,  5577,  5616,  6011,  6183,  6211,  6331,  6923,\n",
       "              7128,  7224,  7565,  8824,  8882,  8911,  9065,  9346,  9429,\n",
       "             10312, 10401, 10453, 10692, 10762, 10857, 11234, 11275, 11323,\n",
       "             11848, 11915, 12018, 12111, 12228, 12292, 12488, 12872, 12922,\n",
       "             14545, 14702, 14798, 14843, 15228, 15666, 16004, 16406, 16740,\n",
       "             16971, 17023, 17049, 17423, 17466, 17615, 17652, 17774, 18633,\n",
       "             18907],\n",
       "            dtype='int64'),\n",
       " 'Praça da Bandeira': Int64Index([  761,   881,  1096,  1218,  2006,  2053,  2562,  2789,  2827,\n",
       "              3692,  3796,  3819,  4050,  4353,  4411,  4598,  5046,  5110,\n",
       "              5244,  6066,  6295,  6446,  6673,  8508,  8990,  9516, 11214,\n",
       "             12561, 12620, 13847, 14052, 14183, 14704, 15161, 15350, 15357,\n",
       "             16501, 16630, 16681, 17431, 19086, 19170, 19441, 19612, 19714,\n",
       "             19731, 19756],\n",
       "            dtype='int64'),\n",
       " 'Quintino Bocaiúva': Int64Index([  295,   368,  4256,  5960,  6585,  7589,  7590,  7896,  8126,\n",
       "              8234,  9019,  9886, 10897, 11208, 11790, 13355, 17206, 17208,\n",
       "             17288, 17370, 17408, 17494, 17510, 17544, 18788, 18856, 18900,\n",
       "             18948, 19014, 19163],\n",
       "            dtype='int64'),\n",
       " 'Ramos': Int64Index([ 1241,  1696,  3507,  4166,  4452,  4602,  8111, 10666, 11298,\n",
       "             13530, 16461, 16950, 17018, 17029, 17097, 17286, 17367, 17643,\n",
       "             17761, 17822, 17954, 18795, 18816, 18837, 18860, 18863, 18875,\n",
       "             18892, 18903, 18918, 18959, 18967, 18971, 19393, 19785],\n",
       "            dtype='int64'),\n",
       " 'Realengo': Int64Index([ 2948,  4098,  7779,  7972,  8589, 10917, 11361, 13050, 16225,\n",
       "             16979, 16983, 17028, 17241, 17319, 17441, 17690, 18527, 18564,\n",
       "             18790, 18828, 18953, 18969, 18990, 19390],\n",
       "            dtype='int64'),\n",
       " 'Recreio dos Bandeirantes': Int64Index([   16,    22,    23,    26,    30,    37,    58,    75,    87,\n",
       "                88,\n",
       "             ...\n",
       "             18724, 18730, 18739, 18743, 18752, 18757, 18758, 18761, 18771,\n",
       "             18775],\n",
       "            dtype='int64', length=1638),\n",
       " 'Riachuelo': Int64Index([  961,  1200,  3082,  3366,  5357,  5695,  5782,  6791,  6920,\n",
       "              7119,  7888,  8060,  8100,  8279,  8648,  8710,  9025,  9423,\n",
       "              9541,  9637, 10255, 11349, 12780, 13096, 13232, 14633, 15497,\n",
       "             15542, 15877, 17070, 17187, 17249, 17343, 18855, 18887, 18915,\n",
       "             18965, 18983, 19000, 19013, 19016],\n",
       "            dtype='int64'),\n",
       " 'Ribeira': Int64Index([8533, 10296, 14146, 17471, 17733], dtype='int64'),\n",
       " 'Rio Comprido': Int64Index([  287,   616,   744,   901,  1236,  1354,  1593,  1869,  1890,\n",
       "              2580,  2699,  2716,  2794,  2843,  3048,  3055,  3491,  3919,\n",
       "              3945,  4158,  4536,  4684,  4841,  6064,  6161,  6169,  6208,\n",
       "              6288,  6507,  6596,  6638,  7260,  8086,  8166,  8270,  8967,\n",
       "              9283,  9291,  9932, 10081, 10283, 10341, 10634, 10898, 10972,\n",
       "             11061, 11807, 12109, 12714, 13537, 13814, 13828, 13882, 14053,\n",
       "             14104, 14206, 14481, 14566, 14698, 14746, 14887, 14907, 15031,\n",
       "             15236, 15280, 15320, 15368, 15440, 15927, 15992, 16073, 16190,\n",
       "             16436, 17424, 17442, 17568, 17599, 17632, 19103],\n",
       "            dtype='int64'),\n",
       " 'Rio da Prata': Int64Index([18510], dtype='int64'),\n",
       " 'Rocha': Int64Index([  597,  1328,  2346,  4711,  4992,  5987,  6940,  8250,  8408,\n",
       "             10418, 10874, 11446, 12673, 12797, 13348, 15587, 16408, 17189,\n",
       "             17601],\n",
       "            dtype='int64'),\n",
       " 'Rocha Miranda': Int64Index([ 2696,  3169,  3493,  4506,  6138,  7763, 10853, 10973, 12378,\n",
       "             13055, 17171, 17405, 17810, 18927, 18931, 19002, 19179],\n",
       "            dtype='int64'),\n",
       " 'Sampaio': Int64Index([2258, 2538, 3553, 9037, 9287, 16444, 16614, 16858], dtype='int64'),\n",
       " 'Santa Cruz': Int64Index([1399, 3140, 5870, 9494, 14404, 15150, 16998], dtype='int64'),\n",
       " 'Santa Teresa': Int64Index([  394,   466,   590,  1051,  1205,  1438,  1537,  1844,  2059,\n",
       "              2354,\n",
       "             ...\n",
       "             19075, 19125, 19143, 19178, 19253, 19574, 19741, 19752, 19757,\n",
       "             19820],\n",
       "            dtype='int64', length=111),\n",
       " 'Santo Cristo': Int64Index([11611, 12335, 17354, 18924], dtype='int64'),\n",
       " 'Santíssimo': Int64Index([3184, 7474, 9225, 16970, 17089, 17454, 17459, 17841], dtype='int64'),\n",
       " 'Saúde': Int64Index([441, 19671], dtype='int64'),\n",
       " 'Senador Camará': Int64Index([17192], dtype='int64'),\n",
       " 'Senador Vasconcelos': Int64Index([ 3023,  3248,  5160,  6148,  6549,  7871,  8794, 13270, 14326,\n",
       "             15309, 16391, 17050, 17090, 17210, 17265, 17966, 19726],\n",
       "            dtype='int64'),\n",
       " 'São Conrado': Int64Index([  284,   396,   429,   476,   493,   545,   632,   641,   837,\n",
       "              1179,\n",
       "             ...\n",
       "             18385, 18390, 18407, 18418, 18464, 18480, 18496, 18634, 18665,\n",
       "             19676],\n",
       "            dtype='int64', length=181),\n",
       " 'São Cristóvão': Int64Index([  156,   839,   933,  1330,  1690,  1698,  1892,  1977,  2016,\n",
       "              2384,  3311,  3443,  3487,  3838,  4346,  4390,  5417,  5723,\n",
       "              5819,  6746,  7206,  7241,  7273,  7299,  7347,  7700,  7817,\n",
       "              7851,  8686,  8759,  9083,  9166,  9211,  9607, 10498, 10553,\n",
       "             10571, 10651, 10911, 10945, 10985, 11331, 11471, 12003, 12030,\n",
       "             12430, 13743, 14688, 14828, 14864, 15036, 15160, 15639, 16253,\n",
       "             16520, 18749, 18756, 18888, 18939, 18995, 19007, 19522, 19631,\n",
       "             19718],\n",
       "            dtype='int64'),\n",
       " 'São Francisco Xavier': Int64Index([  210,   385,   791,  1679,  1915,  2219,  2542,  2986,  3057,\n",
       "              4092,  4604,  6513,  7412,  7872,  7946,  8038,  8354, 10485,\n",
       "             11285, 11554, 12646, 12705, 14387, 14583, 15389, 16583, 16921,\n",
       "             18026, 18991],\n",
       "            dtype='int64'),\n",
       " 'Tanque': Int64Index([ 1260,  2122,  3212,  4174,  5876,  6420,  6443,  6853,  7336,\n",
       "              8146,  9096,  9290, 10229, 11549, 12422, 13016, 13309, 13859,\n",
       "             14677, 15848, 16363, 16932, 17377, 17839, 18014, 18878, 18954,\n",
       "             18981],\n",
       "            dtype='int64'),\n",
       " 'Taquara': Int64Index([   90,   143,   258,   580,   694,   954,  1284,  1285,  1308,\n",
       "              1545,\n",
       "             ...\n",
       "             18735, 18748, 18810, 18827, 18847, 18881, 18936, 18957, 19229,\n",
       "             19576],\n",
       "            dtype='int64', length=171),\n",
       " 'Tauá': Int64Index([8365, 10743, 11607, 11921, 14759, 15257, 15351, 17575], dtype='int64'),\n",
       " 'Tijuca': Int64Index([    8,    10,    12,    61,    62,    95,   110,   113,   120,\n",
       "               123,\n",
       "             ...\n",
       "             19445, 19460, 19525, 19583, 19605, 19662, 19680, 19695, 19710,\n",
       "             19723],\n",
       "            dtype='int64', length=1097),\n",
       " 'Todos os Santos': Int64Index([   52,  1305,  1364,  1832,  2089,  2187,  2299,  2695,  3146,\n",
       "              3234,  4286,  6869,  6948,  7106,  7255,  7548,  7880,  9001,\n",
       "              9248, 10058, 10146, 10282, 10352, 10964, 11461, 12195, 12536,\n",
       "             12601, 12618, 12846, 12993, 13209, 13246, 13531, 13675, 13698,\n",
       "             14087, 14145, 14288, 14675, 15455, 16044, 16266, 16562, 16906,\n",
       "             16996, 17096, 17207, 17474, 18796, 18891, 19015],\n",
       "            dtype='int64'),\n",
       " 'Tomás Coelho': Int64Index([1204, 1868, 3347, 4118, 9028, 9887, 9908, 10011, 16050, 16104], dtype='int64'),\n",
       " 'Turiaçu': Int64Index([5644, 10365, 11775, 17803, 18846], dtype='int64'),\n",
       " 'Urca': Int64Index([  250,   651,  1423,  1608,  1851,  1860,  3707,  3729,  3755,\n",
       "              3893,  4053,  4436,  4631,  5376,  5420,  5658,  6678,  6842,\n",
       "              6865,  7014,  7554,  7814,  8065,  8456,  8889,  8892,  9010,\n",
       "              9522,  9669,  9767, 10111, 10170, 11088, 11452, 11550, 12207,\n",
       "             12383, 12442, 12524, 12589, 12964, 13063, 13849, 14613, 14957,\n",
       "             15196, 15397, 16557, 16683, 16691, 16963, 16969, 17014, 17108,\n",
       "             17380, 17463, 17517, 17596, 17635, 17689, 17717, 17736, 17747,\n",
       "             18669, 19149, 19268, 19759],\n",
       "            dtype='int64'),\n",
       " 'Usina': Int64Index([5342, 10134, 10213, 10244, 12480], dtype='int64'),\n",
       " 'Vargem Grande': Int64Index([  928,  9073,  9352,  9651,  9774, 11821, 12220, 16941, 16953,\n",
       "             17012, 17041, 17112, 17468, 17616, 17682, 17731, 17832, 17835,\n",
       "             17853, 17864, 17868, 17870, 17899, 17907, 17914, 17917, 17929,\n",
       "             17950, 17962, 17970, 18004, 18033, 18042, 18064, 18105, 18121,\n",
       "             18123, 18146, 18176, 18186, 18203, 18232, 18259, 18320, 18349,\n",
       "             18461, 18484, 18505, 18524, 18542, 18545, 18581, 18649, 18658,\n",
       "             18674, 18692, 18702, 18760, 18767, 19190],\n",
       "            dtype='int64'),\n",
       " 'Vargem Pequena': Int64Index([   34,   293,   700,   966,  1288,  1896,  2061,  2184,  2204,\n",
       "              2457,  2721,  2780,  3038,  3442,  3902,  4733,  7035,  7493,\n",
       "             10927, 12793, 14013, 15676, 16277, 16445, 16999, 17075, 17204,\n",
       "             17277, 17309, 17352, 17378, 17416, 17420, 17482, 17515, 17779,\n",
       "             17910, 18133, 18216, 18230, 18271, 18318, 18327, 18338, 18408,\n",
       "             18425, 18445, 18462, 18523, 18643, 18719, 18940],\n",
       "            dtype='int64'),\n",
       " 'Vaz Lobo': Int64Index([  196,  1735,  2264,  5707,  6082,  6938,  7303,  8084,  9304,\n",
       "              9495, 14051, 15279, 15442, 15735, 16573, 16944, 17034, 17115,\n",
       "             17131, 17137, 17278, 17449, 17548, 17550, 18934, 19001],\n",
       "            dtype='int64'),\n",
       " 'Vicente de Carvalho': Int64Index([ 6343,  8498,  8715, 11666, 11703, 12073, 16257, 17132, 17136,\n",
       "             17279, 17478, 17505, 18820, 18858],\n",
       "            dtype='int64'),\n",
       " 'Vidigal': Int64Index([5005, 9378, 10493, 12483], dtype='int64'),\n",
       " 'Vigário Geral': Int64Index([6268, 10583, 17593, 17656, 18998], dtype='int64'),\n",
       " 'Vila Isabel': Int64Index([   77,   201,   223,   253,   321,   364,   467,   491,   505,\n",
       "               515,\n",
       "             ...\n",
       "             18859, 18869, 18913, 18920, 18958, 18985, 19009, 19452, 19704,\n",
       "             19808],\n",
       "            dtype='int64', length=300),\n",
       " 'Vila Kosmos': Int64Index([782, 3833, 6264, 6625, 7939, 12027, 12189, 13262, 17200, 17569,\n",
       "             17666],\n",
       "            dtype='int64'),\n",
       " 'Vila Militar': Int64Index([18849, 18980], dtype='int64'),\n",
       " 'Vila Valqueire': Int64Index([   45,   393,   666,  1086,  1175,  1202,  1289,  2342,  2460,\n",
       "              2474,  2602,  3047,  3504,  3818,  3948,  4114,  4243,  4530,\n",
       "              5839,  7559,  7615,  8308,  9126,  9834,  9910, 10689, 11812,\n",
       "             13408, 14100, 14270, 15062, 16025, 16228, 16918, 16968, 17154,\n",
       "             17205, 17261, 17364, 17509, 17564, 17636, 17662, 17713, 17768,\n",
       "             18574, 18738, 19006],\n",
       "            dtype='int64'),\n",
       " 'Vila da Penha': Int64Index([  172,   645,   774,   952,  1001,  1039,  1087,  1221,  1409,\n",
       "              1611,\n",
       "             ...\n",
       "             18161, 18806, 18922, 19005, 19148, 19353, 19406, 19538, 19571,\n",
       "             19746],\n",
       "            dtype='int64', length=104),\n",
       " 'Vista Alegre': Int64Index([  783,  1609,  2534,  7405,  7626,  8776,  9040,  9868, 10428,\n",
       "             11667, 16215, 16232, 16564, 17347, 17936, 18731],\n",
       "            dtype='int64'),\n",
       " 'Zumbi': Int64Index([7311, 9116], dtype='int64'),\n",
       " 'Água Santa': Int64Index([150, 344, 7341, 10461, 10690, 11009, 12928, 17618, 18839], dtype='int64')}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grupo_bairro.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valor médio do bairro Abolição -> 1195.3333333333333\n",
      "Valor médio do bairro Alto da Boa Vista -> 3966.6666666666665\n",
      "Valor médio do bairro Anchieta -> 875.0\n",
      "Valor médio do bairro Andaraí -> 1464.7113402061855\n",
      "Valor médio do bairro Anil -> 2048.8732394366198\n",
      "Valor médio do bairro Arpoador -> 6742.857142857143\n",
      "Valor médio do bairro Bancários -> 1825.0\n",
      "Valor médio do bairro Bangu -> 1016.0\n",
      "Valor médio do bairro Barra da Tijuca -> 5500.497783032811\n",
      "Valor médio do bairro Barra de Guaratiba -> 5550.0\n",
      "Valor médio do bairro Benfica -> 996.0\n",
      "Valor médio do bairro Bento Ribeiro -> 1030.8695652173913\n",
      "Valor médio do bairro Bonsucesso -> 1225.9322033898304\n",
      "Valor médio do bairro Botafogo -> 3375.4605418138985\n",
      "Valor médio do bairro Braz de Pina -> 1115.0\n",
      "Valor médio do bairro Cachambi -> 1157.1742424242425\n",
      "Valor médio do bairro Cachamorra -> 3000.0\n",
      "Valor médio do bairro Cacuia -> 916.6666666666666\n",
      "Valor médio do bairro Caju -> 850.0\n",
      "Valor médio do bairro Camorim -> 1735.2272727272727\n",
      "Valor médio do bairro Campinho -> 1037.3333333333333\n",
      "Valor médio do bairro Campo Grande -> 1267.71714922049\n",
      "Valor médio do bairro Cascadura -> 948.7096774193549\n",
      "Valor médio do bairro Catete -> 2253.0188679245284\n",
      "Valor médio do bairro Catumbi -> 1112.5\n",
      "Valor médio do bairro Cavalcanti -> 595.0\n",
      "Valor médio do bairro Centro -> 1242.3391812865498\n",
      "Valor médio do bairro Cidade Nova -> 1471.4285714285713\n",
      "Valor médio do bairro Cocotá -> 1883.3333333333333\n",
      "Valor médio do bairro Coelho Neto -> 700.0\n",
      "Valor médio do bairro Colégio -> 885.0\n",
      "Valor médio do bairro Copacabana -> 3351.519427402863\n",
      "Valor médio do bairro Cordovil -> 905.5555555555555\n",
      "Valor médio do bairro Cosme Velho -> 5021.666666666667\n",
      "Valor médio do bairro Cosmos -> 658.3333333333334\n",
      "Valor médio do bairro Curicica -> 1514.5657894736842\n",
      "Valor médio do bairro Del Castilho -> 1261.304347826087\n",
      "Valor médio do bairro Encantado -> 1050.0\n",
      "Valor médio do bairro Engenho Novo -> 1035.8252427184466\n",
      "Valor médio do bairro Engenho da Rainha -> 883.5714285714286\n",
      "Valor médio do bairro Engenho de Dentro -> 1166.3513513513512\n",
      "Valor médio do bairro Estácio -> 1233.9130434782608\n",
      "Valor médio do bairro Flamengo -> 3220.119266055046\n",
      "Valor médio do bairro Freguesia (Ilha do Governador) -> 1720.0\n",
      "Valor médio do bairro Freguesia (Jacarepaguá) -> 2014.9779735682819\n",
      "Valor médio do bairro Fátima -> 956.6666666666666\n",
      "Valor médio do bairro Gamboa -> 1116.6666666666667\n",
      "Valor médio do bairro Gardênia Azul -> 761.1111111111111\n",
      "Valor médio do bairro Glória -> 2113.2075471698113\n",
      "Valor médio do bairro Grajaú -> 1636.111111111111\n",
      "Valor médio do bairro Guadalupe -> 700.0\n",
      "Valor médio do bairro Guaratiba -> 1232.258064516129\n",
      "Valor médio do bairro Gávea -> 5535.259803921569\n",
      "Valor médio do bairro Higienópolis -> 1006.25\n",
      "Valor médio do bairro Honório Gurgel -> 628.5714285714286\n",
      "Valor médio do bairro Humaitá -> 3746.9108280254777\n",
      "Valor médio do bairro Icaraí -> 2393.75\n",
      "Valor médio do bairro Ilha do Governador -> 1728.0\n",
      "Valor médio do bairro Inhaúma -> 821.4285714285714\n",
      "Valor médio do bairro Inhoaíba -> 725.0\n",
      "Valor médio do bairro Ipanema -> 5451.549307253464\n",
      "Valor médio do bairro Irajá -> 1096.012658227848\n",
      "Valor médio do bairro Itanhangá -> 8909.83606557377\n",
      "Valor médio do bairro Jabour -> 723.3333333333334\n",
      "Valor médio do bairro Jacarepaguá -> 2014.3934426229507\n",
      "Valor médio do bairro Jacaré -> 1200.0\n",
      "Valor médio do bairro Jardim América -> 714.2857142857143\n",
      "Valor médio do bairro Jardim Botânico -> 5703.981900452489\n",
      "Valor médio do bairro Jardim Carioca -> 2211.1111111111113\n",
      "Valor médio do bairro Jardim Guanabara -> 2124.6875\n",
      "Valor médio do bairro Jardim Oceânico -> 5337.5\n",
      "Valor médio do bairro Jardim Sulacap -> 1176.6666666666667\n",
      "Valor médio do bairro Joá -> 13182.777777777777\n",
      "Valor médio do bairro Lagoa -> 5203.671122994652\n",
      "Valor médio do bairro Lapa -> 1488.7777777777778\n",
      "Valor médio do bairro Laranjeiras -> 3206.007228915663\n",
      "Valor médio do bairro Largo do Machado -> 1880.0\n",
      "Valor médio do bairro Leblon -> 5661.45393258427\n",
      "Valor médio do bairro Leme -> 3492.3963133640555\n",
      "Valor médio do bairro Lins de Vasconcelos -> 1086.1702127659576\n",
      "Valor médio do bairro Madureira -> 1017.6595744680851\n",
      "Valor médio do bairro Magalhães Bastos -> 640.0\n",
      "Valor médio do bairro Maracanã -> 1945.4961240310076\n",
      "Valor médio do bairro Marechal Hermes -> 844.4444444444445\n",
      "Valor médio do bairro Maria da Graça -> 1073.3333333333333\n",
      "Valor médio do bairro Moneró -> 1900.0\n",
      "Valor médio do bairro Méier -> 1355.8745098039215\n",
      "Valor médio do bairro Olaria -> 1016.5625\n",
      "Valor médio do bairro Oswaldo Cruz -> 823.8888888888889\n",
      "Valor médio do bairro Paciência -> 580.0\n",
      "Valor médio do bairro Padre Miguel -> 786.25\n",
      "Valor médio do bairro Paquetá -> 10550.0\n",
      "Valor médio do bairro Parada de Lucas -> 1062.5\n",
      "Valor médio do bairro Parque Anchieta -> 1287.5\n",
      "Valor médio do bairro Pavuna -> 648.9473684210526\n",
      "Valor médio do bairro Pechincha -> 1357.4257425742574\n",
      "Valor médio do bairro Pedra de Guaratiba -> 1082.1052631578948\n",
      "Valor médio do bairro Penha -> 1023.3333333333334\n",
      "Valor médio do bairro Penha Circular -> 1160.0\n",
      "Valor médio do bairro Península -> 4722.580645161291\n",
      "Valor médio do bairro Piedade -> 956.5384615384615\n",
      "Valor médio do bairro Pilares -> 1077.5\n",
      "Valor médio do bairro Pitangueiras -> 2725.0\n",
      "Valor médio do bairro Portuguesa -> 1272.7272727272727\n",
      "Valor médio do bairro Praia da Bandeira -> 1300.0\n",
      "Valor médio do bairro Praça Seca -> 998.4146341463414\n",
      "Valor médio do bairro Praça da Bandeira -> 1358.723404255319\n",
      "Valor médio do bairro Quintino Bocaiúva -> 924.6666666666666\n",
      "Valor médio do bairro Ramos -> 958.0\n",
      "Valor médio do bairro Realengo -> 908.3333333333334\n",
      "Valor médio do bairro Recreio dos Bandeirantes -> 2921.3522588522587\n",
      "Valor médio do bairro Riachuelo -> 1143.9024390243903\n",
      "Valor médio do bairro Ribeira -> 1420.0\n",
      "Valor médio do bairro Rio Comprido -> 1610.240506329114\n",
      "Valor médio do bairro Rio da Prata -> 1300.0\n",
      "Valor médio do bairro Rocha -> 1086.842105263158\n",
      "Valor médio do bairro Rocha Miranda -> 877.0\n",
      "Valor médio do bairro Sampaio -> 937.5\n",
      "Valor médio do bairro Santa Cruz -> 734.2857142857143\n",
      "Valor médio do bairro Santa Teresa -> 2156.036036036036\n",
      "Valor médio do bairro Santo Cristo -> 1225.0\n",
      "Valor médio do bairro Santíssimo -> 756.25\n",
      "Valor médio do bairro Saúde -> 950.0\n",
      "Valor médio do bairro Senador Camará -> 600.0\n",
      "Valor médio do bairro Senador Vasconcelos -> 867.3529411764706\n",
      "Valor médio do bairro São Conrado -> 5620.7569060773485\n",
      "Valor médio do bairro São Cristóvão -> 1391.71875\n",
      "Valor médio do bairro São Francisco Xavier -> 1237.2413793103449\n",
      "Valor médio do bairro Tanque -> 1335.7142857142858\n",
      "Valor médio do bairro Taquara -> 1642.140350877193\n",
      "Valor médio do bairro Tauá -> 1340.0\n",
      "Valor médio do bairro Tijuca -> 1999.9380127620784\n",
      "Valor médio do bairro Todos os Santos -> 1243.076923076923\n",
      "Valor médio do bairro Tomás Coelho -> 737.0\n",
      "Valor médio do bairro Turiaçu -> 700.0\n",
      "Valor médio do bairro Urca -> 6747.686567164179\n",
      "Valor médio do bairro Usina -> 1520.0\n",
      "Valor médio do bairro Vargem Grande -> 3358.1666666666665\n",
      "Valor médio do bairro Vargem Pequena -> 2703.480769230769\n",
      "Valor médio do bairro Vaz Lobo -> 845.0\n",
      "Valor médio do bairro Vicente de Carvalho -> 1075.7142857142858\n",
      "Valor médio do bairro Vidigal -> 2600.0\n",
      "Valor médio do bairro Vigário Geral -> 970.0\n",
      "Valor médio do bairro Vila Isabel -> 1560.8333333333333\n",
      "Valor médio do bairro Vila Kosmos -> 1311.8181818181818\n",
      "Valor médio do bairro Vila Militar -> 500.0\n",
      "Valor médio do bairro Vila Valqueire -> 1769.5833333333333\n",
      "Valor médio do bairro Vila da Penha -> 1260.576923076923\n",
      "Valor médio do bairro Vista Alegre -> 1114.375\n",
      "Valor médio do bairro Zumbi -> 2150.0\n",
      "Valor médio do bairro Água Santa -> 861.1111111111111\n"
     ]
    }
   ],
   "source": [
    "for bairro, dados, in grupo_bairro:\n",
    "    print(f'Valor médio do bairro {bairro} -> {dados.Valor.mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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",
       "      <th>Condominio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bairro</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Abolição</td>\n",
       "      <td>1195.33</td>\n",
       "      <td>191.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Alto da Boa Vista</td>\n",
       "      <td>3966.67</td>\n",
       "      <td>885.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Anchieta</td>\n",
       "      <td>875.00</td>\n",
       "      <td>19.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andaraí</td>\n",
       "      <td>1464.71</td>\n",
       "      <td>497.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Anil</td>\n",
       "      <td>2048.87</td>\n",
       "      <td>455.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vila Valqueire</td>\n",
       "      <td>1769.58</td>\n",
       "      <td>276.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vila da Penha</td>\n",
       "      <td>1260.58</td>\n",
       "      <td>232.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vista Alegre</td>\n",
       "      <td>1114.38</td>\n",
       "      <td>210.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Zumbi</td>\n",
       "      <td>2150.00</td>\n",
       "      <td>1050.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Água Santa</td>\n",
       "      <td>861.11</td>\n",
       "      <td>269.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Valor  Condominio\n",
       "Bairro                                \n",
       "Abolição           1195.33      191.40\n",
       "Alto da Boa Vista  3966.67      885.00\n",
       "Anchieta            875.00       19.75\n",
       "Andaraí            1464.71      497.71\n",
       "Anil               2048.87      455.85\n",
       "...                    ...         ...\n",
       "Vila Valqueire     1769.58      276.88\n",
       "Vila da Penha      1260.58      232.68\n",
       "Vista Alegre       1114.38      210.06\n",
       "Zumbi              2150.00     1050.00\n",
       "Água Santa          861.11      269.44\n",
       "\n",
       "[151 rows x 2 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grupo_bairro[['Valor','Condominio']].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estatísticas Descritivas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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",
       "    </tr>\n",
       "    <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",
       "      <td>Abolição</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1195.33</td>\n",
       "      <td>425.32</td>\n",
       "      <td>800.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Alto da Boa Vista</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3966.67</td>\n",
       "      <td>5513.59</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>3275.0</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Anchieta</td>\n",
       "      <td>4.0</td>\n",
       "      <td>875.00</td>\n",
       "      <td>132.29</td>\n",
       "      <td>700.0</td>\n",
       "      <td>812.5</td>\n",
       "      <td>900.0</td>\n",
       "      <td>962.5</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andaraí</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1464.71</td>\n",
       "      <td>408.11</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>2950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Anil</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2048.87</td>\n",
       "      <td>1523.97</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>2675.0</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vila Valqueire</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1769.58</td>\n",
       "      <td>2203.01</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vila da Penha</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1260.58</td>\n",
       "      <td>644.66</td>\n",
       "      <td>450.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1302.5</td>\n",
       "      <td>4500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vista Alegre</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1114.38</td>\n",
       "      <td>199.60</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1012.5</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Zumbi</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2150.00</td>\n",
       "      <td>777.82</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>2425.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Água Santa</td>\n",
       "      <td>9.0</td>\n",
       "      <td>861.11</td>\n",
       "      <td>174.60</td>\n",
       "      <td>600.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count     mean      std     min     25%     50%     75%  \\\n",
       "Bairro                                                                       \n",
       "Abolição            15.0  1195.33   425.32   800.0   900.0  1180.0  1200.0   \n",
       "Alto da Boa Vista    6.0  3966.67  5513.59   600.0  1150.0  1725.0  3275.0   \n",
       "Anchieta             4.0   875.00   132.29   700.0   812.5   900.0   962.5   \n",
       "Andaraí             97.0  1464.71   408.11   700.0  1200.0  1400.0  1650.0   \n",
       "Anil                71.0  2048.87  1523.97   300.0  1100.0  1500.0  2675.0   \n",
       "...                  ...      ...      ...     ...     ...     ...     ...   \n",
       "Vila Valqueire      48.0  1769.58  2203.01   800.0  1000.0  1250.0  1700.0   \n",
       "Vila da Penha      104.0  1260.58   644.66   450.0   900.0  1100.0  1302.5   \n",
       "Vista Alegre        16.0  1114.38   199.60   700.0  1012.5  1200.0  1225.0   \n",
       "Zumbi                2.0  2150.00   777.82  1600.0  1875.0  2150.0  2425.0   \n",
       "Água Santa           9.0   861.11   174.60   600.0   800.0   850.0   950.0   \n",
       "\n",
       "                       max  \n",
       "Bairro                      \n",
       "Abolição            2300.0  \n",
       "Alto da Boa Vista  15000.0  \n",
       "Anchieta            1000.0  \n",
       "Andaraí             2950.0  \n",
       "Anil                7000.0  \n",
       "...                    ...  \n",
       "Vila Valqueire     16000.0  \n",
       "Vila da Penha       4500.0  \n",
       "Vista Alegre        1400.0  \n",
       "Zumbi               2700.0  \n",
       "Água Santa          1200.0  \n",
       "\n",
       "[151 rows x 8 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grupo_bairro['Valor'].describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bairro</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Abolição</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Alto da Boa Vista</td>\n",
       "      <td>600.0</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Anchieta</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andaraí</td>\n",
       "      <td>700.0</td>\n",
       "      <td>2950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Anil</td>\n",
       "      <td>300.0</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vila Valqueire</td>\n",
       "      <td>800.0</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vila da Penha</td>\n",
       "      <td>450.0</td>\n",
       "      <td>4500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vista Alegre</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Zumbi</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Água Santa</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      min      max\n",
       "Bairro                            \n",
       "Abolição            800.0   2300.0\n",
       "Alto da Boa Vista   600.0  15000.0\n",
       "Anchieta            700.0   1000.0\n",
       "Andaraí             700.0   2950.0\n",
       "Anil                300.0   7000.0\n",
       "...                   ...      ...\n",
       "Vila Valqueire      800.0  16000.0\n",
       "Vila da Penha       450.0   4500.0\n",
       "Vista Alegre        700.0   1400.0\n",
       "Zumbi              1600.0   2700.0\n",
       "Água Santa          600.0   1200.0\n",
       "\n",
       "[151 rows x 2 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grupo_bairro['Valor'].aggregate(['min','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('figure', figsize = (40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Valor médio do Aluguel por Bairro')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2880x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = grupo_bairro['Valor'].mean().plot.bar()\n",
    "fig.set_ylabel('Valor do Aluguel')\n",
    "fig.set_title('Valor médio do Aluguel por Bairro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamento de Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dados/aluguel.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29d70e73fc8>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot('Valor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Tipo, Bairro, Quartos, Vagas, Suites, Area, Valor, Condominio, IPTU, Valor m2, Tipo Agregado]\n",
       "Index: []"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Valor'] >= 500000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preparando Boxplot\n",
    "\n",
    "valor = df['Valor']\n",
    "\n",
    "Q1 = valor.quantile(.25)\n",
    "Q3 = valor.quantile(.75)\n",
    "IIQ = Q3 - Q1\n",
    "lim_inferior = Q1 - 1.5 * IIQ\n",
    "lim_superior = Q3 + 1.5 * IIQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29d673c3308>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selecao = (valor >= lim_inferior) & (valor <= lim_superior)\n",
    "df_new = df[selecao]\n",
    "\n",
    "plt.rc('figure', figsize = (30,20))\n",
    "df_new.boxplot(['Valor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000029D7B955308>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('figure', figsize = (30,20))\n",
    "df.hist(['Valor'])\n",
    "df_new.hist(['Valor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29d7bccc3c8>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(['Valor'], by = ['Tipo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "grupo_tipo = df.groupby('Tipo')['Valor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento           1600.0\n",
       "Casa                  1000.0\n",
       "Casa de Condomínio    3900.0\n",
       "Casa de Vila           750.0\n",
       "Quitinete              900.0\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = grupo_tipo.quantile(.25)\n",
    "Q3 = grupo_tipo.quantile(.75)\n",
    "IIQ = Q3 - Q1\n",
    "lim_inferior = Q1 - 1.5 * IIQ\n",
    "lim_superior = Q3 + 1.5 * IIQ\n",
    "\n",
    "Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tipo\n",
       "Apartamento            4000.0\n",
       "Casa                   7000.0\n",
       "Casa de Condomínio    15000.0\n",
       "Casa de Vila           1600.0\n",
       "Quitinete              1500.0\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jz00rs\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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yYWJ8WIWmVm5hVRvTh05qqenD7ktWPbWtiyJojDVhY41yXKXRou7auSX0dXe+d0NL7NbrN4ZuGzbGTuLMQVLandN6l/X4jNfFsbl6KO9qHuLsN6x60nVVZbevXxot6sjkbXpp6g4dmbwtkYqsqBVyYfduu3s6DZ3utSymUdavCx5vWLwfpX0vAgAAAAAA+IjEFzIhTtVGWm3r6scovf2J+3ZjjXpc+0oj2rA+H/i6p3/celxPPX8+dNvm1xssBO+32FQBEmcOmn+3UzxM82uGVTG866reqlVcVAW9NHVHS7LAVOO9cFXBFGe/+0ojunvnlpX5yBmju3du0b7SSMN2rpJ/+0ojGtvWWME5tm2o5fVdilohl4WxRlGb/3W54Nn5H7e5rZjtxqU3g9ckC4v3o6j3Yr/zab290yH/DQiLAwAAAACA5LHGFzIjbB2oZmFrYvXayqs8U9H0oZM6NzevTYMFTYwPt4wn6hi7+Z2wNZzirh3W/Hp7y7OBa6IFVY1FHWucdZU6qX/NrZMHA7dJIqnZzdx18kefvKnlmkmCi7HG3e++0kjHh+tXFfKam2+9bq8KSbZGVZ6p6HunLzbEvnf6osozlZ7Oy2DIeIOSw1Gv8fJMRd97qWmsL4WPNcr7jCul0aJ+5+FnAv/u/z11YVXGEIer93r4xcf19khyAQAAAACQLiq+4B0XreBqD9Yqc/OyevvB2mp+qjzOGkxxtn30xCuB24bFo3BVleTTWlxZuGbSFrbMUK/LD33hO89pYbGxn+nCotUXvvNcT/u9/yM3KN+0QGB+wOj+j9zQsm3Ua/z+x5/TQlPv1YUlq/sfbx1rFq4ZGzOepjTar2ZN7YMLi3Z5hhat1YNHz2hveTblka2eqOvtAQAAAAAA1FDxBe+URos6/vIFHTh2VovWKmeM7ry5twqZdg/WVusT5XGqqOJsO7+wFPh6YfGoXFQl3Xr9xsjVaVK61TNZuGbSFlalGBaP6mLI74fFo6rNS9RrJso1HlRBFhbnmokn7nz1owPHzobG10q7w7TaGwMAAAAAAH+R+IJ3yjMVPXqi0vAJ+EdPVLT9mqGuH4hm4cFanIe8/fpAOGztsqB42u2vsnDNpM3HVnSu2khGwTUTX5rzlQW1/85FjfcjH99nAAAAAABAukh8wTsuqiay8mAtzkPefnwgHDQHYfG0q2eycs2kaWJ8WBPffrahLWE+Z3puRRdnLa60Xbkup9ffXAyMN+OaQVw5YwKTXLle+4l6JMk1JQEAAAAAwNrAGl/wTpSqib3lWW3b84SunTyobXue6Lgeisu1ZMozFY1NHdbWyYMamzq8ptaAiivsYW5QPO3qmYnxYeVzTetFJZD08c1i0/pWzT93I85aXGnL54L/MxoUZ80qxLV7x+ZY8X7kak1JAAAAAADQv6j4gnc6VU3sLc82rBO1aO3Kz2FrorhqHRi3HZ+LNauKIeermMEqkzhtvTJRPdM8rLXTfUyS9IXvPKfmPNeSXY73ct361MrztZA1voLiPh0XsqH236z6NS1379i8Ztb3qunHCmcAAAAAAOCOs8SXMea0pJ9KWpT0lrV2uzFmSNLDkq6VdFrSJ6y1F40xRtKfSPqQpEuSftNa+31XY4PfOrU9OnDsbODvHTh2tu3DQhcP1uK043O1ZpVPbaI2rM/r4qXWhMGG9a0t7tI+rulDJ7XQlPVZWLKr1moxC4Lmql08Dl8edMdNwKZ9XD4lwrFsX2lkzSW6AAAAAAAAeuG64utWa+0/1/08KelvrbVTxpjJ6s+/K+nXJF1X/doh6U+rfwItOlVNxKkaci1OO76k16yqrx4bXJ/X5ZcN6LX5hbZVJlEqzmrb1D8871SFELWSLWyKguKl0aKOv3yhoRLizpvdJhXqjyPsakqi1aKLyj+X+13Lbr1+Y0OFaX08SNpzMDE+rHu/9UxDpd6AUSYT4VL65ysL9pZn13zFFwAAAAAAQByr3erwo5I+UP3+a5K+q+XE10clfd1aayUdNcYMGmPeba394SqPD55oVzWRMyY0yTU2dbirB6fdPniMUw0StF27eLsHws3VYxcvLaiQz+mPPnlT6LFHqThr3qamXTvJOJVscyFt44Li5ZmKHj1RWZnrRWv16ImKtl8z5OTBeNixN7usx5UTXVX+udrvWveNgKRXLd7LveBCeaaiiUeeCWxP+cjxM5m7DtI+X1nQTeteAAAAAACAta7HR7RtWUl/Y4w5YYz5TDX2rloyq/rn1dV4UVJ9f7pXqjEgtt07Nof+Xe3BaXmmEnl/tQeP9QmWB4+e0d7ybMffnRgfViGfa4gl0Y6v9kC4Uq08aj6udtVjYaL8TtA29b5xrDUJ0M1YonC13zivF2RhKfnXaXdc5ZmKxqYOa+vkQY1NHQ69tlf7fK0VYdMdFE9zDmrvGWHX55FTF5yPIS6u2fatewEAAAAAABDMZeJrzFr7fi23MfysMeZftdnWBMRaSnaMMZ8xxhw3xhw/f/58UuNETFEftKdlX2lEd+/copwJuqziPzjt5cFjabSoO28urowliXZ85ZmKPv+tZ9s+EA5rt1eZm9e2PU8EJu2itGXs1MavuZIk6n67EXe/vV63SbQw7OV1guKdEqDd7hdupDkHURO3WcI1m63WvQAAAAAAAL5wlviy1p6r/vmqpL+U9MuSfmSMebckVf98tbr5K5Lqy3TeI+lcwD6/Yq3dbq3dvnFj8PopcCvsQfve8mymkmH7SiM6tf9DoX8f1j4wSC8PHsPa8XV7fmrnP+y1aw+Eg1op1oRVrA2uzwduXx9vt98wYb/Tzb663W+cBFHc10tanOOKUxHjah4QXZpz4GOyiGtWoR/gCIsDAAAAAADAUeLLGHOlMeYdte8l/aqkf5D0uKRPVzf7tKS/qn7/uKT/2SzbKek11vfKprAH7Q8dPdNTUsGVJB4attt2tdvLdaraqD0QDmqx2Ky5Yu1nIfutj3fabz7gHcVVu8c4+01iHqKcU0ka2zYUeZ9RXyfsuOJUxLiah7XuuquvjBxPcw58TBZxzYa37m3X0hcAAAAAAGCtc1Xx9S5J/9UY86yk70k6aK39a0lTkm43xrwg6fbqz5L0hKQXJf1A0gOSfsvRuNCjsAftzfVHWVmHJYk2Ue0eMK52e7lOlWrXvnP54XZptKj9u0ZU7FD5VW8+ZPGf+nhtv2G5wJ+7orVqrDRa1Pu3XNUQe/+Wq3pq91g/luJgQUZScbCg/btGAvebxDw0v96GkAq5j2/fEnmfUV6n3XHFqYiJs9+owlLCSdSiZL2las2T936gJcl13dVX6sl7P9CyrYs5iCpq4jZL0jxfWdHcujdnjO7euUX7SiMpjwwAAAAAACC7LnOxU2vti5JuDIj/WNKvBMStpM+6GAuStWmwELlN4Gq01irPVDR96KTOzc1r02BBE+PDDQ9FiyHjbZcQalZ7wHjg2NnQhFkt0df8QDbsfDUnJsozFeWMCdx//VjDtqk5+uLFle9Lo8trib13z8HAtbcGusxOlEaL+tzDzwT+3cVLCy2xveVZHTl1oSF25NQF7S3Pal9ppGEOuxlLlIfgUechzuvd9IW/Cdzm/sefW7UH8xPjw9rz2GxDNVu7iphHjp9ZOQ+VuXk9cvxM12Mtz1Q0EHI99lpdVGtNWTuuWnJZUiaTHp+99bqG96HP3npd6LZRr9mk1V4z7N7NqrTOV5bsK42s+URXp//WAwAAAAAA1HO2xhf6U1DVQFj+xHVrrSjrNiXVKqu2ZtjpqTtCtwlKrNQqsNrFO63btX7d27dpp0q1oL8PSnq1iyetuaVifbx5DsNcfllvb1W3Xh+8JmBYPIq5+dYkX7t4VHHWI4tTEXPXA08HJiDveuDprscYdj32cl6l5FuEupTE+nGrhUQBfOTTPQYAAAAAALKBxBdiCXrQftfOLamswxLl4biLVllx1g17+sULAVs2xjut2/XCq6+vfB+nUi2K1Xhw2K7dZKdjr3nzreA2jFE99fz5WPE0xU36lEaLOjJ5m16aukNHJm8Lvbabk16d4nHHWK/X85p0i1CXfErSAT7iHgMAAAAAAHE5aXWI/hbUemr7NUM9tyGK28oo6sPxpFtlxVk3LEq1VZyH+RPjw5r49rNaWEymXGs12seFtWfMGRP52HutHvQpkeLDWDuNpdexJtWacjX4MF+Az7jHAAAAAABAXFR8IRFRq07CdNPKKOwheBYfjrcTe7xtcl5xWwJ2+6n5sPXBguK7d2wO3Hb3js2Rjj2J6kGfrpWrCvlY8TR0Om+9nteJ8WHlmy6m/IBxXkXaDZ+uLcBH3GMAAAAAACAuEl/IhLitjMozFV16862WeFCSpDxT0djUYW2dPKixqcOhybQo28VtDVjIB99i9fE4D/OnD53UQpvFud4IaAnY6Sbv5lPzv7FjS+T4vtKI7t65ZaUVZM4Y3b1zi/aVRgLXYMsPGG1Yn0+sNaWU3Fpv9ca2DcWKR7WwGNzWMSweVZLjDTqfNflcQgmq5iRq2GKCKXNxbQF4G/cYAAAAAACIi8QXMiFOK6NaddjFSwsN8cFCviVJErWSLMp2tW3iuPPm93SMf/mpFyLvr5skVad0STefmm+XzArb/tT+D+n01B06tf9DK9sFrcF27S+s18VLCyvz8MjxM7HH18zFWm9bN/5crHhUr78ZvHZWWDyqh+65pSXJNbZtSA/dc0vsfdXO54b1AVVoCXThnD50sqWd58KizeSaPi6uLZdOT90RKw6kzbd7DAAAAAAApI81vpAJg+vzLYksSTJG2jp5sGHNr6DqMEm68vLLWh6Etaskq982ynZhr1sTVN311PPnA7etj7/w6uuh+2wWtvZRt7Lwqfn6NdjueuBpHTl1oeHvj5y6oLseeLqrBE3cdePiOHDsbGg8LAGYtm7OYZjavdh83y4s2Zb7Ky7f1vRJeh1B1/74kze13BdAlvl2jwEAAAAAgHSR+OpjLh/6R7W3PKsDx85q0VrljNHuHZsDkwI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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "df = pd.read_csv('dados/aluguel.csv', sep=';')\n",
    "\n",
    "area = plt.figure() \n",
    "\n",
    "g1 = area.add_subplot(2, 2, 1)\n",
    "\n",
    "g1 = area.add_subplot(2, 2, 1)\n",
    "g2 = area.add_subplot(2, 2, 2)\n",
    "g3 = area.add_subplot(2, 2, 3)\n",
    "g4 = area.add_subplot(2, 2, 4)\n",
    "\n",
    "g1.scatter(df.Valor, df.Area)\n",
    "g1.set_title('Valor X Área')\n",
    "\n",
    "g2.hist(df.Valor)\n",
    "g2.set_title('Histograma')\n",
    "\n",
    "dados_g3 = df.Valor.sample(100) \n",
    "dados_g3.index = range(dados_g3.shape[0])\n",
    "g3.plot(dados_g3)\n",
    "g3.set_title('Amostra (Valor)')\n",
    "\n",
    "grupo = df.groupby('Tipo')['Valor']\n",
    "label = grupo.mean().index\n",
    "valores = grupo.mean().values\n",
    "g4.bar(label, valores)\n",
    "g4.set_title('Valor Médio por Tipo')\n",
    "\n",
    "\n",
    "area.savefig('grafico.png', dpi = 300, bbox_inches = 'tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}