Relatório de Análise V.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relatório de Análise V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neste quinto relatório, nosso objetivo consistirá em tratar os valores faltantes de nossa base de dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratando os Valores Faltantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Jardim Botânico</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Centro</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>800.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Higienópolis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>800.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Vista Alegre</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo           Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
       "0    Quitinete       Copacabana        1      0       0    40  1700.0   \n",
       "1         Casa  Jardim Botânico        2      0       1   100  7000.0   \n",
       "2  Apartamento           Centro        1      0       0    15   800.0   \n",
       "3  Apartamento     Higienópolis        1      0       0    48   800.0   \n",
       "4  Apartamento     Vista Alegre        3      1       0    70  1200.0   \n",
       "\n",
       "   Condominio  IPTU  \n",
       "0       500.0  60.0  \n",
       "1         NaN   NaN  \n",
       "2       390.0  20.0  \n",
       "3       230.0   NaN  \n",
       "4         NaN   NaN  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importa o pandas com o apelido pd\n",
    "import pandas as pd\n",
    "\n",
    "# Importa o módulo os\n",
    "import os\n",
    "\n",
    "# Muda para o diretório com os dados\n",
    "os.chdir('dados')\n",
    "\n",
    "# Importa o CSV com os dados de imóveis residenciais\n",
    "imoveis = pd.read_csv('imoveis-residenciais.csv', sep=';')\n",
    "\n",
    "# Exibe as cinco primeiras linhas do dataframe\n",
    "imoveis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "22575    False\n",
       "22576    False\n",
       "22577    False\n",
       "22578    False\n",
       "22579    False\n",
       "Name: Valor, Length: 22580, dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cria uma série booleana\n",
    "# Se o valor do aluguel estiver presente, retorna False\n",
    "# Se o valor do aluguel não estiver presente, retorna True\n",
    "imoveis['Valor'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtém o número de imóveis sem o valor do aluguel\n",
    "imoveis['Valor'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Tipo</th>\n",
       "      <th>Bairro</th>\n",
       "      <th>Quartos</th>\n",
       "      <th>Vagas</th>\n",
       "      <th>Suites</th>\n",
       "      <th>Area</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Barra da Tijuca</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>970.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Leme</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>878.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Botafogo</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Copacabana</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>850.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4941</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Campo Grande</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8568</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Leme</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>878.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8947</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Glória</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>910.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9149</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Gávea</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>880.0</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Tipo           Bairro  Quartos  Vagas  Suites  Area  Valor  \\\n",
       "58    Apartamento  Barra da Tijuca        2      1       1    70    NaN   \n",
       "1492  Apartamento             Leme        2      0       0    75    NaN   \n",
       "1683         Casa     Campo Grande        3      4       3   363    NaN   \n",
       "2012  Apartamento         Botafogo        2      0       0    95    NaN   \n",
       "2034  Apartamento       Copacabana        2      0       0    72    NaN   \n",
       "4941         Casa     Campo Grande        3      2       1   100    NaN   \n",
       "8568  Apartamento             Leme        2      0       1    75    NaN   \n",
       "8947  Apartamento           Glória        3      0       1   135    NaN   \n",
       "9149  Apartamento            Gávea        3      1       1   105    NaN   \n",
       "\n",
       "      Condominio   IPTU  \n",
       "58         970.0   68.0  \n",
       "1492       878.0    NaN  \n",
       "1683         NaN    NaN  \n",
       "2012      1010.0  170.0  \n",
       "2034       850.0    NaN  \n",
       "4941         NaN    NaN  \n",
       "8568       878.0    NaN  \n",
       "8947       910.0  228.0  \n",
       "9149       880.0  221.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleciona todos os registros em que o valor do aluguel está faltando\n",
    "imoveis[imoveis['Valor'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22580 entries, 0 to 22579\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Tipo        22580 non-null  object \n",
      " 1   Bairro      22580 non-null  object \n",
      " 2   Quartos     22580 non-null  int64  \n",
      " 3   Vagas       22580 non-null  int64  \n",
      " 4   Suites      22580 non-null  int64  \n",
      " 5   Area        22580 non-null  int64  \n",
      " 6   Valor       22571 non-null  float64\n",
      " 7   Condominio  20765 non-null  float64\n",
      " 8   IPTU        15795 non-null  float64\n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Obtém um resumo do dataframe\n",
    "imoveis.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com este resumo, notamos que a coluna de valor do aluguel possui 9 registros nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Armazena o número de linhas do dataframe\n",
    "a = imoveis.shape[0]\n",
    "\n",
    "# Remove as linhas em que há valores nulos na coluna Valor\n",
    "imoveis.dropna(subset=['Valor'], inplace=True)\n",
    "\n",
    "# Armazena o número de linhas do dataframe após a remoção de registros nulos\n",
    "b = imoveis.shape[0]\n",
    "\n",
    "# Exibe o número de linhas eliminadas\n",
    "a - b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os imóveis classificados como 'Apartamento' devem obrigatoriamente possuir o valor de seu condomínio informado. Portanto, vamos remover todos os apartamentos em que o condomínio não foi informado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtro para selecionar os apartamentos com valores de condomínio nulos\n",
    "filtro = (imoveis['Tipo'] == 'Apartamento') & (imoveis['Condominio'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "745"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Número de imóveis antes de eliminar registros\n",
    "a = imoveis.shape[0]\n",
    "\n",
    "# Seleciona todos os imóveis que não são do tipo apartamento ou os apartamentos que possuem valor de condomínio informado\n",
    "imoveis = imoveis[~filtro]\n",
    "\n",
    "# Número de imóveis depois de eliminar registros\n",
    "b = imoveis.shape[0]\n",
    "\n",
    "# Número de registros eliminados\n",
    "a - b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1068"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exibe o número de imóveis cujo condomínio é nulo\n",
    "imoveis[imoveis['Condominio'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para estes imóveis com condomínio nulo, vamos atribuir o valor 0 já que de fato não possuem condomínio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "imoveis = imoveis.fillna({'Condominio': 0, 'IPTU': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imoveis['Condominio'].fillna(0, inplace=True)\n",
    "# imoveis['IPTU'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Número de imóveis com condomínio nulo\n",
    "imoveis[imoveis['Condominio'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Número de imóveis com IPTU nulo\n",
    "imoveis[imoveis['IPTU'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21826 entries, 0 to 22579\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Tipo        21826 non-null  object \n",
      " 1   Bairro      21826 non-null  object \n",
      " 2   Quartos     21826 non-null  int64  \n",
      " 3   Vagas       21826 non-null  int64  \n",
      " 4   Suites      21826 non-null  int64  \n",
      " 5   Area        21826 non-null  int64  \n",
      " 6   Valor       21826 non-null  float64\n",
      " 7   Condominio  21826 non-null  float64\n",
      " 8   IPTU        21826 non-null  float64\n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "imoveis.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, notamos que não há mais registros com dados nulos em nossa base de dados. Podemos, pois, exportar esta base para análises posteriores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "imoveis.to_csv('imoveis-residenciais.csv', sep=';', index=False)"
   ]
  }
 ],
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