Tratamento de Dados Faltantes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0aa31bc0",
   "metadata": {},
   "source": [
    "# Relatório de Análise V "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c83810",
   "metadata": {},
   "source": [
    "## Tratamento de Dados Faltantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c3fa44f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be8b377a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = pd.read_csv('data/aluguel_residencial.csv', sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "659b84af",
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   "outputs": [
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       "  <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <td>390.0</td>\n",
       "      <td>20.0</td>\n",
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       "    <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",
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      "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": 3,
     "metadata": {},
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   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6c8d7f1",
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       "        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        True   True\n",
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       "3      False   False    False  False   False  False  False       False   True\n",
       "4      False   False    False  False   False  False  False        True   True\n",
       "...      ...     ...      ...    ...     ...    ...    ...         ...    ...\n",
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       "\n",
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       "       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       False  False\n",
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       "...     ...     ...      ...    ...     ...   ...    ...         ...    ...\n",
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       "\n",
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     "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"
     ]
    }
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  {
   "cell_type": "code",
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       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados[dados['Valor'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec9bf514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = dados.shape[0]\n",
    "dados.dropna(subset = ['Valor'], inplace = True)\n",
    "B = dados.shape[0]\n",
    "A - B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "288db05d",
   "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",
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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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Tipo, Bairro, Quartos, Vagas, Suites, Area, Valor, Condominio, IPTU]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados[dados['Valor'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e59f9a7c",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22575</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Méier</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>900.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22576</th>\n",
       "      <td>Quitinete</td>\n",
       "      <td>Centro</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>800.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22577</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Jacarepaguá</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22578</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>São Francisco Xavier</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22579</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>Leblon</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22571 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Tipo                Bairro  Quartos  Vagas  Suites  Area  \\\n",
       "0        Quitinete            Copacabana        1      0       0    40   \n",
       "1             Casa       Jardim Botânico        2      0       1   100   \n",
       "2      Apartamento                Centro        1      0       0    15   \n",
       "3      Apartamento          Higienópolis        1      0       0    48   \n",
       "4      Apartamento          Vista Alegre        3      1       0    70   \n",
       "...            ...                   ...      ...    ...     ...   ...   \n",
       "22575  Apartamento                 Méier        2      0       0    70   \n",
       "22576    Quitinete                Centro        0      0       0    27   \n",
       "22577  Apartamento           Jacarepaguá        3      1       2    78   \n",
       "22578  Apartamento  São Francisco Xavier        2      1       0    48   \n",
       "22579  Apartamento                Leblon        2      0       0    70   \n",
       "\n",
       "        Valor  Condominio  IPTU  \n",
       "0      1700.0       500.0  60.0  \n",
       "1      7000.0         NaN   NaN  \n",
       "2       800.0       390.0  20.0  \n",
       "3       800.0       230.0   NaN  \n",
       "4      1200.0         NaN   NaN  \n",
       "...       ...         ...   ...  \n",
       "22575   900.0       490.0  48.0  \n",
       "22576   800.0       350.0  25.0  \n",
       "22577  1800.0       800.0  40.0  \n",
       "22578  1400.0       509.0  37.0  \n",
       "22579  3000.0       760.0   NaN  \n",
       "\n",
       "[22571 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3d46934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1813"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total de assinaturas nulas\n",
    "dados[dados['Condominio'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4a72445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "745"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtrar os dados de acordo com o tipo 'Apartamento'\n",
    "selecao = (dados['Tipo']=='Apartamento') & (dados['Condominio'].isnull())\n",
    "A = dados.shape[0]\n",
    "dados = dados[~selecao]\n",
    "B = dados.shape[0]\n",
    "A - B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecd4be07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1068"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verificar quantas assinaturas nulas temos em Condominio\n",
    "dados[dados['Condominio'].isnull()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a8d5340",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:5176: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n"
     ]
    }
   ],
   "source": [
    "#atribuir o valor 0 a eles\n",
    "dados.fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "49517755",
   "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": [
    "dados.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "27e51e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dados.to_csv('data/aluguel_residencial.csv', sep=';', index  = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0272b15b",
   "metadata": {},
   "source": [
    "## Exercicíos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "56b38d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "imoveis = pd.DataFrame([['Apartamento', None, 970, 68], \n",
    "                        ['Apartamento', 2000, 878, 112], \n",
    "                        ['Casa', 5000, None, 500], \n",
    "                        ['Apartamento', None, 1010, 170], \n",
    "                        ['Apartamento', 1500, 850, None], \n",
    "                        ['Casa', None, None, None], \n",
    "                        ['Apartamento', 2000, 878, None], \n",
    "                        ['Apartamento', 1550, None, 228], \n",
    "                        ['Apartamento', 2500, 880, 195]], \n",
    "                        columns = ['Tipo', 'Valor', 'Condominio', 'IPTU'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5db1b544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Condominio</th>\n",
       "      <th>IPTU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>NaN</td>\n",
       "      <td>970.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>878.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Casa</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Casa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>878.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tipo   Valor  Condominio   IPTU\n",
       "0  Apartamento     NaN       970.0   68.0\n",
       "1  Apartamento  2000.0       878.0  112.0\n",
       "2         Casa  5000.0         NaN  500.0\n",
       "3  Apartamento     NaN      1010.0  170.0\n",
       "4  Apartamento  1500.0       850.0    NaN\n",
       "5         Casa     NaN         NaN    NaN\n",
       "6  Apartamento  2000.0       878.0    NaN\n",
       "7  Apartamento  1550.0         NaN  228.0\n",
       "8  Apartamento  2500.0       880.0  195.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imoveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f9e53865",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>Casa</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Apartamento</td>\n",
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       "      <td>878.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Apartamento</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Tipo   Valor  Condominio   IPTU\n",
       "0  Apartamento     NaN       970.0   68.0\n",
       "1  Apartamento  2000.0       878.0  112.0\n",
       "2         Casa  5000.0         NaN  500.0\n",
       "3  Apartamento     NaN      1010.0  170.0\n",
       "4  Apartamento  1500.0       850.0    NaN\n",
       "5         Casa     NaN         NaN    NaN\n",
       "6  Apartamento  2000.0       878.0    NaN\n",
       "7  Apartamento  1550.0         NaN  228.0\n",
       "8  Apartamento  2500.0       880.0  195.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "imoveis.index = range(imoveis.shape[0])\n",
    "imoveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3040aa36",
   "metadata": {},
   "outputs": [
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       "      <td>500.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>Apartamento</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Apartamento</td>\n",
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       "      <td>880.0</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          Tipo   Valor  Condominio   IPTU\n",
       "1  Apartamento  2000.0       878.0  112.0\n",
       "2         Casa  5000.0         NaN  500.0\n",
       "4  Apartamento  1500.0       850.0    NaN\n",
       "6  Apartamento  2000.0       878.0    NaN\n",
       "7  Apartamento  1550.0         NaN  228.0\n",
       "8  Apartamento  2500.0       880.0  195.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "imoveis.dropna(subset = ['Valor'], inplace = True)\n",
    "imoveis"
   ]
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  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a9669d02",
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   "outputs": [
    {
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       "      <td>195.0</td>\n",
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      "text/plain": [
       "          Tipo   Valor  Condominio   IPTU\n",
       "0  Apartamento  2000.0       878.0  112.0\n",
       "1         Casa  5000.0         NaN  500.0\n",
       "2  Apartamento  1500.0       850.0    NaN\n",
       "3  Apartamento  2000.0       878.0    NaN\n",
       "4  Apartamento  1550.0         NaN  228.0\n",
       "5  Apartamento  2500.0       880.0  195.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imoveis.index = range(imoveis.shape[0])\n",
    "imoveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edfd3ad6",
   "metadata": {},
   "outputs": [],
   "source": []
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