exercicio_pandas_relatorio_imoveis.ipynb

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      "source": [
        "# Exercicíos do curso Python Pandas do curso Alura 🐼 💻\n",
        "\n",
        "\n",
        "##### **A base de dados com informações sobre imóveis disponíveis para alugueis, contendo as seguintes informações:**\n",
        "\n",
        "  *  Tipo\t\n",
        "  * Bairro\t\n",
        "  * Quartos\t\n",
        "  * Vagas\n",
        "  * Suites\n",
        "  * Area\n",
        "  * Valor\n",
        "  * Condominio\n",
        "  * IPTU\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "thxoHROjjzoE"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Relatório de análise"
      ],
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        "id": "LgWNPDODAb_k"
      }
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      "execution_count": 2,
      "metadata": {
        "id": "yh3v6LdZl334"
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      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"aluguel.csv\", sep=\";\")"
      ],
      "metadata": {
        "id": "24ykImSL_JMv"
      },
      "execution_count": 5,
      "outputs": []
    },
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        "df.head()"
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              "                      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  1700.0       500.0    60.0  \n",
              "1  7000.0         NaN     NaN  \n",
              "2  5200.0      4020.0  1111.0  \n",
              "3   800.0       390.0    20.0  \n",
              "4   800.0       230.0     NaN  "
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              "      <td>Apartamento</td>\n",
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              "      <td>Apartamento</td>\n",
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        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "## Informações Gerais da Base de Dados"
      ],
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      }
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        "id": "TKoB_LEV_Vtl",
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      "execution_count": 7,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32960 entries, 0 to 32959\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Tipo        32960 non-null  object \n",
            " 1   Bairro      32960 non-null  object \n",
            " 2   Quartos     32960 non-null  int64  \n",
            " 3   Vagas       32960 non-null  int64  \n",
            " 4   Suites      32960 non-null  int64  \n",
            " 5   Area        32960 non-null  int64  \n",
            " 6   Valor       32943 non-null  float64\n",
            " 7   Condominio  28867 non-null  float64\n",
            " 8   IPTU        22723 non-null  float64\n",
            "dtypes: float64(3), int64(4), object(2)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ]
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        "df.shape"
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              "(32960, 9)"
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      "cell_type": "code",
      "source": [
        "imoveis = df.shape[0]\n",
        "variaveis = df.shape[1]\n",
        "print(f\"A base de dados contém {imoveis} registros e {variaveis} variáveis.\")"
      ],
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          "text": [
            "A base de dados contém 32960 registros e 9 variáveis.\n"
          ]
        }
      ]
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        "dados = df.dtypes"
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      "outputs": []
    },
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        "dados"
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      "execution_count": 11,
      "outputs": [
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          "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",
              "dtype: object"
            ]
          },
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        }
      ]
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        "tipos_de_dados = pd.DataFrame(dados, columns=[\"Tipos de Dados\"])"
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      },
      "execution_count": 12,
      "outputs": []
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        "tipos_de_dados.columns.name = \"Variaveis\""
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              "Variaveis  Tipos de Dados\n",
              "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"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1d07f704-5100-430f-9c19-041e840ae4bf');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- - -\n",
        "## Verificar os tipos de imoveis"
      ],
      "metadata": {
        "id": "kU6qnVkEDxuh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tipos_de_imoveis =  [imoveis for imoveis in df[\"Tipo\"].drop_duplicates()]"
      ],
      "metadata": {
        "id": "lAHlqLx3D2R5"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tipos_de_imoveis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFZp4S2cEAY8",
        "outputId": "e564933c-babf-491d-e11f-86fe30dc7754"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Quitinete',\n",
              " 'Casa',\n",
              " 'Conjunto Comercial/Sala',\n",
              " 'Apartamento',\n",
              " 'Casa de Condomínio',\n",
              " 'Prédio Inteiro',\n",
              " 'Flat',\n",
              " 'Loja/Salão',\n",
              " 'Galpão/Depósito/Armazém',\n",
              " 'Casa Comercial',\n",
              " 'Casa de Vila',\n",
              " 'Terreno Padrão',\n",
              " 'Box/Garagem',\n",
              " 'Loft',\n",
              " 'Loja Shopping/ Ct Comercial',\n",
              " 'Chácara',\n",
              " 'Loteamento/Condomínio',\n",
              " 'Sítio',\n",
              " 'Pousada/Chalé',\n",
              " 'Studio',\n",
              " 'Hotel',\n",
              " 'Indústria']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tipo_imoveis = pd.DataFrame(tipos_de_imoveis, columns=[\"Tipo de imoveis\"])"
      ],
      "metadata": {
        "id": "eMoS_uxyEWbt"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tipo_imoveis"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "aamsSjWMFi7O",
        "outputId": "f7b3f1b3-ac29-4bc7-8029-218090206d66"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Tipo de imoveis\n",
              "0                     Quitinete\n",
              "1                          Casa\n",
              "2       Conjunto Comercial/Sala\n",
              "3                   Apartamento\n",
              "4            Casa de Condomínio\n",
              "5                Prédio Inteiro\n",
              "6                          Flat\n",
              "7                    Loja/Salão\n",
              "8       Galpão/Depósito/Armazém\n",
              "9                Casa Comercial\n",
              "10                 Casa de Vila\n",
              "11               Terreno Padrão\n",
              "12                  Box/Garagem\n",
              "13                         Loft\n",
              "14  Loja Shopping/ Ct Comercial\n",
              "15                      Chácara\n",
              "16        Loteamento/Condomínio\n",
              "17                        Sítio\n",
              "18                Pousada/Chalé\n",
              "19                       Studio\n",
              "20                        Hotel\n",
              "21                    Indústria"
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              "      <td>Chácara</td>\n",
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              "      <th>17</th>\n",
              "      <td>Sítio</td>\n",
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              "      <th>18</th>\n",
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              "      <td>Hotel</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 18
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- - -\n",
        "## Eliminando dados de imoveis que não são residenciais"
      ],
      "metadata": {
        "id": "9Mm1TcyqT9V6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_residenciais = ['Quitinete', 'Casa', 'Apartamento', 'Casa de Condomínio', 'Casa de Vila']"
      ],
      "metadata": {
        "id": "nG2DO-04ULT2"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_residencial = df[df.Tipo.isin(imoveis_residenciais)== True]"
      ],
      "metadata": {
        "id": "ipnFUR_IVZmJ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#verificando se os imoveis não selecionados foram removidos\n",
        "df_residencial.Tipo.drop_duplicates()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "XIrGBh3dVikH",
        "outputId": "3879e253-b9fc-47f5-9e51-8f0211fb94d6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0               Quitinete\n",
              "1                    Casa\n",
              "3             Apartamento\n",
              "7      Casa de Condomínio\n",
              "117          Casa de Vila\n",
              "Name: Tipo, dtype: object"
            ]
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          "metadata": {},
          "execution_count": 21
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        "outputId": "23abf035-e193-4bda-b47a-1d068672d6d3"
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      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "3      Apartamento                Centro        1      0       0    15   \n",
              "4      Apartamento          Higienópolis        1      0       0    48   \n",
              "5      Apartamento          Vista Alegre        3      1       0    70   \n",
              "...            ...                   ...      ...    ...     ...   ...   \n",
              "32953  Apartamento                 Méier        2      0       0    70   \n",
              "32955    Quitinete                Centro        0      0       0    27   \n",
              "32956  Apartamento           Jacarepaguá        3      1       2    78   \n",
              "32957  Apartamento  São Francisco Xavier        2      1       0    48   \n",
              "32958  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",
              "3       800.0       390.0  20.0  \n",
              "4       800.0       230.0   NaN  \n",
              "5      1200.0         NaN   NaN  \n",
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              "32953   900.0       490.0  48.0  \n",
              "32955   800.0       350.0  25.0  \n",
              "32956  1800.0       800.0  40.0  \n",
              "32957  1400.0       509.0  37.0  \n",
              "32958  3000.0       760.0   NaN  \n",
              "\n",
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              "      <td>Quitinete</td>\n",
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              "      <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>32957</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>32958</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>22580 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-58d0ddb1-ce68-4265-a577-21e2e8131f15')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-58d0ddb1-ce68-4265-a577-21e2e8131f15 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-58d0ddb1-ce68-4265-a577-21e2e8131f15');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_residenciais = df_residencial.shape[0]\n",
        "variaveis = df_residencial.shape[1]\n",
        "print(f\"A base de dados contém {imoveis_residenciais} registros e {variaveis} variáveis.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NTj3cO2W-sv",
        "outputId": "0ad791fb-2a6b-4186-8499-ce1bfe04bfea"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A base de dados contém 22580 registros e 9 variáveis.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Foram removidos {imoveis - imoveis_residenciais} registros em relação ao arquivo original\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVKdCenDXgwL",
        "outputId": "0826f2ff-3267-4029-e37e-74d3ae2fc2c3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Foram removidos 10380 registros em relação ao arquivo original\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exportando arquivo que consta somente os dados de imoveis residenciais\n",
        "df_residencial.to_csv(\"aluguel_imoveis_residenciais.csv\", index=False, sep=\";\")"
      ],
      "metadata": {
        "id": "1OhCAQOAV7RP"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Verificar seleção e frequência dos imoveis residencias\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jFyMSJa5dcmr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Classificar imoveis do tipo Apartamento"
      ],
      "metadata": {
        "id": "fthtG_HJfNiq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_apartamento = df_residencial[df_residencial.Tipo == \"Apartamento\"]"
      ],
      "metadata": {
        "id": "05PrtyvXeP0q"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_apartamento"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "A4kClEzrfmZ-",
        "outputId": "d43236ec-b32c-4214-e993-628bbbbcfa12"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Tipo                Bairro  Quartos  Vagas  Suites  Area  \\\n",
              "3      Apartamento                Centro        1      0       0    15   \n",
              "4      Apartamento          Higienópolis        1      0       0    48   \n",
              "5      Apartamento          Vista Alegre        3      1       0    70   \n",
              "6      Apartamento              Cachambi        2      0       0    50   \n",
              "10     Apartamento                Centro        1      0       0    36   \n",
              "...            ...                   ...      ...    ...     ...   ...   \n",
              "32951  Apartamento        Vila Valqueire        2      0       0    52   \n",
              "32953  Apartamento                 Méier        2      0       0    70   \n",
              "32956  Apartamento           Jacarepaguá        3      1       2    78   \n",
              "32957  Apartamento  São Francisco Xavier        2      1       0    48   \n",
              "32958  Apartamento                Leblon        2      0       0    70   \n",
              "\n",
              "        Valor  Condominio  IPTU  \n",
              "3       800.0       390.0  20.0  \n",
              "4       800.0       230.0   NaN  \n",
              "5      1200.0         NaN   NaN  \n",
              "6      1300.0       301.0  17.0  \n",
              "10     1200.0         NaN   NaN  \n",
              "...       ...         ...   ...  \n",
              "32951  1000.0       550.0   NaN  \n",
              "32953   900.0       490.0  48.0  \n",
              "32956  1800.0       800.0  40.0  \n",
              "32957  1400.0       509.0  37.0  \n",
              "32958  3000.0       760.0   NaN  \n",
              "\n",
              "[19532 rows x 9 columns]"
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              "      <th>4</th>\n",
              "      <td>Apartamento</td>\n",
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              "      <td>Apartamento</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Apartamento</td>\n",
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              "      <td>Apartamento</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>32951</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Vila Valqueire</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>32953</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>32956</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>32957</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>32958</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>19532 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc7f6ab2-ced0-418d-aec1-a076c105de14')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc7f6ab2-ced0-418d-aec1-a076c105de14 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc7f6ab2-ced0-418d-aec1-a076c105de14');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total de imoveis que são do tipo Apartamento: {imoveis_apartamento.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XSbN9MmesgN",
        "outputId": "da9df715-2c34-4604-bdf5-3bf823e18687"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de imoveis que são do tipo Apartamento: 19532\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Classificar imoveis do tipo Casa"
      ],
      "metadata": {
        "id": "a9lb_pnXfWRD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_casa = df_residencial[(df_residencial.Tipo != \"Apartamento\") & (df_residencial.Tipo != \"Quitinete\")]"
      ],
      "metadata": {
        "id": "wHANxL9DewM2"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_casa"
      ],
      "metadata": {
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              "                     Tipo                   Bairro  Quartos  Vagas  Suites  \\\n",
              "1                    Casa          Jardim Botânico        2      0       1   \n",
              "7      Casa de Condomínio          Barra da Tijuca        5      4       5   \n",
              "8      Casa de Condomínio                    Ramos        2      2       0   \n",
              "18     Casa de Condomínio                  Taquara        3      1       1   \n",
              "22     Casa de Condomínio          Barra da Tijuca        4      3       2   \n",
              "...                   ...                      ...      ...    ...     ...   \n",
              "32908  Casa de Condomínio  Freguesia (Jacarepaguá)        3      3       1   \n",
              "32911                Casa            Bento Ribeiro        2      1       0   \n",
              "32924  Casa de Condomínio          Barra da Tijuca        3      5       3   \n",
              "32931                Casa                   Centro        5      0       1   \n",
              "32952  Casa de Condomínio          Barra da Tijuca        5      3       4   \n",
              "\n",
              "       Area    Valor  Condominio    IPTU  \n",
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              "32931   160   5000.0         NaN    80.0  \n",
              "32952   450  15000.0      1711.0  2332.0  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total de imoveis que são do tipo Casa: {imoveis_casa.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovrcsSONglXG",
        "outputId": "3a1bda0c-d229-4edb-9112-8cd1f5805f67"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de imoveis que são do tipo Casa: 2212\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Selecionar imoveis com área entre 60 e 100 m²"
      ],
      "metadata": {
        "id": "NRYE5xJafego"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_area = df_residencial[(df_residencial.Area >= 60) & (df_residencial.Area <= 100)]"
      ],
      "metadata": {
        "id": "OSAtWOszfgdx"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_area"
      ],
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        "id": "yRC4mgNNhHam",
        "outputId": "90e74564-f7fb-4abb-c635-dff715efc46f"
      },
      "execution_count": 33,
      "outputs": [
        {
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              "                     Tipo                    Bairro  Quartos  Vagas  Suites  \\\n",
              "1                    Casa           Jardim Botânico        2      0       1   \n",
              "5             Apartamento              Vista Alegre        3      1       0   \n",
              "8      Casa de Condomínio                     Ramos        2      2       0   \n",
              "11            Apartamento                    Grajaú        2      1       0   \n",
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              "...                   ...                       ...      ...    ...     ...   \n",
              "32944         Apartamento  Recreio dos Bandeirantes        3      2       1   \n",
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              "32953         Apartamento                     Méier        2      0       0   \n",
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              "      <td>3000.0</td>\n",
              "      <td>760.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8719 rows × 9 columns</p>\n",
              "</div>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total de imoveis com área entre 60 e 100 m²: {imoveis_area.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4hJ8XtXhO9O",
        "outputId": "c3be0238-d7ef-4ed8-a019-483356a316e8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de imoveis com área entre 60 e 100 m²: 8719\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Selecionar imoveis com pelo menos 4 quartos e aluguel menor que R$ 2.000"
      ],
      "metadata": {
        "id": "1_AKSZLAfhuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_quartos = df_residencial[(df_residencial.Quartos >= 4) & (df_residencial.Valor < 2000)]"
      ],
      "metadata": {
        "id": "megeuuxefixi"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_quartos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZvgqATn9hlG5",
        "outputId": "06fb5308-9759-47a6-8f5b-8263c73a7a3b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Tipo                    Bairro  Quartos  Vagas  Suites  \\\n",
              "701           Apartamento  Recreio dos Bandeirantes        4      2       2   \n",
              "1465          Apartamento                   Taquara        4      1       1   \n",
              "2874                 Casa              Campo Grande        4      1       4   \n",
              "3075                 Casa         Engenho de Dentro        4      3       0   \n",
              "3218          Apartamento  Recreio dos Bandeirantes        4      2       2   \n",
              "3419         Casa de Vila                     Méier        4      0       1   \n",
              "5849                 Casa              Campo Grande        4      1       0   \n",
              "5852          Apartamento              Campo Grande        4      2       1   \n",
              "6456          Apartamento  Recreio dos Bandeirantes        4      2       1   \n",
              "7520          Apartamento               Cidade Nova        4      0       0   \n",
              "9716          Apartamento                 Pechincha        4      1       2   \n",
              "10132                Casa                 Riachuelo        4      0       1   \n",
              "10144                Casa                     Méier        4      1       1   \n",
              "11323         Apartamento  Recreio dos Bandeirantes        4      2       3   \n",
              "11367         Apartamento  Recreio dos Bandeirantes        4      3       2   \n",
              "11675         Apartamento  Recreio dos Bandeirantes        4      2       1   \n",
              "14890         Apartamento  Recreio dos Bandeirantes        4      2       1   \n",
              "16078  Casa de Condomínio                   Taquara        4      1       0   \n",
              "16638                Casa              Santo Cristo        5      0       0   \n",
              "17637         Apartamento                      Leme        4      0       1   \n",
              "18453         Apartamento  Recreio dos Bandeirantes        4      2       1   \n",
              "19553         Apartamento                   Camorim        4      1       1   \n",
              "20077         Apartamento           Barra da Tijuca        4      3       4   \n",
              "20633                Casa                Praça Seca        4      1       1   \n",
              "21973  Casa de Condomínio   Freguesia (Jacarepaguá)        4      2       1   \n",
              "22000                Casa        Pedra de Guaratiba        4      1       0   \n",
              "23284         Apartamento              Campo Grande        4      2       2   \n",
              "23453         Apartamento  Recreio dos Bandeirantes        4      2       2   \n",
              "23608         Apartamento              Rio Comprido        4      1       0   \n",
              "24572                Casa              Campo Grande        4      3       0   \n",
              "25993         Apartamento                   Taquara        4      3       1   \n",
              "26178                Casa              Campo Grande        4      0       1   \n",
              "26549                Casa              Campo Grande        4      0       0   \n",
              "26750                Casa              Braz de Pina        4      1       0   \n",
              "27033                Casa              Campo Grande        4      1       3   \n",
              "28421                Casa              Campo Grande        4      1       2   \n",
              "29517         Apartamento              Campo Grande        4      1       1   \n",
              "30670         Apartamento           Barra da Tijuca        4      3       4   \n",
              "31382         Apartamento             Vargem Grande        4      0       1   \n",
              "31840         Apartamento  Recreio dos Bandeirantes        4      2       2   \n",
              "32064                Casa              Campo Grande        4      2       0   \n",
              "\n",
              "       Area   Valor  Condominio   IPTU  \n",
              "701     160  1900.0       830.0    NaN  \n",
              "1465    110  1900.0         NaN    NaN  \n",
              "2874    100  1600.0         NaN    NaN  \n",
              "3075    120  1500.0         NaN    NaN  \n",
              "3218    125  1700.0         NaN   90.0  \n",
              "3419     85  1600.0         NaN    NaN  \n",
              "5849    140  1700.0         NaN    NaN  \n",
              "5852    115  1900.0       500.0    NaN  \n",
              "6456    105  1900.0      1492.0  205.0  \n",
              "7520    110  1300.0         NaN    NaN  \n",
              "9716    178  1900.0      1100.0  270.0  \n",
              "10132   100  1800.0         NaN   40.0  \n",
              "10144   125  1900.0         NaN    NaN  \n",
              "11323   280  1800.0      1400.0  580.0  \n",
              "11367   250  1500.0      1380.0  700.0  \n",
              "11675   160  1900.0      1093.0  183.0  \n",
              "14890   160  1900.0      1038.0  158.0  \n",
              "16078   110  1750.0       550.0  993.0  \n",
              "16638    85  1200.0         NaN    NaN  \n",
              "17637   185  1800.0      2000.0  270.0  \n",
              "18453   160  1900.0       930.0  150.0  \n",
              "19553    96  1350.0       747.0    NaN  \n",
              "20077   148  1600.0      1766.0    NaN  \n",
              "20633   128  1600.0         NaN  228.0  \n",
              "21973   120  1800.0       195.0   30.0  \n",
              "22000   100  1300.0         NaN    NaN  \n",
              "23284   160  1900.0       880.0    NaN  \n",
              "23453   160  1900.0       912.0  160.0  \n",
              "23608   110  1650.0       500.0    NaN  \n",
              "24572   120  1700.0         NaN    NaN  \n",
              "25993   190  1200.0         NaN    NaN  \n",
              "26178   366  1890.0         NaN  208.0  \n",
              "26549   159  1800.0         NaN    NaN  \n",
              "26750    99  1800.0         NaN    NaN  \n",
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              "29517    80  1600.0       950.0    NaN  \n",
              "30670   148  1500.0      1600.0  196.0  \n",
              "31382   180  1900.0         NaN  176.0  \n",
              "31840   160  1900.0      1038.0    NaN  \n",
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              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <th>3218</th>\n",
              "      <td>Apartamento</td>\n",
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              "      <td>4</td>\n",
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              "      <td>1700.0</td>\n",
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              "      <td>90.0</td>\n",
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              "      <td>Casa</td>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>1700.0</td>\n",
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              "      <td>Casa</td>\n",
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              "      <th>11323</th>\n",
              "      <td>Apartamento</td>\n",
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              "      <th>11367</th>\n",
              "      <td>Apartamento</td>\n",
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              "      <td>1380.0</td>\n",
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              "      <th>11675</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Recreio dos Bandeirantes</td>\n",
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              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>1093.0</td>\n",
              "      <td>183.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14890</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Recreio dos Bandeirantes</td>\n",
              "      <td>4</td>\n",
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              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>1038.0</td>\n",
              "      <td>158.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16078</th>\n",
              "      <td>Casa de Condomínio</td>\n",
              "      <td>Taquara</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "      <td>1750.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>993.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16638</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Santo Cristo</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
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              "      <td>85</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17637</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Leme</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>185</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>270.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18453</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Recreio dos Bandeirantes</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>930.0</td>\n",
              "      <td>150.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19553</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Camorim</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>96</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>747.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20077</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Barra da Tijuca</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>148</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>1766.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20633</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Praça Seca</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>128</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>228.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21973</th>\n",
              "      <td>Casa de Condomínio</td>\n",
              "      <td>Freguesia (Jacarepaguá)</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22000</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Pedra de Guaratiba</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23284</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Campo Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23453</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Recreio dos Bandeirantes</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>912.0</td>\n",
              "      <td>160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23608</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Rio Comprido</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "      <td>1650.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24572</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Campo Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25993</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Taquara</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>190</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26178</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Campo Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>366</td>\n",
              "      <td>1890.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>208.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26549</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Campo Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>159</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26750</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Braz de Pina</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27033</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Campo Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>100</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28421</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Campo Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>180</td>\n",
              "      <td>1850.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29517</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Campo Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>80</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>950.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30670</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Barra da Tijuca</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>148</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>196.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31382</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Vargem Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>180</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31840</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Recreio dos Bandeirantes</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>1038.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32064</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Campo Grande</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>1600.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff1f8ee1-f508-4062-85fa-c50117ece2b3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ff1f8ee1-f508-4062-85fa-c50117ece2b3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ff1f8ee1-f508-4062-85fa-c50117ece2b3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total de imoveis com pelo menos de 4 quartos e aluguel menor que R$ 2.000: {imoveis_quartos.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_sDwWkuhtFJ",
        "outputId": "10afd9d3-5073-4c3b-f640-242045d31146"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de imoveis com pelo menos de 4 quartos e aluguel menor que R$ 2.000: 41\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- - -\n",
        "## Tratamento de valores nulos"
      ],
      "metadata": {
        "id": "R5_qv_Nsva_O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_residencial.head(10)"
      ],
      "metadata": {
        "id": "r8r-CcFnh4AB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "0538ba26-444e-4f42-ee47-d51702e8402a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "3          Apartamento           Centro        1      0       0    15   \n",
              "4          Apartamento     Higienópolis        1      0       0    48   \n",
              "5          Apartamento     Vista Alegre        3      1       0    70   \n",
              "6          Apartamento         Cachambi        2      0       0    50   \n",
              "7   Casa de Condomínio  Barra da Tijuca        5      4       5   750   \n",
              "8   Casa de Condomínio            Ramos        2      2       0    65   \n",
              "10         Apartamento           Centro        1      0       0    36   \n",
              "11         Apartamento           Grajaú        2      1       0    70   \n",
              "\n",
              "      Valor  Condominio  IPTU  \n",
              "0    1700.0       500.0  60.0  \n",
              "1    7000.0         NaN   NaN  \n",
              "3     800.0       390.0  20.0  \n",
              "4     800.0       230.0   NaN  \n",
              "5    1200.0         NaN   NaN  \n",
              "6    1300.0       301.0  17.0  \n",
              "7   22000.0         NaN   NaN  \n",
              "8    1000.0         NaN   NaN  \n",
              "10   1200.0         NaN   NaN  \n",
              "11   1500.0       642.0  74.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-387485c7-fa15-4bdf-9e3a-f294850c6078\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>3</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>4</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>5</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>6</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Cachambi</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>301.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Casa de Condomínio</td>\n",
              "      <td>Barra da Tijuca</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>750</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Casa de Condomínio</td>\n",
              "      <td>Ramos</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Centro</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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        "df_residencial.isnull() #Observando quais valores são nulos"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 22580 entries, 0 to 32958\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",
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            " 8   IPTU        15795 non-null  float64\n",
            "dtypes: float64(3), int64(4), object(2)\n",
            "memory usage: 1.7+ MB\n"
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        "df_residencial[df_residencial[\"Valor\"].isnull()]"
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        "id": "G4jB86uUyCEW",
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      "execution_count": 42,
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              "              Tipo           Bairro  Quartos  Vagas  Suites  Area  Valor  \\\n",
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              "      <td>Casa</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Botafogo</td>\n",
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              "      <td>1010.0</td>\n",
              "      <td>170.0</td>\n",
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              "    <tr>\n",
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              "      <td>Apartamento</td>\n",
              "      <td>Copacabana</td>\n",
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              "      <td>0</td>\n",
              "      <td>72</td>\n",
              "      <td>NaN</td>\n",
              "      <td>850.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>7302</th>\n",
              "      <td>Casa</td>\n",
              "      <td>Campo Grande</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12683</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Leme</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>878.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>13229</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Glória</td>\n",
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              "      <td>910.0</td>\n",
              "      <td>228.0</td>\n",
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              "    <tr>\n",
              "      <th>13525</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",
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              "      <td>880.0</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-c67a5bd2-3d6a-4f89-b48f-4e0964190827');\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_residencial[\"Valor\"].isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4H6Q7mr1LzA",
        "outputId": "6aaa5690-e8d7-458d-dd4a-23cf701bd956"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "3        False\n",
              "4        False\n",
              "5        False\n",
              "         ...  \n",
              "32953    False\n",
              "32955    False\n",
              "32956    False\n",
              "32957    False\n",
              "32958    False\n",
              "Name: Valor, Length: 22580, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_residencial.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8i4KAaQB1VBP",
        "outputId": "aefb90de-67f8-453c-83e5-b047e4a30dbc"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22580"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_residencial.dropna(subset=[\"Valor\"], inplace=True) #substituindo os valores nulos por zero"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cz_H69Qx1bt_",
        "outputId": "1c7a29a0-a7f3-4358-8355-bb9afe80dd42"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: 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 func(*args, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_residencial"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tzloG98x1u0M",
        "outputId": "5407b0a3-2557-440e-8657-bb660af2a0bf"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "3      Apartamento                Centro        1      0       0    15   \n",
              "4      Apartamento          Higienópolis        1      0       0    48   \n",
              "5      Apartamento          Vista Alegre        3      1       0    70   \n",
              "...            ...                   ...      ...    ...     ...   ...   \n",
              "32953  Apartamento                 Méier        2      0       0    70   \n",
              "32955    Quitinete                Centro        0      0       0    27   \n",
              "32956  Apartamento           Jacarepaguá        3      1       2    78   \n",
              "32957  Apartamento  São Francisco Xavier        2      1       0    48   \n",
              "32958  Apartamento                Leblon        2      0       0    70   \n",
              "\n",
              "        Valor  Condominio  IPTU  \n",
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              "4       800.0       230.0   NaN  \n",
              "5      1200.0         NaN   NaN  \n",
              "...       ...         ...   ...  \n",
              "32953   900.0       490.0  48.0  \n",
              "32955   800.0       350.0  25.0  \n",
              "32956  1800.0       800.0  40.0  \n",
              "32957  1400.0       509.0  37.0  \n",
              "32958  3000.0       760.0   NaN  \n",
              "\n",
              "[22571 rows x 9 columns]"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        "df_residencial[df_residencial[\"Valor\"].isnull()]"
      ],
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      "execution_count": 48,
      "outputs": [
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Tipo, Bairro, Quartos, Vagas, Suites, Area, Valor, Condominio, IPTU]\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "df_residencial.info()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpF0tyGE1-wB",
        "outputId": "446a88ec-5b88-480c-d996-68116a46a2a5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 22571 entries, 0 to 32958\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Tipo        22571 non-null  object \n",
            " 1   Bairro      22571 non-null  object \n",
            " 2   Quartos     22571 non-null  int64  \n",
            " 3   Vagas       22571 non-null  int64  \n",
            " 4   Suites      22571 non-null  int64  \n",
            " 5   Area        22571 non-null  int64  \n",
            " 6   Valor       22571 non-null  float64\n",
            " 7   Condominio  20758 non-null  float64\n",
            " 8   IPTU        15791 non-null  float64\n",
            "dtypes: float64(3), int64(4), object(2)\n",
            "memory usage: 1.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Tratamento condicional"
      ],
      "metadata": {
        "id": "LxaKpm1X4HFU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Verificar os valores nulos da coluna Condominio\n",
        "df_residencial[df_residencial.Condominio.isnull()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jNl7f7p7TEsF",
        "outputId": "946a8127-e056-4a04-811e-bbfe986974d8"
      },
      "execution_count": 53,
      "outputs": [
        {
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              "                     Tipo                    Bairro  Quartos  Vagas  Suites  \\\n",
              "1                    Casa           Jardim Botânico        2      0       1   \n",
              "5             Apartamento              Vista Alegre        3      1       0   \n",
              "7      Casa de Condomínio           Barra da Tijuca        5      4       5   \n",
              "8      Casa de Condomínio                     Ramos        2      2       0   \n",
              "10            Apartamento                    Centro        1      0       0   \n",
              "...                   ...                       ...      ...    ...     ...   \n",
              "32859                Casa              Campo Grande        1      0       0   \n",
              "32865        Casa de Vila                    Pavuna        2      0       0   \n",
              "32893                Casa  Recreio dos Bandeirantes        4      2       2   \n",
              "32911                Casa             Bento Ribeiro        2      1       0   \n",
              "32931                Casa                    Centro        5      0       1   \n",
              "\n",
              "       Area    Valor  Condominio  IPTU  \n",
              "1       100   7000.0         NaN   NaN  \n",
              "5        70   1200.0         NaN   NaN  \n",
              "7       750  22000.0         NaN   NaN  \n",
              "8        65   1000.0         NaN   NaN  \n",
              "10       36   1200.0         NaN   NaN  \n",
              "...     ...      ...         ...   ...  \n",
              "32859    50    600.0         NaN   NaN  \n",
              "32865    55    600.0         NaN   NaN  \n",
              "32893   352   6500.0         NaN   NaN  \n",
              "32911    60   1400.0         NaN   NaN  \n",
              "32931   160   5000.0         NaN  80.0  \n",
              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Verificar quais tipos de residencia tem valores nulos\n",
        "df_residencial.Tipo[df_residencial.Condominio.isnull()].drop_duplicates()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4ClqTA5TYmL",
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            "text/plain": [
              "1                    Casa\n",
              "5             Apartamento\n",
              "7      Casa de Condomínio\n",
              "117          Casa de Vila\n",
              "536             Quitinete\n",
              "Name: Tipo, dtype: object"
            ]
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              "              Tipo           Bairro  Quartos  Vagas  Suites  Area   Valor  \\\n",
              "5      Apartamento     Vista Alegre        3      1       0    70  1200.0   \n",
              "10     Apartamento           Centro        1      0       0    36  1200.0   \n",
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      "cell_type": "code",
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        "df_residencial.fillna(0, inplace=True)"
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        "outputId": "829458c2-843b-44d2-a413-bae6242a4a30"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:5182: 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",
            "  downcast=downcast,\n"
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      "cell_type": "code",
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        "df_residencial.info()"
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        "id": "i3fPb80_UBpI",
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      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 22571 entries, 0 to 32958\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Tipo        22571 non-null  object \n",
            " 1   Bairro      22571 non-null  object \n",
            " 2   Quartos     22571 non-null  int64  \n",
            " 3   Vagas       22571 non-null  int64  \n",
            " 4   Suites      22571 non-null  int64  \n",
            " 5   Area        22571 non-null  int64  \n",
            " 6   Valor       22571 non-null  float64\n",
            " 7   Condominio  22571 non-null  float64\n",
            " 8   IPTU        22571 non-null  float64\n",
            "dtypes: float64(3), int64(4), object(2)\n",
            "memory usage: 1.7+ MB\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Atribuição de novos valores"
      ],
      "metadata": {
        "id": "NhaRsesMXBxe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcular o valor bruto de cada imovel\n",
        "df_residencial[\"Valor Bruto\"] = df_residencial[\"Valor\"] + df_residencial[\"Condominio\"] + df_residencial[\"IPTU\"]"
      ],
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        "colab": {
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        "id": "RsOjJZKcUFCn",
        "outputId": "d5585060-1678-497c-e7e6-b55ecf091553"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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      "execution_count": 60,
      "outputs": [
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              "              Tipo                Bairro  Quartos  Vagas  Suites  Area  \\\n",
              "0        Quitinete            Copacabana        1      0       0    40   \n",
              "1             Casa       Jardim Botânico        2      0       1   100   \n",
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              "5      Apartamento          Vista Alegre        3      1       0    70   \n",
              "...            ...                   ...      ...    ...     ...   ...   \n",
              "32953  Apartamento                 Méier        2      0       0    70   \n",
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              "32956  Apartamento           Jacarepaguá        3      1       2    78   \n",
              "32957  Apartamento  São Francisco Xavier        2      1       0    48   \n",
              "32958  Apartamento                Leblon        2      0       0    70   \n",
              "\n",
              "        Valor  Condominio  IPTU  Valor Bruto  \n",
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              "      <td>1030.0</td>\n",
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              "      <td>1200.0</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32953</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Méier</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>900.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>1438.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32955</th>\n",
              "      <td>Quitinete</td>\n",
              "      <td>Centro</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>800.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1175.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32956</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Jacarepaguá</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>78</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>800.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32957</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>São Francisco Xavier</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>509.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1946.0</td>\n",
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              "    <tr>\n",
              "      <th>32958</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcular os valor por m²\n",
        "df_residencial[\"Valor por m²\"] = (df_residencial[\"Valor\"] / df_residencial[\"Area\"]).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYtkfvHOXhKv",
        "outputId": "7762b82a-28de-4004-a118-c67344bc970b"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "df_residencial"
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        "id": "ubGtJSkpX0HG",
        "outputId": "49c733f7-c470-46a4-80e0-2003618190ca"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "3      Apartamento                Centro        1      0       0    15   \n",
              "4      Apartamento          Higienópolis        1      0       0    48   \n",
              "5      Apartamento          Vista Alegre        3      1       0    70   \n",
              "...            ...                   ...      ...    ...     ...   ...   \n",
              "32953  Apartamento                 Méier        2      0       0    70   \n",
              "32955    Quitinete                Centro        0      0       0    27   \n",
              "32956  Apartamento           Jacarepaguá        3      1       2    78   \n",
              "32957  Apartamento  São Francisco Xavier        2      1       0    48   \n",
              "32958  Apartamento                Leblon        2      0       0    70   \n",
              "\n",
              "        Valor  Condominio  IPTU  Valor Bruto  Valor por m²  \n",
              "0      1700.0       500.0  60.0       2260.0         42.50  \n",
              "1      7000.0         0.0   0.0       7000.0         70.00  \n",
              "3       800.0       390.0  20.0       1210.0         53.33  \n",
              "4       800.0       230.0   0.0       1030.0         16.67  \n",
              "5      1200.0         0.0   0.0       1200.0         17.14  \n",
              "...       ...         ...   ...          ...           ...  \n",
              "32953   900.0       490.0  48.0       1438.0         12.86  \n",
              "32955   800.0       350.0  25.0       1175.0         29.63  \n",
              "32956  1800.0       800.0  40.0       2640.0         23.08  \n",
              "32957  1400.0       509.0  37.0       1946.0         29.17  \n",
              "32958  3000.0       760.0   0.0       3760.0         42.86  \n",
              "\n",
              "[22571 rows x 11 columns]"
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4089003b-ca66-43d2-bb63-5293fa230cb5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Definir tipos precisos de imoveis\n",
        "casa = ['Casa', 'Casa de Condomínio', 'Casa de Vila', \"Quitinete\"]\n",
        "df_residencial[\"Tipo Agregado\"] = df_residencial.Tipo.apply(lambda x: \"Casa\" if x in casa else \"Apartamento\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sY_DZyi_X2le",
        "outputId": "8149ae6d-3764-4bb8-b827-76f34bbb96a9"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_residencial"
      ],
      "metadata": {
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          "height": 424
        },
        "id": "J80RxtvhZGJK",
        "outputId": "0e60ffa3-576e-4053-decc-bddd74f3bdb6"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "3      Apartamento                Centro        1      0       0    15   \n",
              "4      Apartamento          Higienópolis        1      0       0    48   \n",
              "5      Apartamento          Vista Alegre        3      1       0    70   \n",
              "...            ...                   ...      ...    ...     ...   ...   \n",
              "32953  Apartamento                 Méier        2      0       0    70   \n",
              "32955    Quitinete                Centro        0      0       0    27   \n",
              "32956  Apartamento           Jacarepaguá        3      1       2    78   \n",
              "32957  Apartamento  São Francisco Xavier        2      1       0    48   \n",
              "32958  Apartamento                Leblon        2      0       0    70   \n",
              "\n",
              "        Valor  Condominio  IPTU  Valor Bruto  Valor por m² Tipo Agregado  \n",
              "0      1700.0       500.0  60.0       2260.0         42.50          Casa  \n",
              "1      7000.0         0.0   0.0       7000.0         70.00          Casa  \n",
              "3       800.0       390.0  20.0       1210.0         53.33   Apartamento  \n",
              "4       800.0       230.0   0.0       1030.0         16.67   Apartamento  \n",
              "5      1200.0         0.0   0.0       1200.0         17.14   Apartamento  \n",
              "...       ...         ...   ...          ...           ...           ...  \n",
              "32953   900.0       490.0  48.0       1438.0         12.86   Apartamento  \n",
              "32955   800.0       350.0  25.0       1175.0         29.63          Casa  \n",
              "32956  1800.0       800.0  40.0       2640.0         23.08   Apartamento  \n",
              "32957  1400.0       509.0  37.0       1946.0         29.17   Apartamento  \n",
              "32958  3000.0       760.0   0.0       3760.0         42.86   Apartamento  \n",
              "\n",
              "[22571 rows x 12 columns]"
            ],
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apartamento</td>\n",
              "      <td>Higienópolis</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>230.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>16.67</td>\n",
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              "      <th>5</th>\n",
              "      <td>Apartamento</td>\n",
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              "      <td>Quitinete</td>\n",
              "      <td>Centro</td>\n",
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              "      <td>42.86</td>\n",
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              "<p>22571 rows × 12 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Estatistica Descritiva"
      ],
      "metadata": {
        "id": "vwUqLAWCbIPh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "media_valores_residencias = df_residencial.Valor.mean().round(3)\n",
        "print(f\"O valor dos imoveis em média é de R$ {media_valores_residencias}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjPsbEQdamI9",
        "outputId": "e48966ca-db27-4b5f-fb7b-5482f3c27f3e"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O valor dos imoveis em média é de R$ 4985.73\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Vizualizar os bairros que consta na base de dados\n",
        "df_residencial.Bairro.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjCBYAyAb4IO",
        "outputId": "fa8a2612-b1a1-4421-a0d0-cc872785a648"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Copacabana', 'Jardim Botânico', 'Centro', 'Higienópolis',\n",
              "       'Vista Alegre', 'Cachambi', 'Barra da Tijuca', 'Ramos', 'Grajaú',\n",
              "       'Lins de Vasconcelos', 'Taquara', 'Freguesia (Jacarepaguá)',\n",
              "       'Tijuca', 'Olaria', 'Ipanema', 'Campo Grande', 'Botafogo',\n",
              "       'Recreio dos Bandeirantes', 'Leblon', 'Jardim Oceânico', 'Humaitá',\n",
              "       'Península', 'Méier', 'Vargem Pequena', 'Maracanã', 'Jacarepaguá',\n",
              "       'São Conrado', 'Vila Valqueire', 'Gávea', 'Cosme Velho',\n",
              "       'Bonsucesso', 'Todos os Santos', 'Laranjeiras', 'Itanhangá',\n",
              "       'Flamengo', 'Praça Seca', 'Piedade', 'Lagoa', 'Catete',\n",
              "       'Jardim Carioca', 'Benfica', 'Glória', 'Vila Isabel',\n",
              "       'Rocha Miranda', 'Engenho Novo', 'Engenho de Dentro',\n",
              "       'Cidade Nova', 'Pilares', 'Tomás Coelho', 'Água Santa',\n",
              "       'São Cristóvão', 'Ilha do Governador', 'Jardim Sulacap',\n",
              "       'Oswaldo Cruz', 'Vila da Penha', 'Anil', 'Vargem Grande', 'Tanque',\n",
              "       'Vaz Lobo', 'Madureira', 'São Francisco Xavier', 'Pechincha',\n",
              "       'Leme', 'Irajá', 'Quintino Bocaiúva', 'Urca', 'Penha',\n",
              "       'Gardênia Azul', 'Rio Comprido', 'Andaraí', 'Santa Teresa',\n",
              "       'Inhaúma', 'Marechal Hermes', 'Curicica', 'Santíssimo', 'Moneró',\n",
              "       'Jardim Guanabara', 'Camorim', 'Cascadura', 'Praia da Bandeira',\n",
              "       'Saúde', 'Joá', 'Colégio', 'Guadalupe', 'Realengo', 'Fátima',\n",
              "       'Inhoaíba', 'Rocha', 'Jabour', 'Braz de Pina',\n",
              "       'Vicente de Carvalho', 'Praça da Bandeira', 'Vila Kosmos',\n",
              "       'Encantado', 'Campinho', 'Ricardo de Albuquerque', 'Guaratiba',\n",
              "       'Riachuelo', 'Bangu', 'Lapa', 'Catumbi', 'Penha Circular',\n",
              "       'Parque Anchieta', 'Abolição', 'Pavuna', 'Santa Cruz',\n",
              "       'Alto da Boa Vista', 'Bento Ribeiro', 'Estácio', 'Jardim América',\n",
              "       'Cordovil', 'Caju', 'Pedra de Guaratiba', 'Padre Miguel',\n",
              "       'Paciência', 'Del Castilho', 'Arpoador', 'Sampaio', 'Anchieta',\n",
              "       'Icaraí', 'Senador Vasconcelos', 'Gamboa', 'Maria da Graça',\n",
              "       'Barra de Guaratiba', 'Paquetá', 'Largo do Machado',\n",
              "       'Parada de Lucas', 'Turiaçu', 'Freguesia (Ilha do Governador)',\n",
              "       'Portuguesa', 'Honório Gurgel', 'Santo Cristo', 'Pitangueiras',\n",
              "       'Vila Militar', 'Vidigal', 'Senador Camará', 'Usina',\n",
              "       'Vigário Geral', 'Cosmos', 'Jacaré', 'Cocotá', 'Engenho da Rainha',\n",
              "       'Cachamorra', 'Zumbi', 'Tauá', 'Ribeira', 'Magalhães Bastos',\n",
              "       'Cacuia', 'Bancários', 'Cavalcanti', 'Rio da Prata',\n",
              "       'Cidade Jardim', 'Coelho Neto', 'Grumari'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Média dos valores dos bairros de alto padrão do RJ\n",
        "bairros_alto_padrao = [\"Botafogo\", \"Catete\", \"Copacabana\", \"Ipanema\", \"Leblon\", \"Flamengo\", \"Barra da Tijuca\", \"Lagoa\", \"Joá\"]\n",
        "imoveis_alto_padrao = df_residencial[df_residencial.Bairro.isin(bairros_alto_padrao)].drop_duplicates()\n",
        "media_valores_alto_padrao = imoveis_alto_padrao.Valor.mean().round(2)\n",
        "print(f\"Média dos valores dos imoveis de alto padrão é de R$ {media_valores_alto_padrao}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySEmjLUpbUR8",
        "outputId": "a4dc48f9-7320-4c1d-a966-8212cce56d66"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Média dos valores dos imoveis de alto padrão é de R$ 6866.44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grupo_bairros_alto_padrao = imoveis_alto_padrao.groupby(\"Bairro\")"
      ],
      "metadata": {
        "id": "fSe0B3-Hb27b"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Média por bairros de alto padrão\n",
        "for bairro, dado in grupo_bairros_alto_padrao:\n",
        "  print(f\"Média dos valores dos imoveis no bairro {bairro}: R$ {dado.Valor.mean().round(2)}\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ad7VP-WpgBg7",
        "outputId": "f689bccb-69cc-4bb9-ecbf-4ca52364253a"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Média dos valores dos imoveis no bairro Barra da Tijuca: R$ 7080.56\n",
            "Média dos valores dos imoveis no bairro Botafogo: R$ 8826.83\n",
            "Média dos valores dos imoveis no bairro Catete: R$ 2272.52\n",
            "Média dos valores dos imoveis no bairro Copacabana: R$ 4096.22\n",
            "Média dos valores dos imoveis no bairro Flamengo: R$ 4111.37\n",
            "Média dos valores dos imoveis no bairro Ipanema: R$ 9476.14\n",
            "Média dos valores dos imoveis no bairro Joá: R$ 16773.48\n",
            "Média dos valores dos imoveis no bairro Lagoa: R$ 8183.83\n",
            "Média dos valores dos imoveis no bairro Leblon: R$ 8794.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grupo_bairros_todos = df_residencial.groupby(\"Bairro\")"
      ],
      "metadata": {
        "id": "_HrzAPE9gO1E"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Média de todos os bairros\n",
        "media_valores_dos_bairros = []\n",
        "for bairro, dado in grupo_bairros_todos:\n",
        "  media = dado.Valor.mean().round(2)\n",
        "  media_bairros = {\"Bairro\":bairro, \"Media dos valores\":media}\n",
        "  media_valores_dos_bairros.append(media_bairros) "
      ],
      "metadata": {
        "id": "LOdeu3Bmhb5X"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_media_bairros = pd.DataFrame(media_valores_dos_bairros)"
      ],
      "metadata": {
        "id": "byjAJlXZhjyf"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_media_bairros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "F63P9lwxiUQd",
        "outputId": "d6e7434e-d2f4-40ba-a66f-c20e13fe335b"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Bairro  Media dos valores\n",
              "0             Abolição            1195.62\n",
              "1    Alto da Boa Vista            3966.67\n",
              "2             Anchieta             784.29\n",
              "3              Andaraí            1463.03\n",
              "4                 Anil            1987.37\n",
              "..                 ...                ...\n",
              "149     Vila Valqueire            1741.80\n",
              "150      Vila da Penha            1220.16\n",
              "151       Vista Alegre            1087.14\n",
              "152              Zumbi            2150.00\n",
              "153         Água Santa             860.00\n",
              "\n",
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              "      <td>784.29</td>\n",
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              "      <td>860.00</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "grupo_bairros_todos.Valor.describe().round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "-kJVMHTTijN8",
        "outputId": "22f262ac-443a-499d-9b7e-3dbdb48848fc"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   count     mean      std     min     25%     50%     75%  \\\n",
              "Bairro                                                                       \n",
              "Abolição            16.0  1195.62   410.90   800.0   900.0  1190.0  1200.0   \n",
              "Alto da Boa Vista    6.0  3966.67  5513.59   600.0  1150.0  1725.0  3275.0   \n",
              "Anchieta             7.0   784.29   150.21   600.0   695.0   700.0   900.0   \n",
              "Andaraí             98.0  1463.03   406.34   700.0  1200.0  1400.0  1650.0   \n",
              "Anil                76.0  1987.37  1491.59   300.0  1052.5  1345.0  2600.0   \n",
              "...                  ...      ...      ...     ...     ...     ...     ...   \n",
              "Vila Valqueire      50.0  1741.80  2162.44   800.0  1000.0  1250.0  1675.0   \n",
              "Vila da Penha      124.0  1220.16   611.88   450.0   900.0  1100.0  1300.0   \n",
              "Vista Alegre        21.0  1087.14   196.52   700.0   900.0  1100.0  1200.0   \n",
              "Zumbi                2.0  2150.00   777.82  1600.0  1875.0  2150.0  2425.0   \n",
              "Água Santa          10.0   860.00   164.65   600.0   800.0   850.0   925.0   \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",
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              "      <th>Abolição</th>\n",
              "      <td>16.0</td>\n",
              "      <td>1195.62</td>\n",
              "      <td>410.90</td>\n",
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              "      <td>900.0</td>\n",
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              "      <td>2300.0</td>\n",
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              "      <th>Alto da Boa Vista</th>\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",
              "      <th>Anchieta</th>\n",
              "      <td>7.0</td>\n",
              "      <td>784.29</td>\n",
              "      <td>150.21</td>\n",
              "      <td>600.0</td>\n",
              "      <td>695.0</td>\n",
              "      <td>700.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>1000.0</td>\n",
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              "    <tr>\n",
              "      <th>Andaraí</th>\n",
              "      <td>98.0</td>\n",
              "      <td>1463.03</td>\n",
              "      <td>406.34</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",
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              "    <tr>\n",
              "      <th>Anil</th>\n",
              "      <td>76.0</td>\n",
              "      <td>1987.37</td>\n",
              "      <td>1491.59</td>\n",
              "      <td>300.0</td>\n",
              "      <td>1052.5</td>\n",
              "      <td>1345.0</td>\n",
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              "      <th>Vila Valqueire</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1741.80</td>\n",
              "      <td>2162.44</td>\n",
              "      <td>800.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1250.0</td>\n",
              "      <td>1675.0</td>\n",
              "      <td>16000.0</td>\n",
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              "    <tr>\n",
              "      <th>Vila da Penha</th>\n",
              "      <td>124.0</td>\n",
              "      <td>1220.16</td>\n",
              "      <td>611.88</td>\n",
              "      <td>450.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>4500.0</td>\n",
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              "      <th>Vista Alegre</th>\n",
              "      <td>21.0</td>\n",
              "      <td>1087.14</td>\n",
              "      <td>196.52</td>\n",
              "      <td>700.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1400.0</td>\n",
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              "    <tr>\n",
              "      <th>Zumbi</th>\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",
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              "      <th>Água Santa</th>\n",
              "      <td>10.0</td>\n",
              "      <td>860.00</td>\n",
              "      <td>164.65</td>\n",
              "      <td>600.0</td>\n",
              "      <td>800.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>925.0</td>\n",
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              "<p>154 rows × 8 columns</p>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
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        "grupo_bairros_todos.Valor.aggregate([\"min\", \"max\", \"sum\"]).rename(columns = {'min': 'Mínimo', 'max': 'Máximo'})"
      ],
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        "id": "77HYcYHPrirv",
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      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Mínimo   Máximo       sum\n",
              "Bairro                                      \n",
              "Abolição            800.0   2300.0   19130.0\n",
              "Alto da Boa Vista   600.0  15000.0   23800.0\n",
              "Anchieta            600.0   1000.0    5490.0\n",
              "Andaraí             700.0   2950.0  143377.0\n",
              "Anil                300.0   7000.0  151040.0\n",
              "...                   ...      ...       ...\n",
              "Vila Valqueire      800.0  16000.0   87090.0\n",
              "Vila da Penha       450.0   4500.0  151300.0\n",
              "Vista Alegre        700.0   1400.0   22830.0\n",
              "Zumbi              1600.0   2700.0    4300.0\n",
              "Água Santa          600.0   1200.0    8600.0\n",
              "\n",
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              "      <th>Vila Valqueire</th>\n",
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              "      <th>Vila da Penha</th>\n",
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              "      <th>Zumbi</th>\n",
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              "      <th>Água Santa</th>\n",
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              "      <td>1200.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Visualização dos dados com gráfico"
      ],
      "metadata": {
        "id": "mvlcK1aYs70M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rc('figure', figsize = (20,10))"
      ],
      "metadata": {
        "id": "0lO1Mt_jsrxr"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "figura = grupo_bairros_todos.mean().plot.bar(color = 'blue')\n",
        "figura.set_ylabel('Valor do Aluguel')\n",
        "figura.set_title('Valor Médio do Aluguel por Bairro', {'fontsize': 22})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791
        },
        "id": "M5uT1oJKtEoX",
        "outputId": "0351c819-a7bc-474d-da55-f6abccfea5d8"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Valor Médio do Aluguel por Bairro')"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Na3wKguEtXZ9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}