Oficina_Análise_Descritiva_de_Dados_com_Python.ipynb

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      "source": [
        "# **Oficina: Análise Descritiva de Dados com Python**\n",
        "\n"
      ],
      "metadata": {
        "id": "0t2jjCTNYn3O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***1. Importação e Entendimento dos Dados***\n",
        "\n"
      ],
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      "source": [
        "# importando as bibliotecas para lidar com os dados\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importando os dados\n",
        "houses = pd.read_csv(\"https://raw.githubusercontent.com/ProfLuciano/cd/gh-pages/data/recife.csv\",encoding = \"ISO-8859-1\")"
      ],
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      "execution_count": null,
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      "source": [
        "# apresentando o cabeçalho dos dados\n",
        "houses.head()"
      ],
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              "    price  latitude  longitude  bedrooms  area  pkspaces  bathrooms  ensuites  \\\n",
              "0    1500 -8.059791 -34.889619       4.0  37.0       3.0        3.0       NaN   \n",
              "1  330000 -8.029595 -34.900372       3.0  86.0       1.0        3.0       1.0   \n",
              "2  255000 -8.037247 -34.890986       2.0  82.0       1.0        2.0       NaN   \n",
              "3  380000 -8.047562 -34.876964       3.0  95.0       2.0        3.0       1.0   \n",
              "4  300000 -8.053580 -34.908733       3.0  97.0       1.0        3.0       1.0   \n",
              "\n",
              "         suburb district    city       state   type  \n",
              "0     Boa Vista   Recife  Recife  Pernambuco  apart  \n",
              "1   Tamarineira   Recife  Recife  Pernambuco  apart  \n",
              "2  Encruzilhada   Recife  Recife  Pernambuco  apart  \n",
              "3   Santo Amaro   Recife  Recife  Pernambuco  apart  \n",
              "4      Madalena   Recife  Recife  Pernambuco  apart  "
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      "cell_type": "markdown",
      "source": [
        "**Descrições dos cabeçalhos:**\n",
        "\n",
        "*   price - preço\n",
        "*   latitude - latitude (coordenada)\n",
        "*   longitude - longitude (coordenada)\n",
        "*   bedrooms - quartos\n",
        "*   area - área quadrada\n",
        "*   pkspaces - espaços ???\n",
        "*   bathrooms - banheiros\n",
        "*   ensuites - suites\n",
        "*   suburb - bairro\n",
        "*   district - distrito\n",
        "*   city - cidade\n",
        "*   state - estado\n",
        "*   type - tipo do imóvel\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "aWzV_P4JLh_9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# apresentando o tipo dos dados\n",
        "houses.dtypes"
      ],
      "metadata": {
        "colab": {
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        "id": "xWP7CfAbOIdx",
        "outputId": "1ae51890-270d-41eb-9907-174d912b947e"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "price          int64\n",
              "latitude     float64\n",
              "longitude    float64\n",
              "bedrooms     float64\n",
              "area         float64\n",
              "pkspaces     float64\n",
              "bathrooms    float64\n",
              "ensuites     float64\n",
              "suburb        object\n",
              "district      object\n",
              "city          object\n",
              "state         object\n",
              "type          object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
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    {
      "cell_type": "code",
      "source": [
        "# Apresentando a estrutura da tabela dos dados\n",
        "houses.shape"
      ],
      "metadata": {
        "colab": {
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        "id": "jzVLNaopKL3F",
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          "data": {
            "text/plain": [
              "(1744, 13)"
            ]
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          "metadata": {},
          "execution_count": 4
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Adição de Novas Variáveis**"
      ],
      "metadata": {
        "id": "mCc7elxpKZhC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.1. Adicionar informações sobre o tipo de negócio ao imóvel, exemplo: aluguel ou venda ..."
      ],
      "metadata": {
        "id": "P0VwAQxVOgwG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# adicionando uma nova coluna 'operation'\n",
        "# atribuindo todos os valores para 'sell' = 'venda'\n",
        "\n",
        "houses['operation'] = 'sell'"
      ],
      "metadata": {
        "id": "jzZWKQ-VOstZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# criando uma condição para definir se é venda ou alguel\n",
        "# se o preco > 0 e preco < 30000, então é de aluguel = 'rent'\n",
        "# .loc -> função que acessa um grupo de linhas e colunas ou um vetor booleano\n",
        "\n",
        "houses.loc[ (houses['price'] > 0) & (houses['price'] < 30000),'operation' ] = 'rent'"
      ],
      "metadata": {
        "id": "jXMYE19YO6ag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# verificando a nova coluna\n",
        "\n",
        "houses.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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              "    price  latitude  longitude  bedrooms  area  pkspaces  bathrooms  ensuites  \\\n",
              "0    1500 -8.059791 -34.889619       4.0  37.0       3.0        3.0       NaN   \n",
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              "\n",
              "         suburb district    city       state   type operation  \n",
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              "3   Santo Amaro   Recife  Recife  Pernambuco  apart      sell  \n",
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          },
          "metadata": {},
          "execution_count": 14
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.2. Adicionar o preço por métro quadrado"
      ],
      "metadata": {
        "id": "ryd2RfcKTsMC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# houses['price_square_meter'] -> coluna nova\n",
        "# calculo = preço / area\n",
        "\n",
        "houses['price_square_meter'] = houses['price']/houses['area']"
      ],
      "metadata": {
        "id": "YFQCa8o6Ttgf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# verificando a nova coluna\n",
        "\n",
        "houses.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EHvhOcNTT8HY",
        "outputId": "55a279d0-5e33-4c77-e3da-67fe58a48e0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    price  latitude  longitude  bedrooms  area  pkspaces  bathrooms  ensuites  \\\n",
              "0    1500 -8.059791 -34.889619       4.0  37.0       3.0        3.0       NaN   \n",
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              "4  300000 -8.053580 -34.908733       3.0  97.0       1.0        3.0       1.0   \n",
              "\n",
              "         suburb district    city       state   type operation  \\\n",
              "0     Boa Vista   Recife  Recife  Pernambuco  apart      rent   \n",
              "1   Tamarineira   Recife  Recife  Pernambuco  apart      sell   \n",
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              "3   Santo Amaro   Recife  Recife  Pernambuco  apart      sell   \n",
              "4      Madalena   Recife  Recife  Pernambuco  apart      sell   \n",
              "\n",
              "   price_square_meter  \n",
              "0           40.540541  \n",
              "1         3837.209302  \n",
              "2         3109.756098  \n",
              "3         4000.000000  \n",
              "4         3092.783505  "
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              "      <td>-34.876964</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Santo Amaro</td>\n",
              "      <td>Recife</td>\n",
              "      <td>Recife</td>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>apart</td>\n",
              "      <td>sell</td>\n",
              "      <td>4000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>300000</td>\n",
              "      <td>-8.053580</td>\n",
              "      <td>-34.908733</td>\n",
              "      <td>3.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Madalena</td>\n",
              "      <td>Recife</td>\n",
              "      <td>Recife</td>\n",
              "      <td>Pernambuco</td>\n",
              "      <td>apart</td>\n",
              "      <td>sell</td>\n",
              "      <td>3092.783505</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d491824-196d-44c8-9afd-4b8c06a628e7')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-9d491824-196d-44c8-9afd-4b8c06a628e7 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-9d491824-196d-44c8-9afd-4b8c06a628e7');\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",
              "\n",
              "\n",
              "<div id=\"df-9a33bbf5-58bf-44ef-9330-d4d32403af4f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9a33bbf5-58bf-44ef-9330-d4d32403af4f')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9a33bbf5-58bf-44ef-9330-d4d32403af4f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Utilização de Estatísticas Univariadas**"
      ],
      "metadata": {
        "id": "-iTHq23uPqxn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.1. Calcular a média de preço das casas a venda"
      ],
      "metadata": {
        "id": "rNzgfr5HPzZg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# criando um filtro para definir as casas a venda\n",
        "\n",
        "houses['operation'] == 'sell'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccTX6QT-QCtg",
        "outputId": "7e921b9d-6bfb-411c-a733-36d5ef51ac5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1        True\n",
              "2        True\n",
              "3        True\n",
              "4        True\n",
              "        ...  \n",
              "1739     True\n",
              "1740     True\n",
              "1741    False\n",
              "1742    False\n",
              "1743     True\n",
              "Name: operation, Length: 1744, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# capturando apenas os preços das casas a venda\n",
        "\n",
        "houses[houses['operation'] == 'sell']['price']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvQdaXtePwqR",
        "outputId": "ef9fa61b-d66c-4fad-b01b-56e66d1c59af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1       330000\n",
              "2       255000\n",
              "3       380000\n",
              "4       300000\n",
              "5       320000\n",
              "         ...  \n",
              "1735    490000\n",
              "1736    520000\n",
              "1739    580000\n",
              "1740    390000\n",
              "1743    670000\n",
              "Name: price, Length: 1242, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculando a média usando a função .mean()\n",
        "\n",
        "houses[houses['operation'] == 'sell']['price'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttMBPTN3QWYW",
        "outputId": "ddce93af-320b-4184-bceb-82af2d2dc18b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "652416.6103059581"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.2. Calcular a mediana de preço das casas a venda"
      ],
      "metadata": {
        "id": "ZQx80Ia-Qh_m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# calculando a mediana usando a função .median()\n",
        "\n",
        "houses[houses['operation'] == 'sell']['price'].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FmZR8DfMQCDd",
        "outputId": "cd4c6817-611f-4567-8373-689cf92515f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "470000.0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.3. Calcular a moda de preço das casas a venda"
      ],
      "metadata": {
        "id": "eJ3OVIlGQrNV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# calculando a moda usando a função .mode()\n",
        "\n",
        "houses[houses['operation'] == 'sell']['price'].mode()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHrilgs9P_Bi",
        "outputId": "40f71a69-01b0-4d4a-bcab-309ee4735157"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    350000\n",
              "Name: price, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.4. Calcular a variância do preço das casas a venda"
      ],
      "metadata": {
        "id": "7Ai7xNl9Qql1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# calculando a variância usando a função .mode()\n",
        "\n",
        "houses[houses['operation'] == 'sell']['price'].var()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j4eZXui_Q9-i",
        "outputId": "2b91848b-81f7-43eb-d647-a532b430f183"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "371353283907.4838"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.5. Calcular o desvio padrão do preço das casas a venda"
      ],
      "metadata": {
        "id": "ZNhQgeSwRByC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# calculando o desvio padrão usando a função .std()\n",
        "\n",
        "houses[houses['operation'] == 'sell']['price'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQrZVUsaRJ3y",
        "outputId": "578f5229-3678-4ec9-a47d-fe5816efdd4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "609387.6302547369"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.6. Calcular todas as estatísticas descritivas para a área quadrada das casas"
      ],
      "metadata": {
        "id": "exkXEJ1hRSPG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# usando a função describe() para exibir todas as estatísticas descritivas\n",
        "\n",
        "houses[houses['operation'] == 'sell']['area'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TYflDqeBRS8f",
        "outputId": "34a6924f-7f72-4736-a554-74cb0e16a635"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1220.000000\n",
              "mean      128.066484\n",
              "std       102.078742\n",
              "min        25.000000\n",
              "25%        72.000000\n",
              "50%       100.000000\n",
              "75%       149.000000\n",
              "max      1682.000000\n",
              "Name: area, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# coletando um valor especifico da função describe()\n",
        "\n",
        "houses[houses['operation'] == 'sell']['area'].describe()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4bbNIqFRs0j",
        "outputId": "5acda701-4c34-46ec-e5b6-c1a9d31bf76a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1220.0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Apresentação de Gráficos Univariados**"
      ],
      "metadata": {
        "id": "NkQ5JDDUR7h2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.1. Criar um boxplot da área das casas"
      ],
      "metadata": {
        "id": "4yUQ0oZfSQdJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "houses.boxplot(column=['area'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "i33rOZMfSFMb",
        "outputId": "56327f09-06a3-4158-f25a-9ba56a08d8c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.2. Plotar o posicionamento das casas"
      ],
      "metadata": {
        "id": "wDsztOXsWPxl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# a função scatter() cria um gráfico de dispersão utilizando as coordenadas de x e y\n",
        "\n",
        "houses.plot.scatter(x='longitude',y='latitude')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Y1PE7VglWQD-",
        "outputId": "c842a78c-99ac-4235-f85d-0e47aa210e65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='longitude', ylabel='latitude'>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.3. Criar um histograma sobre o preço por métro quadrado"
      ],
      "metadata": {
        "id": "5qk4GkKnZzlO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# a função hist() cria o histograma para a coluna especificada\n",
        "houses['price_square_meter'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "torAXKDbZ7qw",
        "outputId": "9d0846a2-f1b6-4156-ccba-0078efd47716"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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PxxO3xwmHw/L7/Zq9NUGhiCtux+1t2+cW9/USHJ0ZTpgwQUlJSX29HOuQX+zIMDbkFzsy7F7nOyhfJe4F5t5779WMGTNUUlIiSRo1apQ++ugjVVVVqaysTNnZ2ZKkpqYmDRkyxLlfU1OTxo4dK0nKzs5Wc3Nz1HGPHDmilpYW5/5f5na75Xa7u2xPSkrqlSdHKOJSqMOeAnMy/oD01vfmdEF+sSPD2JBf7Miwq57mEferkNra2pSQEH3Yfv36KRL5/C2XvLw8ZWdna8OGDc7+YDCo+vp6+Xw+SZLP51Nra6saGhqcmY0bNyoSiaigoCDeSwYAAJaJ+ysw119/vR5++GENHTpUI0eO1HvvvaeFCxfqRz/6kSTJ5XJp+vTpeuihh3TuuecqLy9Ps2fPVk5Ojm688UZJ0gUXXKBrr71WU6dO1fLlyxUOh1VRUaGSkhKuQAIAAPEvMEuWLNHs2bN15513qrm5WTk5Ofrxj3+sOXPmODP33XefDh06pGnTpqm1tVVXXnmlqqurlZKS4sysWrVKFRUVGj9+vBISEjR58mQtXrw43ssFAAAWinuBGTBggBYtWqRFixZ1O+NyuTR//nzNnz+/25nMzEytXr063ssDAACnAP4WEgAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYJ1eKTB/+tOf9E//9E8aNGiQUlNTNWrUKG3dutXZb4zRnDlzNGTIEKWmpqqwsFB79uyJOkZLS4tKS0vl8XiUkZGhKVOm6ODBg72xXAAAYJm4F5hPP/1UV1xxhZKSkvTqq69q586d+rd/+zcNHDjQmVmwYIEWL16s5cuXq76+Xv3791dxcbEOHz7szJSWlmrHjh3y+/1at26damtrNW3atHgvFwAAWCgx3gd89NFHlZubqxUrVjjb8vLynH8bY7Ro0SLNmjVLN9xwgyTp2Wefldfr1dq1a1VSUqJdu3apurpaW7ZsUX5+viRpyZIluu666/T4448rJycn3ssGAAAWiXuBefnll1VcXKx//Md/1KZNm3TmmWfqzjvv1NSpUyVJe/fuVSAQUGFhoXOf9PR0FRQUqK6uTiUlJaqrq1NGRoZTXiSpsLBQCQkJqq+v10033dTlcUOhkEKhkHM7GAxKksLhsMLhcNzOr/NY7gQTt2N+HeKZQaw613Iyrckm5Bc7MowN+cWODLvX00ziXmD+8Ic/aNmyZaqsrNRPf/pTbdmyRf/yL/+i5ORklZWVKRAISJK8Xm/U/bxer7MvEAgoKysreqGJicrMzHRmvqyqqkrz5s3rsr2mpkZpaWnxOLUoD+ZH4n7M3rR+/fq+XkIXfr+/r5dgNfKLHRnGhvxiR4ZdtbW19Wgu7gUmEokoPz9fP/vZzyRJF110kbZv367ly5errKws3g/nmDlzpiorK53bwWBQubm5KioqksfjidvjhMNh+f1+zd6aoFDEFbfj9rbtc4v7egmOzgwnTJigpKSkvl6OdcgvdmQYG/KLHRl2r/MdlK8S9wIzZMgQjRgxImrbBRdcoP/+7/+WJGVnZ0uSmpqaNGTIEGemqalJY8eOdWaam5ujjnHkyBG1tLQ49/8yt9stt9vdZXtSUlKvPDlCEZdCHfYUmJPxB6S3vjenC/KLHRnGhvxiR4Zd9TSPuF+FdMUVV2j37t1R2z788EMNGzZM0ucf6M3OztaGDRuc/cFgUPX19fL5fJIkn8+n1tZWNTQ0ODMbN25UJBJRQUFBvJcMAAAsE/dXYO655x5dfvnl+tnPfqabb75Zmzdv1tNPP62nn35akuRyuTR9+nQ99NBDOvfcc5WXl6fZs2crJydHN954o6TPX7G59tprNXXqVC1fvlzhcFgVFRUqKSnhCiQAABD/AnPJJZfopZde0syZMzV//nzl5eVp0aJFKi0tdWbuu+8+HTp0SNOmTVNra6uuvPJKVVdXKyUlxZlZtWqVKioqNH78eCUkJGjy5MlavHhxvJcLAAAsFPcCI0nf+c539J3vfKfb/S6XS/Pnz9f8+fO7ncnMzNTq1at7Y3kAAMBy/C0kAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdRL7egH4epw945W+XoLD3c9owaXShXP/R6EO1zFn//jIpK9pVQAAm/AKDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYJ1eLzCPPPKIXC6Xpk+f7mw7fPiwysvLNWjQIJ1xxhmaPHmympqaou63b98+TZo0SWlpacrKytK9996rI0eO9PZyAQCABXq1wGzZskW/+MUvNHr06Kjt99xzj37729/qxRdf1KZNm/TJJ5/oe9/7nrO/o6NDkyZNUnt7u95++2396le/0sqVKzVnzpzeXC4AALBErxWYgwcPqrS0VP/+7/+ugQMHOtv379+vX/7yl1q4cKG+/e1va9y4cVqxYoXefvttvfPOO5Kkmpoa7dy5U//5n/+psWPHauLEiXrwwQe1dOlStbe399aSAQCAJRJ768Dl5eWaNGmSCgsL9dBDDznbGxoaFA6HVVhY6GwbPny4hg4dqrq6Ol122WWqq6vTqFGj5PV6nZni4mLdcccd2rFjhy666KIujxcKhRQKhZzbwWBQkhQOhxUOh+N2Xp3HcieYuB3zdNOZXU8yjOf37lTRmQnZnDgyjA35xY4Mu9fTTHqlwKxZs0bvvvuutmzZ0mVfIBBQcnKyMjIyorZ7vV4FAgFn5ovlpXN/576jqaqq0rx587psr6mpUVpa2omcxjE9mB+J+zFPNz3JcP369V/DSuzk9/v7egnWI8PYkF/syLCrtra2Hs3FvcB8/PHHuvvuu+X3+5WSkhLvw3dr5syZqqysdG4Hg0Hl5uaqqKhIHo8nbo8TDofl9/s1e2uCQhFX3I57OnEnGD2YH+lRhtvnFn9Nq7JH53NwwoQJSkpK6uvlWIkMY0N+sSPD7nW+g/JV4l5gGhoa1NzcrIsvvtjZ1tHRodraWj355JP6n//5H7W3t6u1tTXqVZimpiZlZ2dLkrKzs7V58+ao43ZepdQ582Vut1tut7vL9qSkpF55coQiLoU6KDCx6EmG/GB3r7ee26cTMowN+cWODLvqaR5x/xDv+PHjtW3bNjU2Njpf+fn5Ki0tdf6dlJSkDRs2OPfZvXu39u3bJ5/PJ0ny+Xzatm2bmpubnRm/3y+Px6MRI0bEe8kAAMAycX8FZsCAAbrwwgujtvXv31+DBg1ytk+ZMkWVlZXKzMyUx+PRXXfdJZ/Pp8suu0ySVFRUpBEjRui2227TggULFAgENGvWLJWXlx/1VRYAAHB66bWrkI7l5z//uRISEjR58mSFQiEVFxfrqaeecvb369dP69at0x133CGfz6f+/furrKxM8+fP74vlAgCAk8zXUmBef/31qNspKSlaunSpli5d2u19hg0bxhUoAADgqPhbSAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOvEvcBUVVXpkksu0YABA5SVlaUbb7xRu3fvjpo5fPiwysvLNWjQIJ1xxhmaPHmympqaomb27dunSZMmKS0tTVlZWbr33nt15MiReC8XAABYKO4FZtOmTSovL9c777wjv9+vcDisoqIiHTp0yJm555579Nvf/lYvvviiNm3apE8++UTf+973nP0dHR2aNGmS2tvb9fbbb+tXv/qVVq5cqTlz5sR7uQAAwEKJ8T5gdXV11O2VK1cqKytLDQ0Nuvrqq7V//3798pe/1OrVq/Xtb39bkrRixQpdcMEFeuedd3TZZZeppqZGO3fu1O9+9zt5vV6NHTtWDz74oO6//37NnTtXycnJ8V42AACwSK9/Bmb//v2SpMzMTElSQ0ODwuGwCgsLnZnhw4dr6NChqqurkyTV1dVp1KhR8nq9zkxxcbGCwaB27NjR20sGAAAnubi/AvNFkUhE06dP1xVXXKELL7xQkhQIBJScnKyMjIyoWa/Xq0Ag4Mx8sbx07u/cdzShUEihUMi5HQwGJUnhcFjhcDgu59N5PElyJ5i4HfN005ldTzKM5/fuVNGZCdmcODKMDfnFjgy719NMerXAlJeXa/v27XrzzTd782Ekff7h4Xnz5nXZXlNTo7S0tLg/3oP5kbgf83TTkwzXr1//NazETn6/v6+XYD0yjA35xY4Mu2pra+vRXK8VmIqKCq1bt061tbU666yznO3Z2dlqb29Xa2tr1KswTU1Nys7OdmY2b94cdbzOq5Q6Z75s5syZqqysdG4Hg0Hl5uaqqKhIHo8nXqelcDgsv9+v2VsTFIq44nbc04k7wejB/EiPMtw+t/hrWpU9Op+DEyZMUFJSUl8vx0pkGBvyix0Zdq/zHZSvEvcCY4zRXXfdpZdeekmvv/668vLyovaPGzdOSUlJ2rBhgyZPnixJ2r17t/bt2yefzydJ8vl8evjhh9Xc3KysrCxJn7dUj8ejESNGHPVx3W633G53l+1JSUm98uQIRVwKdVBgYtGTDPnB7l5vPbdPJ2QYG/KLHRl21dM84l5gysvLtXr1av3mN7/RgAEDnM+spKenKzU1Venp6ZoyZYoqKyuVmZkpj8eju+66Sz6fT5dddpkkqaioSCNGjNBtt92mBQsWKBAIaNasWSovLz9qSQEAAKeXuBeYZcuWSZKuueaaqO0rVqzQP//zP0uSfv7znyshIUGTJ09WKBRScXGxnnrqKWe2X79+Wrdune644w75fD71799fZWVlmj9/fryXCwAALNQrbyF9lZSUFC1dulRLly7tdmbYsGF8gBMAABwVfwsJAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGAdCgwAALAOBQYAAFiHAgMAAKxDgQEAANahwAAAAOtQYAAAgHUoMAAAwDoUGAAAYB0KDAAAsA4FBgAAWIcCAwAArEOBAQAA1qHAAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwzkldYJYuXaqzzz5bKSkpKigo0ObNm/t6SQAA4CRw0haY559/XpWVlXrggQf07rvvasyYMSouLlZzc3NfLw0AAPSxxL5eQHcWLlyoqVOn6oc//KEkafny5XrllVf0zDPPaMaMGX28Onxdzp7xSl8v4bj98ZFJfb0EADjlnZQFpr29XQ0NDZo5c6azLSEhQYWFhaqrqzvqfUKhkEKhkHN7//79kqSWlhaFw+G4rS0cDqutrU2J4QR1RFxxO+7pJDFi1NYWOWUzPOf/vdCrx3cnGM26KKKx//prheKUX/3M8XE5ji06f47/9re/KSkpqa+XYx3yix0Zdu/AgQOSJGPMMedOygLz17/+VR0dHfJ6vVHbvV6v/vd///eo96mqqtK8efO6bM/Ly+uVNSI23+/rBVgu3vkN/rc4HxAAYnTgwAGlp6d3u/+kLDAnYubMmaqsrHRuRyIRtbS0aNCgQXK54vd/+cFgULm5ufr444/l8XjidtzTCRnGhvxiR4axIb/YkWH3jDE6cOCAcnJyjjl3UhaYwYMHq1+/fmpqaora3tTUpOzs7KPex+12y+12R23LyMjorSXK4/HwpIsRGcaG/GJHhrEhv9iR4dEd65WXTiflVUjJyckaN26cNmzY4GyLRCLasGGDfD5fH64MAACcDE7KV2AkqbKyUmVlZcrPz9ell16qRYsW6dChQ85VSQAA4PR10haYW265RX/5y180Z84cBQIBjR07VtXV1V0+2Pt1c7vdeuCBB7q8XYWeI8PYkF/syDA25Bc7Moydy3zVdUoAAAAnmZPyMzAAAADHQoEBAADWocAAAADrUGAAAIB1KDDHaenSpTr77LOVkpKigoICbd68ua+X1Otqa2t1/fXXKycnRy6XS2vXro3ab4zRnDlzNGTIEKWmpqqwsFB79uyJmmlpaVFpaak8Ho8yMjI0ZcoUHTx4MGrmgw8+0FVXXaWUlBTl5uZqwYIFXdby4osvavjw4UpJSdGoUaO0fv36uJ9vvFVVVemSSy7RgAEDlJWVpRtvvFG7d++Omjl8+LDKy8s1aNAgnXHGGZo8eXKXX+S4b98+TZo0SWlpacrKytK9996rI0eORM28/vrruvjii+V2u3XOOedo5cqVXdZj43N42bJlGj16tPNLv3w+n1599VVnP/kdn0ceeUQul0vTp093tpHhsc2dO1culyvqa/jw4c5+8usDBj22Zs0ak5ycbJ555hmzY8cOM3XqVJORkWGampr6emm9av369eZf//Vfza9//Wsjybz00ktR+x955BGTnp5u1q5da95//33z3e9+1+Tl5ZnPPvvMmbn22mvNmDFjzDvvvGPeeOMNc84555hbb73V2b9//37j9XpNaWmp2b59u3nuuedMamqq+cUvfuHMvPXWW6Zfv35mwYIFZufOnWbWrFkmKSnJbNu2rdcziEVxcbFZsWKF2b59u2lsbDTXXXedGTp0qDl48KAzc/vtt5vc3FyzYcMGs3XrVnPZZZeZyy+/3Nl/5MgRc+GFF5rCwkLz3nvvmfXr15vBgwebmTNnOjN/+MMfTFpamqmsrDQ7d+40S5YsMf369TPV1dXOjK3P4Zdfftm88sor5sMPPzS7d+82P/3pT01SUpLZvn27MYb8jsfmzZvN2WefbUaPHm3uvvtuZzsZHtsDDzxgRo4caf785z87X3/5y1+c/eT39aPAHIdLL73UlJeXO7c7OjpMTk6Oqaqq6sNVfb2+XGAikYjJzs42jz32mLOttbXVuN1u89xzzxljjNm5c6eRZLZs2eLMvPrqq8blcpk//elPxhhjnnrqKTNw4EATCoWcmfvvv9+cf/75zu2bb77ZTJo0KWo9BQUF5sc//nFcz7G3NTc3G0lm06ZNxpjP80pKSjIvvviiM7Nr1y4jydTV1RljPi+RCQkJJhAIODPLli0zHo/Hyey+++4zI0eOjHqsW265xRQXFzu3T6Xn8MCBA81//Md/kN9xOHDggDn33HON3+83f//3f+8UGDL8ag888IAZM2bMUfeRX9/gLaQeam9vV0NDgwoLC51tCQkJKiwsVF1dXR+urG/t3btXgUAgKpf09HQVFBQ4udTV1SkjI0P5+fnOTGFhoRISElRfX+/MXH311UpOTnZmiouLtXv3bn366afOzBcfp3PGtvz3798vScrMzJQkNTQ0KBwOR53b8OHDNXTo0KgMR40aFfWLHIuLixUMBrVjxw5n5lj5nCrP4Y6ODq1Zs0aHDh2Sz+cjv+NQXl6uSZMmdTlPMuyZPXv2KCcnR9/85jdVWlqqffv2SSK/vkKB6aG//vWv6ujo6PKbgL1erwKBQB+tqu91nvuxcgkEAsrKyoran5iYqMzMzKiZox3ji4/R3YxN+UciEU2fPl1XXHGFLrzwQkmfn1dycnKXPz765QxPNJ9gMKjPPvvM+ufwtm3bdMYZZ8jtduv222/XSy+9pBEjRpBfD61Zs0bvvvuuqqqquuwjw69WUFCglStXqrq6WsuWLdPevXt11VVX6cCBA+TXR07aPyUAnIrKy8u1fft2vfnmm329FOucf/75amxs1P79+/Vf//VfKisr06ZNm/p6WVb4+OOPdffdd8vv9yslJaWvl2OliRMnOv8ePXq0CgoKNGzYML3wwgtKTU3tw5WdvngFpocGDx6sfv36dflUeVNTk7Kzs/toVX2v89yPlUt2draam5uj9h85ckQtLS1RM0c7xhcfo7sZW/KvqKjQunXr9Nprr+mss85ytmdnZ6u9vV2tra1R81/O8ETz8Xg8Sk1Ntf45nJycrHPOOUfjxo1TVVWVxowZoyeeeIL8eqChoUHNzc26+OKLlZiYqMTERG3atEmLFy9WYmKivF4vGR6njIwMnXfeefr973/Pc7CPUGB6KDk5WePGjdOGDRucbZFIRBs2bJDP5+vDlfWtvLw8ZWdnR+USDAZVX1/v5OLz+dTa2qqGhgZnZuPGjYpEIiooKHBmamtrFQ6HnRm/36/zzz9fAwcOdGa++DidMyd7/sYYVVRU6KWXXtLGjRuVl5cXtX/cuHFKSkqKOrfdu3dr3759URlu27Ytqgj6/X55PB6NGDHCmTlWPqfaczgSiSgUCpFfD4wfP17btm1TY2Oj85Wfn6/S0lLn32R4fA4ePKj/+7//05AhQ3gO9pW+/hSxTdasWWPcbrdZuXKl2blzp5k2bZrJyMiI+lT5qejAgQPmvffeM++9956RZBYuXGjee+8989FHHxljPr+MOiMjw/zmN78xH3zwgbnhhhuOehn1RRddZOrr682bb75pzj333KjLqFtbW43X6zW33Xab2b59u1mzZo1JS0vrchl1YmKiefzxx82uXbvMAw88YMVl1HfccYdJT083r7/+etQlmG1tbc7M7bffboYOHWo2btxotm7danw+n/H5fM7+zkswi4qKTGNjo6murjbf+MY3jnoJ5r333mt27dplli5detRLMG18Ds+YMcNs2rTJ7N2713zwwQdmxowZxuVymZqaGmMM+Z2IL16FZAwZfpWf/OQn5vXXXzd79+41b731liksLDSDBw82zc3Nxhjy6wsUmOO0ZMkSM3ToUJOcnGwuvfRS88477/T1knrda6+9ZiR1+SorKzPGfH4p9ezZs43X6zVut9uMHz/e7N69O+oYf/vb38ytt95qzjjjDOPxeMwPf/hDc+DAgaiZ999/31x55ZXG7XabM8880zzyyCNd1vLCCy+Y8847zyQnJ5uRI0eaV155pdfOO16Olp0ks2LFCmfms88+M3feeacZOHCgSUtLMzfddJP585//HHWcP/7xj2bixIkmNTXVDB482PzkJz8x4XA4aua1114zY8eONcnJyeab3/xm1GN0svE5/KMf/cgMGzbMJCcnm2984xtm/PjxTnkxhvxOxJcLDBke2y233GKGDBlikpOTzZlnnmluueUW8/vf/97ZT35fP5cxxvTNaz8AAAAnhs/AAAAA61BgAACAdSgwAADAOhQYAABgHQoMAACwDgUGAABYhwIDAACsQ4EBAADWocAAAADrUGAAAIB1KDAAAMA6FBgAAGCd/w95XUzAZMiwvQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.4. Criar um gráfico que ordena os bairros com maiores preços por metro quadrado"
      ],
      "metadata": {
        "id": "-h5_ZOKbTWSc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. definindo o filtro de casas a venda\n",
        "houses['operation'] == 'sell'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LEhnBaqzUkEV",
        "outputId": "fc2b2b9c-97d0-432d-84f5-d5d865df061c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1        True\n",
              "2        True\n",
              "3        True\n",
              "4        True\n",
              "        ...  \n",
              "1739     True\n",
              "1740     True\n",
              "1741    False\n",
              "1742    False\n",
              "1743     True\n",
              "Name: operation, Length: 1744, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Agrupando os preços das casas por bairros\n",
        "# a função groupby() agrupa dados de acordo com as informações da coluna especificada\n",
        "houses[houses['operation'] == 'sell'].groupby(['suburb'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XI9BJFzIUr8m",
        "outputId": "42ee9719-ff80-41ec-abc1-9c00fcf9009e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7a24b0c65750>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Calculando o valor médio por metro quadrado de acordo com o bairro\n",
        "houses[houses['operation']=='sell'].groupby(['suburb'])['price_square_meter'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5CyOnBGVKB1",
        "outputId": "2a7740a9-07d0-4c61-aa17-f7bd3aa3eed8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "suburb\n",
              "Aflitos             5057.981293\n",
              "Apipucos            4953.378876\n",
              "Areias              2317.073171\n",
              "Arruda              3599.960047\n",
              "Boa Viagem          5930.295787\n",
              "Boa Vista           4665.206061\n",
              "Campo Grande        4648.761296\n",
              "Casa Amarela        5351.374665\n",
              "Casa Forte          6183.445243\n",
              "CaxangÌÁ            4587.357285\n",
              "Coelhos             4666.666667\n",
              "Cohab               1933.333333\n",
              "Cordeiro            4083.564554\n",
              "Derby               9043.387168\n",
              "Encruzilhada        5052.220649\n",
              "Engenho do Meio     9641.890805\n",
              "Espinheiro          5231.928476\n",
              "FundÌ£o             3920.280333\n",
              "GraÌ¤as             4587.853776\n",
              "HipÌ_dromo          2502.343750\n",
              "Ibura               5990.463333\n",
              "Ilha do Retiro      6055.816062\n",
              "Imbiribeira         9902.509394\n",
              "Ipsep               2777.982481\n",
              "Iputinga            1770.728716\n",
              "Jaqueira            6742.418062\n",
              "Madalena            5794.175868\n",
              "Mangabeira          4138.295095\n",
              "Monteiro            6544.807869\n",
              "Parnamirim          5511.663726\n",
              "Pau-Ferro           2395.826190\n",
              "Pina                7330.712373\n",
              "Ponto de Parada     5217.510616\n",
              "Porto da Madeira    2500.000000\n",
              "PoÌ¤o               6577.684081\n",
              "Prado               5181.694309\n",
              "Rosarinho           6279.017922\n",
              "San Martin          4475.841052\n",
              "Santana             6863.182567\n",
              "Santo Amaro         5127.974333\n",
              "Santo AntÌ«nio      4000.000000\n",
              "Soledade            9607.917369\n",
              "SÌ£o JosÌ©          1667.983122\n",
              "Tamarineira         5584.412500\n",
              "TejipiÌ_            1043.338684\n",
              "Torre               5729.832650\n",
              "TorreÌ£o            4826.267203\n",
              "TorrÌµes            2720.000000\n",
              "Vasco da Gama       3125.000000\n",
              "VÌÁrzea             4225.103022\n",
              "Name: price_square_meter, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Ordenando as médias\n",
        "houses[houses['operation']=='sell'].groupby(['suburb'])['price_square_meter'].mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WMocYLuVWQ9",
        "outputId": "4f877f1b-c149-4339-8884-92bc2ef2ce82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "suburb\n",
              "Imbiribeira         9902.509394\n",
              "Engenho do Meio     9641.890805\n",
              "Soledade            9607.917369\n",
              "Derby               9043.387168\n",
              "Pina                7330.712373\n",
              "Santana             6863.182567\n",
              "Jaqueira            6742.418062\n",
              "PoÌ¤o               6577.684081\n",
              "Monteiro            6544.807869\n",
              "Rosarinho           6279.017922\n",
              "Casa Forte          6183.445243\n",
              "Ilha do Retiro      6055.816062\n",
              "Ibura               5990.463333\n",
              "Boa Viagem          5930.295787\n",
              "Madalena            5794.175868\n",
              "Torre               5729.832650\n",
              "Tamarineira         5584.412500\n",
              "Parnamirim          5511.663726\n",
              "Casa Amarela        5351.374665\n",
              "Espinheiro          5231.928476\n",
              "Ponto de Parada     5217.510616\n",
              "Prado               5181.694309\n",
              "Santo Amaro         5127.974333\n",
              "Aflitos             5057.981293\n",
              "Encruzilhada        5052.220649\n",
              "Apipucos            4953.378876\n",
              "TorreÌ£o            4826.267203\n",
              "Coelhos             4666.666667\n",
              "Boa Vista           4665.206061\n",
              "Campo Grande        4648.761296\n",
              "GraÌ¤as             4587.853776\n",
              "CaxangÌÁ            4587.357285\n",
              "San Martin          4475.841052\n",
              "VÌÁrzea             4225.103022\n",
              "Mangabeira          4138.295095\n",
              "Cordeiro            4083.564554\n",
              "Santo AntÌ«nio      4000.000000\n",
              "FundÌ£o             3920.280333\n",
              "Arruda              3599.960047\n",
              "Vasco da Gama       3125.000000\n",
              "Ipsep               2777.982481\n",
              "TorrÌµes            2720.000000\n",
              "HipÌ_dromo          2502.343750\n",
              "Porto da Madeira    2500.000000\n",
              "Pau-Ferro           2395.826190\n",
              "Areias              2317.073171\n",
              "Cohab               1933.333333\n",
              "Iputinga            1770.728716\n",
              "SÌ£o JosÌ©          1667.983122\n",
              "TejipiÌ_            1043.338684\n",
              "Name: price_square_meter, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotando o gráfico com as médias por bairro ordenadas\n",
        "houses[houses['operation']=='sell'].groupby(['suburb'])['price_square_meter'].mean().sort_values(ascending=False).plot.bar(figsize=(20,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "cz5EohZiTW3c",
        "outputId": "c282fd08-9a04-4db3-d3b9-7b74f8829739"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='suburb'>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5. Estatísticas Bivariadas**"
      ],
      "metadata": {
        "id": "CPd2ZyBaV4zu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.1. Calcular a covariância"
      ],
      "metadata": {
        "id": "Bmvccw_jV8SG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# função .cov() para calcular a covariância\n",
        "\n",
        "houses[houses.operation=='sell'].cov()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "cv9oPv0oV_BP",
        "outputId": "3b2b59cd-c2c2-4f26-9dc1-f921c71ff132"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-a39018129fce>:3: FutureWarning: The default value of numeric_only in DataFrame.cov is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  houses[houses.operation=='sell'].cov()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           price     latitude   longitude       bedrooms  \\\n",
              "price               3.713533e+11 -2171.657900 -425.307071  301614.579684   \n",
              "latitude           -2.171658e+03     0.000992   -0.000045      -0.003230   \n",
              "longitude          -4.253071e+02    -0.000045    0.000233      -0.001055   \n",
              "bedrooms            3.016146e+05    -0.003230   -0.001055       1.092577   \n",
              "area                4.207143e+07    -0.129394   -0.144310      58.151630   \n",
              "pkspaces            3.323702e+05    -0.001840   -0.002335       0.679856   \n",
              "bathrooms           4.119982e+05     0.003016   -0.001252       0.757604   \n",
              "ensuites            3.251021e+05    -0.000310   -0.002352       0.419521   \n",
              "price_square_meter  1.089569e+09    -8.937359    0.890927      71.754388   \n",
              "\n",
              "                            area       pkspaces      bathrooms       ensuites  \\\n",
              "price               4.207143e+07  332370.180148  411998.187635  325102.123490   \n",
              "latitude           -1.293944e-01      -0.001840       0.003016      -0.000310   \n",
              "longitude          -1.443103e-01      -0.002335      -0.001252      -0.002352   \n",
              "bedrooms            5.815163e+01       0.679856       0.757604       0.419521   \n",
              "area                1.042007e+04      59.909799      74.235459      58.567118   \n",
              "pkspaces            5.990980e+01       1.027633       0.740625       0.573380   \n",
              "bathrooms           7.423546e+01       0.740625       1.470918       0.878985   \n",
              "ensuites            5.856712e+01       0.573380       0.878985       0.859048   \n",
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          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.2. Calcular a correlação de pearson para as casas a venda"
      ],
      "metadata": {
        "id": "xdn55lC4X02P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# o método .corr() calcula a correlação dos dados\n",
        "# 'pearson' foi o método adotado para realizar o calculo\n",
        "\n",
        "houses[houses.operation=='sell'].corr(method='pearson')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "3Oa2jkj-X7es",
        "outputId": "12af14d0-7bdd-4cc8-c6cd-5e92be916ac0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-1f61130edcb0>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  houses[houses.operation=='sell'].corr(method='pearson')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       price  latitude  longitude  bedrooms      area  \\\n",
              "price               1.000000 -0.113166  -0.045734  0.473514  0.677350   \n",
              "latitude           -0.113166  1.000000  -0.094013 -0.098142 -0.040105   \n",
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              "                    pkspaces  bathrooms  ensuites  price_square_meter  \n",
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              "      <td>-0.078492</td>\n",
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          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.3. Calcular a correlação de pearson para as casas a venda"
      ],
      "metadata": {
        "id": "wHzAn3siX_Sd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# o método .corr() calcula a correlação dos dados\n",
        "# 'spearman' foi o método adotado para realizar o calculo\n",
        "\n",
        "houses[houses.operation=='sell'].corr(method='spearman')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "3tBTZ3rVYFUi",
        "outputId": "04d1c727-84a6-4647-ab30-e65f85190c55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-474f9cc0c5e6>:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  houses[houses.operation=='sell'].corr(method='spearman')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       price  latitude  longitude  bedrooms      area  \\\n",
              "price               1.000000 -0.066591  -0.069280  0.639660  0.723069   \n",
              "latitude           -0.066591  1.000000  -0.205969 -0.134175 -0.070345   \n",
              "longitude          -0.069280 -0.205969   1.000000 -0.049529 -0.093217   \n",
              "bedrooms            0.639660 -0.134175  -0.049529  1.000000  0.736587   \n",
              "area                0.723069 -0.070345  -0.093217  0.736587  1.000000   \n",
              "pkspaces            0.713786 -0.085252  -0.138025  0.694278  0.701702   \n",
              "bathrooms           0.656935  0.030275  -0.049273  0.701999  0.678649   \n",
              "ensuites            0.718041  0.070180  -0.177310  0.634320  0.658985   \n",
              "price_square_meter  0.443111  0.022080  -0.062570 -0.064802 -0.188167   \n",
              "\n",
              "                    pkspaces  bathrooms  ensuites  price_square_meter  \n",
              "price               0.713786   0.656935  0.718041            0.443111  \n",
              "latitude           -0.085252   0.030275  0.070180            0.022080  \n",
              "longitude          -0.138025  -0.049273 -0.177310           -0.062570  \n",
              "bedrooms            0.694278   0.701999  0.634320           -0.064802  \n",
              "area                0.701702   0.678649  0.658985           -0.188167  \n",
              "pkspaces            1.000000   0.590699  0.626918            0.140522  \n",
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              "ensuites            0.626918   0.803017  1.000000            0.203648  \n",
              "price_square_meter  0.140522   0.074260  0.203648            1.000000  "
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              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2ac6c4be-a2ea-4ec2-969d-682b65730abf 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-2ac6c4be-a2ea-4ec2-969d-682b65730abf');\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",
              "\n",
              "\n",
              "<div id=\"df-8fa3ed61-afff-47f0-a6c6-924aa8086923\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fa3ed61-afff-47f0-a6c6-924aa8086923')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8fa3ed61-afff-47f0-a6c6-924aa8086923 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    }
  ]
}