Profissao_Analista_de_dados_M22_Exercicio.ipynb

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        "<img src=\"https://raw.githubusercontent.com/andre-marcos-perez/ebac-course-utils/main/media/logo/newebac_logo_black_half.png\" alt=\"ebac-logo\">\n",
        "\n",
        "---\n",
        "\n",
        "# **Módulo** | Análise de Dados: Fundamentos de Aprendizado de Máquina\n",
        "Caderno de **Exercícios**<br>\n",
        "Professor [André Perez](https://www.linkedin.com/in/andremarcosperez/)\n",
        "<br>\n",
        "Elaborado por [Douglas Rizzi](https://www.linkedin.com/in/douglas-rizzi/)\n",
        "\n",
        "---"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9jDtUbDOE1-"
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      "source": [
        "# **Tópicos**\n",
        "\n",
        "<ol type=\"1\">\n",
        "  <li>Teoria;</li>\n",
        "  <li>Atributos categóricos;</li>\n",
        "  <li>Atributos numéricos;</li>\n",
        "  <li>Dados faltantes.</li>\n",
        "</ol>"
      ]
    },
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      "cell_type": "markdown",
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      "source": [
        "---"
      ]
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      "source": [
        "# **Exercícios**"
      ]
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      "source": [
        "## 1\\. Pinguins"
      ]
    },
    {
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      "metadata": {
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      },
      "source": [
        "Neste exercício, vamos utilizar uma base de dados com informações sobre penguins. A idéia é preparar a base de dados para prever a espécie do penguin (variável resposta) baseado em suas características físicas e geográficas (variáveis preditivas)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lblH8kIXzPZZ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import re"
      ],
      "execution_count": 1,
      "outputs": []
    },
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      "cell_type": "code",
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      "source": [
        "data = sns.load_dataset('penguins')"
      ],
      "execution_count": 2,
      "outputs": []
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              "    species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0    Adelie  Torgersen            39.1           18.7              181.0   \n",
              "1    Adelie  Torgersen            39.5           17.4              186.0   \n",
              "2    Adelie  Torgersen            40.3           18.0              195.0   \n",
              "3    Adelie  Torgersen             NaN            NaN                NaN   \n",
              "4    Adelie  Torgersen            36.7           19.3              193.0   \n",
              "..      ...        ...             ...            ...                ...   \n",
              "339  Gentoo     Biscoe             NaN            NaN                NaN   \n",
              "340  Gentoo     Biscoe            46.8           14.3              215.0   \n",
              "341  Gentoo     Biscoe            50.4           15.7              222.0   \n",
              "342  Gentoo     Biscoe            45.2           14.8              212.0   \n",
              "343  Gentoo     Biscoe            49.9           16.1              213.0   \n",
              "\n",
              "     body_mass_g     sex  \n",
              "0         3750.0    Male  \n",
              "1         3800.0  Female  \n",
              "2         3250.0  Female  \n",
              "3            NaN     NaN  \n",
              "4         3450.0  Female  \n",
              "..           ...     ...  \n",
              "339          NaN     NaN  \n",
              "340       4850.0  Female  \n",
              "341       5750.0    Male  \n",
              "342       5200.0  Female  \n",
              "343       5400.0    Male  \n",
              "\n",
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              "      <td>Adelie</td>\n",
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              "      <td>39.5</td>\n",
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              "      <td>36.7</td>\n",
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              "      <td>50.4</td>\n",
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              "      <td>49.9</td>\n",
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              "      <td>213.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>Male</td>\n",
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              "        google.colab.notebook.generateWithVariable('data');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 344,\n  \"fields\": [\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Adelie\",\n          \"Chinstrap\",\n          \"Gentoo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Torgersen\",\n          \"Biscoe\",\n          \"Dream\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.4595837139265315,\n        \"min\": 32.1,\n        \"max\": 59.6,\n        \"num_unique_values\": 164,\n        \"samples\": [\n          48.2,\n          49.8,\n          45.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9747931568167816,\n        \"min\": 13.1,\n        \"max\": 21.5,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          16.9,\n          18.7,\n          18.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.061713679356888,\n        \"min\": 172.0,\n        \"max\": 231.0,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          201.0,\n          180.0,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 801.9545356980956,\n        \"min\": 2700.0,\n        \"max\": 6300.0,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          4350.0,\n          4150.0,\n          3525.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrTLgE0tki6P"
      },
      "source": [
        "### **1.1. Valores nulos**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JmRsG6K8SPd"
      },
      "source": [
        "A base de dados possui valores faltantes, utilize os conceitos da aula para trata-los."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kyhQS748SPf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56071477-87f0-418d-dc07-45086130dce3"
      },
      "source": [
        "# convertendo o dataset num dataframe e ferificando a % de valores nulos\n",
        "df = pd.DataFrame(data)\n",
        "missinss_values = df.isnull().mean()*100\n",
        "print(missinss_values)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "species              0.000000\n",
            "island               0.000000\n",
            "bill_length_mm       0.581395\n",
            "bill_depth_mm        0.581395\n",
            "flipper_length_mm    0.581395\n",
            "body_mass_g          0.581395\n",
            "sex                  3.197674\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# iniciando o tratamento removedo o maior % de nulos ('sex')\n",
        "remove_null = df.dropna(subset=['sex'])\n",
        "df = remove_null"
      ],
      "metadata": {
        "id": "DBQ3Ej_KyUVk"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# com a remoção da coluna de maior%, as outras também foram tratadas\n",
        "missinss_values = df.isnull().mean()*100\n",
        "print(missinss_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiUHjdzByq5X",
        "outputId": "32c61972-9690-4af8-8bd9-bd3dcfc97e30"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "species                  0.0\n",
            "island                   0.0\n",
            "bill_length_mm           0.0\n",
            "bill_depth_mm            0.0\n",
            "flipper_length_mm        0.0\n",
            "body_mass_g              0.0\n",
            "sex                      0.0\n",
            "bill_length_mm_std       0.0\n",
            "bill_depth_mm_std        0.0\n",
            "flipper_length_mm_std    0.0\n",
            "body_mass_g_std          0.0\n",
            "island_Biscoe_nom        0.0\n",
            "island_Dream_nom         0.0\n",
            "island_Torgersen_nom     0.0\n",
            "Female_nom               0.0\n",
            "Male_nom                 0.0\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YtwYCuk8SPU"
      },
      "source": [
        "### **1.2. Variáveis numéricas**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tIjNOs8q6aZ"
      },
      "source": [
        "Identifique as variáveis numéricas e crie uma nova coluna **padronizando** seus valores. A nova coluna deve ter o mesmo nome da coluna original acrescidade de \"*_std*\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QchO_rKR8tyx"
      },
      "source": [
        "> **Nota**: Você não deve tratar a variável resposta."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwTTh4VKkdKN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "c9432496-a341-4e2c-dff4-62b449f9f634"
      },
      "source": [
        "# visalizando as variáveis númericas\n",
        "df[['bill_length_mm','bill_depth_mm', 'flipper_length_mm', 'body_mass_g']]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
              "0              39.1           18.7              181.0       3750.0\n",
              "1              39.5           17.4              186.0       3800.0\n",
              "2              40.3           18.0              195.0       3250.0\n",
              "4              36.7           19.3              193.0       3450.0\n",
              "5              39.3           20.6              190.0       3650.0\n",
              "..              ...            ...                ...          ...\n",
              "338            47.2           13.7              214.0       4925.0\n",
              "340            46.8           14.3              215.0       4850.0\n",
              "341            50.4           15.7              222.0       5750.0\n",
              "342            45.2           14.8              212.0       5200.0\n",
              "343            49.9           16.1              213.0       5400.0\n",
              "\n",
              "[333 rows x 4 columns]"
            ],
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              "      <th>bill_length_mm</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39.1</td>\n",
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              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.7</td>\n",
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              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>39.3</td>\n",
              "      <td>20.6</td>\n",
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              "      <td>3650.0</td>\n",
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              "    <tr>\n",
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              "      <td>4925.0</td>\n",
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              "      <td>215.0</td>\n",
              "      <td>4850.0</td>\n",
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              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>50.4</td>\n",
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              "      <td>222.0</td>\n",
              "      <td>5750.0</td>\n",
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              "    <tr>\n",
              "      <th>342</th>\n",
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              "    <tr>\n",
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              "      <td>49.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>213.0</td>\n",
              "      <td>5400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>333 rows × 4 columns</p>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        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-f00f423a-a271-4df5-8d43-47676af8d26b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['bill_length_mm','bill_depth_mm', 'flipper_length_mm', 'body_mass_g']]\",\n  \"rows\": 333,\n  \"fields\": [\n    {\n      \"column\": \"bill_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.468668342647559,\n        \"min\": 32.1,\n        \"max\": 59.6,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          59.6,\n          48.1,\n          46.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9692354633199007,\n        \"min\": 13.1,\n        \"max\": 21.5,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          19.5,\n          18.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.015765288287879,\n        \"min\": 172.0,\n        \"max\": 231.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          188.0,\n          231.0,\n          224.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 805.2158019428965,\n        \"min\": 2700.0,\n        \"max\": 6300.0,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          4725.0,\n          3100.0,\n          3575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# criando um função para padronizar as colunas numéricas\n",
        "def calcular_std(df,column):\n",
        "  mean = df[column].mean()\n",
        "  std = df[column].std()\n",
        "  df[f'{column}_std'] = (df[column]-mean)/std\n",
        "  return df"
      ],
      "metadata": {
        "id": "9nkrOV5FCk4g"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# aplicando em cada uma a funcão\n",
        "lenght = calcular_std(df,'bill_length_mm')\n",
        "depth = calcular_std(df,'bill_depth_mm')\n",
        "flipper = calcular_std(df,'flipper_length_mm')\n",
        "mass = calcular_std(df, 'body_mass_g')"
      ],
      "metadata": {
        "id": "mBrN8cciDMPb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "KJl-bBQiDmyY",
        "outputId": "aada83e0-7ede-4141-c7df-2f3af67e7759"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g  \\\n",
              "count      333.000000     333.000000         333.000000   333.000000   \n",
              "mean        43.992793      17.164865         200.966967  4207.057057   \n",
              "std          5.468668       1.969235          14.015765   805.215802   \n",
              "min         32.100000      13.100000         172.000000  2700.000000   \n",
              "25%         39.500000      15.600000         190.000000  3550.000000   \n",
              "50%         44.500000      17.300000         197.000000  4050.000000   \n",
              "75%         48.600000      18.700000         213.000000  4775.000000   \n",
              "max         59.600000      21.500000         231.000000  6300.000000   \n",
              "\n",
              "       bill_length_mm_std  bill_depth_mm_std  flipper_length_mm_std  \\\n",
              "count        3.330000e+02       3.330000e+02           3.330000e+02   \n",
              "mean         3.840772e-16       5.547781e-16           1.707010e-16   \n",
              "std          1.000000e+00       1.000000e+00           1.000000e+00   \n",
              "min         -2.174715e+00      -2.064184e+00          -2.066742e+00   \n",
              "25%         -8.215515e-01      -7.946560e-01          -7.824736e-01   \n",
              "50%          9.274785e-02       6.862315e-02          -2.830361e-01   \n",
              "75%          8.424733e-01       7.795590e-01           8.585356e-01   \n",
              "max          2.853932e+00       2.201431e+00           2.142804e+00   \n",
              "\n",
              "       body_mass_g_std  \n",
              "count     3.330000e+02  \n",
              "mean     -1.280257e-16  \n",
              "std       1.000000e+00  \n",
              "min      -1.871619e+00  \n",
              "25%      -8.160012e-01  \n",
              "50%      -1.950496e-01  \n",
              "75%       7.053301e-01  \n",
              "max       2.599232e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e93915ac-bea7-490e-9367-3aec78ab4791\" 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>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>bill_length_mm_std</th>\n",
              "      <th>bill_depth_mm_std</th>\n",
              "      <th>flipper_length_mm_std</th>\n",
              "      <th>body_mass_g_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>333.000000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>3.330000e+02</td>\n",
              "      <td>3.330000e+02</td>\n",
              "      <td>3.330000e+02</td>\n",
              "      <td>3.330000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.992793</td>\n",
              "      <td>17.164865</td>\n",
              "      <td>200.966967</td>\n",
              "      <td>4207.057057</td>\n",
              "      <td>3.840772e-16</td>\n",
              "      <td>5.547781e-16</td>\n",
              "      <td>1.707010e-16</td>\n",
              "      <td>-1.280257e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.468668</td>\n",
              "      <td>1.969235</td>\n",
              "      <td>14.015765</td>\n",
              "      <td>805.215802</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>32.100000</td>\n",
              "      <td>13.100000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>2700.000000</td>\n",
              "      <td>-2.174715e+00</td>\n",
              "      <td>-2.064184e+00</td>\n",
              "      <td>-2.066742e+00</td>\n",
              "      <td>-1.871619e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39.500000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>3550.000000</td>\n",
              "      <td>-8.215515e-01</td>\n",
              "      <td>-7.946560e-01</td>\n",
              "      <td>-7.824736e-01</td>\n",
              "      <td>-8.160012e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>44.500000</td>\n",
              "      <td>17.300000</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>4050.000000</td>\n",
              "      <td>9.274785e-02</td>\n",
              "      <td>6.862315e-02</td>\n",
              "      <td>-2.830361e-01</td>\n",
              "      <td>-1.950496e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.600000</td>\n",
              "      <td>18.700000</td>\n",
              "      <td>213.000000</td>\n",
              "      <td>4775.000000</td>\n",
              "      <td>8.424733e-01</td>\n",
              "      <td>7.795590e-01</td>\n",
              "      <td>8.585356e-01</td>\n",
              "      <td>7.053301e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>59.600000</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>231.000000</td>\n",
              "      <td>6300.000000</td>\n",
              "      <td>2.853932e+00</td>\n",
              "      <td>2.201431e+00</td>\n",
              "      <td>2.142804e+00</td>\n",
              "      <td>2.599232e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"bill_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.09816384915693,\n        \"min\": 5.468668342647559,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          43.99279279279279,\n          44.5,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.56529060018214,\n        \"min\": 1.9692354633199007,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.164864864864864,\n          17.3,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87.83322997856419,\n        \"min\": 14.015765288287879,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          200.96696696696696,\n          197.0,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1999.914028923884,\n        \"min\": 333.0,\n        \"max\": 6300.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4207.057057057057,\n          4050.0,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 117.65171541044012,\n        \"min\": -2.174714582715964,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8407715446491904e-16,\n          0.09274784562299009,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 117.67990301316681,\n        \"min\": -2.0641842687577734,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.54778112004883e-16,\n          0.06862314723263963,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 117.69617150833578,\n        \"min\": -2.066741727700941,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.70700957539964e-16,\n          -0.28303605870754106,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 117.66885136092783,\n        \"min\": -1.8716188299095664,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -1.28025718154973e-16,\n          -0.19504964591864177,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTh7w8rgkznY"
      },
      "source": [
        "### **1.3. Variáveis categóricas**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clBerimQs2PY"
      },
      "source": [
        "Identifique as variáveis categóricas nominais e ordinais, crie uma nova coluna aplicando a técnica correta de conversão a seus valores. A nova coluna deve ter o mesmo nome da coluna original acrescidade de \"*_nom*\" ou \"*_ord*\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqpAVY109BWA"
      },
      "source": [
        "> **Nota**: Você não deve tratar a variável resposta."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fohdVuLzkdKP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "79fa9a83-b5da-4aa7-aad4-a1626e97da69"
      },
      "source": [
        "# visuzalizando as variáveis categóricas\n",
        "df[['island', 'sex']]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        island     sex\n",
              "0    Torgersen    Male\n",
              "1    Torgersen  Female\n",
              "2    Torgersen  Female\n",
              "4    Torgersen  Female\n",
              "5    Torgersen    Male\n",
              "..         ...     ...\n",
              "338     Biscoe  Female\n",
              "340     Biscoe  Female\n",
              "341     Biscoe    Male\n",
              "342     Biscoe  Female\n",
              "343     Biscoe    Male\n",
              "\n",
              "[333 rows x 2 columns]"
            ],
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              "\n",
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              "      <th>sex</th>\n",
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              "      <td>Torgersen</td>\n",
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              "      <th>1</th>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Torgersen</td>\n",
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              "      <th>5</th>\n",
              "      <td>Torgersen</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Female</td>\n",
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              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>Biscoe</td>\n",
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              "      <td>Biscoe</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>333 rows × 2 columns</p>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  <script>\n",
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              "      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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['island', 'sex']]\",\n  \"rows\": 333,\n  \"fields\": [\n    {\n      \"column\": \"island\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Torgersen\",\n          \"Biscoe\",\n          \"Dream\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# utilizando o .get_dummies para aplicar 'one hot encoding'\n",
        "island = pd.get_dummies(df['island'])\n",
        "island.columns = ['island' +'_'+ str(col) +'_nom' for col in island.columns]\n",
        "df = pd.concat([df, island], axis=1)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "RZgnQrIuHK1H",
        "outputId": "1f7c385b-8c9c-456d-8bcd-036799cb422f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0    Adelie  Torgersen            39.1           18.7              181.0   \n",
              "1    Adelie  Torgersen            39.5           17.4              186.0   \n",
              "2    Adelie  Torgersen            40.3           18.0              195.0   \n",
              "4    Adelie  Torgersen            36.7           19.3              193.0   \n",
              "5    Adelie  Torgersen            39.3           20.6              190.0   \n",
              "..      ...        ...             ...            ...                ...   \n",
              "338  Gentoo     Biscoe            47.2           13.7              214.0   \n",
              "340  Gentoo     Biscoe            46.8           14.3              215.0   \n",
              "341  Gentoo     Biscoe            50.4           15.7              222.0   \n",
              "342  Gentoo     Biscoe            45.2           14.8              212.0   \n",
              "343  Gentoo     Biscoe            49.9           16.1              213.0   \n",
              "\n",
              "     body_mass_g     sex  bill_length_mm_std  bill_depth_mm_std  \\\n",
              "0         3750.0    Male           -0.894695           0.779559   \n",
              "1         3800.0  Female           -0.821552           0.119404   \n",
              "2         3250.0  Female           -0.675264           0.424091   \n",
              "4         3450.0  Female           -1.333559           1.084246   \n",
              "5         3650.0    Male           -0.858123           1.744400   \n",
              "..           ...     ...                 ...                ...   \n",
              "338       4925.0  Female            0.586470          -1.759497   \n",
              "340       4850.0  Female            0.513326          -1.454811   \n",
              "341       5750.0    Male            1.171621          -0.743875   \n",
              "342       5200.0  Female            0.220750          -1.200905   \n",
              "343       5400.0    Male            1.080191          -0.540750   \n",
              "\n",
              "     flipper_length_mm_std  body_mass_g_std  island_Biscoe_nom  \\\n",
              "0                -1.424608        -0.567621                  0   \n",
              "1                -1.067867        -0.505525                  0   \n",
              "2                -0.425733        -1.188572                  0   \n",
              "4                -0.568429        -0.940192                  0   \n",
              "5                -0.782474        -0.691811                  0   \n",
              "..                     ...              ...                ...   \n",
              "338               0.929884         0.891616                  1   \n",
              "340               1.001232         0.798473                  1   \n",
              "341               1.500670         1.916186                  1   \n",
              "342               0.787187         1.233139                  1   \n",
              "343               0.858536         1.481520                  1   \n",
              "\n",
              "     island_Dream_nom  island_Torgersen_nom  \n",
              "0                   0                     1  \n",
              "1                   0                     1  \n",
              "2                   0                     1  \n",
              "4                   0                     1  \n",
              "5                   0                     1  \n",
              "..                ...                   ...  \n",
              "338                 0                     0  \n",
              "340                 0                     0  \n",
              "341                 0                     0  \n",
              "342                 0                     0  \n",
              "343                 0                     0  \n",
              "\n",
              "[333 rows x 14 columns]"
            ],
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              "\n",
              "  <div id=\"df-548ae32c-1de3-4ae2-ad65-88b81e5c9f5d\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>species</th>\n",
              "      <th>island</th>\n",
              "      <th>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex</th>\n",
              "      <th>bill_length_mm_std</th>\n",
              "      <th>bill_depth_mm_std</th>\n",
              "      <th>flipper_length_mm_std</th>\n",
              "      <th>body_mass_g_std</th>\n",
              "      <th>island_Biscoe_nom</th>\n",
              "      <th>island_Dream_nom</th>\n",
              "      <th>island_Torgersen_nom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>-0.894695</td>\n",
              "      <td>0.779559</td>\n",
              "      <td>-1.424608</td>\n",
              "      <td>-0.567621</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>-0.821552</td>\n",
              "      <td>0.119404</td>\n",
              "      <td>-1.067867</td>\n",
              "      <td>-0.505525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>-0.675264</td>\n",
              "      <td>0.424091</td>\n",
              "      <td>-0.425733</td>\n",
              "      <td>-1.188572</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>-1.333559</td>\n",
              "      <td>1.084246</td>\n",
              "      <td>-0.568429</td>\n",
              "      <td>-0.940192</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.3</td>\n",
              "      <td>20.6</td>\n",
              "      <td>190.0</td>\n",
              "      <td>3650.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>-0.858123</td>\n",
              "      <td>1.744400</td>\n",
              "      <td>-0.782474</td>\n",
              "      <td>-0.691811</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>47.2</td>\n",
              "      <td>13.7</td>\n",
              "      <td>214.0</td>\n",
              "      <td>4925.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.586470</td>\n",
              "      <td>-1.759497</td>\n",
              "      <td>0.929884</td>\n",
              "      <td>0.891616</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>46.8</td>\n",
              "      <td>14.3</td>\n",
              "      <td>215.0</td>\n",
              "      <td>4850.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.513326</td>\n",
              "      <td>-1.454811</td>\n",
              "      <td>1.001232</td>\n",
              "      <td>0.798473</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>50.4</td>\n",
              "      <td>15.7</td>\n",
              "      <td>222.0</td>\n",
              "      <td>5750.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.171621</td>\n",
              "      <td>-0.743875</td>\n",
              "      <td>1.500670</td>\n",
              "      <td>1.916186</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>45.2</td>\n",
              "      <td>14.8</td>\n",
              "      <td>212.0</td>\n",
              "      <td>5200.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.220750</td>\n",
              "      <td>-1.200905</td>\n",
              "      <td>0.787187</td>\n",
              "      <td>1.233139</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>49.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>213.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.080191</td>\n",
              "      <td>-0.540750</td>\n",
              "      <td>0.858536</td>\n",
              "      <td>1.481520</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>333 rows × 14 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 333,\n  \"fields\": [\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Adelie\",\n          \"Chinstrap\",\n          \"Gentoo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Torgersen\",\n          \"Biscoe\",\n          \"Dream\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.468668342647559,\n        \"min\": 32.1,\n        \"max\": 59.6,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          59.6,\n          48.1,\n          46.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9692354633199007,\n        \"min\": 13.1,\n        \"max\": 21.5,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          19.5,\n          18.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.015765288287879,\n        \"min\": 172.0,\n        \"max\": 231.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          188.0,\n          231.0,\n          224.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 805.2158019428965,\n        \"min\": 2700.0,\n        \"max\": 6300.0,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          4725.0,\n          3100.0,\n          3575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.174714582715964,\n        \"max\": 2.853931931745427,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          2.853931931745427,\n          0.7510433893342997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999998,\n        \"min\": -2.0641842687577734,\n        \"max\": 2.2014305632230515,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          1.1858079841799982,\n          0.7795589525627764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.066741727700941,\n        \"max\": 2.1428036511234825,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -0.9251700995451649,\n          2.1428036511234825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -1.8716188299095664,\n        \"max\": 2.599232327399566,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.6432349460768205,\n          -1.3748575902085516\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Biscoe_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Dream_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Torgersen_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# aplicando a mesma ténica anterior\n",
        "sex = pd.get_dummies(df['sex'])\n",
        "sex.columns = [str(col) +'_nom' for col in sex.columns]\n",
        "df = pd.concat([df, sex], axis=1)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "pBVg5ycXIRgM",
        "outputId": "4922b149-6a30-4ea9-a9a7-142384869202"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0    Adelie  Torgersen            39.1           18.7              181.0   \n",
              "1    Adelie  Torgersen            39.5           17.4              186.0   \n",
              "2    Adelie  Torgersen            40.3           18.0              195.0   \n",
              "4    Adelie  Torgersen            36.7           19.3              193.0   \n",
              "5    Adelie  Torgersen            39.3           20.6              190.0   \n",
              "..      ...        ...             ...            ...                ...   \n",
              "338  Gentoo     Biscoe            47.2           13.7              214.0   \n",
              "340  Gentoo     Biscoe            46.8           14.3              215.0   \n",
              "341  Gentoo     Biscoe            50.4           15.7              222.0   \n",
              "342  Gentoo     Biscoe            45.2           14.8              212.0   \n",
              "343  Gentoo     Biscoe            49.9           16.1              213.0   \n",
              "\n",
              "     body_mass_g     sex  bill_length_mm_std  bill_depth_mm_std  \\\n",
              "0         3750.0    Male           -0.894695           0.779559   \n",
              "1         3800.0  Female           -0.821552           0.119404   \n",
              "2         3250.0  Female           -0.675264           0.424091   \n",
              "4         3450.0  Female           -1.333559           1.084246   \n",
              "5         3650.0    Male           -0.858123           1.744400   \n",
              "..           ...     ...                 ...                ...   \n",
              "338       4925.0  Female            0.586470          -1.759497   \n",
              "340       4850.0  Female            0.513326          -1.454811   \n",
              "341       5750.0    Male            1.171621          -0.743875   \n",
              "342       5200.0  Female            0.220750          -1.200905   \n",
              "343       5400.0    Male            1.080191          -0.540750   \n",
              "\n",
              "     flipper_length_mm_std  body_mass_g_std  island_Biscoe_nom  \\\n",
              "0                -1.424608        -0.567621                  0   \n",
              "1                -1.067867        -0.505525                  0   \n",
              "2                -0.425733        -1.188572                  0   \n",
              "4                -0.568429        -0.940192                  0   \n",
              "5                -0.782474        -0.691811                  0   \n",
              "..                     ...              ...                ...   \n",
              "338               0.929884         0.891616                  1   \n",
              "340               1.001232         0.798473                  1   \n",
              "341               1.500670         1.916186                  1   \n",
              "342               0.787187         1.233139                  1   \n",
              "343               0.858536         1.481520                  1   \n",
              "\n",
              "     island_Dream_nom  island_Torgersen_nom  Female_nom  Male_nom  \n",
              "0                   0                     1           0         1  \n",
              "1                   0                     1           1         0  \n",
              "2                   0                     1           1         0  \n",
              "4                   0                     1           1         0  \n",
              "5                   0                     1           0         1  \n",
              "..                ...                   ...         ...       ...  \n",
              "338                 0                     0           1         0  \n",
              "340                 0                     0           1         0  \n",
              "341                 0                     0           0         1  \n",
              "342                 0                     0           1         0  \n",
              "343                 0                     0           0         1  \n",
              "\n",
              "[333 rows x 16 columns]"
            ],
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              "  <div id=\"df-467ea694-eb1c-4c02-97bf-f77121a12def\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>species</th>\n",
              "      <th>island</th>\n",
              "      <th>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex</th>\n",
              "      <th>bill_length_mm_std</th>\n",
              "      <th>bill_depth_mm_std</th>\n",
              "      <th>flipper_length_mm_std</th>\n",
              "      <th>body_mass_g_std</th>\n",
              "      <th>island_Biscoe_nom</th>\n",
              "      <th>island_Dream_nom</th>\n",
              "      <th>island_Torgersen_nom</th>\n",
              "      <th>Female_nom</th>\n",
              "      <th>Male_nom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>-0.894695</td>\n",
              "      <td>0.779559</td>\n",
              "      <td>-1.424608</td>\n",
              "      <td>-0.567621</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>-0.821552</td>\n",
              "      <td>0.119404</td>\n",
              "      <td>-1.067867</td>\n",
              "      <td>-0.505525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>-0.675264</td>\n",
              "      <td>0.424091</td>\n",
              "      <td>-0.425733</td>\n",
              "      <td>-1.188572</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>-1.333559</td>\n",
              "      <td>1.084246</td>\n",
              "      <td>-0.568429</td>\n",
              "      <td>-0.940192</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.3</td>\n",
              "      <td>20.6</td>\n",
              "      <td>190.0</td>\n",
              "      <td>3650.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>-0.858123</td>\n",
              "      <td>1.744400</td>\n",
              "      <td>-0.782474</td>\n",
              "      <td>-0.691811</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>47.2</td>\n",
              "      <td>13.7</td>\n",
              "      <td>214.0</td>\n",
              "      <td>4925.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.586470</td>\n",
              "      <td>-1.759497</td>\n",
              "      <td>0.929884</td>\n",
              "      <td>0.891616</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>46.8</td>\n",
              "      <td>14.3</td>\n",
              "      <td>215.0</td>\n",
              "      <td>4850.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.513326</td>\n",
              "      <td>-1.454811</td>\n",
              "      <td>1.001232</td>\n",
              "      <td>0.798473</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>50.4</td>\n",
              "      <td>15.7</td>\n",
              "      <td>222.0</td>\n",
              "      <td>5750.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.171621</td>\n",
              "      <td>-0.743875</td>\n",
              "      <td>1.500670</td>\n",
              "      <td>1.916186</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>45.2</td>\n",
              "      <td>14.8</td>\n",
              "      <td>212.0</td>\n",
              "      <td>5200.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.220750</td>\n",
              "      <td>-1.200905</td>\n",
              "      <td>0.787187</td>\n",
              "      <td>1.233139</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>49.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>213.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.080191</td>\n",
              "      <td>-0.540750</td>\n",
              "      <td>0.858536</td>\n",
              "      <td>1.481520</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>333 rows × 16 columns</p>\n",
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              "          + ' 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",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\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",
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              "      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-2bc4456f-bb24-43b5-ab20-80002e9122e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_251a1f9a-7791-4a2b-9d3c-c8f7bcc56a91\">\n",
              "    <style>\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_251a1f9a-7791-4a2b-9d3c-c8f7bcc56a91 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 333,\n  \"fields\": [\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Adelie\",\n          \"Chinstrap\",\n          \"Gentoo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Torgersen\",\n          \"Biscoe\",\n          \"Dream\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.468668342647559,\n        \"min\": 32.1,\n        \"max\": 59.6,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          59.6,\n          48.1,\n          46.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9692354633199007,\n        \"min\": 13.1,\n        \"max\": 21.5,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          19.5,\n          18.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.015765288287879,\n        \"min\": 172.0,\n        \"max\": 231.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          188.0,\n          231.0,\n          224.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 805.2158019428965,\n        \"min\": 2700.0,\n        \"max\": 6300.0,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          4725.0,\n          3100.0,\n          3575.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.174714582715964,\n        \"max\": 2.853931931745427,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          2.853931931745427,\n          0.7510433893342997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999998,\n        \"min\": -2.0641842687577734,\n        \"max\": 2.2014305632230515,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          1.1858079841799982,\n          0.7795589525627764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.066741727700941,\n        \"max\": 2.1428036511234825,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -0.9251700995451649,\n          2.1428036511234825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -1.8716188299095664,\n        \"max\": 2.599232327399566,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.6432349460768205,\n          -1.3748575902085516\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Biscoe_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Dream_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Torgersen_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Female_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvkcQD9E9DGE"
      },
      "source": [
        "### **1.4. Limpeza**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D03kCy8-9DGE"
      },
      "source": [
        "Descarte as colunas originais e mantenha apenas a variável resposta e as variáveis preditivas com o sufixo *_std*\", *_nom*\" e \"*_ord*\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuEOO6009DGE"
      },
      "source": [
        "# identificando as colunas de interesse (species + *_std + *_nom)\n",
        "column_final = ['species']\n",
        "\n",
        "for col in df:\n",
        "  if re.findall(\".*_std.*|.*_nom.*\", col):\n",
        "    column_final.append(col)\n",
        "\n",
        "df_final = df[column_final]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "0Q9_irThMYRQ",
        "outputId": "49c6bb53-d738-4cda-b6f1-579a4696c1ec"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    species  bill_length_mm_std  bill_depth_mm_std  flipper_length_mm_std  \\\n",
              "0    Adelie           -0.894695           0.779559              -1.424608   \n",
              "1    Adelie           -0.821552           0.119404              -1.067867   \n",
              "2    Adelie           -0.675264           0.424091              -0.425733   \n",
              "4    Adelie           -1.333559           1.084246              -0.568429   \n",
              "5    Adelie           -0.858123           1.744400              -0.782474   \n",
              "..      ...                 ...                ...                    ...   \n",
              "338  Gentoo            0.586470          -1.759497               0.929884   \n",
              "340  Gentoo            0.513326          -1.454811               1.001232   \n",
              "341  Gentoo            1.171621          -0.743875               1.500670   \n",
              "342  Gentoo            0.220750          -1.200905               0.787187   \n",
              "343  Gentoo            1.080191          -0.540750               0.858536   \n",
              "\n",
              "     body_mass_g_std  island_Biscoe_nom  island_Dream_nom  \\\n",
              "0          -0.567621                  0                 0   \n",
              "1          -0.505525                  0                 0   \n",
              "2          -1.188572                  0                 0   \n",
              "4          -0.940192                  0                 0   \n",
              "5          -0.691811                  0                 0   \n",
              "..               ...                ...               ...   \n",
              "338         0.891616                  1                 0   \n",
              "340         0.798473                  1                 0   \n",
              "341         1.916186                  1                 0   \n",
              "342         1.233139                  1                 0   \n",
              "343         1.481520                  1                 0   \n",
              "\n",
              "     island_Torgersen_nom  Female_nom  Male_nom  \n",
              "0                       1           0         1  \n",
              "1                       1           1         0  \n",
              "2                       1           1         0  \n",
              "4                       1           1         0  \n",
              "5                       1           0         1  \n",
              "..                    ...         ...       ...  \n",
              "338                     0           1         0  \n",
              "340                     0           1         0  \n",
              "341                     0           0         1  \n",
              "342                     0           1         0  \n",
              "343                     0           0         1  \n",
              "\n",
              "[333 rows x 10 columns]"
            ],
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              "\n",
              "  <div id=\"df-07f7b88a-9e81-49a6-aac8-8543c959cae4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>species</th>\n",
              "      <th>bill_length_mm_std</th>\n",
              "      <th>bill_depth_mm_std</th>\n",
              "      <th>flipper_length_mm_std</th>\n",
              "      <th>body_mass_g_std</th>\n",
              "      <th>island_Biscoe_nom</th>\n",
              "      <th>island_Dream_nom</th>\n",
              "      <th>island_Torgersen_nom</th>\n",
              "      <th>Female_nom</th>\n",
              "      <th>Male_nom</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>-0.894695</td>\n",
              "      <td>0.779559</td>\n",
              "      <td>-1.424608</td>\n",
              "      <td>-0.567621</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>-0.821552</td>\n",
              "      <td>0.119404</td>\n",
              "      <td>-1.067867</td>\n",
              "      <td>-0.505525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>-0.675264</td>\n",
              "      <td>0.424091</td>\n",
              "      <td>-0.425733</td>\n",
              "      <td>-1.188572</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>-1.333559</td>\n",
              "      <td>1.084246</td>\n",
              "      <td>-0.568429</td>\n",
              "      <td>-0.940192</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>-0.858123</td>\n",
              "      <td>1.744400</td>\n",
              "      <td>-0.782474</td>\n",
              "      <td>-0.691811</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>0.586470</td>\n",
              "      <td>-1.759497</td>\n",
              "      <td>0.929884</td>\n",
              "      <td>0.891616</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>0.513326</td>\n",
              "      <td>-1.454811</td>\n",
              "      <td>1.001232</td>\n",
              "      <td>0.798473</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>1.171621</td>\n",
              "      <td>-0.743875</td>\n",
              "      <td>1.500670</td>\n",
              "      <td>1.916186</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>0.220750</td>\n",
              "      <td>-1.200905</td>\n",
              "      <td>0.787187</td>\n",
              "      <td>1.233139</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>1.080191</td>\n",
              "      <td>-0.540750</td>\n",
              "      <td>0.858536</td>\n",
              "      <td>1.481520</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>333 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07f7b88a-9e81-49a6-aac8-8543c959cae4')\"\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",
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              "  </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-07f7b88a-9e81-49a6-aac8-8543c959cae4 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-07f7b88a-9e81-49a6-aac8-8543c959cae4');\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-51c1d848-afd9-4b0a-95e8-045c9abf3e63\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-51c1d848-afd9-4b0a-95e8-045c9abf3e63')\"\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",
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              "    </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",
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              "    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-51c1d848-afd9-4b0a-95e8-045c9abf3e63 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2b11db36-422b-420b-ae8a-48c9fb845b9c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_final')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2b11db36-422b-420b-ae8a-48c9fb845b9c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_final');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_final",
              "summary": "{\n  \"name\": \"df_final\",\n  \"rows\": 333,\n  \"fields\": [\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Adelie\",\n          \"Chinstrap\",\n          \"Gentoo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.174714582715964,\n        \"max\": 2.853931931745427,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          2.853931931745427,\n          0.7510433893342997,\n          0.42189561747864424\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999998,\n        \"min\": -2.0641842687577734,\n        \"max\": 2.2014305632230515,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          1.1858079841799982,\n          0.7795589525627764,\n          -0.08372023962381876\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.066741727700941,\n        \"max\": 2.1428036511234825,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          -0.9251700995451649,\n          2.1428036511234825,\n          1.6433660638053305\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g_std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -1.8716188299095664,\n        \"max\": 2.599232327399566,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          0.6432349460768205,\n          -1.3748575902085516,\n          -0.7849536180635968\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Biscoe_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Dream_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island_Torgersen_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Female_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male_nom\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waxn4B2UDHyl"
      },
      "source": [
        "---"
      ]
    }
  ]
}