NikeRun.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/arexdevson/Analises-Python/blob/main/NikeRun.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Objetivos:\n"
      ],
      "metadata": {
        "id": "j-loXWvaxafN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Realizar uma avaliação pessoal dos meus dados de corridas obtidos através do pacote \"nrc-exporter\""
      ],
      "metadata": {
        "id": "-hhWnwr4xg8Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Bibliotecas"
      ],
      "metadata": {
        "id": "MUG88UoVxGNo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4TXJ3HexxC3q"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "from functools import reduce\n",
        "from google.colab import drive"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "warnings.filterwarnings('ignore') # Ignorando avisos padrão do Google Colab"
      ],
      "metadata": {
        "id": "Nt0q9Uhv9c2H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importação dos Dados\n"
      ],
      "metadata": {
        "id": "JJnS5CNRxR38"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
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        },
        "id": "bK5y3_i0zB9U",
        "outputId": "bf989fa6-aa23-446b-ee08-b82bad1cf7eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nk_weather = pd.read_excel('/content/drive/MyDrive/Registros/Geral/PycharmProjects/NikeRun/NikeWeather.xlsx')"
      ],
      "metadata": {
        "id": "jib17gexzK7l"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "nk_metric = pd.read_excel('/content/drive/MyDrive/Registros/Geral/PycharmProjects/NikeRun/NikeRunMetric.xlsx')"
      ],
      "metadata": {
        "id": "7FLY4hcOzc3x"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nk_date = pd.read_excel('/content/drive/MyDrive/Registros/Geral/PycharmProjects/NikeRun/NikeDate.xlsx')"
      ],
      "metadata": {
        "id": "dip1qA88zgzd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pré-Visualização dos dados"
      ],
      "metadata": {
        "id": "wauiZo2kznY9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Weather"
      ],
      "metadata": {
        "id": "mBJ3FyfX0Yt4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nk_weather.head() #Primeiros dados"
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              "         Weather\n",
              "0  NotIdentified\n",
              "1  NotIdentified\n",
              "2         cloudy\n",
              "3    clear_night\n",
              "4  NotIdentified"
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      "source": [
        "nk_weather.info() #informações estrutura"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 107 entries, 0 to 106\n",
            "Data columns (total 1 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   Weather  107 non-null    object\n",
            "dtypes: object(1)\n",
            "memory usage: 984.0+ bytes\n"
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      "source": [
        "nk_weather.describe() #Descrição Geral"
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              "              Weather\n",
              "count             107\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c1d7f8b6-e61d-4b18-8a14-9f8d86493c52');\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-9afdf108-c1a8-489e-bbe9-fb81b8d14b35\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9afdf108-c1a8-489e-bbe9-fb81b8d14b35')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9afdf108-c1a8-489e-bbe9-fb81b8d14b35 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nk_weather\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Weather\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          16,\n          \"21\",\n          \"107\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Date"
      ],
      "metadata": {
        "id": "JUkAeXqD0cdO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nk_date.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_a-1xUB0z_ka",
        "outputId": "7ac2afaf-a7be-4145-fad0-3eeb79c88de6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  start_epoch_ms  DiasSemCorrer\n",
              "0     2021-12-01              0\n",
              "1     2021-12-27             26\n",
              "2     2022-01-04              8\n",
              "3     2022-03-08             64\n",
              "4     2022-04-06             30"
            ],
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>start_epoch_ms</th>\n",
              "      <th>DiasSemCorrer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-12-27</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-01-04</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-03-08</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-04-06</td>\n",
              "      <td>30</td>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "    }\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",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7ba05a61-8d93-42b7-9cb0-5bd54e3fadb8 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-7ba05a61-8d93-42b7-9cb0-5bd54e3fadb8');\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",
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              "\n",
              "\n",
              "<div id=\"df-944f47e6-04c0-4287-89ea-462d0a8c6c26\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-944f47e6-04c0-4287-89ea-462d0a8c6c26')\"\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",
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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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-944f47e6-04c0-4287-89ea-462d0a8c6c26 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "nk_date",
              "summary": "{\n  \"name\": \"nk_date\",\n  \"rows\": 107,\n  \"fields\": [\n    {\n      \"column\": \"start_epoch_ms\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 81,\n        \"samples\": [\n          \"2023-08-16\",\n          \"2021-12-01\",\n          \"2023-06-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiasSemCorrer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 0,\n        \"max\": 124,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          14,\n          25,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nk_date.info() #informações estrutura"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GjUMaKOn09iI",
        "outputId": "e31f290f-7c85-46b5-e401-c5aaa84ed401"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 107 entries, 0 to 106\n",
            "Data columns (total 2 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   start_epoch_ms  107 non-null    object\n",
            " 1   DiasSemCorrer   107 non-null    int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 1.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nk_date.describe() #Descrição Geral"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "J4A7ZynA09Ys",
        "outputId": "a234f584-fb1c-4ba5-f30e-94e93fc0d8e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       DiasSemCorrer\n",
              "count     107.000000\n",
              "mean        9.317757\n",
              "std        16.753258\n",
              "min         0.000000\n",
              "25%         1.000000\n",
              "50%         4.000000\n",
              "75%         9.000000\n",
              "max       124.000000"
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              "      <th>DiasSemCorrer</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>107.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.317757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.753258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>9.000000</td>\n",
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              "    <tr>\n",
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              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e284ee12-11f9-46f6-8198-713d5c01b692');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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              "    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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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nk_date\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"DiasSemCorrer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.85772218733036,\n        \"min\": 0.0,\n        \"max\": 124.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.317757009345794,\n          4.0,\n          107.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Metric"
      ],
      "metadata": {
        "id": "JlwBw1Vx0eOA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nk_metric.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5laVbb9R0Ioi",
        "outputId": "c6f02f86-10cf-43c5-99d3-ab65e3566b35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     metric summary                              source  \\\n",
              "0     speed    mean  com.nike.running.android.fullpower   \n",
              "1  distance   total  com.nike.running.android.fullpower   \n",
              "2     steps   total  com.nike.running.android.fullpower   \n",
              "3  nikefuel    mean    com.nike.nikefuelengine.1.0-A5.0   \n",
              "4  nikefuel   total    com.nike.nikefuelengine.1.0-A5.0   \n",
              "\n",
              "                         app_id        value  \n",
              "0  com.nike.sport.running.droid     6.615161  \n",
              "1  com.nike.sport.running.droid     4.026061  \n",
              "2  com.nike.sport.running.droid  4735.000000  \n",
              "3  com.nike.sport.running.droid    25.970099  \n",
              "4  com.nike.sport.running.droid   948.341464  "
            ],
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              "      <th></th>\n",
              "      <th>metric</th>\n",
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              "      <th>0</th>\n",
              "      <td>speed</td>\n",
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              "      <td>com.nike.running.android.fullpower</td>\n",
              "      <td>com.nike.sport.running.droid</td>\n",
              "      <td>6.615161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>distance</td>\n",
              "      <td>total</td>\n",
              "      <td>com.nike.running.android.fullpower</td>\n",
              "      <td>com.nike.sport.running.droid</td>\n",
              "      <td>4.026061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>steps</td>\n",
              "      <td>total</td>\n",
              "      <td>com.nike.running.android.fullpower</td>\n",
              "      <td>com.nike.sport.running.droid</td>\n",
              "      <td>4735.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nikefuel</td>\n",
              "      <td>mean</td>\n",
              "      <td>com.nike.nikefuelengine.1.0-A5.0</td>\n",
              "      <td>com.nike.sport.running.droid</td>\n",
              "      <td>25.970099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>nikefuel</td>\n",
              "      <td>total</td>\n",
              "      <td>com.nike.nikefuelengine.1.0-A5.0</td>\n",
              "      <td>com.nike.sport.running.droid</td>\n",
              "      <td>948.341464</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dab07637-f330-423d-8215-c2370b4912f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "nk_metric",
              "summary": "{\n  \"name\": \"nk_metric\",\n  \"rows\": 751,\n  \"fields\": [\n    {\n      \"column\": \"metric\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"speed\",\n          \"distance\",\n          \"calories\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"summary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"mean\",\n          \"total\",\n          \"max\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"com.nike.running.android.fullpower\",\n          \"com.nike.nikefuelengine.1.0-A5.0\",\n          \"com.nike.running.manualentry\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"app_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"com.nike.sport.running.droid\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1673.4660219349,\n        \"min\": 0.273211507648106,\n        \"max\": 9943.0,\n        \"num_unique_values\": 750,\n        \"samples\": [\n          6.224862640679447\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nk_metric.info() #informações estrutura"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiuiGzwh0Ld1",
        "outputId": "99668045-3ad5-4bf3-fa2d-3358146b89a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 751 entries, 0 to 750\n",
            "Data columns (total 5 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   metric   751 non-null    object \n",
            " 1   summary  751 non-null    object \n",
            " 2   source   751 non-null    object \n",
            " 3   app_id   751 non-null    object \n",
            " 4   value    751 non-null    float64\n",
            "dtypes: float64(1), object(4)\n",
            "memory usage: 29.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nk_metric.describe() #Descrição Geral"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "EEIzUsM_1WXH",
        "outputId": "9528dfa2-7277-49e8-f791-99df643eeb84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             value\n",
              "count   751.000000\n",
              "mean    803.791756\n",
              "std    1673.466022\n",
              "min       0.273212\n",
              "25%       7.025782\n",
              "50%      25.970099\n",
              "75%     587.330500\n",
              "max    9943.000000"
            ],
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>751.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>803.791756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1673.466022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.273212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.025782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>25.970099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>587.330500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9943.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40a39e98-e8d2-4ce8-b76a-9d58cf5927cf')\"\n",
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              "  </svg>\n",
              "    </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-40a39e98-e8d2-4ce8-b76a-9d58cf5927cf 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-40a39e98-e8d2-4ce8-b76a-9d58cf5927cf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\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",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5c64e5fb-f1b2-4f3a-8b8c-6c464c4412a9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nk_metric\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3368.8188794138478,\n        \"min\": 0.273211507648106,\n        \"max\": 9943.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          803.7917557331921,\n          25.97009942780772,\n          751.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Tratamento dos dados"
      ],
      "metadata": {
        "id": "KZZmr_Bv1dRN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "speed = nk_metric.loc[nk_metric['metric']==\"speed\"]\n",
        "calories = nk_metric.loc[nk_metric['metric']==\"calories\"]\n",
        "distance = nk_metric.loc[nk_metric['metric']==\"distance\"]\n",
        "steps = nk_metric.loc[nk_metric['metric']==\"steps\"]\n",
        "pace = nk_metric.loc[nk_metric['metric']==\"pace\"]\n",
        "nk_weather['Weather'] = nk_weather['Weather'].replace('NotIdentified','NãoIdentificado').replace('cloudy','Nublado - Dia').replace('mostly-cloudy-night','Nublado - Noite').replace('partly-cloudy-night','Nublado - Noite').replace('mostly-clear-night','Céu Limpo - Noite').replace('clear-day','Céu Limpo - Dia').replace('clear-night','Céu Limpo - Noite').replace('sunny','Ensolarado').replace('partly_sunny','Ensolarado').replace('mostly-clear-day','Ensolarado').replace('wind','Nublado').replace('light-thunderstorms','Nublado')\n",
        "nk_weather['Weather'] = nk_weather['Weather'].replace('mostly-cloudy-day','Nublado - Dia').replace('partly-cloudy-day\t','Nublado - Dia').replace('partly_cloudy_night','Nublado - Noite').replace('clear_night','Céu Limpo - Noite').replace('partly-cloudy-day','Nublado - Dia')\n"
      ],
      "metadata": {
        "id": "Pjcf4KDR1gTy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diassemcorrer = nk_date['DiasSemCorrer'].values\n",
        "data = nk_date['start_epoch_ms'].values"
      ],
      "metadata": {
        "id": "Ewu_RIWA6kTN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transformação das tabelas"
      ],
      "metadata": {
        "id": "_S5rHGVw28So"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Função\n",
        "\n",
        "def transf_tabelas(df):\n",
        "  df.drop(columns=['source','app_id'],inplace=True)\n",
        "  df.reset_index(inplace=True)\n",
        "  df.drop(columns='index',inplace=True)\n",
        "  df['date'] = data\n",
        "  df['diassemcorrer'] = diassemcorrer\n",
        "\n",
        "  return df\n",
        "\n"
      ],
      "metadata": {
        "id": "zL22NS6q7cMN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "speed = transf_tabelas(speed)\n",
        "calories = transf_tabelas(calories)\n",
        "distance = transf_tabelas(distance)\n",
        "pace = transf_tabelas(pace)"
      ],
      "metadata": {
        "id": "2dSLvMhW1wPp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nk_weather['date'] = data\n"
      ],
      "metadata": {
        "id": "RrzCeWNQ8SjO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Juntando as tabelas\n"
      ],
      "metadata": {
        "id": "GEAwDi0482c6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nk_analysis = pd.DataFrame()"
      ],
      "metadata": {
        "id": "KLhcI-gW-EAt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nk_analysis['pace'] = pace['value']\n",
        "nk_analysis['calories'] = calories['value']\n",
        "nk_analysis['speed'] = speed['value']\n",
        "nk_analysis['distance'] = distance['value']\n",
        "nk_analysis['date'] = data\n",
        "nk_analysis['DaysOffRun'] = nk_date['DiasSemCorrer'].values\n",
        "nk_analysis['weather'] = nk_weather['Weather']\n",
        "nk_analysis['Ano']= pd.to_datetime(nk_analysis['date']).dt.year\n",
        "# Converte a coluna de datas para o tipo datetime\n",
        "nk_analysis['Data_Ini'] = pd.to_datetime(nk_analysis['date'])\n",
        "\n",
        "# Cria uma nova coluna com o início do mês\n",
        "nk_analysis['ini_mes'] = nk_analysis['Data_Ini'].dt.to_period('M').dt.to_timestamp()\n",
        "\n",
        "nk_analysis"
      ],
      "metadata": {
        "id": "QErhgNoF-fip",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "1b0acbcf-ea87-4306-b56e-8caa20a3b7af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          pace  calories     speed  distance        date  DaysOffRun  \\\n",
              "0     9.070074   306.060  6.615161  4.026061  2021-12-01           0   \n",
              "1     9.338788   526.578  6.424817  7.231488  2021-12-27          26   \n",
              "2     8.705081   555.748  6.892527  7.390320  2022-01-04           8   \n",
              "3     9.525107   526.388  6.299142  7.410240  2022-03-08          64   \n",
              "4     9.728813   544.387  6.167247  7.529181  2022-04-06          30   \n",
              "..         ...       ...       ...       ...         ...         ...   \n",
              "102   6.792834   592.733  8.832837  7.009838  2024-06-12           2   \n",
              "103   6.918779   199.560  8.672051  2.367952  2024-06-18           6   \n",
              "104   7.196996   222.749  8.336811  2.746516  2024-06-18           1   \n",
              "105   6.448318   601.507  9.304752  7.017334  2024-06-22           4   \n",
              "106  11.254041   117.707  5.331418  1.787506  2024-06-24           3   \n",
              "\n",
              "               weather   Ano   Data_Ini inicio_do_mes    ini_mes  \n",
              "0      NãoIdentificado  2021 2021-12-01    2021-12-01 2021-12-01  \n",
              "1      NãoIdentificado  2021 2021-12-27    2021-12-01 2021-12-01  \n",
              "2        Nublado - Dia  2022 2022-01-04    2022-01-01 2022-01-01  \n",
              "3    Céu Limpo - Noite  2022 2022-03-08    2022-03-01 2022-03-01  \n",
              "4      NãoIdentificado  2022 2022-04-06    2022-04-01 2022-04-01  \n",
              "..                 ...   ...        ...           ...        ...  \n",
              "102    NãoIdentificado  2024 2024-06-12    2024-06-01 2024-06-01  \n",
              "103    NãoIdentificado  2024 2024-06-18    2024-06-01 2024-06-01  \n",
              "104    NãoIdentificado  2024 2024-06-18    2024-06-01 2024-06-01  \n",
              "105    Nublado - Noite  2024 2024-06-22    2024-06-01 2024-06-01  \n",
              "106    NãoIdentificado  2024 2024-06-24    2024-06-01 2024-06-01  \n",
              "\n",
              "[107 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pace</th>\n",
              "      <th>calories</th>\n",
              "      <th>speed</th>\n",
              "      <th>distance</th>\n",
              "      <th>date</th>\n",
              "      <th>DaysOffRun</th>\n",
              "      <th>weather</th>\n",
              "      <th>Ano</th>\n",
              "      <th>Data_Ini</th>\n",
              "      <th>inicio_do_mes</th>\n",
              "      <th>ini_mes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.070074</td>\n",
              "      <td>306.060</td>\n",
              "      <td>6.615161</td>\n",
              "      <td>4.026061</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>0</td>\n",
              "      <td>NãoIdentificado</td>\n",
              "      <td>2021</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>2021-12-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.338788</td>\n",
              "      <td>526.578</td>\n",
              "      <td>6.424817</td>\n",
              "      <td>7.231488</td>\n",
              "      <td>2021-12-27</td>\n",
              "      <td>26</td>\n",
              "      <td>NãoIdentificado</td>\n",
              "      <td>2021</td>\n",
              "      <td>2021-12-27</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>2021-12-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.705081</td>\n",
              "      <td>555.748</td>\n",
              "      <td>6.892527</td>\n",
              "      <td>7.390320</td>\n",
              "      <td>2022-01-04</td>\n",
              "      <td>8</td>\n",
              "      <td>Nublado - Dia</td>\n",
              "      <td>2022</td>\n",
              "      <td>2022-01-04</td>\n",
              "      <td>2022-01-01</td>\n",
              "      <td>2022-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.525107</td>\n",
              "      <td>526.388</td>\n",
              "      <td>6.299142</td>\n",
              "      <td>7.410240</td>\n",
              "      <td>2022-03-08</td>\n",
              "      <td>64</td>\n",
              "      <td>Céu Limpo - Noite</td>\n",
              "      <td>2022</td>\n",
              "      <td>2022-03-08</td>\n",
              "      <td>2022-03-01</td>\n",
              "      <td>2022-03-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.728813</td>\n",
              "      <td>544.387</td>\n",
              "      <td>6.167247</td>\n",
              "      <td>7.529181</td>\n",
              "      <td>2022-04-06</td>\n",
              "      <td>30</td>\n",
              "      <td>NãoIdentificado</td>\n",
              "      <td>2022</td>\n",
              "      <td>2022-04-06</td>\n",
              "      <td>2022-04-01</td>\n",
              "      <td>2022-04-01</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>6.792834</td>\n",
              "      <td>592.733</td>\n",
              "      <td>8.832837</td>\n",
              "      <td>7.009838</td>\n",
              "      <td>2024-06-12</td>\n",
              "      <td>2</td>\n",
              "      <td>NãoIdentificado</td>\n",
              "      <td>2024</td>\n",
              "      <td>2024-06-12</td>\n",
              "      <td>2024-06-01</td>\n",
              "      <td>2024-06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>6.918779</td>\n",
              "      <td>199.560</td>\n",
              "      <td>8.672051</td>\n",
              "      <td>2.367952</td>\n",
              "      <td>2024-06-18</td>\n",
              "      <td>6</td>\n",
              "      <td>NãoIdentificado</td>\n",
              "      <td>2024</td>\n",
              "      <td>2024-06-18</td>\n",
              "      <td>2024-06-01</td>\n",
              "      <td>2024-06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>7.196996</td>\n",
              "      <td>222.749</td>\n",
              "      <td>8.336811</td>\n",
              "      <td>2.746516</td>\n",
              "      <td>2024-06-18</td>\n",
              "      <td>1</td>\n",
              "      <td>NãoIdentificado</td>\n",
              "      <td>2024</td>\n",
              "      <td>2024-06-18</td>\n",
              "      <td>2024-06-01</td>\n",
              "      <td>2024-06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>6.448318</td>\n",
              "      <td>601.507</td>\n",
              "      <td>9.304752</td>\n",
              "      <td>7.017334</td>\n",
              "      <td>2024-06-22</td>\n",
              "      <td>4</td>\n",
              "      <td>Nublado - Noite</td>\n",
              "      <td>2024</td>\n",
              "      <td>2024-06-22</td>\n",
              "      <td>2024-06-01</td>\n",
              "      <td>2024-06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>11.254041</td>\n",
              "      <td>117.707</td>\n",
              "      <td>5.331418</td>\n",
              "      <td>1.787506</td>\n",
              "      <td>2024-06-24</td>\n",
              "      <td>3</td>\n",
              "      <td>NãoIdentificado</td>\n",
              "      <td>2024</td>\n",
              "      <td>2024-06-24</td>\n",
              "      <td>2024-06-01</td>\n",
              "      <td>2024-06-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>107 rows × 11 columns</p>\n",
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              "summary": "{\n  \"name\": \"nk_analysis\",\n  \"rows\": 107,\n  \"fields\": [\n    {\n      \"column\": \"pace\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5379149970355503,\n        \"min\": 5.924465153067117,\n        \"max\": 22.93499181394037,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          6.40841844849442,\n          8.411775644933817,\n          9.728813394907075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 161.12914637988217,\n        \"min\": 17.676,\n        \"max\": 849.964,\n        \"num_unique_values\": 106,\n        \"samples\": [\n          434.152,\n          459.291,\n          544.387\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.661334505599283,\n        \"min\": 2.61608988077034,\n        \"max\": 10.12749648277327,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          9.362684487948234,\n          7.132857856965832,\n          6.167247490984803\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9404296320606986,\n        \"min\": 0.273211507648106,\n        \"max\": 10.01043111137931,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          5.019439183816692,\n          5.961880358780608,\n          7.529181311910614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 81,\n        \"samples\": [\n          \"2023-08-16\",\n          \"2021-12-01\",\n          \"2023-06-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DaysOffRun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 0,\n        \"max\": 124,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          14,\n          25,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"N\\u00e3oIdentificado\",\n          \"Nublado - Dia\",\n          \"C\\u00e9u Limpo - Dia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ano\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2022,\n          2024,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Data_Ini\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-12-01 00:00:00\",\n        \"max\": \"2024-06-24 00:00:00\",\n        \"num_unique_values\": 81,\n        \"samples\": [\n          \"2023-08-16 00:00:00\",\n          \"2021-12-01 00:00:00\",\n          \"2023-06-27 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inicio_do_mes\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-12-01 00:00:00\",\n        \"max\": \"2024-06-01 00:00:00\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"2023-01-01 00:00:00\",\n          \"2023-10-01 00:00:00\",\n          \"2021-12-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ini_mes\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-12-01 00:00:00\",\n        \"max\": \"2024-06-01 00:00:00\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"2023-01-01 00:00:00\",\n          \"2023-10-01 00:00:00\",\n          \"2021-12-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nk_analysis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "2jRt-pr3-6pg",
        "outputId": "6f0532b3-7678-4165-8d29-dd46365bd9df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          pace  calories     speed  distance        date  DaysOffRun  \\\n",
              "0     9.070074   306.060  6.615161  4.026061  2021-12-01           0   \n",
              "1     9.338788   526.578  6.424817  7.231488  2021-12-27          26   \n",
              "2     8.705081   555.748  6.892527  7.390320  2022-01-04           8   \n",
              "3     9.525107   526.388  6.299142  7.410240  2022-03-08          64   \n",
              "4     9.728813   544.387  6.167247  7.529181  2022-04-06          30   \n",
              "..         ...       ...       ...       ...         ...         ...   \n",
              "102   6.792834   592.733  8.832837  7.009838  2024-06-12           2   \n",
              "103   6.918779   199.560  8.672051  2.367952  2024-06-18           6   \n",
              "104   7.196996   222.749  8.336811  2.746516  2024-06-18           1   \n",
              "105   6.448318   601.507  9.304752  7.017334  2024-06-22           4   \n",
              "106  11.254041   117.707  5.331418  1.787506  2024-06-24           3   \n",
              "\n",
              "               weather   Ano  \n",
              "0      NãoIdentificado  2021  \n",
              "1      NãoIdentificado  2021  \n",
              "2        Nublado - Dia  2022  \n",
              "3    Céu Limpo - Noite  2022  \n",
              "4      NãoIdentificado  2022  \n",
              "..                 ...   ...  \n",
              "102    NãoIdentificado  2024  \n",
              "103    NãoIdentificado  2024  \n",
              "104    NãoIdentificado  2024  \n",
              "105    Nublado - Noite  2024  \n",
              "106    NãoIdentificado  2024  \n",
              "\n",
              "[107 rows x 8 columns]"
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              "      <td>2022-04-06</td>\n",
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              "    }\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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7eb9ebe9-72d8-470c-9f6c-846976eb1d24\">\n",
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              "      }\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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('nk_analysis')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "nk_analysis",
              "summary": "{\n  \"name\": \"nk_analysis\",\n  \"rows\": 107,\n  \"fields\": [\n    {\n      \"column\": \"pace\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5379149970355503,\n        \"min\": 5.924465153067117,\n        \"max\": 22.93499181394037,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          6.40841844849442,\n          8.411775644933817,\n          9.728813394907075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 161.12914637988217,\n        \"min\": 17.676,\n        \"max\": 849.964,\n        \"num_unique_values\": 106,\n        \"samples\": [\n          434.152,\n          459.291,\n          544.387\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.661334505599283,\n        \"min\": 2.61608988077034,\n        \"max\": 10.12749648277327,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          9.362684487948234,\n          7.132857856965832,\n          6.167247490984803\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9404296320606986,\n        \"min\": 0.273211507648106,\n        \"max\": 10.01043111137931,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          5.019439183816692,\n          5.961880358780608,\n          7.529181311910614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 81,\n        \"samples\": [\n          \"2023-08-16\",\n          \"2021-12-01\",\n          \"2023-06-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DaysOffRun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 0,\n        \"max\": 124,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          14,\n          25,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"N\\u00e3oIdentificado\",\n          \"Nublado - Dia\",\n          \"C\\u00e9u Limpo - Dia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ano\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2022,\n          2024,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Analises\n"
      ],
      "metadata": {
        "id": "cMka4KEtEKdD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#1 ° - Numero de Corridas\n",
        "print(f'O numero de corridas analisadas são: {nk_analysis[\"pace\"].value_counts().sum()}')"
      ],
      "metadata": {
        "id": "ZbUop72m_AVq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "800c979d-9d81-4b91-ade8-076ed3bfd9f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O numero de corridas analisadas são: 107\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2° - Numero de Corridas por Ano\n",
        "\n",
        "print(f'O numero de corridas analisadas por {nk_analysis.groupby(\"Ano\")[\"Ano\"].value_counts()}:')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7Pbx6xtEc49",
        "outputId": "aec3bb7c-35d4-42bb-86e2-167df8db8e72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O numero de corridas analisadas por Ano\n",
            "2021     2\n",
            "2022    12\n",
            "2023    61\n",
            "2024    32\n",
            "Name: count, dtype: int64:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3 - Distribuição das corrida por tipo de clima\n",
        "nk_analysis['weather'] = nk_analysis['weather'].replace('NotIdentified','NãoIdentificado').replace('cloudy','Nublado - Dia').replace('mostly-cloudy-night','Nublado - Noite').replace('partly-cloudy-night','Nublado - Noite').replace('mostly-clear-night','Céu Limpo - Noite').replace('clear-day','Céu Limpo - Dia').replace('clear-night','Céu Limpo - Noite').replace('sunny','Ensolarado').replace('partly_sunny','Ensolarado').replace('mostly-clear-day','Ensolarado').replace('wind','Nublado').replace('light-thunderstorms','Nublado')\n",
        "nk_analysis['weather'] = nk_analysis['weather'].replace('mostly-cloudy-day','Nublado - Dia').replace('partly-cloudy-day\t','Nublado - Dia').replace('partly_cloudy_night','Nublado - Noite').replace('clear_night','Céu Limpo - Noite')\n",
        "weather = nk_analysis['weather'].value_counts().reset_index()\n",
        "weather['% perc'] = (weather['count']/nk_analysis['pace'].value_counts().sum())*100\n",
        "weather"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "xEFgtcUoNIma",
        "outputId": "0de45b57-b0f7-4782-e297-e8088d1761a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             weather  count     % perc\n",
              "0      Nublado - Dia     30  28.037383\n",
              "1    Nublado - Noite     23  21.495327\n",
              "2    NãoIdentificado     21  19.626168\n",
              "3  Céu Limpo - Noite     15  14.018692\n",
              "4    Céu Limpo - Dia      9   8.411215\n",
              "5         Ensolarado      6   5.607477\n",
              "6            Nublado      3   2.803738"
            ],
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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",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c3eff299-0b57-4b6d-b4dc-9b9f71a87dc7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d19c5b62-0f92-4776-9343-53041c19da7a\">\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('weather')\"\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_d19c5b62-0f92-4776-9343-53041c19da7a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('weather');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "weather",
              "summary": "{\n  \"name\": \"weather\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Nublado - Dia\",\n          \"Nublado - Noite\",\n          \"Ensolarado\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 3,\n        \"max\": 30,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          30,\n          23,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"% perc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.231612236615502,\n        \"min\": 2.803738317757009,\n        \"max\": 28.037383177570092,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          28.037383177570092,\n          21.49532710280374,\n          5.607476635514018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#4° - Pace Médio por ano\n",
        "nk_analysis.groupby('Ano')['pace'].mean()"
      ],
      "metadata": {
        "id": "F9P1fsOkFORH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bd57068-20bb-40bc-fc27-532f4612bdc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ano\n",
              "2021    9.204431\n",
              "2022    9.435464\n",
              "2023    8.934033\n",
              "2024    8.793989\n",
              "Name: pace, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#4.1 - Maior Pace por ano\n",
        "nk_analysis.groupby('Ano')['pace'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGWs5_kEEemN",
        "outputId": "da51e343-55f9-4ad3-c276-608a78751086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ano\n",
              "2021     9.338788\n",
              "2022    10.146797\n",
              "2023    16.083321\n",
              "2024    22.934992\n",
              "Name: pace, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#4.2 - Menor pace por ano\n",
        "corrida = nk_analysis.groupby('Ano')['pace'].min().reset_index()\n",
        "corrida['LastYearPace'] = corrida['pace'].shift(1)\n",
        "corrida['% Progresso'] = (corrida['pace']/corrida['LastYearPace'] -1)*100\n",
        "corrida['LastYearPace'].fillna(0,inplace=True)\n",
        "corrida['% Progresso'].fillna(0,inplace=True)\n",
        "\n",
        "corrida"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "7apMvAxTEg-r",
        "outputId": "17bc00a1-66bf-4539-b0f5-1ae6ade3a6fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Ano      pace  LastYearPace  % Progresso\n",
              "0  2021  9.070074      0.000000     0.000000\n",
              "1  2022  8.411776      9.070074    -7.257914\n",
              "2  2023  6.224863      8.411776   -25.998233\n",
              "3  2024  5.924465      6.224863    -4.825769"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ddc0657-514d-4660-8f05-3d02d581b35b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th>Ano</th>\n",
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              "      <th>0</th>\n",
              "      <td>2021</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022</td>\n",
              "      <td>8.411776</td>\n",
              "      <td>9.070074</td>\n",
              "      <td>-7.257914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023</td>\n",
              "      <td>6.224863</td>\n",
              "      <td>8.411776</td>\n",
              "      <td>-25.998233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024</td>\n",
              "      <td>5.924465</td>\n",
              "      <td>6.224863</td>\n",
              "      <td>-4.825769</td>\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        const element = document.querySelector('#df-4ddc0657-514d-4660-8f05-3d02d581b35b');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    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",
              "    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",
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              "      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",
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              "      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",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "corrida",
              "summary": "{\n  \"name\": \"corrida\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Ano\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2022,\n          2024,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pace\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5674548725775228,\n        \"min\": 5.924465153067117,\n        \"max\": 9.070073796042236,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8.411775644933817,\n          5.924465153067117,\n          9.070073796042236\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastYearPace\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.134048606029878,\n        \"min\": 0.0,\n        \"max\": 9.070073796042236,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.070073796042236,\n          6.224862640679447,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"% Progresso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.39174247368465,\n        \"min\": -25.99823267482756,\n        \"max\": 0.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -7.257913947686623,\n          -4.825768935835373,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#5 - Pace médio por weather\n",
        "nk_analysis.groupby('weather')['pace'].min()"
      ],
      "metadata": {
        "id": "31657huiFhnE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "142a92aa-02ba-45be-8f59-8d8e1e55b8e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weather\n",
              "Céu Limpo - Dia      6.551487\n",
              "Céu Limpo - Noite    6.528828\n",
              "Ensolarado           8.268100\n",
              "Nublado              7.412095\n",
              "Nublado - Dia        6.131682\n",
              "Nublado - Noite      5.924465\n",
              "NãoIdentificado      6.039720\n",
              "Name: pace, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#5.1 - Pace médio por weather/ano\n",
        "nk_analysis.groupby(['weather','Ano'])['pace'].min().reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "kMWgw5G4GqV7",
        "outputId": "5bf89226-05e0-45b6-c574-e40ac6b2b8fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              weather   Ano       pace\n",
              "0     Céu Limpo - Dia  2023   6.551487\n",
              "1   Céu Limpo - Noite  2022   9.525107\n",
              "2   Céu Limpo - Noite  2023   6.528828\n",
              "3   Céu Limpo - Noite  2024   6.626421\n",
              "4          Ensolarado  2022   9.000298\n",
              "5          Ensolarado  2023   8.268100\n",
              "6             Nublado  2023   7.412095\n",
              "7             Nublado  2024  10.504559\n",
              "8       Nublado - Dia  2022   8.705081\n",
              "9       Nublado - Dia  2023   6.224863\n",
              "10      Nublado - Dia  2024   6.131682\n",
              "11    Nublado - Noite  2022   8.411776\n",
              "12    Nublado - Noite  2023   6.544300\n",
              "13    Nublado - Noite  2024   5.924465\n",
              "14    NãoIdentificado  2021   9.070074\n",
              "15    NãoIdentificado  2022   9.023597\n",
              "16    NãoIdentificado  2023   7.076800\n",
              "17    NãoIdentificado  2024   6.039720"
            ],
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          "metadata": {},
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      ]
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    {
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      "source": [
        "#6° - Distância somada percorrida por ano\n",
        "distance_run = nk_analysis.groupby('Ano')['distance'].sum().reset_index()\n",
        "runs_made = nk_analysis.groupby(\"Ano\")[\"Ano\"].value_counts().reset_index()\n",
        "#distance_run['Avg Distance - Run'] =\n",
        "distance_run['Runs Made'] = runs_made['count']\n",
        "distance_run['Avg Distance - Made'] = distance_run['distance'] / distance_run['Runs Made']\n",
        "distance_run\n"
      ],
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      "execution_count": null,
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            "text/plain": [
              "    Ano    distance  Runs Made  Avg Distance - Made\n",
              "0  2021   11.257549          2             5.628774\n",
              "1  2022   76.254181         12             6.354515\n",
              "2  2023  205.018471         61             3.360959\n",
              "3  2024  133.665801         32             4.177056"
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              "type": "dataframe",
              "variable_name": "distance_run",
              "summary": "{\n  \"name\": \"distance_run\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Ano\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2022,\n          2024,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82.52230880537327,\n        \"min\": 11.257548627646702,\n        \"max\": 205.0184709700152,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          76.25418124841245,\n          133.66580089450778,\n          11.257548627646702\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Runs Made\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 2,\n        \"max\": 61,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12,\n          32,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg Distance - Made\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3584886420673339,\n        \"min\": 3.360958540492052,\n        \"max\": 6.354515104034371,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6.354515104034371,\n          4.177056277953368,\n          5.628774313823351\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#7 - Distância máxima por weather\n",
        "nk_analysis.groupby('weather')['distance'].max()"
      ],
      "metadata": {
        "id": "M_tBhGrPF3KY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19b1c6c2-88ca-483a-d1ec-39d341d25757"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weather\n",
              "Céu Limpo - Dia       5.100746\n",
              "Céu Limpo - Noite    10.010431\n",
              "Ensolarado            5.421562\n",
              "Nublado               5.554731\n",
              "Nublado - Dia         7.390320\n",
              "Nublado - Noite       7.020714\n",
              "NãoIdentificado       7.529181\n",
              "Name: distance, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#8 - Qual ano eu tive mais dias Off sem correr?\n",
        "nk_analysis.groupby('Ano')['DaysOffRun'].sum().reset_index()"
      ],
      "metadata": {
        "id": "jMsUHk1oF8lQ",
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          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "4e378fa4-0422-43bf-9419-a9d67121ce5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Ano  DaysOffRun\n",
              "0  2021          26\n",
              "1  2022         275\n",
              "2  2023         497\n",
              "3  2024         199"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"nk_analysis\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Ano\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2022,\n          2024,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DaysOffRun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195,\n        \"min\": 26,\n        \"max\": 497,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          275,\n          199,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#9 - grafico entre calorias,distância\n",
        "nk_analysis.groupby('ini_mes')[['calories','distance']].sum().reset_index()\n",
        "#plt.plot(nk_analysis['date'],nk_analysis['calories'])\n",
        "\n",
        "#plt.show()\n"
      ],
      "metadata": {
        "id": "KDyAAUy7GXbp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d4c3a30a-05ca-4ecd-b9bf-0f793acfd69f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ini_mes  calories   distance\n",
              "0  2021-12-01   832.638  11.257549\n",
              "1  2022-01-01   555.748   7.390320\n",
              "2  2022-03-01   526.388   7.410240\n",
              "3  2022-04-01   544.387   7.529181\n",
              "4  2022-06-01  1441.556  19.739989\n",
              "5  2022-07-01   942.185  13.310148\n",
              "6  2022-08-01  1100.687  14.866947\n",
              "7  2022-09-01   445.877   6.007355\n",
              "8  2023-01-01   467.945   6.243302\n",
              "9  2023-02-01   692.329   9.255223\n",
              "10 2023-04-01   390.853   5.338807\n",
              "11 2023-05-01  1114.523  14.327772\n",
              "12 2023-06-01   628.444   8.193428\n",
              "13 2023-07-01  1926.017  25.968085\n",
              "14 2023-08-01  3071.845  39.568163\n",
              "15 2023-09-01  2199.678  28.489222\n",
              "16 2023-10-01  2131.078  26.011662\n",
              "17 2023-11-01   970.198  13.339956\n",
              "18 2023-12-01  2324.046  28.282852\n",
              "19 2024-01-01  2265.962  27.426278\n",
              "20 2024-02-01  2796.585  35.119198\n",
              "21 2024-03-01   630.182   8.079993\n",
              "22 2024-04-01   455.793   6.024504\n",
              "23 2024-05-01  2571.028  31.069894\n",
              "24 2024-06-01  2168.408  25.945934"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ini_mes</th>\n",
              "      <th>calories</th>\n",
              "      <th>distance</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>832.638</td>\n",
              "      <td>11.257549</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-01-01</td>\n",
              "      <td>555.748</td>\n",
              "      <td>7.390320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-03-01</td>\n",
              "      <td>526.388</td>\n",
              "      <td>7.410240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-04-01</td>\n",
              "      <td>544.387</td>\n",
              "      <td>7.529181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-06-01</td>\n",
              "      <td>1441.556</td>\n",
              "      <td>19.739989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022-07-01</td>\n",
              "      <td>942.185</td>\n",
              "      <td>13.310148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-08-01</td>\n",
              "      <td>1100.687</td>\n",
              "      <td>14.866947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-09-01</td>\n",
              "      <td>445.877</td>\n",
              "      <td>6.007355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>467.945</td>\n",
              "      <td>6.243302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2023-02-01</td>\n",
              "      <td>692.329</td>\n",
              "      <td>9.255223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2023-04-01</td>\n",
              "      <td>390.853</td>\n",
              "      <td>5.338807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2023-05-01</td>\n",
              "      <td>1114.523</td>\n",
              "      <td>14.327772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2023-06-01</td>\n",
              "      <td>628.444</td>\n",
              "      <td>8.193428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2023-07-01</td>\n",
              "      <td>1926.017</td>\n",
              "      <td>25.968085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2023-08-01</td>\n",
              "      <td>3071.845</td>\n",
              "      <td>39.568163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2023-09-01</td>\n",
              "      <td>2199.678</td>\n",
              "      <td>28.489222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2023-10-01</td>\n",
              "      <td>2131.078</td>\n",
              "      <td>26.011662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2023-11-01</td>\n",
              "      <td>970.198</td>\n",
              "      <td>13.339956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2023-12-01</td>\n",
              "      <td>2324.046</td>\n",
              "      <td>28.282852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2024-01-01</td>\n",
              "      <td>2265.962</td>\n",
              "      <td>27.426278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2024-02-01</td>\n",
              "      <td>2796.585</td>\n",
              "      <td>35.119198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2024-03-01</td>\n",
              "      <td>630.182</td>\n",
              "      <td>8.079993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2024-04-01</td>\n",
              "      <td>455.793</td>\n",
              "      <td>6.024504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2024-05-01</td>\n",
              "      <td>2571.028</td>\n",
              "      <td>31.069894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2024-06-01</td>\n",
              "      <td>2168.408</td>\n",
              "      <td>25.945934</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-73b2ed5e-faa5-4642-9eeb-09895f666a73');\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-c4ac19cc-1ab9-4663-a990-8676cf97917a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c4ac19cc-1ab9-4663-a990-8676cf97917a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c4ac19cc-1ab9-4663-a990-8676cf97917a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"#plt\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": \"ini_mes\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-12-01 00:00:00\",\n        \"max\": \"2024-06-01 00:00:00\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"2023-01-01 00:00:00\",\n          \"2023-10-01 00:00:00\",\n          \"2021-12-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 868.5724619976544,\n        \"min\": 390.853,\n        \"max\": 3071.8450000000003,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          467.94500000000005,\n          2131.078,\n          832.6379999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.623274257478604,\n        \"min\": 5.338807274962022,\n        \"max\": 39.56816318976479,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          6.243301568245419,\n          26.011662336909545,\n          11.257548627646702\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a22f3b66050>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_19['calories'].plot(kind='hist', bins=20, title='calories')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "SO7/Ad+LMfXBIW15AAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-fd411d92-5843-48b1-835c-6987114c4398\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-fd411d92-5843-48b1-835c-6987114c4398\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_20['distance'].plot(kind='hist', bins=20, title='distance')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "+389paF87uLEAwAAAABJRU5ErkJggg==\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f0411355-1166-41fb-a7dd-91671e5e1614\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f0411355-1166-41fb-a7dd-91671e5e1614\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a22f367dcc0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_21.plot(kind='scatter', x='calories', y='distance', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-9cdea72a-2c00-45d4-bd93-1beb9a6782cd\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-9cdea72a-2c00-45d4-bd93-1beb9a6782cd\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a22f14c87c0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  xs = series['ini_mes']\n",
              "  ys = series['calories']\n",
              "  \n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_22.sort_values('ini_mes', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('ini_mes')\n",
              "_ = plt.ylabel('calories')"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-ffc883c7-ec05-4365-87d8-1af423713faa\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-ffc883c7-ec05-4365-87d8-1af423713faa\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  xs = series['ini_mes']\n",
              "  ys = series['distance']\n",
              "  \n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_23.sort_values('ini_mes', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('ini_mes')\n",
              "_ = plt.ylabel('distance')"
            ],
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              "AAAAwGb+Ar8cB9QE9juaAAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-fc1971e5-5840-4562-9e04-09968f25f4c1\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-fc1971e5-5840-4562-9e04-09968f25f4c1\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  counted = (series['ini_mes']\n",
              "                .value_counts()\n",
              "              .reset_index(name='counts')\n",
              "              .rename({'index': 'ini_mes'}, axis=1)\n",
              "              .sort_values('ini_mes', ascending=True))\n",
              "  xs = counted['ini_mes']\n",
              "  ys = counted['counts']\n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_24.sort_values('ini_mes', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('ini_mes')\n",
              "_ = plt.ylabel('count()')"
            ],
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e04523da-6b7a-4616-9803-c3990fd6a897\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e04523da-6b7a-4616-9803-c3990fd6a897\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a22f14cbee0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_25['calories'].plot(kind='line', figsize=(8, 4), title='calories')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-987cf144-8ee1-4e5b-b02f-add8a24a3878\">\n",
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              "RERERKQoDFCJiIiISFH+P5d0v1w0DJLLAAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-987cf144-8ee1-4e5b-b02f-add8a24a3878\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-987cf144-8ee1-4e5b-b02f-add8a24a3878\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_26['distance'].plot(kind='line', figsize=(8, 4), title='distance')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "+QUDTSIiIiLyCwaaREREROQXDDSJiIiIyC/+P26UNXi65M6SAAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-0a02429f-d624-4632-a70e-20da2daa255d\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-0a02429f-d624-4632-a70e-20da2daa255d\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "from matplotlib import pyplot as plt\n",
        "_df_21.plot(kind='scatter', x='calories', y='distance', s=32, alpha=.8)\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "AOtywFlr_aL_",
        "outputId": "0abea029-a8ae-48ea-87e2-827de1260cb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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6M6J9dQ2qCpoampWuKfl5R3S+9rjKAxwpggwAdBG9A1799sqTNGVcngI+Q5GYpYDP0JRxefpNK4Zet8dVHuBIMY8MAHRBRzLny+GO3RM0NX1R49w0XsMlr+GUGWkMMUOz0lsVkIAjQZABADSppfPD7AmaKizaqWVbylXfEJXf41LBSOaRQccgyAAADtGaWYCZ2RfJQB8ZAMAhWjM/jM/tUu+AlxCDDkWQAQAkYH4Y2AlBBgCQgPlhYCcEGQBAAuaHgZ0QZAAACZgfBnZCnAYAHOLq/GO1bvsXzc4Pc6SzAAPtheHXAIAmMT8M7IAgAwA4LOaHQSrj1hIA4LB8bhcBBimLzr4AAMC2CDIAAMC2CDIAcBRC4aj2BM2kz3KbKnUAHY0+MgDQCi1dGTrZddBRF50do5YA4Ai1ZmXojq5jQM80jc3rrrc+3pPUoAW0N24tAcARas3K0B1ZRw+/W5s+26+Fa8tUZ0ZkOB2qMyMqXLdT0xdt0J6g2SH1AR2BIAMARyBVVoY+XB3768OKWZYisZgyu7mTFrSAjkCQAYAjkCorQzdXR8yyVBOKyOFwSHIo9pXeAx0ZtICOQpABgCOQKitDN1dHLGapMbtYcjgkp8ORsL+jghbQUQgyAHAEUmVl6ObqcDodcjgky5Iy/e5DgkxHBS2goxBkAOAIXZ1/rIZkpasqaGpfXYPqzIj21TWoKmh26MrQTdWx/0BYTofkcjqU6U8MKx0ZtICOwvBrAGiFVFkZuqk6zhraW+vL9mvn3jp5DZe8hlNmpDHEDM1K1286aHg40BEIMgBwFFJlwrmv15EqQQtob0kNMnPmzNGSJUv00Ucfye/367TTTtNDDz2k4cOHx4/5zne+ozVr1iS87r/+67/09NNPt+g9CDIAurJUCVpAe0lqH5k1a9Zo2rRpWrt2rVasWKFwOKxzzjlHdXV1Ccddf/312r17d/zx8MMPJ6liALAXn9ul3gEvIQad1lF1W29oaND27ds1ePBgGcaRn2r58uUJzxcsWKCsrCytX79e48ePj2/v1q2bcnJyjqZUAADQCbXqisyBAwc0depUdevWTSNHjlRZWZkkacaMGZo7d26ri6murpYk9ezZM2H7woUL1bt3b40aNUqzZs3SgQMHmj2HaZqqqalJeAAAgM6pVUFm1qxZ2rhxo1avXi2fzxffPnHiRD3//POtKiQWi2nmzJk6/fTTNWrUqPj2K6+8UoWFhXrzzTc1a9Ys/eUvf9GUKVOaPc+cOXOUmZkZf+Tm5raqHgAAkPpa1dk3Ly9Pzz//vE499VSlp6dr48aNGjRokEpLSzVmzJhWXQW56aabtGzZMr399tvq379/s8etWrVKEyZMUGlpqQYPHnzIftM0ZZpfLohWU1Oj3NxcOvsCANAJtaqPTFVVlbKysg7ZXldX9+/1PY7M9OnT9corr+itt946bIiRpHHjxklSs0HG6/XK62VoIQAAXUGrbi2dfPLJevXVV+PPD4aXZ599Vvn5+S0+j2VZmj59upYuXapVq1Zp4MCB3/ia4uJiSVLfvn2PrGgAANDptOqKzK9+9SsVFBTogw8+UCQS0eOPP64PPvhA77777iFzvhzOtGnTtGjRIr300ktKT09XeXm5JCkzM1N+v1/btm3TokWLdO6556pXr17atGmTbrnlFo0fP16jR49uTekAAKATafWEeNu2bdPcuXO1ceNGBYNBjRkzRnfddZeOP/74lr95M7eh5s+fr2uvvVa7du3SlClTVFJSorq6OuXm5uqiiy7Sz3/+8xb3d2FCPAAAOi+WKAAAALbVqj4yr732ml5//fVDtr/++utatmzZURcFAADQEq0KMnfffbei0egh2y3L0t13333URQEAALREq4LMJ598ouOOO+6Q7SNGjFBpaelRFwUAANASrQoymZmZ+vTTTw/ZXlpaqrS0tKMuCgAAoCVaFWQuuOACzZw5U9u2bYtvKy0t1W233abvf//7bVYcAADA4bQqyDz88MNKS0vTiBEjNHDgQA0cOFDf+ta31KtXL/3P//xPW9cIAADQpFYPv7YsSytWrNDGjRvl9/s1evRojR8/vq3rO2oMvwYAoPNiHhkAAGBbrVqiQJJWrlyplStXqrKyUrFYLGHfn/70p6MuDAAA4Ju0KsjMnj1bDz74oE4++WT17du3VSteAwAAHK1WBZmnn35aCxYs0NVXX93W9QAAALRYq0YtNTQ06LTTTmvrWgAAAI5Iq4LMf/7nf2rRokVtXQsAAMARadWtpVAopN///vd64403NHr0aLnd7oT9jz32WJsUBwAAcDitCjKbNm3SiSeeKEkqKSlJ2EfHXwAA0FGYRwYAANhWq/rIAAAApIJWT4j3/vvv669//avKysrU0NCQsG/JkiVHXRgAAMA3adUVmcWLF+u0007Thx9+qKVLlyocDmvLli1atWqVMjMz27pGAACAJrUqyPzqV7/SvHnz9PLLL8vj8ejxxx/XRx99pB/84AcaMGBAW9cIAADQpFYFmW3btum8886TJHk8HtXV1cnhcOiWW27R73//+zYtEAAAoDmtCjI9evRQbW2tJOmYY46JD8Hev3+/Dhw40HbVAQAAHEarOvuOHz9eK1as0PHHH6/LLrtMP/3pT7Vq1SqtWLFCEyZMaOsaAQAAmtSqeWT27dunUCikfv36KRaL6eGHH9a7776roUOH6uc//7l69OjRHrW2CvPIAADQeTEhHgAAsK1W9ZFxuVyqrKw8ZPvevXvlcrmOuigAAICWaFWQae4ijmma8ng8R1UQAABASx1RZ98nnnhCUuPCkM8++6wCgUB8XzQa1VtvvaURI0a0bYUAAADNOKIgM2/ePEmNV2SefvrphNtIHo9Hxx57rJ5++um2rRAAAKAZRxRktm/fLkn67ne/qyVLlqTU6CQAAND1tKqPzJtvvpkQYqLRqIqLi/XFF1+0WWEAAADfpFVBZubMmfrjH/8oqTHEjB8/XmPGjFFubq5Wr17dlvUBAAA0q1VB5oUXXtAJJ5wgSXr55Ze1Y8cOffTRR7rlllt0zz33tGmBAAAAzWlVkNm7d69ycnIkSa+99pouu+wyDRs2TD/+8Y+1efPmNi0QAACgOa0KMtnZ2frggw8UjUa1fPlynX322ZKkAwcOMCEeAADoMK1aNPK6667TD37wA/Xt21cOh0MTJ06UJK1bt455ZAAAQIdpVZB54IEHNGrUKO3atUuXXXaZvF6vpMalC+6+++42LRAAAKA5LBoJAABsq8VXZJ544gndcMMN8vl88aUKmvOTn/ykReecM2eOlixZoo8++kh+v1+nnXaaHnroIQ0fPjx+TCgU0m233abFixfLNE1NmjRJTz75pLKzs1taOgAA6KRafEVm4MCBev/999WrVy8NHDiw+RM6HPr0009b9OaTJ0/WD3/4Q51yyimKRCL62c9+ppKSEn3wwQdKS0uTJN1000169dVXtWDBAmVmZmr69OlyOp165513WvQeXJEBAKDzSqlbS1VVVcrKytKaNWs0fvx4VVdXq0+fPlq0aJEuvfRSSdJHH32kb33rWyoqKtKpp576jeckyABNC4WjCpoRBbyGfG5GGwKwpxbfWrr11ltbdJzD4dCjjz7aqmKqq6slST179pQkrV+/XuFwOD4qSpJGjBihAQMGNBtkTNOUaZrx5zU1Na2qBeisqmpNFa7doeUlFaoPR+V3u1QwKkdT8vPUO+BNdnkAcERaHGQ2bNiQ8Pyf//ynIpFIvD/Lxx9/LJfLpbFjx7aqkFgsppkzZ+r000/XqFGjJEnl5eXyeDzq3r17wrHZ2dkqLy9v8jxz5szR7NmzW1UD0NlV1Zqa8dwGlVbWyme45DGcqjMjKly3U2u379NvrzyJMAPAVlocZN588834nx977DGlp6frz3/+c3zxyC+++ELXXXedzjzzzFYVMm3aNJWUlOjtt99u1esPmjVrVsLVo5qaGuXm5h7VOYHOonDtDpVW1qpPwCvD1TgfZppXikRjKq2sVWHRTs08e1iSqwSAlmvVzL6PPvqo5syZk7ACdo8ePfSLX/yiVbeVpk+frldeeUVvvvmm+vfvH9+ek5OjhoYG7d+/P+H4ioqK+BIJX+f1epWRkZHwANDYJ2Z5SYV8hiseYg4yXE55DZeWbSlXKBxNUoUAcORaFWRqampUVVV1yPaqqirV1ta2+DyWZWn69OlaunSpVq1adchoqLFjx8rtdmvlypXxbVu3blVZWZny8/NbUzrQZQXNiOrDUXmMpn/svYZT9Q2NHYABwC5aNbPvRRddpOuuu06PPvqovv3tb0tqXJ7gjjvu0MUXX9zi80ybNk2LFi3SSy+9pPT09Hi/l8zMTPn9fmVmZmrq1Km69dZb1bNnT2VkZGjGjBnKz89v0YglAF8KeA353S7VmRGlNdENxozEFPAZCnhb9dcCACRFq4ZfHzhwQLfffrv+9Kc/KRwOS5IMw9DUqVP1yCOPxOeA+cY3dzia3D5//nxde+21kr6cEO+5555LmBCvuVtLX8fwa+BL81Zs1cJ1ZQl9ZKTGPjJVQVNTxuXRRwaArRzVPDJ1dXXatm2bJGnw4MEtDjAdiSADfGlP0NT0RY2jlryGS17DKTMSkxmJamhWun7DqCUANpNSE+K1B4IMkGhP0FRh0U4t21Ku+oao/B6XCkYyjwwAeyLIAF0UM/sC6Azo1Qd0UT63iwADwPZaNfwaAAAgFRBkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbSU1yLz11ls6//zz1a9fPzkcDr344osJ+6+99lo5HI6Ex+TJk5NTLLqUUDiqPUFToXA02aUAAA7DSOab19XV6YQTTtCPf/xjXXzxxU0eM3nyZM2fPz/+3Ov1dlR56IKqak0Vrt2h5SUVqg9H5Xe7VDAqR1Py89Q7wHcPAFJNUoNMQUGBCgoKDnuM1+tVTk5OB1WErqyq1tSM5zaotLJWPsMlj+FUnRlR4bqdWrt9n3575UmEGQBIMSnfR2b16tXKysrS8OHDddNNN2nv3r2HPd40TdXU1CQ8gJYoXLtDpZW16hPwqkeaR2leQz3SPOoT8Kq0slaFRTuTXSIA4GtSOshMnjxZ//u//6uVK1fqoYce0po1a1RQUKBotPl+C3PmzFFmZmb8kZub24EVw65C4aiWl1TIZ7hkuBJ/LAyXU17DpWVbyukzAwApxmFZlpXsIiTJ4XBo6dKluvDCC5s95tNPP9XgwYP1xhtvaMKECU0eY5qmTNOMP6+pqVFubq6qq6uVkZHR1mWjk9gTNHXxk+/KcDqU5j30jmudGVEkZmnJzadxewkAUkhKX5H5ukGDBql3794qLS1t9hiv16uMjIyEB/BNAl5DfrdLDZFYwvaYZSkSjSkUjsrvcSnQRMgBACSPrYLMZ599pr1796pv377JLgWdjM/t0uRR2QpFoopEYwpHYyqvrldpZVClVUFV1IbU3e9W0Iwku1QAwFckNcgEg0EVFxeruLhYkrR9+3YVFxerrKxMwWBQd9xxh9auXasdO3Zo5cqVuuCCCzRkyBBNmjQpmWWjk7o6/1gNyUpXRU1In+6p0766BkWiMcVillwOhz6pDGr6og3aEzS/+WQAgA6R1D4yq1ev1ne/+91Dtl9zzTV66qmndOGFF2rDhg3av3+/+vXrp3POOUf//d//rezs7Ba/R01NjTIzM+kjgxbZEzQ1rXC93t+5X06H5HQ6lOl3q1fAI1lSVdDUlHF5mnn2sGSXCgBQCnX2bS8EGRyJUDiqC377jmrNsDL9bjkdDjkdjvj+fXUNCvgMvTTtdPncriRWCgCQbNZHBmhvQTOi+nC0cRi205kQYiTJazhV3xClrwwApAiCDPAVzY1eOsiMxBi9BAAphCADfMXXRy99VSQakxmJqmBkDreVACBF8M9K4Guuzj9W67Z/odLKWnkNl7yGU2akMcQMzUrXlPy8ZJcIAPg3OvvC1kLhxv4qAa/RpldJ9gRNFRbt1LIt5apvaJwMr2Akq2ADQKohyMA2vhpaakMRFa7doeUlFaoPR+V3u1Qwqu2DRnsFJQBA2yDIIOVV1ZoJocXtdMiMxnSgIapubpc8hlMNkZhCkaiGZKXrt1eexFUTAOgi6OyLlFZVa2rGcxu0cF2Z6syIDKdDlbUh/euLeoUaIkr3G0rzGuqR5lGfgFellbUqLNqZ7LIBAB2EIIOUVrh2h0ora9Un4FWPNI/8HpcaopZcTocaopb2Bhvixxoup7yGS8u2lCsUjiaxagBARyHIIGWFwlEtL6lonJzO1fhVjcUsWZbkdDjkcEjV9WHFvnJ3lAnrAKBrIcggZR2cZddjfPk1dTobA4wlSw6HQ5alhCDDhHUA0LUQZJCymppl1+lwKMNnKGZJsVhMDofiywgwYR0AdD0EGaSs5mbZ7R3wyms4FY1Jnn/fStpX16CqoMmEdQDQxXD9HSmtuVl2fYZTPbt55HM71RC1FPAZumxkfyasA4AuhnlkkPION8tuwGswYR0AdGEEGdgGs+wCAL6OW0uwDZ/bRYABACSgsy8AALAtggwAALAtggwAALAtggwAALAtggwAALAtggwAALAtggwAALAtggwAALAtggzaXSgc1Z6gqVA4muxSAACdDDP7ot1U1ZoqXLtDy0sqVB+Oyu92qWBUDgs7AgDaDGstoV1U1Zqa8dwGlVbWyme45DGcaojEFIpENSQrXb+98iTCDADgqHFrCe2icO0OlVbWqk/Aqx5pHqV5DfVI86hPwKvSyloVFu1MdokAgE6AIIM2FwpHtbykQj7DJcOV+BUzXE55DZeWbSmnzwwA4KgRZNDmgmZE9eGoPEbTXy+v4VR9Q1RBM9LBlQEAOhuCDNpcwGvI73apIRJrcr8ZicnvcSngpa85AODoEGTQ5nxulyaPylYoElUkmhhmItGYzEhUBSNz5HO7klQhAKCz4J/EaBdX5x+rddu/UGllrbyGS17DKTPSGGKGZqVrSn5esksEAHQCDL9Gu9kTNFVYtFPLtpSrviEqv8elgpHMIwMAaDsEGbS7ULixY2/Aa3A7CQDQpri1hHbnc7sIMACAdkFnXwAAYFsEGQAAYFtJDTJvvfWWzj//fPXr108Oh0Mvvvhiwn7LsnTfffepb9++8vv9mjhxoj755JPkFAsAAFJOUoNMXV2dTjjhBP3ud79rcv/DDz+sJ554Qk8//bTWrVuntLQ0TZo0SaFQqIMrBQAAqShlRi05HA4tXbpUF154oaTGqzH9+vXTbbfdpttvv12SVF1drezsbC1YsEA//OEPmzyPaZoyTTP+vKamRrm5uYxaAgCgE0rZPjLbt29XeXm5Jk6cGN+WmZmpcePGqaioqNnXzZkzR5mZmfFHbm5uR5QLAACSIGWDTHl5uSQpOzs7YXt2dnZ8X1NmzZql6urq+GPXrl3tWicAAEieTjePjNfrldfLrLFHggnrAAB2lbJBJicnR5JUUVGhvn37xrdXVFToxBNPTFJVnUtVranCtTu0vKRC9eGo/G6XCkaxhAAAwD5S9tbSwIEDlZOTo5UrV8a31dTUaN26dcrPz09iZZ1DVa2pGc9t0MJ1ZaozIzKcDtWZERWu26npizZoT9D85pMAAJBkSb0iEwwGVVpaGn++fft2FRcXq2fPnhowYIBmzpypX/ziFxo6dKgGDhyoe++9V/369YuPbELrFa7dodLKWvUJeGW4GvNsmleKRGMqraxVYdFOzTx7WJKrBADg8JIaZN5//31997vfjT+/9dZbJUnXXHONFixYoDvvvFN1dXW64YYbtH//fp1xxhlavny5fD5fskruFELhqJaXVMhnuOIh5iDD5ZTXcGnZlnLd+J3B9JkBAKS0lJlHpr2w+vWh9gRNXfzkuzKcDqV5D82ydWZEkZilJTefRl8ZAEBKS9k+Mmg/Aa8hv9ulhkisyf1mJCa/x6VAEyEHAIBUQpDpgnxulyaPylYoElUkmhhmItGYzEhUBSNzuK0EAEh5/JO7i7o6/1it2/6FSitr5TVc8hpOmZHGEDM0K11T8vOSXSIAAN+IPjJd2J6gqcKinVq2pVz1DVH5PS4VjGQeGQCAfRBkwMy+AADb4tZSJ3Uk4cTndhFgAAC2RJDpZL667MCBhog8hlMFI3N07RkDuV0EAOh0uLXUiRxcduDj8hqFozGFIjHFLEmylJPh1/zrTtGw7PRklwkAQJth+HUnUrh2hz4ur1EoEtOBcOOwapfDIYcc+nx/vX684D3WUAIAdCoEmU7i4LID4WhMZiQmt9Mhw+mUy+mQ2+WUyynt3h/Sgne2J7tUAADaDEGmkwiaER1oiCgUicnpkBwOR8J+p7Pxf/XyknKFwtFklAgAQJsjyKS4UDiqPUHzG8NHwGvIYzgVsySHHIfstyxLTmfj8gNBM9Je5QIA0KEYtZSivjr6qD4cld/tUsGo5ier87ldmjwyW0+/9aliluT6SpixLEuWJfk9LnXzGqyhBADoNLgik4I+++KAbixcr8K1ZaozIzKcDtWaYf3v2p26eeE/m+2we90Zg5ST4VM0ZikcjSpqWYrEYorELHlcjX1lWEMJANCZEGRSSFWtqXkrtur7v3lbG8q+UG0orFAkqppQWHuDDaqtD2v9ji80rZkw0zvg1fzrvq1jevhlWQ5Fo40j6/0el3weQ8OzWUMJANC5MI9Mijg4B8wnFTWqro/IUmPKjMQsWZLcToccToei0ZjkkMYO6Kknp4xp8jbTnqCpBe9s1/KScpmRmLp5DdZQAgB0SgSZFDFvxVYtXFemHn63du6rlyTFLEuRxhntZPx7GHU0ZsmSpXSvoR/lH6uZZw9r9pysoQQA6Oy4tZQCDs4B4zNc8rhdcjikmCxFY5YckhxSPMBYsuR0OORzu7Rsy+GHUvvcLvUOeAkxAIBOiyCTAoJmRPXhqDyGU06HQxk+Q5Ylff1SmRWzFLOkTL9bPrdL9Q1RhlIDALo0gkwKCHgN+d0uNUQalxXoHfDKazT+r7G+8ohYks9wqleaR2YkJr/HxVBqAECXRpBJAT63S5NHZSsUiSoSjclwOZXXs5u6eb68JeRyOtQrzaMBPbtJksxIlKHUAIAujyCTIq7OP1ZDstJVFTS1r65BZiSmTL9bhlNyOaTeaR4FvIZqQhFVBU0NzWIoNQAAjFpKIXuCpgqLdmrZlnLVN0Tl97h01tDekqQ1n+yJb2MoNQAAjQgyKaipYdMMpQYA4FD0FO1ALQ0jPrfrkP1NbQMAoKsjyHSAI10AEgAAtAy3ltrZZ18c0IznNmjHnjp1c7vk/fcw61AkqiFZ6frtlScRZgAAaCWuyLSTqlpTz6wpVeHanQpFGrNijTOiHt3c6hPwKl2GSitrVVi087DLDAAAgOYx/LodVNWa+q+/rNeCd78MMVLjMgN7gg3aue+AJMlrfPMyAwAAoHkEmXZQuHaHPtxdrYN37RySHI7G/0pSfTiqvXUN8hpOlhkAAOAoEGTaWCgc1WubyxWOWnI4HfHwIkn6d5ixLKm6PqxQOMoyAwAAHAWCTBsLmhEdaGi8VeSUQy6no3Hxx691qY7GLIXCLDMAAMDRIMi0sYDXiK+RZMmS4XTI6VA8zBzMM5YslhkAAOAoEWRaKRSOak/QPKSjrs/t0rnH58jtcijWuJi1PC6nDOeXN5kckk7O66nfTRnD0GsAAI4CnTOOUEsmt7s6/1i9XbpXG3ftV0M0JofDIcmSHJLhcGh0/0z97ipCDAAAR4sJ8Y5AVa2pGc9tUGllrXyGSx7D2ezkdnuCpn6/Zpv+75//Uk0oLEnK8Ll1yZhjdMNZgwkxAAC0AYLMEZi3YqsWritTn4BXhuvLu3KRaExVQVNTxuUdMrldKBzV3jpTsqReAS8dewEAaEP0kWmhUDiq5SUV8hmuhBAjSYbL2ezkdj63S8d076ZjenQjxAAA0MZSOsg88MADcjgcCY8RI0YkpZagGVF9OCqP0XSTMbkdAAAdL+U7+44cOVJvvPFG/LlhJKfkgNeQ3+1SnRlRWhPdW8xITAGfweR2AAB0oJT/rWsYhnJyclp8vGmaMk0z/rympqZN6vC5XZo8KlsL15UpEo0d0kfGjER12cj+3D4CAKADpfStJUn65JNP1K9fPw0aNEhXXXWVysrKDnv8nDlzlJmZGX/k5ua2WS1X5x+rIVnpqgqa2lfXoDozon11DaoKmkxuBwBAEqT0qKVly5YpGAxq+PDh2r17t2bPnq1//etfKikpUXp6epOvaeqKTG5ubpuMWpIah1UXFu3Usi3lqm9oXCupYGTiPDIAAKBjpHSQ+br9+/crLy9Pjz32mKZOndqi17Tl8OuvCoUbO/YGvAa3kwAASJKU7yPzVd27d9ewYcNUWlqa7FLkc7sIMAAAJFnK95H5qmAwqG3btqlv377JLgUAAKSAlA4yt99+u9asWaMdO3bo3Xff1UUXXSSXy6Urrrgi2aUBAIAUkNK3lj777DNdccUV2rt3r/r06aMzzjhDa9euVZ8+fZJdGgAASAG26uzbGu3V2RcAACRfSt9aAgAAOByCDAAAsC2CDAAAsC2CDAAAsC2CDAAAsK2UHn7dFg4OymqrVbABAEDHSU9Pl8PhaHZ/pw8ytbW1ktSmq2ADAICO8U3Tp3T6eWRisZg+//zzb0x0yXRwhe5du3Yx100boD3bFu3ZtmjPtkV7tq1UbM8uf0XG6XSqf//+yS6jRTIyMlLmi9MZ0J5ti/ZsW7Rn26I925ad2pPOvgAAwLYIMgAAwLYIMinA6/Xq/vvvl9frTXYpnQLt2bZoz7ZFe7Yt2rNt2bE9O31nXwAA0HlxRQYAANgWQQYAANgWQQYAANgWQQYAANgWQaadPPDAA3I4HAmPESNGxPeHQiFNmzZNvXr1UiAQ0CWXXKKKioqEc5SVlem8885Tt27dlJWVpTvuuEORSKSjP0pSvPXWWzr//PPVr18/ORwOvfjiiwn7LcvSfffdp759+8rv92vixIn65JNPEo7Zt2+frrrqKmVkZKh79+6aOnWqgsFgwjGbNm3SmWeeKZ/Pp9zcXD388MPt/dGS4pva89prrz3k+zp58uSEY2jPL82ZM0ennHKK0tPTlZWVpQsvvFBbt25NOKatfsZXr16tMWPGyOv1asiQIVqwYEF7f7wO15L2/M53vnPId/TGG29MOIb2bPTUU09p9OjR8Unt8vPztWzZsvj+TvfdtNAu7r//fmvkyJHW7t2744+qqqr4/htvvNHKzc21Vq5cab3//vvWqaeeap122mnx/ZFIxBo1apQ1ceJEa8OGDdZrr71m9e7d25o1a1YyPk6He+2116x77rnHWrJkiSXJWrp0acL+uXPnWpmZmdaLL75obdy40fr+979vDRw40Kqvr48fM3nyZOuEE06w1q5da/3jH/+whgwZYl1xxRXx/dXV1VZ2drZ11VVXWSUlJdZzzz1n+f1+65lnnumoj9lhvqk9r7nmGmvy5MkJ39d9+/YlHEN7fmnSpEnW/PnzrZKSEqu4uNg699xzrQEDBljBYDB+TFv8jH/66adWt27drFtvvdX64IMPrN/85jeWy+Wyli9f3qGft721pD3POuss6/rrr0/4jlZXV8f3055f+vvf/269+uqr1scff2xt3brV+tnPfma53W6rpKTEsqzO990kyLST+++/3zrhhBOa3Ld//37L7XZbL7zwQnzbhx9+aEmyioqKLMtq/MXjdDqt8vLy+DFPPfWUlZGRYZmm2a61p5qv/+KNxWJWTk6O9cgjj8S37d+/3/J6vdZzzz1nWZZlffDBB5Yk67333osfs2zZMsvhcFj/+te/LMuyrCeffNLq0aNHQnvedddd1vDhw9v5EyVXc0HmggsuaPY1tOfhVVZWWpKsNWvWWJbVdj/jd955pzVy5MiE97r88sutSZMmtfdHSqqvt6dlNQaZn/70p82+hvY8vB49eljPPvtsp/xucmupHX3yySfq16+fBg0apKuuukplZWWSpPXr1yscDmvixInxY0eMGKEBAwaoqKhIklRUVKTjjz9e2dnZ8WMmTZqkmpoabdmypWM/SIrZvn27ysvLE9ovMzNT48aNS2i/7t276+STT44fM3HiRDmdTq1bty5+zPjx4+XxeOLHTJo0SVu3btUXX3zRQZ8mdaxevVpZWVkaPny4brrpJu3duze+j/Y8vOrqaklSz549JbXdz3hRUVHCOQ4ec/AcndXX2/OghQsXqnfv3ho1apRmzZqlAwcOxPfRnk2LRqNavHix6urqlJ+f3ym/m51+0chkGTdunBYsWKDhw4dr9+7dmj17ts4880yVlJSovLxcHo9H3bt3T3hNdna2ysvLJUnl5eUJX6KD+w/u68oOfv6m2uer7ZeVlZWw3zAM9ezZM+GYgQMHHnKOg/t69OjRLvWnosmTJ+viiy/WwIEDtW3bNv3sZz9TQUGBioqK5HK5aM/DiMVimjlzpk4//XSNGjVKktrsZ7y5Y2pqalRfXy+/398eHympmmpPSbryyiuVl5enfv36adOmTbrrrru0detWLVmyRBLt+XWbN29Wfn6+QqGQAoGAli5dquOOO07FxcWd7rtJkGknBQUF8T+PHj1a48aNU15env761792qh8WdA4//OEP438+/vjjNXr0aA0ePFirV6/WhAkTklhZ6ps2bZpKSkr09ttvJ7uUTqG59rzhhhvifz7++OPVt29fTZgwQdu2bdPgwYM7usyUN3z4cBUXF6u6ulp/+9vfdM0112jNmjXJLqtdcGupg3Tv3l3Dhg1TaWmpcnJy1NDQoP379yccU1FRoZycHElSTk7OIb3IDz4/eExXdfDzN9U+X22/ysrKhP2RSET79u2jjVtg0KBB6t27t0pLSyXRns2ZPn26XnnlFb355pvq379/fHtb/Yw3d0xGRkan/AdRc+3ZlHHjxklSwneU9vySx+PRkCFDNHbsWM2ZM0cnnHCCHn/88U753STIdJBgMKht27apb9++Gjt2rNxut1auXBnfv3XrVpWVlSk/P1+SlJ+fr82bNyf88lixYoUyMjJ03HHHdXj9qWTgwIHKyclJaL+amhqtW7cuof3279+v9evXx49ZtWqVYrFY/C/A/Px8vfXWWwqHw/FjVqxYoeHDh3fa2yAt9dlnn2nv3r3q27evJNrz6yzL0vTp07V06VKtWrXqkFtqbfUznp+fn3COg8ccPEdn8U3t2ZTi4mJJSviO0p7Ni8ViMk2zc343O7x7cRdx2223WatXr7a2b99uvfPOO9bEiROt3r17W5WVlZZlNQ5/GzBggLVq1Srr/ffft/Lz8638/Pz46w8OfzvnnHOs4uJia/ny5VafPn26zPDr2tpaa8OGDdaGDRssSdZjjz1mbdiwwdq5c6dlWY3Dr7t372699NJL1qZNm6wLLrigyeHXJ510krVu3Trr7bfftoYOHZowXHj//v1Wdna2dfXVV1slJSXW4sWLrW7dunXK4cKHa8/a2lrr9ttvt4qKiqzt27dbb7zxhjVmzBhr6NChVigUip+D9vzSTTfdZGVmZlqrV69OGA584MCB+DFt8TN+cIjrHXfcYX344YfW7373u045XPib2rO0tNR68MEHrffff9/avn279dJLL1mDBg2yxo8fHz8H7fmlu+++21qzZo21fft2a9OmTdbdd99tORwO6//9v/9nWVbn+24SZNrJ5ZdfbvXt29fyeDzWMcccY11++eVWaWlpfH99fb118803Wz169LC6detmXXTRRdbu3bsTzrFjxw6roKDA8vv9Vu/eva3bbrvNCofDHf1RkuLNN9+0JB3yuOaaayzLahyCfe+991rZ2dmW1+u1JkyYYG3dujXhHHv37rWuuOIKKxAIWBkZGdZ1111n1dbWJhyzceNG64wzzrC8Xq91zDHHWHPnzu2oj9ihDteeBw4csM455xyrT58+ltvttvLy8qzrr78+YeilZdGeX9VUW0qy5s+fHz+mrX7G33zzTevEE0+0PB6PNWjQoIT36Cy+qT3Lysqs8ePHWz179rS8Xq81ZMgQ64477kiYR8ayaM+DfvzjH1t5eXmWx+Ox+vTpY02YMCEeYiyr8303HZZlWR13/QcAAKDt0EcGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGQEp64IEHdOKJJx71eRYsWKDu3bsf9XkApCaCDIBO7fLLL9fHH3+c7DIAtBMj2QUAQHsJh8Py+/3y+/3JLgVAO+GKDIB2E4vF9PDDD2vIkCHyer0aMGCAfvnLX0qS7rrrLg0bNkzdunXToEGDdO+99yocDh/2XA8++KD69+8vr9erE088UcuXL4/v37FjhxwOh55//nmdddZZ8vl8WrhwYZO3ll566SWNGTNGPp9PgwYN0uzZsxWJRCRJlmXpgQce0IABA+T1etWvXz/95Cc/afvGAdAmuCIDoN3MmjVLf/jDHzRv3jydccYZ2r17tz766CNJUnp6uhYsWKB+/fpp8+bNuv7665Wenq4777yzyXM9/vjjevTRR/XMM8/opJNO0p/+9Cd9//vf15YtWzR06ND4cXfffbceffRRnXTSSfL5fHr99dcTzvOPf/xDP/rRj/TEE0/ozDPP1LZt23TDDTdIku6//3793//9n+bNm6fFixdr5MiRKi8v18aNG9uphQActaSsuQ2g06upqbG8Xq/1hz/8oUXHP/LII9bYsWPjz++//37rhBNOiD/v16+f9ctf/jLhNaeccop18803W5ZlWdu3b7ckWb/+9a8Tjpk/f76VmZkZfz5hwgTrV7/6VcIxf/nLX6y+fftalmVZjz76qDVs2DCroaGhRXUDSC6uyABoFx9++KFM09SECROa3P/888/riSee0LZt2xQMBhWJRJSRkdHksTU1Nfr88891+umnJ2w//fTTD7lacvLJJx+2ro0bN+qdd96J3+KSpGg0qlAopAMHDuiyyy7Tr3/9aw0aNEiTJ0/Wueeeq/PPP1+GwV+XQCqijwyAdnG4DrZFRUW66qqrdO655+qVV17Rhg0bdM8996ihoeGo3zctLe2w+4PBoGbPnq3i4uL4Y/Pmzfrkk0/k8/mUm5urrVu36sknn5Tf79fNN9+s8ePHH7b/DoDk4Z8YANrF0KFD5ff7tXLlSv3nf/5nwr53331XeXl5uueee+Lbdu7c2ey5MjIy1K9fP73zzjs666yz4tvfeecdffvb3z6iusaMGaOtW7dqyJAhzR7j9/t1/vnn6/zzz9e0adM0YsQIbd68WWPGjDmi9wLQ/ggyANqFz+fTXXfdpTvvvFMej0enn366qqqq4p1zy8rKtHjxYp1yyil69dVXtXTp0sOe74477tD999+vwYMH68QTT9T8+fNVXFyshQsXHlFd9913n/7jP/5DAwYM0KWXXiqn06mNGzeqpKREv/jFL7RgwQJFo1GNGzdO3bp1U2Fhofx+v/Ly8o6mOQC0E4IMgHZz7733yjAM3Xffffr888/Vt29f3XjjjZo6dapuueUWTZ8+XaZp6rzzztO9996rBx54oNlz/eQnP1F1dbVuu+02VVZW6rjjjtPf//73hBFLLTFp0iS98sorevDBB/XQQw/J7XZrxIgR8atG3bt319y5c3XrrbcqGo3q+OOP18svv6xevXodTVMAaCcOy7KsZBcBAADQGnT2BQAAtkWQAQAAtkWQAQAAtkWQAQAAtkWQAQAAtkWQAQAAtkWQAQAAtkWQAQAAtkWQAQAAtkWQAQAAtkWQAQAAtvX/AXQxnFMuJNctAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#9 - grafico entre calorias,distância\n",
        "nk_cal_dis = nk_analysis.groupby('Ano')[['calories','distance']].sum().reset_index()\n",
        "#plt.plot(nk_analysis['date'],nk_analysis['calories'])\n",
        "\n",
        "#plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "id": "k-auMCTJ-jiK",
        "outputId": "ce44f418-2e9f-4201-b635-9305f60d3154"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Ano   calories    distance\n",
              "0  2021    832.638   11.257549\n",
              "1  2022   5556.828   76.254181\n",
              "2  2023  15916.956  205.018471\n",
              "3  2024  10887.958  133.665801"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"#plt\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Ano\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2022,\n          2024,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6532.024323291823,\n        \"min\": 832.6379999999999,\n        \"max\": 15916.956,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5556.828,\n          10887.958,\n          832.6379999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82.52230880537327,\n        \"min\": 11.257548627646702,\n        \"max\": 205.0184709700152,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          76.25418124841245,\n          133.66580089450778,\n          11.257548627646702\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 61
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              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
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              "</style>"
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              "from matplotlib import pyplot as plt\n",
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f57f61da-fc4e-4496-8c75-d632469a40ae\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f57f61da-fc4e-4496-8c75-d632469a40ae\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_9['calories'].plot(kind='hist', bins=20, title='calories')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-7ac233ea-6aed-43ed-ba22-5e34770f132a\">\n",
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              "RK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-7ac233ea-6aed-43ed-ba22-5e34770f132a\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-7ac233ea-6aed-43ed-ba22-5e34770f132a\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_10['distance'].plot(kind='hist', bins=20, title='distance')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "MGBIJ+gAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-732dd9f0-01d1-4a64-9c46-927f0e64ba22\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-732dd9f0-01d1-4a64-9c46-927f0e64ba22\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a22f382feb0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_11.plot(kind='scatter', x='Ano', y='calories', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-d6eb53ef-2dda-4e94-91b7-37fadbbc4b61\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-d6eb53ef-2dda-4e94-91b7-37fadbbc4b61\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_12.plot(kind='scatter', x='calories', y='distance', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-01b6de40-af57-415a-b4ad-3cec8c648630\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-01b6de40-af57-415a-b4ad-3cec8c648630\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a22f367f6a0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  xs = series['Ano']\n",
              "  ys = series['calories']\n",
              "  \n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_13.sort_values('Ano', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('Ano')\n",
              "_ = plt.ylabel('calories')"
            ],
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              "+OGHBR0XAIASwWIYhmF2CAAAAAAAcPc4zR4AAAAAABdDmQcAAAAAwMVQ5gEAAAAAcDGUeQAAAAAA\n",
              "XAxlHgAAAAAAF0OZBwAAAADAxVDmAQAAAABwMZR5AAAAAABcDGUeAAAAAAAXQ5kHAAAAAMDF/D9v\n",
              "HUc8YoXS7gAAAABJRU5ErkJggg==\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-8d86920c-f8a0-4b53-97b2-67beb90bd844\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-8d86920c-f8a0-4b53-97b2-67beb90bd844\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  xs = series['Ano']\n",
              "  ys = series['distance']\n",
              "  \n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_14.sort_values('Ano', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('Ano')\n",
              "_ = plt.ylabel('distance')"
            ],
            "text/html": [
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              "TkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-b676709f-8963-4907-82c7-36f455c92170\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-b676709f-8963-4907-82c7-36f455c92170\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "import seaborn as sns\n",
              "def _plot_series(series, series_name, series_index=0):\n",
              "  palette = list(sns.palettes.mpl_palette('Dark2'))\n",
              "  counted = (series['Ano']\n",
              "                .value_counts()\n",
              "              .reset_index(name='counts')\n",
              "              .rename({'index': 'Ano'}, axis=1)\n",
              "              .sort_values('Ano', ascending=True))\n",
              "  xs = counted['Ano']\n",
              "  ys = counted['counts']\n",
              "  plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n",
              "df_sorted = _df_15.sort_values('Ano', ascending=True)\n",
              "_plot_series(df_sorted, '')\n",
              "sns.despine(fig=fig, ax=ax)\n",
              "plt.xlabel('Ano')\n",
              "_ = plt.ylabel('count()')"
            ],
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              "AAAURswDAABAYf4fjOCF4qJn9MsAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-1cae389a-04b9-4893-a6ba-3e9b16c40881\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-1cae389a-04b9-4893-a6ba-3e9b16c40881\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7a22f367c1c0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_16['Ano'].plot(kind='line', figsize=(8, 4), title='Ano')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-ba610b94-7beb-484b-a5ff-c2ebe8635984\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-ba610b94-7beb-484b-a5ff-c2ebe8635984\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_17['calories'].plot(kind='line', figsize=(8, 4), title='calories')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "AElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-b2beac7a-6eb7-4fe2-babf-59791981afdd\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-b2beac7a-6eb7-4fe2-babf-59791981afdd\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_18['distance'].plot(kind='line', figsize=(8, 4), title='distance')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e7061cf7-0161-4d68-8175-d7cbd2ce975e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e7061cf7-0161-4d68-8175-d7cbd2ce975e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "\n",
        "_df_12.plot(kind='scatter', x='calories', y='distance', s=32, alpha=.8)\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "sd92ymIC-w4B",
        "outputId": "b75370ee-8288-4bdb-ff58-1b65839ad331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "from matplotlib import pyplot as plt\n",
        "_df_12.plot(kind='scatter', x='calories', y='distance', s=32, alpha=.8)\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "yKu7m_Hb9ynE",
        "outputId": "5b2a7a20-84b0-4576-d9de-da3b4c9a1d6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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mNzp8+LDPMZo1ayabzeazzZw5s4Y7AQAAtV1AQ8/JkyfVsWNHvfrqq+eNnTp1Sps3b9bUqVO1efNmLV26VDk5ObrtttvOm/vss8/qyJEj3m3s2LE1UT4AAKhDAnrLenp6utLT0ysdczqdysjI8Nn317/+Vddee63y8vKUkpLi3R8dHa3ExMRqrRUAANRtdWpNT2FhoWw2mxo0aOCzf+bMmYqLi1Pnzp01e/ZseTwX/0Rgt9stl8vlswEAgOBWZx5OWFJSosmTJ+uee+7xeWryuHHj1KVLF8XGxmr9+vWaMmWKjhw5ohdffPGCx5oxY4amT59eE2UDAIBaotZ8DIXNZtOyZcs0ePDg88bKyso0dOhQHTp0SGvWrLnoR0W89dZb+t3vfqfi4mI5HJV/Zozb7Zbb7fa+drlcSk5O5mMoAAAIYpd1eau0tFQ5OTk/eTnpcpSVlemuu+7SgQMHlJGR8ZOhpEePHvJ4PNq/f/8F5zgcDsXExPhsAAAguFUp9Jw6dUqjRo1SvXr11LZtW+Xl5UmSxo4d69fbxc8Gnj179uizzz5TXFzcT35NVlaWQkJCFB8f77c6AABA3Vel0DNlyhRt3bpVa9asUUREhHd/37599d57713ycYqLi5WVlaWsrCxJ0r59+5SVlaW8vDyVlZXpl7/8pb799lstWrRI5eXlys/PV35+vkpLSyVJmZmZevnll7V161Z9//33WrRokSZMmKDhw4erYcOGVWkNAAAEqSqt6WnatKnee+89XXfddYqOjtbWrVvVvHlz5ebmqkuXLpd8N9SaNWvUu3fv8/aPHDlSzzzzjFJTUyv9us8//1w333yzNm/erEceeUS7d++W2+1WamqqRowYoYkTJ15wPU9lXC6XnE4na3oAAAhiVbp769ixY5VePjp58qRsNtslH+fmm2/WxTLXT+WxLl26aMOGDZf8/QAAgHVV6fJWt27d9NFHH3lfnw06f//735WWluafygAAAPyoSmd6nn/+eaWnp2vnzp3yeDx65ZVXtHPnTq1fv15r1671d40AAACXrUpnem644QZlZWXJ4/Goffv2+te//qX4+HhlZmaqa9eu/q4RAADgstWahxMGEguZAQAIflU60/Pxxx/r008/PW//p59+qk8++eSyiwIAAPC3KoWeJ554QuXl5eftN8boiSeeuOyiAAAA/K1KoWfPnj265pprztvfpk0b5ebmXnZRAAAA/lal0ON0OvX999+ftz83N1f169e/7KIAAAD8rUqh5/bbb9f48eO1d+9e777c3Fw99thjuu222/xWHAAAgL9UKfTMmjVL9evXV5s2bZSamqrU1FRdffXViouL05///Gd/1wgAAHDZqnzLujFGGRkZ2rp1qyIjI9WhQwf16tXL3/XVCG5ZBwAg+PGcHhF6AACwgip9DIUkrVq1SqtWrVJBQYEqKip8xt56663LLgwAAMCfqhR6pk+frmeffVbdunVTkyZNftYnqwMAAARClULP3LlztWDBAo0YMcLf9QAAAFSLKt29VVpaquuvv97ftQAAAFSbKoWe3/72t1q8eLG/awEAAKg2Vbq8VVJSonnz5umzzz5Thw4dFBYW5jP+4osv+qU4AAAAf6lS6Nm2bZs6deokScrOzvYZY1EzAACojXhOj3hODwAAVlClNT0AAAB1TZUfTvjtt9/q/fffV15enkpLS33Gli5detmFAQAA+FOVzvS8++67uv7667Vr1y4tW7ZMZWVl2rFjh1avXi2n0+nvGgEAAC5blULP888/r5deeknLly9XeHi4XnnlFe3evVt33XWXUlJS/F0jAADAZatS6Nm7d68GDhwoSQoPD9fJkydls9k0YcIEzZs3z68FAgAA+EOVQk/Dhg1VVFQkSbriiiu8t62fOHFCp06d8l91AAAAflKlhcy9evVSRkaG2rdvrzvvvFOPPvqoVq9erYyMDPXp08ffNQIAAFy2Kj2n5/jx4yopKVFSUpIqKio0a9YsrV+/Xq1atdJTTz2lhg0bVket1Ybn9AAAEPx4OKEIPQAAWEGV1vSEhoaqoKDgvP0//PCDQkNDL7soAAAAf6tS6LnQySG3263w8PDLKggAAKA6/KyFzHPmzJF05kNF//73vysqKso7Vl5ernXr1qlNmzb+rRAAAMAPflboeemllySdOdMzd+5cn0tZ4eHhatasmebOnXvJx1u3bp1mz56tTZs26ciRI1q2bJkGDx7sHTfGaNq0aXrjjTd04sQJ9ezZU6+//rpatWrlnXP8+HGNHTtWy5cvV0hIiIYOHapXXnnFJ5ABAAD8rNCzb98+SVLv3r21dOnSy75L6+TJk+rYsaPuv/9+DRky5LzxWbNmac6cOXr77beVmpqqqVOnql+/ftq5c6ciIiIkScOGDdORI0eUkZGhsrIy3XfffXrwwQe1ePHiy6oNAAAEF7/cvVVeXq7t27eradOmVQ5CNpvN50yPMUZJSUl67LHH9Pvf/16SVFhYqISEBC1YsEB33323du3apWuuuUbffPONunXrJklauXKlBgwYoEOHDikpKemSvjd3bwEAEPyqtJB5/PjxevPNNyWdCTy9evVSly5dlJycrDVr1vilsH379ik/P199+/b17nM6nerRo4cyMzMlSZmZmWrQoIE38EhS3759FRISoo0bN17w2G63Wy6Xy2cDAADBrUqhZ8mSJerYsaMkafny5dq/f792796tCRMm6Mknn/RLYfn5+ZKkhIQEn/0JCQnesfz8fMXHx/uM2+12xcbGeudUZsaMGXI6nd4tOTnZLzUDAIDaq0qh54cfflBiYqIk6eOPP9add96p1q1b6/7779f27dv9WmB1mDJligoLC73bwYMHA10SAACoZlUKPQkJCdq5c6fKy8u1cuVK3XLLLZKkU6dO+e3hhGdD1dGjR332Hz161DuWmJh43kMSPR6Pjh8/7p1TGYfDoZiYGJ8NAAAEtyqFnvvuu0933XWX2rVrJ5vN5l13s3HjRr89pyc1NVWJiYlatWqVd5/L5dLGjRuVlpYmSUpLS9OJEye0adMm75zVq1eroqJCPXr08EsdAAAgOFTpU9afeeYZtWvXTgcPHtSdd94ph8Mh6czHUzzxxBOXfJzi4mLl5uZ6X+/bt09ZWVmKjY1VSkqKxo8frz/+8Y9q1aqV95b1pKQk7x1eV199tfr3768HHnhAc+fOVVlZmcaMGaO77777ku/cAgAA1hDQDxxds2aNevfufd7+kSNHasGCBd6HE86bN08nTpzQDTfcoNdee02tW7f2zj1+/LjGjBnj83DCOXPm/KyHE3LLOgAAwe+SQ8+cOXP04IMPKiIiwvtxFBcybtw4vxRXUwg9AAAEv0sOPampqfr2228VFxen1NTUCx/QZtP333/vtwJrAqEHAIDgF9DLW7UFoQcAgOB3yQuZJ06ceEnzbDabXnjhhSoXBAAAUB0uOfRs2bLF5/XmzZvl8Xh01VVXSZK+++47hYaGqmvXrv6tEAAAwA8uOfR8/vnn3l+/+OKLio6O1ttvv+39gNH//ve/uu+++3TjjTf6v0oAAIDLVKU1PVdccYX+9a9/qW3btj77s7Ozdeutt+rw4cN+K7AmsKYHAIDgV6UnMrtcLh07duy8/ceOHVNRUdFlFwUAAOBvVQo9d9xxh+677z4tXbpUhw4d0qFDh/SPf/xDo0aN0pAhQ/xdIwAAwGWr0uWtU6dO6fe//73eeustlZWVSZLsdrtGjRql2bNnq379+n4vtDpxeQsAgOB3Wc/pOXnypPbu3StJatGiRZ0LO2cRegAACH5V+sDRs+rXr68OHTr4qxYAAIBqU6U1PQAAAHUNoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQcAAFgCoQeoRUrKyvWfYrdKysoDXQoABB17oAsAIB0rcmvhhv1amX1Up8vKFRkWqvR2iRqe1lSNohyBLg8AgoLNGGMCXUSguVwuOZ1OFRYWKiYmJtDlwGKOFbk19p0tyi0oUoQ9VOH2EJV6KlTiKVfL+Gj99dedCT4A4Adc3gICbOGG/cotKFLjKIca1g9XfYddDeuHq3GUQ7kFRVqYeSDQJQJAUKj1oadZs2ay2WznbaNHj5Yk3XzzzeeNPfTQQwGuGrg0JWXlWpl9VBH2UNlDff862kND5LCH6pMd+azxAQA/qPVrer755huVl///N/zs7GzdcsstuvPOO737HnjgAT377LPe1/Xq1avRGoGqKnZ7dLqsXOH2yv//4bCH6HRpuYrdHkWEhdZwdQAQXGp96GncuLHP65kzZ6pFixa66aabvPvq1aunxMTESz6m2+2W2+32vna5XJdfKFAFUQ67IsNCddLtUf1Klu24PRWKirArylHr/6oCQK1X6y9vnau0tFQLFy7U/fffL5vN5t2/aNEiNWrUSO3atdOUKVN06tSpix5nxowZcjqd3i05Obm6SwcqFREWqv7tElTiKZenvMJnzFNeIbenXOltEznLAwB+UKfu3nr//ff161//Wnl5eUpKSpIkzZs3T02bNlVSUpK2bdumyZMn69prr9XSpUsveJzKzvQkJydz9xYC4j/Fbo1ZfObuLYc9VA57iNyeM4GnVXy0/sLdWwDgF3Uq9PTr10/h4eFavnz5BeesXr1affr0UW5urlq0aHFJx+WWdQTaf4rdWph5QJ/syNfp0nJFhocqvS3P6QEAf6ozoefAgQNq3ry5li5dqttvv/2C806ePKmoqCitXLlS/fr1u6RjE3pQW5SUnVm0HOWwc0kLAPyszqyOnD9/vuLj4zVw4MCLzsvKypIkNWnSpAaqAvwrIiyUsAMA1aROhJ6KigrNnz9fI0eOlN3+/0veu3evFi9erAEDBiguLk7btm3ThAkT1KtXL3Xo0CGAFQMAgNqmToSezz77THl5ebr//vt99oeHh+uzzz7Tyy+/rJMnTyo5OVlDhw7VU089FaBKAQBAbVVn1vRUJ9b0AAAQ/OrUc3oAAACqitADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsgdADAAAsoVaHnmeeeUY2m81na9OmjXe8pKREo0ePVlxcnKKiojR06FAdPXo0gBUDAIDaqlaHHklq27atjhw54t2+/PJL79iECRO0fPlyLVmyRGvXrtXhw4c1ZMiQAFYLAABqK3ugC/gpdrtdiYmJ5+0vLCzUm2++qcWLF+t//ud/JEnz58/X1VdfrQ0bNui6666r6VIBAEAtVuvP9OzZs0dJSUlq3ry5hg0bpry8PEnSpk2bVFZWpr59+3rntmnTRikpKcrMzLzoMd1ut1wul88GAACCW60OPT169NCCBQu0cuVKvf7669q3b59uvPFGFRUVKT8/X+Hh4WrQoIHP1yQkJCg/P/+ix50xY4acTqd3S05OrsYuAABAbVCrL2+lp6d7f92hQwf16NFDTZs21fvvv6/IyMgqH3fKlCmaOHGi97XL5SL4AAAQ5Gr1mZ4fa9CggVq3bq3c3FwlJiaqtLRUJ06c8Jlz9OjRStcAncvhcCgmJsZnAwAAwa1OhZ7i4mLt3btXTZo0UdeuXRUWFqZVq1Z5x3NycpSXl6e0tLQAVgkAAGqjWn156/e//70GDRqkpk2b6vDhw5o2bZpCQ0N1zz33yOl0atSoUZo4caJiY2MVExOjsWPHKi0tjTu3AADAeWp16Dl06JDuuece/fDDD2rcuLFuuOEGbdiwQY0bN5YkvfTSSwoJCdHQoUPldrvVr18/vfbaawGuGgAA1EY2Y4wJdBGB5nK55HQ6VVhYyPoeAACCVJ1a0wMAAFBVhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJhB4AAGAJtTr0zJgxQ927d1d0dLTi4+M1ePBg5eTk+My5+eabZbPZfLaHHnooQBUDAIDaqlaHnrVr12r06NHasGGDMjIyVFZWpltvvVUnT570mffAAw/oyJEj3m3WrFkBqhgAANRW9kAXcDErV670eb1gwQLFx8dr06ZN6tWrl3d/vXr1lJiYWNPlAQCAOqRWn+n5scLCQklSbGysz/5FixapUaNGateunaZMmaJTp05d9Dhut1sul8tnAwAAwa1Wn+k5V0VFhcaPH6+ePXuqXbt23v2//vWv1bRpUyUlJWnbtm2aPHmycnJytHTp0gsea8aMGZo+fXpNlA0AAGoJmzHGBLqIS/Hwww/rk08+0Zdffqkrr7zygvNWr16tPn36KDc3Vy1atKh0jtvtltvt9r52uVxKTk5WYWGhYmJi/F47AAAIvDpxpmfMmDFasWKF1q1bd9HAI0k9evSQpIuGHofDIYfD4fc6AQBA7VWrQ48xRmPHjtWyZcu0Zs0apaam/uTXZGVlSZKaNGlSzdUBAIC6pFaHntGjR2vx4sX68MMPFR0drfz8fEmS0+lUZGSk9u7dq8WLF2vAgAGKi4vTtm3bNGHCBPXq1UsdOnQIcPUAAKA2qdVremw2W6X758+fr3vvvVcHDx7U8OHDlZ2drZMnTyo5OVl33HGHnnrqqZ+1NsflcsnpdLKmBwCAIFarQ09NIfQAABD86tRzegAAAKqK0AMAACyB0AMAACyB0AMAACyB0AMAACyB0AMAACyB0AMAACyB0AMAACyB0AMAACyB0AMAACyB0AMAACyB0AMAACyB0FONSsrK9Z9it0rKygNdCgAAlmcPdAHB6FiRWws37NfK7KM6XVauyLBQpbdL1PC0pmoU5Qh0eQAAWJLNGGMCXUSguVwuOZ1OFRYWKiYm5rKOdazIrbHvbFFuQZEi7KEKt4eo1FOhEk+5WsZH66+/7kzwAQAgALi85WcLN+xXbkGRGkc51LB+uOo77GpYP1yNoxzKLSjSwswDgS4RAABLIvT4UUlZuVZmH1WEPVT2UN8frT00RA57qD7Zkc8aHwAAAoDQ40fFbo9Ol5Ur3F75j9VhD9Hp0nIVuz01XBkAACD0+FGUw67IsFCVeioqHXd7KhQZHqooB+vHAQCoaYQeP4oIC1X/dgkq8ZTLU+4bfDzlFXJ7ypXeNlERYaEBqhAAAOvilIOfjUhrpo37/qvcgiI57KFy2EPk9pwJPK3iozU8rWmgSwQAwJK4ZV3+vWVdkv5T7NbCzAP6ZEe+TpeWKzI8VOlteU4PAACBROiR/0PPWSVlZxYtRznsXNICACDAuLxVjSLCQgk7AADUEixkBgAAlkDoAQAAlkDoAQAAlkDoAQAAlkDoAQAAlkDoAQAAlkDoAQAAlkDoAQAAlkDoAQAAlsATmSWd/SQOl8sV4EoAAMDPFR0dLZvN9pPzCD2SioqKJEnJyckBrgQAAPxcl/rZmXzgqKSKigodPnz4kpNiXeRyuZScnKyDBw/69UNVayv6DV5W6lWi32BHv/7BmZ6fISQkRFdeeWWgy6gRMTExlviLdRb9Bi8r9SrRb7Cj35rBQmYAAGAJhB4AAGAJhB6LcDgcmjZtmhwOR6BLqRH0G7ys1KtEv8GOfmsWC5kBAIAlcKYHAABYAqEHAABYAqEHAABYAqEHAABYAqGnDpkxY4a6d++u6OhoxcfHa/DgwcrJyfGZU1JSotGjRysuLk5RUVEaOnSojh496jMnLy9PAwcOVL169RQfH69JkybJ4/H4zFmzZo26dOkih8Ohli1basGCBdXd3kXNnDlTNptN48eP9+4Ltl7//e9/a/jw4YqLi1NkZKTat2+vb7/91jtujNHTTz+tJk2aKDIyUn379tWePXt8jnH8+HENGzZMMTExatCggUaNGqXi4mKfOdu2bdONN96oiIgIJScna9asWTXS37nKy8s1depUpaamKjIyUi1atNAf/vAHnXtfRV3ud926dRo0aJCSkpJks9n0wQcf+IzXZG9LlixRmzZtFBERofbt2+vjjz+u0X7Lyso0efJktW/fXvXr11dSUpJ+85vf6PDhw3Wy35/6vT3XQw89JJvNppdfftlnf13pVbq0fnft2qXbbrtNTqdT9evXV/fu3ZWXl+cdr1Xv1QZ1Rr9+/cz8+fNNdna2ycrKMgMGDDApKSmmuLjYO+ehhx4yycnJZtWqVebbb7811113nbn++uu94x6Px7Rr18707dvXbNmyxXz88cemUaNGZsqUKd4533//valXr56ZOHGi2blzp/nLX/5iQkNDzcqVK2u037O+/vpr06xZM9OhQwfz6KOPevcHU6/Hjx83TZs2Nffee6/ZuHGj+f77782nn35qcnNzvXNmzpxpnE6n+eCDD8zWrVvNbbfdZlJTU83p06e9c/r37286duxoNmzYYL744gvTsmVLc88993jHCwsLTUJCghk2bJjJzs4277zzjomMjDR/+9vfarTf5557zsTFxZkVK1aYffv2mSVLlpioqCjzyiuvBEW/H3/8sXnyySfN0qVLjSSzbNkyn/Ga6u2rr74yoaGhZtasWWbnzp3mqaeeMmFhYWb79u011u+JEydM3759zXvvvWd2795tMjMzzbXXXmu6du3qc4y60u9P/d6etXTpUtOxY0eTlJRkXnrppTrZ66X0m5uba2JjY82kSZPM5s2bTW5urvnwww/N0aNHvXNq03s1oacOKygoMJLM2rVrjTFn3lzCwsLMkiVLvHN27dplJJnMzExjzJk/wCEhISY/P9875/XXXzcxMTHG7XYbY4x5/PHHTdu2bX2+169+9SvTr1+/6m7pPEVFRaZVq1YmIyPD3HTTTd7QE2y9Tp482dxwww0XHK+oqDCJiYlm9uzZ3n0nTpwwDofDvPPOO8YYY3bu3GkkmW+++cY755NPPjE2m838+9//NsYY89prr5mGDRt6+z/7va+66ip/t3RRAwcONPfff7/PviFDhphhw4YZY4Kr3x//Q1GTvd11111m4MCBPvX06NHD/O53v/Nrj+e6WBA46+uvvzaSzIEDB4wxdbffC/V66NAhc8UVV5js7GzTtGlTn9BTV3s1pvJ+f/WrX5nhw4df8Gtq23s1l7fqsMLCQklSbGysJGnTpk0qKytT3759vXPatGmjlJQUZWZmSpIyMzPVvn17JSQkeOf069dPLpdLO3bs8M459xhn55w9Rk0aPXq0Bg4ceF49wdbrP//5T3Xr1k133nmn4uPj1blzZ73xxhve8X379ik/P9+nVqfTqR49evj026BBA3Xr1s07p2/fvgoJCdHGjRu9c3r16qXw8HDvnH79+iknJ0f//e9/q7tNr+uvv16rVq3Sd999J0naunWrvvzyS6Wnp0sKvn7PVZO91ZY/3z9WWFgom82mBg0aSAqufisqKjRixAhNmjRJbdu2PW882Hr96KOP1Lp1a/Xr10/x8fHq0aOHzyWw2vZeTeipoyoqKjR+/Hj17NlT7dq1kyTl5+crPDzc+0ZyVkJCgvLz871zzv2DdXb87NjF5rhcLp0+fbo62qnUu+++q82bN2vGjBnnjQVbr99//71ef/11tWrVSp9++qkefvhhjRs3Tm+//bZPvZXVem4v8fHxPuN2u12xsbE/62dSE5544gndfffdatOmjcLCwtS5c2eNHz9ew4YN86klWPo9V032dqE5gepdOrO+Y/Lkybrnnnu8HzgZTP3+6U9/kt1u17hx4yodD6ZeCwoKVFxcrJkzZ6p///7617/+pTvuuENDhgzR2rVrvXXWpvdqPmW9jho9erSys7P15ZdfBrqUanHw4EE9+uijysjIUERERKDLqXYVFRXq1q2bnn/+eUlS586dlZ2drblz52rkyJEBrs7/3n//fS1atEiLFy9W27ZtlZWVpfHjxyspKSko+8UZZWVluuuuu2SM0euvvx7ocvxu06ZNeuWVV7R582bZbLZAl1PtKioqJEm33367JkyYIEnq1KmT1q9fr7lz5+qmm24KZHmV4kxPHTRmzBitWLFCn3/+ua688krv/sTERJWWlurEiRM+848eParExETvnB+vmj/7+qfmxMTEKDIy0t/tVGrTpk0qKChQly5dZLfbZbfbtXbtWs2ZM0d2u10JCQlB06skNWnSRNdcc43Pvquvvtp7B8TZeiur9dxeCgoKfMY9Ho+OHz/+s34mNWHSpEnesz3t27fXiBEjNGHCBO9ZvWDr91w12duF5gSi97OB58CBA8rIyPCe5ZGCp98vvvhCBQUFSklJ8b5vHThwQI899piaNWvmrTEYepWkRo0ayW63/+R7V216ryb01CHGGI0ZM0bLli3T6tWrlZqa6jPetWtXhYWFadWqVd59OTk5ysvLU1pamiQpLS1N27dv9/lLd/YN6Owf3LS0NJ9jnJ1z9hg1oU+fPtq+fbuysrK8W7du3TRs2DDvr4OlV0nq2bPneY8f+O6779S0aVNJUmpqqhITE31qdblc2rhxo0+/J06c0KZNm7xzVq9erYqKCvXo0cM7Z926dSorK/POycjI0FVXXaWGDRtWW38/durUKYWE+L79hIaGev/nGGz9nqsme6stf77PBp49e/bos88+U1xcnM94sPQ7YsQIbdu2zed9KykpSZMmTdKnn37qrTEYepWk8PBwde/e/aLvXbXu36WftewZAfXwww8bp9Np1qxZY44cOeLdTp065Z3z0EMPmZSUFLN69Wrz7bffmrS0NJOWluYdP3tr4K233mqysrLMypUrTePGjSu9NXDSpElm165d5tVXXw3oLetnnXv3ljHB1evXX39t7Ha7ee6558yePXvMokWLTL169czChQu9c2bOnGkaNGhgPvzwQ7Nt2zZz++23V3qbc+fOnc3GjRvNl19+aVq1auVzK+yJEydMQkKCGTFihMnOzjbvvvuuqVevXo3fsj5y5EhzxRVXeG9ZX7p0qWnUqJF5/PHHg6LfoqIis2XLFrNlyxYjybz44otmy5Yt3ruVaqq3r776ytjtdvPnP//Z7Nq1y0ybNq1abmu+WL+lpaXmtttuM1deeaXJysryee869+6kutLvT/3e/tiP796qS71eSr9Lly41YWFhZt68eWbPnj3eW8m/+OIL7zFq03s1oacOkVTpNn/+fO+c06dPm0ceecQ0bNjQ1KtXz9xxxx3myJEjPsfZv3+/SU9PN5GRkaZRo0bmscceM2VlZT5zPv/8c9OpUycTHh5umjdv7vM9AuXHoSfYel2+fLlp166dcTgcpk2bNmbevHk+4xUVFWbq1KkmISHBOBwO06dPH5OTk+Mz54cffjD33HOPiYqKMjExMea+++4zRUVFPnO2bt1qbrjhBuNwOMwVV1xhZs6cWe29/ZjL5TKPPvqoSUlJMREREaZ58+bmySef9PlHsC73+/nnn1f6d3XkyJE13tv7779vWrdubcLDw03btm3NRx99VKP97tu374LvXZ9//nmd6/enfm9/rLLQU1d6NebS+n3zzTdNy5YtTUREhOnYsaP54IMPfI5Rm96rbcac8whUAACAIMWaHgAAYAmEHgAAYAmEHgAAYAmEHgAAYAmEHgAAYAmEHgAAYAmEHgAAYAmEHgAAYAmEHgB10jPPPKNOnTpd9nEWLFigBg0aXPZxANR+hB4AlvarX/1K3333XaDLAFAD7IEuAAACpaysTJGRkYqMjAx0KQBqAGd6AARMRUWFZs2apZYtW8rhcCglJUXPPfecJGny5Mlq3bq16tWrp+bNm2vq1KkqKyu76LGeffZZXXnllXI4HOrUqZNWrlzpHd+/f79sNpvee+893XTTTYqIiNCiRYsqvbz14YcfqkuXLoqIiFDz5s01ffp0eTweSZIxRs8884xSUlLkcDiUlJSkcePG+f+HA8DvONMDIGCmTJmiN954Qy+99JJuuOEGHTlyRLt375YkRUdHa8GCBUpKStL27dv1wAMPKDo6Wo8//nilx3rllVf0wgsv6G9/+5s6d+6st956S7fddpt27NihVq1aeec98cQTeuGFF9S5c2dFRETo008/9TnOF198od/85jeaM2eObrzxRu3du1cPPvigJGnatGn6xz/+oZdeeknvvvuu2rZtq/z8fG3durWafkIA/Opnfy47APiBy+UyDofDvPHGG5c0f/bs2aZr167e19OmTTMdO3b0vk5KSjLPPfecz9d0797dPPLII8YYY/bt22ckmZdfftlnzvz5843T6fS+7tOnj3n++ed95vzf//2fadKkiTHGmBdeeMG0bt3alJaWXlLdAGoPzvQACIhdu3bJ7XarT58+lY6/9957mjNnjvbu3avi4mJ5PB7FxMRUOtflcunw4cPq2bOnz/6ePXuedxamW7duF61r69at+uqrr7yX2SSpvLxcJSUlOnXqlO688069/PLLat68ufr3768BAwZo0KBBstt5OwVqO9b0AAiIiy0ezszM1LBhwzRgwACtWLFCW7Zs0ZNPPqnS0tLL/r7169e/6HhxcbGmT5+urKws77Z9+3bt2bNHERERSk5OVk5Ojl577TVFRkbqkUceUa9evS663ghA7cB/TQAERKtWrRQZGalVq1bpt7/9rc/Y+vXr1bRpUz355JPefQcOHLjgsWJiYpSUlKSvvvpKN910k3f/V199pWuvvfZn1dWlSxfl5OSoZcuWF5wTGRmpQYMGadCgQRo9erTatGmj7du3q0uXLj/rewGoWYQeAAERERGhyZMn6/HHH1d4eLh69uypY8eOeRce5+Xl6d1331X37t310UcfadmyZRc93qRJkzRt2jS1aNFCnTp10vz585WVlaVFixb9rLqefvpp/eIXv1BKSop++ctfKiQkRFu3blV2drb++Mc/asGCBSovL1ePHj1Ur149LVy4UJGRkWratOnl/DgA1ABCD4CAmTp1qux2u55++mkdPnxYTZo00UMPPaRRo0ZpwoQJGjNmjNxutwYOHKipU6fqmWeeueCxxo0bp8LCQj322GMqKCjQNddco3/+858+d25din79+mnFihV69tln9ac//UlhYWFq06aN92xUgwYNNHPmTE2cOFHl5eVq3769li9frri4uMv5UQCoATZjjAl0EQAAANWNhcwAAMASCD0AAMASCD0AAMASCD0AAMASCD0AAMASCD0AAMASCD0AAMASCD0AAMASCD0AAMASCD0AAMASCD0AAMAS/h/IYzrReOCw4wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# - grafico entre pace e distancia"
      ],
      "metadata": {
        "id": "HqP3-toEwtQl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Distância máxima por weather - Grafico\n"
      ],
      "metadata": {
        "id": "nYnD2EqIuynQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#10 - Gráfico de linha com speed,pace"
      ],
      "metadata": {
        "id": "SM9QJv7MGAYO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}