Year.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "34bff625",
      "metadata": {
        "id": "34bff625"
      },
      "outputs": [],
      "source": [
        "import pandas, warnings\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "plt.style.use(\"seaborn-whitegrid\")\n",
        "\n",
        "reader = lambda url, **multiargs : pandas.read_table(url, sep=\",\", **multiargs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "dabc14f0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dabc14f0",
        "outputId": "9f3ba045-5539-4236-cd2d-65dfe26c78b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id station_id train_id                        line  \\\n",
              "0  AC-2400         AC     2400  COMMUTER LINE TANJUNGPRIUK   \n",
              "1  AC-2401         AC     2401  COMMUTER LINE TANJUNGPRIUK   \n",
              "2  AC-2402         AC     2402  COMMUTER LINE TANJUNGPRIUK   \n",
              "3  AC-2403         AC     2403  COMMUTER LINE TANJUNGPRIUK   \n",
              "4  AC-2404         AC     2404  COMMUTER LINE TANJUNGPRIUK   \n",
              "\n",
              "                      route    color  \n",
              "0  JAKARTAKOTA-TANJUNGPRIUK  #DD0067  \n",
              "1  TANJUNGPRIUK-JAKARTAKOTA  #DD0067  \n",
              "2  JAKARTAKOTA-TANJUNGPRIUK  #DD0067  \n",
              "3  TANJUNGPRIUK-JAKARTAKOTA  #DD0067  \n",
              "4  JAKARTAKOTA-TANJUNGPRIUK  #DD0067  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7633bb6-53b4-4140-bcd1-ef694cc332d7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>station_id</th>\n",
              "      <th>train_id</th>\n",
              "      <th>line</th>\n",
              "      <th>route</th>\n",
              "      <th>color</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AC-2400</td>\n",
              "      <td>AC</td>\n",
              "      <td>2400</td>\n",
              "      <td>COMMUTER LINE TANJUNGPRIUK</td>\n",
              "      <td>JAKARTAKOTA-TANJUNGPRIUK</td>\n",
              "      <td>#DD0067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AC-2401</td>\n",
              "      <td>AC</td>\n",
              "      <td>2401</td>\n",
              "      <td>COMMUTER LINE TANJUNGPRIUK</td>\n",
              "      <td>TANJUNGPRIUK-JAKARTAKOTA</td>\n",
              "      <td>#DD0067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AC-2402</td>\n",
              "      <td>AC</td>\n",
              "      <td>2402</td>\n",
              "      <td>COMMUTER LINE TANJUNGPRIUK</td>\n",
              "      <td>JAKARTAKOTA-TANJUNGPRIUK</td>\n",
              "      <td>#DD0067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AC-2403</td>\n",
              "      <td>AC</td>\n",
              "      <td>2403</td>\n",
              "      <td>COMMUTER LINE TANJUNGPRIUK</td>\n",
              "      <td>TANJUNGPRIUK-JAKARTAKOTA</td>\n",
              "      <td>#DD0067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AC-2404</td>\n",
              "      <td>AC</td>\n",
              "      <td>2404</td>\n",
              "      <td>COMMUTER LINE TANJUNGPRIUK</td>\n",
              "      <td>JAKARTAKOTA-TANJUNGPRIUK</td>\n",
              "      <td>#DD0067</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7633bb6-53b4-4140-bcd1-ef694cc332d7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d7633bb6-53b4-4140-bcd1-ef694cc332d7 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-d7633bb6-53b4-4140-bcd1-ef694cc332d7');\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-491004d5-fde7-4597-b948-e83e3e9e996b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-491004d5-fde7-4597-b948-e83e3e9e996b')\"\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-491004d5-fde7-4597-b948-e83e3e9e996b 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\": \"origin\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"AC-2401\",\n          \"AC-2404\",\n          \"AC-2402\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"AC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2401\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"line\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"COMMUTER LINE TANJUNGPRIUK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"route\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"TANJUNGPRIUK-JAKARTAKOTA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"#DD0067\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "url = \"/content/trainstati.csv\"\n",
        "\n",
        "tanggal = [\"time_estimated\", \"destination_time\"]\n",
        "\n",
        "origin = reader(url, parse_dates=tanggal)\n",
        "\n",
        "origin.iloc[:5, :6]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "182a39ab",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "182a39ab",
        "outputId": "70696c76-30fe-4cde-effb-05f704a8c2cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    destination      time_estimated    destination_time  \\\n",
              "0  TANJUNGPRIUK 2024-06-02 06:07:00 2024-06-02 06:16:00   \n",
              "1   JAKARTAKOTA 2024-06-02 06:34:00 2024-06-02 06:42:00   \n",
              "2  TANJUNGPRIUK 2024-06-02 06:24:00 2024-06-02 06:33:00   \n",
              "3   JAKARTAKOTA 2024-06-02 06:50:00 2024-06-02 06:58:00   \n",
              "4  TANJUNGPRIUK 2024-06-02 06:43:00 2024-06-02 06:52:00   \n",
              "\n",
              "                         updated_at statiun          asal  terlambat  \n",
              "0  2024-03-16 17:00:08.063000+00:00   ANCOL   JAKARTAKOTA        9.0  \n",
              "1  2024-03-16 17:00:08.063000+00:00   ANCOL  TANJUNGPRIUK        8.0  \n",
              "2  2024-03-16 17:00:08.063000+00:00   ANCOL   JAKARTAKOTA        9.0  \n",
              "3  2024-03-16 17:00:08.063000+00:00   ANCOL  TANJUNGPRIUK        8.0  \n",
              "4  2024-03-16 17:00:08.063000+00:00   ANCOL   JAKARTAKOTA        9.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb82963a-9962-4d2e-a6b1-ba5db8c4647f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>destination</th>\n",
              "      <th>time_estimated</th>\n",
              "      <th>destination_time</th>\n",
              "      <th>updated_at</th>\n",
              "      <th>statiun</th>\n",
              "      <th>asal</th>\n",
              "      <th>terlambat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TANJUNGPRIUK</td>\n",
              "      <td>2024-06-02 06:07:00</td>\n",
              "      <td>2024-06-02 06:16:00</td>\n",
              "      <td>2024-03-16 17:00:08.063000+00:00</td>\n",
              "      <td>ANCOL</td>\n",
              "      <td>JAKARTAKOTA</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>JAKARTAKOTA</td>\n",
              "      <td>2024-06-02 06:34:00</td>\n",
              "      <td>2024-06-02 06:42:00</td>\n",
              "      <td>2024-03-16 17:00:08.063000+00:00</td>\n",
              "      <td>ANCOL</td>\n",
              "      <td>TANJUNGPRIUK</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TANJUNGPRIUK</td>\n",
              "      <td>2024-06-02 06:24:00</td>\n",
              "      <td>2024-06-02 06:33:00</td>\n",
              "      <td>2024-03-16 17:00:08.063000+00:00</td>\n",
              "      <td>ANCOL</td>\n",
              "      <td>JAKARTAKOTA</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>JAKARTAKOTA</td>\n",
              "      <td>2024-06-02 06:50:00</td>\n",
              "      <td>2024-06-02 06:58:00</td>\n",
              "      <td>2024-03-16 17:00:08.063000+00:00</td>\n",
              "      <td>ANCOL</td>\n",
              "      <td>TANJUNGPRIUK</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TANJUNGPRIUK</td>\n",
              "      <td>2024-06-02 06:43:00</td>\n",
              "      <td>2024-06-02 06:52:00</td>\n",
              "      <td>2024-03-16 17:00:08.063000+00:00</td>\n",
              "      <td>ANCOL</td>\n",
              "      <td>JAKARTAKOTA</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb82963a-9962-4d2e-a6b1-ba5db8c4647f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bb82963a-9962-4d2e-a6b1-ba5db8c4647f 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-bb82963a-9962-4d2e-a6b1-ba5db8c4647f');\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-38b084d3-2b6a-4ce6-a10e-068542ede10b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38b084d3-2b6a-4ce6-a10e-068542ede10b')\"\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-38b084d3-2b6a-4ce6-a10e-068542ede10b 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\": \"origin\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"destination\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"JAKARTAKOTA\",\n          \"TANJUNGPRIUK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_estimated\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-06-02 06:07:00\",\n        \"max\": \"2024-06-02 06:50:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-06-02 06:34:00\",\n          \"2024-06-02 06:43:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"destination_time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-06-02 06:16:00\",\n        \"max\": \"2024-06-02 06:58:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-06-02 06:42:00\",\n          \"2024-06-02 06:52:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"updated_at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-03-16 17:00:08.063000+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"statiun\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ANCOL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"asal\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"TANJUNGPRIUK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"terlambat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5477225575051662,\n        \"min\": 8.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "origin.iloc[:5, 6:]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ```General```"
      ],
      "metadata": {
        "id": "eFFiw9SsulHP"
      },
      "id": "eFFiw9SsulHP"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "8080275a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8080275a",
        "outputId": "47c52e45-6139-42c0-fd5d-bfe5cfc7659f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "null = origin.isnull().sum()\n",
        "\n",
        "null.sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "ea25bc19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ea25bc19",
        "outputId": "afd7d09c-8355-4a2f-d1d0-1fa8027d1c49"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "double = origin.duplicated()\n",
        "\n",
        "double.sum()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(origin)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nx1xubVPvzXp",
        "outputId": "4c92d05e-4abf-472d-8db0-f9c50cd30dc5"
      },
      "id": "Nx1xubVPvzXp",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18065"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "427641a0",
      "metadata": {
        "id": "427641a0"
      },
      "outputs": [],
      "source": [
        "assert origin[\"id\"].nunique() == len(origin), \"Failed !\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "origin[\"station_id\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IN8PH4iOnMNn",
        "outputId": "cd7053f5-f6af-4391-b8bc-b5fa91fc074a"
      },
      "id": "IN8PH4iOnMNn",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "origin[\"station_id\"].unique().tolist()[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SdPZekDnWQy",
        "outputId": "6fb9e547-ace9-4f72-d5ba-42a227b4d0d7"
      },
      "id": "2SdPZekDnWQy",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['AC', 'AK', 'BBN', 'BJD', 'BKS']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert origin[\"station_id\"].nunique() == origin[\"statiun\"].nunique(), \"Failed !\""
      ],
      "metadata": {
        "id": "k0yMvYCSnhsa"
      },
      "id": "k0yMvYCSnhsa",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "origin[\"train_id\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "guir67nInRQt",
        "outputId": "ec32c96e-f78d-48a6-879a-aef9b0b8f0dd"
      },
      "id": "guir67nInRQt",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1284"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "origin[\"train_id\"].unique().tolist()[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "di00f91undHb",
        "outputId": "ab713f1a-5735-40c0-844e-3e9ed7083076"
      },
      "id": "di00f91undHb",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2400', '2401', '2402', '2403', '2404']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ```Line General```"
      ],
      "metadata": {
        "id": "LLnJH2OQtU4s"
      },
      "id": "LLnJH2OQtU4s"
    },
    {
      "cell_type": "code",
      "source": [
        "origin[\"line\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQYjsXjtopKU",
        "outputId": "53e2b5e7-4a2c-45cb-ab94-2c45a02c9ee3"
      },
      "id": "uQYjsXjtopKU",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Terdapat 10 Commuter Line Pada Table```"
      ],
      "metadata": {
        "id": "Kh4vP1hjtYCt"
      },
      "id": "Kh4vP1hjtYCt"
    },
    {
      "cell_type": "code",
      "source": [
        "total = origin[\"line\"].value_counts()\n",
        "\n",
        "total.index = total.index.str.replace(\"COMMUTER LINE\", \"\")\n",
        "\n",
        "total.index = total.index.str.strip()\n",
        "\n",
        "total[:5].index.tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MemUH5Ioung",
        "outputId": "63b16e9e-d593-4961-d1b6-6b66666fa7be"
      },
      "id": "_MemUH5Ioung",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['BOGOR', 'CIKARANG', 'RANGKASBITUNG', 'TANGERANG', 'BST']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot = total.iloc[:5].plot.bar(rot=0, figsize=(8, 5))\n",
        "\n",
        "plot.set_title(\"Top 5 Trip Commuter Line\")\n",
        "\n",
        "plot.set_xlabel(\"Line\")\n",
        "\n",
        "plot.set_ylabel(\"Total Trip\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "8ZvPNOwMo2QT",
        "outputId": "d32d3ebd-78a2-446c-882a-2b99c8586d09"
      },
      "id": "8ZvPNOwMo2QT",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Commuter Line Bogor Menjadi Commuter Line Dengan Trip Terbanyak, 7.456 Trip```  \n",
        "```Cikarang Menjadi Commuter Line Trip Terbanyak Kedua, 5.203 Trip```"
      ],
      "metadata": {
        "id": "LuNE5U0qtfoa"
      },
      "id": "LuNE5U0qtfoa"
    },
    {
      "cell_type": "code",
      "source": [
        "lito = origin.groupby(\"line\")[\"route\"].nunique()\n",
        "\n",
        "lito = lito.sort_values(ascending=False)\n",
        "\n",
        "lito.index = lito.index.str.replace(\"COMMUTER LINE\", \"\")\n",
        "\n",
        "lito.index = lito.index.str.strip()"
      ],
      "metadata": {
        "id": "ODTyCiLiqPhA"
      },
      "id": "ODTyCiLiqPhA",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot = lito.iloc[:5].plot.bar(rot=0, figsize=(8, 5))\n",
        "\n",
        "plot.set_title(\"Top 5 Commuter Line With Cross Route\")\n",
        "\n",
        "plot.set_xlabel(\"Line\")\n",
        "\n",
        "plot.set_ylabel(\"Total Route\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "2HbWT92vq54b",
        "outputId": "0ffe131a-87e5-4b0e-e86e-797957fd6bfd"
      },
      "id": "2HbWT92vq54b",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Commuter Line Cikarang Menjadi Commuter Line Total Route Terbanyak : 25 Rute```  "
      ],
      "metadata": {
        "id": "JabDR_ZOtwAv"
      },
      "id": "JabDR_ZOtwAv"
    },
    {
      "cell_type": "code",
      "source": [
        "lito = origin.groupby(\"line\")[\"route\"].nunique()\n",
        "\n",
        "lito = lito.sort_values(ascending=False)\n",
        "\n",
        "lito.index[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "S6IulJbet72q",
        "outputId": "a15f851d-5b8c-4cb4-9d5d-3376daf47459"
      },
      "id": "S6IulJbet72q",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'COMMUTER LINE TANGERANG'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lito = origin.groupby(\"line\")[\"route\"].nunique()\n",
        "\n",
        "lito = lito.sort_values(ascending=False)\n",
        "\n",
        "origin[origin[\"line\"] == lito.index[3]][\"route\"].unique().tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgVK0WgfrEh9",
        "outputId": "6de93cd3-f105-4546-dd0e-600cc2ca5ff6"
      },
      "id": "pgVK0WgfrEh9",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['TANGERANG-DURI', 'DURI-TANGERANG', 'DURI-MANGGARAI', 'MANGGARAI-DURI']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lito = origin.groupby(\"line\")[\"statiun\"].nunique()\n",
        "\n",
        "lito = lito.sort_values(ascending=False)\n",
        "\n",
        "lito.index = lito.index.str.replace(\"COMMUTER LINE\", \"\")\n",
        "\n",
        "lito.index = lito.index.str.strip()"
      ],
      "metadata": {
        "id": "k_P6s4_Frs-_"
      },
      "id": "k_P6s4_Frs-_",
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot = lito.iloc[:5].plot.bar(rot=0, figsize=(8, 5))\n",
        "\n",
        "plot.set_title(\"Top 5 Commuter Line With Source Station\")\n",
        "\n",
        "plot.set_xlabel(\"Line\")\n",
        "\n",
        "plot.set_ylabel(\"Total Source Station\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "TnLtu9U-r8o7",
        "outputId": "dacc35ef-b468-4172-e4dd-6fda40a6d403"
      },
      "id": "TnLtu9U-r8o7",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lito = origin.groupby(\"line\")[\"statiun\"].nunique()\n",
        "\n",
        "lito = lito.sort_values(ascending=False)\n",
        "\n",
        "origin[origin[\"line\"] == lito.index[0]][\"statiun\"].unique().tolist()[:6]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5hd0gm-sFif",
        "outputId": "3bef85fc-3995-4a16-946e-dfe72308685d"
      },
      "id": "O5hd0gm-sFif",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ANGKE', 'BEKASI', 'BEKASITIMUR', 'BUARAN', 'CIBITUNG', 'CIKARANG']"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Commuter Line Cikarang Menjadi Commuter Line Dengan Total Menerima Trip Dari 28 Statiun```  \n",
        "```6 Diantaranya, Angke, Bekasi, Buaran, Cibitung, Cikarang, Bekasi Timur```  "
      ],
      "metadata": {
        "id": "qS3I4C9LuFnc"
      },
      "id": "qS3I4C9LuFnc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ```Rute General```"
      ],
      "metadata": {
        "id": "qTMaiEhQuphq"
      },
      "id": "qTMaiEhQuphq"
    },
    {
      "cell_type": "code",
      "source": [
        "rute = origin[\"route\"].value_counts()\n",
        "\n",
        "plot = rute.iloc[:4].plot.bar(rot=0, figsize=(12, 6))\n",
        "\n",
        "plot.set_title(\"Top 4 Route\")\n",
        "\n",
        "plot.set_xlabel(\"Route\")\n",
        "\n",
        "plot.set_ylabel(\"Total Trip\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "CaKJKn5YoLWK",
        "outputId": "ecda3f3f-2404-45ef-e45e-76f883501f6f"
      },
      "id": "CaKJKn5YoLWK",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot = rute.iloc[4:8].plot.bar(rot=0, figsize=(12, 6))\n",
        "\n",
        "plot.set_title(\"Top 8 Route\")\n",
        "\n",
        "plot.set_xlabel(\"Route\")\n",
        "\n",
        "plot.set_ylabel(\"Total Trip\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "m6r5tO2No9z2",
        "outputId": "4338688b-340c-4079-900c-60146a03447c"
      },
      "id": "m6r5tO2No9z2",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```4 Rute Terbanyak Yang Dilewati : Jakarta - Bogor, Rangkasbitung - Tanah Abang, Kampung Bandan - Cikarang, Bekasi -  Kampung Bandan```  \n",
        "```Masing Masing Rute Menjadi Top 4 Rute Dengan Masing-Masing Memiliki 2 Arah Keberangkatan```  \n",
        "```Contoh : Rangkasbitung - Tanah Abang, 1062 + 1062 Menjadi 2.124 Trip```"
      ],
      "metadata": {
        "id": "S_YZHL1JpKSX"
      },
      "id": "S_YZHL1JpKSX"
    },
    {
      "cell_type": "code",
      "source": [
        "rute = origin.groupby(\"route\")[[\"statiun\"]].nunique()\n",
        "\n",
        "rute.sort_values(\"statiun\", ascending=False).iloc[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "A5V6-1njqet8",
        "outputId": "5a50e791-00e3-4716-b121-ab852b6e3a49"
      },
      "id": "A5V6-1njqet8",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        statiun\n",
              "route                          \n",
              "CIKARANG-KAMPUNGBANDAN       26\n",
              "KAMPUNGBANDAN-CIKARANG       25\n",
              "TAMBUN-KAMPUNGBANDAN         23\n",
              "BOGOR-JAKARTAKOTA            23\n",
              "JAKARTAKOTA-BOGOR            23"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb0ac01c-42bc-46d0-8448-9ddf8a59203e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>statiun</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>route</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CIKARANG-KAMPUNGBANDAN</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KAMPUNGBANDAN-CIKARANG</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAMBUN-KAMPUNGBANDAN</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOGOR-JAKARTAKOTA</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JAKARTAKOTA-BOGOR</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb0ac01c-42bc-46d0-8448-9ddf8a59203e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eb0ac01c-42bc-46d0-8448-9ddf8a59203e 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-eb0ac01c-42bc-46d0-8448-9ddf8a59203e');\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-bb293f49-1f7d-40f9-8587-31784539221a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bb293f49-1f7d-40f9-8587-31784539221a')\"\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-bb293f49-1f7d-40f9-8587-31784539221a 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\": \"rute\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"route\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"KAMPUNGBANDAN-CIKARANG\",\n          \"JAKARTAKOTA-BOGOR\",\n          \"TAMBUN-KAMPUNGBANDAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"statiun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 23,\n        \"max\": 26,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          26,\n          25,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rute.sort_values(\"statiun\").iloc[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "HN32ubigsCsx",
        "outputId": "c927a090-ed6c-49d9-c63d-e806024be36e"
      },
      "id": "HN32ubigsCsx",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          statiun\n",
              "route                            \n",
              "RANGKASBITUNG-MERAK             1\n",
              "DEPOK-BOJONGGEDE                2\n",
              "SERPONG-PARUNGPANJANG           3\n",
              "DEPOK-NAMBO                     3\n",
              "TANJUNGPRIUK-JAKARTAKOTA        3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc571cb4-c436-4753-9175-c684c194f359\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>statiun</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>route</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RANGKASBITUNG-MERAK</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEPOK-BOJONGGEDE</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SERPONG-PARUNGPANJANG</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEPOK-NAMBO</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TANJUNGPRIUK-JAKARTAKOTA</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc571cb4-c436-4753-9175-c684c194f359')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dc571cb4-c436-4753-9175-c684c194f359 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-dc571cb4-c436-4753-9175-c684c194f359');\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-1e338241-6594-43c6-a98d-9cf46ad944e8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1e338241-6594-43c6-a98d-9cf46ad944e8')\"\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-1e338241-6594-43c6-a98d-9cf46ad944e8 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\": \"rute\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"route\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"DEPOK-BOJONGGEDE\",\n          \"TANJUNGPRIUK-JAKARTAKOTA\",\n          \"SERPONG-PARUNGPANJANG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"statiun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t = rute.sort_values(\"statiun\").iloc[:5]\n",
        "\n",
        "origin[origin[\"route\"] == t.index[4]][\"statiun\"].unique().tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQVyuEINsHxv",
        "outputId": "f93e919c-cd15-468e-8901-34f5cf721824"
      },
      "id": "HQVyuEINsHxv",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ANCOL', 'KAMPUNGBANDAN', 'TANJUNGPRIUK']"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ```Destination General```"
      ],
      "metadata": {
        "id": "_SpTcY_uu1tU"
      },
      "id": "_SpTcY_uu1tU"
    },
    {
      "cell_type": "code",
      "source": [
        "tuju = origin[\"destination\"].value_counts()\n",
        "\n",
        "plot = tuju.iloc[:5].plot.bar(rot=0, figsize=(8, 5))\n",
        "\n",
        "plot.set_title(\"Top 5 Destination\")\n",
        "\n",
        "plot.set_xlabel(\"Destination\")\n",
        "\n",
        "plot.set_ylabel(\"Total Trip\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "c8-fLAkCuxX7",
        "outputId": "bbe8b505-5f0c-49f4-c1cb-2bf009e7fc8b"
      },
      "id": "c8-fLAkCuxX7",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Jakarta Kota, Bogor, Kampung Bandan, Cikarang, Tanah Abang Menjadi 5 Tujuan Terbanyak```"
      ],
      "metadata": {
        "id": "RL4197CVxx2o"
      },
      "id": "RL4197CVxx2o"
    },
    {
      "cell_type": "code",
      "source": [
        "tuju = origin.groupby(\"destination\")[\"route\"].nunique()\n",
        "\n",
        "plot = tuju.sort_values(ascending=False).iloc[:5].plot.bar(rot=0, figsize=(8, 5))\n",
        "\n",
        "plot.set_title(\"Top 5 Destination With Cross Route\")\n",
        "\n",
        "plot.set_xlabel(\"Destination\")\n",
        "\n",
        "plot.set_ylabel(\"Total Route\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "wVKRwuk6u8WR",
        "outputId": "663d1c76-9d92-4dae-ab63-bab6dafc11f3"
      },
      "id": "wVKRwuk6u8WR",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqAAAAHPCAYAAACMdmjXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABNTElEQVR4nO3de3zOhf//8edcs83MMDKnomhz2GZzmsNUDplDDjm1ckj4KORMlDllikolQhEiFRtGyCkdyJDzHEo5ZUTOp2Fc2+8Pv11fl21c0/Z+43rcbze32vt6X+/363q/3tf7el7v0+WSkpKSIgAAAMAgOcwuAAAAAM6FAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoAChho8ODB8vf3v+O/9u3bZ9v8FyxYkOF8ly9fnuHzNm7caDdumTJlVLVqVb300kv68ssvdfXq1WyrOb369+/f/9DM65dffpG/v79WrlyZ5rHx48fL399fv/32W5rH3nzzTQUFBenq1asaPHiwatasmeE8Uvu3cePGe6rx/Pnz+uijj9S4cWNVqFBBlStXVvPmzfXZZ58pMTHxnqZplPTW9XLlyumZZ57R22+/rfPnz5tdIuCUXM0uAHAmQ4YMUf/+/W1/Dx8+XLt371ZMTIxtWM6cObO9jnXr1qUZljdv3rs+b9y4cQoNDVVycrLOnDmjuLg4TZ06VdHR0Zo+fboKFSqUpXV+8sknOnbsmMaMGSNJatSokWrVqiUfH58snY/R87pV1apV5e7url9//VX169e3e2z9+vVycXHR+vXrVaVKFbvHNmzYoCpVqsjDw0NDhgzR9evXbY/Nnz9fsbGxmj179n+u7++//1bHjh3l4eGhHj16KCgoSJcvX9aGDRs0adIkLV26VLNmzVK+fPn+87yyS7t27fTaa6/Z/r569aq2bdum9957Tzt27NC8efNksViydJ5HjhxRvXr19Mcff2TpdIGHBQEUMFCePHmUJ08e29/u7u6yWCx65JFHDK3jXufn7e1te66vr6/Kli2rJk2a6IUXXlC/fv301VdfZWWZ2rZtm3x9fW1/e3h4yMPDI0vnYca8buXh4aEqVaro119/tRt+8eJFxcfHKywsTHFxcerdu7ftsYMHD+rYsWN6+eWXJclunZJuvpas0r9/f7m6umru3Ll28ylTpoyqVKmiNm3aaNasWerVq1eWzTOr5cqVK806/+ijjypHjhzq37+/Nm3apOrVq2fpPLOyB8DDiEPwwH3oxx9/VJs2bRQUFKTg4GC9+OKLdgEl9ZDqTz/9pN69e6tixYqqVKmS3nzzTcMPiT7yyCPq3bu3fvvtN23ZssU2fMeOHercubNq1Kih4OBgtW3bVlu3brU9npKSoilTpig8PFxBQUGqVq2aXn/9dR05ckSSVKdOHa1fv14LFy60HT6+/bD44MGD1axZM23cuFEtWrRQhQoV9Oyzz2rhwoV2Nf7yyy968cUXFRwcrJCQED3//PN2h7wdmZfkeF82btyo/v37q3LlygoNDdWgQYPu2JdatWrpyJEjtteeOi2LxaKIiAjFx8fr0qVLtsfWr19ve17qckg9BN++fXtFR0dr06ZN8vf314IFC2zPu3r1qoYMGaLKlSsrODhY/fr1s5vu7TZv3qydO3eqZ8+eaUKuJJUvX17Lly+3hc/UZfbzzz+rbt26atmypW3cBQsWqEmTJgoMDFSlSpXUuXNn7dq1y/Z4UlKSxowZozp16igwMFA1a9bUoEGDdPbsWds4c+fOVZMmTRQcHKwqVaqoU6dO2r17d4b1302ZMmUkSf/8849t2IkTJ9S/f39Vq1ZNAQEBqlevnj755BPduHHDNk6dOnXUt29fu2ndur5MmDBBAwcOlHTzFIDBgwfbXuP48ePVuHFjBQUF6emnn9YHH3ygpKSke34NwIOKAArcZ9avX69u3bqpTJkyiomJ0dy5c+Xr66uuXbum+bAdPXq0nn76aS1cuFBDhw7VkiVLNHbsWMNrrlu3rlxcXBQXFyfp5h66l19+WVarVVOnTtXcuXNVuHBhderUyRboYmJi9Nlnn2ngwIFavny5Pv/8c124cEGvvvqq7XEfHx81bNhQ69atU0hISLrzPnPmjCZOnKjIyEjFxsaqVKlSGjp0qC1U/P333+revbueeOIJxcbGatGiRQoLC1OfPn20Z88eh+eVmb6MGTNG1atX18KFC9W/f3/Fxsbece9wWFiYJNmF2V9//VWBgYGqVq2aJNmdvxkXF6dixYqpVKlSaaY1YcIElS9fXiEhIVq3bp0aNWpke2z8+PEKDg7W/PnzNWTIEC1dulTTpk3LsK6NGzfKxcVFTz/9dIbjPProo2mGffbZZ3rnnXc0ZcoUSTeX75tvvql69eopNjZWM2fO1PXr19WhQwcdP35ckmyH80ePHq2VK1dq/Pjx2rNnjy3IxcXFacSIEXrllVe0dOlSzZ49W3nz5lWnTp105cqVDOu7k9R1sWjRopKka9euqUOHDtq7d68+/PBDLVu2TF26dNHUqVP1/vvvOzzdTp06qV27dpJunu4yZMgQSdLIkSP1xRdf6OWXX9aSJUs0aNAgRUdHa/jw4fdUP/AgI4AC95kvvvhCpUqV0siRI+Xn5yd/f3+999578vLy0tdff203bo0aNdSiRQuVKFFCzZs3V8OGDbVkyRKlpKTccR4fffSRnnvuOYWGhqpVq1ZasWLFf6rZy8tLefLk0cmTJyVJM2fOVI4cOWxhyN/fX++8845y586tmTNnSpJ2796tIkWKqF69eipatKiCgoL08ccf67333lNycrJ8fHyUI0cOeXh46JFHHpGbm1u68/733381dOhQVaxYUY8//rg6d+6s69ev28Klr6+vFi1apCFDhqhkyZJ67LHH9Prrr8tqtdr2JDoyr8z0pVq1amrVqpUeffRRtWnTRsWLF9fOnTszXH6lS5dW0aJF7QLo+vXrVb16dXl5eSkwMNAW7pOTk7Vp0yZbaL1dvnz55Orqqpw5c+qRRx6xO40gNDRUrVu3VokSJdS6dWuVKlXqjnWdOHFCefLkkbe3d4bjpKdRo0YKDQ21HfaeOnWqnnrqKfXu3VulSpVSYGCgPvzwQ129etW2h3b37t3y9/dX9erVVaRIEVWuXFlTp061BdBdu3YpV65catq0qYoVK6YyZcpo9OjR+vzzzzN9/qbVatX27dv1wQcf2C6ok6RVq1bp0KFDGjNmjGrUqKHHHntMERERatOmjebOnevwnsrcuXMrV65ckm4eIciTJ49OnDihBQsWqEuXLmrTpo0ee+wxNWrUSN26ddPChQt14sSJTL0G4EFHAAXuM/Hx8apUqZJcXFxsw9zc3BQQEGALVakqV65s93e5cuV06dKlDK/s9fDwUKFCheTq6qr33ntPkyZN0pNPPqlevXopNjb2P9V948YNubrePK18586dqlChQprzXStWrGjbW1i7dm0dOnRIHTt21MKFC/XPP//Ix8dHAQEBypHD8U2Tp6en/Pz8bH+nXjR04cIF23z/+usvdevWTWFhYQoJCbHtVTx37pzD88lMXypUqGD3t4+Pz12vtg4LC9OGDRtktVp17NgxHTp0yHZeYrVq1WxhedeuXTp//ryeeuoph2tPdfueXR8fH12+fDnD8V1cXO76ZSY9AQEBtv+/dOmSDh06lGZdLViwoB599FHbsqtbt67Wrl2rXr16admyZTp9+rQKFy4sf39/SVLNmjWVnJysF154Qd98840OHjwoT09PVahQIcMvJ6lmzpypkJAQ278KFSqoffv2CggI0PTp023rW3x8vNzd3RUYGGj3/JCQEF25ckUHDhzI9LJItWvXLiUnJ6e5W0H16tWVkpKSZh0CHnZchATcZy5duiQvL680w3Pnzm13jqCkNHumcufOLenmBSzpXZXcqFEju0OyklSpUiUdPnxYEyZMUPPmze+p5n///VeJiYkqVqyY7TX88ccfaQJPUlKSLSA+/fTTmjVrlmbNmqXRo0fr4sWLqlChggYNGqRKlSo5PG9PT890h6cGp1WrVqlXr15q0KCBPv74YxUsWFAuLi5prji/m8z05faabg2tGQkLC9O8efO0a9cu/fXXX7ZwJd0MoJMnT9aJEye0YcMG5cyZ0xaiM+P2i6ruFjCLFi2qixcv6syZM5m6G8CtXzxSzzFNb9l5eXnZAnBERIR8fX319ddf680331RSUpKqVaumIUOGqHTp0ipXrpzmzp2r6dOn65NPPtGIESNUunRp9evXT3Xr1r1jPS1atFDnzp1tf3/44YfasmWLRo4cafceunTpknLnzp2mX6m13yms303qcujUqZPdF6zU5Z969ABwFgRQ4D6TJ0+edC8MuXTpUpoLQW7/QEz9O7OHTMuUKXPHQ7F3k3oIP/WwsLe3twoXLqyoqKg049764Vu5cmVVrlxZN27c0JYtWzRx4kT973//008//ZTp15CRxYsXy9fXVx999JFt3v/++2+mp5OZvtyLGjVqyNXVVZs3b9a+fftUqVIl2y25KlasKA8PD/3222/auHGjQkJC0g10WS11D+zKlSsVERGR7jgrVqxQ6dKl0z0fVfq/8JbRskv90iLd3Cteu3ZtJSUlaf369Ro3bpy6du2qH374QS4uLvL399fYsWOVkpKi+Ph4TZ06VT179tSyZctUsmTJDF+Ht7e3SpQoYfv7rbfeUsOGDTV27FiNHj3abrzLly8rJSXFLoRevHjR9niq24P73S7+S73N2QcffGC3xz5Vdt/uC7jfcAgeuM9UqFBBW7ZssfuAu3btmnbt2pXm0ODtNxbftWuXChYsmOE9PT///HN9+umnaYbHx8fr8ccfv6d6ExISNHHiRNWpU8f2wRocHKyDBw+qSJEiKlGihO1fSkqK7V6ha9eu1V9//SVJcnV1VWhoqN58801dvnzZbo/ivRwCvtX169eVN29eu+CbepX87dO+07wy05d7kSdPHlWoUEFbt27V9u3bVaNGDdtjbm5uqlixon777Tft2LHDdvX7nfzX5SZJQUFBqlKliiZOnJjuOYp79uzRG2+8oW+//TbDaXh5eal06dJpbqb/77//6siRIwoMDFRycrJWrlxpu3DMzc1NzzzzjHr16qWjR4/q/Pnz2rJli3bs2CHp5p7boKAgRUVFyWq1at++fZl6Xb6+vurdu7diYmK0YcMGu9d77dq1NF/GtmzZIi8vL1vI9fb21pkzZ+zG2b59e7rzSu1DQECALBaLjh07ZveeeOSRR5QjR44s+RIDPEgIoMB9pkuXLjpw4IBGjBih/fv3a+/everbt6+uXbuW5leS1q1bp+joaB0+fFixsbFavnz5HQ+j58qVSxMmTNCECRO0f/9+7du3TyNHjtTOnTvVtWvXu9Z24cIFnTx5UidPntT+/fv11VdfqU2bNvL19bXbk9ShQwddvnxZ/fv3V3x8vI4cOaJ58+apefPmmjt3rqSbt63p0aOH1q1bp2PHjmnfvn2aMWOGChQoYNub5u3trT179mjv3r06derUPSzNm2H4r7/+0rJly3TkyBF98cUX2rFjh4oUKaI9e/bY9obebV6Z6cu9CgsL06ZNm3T48OE096WsXr26vv/+e128ePGuAdTb21uHDh1SfHy83S2G7sXYsWPl7u6uNm3aKCYmRocPH9Zff/2lL7/8Uh07dlTFihXT3JLodv/73/+0du1aTZw4UYcOHdL27dvVu3dv5cuXTy1btlSOHDk0bdo09enTR5s3b9Y///yj3bt369tvv5Wfn5/y5cunH3/8Ud27d9fKlSt19OhRHThwQFOmTJGHh8c9fQFo166dypYtq2HDhtl+yatu3boqVaqU3nrrLW3atEl///23Zs+erZiYGL3yyiu2PdJBQUHaunWrVq9erb///lszZ85McyeE1L2lq1ev1oEDB1SwYEG1atVKEydOVGxsrI4cOaIdO3aoV69eateu3T1fyQ88qDgED9xnqlatqsmTJ2vixIl6/vnnZbFYVKFCBc2aNSvNYc7evXvrhx9+0LvvvisXFxc1bdpUPXv2zHDa7du3V65cuTRnzhzNmDFDVqtV/v7++uSTTxQeHn7X2m79FadcuXKpZMmS6tixo226qUqUKKHZs2fro48+UocOHXT9+nWVLFlSgwYN0osvvihJGjVqlD744AMNGTJEp0+flre3typUqKDp06fbzlV89dVXNXr0aL344ot69913M7UcU3Xo0EEHDhzQ8OHD5eLiotq1a+u9995TdHS0Pv74Yw0YMECzZs2667wy05d7VatWLY0fP1758+e33aMyVbVq1TRu3Dg98sgjaR673SuvvKI33nhDL730kvr166dy5crdc03FihVTbGysvvjiC82YMUOjRo2Su7u7SpYsqX79+qlly5Z3/fWu5s2bKzk5WTNmzLCFxqpVq2r06NG2Q8+ffvqpxo4dq969e+v8+fPKnz+/qlatqpEjR0q6ua5bLBaNHTtW//77rzw9PVW2bFlNnTpVRYoUyfTrslgsGjFihCIiImz37XRzc9OMGTM0duxY9ezZU5cvX1axYsU0YMAA203/JalXr146ceKEBg0aJIvFovDwcPXt29fuvde0aVN999136tOnj2rXrq2JEydq2LBhKlSokCZMmKDjx48rd+7cCgsL01dffWX3/gGcgUtKVhynAWCojRs3qkOHDrbb2wAA8CDhEDwAAAAMRQAFAACAoTgEDwAAAEOxBxQAAACGIoACAADAUARQAAAAGOqBuA/ojRs3dP78ebm7u9v9mgkAAADuD8nJybp27Zry5s0rV9c7R8wHIoCeP39ehw4dMrsMAAAA3EXJkiVVoECBO47zQARQd3d3STdfkDP9WkTqbxz7+fnJYrGYXQ6yGf12LvTbudBv5+Ks/b5y5YoOHTpky2138kAE0NTD7rly5ZKnp6fJ1RjHarVKkjw9PZ1qBXZW9Nu50G/nQr+di7P325HTJTmhEgAAAIYigAIAAMBQBFAAAAAY6oE4B9QZ7dmzR2PGjFF8fLxy5cqlGjVq6K233pKPj4/ZpSEb0G8AgDNhD+h96MaNG+ratauCgoI0efJkfffddzpz5oxGjBhhdmnIBvQbAOBsCKD3oZMnT+rkyZNq2rSpcubMqXz58unZZ5/V3r17zS4N2YB+AwCcDQH0PuTr66uyZcsqOjpaV69e1enTp7Vy5Uo988wzZpeGbEC/AQDOhgB6H8qRI4cmTJigNWvWqFOnTqpVq5Zu3Lih/v37m10asgH9BgA4GwLofSgpKUmvvfaawsPDNW3aNP3000/KkyePBgwYYHZpyAb0GwDgbAig96G4uDglJCSoT58+8vT0VKFChdSrVy+tWrVK586dM7s8ZDH6DQBwNgTQ+5DValVycrJSUlJsw5KSkkysCNmJfgMAnI2pAXTPnj3q0KGDKleurJo1a2rAgAE6c+aMmSXdF0JCQuTp6amJEyfq2rVrOnfunCZPnqwqVaooX758ZpeHLEa/AQDOxrQAmnrvw+DgYK1fv15Llizh3of/X/78+fXFF19o+/bt6tGjh5o0aSIPDw+NGzfO7NKQDeg3AMDZmPZLSKn3PmzWrJnc3Nzk5uamZ599VtOnTzerpPtKQECAZs6cqe3btys4OFgWi8XskpCN6DcAwJmYFkBT7304d+5c9e7dW1evXr3rvQ+tVqusVqtxRZos9bU602t2ZvTbudBv50K/nYuz9jszr9cl5dYrHwx25MgRdezYUQkJCZKkqlWraurUqfLw8LAbLzEx0dRfhWkZfdy0eZtpfuvCZpdgCvoNAMC9K1u2rDw9Pe84jml7QFPvfdigQQO99tprSkxM1MiRIzVgwABNnDgx3ef4+fnd9QVli+jlxs/zPhAcHGx2Ceag3zCA1WpVfHy8AgMDOeXCCdBv5+Ks/U5MTNS+ffscGte0AJp678N+/frJYrEoT5486tWrl5o1a6Zz586le/WvxWJxqkaajWXtXOi3OdiuORf67Vycrd+Zea2mXQXPvQ8BAACck2kBNPXehxMmTNCVK1d09uxZ7n0IAADgBEwLoKn3Pty6daueeuopPffcc9z7EAAAwAmYdg6odPPeh7NnzzazBAAAABiM34IHAACAoQigAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChCKAAAAAwFAEUAAAAhiKAAgAAwFAEUAAAABiKAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMRQAFAACAoQigAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChCKAAAAAwFAEUAAAAhnI1a8a//fabOnXqZDcsJSVF169f1x9//GFSVQAAAMhupgXQKlWqKD4+3m7YlClT9Pvvv5tUEQAAAIxgWgC93bFjxzRjxgwtXLjQ7FIAAACQje6bADp+/Hi1bNlSRYsWzXAcq9Uqq9VqYFXOjWXtXOi3sVKXN8vdOdBv5+Ks/c7M670vAmhCQoJWrlyplStX3nG8ffv2GVQRJGn79u1mlwAD0W9z3H4qEh5u9Nu50O+M3RcBdM6cOapfv74eeeSRO47n5+cnT09Pg6q6RfRy4+d5HwgODja7BHPQbxjAarUqPj5egYGBslgsZpeDbEa/nYuz9jsxMdHhnYX3RQBdsWKFBg0adNfxLBaLUzXSbCxr50K/zcF2zbnQb+fibP3OzGs1/T6ge/fu1dGjR1WzZk2zSwEAAIABTA+ge/bsUb58+eTl5WV2KQAAADCA6QH01KlTdz33EwAAAA8P0wPoq6++qiVLlphdBgAAAAxiegAFAACAcyGAAgAAwFAEUAAAABiKAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMRQAFAACAoQigAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChCKAAAAAwFAEUAAAAhiKAAgAAwFAEUAAwwZQpU9S9e3dVqlRJHTt2VEJCgtklAYBhCKAAYLA5c+ZoyZIlioyM1M8//6zSpUtr5syZZpcFAIZxNbsAAHA206dP18CBA/XII4/Iy8tLkZGRZpcEAIZiDygAGOjEiRNKSEjQhQsXNHDgQFWvXl29evXSmTNnzC4NAAxDAAUAAx0/flyStGLFCr311ltauHChjh8/zl5QAE6FAAoABkpJSZEkderUSfnz51fhwoXVs2dPrVmzRteuXTO5OgAwBgEUAAxUsGBBSZK3t7dtWLFixZSSkqLTp0+bVRYAGIoACgAGKly4sLy8vLR3717bsKNHjypnzpwqVKiQiZUBgHEIoABgIFdXV7Vq1Uqff/65jh8/rtOnT+vTTz9VkyZN5OrKjUkAOAfTt3aTJ0/WnDlzdOnSJQUHBysqKkrFixc3uywAyDb9+/fXtWvXNHToUKWkpCg8PJyLkAA4FVP3gM6ZM0eLFy/WrFmztG7dOm7GDMApuLm5aejQoZo6dao2b96sMWPGKHfu3GaXBQCGMXUP6PTp0zVo0CA98cQTksQeAAAAACdgWgBNvRnz+fPn1ahRI50+fVqhoaEaMWKEfHx80n2O1WqV1Wo1uFLnxbJ2LvTbWKnLm+XuHOi3c3HWfmfm9ZoWQFNvxrx8+XLNmDFDKSkp6tWrlyIjIzVp0qR0n7Nv3z4jS3R627dvN7sEGMhZ+90y+ri5BZg0//mtC5syX2cXHx9vdgkwEP3OmGkBNPVmzF26dJGvr68kqWfPnvrf//6na9euyd3dPc1z/Pz85OnpaWidkqTo5cbP8z4QHBxsdgnmoN/OhX7DAFarVfHx8QoMDJTFYjG7HGQzZ+13YmKiwzsLTQugd7sZc9GiRdM8x2KxOFUjzcaydi7027nQb3PwOeZcnK3fmXmtpl0Fz82YAQAAnJNpATT1ZsxTpkzR4cOHuRkzAACAkzA16fXv319JSUlq3bq1rl+/zs2YAQAAnICpAdTNzU3Dhw/X8OHDzSwDAAAABuK34AEAAGAoAigAAAAMRQAFAACAoQigAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChCKAAAAAwFAEUAAAAhiKAAgAAwFAEUAAAABiKAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMRQAFAACAoQigAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChXM2cub+/v3LmzCkXFxfbsDZt2mjo0KEmVgUAAIDsZGoAlaTly5erePHiZpcBAAAAg5geQDPDarXKarWaXYbTYFk7F/rtXOi3sVKXN8vdOThrvzPzek0PoOPGjdO2bdt06dIlNWzYUIMHD1bu3LnTHXffvn0GV+fctm/fbnYJMBD9di702xzx8fFmlwAD0e+MmRpAg4ODVaNGDY0dO1ZHjhxRnz59NHLkSL333nvpju/n5ydPT0+Dq5QUvdz4ed4HgoODzS7BHPTbudBvGMBqtSo+Pl6BgYGyWCxml4Ns5qz9TkxMdHhnoakBdO7cubb/L1WqlAYMGKBu3bopKipKbm5uaca3WCxO1UizsaydC/12LvTbHHyOORdn63dmXut9dRum4sWLy2q16vTp02aXAgAAgGxyTwF06dKl+t///qfmzZtLkpKSkvTFF18oJSXF4Wns2bNHY8aMsRu2f/9+ubm5qVChQvdSFgAAAB4AmQ6gkyZN0nvvvaeQkBAdOHBAknThwgXFxsZq/PjxDk+nQIECmjt3rj7//HMlJSXp4MGDGj9+vF544QWn2l0NAADgbDIdQOfOnatp06ape/futhvIFyxYUJMmTdKiRYscno6vr68+//xzrVmzRqGhoYqIiFCtWrU0cODAzJYEAACAB0imL0K6ePGinnzyyTTDCxUqpDNnzmRqWlWqVNG3336b2RIAAADwAMv0HlA/Pz8tXrw4zfDp06erVKlSWVIUAAAAHl6Z3gPau3dv9ejRQ19//bWuX7+ubt26ad++fTp//rwmTZqUHTUCAADgIZLpAFq9enV9//33WrJkifz9/eXh4aGwsDA1btxY+fLly4YSAQAA8DDJdAD99NNP1aNHD3Xu3Nlu+OXLlzV69GgNGTIky4oDAADAw8fhAHru3DmdOXNGn332mRo3bpzmnp8HDx7U3LlzCaAAAAC4I4cD6A8//KAxY8bo+vXratCgQbrj1K9fP8sKAwAAwMPJ4QDasmVLNW/eXKGhoene79PDw0MFChTI0uIAAADw8MnUOaAWi0WbN2/OrloAAADgBDJ9EVKdOnVsv4CUnh9++OE/FQQAAICHW6YDaNeuXe3+tlqt+vvvv/XTTz+pS5cuWVYYAAAAHk6ZDqARERHpDn/uuec0YcIEtW7d+j8XBQAAgIdXpn+KMyPlypXTb7/9llWTAwAAwEMq03tA161bl2bY1atXtXr1ahUqVChLigIAAMDDK9MBNL3zPN3c3FSiRAmNGDEiK2oCAADAQyzTAfT333/PjjoAAADgJDIdQCXpzJkz+vnnn3X06FFJUokSJVS7dm15eXllaXEAAAB4+GQ6gMbFxalHjx7KlSuXHn30UUnS119/raioKH311Vd68skns7xIAAAAPDwyHUDff/999ezZU6+88optmNVq1eTJkxUVFaUvv/wySwsEAADAwyXTt2E6cOCA2rdvbzfMYrGoa9eu2rt3b5YVBgAAgIdTpgNooUKFdOjQoTTDjxw5wjmgAAAAuKtMH4Jv1qyZunbtqrZt2+qJJ56QdHOv6Jw5c9SgQYMsLxAAAAAPl0wH0O7du8vb21vz589XQkKCkpKS9NhjjykiIkKdO3fOjhoBAADwEMl0AHVxcVH79u3TnAcKAAAAOCJT54BeunRJv/zyi3755RclJSWleXzOnDlZVhgAAAAeTg7vAd27d6+6dOmiixcvKjk5WT4+Ppo9e7ZKlCihgwcPasiQIdq/f7/atm2bnfUCAADgAefwHtAPPvhAjRo10rZt27R161Y988wzev/99zVlyhQ1a9ZMRYoU0dKlS7OzVgAAADwEHA6gu3fvVu/evWWxWOTm5qZ+/fpp9erVWrBggSZNmqRx48apYMGC2VkrAAAPtDFjxsjf39/sMmAQ+p0xhw/BX7lyxe4+n/ny5ZObm5uWLFkiNze3bCkOAICHxaFDh7Ro0SKzy4BB6PedZfpG9LdycXEhfAIAcBfJycmaPn26Xn75ZbNLgQHo9939pwAKAADubt68ecqZM6eaNGlidikwAP2+O4cPwSclJSkiIuKuw7799tusqQwAgIfAqVOnNHHiRA0ePNjsUmAA+u0YhwNojx490gwLCwvL0mIAAHjYvPvuu3r++edVvHhxs0uBAei3YxwOoK+//np21gEAwEMnLi5O27Zt06JFi7Rv3z6zy0E2o9+Oy/RPcQIAAMcsXrxYp0+fVr169XTjxg3lyHHz0ovQ0FANGzZMjRs3NrlCZCX67TgCKAAA2WTw4MHq3bu3rFardu/erYIFC+rFF1/UokWLlDdvXrPLQxaj3467b66Cf+edd7hZKwDgoZI3b14VLlxYhQsXVoECBWw/2FK4cGHlypXL5OqQ1ei34+6LALp3715u1goAeOgVK1ZMf/zxh9llwCD0O2MOHYL/8MMPHZ5gv379MlVAcnKyhg8fro4dO+rjjz++47hWq1VWqzVT08e9Y1k7F/rtXOi3sVKXN8vdOThrvzPzeh0KoNu2bXNoYi4uLg7PONW3334rd3d3NWnS5K4BlCvKjLV9+3azS4CB6LdzcdZ+t4w+bm4BJs1/fuvCpszXbPT7/uVQAJ09e7ZDE/vxxx8zNfNTp05pwoQJDk/fz89Pnp6emZpHlohebvw87wPBwcFml2AO+u1c6Ldzod/OhX4bKjEx0eGdhfd0FfzZs2f1559/KikpyTbsxIkTioqKcnhvqXTzZq0tWrRQ6dKllZCQcNfxLRaLLBbLvZSMe8Cydi7027nQb+dCv52LWf3OzHwzHUBXrVqlAQMG6Nq1a3JxcVFKSookydvbW61bt3Z4Oqk3a12yZElmSwAAAMADLNNXwX/88ccaOXKkdu7cqZw5c2rPnj2aN2+eqlWrphdeeMHh6aTerLV27doKDQ1VixYtJN28WevSpUszWxYAAAAeEJneA3rs2DE1b95c0s2LjnLkyKGgoCD16tVLb775pubNm+fQdFJv1prq+PHjeuGFF7hZKwAAwEMu0wG0YMGC2r9/v0qVKqX8+fPr999/V5kyZVS8eHH9+eefDk8nb968dkHzxo0bkm7erBUAAAAPr0wH0LZt26pFixb69ddfFR4ertdee01169bV77//Lj8/v3supHjx4tysFQAAwAlkOoB27NhRAQEB8vLy0sCBA5UrVy7Fx8erVKlS6tatW3bUCAAAgIdIpgNobGys7RxQV1dX9enTR5J05coVffvtt3rllVeysj4AAAA8ZBy+Cj45OVlJSUkaPny4rl+/rqSkJLt/f//9tz766KPsrBUAAAAPAYf3gM6aNUtjx46VJAUFBaU7jtP+0gIAAAAc5nAA7dixo5o2baqnnnpK06dPT/O4h4eHypYtm6XFAQAA4OGTqXNAfXx89PPPP6tAgQK6fv26/v33X7m4uMjX15ef+QIAAIBDMn0RUs6cOdW3b1+tXr3adu9Od3d3Pffccxo6dKjc3d2zvEgAAAA8PDIdQEeMGKGTJ09q4sSJKlGihCRp//79mjJlij744AMNGTIky4sEAADAwyPTAXTt2rVasWKFfHx8bMNKliypgIAARUREEEABAABwRw7fhimVxWJRrly50gz39vZWYmJilhQFAACAh1emA2jFihX19ttv68yZM7ZhZ86c0ahRoxQYGJilxQEAAODh4/Ah+H/++UdFihTR8OHD1a1bN9WsWVPe3t6SpAsXLqhUqVKaNGlSthUKAACAh4PDAbRBgwbasWOHfH19tWDBAv3+++9KSEhQUlKSHn30UfZ+AgAAwCEOB9CUlBS7v8uUKaMyZcpkeUEAAAB4uDl8DqiLi0t21gEAAAAn4fAe0KSkJEVERNx1vG+//fY/FQQAAICHm8MBNEeOHAoLC8vOWgAAAOAEHA6grq6uev3117OzFgAAADgBh88Bvf0iJAAAAOBeOBxAq1Spkp11AAAAwEk4HEC/+OKL7KwDAAAATiLTP8UJAAAA/BcEUAAAABiKAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMZWoA/f333/Xyyy+rUqVKqlGjhvr06aOTJ0+aWRIAAACymWkBNCkpSZ06dVLVqlUVFxenJUuW6PTp0xoxYoRZJQEAAMAApgXQK1euqG/fvnr11Vfl5uYmHx8fPfvss/rzzz/NKgkAAAAGcDVrxnnz5lXr1q1tfx84cEALFy5Uw4YNM3yO1WqV1Wo1ojxILGsnQ7+dC/12LvTbuZjV78zM17QAmuro0aMKDw/XjRs31KZNG/Xq1SvDcfft22dgZdi+fbvZJcBA9Nu50G/nQr+dy4PQb9MDaLFixRQfH6/Dhw9r2LBheuONNzRu3Lh0x/Xz85Onp6fBFUqKXm78PO8DwcHBZpdgDvrtXOi3c6HfzoV+GyoxMdHhnYWmB1BJcnFxUcmSJdW3b19FRERoyJAh8vHxSTOexWKRxWIxoULnxLJ2LvTbudBv50K/nYtZ/c7MfE27CCkuLk7h4eFKTk7+v2Jy3CwnZ86cZpUFAACAbGZaAA0ICNClS5f0/vvv68qVKzpz5owmTJigypUrK0+ePGaVBQAAgGxmWgDNkyePpk+frl27dqlatWpq3Lix8uTJow8//NCskgAAAGAAU88B9ff31+zZs80sAQAAAAbjt+ABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMRQAFAACAoQigAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChCKAAAAAwFAEUAAAAhiKAAgAAwFAEUAAAABiKAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMRQAFAACAoUwNoEePHlWPHj0UGhqqGjVqaPDgwbpw4YKZJQEAACCbmRpAX3vtNXl7e2vNmjVasGCB/vzzT40dO9bMkgAAAJDNTAugFy5cUEBAgPr376/cuXOrcOHCev7557V582azSgIAAIABXM2asbe3t9599127Yf/8848KFSqU4XOsVqusVmt2l4b/j2XtXOi3c6HfzoV+Oxez+p2Z+ZoWQG8XHx+vr776SpMnT85wnH379hlYEbZv3252CTAQ/XYu9Nu50G/n8iD0+74IoFu2bFG3bt3Uv39/1ahRI8Px/Pz85OnpaWBl/1/0cuPneR8IDg42uwRz0G/nQr+dC/12LvTbUImJiQ7vLDQ9gK5Zs0YDBw7U0KFD1bx58zuOa7FYZLFYjCkMLGsnQ7+dC/12LvTbuZjV78zM19QAunXrVg0aNEjjx49XWFiYmaUAAADAIKZdBX/jxg1FRkZqwIABhE8AAAAnYloA3b59u/bv36+oqCgFBgba/Tt69KhZZQEAACCbmXYIvnLlyvrjjz/Mmj0AAABMwm/BAwAAwFAEUAAAABiKAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMRQAFAACAoQigAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChCKAAAAAwFAEUAAAAhiKAAgAAwFAEUAAAABiKAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEOZHkDXrl2rGjVqqG/fvmaXAgAAAAO4mjnzqVOnKiYmRiVKlDCzDAAAABjI1D2g7u7uBFAAAAAnY+oe0A4dOmRqfKvVKqvVmk3V4HYsa+dCv50L/XYu9Nu5mNXvzMzX1ACaWfv27TO7BKeyfft2s0uAgei3c6HfzoV+O5cHod8PVAD18/OTp6en8TOOXm78PO8DwcHBZpdgDvrtXOi3c6HfzoV+GyoxMdHhnYUPVAC1WCyyWCxml+E0WNbOhX47F/rtXOi3czGr35mZr+m3YQIAAIBzIYACAADAUKYegg8MDJQk3bhxQ5K0evVqSVJ8fLxpNQEAACB7mRpACZoAAADOh0PwAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChCKAAAAAwFAEUAAAAhiKAAgAAwFAEUAAAABiKAAoAAABDEUABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMRQAFAACAoQigAAAAMBQBFAAAAIYigAIAAMBQBFAAAAAYigAKAAAAQxFAAQAAYCgCKAAAAAxFAAUAAIChCKAAAAAwFAEUAAAAhiKAAgAAwFCmBtCjR4+qa9euCg0NVe3atfX+++8rOTnZzJIAAACQzVzNnHnPnj1Vvnx5rV69WqdPn9arr76qggUL6pVXXjGzLAAAAGQj0/aAxsfH6/fff9eAAQOUJ08elSxZUh07dtTcuXPNKgkAAAAGMG0P6O7du1WsWDHlzZvXNqx8+fI6ePCgLl26JC8vL9vw1MPyly9fltVqNbzWx/OZuqPYNBcvXjS7BFPQb+dCv50L/XYu9NtYV69elSSHTqd0SUlJScnugtIzZcoUrVq1SvPnz7cNO3z4sOrXr6/Vq1fr0UcftQ0/ffq0Dh06ZEKVAAAAyIySJUuqQIECdxzH1K8GjmbfvHnzqmTJknJ3d1eOHFy4DwAAcL9JTk7WtWvX7I5uZ8S0AOrj46Nz587ZDTt37pxcXFzk4+NjN9zV1fWuSRoAAADmuvUUyjsxbXdiQECA/vnnH505c8Y2LD4+XqVLl1bu3LnNKgsAAADZzLQAWq5cOQUGBmrcuHG6dOmS9u/frxkzZujFF180qyQAAAAYwLSLkCTp+PHjGjp0qDZt2iQvLy9FRETo9ddfl4uLi1klAQAAIJuZekVP4cKFNXXqVO3YsUO//vqrevbsmW3hc/fu3QoMDFT58uVVs2ZNu8fmzp0rf39/TZs2Lc3z2rdvrw8++MBu2PLly1WxYkVt2bLFbnhycrKefvpphYaGKikpye6xjRs3yt/fX4GBgbZ/ISEhatGihb7//ntJUmxsrO2xgIAA+fv7KyAgwDZs0qRJWVrzxYsXNW7cONWvX18VKlRQaGiounXrpp07d9rGiYyMtM2/fPnyaV7Db7/9Zhu3f//+8vf3t3v+/aROnToKDg7W5cuX0zw2c+ZM+fv7a8GCBXbDIyIiVL58eZ08edJueEJCgvz9/dW9e/c005owYYIGDx5sNywuLk5dunRR1apVVaFCBdWpU0dvv/22Tp8+neb533//vdq1a6dKlSopJCRE4eHh+vDDD5WYmJju67pbjbeuQ8HBwWrcuLG+/vrrdKf14Ycfyt/f37ZO3iq99epBduDAAfXv3181atSw9SQqKsp2bvrtr9ff31+//PKL3TSioqIUHh6epo8HDx5UmTJl1LVr1zTznTBhgsqUKWP3PgoNDVXnzp21Z8+eNOM7sl0ZPXp0mucNHjxYEyZMsBuW2XXL2dSpU0fly5dXYGCggoKCVLNmTfXv318nTpyQdHOZli1b1q53qf+GDx8uSVqwYEGaz5jTp08rPDw83T7d6T13/fp1ffLJJwoPD1dwcLBCQkLUvn17bd682TbOhAkT1KZNm6xcDPeVTp062ZZxuXLl0rx3jh49KinrttXSnXtSp04dffPNN2mGf/PNN6pTp06a4evWrZO/v79GjhyZ5rHb16fQ0FB16NBBW7duTXdZmPF5lJ2c5pLy8uXLKz4+XqNGjUrzWHR0tBo3bpwmfKQnLi5Ob731lsaPH69KlSrZPbZ27VrlypVLRYoU0erVq9N9/ubNmxUfH6/4+Hht3LhRL7/8sgYMGKAtW7aoefPmtseWL18uSVq0aJFt2K0r13+t+fLly3rppZe0Z88eTZw4Udu3b9fKlStVtWpVdejQwfZBGxUVZZv/qFGjVLBgQdvf8fHxqlKliiTp/PnzWr16tRo2bGh3a637jaenZ7q9+e6779Jc/PbXX3/pzz//VM2aNbVw4cJ0p7d161atXbv2jvOMjo5Wjx491KhRI/3000/asmWLJkyYoD///FNt2rTRpUuXbOOOHz9eo0ePVseOHRUXF6eNGzdq9OjR+vHHH/Xyyy/rxo0bma7x1nVo8+bNGjRokN5//30tWbLEbjyr1aqFCxeqcePG93UPs8LevXvVqlUrFS5cWIsXL9bWrVv16aef6o8//tCLL75ou5fdnXz66adavXq1ZsyYkeYiyejoaNWvX19xcXG28HKroKAgu/fRqlWr9Pjjj6tLly66cOGC3bh3267kzp1bMTEx2rdv3x3rzey65awiIyMVHx+vnTt3asGCBTp16pSGDRtme7xBgwZ2vUv9l17AkKRLly6pS5cuqlixot566y27x+72nhszZozWrFmjTz75RFu2bNHatWtVo0YNderUSUeOHMnaF36fmj59um0Zd+vWLc17p1ixYlm2rZayfjuY+lm9dOlSXbt2Lc3jt65Pa9asUfny5fXqq6/e07Y+qz+PspvTBNCM/PHHH/rzzz81ZMgQHT9+XNu2bctw3J07d6pnz5565513VKtWrTSPx8TEKDw8XOHh4Q6tuG5ubmrWrJmqVKmiH374wdCap0+frosXL2ry5Mny8/OTi4uL8ubNq1deeUUdOnTQsGHDMvWBtHjxYpUrV07t27fX0qVLHfoAN8PTTz+txYsX2w07fPiwzp49q9KlS9sNj4mJUe3atfXcc89lGPT79u2rqKioNHumUl24cEHvvPOO3njjDbVo0UKenp5ydXVV+fLlNXnyZLVq1cq29+ngwYOaPHmy3n33XdWrV09ubm5yc3NT5cqV9fnnnys8PDzNcnWkxlu5urrqqaeeUqNGjbRq1Sq7x37++WdZLBYNGDBAcXFxOn78+F2n96B6++23FRYWpoEDB6pgwYKyWCwqW7asJk+erODgYP377793fP4333yjOXPmaPr06SpatKjdYzdu3NCiRYsUERGhypUrKzY29q71eHt7a/Dgwbp48WKa9/Pdtiv58uVT+/bt0/1ynepe1i1Ivr6+ql+/vg4ePHhPz7927Zq6deum4sWLKyoqKs0Rvru953799Vc1btxY/v7+slgs8vLyUrdu3RQVFSU3N7d7fl0Pm6zYVqfKyu3g2bNntWbNGvXq1Uv58+dPs829Xe7cudWiRQtduHAhzd5Ioz+PjOD0ATQ6Olp16tRRgQIFFB4erpiYmHTH279/v1599VUNHjxYDRo0SPP46dOn9eOPP6pp06Zq0qSJ4uLidOzYMYdquH79uuE1r1ixQi+88EK6G7EOHTron3/+0Y4dOxyuKSYmRk2bNlXFihWVN29erVixIlOvySh16tTRli1bdOrUKduw7777TuHh4XbjJSUladGiRWratKnq1aunEydO2B32StW6dWt5eXlpxowZ6c5v3bp1SklJUcuWLdM8lvphUqhQIUnS6tWr9dhjj6X75aZIkSLq0qWL3e0tHK0xPemtc9HR0XruuedUtGhRVaxY0aFA+yA6ffq0tm7dqnbt2qV5zMvLS++++64ee+yxDJ+/bNkyffTRR5o2bZqeeOKJNI//+OOPypEjh6pVq6amTZs6vByTk5PT3BvZ0e3Ka6+9piNHjqTZq50qs+sWbt6n+siRI1q0aJGee+65TD/farWqb9++cnV11bhx42SxWNKMc7f33OOPP66FCxdq7969dsObNm0qX1/fTNf0MMqqbXWqrNwOLlq0SGXLllXJkiXVpEmTDD+rU507d06zZs1S1apV7fprxueREZw6gCYlJem7775Ts2bNJEnNmjXT999/n+YbwD///KPOnTurUKFCatGiRbrTio2Nlb+/v0qVKqXixYs7tOJeuXJFMTEx2r59uxo1amRozX///bdKlCiR7jwKFiyoPHnyOHyIJz4+Xvv371fDhg3l4uKiZs2a3beHcL29vRUWFqZly5bZhi1dulRNmza1G2/NmjWyWCyqWbOmPD09Vb9+/XRfU44cOTRs2DB99tln6X5TTkhIUNGiRZUzZ8671nbkyJEMe5IeR2u8VVJSktasWaPly5erSZMmtuEnT57UL7/8YluvmjdvrgULFjj8YxEPktT1+vHHH8/0c9evX6833nhDTZs2Vbly5dIdJyYmRs8995xy5Mih+vXr68SJE3bnSqfn7NmzioqKUr58+RQaGmob7uh2xdPTU4MGDdJ7772X7jnOmV23nFlUVJTtnLx69erJ09NTbdu2tT2+fPnydM8B3b17t910IiMj9cMPP6h///7pftF35D03dOhQeXt7q3nz5qpTp44GDhyoJUuW3HUvnjPJqm215Ph28NZ1JPVfVFRUmunFxMTYfVZv3LhRCQkJduPcuj6FhoYqLi5OAwcOzJbXmJnPIyM4dQBdtWqVcuTIobCwMElSaGiovL2905x4vGLFCr388su6cOGCPvzww3SndeuKJt1c2dJbcStXrmx3EdJXX32lyZMnKyAgwPCarVarQ/O8m+joaD3zzDPKly+fpJuvfdOmTfftOUrNmze3HYbfs2ePcuTIobJly9qNk3reTupei2bNmmn58uXpfrhXqFBBDRo00Lvvvpvu/G5fzpMmTbK72Cz1vDAXF5c0495+EdjEiRMzXWOzZs1s06hQoYLGjBmjUaNGqV69erZxFi5cKD8/Pz355JOSpPDwcJ08eVIbN27MYCk+uFIPgzryW8W3W7JkiQYPHqy5c+fq559/TvP4iRMntHbtWtu2IHfu3KpXr16aPR87d+60+/CqVq2aTp06pdmzZ8vDw8M2nqPbFUlq2LChnnjiCX366afpvubMrFvOLPUc0F27dum3335TlSpV1Lx5c509e1ZSxueAli9f3jaNU6dO6fz582rVqpUGDBiQ7u9yO/KeK1q0qL799lstXbpUHTt21NWrVxUZGanGjRune26xM8rKbbWj28HUdeTWf5GRkXbjbN++XYcOHVLDhg0lSY8++qiCg4PTfIG8dX3auXOnBg8erM6dO2vTpk3Z8hod/Twygqk/xWmW1A+g6OhonT9/3nYhjXTznJ358+fb7aJu3769XnnlFVWuXFlt27ZVmTJl7A7JbN68WQcOHNC4ceP00UcfSbr54Xb16lVt2LBB1atXtxvX3d1d0s2rxs+cOaOnnnrK4dqzqubHH39c+/fvT3ceJ0+e1MWLF9M9vHi7K1eu2E6uDgkJsQ1PSUnR/Pnz1adPH4dfm1GeeuopDRkyRIcOHdJ3331ntydQko4dO6b169dr06ZNmjdvnm14YmKili1bptatW6eZ5oABAxQeHq64uDi74U888YSOHj2qq1ev2oJF9+7dbReUDR482BaEHn/8cf38889KSUmxraNRUVG2b9bt27e3BY/M1Lho0SKVKlVK0s2rO3/44QfbRjHV/PnzlZCQYNfDpKQkzZ8/X9WqVbvrMn2QpB5e//PPPzN9GHPUqFGqXbu2JKlfv36aN2+ebdlKN5ej1Wq1u5/x9evX5erqqqFDh9oOcwcFBdn6dvXqVT333HMKCAiw2yubme1KqsjISLVq1SrNIbbMrFv4P97e3urRo4fmz5+f7hXRGcmfP78+/fRTXb9+XR06dFC/fv302Wef2f2UdGbec6VLl1bp0qXVoUMHnTx5Uq1bt9aXX36pN95447+/yAdYVm6rpazdDkZHR+vGjRuqW7eubdj169d14sQJvf766+n+rLi7u7vq1aunH374QV9//bWqVq1q2ueRER76PaBz5szRV199Zfv74sWLyp8/v44cOaINGzZo0qRJio2Ntf2bNm2atmzZYnfSeeq3jsDAQA0bNkxDhgzRrl27bI/Pnz9fYWFhWrx4sW06ixcvVu3ate94zsebb76pXbt2OXy4OitrbtCggWJiYtK98ODrr79WiRIlHNoru3z5crm6uuq7776zq6lPnz6KjY01dGV2lJubmxo2bKgVK1ZoxYoVac7vWrBggUqVKqUlS5bYvaaIiIgMe+Xj46NevXopKirK7uKtGjVqKHfu3Jo9e3a6z7t1+dSvX1+nTp3K8IPu1nHvpUbp5sbm2rVrmjJlim3Ypk2blJCQoLlz59pNa/To0Vq5cmW6e28eZPnz51fVqlXTPU/qypUratGiRZpbrKVKfV+1a9dO9erVU/fu3W1XraekpGjBggXq3r273XJcsmSJcufObXfax608PDw0fPhwTZ482e5L4b1sV0qXLq2IiIg0t/vJzLqF9KV3BXNGLBaLXFxc5ObmpvHjx2vv3r12t/Ry5D13/PhxjRgxIs1VyY888ojKlCmjK1euZNlre1Bl5bY6K7eDly9f1rJlyzRy5Ei7acXExOjff/9NN/zeLvWz2azPIyM89AE0OTlZEydO1IEDB3T27FktWrRITz31lObPny8/Pz8988wzKlGihO1fjRo1FBAQkGFjW7VqpWbNmun111/XqVOndOnSJS1fvlwvvPCC3XRKlCihiIgIrVq1Ks1tVVIVLFhQ/fv319ixY9Pc1ys9WVWzJHXs2FE+Pj5q166dfv/9d6WkpOjChQuaNGmSZs6cqVGjRqX7De120dHRatKkiR5//HG7ml588UWdPn1a69atu+s0zNC8eXPNnTtXvr6+Kl68uG14cnKyFixYoJYtW6bpZ7t27bRt27YM9xy/9NJLslgsdn3w9PTU8OHDNX78eE2aNEnnz59XSkqKEhIS9NFHH2nZsmUKDAyUdPNwW9++ffXWW2/pm2++UWJiopKTk7V//36NGDFCO3bsULly5f5Tjalh5/PPP7fdtic6Olq1atVSQECA3bSaNm2qPHnyZHhhy4NsyJAh2r59u/r166fjx48rOTlZe/fuVZcuXeTh4aGgoKC7TmPkyJHy9PRUv379lJycrA0bNuiff/5R27Zt7ZZjyZIl1bRp0zt+Ga1Vq5aeffZZRUZGKjk5+T9tV15//XXt27fP7r3n6LoFe9euXdOMGTN09uxZuz1ZmeHr66uPP/5Ys2bN0nfffSfJsfecj4+P1q9fr4EDB+rAgQNKTk7WlStXtGTJEsXFxaV7z0lnktXb6qzcDi5btkzu7u56/vnn7aZVpkwZ1alTJ8NtgdVqVVxcnJYvX66mTZua+nlkhIc+gLZt21bNmjVT27Zt1bBhQ5UrV049evTQwoULM7ygqGXLloqNjc3wHMnIyEj5+vqqZ8+eWrp0qdzd3W2H5W5Vq1Yt5c2b17bRSc8LL7ygJ554Qm+//fYdX0dycnKW1ZyUlKRcuXJpzpw5qlKlil5//XVVqFBBzz77rPbs2aM5c+bYXQiRkQMHDmjLli3pXlGXL18+1a1b9769GCk4OFg5c+ZMc/jdarXq33//tTvvLtWTTz6poKCgDF+TxWLRsGHD0nyZaNSokWbOnKlt27bp2WefVYUKFdS2bVslJCRo9uzZdhc4dO7cWR988IGWLVumWrVqKSQkRF27dpXValVsbKzq1q2r9evX33ON0v+FnSFDhujixYtauXKlWrVqlWY8V1fX+/qCsv+iTJkymjdvnpKTk/X8888rJCREffr0UbVq1TR9+nSHTtL38PDQhAkTFB8fr/fff18xMTF6+umnVbBgwTTjtmrVSjt27NBff/2V4fTefPNN/fXXX5ozZ85/2q54eXlp4MCBadZDR9Yt2F9gUrNmTf3444+aNm2a7dSNjC5CevbZZzOcZuXKlTVo0CANGTJEmzZtcug95+bmptmzZ6tAgQLq3LmzQkJCVKNGDX399dcaN25cunc0cCb/ZTt4+7Y6q7eD8+fPV5MmTdK9+Kxly5ZavXq17Qcvbl2fKlWqpHfeeUdDhw5Vo0aNsvQ1psrM51F2M/WnOAEAAOB8Hvo9oAAAALi/EEABAABgKAIoAAAADEUABQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgDZLDIyUm+88UaWT/fo0aMKDAzUwYMHs3zaAJCd+CUkAE6pTp06OnHihHLkuPk9vGDBggoNDVWXLl1UunTp/zRtq9WqWbNm6ZVXXsmKUu3ExcXJy8vL0N9sBoCsxh5QAE4rMjJS8fHx2rp1q6ZNm6b8+fOrZcuWiouL+0/T3bNnj6ZNm5ZFVdqbOXOmdu3alS3TBgCjEEABOL2cOXOqVKlSGjRokNq3b6/IyEhZrVYdPXpUr732mkJDQ1WlShW98cYbunTpkiTpypUrGjRokKpXr66QkBBFRERo165d2rlzpyIiInTq1CkFBgZqw4YNGjx4sPr27StJWrBggZo2barY2FjVqVNHISEh6tu3r65fvy5JunbtmiIjIxUWFqaKFSvqpZde0r59+yRJr732mn766SdFRUXp5ZdfVkJCgvz9/bV//35J0vnz5/XGG28oLCxMISEh6tq1qxISEiTJNu6vv/6q5s2bKzg4WBEREbbHAcBIBFAAuEXHjh2VkJCgXbt2qXv37ipSpIh++uknLV++XCdOnNDYsWMlSV9++aVOnTqlVatWaePGjapVq5aGDh2qoKAgjRo1SgULFlR8fLyqVauWZh5Hjx7Vrl27tGTJEs2bN0+rV6/WqlWrJElTp07Vjh07tGTJEm3YsEFPPPGEBg8eLEmaMmWKihUrpsjISH355ZdpphsZGamTJ09q8eLFWrt2rTw8PNSnTx+7cWbNmqXPPvtMP/30kxITE7NtTy0A3AkBFABuUbBgQXl7e2vjxo36888/NXDgQOXKlUsFChRQz549tXjxYqWkpOjChQvKmTOnPDw85Obmpu7du2vBggUOzePy5cvq06ePPD099eSTT8rf318HDhyQJL366qv65ptvlC9fPrm5ualBgwb6/fffdePGjTtO89y5c1q1apX69OkjHx8feXl5qVevXoqPj9eRI0ds47344ovy9fVVvnz5FBYWZtt7CgBGcjW7AAC439y4cUO+vr6yWq0KDQ21e8xqters2bN66aWX1LlzZz399NOqVauW6tWrp7p16zo0/fz588vLy8v2d65cuXT16lVJ0pkzZxQVFaVNmzbp8uXLtnlarVa5uma8yT527JhSUlJUqlQp27DHHntM0s09rsWLF5ck239T53vt2jWHagaArEQABYBbHD58WImJiTp37pw8PT21bdu2dMfz8fHRsmXLtHHjRq1Zs0bDhg3T4sWL9cknn9x1HqlX3qenb9++cnd316JFi1S4cGHFxcWpY8eOd51mUlJSho+5uLik+/8AYBYOwQPALSZMmCA/Pz9Vr15diYmJdoevL126pLNnz0q6eRjdarWqRo0aioyMVHR0tFasWGF7/F7t3LlTbdq0UeHChSVJu3fvduh5jz76qCTZDuXf+v+pe0IB4H5BAAUASSdOnNC7776rH374QaNHj5afn59CQkI0evRonTlzRhcuXNDw4cNtN5Tv1auXxo4dq0uXLik5OVnbtm1Tvnz5lDdvXnl4eOjixYs6ceKE7dC6o4oVK6adO3fq+vXr+uWXX/Trr7/a6pMkd3d3/f3337p48aLd8woUKKCwsDCNHz9e586d0/nz5/Xxxx8rNDRURYoUyYIlBABZhwAKwGlFRUUpMDBQAQEBatq0qU6cOKHo6GgFBQVJksaNG6eUlBTVrVtXzz77rKxWq8aMGSNJGjVqlA4fPqynnnpKVapU0VdffaVPP/1UOXLkULVq1VS8eHHVq1dPa9asyVRNw4YN08qVK1W1alXFxMToww8/VIUKFdSiRQudOnVKbdq00ddff6127dqlee7YsWPl6emphg0bqlGjRvLy8tL48eP/+4ICgCzGLyEBAADAUOwBBQAAgKEIoAAAADAUARQAAACGIoACAADAUARQAAAAGIoACgAAAEMRQAEAAGAoAigAAAAMRQAFAACAoQigAAAAMBQBFAAAAIb6f6B4js8X9jvzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Jakarta Kota, Manggarai, Cikarang Menjadi Tiga Diantara 5 Statiun Dengan Total Statiun Menuju Tujuan Terbanyak```  \n",
        "```Contoh : Jika Budi Mau Ke Cikarang, Budi Bisa Naik Dari 6 Statiun Berbeda```"
      ],
      "metadata": {
        "id": "tGrJ2kW9yMvK"
      },
      "id": "tGrJ2kW9yMvK"
    },
    {
      "cell_type": "code",
      "source": [
        "origin[origin[\"destination\"] == \"CIKARANG\"][\"route\"].unique().tolist()[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dxa4WUAJvJaQ",
        "outputId": "c23245b2-3f65-4c28-8c2b-c1083d952533"
      },
      "id": "Dxa4WUAJvJaQ",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['KAMPUNGBANDAN-CIKARANG', 'ANGKE-CIKARANG', 'JAKARTAKOTA-CIKARANG']"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "origin[origin[\"destination\"] == \"CIKARANG\"][\"route\"].unique().tolist()[3:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egSvem1RvOFF",
        "outputId": "70f2c77b-19ec-444c-cb32-f7fc97a1f443"
      },
      "id": "egSvem1RvOFF",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['BEKASI-CIKARANG', 'MANGGARAI-CIKARANG', 'CAKUNG-CIKARANG']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ```Hour General```"
      ],
      "metadata": {
        "id": "KN9coZHoutWx"
      },
      "id": "KN9coZHoutWx"
    },
    {
      "cell_type": "code",
      "source": [
        "jamly = origin.groupby(origin[\"time_estimated\"].dt.hour)[\"id\"].count()\n",
        "\n",
        "jamly = jamly.sort_values(ascending=False)\n",
        "\n",
        "plot = jamly.iloc[:6].plot.bar(rot=0)\n",
        "\n",
        "plot.set_title(\"Top 6 Highest Trip By Hour\")\n",
        "\n",
        "plot.set_xlabel(\"Hour\")\n",
        "\n",
        "plot.set_ylabel(\"Total Trip\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "id": "Ukg7YsgQxr9T",
        "outputId": "3cfb9542-75cf-474f-c2d2-7e39ff42f869",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "id": "Ukg7YsgQxr9T",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Jam Perjalanan Pulang Pergi Kantor / Sekolah Menjadi Total Hour Tertinggi, Dimana Pukul 7 Memiliki 1.154 Total Trip```"
      ],
      "metadata": {
        "id": "YVBUFoANylyV"
      },
      "id": "YVBUFoANylyV"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ```Station General```"
      ],
      "metadata": {
        "id": "dOkx097qvAoP"
      },
      "id": "dOkx097qvAoP"
    },
    {
      "cell_type": "code",
      "source": [
        "origin[\"statiun\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADj6g2VuzM5t",
        "outputId": "40ebac91-0a9d-4816-d091-f6f60edb86c8"
      },
      "id": "ADj6g2VuzM5t",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sati = origin[\"statiun\"].value_counts()\n",
        "\n",
        "plot = sati.iloc[:5].plot.bar(title=\"Top 5 Busiest Station\", rot=0)\n",
        "\n",
        "plot.set_xlabel(\"Station\")\n",
        "\n",
        "plot.set_ylabel(\"Total Trip\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "9ac1HTWKs6ja",
        "outputId": "91cd7429-6d09-4d06-a2d3-fa14d84cd49a"
      },
      "id": "9ac1HTWKs6ja",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Manggarai Menjadi Statiun Dengan Total Trip Terbanyak, Yaitu : 532 Trip```"
      ],
      "metadata": {
        "id": "yJ9NR7yRy7sf"
      },
      "id": "yJ9NR7yRy7sf"
    },
    {
      "cell_type": "code",
      "source": [
        "satir = origin.groupby(\"statiun\")[\"route\"].nunique()\n",
        "\n",
        "plot = satir.sort_values(ascending=False).iloc[:5].plot.bar(title=\"Top 5 Station With High Total Route\", rot=0)\n",
        "\n",
        "plot.set_xlabel(\"Station\")\n",
        "\n",
        "plot.set_ylabel(\"Total Route\")\n",
        "\n",
        "plot.bar_label(plot.containers[0]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "LGv8zbgitefC",
        "outputId": "c8301844-56e0-4675-f774-c73629015ab9"
      },
      "id": "LGv8zbgitefC",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "```Dimana Manggarai Menjadi Statiun Dengan Total Dilintasi Rute Terbanyak, Yaitu : 27 Rute Melewati Statiun Manggarai```"
      ],
      "metadata": {
        "id": "8Q8TITDNzVee"
      },
      "id": "8Q8TITDNzVee"
    },
    {
      "cell_type": "code",
      "source": [
        "origin[origin[\"statiun\"] == \"GROGOL\"][\"route\"].unique().tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "macVmprAt6cS",
        "outputId": "e78188e2-882f-4494-bce1-d3919bd3fddf"
      },
      "id": "macVmprAt6cS",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['TANGERANG-DURI', 'DURI-TANGERANG']"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ```Final```\n",
        "\n",
        "```Kereta Menjadi Salah Satu Moda Transportasi Vital Daerah Jabodetabek, Terutama Bogor - Jakarta dan Antar Daerah Jakarta, Statiun Seperti Manggarai, Tanah Abang Menjadi Salah Dua Dari 100 Statiun Yang Akan Mengalami 'Rush Hour' Dalam Moment Tertentu, Contoh : Manggarai, Jam Kerja (Pulang / Pergi), Tanah Abang (Hari Lebaran Idul Fitri), Penting Bagi Pemerintah Untuk Menjaga Agar Tidak Ada Keterlambatan dan Menjaga Fasilitas Stasiun dan Kereta```"
      ],
      "metadata": {
        "id": "VhYUGYZxz24N"
      },
      "id": "VhYUGYZxz24N"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}