New York Bus Rides.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Capstone.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.7"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWDijqRGKn8I"
      },
      "source": [
        "# CAPSTONE PROJECT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UEnIzR1tlWhB"
      },
      "source": [
        "## Connect Colab to GDrive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mD-C7O-YU7Mz",
        "outputId": "f21ccc90-9cbf-420c-b5da-6054d8998f98"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6YWX-NtKn8K"
      },
      "source": [
        "## IMPORT PACKAGES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHUsnon5l5yo"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import sqlite3\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTQgQJxEViRF"
      },
      "source": [
        "def detail(df):\n",
        "  print(f\"Shape of data: {df.shape}\")\n",
        "  print(f\"Number of missing values in data: {df.isnull().sum().sum()}\")\n",
        "def missing(df):\n",
        "  miss = df.isnull().sum()\n",
        "  return miss\n",
        "def unique(df):\n",
        "  unik = df.apply(lambda x: len(x.unique()))\n",
        "  return unik"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmfuYV3clcWk"
      },
      "source": [
        "## Connect SQLite to Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v31KzqKnling"
      },
      "source": [
        "conn = sqlite3.connect(\"/content/drive/MyDrive/Narasio/Capstone_Project/6. New York Bus Rides/New York Bus Rides.db\")\n",
        "cur = conn.cursor()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "031LDaaFlk2t"
      },
      "source": [
        "## Data Preparation Using SQL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjnGaIillubA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b3c9902-7bce-4ffa-fe84-79bae8eb4689"
      },
      "source": [
        "cur.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
        "print(cur.fetchall())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('bookings',), ('segments',), ('searches',)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4QVs_MdVeBF"
      },
      "source": [
        "df_total1 = pd.read_sql_query(\"\"\"SELECT  a.*, b.*, c.*\n",
        "FROM    segments a\n",
        "        INNER JOIN searches b\n",
        "            ON a.user_id = b.user_id\n",
        "        LEFT JOIN bookings c\n",
        "            ON b.session_id = c.search_session_id\"\"\", conn)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "WQ0_OWfKV0g5",
        "outputId": "29a3d9e6-b62e-41e8-c25a-342b7a04d236"
      },
      "source": [
        "df_total1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>user_id</th>\n",
              "      <th>yr_wk</th>\n",
              "      <th>new_segment_tag</th>\n",
              "      <th>session_id</th>\n",
              "      <th>search_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>search_city</th>\n",
              "      <th>search_time</th>\n",
              "      <th>num_of_results</th>\n",
              "      <th>is_result</th>\n",
              "      <th>median_pickup_walk_time</th>\n",
              "      <th>median_dropoff_walk_time</th>\n",
              "      <th>median_bus_travel_time</th>\n",
              "      <th>high_demand_val</th>\n",
              "      <th>user_device_os</th>\n",
              "      <th>user_device_app_version</th>\n",
              "      <th>search_session_id</th>\n",
              "      <th>booking_id</th>\n",
              "      <th>booking_timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>2019-24</td>\n",
              "      <td>New</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>6d2b4ed4-88bf-47bb-95fd-0bbecd824775</td>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>New York</td>\n",
              "      <td>2020-02-24 13:36:19.625 UTC</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>600.0</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>3336.0</td>\n",
              "      <td>0</td>\n",
              "      <td>android</td>\n",
              "      <td>291.0</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>5e53b5664dc0e1001a423476</td>\n",
              "      <td>2020-02-24 13:37:10.395 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>2019-29</td>\n",
              "      <td>Inactive</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>6d2b4ed4-88bf-47bb-95fd-0bbecd824775</td>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>New York</td>\n",
              "      <td>2020-02-24 13:36:19.625 UTC</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>600.0</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>3336.0</td>\n",
              "      <td>0</td>\n",
              "      <td>android</td>\n",
              "      <td>291.0</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>5e53b5664dc0e1001a423476</td>\n",
              "      <td>2020-02-24 13:37:10.395 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>2019-39</td>\n",
              "      <td>Inactive</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>6d2b4ed4-88bf-47bb-95fd-0bbecd824775</td>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>New York</td>\n",
              "      <td>2020-02-24 13:36:19.625 UTC</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>600.0</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>3336.0</td>\n",
              "      <td>0</td>\n",
              "      <td>android</td>\n",
              "      <td>291.0</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>5e53b5664dc0e1001a423476</td>\n",
              "      <td>2020-02-24 13:37:10.395 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>2019-26</td>\n",
              "      <td>Retained</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>6d2b4ed4-88bf-47bb-95fd-0bbecd824775</td>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>New York</td>\n",
              "      <td>2020-02-24 13:36:19.625 UTC</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>600.0</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>3336.0</td>\n",
              "      <td>0</td>\n",
              "      <td>android</td>\n",
              "      <td>291.0</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>5e53b5664dc0e1001a423476</td>\n",
              "      <td>2020-02-24 13:37:10.395 UTC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>2019-47</td>\n",
              "      <td>Retained</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>6d2b4ed4-88bf-47bb-95fd-0bbecd824775</td>\n",
              "      <td>5cfe072e5612de0019c6f4b5</td>\n",
              "      <td>New York</td>\n",
              "      <td>2020-02-24 13:36:19.625 UTC</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>600.0</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>3336.0</td>\n",
              "      <td>0</td>\n",
              "      <td>android</td>\n",
              "      <td>291.0</td>\n",
              "      <td>7c80fae5-210c-4ace-a5df-5221d6a5a008</td>\n",
              "      <td>5e53b5664dc0e1001a423476</td>\n",
              "      <td>2020-02-24 13:37:10.395 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    user_id  ...            booking_timestamp\n",
              "0  5cfe072e5612de0019c6f4b5  ...  2020-02-24 13:37:10.395 UTC\n",
              "1  5cfe072e5612de0019c6f4b5  ...  2020-02-24 13:37:10.395 UTC\n",
              "2  5cfe072e5612de0019c6f4b5  ...  2020-02-24 13:37:10.395 UTC\n",
              "3  5cfe072e5612de0019c6f4b5  ...  2020-02-24 13:37:10.395 UTC\n",
              "4  5cfe072e5612de0019c6f4b5  ...  2020-02-24 13:37:10.395 UTC\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYjP2vXwVqS8",
        "outputId": "7be27a5f-f320-4a7a-d890-6e570871e780"
      },
      "source": [
        "detail(df_total1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data: (161469, 19)\n",
            "Number of missing values in data: 331620\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z10mpWXPV5O6",
        "outputId": "ad8f4eca-e6fb-49ef-e4a9-316b41506f45"
      },
      "source": [
        "unique(df_total1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id                     6331\n",
              "yr_wk                         42\n",
              "new_segment_tag                4\n",
              "session_id                  7000\n",
              "search_id                   7000\n",
              "user_id                     6331\n",
              "search_city                    1\n",
              "search_time                 7000\n",
              "num_of_results                58\n",
              "is_result                      1\n",
              "median_pickup_walk_time      310\n",
              "median_dropoff_walk_time     297\n",
              "median_bus_travel_time      5160\n",
              "high_demand_val               11\n",
              "user_device_os                 4\n",
              "user_device_app_version       76\n",
              "search_session_id           2146\n",
              "booking_id                  2151\n",
              "booking_timestamp           2141\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DH2ISL0Kn8L"
      },
      "source": [
        "## PROBLEM STATEMENT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QurXOCkyIYbF"
      },
      "source": [
        "\n",
        "\n",
        "1.   Pada jam berapakah jumlah search mengalami kenaikan/penurunan ?\n",
        "2.   Pada jam berapakah jumlah booking mengalami kenaikan/penurunan ?\n",
        "3. Segmentasi user apa yang paling mendominasi?\n",
        "4. Device apa yang paling banyak digunakan oleh user?\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlX3RVe4Kn8L"
      },
      "source": [
        "## DATA CLEANSING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAGzDisgKn8M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1fe2ac9-99a4-46c9-9fcc-636eb25aaea3"
      },
      "source": [
        "missing(df_total1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id                          0\n",
              "yr_wk                            0\n",
              "new_segment_tag                  0\n",
              "session_id                       0\n",
              "search_id                        0\n",
              "user_id                          0\n",
              "search_city                      0\n",
              "search_time                      0\n",
              "num_of_results                   0\n",
              "is_result                        0\n",
              "median_pickup_walk_time       1037\n",
              "median_dropoff_walk_time      1037\n",
              "median_bus_travel_time           0\n",
              "high_demand_val                  0\n",
              "user_device_os                   9\n",
              "user_device_app_version          9\n",
              "search_session_id           109744\n",
              "booking_id                  109744\n",
              "booking_timestamp           110040\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gx4o58iTYPEg",
        "outputId": "b77790eb-5ce5-4c33-8d40-0f7e212118b3"
      },
      "source": [
        "df_total1.user_device_os.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['android', 'ios', None, 'iOS'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qPwQObUWglx"
      },
      "source": [
        "* Drop missing value pada \n",
        "> 1.   median_pickup_walk_time\n",
        "> 2.   median_dropoff_walk_time\n",
        "* Drop salah satu kolom user_id\n",
        "* Drop kolom search_session_id karena sama dengan kolom session_id\n",
        "* Isi missing value pada user_device_os menggunakan mode\n",
        "* Rename user_device_os\n",
        "\n",
        "Untuk nilai dari tabel booking saya biarkan karena mengandung informasi penting tentang data pemesanan dan apabila missing value pada kolom tersebut dihapus bisa mengurangi banyak sekali data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8iDMOVLGXGIk",
        "outputId": "43615d86-8ac8-46a6-afd6-dfdc1f0dedbe"
      },
      "source": [
        "# drop missing value pada median_pickup_walk_time dan median_dropoff_walk_time\n",
        "df_total2 = df_total1.dropna(subset=['median_pickup_walk_time', 'median_dropoff_walk_time'])\n",
        "# drop kolom search_session_id\n",
        "df_total2 = df_total1.drop('search_session_id', axis=1)\n",
        "# drop salah satu kolom user_id\n",
        "df_total2 = df_total1.loc[:,~df_total1.columns.duplicated()]\n",
        "\n",
        "# fill missing value pada user_device_os\n",
        "df_total2['user_device_os'] = df_total2['user_device_os'].fillna(df_total2['user_device_os'].mode().iloc[0])\n",
        "df_total2['user_device_app_version'] = df_total2['user_device_app_version'].fillna(df_total2['user_device_app_version'].mode().iloc[0])\n",
        "# rename kolom user_device_os\n",
        "map_device = {'android': 'android', 'ios': 'iOS', 'iOS': 'iOS'}\n",
        "df_total2['user_device_os'] = df_total2['user_device_os'].apply(lambda x: map_device[x])\n",
        "detail(df_total2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data: (161469, 18)\n",
            "Number of missing values in data: 331602\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Fy_DXkHbeJU",
        "outputId": "f849c387-f124-418e-9717-6a88281cc7e4"
      },
      "source": [
        "missing(df_total2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id                          0\n",
              "yr_wk                            0\n",
              "new_segment_tag                  0\n",
              "session_id                       0\n",
              "search_id                        0\n",
              "search_city                      0\n",
              "search_time                      0\n",
              "num_of_results                   0\n",
              "is_result                        0\n",
              "median_pickup_walk_time       1037\n",
              "median_dropoff_walk_time      1037\n",
              "median_bus_travel_time           0\n",
              "high_demand_val                  0\n",
              "user_device_os                   0\n",
              "user_device_app_version          0\n",
              "search_session_id           109744\n",
              "booking_id                  109744\n",
              "booking_timestamp           110040\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUttfhqCcy3w"
      },
      "source": [
        "Mengubah kolom datetime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_yZtgiXXHbb"
      },
      "source": [
        "df_total2['search_time'] = pd.to_datetime(df_total2['search_time'])\n",
        "df_total2['booking_timestamp'] = pd.to_datetime(df_total2['booking_timestamp'])\n",
        "df_total2['hour_search'] = df_total2.search_time.dt.hour\n",
        "df_total2['hour_books'] = df_total2.booking_timestamp.dt.hour\n",
        "df_total2['day_search'] = df_total2['search_time'].apply(lambda x: x.strftime('%A'))\n",
        "df_total2['day_books'] = df_total2['booking_timestamp'].apply(lambda x: x.strftime('%A'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUlpaqeqMFn2"
      },
      "source": [
        "## EXPLORATORY DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "gUs1DBrS0sLC",
        "outputId": "89bf4203-311b-496c-f6bf-6eb23cdb6e47"
      },
      "source": [
        "sns.catplot(x=\"new_segment_tag\", kind=\"count\",\n",
        "            data=df_total2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa37c7dadd0>"
            ]
          },
          "metadata": {},
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lpgz0CHc0weo"
      },
      "source": [
        "Jumlah user paling banyak yaitu user retained."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AayCOMwWmVYc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "d87fd6c3-f116-4f13-b2ab-e1fec3b7acdc"
      },
      "source": [
        "a = pd.DataFrame(df_total2.groupby([pd.Grouper(key='search_time', freq='h'), 'hour_search', 'day_search'])[['search_id']].count())\n",
        "hour = []\n",
        "day = []\n",
        "for value in a.index.get_level_values(1):\n",
        "  hour.append(value)\n",
        "for value in a.index.get_level_values(2):\n",
        "  day.append(value)\n",
        "a['hour'] = hour\n",
        "a['day'] = day\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th></th>\n",
              "      <th>search_id</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>search_time</th>\n",
              "      <th>hour_search</th>\n",
              "      <th>day_search</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-10-14 03:00:00+00:00</th>\n",
              "      <th>3</th>\n",
              "      <th>Monday</th>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 04:00:00+00:00</th>\n",
              "      <th>4</th>\n",
              "      <th>Monday</th>\n",
              "      <td>32</td>\n",
              "      <td>4</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 07:00:00+00:00</th>\n",
              "      <th>7</th>\n",
              "      <th>Monday</th>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 08:00:00+00:00</th>\n",
              "      <th>8</th>\n",
              "      <th>Monday</th>\n",
              "      <td>82</td>\n",
              "      <td>8</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 09:00:00+00:00</th>\n",
              "      <th>9</th>\n",
              "      <th>Monday</th>\n",
              "      <td>67</td>\n",
              "      <td>9</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-22 15:00:00+00:00</th>\n",
              "      <th>15</th>\n",
              "      <th>Sunday</th>\n",
              "      <td>29</td>\n",
              "      <td>15</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-22 17:00:00+00:00</th>\n",
              "      <th>17</th>\n",
              "      <th>Sunday</th>\n",
              "      <td>61</td>\n",
              "      <td>17</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-22 20:00:00+00:00</th>\n",
              "      <th>20</th>\n",
              "      <th>Sunday</th>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-22 22:00:00+00:00</th>\n",
              "      <th>22</th>\n",
              "      <th>Sunday</th>\n",
              "      <td>10</td>\n",
              "      <td>22</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-22 23:00:00+00:00</th>\n",
              "      <th>23</th>\n",
              "      <th>Sunday</th>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2676 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  search_id  hour     day\n",
              "search_time               hour_search day_search                         \n",
              "2019-10-14 03:00:00+00:00 3           Monday             12     3  Monday\n",
              "2019-10-14 04:00:00+00:00 4           Monday             32     4  Monday\n",
              "2019-10-14 07:00:00+00:00 7           Monday             12     7  Monday\n",
              "2019-10-14 08:00:00+00:00 8           Monday             82     8  Monday\n",
              "2019-10-14 09:00:00+00:00 9           Monday             67     9  Monday\n",
              "...                                                     ...   ...     ...\n",
              "2020-03-22 15:00:00+00:00 15          Sunday             29    15  Sunday\n",
              "2020-03-22 17:00:00+00:00 17          Sunday             61    17  Sunday\n",
              "2020-03-22 20:00:00+00:00 20          Sunday              4    20  Sunday\n",
              "2020-03-22 22:00:00+00:00 22          Sunday             10    22  Sunday\n",
              "2020-03-22 23:00:00+00:00 23          Sunday             17    23  Sunday\n",
              "\n",
              "[2676 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "hvC-B4yDfN1_",
        "outputId": "d09c1080-9e09-490c-aced-9224fbe2c906"
      },
      "source": [
        "sns.set_theme(style=\"darkgrid\")\n",
        "hue_ords = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.pointplot(data=a, x='hour', y='search_id', hue='day', hue_order=hue_ords, ci=None, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa37d4ea650>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aAeWr4XahAok"
      },
      "source": [
        "Dari grafik di atas terlihat bahwa terdapat pola antara jam dan banyaknya search. Dalam hari kerja mulai jam 5 terjadi kenaikan search. Mulai jam 4 sore jumlah search sudah mulai menurun. Hal ini bisa dilihat bahwa pada jam 5 orang orang sudah mulai beraktivitas seperti berangkat kerja, sehingga terjadi lonjakan search. Untuk jam 4 orang orang sudah mulai pulang dari pekerjaannya sehingga sudah tidak ada lagi user yang melakukan searching."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "NHpQAJW-ftnq",
        "outputId": "8b3bd7c6-a94b-4cdf-f214-c6c0891cdc4d"
      },
      "source": [
        "b = df_total2.groupby([pd.Grouper(key='booking_timestamp', freq='H'), 'hour_books', 'day_books', 'new_segment_tag']).agg(booking_count=('booking_id', 'count'),\n",
        "          #segment_user=('new_segment_tag', 'nunique'),\n",
        "         median_pickup_walk_time=('median_pickup_walk_time','mean'),\n",
        "         median_dropoff_walk_time=('median_dropoff_walk_time','mean'),\n",
        "        median_bus_travel_time=('median_bus_travel_time', 'mean'))\n",
        "hour = []\n",
        "day = []\n",
        "user = []\n",
        "for value in b.index.get_level_values(1):\n",
        "  hour.append(value)\n",
        "for value in b.index.get_level_values(2):\n",
        "  day.append(value)\n",
        "for value in b.index.get_level_values(3):\n",
        "  user.append(value)\n",
        "b['hour'] = hour\n",
        "b['day'] = day\n",
        "b['user'] = user\n",
        "b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>booking_count</th>\n",
              "      <th>median_pickup_walk_time</th>\n",
              "      <th>median_dropoff_walk_time</th>\n",
              "      <th>median_bus_travel_time</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
              "      <th>user</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>booking_timestamp</th>\n",
              "      <th>hour_books</th>\n",
              "      <th>day_books</th>\n",
              "      <th>new_segment_tag</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-10-14 08:00:00+00:00</th>\n",
              "      <th>8.0</th>\n",
              "      <th>Monday</th>\n",
              "      <th>Retained</th>\n",
              "      <td>82</td>\n",
              "      <td>1576.097561</td>\n",
              "      <td>693.658537</td>\n",
              "      <td>1647.317073</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2019-10-14 09:00:00+00:00</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">9.0</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Monday</th>\n",
              "      <th>Explorer</th>\n",
              "      <td>1</td>\n",
              "      <td>390.000000</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>3026.000000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Explorer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New</th>\n",
              "      <td>1</td>\n",
              "      <td>390.000000</td>\n",
              "      <td>600.000000</td>\n",
              "      <td>3026.000000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>New</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 10:00:00+00:00</th>\n",
              "      <th>10.0</th>\n",
              "      <th>Monday</th>\n",
              "      <th>Retained</th>\n",
              "      <td>30</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>2160.000000</td>\n",
              "      <td>4367.500000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 14:00:00+00:00</th>\n",
              "      <th>14.0</th>\n",
              "      <th>Monday</th>\n",
              "      <th>Explorer</th>\n",
              "      <td>3</td>\n",
              "      <td>1380.000000</td>\n",
              "      <td>2160.000000</td>\n",
              "      <td>8060.000000</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Explorer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2020-03-22 17:00:00+00:00</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">17.0</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">Sunday</th>\n",
              "      <th>Inactive</th>\n",
              "      <td>3</td>\n",
              "      <td>21120.000000</td>\n",
              "      <td>1170.000000</td>\n",
              "      <td>2901.000000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New</th>\n",
              "      <td>1</td>\n",
              "      <td>21120.000000</td>\n",
              "      <td>1170.000000</td>\n",
              "      <td>2901.000000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>New</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>15</td>\n",
              "      <td>21120.000000</td>\n",
              "      <td>1170.000000</td>\n",
              "      <td>2901.000000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2020-03-22 20:00:00+00:00</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">20.0</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sunday</th>\n",
              "      <th>New</th>\n",
              "      <td>1</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>540.000000</td>\n",
              "      <td>4116.000000</td>\n",
              "      <td>20.0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>New</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>3</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>540.000000</td>\n",
              "      <td>4116.000000</td>\n",
              "      <td>20.0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3424 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                booking_count  ...      user\n",
              "booking_timestamp         hour_books day_books new_segment_tag                 ...          \n",
              "2019-10-14 08:00:00+00:00 8.0        Monday    Retained                    82  ...  Retained\n",
              "2019-10-14 09:00:00+00:00 9.0        Monday    Explorer                     1  ...  Explorer\n",
              "                                               New                          1  ...       New\n",
              "2019-10-14 10:00:00+00:00 10.0       Monday    Retained                    30  ...  Retained\n",
              "2019-10-14 14:00:00+00:00 14.0       Monday    Explorer                     3  ...  Explorer\n",
              "...                                                                       ...  ...       ...\n",
              "2020-03-22 17:00:00+00:00 17.0       Sunday    Inactive                     3  ...  Inactive\n",
              "                                               New                          1  ...       New\n",
              "                                               Retained                    15  ...  Retained\n",
              "2020-03-22 20:00:00+00:00 20.0       Sunday    New                          1  ...       New\n",
              "                                               Retained                     3  ...  Retained\n",
              "\n",
              "[3424 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "HxUu9IJBwl8s",
        "outputId": "2094f40c-1092-4ac1-d47a-dd2ebe2891fc"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=b, x='day', y='booking_count', hue='user', ci=25, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa36e77ce10>"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oLXj99Vl573vfU3j/inxr21FHHZUxY8Zk8eLF6devX5Kkuro6xx9/fE4//fTNPt6iRStSX9+0HrYlZ4U0B2eFbLuaeiZRc1qwYHmlR2ArU1fX0PjT+qEp/L8PAFrPO+HP3fr6+qxfX7/BtsVL1yRZs0XHbU319Q2pr2940+doK+rr69/036lcLqVnz84b3b9VQtLKlSuzbNmyxmj0xBNPpFu3bqmpqcny5cvTpUuXNDQ05OGHH87gwS1/Gtb/5dtjAAAAgJby+c9/sdIjNKtWCUm1tbU555xzUltbm3K5nG7duuXGG2/MokWLctZZZ6Wuri719fXZddddM3bs2NYYCQAAAIDN1CohqVevXvn+97+/0ecmT57cGiMAAFu5UlX7DX4CAND6ypUeAACgiM79h6Z9577p3H9opUcBANhmVeRm28CW8bfybIkt+ZKBqqpS48+m3rzRlwzQVDXdBqam28BKjwEAsE0TkmAr1Ln/0Kyc+z/p1HfvSo/CVsiXDAAAsDXr2q0mNdXVzX7cNWvXZtnSrefb4CpFSIKtkL+VBwAAtlU11dX53PfOafbj3nHKtUk2HZJGjBie7bbbLv/+7/enXC43bps48Zrsssu7m32utsY9kgAAAAA2Q21tbaZNe7jSY1SEkLQF3KcGAAAAtj2nnnpabr/9lqxbt26D7QsXLszo0RfkC184OSeffGzuvPP2JMmvf/3LnH/+X8+iWrz4jRx44P554onHkiT33PPvuemm61v3A2wBIWkL+PYYAAAA2Pbsuefg7LHHnnnwwQc22D5+/JiMGHFcbrnlztx229351a/+O7/5za8yZMi++d3vXsj69evz9NNP5b3v3TvPPPNUkuSZZ36T/fbbvxIfo0ncI2kLuE8NAAAAbJtOO+30nHXWl3L44UcmSerq6jN9+jNZsmRJ4z6rVq3MzJkzs//+H8guu+ya3/3uhTz99FP53OdG5oYbrs3atWvz0ksvZu+9h1TqY2w2IQkAAABgM+2008754Af/If/xH3/9NuNyuZxSqZRbb70z7dq9ObcMHToszzzzVH73uxcyatRXs/32PfP44z/JbrvtnpqamtYev8lc2gZAYe4NBwAA/+vUU0/LD384KatWrUqplAwZsm/uvvuOxufnzZubRYsWJkn22+/9efjhKdlhhz5p3759hg3bP7fddtNWdVlb4owkADZD5/5Ds3Lu/6RT370rPQoAANuoNWvX5o5Trm2R426uHXbok0MOOSz33393kmTMmMty3XVX5+STj02SdOzYKV/96pj07Nkr733vXlm6dEmGDftrONpvv/1z003XC0kAvHO5NxwAAJW2bOmaJGsq9v4PPDBlg8dnnvmVnHnmVxofX3LJFRt9Xbt27fLoo//V+Pg979krv/jF0y0zZAtyaRsAAAAAhQhJAAAAABQiJAEAAABQiJAEAAAAQCFCEgAAAACFCEkAAAAAFNKu0gMAAAAAFNW9S3Xad6hp9uOuW70mS5av3eR+I0YMz8SJ12SXXd7drO///e/fm0984tBsv32PJMnkyQ9kzZo1OfbYE5r1fbaUkAQAAABsNdp3qMnDJ5/S7Mc97M7vJQVCUkv5/vfvy7Bh728MSUcdNaJis7wdIQkAAABgM5155mkZPPi9eeGF57Nw4cIcfPDHc/rpZyVJ7rvv7jz++E9SV7c+1dU1GTXqwuy22x5JkhdeeD7XX39tVq1alST58pfPyUsv/S4LFy7I6NH/lurqmowdOz5PPPFoamtrc+aZX8lxxx2dyy6bkN122z1J8oMf/Edefvn3ueiisXn11Zm59tqrs3Tpkqxbty6f+cxn8+lPH9Fin1tIAgAAAGiCefPm5vrrb8mqVaty7LFH5vDDj8zAgTvl0EM/nc9+9sQkyW9+8+tceeXXc/PNd2TZsqW56KLzc/nlE7P33kNSV1eXlStX5v3v/0CmTJmc8eMnbPSSuUMP/XQeeWRqdtvtvCTJQw9Nydlnn5f169dn3LjRGTt2fAYN2jmrVq3M5z9/Uvba630ZNGjnFvnMQhIAAABAExx00MdSLpfTuXPnDBr0rrz22uwMHLhTXn75pdx11/eybNnSlMvl/OUvryZJXnjhf7Lzzu/K3nsPSZJUVVWla9eum3yfQw89PF/84r/k9NPPzqxZM7NixfIMGbJvZs6ckVmzZmTs2Isa9123bl1mzpwhJAEAAAC0JdXV/3vT73K5nLq6uqxbty5f+9q/5TvfuSV77LFnFi5ckKOO+tQWvU/fvn2z88675le/+u9Mn/5MDjtseEqlUhoaGtKtW/fccce9W/pRCiu32jsBAAAAvMOtXbsmdXV12WGHPkmSH/5wUuNze+21d2bOnJEXXng+SVJXV5dly5YlSTp16pQVK1a85XEPO+zwTJ06OY89Ni2HHnp4kmSnnQalQ4cOeeSRhxr3mzVrZlaufOvjbClnJAEAAAA0k06dOufzn/9ivvCFk9O1a7ccdNDHGp/r2rVbLr98Yr797WuyenVtSqVyvvzlc7L//gdkxIjjcsUVl6ZDhw4ZO3b8m4770Y8enKuvnpA993xv+vbtmyRp165dJky4Jtdd983cd99dqaurT48ePXLppd9osc8nJAEAAABbjXWr1+SwO7/XIsct4oEHpiRJvvOdmzfY/n8fn3DCv+SEE/6l8fFJJ53S+Pveew/JTTe9ef7hw4/K8OFHNT7+/Oe/uMHzHTp0yLRp/+9Nrxs4cKdceeW1hWZvDkISAAAAsNVYsnxtsnxtpcfYZrlHEgAAAACFCEkAAAAAFCIkAQAAAG1UKQ0N9ZUe4h2roaFhs18jJAEAAABtUnV1hyxZsjDr169rUvTgrTU0NGTlymVp1656s17nZtsAAABAm7T99r2zYsXSvPHGvNTX11V6nHecdu2qs/32vTfvNS00CwAAAMAWKZVK6dKle7p06V7pUfj/ubQNAAAAgEKEJAAAAAAKEZIAAAAAKERIAgAAAKAQIQkAAACAQoQkAAAAAAoRkgAAAAAoREgCAAAAoBAhCQAAAIBChCQAAAAAChGSAAAAAChESAIAAACgECEJAAAAgEKEJAAAAAAKEZIAAAAAKERIAgAAAKAQIQkAAACAQoQkAAAAAAoRkgAAAAAoREgCAAAAoBAhCQAAAIBChCQAAAAAChGSAAAAAChESAIAAACgECEJAAAAgEKEJAAAAAAKEZIAAAAAKKRda73RGWeckdmzZ6dcLqdjx4752te+lsGDB2fGjBm58MILs2TJknTv3j0TJkzIzjvv3FpjAQAAAFBQq4WkCRMmpEuXLkmSxx57LBdddFEefPDBjB07Nscff3yOPPLI/OhHP8qYMWNy5513ttZYAAAAABTUape2/S0iJcmKFStSKpWyaNGivPjiizn88MOTJIcffnhefPHFvPHGG601FgAAAAAFtdoZSUly8cUX58knn0xDQ0NuvfXWzJkzJ3369ElVVVWSpKqqKjvssEPmzJmTHj16tOZoAAAAAGxCq4akyy+/PEkyefLkTJw4Meecc06zHLdnz87NcpxK6d27y6Z3ghZg7VEp1h6VZP0BQOvx5+47T6uGpL856qijMmbMmPTt2zfz5s1LXV1dqqqqUldXl/nz56dfv36bdbxFi1akvr6hSbO0hUW9YMHySo9ABVh7VIq1RyVZfwDQevy5S1OVy6W3PGmnVe6RtHLlysyZM6fx8RNPPJFu3bqlZ8+eGTx4cKZOnZokmTp1agYPHuyyNgAAAIA2qFXOSKqtrc0555yT2tralMvldOvWLTfeeGNKpVLGjRuXCy+8MDfccEO6du2aCRMmtMZIAAAAbd6zzz6dKVMezPDhR2fo0GGVHgegdUJSr1698v3vf3+jz+26666ZNGlSa4wBAACwVZk06d7MmPFKVq+uFZKANqFVLm0DAABg89XWrt7gJ0ClCUkAAAAAFCIkAQAAAFCIkAQAAABAIUISAAAAAIUISQAAAAAUIiQBAAAAUIiQBAAAAEAh7So9AAAAwDtZl64d0qGmfZNeW1VVavzZu3eXJh1j9Zp1Wb5sdZNeC/D3hCQAAIAW1KGmfY6/4J4mvXbhwuVJkrkLlzf5GPdOPCHLIyQBzcOlbQAAAAAUIiQBAAAAUIiQBAAAAEAhQhIAAAAAhQhJAAAAABQiJAEAAABQiJAEAADQRpWq2m/wE6DShCQAAIA2qnP/oWnfuW869x9a6VEAkiTtKj0AAAAAG1fTbWBqug2s9BgAjZyRBAAAAEAhQhIAAAAAhQhJAAAAABQiJAEAAABQiJAEAAAAQCFCEgAAAACFCEkAAAAAFCIkAQAAAFCIkAQAAABAIUISAAAAAIUISQAAAAAUIiQBAAAAUIiQBAAAAEAhQhIAAAAAhQhJAAAAABQiJAEAAABQiJAEAAAAQCFCEgAAAACFCEkAAAAAFCIkAQAAAFCIkAQAAABAIUISAAAAAIUISQAAAAAUIiQBAAAAUIiQBAAAAEAhQhIAAAAAhQhJAAAAABQiJAEAAABQiJAEAAAAQCFCEgAAAACFCEkAAPA2nn326VxyycV59tmnKz0KAFRcu0oPAAAAbdmkSfdmxoxXsnp1bYYOHVbpcQCgopyRBAAAb6O2dvUGPwFgWyYkAQAAAFCIkAQAAABAIUISAAAAAIUISQAAAAAUIiQBAAAAUIiQBAAAAEAh7So9AAAAtLQuXTukQ037Jr22qqrU+LN37y5NOsbqNeuyfNnqJr0WANoSIQkAgHe8DjXtc/wF9zTptQsXLk+SzF24vMnHuHfiCVkeIQmArZ9L2wAAAAAoREgCAAAAoJBWubRt8eLFueCCC/Lqq6+muro6gwYNyqWXXpoePXpkjz32yO67755y+a9Na+LEidljjz1aYywAAAAANkOrhKRSqZSRI0fmgAMOSJJMmDAhV111Va644ookyf33359OnTq1xigAAAAANFGrXNrWvXv3xoiUJPvss09ef/311nhrAAAAAJpJq39rW319fe67774cfPDBjdtOOumk1NXV5SMf+UjOOuusVFdXb9Yxe/bs3Nxjtqqmfo0sbClrj0qx9qgk649KsfaoJOuPSrH23nlaPSRddtll6dixY0488cQkyc9+9rP069cvK1asyPnnn5/rr78+55577mYdc9GiFamvb2jSPG1hUS9YsLzSI1AB1h6VYu1RSdYflWLtUUnWH5Vi7dFU5XLpLU/aadVvbZswYUJmzZqVb33rW4031+7Xr1+SpHPnzjnmmGPy7LPPtuZIAADwtkpV7Tf4CQDbslYLSVdffXVeeOGFXH/99Y2Xri1dujSrV69Okqxfvz7Tpk3L4MGDW2skAADYpM79h6Z9577p3H9opUcBgIprlUvb/vjHP+amm27KzjvvnOOOOy5JsuOOO2bkyJEZM2ZMSqVS1q9fn3333TfnnHNOa4wEAACF1HQbmJpuAys9BgC0Ca0Sknbbbbe8/PLLG31uypQprTECAAAAAFuoVe+RBAAAAMDWS0gCAAAAoBAhCQAAAIBChCQAAAAAChGSAAAAAChESAIAAACgECEJAAAAgEKEJAAAAAAKEZIAAAAAKERIAgAAAKAQIQkAAACAQoQkAAAAAAoRkgAAAAAoREgCAAAAoBAhCQAAAIBChCQAAAAAChGSAAAAAChESAIAAACgECEJAAAAgEKEJAAAAAAKEZIAAAAAKERIAgAAAKAQIQkAAACAQoQkAAAAAAoRkgAAAAAoREgCAAAAoBAhCQAAAIBChCQAAAAAChGSAAAAAChESAIAAACgECEJAAAAgEKEJAAAAAAKEZIAAAAAKKRwSHruuec2uv35559vtmEAAAAAaLsKh6RTTjllo9tHjhzZbMMAAAAA0Ha129QO9fX1aWho2OCfv3n11VdTVVXVogMCAAAA0DZsMiS95z3vSalUavz9/yqXy/nSl77UMpMBAAAA0KZsMiQ9/vjjaWhoyEknnZS77767cXupVEqPHj3SoUOHFh0QAAAAgLZhkyFpwIABSZKf/vSnLT4MAAAAAG3XJkPS3yxZsiS33357XnrppaxatWqD5+65555mHwwAAACAtqVwSPrXf/3XrF27Np/61Key3XbbteRMAAAAALRBhUPS9OnT86tf/SrV1dULCjUAACAASURBVNUtOQ8AAAAAbVS56I577LFH5s6d25KzAAAAANCGFT4j6QMf+EBGjhyZf/qnf0qvXr02eG7EiBHNPhgAAAAAbUvhkPT000+nT58+efLJJzfYXiqVhCQAAACAbUDhkHTXXXe15BwAAAAAtHGFQ1J9ff1bPlcuF77VEgAAAABbqcIh6T3veU9KpdJGn3vppZeabSAAAAAA2qbCIenxxx/f4PGCBQty880356CDDmr2oQAAAABoewqHpAEDBrzp8YQJEzJixIgcc8wxzT4YAAAAAG3LFt3caMWKFXnjjTeaaxYAAAAA2rDCZySdf/75G9wjafXq1fnNb36TI444okUGAwAAAKBtKRySBg0atMHj7bbbLscdd1w+9KEPNftQAAAAALQ9hUPSmWee2ZJzAAAAANDGFQ5JSfKDH/wgP/rRjzJv3rz06dMnRx55ZP75n/+5pWYDAAAAoA0pHJK++93vZvLkyTn11FPTv3//vP7667n11lszf/78nH766S05IwAAAABtQOGQNGnSpNx1110ZMGBA47YPf/jDOfHEE4UkAAAAgG1AueiOtbW16dGjxwbbunfvntWrVzf7UAAAAAC0PYVD0oEHHphRo0bllVdeyerVq/PnP/85F154YT784Q+35HwAAAAAtBGFQ9KYMWPSqVOnHHHEEdl3331z1FFHZbvttsvXvva1lpwPAAAAgDai8D2SOnfunIkTJ+Yb3/hGFi9enO233z7lcuEOBQAAAMBWrnAJmjx5cn7/+9+nXC6nZ8+eKZfL+f3vf5/Jkydv8rWLFy/OF77whRxyyCEZPnx4zjzzzLzxxhtJkt/+9rc54ogjcsghh+TUU0/NokWLmv5pAAAAAGgxhUPStddem379+m2wrW/fvrn22ms3+dpSqZSRI0dm2rRpmTJlSgYOHJirrroq9fX1Of/88zNmzJhMmzYtw4YNy1VXXbX5nwIAAACAFlc4JK1YsSKdO3feYFuXLl2ybNmyTb62e/fuOeCAAxof77PPPnn99dfzwgsvpKamJsOGDUuSHHfccXnkkUeKjgQAAABAKyp8j6Rdd90106ZNy2GHHda47dFHH82uu+66WW9YX1+f++67LwcffHDmzJmT/v37Nz7Xo0eP1NfXZ8mSJenevXvhY/bs2XnTO7VhvXt3qfQIbKOsPSrF2qOSrD8qxdqjkqw/KsXae+cpHJJGjRqV0047LT/+8Y8zcODAvPrqq/nlL3+Zm2++ebPe8LLLLkvHjh1z4okn5tFHH93sgTdm0aIVqa9vaNJr28KiXrBgeaVHoAKsPSrF2qOSrD8qxdqjkqw/KsXao6nK5dJbnrRT+NK2YcOGZerUqdl7771TW1ub973vfZk6dWr222+/woNMmDAhs2bNyre+9a2Uy+X069cvr7/+euPzb7zxRsrl8madjQQAAABA6yh8RlKS9O/fP6eddtpbPj98+PBMmTJlo89dffXVeeGFF3LzzTenuro6SbLXXntl9erVefrppzNs2LDcf//9OfTQQzdnJAAAAABayWaFpE2ZPXv2Rrf/8Y9/zE033ZSdd945xx13XJJkxx13zPXXX5+JEydm7NixWbNmTQYMGJArr7yyOUcCAAAAoJk0a0gqlUob3b7bbrvl5Zdf3uhzQ4cOfcuzmAAAAABoOwrfIwkAAACAbZuQBAAAAEAhzRqSGhoamvNwAAAAALQhzRqSLr300uY8HAAAAABtSOGbbZ9//vkbvZl2dXV1+vbtm49//OMZPnx4sw4HAAAAQNtR+IykLl265PHHH09DQ0P69u2bhoaGPPHEEymXy/nzn/+cY489NpMnT27JWQEAAACooMJnJM2cOTM333xz9ttvv8Zt06dPz3XXXZfvfe97+fnPf54rrrgiRx11VIsMCgAAAEBlFT4j6bnnnsuQIUM22LbXXnvl+eefT5IceOCBmTdvXvNOBwAAAECbUTgkDR48ONdcc03WrFmTJFmzZk2uvfba7LnnnkmS2bNnp1u3bi0zJQAAAAAVV/jStm984xsZNWpU9ttvv3Tr1i1Lly7NXnvtlSuvvDJJsmTJkowdO7bFBgUAAACgsgqHpB133DH3339/5syZk/nz56d3797p379/4/N77713iwwIAAAAQNtQ+NK2v6murk6PHj1SV1eXv/zlL/nLX/7SEnMBAAAA0MYUPiPp5z//eS6++OIsWLBgg+2lUikvvfRSsw8GAAAAQNtSOCRdeumlOeOMM3L00UenQ4cOLTkTAAAAAG1Q4ZC0bNmyHHfccSmVSi05DwAAAABtVOF7JP3zP/9zfvCDH7TkLAAAAAC0YYXPSHruuedy11135ZZbbkmvXr02eO6ee+5p9sEAAAAAaFsKh6RjjjkmxxxzTEvOAgAAAEAbVjgkHX300S05BwAAAABt3NuGpMmTJ+eoo45KkjzwwANvud+IESOadyoAAAAA2py3DUkPPfRQY0j60Y9+tNF9SqWSkAQAAACwDXjbkHTLLbc0/n7XXXdtdJ+GhobmnQgAAACANqlcdMfbb799o9svuuiiZhsGAAAAgLarcEiaPHlyJk2a1Pi4oaEho0aNyrx581pkMAAAAADalsLf2nbrrbfmpJNOSqdOnXLIIYfkvPPOS21tbW688caWnA8AAACANqJwSNphhx1y22235eSTT87dd9+d7bffPjfccEPatSt8CAAAAAC2Ym9bgR544IE3bfvkJz+ZqVOn5ogjjsjkyZOTxLe2AQAAAGwD3jYk/ehHP9ro9ne961156KGHkiSlUklIAgAAANgGvG1Iuuuuu1prDgAAAADauM26wdHSpUvz05/+NPPmzUufPn1y0EEHpVu3bi01GwAAAABtSLnojtOnT88nPvGJ3H///Xn55Zdz//335xOf+ESmT5/ekvMBAAAA0EYUPiPpiiuuyNixY/PpT3+6cdvDDz+c8ePH5wc/+EGLDAcAAABA21H4jKSZM2fmU5/61AbbDjnkkLz66qvNPhQAAAAAbU/hkDRo0KDGb2r7m0ceeSQDBw5s9qEAAAAAaHsKX9p20UUX5Utf+lLuuuuu9O/fP6+99lpmzZqVG2+8sSXnAwAAAKCNKByShg4dmkcffTQ/+9nPMn/+/Bx00EH56Ec/mu7du7fkfAAAAAC0EYVDUpJ069Yt+++/f+bNm5c+ffqISAAAAADbkMIhaf78+TnvvPPy29/+Nt27d8+SJUsyZMiQXH311enTp09LzggAAABAG1D4Ztvjxo3Lnnvumaeeeiq/+MUv8tRTT2Xw4MEZO3ZsS84HAAAAQBtR+IykZ555Jtdee23at2+fJOnYsWMuuOCCHHjggS02HAAAAABtR+Ezkrp165Y///nPG2x75ZVX0rVr12YfCgAAAIC2p/AZSSNHjsznPve5jBgxIv37989rr72WBx98MOecc05LzgcAAABAG1E4JH3mM5/JTjvtlClTpuQPf/hDdthhh3zzm9/MBz/4wZacDwAAAIA2onBIWrt2bX7961/nqaeeyvz589OnT5/06tUrQ4cOTU1NTUvOCAAAAEAbUDgkjRs3LjNmzMjo0aMzYMCAvP7667nxxhszb968fP3rX2/JGQEAAABoAwqHpMcffzyPPvpo48213/3ud+d973tfPvnJT7bYcAAAAAC0HYW/ta1Xr16pra3dYNuaNWvSu3fvZh8KAAAAgLbnbc9I+uUvf9n4+5FHHpmRI0fmpJNOSp8+fTJ37tzcc889OfLII1t8SAAAAAAq721D0sUXX/ymbTfeeOMGj//jP/4jp512WvNOBQAAAECb87Yh6YknnmitOQAAAABo4wrfIwkAAACAbZuQBAAAAEAhQhIAAAAAhQhJAAAAABQiJAEAAABQiJAEAAAAQCF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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLHx627DwwsB"
      },
      "source": [
        "Pemesanan didominasi oleh user retained. User selain retained tetap melakukan pemesanan, namun jumlah dari user tersebut sangatlah kecil."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "IQmUlFAmf6EO",
        "outputId": "c71505c1-a2da-442a-bb56-a21f0e358d9b"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=b, x='hour', y='booking_count', ci=25, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa36331b890>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKSI1FN5tpvp"
      },
      "source": [
        "Jumlah pemesanan bus ride mulai meningkat pada jam 4 pagi. Jumlah pemesanan bus ride mulai menurun pada jam 7 malam."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "PKEpJwvItH_8",
        "outputId": "337cf038-5e8a-4f43-95e9-21c35f7eb8de"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=b, x='hour', y='median_pickup_walk_time', ci=None, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3637d2f90>"
            ]
          },
          "metadata": {},
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxNEZ9ZZuZpm"
      },
      "source": [
        "Grafik di atas menunjukkan nilai rata rata median_pickup_walk_time terhadap jam. Pada pagi hari tepatnya jam 6 pagi nilai rata rata pickup time mulai naik sampai jam 7. Hal ini dikarenakan pada jam tersebut orang orang sudah mulai berangkat kerja, sehingga waktu yang dibutuhkan juga bertambah seiringnya user."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "TGVu-8OgtY2Q",
        "outputId": "6e25d678-45b7-4f9e-f22a-5ac26fe33a69"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=b, x='hour', y='median_dropoff_walk_time', ci=None, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa36b04e390>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6ZEByVzuR1I"
      },
      "source": [
        "Perkiraan waktu yang dibutuhkan pelanggan untuk berjalan kaki dari stasiun bus tujuan yang diinginkan pada grafik di atas tidak memiliki pola tertentu dikarenakan hal ini tidak memiliki keterkaitan antara jam sibuk atau tidak. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "nswa-zKgthbq",
        "outputId": "9edbb2ad-85a7-42bd-cee7-e013613602c2"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=b, x='hour', y='median_bus_travel_time', ci=None, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa365432510>"
            ]
          },
          "metadata": {},
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ttofq_KewROm"
      },
      "source": [
        "Perkiraan waktu tempuh bus dalam perjalanan juga tidak memiliki pola tertentu. Hal ini berarti tidak ada suatu delay dalam perjalanan yang bisa menambah waktu tempuh bus."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "4ia7AsEUlOuY",
        "outputId": "1bb50b49-abee-47ce-eedd-2032bc6dfec8"
      },
      "source": [
        "c = df_total2.groupby([pd.Grouper(key='search_time', freq='H'), 'hour_search', 'day_search', 'new_segment_tag']).agg(search_count=('search_id', 'count'),\n",
        "          high_demand=('high_demand_val', 'sum'),\n",
        "         median_pickup_walk_time=('median_pickup_walk_time','mean'),\n",
        "         median_dropoff_walk_time=('median_dropoff_walk_time','mean'),\n",
        "        median_bus_travel_time=('median_bus_travel_time', 'mean'))\n",
        "hour = []\n",
        "day = []\n",
        "user = []\n",
        "for value in c.index.get_level_values(1):\n",
        "  hour.append(value)\n",
        "for value in c.index.get_level_values(2):\n",
        "  day.append(value)\n",
        "for value in c.index.get_level_values(3):\n",
        "  user.append(value) \n",
        "c['hour'] = hour\n",
        "c['day'] = day\n",
        "c['user'] = user\n",
        "c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>search_count</th>\n",
              "      <th>high_demand</th>\n",
              "      <th>median_pickup_walk_time</th>\n",
              "      <th>median_dropoff_walk_time</th>\n",
              "      <th>median_bus_travel_time</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
              "      <th>user</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>search_time</th>\n",
              "      <th>hour_search</th>\n",
              "      <th>day_search</th>\n",
              "      <th>new_segment_tag</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">2019-10-14 03:00:00+00:00</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">Monday</th>\n",
              "      <th>Explorer</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1380.0</td>\n",
              "      <td>1890.0</td>\n",
              "      <td>3303.000</td>\n",
              "      <td>3</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Explorer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1380.0</td>\n",
              "      <td>1890.0</td>\n",
              "      <td>3303.000</td>\n",
              "      <td>3</td>\n",
              "      <td>Monday</td>\n",
              "      <td>New</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1380.0</td>\n",
              "      <td>1890.0</td>\n",
              "      <td>3303.000</td>\n",
              "      <td>3</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2019-10-14 04:00:00+00:00</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Monday</th>\n",
              "      <th>Inactive</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1080.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>4194.500</td>\n",
              "      <td>4</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>24</td>\n",
              "      <td>132</td>\n",
              "      <td>585.0</td>\n",
              "      <td>7320.0</td>\n",
              "      <td>1314.875</td>\n",
              "      <td>4</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-22 20:00:00+00:00</th>\n",
              "      <th>20</th>\n",
              "      <th>Sunday</th>\n",
              "      <th>Retained</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>4116.000</td>\n",
              "      <td>20</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2020-03-22 22:00:00+00:00</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">22</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sunday</th>\n",
              "      <th>New</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2730.0</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>5957.500</td>\n",
              "      <td>22</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>New</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>2730.0</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>5957.500</td>\n",
              "      <td>22</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2020-03-22 23:00:00+00:00</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">23</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sunday</th>\n",
              "      <th>Inactive</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1140.0</td>\n",
              "      <td>8700.0</td>\n",
              "      <td>8204.000</td>\n",
              "      <td>23</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1140.0</td>\n",
              "      <td>8700.0</td>\n",
              "      <td>8204.000</td>\n",
              "      <td>23</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Retained</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7826 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                  search_count  ...      user\n",
              "search_time               hour_search day_search new_segment_tag                ...          \n",
              "2019-10-14 03:00:00+00:00 3           Monday     Explorer                    3  ...  Explorer\n",
              "                                                 New                         1  ...       New\n",
              "                                                 Retained                    8  ...  Retained\n",
              "2019-10-14 04:00:00+00:00 4           Monday     Inactive                    8  ...  Inactive\n",
              "                                                 Retained                   24  ...  Retained\n",
              "...                                                                        ...  ...       ...\n",
              "2020-03-22 20:00:00+00:00 20          Sunday     Retained                    3  ...  Retained\n",
              "2020-03-22 22:00:00+00:00 22          Sunday     New                         1  ...       New\n",
              "                                                 Retained                    9  ...  Retained\n",
              "2020-03-22 23:00:00+00:00 23          Sunday     Inactive                    3  ...  Inactive\n",
              "                                                 Retained                   14  ...  Retained\n",
              "\n",
              "[7826 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "Ja7bAkZK0Su5",
        "outputId": "de6f4a55-9a4b-48ae-8b59-86daa343da40"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=c, x='day', y='search_count', hue='user', ci=25, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa36c6e0b50>"
            ]
          },
          "metadata": {},
          "execution_count": 140
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bE_AftPu0cmz"
      },
      "source": [
        "Sama halnya dengan pemesanan, pencarian didominasi oleh user retained dan user lainnya hanya sedikit saja."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "-NMCAGWNpxRw",
        "outputId": "3f5b375a-8c09-4b6c-b039-c783abca99ad"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=c, x='hour', y='median_pickup_walk_time', ci=None, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa36c13f090>"
            ]
          },
          "metadata": {},
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "uqJD_8d1qk4p",
        "outputId": "7f09b9c4-659c-4a6a-ccde-4f450f85defb"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=c, x='hour', y='median_dropoff_walk_time', ci=None, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa368d344d0>"
            ]
          },
          "metadata": {},
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "kJWOofttqrlb",
        "outputId": "7bd648bc-f18c-4bf9-ab0c-4044139fc043"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=c, x='hour', y='median_bus_travel_time', ci=None, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa36af1c410>"
            ]
          },
          "metadata": {},
          "execution_count": 143
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BDOsAtXDhwiTJ8uXLs3jx4syePXs4v/xOxo/fp6Zff3dNmDBm8DfVobJ2J+VtL2t3Ut72snYn5W0va3eZlfn3vKztZe1Oytte1u6kvO1l7U7K217W7qS87WXtTsrbXtbupLztu9tdk++C9/73vz/z5s3L/vvvn40bN6avry8jRoxIX19fHn/88XR2dqa/v3+PzqrR0/NMKpX+JPX9B/3EE5t3eV6v7WXtTsrbXtbupLztZe1Oyts+WHeZlfX3vF67k/K2l7U7KW97WbuT8raXtTspb3tZu5Pytpe1Oylve1m7k/K2v7S7ubnpFS/2GZZnQD377LPZsGHDwI9vv/32jBs3LuPHj8/UqVNz4403JkluvPHGTJ06NR0dHXt8BgAAAEB9GZYroJ5//vnMnj07zz//fJqbmzNu3LhcccUVaWpqygUXXJBzzjknX/rSlzJ27NgsWrRo4PP29AyA166x7a1pa2mrdcZOtm7fmj9u2lbrDAAAqIlhGaBe//rX57vf/e7Lnk2ePDnXXnvtkJ4B8NrV1tKWj35vZq0zdnL1365KYoACAOC1aVhuwQMAAADgtcsABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFMoABQAAAEChDFAAAAAAFGpktZ/w4IMPZtWqVXnyySczf/78PPjgg9m+fXsOPvjgIvoAAAAAKLmqroC65ZZbcvLJJ2fjxo1ZsWJFkuS5557LF77whULiAAAAACi/qq6Auuyyy3L11Vfn4IMPzi233JIkOfjgg/OrX/2qkDgAAAAAyq+qK6B6e3szZcqUJElTU9PA///TxwAAAADwv1U1QL35zW8euPXuT2666aa85S1vGdIoAAAAABpHVbfgnXfeeTn11FNz3XXX5bnnnsupp56ahx56KF//+teL6gMAAACg5KoaoCZPnpxbbrklP/zhD3P00Uens7MzRx99dPbee++i+gAAAAAouaoGqCTZa6+98u53v7uIFgAAAAAaUFUD1Pr16/PFL34x999/f5577rkdzlavXj2kYQAAAAA0hqoGqNmzZ+fP/uzP8slPfjKjRo0qqgkAAACABlLVAPXb3/423/nOd9LcXNU3zwMAAADgNayqJemv//qvc/fddxfVAgAAAEADquoKqPPPPz+zZs3KgQcemPHjx+9wdtFFFw1pGAAAAACNoaoB6l/+5V8yYsSITJ48OW1tbUU1AQAAANBAqhqg7rrrrtx5553ZZ599iuoBAAAAoMFU9QyoKVOmZNOmTUW1AAAAANCAqroC6i/+4i9y6qmn5gMf+MBOz4A68cQThzQMAAAAgMZQ1QD1P//zP9l3333z4x//eIfXm5qaDFAAAAAAvKyqBqhvfvObRXUAAAAA0KAGHaD6+/vT1NSUJKlUKq/4vubmqh4nBQAAAMBrxKAD1PTp03PPPfckSQ455JCBMepP/jRQ3X///cUUAgAAAFBqgw5QN91008DHt912W6ExAAAAADSeQe+b6+zsHPh41apV6erq2un/br311kIjAQAAACivqh7cdPnll7/s61/+8peHJAYAAACAxrNb3wXvpz/9aZIXH0J+1113pb+/f+Ds0Ucfzd57711MHQAAAAClt1sD1HnnnZck2bp1a84999yB15uamjJhwoScf/75xdQBAAAAUHq7NUDdfvvtSZKzzz47ixcv3uV7H3vssey///6vvgwAAACAhlDVM6AGG5+S5N3vfvcexwAAAADQeHbrCqhqvPT5UH/y1FNP5eyzz87vf//7tLa25g1veEMWLFiQjo6OTJkyJW9605vS3PziFrZ48eJMmTIlyYtXXi1evDh9fX1585vfnIsuuih77bXXoGcAAAAA1I+qroDaHU1NTS/72mmnnZbVq1dn5cqVOeCAA7J06dKB82XLlmXFihVZsWLFwPj07LPP5rOf/WyuuOKKfP/738/ee++dq666atAzAAAAAOrLkA9QL6e9vT1vf/vbB3582GGHZf369bv8nP/6r//KtGnTctBBByVJZs2alVtuuWXQMwAAAADqy5DfgjeYSqWSb3/725kxY8bAax/+8IfT19eXv/qrv8qZZ56Z1tbWbNiwIRMnThx4z8SJE7Nhw4Yk2eUZAAAAAPVlWJ4B9VIXXnhhRo8enZNPPjlJcscdd6SzszPPPPNM5s6dm8svvzyf/vSnhzrrZY0fv8+wfJ1Xa8KEMbVO2CNl7U7K217W7qS87WXtTsrbXtbupLztZe1Oytte1u6kvO1l7U7K217W7qS87WXtTsrbXtbupLztZe1Oytu+u91DPkDdfPPNr3i2aNGiPPzww7niiisGHjre2dmZJNlnn33yd3/3d7n66qsHXv/Zz3428Lnr168feO+uzqrR0/NMKpUXB7N6/oN+4onNuzyv1/aydiflbS9rd1Le9rJ2J+VtL2t3Ut72snYn5W0va3dS3vaydiflbS9rd1Le9rJ2J+VtL2t3Ut72snYn5W1/aXdzc9MrXuwz6AB11FFHveyDxf+3O+64I0lecQi6+OKLs27dunz1q19Na2trkuTpp59OW1tbRo0alRdeeCGrV6/O1KlTkyRHHnlkLrzwwvzud7/LQQcdlGXLluVd73rXoGcAAAAA1JdBB6glS5a86i/ym9/8Jl/5yldy0EEHZdasWUmSSZMm5bTTTsu8efPS1NSUF154IW9961sze/bsJC9eEbVgwYJ84hOfSKVSydSpU3PeeecNegYAAABAfRl0gHrb2972qr/IG9/4xvz6179+2bOVK1e+4ue9853vzDvf+c6qzwAAAACoH83VvHnbtm255JJLcswxx2T69OlJkh//+Mf51re+VUgcAAAAAOVX1QD1+c9/Pg888ECWLl068FyoN77xjfn2t79dSBwAAAAA5VfVd8H7wQ9+kFtvvTWjR48e+C52++23XzZu3FhIHAAAAADlV9UVUC0tLenr69vhtd7e3rS3tw9pFAAAAACNo6oBaubMmfnMZz6TRx55JEny+OOPZ8GCBXnPe95TSBwAAAAA5VfVAPXpT386kyZNygknnJA//vGPOe6447Lvvvvmn//5n4vqAwAAAKDkqnoGVGtra84999yce+656e3tzete97qBh5EDAAAAwMup6gqo008/Pbfccku2bduWjo4O4xMAAAAAg6pqgHrb296Wq666KkcccUQ+85nP5M4770ylUimqDQAAAIAGUNUA9Y//+I+57rrrcv311+eAAw7I5z//+Rx55JH53Oc+V1QfAAAAACVX1QD1JwcddFDOOOOMXHLJJZkyZUquueaaoe4CAAAAoEFU9RDyJPn973+fG2+8MTfddFN6e3szc+bMnH766UW0AQAAANAAqhqgPvjBD+Z3v/tdjjnmmJx99tn5y7/8y4wcWfWGBQAAAMBrSFXr0amnnpoZM2Zk1KhRRfUAAAAA0GCqGqDe/e53D3zc39+f/v7+gR83N+/R46QAAAAAaHBVDVAbN27MggULsmbNmvzxj3/c4ez+++8f0jAAAAAAGkNVly3Nnz8/LS0t+cY3vpHRo0fne9/7XmbMmJF//dd/LaoPAAAAgJKr6gqoe++9Nz/84Q8zevToNDU15eCDD87ChQsza9as/P3f/31RjQAAAACUWFVXQDU3Nw9817uxY8emt7c3o0ePzsaNGwuJAwAAAKD8qroC6tBDD82PfvSjHHvssXnHO96RT33qUxk1alSmTZtWVB8AAAAAJVfVALV48eJUKpUkybnnnpuvf/3refbZZ/ORj3ykkDgAAAAAym+3B6i+vr4sXLgwF154YZJk1KhROf300wsLAwAAAKAx7PYzoEaMGJGf/OQnaWpqKrIHAAAAgAZT1UPIP/KRj+Q//uM/sn379qJ6AAAAAGgwVT0D6lvf+laefPLJXH311eno6Njhaqg77rhjqNsAAAAAaABVDVBLliwpqgMAAACABlXVANXT05N3vetdO72+atWqIQsCAAAAoLFU9Qyo884772Vfnzdv3pDEAAAAANB4dusKqEceeSRJ0t/fP/DxS89aW1uHvgwAAACAhrBbA9Sxxx6bpqam9Pf359hjj93h7PWvf33OPPPMQuIAAAAAKL/dGqB+9atfJUlOPvnkfOtb3yo0CAAAAIDGUtUzoHZnfDr88MP3OAYAAACAxlPVALU7+vv7h/qnBAAAAKDEhnyAampqGuqfEgAAAIASG/IBCgAAAABeygAFAAAAQKE8AwoAAACAQr2qAeqRRx7Jo48+usNrX/va115VEAAAAACNpaoB6qyzzso999yTJLn++uvznve8J8cff3yuvfbagfd0d3cPbSEAAAAApVbVAPXTn/4006ZNS5J84xvfyNVXX51rr73WVU8AAAAAvKKR1bx5+/btaW1tzcaNG7Np06ZMnz49SfLkk08WEgcAAABA+VU1QE2dOjVf+cpX8oc//CFHH310kmTjxo3ZZ599imgDAAAAoAFUdQvewoUL88ADD2Tr1q351Kc+lSS599578973vreQOAAAAADKr6oroA488MD827/92w6vzZw5MzNnzhzSKAAAAAAaR1UD1HXXXfeKZyeeeOKrjgEAAACg8VQ1QK1YsWKHHz/55JN55JFH8ta3vtUABQAAAMDLqmqA+uY3v7nTa9ddd10efPDBIQsCAAAAoLFU9RDyl/OBD3wg119//VC0AAAAANCAqroCqlKp7PDj559/PjfccEPGjBkzpFEAAAAANI6qBqhDDjkkTU1NO7y233775cILLxzSKAAAAAAaR1UD1G233bbDj/faa690dHQMaRAAAAAAjaWqZ0B1dXWlq6srI0eOTE9PT7Zv315UFwAAAAANoqoroNavX585c+Zk7dq1GTduXJ5++ukcdthhWbJkSbq6uopqBAAAAKDEqroC6jOf+Uze/OY3Z82aNfnpT3+an//855k2bVrOOeecXX7eU089lY997GM57rjj8t73vjdnnHFGent7kyRr167NCSeckOOOOy6nnHJKenp6Bj5vT88AAAAAqB9VDVC//OUvc/bZZ2f06NFJkr333jtz5szJunXrdvl5TU1NOe2007J69eqsXLkyBxxwQJYuXZpKpZK5c+dm3rx5Wb16dbq7u7N06dIk2eMzAAAAAOpLVQPUYYcdll/84hc7vLZu3bq89a1v3eXntbe35+1vf/sOP8/69euzbt26tLW1pbu7O0kya9asrFq1auDn3ZMzAAAAAOrLoM+AuvTSSwc+PuCAA/Lxj388Rx99dPbff/889thj+dGPfpTjjz9+t79gpVLJt7/97cyYMSMbNmzIxIkTB846OjpSqVSyadOmPT5rb2/f7RYAAAAAijfoAPXYY4/t8OO/+Zu/SZL09vamtbU1xx57bLZu3brbX/DCCy/M6NGjc/LJJ+f73/9+lblDa/z4fWr69XfXhAljap2wR8ranZS3vazdSXnby9qdlLe9rN1JedvL2p2Ut72s3Ul528vanZS3vazdSXnby9qdlLe9rN1JedvL2p2Ut313uwcdoC666KKqvvCNN974ildELVq0KA8//HCuuOKKNDc3p7OzM+vXrx847+3tTXNzc9rb2/f4rBo9Pc+kUulPUt9/0E88sXmX5/XaXtbupLztZe1Oytte1u6kvO1l7U7K217W7qS87WXtTsrbXtbupLztZe1Oytte1u6kvO1l7U7K217W7qS87S/tbm5uesWLfap6BtTumDdv3su+fvHFF2fdunW5/PLL09ramiSZNm1atmzZkjVr1iRJli1blpkzZ76qMwAAAADqy6BXQFWrv79/p9d+85vf5Ctf+UoOOuigzJo1K0kyadKkXH755Vm8eHHmz5+frVu3pqurK0uWLEmSNDc379EZAAAAAPVlyAeopqamnV574xvfmF//+tcv+/7DDz88K1euHNIzAAAAAOrHkN+CBwAAAAAvZYACAAAAoFBDPkBNnDhxqH9KAMPJvboAAB/aSURBVAAAAEqs6mdAbd68OQ899FCeffbZHV4/4ogjkiQ33njj0JQBAAAA0BCqGqD+8z//MwsWLMjo0aMzatSogdebmppy2223DXkcAAAAAOVX1QB1ySWX5NJLL81RRx1VVA8AAAAADaaqZ0D19fXlHe94R1EtAAAAADSgqgaoj33sY/nyl7+cSqVSVA8AAAAADaaqW/C+8Y1v5Mknn8yVV16Z9vb2Hc7uuOOOoewCAAAAoEFUNUAtWbKkqA4AAAAAGlRVA9Tb3va2ojoAAAAAaFBVDVBJcv/992fNmjV56qmn0t/fP/D67NmzhzQMAAAAgMZQ1UPIv/Od7+Qf/uEfctddd+VrX/taHnjggVx99dX5/e9/X1QfAAAAACVX1QB15ZVX5sorr8zll1+eUaNG5fLLL8+ll16akSOrvpAKAAAAgNeIqgaonp6edHd3v/iJzc2pVCo56qij8sMf/rCQOAAAAADKr6pLl/bff/88+uijmTRpUg466KDcdttted3rXpeWlpai+gAAAAAouaoGqNNOOy0PPvhgJk2alNNPPz2zZ8/O9u3bc9555xXVBwAAAEDJVTVAfeADHxj4+Kijjsrdd9+d7du3Z++99x7yMAAAAAAaw6ADVH9/f5qampIklUplx08eOTIjR45MpVJJc3NVj5MCAAAA4DVi0AFq+vTpueeee5IkhxxyyMAY9Sd/Gqjuv//+YgoBAAAAKLVBB6ibbrpp4OPbbrut0BgAAAAAGs+gA1RnZ+fAx11dXYXGAAAAANB4Bh2g5s6du9Ntdy9n8eLFQxIEAAAAQGMZ9Mnhb3jDG3LggQfmwAMPzJgxY/KDH/wgfX192X///VOpVHLbbbdl7Nixw9EKAAAAQAkNegXUGWecMfDxqaeemq9+9avp7u4eeG3NmjX58pe/XEwdAAAAAKU36BVQL7V27doceuihO7x26KGH5t577x3SKAAAAAAaR1UD1CGHHJKLL744W7ZsSZJs2bIll1xySaZOnVpIHAAAAADlN+gteC910UUXZc6cOenu7s7YsWPzxz/+MdOmTcuSJUuK6gMAAACg5KoaoCZNmpRly5Zlw4YNefzxxzNhwoRMnDixqDYAAAAAGkBVt+AlyVNPPZWf/exnufvuuzNx4sRs3Lgxjz32WBFtAAAAADSAqgaou+++OzNnzszKlSvzpS99KUny8MMP54ILLiiiDQAAAIAGUNUA9fnPfz7//u//nquuuiojR754996hhx6aX/ziF4XEAQAAAFB+VQ1Qf/jDH3LEEUckSZqampIkLS0t6evrG/oyAAAAABpCVQPU5MmTc+edd+7w2n//93/nTW9605BGAQAAANA4qvoueOecc04+8YlP5Oijj86WLVsyb9683H777QPPgwIAAACA/62qK6AOO+yw3HDDDfnzP//zfPCDH8ykSZNy/fXX5y1veUtRfQAAAACUXFVXQG3evDnXXXdd7rvvvjz33HN5+OGHc9dddyVJvv71rxcSCAAAAEC5VTVAzZ49O319fTn22GPT1tZWVBMAAAAADaSqAWrt2rW566670traWlQPAAAAAA2mqmdATZ8+Pb/97W+LagEAAACgAVV1BdQXvvCFfOxjH8uhhx6a8ePH73B2xhlnDGkYAAAAAI2hqgHqkksuyWOPPZZJkyblmWeeGXi9qalpyMMAAAAAaAxVDVA33XRTVq9enX333beoHgAAAAAaTFXPgDrggAMycmRVmxUAAAAAr3FVrUnve9/7cvrpp+fkk0/e6RlQRxxxxJCGAQAAANAYqhqgrrnmmiTJxRdfvMPrTU1Nue2224auCgAAAICGUdUAdfvttxfVAQAAAECDquoZUAAAAABQLQMUAAAAAIUyQAEAAABQKAMUAAAAAIUyQAEAAABQKAMUAAAAAIUatgFq0aJFmTFjRqZMmZIHHnhg4PUZM2Zk5syZed/73pf3ve99ufPOOwfO1q5dmxNOOCHHHXdcTjnllPT09OzWGQAAAAD1Y9gGqGOOOSbXXHNNurq6djq77LLLsmLFiqxYsSJHHnlkkqRSqWTu3LmZN29eVq9ene7u7ixdunTQMwAAAADqy7ANUN3d3ens7Nzt969bty5tbW3p7u5OksyaNSurVq0a9AwAAACA+jKy1gFJMmfOnPT392f69Ok566yzMnbs2GzYsCETJ04ceE9HR0cqlUo2bdq0y7P29vbd/rrjx+8zpL+OokyYMKbWCXukrN1JedvL2p2Ut72s3Ul528vanZS3vazdSXnby9qdlLe9rN1JedvL2p2Ut72s3Ul528vanZS3vazdSXnbd7e75gPUNddck87Ozmzbti0LFy7MggULhu12up6eZ1Kp9Cep7z/oJ57YvMvzem0va3dS3vaydiflbS9rd1Le9rJ2J+VtL2t3Ut72snYn5W0va3dS3vaydiflbS9rd1Le9rJ2J+VtL2t3Ut72l3Y3Nze94sU+Nf8ueH+6La+1tTUnnXRS7rnnnoHX169fP/C+3t7eNDc3p729fZdnAAAAANSXmg5Qzz33XDZvfnEp6+/vz80335ypU6cmSaZNm5YtW7ZkzZo1SZJly5Zl5syZg54BAAAAUF+G7Ra8z33uc7n11lvz5JNP5qMf/Wja29tzxRVX5Mwzz0xfX18qlUomT56c+fPnJ0mam5uzePHizJ8/P1u3bk1XV1eWLFky6BkAAAAA9WXYBqjzzz8/559//k6vL1++/BU/5/DDD8/KlSurPgMAAACgftT8GVAAAAAANDYDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUCgDFAAAAACFMkABAAAAUKhhGaAWLVqUGTNmZMqUKXnggQcGXn/ooYfyoQ99KMcdd1w+9KEP5Xe/+92rPgMAAACgvgzLAHXMMcfkmmuuSVdX1w6vz58/PyeddFJWr16dk046KfPmzXvVZwAAAADUl2EZoLq7u9PZ2bnDaz09Pbnvvvty/PHHJ0mOP/743Hfffent7d3jMwAAAADqz8hafeENGzZkv/32y4gRI5IkI0aMyL777psNGzakv79/j846Ojqqahg/fp+h/UUVZMKEMbVO2CNl7U7K217W7qS87WXtTsrbXtbupLztZe1Oytte1u6kvO1l7U7K217W7qS87WXtTsrbXtbupLztZe1Oytu+u901G6DqQU/PM6lU+pPU9x/0E09s3uV5vbaXtTspb3tZu5Pytpe1Oylve1m7k/K2l7U7KW97WbuT8raXtTspb3tZu5Pytpe1Oylve1m7k/K2l7U7KW/7S7ubm5te8WKfmg1QnZ2d2bhxY/r6+jJixIj09fXl8ccfT2dnZ/r7+/foDAAAAID6MyzPgHo548ePz9SpU3PjjTcmSW688cZMnTo1HR0de3wGAAAAQP0ZliugPve5z+XWW2/Nk08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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5O4zM2NrrQEU"
      },
      "source": [
        "Pada grafik di atas terlihat bahwa tidak ada pola khusus yang terbentuk antara kolom median_pickup_walk_time, median_dropoff_walk_time, median_bus_travel_time. Semua berjalan normal pada jam berapapun."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "1aH6n6X7LItF",
        "outputId": "920f1a77-ba50-4bf1-cf07-3a9203ea4669"
      },
      "source": [
        "d = df_total2.groupby([pd.Grouper(key='search_time', freq='H'), 'hour_search', 'day_search']).agg(search_count=('search_id', 'count'),\n",
        "          high_demand=('high_demand_val', 'sum'),\n",
        "         median_pickup_walk_time=('median_pickup_walk_time','mean'),\n",
        "         median_dropoff_walk_time=('median_dropoff_walk_time','mean'),\n",
        "        median_bus_travel_time=('median_bus_travel_time', 'mean'))\n",
        "hour = []\n",
        "day = []\n",
        "user = []\n",
        "for value in d.index.get_level_values(1):\n",
        "  hour.append(value)\n",
        "for value in d.index.get_level_values(2):\n",
        "  day.append(value)\n",
        "\n",
        "d['hour'] = hour\n",
        "d['day'] = day\n",
        "d.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th></th>\n",
              "      <th>search_count</th>\n",
              "      <th>high_demand</th>\n",
              "      <th>median_pickup_walk_time</th>\n",
              "      <th>median_dropoff_walk_time</th>\n",
              "      <th>median_bus_travel_time</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>search_time</th>\n",
              "      <th>hour_search</th>\n",
              "      <th>day_search</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-10-14 03:00:00+00:00</th>\n",
              "      <th>3</th>\n",
              "      <th>Monday</th>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>1380.000000</td>\n",
              "      <td>1890.000000</td>\n",
              "      <td>3303.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 04:00:00+00:00</th>\n",
              "      <th>4</th>\n",
              "      <th>Monday</th>\n",
              "      <td>32</td>\n",
              "      <td>140</td>\n",
              "      <td>708.750000</td>\n",
              "      <td>5790.000000</td>\n",
              "      <td>2034.781250</td>\n",
              "      <td>4</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 07:00:00+00:00</th>\n",
              "      <th>7</th>\n",
              "      <th>Monday</th>\n",
              "      <td>12</td>\n",
              "      <td>36</td>\n",
              "      <td>10620.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>1408.000000</td>\n",
              "      <td>7</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 08:00:00+00:00</th>\n",
              "      <th>8</th>\n",
              "      <th>Monday</th>\n",
              "      <td>82</td>\n",
              "      <td>42</td>\n",
              "      <td>1576.097561</td>\n",
              "      <td>693.658537</td>\n",
              "      <td>1647.317073</td>\n",
              "      <td>8</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14 09:00:00+00:00</th>\n",
              "      <th>9</th>\n",
              "      <th>Monday</th>\n",
              "      <td>67</td>\n",
              "      <td>38</td>\n",
              "      <td>620.597015</td>\n",
              "      <td>1230.447761</td>\n",
              "      <td>2378.298507</td>\n",
              "      <td>9</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  search_count  ...     day\n",
              "search_time               hour_search day_search                ...        \n",
              "2019-10-14 03:00:00+00:00 3           Monday                12  ...  Monday\n",
              "2019-10-14 04:00:00+00:00 4           Monday                32  ...  Monday\n",
              "2019-10-14 07:00:00+00:00 7           Monday                12  ...  Monday\n",
              "2019-10-14 08:00:00+00:00 8           Monday                82  ...  Monday\n",
              "2019-10-14 09:00:00+00:00 9           Monday                67  ...  Monday\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "UZ7YRzvpLyjy",
        "outputId": "5778f4eb-28d2-4610-f3eb-0119313828bf"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(data=d, x='hour', y='high_demand', ci=25, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa36a816b50>"
            ]
          },
          "metadata": {},
          "execution_count": 145
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VnV1sv3Mc55"
      },
      "source": [
        "Grafik di atas menunjukkan jumlah kursi kosong pada tiap jamnya.\n",
        "* Pada jam 7 sampai jam 10 terjadi penurunan pada grafik. Hal ini menunjukkan menurunnya jumlah kursi kosong pada jam tersebut. Berarti pada jam 7 sampai jam 10 merupakan waktu waktu yang sibuk/ramai. Pada jam 7 sampai jam 10 merupakan waktu waktu berangkat jam kerja.\n",
        "* Mulai jam 3 sore sampai 8 malam terjadi penurunan pada grafik. Terjadi penurunan pada grafik yang berarti menurunnya jumlah kursi kosong pada jam tersebut. Hal ini bisa diasumsikan bahwa pada jam 3 jam sore sampai jam 8 malam merupakan waktu pulang kerja."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2wE-4KurPTN",
        "outputId": "88ffc39a-9faf-4d11-89d3-ecac92b6d94d"
      },
      "source": [
        "df_total2.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['user_id', 'yr_wk', 'new_segment_tag', 'session_id', 'search_id',\n",
              "       'search_city', 'search_time', 'num_of_results', 'is_result',\n",
              "       'median_pickup_walk_time', 'median_dropoff_walk_time',\n",
              "       'median_bus_travel_time', 'high_demand_val', 'user_device_os',\n",
              "       'user_device_app_version', 'search_session_id', 'booking_id',\n",
              "       'booking_timestamp', 'hour_search', 'hour_books', 'day_search',\n",
              "       'day_books'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cpp8GJgBlS4r",
        "outputId": "364efa44-a0db-4312-c3ef-c4ad77f42230"
      },
      "source": [
        "df_total2.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['user_id', 'yr_wk', 'new_segment_tag', 'session_id', 'search_id',\n",
              "       'search_city', 'search_time', 'num_of_results', 'is_result',\n",
              "       'median_pickup_walk_time', 'median_dropoff_walk_time',\n",
              "       'median_bus_travel_time', 'high_demand_val', 'user_device_os',\n",
              "       'user_device_app_version', 'search_session_id', 'booking_id',\n",
              "       'booking_timestamp', 'hour_search', 'hour_books', 'day_search',\n",
              "       'day_books'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miAcdc4djpDB",
        "outputId": "a132ff1e-99b8-4738-d291-1fe654dccca2"
      },
      "source": [
        "# hanya ada satu search_city sehingga tidak terlalu membantu dalam visualisasi data\n",
        "# Dan rute perjalanan sudah ditetapkan sehingga tidak bisa melihat rute mana yang paling ramai\n",
        "df_total2.search_city.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New York'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "GpqQ0rRodirZ",
        "outputId": "f50e4890-1d62-4e0a-e427-046d6aead7a8"
      },
      "source": [
        "sns.catplot(x=\"user_device_os\", hue=\"new_segment_tag\", kind=\"count\",\n",
        "            data=df_total2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa369d11dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 478.425x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "knphfK8D08rQ"
      },
      "source": [
        "Ternyata user pada Apps Bus Ride paling banyak menggunakan device android. Terlebih user tersebut merupakan user retained."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6j3Fq0i25Rf"
      },
      "source": [
        "df_total2.to_csv('df_total_fix.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVYEbWFcLFm5"
      },
      "source": [
        "## REKOMENDASI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKCnqg6VKn8P"
      },
      "source": [
        "## KESIMPULAN - Create the story"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4psNNh3_1eER"
      },
      "source": [
        "\n",
        "\n",
        "1.   Perusahaan bisa memberikan promo yang dibagi menjadi 3 jenis, yaitu\n",
        "> * Promo untuk user baru, dimana user baru bisa mendapatkan potongan harga \n",
        "> * Promo untuk user retained agar tetap menggunakan apps ini. Semakin banyak user retained melakukan pemesanan, maka bisa mendapatkan diskon yang lebih menggiurkan.\n",
        "> * Promo untuk user inactive yang telah inactive beberapa minggu sehingga ketika user kembali bisa tertarik untuk melakukan pemesanan\n",
        "2.   Mengurangi jumlah armada pada hari jumat.<br> Berdasarkan data di atas, hari jumat selalu mengalami penurunan entah itu dalam jumlah pencarian maupun pemesanan. Dengan mengurangi jumlah armada pada hari jumat bisa menekan biaya operasi.\n",
        "3.   Menambah Armada pada jam tertentu. <br>\n",
        "Pada jam berangkat kerja mulai dari jam 7 sampai 10 pagi dan jam pulang kerja dari jam 3 sore sampai jam 8 malam. Terjadi peningkatan jumlah pencarian, jumlah pemesanan dan juga menurunnya jumlah kursi kosong. Sehingga, diperlukan penambahan armada pada jam tersebut untuk memenuhi permintaan.\n",
        "\n"
      ]
    }
  ]
}