notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9wADwK78DCz"
      },
      "source": [
        "# Proyek Analisis Data: Bike Sharing Dataset\n",
        "- **Nama:** Muchamad Yuda Tri Ananda\n",
        "- **Email:** yudaplzhacker@gmail.com\n",
        "- **ID Dicoding:** myudak"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eE0raob58DC0"
      },
      "source": [
        "## Menentukan Pertanyaan Bisnis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmQeQ5YF8DC0"
      },
      "source": [
        "- Berapa jumlah rata-rata persewaan sepeda per jam, dan bagaimana variasinya sepanjang hari?\n",
        "  \n",
        "- Bagaimana hubungan kondisi cuaca dengan jumlah rata-rata persewaan?\n",
        "  \n",
        "- Bagaaimana hubungan hari libur tertentu yang terkait dengan kenaikan atau penurunan rata-rata Penyewaan?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-z4QGlO8DC1"
      },
      "source": [
        "## Import Semua Packages/Library yang Digunakan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "FVYwaObI8DC1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_Sh51Xy8DC1"
      },
      "source": [
        "## Data Wrangling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FfCCISYqfVLx"
      },
      "outputs": [],
      "source": [
        "link = lambda x : f\"https://raw.githubusercontent.com/myudak/Bike-Exploratory-Data-Analysis/main/data/{x}.csv\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXU2GBYu8DC1"
      },
      "source": [
        "### Gathering Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "zjCBk1BI8DC1",
        "outputId": "e1a9bb81-b842-449b-e9eb-efc259053dd6"
      },
      "outputs": [
        {
          "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>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-02</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.363478</td>\n",
              "      <td>0.353739</td>\n",
              "      <td>0.696087</td>\n",
              "      <td>0.248539</td>\n",
              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "0        1  2011-01-01       1   0     1        0        6           0   \n",
              "1        2  2011-01-02       1   0     1        0        0           0   \n",
              "2        3  2011-01-03       1   0     1        0        1           1   \n",
              "3        4  2011-01-04       1   0     1        0        2           1   \n",
              "4        5  2011-01-05       1   0     1        0        3           1   \n",
              "\n",
              "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
              "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
              "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
              "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
              "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
              "\n",
              "    cnt  \n",
              "0   985  \n",
              "1   801  \n",
              "2  1349  \n",
              "3  1562  \n",
              "4  1600  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day_df = pd.read_csv(link(\"day\"))\n",
        "day_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "7wC_VGQhfVLx",
        "outputId": "4ec57c57-e9b6-4547-a258-9cdb3ad093e5"
      },
      "outputs": [
        {
          "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>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
              "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
              "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
              "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
              "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
              "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
              "\n",
              "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
              "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
              "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
              "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
              "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
              "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour_df = pd.read_csv(link(\"hour\"))\n",
        "hour_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHSiqaZp8DC1"
      },
      "source": [
        "### Assessing Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_s-Tk2UfVLy"
      },
      "source": [
        "#### Memeriksa tipe data dari tiap kolom yang terdapat dalam bike_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyGuZaotfVLy",
        "outputId": "e0408136-9953-4bca-9ba8-8460cecfd72d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 16 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     731 non-null    int64  \n",
            " 1   dteday      731 non-null    object \n",
            " 2   season      731 non-null    int64  \n",
            " 3   yr          731 non-null    int64  \n",
            " 4   mnth        731 non-null    int64  \n",
            " 5   holiday     731 non-null    int64  \n",
            " 6   weekday     731 non-null    int64  \n",
            " 7   workingday  731 non-null    int64  \n",
            " 8   weathersit  731 non-null    int64  \n",
            " 9   temp        731 non-null    float64\n",
            " 10  atemp       731 non-null    float64\n",
            " 11  hum         731 non-null    float64\n",
            " 12  windspeed   731 non-null    float64\n",
            " 13  casual      731 non-null    int64  \n",
            " 14  registered  731 non-null    int64  \n",
            " 15  cnt         731 non-null    int64  \n",
            "dtypes: float64(4), int64(11), object(1)\n",
            "memory usage: 91.5+ KB\n"
          ]
        }
      ],
      "source": [
        "day_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVx45LJafVLy",
        "outputId": "ba37f12e-6f9b-41c5-cbb8-978d050f1155"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17379 entries, 0 to 17378\n",
            "Data columns (total 17 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     17379 non-null  int64  \n",
            " 1   dteday      17379 non-null  object \n",
            " 2   season      17379 non-null  int64  \n",
            " 3   yr          17379 non-null  int64  \n",
            " 4   mnth        17379 non-null  int64  \n",
            " 5   hr          17379 non-null  int64  \n",
            " 6   holiday     17379 non-null  int64  \n",
            " 7   weekday     17379 non-null  int64  \n",
            " 8   workingday  17379 non-null  int64  \n",
            " 9   weathersit  17379 non-null  int64  \n",
            " 10  temp        17379 non-null  float64\n",
            " 11  atemp       17379 non-null  float64\n",
            " 12  hum         17379 non-null  float64\n",
            " 13  windspeed   17379 non-null  float64\n",
            " 14  casual      17379 non-null  int64  \n",
            " 15  registered  17379 non-null  int64  \n",
            " 16  cnt         17379 non-null  int64  \n",
            "dtypes: float64(4), int64(12), object(1)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ],
      "source": [
        "hour_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHp9b-EsfVLz"
      },
      "source": [
        "Semua tipe data sesuai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_30O3g7nfVLz"
      },
      "source": [
        "#### Menilai data bike_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ax-3tEjc9Cj1",
        "outputId": "45931d59-3bb9-426a-88c6-810b9a7f6f0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "instant       0\n",
              "dteday        0\n",
              "season        0\n",
              "yr            0\n",
              "mnth          0\n",
              "holiday       0\n",
              "weekday       0\n",
              "workingday    0\n",
              "weathersit    0\n",
              "temp          0\n",
              "atemp         0\n",
              "hum           0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "cnt           0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDtVu1xafVLz",
        "outputId": "f8fb60f1-e807-4e5a-fb96-9299b8be9d26"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "instant       0\n",
              "dteday        0\n",
              "season        0\n",
              "yr            0\n",
              "mnth          0\n",
              "hr            0\n",
              "holiday       0\n",
              "weekday       0\n",
              "workingday    0\n",
              "weathersit    0\n",
              "temp          0\n",
              "atemp         0\n",
              "hum           0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "cnt           0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour_df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJgRuxWnfVLz"
      },
      "source": [
        "Berdasarkan hasil di atas, tidak ada masalah pada tipe data tiap kolom dalam day_df dan hour_df."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5rAA1yefVLz",
        "outputId": "8c6dca46-fa5e-496b-fb42-0c196b18cb6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        }
      ],
      "source": [
        "print(\"Jumlah duplikasi: \", day_df.duplicated().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F69NUROyfVLz",
        "outputId": "1633cb2e-4d24-440f-86d4-73d80db3a255"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        }
      ],
      "source": [
        "print(\"Jumlah duplikasi: \", hour_df.duplicated().sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhN5R4hr8DC1"
      },
      "source": [
        "### Cleaning Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8cB8G9uvfVL0"
      },
      "source": [
        "Semua tipe data sudah sesuai serta tidak ada masalah"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gp-Y6wU38DC1"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW7WF2kr8DC1"
      },
      "source": [
        "### Explore ..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ZYLKSplfVL0"
      },
      "source": [
        "<h5>Merge DataFrame</h5>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "N9F6jPR8fVL0",
        "outputId": "9b5e2078-2ebe-49c7-f069-a50f86a14b45"
      },
      "outputs": [
        {
          "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>instant_hour</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season_hour</th>\n",
              "      <th>yr_hour</th>\n",
              "      <th>mnth_hour</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday_hour</th>\n",
              "      <th>weekday_hour</th>\n",
              "      <th>workingday_hour</th>\n",
              "      <th>weathersit_hour</th>\n",
              "      <th>...</th>\n",
              "      <th>weekday_day</th>\n",
              "      <th>workingday_day</th>\n",
              "      <th>weathersit_day</th>\n",
              "      <th>temp_day</th>\n",
              "      <th>atemp_day</th>\n",
              "      <th>hum_day</th>\n",
              "      <th>windspeed_day</th>\n",
              "      <th>casual_day</th>\n",
              "      <th>registered_day</th>\n",
              "      <th>cnt_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.344167</td>\n",
              "      <td>0.363625</td>\n",
              "      <td>0.805833</td>\n",
              "      <td>0.160446</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   instant_hour      dteday  season_hour  yr_hour  mnth_hour  hr  \\\n",
              "0             1  2011-01-01            1        0          1   0   \n",
              "1             2  2011-01-01            1        0          1   1   \n",
              "2             3  2011-01-01            1        0          1   2   \n",
              "3             4  2011-01-01            1        0          1   3   \n",
              "4             5  2011-01-01            1        0          1   4   \n",
              "\n",
              "   holiday_hour  weekday_hour  workingday_hour  weathersit_hour  ...  \\\n",
              "0             0             6                0                1  ...   \n",
              "1             0             6                0                1  ...   \n",
              "2             0             6                0                1  ...   \n",
              "3             0             6                0                1  ...   \n",
              "4             0             6                0                1  ...   \n",
              "\n",
              "   weekday_day  workingday_day  weathersit_day  temp_day  atemp_day   hum_day  \\\n",
              "0            6               0               2  0.344167   0.363625  0.805833   \n",
              "1            6               0               2  0.344167   0.363625  0.805833   \n",
              "2            6               0               2  0.344167   0.363625  0.805833   \n",
              "3            6               0               2  0.344167   0.363625  0.805833   \n",
              "4            6               0               2  0.344167   0.363625  0.805833   \n",
              "\n",
              "   windspeed_day  casual_day  registered_day  cnt_day  \n",
              "0       0.160446         331             654      985  \n",
              "1       0.160446         331             654      985  \n",
              "2       0.160446         331             654      985  \n",
              "3       0.160446         331             654      985  \n",
              "4       0.160446         331             654      985  \n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_df = hour_df.merge(day_df, on='dteday', how='inner', suffixes=('_hour', '_day'))\n",
        "bike_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skVspL8WfVL0"
      },
      "source": [
        "<h5>Tambah Weather Labels</h5>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "aKBAGXXrfVL0"
      },
      "outputs": [],
      "source": [
        "weather_labels = {\n",
        "    1: 'Jernih',\n",
        "    2: 'Kabut',\n",
        "    3: 'Curah Hujan Ringan',\n",
        "    4: 'Curah Hujan Lebat'\n",
        "}\n",
        "\n",
        "bike_df['weather_label'] = bike_df['weathersit_day'].map(weather_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "e9CQCZjk8DC2",
        "outputId": "7ff04881-11f9-4952-81a5-b86253684683"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b7fcb27a-823f-4f2c-8daa-5df48a2277a7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant_hour</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season_hour</th>\n",
              "      <th>yr_hour</th>\n",
              "      <th>mnth_hour</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday_hour</th>\n",
              "      <th>weekday_hour</th>\n",
              "      <th>workingday_hour</th>\n",
              "      <th>weathersit_hour</th>\n",
              "      <th>...</th>\n",
              "      <th>workingday_day</th>\n",
              "      <th>weathersit_day</th>\n",
              "      <th>temp_day</th>\n",
              "      <th>atemp_day</th>\n",
              "      <th>hum_day</th>\n",
              "      <th>windspeed_day</th>\n",
              "      <th>casual_day</th>\n",
              "      <th>registered_day</th>\n",
              "      <th>cnt_day</th>\n",
              "      <th>weather_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17379.0000</td>\n",
              "      <td>17379</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>731</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jernih</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.501640</td>\n",
              "      <td>0.502561</td>\n",
              "      <td>6.537775</td>\n",
              "      <td>11.546752</td>\n",
              "      <td>0.028770</td>\n",
              "      <td>3.003683</td>\n",
              "      <td>0.682721</td>\n",
              "      <td>1.425283</td>\n",
              "      <td>...</td>\n",
              "      <td>0.682721</td>\n",
              "      <td>1.391680</td>\n",
              "      <td>0.496987</td>\n",
              "      <td>0.475775</td>\n",
              "      <td>0.627229</td>\n",
              "      <td>0.190098</td>\n",
              "      <td>854.339720</td>\n",
              "      <td>3679.353242</td>\n",
              "      <td>4533.692963</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5017.0295</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.106918</td>\n",
              "      <td>0.500008</td>\n",
              "      <td>3.438776</td>\n",
              "      <td>6.914405</td>\n",
              "      <td>0.167165</td>\n",
              "      <td>2.005771</td>\n",
              "      <td>0.465431</td>\n",
              "      <td>0.639357</td>\n",
              "      <td>...</td>\n",
              "      <td>0.465431</td>\n",
              "      <td>0.540501</td>\n",
              "      <td>0.182445</td>\n",
              "      <td>0.162426</td>\n",
              "      <td>0.141779</td>\n",
              "      <td>0.077204</td>\n",
              "      <td>685.686754</td>\n",
              "      <td>1544.953518</td>\n",
              "      <td>1917.376947</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.059130</td>\n",
              "      <td>0.079070</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022392</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4345.5000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.338363</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.134950</td>\n",
              "      <td>318.000000</td>\n",
              "      <td>2545.000000</td>\n",
              "      <td>3214.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.502500</td>\n",
              "      <td>0.490122</td>\n",
              "      <td>0.625833</td>\n",
              "      <td>0.180967</td>\n",
              "      <td>724.000000</td>\n",
              "      <td>3681.000000</td>\n",
              "      <td>4563.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13034.5000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.655833</td>\n",
              "      <td>0.610488</td>\n",
              "      <td>0.729583</td>\n",
              "      <td>0.233204</td>\n",
              "      <td>1100.000000</td>\n",
              "      <td>4801.000000</td>\n",
              "      <td>5986.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17379.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.861667</td>\n",
              "      <td>0.840896</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.507463</td>\n",
              "      <td>3410.000000</td>\n",
              "      <td>6946.000000</td>\n",
              "      <td>8714.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 33 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7fcb27a-823f-4f2c-8daa-5df48a2277a7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b7fcb27a-823f-4f2c-8daa-5df48a2277a7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b7fcb27a-823f-4f2c-8daa-5df48a2277a7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2be0a4c0-ab7e-49f1-b528-f1bfd5c233c1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2be0a4c0-ab7e-49f1-b528-f1bfd5c233c1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2be0a4c0-ab7e-49f1-b528-f1bfd5c233c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        instant_hour      dteday   season_hour       yr_hour     mnth_hour  \\\n",
              "count     17379.0000       17379  17379.000000  17379.000000  17379.000000   \n",
              "unique           NaN         731           NaN           NaN           NaN   \n",
              "top              NaN  2011-01-01           NaN           NaN           NaN   \n",
              "freq             NaN          24           NaN           NaN           NaN   \n",
              "mean       8690.0000         NaN      2.501640      0.502561      6.537775   \n",
              "std        5017.0295         NaN      1.106918      0.500008      3.438776   \n",
              "min           1.0000         NaN      1.000000      0.000000      1.000000   \n",
              "25%        4345.5000         NaN      2.000000      0.000000      4.000000   \n",
              "50%        8690.0000         NaN      3.000000      1.000000      7.000000   \n",
              "75%       13034.5000         NaN      3.000000      1.000000     10.000000   \n",
              "max       17379.0000         NaN      4.000000      1.000000     12.000000   \n",
              "\n",
              "                  hr  holiday_hour  weekday_hour  workingday_hour  \\\n",
              "count   17379.000000  17379.000000  17379.000000     17379.000000   \n",
              "unique           NaN           NaN           NaN              NaN   \n",
              "top              NaN           NaN           NaN              NaN   \n",
              "freq             NaN           NaN           NaN              NaN   \n",
              "mean       11.546752      0.028770      3.003683         0.682721   \n",
              "std         6.914405      0.167165      2.005771         0.465431   \n",
              "min         0.000000      0.000000      0.000000         0.000000   \n",
              "25%         6.000000      0.000000      1.000000         0.000000   \n",
              "50%        12.000000      0.000000      3.000000         1.000000   \n",
              "75%        18.000000      0.000000      5.000000         1.000000   \n",
              "max        23.000000      1.000000      6.000000         1.000000   \n",
              "\n",
              "        weathersit_hour  ...  workingday_day  weathersit_day      temp_day  \\\n",
              "count      17379.000000  ...    17379.000000    17379.000000  17379.000000   \n",
              "unique              NaN  ...             NaN             NaN           NaN   \n",
              "top                 NaN  ...             NaN             NaN           NaN   \n",
              "freq                NaN  ...             NaN             NaN           NaN   \n",
              "mean           1.425283  ...        0.682721        1.391680      0.496987   \n",
              "std            0.639357  ...        0.465431        0.540501      0.182445   \n",
              "min            1.000000  ...        0.000000        1.000000      0.059130   \n",
              "25%            1.000000  ...        0.000000        1.000000      0.340000   \n",
              "50%            1.000000  ...        1.000000        1.000000      0.502500   \n",
              "75%            2.000000  ...        1.000000        2.000000      0.655833   \n",
              "max            4.000000  ...        1.000000        3.000000      0.861667   \n",
              "\n",
              "           atemp_day       hum_day  windspeed_day    casual_day  \\\n",
              "count   17379.000000  17379.000000   17379.000000  17379.000000   \n",
              "unique           NaN           NaN            NaN           NaN   \n",
              "top              NaN           NaN            NaN           NaN   \n",
              "freq             NaN           NaN            NaN           NaN   \n",
              "mean        0.475775      0.627229       0.190098    854.339720   \n",
              "std         0.162426      0.141779       0.077204    685.686754   \n",
              "min         0.079070      0.000000       0.022392      2.000000   \n",
              "25%         0.338363      0.520000       0.134950    318.000000   \n",
              "50%         0.490122      0.625833       0.180967    724.000000   \n",
              "75%         0.610488      0.729583       0.233204   1100.000000   \n",
              "max         0.840896      0.972500       0.507463   3410.000000   \n",
              "\n",
              "        registered_day       cnt_day  weather_label  \n",
              "count     17379.000000  17379.000000          17379  \n",
              "unique             NaN           NaN              3  \n",
              "top                NaN           NaN         Jernih  \n",
              "freq               NaN           NaN          11040  \n",
              "mean       3679.353242   4533.692963            NaN  \n",
              "std        1544.953518   1917.376947            NaN  \n",
              "min          20.000000     22.000000            NaN  \n",
              "25%        2545.000000   3214.000000            NaN  \n",
              "50%        3681.000000   4563.000000            NaN  \n",
              "75%        4801.000000   5986.000000            NaN  \n",
              "max        6946.000000   8714.000000            NaN  \n",
              "\n",
              "[11 rows x 33 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_df.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "znn87v2dfVL1",
        "outputId": "faceae41-475d-4e3d-89f2-0fab98d5879b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-52ad1608-8120-4957-925b-ec00619ae869\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>weather_label</th>\n",
              "      <th>cnt_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Curah Hujan Ringan</td>\n",
              "      <td>1914.700855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Kabut</td>\n",
              "      <td>4062.717595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jernih</td>\n",
              "      <td>4895.177083</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52ad1608-8120-4957-925b-ec00619ae869')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-52ad1608-8120-4957-925b-ec00619ae869 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-52ad1608-8120-4957-925b-ec00619ae869');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9f86a1e3-cc29-4344-9807-10d784832e2a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f86a1e3-cc29-4344-9807-10d784832e2a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9f86a1e3-cc29-4344-9807-10d784832e2a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        weather_label      cnt_day\n",
              "0  Curah Hujan Ringan  1914.700855\n",
              "2               Kabut  4062.717595\n",
              "1              Jernih  4895.177083"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_df.groupby('weather_label')['cnt_day'].mean().reset_index().sort_values(\"cnt_day\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "dO_e2Yq3fVL1",
        "outputId": "1183f02e-8304-489a-978e-fe1aec0d7adb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-500fdbfd-9c69-4cdf-81d0-089b7535e2f1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hr</th>\n",
              "      <th>cnt_hour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>53.898072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>33.375691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>22.869930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>11.727403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>6.352941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>19.889819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>76.044138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>212.064649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>359.011004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>219.309491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>173.668501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11</td>\n",
              "      <td>208.143054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12</td>\n",
              "      <td>253.315934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>13</td>\n",
              "      <td>253.661180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>14</td>\n",
              "      <td>240.949246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>15</td>\n",
              "      <td>251.233196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>16</td>\n",
              "      <td>311.983562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>17</td>\n",
              "      <td>461.452055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>18</td>\n",
              "      <td>425.510989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>311.523352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>20</td>\n",
              "      <td>226.030220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>21</td>\n",
              "      <td>172.314560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>22</td>\n",
              "      <td>131.335165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>23</td>\n",
              "      <td>87.831044</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-500fdbfd-9c69-4cdf-81d0-089b7535e2f1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-500fdbfd-9c69-4cdf-81d0-089b7535e2f1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-500fdbfd-9c69-4cdf-81d0-089b7535e2f1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bfbc4a7c-192a-4601-bda6-94a62ee03005\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bfbc4a7c-192a-4601-bda6-94a62ee03005')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bfbc4a7c-192a-4601-bda6-94a62ee03005 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    hr    cnt_hour\n",
              "0    0   53.898072\n",
              "1    1   33.375691\n",
              "2    2   22.869930\n",
              "3    3   11.727403\n",
              "4    4    6.352941\n",
              "5    5   19.889819\n",
              "6    6   76.044138\n",
              "7    7  212.064649\n",
              "8    8  359.011004\n",
              "9    9  219.309491\n",
              "10  10  173.668501\n",
              "11  11  208.143054\n",
              "12  12  253.315934\n",
              "13  13  253.661180\n",
              "14  14  240.949246\n",
              "15  15  251.233196\n",
              "16  16  311.983562\n",
              "17  17  461.452055\n",
              "18  18  425.510989\n",
              "19  19  311.523352\n",
              "20  20  226.030220\n",
              "21  21  172.314560\n",
              "22  22  131.335165\n",
              "23  23   87.831044"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_df.groupby('hr')['cnt_hour'].mean().reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "8sl8N4dafVL1",
        "outputId": "62bcde66-1fd6-4479-95c6-88ce9bfe97c8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cf32e3ba-0d41-4b73-82cd-c5d014931713\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>holiday_day</th>\n",
              "      <th>cnt_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3750.060000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4556.906215</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf32e3ba-0d41-4b73-82cd-c5d014931713')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cf32e3ba-0d41-4b73-82cd-c5d014931713 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cf32e3ba-0d41-4b73-82cd-c5d014931713');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b51e386e-6194-4f46-8d1f-2c3ce3a6fbad\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b51e386e-6194-4f46-8d1f-2c3ce3a6fbad')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b51e386e-6194-4f46-8d1f-2c3ce3a6fbad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   holiday_day      cnt_day\n",
              "1            1  3750.060000\n",
              "0            0  4556.906215"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_df.groupby('holiday_day')['cnt_day'].mean().reset_index().sort_values(\"cnt_day\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2672662"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_df.registered_hour.sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsyZjqak8DC2"
      },
      "source": [
        "## Visualization & Explanatory Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FxmuibeyfVL1"
      },
      "source": [
        "- Berapa jumlah rata-rata persewaan sepeda per jam, dan bagaimana variasinya sepanjang hari?\n",
        "  \n",
        "- Bagaimana hubungan kondisi cuaca dengan jumlah rata-rata persewaan?\n",
        "  \n",
        "- Bagaaimana hubungan hari libur tertentu yang terkait dengan kenaikan atau penurunan rata-rata Penyewaan?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZxOiQ6n8DC2"
      },
      "source": [
        "### Pertanyaan 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "XMU9YH4ZfVL2",
        "outputId": "2893b8ee-f3d1-45d6-e3c4-1f4ab6e37f54"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rental_jam = bike_df.groupby('hr')['cnt_hour'].mean()\n",
        "\n",
        "plt.bar(rental_jam.index, rental_jam.values, color='#1f77b4')\n",
        "\n",
        "plt.title('Rata - Rata Penyewaan per Jam')\n",
        "plt.xlabel('Jam')\n",
        "plt.ylabel('Rata - Rata Penyewaan')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LySPYPY9Joqk"
      },
      "source": [
        "Rata - Rata Penyewaan sepeda paling banyak terjadi pada jam 17 dan 18 atau jam 5 PM dan 6 PM. Paling sedikit jam 4 AM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgHI7CiU8DC2"
      },
      "source": [
        "### Pertanyaan 2:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Go0lCsvO8DC2",
        "outputId": "80609d0f-0344-41d3-eb15-81baa3ac65bd"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "avg_weather = bike_df.groupby('weather_label')['cnt_day'].mean().reset_index().sort_values(\"cnt_day\")\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='cnt_day', y='weather_label', data=avg_weather, palette='viridis')\n",
        "\n",
        "plt.title('Rata - Rata Penyewaan Sepeda berdasarkan Kondisi Cuaca')\n",
        "plt.xlabel('Rata - Rata Penyewaan')\n",
        "plt.ylabel('Kondisi Cuaca')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1aPb1aCKyEd"
      },
      "source": [
        "kondisi cuaca dengan jumlah rata-rata persewaan berhubungan erat seperti visualisasi diatas. Rata-rata persewaan paling banyak terjadi pada saat cuaca jernih dan paling sedikit terjadi saat cuaca hujan ringan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rga0fC36fVL7"
      },
      "source": [
        "### Pertanyaan 3:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "rq_L5AhYfVL7",
        "outputId": "362ade61-fd6e-45d8-f09a-8b5c7bcec5fd"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "avg_holiday = bike_df.groupby('holiday_day')['cnt_day'].mean().reset_index().sort_values(\"cnt_day\")\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.barplot(x='holiday_day', y='cnt_day', data=avg_holiday, palette='Set2')\n",
        "\n",
        "plt.title('Rata-rata Penyewaan Sepeda pada Hari Libur')\n",
        "plt.xlabel('Hari Libur')\n",
        "plt.ylabel('Rata-rata Penyewaan')\n",
        "plt.xticks([0, 1], ['Tidak Libur', 'Libur'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GilHsYcsL7FE"
      },
      "source": [
        "Rate - rata penyewaan terjadi saat hari tidak libur ( Senin - Jumat )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WeHlCeX8DC2"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTcyR48Y8DC2"
      },
      "source": [
        "- **Conclution pertanyaan 1** :\n",
        "Rata rata penyewaan sepeda paling banyak terjadi pada saat pagi hari dan sore hari yaitu jam 8 AM dan 10 PM.\n",
        "  - **Pola Transportasi Harian** :\n",
        "  Jam 8 pagi adalah waktu yang umumnya sesuai dengan awal hari kerja, dan banyak orang menggunakan sepeda sebagai sarana transportasi menuju tempat kerja atau sekolah pada saat tersebut. Peningkatan penyewaan sepeda pada jam ini mencerminkan kebutuhan mobilitas pagi hari. Jam 6 sore juga mencerminkan waktu pulang kerja atau sekolah, di mana banyak orang kembali ke rumah atau melakukan kegiatan rekreasi. Penggunaan sepeda sebagai sarana pulang mungkin menjadi pilihan yang populer.\n",
        "  - **Aktivitas Rekreasi dan Kesehatan** :\n",
        "  Jam 8 pagi dapat mencerminkan saat orang-orang sedang memulai hari dengan olahraga atau aktivitas fisik, seperti bersepeda. Jam 6 sore adalah waktu yang cocok untuk aktivitas rekreasi dan olahraga setelah jam kerja. Aktivitas fisik seperti bersepeda dapat dijadikan pilihan.\n",
        "  - **Kondisi Cuaca:** :\n",
        "  Pada pagi dan sore hari, kondisi cuaca cenderung lebih bersahabat, yang dapat memotivasi orang untuk bersepeda. Kondisi cuaca yang baik pada jam-jam tersebut dapat meningkatkan minat pengguna untuk melakukan aktivitas di luar ruangan, termasuk bersepeda.\n",
        "  \n",
        "\n",
        "- **Conclution pertanyaan 2** :\n",
        "Kondisi cuaca berhubungan erat dengan rata - rata penyewaan sepeda. Rata - rata penyewaan sepeda terjadi saat cuaca cerah\n",
        "  - **Kondisi Cuaca yang Mendukung Aktivitas Luar Ruangan** :\n",
        "   Cuaca cerah cenderung menciptakan kondisi yang lebih nyaman untuk aktivitas di luar ruangan, termasuk bersepeda. Orang-orang lebih cenderung memanfaatkan sepeda saat cuaca cerah karena keberlangsungan aktivitas tersebut tidak terganggu oleh hujan atau kondisi cuaca yang kurang bersahabat.\n",
        "  - **Minat Tinggi pada Cuaca Baik** :\n",
        "  Penggunaan sepeda seringkali berkaitan dengan hobi atau rekreasi, dan cuaca cerah dapat meningkatkan minat untuk bersepeda. Pengguna mungkin lebih termotivasi untuk menyewa sepeda dan menikmati perjalanan di bawah sinar matahari saat cuaca sedang baik.\n",
        "  \n",
        "  \n",
        "- **Conclution pertanyaan 3** :\n",
        "Rate - rata penyewaan terjadi saat hari tidak libur ( Senin - Jumat ).\n",
        "  - **Keterkaitan dengan Hari Kerja** :\n",
        "   Hal ini kemungkinan dapat disebabkan oleh tingginya aktivitas transportasi pada hari-hari kerja ketika orang-orang pergi atau pulang dari pekerjaan.\n",
        "  - **Pengaruh Kegiatan Harian** :\n",
        "  Kenaikan penyewaan pada hari-hari kerja mungkin mencerminkan kebutuhan orang-orang untuk beraktivitas sehari-hari, seperti bekerja, bersekolah, atau berbelanja. Penggunaan sepeda dapat menjadi pilihan yang populer untuk perjalanan pendek, terutama pada hari-hari di mana orang-orang memiliki rutinitas harian.\n",
        "  - **Penyewaan oleh Pengguna Tertentu** :\n",
        "  Mungkin ada kelompok pengguna tertentu, seperti para pekerja atau pelajar, yang secara khusus menggunakan layanan penyewaan sepeda pada hari-hari kerja.\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "H-z4QGlO8DC1",
        "h_Sh51Xy8DC1",
        "c_s-Tk2UfVLy",
        "_30O3g7nfVLz",
        "EhN5R4hr8DC1",
        "MW7WF2kr8DC1"
      ],
      "provenance": []
    },
    "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": "3.10.0"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "972b3bf27e332e87b5379f2791f6ef9dfc79c71018c370b0d7423235e20fe4d7"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}