bike_sharing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPenwNWIwPjQ"
      },
      "source": [
        "# Data Analytics Project: Bike Sharing Dataset\n",
        "- Email: anas.f.hanif@students.amikom.ac.id\n",
        "- Id Dicoding: anashaneef"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaIi3Hpssh2E"
      },
      "source": [
        "## **Business Questions**\n",
        "* What are the types of Bike Sharing users on a daily basis?\n",
        "* How productive is Bike Sharing in 24 hours?\n",
        "* How productive is Bike Sharing on a monthly basis?\n",
        "* How do weather and seasons affect Bike Sharing productivity?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcjWTK9zsfEB"
      },
      "source": [
        "## **Import Library**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VFNEX1miRi9G"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib.patches as mpatches"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "adPPn_TBwcL-"
      },
      "source": [
        "## **Data Wrangling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-A0uSWFwkGR"
      },
      "source": [
        "### **Gathering Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "wazwuNMVwe0S",
        "outputId": "929ed8c1-a788-459d-883b-86db13d09c89"
      },
      "outputs": [
        {
          "data": {
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              "<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": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day = pd.read_csv('./data/day.csv')\n",
        "day.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
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      "outputs": [
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              "<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",
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              "      <td>1</td>\n",
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              "      <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",
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            "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": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour = pd.read_csv('./data/hour.csv')\n",
        "hour.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49quRb63xD4E"
      },
      "source": [
        "### **Assessing Data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1o1-o3A3zpE"
      },
      "source": [
        "**Dataset Information**\n",
        "* instant: record index\n",
        "* dteday : date\n",
        "* season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
        "* yr : year (0: 2011, 1:2012)\n",
        "* mnth : month ( 1 to 12)\n",
        "* hr : hour (0 to 23)\n",
        "* holiday : weather day is holiday or not (extracted from [Web Link])\n",
        "* weekday : day of the week\n",
        "* workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
        "* weathersit :\n",
        "  * Clear, Few clouds, Partly cloudy, Partly cloudy\n",
        "  * Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
        "  * Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
        "  * Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
        "* temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)\n",
        "* atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)\n",
        "* hum: Normalized humidity. The values are divided to 100 (max)\n",
        "* windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
        "* casual: count of casual users\n",
        "* registered: count of registered users\n",
        "* cnt: count of total rental bikes including both casual and registered"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_DHN-C5xSzK"
      },
      "source": [
        "#### **Day Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7uoq5PaxHKY",
        "outputId": "9ced2916-c575-4293-9f39-96a31ca2dd38"
      },
      "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.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "60qGTsQAxJJK",
        "outputId": "fe2b94b8-2fca-409d-d312-0a1aa7308942"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of duplications:  0\n"
          ]
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</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>count</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>2.496580</td>\n",
              "      <td>0.500684</td>\n",
              "      <td>6.519836</td>\n",
              "      <td>0.028728</td>\n",
              "      <td>2.997264</td>\n",
              "      <td>0.683995</td>\n",
              "      <td>1.395349</td>\n",
              "      <td>0.495385</td>\n",
              "      <td>0.474354</td>\n",
              "      <td>0.627894</td>\n",
              "      <td>0.190486</td>\n",
              "      <td>848.176471</td>\n",
              "      <td>3656.172367</td>\n",
              "      <td>4504.348837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>211.165812</td>\n",
              "      <td>1.110807</td>\n",
              "      <td>0.500342</td>\n",
              "      <td>3.451913</td>\n",
              "      <td>0.167155</td>\n",
              "      <td>2.004787</td>\n",
              "      <td>0.465233</td>\n",
              "      <td>0.544894</td>\n",
              "      <td>0.183051</td>\n",
              "      <td>0.162961</td>\n",
              "      <td>0.142429</td>\n",
              "      <td>0.077498</td>\n",
              "      <td>686.622488</td>\n",
              "      <td>1560.256377</td>\n",
              "      <td>1937.211452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>183.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.337083</td>\n",
              "      <td>0.337842</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.134950</td>\n",
              "      <td>315.500000</td>\n",
              "      <td>2497.000000</td>\n",
              "      <td>3152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.498333</td>\n",
              "      <td>0.486733</td>\n",
              "      <td>0.626667</td>\n",
              "      <td>0.180975</td>\n",
              "      <td>713.000000</td>\n",
              "      <td>3662.000000</td>\n",
              "      <td>4548.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>548.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.655417</td>\n",
              "      <td>0.608602</td>\n",
              "      <td>0.730209</td>\n",
              "      <td>0.233214</td>\n",
              "      <td>1096.000000</td>\n",
              "      <td>4776.500000</td>\n",
              "      <td>5956.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          instant      season          yr        mnth     holiday     weekday  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
              "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
              "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
              "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
              "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
              "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
              "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
              "\n",
              "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
              "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
              "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
              "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
              "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
              "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
              "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
              "\n",
              "            casual   registered          cnt  \n",
              "count   731.000000   731.000000   731.000000  \n",
              "mean    848.176471  3656.172367  4504.348837  \n",
              "std     686.622488  1560.256377  1937.211452  \n",
              "min       2.000000    20.000000    22.000000  \n",
              "25%     315.500000  2497.000000  3152.000000  \n",
              "50%     713.000000  3662.000000  4548.000000  \n",
              "75%    1096.000000  4776.500000  5956.000000  \n",
              "max    3410.000000  6946.000000  8714.000000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Number of duplications: ', day.duplicated().sum())\n",
        "day.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lm6XRm4WxYYR"
      },
      "source": [
        "#### **Hour Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jAPNKRkxak-",
        "outputId": "fd6e5867-eba3-44e7-df41-5a91f6ce1667"
      },
      "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.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "sj7ENZx8xekj",
        "outputId": "c21169b5-49cc-4a75-87d8-eb4f970fe1a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of duplications:  0\n"
          ]
        },
        {
          "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>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>count</th>\n",
              "      <td>17379.0000</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>17379.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8690.0000</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>0.496987</td>\n",
              "      <td>0.475775</td>\n",
              "      <td>0.627229</td>\n",
              "      <td>0.190098</td>\n",
              "      <td>35.676218</td>\n",
              "      <td>153.786869</td>\n",
              "      <td>189.463088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5017.0295</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>0.192556</td>\n",
              "      <td>0.171850</td>\n",
              "      <td>0.192930</td>\n",
              "      <td>0.122340</td>\n",
              "      <td>49.305030</td>\n",
              "      <td>151.357286</td>\n",
              "      <td>181.387599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0000</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>0.020000</td>\n",
              "      <td>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4345.5000</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>0.340000</td>\n",
              "      <td>0.333300</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>0.104500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8690.0000</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>0.500000</td>\n",
              "      <td>0.484800</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>0.194000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>142.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13034.5000</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>0.660000</td>\n",
              "      <td>0.621200</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.253700</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>281.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17379.0000</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>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.850700</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>977.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          instant        season            yr          mnth            hr  \\\n",
              "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
              "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
              "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
              "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
              "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
              "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
              "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
              "\n",
              "            holiday       weekday    workingday    weathersit          temp  \\\n",
              "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
              "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
              "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
              "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
              "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
              "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
              "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
              "\n",
              "              atemp           hum     windspeed        casual    registered  \\\n",
              "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
              "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
              "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
              "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
              "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
              "\n",
              "                cnt  \n",
              "count  17379.000000  \n",
              "mean     189.463088  \n",
              "std      181.387599  \n",
              "min        1.000000  \n",
              "25%       40.000000  \n",
              "50%      142.000000  \n",
              "75%      281.000000  \n",
              "max      977.000000  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Number of duplications: ', hour.duplicated().sum())\n",
        "hour.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZX8N6Q4-xtPL"
      },
      "source": [
        "### **Cleaning Data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jykayUjCxz_Q"
      },
      "source": [
        "#### **Day Dataframe**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivzlRQCC2--A"
      },
      "source": [
        "Delete the holiday column because the information in the column is already represented in the workingday column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "YRfsPBm03OKm"
      },
      "outputs": [],
      "source": [
        "day.drop(['holiday'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKS0iSAa0En5"
      },
      "source": [
        "Changed some column header names to make it easier to understand and avoid writing errors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Ps5_yRUFxvg_"
      },
      "outputs": [],
      "source": [
        "day.rename(columns={'dteday':'date', 'yr':'year', 'mnth':'month', 'weekday':'day', 'weathersit':'weather',\n",
        "                    'temp':'temperature', 'atemp':'temperature_feel', 'hum':'humidity', 'casual':'casual_user',\n",
        "                    'registered':'registered_user', 'cnt':'total_user'}, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O5WkpUc84pSt"
      },
      "source": [
        "Change the data type of the date column from object to datetime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ZE5-yHDN2bU9"
      },
      "outputs": [],
      "source": [
        "datetime_columns = ['date']\n",
        "\n",
        "for column in datetime_columns:\n",
        "  day[column] = pd.to_datetime(day[column])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vpaFBqmg_9V6",
        "outputId": "86983f3d-227f-4d90-c3c8-03107a194244"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 15 columns):\n",
            " #   Column            Non-Null Count  Dtype         \n",
            "---  ------            --------------  -----         \n",
            " 0   instant           731 non-null    int64         \n",
            " 1   date              731 non-null    datetime64[ns]\n",
            " 2   season            731 non-null    int64         \n",
            " 3   year              731 non-null    int64         \n",
            " 4   month             731 non-null    int64         \n",
            " 5   day               731 non-null    int64         \n",
            " 6   workingday        731 non-null    int64         \n",
            " 7   weather           731 non-null    int64         \n",
            " 8   temperature       731 non-null    float64       \n",
            " 9   temperature_feel  731 non-null    float64       \n",
            " 10  humidity          731 non-null    float64       \n",
            " 11  windspeed         731 non-null    float64       \n",
            " 12  casual_user       731 non-null    int64         \n",
            " 13  registered_user   731 non-null    int64         \n",
            " 14  total_user        731 non-null    int64         \n",
            "dtypes: datetime64[ns](1), float64(4), int64(10)\n",
            "memory usage: 85.8 KB\n"
          ]
        }
      ],
      "source": [
        "day.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PyCsR8585RhJ"
      },
      "source": [
        "Convert seasons from numeric to actual values. The numbers in the season represent:\n",
        "* 1 is springer\n",
        "* 2 is summer\n",
        "* 3 is fall\n",
        "* 4 is winter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "E_xG3LBH51ax",
        "outputId": "c9bd3ec2-6952-4c27-e38d-411211bd2d66"
      },
      "outputs": [
        {
          "data": {
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>springer</td>\n",
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              "      <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>springer</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>springer</td>\n",
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              "      <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>springer</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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       date    season  year  month  day  workingday  weather  \\\n",
              "0        1 2011-01-01  springer     0      1    6           0        2   \n",
              "1        2 2011-01-02  springer     0      1    0           0        2   \n",
              "2        3 2011-01-03  springer     0      1    1           1        1   \n",
              "3        4 2011-01-04  springer     0      1    2           1        1   \n",
              "4        5 2011-01-05  springer     0      1    3           1        1   \n",
              "\n",
              "   temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0     0.344167          0.363625  0.805833   0.160446          331   \n",
              "1     0.363478          0.353739  0.696087   0.248539          131   \n",
              "2     0.196364          0.189405  0.437273   0.248309          120   \n",
              "3     0.200000          0.212122  0.590435   0.160296          108   \n",
              "4     0.226957          0.229270  0.436957   0.186900           82   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0              654         985  \n",
              "1              670         801  \n",
              "2             1229        1349  \n",
              "3             1454        1562  \n",
              "4             1518        1600  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def change_season(x):\n",
        "    if x == 1:\n",
        "        return 'springer'\n",
        "    elif x == 2:\n",
        "        return 'summer'\n",
        "    elif x == 3:\n",
        "        return 'fall'\n",
        "    else:\n",
        "        return 'winter'\n",
        "\n",
        "day['season'] = day['season'].apply(change_season)\n",
        "day.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsWlWQYx6YwT"
      },
      "source": [
        "Change the year with the following conditions:\n",
        "* 0 means it is 2011\n",
        "* 1 means it is 2012"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "M4NNx_QD6nzs",
        "outputId": "fd87896e-38b7-485a-bcf9-d43cb3d7fd31"
      },
      "outputs": [
        {
          "data": {
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              "      <th>day</th>\n",
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              "      <th>0</th>\n",
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              "      <td>2011-01-01</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
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              "      <td>120</td>\n",
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              "      <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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</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       date    season  year  month  day  workingday  weather  \\\n",
              "0        1 2011-01-01  springer  2011      1    6           0        2   \n",
              "1        2 2011-01-02  springer  2011      1    0           0        2   \n",
              "2        3 2011-01-03  springer  2011      1    1           1        1   \n",
              "3        4 2011-01-04  springer  2011      1    2           1        1   \n",
              "4        5 2011-01-05  springer  2011      1    3           1        1   \n",
              "\n",
              "   temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0     0.344167          0.363625  0.805833   0.160446          331   \n",
              "1     0.363478          0.353739  0.696087   0.248539          131   \n",
              "2     0.196364          0.189405  0.437273   0.248309          120   \n",
              "3     0.200000          0.212122  0.590435   0.160296          108   \n",
              "4     0.226957          0.229270  0.436957   0.186900           82   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0              654         985  \n",
              "1              670         801  \n",
              "2             1229        1349  \n",
              "3             1454        1562  \n",
              "4             1518        1600  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def change_year(x):\n",
        "    if x == 0:\n",
        "        return 2011\n",
        "    else:\n",
        "        return 2012\n",
        "\n",
        "day['year'] = day['year'].apply(change_year)\n",
        "day.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0hDK3_Y66gO"
      },
      "source": [
        "Change month data in month order"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "SH2KXXNt7OBl",
        "outputId": "83bdbed1-2832-4237-c58d-86b96e24ed82"
      },
      "outputs": [
        {
          "data": {
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              "      <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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</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       date    season  year    month  day  workingday  weather  \\\n",
              "0        1 2011-01-01  springer  2011  january    6           0        2   \n",
              "1        2 2011-01-02  springer  2011  january    0           0        2   \n",
              "2        3 2011-01-03  springer  2011  january    1           1        1   \n",
              "3        4 2011-01-04  springer  2011  january    2           1        1   \n",
              "4        5 2011-01-05  springer  2011  january    3           1        1   \n",
              "\n",
              "   temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0     0.344167          0.363625  0.805833   0.160446          331   \n",
              "1     0.363478          0.353739  0.696087   0.248539          131   \n",
              "2     0.196364          0.189405  0.437273   0.248309          120   \n",
              "3     0.200000          0.212122  0.590435   0.160296          108   \n",
              "4     0.226957          0.229270  0.436957   0.186900           82   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0              654         985  \n",
              "1              670         801  \n",
              "2             1229        1349  \n",
              "3             1454        1562  \n",
              "4             1518        1600  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "month_change = {1: 'january', 2: 'february', 3: 'march', 4:'april', 5:'may', 6:'june', 7:'july',\n",
        "                8:'august', 9:'september', 10:'october', 11:'november', 12:'december'}\n",
        "\n",
        "day['month'] = day['month'].map(month_change)\n",
        "day.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SSnF0jEz7ra3"
      },
      "source": [
        "Convert weekday data to day names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "6iwvrHzp7vA6",
        "outputId": "19dc918a-91c9-41d8-9742-617e68c29726"
      },
      "outputs": [
        {
          "data": {
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              "      <td>801</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>tuesday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
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              "      <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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>thursday</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       date    season  year    month        day  workingday  \\\n",
              "0        1 2011-01-01  springer  2011  january     sunday           0   \n",
              "1        2 2011-01-02  springer  2011  january     monday           0   \n",
              "2        3 2011-01-03  springer  2011  january    tuesday           1   \n",
              "3        4 2011-01-04  springer  2011  january  wednesday           1   \n",
              "4        5 2011-01-05  springer  2011  january   thursday           1   \n",
              "\n",
              "   weather  temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0        2     0.344167          0.363625  0.805833   0.160446          331   \n",
              "1        2     0.363478          0.353739  0.696087   0.248539          131   \n",
              "2        1     0.196364          0.189405  0.437273   0.248309          120   \n",
              "3        1     0.200000          0.212122  0.590435   0.160296          108   \n",
              "4        1     0.226957          0.229270  0.436957   0.186900           82   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0              654         985  \n",
              "1              670         801  \n",
              "2             1229        1349  \n",
              "3             1454        1562  \n",
              "4             1518        1600  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day_change = {0: 'monday', 1: 'tuesday', 2: 'wednesday', 3: 'thursday', 4:'friday', 5:'saturday', 6:'sunday'}\n",
        "\n",
        "day['day'] = day['day'].map(day_change)\n",
        "day.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOfxt2sG8c5v"
      },
      "source": [
        "Changing data on working days that were originally numeric to clarify the meaning of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "GzylUMHy8loH",
        "outputId": "6af6a666-7982-4e29-ce08-27a1ce937f7b"
      },
      "outputs": [
        {
          "data": {
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              "      <td>january</td>\n",
              "      <td>tuesday</td>\n",
              "      <td>working day</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
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              "      <td>5</td>\n",
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              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>thursday</td>\n",
              "      <td>working day</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
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              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
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            "text/plain": [
              "   instant       date    season  year    month        day       workingday  \\\n",
              "0        1 2011-01-01  springer  2011  january     sunday  weekend/holiday   \n",
              "1        2 2011-01-02  springer  2011  january     monday  weekend/holiday   \n",
              "2        3 2011-01-03  springer  2011  january    tuesday      working day   \n",
              "3        4 2011-01-04  springer  2011  january  wednesday      working day   \n",
              "4        5 2011-01-05  springer  2011  january   thursday      working day   \n",
              "\n",
              "   weather  temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0        2     0.344167          0.363625  0.805833   0.160446          331   \n",
              "1        2     0.363478          0.353739  0.696087   0.248539          131   \n",
              "2        1     0.196364          0.189405  0.437273   0.248309          120   \n",
              "3        1     0.200000          0.212122  0.590435   0.160296          108   \n",
              "4        1     0.226957          0.229270  0.436957   0.186900           82   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0              654         985  \n",
              "1              670         801  \n",
              "2             1229        1349  \n",
              "3             1454        1562  \n",
              "4             1518        1600  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def change_workingday(x):\n",
        "    if x == 0:\n",
        "        return 'weekend/holiday'\n",
        "    else:\n",
        "        return 'working day'\n",
        "\n",
        "day['workingday'] = day['workingday'].apply(change_workingday)\n",
        "day.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SY8GbBYY9ADb"
      },
      "source": [
        "Change the weather data to better represent the real data with the following conditions\n",
        "* 1 is clear\n",
        "* 2 is mist\n",
        "* 3 is light snow\n",
        "* 4 is heavy rain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "4oL1JAC09Rtn",
        "outputId": "70fbdd48-5a41-4db1-a48a-962562ef8d17"
      },
      "outputs": [
        {
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              "      <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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>thursday</td>\n",
              "      <td>working day</td>\n",
              "      <td>clear</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       date    season  year    month        day       workingday  \\\n",
              "0        1 2011-01-01  springer  2011  january     sunday  weekend/holiday   \n",
              "1        2 2011-01-02  springer  2011  january     monday  weekend/holiday   \n",
              "2        3 2011-01-03  springer  2011  january    tuesday      working day   \n",
              "3        4 2011-01-04  springer  2011  january  wednesday      working day   \n",
              "4        5 2011-01-05  springer  2011  january   thursday      working day   \n",
              "\n",
              "  weather  temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0    mist     0.344167          0.363625  0.805833   0.160446          331   \n",
              "1    mist     0.363478          0.353739  0.696087   0.248539          131   \n",
              "2   clear     0.196364          0.189405  0.437273   0.248309          120   \n",
              "3   clear     0.200000          0.212122  0.590435   0.160296          108   \n",
              "4   clear     0.226957          0.229270  0.436957   0.186900           82   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0              654         985  \n",
              "1              670         801  \n",
              "2             1229        1349  \n",
              "3             1454        1562  \n",
              "4             1518        1600  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def change_weather(x):\n",
        "    if x == 1:\n",
        "        return 'clear'\n",
        "    elif x == 2:\n",
        "        return 'mist'\n",
        "    elif x == 3:\n",
        "        return 'light snow'\n",
        "    else:\n",
        "        return 'heavy rain'\n",
        "\n",
        "day['weather'] = day['weather'].apply(change_weather)\n",
        "day.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wwt4jbm9rbZ"
      },
      "source": [
        "#### **Hour Dataframe**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cI9m7pY792Np"
      },
      "source": [
        "Delete the holiday column because the information in the column is already represented in the workingday column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "XKnL0cgX9wQO"
      },
      "outputs": [],
      "source": [
        "hour.drop(['holiday'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94qM5_XG99QU"
      },
      "source": [
        "Changed some column header names to make it easier to understand and avoid writing errors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "yPtvM2NS9-2z"
      },
      "outputs": [],
      "source": [
        "hour.rename(columns={'dteday':'date', 'yr':'year', 'mnth':'month', 'hr':'hour', 'weekday':'day',\n",
        "                    'weathersit':'weather', 'temp':'temperature', 'atemp':'temperature_feel',\n",
        "                    'hum':'humidity', 'casual':'casual_user', 'registered':'registered_user', 'cnt':'total_user'}, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkVvSZNuAMP1"
      },
      "source": [
        "Change the data type of the date column from object to datetime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "nNYkPOFFAQpD"
      },
      "outputs": [],
      "source": [
        "datetime_columns = ['date']\n",
        "\n",
        "for column in datetime_columns:\n",
        "  hour[column] = pd.to_datetime(hour[column])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FW_AtnTJAQpE",
        "outputId": "e67ed0ac-883d-4461-80d2-9f1240063314"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17379 entries, 0 to 17378\n",
            "Data columns (total 16 columns):\n",
            " #   Column            Non-Null Count  Dtype         \n",
            "---  ------            --------------  -----         \n",
            " 0   instant           17379 non-null  int64         \n",
            " 1   date              17379 non-null  datetime64[ns]\n",
            " 2   season            17379 non-null  int64         \n",
            " 3   year              17379 non-null  int64         \n",
            " 4   month             17379 non-null  int64         \n",
            " 5   hour              17379 non-null  int64         \n",
            " 6   day               17379 non-null  int64         \n",
            " 7   workingday        17379 non-null  int64         \n",
            " 8   weather           17379 non-null  int64         \n",
            " 9   temperature       17379 non-null  float64       \n",
            " 10  temperature_feel  17379 non-null  float64       \n",
            " 11  humidity          17379 non-null  float64       \n",
            " 12  windspeed         17379 non-null  float64       \n",
            " 13  casual_user       17379 non-null  int64         \n",
            " 14  registered_user   17379 non-null  int64         \n",
            " 15  total_user        17379 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(4), int64(11)\n",
            "memory usage: 2.1 MB\n"
          ]
        }
      ],
      "source": [
        "hour.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCXm_n0WAnqM"
      },
      "source": [
        "Convert seasons from numeric to actual values. The numbers in the season represent:\n",
        "* 1 is springer\n",
        "* 2 is summer\n",
        "* 3 is fall\n",
        "* 4 is winter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "epjHYi5mAnqS",
        "outputId": "a4be94c9-8eb4-43af-d031-f2aba4e3b000"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>date</th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temperature</th>\n",
              "      <th>temperature_feel</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th>total_user</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>springer</td>\n",
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              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>2011-01-01</td>\n",
              "      <td>springer</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <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>springer</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</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>springer</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</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>springer</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</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       date    season  year  month  hour  day  workingday  weather  \\\n",
              "0        1 2011-01-01  springer     0      1     0    6           0        1   \n",
              "1        2 2011-01-01  springer     0      1     1    6           0        1   \n",
              "2        3 2011-01-01  springer     0      1     2    6           0        1   \n",
              "3        4 2011-01-01  springer     0      1     3    6           0        1   \n",
              "4        5 2011-01-01  springer     0      1     4    6           0        1   \n",
              "\n",
              "   temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0         0.24            0.2879      0.81        0.0            3   \n",
              "1         0.22            0.2727      0.80        0.0            8   \n",
              "2         0.22            0.2727      0.80        0.0            5   \n",
              "3         0.24            0.2879      0.75        0.0            3   \n",
              "4         0.24            0.2879      0.75        0.0            0   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0               13          16  \n",
              "1               32          40  \n",
              "2               27          32  \n",
              "3               10          13  \n",
              "4                1           1  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def change_season(x):\n",
        "    if x == 1:\n",
        "        return 'springer'\n",
        "    elif x == 2:\n",
        "        return 'summer'\n",
        "    elif x == 3:\n",
        "        return 'fall'\n",
        "    else:\n",
        "        return 'winter'\n",
        "\n",
        "hour['season'] = hour['season'].apply(change_season)\n",
        "hour.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZI-KQFXOAnqT"
      },
      "source": [
        "Change the year with the following conditions:\n",
        "* 0 means it is 2011\n",
        "* 1 means it is 2012"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "QrpqwgkpAnqT",
        "outputId": "98c8ceb2-9cb6-480d-8087-4478334204c9"
      },
      "outputs": [
        {
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>date</th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temperature</th>\n",
              "      <th>temperature_feel</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th>total_user</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>springer</td>\n",
              "      <td>2011</td>\n",
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              "      <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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>2</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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>3</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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>4</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       date    season  year  month  hour  day  workingday  weather  \\\n",
              "0        1 2011-01-01  springer  2011      1     0    6           0        1   \n",
              "1        2 2011-01-01  springer  2011      1     1    6           0        1   \n",
              "2        3 2011-01-01  springer  2011      1     2    6           0        1   \n",
              "3        4 2011-01-01  springer  2011      1     3    6           0        1   \n",
              "4        5 2011-01-01  springer  2011      1     4    6           0        1   \n",
              "\n",
              "   temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0         0.24            0.2879      0.81        0.0            3   \n",
              "1         0.22            0.2727      0.80        0.0            8   \n",
              "2         0.22            0.2727      0.80        0.0            5   \n",
              "3         0.24            0.2879      0.75        0.0            3   \n",
              "4         0.24            0.2879      0.75        0.0            0   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0               13          16  \n",
              "1               32          40  \n",
              "2               27          32  \n",
              "3               10          13  \n",
              "4                1           1  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def change_year(x):\n",
        "    if x == 0:\n",
        "        return 2011\n",
        "    else:\n",
        "        return 2012\n",
        "\n",
        "hour['year'] = hour['year'].apply(change_year)\n",
        "hour.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0FvFSBJ8AnqT"
      },
      "source": [
        "Change month data in month order"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "pWE3e7mmAnqT",
        "outputId": "f38d3834-6ccf-4726-a37a-73aa97d45b63"
      },
      "outputs": [
        {
          "data": {
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              "      <th>date</th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
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              "      <th>temperature</th>\n",
              "      <th>temperature_feel</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
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              "  <tbody>\n",
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              "      <td>2011-01-01</td>\n",
              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
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              "      <td>6</td>\n",
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              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
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              "      <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",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>2</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",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>3</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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>4</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       date    season  year    month  hour  day  workingday  \\\n",
              "0        1 2011-01-01  springer  2011  january     0    6           0   \n",
              "1        2 2011-01-01  springer  2011  january     1    6           0   \n",
              "2        3 2011-01-01  springer  2011  january     2    6           0   \n",
              "3        4 2011-01-01  springer  2011  january     3    6           0   \n",
              "4        5 2011-01-01  springer  2011  january     4    6           0   \n",
              "\n",
              "   weather  temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0        1         0.24            0.2879      0.81        0.0            3   \n",
              "1        1         0.22            0.2727      0.80        0.0            8   \n",
              "2        1         0.22            0.2727      0.80        0.0            5   \n",
              "3        1         0.24            0.2879      0.75        0.0            3   \n",
              "4        1         0.24            0.2879      0.75        0.0            0   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0               13          16  \n",
              "1               32          40  \n",
              "2               27          32  \n",
              "3               10          13  \n",
              "4                1           1  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "month_change = {1: 'january', 2: 'february', 3: 'march', 4:'april', 5:'may', 6:'june', 7:'july',\n",
        "                8:'august', 9:'september', 10:'october', 11:'november', 12:'december'}\n",
        "\n",
        "hour['month'] = hour['month'].map(month_change)\n",
        "hour.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ckvbz8sAnqT"
      },
      "source": [
        "Convert weekday data to day names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "KnP1PLhdAnqU",
        "outputId": "280379ff-401c-49fd-9d54-d82766299a45"
      },
      "outputs": [
        {
          "data": {
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              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>2</td>\n",
              "      <td>sunday</td>\n",
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              "      <td>sunday</td>\n",
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              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>4</td>\n",
              "      <td>sunday</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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            ],
            "text/plain": [
              "   instant       date    season  year    month  hour     day  workingday  \\\n",
              "0        1 2011-01-01  springer  2011  january     0  sunday           0   \n",
              "1        2 2011-01-01  springer  2011  january     1  sunday           0   \n",
              "2        3 2011-01-01  springer  2011  january     2  sunday           0   \n",
              "3        4 2011-01-01  springer  2011  january     3  sunday           0   \n",
              "4        5 2011-01-01  springer  2011  january     4  sunday           0   \n",
              "\n",
              "   weather  temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0        1         0.24            0.2879      0.81        0.0            3   \n",
              "1        1         0.22            0.2727      0.80        0.0            8   \n",
              "2        1         0.22            0.2727      0.80        0.0            5   \n",
              "3        1         0.24            0.2879      0.75        0.0            3   \n",
              "4        1         0.24            0.2879      0.75        0.0            0   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0               13          16  \n",
              "1               32          40  \n",
              "2               27          32  \n",
              "3               10          13  \n",
              "4                1           1  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day_change = {0: 'monday', 1: 'tuesday', 2: 'wednesday', 3: 'thursday', 4:'friday', 5:'saturday', 6:'sunday'}\n",
        "\n",
        "hour['day'] = hour['day'].map(day_change)\n",
        "hour.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1jxC0F0MAnqU"
      },
      "source": [
        "Changing data on working days that were originally numeric to clarify the meaning of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "IwM8-tTmAnqU",
        "outputId": "0d36cc39-6f0a-4d05-defb-936e2ff9e7ab"
      },
      "outputs": [
        {
          "data": {
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              "      <td>january</td>\n",
              "      <td>2</td>\n",
              "      <td>sunday</td>\n",
              "      <td>weekend/holiday</td>\n",
              "      <td>1</td>\n",
              "      <td>0.22</td>\n",
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              "      <td>0.80</td>\n",
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              "      <td>january</td>\n",
              "      <td>3</td>\n",
              "      <td>sunday</td>\n",
              "      <td>weekend/holiday</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",
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              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>4</td>\n",
              "      <td>sunday</td>\n",
              "      <td>weekend/holiday</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",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   instant       date    season  year    month  hour     day       workingday  \\\n",
              "0        1 2011-01-01  springer  2011  january     0  sunday  weekend/holiday   \n",
              "1        2 2011-01-01  springer  2011  january     1  sunday  weekend/holiday   \n",
              "2        3 2011-01-01  springer  2011  january     2  sunday  weekend/holiday   \n",
              "3        4 2011-01-01  springer  2011  january     3  sunday  weekend/holiday   \n",
              "4        5 2011-01-01  springer  2011  january     4  sunday  weekend/holiday   \n",
              "\n",
              "   weather  temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0        1         0.24            0.2879      0.81        0.0            3   \n",
              "1        1         0.22            0.2727      0.80        0.0            8   \n",
              "2        1         0.22            0.2727      0.80        0.0            5   \n",
              "3        1         0.24            0.2879      0.75        0.0            3   \n",
              "4        1         0.24            0.2879      0.75        0.0            0   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0               13          16  \n",
              "1               32          40  \n",
              "2               27          32  \n",
              "3               10          13  \n",
              "4                1           1  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def change_workingday(x):\n",
        "    if x == 0:\n",
        "        return 'weekend/holiday'\n",
        "    else:\n",
        "        return 'working day'\n",
        "\n",
        "hour['workingday'] = hour['workingday'].apply(change_workingday)\n",
        "hour.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JDYlezOWAnqU"
      },
      "source": [
        "Change the weather data to better represent the real data with the following conditions\n",
        "* 1 is clear\n",
        "* 2 is mist\n",
        "* 3 is light snow\n",
        "* 4 is heavy rain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "JvahVFNDAnqU",
        "outputId": "4a48cea4-870c-47b7-8574-06b5804385cf"
      },
      "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>date</th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temperature</th>\n",
              "      <th>temperature_feel</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th>total_user</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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>0</td>\n",
              "      <td>sunday</td>\n",
              "      <td>weekend/holiday</td>\n",
              "      <td>clear</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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>1</td>\n",
              "      <td>sunday</td>\n",
              "      <td>weekend/holiday</td>\n",
              "      <td>clear</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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>2</td>\n",
              "      <td>sunday</td>\n",
              "      <td>weekend/holiday</td>\n",
              "      <td>clear</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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>3</td>\n",
              "      <td>sunday</td>\n",
              "      <td>weekend/holiday</td>\n",
              "      <td>clear</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>springer</td>\n",
              "      <td>2011</td>\n",
              "      <td>january</td>\n",
              "      <td>4</td>\n",
              "      <td>sunday</td>\n",
              "      <td>weekend/holiday</td>\n",
              "      <td>clear</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       date    season  year    month  hour     day       workingday  \\\n",
              "0        1 2011-01-01  springer  2011  january     0  sunday  weekend/holiday   \n",
              "1        2 2011-01-01  springer  2011  january     1  sunday  weekend/holiday   \n",
              "2        3 2011-01-01  springer  2011  january     2  sunday  weekend/holiday   \n",
              "3        4 2011-01-01  springer  2011  january     3  sunday  weekend/holiday   \n",
              "4        5 2011-01-01  springer  2011  january     4  sunday  weekend/holiday   \n",
              "\n",
              "  weather  temperature  temperature_feel  humidity  windspeed  casual_user  \\\n",
              "0   clear         0.24            0.2879      0.81        0.0            3   \n",
              "1   clear         0.22            0.2727      0.80        0.0            8   \n",
              "2   clear         0.22            0.2727      0.80        0.0            5   \n",
              "3   clear         0.24            0.2879      0.75        0.0            3   \n",
              "4   clear         0.24            0.2879      0.75        0.0            0   \n",
              "\n",
              "   registered_user  total_user  \n",
              "0               13          16  \n",
              "1               32          40  \n",
              "2               27          32  \n",
              "3               10          13  \n",
              "4                1           1  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def change_weather(x):\n",
        "    if x == 1:\n",
        "        return 'clear'\n",
        "    elif x == 2:\n",
        "        return 'mist'\n",
        "    elif x == 3:\n",
        "        return 'light snow'\n",
        "    else:\n",
        "        return 'heavy rain'\n",
        "\n",
        "hour['weather'] = hour['weather'].apply(change_weather)\n",
        "hour.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kv6tthSbBTLG"
      },
      "source": [
        "## **Exploratory Data Analysis (EDA)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PecdwITYtMq1"
      },
      "source": [
        "### **Day Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "4dB8AhyrtSHp",
        "outputId": "6979baef-0964-4833-ff7c-d39e68b76d47"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "\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>date</th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temperature</th>\n",
              "      <th>temperature_feel</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th>total_user</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>731</td>\n",
              "      <td>731</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731</td>\n",
              "      <td>731</td>\n",
              "      <td>731</td>\n",
              "      <td>731</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>3</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>fall</td>\n",
              "      <td>NaN</td>\n",
              "      <td>january</td>\n",
              "      <td>sunday</td>\n",
              "      <td>working day</td>\n",
              "      <td>clear</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>188</td>\n",
              "      <td>NaN</td>\n",
              "      <td>62</td>\n",
              "      <td>105</td>\n",
              "      <td>500</td>\n",
              "      <td>463</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>2012-01-01 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.500684</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.495385</td>\n",
              "      <td>0.474354</td>\n",
              "      <td>0.627894</td>\n",
              "      <td>0.190486</td>\n",
              "      <td>848.176471</td>\n",
              "      <td>3656.172367</td>\n",
              "      <td>4504.348837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>183.500000</td>\n",
              "      <td>2011-07-02 12:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.337083</td>\n",
              "      <td>0.337842</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.134950</td>\n",
              "      <td>315.500000</td>\n",
              "      <td>2497.000000</td>\n",
              "      <td>3152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>2012-01-01 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.498333</td>\n",
              "      <td>0.486733</td>\n",
              "      <td>0.626667</td>\n",
              "      <td>0.180975</td>\n",
              "      <td>713.000000</td>\n",
              "      <td>3662.000000</td>\n",
              "      <td>4548.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>548.500000</td>\n",
              "      <td>2012-07-01 12:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.655417</td>\n",
              "      <td>0.608602</td>\n",
              "      <td>0.730209</td>\n",
              "      <td>0.233214</td>\n",
              "      <td>1096.000000</td>\n",
              "      <td>4776.500000</td>\n",
              "      <td>5956.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>2012-12-31 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>211.165812</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.500342</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.183051</td>\n",
              "      <td>0.162961</td>\n",
              "      <td>0.142429</td>\n",
              "      <td>0.077498</td>\n",
              "      <td>686.622488</td>\n",
              "      <td>1560.256377</td>\n",
              "      <td>1937.211452</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           instant                 date season         year    month     day  \\\n",
              "count   731.000000                  731    731   731.000000      731     731   \n",
              "unique         NaN                  NaN      4          NaN       12       7   \n",
              "top            NaN                  NaN   fall          NaN  january  sunday   \n",
              "freq           NaN                  NaN    188          NaN       62     105   \n",
              "mean    366.000000  2012-01-01 00:00:00    NaN  2011.500684      NaN     NaN   \n",
              "min       1.000000  2011-01-01 00:00:00    NaN  2011.000000      NaN     NaN   \n",
              "25%     183.500000  2011-07-02 12:00:00    NaN  2011.000000      NaN     NaN   \n",
              "50%     366.000000  2012-01-01 00:00:00    NaN  2012.000000      NaN     NaN   \n",
              "75%     548.500000  2012-07-01 12:00:00    NaN  2012.000000      NaN     NaN   \n",
              "max     731.000000  2012-12-31 00:00:00    NaN  2012.000000      NaN     NaN   \n",
              "std     211.165812                  NaN    NaN     0.500342      NaN     NaN   \n",
              "\n",
              "         workingday weather  temperature  temperature_feel    humidity  \\\n",
              "count           731     731   731.000000        731.000000  731.000000   \n",
              "unique            2       3          NaN               NaN         NaN   \n",
              "top     working day   clear          NaN               NaN         NaN   \n",
              "freq            500     463          NaN               NaN         NaN   \n",
              "mean            NaN     NaN     0.495385          0.474354    0.627894   \n",
              "min             NaN     NaN     0.059130          0.079070    0.000000   \n",
              "25%             NaN     NaN     0.337083          0.337842    0.520000   \n",
              "50%             NaN     NaN     0.498333          0.486733    0.626667   \n",
              "75%             NaN     NaN     0.655417          0.608602    0.730209   \n",
              "max             NaN     NaN     0.861667          0.840896    0.972500   \n",
              "std             NaN     NaN     0.183051          0.162961    0.142429   \n",
              "\n",
              "         windspeed  casual_user  registered_user   total_user  \n",
              "count   731.000000   731.000000       731.000000   731.000000  \n",
              "unique         NaN          NaN              NaN          NaN  \n",
              "top            NaN          NaN              NaN          NaN  \n",
              "freq           NaN          NaN              NaN          NaN  \n",
              "mean      0.190486   848.176471      3656.172367  4504.348837  \n",
              "min       0.022392     2.000000        20.000000    22.000000  \n",
              "25%       0.134950   315.500000      2497.000000  3152.000000  \n",
              "50%       0.180975   713.000000      3662.000000  4548.000000  \n",
              "75%       0.233214  1096.000000      4776.500000  5956.000000  \n",
              "max       0.507463  3410.000000      6946.000000  8714.000000  \n",
              "std       0.077498   686.622488      1560.256377  1937.211452  "
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1ZA8wKXt9sX"
      },
      "source": [
        "From the data above it can be seen that users use Bike Sharing more frequently in autumn."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rP7BER9QpdoX"
      },
      "source": [
        "**Number of Daily Users**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "qmxjF-D4pjM0",
        "outputId": "07ad3105-172b-4448-8a93-77771d77c80d"
      },
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th colspan=\"4\" halign=\"left\">total_user</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>nunique</th>\n",
              "      <th>sum</th>\n",
              "      <th>sum</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>friday</th>\n",
              "      <td>104</td>\n",
              "      <td>61460</td>\n",
              "      <td>423935</td>\n",
              "      <td>7804</td>\n",
              "      <td>431</td>\n",
              "      <td>4667.259615</td>\n",
              "      <td>485395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>monday</th>\n",
              "      <td>105</td>\n",
              "      <td>140521</td>\n",
              "      <td>303506</td>\n",
              "      <td>8227</td>\n",
              "      <td>605</td>\n",
              "      <td>4228.828571</td>\n",
              "      <td>444027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>saturday</th>\n",
              "      <td>104</td>\n",
              "      <td>78238</td>\n",
              "      <td>409552</td>\n",
              "      <td>8362</td>\n",
              "      <td>1167</td>\n",
              "      <td>4690.288462</td>\n",
              "      <td>487790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sunday</th>\n",
              "      <td>105</td>\n",
              "      <td>153852</td>\n",
              "      <td>323955</td>\n",
              "      <td>8714</td>\n",
              "      <td>627</td>\n",
              "      <td>4550.542857</td>\n",
              "      <td>477807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thursday</th>\n",
              "      <td>104</td>\n",
              "      <td>57319</td>\n",
              "      <td>415729</td>\n",
              "      <td>8173</td>\n",
              "      <td>441</td>\n",
              "      <td>4548.538462</td>\n",
              "      <td>473048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tuesday</th>\n",
              "      <td>105</td>\n",
              "      <td>70784</td>\n",
              "      <td>384719</td>\n",
              "      <td>7525</td>\n",
              "      <td>22</td>\n",
              "      <td>4338.123810</td>\n",
              "      <td>455503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wednesday</th>\n",
              "      <td>104</td>\n",
              "      <td>57843</td>\n",
              "      <td>411266</td>\n",
              "      <td>7767</td>\n",
              "      <td>683</td>\n",
              "      <td>4510.663462</td>\n",
              "      <td>469109</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date casual_user registered_user total_user                     \\\n",
              "          nunique         sum             sum        max   min         mean   \n",
              "day                                                                           \n",
              "friday        104       61460          423935       7804   431  4667.259615   \n",
              "monday        105      140521          303506       8227   605  4228.828571   \n",
              "saturday      104       78238          409552       8362  1167  4690.288462   \n",
              "sunday        105      153852          323955       8714   627  4550.542857   \n",
              "thursday      104       57319          415729       8173   441  4548.538462   \n",
              "tuesday       105       70784          384719       7525    22  4338.123810   \n",
              "wednesday     104       57843          411266       7767   683  4510.663462   \n",
              "\n",
              "                   \n",
              "              sum  \n",
              "day                \n",
              "friday     485395  \n",
              "monday     444027  \n",
              "saturday   487790  \n",
              "sunday     477807  \n",
              "thursday   473048  \n",
              "tuesday    455503  \n",
              "wednesday  469109  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day.groupby(by=\"day\").agg({\n",
        "    \"date\": \"nunique\",\n",
        "    \"casual_user\": \"sum\",\n",
        "    \"registered_user\": \"sum\",\n",
        "    \"total_user\": [\"max\", \"min\", \"mean\", \"sum\"]\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXTnuWktuT8n"
      },
      "source": [
        "Based on the day, the most Bike Sharing users are on Saturdays with an average number of users of 4,690,288 people."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xlmRF3uzrJ55"
      },
      "source": [
        "**Number of Monthly and Annual Users**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "rofRaQPBrPcf",
        "outputId": "d796d16b-25ff-4bb4-ea6d-de9dded40245"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th colspan=\"4\" halign=\"left\">total_user</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>nunique</th>\n",
              "      <th>sum</th>\n",
              "      <th>sum</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"12\" valign=\"top\">2011</th>\n",
              "      <th>april</th>\n",
              "      <td>30</td>\n",
              "      <td>22346</td>\n",
              "      <td>72524</td>\n",
              "      <td>5312</td>\n",
              "      <td>795</td>\n",
              "      <td>3162.333333</td>\n",
              "      <td>94870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>august</th>\n",
              "      <td>31</td>\n",
              "      <td>28842</td>\n",
              "      <td>107849</td>\n",
              "      <td>5895</td>\n",
              "      <td>1115</td>\n",
              "      <td>4409.387097</td>\n",
              "      <td>136691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>december</th>\n",
              "      <td>31</td>\n",
              "      <td>8448</td>\n",
              "      <td>78875</td>\n",
              "      <td>3940</td>\n",
              "      <td>705</td>\n",
              "      <td>2816.870968</td>\n",
              "      <td>87323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>february</th>\n",
              "      <td>28</td>\n",
              "      <td>6242</td>\n",
              "      <td>41973</td>\n",
              "      <td>2927</td>\n",
              "      <td>1005</td>\n",
              "      <td>1721.964286</td>\n",
              "      <td>48215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>january</th>\n",
              "      <td>31</td>\n",
              "      <td>3073</td>\n",
              "      <td>35116</td>\n",
              "      <td>1985</td>\n",
              "      <td>431</td>\n",
              "      <td>1231.903226</td>\n",
              "      <td>38189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>july</th>\n",
              "      <td>31</td>\n",
              "      <td>36452</td>\n",
              "      <td>104889</td>\n",
              "      <td>6043</td>\n",
              "      <td>3285</td>\n",
              "      <td>4559.387097</td>\n",
              "      <td>141341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>june</th>\n",
              "      <td>30</td>\n",
              "      <td>30612</td>\n",
              "      <td>112900</td>\n",
              "      <td>5515</td>\n",
              "      <td>3767</td>\n",
              "      <td>4783.733333</td>\n",
              "      <td>143512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>march</th>\n",
              "      <td>31</td>\n",
              "      <td>12826</td>\n",
              "      <td>51219</td>\n",
              "      <td>3239</td>\n",
              "      <td>605</td>\n",
              "      <td>2065.967742</td>\n",
              "      <td>64045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>may</th>\n",
              "      <td>31</td>\n",
              "      <td>31050</td>\n",
              "      <td>104771</td>\n",
              "      <td>5805</td>\n",
              "      <td>2633</td>\n",
              "      <td>4381.322581</td>\n",
              "      <td>135821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>november</th>\n",
              "      <td>30</td>\n",
              "      <td>15594</td>\n",
              "      <td>86573</td>\n",
              "      <td>4486</td>\n",
              "      <td>1495</td>\n",
              "      <td>3405.566667</td>\n",
              "      <td>102167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>october</th>\n",
              "      <td>31</td>\n",
              "      <td>25222</td>\n",
              "      <td>98289</td>\n",
              "      <td>5511</td>\n",
              "      <td>627</td>\n",
              "      <td>3984.225806</td>\n",
              "      <td>123511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>september</th>\n",
              "      <td>30</td>\n",
              "      <td>26545</td>\n",
              "      <td>100873</td>\n",
              "      <td>5423</td>\n",
              "      <td>1842</td>\n",
              "      <td>4247.266667</td>\n",
              "      <td>127418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"12\" valign=\"top\">2012</th>\n",
              "      <th>april</th>\n",
              "      <td>30</td>\n",
              "      <td>38456</td>\n",
              "      <td>135768</td>\n",
              "      <td>7460</td>\n",
              "      <td>1027</td>\n",
              "      <td>5807.466667</td>\n",
              "      <td>174224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>august</th>\n",
              "      <td>31</td>\n",
              "      <td>43197</td>\n",
              "      <td>171306</td>\n",
              "      <td>7865</td>\n",
              "      <td>4549</td>\n",
              "      <td>6919.451613</td>\n",
              "      <td>214503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>december</th>\n",
              "      <td>31</td>\n",
              "      <td>13245</td>\n",
              "      <td>110468</td>\n",
              "      <td>6606</td>\n",
              "      <td>441</td>\n",
              "      <td>3990.741935</td>\n",
              "      <td>123713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>february</th>\n",
              "      <td>29</td>\n",
              "      <td>8721</td>\n",
              "      <td>94416</td>\n",
              "      <td>5062</td>\n",
              "      <td>1529</td>\n",
              "      <td>3556.448276</td>\n",
              "      <td>103137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>january</th>\n",
              "      <td>31</td>\n",
              "      <td>8969</td>\n",
              "      <td>87775</td>\n",
              "      <td>4521</td>\n",
              "      <td>1301</td>\n",
              "      <td>3120.774194</td>\n",
              "      <td>96744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>july</th>\n",
              "      <td>31</td>\n",
              "      <td>41705</td>\n",
              "      <td>161902</td>\n",
              "      <td>8173</td>\n",
              "      <td>4459</td>\n",
              "      <td>6567.967742</td>\n",
              "      <td>203607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>june</th>\n",
              "      <td>30</td>\n",
              "      <td>43294</td>\n",
              "      <td>159536</td>\n",
              "      <td>8120</td>\n",
              "      <td>4127</td>\n",
              "      <td>6761.000000</td>\n",
              "      <td>202830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>march</th>\n",
              "      <td>31</td>\n",
              "      <td>31618</td>\n",
              "      <td>133257</td>\n",
              "      <td>8362</td>\n",
              "      <td>3194</td>\n",
              "      <td>5318.548387</td>\n",
              "      <td>164875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>may</th>\n",
              "      <td>31</td>\n",
              "      <td>44235</td>\n",
              "      <td>151630</td>\n",
              "      <td>8294</td>\n",
              "      <td>2843</td>\n",
              "      <td>6318.225806</td>\n",
              "      <td>195865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>november</th>\n",
              "      <td>30</td>\n",
              "      <td>21009</td>\n",
              "      <td>131655</td>\n",
              "      <td>6852</td>\n",
              "      <td>2277</td>\n",
              "      <td>5088.800000</td>\n",
              "      <td>152664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>october</th>\n",
              "      <td>31</td>\n",
              "      <td>34538</td>\n",
              "      <td>164303</td>\n",
              "      <td>8156</td>\n",
              "      <td>22</td>\n",
              "      <td>6414.225806</td>\n",
              "      <td>198841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>september</th>\n",
              "      <td>30</td>\n",
              "      <td>43778</td>\n",
              "      <td>174795</td>\n",
              "      <td>8714</td>\n",
              "      <td>4073</td>\n",
              "      <td>7285.766667</td>\n",
              "      <td>218573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  date casual_user registered_user total_user        \\\n",
              "               nunique         sum             sum        max   min   \n",
              "year month                                                            \n",
              "2011 april          30       22346           72524       5312   795   \n",
              "     august         31       28842          107849       5895  1115   \n",
              "     december       31        8448           78875       3940   705   \n",
              "     february       28        6242           41973       2927  1005   \n",
              "     january        31        3073           35116       1985   431   \n",
              "     july           31       36452          104889       6043  3285   \n",
              "     june           30       30612          112900       5515  3767   \n",
              "     march          31       12826           51219       3239   605   \n",
              "     may            31       31050          104771       5805  2633   \n",
              "     november       30       15594           86573       4486  1495   \n",
              "     october        31       25222           98289       5511   627   \n",
              "     september      30       26545          100873       5423  1842   \n",
              "2012 april          30       38456          135768       7460  1027   \n",
              "     august         31       43197          171306       7865  4549   \n",
              "     december       31       13245          110468       6606   441   \n",
              "     february       29        8721           94416       5062  1529   \n",
              "     january        31        8969           87775       4521  1301   \n",
              "     july           31       41705          161902       8173  4459   \n",
              "     june           30       43294          159536       8120  4127   \n",
              "     march          31       31618          133257       8362  3194   \n",
              "     may            31       44235          151630       8294  2843   \n",
              "     november       30       21009          131655       6852  2277   \n",
              "     october        31       34538          164303       8156    22   \n",
              "     september      30       43778          174795       8714  4073   \n",
              "\n",
              "                                     \n",
              "                       mean     sum  \n",
              "year month                           \n",
              "2011 april      3162.333333   94870  \n",
              "     august     4409.387097  136691  \n",
              "     december   2816.870968   87323  \n",
              "     february   1721.964286   48215  \n",
              "     january    1231.903226   38189  \n",
              "     july       4559.387097  141341  \n",
              "     june       4783.733333  143512  \n",
              "     march      2065.967742   64045  \n",
              "     may        4381.322581  135821  \n",
              "     november   3405.566667  102167  \n",
              "     october    3984.225806  123511  \n",
              "     september  4247.266667  127418  \n",
              "2012 april      5807.466667  174224  \n",
              "     august     6919.451613  214503  \n",
              "     december   3990.741935  123713  \n",
              "     february   3556.448276  103137  \n",
              "     january    3120.774194   96744  \n",
              "     july       6567.967742  203607  \n",
              "     june       6761.000000  202830  \n",
              "     march      5318.548387  164875  \n",
              "     may        6318.225806  195865  \n",
              "     november   5088.800000  152664  \n",
              "     october    6414.225806  198841  \n",
              "     september  7285.766667  218573  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day.groupby(by=[\"year\", \"month\"]).agg({\n",
        "    \"date\": \"nunique\",\n",
        "    \"casual_user\": \"sum\",\n",
        "    \"registered_user\": \"sum\",\n",
        "    \"total_user\": [\"max\", \"min\", \"mean\", \"sum\"]\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wx4Slp4Du6vU"
      },
      "source": [
        "In 2011 the most Bike Sharing users were in June with a total of 143,512 users. In 2012 the most Bike Sharing users were in September with a total of 218,573 users."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ovEeGSRFt9vY"
      },
      "source": [
        "### **Hour Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "TUAFCs29tzhL",
        "outputId": "cb0ea546-8b14-4caa-b42a-569beddda46d"
      },
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>date</th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>hour</th>\n",
              "      <th>day</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "      <th>temperature</th>\n",
              "      <th>temperature_feel</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th>total_user</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</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379</td>\n",
              "      <td>17379</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>4</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>fall</td>\n",
              "      <td>NaN</td>\n",
              "      <td>may</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sunday</td>\n",
              "      <td>working day</td>\n",
              "      <td>clear</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4496</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1488</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2512</td>\n",
              "      <td>11865</td>\n",
              "      <td>11413</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>2012-01-02 04:08:34.552045568</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.502561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.546752</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.496987</td>\n",
              "      <td>0.475775</td>\n",
              "      <td>0.627229</td>\n",
              "      <td>0.190098</td>\n",
              "      <td>35.676218</td>\n",
              "      <td>153.786869</td>\n",
              "      <td>189.463088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4345.5000</td>\n",
              "      <td>2011-07-04 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.333300</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>0.104500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>2012-01-02 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.484800</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>0.194000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>142.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13034.5000</td>\n",
              "      <td>2012-07-02 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>0.621200</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.253700</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>281.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17379.0000</td>\n",
              "      <td>2012-12-31 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.850700</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>977.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5017.0295</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.500008</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.914405</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.192556</td>\n",
              "      <td>0.171850</td>\n",
              "      <td>0.192930</td>\n",
              "      <td>0.122340</td>\n",
              "      <td>49.305030</td>\n",
              "      <td>151.357286</td>\n",
              "      <td>181.387599</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           instant                           date season          year  month  \\\n",
              "count   17379.0000                          17379  17379  17379.000000  17379   \n",
              "unique         NaN                            NaN      4           NaN     12   \n",
              "top            NaN                            NaN   fall           NaN    may   \n",
              "freq           NaN                            NaN   4496           NaN   1488   \n",
              "mean     8690.0000  2012-01-02 04:08:34.552045568    NaN   2011.502561    NaN   \n",
              "min         1.0000            2011-01-01 00:00:00    NaN   2011.000000    NaN   \n",
              "25%      4345.5000            2011-07-04 00:00:00    NaN   2011.000000    NaN   \n",
              "50%      8690.0000            2012-01-02 00:00:00    NaN   2012.000000    NaN   \n",
              "75%     13034.5000            2012-07-02 00:00:00    NaN   2012.000000    NaN   \n",
              "max     17379.0000            2012-12-31 00:00:00    NaN   2012.000000    NaN   \n",
              "std      5017.0295                            NaN    NaN      0.500008    NaN   \n",
              "\n",
              "                hour     day   workingday weather   temperature  \\\n",
              "count   17379.000000   17379        17379   17379  17379.000000   \n",
              "unique           NaN       7            2       4           NaN   \n",
              "top              NaN  sunday  working day   clear           NaN   \n",
              "freq             NaN    2512        11865   11413           NaN   \n",
              "mean       11.546752     NaN          NaN     NaN      0.496987   \n",
              "min         0.000000     NaN          NaN     NaN      0.020000   \n",
              "25%         6.000000     NaN          NaN     NaN      0.340000   \n",
              "50%        12.000000     NaN          NaN     NaN      0.500000   \n",
              "75%        18.000000     NaN          NaN     NaN      0.660000   \n",
              "max        23.000000     NaN          NaN     NaN      1.000000   \n",
              "std         6.914405     NaN          NaN     NaN      0.192556   \n",
              "\n",
              "        temperature_feel      humidity     windspeed   casual_user  \\\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     35.676218   \n",
              "min             0.000000      0.000000      0.000000      0.000000   \n",
              "25%             0.333300      0.480000      0.104500      4.000000   \n",
              "50%             0.484800      0.630000      0.194000     17.000000   \n",
              "75%             0.621200      0.780000      0.253700     48.000000   \n",
              "max             1.000000      1.000000      0.850700    367.000000   \n",
              "std             0.171850      0.192930      0.122340     49.305030   \n",
              "\n",
              "        registered_user    total_user  \n",
              "count      17379.000000  17379.000000  \n",
              "unique              NaN           NaN  \n",
              "top                 NaN           NaN  \n",
              "freq                NaN           NaN  \n",
              "mean         153.786869    189.463088  \n",
              "min            0.000000      1.000000  \n",
              "25%           34.000000     40.000000  \n",
              "50%          115.000000    142.000000  \n",
              "75%          220.000000    281.000000  \n",
              "max          886.000000    977.000000  \n",
              "std          151.357286    181.387599  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour.describe(include=\"all\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vN6NVO25v9vg"
      },
      "source": [
        "From the data above it can be seen that users use Bike Sharing more frequently in autumn."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hgdG1Cbr9NS"
      },
      "source": [
        "**Number of Users Every Hour Based on Working Days**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lTf2akPqtIlV",
        "outputId": "1c7b6734-7658-4fc5-afbc-5b3722d9b7ed"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th colspan=\"4\" halign=\"left\">total_user</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>nunique</th>\n",
              "      <th>sum</th>\n",
              "      <th>sum</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>workingday</th>\n",
              "      <th>hour</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"24\" valign=\"top\">weekend/holiday</th>\n",
              "      <th>0</th>\n",
              "      <td>230</td>\n",
              "      <td>3829</td>\n",
              "      <td>17055</td>\n",
              "      <td>249</td>\n",
              "      <td>6</td>\n",
              "      <td>90.800000</td>\n",
              "      <td>20884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>230</td>\n",
              "      <td>2876</td>\n",
              "      <td>13111</td>\n",
              "      <td>168</td>\n",
              "      <td>4</td>\n",
              "      <td>69.508696</td>\n",
              "      <td>15987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>228</td>\n",
              "      <td>2371</td>\n",
              "      <td>9752</td>\n",
              "      <td>132</td>\n",
              "      <td>2</td>\n",
              "      <td>53.171053</td>\n",
              "      <td>12123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>227</td>\n",
              "      <td>1465</td>\n",
              "      <td>4386</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>25.775330</td>\n",
              "      <td>5851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>227</td>\n",
              "      <td>514</td>\n",
              "      <td>1362</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>8.264317</td>\n",
              "      <td>1876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>222</td>\n",
              "      <td>383</td>\n",
              "      <td>1546</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>8.689189</td>\n",
              "      <td>1929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>229</td>\n",
              "      <td>875</td>\n",
              "      <td>3417</td>\n",
              "      <td>133</td>\n",
              "      <td>1</td>\n",
              "      <td>18.742358</td>\n",
              "      <td>4292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>231</td>\n",
              "      <td>1988</td>\n",
              "      <td>8039</td>\n",
              "      <td>387</td>\n",
              "      <td>1</td>\n",
              "      <td>43.406926</td>\n",
              "      <td>10027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>231</td>\n",
              "      <td>4718</td>\n",
              "      <td>19688</td>\n",
              "      <td>597</td>\n",
              "      <td>5</td>\n",
              "      <td>105.653680</td>\n",
              "      <td>24406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>231</td>\n",
              "      <td>10399</td>\n",
              "      <td>29246</td>\n",
              "      <td>397</td>\n",
              "      <td>14</td>\n",
              "      <td>171.623377</td>\n",
              "      <td>39645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>231</td>\n",
              "      <td>18311</td>\n",
              "      <td>40804</td>\n",
              "      <td>539</td>\n",
              "      <td>12</td>\n",
              "      <td>255.909091</td>\n",
              "      <td>59115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>231</td>\n",
              "      <td>24637</td>\n",
              "      <td>48201</td>\n",
              "      <td>663</td>\n",
              "      <td>27</td>\n",
              "      <td>315.316017</td>\n",
              "      <td>72838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>231</td>\n",
              "      <td>29163</td>\n",
              "      <td>55443</td>\n",
              "      <td>776</td>\n",
              "      <td>32</td>\n",
              "      <td>366.259740</td>\n",
              "      <td>84606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>231</td>\n",
              "      <td>31514</td>\n",
              "      <td>54587</td>\n",
              "      <td>760</td>\n",
              "      <td>30</td>\n",
              "      <td>372.731602</td>\n",
              "      <td>86101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>231</td>\n",
              "      <td>32246</td>\n",
              "      <td>51987</td>\n",
              "      <td>750</td>\n",
              "      <td>23</td>\n",
              "      <td>364.645022</td>\n",
              "      <td>84233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>231</td>\n",
              "      <td>31532</td>\n",
              "      <td>51354</td>\n",
              "      <td>750</td>\n",
              "      <td>7</td>\n",
              "      <td>358.813853</td>\n",
              "      <td>82886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>231</td>\n",
              "      <td>29710</td>\n",
              "      <td>51770</td>\n",
              "      <td>783</td>\n",
              "      <td>11</td>\n",
              "      <td>352.727273</td>\n",
              "      <td>81480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>231</td>\n",
              "      <td>25829</td>\n",
              "      <td>48911</td>\n",
              "      <td>731</td>\n",
              "      <td>15</td>\n",
              "      <td>323.549784</td>\n",
              "      <td>74740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>230</td>\n",
              "      <td>19371</td>\n",
              "      <td>45272</td>\n",
              "      <td>676</td>\n",
              "      <td>23</td>\n",
              "      <td>281.056522</td>\n",
              "      <td>64643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>230</td>\n",
              "      <td>15076</td>\n",
              "      <td>38209</td>\n",
              "      <td>554</td>\n",
              "      <td>11</td>\n",
              "      <td>231.673913</td>\n",
              "      <td>53285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>230</td>\n",
              "      <td>10756</td>\n",
              "      <td>29434</td>\n",
              "      <td>551</td>\n",
              "      <td>11</td>\n",
              "      <td>174.739130</td>\n",
              "      <td>40190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>230</td>\n",
              "      <td>8314</td>\n",
              "      <td>24360</td>\n",
              "      <td>584</td>\n",
              "      <td>7</td>\n",
              "      <td>142.060870</td>\n",
              "      <td>32674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>230</td>\n",
              "      <td>6453</td>\n",
              "      <td>20241</td>\n",
              "      <td>502</td>\n",
              "      <td>9</td>\n",
              "      <td>116.060870</td>\n",
              "      <td>26694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>230</td>\n",
              "      <td>4402</td>\n",
              "      <td>15362</td>\n",
              "      <td>239</td>\n",
              "      <td>2</td>\n",
              "      <td>85.930435</td>\n",
              "      <td>19764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"24\" valign=\"top\">working day</th>\n",
              "      <th>0</th>\n",
              "      <td>496</td>\n",
              "      <td>3546</td>\n",
              "      <td>14700</td>\n",
              "      <td>283</td>\n",
              "      <td>2</td>\n",
              "      <td>36.786290</td>\n",
              "      <td>18246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>494</td>\n",
              "      <td>1833</td>\n",
              "      <td>6344</td>\n",
              "      <td>114</td>\n",
              "      <td>1</td>\n",
              "      <td>16.552632</td>\n",
              "      <td>8177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>487</td>\n",
              "      <td>1041</td>\n",
              "      <td>3188</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>8.683778</td>\n",
              "      <td>4229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>470</td>\n",
              "      <td>428</td>\n",
              "      <td>1895</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>4.942553</td>\n",
              "      <td>2323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>470</td>\n",
              "      <td>360</td>\n",
              "      <td>2192</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>5.429787</td>\n",
              "      <td>2552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>495</td>\n",
              "      <td>629</td>\n",
              "      <td>11703</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>24.913131</td>\n",
              "      <td>12332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>496</td>\n",
              "      <td>2142</td>\n",
              "      <td>48698</td>\n",
              "      <td>213</td>\n",
              "      <td>7</td>\n",
              "      <td>102.500000</td>\n",
              "      <td>50840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>496</td>\n",
              "      <td>6049</td>\n",
              "      <td>138095</td>\n",
              "      <td>596</td>\n",
              "      <td>18</td>\n",
              "      <td>290.612903</td>\n",
              "      <td>144144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>496</td>\n",
              "      <td>11043</td>\n",
              "      <td>225552</td>\n",
              "      <td>839</td>\n",
              "      <td>31</td>\n",
              "      <td>477.006048</td>\n",
              "      <td>236595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>496</td>\n",
              "      <td>12059</td>\n",
              "      <td>107734</td>\n",
              "      <td>426</td>\n",
              "      <td>23</td>\n",
              "      <td>241.518145</td>\n",
              "      <td>119793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>496</td>\n",
              "      <td>15478</td>\n",
              "      <td>51664</td>\n",
              "      <td>275</td>\n",
              "      <td>8</td>\n",
              "      <td>135.366935</td>\n",
              "      <td>67142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>496</td>\n",
              "      <td>18649</td>\n",
              "      <td>59833</td>\n",
              "      <td>361</td>\n",
              "      <td>10</td>\n",
              "      <td>158.229839</td>\n",
              "      <td>78482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>497</td>\n",
              "      <td>20555</td>\n",
              "      <td>79253</td>\n",
              "      <td>471</td>\n",
              "      <td>3</td>\n",
              "      <td>200.820926</td>\n",
              "      <td>99808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>498</td>\n",
              "      <td>21199</td>\n",
              "      <td>77619</td>\n",
              "      <td>428</td>\n",
              "      <td>11</td>\n",
              "      <td>198.429719</td>\n",
              "      <td>98818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>498</td>\n",
              "      <td>22843</td>\n",
              "      <td>68576</td>\n",
              "      <td>651</td>\n",
              "      <td>12</td>\n",
              "      <td>183.572289</td>\n",
              "      <td>91419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>498</td>\n",
              "      <td>23074</td>\n",
              "      <td>77189</td>\n",
              "      <td>601</td>\n",
              "      <td>13</td>\n",
              "      <td>201.331325</td>\n",
              "      <td>100263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>499</td>\n",
              "      <td>24124</td>\n",
              "      <td>122144</td>\n",
              "      <td>634</td>\n",
              "      <td>13</td>\n",
              "      <td>293.122244</td>\n",
              "      <td>146268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>499</td>\n",
              "      <td>28391</td>\n",
              "      <td>233729</td>\n",
              "      <td>976</td>\n",
              "      <td>26</td>\n",
              "      <td>525.290581</td>\n",
              "      <td>262120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>498</td>\n",
              "      <td>25125</td>\n",
              "      <td>220004</td>\n",
              "      <td>977</td>\n",
              "      <td>26</td>\n",
              "      <td>492.226908</td>\n",
              "      <td>245129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>498</td>\n",
              "      <td>20429</td>\n",
              "      <td>153075</td>\n",
              "      <td>743</td>\n",
              "      <td>18</td>\n",
              "      <td>348.401606</td>\n",
              "      <td>173504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>498</td>\n",
              "      <td>15622</td>\n",
              "      <td>108738</td>\n",
              "      <td>567</td>\n",
              "      <td>23</td>\n",
              "      <td>249.718876</td>\n",
              "      <td>124360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>498</td>\n",
              "      <td>12256</td>\n",
              "      <td>80515</td>\n",
              "      <td>398</td>\n",
              "      <td>6</td>\n",
              "      <td>186.287149</td>\n",
              "      <td>92771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>498</td>\n",
              "      <td>9747</td>\n",
              "      <td>59171</td>\n",
              "      <td>321</td>\n",
              "      <td>11</td>\n",
              "      <td>138.389558</td>\n",
              "      <td>68918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>498</td>\n",
              "      <td>6663</td>\n",
              "      <td>37514</td>\n",
              "      <td>256</td>\n",
              "      <td>9</td>\n",
              "      <td>88.708835</td>\n",
              "      <td>44177</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        date casual_user registered_user total_user      \\\n",
              "                     nunique         sum             sum        max min   \n",
              "workingday      hour                                                      \n",
              "weekend/holiday 0        230        3829           17055        249   6   \n",
              "                1        230        2876           13111        168   4   \n",
              "                2        228        2371            9752        132   2   \n",
              "                3        227        1465            4386         79   2   \n",
              "                4        227         514            1362         28   1   \n",
              "                5        222         383            1546         55   1   \n",
              "                6        229         875            3417        133   1   \n",
              "                7        231        1988            8039        387   1   \n",
              "                8        231        4718           19688        597   5   \n",
              "                9        231       10399           29246        397  14   \n",
              "                10       231       18311           40804        539  12   \n",
              "                11       231       24637           48201        663  27   \n",
              "                12       231       29163           55443        776  32   \n",
              "                13       231       31514           54587        760  30   \n",
              "                14       231       32246           51987        750  23   \n",
              "                15       231       31532           51354        750   7   \n",
              "                16       231       29710           51770        783  11   \n",
              "                17       231       25829           48911        731  15   \n",
              "                18       230       19371           45272        676  23   \n",
              "                19       230       15076           38209        554  11   \n",
              "                20       230       10756           29434        551  11   \n",
              "                21       230        8314           24360        584   7   \n",
              "                22       230        6453           20241        502   9   \n",
              "                23       230        4402           15362        239   2   \n",
              "working day     0        496        3546           14700        283   2   \n",
              "                1        494        1833            6344        114   1   \n",
              "                2        487        1041            3188         74   1   \n",
              "                3        470         428            1895         36   1   \n",
              "                4        470         360            2192         17   1   \n",
              "                5        495         629           11703         66   1   \n",
              "                6        496        2142           48698        213   7   \n",
              "                7        496        6049          138095        596  18   \n",
              "                8        496       11043          225552        839  31   \n",
              "                9        496       12059          107734        426  23   \n",
              "                10       496       15478           51664        275   8   \n",
              "                11       496       18649           59833        361  10   \n",
              "                12       497       20555           79253        471   3   \n",
              "                13       498       21199           77619        428  11   \n",
              "                14       498       22843           68576        651  12   \n",
              "                15       498       23074           77189        601  13   \n",
              "                16       499       24124          122144        634  13   \n",
              "                17       499       28391          233729        976  26   \n",
              "                18       498       25125          220004        977  26   \n",
              "                19       498       20429          153075        743  18   \n",
              "                20       498       15622          108738        567  23   \n",
              "                21       498       12256           80515        398   6   \n",
              "                22       498        9747           59171        321  11   \n",
              "                23       498        6663           37514        256   9   \n",
              "\n",
              "                                          \n",
              "                            mean     sum  \n",
              "workingday      hour                      \n",
              "weekend/holiday 0      90.800000   20884  \n",
              "                1      69.508696   15987  \n",
              "                2      53.171053   12123  \n",
              "                3      25.775330    5851  \n",
              "                4       8.264317    1876  \n",
              "                5       8.689189    1929  \n",
              "                6      18.742358    4292  \n",
              "                7      43.406926   10027  \n",
              "                8     105.653680   24406  \n",
              "                9     171.623377   39645  \n",
              "                10    255.909091   59115  \n",
              "                11    315.316017   72838  \n",
              "                12    366.259740   84606  \n",
              "                13    372.731602   86101  \n",
              "                14    364.645022   84233  \n",
              "                15    358.813853   82886  \n",
              "                16    352.727273   81480  \n",
              "                17    323.549784   74740  \n",
              "                18    281.056522   64643  \n",
              "                19    231.673913   53285  \n",
              "                20    174.739130   40190  \n",
              "                21    142.060870   32674  \n",
              "                22    116.060870   26694  \n",
              "                23     85.930435   19764  \n",
              "working day     0      36.786290   18246  \n",
              "                1      16.552632    8177  \n",
              "                2       8.683778    4229  \n",
              "                3       4.942553    2323  \n",
              "                4       5.429787    2552  \n",
              "                5      24.913131   12332  \n",
              "                6     102.500000   50840  \n",
              "                7     290.612903  144144  \n",
              "                8     477.006048  236595  \n",
              "                9     241.518145  119793  \n",
              "                10    135.366935   67142  \n",
              "                11    158.229839   78482  \n",
              "                12    200.820926   99808  \n",
              "                13    198.429719   98818  \n",
              "                14    183.572289   91419  \n",
              "                15    201.331325  100263  \n",
              "                16    293.122244  146268  \n",
              "                17    525.290581  262120  \n",
              "                18    492.226908  245129  \n",
              "                19    348.401606  173504  \n",
              "                20    249.718876  124360  \n",
              "                21    186.287149   92771  \n",
              "                22    138.389558   68918  \n",
              "                23     88.708835   44177  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour.groupby(by=[\"workingday\", \"hour\"]).agg({\n",
        "    \"date\": \"nunique\",\n",
        "    \"casual_user\": \"sum\",\n",
        "    \"registered_user\": \"sum\",\n",
        "    \"total_user\": [\"max\", \"min\", \"mean\", \"sum\"]\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PB73-Ez8yIwt"
      },
      "source": [
        "On weekends or holidays, the most users are at 13.00 with a total of 86,101 users. While on weekdays, the most users are at 17.00 with a total of 262,120 users."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GwLm1I3es62t"
      },
      "source": [
        "**Number of Users by Weather and Season**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "bHfD3bkxsAZy",
        "outputId": "e76cfeab-eda8-48e3-9a77-646c18e77b16"
      },
      "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 tr th {\n",
              "        text-align: left;\n",
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              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>casual_user</th>\n",
              "      <th>registered_user</th>\n",
              "      <th colspan=\"4\" halign=\"left\">total_user</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>nunique</th>\n",
              "      <th>sum</th>\n",
              "      <th>sum</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <th>weather</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">fall</th>\n",
              "      <th>clear</th>\n",
              "      <td>183</td>\n",
              "      <td>174014</td>\n",
              "      <td>627927</td>\n",
              "      <td>977</td>\n",
              "      <td>1</td>\n",
              "      <td>244.494207</td>\n",
              "      <td>801941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>light snow</th>\n",
              "      <td>72</td>\n",
              "      <td>9727</td>\n",
              "      <td>36529</td>\n",
              "      <td>891</td>\n",
              "      <td>1</td>\n",
              "      <td>171.955390</td>\n",
              "      <td>46256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mist</th>\n",
              "      <td>140</td>\n",
              "      <td>42350</td>\n",
              "      <td>170582</td>\n",
              "      <td>868</td>\n",
              "      <td>2</td>\n",
              "      <td>224.848997</td>\n",
              "      <td>212932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">springer</th>\n",
              "      <th>clear</th>\n",
              "      <td>174</td>\n",
              "      <td>44462</td>\n",
              "      <td>280816</td>\n",
              "      <td>801</td>\n",
              "      <td>1</td>\n",
              "      <td>122.055535</td>\n",
              "      <td>325278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heavy rain</th>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>215</td>\n",
              "      <td>164</td>\n",
              "      <td>23</td>\n",
              "      <td>74.333333</td>\n",
              "      <td>223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>light snow</th>\n",
              "      <td>75</td>\n",
              "      <td>1392</td>\n",
              "      <td>20250</td>\n",
              "      <td>520</td>\n",
              "      <td>1</td>\n",
              "      <td>58.650407</td>\n",
              "      <td>21642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mist</th>\n",
              "      <td>146</td>\n",
              "      <td>14760</td>\n",
              "      <td>109445</td>\n",
              "      <td>712</td>\n",
              "      <td>1</td>\n",
              "      <td>103.074689</td>\n",
              "      <td>124205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">summer</th>\n",
              "      <th>clear</th>\n",
              "      <td>177</td>\n",
              "      <td>151197</td>\n",
              "      <td>513266</td>\n",
              "      <td>873</td>\n",
              "      <td>1</td>\n",
              "      <td>232.410983</td>\n",
              "      <td>664463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>light snow</th>\n",
              "      <td>91</td>\n",
              "      <td>7413</td>\n",
              "      <td>39350</td>\n",
              "      <td>782</td>\n",
              "      <td>1</td>\n",
              "      <td>115.179803</td>\n",
              "      <td>46763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mist</th>\n",
              "      <td>146</td>\n",
              "      <td>44912</td>\n",
              "      <td>162451</td>\n",
              "      <td>957</td>\n",
              "      <td>1</td>\n",
              "      <td>181.261364</td>\n",
              "      <td>207363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">winter</th>\n",
              "      <th>clear</th>\n",
              "      <td>166</td>\n",
              "      <td>93072</td>\n",
              "      <td>453419</td>\n",
              "      <td>967</td>\n",
              "      <td>1</td>\n",
              "      <td>209.463779</td>\n",
              "      <td>546491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>light snow</th>\n",
              "      <td>62</td>\n",
              "      <td>4251</td>\n",
              "      <td>39419</td>\n",
              "      <td>856</td>\n",
              "      <td>1</td>\n",
              "      <td>116.453333</td>\n",
              "      <td>43670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mist</th>\n",
              "      <td>135</td>\n",
              "      <td>32459</td>\n",
              "      <td>218993</td>\n",
              "      <td>905</td>\n",
              "      <td>1</td>\n",
              "      <td>201.483974</td>\n",
              "      <td>251452</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       date casual_user registered_user total_user      \\\n",
              "                    nunique         sum             sum        max min   \n",
              "season   weather                                                         \n",
              "fall     clear          183      174014          627927        977   1   \n",
              "         light snow      72        9727           36529        891   1   \n",
              "         mist           140       42350          170582        868   2   \n",
              "springer clear          174       44462          280816        801   1   \n",
              "         heavy rain       3           8             215        164  23   \n",
              "         light snow      75        1392           20250        520   1   \n",
              "         mist           146       14760          109445        712   1   \n",
              "summer   clear          177      151197          513266        873   1   \n",
              "         light snow      91        7413           39350        782   1   \n",
              "         mist           146       44912          162451        957   1   \n",
              "winter   clear          166       93072          453419        967   1   \n",
              "         light snow      62        4251           39419        856   1   \n",
              "         mist           135       32459          218993        905   1   \n",
              "\n",
              "                                         \n",
              "                           mean     sum  \n",
              "season   weather                         \n",
              "fall     clear       244.494207  801941  \n",
              "         light snow  171.955390   46256  \n",
              "         mist        224.848997  212932  \n",
              "springer clear       122.055535  325278  \n",
              "         heavy rain   74.333333     223  \n",
              "         light snow   58.650407   21642  \n",
              "         mist        103.074689  124205  \n",
              "summer   clear       232.410983  664463  \n",
              "         light snow  115.179803   46763  \n",
              "         mist        181.261364  207363  \n",
              "winter   clear       209.463779  546491  \n",
              "         light snow  116.453333   43670  \n",
              "         mist        201.483974  251452  "
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour.groupby(by=[\"season\", \"weather\"]).agg({\n",
        "    \"date\": \"nunique\",\n",
        "    \"casual_user\": \"sum\",\n",
        "    \"registered_user\": \"sum\",\n",
        "    \"total_user\": [\"max\", \"min\", \"mean\", \"sum\"]\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZwvH1Bwy06G"
      },
      "source": [
        "Based on the season and weather conditions, the most users are in autumn during sunny weather with a total of 801,941 users. While the least users are in spring during heavy rainy weather with a total of 223 users."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0UlB-0Rs_p5"
      },
      "source": [
        "## **Data Visualization & Explanatory Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThxXsq39CIlk"
      },
      "source": [
        "### **Type of Bike Sharing User by Day (Question 1)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "jI13b0vkBN_P",
        "outputId": "7f671f5f-04a7-41e0-ecb5-f9873b45fc00"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>day</th>\n",
              "      <th>casual_user</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sunday</td>\n",
              "      <td>153852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>monday</td>\n",
              "      <td>140521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>saturday</td>\n",
              "      <td>78238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tuesday</td>\n",
              "      <td>70784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>friday</td>\n",
              "      <td>61460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>wednesday</td>\n",
              "      <td>57843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>thursday</td>\n",
              "      <td>57319</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         day  casual_user\n",
              "0     sunday       153852\n",
              "1     monday       140521\n",
              "2   saturday        78238\n",
              "3    tuesday        70784\n",
              "4     friday        61460\n",
              "5  wednesday        57843\n",
              "6   thursday        57319"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum_casual_user = day.groupby(\"day\").casual_user.sum().sort_values(ascending=False).reset_index()\n",
        "sum_casual_user"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "BWVa0mtaBccr",
        "outputId": "4c0534d1-9223-49db-d2dc-40cb0cab70bd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>day</th>\n",
              "      <th>registered_user</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>friday</td>\n",
              "      <td>423935</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>thursday</td>\n",
              "      <td>415729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>wednesday</td>\n",
              "      <td>411266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>saturday</td>\n",
              "      <td>409552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tuesday</td>\n",
              "      <td>384719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>sunday</td>\n",
              "      <td>323955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>monday</td>\n",
              "      <td>303506</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         day  registered_user\n",
              "0     friday           423935\n",
              "1   thursday           415729\n",
              "2  wednesday           411266\n",
              "3   saturday           409552\n",
              "4    tuesday           384719\n",
              "5     sunday           323955\n",
              "6     monday           303506"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sum_registered_user = day.groupby(\"day\").registered_user.sum().sort_values(ascending=False).reset_index()\n",
        "sum_registered_user"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "uczN7JPzaXx-",
        "outputId": "9cd11d51-53fc-45cb-8ed2-b8407ffe935d"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>day</th>\n",
              "      <th>casual_user</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sunday</td>\n",
              "      <td>153852</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>monday</td>\n",
              "      <td>140521</td>\n",
              "      <td>303506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>saturday</td>\n",
              "      <td>78238</td>\n",
              "      <td>409552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tuesday</td>\n",
              "      <td>70784</td>\n",
              "      <td>384719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>friday</td>\n",
              "      <td>61460</td>\n",
              "      <td>423935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>wednesday</td>\n",
              "      <td>57843</td>\n",
              "      <td>411266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>thursday</td>\n",
              "      <td>57319</td>\n",
              "      <td>415729</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         day  casual_user  registered_user\n",
              "0     sunday       153852           323955\n",
              "1     monday       140521           303506\n",
              "2   saturday        78238           409552\n",
              "3    tuesday        70784           384719\n",
              "4     friday        61460           423935\n",
              "5  wednesday        57843           411266\n",
              "6   thursday        57319           415729"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "daily_user = pd.merge(\n",
        "    left=sum_casual_user,\n",
        "    right=sum_registered_user,\n",
        "    how=\"left\",\n",
        "    left_on=\"day\",\n",
        "    right_on=\"day\"\n",
        ")\n",
        "daily_user"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "nyFhacREeOGu",
        "outputId": "004427c7-9504-4bcb-f25c-12a1a123da73"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>day</th>\n",
              "      <th>user_type</th>\n",
              "      <th>user_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sunday</td>\n",
              "      <td>casual_user</td>\n",
              "      <td>153852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>monday</td>\n",
              "      <td>casual_user</td>\n",
              "      <td>140521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>saturday</td>\n",
              "      <td>casual_user</td>\n",
              "      <td>78238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tuesday</td>\n",
              "      <td>casual_user</td>\n",
              "      <td>70784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>friday</td>\n",
              "      <td>casual_user</td>\n",
              "      <td>61460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>wednesday</td>\n",
              "      <td>casual_user</td>\n",
              "      <td>57843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>thursday</td>\n",
              "      <td>casual_user</td>\n",
              "      <td>57319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>sunday</td>\n",
              "      <td>registered_user</td>\n",
              "      <td>323955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>monday</td>\n",
              "      <td>registered_user</td>\n",
              "      <td>303506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>saturday</td>\n",
              "      <td>registered_user</td>\n",
              "      <td>409552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>tuesday</td>\n",
              "      <td>registered_user</td>\n",
              "      <td>384719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>friday</td>\n",
              "      <td>registered_user</td>\n",
              "      <td>423935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>wednesday</td>\n",
              "      <td>registered_user</td>\n",
              "      <td>411266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>thursday</td>\n",
              "      <td>registered_user</td>\n",
              "      <td>415729</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          day        user_type  user_count\n",
              "0      sunday      casual_user      153852\n",
              "1      monday      casual_user      140521\n",
              "2    saturday      casual_user       78238\n",
              "3     tuesday      casual_user       70784\n",
              "4      friday      casual_user       61460\n",
              "5   wednesday      casual_user       57843\n",
              "6    thursday      casual_user       57319\n",
              "7      sunday  registered_user      323955\n",
              "8      monday  registered_user      303506\n",
              "9    saturday  registered_user      409552\n",
              "10    tuesday  registered_user      384719\n",
              "11     friday  registered_user      423935\n",
              "12  wednesday  registered_user      411266\n",
              "13   thursday  registered_user      415729"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "daily_user_type = daily_user.melt(id_vars='day', var_name='user_type', value_name='user_count')\n",
        "daily_user_type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "vNylzqH-ZPbA",
        "outputId": "f505c494-ec4d-4103-9644-3fa1a96066da"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 6))\n",
        "\n",
        "\n",
        "sns.barplot(x=\"day\", y=\"user_count\", hue=\"user_type\", data=daily_user_type, palette=\"muted\")\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.title(\"Comparison of Number of Casual Users and Registered Users by Day\")\n",
        "casual_patch = mpatches.Patch(color=sns.color_palette(\"muted\")[0], label='Casual User')\n",
        "registered_patch = mpatches.Patch(color=sns.color_palette(\"muted\")[1], label='Registered User')\n",
        "plt.legend(handles=[casual_patch, registered_patch], title=\"User Type\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "Lt0kYj9hA2rf",
        "outputId": "f7e9139a-382a-49b8-c236-6c81af5f093a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2400x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(24, 6))\n",
        "\n",
        "colors = [\"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
        "\n",
        "sns.barplot(x=\"casual_user\", y=\"day\", data=sum_casual_user, palette=colors, hue=\"day\", legend=False, ax=ax[0])\n",
        "ax[0].set_ylabel(None)\n",
        "ax[0].set_xlabel(None)\n",
        "ax[0].set_title(\"Casual User\", loc=\"center\", fontsize=15)\n",
        "ax[0].tick_params(axis ='y', labelsize=12)\n",
        "\n",
        "sns.barplot(x=\"registered_user\", y=\"day\", data=sum_registered_user, hue=\"day\", legend=False, palette=colors, ax=ax[1])\n",
        "ax[1].set_ylabel(None)\n",
        "ax[1].set_xlabel(None)\n",
        "ax[1].invert_xaxis()\n",
        "ax[1].yaxis.set_label_position(\"right\")\n",
        "ax[1].yaxis.tick_right()\n",
        "ax[1].set_title(\"Registered User\", loc=\"center\", fontsize=15)\n",
        "ax[1].tick_params(axis='y', labelsize=12)\n",
        "\n",
        "plt.suptitle(\"Number of Casual Users and Registered Users by Day\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5rg8e35fV3rf"
      },
      "source": [
        "From the visualisation above, it can be seen that every day Bike Sharing users with the type \"registered user\" have more numbers than users with the type \"casual user\".\n",
        "\n",
        "When viewed based on each type of user, users with the type \"casual user\" are most numerous on Sundays with a total of 153,852 users. Meanwhile, users of the \"registered_user\" type are most numerous on Friday with a total of 423,935 users."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hbwffouwFmOq"
      },
      "source": [
        "### **Bike Sharing Productivity Based on Time (Question 2)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "gytug6E1F2fl",
        "outputId": "564fa8c5-3961-4010-afbe-03a0753e3c74"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(20,5))\n",
        "sns.pointplot(data=hour, x='hour', y='total_user', hue='workingday', errorbar=None, ax=ax)\n",
        "ax.set(title='Bike Sharing Productivity Based on Time')\n",
        "ax.set_ylabel('Total User')\n",
        "ax.set_xlabel('Hour')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxpfK6YDkirH"
      },
      "source": [
        "On weekends or holidays, the number of users starts to increase at 6.00 am and continues to increase until the peak number of users occurs at 1.00 pm. From 4.00pm the number of users starts to decline until 5.00am.\n",
        "\n",
        "While on weekdays the number of users experienced a significant increase during the hours of leaving and returning from work, namely from 6.00 to 8.00 am and 15.00 to 17.00 pm. While a significant decrease in users occurs at 9.00 am when people are already actively working in the office and at night starting at 7.00 pm. The number of users tends to be low from midnight to 5 am."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lg50u4gQdLW"
      },
      "source": [
        "### **Bike Sharing Productivity by Month (Question 3)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "rDSTv_VwTyoD",
        "outputId": "1e64c3be-8eae-4b15-b525-69c7f553a9ca"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(20,5))\n",
        "sns.pointplot(data=day, x='month', y='total_user', errorbar=None, ax=ax)\n",
        "ax.set(title='Bike Sharing Productivity by Month')\n",
        "ax.set_ylabel('Total User')\n",
        "ax.set_xlabel('Month')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkhNUH4ZozJb"
      },
      "source": [
        "Bike Sharing has a very high productivity in June and September with many users renting bicycles in these months. While starting in October the number of users continued to decline until the lowest point in January. The number of users increased again starting in February."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fz9zYh0vIXBD"
      },
      "source": [
        "### **Weather and Season Impact on Bike Sharing Productivity (Question 4)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Vrmv67jNN52M",
        "outputId": "fb55d83d-a12a-4131-c757-3275d8d9c843"
      },
      "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>weather</th>\n",
              "      <th>total_user</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>clear</td>\n",
              "      <td>2338173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>mist</td>\n",
              "      <td>795952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>light snow</td>\n",
              "      <td>158331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>heavy rain</td>\n",
              "      <td>223</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      weather  total_user\n",
              "0       clear     2338173\n",
              "1        mist      795952\n",
              "2  light snow      158331\n",
              "3  heavy rain         223"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "byweather = hour.groupby(\"weather\").total_user.sum().sort_values(ascending=False).reset_index()\n",
        "byweather"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "voyT3-nIPxWY",
        "outputId": "828188d9-53e5-4681-f2c7-2d144598f68e"
      },
      "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>season</th>\n",
              "      <th>total_user</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>fall</td>\n",
              "      <td>1061129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>summer</td>\n",
              "      <td>918589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>winter</td>\n",
              "      <td>841613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>springer</td>\n",
              "      <td>471348</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     season  total_user\n",
              "0      fall     1061129\n",
              "1    summer      918589\n",
              "2    winter      841613\n",
              "3  springer      471348"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "byseason = hour.groupby(\"season\").total_user.sum().sort_values(ascending=False).reset_index()\n",
        "byseason"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "tB7-0VGYIfiN",
        "outputId": "d494e7ba-6228-4de5-8127-60713f04627b"
      },
      "outputs": [
        {
          "data": {
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LpTdyNdecSF88ioiIcHrV56pVq6wromvVqpWh/Z///KdVkDty5IjT7Zw4ccLal379+t1Urq+88oqV641X3s6ePdtIMqVLl7aWTZw40UgyNWvWzLCuunXrGun6VZ/pX0+///67CQoKMpLMs88+m+mVzUOGDLH22Vkxdf/+/Vnuy8cff2zty4oVK5zG2Nuzm7XgxqtTR4wYkSEmLS3NtGjRwkjXZ1FwVtC071NwcLDZunWr020dPHjQlCxZ0kjXv7Bzo/Sv1YCAALNjx44sc89OWlqa+fnnn7OMGTp0qFWAdHbc8+r4pJ8hY/bs2RnaU1JSMsyIcDMOHz5svf9KMr6+vqZjx45m4sSJZvPmzTn+4sbUqVOtL41k9gWqU6dOWV9uql+/fob233//3Zw+fTrTbaSmppq//e1v1vvH1atXHdpXrlxp7UdmVzYbc/1LBsnJyQ7L8uJz6Ma/CXr16mXS0tIyrKdnz55WzLZt2zLNEwAAACgouOczAAAA7golSpRQXFycpOv3l1y9enW+5hMeHq5Ro0ZlWO7n56fY2FhJ0p9//qk6deqoX79+GeJCQ0P12GOPScr8HrPpTZ06VQEBARmWP/3003r44YclSQsWLFBSUpJD+1tvvSVJ6tChg3r06OF03T4+PnrvvfckXb9/clbH9qGHHsp0PTk1ffp0XbhwQZI0duxYhYeHZ4ipXr26XnvtNUnSb7/9poULF7q0zdvl1KlT1v2+099H2JkiRYo4/H7w4EHNmTNHkvTee++pbNmyTvtVr15dffv2lSTrPqOZeeutt1S6dOk8z/VmjBkzRqGhoRmWN23aVL169ZJ0/Z7QW7dudWh/9tlnZbPZlJaWZt3n+0azZs2y9uXZZ5+9qfyyuu+z/Xf7vZ7T/7xjxw6dPXvWWn7x4kUlJiZKkqKjo+Xh4WG1TZ48WcnJySpVqpTef/99h7b03njjDZUqVUppaWn65JNPMrSXK1cuy3155plnFBkZKUl5+tqpWbOmhgwZkmG5zWbTgAEDJF2/9/amTZsc2s+dO6dJkyZJkt58803VrFnT6fojIiL0z3/+U5I0d+5cnT9/PtNcBg8erGrVqt3UfqTPO7v7Hg8dOlTFihWTMUaLFi3KMvZmj09SUpIWLFggSWrTpo06d+6cYR2FChXS1KlTs9x+ToSHh2vOnDnW58nFixc1b948vfDCC6pdu7YCAwPVqFEjjRs3LtN7VBtj9Pbbb0uSXnzxRbVq1cppXOHChTV69GhJ0oYNG/Tzzz87tBcrVkzBwcGZ5url5WX1P3TokHbs2OHQnv7zLqv3MA8PDwUGBjosy+vPoZIlS2rixIkO91O3e/nll62fc/J5DwAAAOQ3is8AAAC4awwaNEiFChWSJKtAkV/at28vT09Pp23pCyJPPvlkpuuwx50+fVpnzpzJNK5q1aqZFmuk/xXbrl69qjVr1ljLU1JSrN87duyYaX9JqlixoooVKyZJGQoj6XXt2jXL9eTEihUrJEnBwcFq3759pnE9e/bM0KegK1q0qLy8vCRJ//d//6erV6/muO+SJUt07do1+fn5WV8oyIy90HLs2DEdPnzYaYyXl5cef/zxW5JrbhUuXFjt2rXLtD19wfjGc12mTBk1b95cUubF9unTp0u6Xvx78MEHbyrH+vXry83t+hB73bp1Dm3239MXnx988EEFBgbq2rVr2rhxo7X8+++/z7Soby9etmnTRt7e3pnm4uHhoejoaElZvx6l64XApKQk7d+/X7t377YepUqVkiT98MMPWfbPjS5dujgtrklyeI/69ddfHdrWrl2r5ORkSdm/F9mP2ZUrV6wivjN58V50o7S0NB07dkz79u2zjuNPP/2ke+65R1L2x/Jmj8/q1at17do1Sde/OJCZ2rVrq3Llyjnal6y0adNGP/30k1588UXrfd8uNTVV69ev14ABA3Tfffc5/fLDjz/+qP/+97+Scn4+peyfy6mpqTp8+LB+/PFH6/gbY6z2G49/yZIlrZ/t7wE5ldefQx07dsz0NV2+fHmr2H/juQcAAAAKIorPAAAAuGsULVpU/fv3l3T9Kqply5blWy4PPPBApm3pr+TKaVz6KydvVKtWrSxzqV27tvXzrl27rJ+3b9+utLQ0SVLnzp1ls9myfPzxxx+SlOHq6fRutrCX3u7duyVJNWrUyLSAL0khISEqU6aMQ5+Cztvb2/rCwbx583T//fdr8ODBWrp0aZZfMJBkXfF74cIFeXh4ZHmu2rRpY/XL7HyVK1dOPj4+tyTX3KpevXqmV/lKUmRkpFUIT/8ctrMXgH755ZcMVw5u3brVen7c7FXPkhQUFGQ9vzds2GAVvU6dOqUff/xRktSwYUMr3s3NTfXq1ZPkeDVj+p/Tx1+7ds26cvODDz7I9vU4b948SZmf3yVLlqhNmzYKCgpSyZIlVb58eVWtWtV6LFmyRJKs13VeqFChQqZt6a+Ov/H9LP3V7CVLlsxyv6tUqWLFZrbvAQEBuvfee292NxwYYzRr1iw1bdpUAQEBKlWqlCpUqOBwLO3nLbtjebPHJ/1zPjfv96645557NH78eJ04cUKJiYmaNGmSnn32WYer6s+cOaPY2NgMhd305zM6OjrL85l+xg5n5/P8+fMaOXKkqlWrJn9/f0VERKhy5crWsa9evboVe+Pxb9CggfU86N+/v2rXrq2RI0dqw4YNunz5cpb7n9efQ1mde+n6F3CkrD/rAQAAgIKC4jMAAADuKgMGDLCKtsOGDcu3PPz8/DJts189mZs4+1VvzpQoUSLLXEJCQqyf00+TevLkySz7ZcY+Fakz9n+gu8KeY3b7Jcmapjmz6V/zQvorFdNfZZcVe5yzqxzfe+89tW3bVtL1qWJHjx6t1q1bq2jRoqpVq5ZGjx5tXQWaXl6fr5ycq5vNNbeyO9ceHh5Wcc7ZuY6JibGu0LyxEPbxxx9Luj59fJcuXVzK036V5unTp61C03fffSdjjAoXLpzhqlP7ldDOis9eXl6qW7eutfzUqVM3dXX5jefXGKOePXuqTZs2WrJkSbbFrIsXL+Z6m5m52fezvH5uZzVVc25cunRJrVu31tNPP601a9Zke6yya7/Z45P+OZ+b9/u84Obmpho1aujvf/+7PvroI+3fv19bt251uMp/4MCBDs+zvDqfBw8eVNWqVTVkyBDt3Lkzy89BKePx9/T01Ndff62KFStKuj5t/5AhQ9SgQQMFBwerVatWmj17ttP15vXnUFbnXvrf+c9uHwEAAICCIPOvjgMAAAB/QcHBwRowYICGDh2qhIQELV682OEq0L+izKZxzU76f3J/8MEH1lWa2cmqaOnu7n5TuThzs/uV13x9fa2fc1qos9+L1t/fP0NbYGCgFi1apM2bN+uLL77QmjVrtGPHDl27dk1bt27V1q1b9c4772jhwoXW1MrS/85XsWLFcnVP88zuDZ2Tc3WzueaWq+fay8tLTz/9tMaNG6e5c+dq4sSJ8vf316VLl/TZZ59Jkh577DGXi5KNGjXShAkTJF2fartq1arWlNv16tXLsB/2At3mzZuVmpoqd3d3a2rhWrVqOVx5nv712LNnT6f3gnfGfkW43ccff6yPPvpI0vUrxvv37686deqoVKlS8vPzs857t27d9H//9385/kLFrZR+37dt25bllabp2ae7vlFevQ/9+9//1jfffCNJaty4sfr27asaNWooNDRUvr6+VsGwUaNGWr9+/W05lgXhfbFmzZqKj49XZGSkfvnlF50+fVorVqzQY489JsnxfH799dfWlcHZubHQ+/TTT+vAgQOy2Wx65pln1KlTJ1WsWFHFixeXl5eXda93+/l2dvwrVaqkXbt26euvv9bXX3+tdevW6ZdfftHFixe1bNkyLVu2TGPHjtXSpUudFpoLwvEGAAAAChqKzwAAALjr9O/fX+PHj9eff/6pYcOG5aj4bC8i2Kehzoy9qFiQnDhxIsft6ad2LVq0qPWzn5+fw3S2+alIkSI6fvx4tvsl/W+a1vT7dSvySb+9SpUqZRmfmppqTUudVV61a9e2psg9e/as1qxZoxkzZmj+/Pk6efKkOnTooP/+979W8dt+vs6ePauKFSvmaaE/O7nNNbeyO9dXr161rirM7Jj27NlT48aN07lz5zR37lx1795dCxcutM6FK1Nu26WfJnv9+vXq27evdSVz+ja72rVry8vLS5cuXdKWLVvk5eVlvYfceL/n9PtljLnp1+O0adMkSffff782btyY6Tm5lbMF5Fb696LixYtnWlS+nYwx+vDDDyVdP7erVq1yuDo5vVt9LNN/4efEiRMKDw/PNDYn75t5wd/fX507d9abb74p6fqU93bpz2dwcPBNPZf37t2r7777TpI0ZMgQjRgxwmlcTo69u7u7YmJiFBMTI0k6fvy44uPjNWnSJCUmJioxMVHPPfecFixYYPUpaJ9DAAAAQEHCtNsAAAC46xQqVEiDBg2SdP0quvT/UM6qj3R9Ot2s7N+/3/UE89iWLVty3J6+CBAZGWld1bVhw4Zbk9xNsOe4bdu2LKchPnnypA4dOuTQ51ZIfx/rxMTEbON/+OEH68q/nN4Du1ChQmrbtq2+/PJLvfjii5KuF0jsxRdJ1r1NU1NTHe6pervlJNfc2rFjR5bn+ocffrDu0ZrZua5UqZJ19bV96m37lNsRERFq1qzZTednV6JECZUvX17S9eLz+fPntW3bNklymIbYztfXVzVq1LDi00+/fWPx2cvLy5q225XX4549eyRJjz76aKaFZ2OMlXdBkP6+vQXlvejUqVNWUfHxxx/PtPB87tw57du375bmUrVqVevn3Lzf32phYWHWz+mvEM6L82l/Hkuy7j3vzM28F5YsWVLPPPOMNm3aZL0+Fy9e7DCzRUH7HAIAAAAKEorPAAAAuCvFxcVZU2gOGzYs2+lQ7VMTb9u2LdPYPXv2aOfOnXmbaB7YtWuXtm/fnmm7vQDn7u6uJk2aWMuLFy9u3XN29uzZ+v33329pnjnVvHlzSdKZM2c0f/78TOM++ugj61zZ+9wKjRs3lofH9UmlPv/882yfS7NmzbJ+vpmCZ/o+f/zxh/Vz27ZtrQLPu+++m+v13gqZ5Zpbp06d0tdff51pu/05LGV9rnv27CnpeqF39erVWrlypSSpe/fueTZ9rr1ofOzYMc2aNUtXr16Vt7e3oqKinMbbi9Lr1q2zis/u7u6qX79+hthHH31U0vWrPpctW3ZT+dkLZVnN0vDVV1/p+PHjWa7HPiV4amrqTeWRG82bN7fuiTthwoQCMRV4+oJjVsfyww8/vKl7dedG06ZNrZkOZs6cmWncli1brHuR36zcHPv0hd97773X+rlGjRrW1etTp07VpUuXcp1HTo//lClTcr1uO09PTzVu3Njann2WBKngfQ4BAAAABQnFZwAAANyV/P399corr0i6XpxdunRplvH2f0AfO3bMukdsemfPnlWPHj3yPtE80rt3b6f/oJ89e7a17zExMSpZsqRD++uvvy5JSklJUceOHR3++X6j1NRUTZo06aYKCbnxzDPPWIWogQMH6rfffssQ88MPP+g///mPJKlUqVLWdKq3QkhIiB5//HFJ17+c8NZbb2Uau2rVKqsYUqZMGauYaPfrr79q7dq1WW7v22+/tX5Of7/m8uXLW3l8/vnnGjt2bJbrOXDggNPnck65kuvNGDBggNMpbteuXaupU6dKun6v2Vq1amW6jieffFKFChWSMUZdunRRWlqadb/YvJL+iuVRo0ZJuj69tre3t9N4e/F548aN1tXhkZGR1mwL6fXr108BAQGSrr8O0l/96cySJUsyfCGmXLlykq7fa9fZlMT//e9/1bdv3yzXK8l6r/jvf/+bbayrgoODFRcXJ+n6cXrppZeyvAXCiRMnrCmxb5XixYtb9wj/7LPPnBbht2zZon/+85+3NA/p+rlo166dJGnRokX64osvMsScO3dOzz33nMvb6tOnj/7zn/9kO5318uXLrUK4v7+/Q+HVzc1NQ4YMkXT9faRbt25ZfokhJSVF7733nsMy+/NYkmbMmOG03+TJk/XVV19lut7169c7TAd+o8uXL1vvcQEBASpevLjVVtA+hwAAAICChHs+AwAA4K7Vp08fvfPOOzp+/Hi2V2U+9dRTGj58uFJSUtSjRw/98ssvatmypWw2mxITEzV27FgdPXpU1atXz/Iq4/wQFRWlrVu3KioqSq+88oqqVq2q5ORkzZs3Tx988IGk61Mlv/POOxn6PvLII+rXr5/Gjx+vdevWqWLFinr++efVoEEDFS1aVOfPn9cvv/yi9evXa/78+Tp9+rRiY2Nv6f4UL15co0ePVt++fXX06FHVrFlTr776qurVq6erV69qxYoVGj16tM6dOyebzaapU6fK09PzluY0ZswYrVy5UidPntSQIUO0Zs0aPfXUU3rggQfk4eGho0eP6uuvv9bMmTN19epVubm56eOPP85wX+bDhw+radOmqlSpkh577DFFRUWpVKlSkqQjR45ozpw5VmEpMjJSderUceg/efJkbd26Vb/++qsGDhyor776St26dVPlypXl7e2tP//8Uz/88IPi4+O1atUqPfbYY+rcufNN7bOrueZGtWrV9OOPP6pmzZp67bXXVLt2baWmpmrp0qUaN26crl69Kg8PD02aNCnL9fj7+6tTp06aNm2aNWXyQw89pIiIiJvO7Ubp7+3866+/SnI+5bad/QrnlJQUa9mNU27bhYSEaObMmerYsaOOHz+uqKgode/eXQ8//LDuueceXblyRUePHtXmzZs1b948/frrr/r6668dpnfv1q2bBg0apGPHjik6OlqvvPKKqlSpokuXLmnVqlV69913lZqaqho1amQ59Xa9evV04MABLVq0SB988IHq169vXQ0dGBhozSyRV/71r39p7dq1SkhI0Pjx47VmzRr16tVLkZGR8vf31+nTp7Vnzx6tWLFC33zzjapWrWpd6X4ruLm5qWvXrpo0aZJ27typBg0aaMCAASpXrpySk5O1dOlSvf/++woICFBYWNgtvyXDmDFjtHz5cp09e1ZdunTR2rVr1bFjRwUGBmrnzp166623tH//fuvz4Gb98ccf+uCDD/TGG2/okUceUePGjVWlShUVLVpUV69e1S+//GIVwO1fEBgxYoQCAwMd1vP8889r+fLlWrBggebOnatt27bpueeeU+3atRUUFKSUlBTt3btXa9as0aJFi+Tj42N9AUG6PnV3lSpVtHv3bn3wwQc6ffq0nn76aZUsWVJHjx7VrFmzNG/ePNWvXz/Tqb1XrlypN998Uw0bNlTr1q314IMPqnjx4rp48aL279+vKVOmWK+BHj16WDNcSAXzcwgAAAAoMAwAAADwF7N69WojyUgy06dPzzJ24sSJVqz9kZkvvvjCuLu7Z4iXZHx9fc3cuXNNbGyskWQiIiIy9D9w4ECO8kqf/+rVqzONmz59uhV34MCBDO32tmHDhplhw4Y5zVuSCQwMNGvWrMl0O2lpaeaNN94wHh4ema7D/vD39zcXLlzIVZ4369///rdxc3PLNBdvb28zc+bMTPtnda5uxt69e03FihWzPUbBwcFm8eLFTteR/txn9ahQoYL59ddfna7j+PHjpmHDhjlazzPPPJOhf+PGjY0k07hx4yz3Ny9yzU5ERISRZGJjY820adMyfQ56eXmZzz77LEfrTEhIcOj76aef3lRuWSldurTDNpYsWZJlfIUKFRziFyxYkGX8okWLTJEiRbI99m5ubmbVqlUOfS9fvmxatGiRaR9fX1/zxRdfZPv62L59u/H29na6jtjYWCsup+9nxji+ZzmTkpJi2rdvn6PnXdOmTTP0z+vX/JkzZ0xkZGSmORQpUsSsXbs2y9dUXh6f1atXm0KFCmWaz9ChQx0+C27Giy++mKPjL8n4+PiYUaNGZbquy5cvmz59+hibzZbtusqWLZuh//bt203hwoUz7VO1alVz7NixTI9bVp+L6R/t2rXL8Llm58rnUE7/JjDG8b0QAAAAKOiYdhsAAAB3tV69eik8PDxHsY8//rg2btyoxx57TMWLF5eXl5fCw8MVGxurLVu2qGPHjrc425s3fPhwxcfHq3Xr1goJCZGXl5fKlCmjv//979qzZ481rbgzNptNQ4cO1f79+zV48GBFRUWpSJEicnd3V6FChVSpUiV17dpVM2fO1PHjx+Xr63tb9mnIkCHavn27evXqpfvuu0++vr7y9/dXxYoV1a9fP+3du1fdunW7LblI16e93rlzp2bNmqWOHTsqIiJCfn5+8vLyUmhoqJo1a6bRo0fr4MGDat26tdN1NGzYUGvWrNFrr72mpk2b6v7771ehQoXk6empkJAQtWjRQlOmTNGOHTsyncY6NDRU69at0+LFi9W1a1fde++98vPzk6enp4oXL6569epp4MCBWrt2rcO9knMrL3LNjZ49e2r9+vV64oknFBYWJi8vL5UqVUrdunXT9u3b1alTpxytp3bt2nrggQckXZ/OuX379i7ndqP0Vy67ubmpXr16Wcanv1raZrM5/O5M27ZtdeDAAb3zzjt66KGHFBISIk9PT/n6+qps2bJq06aNxo4dq4MHD6pp06YOfT09PbVkyRJNmDBBUVFR8vPzk6+vr+6//349//zz2rZtmzV9e1YiIyO1adMmde7cWaVLl850WvG8VKhQIX355Zdav369evbsqfLly6tQoULy8PBQkSJFVKtWLfXt21dLly7V8uXLb3k+QUFB2rBhg958801VrVpVPj4+CggIUMWKFfXyyy/rhx9+yPQq9luhSZMm2rNnj/r06aOIiAh5eXkpJCRErVu3Vnx8vN544w2XtzF+/HgdOnRIH3zwgZ566ilFRkaqcOHC8vDwkK+vr8LCwtS8eXP9+9//1v79+zVo0KBM1+Xp6an3339fP/zwg1544QVVrVpVQUFBcnd3V1BQkCIjI9WjRw/NmzdPP/30U4b+kZGR2rFjh55//nlFRETI09NTRYoUUe3atfXOO+9o8+bNGW4lkd7LL7+sL7/8Un369FHdunVVunRp+fj4yMfHR2XKlNETTzyhxYsXa+HChZl+rhW0zyEAAACgILAZY0x+JwEAAAAAwO2QkpKi0NBQXbx4UX369NH777+f3ykBAAAAAPCXwZXPAAAAAIC7xmeffaaLFy9Kun4fVwAAAAAAkHe48hkAAAAAcFe4evWqKleurP379ysqKkpbtmzJ75QAAAAAAPhL8cjvBAAAAAAAuFVOnTqlU6dO6c8//9SYMWO0f/9+Sdfv1QoAAAAAAPIWxWcAAAAAwF/WhAkT9MYbbzgsa9OmjR577LF8yggAAAAAgL8uis8AAAAAgL88Dw8PRUREqHPnznrttdfyOx0AAAAAAP6SuOczAAAAAAAAAAAAAMBlbvmdAAAAAAAAAAAAAADgzkfxGQAAAAAAAAAAAADgMorPAAAAAAAAAAAAAACXUXwGAAAAAAAAAAAAALiM4jMAAAAAAAAAAAAAwGUUnwEAAAAAAAAAAAAALqP4DAAAAAAAAAAAAABwGcVnACigbDabbDabgoODdebMGacxb731lmw2m4YPH35bc8uJNWvWyGazqXv37vmdyi0xYcIEVa5cWd7e3rLZbGrSpEmW8QcPHrTOaVa6d+9eYM9pZpo0aSKbzaaDBw/e9m3369dPNptNQ4YMcdo+f/5867hv3LjRacyzzz4rm82m0aNH38pUc2XGjBl33PMAAAAAQM4x5i/YGPP/T36O+e1SU1M1btw4RUdHKygoSF5eXipZsqSioqL0wgsvaMmSJfmWGwAgI4rPAFDAJScna+zYsfmdBtKZP3+++vXrp+PHj+vRRx9VbGysWrVqld9p3ZUaNmwoSfruu++ctq9fv976ObsY+7puh+HDh8tms2nGjBm3bZsAAAAACh7G/AUPY/6C5cyZM6pXr54GDBigxMRERUZGqkOHDoqKitKxY8f03nvvaeDAgfmdJgAgHY/8TgAAkDmbzSZvb2+NHz9eL730kgoXLpzfKUHSwoULJUnz5s3TQw89lL/J3OXsBeMtW7bo8uXL8vLycmhfv369SpcurZSUFH333XcaPHiwQ3tSUpJ++eUX+fr6qmbNmrctbwAAAABgzF8wMeYvWIYOHapt27YpMjJSixcvVqlSpRzaN2/erPj4+HzKDgDgDFc+A0AB5ubmpt69eyslJUXvvPNOfqeD/+/o0aOSpHvvvTefM0FISIjKlSunS5cuaevWrQ5t586d044dO9SwYUNFR0drw4YNMsY4xNivhq5bt648PT1vW94AAAAAwJi/YGLMX7B8+eWXkqTRo0dnKDxLUu3atTV06NDbnRYAIAsUnwGggHv11Vfl6+uriRMn6s8//8xRn6zux2O/D9GN9ytKPw1wYmKiHn74YQUHB6tIkSJ64oknrMHX+fPnNXjwYJUpU0Y+Pj6qUqWK5s2bl2U+x48fV/fu3RUSEiJfX1/VqFFDn3zySabxp06d0muvvaZKlSrJ19dXQUFBeuihh7R48eIs9yclJUUDBgxQ2bJl5enpqf79+2d7rCTpyJEjeu655xQRESFvb2+VKFFC7du315YtW5weo9WrV0uSypYta93Tac2aNTna1s06d+6cRo4cqWrVqikoKEgBAQG677779Pjjj2vZsmUZ4i9cuKCRI0eqevXqCggIUEBAgOrWrauZM2c6Xb/NZlOZMmV0+fJl/etf/1KFChXk7e2tmJiYXOU5a9Ys1axZU35+fipRooRiY2P122+/OcTExcXJZrNp6tSpma6nfPnycnNz06+//prtNu1XP6efYluSNm3apGvXrqlBgwaqX7++Tp06pR9//NEhJrMpt48cOaK4uDjdd9998vHxUZEiRdSmTRun9402xuizzz5Tp06d9MADD8jf31+FChVS7dq19f777ystLc0hvkyZMnrjjTckSc8884z1HMrseXT48GF16dJFxYsXl6+vr6KiovT1119nejx++uknde/eXeHh4fL29lZISIg6deqkPXv2ZIhNf2/p/fv3q1OnTgoJCZGbm5v1bX8AAAAAtw5jfsb8EmP+rPz++++SpOLFi+cqVyl34+NLly7po48+Urt27XTvvffK19dXwcHBatSokT7//HOn6798+bLef/991apVS0WLFpWfn5/KlCmjNm3aOO3z559/atCgQSpXrpz1v4ZWrVrp22+/dbp++3m7du2a3n77bT3wwAPy9vZWeHi4XnnlFaWmpub6mADA7UDxGQAKuJIlS+r555/X2bNnNXr06Fu+vYSEBNWvX1+///67WrZsqaJFi2ru3Llq1qyZkpOT1bRpU82cOVO1atVSdHS0fvzxRz3xxBNOB0PS9UFl3bp1FR8fryZNmqhhw4batWuXYmNjNXz48Azx+/fvV2RkpN566y1dvHhRLVu2VFRUlBISEtS2bdtMvw1+8eJFNW7cWDNmzFBkZKQeffTRHE1ZtmvXLtWoUUNTp06Vr6+v2rdvr3LlymnBggWqV6+e5s6da8VGRkYqNjZWISEhkqQOHTooNjZWsbGxCg0NzcHRvTnXrl1T8+bNNWTIEB07dkxNmjRR69atFRoaqqVLl+qzzz5ziD958qSio6M1ZMgQJSUlqXHjxmrUqJH27t2r7t2764UXXnC6nbS0NMXExGjUqFG677771K5dO5UsWTLHeb7zzjvq1q2bAgIC1K5dO/n7++uTTz5R3bp1rX9kSNJzzz0nSZo2bZrT9axdu1b79+9Xs2bNcvRN88zu+2wvLNuLz1nFpC8+b9q0SdWqVdOkSZPk6emp1q1bq0qVKlq2bJkaNWqkOXPmOKwjNTVVXbp00YoVKxQaGqq2bduqbt262rNnj/r27atnn33WIb5jx46qVq2aJKl+/frWc8jZ8+jgwYOqVauWNm/erGbNmql69epKTExUTEyM08HpwoULVb16dc2cOVPFihXTo48+qrJly+qLL75Q7dq1tW7dOqfHcN++fdZ2mjZtqr/97W9cCQ4AAADcBoz5GfMz5s9aeHi4JGnKlCkZZjPLSm7HxwcPHlTPnj21detWlSlTRu3atVNkZKS+//57de7c2enzuWvXrurbt6/27dununXrql27dipdurS+++47TZkyxSH2t99+U+3atfXOO+/o8uXLiomJUfXq1bVixQq1bNlS48aNy3RfunTpohEjRqh8+fJq0aKFzp49q1GjRqlHjx45Ph4AcFsZAECBJMm4u7sbY4xJSkoyfn5+xt/f35w8edKKGTlypJFkhg0b5tC3cePGRpI5cOBAhvUeOHDASDKNGzd2WD5s2DAjyUgykydPtpZfvnzZNG/e3EgylSpVMg899JA5d+6c1f7hhx8aSaZRo0YO61u9erW1vr/97W8OfTZv3mwCAgKMm5ubSUxMtJZfvXrVVK1a1Ugyo0aNMteuXbPafv75Z1O2bFnj7u5udu3alWF/JJno6Ghz+vTpzA/qDdLS0qztDR482KSlpVlt8+bNM25ubiYgIMAcO3bMoV9Wxzcz6fPMSmxsbIZzumrVKiPJ1KpVy1y8eNEhPjk52WzdutVh2SOPPGIkmX79+plLly5Zy5OSkkxUVJSRZL755huHPvbc7r//fnP06NEc75cx/zseHh4eZsmSJdbyy5cvm65duxpJpl27dg596tWrZySZ7du3Z1ifvc+cOXNytP1ffvnFSDJFihRxOIdNmzY1hQsXNmlpaeb8+fPGw8PDPPXUU1Z7SkqKcXd3Nx4eHtbzMzk52ZQsWdK4u7ubWbNmOWxny5YtpnDhwiYgIMDhdXjlyhWzYMECc/nyZYf4kydPWsd77dq1Dm3219v06dOd7tP06dOtczJw4ECH18K4ceOMJNOwYUOHPgcOHDD+/v4mICDALF++3KHtm2++MZ6eniY8PNykpqY63U5cXJy5evWq03wAAAAA5C3G/Iz57RjzZ83+OpBkKlSoYF599VWzYMECc+TIkUz73Mz4+I8//jDLly93eJ4YY8yvv/5qypQpY9zc3ByeE7/++quRZCIiIswff/zh0OfixYtm48aNDsvatGljJJkuXbo4bHf9+vXGz8/PuLu7Zzhe9v2uWLGiOX78uMO2g4ODjSTzyy+/ZHocACC/UHwGgAIq/UDUGGMGDhxoFaLsbsVAtEGDBhn6fPXVV0aScXNzM/v27XNou3r1qilWrJjx9PR0KL7ZB6Jubm5m7969Gdb5yiuvGEmmR48e1rIFCxYYSaZDhw5Oj8n8+fONJPPiiy9m2B9JZsuWLU77ZcY+wCtdunSGwqExxrRv395IMiNGjHBYfrsHonPmzDGSTP/+/bPdzvbt261Ba/qBvN22bduMJPPoo486LLfnNnfu3JztUDr249GlS5cMbX/88Yfx8/MzNpvNHD582Fr+ySefGEnm73//u0P8qVOnjI+PjylevLjDYCw7JUuWNJKsf1JcvnzZ+Pn5mUceecSKqVWrlomIiLB+j4+Pt46Vnb2wm/51lt7YsWONJDN27Ngc5bV8+XIjyQwYMMBheU6Lz2XLls1wHK5cuWIKFy5sPD09Hdr69etnJJmJEyc6XeeLL75oJJn58+dn2E7x4sXN+fPnc7RPAAAAAFzHmD8jxvyM+Z25du2aGTx4sPH09LT2w/6oXLmymTx5coZjcTPj46xMmzbNSDITJkywliUkJBhJJiYmJtv+//3vf40kExAQYP78888M7QMGDDCSTM+ePR2W2/fzxgK6McbExcVl+X8FAMhPTLsNAHeIV155Rf7+/po8ebJOnDhxy7bTokWLDMvs0yCVKVNGDzzwgEObu7u7IiIidOXKFf3xxx8Z+kZGRqp8+fIZlnfu3FmS43167dMIt2/f3mlu9qmRN2/enKGtZMmSioqKctovM/ZtP/HEE06nGH766acz5JgfIiMj5ebmpunTp2vatGlZ3gfMfgxjYmLk5pbxY95+Pyhnx9Bms6lt27Y3nWenTp0yLCtatKhatGghY4zDlNePP/64ihYtqk8//VQXLlywls+aNUuXLl1SbGysvLy8crztG6fe3rZtmy5cuKAGDRpYMfXr19ehQ4es+1HZY9NPue3Kc3DHjh0aNWqU+vbtq2eeeUbdu3fX5MmTJUk///xzjvclvSZNmmQ4Dh4eHipbtqyuXLni8FxwJffmzZvLz8/vpnIEAAAA4DrG/Iz5GfM75+bmprffflsHDx7U+PHj1aFDB5UuXVqStGfPHvXp00cdOnRQWlqa1ceV59p3332nESNGqE+fPtbY3j49e/qxfYUKFeTv768lS5Zo9OjROnbsWKb7YD82rVq1UpEiRTK0Z/Vc9PT0VNOmTTMst79Wjx8/nul2ASC/UHwGgDtE8eLF1bdvX124cEFvvfXWLdtOqVKlMiwLCAjItC19e2pqaoa2iIgIp33KlCkjSQ5/nB88eFDS9Xvm2Gy2DI/ixYtLktMBr33gkRv2bdtzySxHe7HSFTabLUdx5v/fvyh9/AMPPKBRo0bpwoUL6t27t0qUKKFq1appwIAB2rlzp0N/+zH8xz/+4fQY2mw2nTt3zukxLFGihLy9vW9yD3N3rn18fBQbG6vk5GSHe2x9+OGHkqSePXvmats3Fp/T3+/Zzn7fZ3ubs/s9249f/fr1nR67WrVqSXJ8Dl6+fFldunRR9erV9corr+j999/XjBkzNHPmTM2fP1+SdPbs2Vztj90999zjdHmhQoUkOb7m7LmXKlXKae6PP/54htztbub1AwAAACDvMOZnzM+YP2thYWF68cUXNW/ePB06dEg//vijnn32WUnX7++c/t7YNzM+Tk5OVrNmzdSwYUP985//1JQpU6yxvb2YnX5sHxgYqGnTpsnb21uDBw9WqVKlVL58eT3//PPasGGDQ+6uPBdDQ0Pl7u6eYbmz/wsAQEHhkd8JAABybtCgQXr//fc1ZcoUDR48+KbWkf6boM44++ZsTtrygj23Vq1aKSQkJNO4YsWKZVjm4+OT5/nkdPCYE+mvKr1w4UK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            "text/plain": [
              "<Figure size 2400x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(24, 6))\n",
        "colors = [\"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
        "\n",
        "sns.barplot(y=\"total_user\", x=\"weather\", data=byweather.sort_values(by=\"total_user\", ascending=False), palette=colors, hue=\"weather\", legend=False, ax=ax[0])\n",
        "ax[0].set_title(\"Number of User by Weather\", loc=\"center\", fontsize=15)\n",
        "ax[0].set_ylabel(None)\n",
        "ax[0].set_xlabel(None)\n",
        "ax[0].ticklabel_format(style='plain', axis='y')\n",
        "\n",
        "sns.barplot(y=\"total_user\", x=\"season\", data=byseason.sort_values(by=\"total_user\", ascending=False), palette=colors, hue=\"season\", legend=False, ax=ax[1])\n",
        "ax[1].set_title(\"Number of User by Season\", loc=\"center\", fontsize=15)\n",
        "ax[1].set_ylabel(None)\n",
        "ax[1].set_xlabel(None)\n",
        "ax[1].ticklabel_format(style='plain', axis='y')\n",
        "\n",
        "plt.suptitle(\"Number of Users by Weather and Season\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkY1LCYMpwUw"
      },
      "source": [
        "Based on weather conditions, Bike Sharing has a very high productivity during sunny or slightly cloudy weather with 2,338,173 users. While in times of heavy rain, storms, snow, or fog Bike Sharing productivity is very low with only 223 users.\n",
        "\n",
        "When viewed by season, the highest productivity occurs in autumn with a total of 1,061,129 users. While the lowest productivity occurred in spring with 471,348 users."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tzdcg4-HzhYn"
      },
      "source": [
        "### **RFM Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "MP1gC3v3zltW",
        "outputId": "32455fd4-7291-43b9-c87e-9b110484e1b5"
      },
      "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>day</th>\n",
              "      <th>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>friday</td>\n",
              "      <td>104</td>\n",
              "      <td>485395</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>monday</td>\n",
              "      <td>105</td>\n",
              "      <td>444027</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>saturday</td>\n",
              "      <td>104</td>\n",
              "      <td>487790</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sunday</td>\n",
              "      <td>105</td>\n",
              "      <td>477807</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>thursday</td>\n",
              "      <td>104</td>\n",
              "      <td>473048</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>tuesday</td>\n",
              "      <td>105</td>\n",
              "      <td>455503</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>wednesday</td>\n",
              "      <td>104</td>\n",
              "      <td>469109</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         day  frequency  monetary  recency\n",
              "0     friday        104    485395        4\n",
              "1     monday        105    444027        1\n",
              "2   saturday        104    487790        3\n",
              "3     sunday        105    477807        2\n",
              "4   thursday        104    473048        5\n",
              "5    tuesday        105    455503        0\n",
              "6  wednesday        104    469109        6"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfm_df = day.groupby(by=\"day\", as_index=False).agg({\n",
        "    \"date\": \"max\", # retrieve the date of the last order\n",
        "    \"instant\": \"nunique\", # calculate the order quantity\n",
        "    \"total_user\": \"sum\" # calculate the amount of revenue generated\n",
        "})\n",
        "\n",
        "rfm_df.columns = [\"day\", \"max_order_timestamp\", \"frequency\", \"monetary\"]\n",
        "\n",
        "# calculates when the customer last made a transaction (days)\n",
        "rfm_df[\"max_order_timestamp\"] = rfm_df[\"max_order_timestamp\"].dt.date\n",
        "recent_date = day[\"date\"].dt.date.max()\n",
        "rfm_df[\"recency\"] = rfm_df[\"max_order_timestamp\"].apply(lambda x: (recent_date - x).days)\n",
        "\n",
        "rfm_df.drop(\"max_order_timestamp\", axis=1, inplace=True)\n",
        "rfm_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "YtaElgUt1voU",
        "outputId": "28f906b3-eea7-4354-f4b9-9bac3ed922a8"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 3000x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(30, 6))\n",
        "\n",
        "colors = [\"#72BCD4\", \"#72BCD4\", \"#72BCD4\", \"#72BCD4\", \"#72BCD4\"]\n",
        "\n",
        "sns.barplot(y=\"recency\", x=\"day\", data=rfm_df.sort_values(by=\"recency\", ascending=True).head(5), palette=colors, hue=\"day\", legend=False, ax=ax[0])\n",
        "ax[0].set_ylabel(None)\n",
        "ax[0].set_xlabel(None)\n",
        "ax[0].set_title(\"By Recency (days)\", loc=\"center\", fontsize=18)\n",
        "ax[0].tick_params(axis ='x', labelsize=15)\n",
        "\n",
        "sns.barplot(y=\"frequency\", x=\"day\", data=rfm_df.sort_values(by=\"frequency\", ascending=False).head(5), palette=colors, hue=\"day\", legend=False, ax=ax[1])\n",
        "ax[1].set_ylabel(None)\n",
        "ax[1].set_xlabel(None)\n",
        "ax[1].set_title(\"By Frequency\", loc=\"center\", fontsize=18)\n",
        "ax[1].tick_params(axis='x', labelsize=15)\n",
        "\n",
        "sns.barplot(y=\"monetary\", x=\"day\", data=rfm_df.sort_values(by=\"monetary\", ascending=False).head(5), palette=colors, hue=\"day\", legend=False, ax=ax[2])\n",
        "ax[2].set_ylabel(None)\n",
        "ax[2].set_xlabel(None)\n",
        "ax[2].set_title(\"By Monetary\", loc=\"center\", fontsize=18)\n",
        "ax[2].tick_params(axis='x', labelsize=15)\n",
        "\n",
        "plt.suptitle(\"Best Customer Based on RFM Parameters (day)\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwwsK7TqvrO2"
      },
      "source": [
        "**Conclusion**\n",
        "\n",
        "* Overall, there were more registered users than casual users. Registered users were most prevalent on Friday, while casual users were most prevalent on Sunday.\n",
        "* On weekdays, users were most prevalent during commuting and work hours, while on holidays/weekends users were most prevalent during the afternoon to evening around 12.00 to 16.00.\n",
        "* Bike Sharing users are relatively few from midnight to 5am.\n",
        "* September and June are the months with the most Bike Sharing users.\n",
        "* Bike Sharing users are more in sunny weather and very few in heavy rainy weather.\n",
        "* Bike Sharing users are most abundant in autumn."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znYCWTxt7D45"
      },
      "source": [
        "## **Export Clean Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "WP0Jwpph7GdJ"
      },
      "outputs": [],
      "source": [
        "day.to_csv(\"dashboard/day_clean.csv\", index=False)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "WcjWTK9zsfEB",
        "adPPn_TBwcL-",
        "k-A0uSWFwkGR",
        "49quRb63xD4E",
        "ZX8N6Q4-xtPL",
        "Kv6tthSbBTLG",
        "ThxXsq39CIlk",
        "hbwffouwFmOq",
        "8lg50u4gQdLW"
      ],
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}