Calendars_dataset.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Seasonal patterns of prices"
      ],
      "metadata": {
        "id": "pcYJ1vXQRKru"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "_r_65cX4iHVv"
      },
      "outputs": [],
      "source": [
        "# Import all necessary libraries\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "import datetime as dt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn import linear_model, metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "NhG9DaykiWWX"
      },
      "outputs": [],
      "source": [
        "# Read in the dataset\n",
        "calendar_df = pd.read_csv('/content/drive/MyDrive/calendar.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzZ5ZpauCzf7"
      },
      "source": [
        "## Data Cleaning and Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bg5uYfe0ijxH",
        "outputId": "8f5fd01b-1853-4bfd-aa72-f8b6e415823c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2554278, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Data Understanding\n",
        "calendar_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "A-J5Canai1jU",
        "outputId": "70fb1cf4-3d32-4036-b827-229f55e05283"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   listing_id        date available   price adjusted_price  minimum_nights  \\\n",
              "0        2818  2023-03-09         f  $69.00         $69.00             3.0   \n",
              "1        2818  2023-03-10         f  $69.00         $69.00             3.0   \n",
              "2        2818  2023-03-11         f  $69.00         $69.00             3.0   \n",
              "3        2818  2023-03-12         f  $69.00         $69.00             3.0   \n",
              "4        2818  2023-03-13         t  $69.00         $69.00             3.0   \n",
              "\n",
              "   maximum_nights  \n",
              "0          1125.0  \n",
              "1          1125.0  \n",
              "2          1125.0  \n",
              "3          1125.0  \n",
              "4          1125.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f8b44db8-3fa6-4048-a5b0-305011fed8c5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>listing_id</th>\n",
              "      <th>date</th>\n",
              "      <th>available</th>\n",
              "      <th>price</th>\n",
              "      <th>adjusted_price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>f</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-10</td>\n",
              "      <td>f</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-11</td>\n",
              "      <td>f</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-12</td>\n",
              "      <td>f</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-13</td>\n",
              "      <td>t</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>$69.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8b44db8-3fa6-4048-a5b0-305011fed8c5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8b44db8-3fa6-4048-a5b0-305011fed8c5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8b44db8-3fa6-4048-a5b0-305011fed8c5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "calendar_df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Le4KJZX0i-G2",
        "outputId": "31bc6535-8f7b-4f75-b8cf-c33078183cfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2554278 entries, 0 to 2554277\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   listing_id      int64  \n",
            " 1   date            object \n",
            " 2   available       object \n",
            " 3   price           object \n",
            " 4   adjusted_price  object \n",
            " 5   minimum_nights  float64\n",
            " 6   maximum_nights  float64\n",
            "dtypes: float64(2), int64(1), object(4)\n",
            "memory usage: 136.4+ MB\n"
          ]
        }
      ],
      "source": [
        "calendar_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gY4Syi6i6ia",
        "outputId": "9177919a-07ef-4d0a-e525-9df3c62d0a97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "listing_id          0\n",
              "date                0\n",
              "available           0\n",
              "price             759\n",
              "adjusted_price    759\n",
              "minimum_nights      2\n",
              "maximum_nights      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Print number of missing values\n",
        "calendar_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "wcwWpUSojwyk",
        "outputId": "54b33b89-03b8-4292-bb99-6aa1f8808019"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         listing_id  minimum_nights  maximum_nights\n",
              "count  2.554278e+06    2.554276e+06    2.554276e+06\n",
              "mean   1.867998e+17    4.261859e+00    6.109487e+05\n",
              "std    3.138151e+17    1.942372e+01    3.619949e+07\n",
              "min    2.818000e+03    1.000000e+00    1.000000e+00\n",
              "25%    1.578502e+07    2.000000e+00    3.000000e+01\n",
              "50%    3.534298e+07    3.000000e+00    3.650000e+02\n",
              "75%    5.822990e+17    4.000000e+00    1.125000e+03\n",
              "max    8.427546e+17    1.001000e+03    2.147484e+09"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b952ff5e-bb97-47f5-ae1b-3fbbed0148c9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>listing_id</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.554278e+06</td>\n",
              "      <td>2.554276e+06</td>\n",
              "      <td>2.554276e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.867998e+17</td>\n",
              "      <td>4.261859e+00</td>\n",
              "      <td>6.109487e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.138151e+17</td>\n",
              "      <td>1.942372e+01</td>\n",
              "      <td>3.619949e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.818000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.578502e+07</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.000000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.534298e+07</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>3.650000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.822990e+17</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>1.125000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.427546e+17</td>\n",
              "      <td>1.001000e+03</td>\n",
              "      <td>2.147484e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b952ff5e-bb97-47f5-ae1b-3fbbed0148c9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b952ff5e-bb97-47f5-ae1b-3fbbed0148c9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b952ff5e-bb97-47f5-ae1b-3fbbed0148c9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Print description of DataFrame\n",
        "calendar_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hk-gzbi3lCIV",
        "outputId": "2ce20dfb-8894-4796-f6d5-b430700e1920"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   listing_id        date available  price  adjusted_price  minimum_nights  \\\n",
              "0        2818  2023-03-09         f   69.0            69.0             3.0   \n",
              "1        2818  2023-03-10         f   69.0            69.0             3.0   \n",
              "2        2818  2023-03-11         f   69.0            69.0             3.0   \n",
              "3        2818  2023-03-12         f   69.0            69.0             3.0   \n",
              "4        2818  2023-03-13         t   69.0            69.0             3.0   \n",
              "\n",
              "   maximum_nights  \n",
              "0          1125.0  \n",
              "1          1125.0  \n",
              "2          1125.0  \n",
              "3          1125.0  \n",
              "4          1125.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4065de63-9231-4d3a-9f37-eea201d4fe57\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>listing_id</th>\n",
              "      <th>date</th>\n",
              "      <th>available</th>\n",
              "      <th>price</th>\n",
              "      <th>adjusted_price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>f</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-10</td>\n",
              "      <td>f</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-11</td>\n",
              "      <td>f</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-12</td>\n",
              "      <td>f</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-13</td>\n",
              "      <td>t</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4065de63-9231-4d3a-9f37-eea201d4fe57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4065de63-9231-4d3a-9f37-eea201d4fe57 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4065de63-9231-4d3a-9f37-eea201d4fe57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Remove $ and convert price to float\n",
        "calendar_df['price'] = calendar_df['price'].replace('[\\$,]', '', regex=True).astype(float)\n",
        "calendar_df['adjusted_price'] = calendar_df['adjusted_price'].replace('[\\$,]', '', regex=True).astype(float)\n",
        "calendar_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-Z-FWnanh6K",
        "outputId": "8eb650de-181a-4017-e152-9fff75181ce8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['f', 't'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Find number of unique values in room_type column\n",
        "calendar_df['available'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "k6epF_qLqKE1",
        "outputId": "95237b56-1507-403a-dcc5-4aae0f14833b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   listing_id        date  available  price  adjusted_price  minimum_nights  \\\n",
              "0        2818  2023-03-09          0   69.0            69.0             3.0   \n",
              "1        2818  2023-03-10          0   69.0            69.0             3.0   \n",
              "2        2818  2023-03-11          0   69.0            69.0             3.0   \n",
              "3        2818  2023-03-12          0   69.0            69.0             3.0   \n",
              "4        2818  2023-03-13          1   69.0            69.0             3.0   \n",
              "\n",
              "   maximum_nights  \n",
              "0          1125.0  \n",
              "1          1125.0  \n",
              "2          1125.0  \n",
              "3          1125.0  \n",
              "4          1125.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f41fa48-0a83-420a-9ed2-9543d9e5c233\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>listing_id</th>\n",
              "      <th>date</th>\n",
              "      <th>available</th>\n",
              "      <th>price</th>\n",
              "      <th>adjusted_price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-10</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-11</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-12</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-13</td>\n",
              "      <td>1</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f41fa48-0a83-420a-9ed2-9543d9e5c233')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4f41fa48-0a83-420a-9ed2-9543d9e5c233 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4f41fa48-0a83-420a-9ed2-9543d9e5c233');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Create a label encoder object\n",
        "le = LabelEncoder()\n",
        "\n",
        "# Fit the label encoder to the categorical column\n",
        "calendar_df['available'] = le.fit_transform(calendar_df['available'])\n",
        "calendar_df['available'] = calendar_df['available'].astype(bool).astype(int)\n",
        "\n",
        "# Print the updated dataframe with the categorical column in integer format\n",
        "calendar_df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNtHxWCnrqWS",
        "outputId": "b8b5328f-daca-400a-ab72-b3f7f7d54759"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [listing_id, date, available, price, adjusted_price, minimum_nights, maximum_nights]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "# Checking for duplicate entries or inconsistent data\n",
        "duplicates = calendar_df[calendar_df.duplicated()]\n",
        "print(duplicates)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "LOH7Yav-vclV"
      },
      "outputs": [],
      "source": [
        "# Convert date to proper format\n",
        "calendar_df[['Year', 'Month', 'Date']] = calendar_df['date'].astype(str).str.split('-', n=2, expand=True)\n",
        "\n",
        "#Add Day to every row\n",
        "calendar_df['date'] = pd.to_datetime(calendar_df['date'], errors='coerce')\n",
        "calendar_df['Day'] = calendar_df['date'].dt.day_name()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "3TSRRx3_6t6d"
      },
      "outputs": [],
      "source": [
        "# Removing missing values\n",
        "calendar_df.dropna(subset=['price', 'adjusted_price'], inplace=True)\n",
        "calendar_df.dropna(subset=['minimum_nights', 'maximum_nights'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "exymkVDp6RWZ"
      },
      "outputs": [],
      "source": [
        "calendar_df.dropna(subset=['price', 'adjusted_price'], inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "gLtFY5kWJ0jR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d078450b-322a-4fa7-c0af-9d9412839603"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   listing_id       date  available  price  adjusted_price  minimum_nights  \\\n",
              "0        2818 2023-03-09          0   69.0            69.0             3.0   \n",
              "1        2818 2023-03-10          0   69.0            69.0             3.0   \n",
              "2        2818 2023-03-11          0   69.0            69.0             3.0   \n",
              "3        2818 2023-03-12          0   69.0            69.0             3.0   \n",
              "4        2818 2023-03-13          1   69.0            69.0             3.0   \n",
              "\n",
              "   maximum_nights  Year Month Date       Day  day_num  \n",
              "0          1125.0  2023    03   09  Thursday        4  \n",
              "1          1125.0  2023    03   10    Friday        5  \n",
              "2          1125.0  2023    03   11  Saturday        6  \n",
              "3          1125.0  2023    03   12    Sunday        7  \n",
              "4          1125.0  2023    03   13    Monday        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c38a4e09-f242-4777-9d6a-2fd44e633bc0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>listing_id</th>\n",
              "      <th>date</th>\n",
              "      <th>available</th>\n",
              "      <th>price</th>\n",
              "      <th>adjusted_price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Date</th>\n",
              "      <th>Day</th>\n",
              "      <th>day_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>09</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-10</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-11</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>11</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-12</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>12</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-13</td>\n",
              "      <td>1</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>13</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c38a4e09-f242-4777-9d6a-2fd44e633bc0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c38a4e09-f242-4777-9d6a-2fd44e633bc0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c38a4e09-f242-4777-9d6a-2fd44e633bc0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "\n",
        "lookupDay = {\n",
        "    'Monday': 1,\n",
        "    'Tuesday': 2,\n",
        "    'Wednesday': 3,\n",
        "    'Thursday': 4,\n",
        "    'Friday': 5,\n",
        "    'Saturday': 6,\n",
        "    'Sunday': 7\n",
        "}\n",
        "calendar_df['day_num'] = calendar_df['Day'].apply(lambda x: lookupDay[x])\n",
        "calendar_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "29ksIRWUPtK8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b8983cf9-f79e-4f92-e084-32f92782026e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   listing_id       date  available  price  adjusted_price  minimum_nights  \\\n",
              "0        2818 2023-03-09          0   69.0            69.0             3.0   \n",
              "1        2818 2023-03-10          0   69.0            69.0             3.0   \n",
              "2        2818 2023-03-11          0   69.0            69.0             3.0   \n",
              "3        2818 2023-03-12          0   69.0            69.0             3.0   \n",
              "4        2818 2023-03-13          1   69.0            69.0             3.0   \n",
              "\n",
              "   maximum_nights  Year Month Date       Day  day_num  season  \n",
              "0          1125.0  2023    03   09  Thursday        4  Spring  \n",
              "1          1125.0  2023    03   10    Friday        5  Spring  \n",
              "2          1125.0  2023    03   11  Saturday        6  Spring  \n",
              "3          1125.0  2023    03   12    Sunday        7  Spring  \n",
              "4          1125.0  2023    03   13    Monday        1  Spring  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-843b486c-e6d2-40cd-ba17-eb8604ccc7af\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>listing_id</th>\n",
              "      <th>date</th>\n",
              "      <th>available</th>\n",
              "      <th>price</th>\n",
              "      <th>adjusted_price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Date</th>\n",
              "      <th>Day</th>\n",
              "      <th>day_num</th>\n",
              "      <th>season</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>09</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>4</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-10</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>5</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-11</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>11</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>6</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-12</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>12</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-13</td>\n",
              "      <td>1</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>13</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "      <td>Spring</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-843b486c-e6d2-40cd-ba17-eb8604ccc7af')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-843b486c-e6d2-40cd-ba17-eb8604ccc7af button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-843b486c-e6d2-40cd-ba17-eb8604ccc7af');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Assign Season to every entry\n",
        "lookup = {\n",
        "    '01': 'Winter',\n",
        "    '02': 'Spring',\n",
        "    '03': 'Spring',\n",
        "    '04': 'Spring',\n",
        "    '05': 'Summer',\n",
        "    '06': 'Summer',\n",
        "    '07': 'Summer',\n",
        "    '08': 'Autumn',\n",
        "    '09': 'Autumn',\n",
        "    '10': 'Autumn',\n",
        "    '11': 'Winter',\n",
        "    '12': 'Winter'\n",
        "}\n",
        "calendar_df['season'] = calendar_df['Month'].apply(lambda x: lookup[x])\n",
        "calendar_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Zsb1AZZ0PBlK"
      },
      "outputs": [],
      "source": [
        "lookupNum = {\n",
        "    '01': 4,\n",
        "    '02': 1,\n",
        "    '03': 1,\n",
        "    '04': 1,\n",
        "    '05': 2,\n",
        "    '06': 2,\n",
        "    '07': 2,\n",
        "    '08': 3,\n",
        "    '09': 3,\n",
        "    '10': 3,\n",
        "    '11': 4,\n",
        "    '12': 4\n",
        "}\n",
        "\n",
        "calendar_df['season_num'] = calendar_df['Month'].apply(lambda x: lookupNum[x])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "a6mP00jhTwzr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "1cce8a3c-9e72-43eb-abe3-8099cc4e06d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   listing_id       date  available  price  adjusted_price  minimum_nights  \\\n",
              "0        2818 2023-03-09          0   69.0            69.0             3.0   \n",
              "1        2818 2023-03-10          0   69.0            69.0             3.0   \n",
              "2        2818 2023-03-11          0   69.0            69.0             3.0   \n",
              "3        2818 2023-03-12          0   69.0            69.0             3.0   \n",
              "4        2818 2023-03-13          1   69.0            69.0             3.0   \n",
              "\n",
              "   maximum_nights  Year Month Date       Day  day_num  season  season_num  \n",
              "0          1125.0  2023    03   09  Thursday        4  Spring           1  \n",
              "1          1125.0  2023    03   10    Friday        5  Spring           1  \n",
              "2          1125.0  2023    03   11  Saturday        6  Spring           1  \n",
              "3          1125.0  2023    03   12    Sunday        7  Spring           1  \n",
              "4          1125.0  2023    03   13    Monday        1  Spring           1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-18569219-1d1a-4049-9bcb-114751d6ce56\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>listing_id</th>\n",
              "      <th>date</th>\n",
              "      <th>available</th>\n",
              "      <th>price</th>\n",
              "      <th>adjusted_price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Date</th>\n",
              "      <th>Day</th>\n",
              "      <th>day_num</th>\n",
              "      <th>season</th>\n",
              "      <th>season_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>09</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>4</td>\n",
              "      <td>Spring</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-10</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>10</td>\n",
              "      <td>Friday</td>\n",
              "      <td>5</td>\n",
              "      <td>Spring</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-11</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>11</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>6</td>\n",
              "      <td>Spring</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-12</td>\n",
              "      <td>0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>12</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>7</td>\n",
              "      <td>Spring</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2818</td>\n",
              "      <td>2023-03-13</td>\n",
              "      <td>1</td>\n",
              "      <td>69.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>03</td>\n",
              "      <td>13</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "      <td>Spring</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18569219-1d1a-4049-9bcb-114751d6ce56')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-18569219-1d1a-4049-9bcb-114751d6ce56 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-18569219-1d1a-4049-9bcb-114751d6ce56');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "calendar_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7begU8gc_QSI"
      },
      "source": [
        "## Data Visualization and Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Average price by season\n"
      ],
      "metadata": {
        "id": "YJRUa40cHQMN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "0m4nHv2CT4zn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "b1c3b483-4218-4b59-a94a-4f84f4b30008"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season  Average Price\n",
              "0  Autumn     251.408176\n",
              "1  Spring     258.998627\n",
              "2  Summer     242.015976\n",
              "3  Winter     247.757050"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae36d196-bbb5-40eb-9ee0-e8361fd7d287\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>Average Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Autumn</td>\n",
              "      <td>251.408176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Spring</td>\n",
              "      <td>258.998627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Summer</td>\n",
              "      <td>242.015976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Winter</td>\n",
              "      <td>247.757050</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae36d196-bbb5-40eb-9ee0-e8361fd7d287')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae36d196-bbb5-40eb-9ee0-e8361fd7d287 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae36d196-bbb5-40eb-9ee0-e8361fd7d287');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "seasonDF = calendar_df.groupby(['season']).price.mean()\n",
        "seasonDF = seasonDF.reset_index()\n",
        "seasonDF = seasonDF.rename(columns={'price':'Average Price'})\n",
        "seasonDF"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up the figure and axis\n",
        "fig, ax = plt.subplots(figsize=(5,6))\n",
        "\n",
        "# Create the bar plot with a color palette\n",
        "sns.barplot(x='season', y='Average Price', data=seasonDF, color=sns.color_palette('pastel')[0], ax=ax)\n",
        "\n",
        "# Add x-axis and y-axis labels\n",
        "ax.set_xlabel('Season')\n",
        "ax.set_ylabel('Average Price')\n",
        "\n",
        "# Add a title to the plot\n",
        "ax.set_title('Average Price by Season')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "pJGrinppDKnp",
        "outputId": "b1d654d8-ccaf-44b9-e9c2-201f3d7adaba"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Average price by Month"
      ],
      "metadata": {
        "id": "CQ9YPVGRHMbg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "u7X22rkPT_4W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "9007cee4-19e2-4a18-d54d-1324b66b785a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Month  Average Price\n",
              "0     January     259.333143\n",
              "1    February     285.513225\n",
              "2       March     243.324878\n",
              "3       April     249.034568\n",
              "4         May     243.197132\n",
              "5        June     239.026433\n",
              "6        July     243.727925\n",
              "7      August     245.934967\n",
              "8   September     250.801047\n",
              "9     October     257.467292\n",
              "10   November     240.360826\n",
              "11   December     243.338592"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe043ed4-a1d7-456a-be01-64ac00ffb8c7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Average Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>January</td>\n",
              "      <td>259.333143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>February</td>\n",
              "      <td>285.513225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>March</td>\n",
              "      <td>243.324878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>April</td>\n",
              "      <td>249.034568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>May</td>\n",
              "      <td>243.197132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>June</td>\n",
              "      <td>239.026433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>July</td>\n",
              "      <td>243.727925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>August</td>\n",
              "      <td>245.934967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>September</td>\n",
              "      <td>250.801047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>October</td>\n",
              "      <td>257.467292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>November</td>\n",
              "      <td>240.360826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>December</td>\n",
              "      <td>243.338592</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe043ed4-a1d7-456a-be01-64ac00ffb8c7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fe043ed4-a1d7-456a-be01-64ac00ffb8c7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fe043ed4-a1d7-456a-be01-64ac00ffb8c7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "monthDF = calendar_df.groupby(['Month']).price.mean()\n",
        "monthDF = monthDF.reset_index()\n",
        "monthDF = monthDF.rename(columns={'price':'Average Price'})\n",
        "lookupMonth = {\n",
        "    '01': 'January',\n",
        "    '02': 'February',\n",
        "    '03': 'March',\n",
        "    '04': 'April',\n",
        "    '05': 'May',\n",
        "    '06': 'June',\n",
        "    '07': 'July',\n",
        "    '08': 'August',\n",
        "    '09': 'September',\n",
        "    '10': 'October',\n",
        "    '11': 'November',\n",
        "    '12': 'December'\n",
        "}\n",
        "monthDF['Month'] = monthDF['Month'].apply(lambda x: lookupMonth[x])\n",
        "monthDF"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up the figure and axis\n",
        "fig, ax = plt.subplots(figsize=(5,6))\n",
        "\n",
        "# Create the bar plot with a color palette\n",
        "sns.barplot(x='Average Price', y='Month', data=monthDF, color=sns.color_palette('pastel')[0], orient='h', ax=ax)\n",
        "\n",
        "# Add x-axis and y-axis labels\n",
        "ax.set_xlabel('Average Price')\n",
        "ax.set_ylabel('Month')\n",
        "\n",
        "# Add a title to the plot\n",
        "ax.set_title('Average Price by Month')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "nJq3FnlhD64g",
        "outputId": "b5818b83-7420-43a9-ed54-cea290c0d737"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Average price by Day\n"
      ],
      "metadata": {
        "id": "njVfYfu5HIR5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "uYXPG0GHUHjs",
        "outputId": "2b7b653d-7947-45a3-d39f-c8ef8ea41c62"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Day  Average Price  day num\n",
              "1     Monday     246.567917        0\n",
              "5    Tuesday     246.457925        1\n",
              "6  Wednesday     246.643025        2\n",
              "4   Thursday     248.498792        3\n",
              "0     Friday     256.794463        4\n",
              "2   Saturday     256.893177        5\n",
              "3     Sunday     247.859589        6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c25368ff-6c20-4d8e-bf1f-3696a72989ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day</th>\n",
              "      <th>Average Price</th>\n",
              "      <th>day num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Monday</td>\n",
              "      <td>246.567917</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Tuesday</td>\n",
              "      <td>246.457925</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Wednesday</td>\n",
              "      <td>246.643025</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thursday</td>\n",
              "      <td>248.498792</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Friday</td>\n",
              "      <td>256.794463</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Saturday</td>\n",
              "      <td>256.893177</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunday</td>\n",
              "      <td>247.859589</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c25368ff-6c20-4d8e-bf1f-3696a72989ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c25368ff-6c20-4d8e-bf1f-3696a72989ec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c25368ff-6c20-4d8e-bf1f-3696a72989ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "dayDF = calendar_df.groupby(['Day']).price.mean()\n",
        "dayDF = dayDF.reset_index()\n",
        "dayDF = dayDF.rename(columns={'price':'Average Price'})\n",
        "lookupDayNum = {\n",
        "    'Monday': 0,\n",
        "    'Tuesday': 1,\n",
        "    'Wednesday': 2,\n",
        "    'Thursday': 3,\n",
        "    'Friday': 4,\n",
        "    'Saturday': 5,\n",
        "    'Sunday': 6,\n",
        "}\n",
        "dayDF['day num'] = dayDF['Day'].apply(lambda x: lookupDayNum[x])\n",
        "dayDF = dayDF.sort_values(by=['day num'])\n",
        "dayDF"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up the figure and axis\n",
        "fig, ax = plt.subplots(figsize=(5,6))\n",
        "\n",
        "# Create the bar plot with a color palette\n",
        "sns.barplot(x='Average Price', y='Day', data=dayDF, color=sns.color_palette('pastel')[0], orient='h', ax=ax)\n",
        "\n",
        "# Add x-axis and y-axis labels\n",
        "ax.set_xlabel('Average Price')\n",
        "ax.set_ylabel('Day')\n",
        "\n",
        "# Add a title to the plot\n",
        "ax.set_title('Average Price by Day')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Ig_4o_D_HVEr",
        "outputId": "df87142d-9fbf-4527-f11c-9a392d582ca5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis of average price during the holidays\n"
      ],
      "metadata": {
        "id": "TnGei_KcG_gk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "bO8fBI0HUS0C"
      },
      "outputs": [],
      "source": [
        "from pandas.tseries.holiday import USFederalHolidayCalendar as calendar\n",
        "import holidays\n",
        "\n",
        "holidays_pandas = calendar().holidays()\n",
        "holidays_us = holidays.Netherlands()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "U8FmHzbqUxcv"
      },
      "outputs": [],
      "source": [
        "calendar_df['holiday'] = calendar_df['date'].isin(holidays_pandas)\n",
        "calendar_df['holiday name'] = calendar_df['date'].apply(lambda x: holidays_us.get(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "nJJID1D7fe6T"
      },
      "outputs": [],
      "source": [
        "\n",
        "holidayDf=calendar_df.groupby('holiday name').price.mean()\n",
        "holidayDf=holidayDf.reset_index()\n",
        "holidayDf=holidayDf.sort_values('price',ascending=[1])\n",
        "holidayDf = holidayDf.rename(columns={'price':'Average Price'})\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "holidayDf\n",
        "\n",
        "# Set up the figure and axis\n",
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "\n",
        "# Create the bar plot with a color palette\n",
        "sns.barplot(y='holiday name', x='Average Price', data=holidayDf, color=sns.color_palette('pastel')[0], orient='h', ax=ax)\n",
        "\n",
        "# Add x-axis and y-axis labels\n",
        "ax.set_ylabel('Season')\n",
        "ax.set_xlabel('Average Price')\n",
        "\n",
        "# Add a title to the plot\n",
        "ax.set_title('Average Price by Holiday')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "HFbI5OtuFKry",
        "outputId": "e0c365ee-dd98-4e72-be88-f45c4144a4a4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seasonal trends in prices"
      ],
      "metadata": {
        "id": "z4ICWFAhI9Jf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new DataFrame with the average price by season\n",
        "season_df = calendar_df.groupby('season')['price'].mean().reset_index()\n",
        "\n",
        "# Plot the line chart\n",
        "sns.lineplot(data=season_df, x='season', y='price')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Dz4Yb5vBI8i_",
        "outputId": "f364072f-7c31-4f8d-a2d1-902383c90f2c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='season', ylabel='price'>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calendar_df.to_csv('calendar_cleaned.csv', index=False)\n"
      ],
      "metadata": {
        "id": "dD6xN7BCaXHg"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "calendar_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbV-Eb_E3IGa",
        "outputId": "92ced167-3185-4d09-dbcf-ed30b4e69741"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2553517 entries, 0 to 2554277\n",
            "Data columns (total 16 columns):\n",
            " #   Column          Dtype         \n",
            "---  ------          -----         \n",
            " 0   listing_id      int64         \n",
            " 1   date            datetime64[ns]\n",
            " 2   available       int64         \n",
            " 3   price           float64       \n",
            " 4   adjusted_price  float64       \n",
            " 5   minimum_nights  float64       \n",
            " 6   maximum_nights  float64       \n",
            " 7   Year            object        \n",
            " 8   Month           object        \n",
            " 9   Date            object        \n",
            " 10  Day             object        \n",
            " 11  day_num         int64         \n",
            " 12  season          object        \n",
            " 13  season_num      int64         \n",
            " 14  holiday         bool          \n",
            " 15  holiday name    object        \n",
            "dtypes: bool(1), datetime64[ns](1), float64(4), int64(4), object(6)\n",
            "memory usage: 314.1+ MB\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}