{
  "cells": [
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      "metadata": {
        "id": "kd8CTiQ9FHjJ"
      },
      "source": [
        "# 2a_Python_Analysis_Italy_Hospitality\n",
        "\n",
        "This notebook follows the same simple idea as the course analysis notebook, but it is adapted to the **Italy Hospitality Market Insight Assistant** project.\n",
        "\n",
        "**Goal:** load the cleaned CSV from Notebook 1, explore it, visualize the main market patterns, and create simple insight tables that can later be used by the Hugging Face app and the n8n workflow."
      ],
      "id": "kd8CTiQ9FHjJ"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbrrlHyHFHjL"
      },
      "source": [
        "## 1. Install Required Packages\n",
        "We install the packages needed for analysis and visualization."
      ],
      "id": "CbrrlHyHFHjL"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfhUfk8jFHjL"
      },
      "execution_count": 17,
      "outputs": [],
      "source": [
        "!pip -q install pandas numpy matplotlib seaborn"
      ],
      "id": "hfhUfk8jFHjL"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6TlBcTiFHjL"
      },
      "source": [
        "## 2a. Initial Setup\n",
        "We import the libraries used for analysis."
      ],
      "id": "T6TlBcTiFHjL"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Td9iVmeqFHjM"
      },
      "execution_count": 18,
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_theme(style='whitegrid')"
      ],
      "id": "Td9iVmeqFHjM"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybGNCyHfFHjM"
      },
      "source": [
        "## 2b. Load the Cleaned Dataset\n",
        "We load the CSV that was created in Notebook 1."
      ],
      "id": "ybGNCyHfFHjM"
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 590
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        "id": "lzyBtE5NFHjM",
        "outputId": "845c25e0-6550-451f-c4e2-5912268d1e2c"
      },
      "execution_count": 19,
      "outputs": [
        {
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          "data": {
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              "      section location  entity                  metric_name   value     unit  \\\n",
              "0  investment   Europe  market      hotel_investment_volume   10.00   eur_bn   \n",
              "1  investment   Europe  market  avg_hotel_investment_volume   17.30   eur_bn   \n",
              "2  investment    Italy  market      hotel_investment_volume  950.00    eur_m   \n",
              "3  investment    Italy  market  avg_hotel_investment_volume    1.75   eur_bn   \n",
              "4  investment    Italy  market      share_of_cre_investment   30.00  percent   \n",
              "\n",
              "              period  source_page  \\\n",
              "0            H1 2024            2   \n",
              "1  2017-2023 average            2   \n",
              "2            H1 2024            3   \n",
              "3  2017-2023 average            3   \n",
              "4            H1 2024            3   \n",
              "\n",
              "                                                note  \n",
              "0  European hospitality investment volume in H1 2024  \n",
              "1   Average annual hotel investment volume in Europe  \n",
              "2              Italian hospitality investment volume  \n",
              "3    Average annual hotel investment volume in Italy  \n",
              "4  Hospitality share of commercial real estate in...  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 61,\n  \"fields\": [\n    {\n      \"column\": \"section\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"chains\",\n          \"city_performance\",\n          \"accommodation_shift\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Toscana\",\n          \"Trentino Alto Adige / S\\u00fcdtirol\",\n          \"Europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"entity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"market\",\n          \"city\",\n          \"management_model\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metric_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"management_contract_share\",\n          \"room_supply_growth_3_star\",\n          \"demand_supply_ratio_2023\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21562.461867466463,\n        \"min\": -24.7,\n        \"max\": 155258.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          -9.6,\n          41954.0,\n          12.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"eur_m\",\n          \"thousand_nights\",\n          \"eur_bn\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"period\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"H1 2024\",\n          \"2017-2023 average\",\n          \"2023 vs 2013\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"source_page\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2,\n        \"max\": 21,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          18,\n          17,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"note\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"Domestic revenues in Italy\",\n          \"Room supply growth in 5-star category\",\n          \"Chain-operated rooms share of total hotel rooms\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df = pd.read_csv('italy_hospitality_market_cleaned.csv')\n",
        "df.head()"
      ],
      "id": "lzyBtE5NFHjM"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6_xRBOBFHjM"
      },
      "source": [
        "## 2c. Inspect the Dataset\n",
        "We verify the structure before running the analysis."
      ],
      "id": "B6_xRBOBFHjM"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MqJRQ8hFHjM",
        "outputId": "3893a2ac-f1df-4e0c-f26d-9663ed0876fa"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (61, 9)\n",
            "\n",
            "Columns:\n",
            "['section', 'location', 'entity', 'metric_name', 'value', 'unit', 'period', 'source_page', 'note']\n",
            "\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 61 entries, 0 to 60\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   section      61 non-null     object \n",
            " 1   location     61 non-null     object \n",
            " 2   entity       61 non-null     object \n",
            " 3   metric_name  61 non-null     object \n",
            " 4   value        61 non-null     float64\n",
            " 5   unit         61 non-null     object \n",
            " 6   period       61 non-null     object \n",
            " 7   source_page  61 non-null     int64  \n",
            " 8   note         61 non-null     object \n",
            "dtypes: float64(1), int64(1), object(7)\n",
            "memory usage: 4.4+ KB\n",
            "None\n",
            "\n",
            "Missing values:\n",
            "section        0\n",
            "location       0\n",
            "entity         0\n",
            "metric_name    0\n",
            "value          0\n",
            "unit           0\n",
            "period         0\n",
            "source_page    0\n",
            "note           0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print('Shape:', df.shape)\n",
        "print('\\nColumns:')\n",
        "print(df.columns.tolist())\n",
        "print('\\nInfo:')\n",
        "print(df.info())\n",
        "print('\\nMissing values:')\n",
        "print(df.isnull().sum())"
      ],
      "id": "3MqJRQ8hFHjM"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-NOkmtE6FHjM"
      },
      "source": [
        "## 3a. Summary Statistics by Section\n",
        "We check how many indicators we have in each section."
      ],
      "id": "-NOkmtE6FHjM"
    },
    {
      "cell_type": "code",
      "metadata": {
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      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               section  indicator_count\n",
              "2     city_performance               12\n",
              "7      regional_demand               12\n",
              "4    holiday_dwellings               12\n",
              "3               demand                5\n",
              "1               chains                5\n",
              "8               supply                5\n",
              "5           investment                5\n",
              "0  accommodation_shift                3\n",
              "6       market_outlook                2"
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              "\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-363e60f1-d49a-47a6-9c39-3e87de909ef8 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-363e60f1-d49a-47a6-9c39-3e87de909ef8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_946cf15a-dc8e-41ab-a581-22af5b8eaa77\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('section_summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_946cf15a-dc8e-41ab-a581-22af5b8eaa77 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('section_summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "section_summary",
              "summary": "{\n  \"name\": \"section_summary\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"section\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"accommodation_shift\",\n          \"regional_demand\",\n          \"supply\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"indicator_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2,\n        \"max\": 12,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          2,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "section_summary = df.groupby('section').size().reset_index(name='indicator_count').sort_values('indicator_count', ascending=False)\n",
        "section_summary"
      ],
      "id": "WNQkP4B9FHjN"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "zKg2EIb-FHjN",
        "outputId": "cf5c40be-e85c-4405-db76-7bd47196f0bb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(section_summary['section'], section_summary['indicator_count'])\n",
        "plt.title('Number of Indicators by Section')\n",
        "plt.xlabel('Section')\n",
        "plt.ylabel('Indicator Count')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "zKg2EIb-FHjN"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JxtLXzhzFHjN"
      },
      "source": [
        "## 3b. City Performance Heatmap\n",
        "We compare the main Italian tourism cities on occupancy, ADR, and RevPAR."
      ],
      "id": "JxtLXzhzFHjN"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VU1byFuDFHjN",
        "outputId": "8a2b8003-53e0-42d7-adda-648252f6227e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "metric_name  adr_yoy  occupancy_yoy  revpar_yoy\n",
              "location                                       \n",
              "Florence         4.0           -2.0         3.0\n",
              "Milan            8.0            1.0         2.0\n",
              "Rome            17.0            0.0        21.0\n",
              "Venice           4.0           -2.0         3.0"
            ],
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              "      <th>adr_yoy</th>\n",
              "      <th>occupancy_yoy</th>\n",
              "      <th>revpar_yoy</th>\n",
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              "  <tbody>\n",
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              "      <th>Florence</th>\n",
              "      <td>4.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Milan</th>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rome</th>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Venice</th>\n",
              "      <td>4.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_0fd210bd-b3ed-4e5c-ba7e-0be41b6cc793\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('city_pivot')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0fd210bd-b3ed-4e5c-ba7e-0be41b6cc793 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('city_pivot');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "city_pivot",
              "summary": "{\n  \"name\": \"city_pivot\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Milan\",\n          \"Venice\",\n          \"Florence\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adr_yoy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.13052471924984,\n        \"min\": 4.0,\n        \"max\": 17.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4.0,\n          8.0,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupancy_yoy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5,\n        \"min\": -2.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -2.0,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revpar_yoy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.17877987534291,\n        \"min\": 2.0,\n        \"max\": 21.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0,\n          2.0,\n          21.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "city_perf = df[(df['section'] == 'city_performance') & (df['period'] == 'YTD June 2024')]\n",
        "city_pivot = city_perf.pivot(index='location', columns='metric_name', values='value')\n",
        "city_pivot"
      ],
      "id": "VU1byFuDFHjN"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "e_2VvcWyFHjN",
        "outputId": "be0e3347-a774-49bf-b39a-2483d79c27d1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "sns.heatmap(city_pivot, annot=True, cmap='YlGnBu', fmt='.1f')\n",
        "plt.title('Main Italian Cities Performance Indicators (YTD June 2024)')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "e_2VvcWyFHjN"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qmwu7KPbFHjN"
      },
      "source": [
        "## 3c. Regional Demand Comparison\n",
        "We compare domestic and international demand growth by region."
      ],
      "id": "Qmwu7KPbFHjN"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Y04qfQKlFHjO",
        "outputId": "3d4ed0ac-f300-4ed9-9de8-dc1a6ae052f7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "metric_name                        location  domestic_demand_growth  \\\n",
              "0                                     Lazio                    21.0   \n",
              "1                                    Puglia                    -7.0   \n",
              "2                                   Sicilia                    -7.0   \n",
              "3                                   Toscana                   -13.0   \n",
              "4            Trentino Alto Adige / Südtirol                     2.0   \n",
              "5                             Valle d'Aosta                    12.0   \n",
              "\n",
              "metric_name  international_demand_growth  \n",
              "0                                   18.0  \n",
              "1                                   13.0  \n",
              "2                                    8.0  \n",
              "3                                  -13.0  \n",
              "4                                    3.0  \n",
              "5                                   -6.0  "
            ],
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              "      <td>Lazio</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        google.colab.notebook.generateWithVariable('regional_pivot');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "regional_pivot",
              "summary": "{\n  \"name\": \"regional_pivot\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Lazio\",\n          \"Puglia\",\n          \"Valle d'Aosta\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"domestic_demand_growth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.002563849743892,\n        \"min\": -13.0,\n        \"max\": 21.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -7.0,\n          12.0,\n          -13.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"international_demand_growth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.68617416722285,\n        \"min\": -13.0,\n        \"max\": 18.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          18.0,\n          13.0,\n          -6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "regional = df[df['section'] == 'regional_demand'].copy()\n",
        "regional_pivot = regional.pivot(index='location', columns='metric_name', values='value').reset_index()\n",
        "regional_pivot"
      ],
      "id": "Y04qfQKlFHjO"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "jO59R4BnFHjO",
        "outputId": "de46090c-705a-45cc-fe68-9afc5d4906f7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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9e+XIkUMRERHy9vZWXFycZbmLpM+Y7777rtauXauePXuqR48e2rZtm7p27WoZEYXMj1ceZKikK4IZDAaFh4fr559/VnR0tDw8PNSzZ0917dpVFy9e1LJlyxQcHKwXXnhBBoNBQUFBCgsLU8uWLSUxBQ+Zx4kTJzR//nyNGTNGefLkkSQdPXpUS5cu1eXLl/Xmm29aLtmdmJiosLAw3bx5Uz4+PoqIiFBMTIxee+011atXz9ImpUslA0+S0WhUhw4dLIsvm0wm1axZU126dJG7u7tlsfEPP/xQ5cqVU9++fXX+/HktWLBAJpNJOXLk0BdffKGcOXPa+6kAVpIufiJJhw4dsvTbnDlz6sCBAxo3bpwiIiI0aNAgFS9eXGPGjNHChQu1Zs0a5c6dW8uWLZO/v7+dnwWQXNL7B5PJpO+//17FixdXgwYN5O/vr2HDhuntt9/WsmXL1KtXL8tr8/Xr15UrVy75+PjYt3jgAZL6tdlsVkxMjKpXr67atWvrhx9+0MiRIzV58mSVKFFCCQkJMhqNatOmjXx9ffXTTz/J1dVV7du3V7Fixez9NJAGBjNDTZBB7v1gfeDAAVWvXl3nzp3TpEmTdOnSJXXu3Fm9evWSJC1evFibNm3S6dOnlSdPHvn4+GjKlCkqXbq0HZ8BkNyuXbv05ptvqnnz5ho3bpxy5colSdq+fbsGDx6s5s2b680331Tp0qWVkJCgV155RWfOnFFgYKDCwsLk7u6uDRs2WP64Eq7CnqKjo3Xp0iXLJeZ/+OEHjR8/XqVLl9Zrr72mWrVqSZK++eYbrVixQiaTSRMnTlSFChV048YNnT59WtHR0SpTpozy5ctnz6cCPNQXX3yhyMhI3blzR2PHjrWM9vj+++81YsQItWnTRiNGjJCfn5/MZrMuX74sLy8vRj4hU4mNjVW2bNkst2NiYjRr1iydPn1alSpV0oABA2Q0GhUfH68lS5Zo5syZatSokWrVqiU3NzetWbNGBoNBa9as4csvZDr3fnbcsmWLihcvbvksuHHjRi1dulSurq6WECoxMVEJCQmKiYmRt7e3PUvHYyCAQoa49wVk8ODB2r59u5YuXao6dero3Llz+vjjj3Xp0iW99NJLlhDqypUrunHjhlxdXZUzZ06+SUemlJiYqN27d2vkyJGqWbOmJkyYoNy5c0uStm3bprfeessqhLp586Zmzpyp8PBw+fr66r333pOzszMjn2B3JpNJ7du3V+3atTVy5EhLf9y2bZsmTpz40BDqo48+Urly5exZPpBqBw8e1Ntvv607d+7oxRdf1Ntvv62EhAQ5OTnJyclJW7Zs0fDhw9W+fXu99tprKl68uL1LBpL5448/tGHDBo0YMUK+vr6SpLVr12rq1KmSpLffflsvvvii1ciooKAgy3uQPHnyqEiRIpo9e7ZcXFx4H4JM5d7+OHz4cP39999q3Lix+vTpYwmX1q9frxUrVsjV1VWTJk1SsWLFNGnSJF29elWTJ0+Wm5sbX+w6IAIoPLaEhATLN4v9+/fX4cOHZTQa9frrr1vCpvPnz+ujjz5KFkIBjiAxMVE//vijRo0a9cAQ6tlnn9WgQYMs0/HunQZy7+8IYE+nT59WwYIFlSNHDl2+fFn58+eX9PAQauXKlQoPD9fcuXMVGBhoz/KBVNu4caMWLVqkq1evasWKFSpdurQSExPl5OQkg8GgLVu2aNiwYerSpYvee+89ubi42LtkwMrGjRt19OhRjRs3zmoE9dKlS/X5558rV65c+uKLL1SiRAnL2qlJ66pGRUXJYDCoQIECMhgMvA9BpjVy5EjL9Ohy5copV65cVu+h161bpy+//FIXL15UuXLldPToUa1atUplypSxc+VILwIoPJZ7XyAGDBigEydOaNasWfroo48UEBCgjz76yNImKYQKCwvT888/r9dff93O1QMPdv83hTExMfrll18eGkK1bNlSffr0UYUKFexVNpAqEydO1IEDB/TJJ59Yhrs/KIRau3atNm3apI8//liFChWyZ9lAMg8b1fHtt99q4cKFlikcpUqVsgqhtm/froCAAMuUVCAzioqK0oIFC9SyZUvL6/XSpUv15ZdfqkyZMhoxYoSKFSsmk8lkubjPve59rw5kJn/88YfefvttjRs3To0bN5Z094rTZ8+eVVRUlBo1aiRJ2r17t/bv36+bN2+qb9++vGY7OF6N8FiS/qC9+uqrOnr0qGbOnKkKFSqocOHCunXrlqS7f/jMZrNlsU8PDw/98MMPioiIsGfpwAMlXWUmJiZGgwYNUlxcnNzc3NSgQQNNnjxZv//+u8aOHatr165Jklq0aKFZs2YpKChIO3bssHP1QHJJV5hJ0qxZM12+fFlTpkzRqVOnJN3tx++//75OnTqlhQsXWi5Z36lTJ82dO5fwCZnOveHTH3/8oW3btuno0aOWK4G1a9dOvXv3lslk0qhRo3TmzBnLVCWz2azmzZvzQQaZ0r1Xrdu/f7/mzZun5cuX6+zZs5Luvu/u0qWL/v77b02dOlXBwcEPDJkIn5BZRUZGKioqSvnz59etW7e0Z88edezYUW+99Zb69eunkSNHSpIaN26skSNHauLEibxmZwGMgMJjCw8P14IFC9S6dWvLZeanT5+u3bt365tvvpHJZJLRaFRcXJxcXV3133//yWg0qkCBAnauHEguaZh6YmKifvvtN/Xp00dVq1bV8uXL5eLiotjYWP38888pjoTav3+/qlWrxjB3ZCr3fkj/559/lDNnTnl5eenQoUN64403VKZMGY0aNcryzfr27ds1adIk+fn5acSIEapevbo9ywdSdO+ojlGjRun3339XQkKCrl+/rueff14vvPCCpe9u2LBBK1asULZs2SyL7gOZTUREhK5evaoCBQrIw8NDt2/f1smTJ1WjRg1t3LhRo0ePVrt27fTaa6+pRIkSkqQFCxZo/fr1CgwM1ODBgy3bgcwmpQvxXL58Wa1bt1bhwoXl5uamU6dOqU2bNmrdurUuXryo999/X8uXL7eMyEbWQCSONLs/s/T19dXw4cMt4ZMkZc+eXTdu3JDBYJDRaNTt27c1ZcoUffvttypUqBDhEzKlxMREOTs76/bt2xo9erS+/PJL+fv769ChQ+rSpYvi4uKULVs2y0ioAwcOaPz48bpy5YokqVatWnJ2dlZCQoKdnwlw173h09ixYzVp0iTt2LFDcXFxqlq1qubOnauTJ09q8uTJlpFQzZs31/DhwxUREcFrNTKtpPBp9OjRltfin376Sc8++6y2bNmiRYsW6eDBg5Kkjh07qlevXrp69ao+/vhjxcXFJXsvA9jbjz/+qHfeeUd79+5VeHi4mjRpotX/r707j4/xXt84/pmZZLJISMhiV2ItQq21L0f1FEGlmtpqC0qUqq321r7vqhTHrijR4nSzVPmhpbZaS+yxJiKbJJNk5vdHX5kjxWnPOWUG1/ufMjPP5H68ppN5rrm/93ftWiwWC6+//jrjxo2zLyvN6oTq3r07b7zxBnv27OHLL7908BmIPFxmZqY9fEpLSyMlJYWUlBTy5cvH0qVLyZs3L5UqVWL8+PGMHTuWGjVqkD9/fgoVKmTffVqeHeqAkv/I72ctJCYm4u3t/cD9mzdvZuLEiXzzzTcATJkyhbVr17Jp0yZ98yhOLS0tjbCwMHLlykWHDh0oVaoU69ev5/PPPydPnjxs2LABs9lMWloae/bsISIigvDwcAYMGODo0kUeqV+/fhw7doyePXtSp04dAgMD7ffd3wk1ZMgQSpYsicFgICkpSVvSi1PbuXMnCxYs4L333uPll19m4cKFzJ49m7CwMNatW0e1atXo1asXlStXBmDz5s1UrFhRy0nFKaWnpxMWFsb169cxGAyUKFGCGTNmkCtXLvtn7w0bNjBs2LAHOqG++OILmjVrpl3uxOncf+04bdo0Tp06xfXr1yldujTt2rWjUqVKD1xfxsTEMGvWLI4fP86iRYvsu0DKs0HrRORPu//NYcaMGRw/fpxz587RtGlTQkNDCQoKst8fEBBAfHw8Z86c4csvv2TTpk1ERkYqfBKnd/jwYa5du0b//v2pU6cOAD179qRUqVKMHTuWNm3asGbNGtzc3KhduzYrV66kYsWKji1a5N/45z//yYEDB5g0aRI1atTAaDRma4XP6oTq3bs3Q4cOZeLEiZQoUYIcOXI4uHKR7H6/hCNnzpyUK1eO6tWrs3HjRj755BPGjh1Ly5YtKVy4MBMmTCBHjhykpqZSq1YtQkJCHFi9yINOnjzJ5cuXadCgAW5ubixbtoyqVavi4eFB48aN8fLywmQy2ccDhIaGAjBs2DCMRiMdO3akdOnStGjRAvj3Q/lFHCHr9di3b18OHTpEw4YN8fHx4dSpU3To0IEpU6bQpEkT++M3btzITz/9xI4dO1i+fLnCp2eQAij5U7KGMgO89957nDx5kldffZUmTZowYsQIrl27xttvv02lSpUAcHd3x8vLiwkTJhAVFcXq1au1XaY8FaxWK+np6Xh4eADY/9ywYUNOnjzJ4sWL6dChAytWrMDNzY2XXnop24dDEUdJTU0lKiqKsmXLZrv94sWLeHp6UrVqVfuyJYPB8EAINX36dIYNG2YPnn4/q0HEke6/sL558yaBgYFUrlyZ4sWLYzAYiIyMJCQkhEaNGgFQp04d5s6dy86dO3FxcaFy5cq4u7s78hREsrFaraxYsYLIyEhmzJjB3/72Ny5cuEC1atWIj49n7ty55MyZk8aNG+Pm5pYthDIajQwZMoSCBQtm+3JX4ZM4o2+//ZajR48ydepU+zynM2fOsGjRIgYOHIifnx/VqlXjl19+Yd26dbi6urJq1SpKlCjh4MrlcdDVkvwpWRcic+fO5ezZs0yePJmKFSvy2WefYbVa2bZtG7GxsfTr149KlSpRsGBBvL29iYqKYs2aNep8EqeUdUFz/4V4kSJFMJlM7NixgypVquDq6moPoUJDQ/n666+5fPky4eHhLFmyxB46KXwSR8rIyKBt27bUrFmTMmXKZNv1yGKxkJiYSEZGBmaz2X571mv+//7v/yhTpgwvv/wyW7du1UW6OJ37w6fx48eTmJhI/fr1efXVV8mVKxe3b9/m7Nmz1KlTx75s9M6dO9SuXZsGDRpQvnx5va7F6WSFSBkZGQwdOpTRo0cTEhLC8uXLgd92IB0/frx9x0Z3d3f7l2Svv/46efLkoWbNmg4+C5E/FhsbS0pKSra5kqVKlaJXr15ER0czdepUlixZQtmyZZkyZQo5cuTQ7KdnmIaQy5+WmJjIhQsXaNWqFRUrVmTp0qWMHTuWRYsWsXjxYg4fPswnn3zCoUOHyJMnD4MHD2bz5s0Kn8RpmUwmUlNTiYiI4NChQ2RmZlKgQAF69uzJkiVLWL16NQCurq4A/PrrrxQrVoyePXty8eJF9uzZ48jyRexcXFwYMGAA77zzDkajkcuXL9vvK1y4MImJiXz11VekpaVlO+7EiRNERkZy8eJFAF2ki1O6fwnHtm3bqFChQraNT/z9/alUqRJr1qzh0qVLnD17li+++ILY2FiaNGlC0aJFHVW6yCNlZmaSM2dORo4cSbNmzRgzZgzffPMN9+7dA2DdunUULFiQCRMmsGPHDu7du8fdu3cZOHAgBw8epG7dutr4RJzOw8ZLp6WlZXudWiwWAIoWLUrDhg25dOkSCQkJGI1GDR5/DiiAkkf6/RuIt7c3vXr1onHjxpw4cYKFCxcybNgwqlWrRvXq1WnWrBl79uxh8uTJnD59mkaNGlGwYEEHVS/y58TFxXH8+HFGjBjByZMnAWjatCmtWrVi9OjRTJw4kX379rFz506WLFlCoUKFaNmyJXfv3iUqKsrB1cvzzmazkZmZCUDNmjXx8vJi/PjxDBo0iEOHDgG/7QBWqVIlZsyYwY4dO0hJSQHgxo0bfPbZZxw/fpwCBQo47BxE/oxVq1Zx+PBhJk+ezBtvvEG+fPmy3d++fXt8fHx49dVX6dKlC99++y1Dhw5Vd6o4payuvnv37rFo0SLc3NxISEhg9OjR7Nq1i9TUVAwGA+vXr6dgwYJ89NFHDBkyhB49enDkyJFssyf1GhdHu3fvHuPHj+fo0aPZlu9nfT5p1qwZHh4ejB8/HiBbN7afnx8eHh5YrdYnW7Q4jAIoyebevXvs2rUL+NeMkCw2m42goCAKFSrE6dOncXd3p0GDBvY3EU9PT6pVq8bVq1e1c5I4rd+/pvPly8fKlSsxm80MGDCA48ePkzdvXvr27cvgwYPZuHEjPXv2ZNCgQRiNRgYPHkx8fDx58uTRNzTiUCkpKcyZM4clS5aQmJhov71kyZJcvXqVRYsWcfDgQQA+/vhjChcuzAcffEDnzp3p06cP7777Lt9++y0zZ87MtiueiDM6d+4c+fPnp2zZstkuuO8PYBcuXMjo0aPp27cvn3/+uTqwxWllhU+vv/46hw8fJjAwkF69epEvXz6GDRvGjh07SE1NBWD9+vXUrl2bpKQkChUqxI4dO3BxcbG/9kUcyWazMWbMGJYvX06bNm0YOXIkW7duBf7VvZozZ0569uzJ3r176du3L0lJSaSnpxMbG8vOnTvx8/MjV65cjjwNeYIMtof1yclza8qUKSxevJgpU6bYd4v5/a4zAJs2beKDDz5g06ZNlC5dmpiYGEaPHk27du2oUqWKhiCKU7JarRiNRiwWiz04zXp9X7p0ib59+5KSksK0adMoV64cANevX+fy5ct4eHhQvnx5MjMzGTp0KPv27eOzzz5T54g4RFJSEl27diUtLY38+fMzbdo0++B8gK1btzJ+/HjKly9Pt27d7NvQz549m7NnzxIXF0fp0qVp27YtxYoVc9RpiDzU/Z87sv4cHh5OYmIia9eufeAxAIcOHbJvhCLyNPjHP/7B8uXLWbBgASVLlgR++8wxceJEdu/ezbhx46hXrx6enp4AJCcn2zeJ0MYn4iysVitr1qzh448/xtfXl7t375KYmEiNGjUICwujatWqeHl5ER8fT2RkJHPnzsXb25s8efLg4uLC+fPnWb58ub4weI4ogJJsbt68yeTJk/nuu+8YM2aMfVvX33/QO3/+PCNGjODSpUvUr1+f69evc/LkSdasWcMLL7zgoOpFHnTt2jUuXbpEXFwc5cuXx9/fn1GjRlG8eHG6desGZA+hevfujdVqZcKECZQpU8Y+/wlg3759LF26lF9++YXFixdrZ0dxiHv37vHWW2/h6+vLgAEDKFWqFGazGavVisFgsL9Xb968mYkTJ1K+fHnCw8OpUqUK8NvrPSuM1U534mzuHzh+/5cFn3zyCcuWLWPChAnUr18/2zFHjx5l7dq1dOrUyX4hL+Ls5syZw+rVq/nuu++yrRy4du0avXv35tatW4waNYo6depkm8/3sC+GRRzp5s2bvPnmm1SrVo133nmH77//nhUrVhAXF0dQUBB9+/alQoUK+Pj4EBUVxfLly0lISCBfvny0bt1ac/qeM1qCJ9kEBgbywQcf0LBhQ4YPH84XX3wBPLgcr1ixYvTq1Ys6derw888/YzQaWbp0qcIncSqHDx+mS5cu9OnTh/fff5/27dszatQoTp06xbfffmsfMp71+i5SpAgDBgzg8uXLTJo0iWPHjtmfy2KxkJycTKFChVixYoXCJ3GYxYsX4+HhwciRIylXrpz9Av33gVJISAgDBw7kl19+YdGiRfaZUAaDAZPJpAsYcTr3h09z585l0qRJxMXFAfDKK69gMplYvHgx+/btsx9z584dPvvsM86cOYOfn59D6hZ5lIMHDzJp0qSHLpcLCAjAYDBw6tSpbPNv8ufPT+3atYmJieHdd9/l8OHD2Y7Te7c4g6zXtNVqJTAwkIiICDZv3kx0dDRdu3Zly5YtREREYDabiYiIoHfv3qxbt46goCA++ugjZsyYwcCBAxU+PYfUuykP8Pf3Z9iwYQAMHz4cgBYtWmAwGLJ9OCxSpAglS5akefPmBAcH29uCRZzBgQMH6Ny5M2FhYdSvX59cuXIxbNgwjhw5QuPGjfnll19YtWoVBoOBNm3a2D/QBQUFUbhwYX7++WeWL19uX7pkNptp0KABdevWzTY8UeRJO3nyJAEBAQQFBWW7ffv27Rw6dIh79+5RvHhx2rVrR8uWLQGYMWMG06dPZ9CgQdl2DxNxFlar1f75ok+fPpw5c4aQkBCSk5Px9fUlKCiIWbNm8c477zBq1CiqVq1Krly5OH36NMePH2f58uWayydOJTMzkxMnTmCz2ewzn7744gsaNmxIYGAgr7zyCnPnzuXTTz/lhRdeIE+ePBiNv/UGeHh40KlTJwICAqhataqDz0TkX1JTUzEajZjN5mxLQStVqkSRIkVYtWoVZcqUwd/fn+7du7Nv3z5y5MjBvXv3+Oijj/j00095++236dChg7r5nlMKoOShHhVCZX04vHTpEhMnTmTnzp3s3LlT4ZM4lRMnTtChQwd69+5N165dcXNzw2g0smTJEho3bkxSUhIffPABEyZMYOXKlVitVtq1awf81kZctmxZFi5c+MAuSyaTSfPNxKEyMjKwWq2kpqaSlJSEl5cXt2/fZuTIkfzf//0fGRkZGI1GbDYbx44dY9KkSbRs2ZLMzEwWLlyIv7+/o09B5KGyLrwnTZrEyZMnmTRpEmXKlMHT05P09HQMBgOVK1dmxYoVLFu2jAMHDuDm5kaxYsVYvXo1xYsXd/AZiPzLwYMHOXv2LGFhYfal/CtXrmT69OnExsbyxhtvkDdvXiZPnkxERARDhw4lPDyc0qVLEx0dza5du6hVqxZdunQBNPNJnMeoUaPYsWMHu3fvxt3dnfT0dFxdXSlevDjNmzdnzpw5xMTE4O/vT9++fTl58iTz588nICCA8+fP8+mnn1KrVi3gX+/78nzRDCj5t27fvs24cePYvn0748ePJyQkxL48af/+/axYsYIXX3zR0WWK2KWnpzN69GjWr1/PuHHjCA0NBX7bMczDw4OuXbuSmprKqlWrOHPmDJMnT+bChQvUrFmTcuXKsX79evz9/Zk/f/4DXX8izuDIkSO0bduWv/3tb3h5ebFv3z7i4+N55ZVX6NGjBwDTpk1j9+7dzJ07l3r16gHYAysRZ5WcnEzXrl2pWrUq/fv3B+DKlSt89tln3Lx5k4YNG9KkSRN7EAu/LUe6f1afiKNlZmYydepUbDYbAwYMyBYcjR07lsjISDp27Ejbtm3x8/Pjxx9/ZODAgaSnp2O1WjGbzfj6+rJx40aFTuJUbDYb58+fZ9SoUdy5c4cNGzbg4eFh72SKjY2lXbt2vPDCC5jNZvbv38+0adOoXbv2AxtLyPNL72ryb93fCTV06FBiYmI4ffo0e/fuZfXq1ZqDI07H1dWV3r17c+/ePT788ENcXFxo0aKFfYew8+fP8/LLLwNQqlQphg0bxpo1a/jqq6/Yu3cvL7zwAnPmzLHPhVL4JM6mYsWKLFu2jKFDh3Lnzh2qV69u32kma7ekIUOGsGPHDm7evGk/Tp2q4sxsNhtpaWkkJCSQlpbGwYMHOXPmDNOmTSMgIACj0chXX31Fjhw57KGqiDMymUxERERkC/xTU1Nxd3dn+PDhZGZmsnTpUgDatm1L9erV+eKLL9i2bRu3b9/G29ubNm3a4OLioi/BxCmkpKRw/vx5ypYtS7FixRg3bhzdunVj3LhxjB071h4o+fr6Urt2bVauXEmRIkWYNWsWVatWVeAk2agDSv6U27dvM2nSJLZs2YKLiwvr169X+CRO7f7uvcmTJ/Paa6/Ro0cPLly4wKZNm/D09LR/sEtLSyM5OZk7d+5QrFgxjEaj2t3F6SUnJ5OYmEjevHmz3W61Wtm9ezfDhw9n6tSpVK9e3UEVijxa1k6Mvzdv3jzmz5+Pt7c3ZrOZN954g86dO2M2mwkNDeWll15i9OjRDqhY5M/J6vBISUlh+vTp9i9y09LScHNzA+Cjjz5i/fr1dO/enTfffPOB93FA4ZM4BavVyogRI9i6dSuLFi2iSpUq2Gw2bt26RWBgoP1xWa/7mJgYQkNDKV68OIsXL852nwioA0r+JH9/fwYOHIi3tzft27d/YPitiLO5v3tv0KBBzJ07F4vFwpw5c/D09Mw28NbNzQ03Nzf7AFur1arwSZxejhw57F1NWTMYABITE/nmm28eOqhcxJGyLkLuD59OnDhBUlISrq6uVKpUiYiICIKDg3FxcSFHjhz2ofk3btzAzc1Nu+2KU7px4wanT5/mxRdfJCAgAIBt27axYsUKrl27xrx583Bzc7OHUKNGjSIuLo6lS5diNBrtM6Hup/BJnIHRaKRBgwZcuXKFwYMHM2HCBKpVq2Z/nWcFpVnv7Xny5KF58+asXbuW/fv38/LLLyt8kmw0+Uv+tMDAQIYNG6YLGnlqZIVQf//737l8+TKvv/46pUuX/sPjNBRRnjZZ4dPRo0eZOHEi3333HePHj9e29OI0kpKSmD17NleuXLG/xw4cOJD33nuPjh070r17dzp06MCVK1eoU6cONWrUsIdPV69eZd68edy+fZtGjRo58jREHnDs2DH69OnDvHnz+O677+y3169fn6FDh3Lw4EHeeecd4LcvvFJSUgB4/fXXSU9PZ+7cuezevdshtYv8O1kLpRo1akS3bt0oXrw4Q4YM4ciRIw8dVWE0GjEYDLz66qskJCSwb98++7w+kSxagiciz7xbt27ZL8rHjRtH8+bNAbUEy7MjPT2d7t27k5iYiM1mY/z48ZQqVcrRZYkAv4VPf//73wkKCuLjjz8mR44cjBw5kl27djFo0CACAgL49ddfWbZsGS4uLsyePZvixYtjs9mYNGkS586d4+zZsyxYsOBPfYkg8qQcPnyY8PBwXnnlFRo3bkzDhg2Bfy0xTUpKYsOGDXz88ce89NJLfPLJJ/ZjN23axJUrV/Dx8bHPfBJxJvcvA/3xxx/5+uuv+fLLL/Hx8WH69OlUqFDhkZ+lZ8+eTZMmTbRDqTxAAZSIPBeyZkLt2LGDsWPH2kMokWfF0aNHOXr0KI0bN37oPBERR0hKSqJFixa88MILjBs3jrx583Lt2jW6d+9OWFgYHTp0AH670ImKiqJfv354e3vz2WefAfCPf/yDX3/9lR49emj5nTiV6Oho3nnnHapXr07v3r3x8fEBHvxyKyEhgcjISObOnUv58uUZPHgwd+/etQ9o7tevH4BmT4rT6tWrF9evXydPnjy4urry/fffkz9/fiZNmmSfCfX7EOpRc/5E9C4nIs8Ff39/hg8fjslkYtCgQfj6+lKnTh1HlyXyl6lQoQLBwcHq6hOnkZycTPv27TGZTIwePdoejGZmZnLt2rVs23KbTCaKFy/Oe++9x4ABA9i8eTMhISF07tw52/BmEWdx/vx50tLSCAkJwcfHx37BnZycTExMDOfOneOFF16gePHivPXWW3h7ezNv3jxatGhBrly5KFiwIL1797Y/n8IncUarVq3i4MGDLFiwgDJlyuDu7s7WrVtZsWIFgwcPZurUqbz00ksPhFAKn+RR9E4nIs8NPz8/Bg0aROHChalRo4ajyxH5yyl8EmeR1fkUHR1NYGAgx44dIzAwEBcXF1xcXPDy8uLkyZMA9lkiRqORSpUqYTKZiImJsT+XwidxRr/++iu3bt2iQoUKwG8X3JcuXWLmzJn89NNPxMbG4uHhQXh4OJ06daJly5Y0btyYXbt24eXlRe3atTGZTOp8EqcWFxeHl5cXhQoVwt3dHYCmTZtiNpsZNWoUAwYMYMaMGQQHB2u0hfwpiiZF5LkSGBhI3759cXFxISMjw9HliIg8c5KSkmjVqhXFihVj27Zt+Pj4MH78eL799lssFgv58uWjS5cuREZGsn79euBf4WlCQgIBAQH25UwizqpWrVoAjBo1iqNHj7Js2TJat27Njh07qFSpEgMHDqRGjRrMmzePr7/+GqPRiJeXF02bNqVevXqYTCYyMzMVPolTs1qt3Lt3z77rrsViAeCVV16hSZMmREdH07lzZw4dOqTwSf4UzYASERERkb9EZmYmVatWpWzZskyePJl8+fKRnp5OaGgocXFxDBkyhFdeeYXU1FQmTpxIZGQk4eHh1K1bF6PRyLp169i9ezfr1q2jQIECjj4dkUdKTExk5cqVLFy40L6zXZ06dWjZsiVNmzYFIDY2lm7dumG1Wlm3bh0uLi5amiRPlYsXL9K+fXuqV6/OtGnTgH/NK5s+fTo//fQT+fPnp2/fvhQpUsTB1crTQAGUiIiIiPxlTp8+ja+vL4GBgfZdlCwWC2+88QZxcXEMGzaMV199lbi4OFatWsWCBQswmUzkzJkTDw8PZs2aRZkyZRx9GiJ/KCkpiQsXLvDLL79QoEABatWqla2jyWKx8PbbbxMQEMDs2bMdWKnIfyctLY1ly5axfPly6tSpw4QJE4DfulXHjBlDmTJlCAsLs3dIifwRBVAiIiIi8thkfVueFULduXOH4cOH06hRI1xcXDhz5gzXrl3D3d2doKAgAgICHF2yiN3FixeJjo62L7n7I1lzcGw2GydPnmTYsGE0bdqUbt26PeZKRR6P2NhYVq5cyZo1a8idOzeFCxfm7t27nDlzho0bN1K0aFFHlyhPEQVQIiIiIvJYPSyEGjp0KI0aNcJsNju6PJEH2Gw20tPTiYiIIGfOnPblR//O/R1/Fy5c4KOPPsJqtbJq1SpMJtMTqFrkz7t/aHhWJPD7OU5Zj0lOTubkyZN89tlnxMTE4OvrS0REBCVKlHjidcvTTQGUiIiIiDx2vw+hkpKS6NOnD02bNsXV1dXR5Yk81I8//kjXrl2ZPXs2DRs2/MPHX79+nblz5xIVFQXAihUrcHV1tYdTIs7g96/H3+9g90c72mn3RvlvKYASERERkSfi/hDq1VdfxcPDg3Xr1uHl5eXo0kQeYLVaMRqNjB07luvXrzNy5EgCAwMf+fjU1FTWrl3L7t27KVOmTLZdd3WxLs7i/vBp9uzZXLhwgWvXrtG8eXOqVav2yK6mrP8f4I8DKpFHUQAlIiIiIk/M/SHUzZs3KVSokKNLEvm3du7cyZQpUxgyZAh16tT5t91MiYmJpKWl4efnBzzYaSLiLPr06cPRo0epWrUq6enpHD9+nMKFC9OxY0fq16/v6PLkGaV9QEVERETkicnqCDGbzQqf5KnQoEEDKlasyNixY7FYLJhMJh71Hb63t7c9fLLZbAqfxClt2rSJo0ePMmvWLMaMGcOsWbPo3Lkz+/bt4/Tp01itVkeXKM8oBVAiIiIi8kRpOZI8LbIuxHv16kXu3LlZtmzZn15+pCVK4mhpaWn2eWT3u3r1Kn5+fpQsWRIPDw8uXbrE3Llzadq0KR07dsRoNBIbG+uAiuVZpwBKRERERETkIbJm3gQEBBAcHMzevXtJTU0FeGQXlIgzyMjI4P3332fQoEGcPHky232JiYnExcXh6enJ9evXad26NTVq1GDMmDH22Xzbtm0jPT3dQdXLs0oBlIiIiIiIyCPYbDbMZjM9evTg5MmTLFiwAFCHkzg3FxcXKleuTFpaGtOmTePEiRP2+8qVK4fNZmPBggW0aNGC2rVrM3r0aHsgtX37dq5du+bA6uVZpQBKRERERETkEQwGA5mZmeTOnZshQ4bwww8/cPjwYUeXJfJQFovFvuyuS5cutGvXjujoaKZPn87x48cBaNiwIf7+/syYMYOSJUsyZswYvL29uX37NnPnzuXcuXOEhobi6urqyFORZ5B2wRMRERERkefS/VvLZ/l3M56ioqL48MMPqVOnDt27d9cud+JUrFYroaGheHp6MmzYMF588UUAVq9ezfLlyylQoAB9+/YlODiYxMREOnbsSHx8PC+99BI+Pj5ERUVx6tQpli5dSunSpR18NvIsUgeUiIiIiIg8dzIyMjAajVgsFo4fP84PP/xAUlISaWlpwMNnPAUFBdG8eXOmT5/OhQsXFD6JUzEajfTo0YOjR48yb948+7K7tm3b8vbbbxMdHc2sWbM4evQo3t7eLF++nGbNmpGUlMTp06cpWbIkq1evVvgkj406oERERERE5LmS1fmUlJREx44diY2N5datW/j7+9OyZUveeOMNChUqlO2YrM4om81GREQEXbp0oUqVKg46A5EHZb2uv//+e9555x0aNGhAREQE5cqVA2DNmjUsW7aMAgUK0KdPHypUqPCnd3UU+SsogBIRERERkeeOxWKhQ4cO5MiRg65du1KiRAk2bdrE8uXLKV++PFOmTMHLy+uhx8bHx5MrVy5dvIvTyXpN/pkQql+/fvbb7z9W5HHREjwREREREXnunDx5ktjYWLp160bVqlUJCAigSJEixMTEULt27UeGTwC5cuUCtBOeOIf7e0qsVis2m4369evzySefsHPnTubNm2cfQN6mTRs6duzIrVu3GD16NKdPn7Yfq9ezPG4KoERERERE5Llz6dIlrl+/TsWKFTGbzWzatIm+ffvSr18/2rVrR1JSEt9//z2pqamOLlXkkTIzM7MFRxkZGfa/169fn/nz5z80hAoNDcVms5EzZ06H1C3PJxdHFyAiIiIiIvI4PWy3uhIlSuDl5cXhw4e5e/cuH3zwAf369aNHjx7YbDY2btzImTNnKF++PO7u7g6qXOTR7n9df/zxxxw7doyEhAQqVqzIwIEDAWjQoAHz58+nZ8+eAPTu3ZuyZcvSqVMnWrVqpQBKnih1QImIiIiIyDPNZDKRmprK1KlTiY2NBcDHx4e8efPy0UcfMWjQIN5//317+HTx4kW++eYbzGYzuXPndnD1Iv+SmppKQkICgD186tOnDxs3bqRgwYI0aNCAJUuWMGTIEG7duoXNZrOHULt372bixIn2ZXcKn+RJUwAlIiIiIiLPpIyMDOC3GTkbNmzg888/Z9KkScTFxZE/f36GDx/OjRs3CAwM5IUXXiA1NZUffviBIUOGkJKSwrBhw+w734k4WmZmJp06deKDDz6w3zZz5kzOnj3L1KlTGT58uH2Q+JYtWxg5cmS2EGr69On8+uuv+Pj4OO4k5LmmXfBEREREROSZk7U8KSkpiblz53L16lWOHDlCTEwMTZs2ZciQIfj5+XHw4EHGjBlDYmIid+7coUCBAuTNm5dPPvkEV1fXhy7fE3GE1NRUxo8fz/79+5k2bRqFChVi8uTJlChRgs6dO/OPf/yDadOmMXHiREwmEwMHDqRx48YMGjSIwMBADAYDycnJ5MiRw9GnIs8pBVAiIiIiIvLUe9gW8qmpqbRu3Rp/f3+aN29OUFAQixYt4vDhw1SuXJlhw4bh5+dHdHQ0iYmJXLhwgWLFilGiRAmMRiMZGRm4uGhsrjiPY8eO0bZtWyIiIujZsydHjhyhQIECXLt2jZ49e9K7d2/atm1LYmIiPXv25ODBg9SoUYPJkyfj7+//0P9PRJ4UvZuKiIiIiMgz4/4L7L1793Ljxg1GjRpFlSpVAJgxYwYzZ84kMjKSiRMnMnToUAoUKABA6dKl7c9jtVoVPolTsdlsBAcH89Zbb7F06VIaN25MxYoVAdi6dSve3t40atQIAG9vb1544QU8PT25fPky6enpAAqfxKE0A0pERERERJ5qR48epU2bNtm2oAeIi4vj3r17FC9eHIC0tDSMRiPvvfceL730Elu2bGH8+PHExcUBZJv1ZDTqUkmcS9Zru06dOgBs37492/1JSUlcv34dgJiYGGJiYggJCeHLL78kf/78T7ZYkYfQu6qIiIiIiDyVbDYbNpuNc+fOUb169Qc6lqpWrYrJZGL9+vUAuLm52UOojh074uvry5kzZ5g/fz6pqanqDhGnkZKSwuHDh7PdljVUv169etSoUYM1a9ZgsVgAKF++PF5eXkyZMoUxY8YwZswYjh8/TsWKFTGbzU+8fpGHUQAlIiIiIiJPpbS0NAwGAyEhIfTr14/U1FQGDx5MWloaAF5eXoSEhBAZGcmXX34J/BZCAZw/f57KlStTsWJFvvvuO27fvu2w8xC5n81mo3///rRp04YRI0awa9cugGwBa6dOnbh37x4LFiwAoHLlygwcOBBXV1d27drFzZs3WbRoEYUKFXLIOYg8jIaQi4iIiIjIU+fcuXNMnDiRvn37Ur58eWw2G9u3b6dv374EBwezdOlS3NzcOHz4MNOmTSM2NpbXX3+dli1bEhUVxaxZs6hVqxYdOnTg5ZdfZuzYsbzxxhuOPi0RAK5cucL27duZP38+AMHBwbzzzjsUK1YMX19fkpKSePfdd0lISGDx4sX4+PgAcPfuXVxcXLDZbHh7ezvwDEQepA4oERERERF56hw7dozz588zY8YMTp8+jcFgoFatWowZM4br16/Tvn170tLSeOmll+jfvz9VqlRhzpw51KtXj169epGZmUmPHj24du0avr6+BAYGOvqUROwKFSpEp06d2LhxI127duXatWt069aNfv36sWfPHry8vBg0aBDnzp2zd/cB5MqVCy8vL4VP4pTUASUiIiIiIk+lDRs2sHz5cnLlysXgwYMpW7YsqampbNmyhTlz5hAQEMDKlStxc3OzDyT/+eef8ff3p3r16hgMBgYPHszPP//M6tWrFUKJU7JardhsNhYsWMAPP/zAkSNHaNCgAdWqVePUqVNcuXKFqVOn2ndzFHFWCqBEREREROSpYrFY7IOVly1bxoYNG/Dx8WH48OGULFnykSHU/fbs2cP69evZv38/y5Yto3Tp0o44FZE/ZLPZ7APy79y5w/fff8+yZcu4ceMG8fHxACxYsIB69eo5skyRP6QleCIiIiIi8tTIzMzEbDYTHx/PgAED2L9/P7dv3+ann35i7NixnDp1Cnd3d5o1a8a7777L7du36dixo30wedZzREVF4erqysqVKxU+iVMzGAxk9Y3kzp2bVq1a8fHHHzNp0iT7LndFihRxcJUif0wdUCIiIiIi8lRJS0sjLCwMHx8fOnbsSFBQEOvXr2fr1q0ULFiQIUOGUKZMGVJTU9m6dSsjR46kVatWjBkzBqvVitFoJDMzk9TUVHLkyOHo0xH5r1mtVu7cuYOfn5+jSxH5QwqgRERERETkqXLkyBHCw8OZPHkyDRs2tN++Zs0aPv30UwoWLGhfjpeSksKPP/5InTp1MJlMQPYlTSJPq8zMTPtrWuRpoCV4IiIiIiLyVElISCApKYnChQsD2JfXtWnThtdee42ffvqJCRMmcPToUTw8PKhfvz4mk4nMzEwAhU/yTFD4JE8bBVAiIiIiIuJ07ty5Q2xsbLbbshZvVKlShYCAABYvXgyAm5ubPYR6/fXXyZ07N2fPnmXz5s3ZjtMFu4iI4yiAEhERERERp3LmzBlatmxJ37592bVrFykpKcBvnUsZGRmYTCZCQ0M5ePAgn3zyCYB9l7sLFy5QpUoVhg4dypAhQ+zHiYiIY7k4ugAREREREZEsNpuNffv2cevWLRITE+nRowe1atWiYcOGtGvXDhcXF1xcXAgNDeXcuXOsX7+eixcv0qlTJy5dusSSJUsoVqwYTZo0ATQnR0TEWWgIuYiIiIiIOJW4uDhat26Nh4cHPXr0YM6cOVy9epXy5csTGhpKvXr1CAgI4ObNm6xfv57PPvuMmJgYvLy8KF68OCtWrMDV1VXDxkVEnIgCKBERERERcRpZHUurV69m9OjRrF69mqJFi7Jr1y6WLFlCdHQ0uXLlok+fPlSrVo38+fNjsVg4dOgQnp6elCtXDqPRSEZGBi4uWvAhIuIsFECJiIiIiIjTOX36ND169KBChQpMnToVs9kMwKJFi5g6dSpubm4ULFiQkJAQWrRoQWBgIEbjbyNutexORMT5aAi5iIiIiIg4ndKlS9O6dWt27tzJ6dOnAUhKSmLDhg3UqlWL/v37U6RIEWbOnMmsWbPs4RNotzsREWekDigREREREXEqVqsVo9HIpUuX6NKlC8HBwQwaNIjw8HDc3NyYMmUKQUFBpKamcvnyZYKCghQ6iYg4OQVQIiIiIiLilGw2G6NGjWLz5s2YzWaKFCnC+PHjCQoKemC4uJbdiYg4NwVQIiIiIiLidLJ2sIuNjeWtt94iLS2NtWvXki9fPkeXJiIi/wXNgBIREREREadjMBiwWq14eXlRr1490tLSuHjxoqPLEhGR/5ICKBEREREReaKsVuufus1oNOLm5kZISAipqans3bv3SZQnIiKPgQIoERERERF5YjIyMjAajaSnp3P16lVOnz5NWlpatl3sfq9ChQrUr1+fI0eOPLlCRUTkL6UZUCIiIiIi8kRkDQpPSkqiT58+XLhwgbi4OPLmzUtERAS1atUid+7cDz329OnTlChRApPJZJ8PJSIiTw8FUCIiIiIi8sSkpqbSunVrfHx8aNGiBW5ubmzdupU9e/bQvXt32rdv/8gQCn5bqvfvuqVERMQ5uTi6ABEREREReX58//33JCcnM27cOIKDgwEICQlhzJgxLFy4kKCgIJo2bfrIoEnhk4jI00nv3iIiIiIi8thkZGRk+3t0dDR37tyhVKlSAFgsFgBGjBhBzZo1mTdvHhaLRUGTiMgzRu/qIiIiIiLyl0pISODq1asAuLi4kJyczL59+wAoVKgQLi4u/PDDD9hsNsxmsz2E+vvf/87169e5cuWKw2oXEZHHQwGUiIiIiIj8ZdLT09m1axdDhgyx71rXokULduzYAUDFihXx9PRk1apVnDt3zh5CwW/dULlz5yZHjhyOKl9ERB4TBVAiIiIiIvKXcXV1xcfHh/T0dAYPHkzdunUpXLgw4eHhZGRkEBAQwIwZMzh69Chjx47lu+++Izk5mUOHDrFx40ZKlChBYGCgo09DRET+YtoFT0RERERE/mdRUVFcuXKF+vXrA/DPf/6TgQMHYjabGTt2LE2bNgV+mwnl4uLCoUOH6NevH8nJyVgsFvLkyUOePHlYs2YNrq6u2u1OROQZo13wRERERETkv2az2UhJSeHdd9/FZDKRkZFBo0aNiIuLo0qVKty7d4/Zs2fj6+tLzZo1cXFxISMjg0qVKvH555/zyy+/EB0dTb58+WjQoIH9OVxcdKkiIvIsUQeUiIiIiIj8zw4cOMDw4cPx9fWlb9++1KhRA4AdO3awePFiYmJiGDlyJLVq1QIgMzMTo9GIwWDI9jyZmZmYTKYnXr+IiDxe6mkVEREREZH/idVqpWrVqkyYMIHbt28zc+ZM+9Dxhg0b0rVrV/z8/Bg9ejT79+8HICYmhjFjxvDTTz9ley6FTyIizyZ1QImIiIiIyH8tq2Mpa2bToUOHGDhwIAEBAXTt2pVGjRoB/+qEunTpEi1btuTAgQMkJyfzxRdfKHQSEXkOKIASEREREZH/SFRUFKdOnaJZs2b226xWK8C/DaH27NnD559/zqlTpyhRogQzZszA1dVVy+5ERJ4DCqBERERERORPu3nzJk2aNCE5OZnatWvz1ltvERwcTEBAQLad6x4VQiUlJWGxWPD19cVgMGjguIjIc0IBlIiIiIiI/GkXL17k3Xff5datW7i4uJCYmEixYsXo1asXFSpUIDAw0P7YgwcPMnjwYAIDAwkPD6dhw4bZnuv+wEpERJ5tCqBEREREROQ/8umnnzJt2jSWLl3KpUuXiIyM5MiRI1SoUIFXXnmFjh07YrPZMJvN9t3xACZMmEClSpUcXL2IiDiCAigREREREflTsjqWEhMTad26NYULF2b+/PlYLBa++eYbNmzYwIEDB6hWrRpVqlShbdu2+Pn5cfjwYf7xj38wY8YMzXoSEXlOKYASEREREZH/SGZmJjNmzGDVqlXMnDmT2rVrYzKZOH36NG3btsXNzY2MjAw8PT1p2rQpvXr1wsvLy36sQigRkeePFlyLiIiIiMifZrPZMJlMdOnSBS8vLzZu3IjJZCI+Pp7+/ftTunRpli5dyrhx4yhfvjyHDh3C09PTfrzCJxGR55M6oERERERE5D+S1cU0f/58Fi1axKhRo1i4cCFms5lJkyZRokQJ+2NtNhsGg0EDx0VEnnP6DSAiIiIiIv+RrC6mWrVqYbPZGDRoEP7+/kyePNkePlmtVgAMBgM2m03hk4jIc06/BURERERE5L8SHBxMeHg4AO3bt6d48eL2++4PnAwGwxOvTUREnIsCKBERERER+Y9lTfKoUaMGgYGBbNu2jfT0dAdXJSIizkoBlIiIiIiI2GVmZj7yvvvHx2Z1Nb300ktUr16drVu3cuvWrcden4iIPJ1cHF2AiIiIiIg4h4yMDFxcXEhNTeXbb7/l9u3bBAQEUL16dQICAh4YJp715/bt25OQkEDevHkdfAYiIuKstAueiIiIiIjYw6SkpCTatGlDcnIyycnJJCYmEhwcTNu2bWnevDnwr53tslgsFlxdXTEYDPYd8kRERO6nAEpERERERABIT08nPDyczMxMRowYQZ48ebh27RpDhw7FYDDQvXt3QkJCHF2miIg8hTQDSkREREREALhz5w7Xr1+nRYsWlCpVCj8/P4KDg5k/fz5Wq5VVq1YRGxvr6DJFROQppABKREREREQASEpK4vLly7i7uwO/LcvLyMigUKFCjB8/nmPHjvHjjz86uEoREXkaKYASEREREXkOPWwSR548eShXrhybN28mOjoao9FoHzieN29evLy8uHPnzpMuVUREngEKoEREREREnjMZGRkYDAYyMjJISEggLS0NAB8fHxo1asTevXtZt26dPYSy2WzExMTg6elJ7ty5HVy9iIg8jTSEXERERETkOXL/bndDhw4lKiqKQoUKUbduXdq2bQvAhx9+yMaNG6latSrNmjXj3r17REZGArB27VrtciciIv8xBVAiIiIiIs8Jm82GwWDAYrHQvn17bDYb5cqV4+TJk9y4cYMmTZowePBgABYuXMj27ds5evQoRYsWpXDhwsydOxdXV1cyMzMVQomIyH9EAZSIiIiIyDMu6yN/1rK7o0ePsmjRIvr370/x4sW5efMmixYtYuvWrTRr1oyhQ4cCEB8fz927d/H09MTPz89+vIuLiyNPR0REnkL6zSEiIiIi8oy6fPkyhQsXxmAwAJCenk5YWBgGgwF/f3+KFy8OQGBgIF27dsVgMLBlyxZMJhODBw8mV65c5MqVy/58VqtV4ZOIiPxXNIRcREREROQZdPPmTZo2bcpXX31lv83V1ZVGjRpx4sQJzp07x/nz5+335c2bly5duhASEsLmzZsZOXLkA8+ZtSOeiIjIf0q/QUREREREnkHu7u4sXLiQ1157DYvFYr+9V69eDB8+nKtXr7Ju3TpiY2Pt9+XNm5fOnTtTt25dbt68iaZ1iIjIX0UzoEREREREnmEZGRm8+eabBAUFMWXKFPvtixcvZsqUKXTt2pUuXbqQJ08e+32xsbH4+vpiNBrtg8tFRET+F1rALSIiIiLyjMjanc5isWA2m+0zm+rVq8fixYvJmTMnI0aMAKBr167YbDamTp2KwWCgc+fO9hAq679Wq1XL7kRE5C+hAEpERERE5BmQlJTExo0bqVatGqVLlyY+Pp4ePXowceJEunTpgpeXF9OnT8dms9nnO4WHh2MwGJg+fTqJiYm8//772YaOK3wSEZG/igIoEREREZFnQEpKCgcPHmT69OksWrSIoUOH4ufnh7e3N97e3oSGhgIwffp0AHsI1bVrV5KTk9m3bx85c+Z0WP0iIvJs0wwoEREREZFngM1m4/z58wwbNowTJ05QpkwZli5diru7u72T6e7du2zYsIHp06fz1ltv2ZfjZR1vMBg080lERB4LdUCJiIiIiDwDDAYDhQsXJiMjA5vNxqVLl7h48SIvvviifTaUj48PoaGhGAwGZsyYQUJCgn0wucInERF5nNQBJSIiIiLyFLs/NEpISODw4cMYDAY++eQToqKiWLJkCWXLlrWHUACJiYksWbKEH3/8kZUrV2rWk4iIPHYKoEREREREnlIZGRm4uLiQmZmJ1WrFYDDg4vLbIodjx44xceJEoqKiWLx4MeXKlQMgJiaGhIQEihUrZg+vtNudiIg8bgqgRERERESeQlmhUVJSEsOHD+fq1avkz5+f5s2b06hRI2w2G8eOHWPSpElcvHiRadOm4evry6hRozCbzaxYsQJAy+5EROSJUAAlIiIiIvKUyQqNLBYLYWFhuLq6UqBAAa5du8bp06cZNmwYb775JgBHjhxh5syZ7N+/nwIFCpArVy7Wrl2Lq6urg89CRESeJxpCLiIiIiLyFMnqfMrMzOSnn34ib968DBo0iKJFi9pnPo0cORKr1cpbb71FxYoVmTp1Knv27MFisRAaGorJZLIv3xMREXkS9BtHREREROQpYjQasVgsdOjQgRw5cuDl5UXRokUBCAoKokePHhgMBj788EMMBgNhYWH4+fnRsmVL+3NkZmYqfBIRkSdKv3VERERERJ4yZrOZQoUKsWXLFooVK8bt27fx9/cHoHDhwnTv3h2DwcCoUaNISUmhU6dO2Y7P2g1PRETkSdFWFyIiIiIiTu7+sa0ZGRkATJ06lY4dO3L+/HnWrl3LnTt37I/JCqEaN27MV199hca+ioiIo2kIuYiIiIiIE8ua1ZSRkUF6ejopKSnkzp3bfv+IESNYv349ffv2JSwsLNt9N2/exN/fH6PRqN3uRETEobQET0RERETESWXNakpKSuL999/nxo0bXL16lSZNmtCoUSPq16/PmDFjMBgMzJo1CyBbCBUYGAj8a3C5iIiIoyiAEhERERFxUiaTiZSUFHuo1LJlS9zd3dm0aROHDh0iOjqadu3aMXr0aIxGI/PmzSM5OZnu3buTM2dO+/MofBIREUdTACUiIiIi4sQ2bNiAzWbjo48+onDhwri4uBAfH8+cOXPw8PCwdzd9+OGHxMfH8/PPP+Pt7e3oskVERLJRACUiIiIi4kQyMzMxmUz22U/nz5/Hw8ODYsWKAbB582Zmz55Nv379aNWqFffu3ePq1auULFmSGTNmYLVaMRgMmvkkIiJORb24IiIiIiIOlpqaCvy2253JZCIpKYk1a9aQmJhI7ty5iYuLA+Dbb79l4MCB9OvXj+7du2OxWFiyZAn79++3P4fRaLSHUCIiIs5CAZSIiIiIiAMdPHiQqVOncvPmTQwGAxaLhVatWrF3714SExMpVqwY6enp9O/fn/fee48BAwbQrVs3AC5evMiePXtITU3F3d3d/pya+SQiIs5Gv5lERERERBzk4MGDtG/fHvhtxzqLxUJ8fDylS5fm/fffJ3/+/DRp0oRq1aqxdetW6tatS1hYGAaDgZMnTzJy5EgMBgNdu3Z18JmIiIj8e5oBJSIiIiLiAIcOHaJTp06Eh4cTERFBRkYGb7/9NlevXiV//vzkz5/f/thp06YBcODAAdq3b4+7uztJSUnkyJGDVatWYTKZ7LOjREREnJECKBERERGRJ+zYsWN06dKFatWq0aNHDzw8PLh79y6VKlUiJiaG5ORke5iUlpaGm5sb06ZN45///Cfnz58nJSWFEiVKEBISkm1guYiIiLMy2Gw2m6OLEBERERF5Xhw4cIDOnTsD4O/vz6BBg/jb3/6G2Wzm1q1bfP7558ybN4+///3v9s4ni8WC2Wx+6POp80lERJ4GmgElIiIiIvKEHDhwgPDwcLp27crevXvJmTMn48ePZ9u2bVgsFgICAggLCyMiIoLt27czZMgQAMxmMxkZGQ99ToVPIiLyNFAHlIiIiIjIE5CYmEjVqlVp37497733Hl5eXqSnpxMaGkpcXBxDhgyhUaNGmM1mYmJiWLduHZ9++imvvfYa48ePB8BqtWqHOxEReSopgBIREREReUIuXbpEQEAAHh4e9qVzFouFN95445Eh1JIlS6hevTrz5s1zdPkiIiL/NX19IiIiIiLyhBQpUgQPDw8A+/Bws9nM559/jq+vLxMmTLAvx/Pz8+PNN98kLCyM5ORkrFarg6sXERH576kDSkRERETEgbJ2sHtUJ1R8fDw5c+bEYDBoCZ6IiDy1FECJiIiIiDjY/SHUm2++SXx8PO+++y4hISG4uroCYLPZMBgMDq5URETkv6OvT0REREREHMzFxcW+HG/dunWkp6ezbds2e/gEKHwSEZGnmjqgREREREScRFYnVEZGBgaDAZPJ5OiSRERE/hIKoEREREREnEjW7ni//7OIiMjTTAGUiIiIiIiIiIg8VpoBJSIiIiIiIiIij5UCKBEREREREREReawUQImIiIiIiIiIyGOlAEpERERERERERB4rBVAiIiIiIiIiIvJYKYASEREREREREZHHSgGUiIiIiIiIiIg8VgqgRERERERERETksVIAJSIiIvIM6dChAx06dHB0GSIiIiLZuDi6ABEREZFnzcaNGxkyZIj97yaTiTx58lCrVi369etHYGCgA6sTERERefIUQImIiIg8Jn369KFgwYJYLBaOHDlCZGQkP//8M1u2bMHNze2x/MzFixc/lucVERER+V8ogBIRERF5TOrWrUv58uUBaN26Nb6+vnz66ads376dJk2aPJafaTabH8vzioiIiPwvNANKRERE5AmpUqUKAFeuXLHfFhUVRZ8+fahWrRrly5enVatWbN++/YFjT58+Tfv27QkODqZu3bp8/PHHbNiwgVKlSnH16lX74x42Ayo2NpahQ4dSs2ZNypcvT/PmzYmMjMz2mKtXr1KqVCkWL17M2rVradSoEeXKlSM0NJRjx479lf8MIiIi8hxSB5SIiIjIExIdHQ1Azpw5ATh79ixt2rQhMDCQbt264enpyVdffUVERARz5szhlVdeAeDmzZt07NgRgO7du+Pp6cn69ev/VLdTamoqHTp04PLly7Rr146CBQvy9ddf88EHH5CQkGB/3ixbtmwhOTmZsLAwDAYDixYt4t1332Xbtm24urr+lf8cIiIi8hxRACUiIiLymCQlJXHnzh0sFgtHjx5l7ty5mM1mGjRoAMC4cePIly8fGzZssIdJbdu2pU2bNkydOtUeQH366afEx8cTGRlJmTJlAGjVqhWvvvrqH9awdu1aoqKimDJlCs2bNwfgrbfeokOHDsycOZPQ0FC8vLzsj7927RrffvstuXLlAqBo0aL06tWLPXv22OsWERER+U9pCZ6IiIjIY9KpUydq1KhBvXr16NOnDx4eHsyfP5+8efNy9+5d9u/fz2uvvWYPqu7cuUNcXBy1a9fm4sWL3Lx5E4Ddu3dTsWJFe/gE4OPjQ0hIyB/W8MMPP+Dv70+zZs3st7m6utKhQwfu3bvHgQMHsj2+SZMm9vAJHr5sUEREROQ/pQ4oERERkcdk5MiRFC1alMTERDZs2MCBAwfsnU6XL1/GZrMxa9YsZs2a9dDjY2NjCQwMJDo6mooVKz5wf+HChf+whujoaIoUKYLRmP17x6CgIOC3jqf75cuXL9vfs8KohISEP/xZIiIiIo+iAEpERETkMQkODrbvgteoUSPatm1L//79+frrr7FarQB06dKFOnXqPPT4PxMw/dVMJtNDb7fZbE+4EhEREXmWKIASEREReQJMJhPvv/8+b7/9NqtWrSI0NBT4bTlczZo1/+2xBQoU4NKlSw/cfvny5T/8uQUKFODMmTNYrdZsXVDnz58HIH/+/P/JaYiIiIj8VzQDSkREROQJqV69OsHBwSxbtgwvLy+qVavG2rVruXXr1gOPvXPnjv3PtWvX5siRI5w6dcp+2927d9m8efMf/sy6dety+/Zt/vnPf9pvy8jIYMWKFXh6elK1atX/8axERERE/pg6oERERESeoK5du9K3b182btzIqFGjaNu2LSEhIbz55psUKlSImJgYjhw5wo0bN/jyyy8BCA8P58svv6Rz5860b98eT09P1q9fT758+bh79y4Gg+GRPy8sLIy1a9fywQcfcOLECQoUKMA333zDoUOHGDp0aLYd8EREREQeFwVQIiIiIk9Q48aNKVy4MEuWLOHNN99kw4YNzJ07l8jISO7evUvu3Ll58cUXiYiIsB+TL18+li9fztixY1mwYAG5c+emXbt2eHh4MHbsWNzc3B7589zd3VmxYgVTp04lMjKSpKQkihYtyoQJE2jVqtWTOGURERERDDZNlBQRERF5Ko0bN461a9dy+PDhRw4PFxEREXEGmgElIiIi8hRITU3N9ve4uDi+/PJLKleurPBJREREnJ6W4ImIiIg8BcLCwqhWrRpBQUHExMSwYcMGkpKS6NWrl6NLExEREflDWoInIiIi8hSYPn0633zzDTdu3MBgMPDiiy/Su3dvatas6ejSRERERP6QAigREREREREREXmsNANKREREREREREQeKwVQIiIiIiIiIiLyWCmAEhERERERERGRx0oBlIiIiIiIiIiIPFYKoERERERERERE5LFSACUiIiIiIiIiIo+VAigREREREREREXmsFECJiIiIiIiIiMhjpQBKREREREREREQeq/8HoZ9evR1capUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "regional = df[df['section'] == 'regional_demand'].copy()\n",
        "\n",
        "regional_long = regional[['location', 'metric_name', 'value']].copy()\n",
        "regional_long = regional_long.rename(columns={'value': 'growth_rate'})\n",
        "\n",
        "regional_long['demand_type'] = regional_long['metric_name'].str.replace('_demand_growth', '', regex=False)\n",
        "regional_long['demand_type'] = regional_long['demand_type'].str.replace('_', ' ').str.title()\n",
        "\n",
        "pivot_plot = regional_long.pivot(index='location', columns='demand_type', values='growth_rate')\n",
        "\n",
        "ax = pivot_plot.plot(kind='bar', figsize=(12, 6))\n",
        "ax.set_title('Regional Demand Growth in Italy (2023 vs 2019)')\n",
        "ax.set_xlabel('Region')\n",
        "ax.set_ylabel('Growth Rate (%)')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "jO59R4BnFHjO"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xw1V2kJUFHjO"
      },
      "source": [
        "## 3d. Supply Trend by Hotel Category\n",
        "We examine which hotel categories gained or lost room supply."
      ],
      "id": "Xw1V2kJUFHjO"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qYUu-gU4FHjO",
        "outputId": "ad70732f-d026-457d-8f00-05e75fd87f16"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   category  value\n",
              "34   5 Star   44.2\n",
              "35   4 Star   12.2\n",
              "36   3 Star   -9.6\n",
              "37   2 Star  -22.6\n",
              "38   1 Star  -24.7"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b010d2f-b33c-49d3-a438-406e99ba9bc0\" 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>category</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>5 Star</td>\n",
              "      <td>44.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>4 Star</td>\n",
              "      <td>12.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>3 Star</td>\n",
              "      <td>-9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2 Star</td>\n",
              "      <td>-22.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1 Star</td>\n",
              "      <td>-24.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b010d2f-b33c-49d3-a438-406e99ba9bc0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-4b010d2f-b33c-49d3-a438-406e99ba9bc0 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-4b010d2f-b33c-49d3-a438-406e99ba9bc0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"supply[['category', 'value']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"4 Star\",\n          \"1 Star\",\n          \"3 Star\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.789928794632335,\n        \"min\": -24.7,\n        \"max\": 44.2,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          12.2,\n          -24.7,\n          -9.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "supply = df[df['section'] == 'supply'].copy()\n",
        "supply['category'] = supply['metric_name'].str.replace('room_supply_growth_', '', regex=False).str.replace('_', ' ').str.title()\n",
        "supply[['category', 'value']].sort_values('value', ascending=False)"
      ],
      "id": "qYUu-gU4FHjO"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "Sh9IJkkkFHjO",
        "outputId": "6e525d71-712a-4abf-806a-943fbb456610"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.bar(supply['category'], supply['value'])\n",
        "plt.title('Room Supply Growth by Hotel Category (2023 vs 2013)')\n",
        "plt.xlabel('Hotel Category')\n",
        "plt.ylabel('Growth Rate (%)')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "Sh9IJkkkFHjO"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6qdPNrvFHjO"
      },
      "source": [
        "## 3e. Holiday Dwellings Market by City\n",
        "We compare holiday dwellings nights spent, bed supply, and demand-supply ratio across the main markets."
      ],
      "id": "b6qdPNrvFHjO"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "oeVp2OlUFHjO",
        "outputId": "31e53204-86c4-435d-f52f-84dcdc1388bf"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "metric_name  location  beds_2023  demand_supply_ratio_2023  nights_spent_2023\n",
              "0            Florence    33233.0                      81.0             2926.0\n",
              "1               Milan    47440.0                      81.0             3530.0\n",
              "2                Rome    41954.0                     116.0             9873.0\n",
              "3              Venice   155258.0                      61.0             9472.0"
            ],
            "text/html": [
              "\n",
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              "      <th>metric_name</th>\n",
              "      <th>location</th>\n",
              "      <th>beds_2023</th>\n",
              "      <th>demand_supply_ratio_2023</th>\n",
              "      <th>nights_spent_2023</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>81.0</td>\n",
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              "        google.colab.notebook.generateWithVariable('holiday_pivot');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "holiday_pivot",
              "summary": "{\n  \"name\": \"holiday_pivot\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Milan\",\n          \"Venice\",\n          \"Florence\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"beds_2023\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57489.570830281904,\n        \"min\": 33233.0,\n        \"max\": 155258.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          47440.0,\n          155258.0,\n          33233.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"demand_supply_ratio_2023\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.86737122335374,\n        \"min\": 61.0,\n        \"max\": 116.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          81.0,\n          116.0,\n          61.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nights_spent_2023\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3732.4874971882577,\n        \"min\": 2926.0,\n        \"max\": 9873.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3530.0,\n          9472.0,\n          2926.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "holiday = df[df['section'] == 'holiday_dwellings'].copy()\n",
        "holiday_pivot = holiday.pivot(index='location', columns='metric_name', values='value').reset_index()\n",
        "holiday_pivot"
      ],
      "id": "oeVp2OlUFHjO"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "mqaT4X5JFHjO",
        "outputId": "cac09df4-6c4b-4112-d897-224f1ee57856"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.bar(holiday_pivot['location'], holiday_pivot['demand_supply_ratio_2023'])\n",
        "plt.title('Holiday Dwellings Demand-Supply Ratio by City (2023)')\n",
        "plt.xlabel('City')\n",
        "plt.ylabel('Nights per Bed')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "mqaT4X5JFHjO"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkDgoFlhFHjP"
      },
      "source": [
        "## 3f. Chains Management Model\n",
        "We visualize the main hotel chain management models in Italy."
      ],
      "id": "xkDgoFlhFHjP"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "47C2VCpQFHjP",
        "outputId": "8aadd964-845c-4e6f-c350-b55d8d597d48"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  model  value\n",
              "55       Lease Contract   38.0\n",
              "56            Ownership   33.0\n",
              "57  Management Contract   21.0\n",
              "58          Franchising    8.0"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Lease Contract</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Ownership</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Management Contract</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Franchising</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-76ba1186-6059-4ce1-94b8-133d3c312ddb 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-76ba1186-6059-4ce1-94b8-133d3c312ddb');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"chains[['model', 'value']]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Ownership\",\n          \"Franchising\",\n          \"Lease Contract\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.391539617733779,\n        \"min\": 8.0,\n        \"max\": 38.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          33.0,\n          8.0,\n          38.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "chains = df[(df['section'] == 'chains') & (df['entity'] == 'management_model')].copy()\n",
        "chains['model'] = chains['metric_name'].str.replace('_share', '', regex=False).str.replace('_', ' ').str.title()\n",
        "chains[['model', 'value']]"
      ],
      "id": "47C2VCpQFHjP"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "v-2pyHu-FHjP",
        "outputId": "464d0963-ca56-46e8-b9e5-825585e9b4f4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(7, 7))\n",
        "plt.pie(chains['value'], labels=chains['model'], autopct='%1.1f%%', startangle=90)\n",
        "plt.title('Hotel Chain Management Models in Italy (2023)')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "v-2pyHu-FHjP"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfDX561HFHjP"
      },
      "source": [
        "## 4a. Create a Simple Market Opportunity Score\n",
        "We build a simple score for the four main cities using the three YTD 2024 city performance indicators.\n",
        "\n",
        "This is not a forecast. It is only a simple descriptive score to summarize current market momentum."
      ],
      "id": "qfDX561HFHjP"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "9jmCXus5FHjP",
        "outputId": "a3404dea-261b-415b-da9a-d0702c234d2b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "metric_name  location  adr_yoy  occupancy_yoy  revpar_yoy  opportunity_score\n",
              "0                Rome     17.0            0.0        21.0               38.0\n",
              "1               Milan      8.0            1.0         2.0               11.0\n",
              "2            Florence      4.0           -2.0         3.0                5.0\n",
              "3              Venice      4.0           -2.0         3.0                5.0"
            ],
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              "      <td>Milan</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Milan\",\n          \"Venice\",\n          \"Rome\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adr_yoy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.13052471924984,\n        \"min\": 4.0,\n        \"max\": 17.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          17.0,\n          8.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupancy_yoy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5,\n        \"min\": -2.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          -2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revpar_yoy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.17877987534291,\n        \"min\": 2.0,\n        \"max\": 21.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          21.0,\n          2.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"opportunity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.75595125658873,\n        \"min\": 5.0,\n        \"max\": 38.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          38.0,\n          11.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "score_df = city_pivot.copy()\n",
        "score_df['opportunity_score'] = score_df[['occupancy_yoy', 'adr_yoy', 'revpar_yoy']].sum(axis=1)\n",
        "score_df = score_df.sort_values('opportunity_score', ascending=False).reset_index()\n",
        "score_df"
      ],
      "id": "9jmCXus5FHjP"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "ozxb9oXnFHjP",
        "outputId": "c594747e-9b9a-47f4-b3ee-d01c46d8a3af"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.bar(score_df['location'], score_df['opportunity_score'])\n",
        "plt.title('Simple Market Opportunity Score by City')\n",
        "plt.xlabel('City')\n",
        "plt.ylabel('Opportunity Score')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "ozxb9oXnFHjP"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2rAYX6OFHjP"
      },
      "source": [
        "## 4b. Export Summary Tables for the App and Workflow\n",
        "These files can later be reused in the Hugging Face Space and the n8n workflow."
      ],
      "id": "T2rAYX6OFHjP"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZeK4N4wFHjP",
        "outputId": "980c76eb-127a-4c76-b629-5d232e2e016e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved: section_summary.csv, regional_demand_summary.csv, city_opportunity_score.csv\n"
          ]
        }
      ],
      "source": [
        "section_summary.to_csv('section_summary.csv', index=False)\n",
        "regional_pivot.to_csv('regional_demand_summary.csv', index=False)\n",
        "score_df.to_csv('city_opportunity_score.csv', index=False)\n",
        "print('Saved: section_summary.csv, regional_demand_summary.csv, city_opportunity_score.csv')"
      ],
      "id": "tZeK4N4wFHjP"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8zG0054FHjQ"
      },
      "source": [
        "## 5. Final Quick Interpretation\n",
        "This cell prints a few quick takeaways that can later be reused in the report or the app."
      ],
      "id": "b8zG0054FHjQ"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCCQiLX-FHjQ",
        "outputId": "2e5b6b56-acea-4bcf-f7fc-592dcc127bb3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top city based on the simple opportunity score: Rome (38.0)\n",
            "Region with the weakest domestic demand growth in this extracted dataset: Toscana\n",
            "Use these outputs only as simple descriptive project insights, not as a causal model.\n"
          ]
        }
      ],
      "source": [
        "best_city = score_df.loc[0, 'location']\n",
        "best_score = score_df.loc[0, 'opportunity_score']\n",
        "worst_region = regional_pivot.sort_values('domestic_demand_growth').iloc[0]['location']\n",
        "\n",
        "print(f'Top city based on the simple opportunity score: {best_city} ({best_score:.1f})')\n",
        "print(f'Region with the weakest domestic demand growth in this extracted dataset: {worst_region}')\n",
        "print('Use these outputs only as simple descriptive project insights, not as a causal model.')"
      ],
      "id": "vCCQiLX-FHjQ"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11"
    },
    "colab": {
      "provenance": []
    }
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