6_report_heat_map.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vYjhT_Pfe9hQ"
      },
      "source": [
        "# Vẽ các loại heat map trong bất động sản.\n",
        "Tài liệu Kham Khảo : https://inforiver.com/insights/heatmaps-in-data-visualization-a-comprehensive-introduction/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kKypciTz9MJ"
      },
      "source": [
        "## 1. Import Dữ liệu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "TuUTCCDjRoof"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "933gIhzmH2i2"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/KhiemDangLe/Final-Project/main/DataFolder/5_preprocessed_data.csv')\n",
        "\n",
        "df_location = pd.read_csv('https://raw.githubusercontent.com/KhiemDangLe/Final-Project/main/DataFolder/7_coordinates_by_street_name_1_5000.csv')\n",
        "df_location2 = pd.read_csv('https://raw.githubusercontent.com/KhiemDangLe/Final-Project/main/DataFolder/8_coordinates_by_street_nam_5000_end.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tn8LzOkjjPdA",
        "outputId": "56b4ffce-0dd2-40de-e5d4-4ab381380ea4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The most common longitude value is 106.7315547 and it occurs 184 times.\n",
            "The most common latitude value is 10.7862056 and it occurs 184 times.\n",
            "There are 184 rows where the 'longitude' and 'latitude' values are the most common.\n"
          ]
        }
      ],
      "source": [
        "# prompt: tôi muốn coi thử cái df_location['longitude'] với df_location['latitude'] nào trùng lặp nhiều nhất\n",
        "\n",
        "# Find the most common values in the 'longitude' and 'latitude' columns\n",
        "most_common_longitude = df_location['longitude'].mode()[0]\n",
        "most_common_latitude = df_location['latitude'].mode()[0]\n",
        "\n",
        "# Count the number of occurrences of the most common values\n",
        "count_longitude = df_location['longitude'].value_counts()[most_common_longitude]\n",
        "count_latitude = df_location['latitude'].value_counts()[most_common_latitude]\n",
        "\n",
        "# Print the results\n",
        "print(f\"The most common longitude value is {most_common_longitude} and it occurs {count_longitude} times.\")\n",
        "print(f\"The most common latitude value is {most_common_latitude} and it occurs {count_latitude} times.\")\n",
        "\n",
        "# Find the rows where the 'longitude' and 'latitude' values are the most common\n",
        "df_filtered = df_location[\n",
        "    (df_location['longitude'] == most_common_longitude) &\n",
        "    (df_location['latitude'] == most_common_latitude)\n",
        "]\n",
        "\n",
        "# Print the number of rows in the filtered DataFrame\n",
        "print(f\"There are {len(df_filtered)} rows where the 'longitude' and 'latitude' values are the most common.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "VBE-EDymoBhB"
      },
      "outputs": [],
      "source": [
        "merged_df1 = pd.merge(df[['article_id', 'price','district','street_name','price_per_m2']], df_location[['article_id', 'longitude', 'latitude']], on='article_id', how='inner')\n",
        "merged_df2 = pd.merge(df[['article_id', 'price','district','street_name','price_per_m2']], df_location2[['article_id', 'longitude', 'latitude']], on='article_id', how='inner')\n",
        "merged_df = pd.concat([merged_df1, merged_df2], ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5mqUhJUtjU9i",
        "outputId": "695ce7d3-f1d8-40fb-ddc4-8a06ff2c0b9e"
      },
      "outputs": [
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      article_id  price    district               street_name  price_per_m2  \\\n",
              "0         285164  3.899      Nhà Bè                       NaN      0.025993   \n",
              "1         285163  5.500    Tân Bình                     Âu Cơ      0.107843   \n",
              "2         285162  4.600   Phú Nhuận             Phan Đăng Lưu      0.102222   \n",
              "3         285161  7.350      Quận 5             LÊ HỒNG PHONG      0.179268   \n",
              "4         285129  4.000  Bình Thạnh                 Bạch Đằng      0.121212   \n",
              "...          ...    ...         ...                       ...           ...   \n",
              "5384      250314  3.800    Bình Tân               ĐƯỜNG SỐ 29      0.097436   \n",
              "5385      250309  0.420     Hóc Môn                       NaN      0.010500   \n",
              "5386      250300  5.800      Gò Vấp  Đường 237 PHẠM VĂN CHIÊU      0.069880   \n",
              "5387      250298  4.700      Quận 7            Huỳnh Tấn Phát      0.055294   \n",
              "5388      250296  6.490      Gò Vấp          Đường Lê Đức Thọ      0.078193   \n",
              "\n",
              "       longitude   latitude  \n",
              "0            NaN        NaN  \n",
              "1     106.648074  10.774784  \n",
              "2     106.680425  10.799369  \n",
              "3     106.676687  10.761255  \n",
              "4     106.709006  10.803060  \n",
              "...          ...        ...  \n",
              "5384  106.612295  10.756475  \n",
              "5385         NaN        NaN  \n",
              "5386         NaN        NaN  \n",
              "5387  106.722940  10.755247  \n",
              "5388  106.652185  10.858648  \n",
              "\n",
              "[5389 rows x 7 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "merged_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "_jtvmrLxoDvw",
        "outputId": "e6738481-9e4c-4252-f4fb-cb9cb6393646"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"merged_df\",\n  \"rows\": 4097,\n  \"fields\": [\n    {\n      \"column\": \"article_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10510,\n        \"min\": 250296,\n        \"max\": 285163,\n        \"num_unique_values\": 4097,\n        \"samples\": [\n          274320,\n          273763,\n          251661\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.052848450488536,\n        \"min\": 0.105,\n        \"max\": 10.0,\n        \"num_unique_values\": 370,\n        \"samples\": [\n          2.97,\n          3.65,\n          9.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"district\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"Qu\\u1eadn 6\",\n          \"B\\u00ecnh T\\u00e2n\",\n          \"T\\u00e2n B\\u00ecnh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"street_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 935,\n        \"samples\": [\n          \"NGUY\\u1ec4N QU\\u00dd Y\\u00caM\",\n          \"T\\u00e2n Ch\\u00e1nh Hi\\u1ec7p\",\n          \"Tho\\u1ea1i Ng\\u1ecdc H\\u1ea7u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_per_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04023675959588939,\n        \"min\": 0.0020192307692307,\n        \"max\": 0.3666666666666667,\n        \"num_unique_values\": 2004,\n        \"samples\": [\n          0.0287671232876712,\n          0.1323255813953488,\n          0.0725190839694656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.048950475386383654,\n        \"min\": 106.4733589,\n        \"max\": 106.8603522,\n        \"num_unique_values\": 826,\n        \"samples\": [\n          106.6258174,\n          106.7281634,\n          106.7810147\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.038431280266966884,\n        \"min\": 10.6483413,\n        \"max\": 10.98002,\n        \"num_unique_values\": 826,\n        \"samples\": [\n          10.8055876,\n          10.8679065,\n          10.8446911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
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              "      <td>Quận 5</td>\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
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              "\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-1e9f4697-429c-4795-80e2-5f72cd172170 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_08430832-4069-4df3-b137-354c4136e184\">\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('merged_df')\"\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",
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              "        document.querySelector('#id_08430832-4069-4df3-b137-354c4136e184 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('merged_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      article_id  price    district       street_name  price_per_m2  \\\n",
              "1         285163   5.50    Tân Bình             Âu Cơ      0.107843   \n",
              "2         285162   4.60   Phú Nhuận     Phan Đăng Lưu      0.102222   \n",
              "3         285161   7.35      Quận 5     LÊ HỒNG PHONG      0.179268   \n",
              "4         285129   4.00  Bình Thạnh         Bạch Đằng      0.121212   \n",
              "5         285109   3.40      Quận 8            Mễ Cốc      0.030909   \n",
              "...          ...    ...         ...               ...           ...   \n",
              "5381      250320   6.60     Tân Phú       Khuông Việt      0.150000   \n",
              "5382      250319   3.80     Tân Phú     LÝ THÁNH TÔNG      0.088372   \n",
              "5384      250314   3.80    Bình Tân       ĐƯỜNG SỐ 29      0.097436   \n",
              "5387      250298   4.70      Quận 7    Huỳnh Tấn Phát      0.055294   \n",
              "5388      250296   6.49      Gò Vấp  Đường Lê Đức Thọ      0.078193   \n",
              "\n",
              "       longitude   latitude  \n",
              "1     106.648074  10.774784  \n",
              "2     106.680425  10.799369  \n",
              "3     106.676687  10.761255  \n",
              "4     106.709006  10.803060  \n",
              "5     106.626348  10.712975  \n",
              "...          ...        ...  \n",
              "5381  106.637698  10.769343  \n",
              "5382  106.626718  10.766911  \n",
              "5384  106.612295  10.756475  \n",
              "5387  106.722940  10.755247  \n",
              "5388  106.652185  10.858648  \n",
              "\n",
              "[4097 rows x 7 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "merged_df = merged_df.dropna(subset=['longitude', 'latitude'])\n",
        "merged_df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zw7RbHiirhip"
      },
      "source": [
        "hm_df = df[['district', 'street_name', 'price', 'latitude', 'longitude']].groupby(['district', 'street_name']).mean().reset_index()\n",
        "# 'latitude' and 'longitude' are included in the grouping to calculate mean based on district and street name.\n",
        "hm_table_data = hm_df[['latitude', 'longitude', 'district', 'street_name', 'price']].values.tolist()\n",
        "hm_table_data = pd.DataFrame(hm_table_data, columns=['latitude', 'longitude', 'district', 'street_name', 'price']).dropna(subset=['longitude', 'latitude'])\n",
        "# Convert hm_table_data to a DataFrame and then drop rows with missing latitude and longitude"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkrviqAUi8ht"
      },
      "source": [
        "# 1. Standard spatial heatmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rFK2mMpxoTMQ",
        "outputId": "72d5ccd0-eef9-4fdb-c727-93080106687e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "article_id      0\n",
            "price           0\n",
            "district        0\n",
            "street_name     0\n",
            "price_per_m2    0\n",
            "longitude       0\n",
            "latitude        0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import folium\n",
        "from folium import plugins\n",
        "\n",
        "# Load data (replace with your data loading logic)\n",
        "df = merged_df.copy()  # Assuming merged_df contains your location and price data\n",
        "\n",
        "# Check for NaN values (optional)\n",
        "print(df.isna().sum())  # Check for missing values in each column\n",
        "\n",
        "#Handle NaN values (consider dropping rows here if necessary)\n",
        "df = df.dropna(subset=['latitude', 'longitude'])  # Uncomment to drop rows with NaN locations\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjuvQe7IicbE",
        "outputId": "cd8c0dc0-9b5c-498d-d53c-02a38d6d67e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: folium in /usr/local/lib/python3.10/dist-packages (0.14.0)\n",
            "Requirement already satisfied: branca>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from folium) (0.7.2)\n",
            "Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from folium) (3.1.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from folium) (1.25.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from folium) (2.31.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.9->folium) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (2024.6.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install folium -- quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpyfKAzvx_0g",
        "outputId": "741ad023-80ef-40d8-8274-110f8a9bd685"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-06-21 00:58:17--  https://raw.githubusercontent.com/huytrao/VietNam-Intern-Map/main/hochiminh.geojson\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 85733 (84K) [text/plain]\n",
            "Saving to: ‘hochiminh.geojson’\n",
            "\n",
            "\rhochiminh.geojson     0%[                    ]       0  --.-KB/s               \rhochiminh.geojson   100%[===================>]  83.72K  --.-KB/s    in 0.003s  \n",
            "\n",
            "2024-06-21 00:58:18 (31.0 MB/s) - ‘hochiminh.geojson’ saved [85733/85733]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://raw.githubusercontent.com/huytrao/VietNam-Intern-Map/main/hochiminh.geojson -O hochiminh.geojson"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "biRJuZul0DIx"
      },
      "source": [
        "##2. Vẽ Heatmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        },
        "id": "73ansI9H1j4K",
        "outputId": "28f356d1-a7c1-4d0c-de74-91a251a326fd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-10-a04c12e98054>:15: UserWarning: The `max_val` parameter is no longer necessary. The largest intensity is calculated automatically.\n",
            "  hm = plugins.HeatMap(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;html&gt;\n",
              "&lt;head&gt;\n",
              "    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    \n",
              "        &lt;script&gt;\n",
              "            L_NO_TOUCH = false;\n",
              "            L_DISABLE_3D = false;\n",
              "        &lt;/script&gt;\n",
              "    \n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
              "    \n",
              "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "            &lt;style&gt;\n",
              "                #map_bca4cccc2e3b2e5e0aaf7cce4d08e61a {\n",
              "                    position: relative;\n",
              "                    width: 100.0%;\n",
              "                    height: 100.0%;\n",
              "                    left: 0.0%;\n",
              "                    top: 0.0%;\n",
              "                }\n",
              "                .leaflet-container { font-size: 1rem; }\n",
              "            &lt;/style&gt;\n",
              "        \n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium@main/folium/templates/leaflet_heat.min.js&quot;&gt;&lt;/script&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;\n",
              "    \n",
              "    \n",
              "&lt;div style=&quot;position: fixed;\n",
              "     bottom: 50px; left: 50px; width: 200px; height: 200px;\n",
              "     background-color: white; z-index:9999; font-size:14px;\n",
              "     border:2px solid grey; padding: 10px;&quot;&gt;\n",
              "     &lt;b&gt; HEATMAP GIÁ TRÊN M2 Ở THÀNH PHỐ HỒ CHÍ MINH&lt;/b&gt;&lt;br&gt;\n",
              "     &lt;i style=&quot;background:green; display:inline-block; width:10px; height:10px;&quot;&gt;&lt;/i&gt;&amp;nbsp; 0.00 - 0.07 tỷ /m2 green &lt;br&gt;\n",
              "     &lt;i style=&quot;background:lime; display:inline-block; width:10px; height:10px;&quot;&gt;&lt;/i&gt;&amp;nbsp; 0.07 - 0.15 tỷ /m2 lime &lt;br&gt;\n",
              "     &lt;i style=&quot;background:orange; display:inline-block; width:10px; height:10px;&quot;&gt;&lt;/i&gt;&amp;nbsp; 0.15 - 0.22 tỷ /m2 orange &lt;br&gt;\n",
              "     &lt;i style=&quot;background:red; display:inline-block; width:10px; height:10px;&quot;&gt;&lt;/i&gt;&amp;nbsp; 0.29 - 0.37  tỷ /m2 red &lt;br&gt;\n",
              "&lt;/div&gt;\n",
              "    \n",
              "            &lt;div class=&quot;folium-map&quot; id=&quot;map_bca4cccc2e3b2e5e0aaf7cce4d08e61a&quot; &gt;&lt;/div&gt;\n",
              "        \n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;\n",
              "    \n",
              "    \n",
              "            var map_bca4cccc2e3b2e5e0aaf7cce4d08e61a = L.map(\n",
              "                &quot;map_bca4cccc2e3b2e5e0aaf7cce4d08e61a&quot;,\n",
              "                {\n",
              "                    center: [10.800234639419088, 106.66954907091795],\n",
              "                    crs: L.CRS.EPSG3857,\n",
              "                    zoom: 11,\n",
              "                    zoomControl: true,\n",
              "                    preferCanvas: false,\n",
              "                }\n",
              "            );\n",
              "\n",
              "            \n",
              "\n",
              "        \n",
              "    \n",
              "            var tile_layer_ed878edf7017f58f572754d5d8a999ca = L.tileLayer(\n",
              "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
              "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
              "            ).addTo(map_bca4cccc2e3b2e5e0aaf7cce4d08e61a);\n",
              "        \n",
              "    \n",
              "            var heat_map_85cf6117856f9cb026ba53ac80eec62d = L.heatLayer(\n",
              "                [[10.7747842, 106.6480738, 0.1078431372549019], [10.7993691, 106.6804249, 0.1022222222222222], [10.761255, 106.6766869, 0.1792682926829268], [10.80306, 106.7090062, 0.1212121212121212], [10.7129752, 106.626348, 0.0309090909090909], [10.7727358, 106.6242973, 0.1057647058823529], [10.8373485, 106.6362851, 0.1394339622641509], [10.8482918, 106.7710403, 0.095], [10.8116821, 106.6244037, 0.1070175438596491], [10.7956238, 106.6639667, 0.1045454545454545], [10.786296, 106.6424777, 0.1571428571428571], [10.7971015, 106.8234697, 0.10375], [10.8050282, 106.6353835, 0.067], [10.8351122, 106.6396031, 0.1053191489361702], [10.7927646, 106.6839379, 0.1345454545454545], [10.7817734, 106.6742377, 0.085], [10.842117, 106.6598368, 0.1672413793103448], [10.761689, 106.5714128, 0.0947916666666666], [10.7669112, 106.626718, 0.1146666666666666], [10.7750421, 106.6482618, 0.0875], [10.8537373, 106.6642851, 0.094], [10.7956238, 106.6639667, 0.1136363636363636], [10.8307717, 106.6694581, 0.088235294117647], [10.7881603, 106.6297821, 0.11625], [10.8535754, 106.7513277, 0.0743801652892562], [10.8150491, 106.6878297, 0.1075], [10.787108, 106.6381048, 0.059375], [10.7752443, 106.6476765, 0.0596491228070175], [10.7752443, 106.6476765, 0.0596491228070175], [10.842117, 106.6598368, 0.1272727272727273], [10.8260967, 106.6337752, 0.074390243902439], [10.8900889, 106.6820288, 0.1015384615384615], [10.71976875, 106.61103987272804, 0.0769841269841269], [10.8307717, 106.6694581, 0.0833333333333333], [10.7790986, 106.624253, 0.0655737704918032], [10.7790986, 106.624253, 0.0655737704918032], [10.787108, 106.6381048, 0.055], [10.787108, 106.6381048, 0.055], [10.7930699, 106.6205554, 0.0907692307692307], [10.737689, 106.6566865, 0.1245454545454545], [10.8410073, 106.6507062, 0.1264864864864864], [10.842117, 106.6598368, 0.1115384615384615], [10.8023275, 106.6900889, 0.1659090909090909], [10.8202678, 106.6942034, 0.106], [10.787108, 106.6381048, 0.0857142857142857], [10.8364264, 106.658344, 0.0912280701754386], [10.7927004, 106.6532078, 0.1766666666666666], [10.8050282, 106.6353835, 0.12], [10.788936, 106.6827396, 0.0907692307692307], [10.7736823, 106.6215157, 0.1061290322580645], [10.7669112, 106.626718, 0.1162162162162162], [10.8364264, 106.658344, 0.0916666666666666], [10.8732795, 106.6320987, 0.0436363636363636], [10.7961278, 106.7750575, 0.0493333333333333], [10.786296, 106.6424777, 0.108695652173913], [10.7993691, 106.6804249, 0.2090909090909091], [10.7396064, 106.6055133, 0.064], [10.8024689, 106.7114321, 0.1125], [10.758912, 106.6029022, 0.07], [10.7750421, 106.6482618, 0.1340909090909091], [10.783532, 106.648631, 0.1076923076923077], [10.7595344, 106.6056798, 0.0979166666666666], [10.7668548, 106.6097136, 0.0833333333333333], [10.8456138, 106.7663383, 0.1046153846153846], [10.7728443, 106.6141073, 0.06796875], [10.821385, 106.6382736, 0.0594594594594594], [10.7790986, 106.624253, 0.0723684210526315], [10.7396736, 106.5418044, 0.1142857142857142], [10.7857536, 106.6669098, 0.1330555555555555], [10.8216008, 106.7036748, 0.065], [10.8549056, 106.6146723, 0.026], [10.8171922, 106.6318809, 0.175], [10.8447022, 106.7618557, 0.0510204081632653], [10.8447022, 106.7618557, 0.0978260869565217], [10.8364264, 106.658344, 0.0818181818181818], [10.8287546, 106.6838399, 0.1160714285714285], [10.7895616, 106.6403111, 0.1067796610169491], [10.7369433, 106.7182479, 0.0479166666666666], [10.7595344, 106.6056798, 0.1130434782608695], [10.795733, 106.6769266, 0.105], [10.7886568, 106.7764557, 0.0491803278688524], [10.8077196, 106.6034316, 0.0679591836734694], [10.7956238, 106.6639667, 0.0997777777777777], [10.8592729, 106.6514144, 0.05], [10.8116821, 106.6244037, 0.065625], [10.8377959, 106.665056, 0.09125], [10.8307717, 106.6694581, 0.084090909090909], [10.783532, 106.648631, 0.0833333333333333], [10.7667344, 106.6068788, 0.0945454545454545], [10.8505281, 106.7662994, 0.1], [10.8446911, 106.7810147, 0.1], [10.8363523, 106.6290671, 0.05], [10.8678527, 106.6231544, 0.055], [10.728024, 106.6071343, 0.0659090909090909], [10.8532806, 106.6560487, 0.11], [10.8150491, 106.6878297, 0.1055555555555555], [10.8511027, 106.608441, 0.0353932584269662], [10.7983053, 106.6588751, 0.1131818181818182], [10.8005079, 106.6462384, 0.1733333333333333], [10.7693609, 106.5956394, 0.0516666666666666], [10.8377959, 106.665056, 0.065], [10.8246101, 106.692635, 0.1071428571428571], [10.7750421, 106.6482618, 0.0872727272727272], [10.8537373, 106.6642851, 0.0666666666666666], [10.80306, 106.7090062, 0.0841463414634146], [10.7750421, 106.6482618, 0.0810714285714285], [10.7918311, 106.6716136, 0.146551724137931], [10.7865347, 106.6434652, 0.0875], [10.8416455, 106.6360065, 0.1], [10.8222058, 106.6867379, 0.064], [10.8307717, 106.6694581, 0.0844827586206896], [10.842117, 106.6598368, 0.03625], [10.803928, 106.6324795, 0.0710526315789473], [10.7895616, 106.6403111, 0.1173333333333333], [10.7865347, 106.6434652, 0.1846153846153846], [10.7929032, 106.6799899, 0.135], [10.7915388, 106.6396806, 0.0948717948717948], [10.7926943, 106.6960027, 0.1261904761904762], [10.7958848, 106.6320491, 0.15625], [10.8600716, 106.7611736, 0.0433333333333333], [10.7625272, 106.6577416, 0.1036363636363636], [10.8105021, 106.6784896, 0.1], [10.7554716, 106.6061042, 0.0505681818181818], [10.842117, 106.6598368, 0.1089743589743589], [10.8537373, 106.6642851, 0.1553488372093023], [10.842117, 106.6598368, 0.1433333333333333], [10.7750421, 106.6482618, 0.0703703703703703], [10.8592729, 106.6514144, 0.0738461538461538], [10.842117, 106.6598368, 0.084090909090909], [10.8610436, 106.79265, 0.0571428571428571], [10.7336933, 106.6337846, 0.0939622641509434], [10.823658, 106.694678, 0.1], [10.7857422, 106.769879, 0.0755555555555555], [10.7758961, 106.6409672, 0.0890909090909091], [10.8005079, 106.6462384, 0.2194444444444444], [10.7750421, 106.6482618, 0.1428571428571428], [10.7961278, 106.7750575, 0.0772727272727272], [10.8351777, 106.7139547, 0.08], [10.8181842, 106.7762964, 0.0818181818181818], [10.8375039, 106.7305201, 0.0661290322580645], [10.8132623, 106.7687019, 0.074], [10.8364264, 106.658344, 0.096], [10.7790986, 106.624253, 0.0791176470588235], [10.803928, 106.6324795, 0.075], [10.8182378, 106.7133028, 0.11875], [10.8005079, 106.6462384, 0.0649350649350649], [10.8446911, 106.7810147, 0.098], [10.8243618, 106.8255164, 0.0867924528301886], [10.8434009, 106.7731237, 0.098], [10.7525334, 106.60579, 0.1175], [10.7506285, 106.5854885, 0.063953488372093], [10.7554716, 106.6061042, 0.0910714285714285], [10.8505281, 106.7662994, 0.0909090909090909], [10.8171922, 106.6318809, 0.075], [10.840036, 106.6462684, 0.079090909090909], [10.7522281, 106.5962828, 0.098], [10.8011386, 106.7905766, 0.1630434782608695], [10.8537373, 106.6642851, 0.145625], [10.7436081, 106.6190643, 0.098], [10.7790986, 106.624253, 0.0951388888888889], [10.8258485, 106.6912801, 0.1232876712328767], [10.820624, 106.694022, 0.0816666666666666], [10.7530705, 106.6986688, 0.0785714285714285], [10.7983053, 106.6588751, 0.1], [10.7750421, 106.6482618, 0.0662337662337662], [10.7825407, 106.6208215, 0.0807692307692307], [10.7883841, 106.6198132, 0.1119402985074626], [10.7993691, 106.6804249, 0.07], [10.7987031, 106.732915, 0.1862745098039216], [10.7552468, 106.7229401, 0.0625], [10.7314492, 106.7258947, 0.0740384615384615], [10.7449756, 106.7117146, 0.125], [10.7488477, 106.6847776, 0.111875], [10.8216008, 106.7036748, 0.098], [10.8736324, 106.7368386, 0.0525], [10.8488374, 106.7645803, 0.1630434782608695], [10.842117, 106.6598368, 0.1571428571428571], [10.7881603, 106.6297821, 0.1054285714285714], [10.7256139, 106.60499, 0.0573333333333333], [10.7834517, 106.66768037225592, 0.0598333333333333], [10.7522281, 106.5962828, 0.0792], [10.8216295, 106.6936487, 0.1309090909090909], [10.8477358, 106.7751033, 0.1933333333333333], [10.7493297, 106.6836942, 0.0709523809523809], [10.8255677, 106.6908885, 0.1066666666666666], [10.7488477, 106.6847776, 0.0933333333333333], [10.8307717, 106.6694581, 0.0444444444444444], [10.7619769, 106.6522204, 0.1975], [10.7721063, 106.6461488, 0.14], [10.8447022, 106.7618557, 0.0957095709570957], [10.7956238, 106.6639667, 0.125], [10.8123441, 106.6930962, 0.098076923076923], [10.79829, 106.7858675, 0.1633333333333333], [10.80905, 106.6856488, 0.0702127659574468], [10.7544946, 106.6412277, 0.1188679245283018], [10.7620835, 106.6694455, 0.1016129032258064], [10.7895616, 106.6403111, 0.14], [10.7791189, 106.6740958, 0.1448333333333333], [10.7949416, 106.6866371, 0.2325], [10.8713194, 106.7427385, 0.045], [10.7855451, 106.6336291, 0.08375], [10.7855451, 106.6336291, 0.08375], [10.7943561, 106.757913, 0.1483333333333333], [10.8408914, 106.6430465, 0.1203703703703703], [10.7910379, 106.65811328662778, 0.086], [10.7582822, 106.6336006, 0.067], [10.7582822, 106.6336006, 0.09], [10.8051163, 106.6477549, 0.0945652173913043], [10.8485166, 106.6338747, 0.0409722222222222], [10.8219618, 106.6885014, 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0.0747126436781609], [10.8015581, 106.6922377, 0.0773067331670823], [10.8307717, 106.6694581, 0.0989583333333333], [10.7750421, 106.6482618, 0.1215625], [10.8447022, 106.7618557, 0.08], [10.8725427, 106.599315, 0.0176], [10.8171922, 106.6318809, 0.0945652173913043], [10.8501525, 106.8337315, 0.09], [10.7750421, 106.6482618, 0.1444444444444444], [10.7736987, 106.6212649, 0.075], [10.783532, 106.648631, 0.1183673469387755], [10.8181842, 106.7762964, 0.0558823529411764], [10.7129752, 106.626348, 0.0281818181818181], [10.761255, 106.6766869, 0.0930232558139534], [10.7514267, 106.674553, 0.2369565217391304], [10.7750421, 106.6482618, 0.1103571428571428], [10.7747842, 106.6480738, 0.0906818181818182], [10.7727358, 106.6242973, 0.069047619047619], [10.7687649, 106.776821, 0.0708333333333333], [10.7948666, 106.6573928, 0.1688311688311688], [10.7993691, 106.6804249, 0.1825], [10.7865347, 106.6434652, 0.1590909090909091], [10.8171922, 106.6318809, 0.2275], [10.7554716, 106.6061042, 0.083], [10.7956238, 106.6639667, 0.0926785714285714], [10.774434, 106.6482271, 0.0931666666666666], [10.7493297, 106.6836942, 0.1166666666666666], [10.8024689, 106.7114321, 0.0983333333333333], [10.7687649, 106.776821, 0.115], [10.8077094, 106.6793747, 0.138], [10.7833504, 106.6248902, 0.0954545454545454], [10.8202678, 106.6942034, 0.1979166666666666], [10.7992303, 106.6321471, 0.1], [10.8376455, 106.6699856, 0.1185714285714285], [10.8287546, 106.6838399, 0.1304347826086956], [10.8446911, 106.7810147, 0.095], [10.7658922, 106.561423, 0.12125], [10.7747842, 106.6480738, 0.0822916666666666], [10.8307717, 106.6694581, 0.0988372093023255], [10.80905, 106.6856488, 0.1145833333333333], [10.7797669, 106.6041103, 0.1017857142857142], [10.786296, 106.6424777, 0.1173469387755102], [10.8679065, 106.7281634, 0.09921875], [10.8516308, 106.6652331, 0.16], [10.8123441, 106.6930962, 0.0819999999999999], [10.8236349, 106.6295511, 0.0833333333333333], [10.786296, 106.6424777, 0.1173469387755102], [10.7747842, 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0.1077777777777777], [10.7667344, 106.6068788, 0.12], [10.7693434, 106.6376979, 0.15], [10.7669112, 106.626718, 0.0883720930232558], [10.7564751, 106.6122945, 0.0974358974358974], [10.7552468, 106.7229401, 0.0552941176470588], [10.8586484, 106.6521853, 0.0781927710843373]],\n",
              "                {&quot;blur&quot;: 15, &quot;gradient&quot;: {&quot;0.2&quot;: &quot;green&quot;, &quot;0.4&quot;: &quot;lime&quot;, &quot;0.6&quot;: &quot;orange&quot;, &quot;1.0&quot;: &quot;red&quot;}, &quot;maxZoom&quot;: 18, &quot;minOpacity&quot;: 0.2, &quot;radius&quot;: 15}\n",
              "            ).addTo(map_bca4cccc2e3b2e5e0aaf7cce4d08e61a);\n",
              "        \n",
              "    \n",
              "\n",
              "        function geo_json_6c5149e859a2d477f9188456bdd88bfc_onEachFeature(feature, layer) {\n",
              "            layer.on({\n",
              "            });\n",
              "        };\n",
              "        var geo_json_6c5149e859a2d477f9188456bdd88bfc = L.geoJson(null, {\n",
              "                onEachFeature: geo_json_6c5149e859a2d477f9188456bdd88bfc_onEachFeature,\n",
              "            \n",
              "        });\n",
              "\n",
              "        function geo_json_6c5149e859a2d477f9188456bdd88bfc_add (data) {\n",
              "            geo_json_6c5149e859a2d477f9188456bdd88bfc\n",
              "                .addData(data)\n",
              "                .addTo(map_bca4cccc2e3b2e5e0aaf7cce4d08e61a);\n",
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H\\u1ed3 Ch\\u00ed Minh&quot;, &quot;f_code&quot;: &quot;AD02&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
              "\n",
              "        \n",
              "&lt;/script&gt;\n",
              "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ],
            "text/plain": [
              "<folium.folium.Map at 0x78c3b119f9a0>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import folium\n",
        "import folium.plugins as plugins\n",
        "import pandas as pd\n",
        "import branca\n",
        "\n",
        "\n",
        "\n",
        "# Create a base map\n",
        "heatmap = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()], zoom_start=11)\n",
        "\n",
        "# Create heatmap data\n",
        "heat_data = [[row['latitude'], row['longitude'], row['price_per_m2']] for index, row in df.iterrows()]\n",
        "\n",
        "# Add HeatMap to base map\n",
        "hm = plugins.HeatMap(\n",
        "    heat_data,\n",
        "    min_opacity=0.2,\n",
        "    max_val=df['price_per_m2'].max(),\n",
        "    radius=15,\n",
        "    blur=15,\n",
        "    gradient={0.2: 'green', 0.4: 'lime', 0.6: 'orange', 1.0: 'red'}\n",
        ")\n",
        "hm.add_to(heatmap)\n",
        "max = df['price_per_m2'].max()\n",
        "min = df['price_per_m2'].min()\n",
        "abs = df['price_per_m2'].max() - df['price_per_m2'].min()\n",
        "# Create legend HTML\n",
        "legend_html = f'''\n",
        "<div style=\"position: fixed;\n",
        "     bottom: 50px; left: 50px; width: 200px; height: 200px;\n",
        "     background-color: white; z-index:9999; font-size:14px;\n",
        "     border:2px solid grey; padding: 10px;\">\n",
        "     <b> HEATMAP GIÁ TRÊN M2 Ở THÀNH PHỐ HỒ CHÍ MINH</b><br>\n",
        "     <i style=\"background:green; display:inline-block; width:10px; height:10px;\"></i>&nbsp; {min:.2f} - {min + 0.2*abs:.2f} tỷ /m2 green <br>\n",
        "     <i style=\"background:lime; display:inline-block; width:10px; height:10px;\"></i>&nbsp; { min + 0.2*abs:.2f} - { min +0.4*abs:.2f} tỷ /m2 lime <br>\n",
        "     <i style=\"background:orange; display:inline-block; width:10px; height:10px;\"></i>&nbsp; {min + 0.4*abs:.2f} - { min + 0.6*abs:.2f} tỷ /m2 orange <br>\n",
        "     <i style=\"background:red; display:inline-block; width:10px; height:10px;\"></i>&nbsp; {min + 0.8*abs:.2f} - {max:.2f}  tỷ /m2 red <br>\n",
        "</div>\n",
        "'''\n",
        "\n",
        "# Add legend to map\n",
        "heatmap.get_root().html.add_child(folium.Element(legend_html))\n",
        "\n",
        "# Add GeoJson to map\n",
        "geojson_file = 'https://raw.githubusercontent.com/huytrao/VietNam-Intern-Map/main/hochiminh.geojson'\n",
        "folium.GeoJson(geojson_file).add_to(heatmap)\n",
        "\n",
        "# Display the map\n",
        "heatmap.save('heatmap_HousePrice.html')\n",
        "heatmap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6n8Wb0MvpsO"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JwWD1ygawGKd"
      },
      "source": [
        "## 3. Biểu Đồ Location"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "5rxBYL22ogIS"
      },
      "outputs": [],
      "source": [
        "location_detail = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()], zoom_start=16)\n",
        "for index, row in df.iterrows(): # Change hm_df to df\n",
        "    # Create popup text with desired information\n",
        "    popup_text = f\"District: {row['district']}<br> Street Name: {row['street_name']}<br> Avg. Price: ${row['price']:.2f} tỷ\"  # Format price with 2 decimals\n",
        "    folium.Marker([row['latitude'], row['longitude']], popup=popup_text,opacity=0.6,icon=folium.Icon(color='blue', icon='home', prefix='fa'),rise_on_hover=True,\n",
        "              rise_offset=250,\n",
        "              zindex_offset=1000).add_to(location_detail)\n",
        "legend_html = '''\n",
        "<div style=\"position: fixed;\n",
        "     bottom: 50px; left: 50px; width: 150px; height: 150px;\n",
        "     background-color: white; z-index:9999; font-size:14px;\n",
        "     border:2px solid grey; padding: 10px;\">\n",
        "     <b>Biểu đồ thể hiện vị trí chi tiết của các bất động sản trong khu vực</b><br>\n",
        "</div>\n",
        "'''\n",
        "location_detail.get_root().html.add_child(folium.Element(legend_html))\n",
        "location_detail.save('location_detail_map.html')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "yA5I_T7EubGx"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}