{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "HDTv5wjRGetj",
        "outputId": "cbc700fe-6898-41f3-fdbb-6bfe942728ed"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-df75c35e-b900-49af-9480-ea30de680543\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-df75c35e-b900-49af-9480-ea30de680543\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving real_estate_listings.csv to real_estate_listings.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_v95Eaj1GMWW",
        "outputId": "d43997ee-7ddc-419e-c333-4b0709063025"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Location    Price    Type of House  \\\n",
              "0     Phường 15, Quận Bình Thạnh  4,16 tỷ     Nhà hẻm, ngõ   \n",
              "1  Xã Tân Kiên, Huyện Bình Chánh     1 tỷ     Nhà mặt tiền   \n",
              "2     Phường Thạnh Xuân, Quận 12   6,5 tỷ     Nhà hẻm, ngõ   \n",
              "3          Phường 5, Quận Gò Vấp    23 tỷ     Nhà hẻm, ngõ   \n",
              "4        Phường 2, Quận Tân Bình      NaN  Biệt thự, Villa   \n",
              "\n",
              "            Land Area  Bedrooms Toilets  Total Floors Main Door Direction  \\\n",
              "0    36 m² (3,2x12,0)   4 phòng    3 WC           2.0                 NaN   \n",
              "1  93,9 m² (5,0x18,8)   3 phòng    2 WC           1.0                 NaN   \n",
              "2   138 m² (8,2x17,0)   3 phòng    3 WC           2.0                 NaN   \n",
              "3   135 m² (5,0x27,0)  21 phòng   21 WC           5.0             Tây Nam   \n",
              "4   100 m² (5,0x20,0)   5 phòng     NaN           NaN                 NaN   \n",
              "\n",
              "  Balcony Direction Legal Documents  \n",
              "0               NaN         Sổ hồng  \n",
              "1               NaN         Sổ hồng  \n",
              "2               NaN         Sổ hồng  \n",
              "3               NaN         Sổ hồng  \n",
              "4               NaN         Sổ hồng  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00373e84-7808-4b3b-8392-d4914c39fb64\" 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>Location</th>\n",
              "      <th>Price</th>\n",
              "      <th>Type of House</th>\n",
              "      <th>Land Area</th>\n",
              "      <th>Bedrooms</th>\n",
              "      <th>Toilets</th>\n",
              "      <th>Total Floors</th>\n",
              "      <th>Main Door Direction</th>\n",
              "      <th>Balcony Direction</th>\n",
              "      <th>Legal Documents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Phường 15, Quận Bình Thạnh</td>\n",
              "      <td>4,16 tỷ</td>\n",
              "      <td>Nhà hẻm, ngõ</td>\n",
              "      <td>36 m² (3,2x12,0)</td>\n",
              "      <td>4 phòng</td>\n",
              "      <td>3 WC</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sổ hồng</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Xã Tân Kiên, Huyện Bình Chánh</td>\n",
              "      <td>1 tỷ</td>\n",
              "      <td>Nhà mặt tiền</td>\n",
              "      <td>93,9 m² (5,0x18,8)</td>\n",
              "      <td>3 phòng</td>\n",
              "      <td>2 WC</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sổ hồng</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Phường Thạnh Xuân, Quận 12</td>\n",
              "      <td>6,5 tỷ</td>\n",
              "      <td>Nhà hẻm, ngõ</td>\n",
              "      <td>138 m² (8,2x17,0)</td>\n",
              "      <td>3 phòng</td>\n",
              "      <td>3 WC</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sổ hồng</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Phường 5, Quận Gò Vấp</td>\n",
              "      <td>23 tỷ</td>\n",
              "      <td>Nhà hẻm, ngõ</td>\n",
              "      <td>135 m² (5,0x27,0)</td>\n",
              "      <td>21 phòng</td>\n",
              "      <td>21 WC</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Tây Nam</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sổ hồng</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Phường 2, Quận Tân Bình</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biệt thự, Villa</td>\n",
              "      <td>100 m² (5,0x20,0)</td>\n",
              "      <td>5 phòng</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sổ hồng</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-00373e84-7808-4b3b-8392-d4914c39fb64')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-00373e84-7808-4b3b-8392-d4914c39fb64 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-00373e84-7808-4b3b-8392-d4914c39fb64');\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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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 26048,\n  \"fields\": [\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 270,\n        \"samples\": [\n          \"Ph\\u01b0\\u1eddng An Kh\\u00e1nh, TP. Th\\u1ee7 \\u0110\\u1ee9c - Qu\\u1eadn 2\",\n          \"X\\u00e3 T\\u00e2n Th\\u1ea1nh T\\u00e2y, Huy\\u1ec7n C\\u1ee7 Chi\",\n          \"Ph\\u01b0\\u1eddng T\\u0103ng Nh\\u01a1n Ph\\u00fa B, TP. Th\\u1ee7 \\u0110\\u1ee9c - Qu\\u1eadn 9\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 378,\n        \"samples\": [\n          \"3 t\\u1ef7 850 tri\\u1ec7u\",\n          \"5 t\\u1ef7 690 tri\\u1ec7u\",\n          \"2,89 t\\u1ef7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type of House\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Nh\\u00e0 m\\u1eb7t ti\\u1ec1n\",\n          \"Chung c\\u01b0\",\n          \"Nh\\u00e0 h\\u1ebbm, ng\\u00f5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 695,\n        \"samples\": [\n          \"321 m\\u00b2\",\n          \"66 m\\u00b2 (4,7x14,0)\",\n          \"45,7 m\\u00b2 (4,0x11,4)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedrooms\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"6 ph\\u00f2ng\",\n          \"20 ph\\u00f2ng\",\n          \"4 ph\\u00f2ng\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Toilets\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"6 WC\",\n          \"15 WC\",\n          \"8 WC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Floors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4205999015910982,\n        \"min\": 1.0,\n        \"max\": 33.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          9.0,\n          15.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Main Door Direction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"\\u0110\\u00f4ng Nam\",\n          \"\\u0110\\u00f4ng B\\u1eafc\",\n          \"T\\u00e2y Nam\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balcony Direction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"\\u0110\\u00f4ng\",\n          \"Nam\",\n          \"B\\u1eafc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Legal Documents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"S\\u1ed5 h\\u1ed3ng\",\n          \"Gi\\u1ea5y t\\u1edd h\\u1ee3p l\\u1ec7\",\n          \"Gi\\u1ea5y t\\u1edd kh\\u00e1c\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"real_estate_listings.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.info())\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYm7p2dNGqU2",
        "outputId": "9f92e200-e4a6-4d92-8a6d-3875e47c898c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26048 entries, 0 to 26047\n",
            "Data columns (total 10 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Location             26048 non-null  object \n",
            " 1   Price                25026 non-null  object \n",
            " 2   Type of House        26048 non-null  object \n",
            " 3   Land Area            25993 non-null  object \n",
            " 4   Bedrooms             13057 non-null  object \n",
            " 5   Toilets              10805 non-null  object \n",
            " 6   Total Floors         6011 non-null   float64\n",
            " 7   Main Door Direction  4639 non-null   object \n",
            " 8   Balcony Direction    2596 non-null   object \n",
            " 9   Legal Documents      26048 non-null  object \n",
            "dtypes: float64(1), object(9)\n",
            "memory usage: 2.0+ MB\n",
            "None\n",
            "                        Location    Price    Type of House  \\\n",
            "0     Phường 15, Quận Bình Thạnh  4,16 tỷ     Nhà hẻm, ngõ   \n",
            "1  Xã Tân Kiên, Huyện Bình Chánh     1 tỷ     Nhà mặt tiền   \n",
            "2     Phường Thạnh Xuân, Quận 12   6,5 tỷ     Nhà hẻm, ngõ   \n",
            "3          Phường 5, Quận Gò Vấp    23 tỷ     Nhà hẻm, ngõ   \n",
            "4        Phường 2, Quận Tân Bình      NaN  Biệt thự, Villa   \n",
            "\n",
            "            Land Area  Bedrooms Toilets  Total Floors Main Door Direction  \\\n",
            "0    36 m² (3,2x12,0)   4 phòng    3 WC           2.0                 NaN   \n",
            "1  93,9 m² (5,0x18,8)   3 phòng    2 WC           1.0                 NaN   \n",
            "2   138 m² (8,2x17,0)   3 phòng    3 WC           2.0                 NaN   \n",
            "3   135 m² (5,0x27,0)  21 phòng   21 WC           5.0             Tây Nam   \n",
            "4   100 m² (5,0x20,0)   5 phòng     NaN           NaN                 NaN   \n",
            "\n",
            "  Balcony Direction Legal Documents  \n",
            "0               NaN         Sổ hồng  \n",
            "1               NaN         Sổ hồng  \n",
            "2               NaN         Sổ hồng  \n",
            "3               NaN         Sổ hồng  \n",
            "4               NaN         Sổ hồng  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Clean cột PRICE"
      ],
      "metadata": {
        "id": "kwutZ4mhIbZW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def clean_price(x):\n",
        "    if pd.isna(x):\n",
        "        return None\n",
        "\n",
        "    x = str(x).lower()\n",
        "\n",
        "    # Xóa ký tự thừa\n",
        "    x = x.replace(\",\", \".\")\n",
        "    x = re.sub(r\"\\s+\", \" \", x)  # xóa khoảng trắng dư\n",
        "\n",
        "    try:\n",
        "        # Trường hợp có cả tỷ và triệu (vd: 6 tỷ 500 triệu)\n",
        "        if \"tỷ\" in x and \"triệu\" in x:\n",
        "            parts = re.findall(r\"[\\d.]+\", x)\n",
        "            if len(parts) >= 2:\n",
        "                ty = float(parts[0])\n",
        "                trieu = float(parts[1])\n",
        "                return ty * 1e9 + trieu * 1e6\n",
        "\n",
        "        elif \"tỷ\" in x:\n",
        "            num = re.findall(r\"[\\d.]+\", x)[0]\n",
        "            return float(num) * 1e9\n",
        "\n",
        "        elif \"triệu\" in x:\n",
        "            num = re.findall(r\"[\\d.]+\", x)[0]\n",
        "            return float(num) * 1e6\n",
        "\n",
        "        else:\n",
        "            return None\n",
        "    except:\n",
        "        return None"
      ],
      "metadata": {
        "id": "tTcOgMISG1RW"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"price_vnd\"] = df[\"Price\"].apply(clean_price)"
      ],
      "metadata": {
        "id": "yhXUGJTkHJlz"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna(subset=[\"price_vnd\"])"
      ],
      "metadata": {
        "id": "oAGKzP0THZJV"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"Price\", \"price_vnd\"]].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "DF6QyuVIHK3x",
        "outputId": "ac1c3e81-0575-4f9f-d537-e6eed555cc70"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Price     price_vnd\n",
              "0          4,16 tỷ  4.160000e+09\n",
              "1             1 tỷ  1.000000e+09\n",
              "2           6,5 tỷ  6.500000e+09\n",
              "3            23 tỷ  2.300000e+10\n",
              "5            38 tỷ  3.800000e+10\n",
              "6   6 tỷ 500 triệu  6.500000e+09\n",
              "7          43,5 tỷ  4.350000e+10\n",
              "9            19 tỷ  1.900000e+10\n",
              "10          9,6 tỷ  9.600000e+09\n",
              "11         1,49 tỷ  1.490000e+09"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd657273-0988-4f75-89db-3c039fd2da46\" 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>Price</th>\n",
              "      <th>price_vnd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4,16 tỷ</td>\n",
              "      <td>4.160000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1 tỷ</td>\n",
              "      <td>1.000000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6,5 tỷ</td>\n",
              "      <td>6.500000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23 tỷ</td>\n",
              "      <td>2.300000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>38 tỷ</td>\n",
              "      <td>3.800000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6 tỷ 500 triệu</td>\n",
              "      <td>6.500000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>43,5 tỷ</td>\n",
              "      <td>4.350000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>19 tỷ</td>\n",
              "      <td>1.900000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9,6 tỷ</td>\n",
              "      <td>9.600000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1,49 tỷ</td>\n",
              "      <td>1.490000e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bd657273-0988-4f75-89db-3c039fd2da46 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-bd657273-0988-4f75-89db-3c039fd2da46');\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",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[[\\\"Price\\\", \\\"price_vnd\\\"]]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"9,6 t\\u1ef7\",\n          \"1 t\\u1ef7\",\n          \"6 t\\u1ef7 500 tri\\u1ec7u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_vnd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15239493466.356129,\n        \"min\": 1000000000.0,\n        \"max\": 43500000000.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          9600000000.0,\n          1000000000.0,\n          43500000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Clean diện tích"
      ],
      "metadata": {
        "id": "VdBpI83SIdlk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def clean_area(x):\n",
        "    if pd.isna(x):\n",
        "        return None\n",
        "\n",
        "    x = str(x)\n",
        "\n",
        "    # Lấy số đầu tiên trong chuỗi\n",
        "    nums = re.findall(r\"[\\d.,]+\", x)\n",
        "\n",
        "    if not nums:\n",
        "        return None\n",
        "\n",
        "    num = nums[0]\n",
        "\n",
        "    # Chuẩn hóa:\n",
        "    num = num.replace(\",\", \".\")\n",
        "\n",
        "    # Nếu có nhiều dấu chấm → giữ lại dấu chấm cuối\n",
        "    if num.count(\".\") > 1:\n",
        "        parts = num.split(\".\")\n",
        "        num = \"\".join(parts[:-1]) + \".\" + parts[-1]\n",
        "\n",
        "    try:\n",
        "        return float(num)\n",
        "    except:\n",
        "        return None"
      ],
      "metadata": {
        "id": "0vYMvnFtHcVp"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"area\"] = df[\"Land Area\"].apply(clean_area)"
      ],
      "metadata": {
        "id": "tMA_MwakHlj5"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"Land Area\", \"area\"]].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "62680EwaHmnG",
        "outputId": "aa9dc31a-5c3a-453f-d918-6b35a412fb55"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Land Area   area\n",
              "0      36 m² (3,2x12,0)   36.0\n",
              "1    93,9 m² (5,0x18,8)   93.9\n",
              "2     138 m² (8,2x17,0)  138.0\n",
              "3     135 m² (5,0x27,0)  135.0\n",
              "5    900 m² (12,0x50,0)  900.0\n",
              "6      48 m² (4,0x12,0)   48.0\n",
              "7     160 m² (8,0x20,0)  160.0\n",
              "9     120 m² (8,0x15,0)  120.0\n",
              "10  100,1 m² (5,0x20,2)  100.1\n",
              "11      11 m² (3,0x4,0)   11.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9683a177-14ec-4029-bccf-1fc05fc5df7e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Land Area</th>\n",
              "      <th>area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>36 m² (3,2x12,0)</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>93,9 m² (5,0x18,8)</td>\n",
              "      <td>93.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>138 m² (8,2x17,0)</td>\n",
              "      <td>138.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>135 m² (5,0x27,0)</td>\n",
              "      <td>135.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>900 m² (12,0x50,0)</td>\n",
              "      <td>900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>48 m² (4,0x12,0)</td>\n",
              "      <td>48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>160 m² (8,0x20,0)</td>\n",
              "      <td>160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>120 m² (8,0x15,0)</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>100,1 m² (5,0x20,2)</td>\n",
              "      <td>100.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11 m² (3,0x4,0)</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "          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",
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              "\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[[\\\"Land Area\\\", \\\"area\\\"]]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Land Area\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"100,1 m\\u00b2 (5,0x20,2)\",\n          \"93,9 m\\u00b2 (5,0x18,8)\",\n          \"48 m\\u00b2 (4,0x12,0)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 259.56605239429047,\n        \"min\": 11.0,\n        \"max\": 900.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          100.1,\n          93.9,\n          48.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tính VND/m2"
      ],
      "metadata": {
        "id": "xXHL46nzHskN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"price_per_m2\"] = df[\"price_vnd\"] / df[\"area\"]"
      ],
      "metadata": {
        "id": "1P3RgS_-HzSI"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"price_vnd\", \"area\", \"price_per_m2\"]].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UDpSvG7aH0sH",
        "outputId": "cadcecbe-f162-40be-bc24-52b05eb3df41"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price_vnd   area  price_per_m2\n",
              "0  4.160000e+09   36.0  1.155556e+08\n",
              "1  1.000000e+09   93.9  1.064963e+07\n",
              "2  6.500000e+09  138.0  4.710145e+07\n",
              "3  2.300000e+10  135.0  1.703704e+08\n",
              "5  3.800000e+10  900.0  4.222222e+07"
            ],
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              "      <th>2</th>\n",
              "      <td>6.500000e+09</td>\n",
              "      <td>138.0</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[[\\\"price_vnd\\\", \\\"area\\\", \\\"price_per_m2\\\"]]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"price_vnd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15633781372.399961,\n        \"min\": 1000000000.0,\n        \"max\": 38000000000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1000000000.0,\n          38000000000.0,\n          6500000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 359.81680338750164,\n        \"min\": 36.0,\n        \"max\": 900.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          93.9,\n          900.0,\n          138.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_per_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64633939.09398,\n        \"min\": 10649627.263045793,\n        \"max\": 170370370.37037036,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10649627.263045793,\n          42222222.222222224,\n          47101449.27536232\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lấy Quận"
      ],
      "metadata": {
        "id": "zGnfESozH7ki"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_district(loc):\n",
        "    if pd.isna(loc):\n",
        "        return None\n",
        "    parts = loc.split(\",\")\n",
        "    for p in parts:\n",
        "        if \"Quận\" in p or \"Huyện\" in p:\n",
        "            return p.strip()\n",
        "    return None\n",
        "\n",
        "df[\"district\"] = df[\"Location\"].apply(extract_district)"
      ],
      "metadata": {
        "id": "BPdCkQ2vH12g"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"Location\", \"district\"]].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "y9kTrkx2H3vK",
        "outputId": "69a26c4d-9a20-4bc8-9ccb-f6a6da0a2a5c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Location          district\n",
              "0         Phường 15, Quận Bình Thạnh   Quận Bình Thạnh\n",
              "1      Xã Tân Kiên, Huyện Bình Chánh  Huyện Bình Chánh\n",
              "2         Phường Thạnh Xuân, Quận 12           Quận 12\n",
              "3              Phường 5, Quận Gò Vấp       Quận Gò Vấp\n",
              "5  Xã Phạm Văn Hai, Huyện Bình Chánh  Huyện Bình Chánh"
            ],
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              "      <th>0</th>\n",
              "      <td>Phường 15, Quận Bình Thạnh</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Xã Tân Kiên, Huyện Bình Chánh</td>\n",
              "      <td>Huyện Bình Chánh</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
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              "      <td>Quận 12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Phường 5, Quận Gò Vấp</td>\n",
              "      <td>Quận Gò Vấp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Xã Phạm Văn Hai, Huyện Bình Chánh</td>\n",
              "      <td>Huyện Bình Chánh</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[[\\\"Location\\\", \\\"district\\\"]]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"X\\u00e3 T\\u00e2n Ki\\u00ean, Huy\\u1ec7n B\\u00ecnh Ch\\u00e1nh\",\n          \"X\\u00e3 Ph\\u1ea1m V\\u0103n Hai, Huy\\u1ec7n B\\u00ecnh Ch\\u00e1nh\",\n          \"Ph\\u01b0\\u1eddng Th\\u1ea1nh Xu\\u00e2n, Qu\\u1eadn 12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"district\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Huy\\u1ec7n B\\u00ecnh Ch\\u00e1nh\",\n          \"Qu\\u1eadn G\\u00f2 V\\u1ea5p\",\n          \"Qu\\u1eadn B\\u00ecnh Th\\u1ea1nh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tạo ROI"
      ],
      "metadata": {
        "id": "WlwxwQ4dH9Uc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "df[\"purchase_price\"] = df[\"price_vnd\"] * np.random.uniform(0.8, 0.95, len(df))\n",
        "df[\"current_price\"] = df[\"price_vnd\"]\n",
        "\n",
        "df[\"ROI\"] = (df[\"current_price\"] - df[\"purchase_price\"]) / df[\"purchase_price\"]"
      ],
      "metadata": {
        "id": "Zgtvy8PTH_cx"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna(subset=[\"price_vnd\", \"area\", \"district\"])\n",
        "df = df[df[\"area\"] > 0]"
      ],
      "metadata": {
        "id": "7q0sVnToIBoj"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Tổng tài sản:\", df[\"current_price\"].sum())\n",
        "print(\"ROI trung bình:\", df[\"ROI\"].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMewFtHDICjY",
        "outputId": "b2170f46-f832-4cd9-ad31-d9dfa64e4d61"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tổng tài sản: 160534635000000.0\n",
            "ROI trung bình: 0.14574777175507286\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vẽ biểu đồ Roi theo quận"
      ],
      "metadata": {
        "id": "3-vsachdIIG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df.groupby(\"district\")[\"ROI\"].mean().sort_values().plot(kind=\"bar\")\n",
        "plt.title(\"ROI theo khu vực\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "vcPU_d7tIENu",
        "outputId": "a99fbfec-0d7b-4306-c12d-40f956a00778"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = [\"district\", \"price_vnd\", \"area\", \"price_per_m2\", \"ROI\"]\n",
        "df[cols].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MiYiTyk8Inkk",
        "outputId": "8a96a631-fdd9-4796-e3dc-7b9270f004d7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           district     price_vnd   area  price_per_m2       ROI\n",
              "0   Quận Bình Thạnh  4.160000e+09   36.0  1.155556e+08  0.122625\n",
              "1  Huyện Bình Chánh  1.000000e+09   93.9  1.064963e+07  0.112596\n",
              "2           Quận 12  6.500000e+09  138.0  4.710145e+07  0.069988\n",
              "3       Quận Gò Vấp  2.300000e+10  135.0  1.703704e+08  0.111655\n",
              "5  Huyện Bình Chánh  3.800000e+10  900.0  4.222222e+07  0.138406"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>district</th>\n",
              "      <th>price_vnd</th>\n",
              "      <th>area</th>\n",
              "      <th>price_per_m2</th>\n",
              "      <th>ROI</th>\n",
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              "      <td>Quận Bình Thạnh</td>\n",
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              "      <th>1</th>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[cols]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"district\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Huy\\u1ec7n B\\u00ecnh Ch\\u00e1nh\",\n          \"Qu\\u1eadn G\\u00f2 V\\u1ea5p\",\n          \"Qu\\u1eadn B\\u00ecnh Th\\u1ea1nh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_vnd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15633781372.399961,\n        \"min\": 1000000000.0,\n        \"max\": 38000000000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1000000000.0,\n          38000000000.0,\n          6500000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 359.81680338750164,\n        \"min\": 36.0,\n        \"max\": 900.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          93.9,\n          900.0,\n          138.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_per_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64633939.09398,\n        \"min\": 10649627.263045793,\n        \"max\": 170370370.37037036,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10649627.263045793,\n          42222222.222222224,\n          47101449.27536232\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025353503567059378,\n        \"min\": 0.06998796446649354,\n        \"max\": 0.13840641720021715,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.11259565302784565,\n          0.13840641720021715,\n          0.06998796446649354\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TÍNH KPI CHO CEO"
      ],
      "metadata": {
        "id": "kP8hyWeLIvw-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Tổng tài sản:\", df[\"price_vnd\"].sum())\n",
        "print(\"ROI trung bình:\", df[\"ROI\"].mean())\n",
        "print(\"Giá trung bình / m2:\", df[\"price_per_m2\"].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwbXslI6Ivci",
        "outputId": "63779d57-6eb9-488e-ec5f-8cfb986e9044"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tổng tài sản: 160534635000000.0\n",
            "ROI trung bình: 0.14574777175507286\n",
            "Giá trung bình / m2: 103798604.1966992\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROI theo khu"
      ],
      "metadata": {
        "id": "JUpwr6syI0BQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roi_by_district = df.groupby(\"district\")[\"ROI\"].mean().sort_values(ascending=False)\n",
        "print(roi_by_district)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6_H699NIzn4",
        "outputId": "6eaf0718-9d6c-44d6-aa58-6f750784d286"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "district\n",
            "Quận 11                       0.162277\n",
            "TP. Thủ Đức - Quận 2          0.154289\n",
            "Quận 12                       0.151183\n",
            "TP. Thủ Đức - Quận 9          0.151065\n",
            "Huyện Hóc Môn                 0.150132\n",
            "Quận Tân Phú                  0.149796\n",
            "Quận Gò Vấp                   0.147685\n",
            "Quận 8                        0.146776\n",
            "Quận 5                        0.146590\n",
            "Huyện Bình Chánh              0.145978\n",
            "Quận Phú Nhuận                0.145943\n",
            "Quận 4                        0.145867\n",
            "TP. Thủ Đức - Quận Thủ Đức    0.145655\n",
            "Quận Bình Tân                 0.145634\n",
            "Quận Bình Thạnh               0.144749\n",
            "Quận Tân Bình                 0.144550\n",
            "Huyện Củ Chi                  0.144151\n",
            "Huyện Nhà Bè                  0.143857\n",
            "Quận 10                       0.142393\n",
            "Quận 7                        0.139014\n",
            "Quận 6                        0.135875\n",
            "Quận 3                        0.130488\n",
            "Quận 1                        0.126975\n",
            "Name: ROI, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Giá theo khu"
      ],
      "metadata": {
        "id": "nR8cYF0eI3QQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "price_m2_by_district = df.groupby(\"district\")[\"price_per_m2\"].mean().sort_values(ascending=False)\n",
        "print(price_m2_by_district)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwoXOnZBI2Ce",
        "outputId": "e24ee6ae-bb41-4191-84c1-d969510983c0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "district\n",
            "Quận 1                        5.369520e+08\n",
            "Huyện Bình Chánh              3.927583e+08\n",
            "Quận 5                        3.437876e+08\n",
            "Quận 3                        2.369202e+08\n",
            "Quận 10                       2.021578e+08\n",
            "Quận Bình Thạnh               1.980620e+08\n",
            "Quận 6                        1.729724e+08\n",
            "Quận 7                        1.519719e+08\n",
            "Quận 11                       1.342663e+08\n",
            "TP. Thủ Đức - Quận 2          1.319468e+08\n",
            "Quận Tân Bình                 1.166243e+08\n",
            "Quận 8                        1.152454e+08\n",
            "Quận Tân Phú                  1.147153e+08\n",
            "Quận Gò Vấp                   1.081101e+08\n",
            "Quận Phú Nhuận                9.026848e+07\n",
            "TP. Thủ Đức - Quận Thủ Đức    7.127407e+07\n",
            "Quận Bình Tân                 6.870012e+07\n",
            "Quận 12                       5.425433e+07\n",
            "Quận 4                        5.100923e+07\n",
            "TP. Thủ Đức - Quận 9          4.969005e+07\n",
            "Huyện Nhà Bè                  4.535858e+07\n",
            "Huyện Hóc Môn                 4.477799e+07\n",
            "Huyện Củ Chi                  8.744082e+06\n",
            "Name: price_per_m2, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bar chart – ROI"
      ],
      "metadata": {
        "id": "tBdvwkkXI8gC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "roi_by_district.plot(kind=\"bar\")\n",
        "plt.title(\"ROI theo khu vực\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "CzBO5UbkI7B0",
        "outputId": "0e5e49c9-6d75-4ddb-a939-643f4c5af4f4"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pie chart – vốn"
      ],
      "metadata": {
        "id": "4zUx4hOmI-v9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data = df.groupby(\"district\")[\"price_vnd\"].sum()\n",
        "\n",
        "def autopct_func(pct):\n",
        "    return ('%1.1f%%' % pct) if pct > 5 else ''   # ❗ chỉ hiện >5%\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "\n",
        "plt.pie(\n",
        "    data,\n",
        "    labels=None,\n",
        "    autopct=autopct_func,\n",
        "    startangle=140\n",
        ")\n",
        "\n",
        "plt.legend(\n",
        "    data.index,\n",
        "    loc=\"center left\",\n",
        "    bbox_to_anchor=(1, 0.5)\n",
        ")\n",
        "\n",
        "plt.title(\"Phân bổ vốn theo khu vực\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "Q4NmZCS-JADF",
        "outputId": "85f45bad-247d-4191-f97b-c416ebb9f2c4"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Line chart – xu hướng"
      ],
      "metadata": {
        "id": "7m0kHu2fJGJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"date\"] = pd.date_range(start=\"2022-01-01\", periods=len(df))\n",
        "\n",
        "df.sort_values(\"date\").set_index(\"date\")[\"price_vnd\"].plot()\n",
        "plt.title(\"Xu hướng tài sản\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "ljb-00HrJGoB",
        "outputId": "965a9151-ccfd-48a6-a45f-e80ab9c9022c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Top 3 khu nên đầu tư"
      ],
      "metadata": {
        "id": "7-BUpQ1eJKI3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "score = df.groupby(\"district\").agg({\n",
        "    \"ROI\": \"mean\",\n",
        "    \"price_per_m2\": \"mean\"\n",
        "})\n",
        "\n",
        "score[\"score\"] = score[\"ROI\"] / score[\"price_per_m2\"]\n",
        "print(score.sort_values(\"score\", ascending=False).head(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9s_kHY_JKkT",
        "outputId": "296d24c3-df28-4a09-a2f4-c09fad9bf0b5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    ROI  price_per_m2         score\n",
            "district                                           \n",
            "Huyện Củ Chi   0.144151  8.744082e+06  1.648555e-08\n",
            "Huyện Hóc Môn  0.150132  4.477799e+07  3.352808e-09\n",
            "Huyện Nhà Bè   0.143857  4.535858e+07  3.171547e-09\n"
          ]
        }
      ]
    }
  ]
}