Chimnie.ipynb

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      "source": [
        "# Senior Data Scientist - Chimnie\n",
        "\n",
        "Things to note:\n",
        " * developed on [Google Colab](https://colab.research.google.com/)\n",
        "    * with 'high-RAM' enabled\n",
        "    * 'Hardware accelerator' set to CPU\n",
        "    * it's possible to load complete price paid data with pandas\n",
        "    * later data manipulation with pandas causes out of RAM crashes\n",
        " * not tested in Jupyter\n",
        "\n",
        "Load standard modules:"
      ],
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      "metadata": {
        "id": "O8JMoiJyJnP7"
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      "source": [
        "import os.path\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load price paid data subset:"
      ],
      "metadata": {
        "id": "87GLzGdIKQs_"
      }
    },
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      "cell_type": "code",
      "source": [
        "pp_may_24_url = 'http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-monthly-update-new-version.csv'\n",
        "pp_23 = 'http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-2023.csv'\n",
        "\n",
        "pp_may_24 = pd.read_csv(pp_may_24_url, header=None)\n",
        "\n",
        "pp_cols = ['id', 'price', 'date', 'postcode', 'type', 'new', 'duration',\n",
        "           'paon', 'saon', 'street', 'locality', 'town', 'district', 'county',\n",
        "           'ppd_category_type', 'record_status']\n",
        "pp_may_24.columns = pp_cols\n",
        "\n",
        "pp_may_24['date'] = pd.to_datetime(pp_may_24['date'])\n",
        "\n",
        "# str_cols = ['paon', 'saon', 'street', 'locality', 'town', 'district', 'county']\n",
        "# for str_col in str_cols:\n",
        "#   pp_may_24[str_col] = pp_may_24[str_col].astype(str)"
      ],
      "metadata": {
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      "execution_count": 2,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "Summarise data subset:"
      ],
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        "id": "z9aalyo5KuxD"
      }
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      "source": [
        "def print_df_summary(df):\n",
        "  print(\"Shape:\")\n",
        "  display(df.shape)\n",
        "\n",
        "  total_nas = df.isna().sum().sum()\n",
        "  rows_nas  = df.isnull().any(axis=1).sum()\n",
        "  cols_nas  = df.isnull().any().sum()\n",
        "  print('\\nTotal NAs:', total_nas)\n",
        "  print('Rows with NAs:', rows_nas)\n",
        "  print('Cols with NAs:', cols_nas)\n",
        "\n",
        "  print(\"\\nInfo:\")\n",
        "  display(df.info())\n",
        "\n",
        "  print(\"\\nSummary stats:\")\n",
        "  display(df.describe())\n",
        "\n",
        "  print(\"\\nRaw data:\")\n",
        "  display(df)\n",
        "  print(\"\\n\")\n",
        "\n",
        "\n",
        "print_df_summary(pp_may_24)\n",
        "\n",
        "\n",
        "for grp in ['type', 'new', 'duration', 'ppd_category_type', 'record_status']:\n",
        "  print('\\n')\n",
        "  display(pp_may_24[[grp, 'price']].groupby(grp).describe())\n",
        "\n",
        "\n",
        "print('\\n\\nNaNs:')\n",
        "pp_may_24.isnull().sum()"
      ],
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          "text": [
            "Shape:\n"
          ]
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              "(78452, 16)"
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          "text": [
            "\n",
            "Total NAs: 115542\n",
            "Rows with NAs: 76402\n",
            "Cols with NAs: 4\n",
            "\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 78452 entries, 0 to 78451\n",
            "Data columns (total 16 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   id                 78452 non-null  object        \n",
            " 1   price              78452 non-null  int64         \n",
            " 2   date               78452 non-null  datetime64[ns]\n",
            " 3   postcode           78176 non-null  object        \n",
            " 4   type               78452 non-null  object        \n",
            " 5   new                78452 non-null  object        \n",
            " 6   duration           78452 non-null  object        \n",
            " 7   paon               78452 non-null  object        \n",
            " 8   saon               12521 non-null  object        \n",
            " 9   street             77217 non-null  object        \n",
            " 10  locality           30352 non-null  object        \n",
            " 11  town               78452 non-null  object        \n",
            " 12  district           78452 non-null  object        \n",
            " 13  county             78452 non-null  object        \n",
            " 14  ppd_category_type  78452 non-null  object        \n",
            " 15  record_status      78452 non-null  object        \n",
            "dtypes: datetime64[ns](1), int64(1), object(14)\n",
            "memory usage: 9.6+ MB\n"
          ]
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              "None"
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          "text": [
            "\n",
            "Summary stats:\n"
          ]
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          "data": {
            "text/plain": [
              "              price                           date\n",
              "count  7.845200e+04                          78452\n",
              "mean   4.357407e+05  2023-06-04 22:15:07.112629248\n",
              "min    1.000000e+02            1995-01-06 00:00:00\n",
              "25%    1.760000e+05            2023-03-31 00:00:00\n",
              "50%    2.750000e+05            2024-01-16 00:00:00\n",
              "75%    4.180000e+05            2024-04-12 00:00:00\n",
              "max    9.000000e+08            2024-05-31 00:00:00\n",
              "std    4.601969e+06                            NaN"
            ],
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              "      <th>price</th>\n",
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              "      <th>count</th>\n",
              "      <td>7.845200e+04</td>\n",
              "      <td>78452</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.357407e+05</td>\n",
              "      <td>2023-06-04 22:15:07.112629248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>1995-01-06 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.760000e+05</td>\n",
              "      <td>2023-03-31 00:00:00</td>\n",
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              "      <th>50%</th>\n",
              "      <td>2.750000e+05</td>\n",
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              "      <th>75%</th>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pp_may_24\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 317899466.05927837,\n        \"min\": 100.0,\n        \"max\": 900000000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          435740.6547570489,\n          418000.0,\n          78452.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000078452\",\n        \"max\": \"2024-05-31 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"78452\",\n          \"2023-06-04 22:15:07.112629248\",\n          \"2024-04-12 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "                                           id   price       date  postcode  \\\n",
              "0      {1A0C5C63-265E-7CBE-E063-4804A8C06C96}  249955 2021-06-24  CF72 9WU   \n",
              "1      {1A0C5C63-26CF-7CBE-E063-4804A8C06C96}  365000 2021-08-31  LL67 0LT   \n",
              "2      {1A0C5C63-270D-7CBE-E063-4804A8C06C96}  202000 2021-09-30   SA4 6PW   \n",
              "3      {1A0C5C63-2789-7CBE-E063-4804A8C06C96}  300000 2021-06-30  SA11 3EU   \n",
              "4      {1A0C5C63-27A6-7CBE-E063-4804A8C06C96}  115000 2021-08-11  NP10 8BL   \n",
              "...                                       ...     ...        ...       ...   \n",
              "78447  {1061746E-A86B-3C34-E063-4804A8C0F9E7}   55462 2024-01-19  SN13 0QU   \n",
              "78448  {1061746E-6EA8-3C34-E063-4804A8C0F9E7}  295000 2024-01-02  BS23 4JL   \n",
              "78449  {1061746E-6EDD-3C34-E063-4804A8C0F9E7}  290000 2024-01-12   TA6 7BZ   \n",
              "78450  {1061746E-8AB6-3C34-E063-4804A8C0F9E7}   81735 2024-01-19   DH4 4QL   \n",
              "78451  {1061746E-73DD-3C34-E063-4804A8C0F9E7}  193670 2024-01-26   CR3 5PJ   \n",
              "\n",
              "      type new duration paon    saon               street    locality  \\\n",
              "0        D   N        F   36     NaN     BLACKTHORN COURT   LLANHARRY   \n",
              "1        D   N        F   12     NaN           MAES CAPEL         NaN   \n",
              "2        T   N        F   31     NaN    HEOL CAE TYNEWYDD     LOUGHOR   \n",
              "3        D   N        F   14     NaN  DAVIES ANDREWS ROAD       TONNA   \n",
              "4        T   N        F   12     NaN           SKYE GROVE     DUFFRYN   \n",
              "...    ...  ..      ...  ...     ...                  ...         ...   \n",
              "78447    T   N        L   52     NaN          STONE CLOSE         NaN   \n",
              "78448    F   N        L   25  FLAT 2        FRENCHAY ROAD         NaN   \n",
              "78449    D   N        F    8     NaN     STAG VALLEY ROAD         NaN   \n",
              "78450    T   N        F    3     NaN         WOODHALL SPA  SHINEY ROW   \n",
              "78451    S   N        F   83     NaN         CHALDON ROAD         NaN   \n",
              "\n",
              "                     town            district              county  \\\n",
              "0               PONTYCLUN  RHONDDA CYNON TAFF  RHONDDA CYNON TAFF   \n",
              "1              CEMAES BAY    ISLE OF ANGLESEY    ISLE OF ANGLESEY   \n",
              "2                 SWANSEA             SWANSEA             SWANSEA   \n",
              "3                   NEATH   NEATH PORT TALBOT   NEATH PORT TALBOT   \n",
              "4                 NEWPORT             NEWPORT             NEWPORT   \n",
              "...                   ...                 ...                 ...   \n",
              "78447             CORSHAM           WILTSHIRE           WILTSHIRE   \n",
              "78448   WESTON-SUPER-MARE      NORTH SOMERSET      NORTH SOMERSET   \n",
              "78449          BRIDGWATER            SOMERSET            SOMERSET   \n",
              "78450  HOUGHTON LE SPRING          SUNDERLAND       TYNE AND WEAR   \n",
              "78451            CATERHAM           TANDRIDGE              SURREY   \n",
              "\n",
              "      ppd_category_type record_status  \n",
              "0                     A             A  \n",
              "1                     A             A  \n",
              "2                     A             A  \n",
              "3                     A             A  \n",
              "4                     A             A  \n",
              "...                 ...           ...  \n",
              "78447                 A             D  \n",
              "78448                 A             D  \n",
              "78449                 A             D  \n",
              "78450                 A             D  \n",
              "78451                 A             D  \n",
              "\n",
              "[78452 rows x 16 columns]"
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              "        price                                                            \\\n",
              "        count          mean           std      min        25%       50%   \n",
              "type                                                                      \n",
              "D     17572.0  4.877258e+05  5.764996e+05   2000.0  300875.25  400000.0   \n",
              "F     16214.0  3.243198e+05  5.104410e+05   2500.0  147500.00  235000.0   \n",
              "O      5392.0  1.537092e+06  1.523031e+07    100.0  115000.00  298100.0   \n",
              "S     20003.0  3.007591e+05  2.245559e+05  20000.0  186150.00  255000.0   \n",
              "T     19271.0  3.140368e+05  4.512705e+06    750.0  138000.00  215000.0   \n",
              "\n",
              "                             \n",
              "           75%          max  \n",
              "type                         \n",
              "D     550250.0   49872000.0  \n",
              "F     370000.0   18000000.0  \n",
              "O     745393.0  900000000.0  \n",
              "S     355000.0   10350000.0  \n",
              "T     325000.0  620000000.0  "
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              "            price                                                          \\\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>57834.0</td>\n",
              "      <td>460113.849812</td>\n",
              "      <td>5.019737e+06</td>\n",
              "      <td>100.0</td>\n",
              "      <td>194995.0</td>\n",
              "      <td>294995.0</td>\n",
              "      <td>435000.0</td>\n",
              "      <td>900000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L</th>\n",
              "      <td>20618.0</td>\n",
              "      <td>367373.239742</td>\n",
              "      <td>3.145992e+06</td>\n",
              "      <td>100.0</td>\n",
              "      <td>140000.0</td>\n",
              "      <td>224175.0</td>\n",
              "      <td>352000.0</td>\n",
              "      <td>414108660.0</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      border-color: transparent;\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      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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            "text/plain": [
              "                     price                                                \\\n",
              "                     count           mean           std    min       25%   \n",
              "ppd_category_type                                                          \n",
              "A                  63001.0  365273.105506  2.508848e+06  750.0  190000.0   \n",
              "B                  15451.0  723070.023105  9.042562e+06  100.0  119000.0   \n",
              "\n",
              "                                                    \n",
              "                        50%       75%          max  \n",
              "ppd_category_type                                   \n",
              "A                  285000.0  422500.0  620000000.0  \n",
              "B                  215596.0  385000.0  900000000.0  "
            ],
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              "      <th>A</th>\n",
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              "      <td>750.0</td>\n",
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              "        const element = document.querySelector('#df-f5010191-2c2d-4ef6-bb70-192a066d25d3');\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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-031b2b78-1062-45b5-81c8-93a72ceb8e6e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-031b2b78-1062-45b5-81c8-93a72ceb8e6e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      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",
              "      } catch (error) {\n",
              "        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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-031b2b78-1062-45b5-81c8-93a72ceb8e6e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pp_may_24\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": [\n        \"ppd_category_type\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"B\",\n          \"A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33622.92744542033,\n        \"min\": 15451.0,\n        \"max\": 63001.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          15451.0,\n          63001.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 253000.6267219247,\n        \"min\": 365273.1055062618,\n        \"max\": 723070.0231053006,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          723070.0231053006,\n          365273.1055062618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4620033.47674632,\n        \"min\": 2508847.5125118364,\n        \"max\": 9042561.513944205,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9042561.513944205,\n          2508847.5125118364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 459.6194077712559,\n        \"min\": 100.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100.0,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"25%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50204.581464244875,\n        \"min\": 119000.0,\n        \"max\": 190000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          119000.0,\n          190000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"50%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49076.039041471144,\n        \"min\": 215596.0,\n        \"max\": 285000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          215596.0,\n          285000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"75%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26516.50429449553,\n        \"min\": 385000.0,\n        \"max\": 422500.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          385000.0,\n          422500.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 197989898.73223332,\n        \"min\": 620000000.0,\n        \"max\": 900000000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          900000000.0,\n          620000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 price                                                \\\n",
              "                 count           mean           std    min       25%   \n",
              "record_status                                                          \n",
              "A              71383.0  427539.129583  4.484793e+06  100.0  179995.5   \n",
              "C               2151.0  873225.216179  1.013442e+07  500.0  132997.5   \n",
              "D               4918.0  363438.942660  9.514494e+05  500.0  170000.0   \n",
              "\n",
              "                                                \n",
              "                    50%       75%          max  \n",
              "record_status                                   \n",
              "A              275000.0  419995.0  900000000.0  \n",
              "C              247000.0  433500.0  414108660.0  \n",
              "D              265500.0  400000.0   52000000.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1b9deca-4846-4cbb-8edc-f74ff33e25fd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe thead tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>record_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>71383.0</td>\n",
              "      <td>427539.129583</td>\n",
              "      <td>4.484793e+06</td>\n",
              "      <td>100.0</td>\n",
              "      <td>179995.5</td>\n",
              "      <td>275000.0</td>\n",
              "      <td>419995.0</td>\n",
              "      <td>900000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>2151.0</td>\n",
              "      <td>873225.216179</td>\n",
              "      <td>1.013442e+07</td>\n",
              "      <td>500.0</td>\n",
              "      <td>132997.5</td>\n",
              "      <td>247000.0</td>\n",
              "      <td>433500.0</td>\n",
              "      <td>414108660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>4918.0</td>\n",
              "      <td>363438.942660</td>\n",
              "      <td>9.514494e+05</td>\n",
              "      <td>500.0</td>\n",
              "      <td>170000.0</td>\n",
              "      <td>265500.0</td>\n",
              "      <td>400000.0</td>\n",
              "      <td>52000000.0</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-a1b9deca-4846-4cbb-8edc-f74ff33e25fd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\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-a1b9deca-4846-4cbb-8edc-f74ff33e25fd 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-a1b9deca-4846-4cbb-8edc-f74ff33e25fd');\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",
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              "\n",
              "\n",
              "<div id=\"df-f2058029-8365-40be-97af-9c09c857c3ba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2058029-8365-40be-97af-9c09c857c3ba')\"\n",
              "            title=\"Suggest charts\"\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      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",
              "      } catch (error) {\n",
              "        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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f2058029-8365-40be-97af-9c09c857c3ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pp_may_24\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": [\n        \"record_status\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"A\",\n          \"C\",\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39196.77354494032,\n        \"min\": 2151.0,\n        \"max\": 71383.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          71383.0,\n          2151.0,\n          4918.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 277676.96051856823,\n        \"min\": 363438.94265961775,\n        \"max\": 873225.2161785216,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          427539.1295826737,\n          873225.2161785216,\n          363438.94265961775\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4631951.707965372,\n        \"min\": 951449.4064247117,\n        \"max\": 10134423.624860242,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4484792.738818525,\n          10134423.624860242,\n          951449.4064247117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 230.94010767585033,\n        \"min\": 100.0,\n        \"max\": 500.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          500.0,\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"25%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24758.523887407613,\n        \"min\": 132997.5,\n        \"max\": 179995.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          179995.5,\n          132997.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"50%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14239.030865898143,\n        \"min\": 247000.0,\n        \"max\": 275000.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          275000.0,\n          247000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"75%\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16854.450698059944,\n        \"min\": 400000.0,\n        \"max\": 433500.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          419995.0,\n          433500.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"price\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 425503050.5049271,\n        \"min\": 52000000.0,\n        \"max\": 900000000.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          900000000.0,\n          414108660.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "NaNs:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                       0\n",
              "price                    0\n",
              "date                     0\n",
              "postcode               276\n",
              "type                     0\n",
              "new                      0\n",
              "duration                 0\n",
              "paon                     0\n",
              "saon                 65931\n",
              "street                1235\n",
              "locality             48100\n",
              "town                     0\n",
              "district                 0\n",
              "county                   0\n",
              "ppd_category_type        0\n",
              "record_status            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "Download complete price paid data set from the Land Registry.\n",
        "\n",
        "First, install [aria2c](https://aria2.github.io/) command line utility for multi-connection (faster) downloads."
      ],
      "metadata": {
        "id": "YlQ7gAgiCVoc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pp_file = '/content/pp-complete.1.csv'\n",
        "\n",
        "if not os.path.exists(pp_file):\n",
        "  !apt-get install aria2\n",
        "  !aria2c -x 16 -s 16 'http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv' -o pp-complete.1.csv\n",
        "else:\n",
        "  print(f'{pp_file} already downloaded')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jRy7doBcoDn8",
        "outputId": "8f60445e-02ff-4089-e046-7549876f88b3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libaria2-0 libc-ares2\n",
            "The following NEW packages will be installed:\n",
            "  aria2 libaria2-0 libc-ares2\n",
            "0 upgraded, 3 newly installed, 0 to remove and 45 not upgraded.\n",
            "Need to get 1,513 kB of archives.\n",
            "After this operation, 5,441 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libc-ares2 amd64 1.18.1-1ubuntu0.22.04.3 [45.1 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libaria2-0 amd64 1.36.0-1 [1,086 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 aria2 amd64 1.36.0-1 [381 kB]\n",
            "Fetched 1,513 kB in 2s (749 kB/s)\n",
            "Selecting previously unselected package libc-ares2:amd64.\n",
            "(Reading database ... 123589 files and directories currently installed.)\n",
            "Preparing to unpack .../libc-ares2_1.18.1-1ubuntu0.22.04.3_amd64.deb ...\n",
            "Unpacking libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n",
            "Selecting previously unselected package libaria2-0:amd64.\n",
            "Preparing to unpack .../libaria2-0_1.36.0-1_amd64.deb ...\n",
            "Unpacking libaria2-0:amd64 (1.36.0-1) ...\n",
            "Selecting previously unselected package aria2.\n",
            "Preparing to unpack .../aria2_1.36.0-1_amd64.deb ...\n",
            "Unpacking aria2 (1.36.0-1) ...\n",
            "Setting up libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n",
            "Setting up libaria2-0:amd64 (1.36.0-1) ...\n",
            "Setting up aria2 (1.36.0-1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "\n",
            "07/23 13:24:51 [\u001b[1;32mNOTICE\u001b[0m] Downloading 1 item(s)\n",
            "\n",
            "07/23 13:24:52 [\u001b[1;32mNOTICE\u001b[0m] CUID#7 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\u001b[0m\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#9 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#18 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#12 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#15 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#13 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#22 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#17 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#19 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#20 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#21 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#10 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#11 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#23 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#16 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#14 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\n",
            "07/23 13:24:53 [\u001b[1;32mNOTICE\u001b[0m] CUID#21 - Redirecting to http://prod2.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv\n",
            "\u001b[0m\n",
            "07/23 13:25:24 [\u001b[1;32mNOTICE\u001b[0m] Download complete: /content/pp-complete.1.csv\n",
            "\n",
            "Download Results:\n",
            "gid   |stat|avg speed  |path/URI\n",
            "======+====+===========+=======================================================\n",
            "7c43b4|\u001b[1;32mOK\u001b[0m  |   156MiB/s|/content/pp-complete.1.csv\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install the python [datatable](https://datatable.readthedocs.io/) package.\n",
        "\n",
        "Datatable is a python library for manipulating tabular data, which supports out-of-memory datasets and multi-threaded data processing.\n",
        "Using datatable instead of pandas should avoid out of RAM problems on [Google Colab](https://colab.research.google.com/)."
      ],
      "metadata": {
        "id": "JowsTJR3unnH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datatable"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CdapKtwupWz",
        "outputId": "37ceeea4-dda9-475a-e902-a35560b99997"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datatable\n",
            "  Downloading datatable-1.1.0-cp310-cp310-manylinux_2_35_x86_64.whl (82.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.0/82.0 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: datatable\n",
            "Successfully installed datatable-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load price paid data with datatable and rename columns."
      ],
      "metadata": {
        "id": "c5XnD7sQvvnW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import datatable as dt\n",
        "from datatable import (dt, f, by, ifelse, update, sort, join,\n",
        "                       count, min, max, mean, sum, rowsum)\n",
        "\n",
        "\n",
        "DT = dt.fread(pp_file)\n",
        "pp_cols = ['id', 'price', 'date_str', 'postcode', 'type', 'new', 'duration',\n",
        "           'paon', 'saon', 'street', 'locality', 'town', 'district', 'county',\n",
        "           'ppd_category_type', 'record_status']\n",
        "DT.names = pp_cols\n",
        "\n",
        "display(DT)\n",
        "print('\\n')\n",
        "display(DT.stypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NLpzhvspvwiw",
        "outputId": "b2258cb3-7b04-414d-9cf2-a89e54b8a538"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style type='text/css'>\n",
              ".datatable table.frame { margin-bottom: 0; }\n",
              ".datatable table.frame thead { border-bottom: none; }\n",
              ".datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n",
              ".datatable .bool    { background: #DDDD99; }\n",
              ".datatable .object  { background: #565656; }\n",
              ".datatable .int     { background: #5D9E5D; }\n",
              ".datatable .float   { background: #4040CC; }\n",
              ".datatable .str     { background: #CC4040; }\n",
              ".datatable .time    { background: #40CC40; }\n",
              ".datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n",
              ".datatable .frame tbody td { text-align: left; }\n",
              ".datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n",
              ".datatable th:nth-child(2) { padding-left: 12px; }\n",
              ".datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n",
              ".datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n",
              ".datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n",
              ".datatable .sp {  opacity: 0.25;}\n",
              ".datatable .footer { font-size: 9px; }\n",
              ".datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Frame#7d4d6a806cd0 29212384x16>"
            ],
            "text/html": [
              "<div class='datatable'>\n",
              "  <table class='frame'>\n",
              "  <thead>\n",
              "    <tr class='colnames'><td class='row_index'></td><th>id</th><th>price</th><th>date_str</th><th>postcode</th><th>type</th><th>new</th><th>duration</th><th>paon</th><th>saon</th><th>street</th><th class='vellipsis'>&hellip;</th><th>town</th><th>district</th><th>county</th><th>ppd_category_type</th><th>record_status</th></tr>\n",
              "    <tr class='coltypes'><td class='row_index'></td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td></td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><td class='row_index'>0</td><td>{109BBF80-1E51-4910-8E2C-B124E1117A7A}</td><td>34950</td><td>1995-06-09 00:00</td><td>BA3 3AH</td><td>F</td><td>N</td><td>L</td><td>COOMBEND HOUSE</td><td>7</td><td>COOMBEND</td><td class=vellipsis>&hellip;</td><td>RADSTOCK</td><td>WANSDYKE</td><td>AVON</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>1</td><td>{D5233D67-2975-426B-B98D-B124E39331D9}</td><td>133000</td><td>1995-06-19 00:00</td><td>SW18 5AG</td><td>T</td><td>N</td><td>F</td><td>142</td><td></td><td>ASTONVILLE STREET</td><td class=vellipsis>&hellip;</td><td>LONDON</td><td>WANDSWORTH</td><td>GREATER LONDON</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>2</td><td>{718BD35C-25E1-431C-8AF7-B124E63ED4E1}</td><td>83000</td><td>1995-08-21 00:00</td><td>IP11 7PU</td><td>D</td><td>N</td><td>F</td><td>3</td><td></td><td>GARFIELD ROAD</td><td class=vellipsis>&hellip;</td><td>FELIXSTOWE</td><td>SUFFOLK COASTAL</td><td>SUFFOLK</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>3</td><td>{E127F626-6247-4D95-A392-B124F4C0A558}</td><td>59000</td><td>1995-07-27 00:00</td><td>PE21 0SF</td><td>D</td><td>Y</td><td>F</td><td>BEAUCADRE</td><td></td><td>GAYSFIELD ROAD</td><td class=vellipsis>&hellip;</td><td>BOSTON</td><td>BOSTON</td><td>LINCOLNSHIRE</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>4</td><td>{B10762A0-2C9C-4C82-A885-B1250B55FFF9}</td><td>51000</td><td>1995-02-28 00:00</td><td>ME7 4DF</td><td>T</td><td>N</td><td>F</td><td>41</td><td></td><td>FRANKLIN ROAD</td><td class=vellipsis>&hellip;</td><td>GILLINGHAM</td><td>GILLINGHAM</td><td>KENT</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>5</td><td>{BBAB55E8-216F-4FFA-B9EC-B4AD71F833EB}</td><td>37000</td><td>1995-06-02 00:00</td><td>TN1 2ET</td><td>F</td><td>N</td><td>L</td><td>48</td><td>TOP FLOOR FLAT</td><td>UPPER GROSVENOR ROAD</td><td class=vellipsis>&hellip;</td><td>TUNBRIDGE WELLS</td><td>TUNBRIDGE WELLS</td><td>KENT</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>6</td><td>{FD34F0B9-8697-481D-A231-B4AD7716F2F5}</td><td>55000</td><td>1995-05-12 00:00</td><td>ST19 5AP</td><td>D</td><td>N</td><td>F</td><td>GAS HOUSE</td><td></td><td>PINFOLD LANE</td><td class=vellipsis>&hellip;</td><td>STAFFORD</td><td>SOUTH STAFFORDSHIRE</td><td>STAFFORDSHIRE</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>7</td><td>{657B1EA4-A147-4E1B-B70F-B4AD98A15237}</td><td>47750</td><td>1995-12-12 00:00</td><td>LL18 5SZ</td><td>S</td><td>Y</td><td>F</td><td>18</td><td></td><td>FFORDD TERFYN</td><td class=vellipsis>&hellip;</td><td>RHYL</td><td>RHUDDLAN</td><td>CLWYD</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>8</td><td>{FF6684D0-67EF-4FE1-AC8F-B4AD99ACE2CD}</td><td>53000</td><td>1995-05-25 00:00</td><td>BS30 5XW</td><td>S</td><td>N</td><td>F</td><td>4</td><td></td><td>GRASMERE GARDENS</td><td class=vellipsis>&hellip;</td><td>BRISTOL</td><td>KINGSWOOD</td><td>AVON</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>9</td><td>{4EED77AD-7F8A-42F2-91C4-AA05EA71F74F}</td><td>60000</td><td>1995-07-14 00:00</td><td>TR12 7PH</td><td>D</td><td>N</td><td>F</td><td>THE BOAT HOUSE</td><td></td><td></td><td class=vellipsis>&hellip;</td><td>HELSTON</td><td>KERRIER</td><td>CORNWALL</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>10</td><td>{EC31B7EF-FBD5-459D-8E3C-AA05EA82F701}</td><td>96950</td><td>1995-05-05 00:00</td><td>SW6 6RW</td><td>F</td><td>N</td><td>L</td><td>13</td><td>FLAT E</td><td>VERA ROAD</td><td class=vellipsis>&hellip;</td><td>LONDON</td><td>HAMMERSMITH AND FULHAM</td><td>GREATER LONDON</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>11</td><td>{FEF50EAF-0816-43C6-98F2-AA05FB12EA0A}</td><td>76000</td><td>1995-06-23 00:00</td><td>DE74 2XH</td><td>D</td><td>N</td><td>F</td><td>20</td><td></td><td>CAMPION HILL</td><td class=vellipsis>&hellip;</td><td>DERBY</td><td>NORTH WEST LEICESTERSHIRE</td><td>LEICESTERSHIRE</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>12</td><td>{FD4959D2-3EE1-4078-B41D-AA0602C1FBA7}</td><td>58000</td><td>1995-06-27 00:00</td><td>RH13 5PS</td><td>T</td><td>N</td><td>F</td><td>59</td><td></td><td>KINGSLEA</td><td class=vellipsis>&hellip;</td><td>HORSHAM</td><td>HORSHAM</td><td>WEST SUSSEX</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>13</td><td>{92706092-0A25-47DF-AFED-AA06085FF9BF}</td><td>129500</td><td>1995-06-14 00:00</td><td>ME15 0DF</td><td>S</td><td>Y</td><td>F</td><td>10</td><td></td><td>FAIRHURST DRIVE</td><td class=vellipsis>&hellip;</td><td>MAIDSTONE</td><td>MAIDSTONE</td><td>KENT</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>14</td><td>{D352F749-2E41-4D24-A4D0-AD9C59B40B37}</td><td>41500</td><td>1995-03-03 00:00</td><td>OX12 7DQ</td><td>S</td><td>Y</td><td>F</td><td>5</td><td></td><td>HARCOURT ROAD</td><td class=vellipsis>&hellip;</td><td>WANTAGE</td><td>VALE OF WHITE HORSE</td><td>OXFORDSHIRE</td><td>A</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22F1;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td></tr>\n",
              "    <tr><td class='row_index'>29,212,379</td><td>{152AB734-8318-E651-E063-4704A8C061D9}</td><td>85000</td><td>2024-03-05 00:00</td><td>OL10 2AE</td><td>F</td><td>N</td><td>L</td><td>BIWATER HOUSE</td><td>APARTMENT 47</td><td>GREGGE STREET</td><td class=vellipsis>&hellip;</td><td>HEYWOOD</td><td>ROCHDALE</td><td>GREATER MANCHESTER</td><td>B</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>29,212,380</td><td>{152AB734-831A-E651-E063-4704A8C061D9}</td><td>60000</td><td>2024-03-13 00:00</td><td>BL1 1AU</td><td>F</td><td>N</td><td>L</td><td>HUNTINGDON HOUSE</td><td>APARTMENT 13</td><td>PRINCESS STREET</td><td class=vellipsis>&hellip;</td><td>BOLTON</td><td>BOLTON</td><td>GREATER MANCHESTER</td><td>B</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>29,212,381</td><td>{152AB734-831C-E651-E063-4704A8C061D9}</td><td>270000</td><td>2024-03-08 00:00</td><td>WN7 4BJ</td><td>D</td><td>N</td><td>F</td><td>21</td><td></td><td>WILKINSON PARK DRIVE</td><td class=vellipsis>&hellip;</td><td>LEIGH</td><td>WIGAN</td><td>GREATER MANCHESTER</td><td>B</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>29,212,382</td><td>{152AB734-831F-E651-E063-4704A8C061D9}</td><td>160000</td><td>2024-03-18 00:00</td><td>WN6 0HF</td><td>T</td><td>N</td><td>F</td><td>44</td><td></td><td>HIGH STREET</td><td class=vellipsis>&hellip;</td><td>WIGAN</td><td>WIGAN</td><td>GREATER MANCHESTER</td><td>B</td><td>A</td></tr>\n",
              "    <tr><td class='row_index'>29,212,383</td><td>{152AB734-8321-E651-E063-4704A8C061D9}</td><td>233000</td><td>2024-02-29 00:00</td><td>M19 1PT</td><td>S</td><td>N</td><td>F</td><td>7</td><td></td><td>MOSSBRAY AVENUE</td><td class=vellipsis>&hellip;</td><td>MANCHESTER</td><td>MANCHESTER</td><td>GREATER MANCHESTER</td><td>B</td><td>A</td></tr>\n",
              "  </tbody>\n",
              "  </table>\n",
              "  <div class='footer'>\n",
              "    <div class='frame_dimensions'>29,212,384 rows &times; 16 columns</div>\n",
              "  </div>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(stype.str32,\n",
              " stype.int32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32,\n",
              " stype.str32)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.\n",
        "\n",
        "Find two land registry records that are likely to be errors:"
      ],
      "metadata": {
        "id": "PaPN_IMvE42N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sel_cols = ['price', 'date_str', 'postcode', 'type', 'new', 'duration', 'paon',\n",
        "            'saon', 'street', 'town', 'district', 'county']\n",
        "DT[f.price > 600000000, sel_cols]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "ruOfj-_sv97w",
        "outputId": "3767ea32-2baf-4586-c3a9-07fc35bac65d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Frame#7d4d6a806b80 2x12>"
            ],
            "text/html": [
              "<div class='datatable'>\n",
              "  <table class='frame'>\n",
              "  <thead>\n",
              "    <tr class='colnames'><td class='row_index'></td><th>price</th><th>date_str</th><th>postcode</th><th>type</th><th>new</th><th>duration</th><th>paon</th><th>saon</th><th>street</th><th>town</th><th>district</th><th>county</th></tr>\n",
              "    <tr class='coltypes'><td class='row_index'></td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><td class='row_index'>0</td><td>900000000</td><td>2021-03-29 00:00</td><td>TN23 7HE</td><td>O</td><td>N</td><td>F</td><td>PENROSE HOUSE, 46</td><td>APARTMENT 1</td><td>VICTORIA ROAD</td><td>ASHFORD</td><td>ASHFORD</td><td>KENT</td></tr>\n",
              "    <tr><td class='row_index'>1</td><td>620000000</td><td>2023-01-06 00:00</td><td>CV33 9FR</td><td>T</td><td>N</td><td>F</td><td>OAKLEY WOOD BARN</td><td>2</td><td>BANBURY ROAD</td><td>LEAMINGTON SPA</td><td>WARWICK</td><td>WARWICKSHIRE</td></tr>\n",
              "  </tbody>\n",
              "  </table>\n",
              "  <div class='footer'>\n",
              "    <div class='frame_dimensions'>2 rows &times; 12 columns</div>\n",
              "  </div>\n",
              "</div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The two most expensive properties recorded are unlikely to be an appartment or part of a barn conversion.  Assuming these aren't transcription errors, then they may be [copyright traps](https://en.wikipedia.org/wiki/Fictitious_entry#Copyright_traps) to detect plagiarism or copyright violations.\n",
        "\n",
        "---\n",
        "\n",
        "## 2.\n",
        "\n",
        "Complete the following table by calculating the number of sales and average sale price for all London Boroughs in 2023."
      ],
      "metadata": {
        "id": "BkW1vATBE-_Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add time64 'Date' column based on str32 'date_str' column\n",
        "DT[:, dt.update(Date =\n",
        "             dt.time.ymd(dt.as_type(dt.str.slice(f.date_str, 0, 4), int),\n",
        "                         dt.as_type(dt.str.slice(f.date_str, 5, 7), int),\n",
        "                         dt.as_type(dt.str.slice(f.date_str, 8, 10), int)))]\n",
        "DT['year'] = DT[:, dt.time.year(f.Date)]\n",
        "\n",
        "l23_cols = ['id', 'price', 'Date', 'year', 'district', 'county']\n",
        "l23_mask = dt.re.match(f.county, 'GREATER LONDON') & (f.year == 2023)\n",
        "london_23 = DT[l23_mask, l23_cols]\n",
        "\n",
        "london_23_stats = london_23[:, {'count_sales': count(f.id),\n",
        "                                'mean_price': mean(f.price)},\n",
        "                            by('district')]\n",
        "display(london_23_stats)\n",
        "london_23_stats.to_csv('q2.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "oJRIeqjR19I6",
        "outputId": "c02a19c2-f6c0-404a-c9e7-8d8c5683d49a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Frame#7d4d69e57690 33x3>"
            ],
            "text/html": [
              "<div class='datatable'>\n",
              "  <table class='frame'>\n",
              "  <thead>\n",
              "    <tr class='colnames'><td class='row_index'></td><th>district</th><th>count_sales</th><th>mean_price</th></tr>\n",
              "    <tr class='coltypes'><td class='row_index'></td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='float' title='float64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><td class='row_index'>0</td><td>BARKING AND DAGENHAM</td><td>1355</td><td>397119</td></tr>\n",
              "    <tr><td class='row_index'>1</td><td>BARNET</td><td>3222</td><td>809265</td></tr>\n",
              "    <tr><td class='row_index'>2</td><td>BEXLEY</td><td>2543</td><td>432761</td></tr>\n",
              "    <tr><td class='row_index'>3</td><td>BRENT</td><td>1864</td><td>678122</td></tr>\n",
              "    <tr><td class='row_index'>4</td><td>BROMLEY</td><td>3741</td><td>603442</td></tr>\n",
              "    <tr><td class='row_index'>5</td><td>CAMDEN</td><td>1926</td><td>1.49899e+06</td></tr>\n",
              "    <tr><td class='row_index'>6</td><td>CITY OF LONDON</td><td>202</td><td>4.39989e+06</td></tr>\n",
              "    <tr><td class='row_index'>7</td><td>CITY OF WESTMINSTER</td><td>2648</td><td>2.36014e+06</td></tr>\n",
              "    <tr><td class='row_index'>8</td><td>CROYDON</td><td>3709</td><td>481384</td></tr>\n",
              "    <tr><td class='row_index'>9</td><td>EALING</td><td>2808</td><td>733168</td></tr>\n",
              "    <tr><td class='row_index'>10</td><td>ENFIELD</td><td>2428</td><td>600082</td></tr>\n",
              "    <tr><td class='row_index'>11</td><td>GREENWICH</td><td>2383</td><td>545723</td></tr>\n",
              "    <tr><td class='row_index'>12</td><td>HACKNEY</td><td>2017</td><td>735308</td></tr>\n",
              "    <tr><td class='row_index'>13</td><td>HAMMERSMITH AND FULHAM</td><td>2081</td><td>995954</td></tr>\n",
              "    <tr><td class='row_index'>14</td><td>HARINGEY</td><td>2128</td><td>817637</td></tr>\n",
              "    <tr><td class='row_index'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td></tr>\n",
              "    <tr><td class='row_index'>28</td><td>SOUTHWARK</td><td>2564</td><td>744741</td></tr>\n",
              "    <tr><td class='row_index'>29</td><td>SUTTON</td><td>2277</td><td>492206</td></tr>\n",
              "    <tr><td class='row_index'>30</td><td>TOWER HAMLETS</td><td>2397</td><td>843265</td></tr>\n",
              "    <tr><td class='row_index'>31</td><td>WALTHAM FOREST</td><td>2525</td><td>585994</td></tr>\n",
              "    <tr><td class='row_index'>32</td><td>WANDSWORTH</td><td>4368</td><td>840768</td></tr>\n",
              "  </tbody>\n",
              "  </table>\n",
              "  <div class='footer'>\n",
              "    <div class='frame_dimensions'>33 rows &times; 3 columns</div>\n",
              "  </div>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See 'q2.csv' file for complete table.\n",
        "\n",
        "---\n",
        "\n",
        "## 3.\n",
        "\n",
        "Count the number of new build Flats sold in each UK region since the start of 2020."
      ],
      "metadata": {
        "id": "G9hnFVssXb-w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "flats_since_2020 = DT[(f.year >= 2020) &\n",
        "                      (f.type == 'F') &\n",
        "                      (f.new == 'Y'), :]\n",
        "\n",
        "flats_since_2020_stats = flats_since_2020[:, {'count_flats': count(f.id)},\n",
        "                                          by('county')]\n",
        "display(flats_since_2020_stats)\n",
        "flats_since_2020_stats.to_csv('q3.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "HUaX76xvXfj4",
        "outputId": "2129c020-52e6-40a7-85c1-c85a8b2f1321"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Frame#7d4d69e54780 110x2>"
            ],
            "text/html": [
              "<div class='datatable'>\n",
              "  <table class='frame'>\n",
              "  <thead>\n",
              "    <tr class='colnames'><td class='row_index'></td><th>county</th><th>count_flats</th></tr>\n",
              "    <tr class='coltypes'><td class='row_index'></td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr><td class='row_index'>0</td><td>BATH AND NORTH EAST SOMERSET</td><td>327</td></tr>\n",
              "    <tr><td class='row_index'>1</td><td>BEDFORD</td><td>291</td></tr>\n",
              "    <tr><td class='row_index'>2</td><td>BLACKBURN WITH DARWEN</td><td>5</td></tr>\n",
              "    <tr><td class='row_index'>3</td><td>BLACKPOOL</td><td>53</td></tr>\n",
              "    <tr><td class='row_index'>4</td><td>BLAENAU GWENT</td><td>2</td></tr>\n",
              "    <tr><td class='row_index'>5</td><td>BOURNEMOUTH, CHRISTCHURCH AND POOLE</td><td>1025</td></tr>\n",
              "    <tr><td class='row_index'>6</td><td>BRACKNELL FOREST</td><td>751</td></tr>\n",
              "    <tr><td class='row_index'>7</td><td>BRIDGEND</td><td>55</td></tr>\n",
              "    <tr><td class='row_index'>8</td><td>BRIGHTON AND HOVE</td><td>257</td></tr>\n",
              "    <tr><td class='row_index'>9</td><td>BUCKINGHAMSHIRE</td><td>1142</td></tr>\n",
              "    <tr><td class='row_index'>10</td><td>CAMBRIDGESHIRE</td><td>983</td></tr>\n",
              "    <tr><td class='row_index'>11</td><td>CARDIFF</td><td>668</td></tr>\n",
              "    <tr><td class='row_index'>12</td><td>CARMARTHENSHIRE</td><td>2</td></tr>\n",
              "    <tr><td class='row_index'>13</td><td>CENTRAL BEDFORDSHIRE</td><td>344</td></tr>\n",
              "    <tr><td class='row_index'>14</td><td>CHESHIRE EAST</td><td>245</td></tr>\n",
              "    <tr><td class='row_index'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td></tr>\n",
              "    <tr><td class='row_index'>105</td><td>WOKINGHAM</td><td>447</td></tr>\n",
              "    <tr><td class='row_index'>106</td><td>WORCESTERSHIRE</td><td>276</td></tr>\n",
              "    <tr><td class='row_index'>107</td><td>WREKIN</td><td>16</td></tr>\n",
              "    <tr><td class='row_index'>108</td><td>WREXHAM</td><td>60</td></tr>\n",
              "    <tr><td class='row_index'>109</td><td>YORK</td><td>363</td></tr>\n",
              "  </tbody>\n",
              "  </table>\n",
              "  <div class='footer'>\n",
              "    <div class='frame_dimensions'>110 rows &times; 2 columns</div>\n",
              "  </div>\n",
              "</div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See 'q3.csv' file for complete table.\n",
        "\n",
        "---\n",
        "\n",
        "## 4.\n",
        "\n",
        "Plot the number of sales per week since the start of 2020 as a line chart."
      ],
      "metadata": {
        "id": "4jkc2oq2bxYJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Currently no datatable.time.week() method :-(\n",
        "# https://datatable.readthedocs.io/en/latest/api/time.html\n",
        "# Use datetime.isocalendar().week instead\n",
        "weeks_list = [_date.isocalendar().week for _date in DT[\"Date\"].to_list()[0]]\n",
        "DT[:, \"week\"] = dt.Frame(weeks_list)\n",
        "\n",
        "DT_2020 = DT[f.year >= 2020, :]\n",
        "sales_per_week = DT_2020[:, {'count_sales': count(f.id)}, by('year', 'week')]\n",
        "sales_per_week[:, update(year_week = f.year + '-' + f.week)]\n",
        "\n",
        "# Avoid over-plotting labels\n",
        "sparse_labels = []\n",
        "sales_labels = sales_per_week['year_week'].to_list()[0]\n",
        "for index, item in enumerate(sales_labels):\n",
        "    if index % 10 == 0:\n",
        "        sparse_labels.append(item)\n",
        "    else:\n",
        "        sparse_labels.append('')\n",
        "\n",
        "# Plot sales data\n",
        "plt.plot(sales_labels, sales_per_week['count_sales'])\n",
        "plt.axvline(x='2020-52', color='grey', linestyle='--', linewidth=0.8)\n",
        "plt.axvline(x='2021-52', color='grey', linestyle='--', linewidth=0.8)\n",
        "plt.axvline(x='2022-52', color='grey', linestyle='--', linewidth=0.8)\n",
        "plt.axvline(x='2023-52', color='grey', linestyle='--', linewidth=0.8)\n",
        "plt.xticks(range(0, len(sparse_labels)), sparse_labels, rotation=45)\n",
        "plt.xlabel('Year-Week')\n",
        "plt.ylabel('Sales')\n",
        "plt.title('Property Sales per Week Since 2020')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "Y1gFspYV1oyx",
        "outputId": "838691b7-134e-4f20-beae-0430516ba538"
      },
      "execution_count": 35,
      "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": [
        "There is a clear annual seasonality in this property sales data.  There are approximately 12 sales peaks per annual cycle (likely corresponding to monthly reporting).  Spring and early summer are typically the busiest periods for house sales.  Additioinally, there is a large sales decline at the end of each year.  The largest sales peaks and declines may correspond to covid 19 lockdowns and/or the so-called \"race for space\" rush from crowded cities to more spacious suburbs and countryside.  The reduction in sales during 2023, compared to 2022, may potentially reflect increased interest rates and/or reduced market confidence.\n",
        "\n",
        "These trends may vary regionally and by property type.  \n",
        "\n",
        "---\n",
        "\n",
        "## 5.\n",
        "\n",
        "Plot a histogram of sale prices and discuss which distribution best represents the data.\n",
        "\n",
        "[Vaex](https://vaex.readthedocs.io/) is a python library for lazy Out-of-Core DataFrames, to visualize and explore big tabular datasets.  This package has a convenient [histogram plotting function](https://vaex.readthedocs.io/en/latest/api.html#vaex.viz.DataFrameAccessorViz.histogram).\n",
        "\n",
        "First, install vaex:"
      ],
      "metadata": {
        "id": "tMitTH1Jb1Vd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install vaex"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TiPu7KtbCYoh",
        "outputId": "7fe2bcaa-1418-4864-c5d3-49b68fe23229"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<9,>=7.5.0->bqplot>=0.10.1->vaex-jupyter<0.9,>=0.8.2->vaex) (0.5.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<9,>=7.5.0->bqplot>=0.10.1->vaex-jupyter<0.9,>=0.8.2->vaex) (2.22)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<9,>=7.5.0->bqplot>=0.10.1->vaex-jupyter<0.9,>=0.8.2->vaex) (1.8.0)\n",
            "Building wheels for collected packages: aplus\n",
            "  Building wheel for aplus (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for aplus: filename=aplus-0.11.0-py3-none-any.whl size=4367 sha256=d08da5df102da57726c56c4fe0480c63ad0bba41287efc602bc5bf34aead15d8\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/ed/14/4d7bffd9475a07582e0c256b9b1ddbbc1f49c58e3150d356f4\n",
            "Successfully built aplus\n",
            "Installing collected packages: ipywebrtc, blake3, aplus, websockets, uvloop, python-multipart, python-dotenv, jedi, httptools, h11, dnspython, watchfiles, uvicorn, starlette, httpcore, email_validator, vaex-core, httpx, vaex-viz, vaex-ml, vaex-hdf5, vaex-astro, fastapi-cli, fastapi, vaex-server, ipyvue, ipympl, ipydatawidgets, pythreejs, ipyvuetify, ipyvolume, vaex-jupyter, vaex\n",
            "Successfully installed aplus-0.11.0 blake3-0.4.1 dnspython-2.6.1 email_validator-2.2.0 fastapi-0.111.1 fastapi-cli-0.0.4 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 ipydatawidgets-4.3.5 ipympl-0.9.4 ipyvolume-0.6.3 ipyvue-1.11.1 ipyvuetify-1.9.4 ipywebrtc-0.6.0 jedi-0.19.1 python-dotenv-1.0.1 python-multipart-0.0.9 pythreejs-2.4.2 starlette-0.37.2 uvicorn-0.30.3 uvloop-0.19.0 vaex-4.17.0 vaex-astro-0.9.3 vaex-core-4.17.1 vaex-hdf5-0.14.1 vaex-jupyter-0.8.2 vaex-ml-0.18.3 vaex-server-0.9.0 vaex-viz-0.5.4 watchfiles-0.22.0 websockets-12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the complete price paid data set with vaex and set the column names.\n",
        "\n",
        "**NOTE**: The first row of data is lost."
      ],
      "metadata": {
        "id": "TiEpYvusW7Nk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import vaex\n",
        "\n",
        "\n",
        "df = vaex.open(pp_file)\n",
        "\n",
        "pp_cols = ['id', 'price', 'date', 'postcode', 'type', 'new', 'duration',\n",
        "           'paon', 'saon', 'street', 'locality', 'town', 'county',\n",
        "           'record_status']\n",
        "\n",
        "old_names = df.get_column_names()\n",
        "new_names = pp_cols\n",
        "pp_names = dict(zip(old_names, new_names))\n",
        "\n",
        "for old_name in pp_names:\n",
        "  new_name = pp_names[old_name]\n",
        "  df.rename(old_name, new_name)\n",
        "\n",
        "display(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0RHih4TokSTx",
        "outputId": "245ed0c4-2b2e-430c-9e06-3707c3107555"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>.vaex-description pre {\n",
              "          max-width : 450px;\n",
              "          white-space : nowrap;\n",
              "          overflow : hidden;\n",
              "          text-overflow: ellipsis;\n",
              "        }\n",
              "\n",
              "        .vex-description pre:hover {\n",
              "          max-width : initial;\n",
              "          white-space: pre;\n",
              "        }</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><h2>pp-complete.1</h2> <b>rows</b>: 29,212,383</div><h2>Columns:</h2><table class='table-striped'><thead><tr><th>column</th><th>type</th><th>unit</th><th>description</th><th>expression</th></tr></thead><tr><td>id</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>price</td><td>int64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>date</td><td>datetime64[s]</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>postcode</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>type</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>new</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>duration</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>paon</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>saon</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>street</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>locality</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>town</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>county</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>record_status</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr></table><h2>Data:</h2><table>\n",
              "<thead>\n",
              "<tr><th>#                                     </th><th>id                                    </th><th>price  </th><th>date               </th><th>postcode  </th><th>type  </th><th>new  </th><th>duration  </th><th>paon            </th><th>saon          </th><th>street              </th><th>locality       </th><th>town           </th><th>county            </th><th>record_status  </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td><i style='opacity: 0.6'>0</i>         </td><td>{D5233D67-2975-426B-B98D-B124E39331D9}</td><td>133000 </td><td>1995-06-19 00:00:00</td><td>SW18 5AG  </td><td>T     </td><td>N    </td><td>F         </td><td>142             </td><td>              </td><td>ASTONVILLE STREET   </td><td>LONDON         </td><td>WANDSWORTH     </td><td>GREATER LONDON    </td><td>A              </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>1</i>         </td><td>{718BD35C-25E1-431C-8AF7-B124E63ED4E1}</td><td>83000  </td><td>1995-08-21 00:00:00</td><td>IP11 7PU  </td><td>D     </td><td>N    </td><td>F         </td><td>3               </td><td>              </td><td>GARFIELD ROAD       </td><td>FELIXSTOWE     </td><td>SUFFOLK COASTAL</td><td>SUFFOLK           </td><td>A              </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>2</i>         </td><td>{E127F626-6247-4D95-A392-B124F4C0A558}</td><td>59000  </td><td>1995-07-27 00:00:00</td><td>PE21 0SF  </td><td>D     </td><td>Y    </td><td>F         </td><td>BEAUCADRE       </td><td>              </td><td>GAYSFIELD ROAD      </td><td>FISHTOFT       </td><td>BOSTON         </td><td>LINCOLNSHIRE      </td><td>A              </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>3</i>         </td><td>{B10762A0-2C9C-4C82-A885-B1250B55FFF9}</td><td>51000  </td><td>1995-02-28 00:00:00</td><td>ME7 4DF   </td><td>T     </td><td>N    </td><td>F         </td><td>41              </td><td>              </td><td>FRANKLIN ROAD       </td><td>GILLINGHAM     </td><td>GILLINGHAM     </td><td>KENT              </td><td>A              </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>4</i>         </td><td>{BBAB55E8-216F-4FFA-B9EC-B4AD71F833EB}</td><td>37000  </td><td>1995-06-02 00:00:00</td><td>TN1 2ET   </td><td>F     </td><td>N    </td><td>L         </td><td>48              </td><td>TOP FLOOR FLAT</td><td>UPPER GROSVENOR ROAD</td><td>TUNBRIDGE WELLS</td><td>TUNBRIDGE WELLS</td><td>KENT              </td><td>A              </td></tr>\n",
              "<tr><td>...                                   </td><td>...                                   </td><td>...    </td><td>...                </td><td>...       </td><td>...   </td><td>...  </td><td>...       </td><td>...             </td><td>...           </td><td>...                 </td><td>...            </td><td>...            </td><td>...               </td><td>...            </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>29,212,378</i></td><td>{152AB734-8318-E651-E063-4704A8C061D9}</td><td>85000  </td><td>2024-03-05 00:00:00</td><td>OL10 2AE  </td><td>F     </td><td>N    </td><td>L         </td><td>BIWATER HOUSE   </td><td>APARTMENT 47  </td><td>GREGGE STREET       </td><td>               </td><td>ROCHDALE       </td><td>GREATER MANCHESTER</td><td>B              </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>29,212,379</i></td><td>{152AB734-831A-E651-E063-4704A8C061D9}</td><td>60000  </td><td>2024-03-13 00:00:00</td><td>BL1 1AU   </td><td>F     </td><td>N    </td><td>L         </td><td>HUNTINGDON HOUSE</td><td>APARTMENT 13  </td><td>PRINCESS STREET     </td><td>               </td><td>BOLTON         </td><td>GREATER MANCHESTER</td><td>B              </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>29,212,380</i></td><td>{152AB734-831C-E651-E063-4704A8C061D9}</td><td>270000 </td><td>2024-03-08 00:00:00</td><td>WN7 4BJ   </td><td>D     </td><td>N    </td><td>F         </td><td>21              </td><td>              </td><td>WILKINSON PARK DRIVE</td><td>               </td><td>WIGAN          </td><td>GREATER MANCHESTER</td><td>B              </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>29,212,381</i></td><td>{152AB734-831F-E651-E063-4704A8C061D9}</td><td>160000 </td><td>2024-03-18 00:00:00</td><td>WN6 0HF   </td><td>T     </td><td>N    </td><td>F         </td><td>44              </td><td>              </td><td>HIGH STREET         </td><td>STANDISH       </td><td>WIGAN          </td><td>GREATER MANCHESTER</td><td>B              </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>29,212,382</i></td><td>{152AB734-8321-E651-E063-4704A8C061D9}</td><td>233000 </td><td>2024-02-29 00:00:00</td><td>M19 1PT   </td><td>S     </td><td>N    </td><td>F         </td><td>7               </td><td>              </td><td>MOSSBRAY AVENUE     </td><td>               </td><td>MANCHESTER     </td><td>GREATER MANCHESTER</td><td>B              </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Square root transformation is applied to sale price data for several reasons:\n",
        "\n",
        " * **Normalisation**: It can help normalise right-skewed distributions.\n",
        " * **Variance stabilisation**: It can stabilise variance across different price ranges.\n",
        " * **Linearity**: It can improve linear relationships between variables.\n",
        " * **Outlier impact reduction**: It reduces the impact of extreme high values.\n",
        " * **Statistical assumptions**: It helps meet assumptions for many statistical tests.\n",
        " * **Interpretability**: The transformed data retains a more intuitive relationship to the original values compared to log transformations."
      ],
      "metadata": {
        "id": "oPN1jX1ZWL_u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['price_float'] = df.price.astype('float64')\n",
        "df['price_sqrt'] = df.price_float ** 0.5\n",
        "\n",
        "df.viz.histogram(df.price_sqrt, limits='99.7%')\n",
        "plt.title('Sale Price (sqrt)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "zMciVJ6TWPEE",
        "outputId": "4f0376ec-ff81-46b5-fc86-df859ab2cc6a"
      },
      "execution_count": 39,
      "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": [
        "\n",
        "Discuss which distribution best represents the data.\n",
        "\n",
        "The complete price paid data exhibits a right-skewed distribution.  This means there are more lower-priced houses and fewer higher-priced ones, with a long tail extending to the right representing luxury properties.\n",
        "\n",
        "There are 2 common distributions to consider:\n",
        "\n",
        " 1. **Log-normal distribution**:\n",
        "It assumes that the logarithm of house prices follows a normal distribution. The log-normal distribution can capture the right-skewness and non-negative nature of house prices.\n",
        " 2. **Gamma distribution**:\n",
        "Another right-skewed distribution that can model house prices well. It's flexible and can represent various shapes depending on its parameters.\n",
        "\n",
        "The best-fitting distribution may vary over time or across regions.\n",
        "A mixture of distributions might be more appropriate for nationwide data.\n",
        "\n",
        "Based on literature and common practices, the log-normal distribution is sometimes cited as a good representation for house price data.\n",
        "\n",
        "To determine which distribution best represents the data:\n",
        " * Plot the empirical distribution\n",
        " * Fit different distributions to the data\n",
        " * Use goodness-of-fit tests (e.g., Kolmogorov-Smirnov)\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## 6.\n",
        "\n",
        "Using the BNG tiles and an appropriate scale, plot a map showing the number of sales per 10km grid square.\n",
        "\n",
        "First download the [NSPL postcode data](https://geoportal.statistics.gov.uk/datasets/f7464f3658ba439ba577651b32014cfe/about) and extract postcode, Ordinance Survey (OS) eastings and northings plus latitude and longitude:"
      ],
      "metadata": {
        "id": "nm4EtzqfWPqo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget 'https://www.arcgis.com/sharing/rest/content/items/f7464f3658ba439ba577651b32014cfe/data' -O 'NSPL_2021_MAY_2024.zip'\n",
        "!unzip NSPL_2021_MAY_2024.zip\n",
        "!awk -F',' '{print $3\",\"$7\",\"$8\",\"$29\",\"$30}' Data/NSPL21_MAY_2024_UK.csv | tr -d '\"' > pcd_os_lat_long.csv\n",
        "!ls -l pcd_os_lat_long.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Obfgoa-YbAyz",
        "outputId": "b3c524b7-bb15-4f2e-d275-922432096cc8"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-23 15:00:51--  https://www.arcgis.com/sharing/rest/content/items/f7464f3658ba439ba577651b32014cfe/data\n",
            "Resolving www.arcgis.com (www.arcgis.com)... 18.234.22.44, 34.233.149.104, 18.234.22.250\n",
            "Connecting to www.arcgis.com (www.arcgis.com)|18.234.22.44|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ago-item-storage.s3.amazonaws.com/f7464f3658ba439ba577651b32014cfe/NSPL_2021_MAY_2024.zip?X-Amz-Security-Token=IQoJb3JpZ2luX2VjELb%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIGq61N8cWX9fwkNnNDi2trGUou8j%2B%2Boj3yE5E9SbBnFqAiEA9w%2FaDJErBetwBO6LxHDcdsiifRySqTc04GV38fq8aiIqvAUIj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAAGgw2MDQ3NTgxMDI2NjUiDMpizJrgWbsCqTjnxiqQBXZ%2BLOTdFSEH%2B77ee4IyQUtwYn6Vb7VLn7JETZzvog9RHTAZg%2BJqFARP53C49aabOY9%2FnWx1feflBmyJgDl%2FYZI5WXs0puuJ8tsQM%2BEL1I8oCCnVikEuRYT%2FxlHWH1rkyegPONCW0JbDA0tKGSkXAqaEIY3K1NCZkJdlr9d0ZCJynnVCSnARa9bOdt5A7a6ztu%2FeB6XSbEg100cVW%2FO4cKExsN8RXHjHZEy9tmOOMavD91v6qfJ8qQ8edJu0uGiOCR2Kvr72x4hoyyUa7fbQtkpKKRkH%2BSBTSgwLEhAmR8HttICsw2FIRTkKBLy2qMQlcjyDeLnULfLphPXv4xJtw9OlECHselCtFO2lwmJmS9LDcahSOT%2B51bzhX%2BKSsYrb4LCqc%2BcJDWHMsvKouy6E84er%2BPmLesqY8o0FPzobq5CPCJtPNTDRyGPIKEmhzub63P%2FmlZ%2FyzdrieATdv7q3%2BhSKzkiwwPObDP1dQOkOasCYFb7xPwQ%2F2RWmgdn7ykFMlXe5mOR%2BvO60SssN4jlv2aMMtJ8Xd1jHI6VK2rs7RffVGxezBQ5kRavpkypQuyo%2FaRyxh5rtS%2FLM2IvX%2BBboZNb%2FFCTimWnDFgc7OVqOtQDINYgae4LMy3HK0KVY0mdY1cM8ae2J8jCNQ0VjPnH%2F7BjvvKDXLj%2BQF8FEd1OGzPS789gDPAewtQBRnvr%2B5A%2FcPphqSCGBh0sPIxAoaPJgHqCQjE2JHvN7V91IyT%2BJZT%2BaVM5MJ7AZti3Z347sAKKL%2B4XXCGCN%2Fg2JG2D9RPK8LKVvPa5DH0kHoGSajPV4P%2BW%2BR9rzTybjEDkeaQ1hulJw333LaaAmsailwO8H5z7tjjISLaky%2Bt%2FxqGmsEa%2BMqD7TMITl%2FrQGOrEBE%2BhkqowBdNjoffeTotgae5li2%2FNDQyC4dRVwLBUehucb6oyxm5YVHuwvETQVK9XYWPRxkEdD6ZnVHhUGKCJIR178PSufIe4kwS6etOhq7qOcVv7bZoktl%2By5upEemZf8sS7Nlbu06IT3i%2F9lD96I1Dn87vu1aabvKooe%2BFrJnwiRlF1VCkK3BPURFyTMBrGDcOFlHYCQj2SL0xZ1iaI6P7dhGgetP5YgHqexpKp3yoLa&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240723T150051Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAYZTTEKKE4DY4MY6V%2F20240723%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=a43068bdf7a22299b4605e1a31e876698a967dac5410a37717f2bd6feffbbf71 [following]\n",
            "--2024-07-23 15:00:52--  https://ago-item-storage.s3.amazonaws.com/f7464f3658ba439ba577651b32014cfe/NSPL_2021_MAY_2024.zip?X-Amz-Security-Token=IQoJb3JpZ2luX2VjELb%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIGq61N8cWX9fwkNnNDi2trGUou8j%2B%2Boj3yE5E9SbBnFqAiEA9w%2FaDJErBetwBO6LxHDcdsiifRySqTc04GV38fq8aiIqvAUIj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAAGgw2MDQ3NTgxMDI2NjUiDMpizJrgWbsCqTjnxiqQBXZ%2BLOTdFSEH%2B77ee4IyQUtwYn6Vb7VLn7JETZzvog9RHTAZg%2BJqFARP53C49aabOY9%2FnWx1feflBmyJgDl%2FYZI5WXs0puuJ8tsQM%2BEL1I8oCCnVikEuRYT%2FxlHWH1rkyegPONCW0JbDA0tKGSkXAqaEIY3K1NCZkJdlr9d0ZCJynnVCSnARa9bOdt5A7a6ztu%2FeB6XSbEg100cVW%2FO4cKExsN8RXHjHZEy9tmOOMavD91v6qfJ8qQ8edJu0uGiOCR2Kvr72x4hoyyUa7fbQtkpKKRkH%2BSBTSgwLEhAmR8HttICsw2FIRTkKBLy2qMQlcjyDeLnULfLphPXv4xJtw9OlECHselCtFO2lwmJmS9LDcahSOT%2B51bzhX%2BKSsYrb4LCqc%2BcJDWHMsvKouy6E84er%2BPmLesqY8o0FPzobq5CPCJtPNTDRyGPIKEmhzub63P%2FmlZ%2FyzdrieATdv7q3%2BhSKzkiwwPObDP1dQOkOasCYFb7xPwQ%2F2RWmgdn7ykFMlXe5mOR%2BvO60SssN4jlv2aMMtJ8Xd1jHI6VK2rs7RffVGxezBQ5kRavpkypQuyo%2FaRyxh5rtS%2FLM2IvX%2BBboZNb%2FFCTimWnDFgc7OVqOtQDINYgae4LMy3HK0KVY0mdY1cM8ae2J8jCNQ0VjPnH%2F7BjvvKDXLj%2BQF8FEd1OGzPS789gDPAewtQBRnvr%2B5A%2FcPphqSCGBh0sPIxAoaPJgHqCQjE2JHvN7V91IyT%2BJZT%2BaVM5MJ7AZti3Z347sAKKL%2B4XXCGCN%2Fg2JG2D9RPK8LKVvPa5DH0kHoGSajPV4P%2BW%2BR9rzTybjEDkeaQ1hulJw333LaaAmsailwO8H5z7tjjISLaky%2Bt%2FxqGmsEa%2BMqD7TMITl%2FrQGOrEBE%2BhkqowBdNjoffeTotgae5li2%2FNDQyC4dRVwLBUehucb6oyxm5YVHuwvETQVK9XYWPRxkEdD6ZnVHhUGKCJIR178PSufIe4kwS6etOhq7qOcVv7bZoktl%2By5upEemZf8sS7Nlbu06IT3i%2F9lD96I1Dn87vu1aabvKooe%2BFrJnwiRlF1VCkK3BPURFyTMBrGDcOFlHYCQj2SL0xZ1iaI6P7dhGgetP5YgHqexpKp3yoLa&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240723T150051Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAYZTTEKKE4DY4MY6V%2F20240723%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=a43068bdf7a22299b4605e1a31e876698a967dac5410a37717f2bd6feffbbf71\n",
            "Resolving ago-item-storage.s3.amazonaws.com (ago-item-storage.s3.amazonaws.com)... 54.231.128.57, 52.217.128.177, 52.216.145.51, ...\n",
            "Connecting to ago-item-storage.s3.amazonaws.com (ago-item-storage.s3.amazonaws.com)|54.231.128.57|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 184893508 (176M) [application/zip]\n",
            "Saving to: ‘NSPL_2021_MAY_2024.zip’\n",
            "\n",
            "NSPL_2021_MAY_2024. 100%[===================>] 176.33M  18.8MB/s    in 11s     \n",
            "\n",
            "2024-07-23 15:01:03 (16.3 MB/s) - ‘NSPL_2021_MAY_2024.zip’ saved [184893508/184893508]\n",
            "\n",
            "Archive:  NSPL_2021_MAY_2024.zip\n",
            "   creating: Data/\n",
            "   creating: Data/multi_csv/\n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_AB.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_AL.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_B.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BB.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BD.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BH.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BL.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BN.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BR.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BS.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_BT.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CB.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CF.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CH.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CM.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CO.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CR.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CT.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CV.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_CW.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DD.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DE.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DG.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DH.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DL.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DN.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DT.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_DY.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_E.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_EC.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_EH.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_EN.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_EX.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_FK.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_FY.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_G.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_GL.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_GU.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_GY.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_HA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_HD.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_HG.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_HP.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_HR.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_HS.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_HU.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_HX.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_IG.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_IM.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_IP.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_IV.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_JE.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_KA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_KT.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_KW.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_KY.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_L.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_LA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_LD.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_LE.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_LL.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_LN.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_LS.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_LU.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_M.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_ME.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_MK.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_ML.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_N.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_NE.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_NG.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_NN.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_NP.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_NR.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_NW.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_OL.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_OX.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_PA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_PE.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_PH.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_PL.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_PO.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_PR.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_RG.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_RH.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_RM.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_S.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SE.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SG.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SK.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SL.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SM.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SN.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SO.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SP.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SR.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SS.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_ST.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SW.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_SY.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_TA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_TD.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_TF.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_TN.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_TQ.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_TR.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_TS.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_TW.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_UB.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_W.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_WA.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_WC.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_WD.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_WF.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_WN.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_WR.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_WS.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_WV.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_YO.csv  \n",
            "  inflating: Data/multi_csv/NSPL21_MAY_2024_UK_ZE.csv  \n",
            "  inflating: Data/NSPL21_MAY_2024_UK.csv  \n",
            "  inflating: Data/NSPL21_MAY_2024_UK.txt  \n",
            "   creating: Documents/\n",
            "  inflating: Documents/2011 Census Output Area Classification Names and Codes UK.csv  \n",
            "  inflating: Documents/2011 Census Output Area Classification Names and Codes UK.xlsx  \n",
            "  inflating: Documents/BUASD_names and codes UK as at 12_13.csv  \n",
            "  inflating: Documents/BUASD_names and codes UK as at 12_13.xlsx  \n",
            "  inflating: Documents/BUA_names and codes EW as at 12_22.csv  \n",
            "  inflating: Documents/BUA_names and codes EW as at 12_22.xlsx  \n",
            "  inflating: Documents/CALNCV names and codes EN as at 04_20.csv  \n",
            "  inflating: Documents/CALNCV names and codes EN as at 04_20.xlsx  \n",
            "  inflating: Documents/Country names and codes UK as at 08_12.csv  \n",
            "  inflating: Documents/Country names and codes UK as at 08_12.xlsx  \n",
            "  inflating: Documents/County Electoral Division names and codes EN as at 05_23.csv  \n",
            "  inflating: Documents/County Electoral Division names and codes EN as at 05_23.xlsx  \n",
            "  inflating: Documents/County names and codes UK as at 12_23_NSPL.csv  \n",
            "  inflating: Documents/County names and codes UK as at 12_23_NSPL.xlsx  \n",
            "  inflating: Documents/EER names and codes UK as at 12_10.csv  \n",
            "  inflating: Documents/EER names and codes UK as at 12_10.xlsx  \n",
            "  inflating: Documents/HLTHAU names and codes UK as at 04_19.csv  \n",
            "  inflating: Documents/HLTHAU names and codes UK as at 04_19.xlsx  \n",
            "  inflating: Documents/ICB names and codes UK as at 04_23.csv  \n",
            "  inflating: Documents/ICB names and codes UK as at 04_23.xlsx  \n",
            "  inflating: Documents/IMD lookup EN as at 12_19.csv  \n",
            "  inflating: Documents/IMD lookup EN as at 12_19.xlsx  \n",
            "  inflating: Documents/IMD lookup NI as at 12_17.csv  \n",
            "  inflating: Documents/IMD lookup NI as at 12_17.xlsx  \n",
            "  inflating: Documents/IMD lookup SC as at 12_20.csv  \n",
            "  inflating: Documents/IMD lookup SC as at 12_20.xlsx  \n",
            "  inflating: Documents/IMD lookup WA as at 12_19.csv  \n",
            "  inflating: Documents/IMD lookup WA as at 12_19.xlsx  \n",
            "  inflating: Documents/LAU121_ITL321_ITL221_ITL121_UK_LU.csv  \n",
            "  inflating: Documents/LAU121_ITL321_ITL221_ITL121_UK_LU.xlsx  \n",
            "  inflating: Documents/LA_UA names and codes UK as at 12_23.csv  \n",
            "  inflating: Documents/LA_UA names and codes UK as at 12_23.xlsx  \n",
            "  inflating: Documents/LEP names and codes EN as at 04_21 v2.csv  \n",
            "  inflating: Documents/LEP names and codes EN as at 04_21 v2.xlsx  \n",
            "  inflating: Documents/LOC names and codes UK as at 07_22.csv  \n",
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            "  inflating: Documents/Rural Urban (2011) Indicator names and codes GB as at 12_16.xlsx  \n",
            "  inflating: Documents/SDZ names and codes NI as at 03_21.csv  \n",
            "  inflating: Documents/SDZ names and codes NI as at 03_21.xlsx  \n",
            "  inflating: Documents/Sub_ICB Location and Local Health Board names and codes EW as at 04_23.csv  \n",
            "  inflating: Documents/Sub_ICB Location and Local Health Board names and codes EW as at 04_23.xlsx  \n",
            "  inflating: Documents/TECLEC names and codes UK as at 12_16.csv  \n",
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            "   creating: User Guide/\n",
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            "  inflating: User Guide/NSPL User Guide May 2024.pdf  \n",
            "-rw-r--r-- 1 root root 116743555 Jul 23 15:01 pcd_os_lat_long.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load postcode, OS eastings, OS northings, latitude, longitude data and join to price paid table.  Then check missing values."
      ],
      "metadata": {
        "id": "EjU8ICZ0T8Cx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pcd = dt.fread('pcd_os_lat_long.csv')\n",
        "pcd.names = ['postcode', 'oseast1m', 'osnrth1m', 'lat', 'long']\n",
        "pcd.key = \"postcode\"\n",
        "\n",
        "display(pcd)\n",
        "print('\\n')\n",
        "display(pcd.stypes)\n",
        "\n",
        "DT_ll = DT[:, :, join(pcd)]\n",
        "print('\\n')\n",
        "display(DT_ll)\n",
        "\n",
        "print('\\nmissing:')\n",
        "display(DT_ll['oseast1m'].to_pandas().isna().sum())\n",
        "display(DT_ll['osnrth1m'].to_pandas().isna().sum())\n",
        "display(DT_ll['lat'].to_pandas().isna().sum())\n",
        "display(DT_ll['long'].to_pandas().isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "N2HzMHnXMZfg",
        "outputId": "20a524cc-3131-4789-e130-51ba7843bc31"
      },
      "execution_count": 41,
      "outputs": [
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              "    <tr><td class='row_index'>5</td><td>{BBAB55E8-216F-4FFA-B9EC-B4AD71F833EB}</td><td>37000</td><td>1995-06-02 00:00</td><td>TN1 2ET</td><td>F</td><td>N</td><td>L</td><td>48</td><td>TOP FLOOR FLAT</td><td>UPPER GROSVENOR ROAD</td><td class=vellipsis>&hellip;</td><td>22</td><td>558611</td><td>140091</td><td>51.1381</td><td>0.265812</td></tr>\n",
              "    <tr><td class='row_index'>6</td><td>{FD34F0B9-8697-481D-A231-B4AD7716F2F5}</td><td>55000</td><td>1995-05-12 00:00</td><td>ST19 5AP</td><td>D</td><td>N</td><td>F</td><td>GAS HOUSE</td><td></td><td>PINFOLD LANE</td><td class=vellipsis>&hellip;</td><td>19</td><td>392104</td><td>314238</td><td>52.7257</td><td>&minus;2.11835</td></tr>\n",
              "    <tr><td class='row_index'>7</td><td>{657B1EA4-A147-4E1B-B70F-B4AD98A15237}</td><td>47750</td><td>1995-12-12 00:00</td><td>LL18 5SZ</td><td>S</td><td>Y</td><td>F</td><td>18</td><td></td><td>FFORDD TERFYN</td><td class=vellipsis>&hellip;</td><td>50</td><td>298395</td><td>376168</td><td>53.2727</td><td>&minus;3.52508</td></tr>\n",
              "    <tr><td class='row_index'>8</td><td>{FF6684D0-67EF-4FE1-AC8F-B4AD99ACE2CD}</td><td>53000</td><td>1995-05-25 00:00</td><td>BS30 5XW</td><td>S</td><td>N</td><td>F</td><td>4</td><td></td><td>GRASMERE GARDENS</td><td class=vellipsis>&hellip;</td><td>21</td><td>367601</td><td>172844</td><td>51.4536</td><td>&minus;2.46766</td></tr>\n",
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              "    <tr><td class='row_index'>11</td><td>{FEF50EAF-0816-43C6-98F2-AA05FB12EA0A}</td><td>76000</td><td>1995-06-23 00:00</td><td>DE74 2XH</td><td>D</td><td>N</td><td>F</td><td>20</td><td></td><td>CAMPION HILL</td><td class=vellipsis>&hellip;</td><td>25</td><td>444289</td><td>327733</td><td>52.8453</td><td>&minus;1.34386</td></tr>\n",
              "    <tr><td class='row_index'>12</td><td>{FD4959D2-3EE1-4078-B41D-AA0602C1FBA7}</td><td>58000</td><td>1995-06-27 00:00</td><td>RH13 5PS</td><td>T</td><td>N</td><td>F</td><td>59</td><td></td><td>KINGSLEA</td><td class=vellipsis>&hellip;</td><td>26</td><td>518436</td><td>131293</td><td>51.0688</td><td>&minus;0.311022</td></tr>\n",
              "    <tr><td class='row_index'>13</td><td>{92706092-0A25-47DF-AFED-AA06085FF9BF}</td><td>129500</td><td>1995-06-14 00:00</td><td>ME15 0DF</td><td>S</td><td>Y</td><td>F</td><td>10</td><td></td><td>FAIRHURST DRIVE</td><td class=vellipsis>&hellip;</td><td>24</td><td>573722</td><td>151603</td><td>51.2372</td><td>0.487165</td></tr>\n",
              "    <tr><td class='row_index'>14</td><td>{D352F749-2E41-4D24-A4D0-AD9C59B40B37}</td><td>41500</td><td>1995-03-03 00:00</td><td>OX12 7DQ</td><td>S</td><td>Y</td><td>F</td><td>5</td><td></td><td>HARCOURT ROAD</td><td class=vellipsis>&hellip;</td><td>9</td><td>440370</td><td>188514</td><td>51.594</td><td>&minus;1.41862</td></tr>\n",
              "    <tr><td class='row_index'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22F1;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td><td class='hellipsis'>&#x22EE;</td></tr>\n",
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              "    <tr><td class='row_index'>29,212,380</td><td>{152AB734-831A-E651-E063-4704A8C061D9}</td><td>60000</td><td>2024-03-13 00:00</td><td>BL1 1AU</td><td>F</td><td>N</td><td>L</td><td>HUNTINGDON HOUSE</td><td>APARTMENT 13</td><td>PRINCESS STREET</td><td class=vellipsis>&hellip;</td><td>11</td><td>371933</td><td>409252</td><td>53.5791</td><td>&minus;2.42537</td></tr>\n",
              "    <tr><td class='row_index'>29,212,381</td><td>{152AB734-831C-E651-E063-4704A8C061D9}</td><td>270000</td><td>2024-03-08 00:00</td><td>WN7 4BJ</td><td>D</td><td>N</td><td>F</td><td>21</td><td></td><td>WILKINSON PARK DRIVE</td><td class=vellipsis>&hellip;</td><td>10</td><td>363349</td><td>399685</td><td>53.4926</td><td>&minus;2.55389</td></tr>\n",
              "    <tr><td class='row_index'>29,212,382</td><td>{152AB734-831F-E651-E063-4704A8C061D9}</td><td>160000</td><td>2024-03-18 00:00</td><td>WN6 0HF</td><td>T</td><td>N</td><td>F</td><td>44</td><td></td><td>HIGH STREET</td><td class=vellipsis>&hellip;</td><td>12</td><td>356291</td><td>410060</td><td>53.5853</td><td>&minus;2.66172</td></tr>\n",
              "    <tr><td class='row_index'>29,212,383</td><td>{152AB734-8321-E651-E063-4704A8C061D9}</td><td>233000</td><td>2024-02-29 00:00</td><td>M19 1PT</td><td>S</td><td>N</td><td>F</td><td>7</td><td></td><td>MOSSBRAY AVENUE</td><td class=vellipsis>&hellip;</td><td>9</td><td>385950</td><td>391895</td><td>53.4237</td><td>&minus;2.21289</td></tr>\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "missing:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "oseast1m    49184\n",
              "dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "osnrth1m    49184\n",
              "dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "lat    49184\n",
              "dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "long    49184\n",
              "dtype: int64"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The 49,184 missing values are likely from the channel islands and/or the Isle of Man.  Also note, Northern Ireland is not included because apparently they use the Irish National Grid system.\n",
        "\n",
        "---\n",
        "\n",
        "Download [Ordance Survey National Grids](https://github.com/charlesroper/OSGB_Grids) in 10 Km squares."
      ],
      "metadata": {
        "id": "JXKgaThNUHIP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget 'https://github.com/charlesroper/OSGB_Grids/blob/master/Shapefile/OSGB_Grid_10km.shp?raw=true' -O 'OSGB_Grid_10km.shp'\n",
        "!ls -l OSGB_Grid_10km.shp\n",
        "\n",
        "!wget 'https://github.com/charlesroper/OSGB_Grids/blob/master/Shapefile/OSGB_Grid_10km.shx?raw=true' -O 'OSGB_Grid_10km.shx'\n",
        "!ls -l OSGB_Grid_10km.shx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TL2Mbb8PSARh",
        "outputId": "ad2085e0-b04b-48f2-a4b5-d097d19859bd"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-23 15:02:10--  https://github.com/charlesroper/OSGB_Grids/blob/master/Shapefile/OSGB_Grid_10km.shp?raw=true\n",
            "Resolving github.com (github.com)... 20.27.177.113\n",
            "Connecting to github.com (github.com)|20.27.177.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github.com/charlesroper/OSGB_Grids/raw/master/Shapefile/OSGB_Grid_10km.shp [following]\n",
            "--2024-07-23 15:02:10--  https://github.com/charlesroper/OSGB_Grids/raw/master/Shapefile/OSGB_Grid_10km.shp\n",
            "Reusing existing connection to github.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/charlesroper/OSGB_Grids/master/Shapefile/OSGB_Grid_10km.shp [following]\n",
            "--2024-07-23 15:02:10--  https://raw.githubusercontent.com/charlesroper/OSGB_Grids/master/Shapefile/OSGB_Grid_10km.shp\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: 392188 (383K) [application/octet-stream]\n",
            "Saving to: ‘OSGB_Grid_10km.shp’\n",
            "\n",
            "OSGB_Grid_10km.shp  100%[===================>] 383.00K  2.41MB/s    in 0.2s    \n",
            "\n",
            "2024-07-23 15:02:11 (2.41 MB/s) - ‘OSGB_Grid_10km.shp’ saved [392188/392188]\n",
            "\n",
            "-rw-r--r-- 1 root root 392188 Jul 23 15:02 OSGB_Grid_10km.shp\n",
            "--2024-07-23 15:02:11--  https://github.com/charlesroper/OSGB_Grids/blob/master/Shapefile/OSGB_Grid_10km.shx?raw=true\n",
            "Resolving github.com (github.com)... 20.27.177.113\n",
            "Connecting to github.com (github.com)|20.27.177.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github.com/charlesroper/OSGB_Grids/raw/master/Shapefile/OSGB_Grid_10km.shx [following]\n",
            "--2024-07-23 15:02:12--  https://github.com/charlesroper/OSGB_Grids/raw/master/Shapefile/OSGB_Grid_10km.shx\n",
            "Reusing existing connection to github.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/charlesroper/OSGB_Grids/master/Shapefile/OSGB_Grid_10km.shx [following]\n",
            "--2024-07-23 15:02:12--  https://raw.githubusercontent.com/charlesroper/OSGB_Grids/master/Shapefile/OSGB_Grid_10km.shx\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: 23164 (23K) [application/octet-stream]\n",
            "Saving to: ‘OSGB_Grid_10km.shx’\n",
            "\n",
            "OSGB_Grid_10km.shx  100%[===================>]  22.62K  --.-KB/s    in 0.007s  \n",
            "\n",
            "2024-07-23 15:02:13 (3.27 MB/s) - ‘OSGB_Grid_10km.shx’ saved [23164/23164]\n",
            "\n",
            "-rw-r--r-- 1 root root 23164 Jul 23 15:02 OSGB_Grid_10km.shx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, load British National Grid (BNG) 10 Km grid squares with [GeoPandas](https://geopandas.org/en/stable/).\n",
        "\n",
        "Second, make a [GeoDataFrame](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html) from the price paid data including OS eastings and northings.\n",
        "\n",
        "Third, perform spatial join between `sales_data` (points) and `bng_tiles`(polygons) and count number of sales in each grid square.\n",
        "\n",
        "Finally, plot the sales map."
      ],
      "metadata": {
        "id": "ImgXl5VSV_LF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "\n",
        "\n",
        "# Projected coordinate system for United Kingdom\n",
        "crs = 'EPSG:27700'\n",
        "\n",
        "# Step 1: Load BNG tiles\n",
        "bng_tiles = gpd.read_file('OSGB_Grid_10km.shp', crs=crs)\n",
        "bng_tiles.set_crs(crs, inplace=True)\n",
        "\n",
        "# Step 2: Create a GeoDataFrame from the price paid data including\n",
        "#         OS eastings and northings\n",
        "# Assuming 'sales' data is a DataFrame with 'BNG' column representing\n",
        "# point locations and a 'sales' column representing sales figures\n",
        "sales_data = gpd.GeoDataFrame({'geometry': gpd.points_from_xy(DT_ll['oseast1m'].to_list()[0],\n",
        "                                                              DT_ll['osnrth1m'].to_list()[0]),\n",
        "                               'sales': DT_ll['price'].to_list()[0]},\n",
        "                               crs = crs)\n",
        "\n",
        "\n",
        "# Step 3: Perform spatial join between sales_data (points) and bng_tiles (polys)\n",
        "#         and count number of sales\n",
        "# WARNING: Bottleneck here\n",
        "sales_grid = bng_tiles.join(\n",
        "    gpd.sjoin(sales_data, bng_tiles).groupby(\"index_right\").size().rename(\"sales_count\"),\n",
        "    how=\"left\",\n",
        ")\n",
        "\n",
        "# Step 4: Plot the map\n",
        "fig, ax = plt.subplots(figsize=(12, 12))\n",
        "sales_grid.plot(column='sales_count', cmap='YlOrRd', linewidth=0.8,\n",
        "                 ax=ax, legend=True,  # edgecolor='0.8',\n",
        "                 legend_kwds={'label': 'Number of Sales'}\n",
        "                 )\n",
        "plt.title('Sales per 10km Grid Square (BNG)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 961
        },
        "id": "29JIRtFEM1WQ",
        "outputId": "9882a83a-89fe-4c94-d4df-0d3f9407612c"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7.\n",
        "\n",
        "Plot a map showing the average sale price per 10km grid square.\n",
        "\n",
        "**NOTE**: I assume the previous cell has already been ran to generate the `bng_tiles` and `sales_data` dataframes."
      ],
      "metadata": {
        "id": "NfzUv8HbEyNa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform spatial join between sales_data (points) and bng_tiles (polys)\n",
        "# and find mean price paid in each grid square\n",
        "# WARNING: Bottleneck here\n",
        "sales_grid = bng_tiles.join(\n",
        "    gpd.sjoin(sales_data, bng_tiles).groupby(\"index_right\").mean('sales'),#.rename(\"price_mean\"),\n",
        "    how=\"left\",\n",
        ")\n",
        "\n",
        "# Plot the map\n",
        "fig, ax = plt.subplots(figsize=(12, 12))\n",
        "sales_grid.plot(column='sales', cmap='YlOrRd', linewidth=0.8,\n",
        "                 ax=ax, legend=True,  # edgecolor='0.8',\n",
        "                 legend_kwds={'label': 'Mean sale price'}\n",
        "                 )\n",
        "plt.title('Mean Sale Price per 10km Grid Square (BNG)')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "NUG-I6a_E56K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "outputId": "b222c37d-6806-4657-919f-e99ff430e3c3"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 2 notable outliers:\n",
        " 1. North of Carlise on the Scottish border\n",
        " 2. West of Cardiff\n",
        "\n",
        "These outliers justify further investigation.\n",
        "\n",
        "---\n",
        "\n",
        "## 8.\n",
        "\n",
        "Comment on your findings.\n",
        "\n",
        "House sales tend to be concentrated in and around major urban centers, particularly:\n",
        "\n",
        " * **London and the Southeast**: This region typically sees the highest volume of sales and highest average prices.\n",
        " * **Other major cities**: Areas like Birmingham, Manchester, Leeds and Cardiff/Bristol also experience significant sales activity.\n",
        " * **Commuter belts**: Towns and areas within commuting distance of major cities often have active housing markets.\n",
        "\n",
        "Rural areas and smaller towns generally have lower volumes of sales compared to urban centers.  Certain coastal areas, especially in the Southwest (e.g., Cornwall, Devon) and parts of Wales, may see sales related to holiday homes and retirement properties.\n",
        "\n",
        "There are also regional variations in pricing and sales volumes, with the North-South divide being a notable factor.   The Southeast and London typically have higher prices and more sales than regions in the North of England and Wales.\n",
        "\n",
        "The distribution of house sales also depends on the types of properties available.  Urban areas may see more transactions involving flats and terraced houses, while rural and suburban areas might have higher sales of detached and semi-detached houses.\n",
        "\n",
        "Finally, the geographic distribution of house sales and prices may vary over time."
      ],
      "metadata": {
        "id": "hgV7jcB3vbcR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## 9.\n",
        "\n",
        "Given everything you have learned from the exercises above, discuss the following model.\n",
        "  * In your discussion, provide an approximate R value that you would expect from the model\n",
        "  * How would you improve the model?\n",
        "  * What range of R would you be happy with?\n",
        "\n",
        "---\n",
        "\n",
        "The house price model includes:\n",
        " * Year of sale (continuous variable)\n",
        " * Postcode area (categorical variable)\n",
        " * Property type (categorical variable)\n",
        "\n",
        "These factors are significant predictors of house prices in the UK:\n",
        " * Year typically captures price trends over time\n",
        " * Postcode area accounts for location, which is crucial in real estate\n",
        " * Property type (e.g., detached, semi-detached, flat) affects price considerably\n",
        "\n",
        "However, the model is missing some potentially important variables:\n",
        " * Number of bedrooms/bathrooms\n",
        " * Floor area\n",
        " * Condition of the property\n",
        " * Construction year\n",
        " * Local crime rates\n",
        " * Local amenities (e.g., schools, parks, transportation)\n",
        " * Economic factors (e.g., interest rates, unemployment, inflation)\n",
        "\n",
        "Further model improvements:\n",
        " * **Interaction Terms**: Consider interaction terms between variables. For example, the effect of year on price might differ by postcode area.\n",
        " * **Non-linear Relationships**: Explore non-linear relationships using polynomial terms or transformations of variables if the relationship between predictors and the price is not strictly linear.\n",
        " * **Handle outliers and influential points**: Identify and address outliers or influential data points that can significantly impact the model.\n",
        " * **Diagnostics**: Check for heteroscedasticity, multicollinearity, and autocorrelation to ensure model assumptions are met.\n",
        " * **Regularization Techniques**: To handle multicollinearity and improve model generalizability, techniques such as Ridge Regression, Lasso Regression, or Elastic Net can be employed.\n",
        " * **Model Evaluation and Selection**: Use cross-validation to evaluate model performance and select the best model. Techniques such as k-fold cross-validation help in assessing how the model generalizes to an independent dataset.\n",
        "\n",
        "R-squared values in real estate models can vary widely, but:\n",
        " * Models with only location can achieve R-squared values of 0.5-0.6\n",
        " * Adding property characteristics could push this to 0.7-0.8\n",
        " * More comprehensive models might reach 0.8-0.9\n",
        "\n",
        "This model has good predictors but isn't comprehensive, so I'd expect it to fall in the middle range.\n",
        "\n",
        "Given these considerations, I would estimate an R-squared value for this model to be approximately 0.6 to 0.8.\n",
        "This suggests the model would explain about 60-80% of the variance in house prices, which is respectable but leaves room for improvement by incorporating additional variables."
      ],
      "metadata": {
        "id": "PACkzEkd0T88"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## 9.\n",
        "\n",
        "Energy Performance Certificates (EPCs) are published for each property transaction recorded by the Land Registry.  The data schema for these is available here\n",
        "  * I. Which fields from this dataset would be useful in determining sale price?\n",
        "  * II. How would you approach the task of joining the EPC database with the Land Registry?\n",
        "    * N.B. We don't expect you to actually join the databases - but please provide as much detail as possible about how you would approach the task.\n",
        "\n",
        "---\n",
        "\n",
        "### I.\n",
        "\n",
        "The following EPC fields could be relevant for determining sale price:\n",
        "\n",
        " * Core EPC Data\n",
        "   * **Current Energy Rating**: This indicates the property's current energy efficiency level.\n",
        "   * **Potential Energy Rating**: This shows the property's potential energy efficiency if recommended improvements are made.\n",
        "\n",
        " * Property Details\n",
        "   * **Total Floor Area**: Larger properties generally consume more energy and might be priced differently.\n",
        "   * **Construction Age Band**: Older properties tend to be less energy efficient and might be priced lower.\n",
        "   * **Number Habitable Rooms**: Number of rooms used for dwelling, or the number of rooms that provide living accommodation.  Rooms must have a window.\n",
        "\n",
        "\n",
        "### II.\n",
        "\n",
        "Proposed Approach\n",
        "\n",
        " * Data Acquisition and Preparation:\n",
        "\n",
        "   * **Obtain datasets**: Secure access to both EPC and Land Registry datasets in a suitable format (e.g., CSV).\n",
        "\n",
        "   * **Data cleaning**: Identify and address data quality issues such as missing values, inconsistencies and outliers.\n",
        "\n",
        "   * **Data standardization**: Ensure consistency in data formats, especially for fields that will be used for joining (e.g., property address, transaction date).\n",
        "\n",
        "   * **Data profiling**: Understand the structure, content and quality of each dataset.\n",
        "\n",
        " * Identify Common Key(s):\n",
        "\n",
        "   * **Property address**: This is the most likely common key, but it can be problematic due to variations in address formats.\n",
        "   * **Unique property identifier**: If available, a unique property identifier would be ideal.\n",
        "   * **Transaction ID**: If EPCs are linked to specific transactions, this could be a potential join key.\n",
        "\n",
        " * Data Matching:\n",
        "\n",
        "   * **Address standardization**: Use address standardization techniques (e.g., fuzzy matching, geocoding) to improve address matching accuracy.\n",
        "      * Use consistent casing and remove special characters.\n",
        "      * Employ address parsing libraries or services to break down addresses into components (e.g., house number, street name).\n",
        "      * Ensure all postcodes are in a standard format (e.g., upper case, removing spaces).\n",
        "      * Remove or correct any invalid postcodes.\n",
        "   * **Probabilistic matching**: Consider probabilistic matching algorithms (e.g., Levenshtein distance) for handling ambiguous or incomplete addresses.\n",
        "   * **Reference data**: Use external reference data (e.g., postcode lookup, Ordnance Survey data) to enhance matching accuracy.\n",
        "\n",
        " * Data Enrichment:\n",
        "\n",
        "   * **Create additional fields**: Derive new fields from existing data to facilitate the join (e.g., postcode, property type).\n",
        "   * **Data imputation**: Fill in missing values using appropriate imputation techniques.\n",
        "   * **Consistency checks**: Ensure common data types (e.g., dates) from each data set are in a single consistent format.\n",
        "\n",
        " * Join the Datasets:\n",
        "\n",
        "   * **Initial join**: Perform an initial join based on the chosen common key(s).\n",
        "   * **Error analysis**: Evaluate the join results for errors and inconsistencies.\n",
        "      * Perform manual checks on a sample of the matched records to verify accuracy.\n",
        "      * Validate against known transactions or external data sources.\n",
        "      * Compare aggregated statistics (e.g., average sale price by postcode) before and after merging to ensure consistency.\n",
        "      * Identify valid and resolve invalid duplicates by determining criteria for the primary record (e.g., latest transaction date).\n",
        "   * **Iterative refinement**: Refine the join process based on error analysis findings.\n",
        "      * For addresses, use fuzzy matching techniques (e.g., Levenshtein distance) to match records that do not align perfectly.\n",
        "\n",
        " * Data Validation and Quality Assurance:\n",
        "\n",
        "   * **Data profiling**: Analyze the joined dataset for consistency and completeness.\n",
        "   * **Data quality checks**: Implement data quality checks to ensure data integrity.\n",
        "\n",
        " * Technical Considerations:\n",
        "\n",
        "   * **Data processing tools**: Utilise compatable data processing tools (e.g., Python) for data manipulation and analysis.\n",
        "     * The [fuzzywuzzy](https://github.com/seatgeek/fuzzywuzzy) approximate string matching library may or may not be compatible with the [datatable](https://datatable.readthedocs.io/) library.\n",
        "     * I have a `sanity_check_before_after_dfs` function in [this notebook](https://github.com/makeyourownmaker/CambridgeTemperatureNotebooks/blob/main/notebooks/feature_engineering.ipynb).\n",
        "        * It summarises the differences between two pandas dataframes.\n",
        "        * With some adaptation it may prove useful for Error analysis.\n",
        "        * Examples can be found in the above notebook by searching for \"before.index.equals(after.index):\"\n"
      ],
      "metadata": {
        "id": "l0iSwFUa0-67"
      }
    }
  ]
}