723 lines
282 KiB
Text
723 lines
282 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# EPC Data Analysis\n",
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"\n",
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"Exploratory analysis of the Energy Performance Certificate (EPC) dataset (~29M rows, converted from CSV to parquet for memory efficiency)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-01-28T22:06:26.543050Z",
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"iopub.status.busy": "2026-01-28T22:06:26.542977Z",
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"iopub.status.idle": "2026-01-28T22:06:27.083569Z",
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"shell.execute_reply": "2026-01-28T22:06:27.083187Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Rows: 28,847,961\n",
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"Columns: LMK_KEY\n",
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"ADDRESS1\n",
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"ADDRESS2\n",
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"ADDRESS3\n",
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"POSTCODE\n",
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"BUILDING_REFERENCE_NUMBER\n",
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"CURRENT_ENERGY_RATING\n",
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"POTENTIAL_ENERGY_RATING\n",
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"CURRENT_ENERGY_EFFICIENCY\n",
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"POTENTIAL_ENERGY_EFFICIENCY\n",
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"PROPERTY_TYPE\n",
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"BUILT_FORM\n",
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"INSPECTION_DATE\n",
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"LOCAL_AUTHORITY\n",
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"CONSTITUENCY\n",
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"COUNTY\n",
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"LODGEMENT_DATE\n",
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"TRANSACTION_TYPE\n",
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"ENVIRONMENT_IMPACT_CURRENT\n",
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"ENVIRONMENT_IMPACT_POTENTIAL\n",
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"ENERGY_CONSUMPTION_CURRENT\n",
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"ENERGY_CONSUMPTION_POTENTIAL\n",
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"CO2_EMISSIONS_CURRENT\n",
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"CO2_EMISS_CURR_PER_FLOOR_AREA\n",
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"CO2_EMISSIONS_POTENTIAL\n",
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"LIGHTING_COST_CURRENT\n",
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"LIGHTING_COST_POTENTIAL\n",
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"HEATING_COST_CURRENT\n",
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"HEATING_COST_POTENTIAL\n",
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"HOT_WATER_COST_CURRENT\n",
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"HOT_WATER_COST_POTENTIAL\n",
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"TOTAL_FLOOR_AREA\n",
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"ENERGY_TARIFF\n",
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"MAINS_GAS_FLAG\n",
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"FLOOR_LEVEL\n",
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"FLAT_TOP_STOREY\n",
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"FLAT_STOREY_COUNT\n",
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"MAIN_HEATING_CONTROLS\n",
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"MULTI_GLAZE_PROPORTION\n",
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"GLAZED_TYPE\n",
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"GLAZED_AREA\n",
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"EXTENSION_COUNT\n",
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"NUMBER_HABITABLE_ROOMS\n",
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"NUMBER_HEATED_ROOMS\n",
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"LOW_ENERGY_LIGHTING\n",
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"NUMBER_OPEN_FIREPLACES\n",
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"HOTWATER_DESCRIPTION\n",
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"HOT_WATER_ENERGY_EFF\n",
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"HOT_WATER_ENV_EFF\n",
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"FLOOR_DESCRIPTION\n",
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"FLOOR_ENERGY_EFF\n",
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"FLOOR_ENV_EFF\n",
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"WINDOWS_DESCRIPTION\n",
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"WINDOWS_ENERGY_EFF\n",
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"WINDOWS_ENV_EFF\n",
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"WALLS_DESCRIPTION\n",
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"WALLS_ENERGY_EFF\n",
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"WALLS_ENV_EFF\n",
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"SECONDHEAT_DESCRIPTION\n",
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"SHEATING_ENERGY_EFF\n",
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"SHEATING_ENV_EFF\n",
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"ROOF_DESCRIPTION\n",
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"ROOF_ENERGY_EFF\n",
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"ROOF_ENV_EFF\n",
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"MAINHEAT_DESCRIPTION\n",
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"MAINHEAT_ENERGY_EFF\n",
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"MAINHEAT_ENV_EFF\n",
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"MAINHEATCONT_DESCRIPTION\n",
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"MAINHEATC_ENERGY_EFF\n",
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"MAINHEATC_ENV_EFF\n",
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"LIGHTING_DESCRIPTION\n",
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"LIGHTING_ENERGY_EFF\n",
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"LIGHTING_ENV_EFF\n",
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"MAIN_FUEL\n",
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"WIND_TURBINE_COUNT\n",
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"HEAT_LOSS_CORRIDOR\n",
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"UNHEATED_CORRIDOR_LENGTH\n",
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"FLOOR_HEIGHT\n",
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"PHOTO_SUPPLY\n",
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"SOLAR_WATER_HEATING_FLAG\n",
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"MECHANICAL_VENTILATION\n",
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"ADDRESS\n",
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"LOCAL_AUTHORITY_LABEL\n",
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"CONSTITUENCY_LABEL\n",
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"POSTTOWN\n",
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"CONSTRUCTION_AGE_BAND\n",
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"LODGEMENT_DATETIME\n",
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"TENURE\n",
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"FIXED_LIGHTING_OUTLETS_COUNT\n",
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"LOW_ENERGY_FIXED_LIGHT_COUNT\n",
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"UPRN\n",
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"UPRN_SOURCE\n",
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"REPORT_TYPE\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><style>\n",
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".dataframe > thead > tr,\n",
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".dataframe > tbody > tr {\n",
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" text-align: right;\n",
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" white-space: pre-wrap;\n",
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"}\n",
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"</style>\n",
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"<small>shape: (3, 93)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>LMK_KEY</th><th>ADDRESS1</th><th>ADDRESS2</th><th>ADDRESS3</th><th>POSTCODE</th><th>BUILDING_REFERENCE_NUMBER</th><th>CURRENT_ENERGY_RATING</th><th>POTENTIAL_ENERGY_RATING</th><th>CURRENT_ENERGY_EFFICIENCY</th><th>POTENTIAL_ENERGY_EFFICIENCY</th><th>PROPERTY_TYPE</th><th>BUILT_FORM</th><th>INSPECTION_DATE</th><th>LOCAL_AUTHORITY</th><th>CONSTITUENCY</th><th>COUNTY</th><th>LODGEMENT_DATE</th><th>TRANSACTION_TYPE</th><th>ENVIRONMENT_IMPACT_CURRENT</th><th>ENVIRONMENT_IMPACT_POTENTIAL</th><th>ENERGY_CONSUMPTION_CURRENT</th><th>ENERGY_CONSUMPTION_POTENTIAL</th><th>CO2_EMISSIONS_CURRENT</th><th>CO2_EMISS_CURR_PER_FLOOR_AREA</th><th>CO2_EMISSIONS_POTENTIAL</th><th>LIGHTING_COST_CURRENT</th><th>LIGHTING_COST_POTENTIAL</th><th>HEATING_COST_CURRENT</th><th>HEATING_COST_POTENTIAL</th><th>HOT_WATER_COST_CURRENT</th><th>HOT_WATER_COST_POTENTIAL</th><th>TOTAL_FLOOR_AREA</th><th>ENERGY_TARIFF</th><th>MAINS_GAS_FLAG</th><th>FLOOR_LEVEL</th><th>FLAT_TOP_STOREY</th><th>FLAT_STOREY_COUNT</th><th>…</th><th>WALLS_ENERGY_EFF</th><th>WALLS_ENV_EFF</th><th>SECONDHEAT_DESCRIPTION</th><th>SHEATING_ENERGY_EFF</th><th>SHEATING_ENV_EFF</th><th>ROOF_DESCRIPTION</th><th>ROOF_ENERGY_EFF</th><th>ROOF_ENV_EFF</th><th>MAINHEAT_DESCRIPTION</th><th>MAINHEAT_ENERGY_EFF</th><th>MAINHEAT_ENV_EFF</th><th>MAINHEATCONT_DESCRIPTION</th><th>MAINHEATC_ENERGY_EFF</th><th>MAINHEATC_ENV_EFF</th><th>LIGHTING_DESCRIPTION</th><th>LIGHTING_ENERGY_EFF</th><th>LIGHTING_ENV_EFF</th><th>MAIN_FUEL</th><th>WIND_TURBINE_COUNT</th><th>HEAT_LOSS_CORRIDOR</th><th>UNHEATED_CORRIDOR_LENGTH</th><th>FLOOR_HEIGHT</th><th>PHOTO_SUPPLY</th><th>SOLAR_WATER_HEATING_FLAG</th><th>MECHANICAL_VENTILATION</th><th>ADDRESS</th><th>LOCAL_AUTHORITY_LABEL</th><th>CONSTITUENCY_LABEL</th><th>POSTTOWN</th><th>CONSTRUCTION_AGE_BAND</th><th>LODGEMENT_DATETIME</th><th>TENURE</th><th>FIXED_LIGHTING_OUTLETS_COUNT</th><th>LOW_ENERGY_FIXED_LIGHT_COUNT</th><th>UPRN</th><th>UPRN_SOURCE</th><th>REPORT_TYPE</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>date</td><td>str</td><td>str</td><td>str</td><td>date</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>…</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>datetime[μs]</td><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>"0013ac0dc0f80dd06448efce74287d…</td><td>"43 GAYAL CROFT"</td><td>"MILTON KEYNES"</td><td>null</td><td>"MK5 7HX"</td><td>10003769440</td><td>"C"</td><td>"B"</td><td>75</td><td>89</td><td>"House"</td><td>"Mid-Terrace"</td><td>2022-08-22</td><td>"E06000042"</td><td>"E14000822"</td><td>null</td><td>2022-12-08</td><td>"rental"</td><td>75</td><td>90</td><td>175</td><td>61</td><td>1.9</td><td>31</td><td>0.7</td><td>55</td><td>44</td><td>340</td><td>342</td><td>98</td><td>63</td><td>61.0</td><td>"Single"</td><td>"Y"</td><td>null</td><td>null</td><td>null</td><td>…</td><td>"Good"</td><td>"Good"</td><td>"None"</td><td>"N/A"</td><td>"N/A"</td><td>"Pitched, insulated (assumed)"</td><td>"Good"</td><td>"Good"</td><td>"Boiler and radiators, mains ga…</td><td>"Good"</td><td>"Good"</td><td>"Programmer, room thermostat an…</td><td>"Good"</td><td>"Good"</td><td>"Low energy lighting in 75% of …</td><td>"Very Good"</td><td>"Very Good"</td><td>"mains gas (not community)"</td><td>0</td><td>null</td><td>null</td><td>2.4</td><td>0.0</td><td>"N"</td><td>"natural"</td><td>"43 GAYAL CROFT, MILTON KEYNES"</td><td>"Milton Keynes"</td><td>"Milton Keynes South"</td><td>null</td><td>"England and Wales: 1996-2002"</td><td>2022-12-08 22:29:20</td><td>"Rented (social)"</td><td>8</td><td>null</td><td>25018528</td><td>"Energy Assessor"</td><td>100</td></tr><tr><td>"20daddd4e5ce9abcca2efab2270af5…</td><td>"30 Ditton Road"</td><td>null</td><td>null</td><td>"DA6 8JL"</td><td>10003624703</td><td>"D"</td><td>"B"</td><td>59</td><td>84</td><td>"House"</td><td>"Mid-Terrace"</td><td>2022-11-03</td><td>"E09000004"</td><td>"E14000558"</td><td>null</td><td>2022-11-03</td><td>"marketed sale"</td><td>51</td><td>81</td><td>279</td><td>93</td><td>4.6</td><td>49</td><td>1.6</td><td>80</td><td>80</td><td>755</td><td>452</td><td>117</td><td>68</td><td>93.0</td><td>"Unknown"</td><td>"Y"</td><td>null</td><td>null</td><td>null</td><td>…</td><td>"Poor"</td><td>"Poor"</td><td>"None"</td><td>"N/A"</td><td>"N/A"</td><td>"Pitched, 100 mm loft insulatio…</td><td>"Average"</td><td>"Average"</td><td>"Boiler and radiators, mains ga…</td><td>"Good"</td><td>"Good"</td><td>"Programmer and room thermostat"</td><td>"Average"</td><td>"Average"</td><td>"Low energy lighting in all fix…</td><td>"Very Good"</td><td>"Very Good"</td><td>"mains gas (not community)"</td><td>0</td><td>null</td><td>null</td><td>2.64</td><td>0.0</td><td>"N"</td><td>"natural"</td><td>"30 Ditton Road"</td><td>"Bexley"</td><td>"Bexleyheath and Crayford"</td><td>"BEXLEYHEATH"</td><td>"England and Wales: 1950-1966"</td><td>2022-11-03 16:38:48</td><td>"Owner-occupied"</td><td>11</td><td>null</td><td>100020205591</td><td>"Energy Assessor"</td><td>100</td></tr><tr><td>"0019793879cf7ab92be22ba552e992…</td><td>"The Paddocks"</td><td>"Bridge Road"</td><td>"Stoke Bruerne"</td><td>"NN12 7SE"</td><td>10003816660</td><td>"D"</td><td>"C"</td><td>65</td><td>74</td><td>"House"</td><td>"Detached"</td><td>2022-12-16</td><td>"E06000062"</td><td>"E14000942"</td><td>null</td><td>2022-12-16</td><td>"marketed sale"</td><td>57</td><td>67</td><td>146</td><td>103</td><td>8.3</td><td>38</td><td>6.4</td><td>130</td><td>130</td><td>1008</td><td>959</td><td>184</td><td>93</td><td>217.0</td><td>"Single"</td><td>"N"</td><td>null</td><td>null</td><td>null</td><td>…</td><td>"Good"</td><td>"Good"</td><td>"None"</td><td>"N/A"</td><td>"N/A"</td><td>"Pitched, insulated at rafters"</td><td>"Average"</td><td>"Average"</td><td>"Boiler and radiators, oil"</td><td>"Average"</td><td>"Good"</td><td>"Programmer, room thermostat an…</td><td>"Good"</td><td>"Good"</td><td>"Low energy lighting in all fix…</td><td>"Very Good"</td><td>"Very Good"</td><td>"oil (not community)"</td><td>0</td><td>null</td><td>null</td><td>2.36</td><td>0.0</td><td>"N"</td><td>"natural"</td><td>"The Paddocks, Bridge Road, Sto…</td><td>"West Northamptonshire"</td><td>"South Northamptonshire"</td><td>"TOWCESTER"</td><td>"England and Wales: 1983-1990"</td><td>2022-12-16 10:47:55</td><td>"Owner-occupied"</td><td>22</td><td>null</td><td>10023964427</td><td>"Energy Assessor"</td><td>100</td></tr></tbody></table></div>"
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],
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"text/plain": [
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"shape: (3, 93)\n",
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"┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
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"│ LMK_KEY ┆ ADDRESS1 ┆ ADDRESS2 ┆ ADDRESS3 ┆ … ┆ LOW_ENERG ┆ UPRN ┆ UPRN_SOUR ┆ REPORT_T │\n",
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"│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ Y_FIXED_L ┆ --- ┆ CE ┆ YPE │\n",
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"│ str ┆ str ┆ str ┆ str ┆ ┆ IGHT_COUN ┆ i64 ┆ --- ┆ --- │\n",
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"│ ┆ ┆ ┆ ┆ ┆ T ┆ ┆ str ┆ i64 │\n",
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"│ ┆ ┆ ┆ ┆ ┆ --- ┆ ┆ ┆ │\n",
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"│ ┆ ┆ ┆ ┆ ┆ i64 ┆ ┆ ┆ │\n",
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"╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
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"│ 0013ac0dc ┆ 43 GAYAL ┆ MILTON ┆ null ┆ … ┆ null ┆ 25018528 ┆ Energy ┆ 100 │\n",
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"│ 0f80dd064 ┆ CROFT ┆ KEYNES ┆ ┆ ┆ ┆ ┆ Assessor ┆ │\n",
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"│ 48efce742 ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ 87d… ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ 20daddd4e ┆ 30 Ditton ┆ null ┆ null ┆ … ┆ null ┆ 100020205 ┆ Energy ┆ 100 │\n",
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"│ 5ce9abcca ┆ Road ┆ ┆ ┆ ┆ ┆ 591 ┆ Assessor ┆ │\n",
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"│ 2efab2270 ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ af5… ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ 001979387 ┆ The ┆ Bridge ┆ Stoke ┆ … ┆ null ┆ 100239644 ┆ Energy ┆ 100 │\n",
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"│ 9cf7ab92b ┆ Paddocks ┆ Road ┆ Bruerne ┆ ┆ ┆ 27 ┆ Assessor ┆ │\n",
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"│ e22ba552e ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"│ 992… ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
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"└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"import polars as pl\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.ticker as ticker\n",
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"\n",
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"plt.rcParams[\"figure.figsize\"] = (12, 5)\n",
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"plt.rcParams[\"figure.dpi\"] = 100\n",
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"\n",
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"PARQUET = \"../data/epc/certificates.parquet\"\n",
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"\n",
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"\n",
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"def scan():\n",
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" return pl.scan_parquet(PARQUET)\n",
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"\n",
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"\n",
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"# Quick row count\n",
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"row_count = scan().select(pl.len()).collect().item()\n",
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"print(f\"Rows: {row_count:,}\")\n",
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"print(f\"Columns: {'\\n'.join(scan().collect_schema().names())}\")\n",
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"\n",
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"scan().head(3).collect()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Schema Overview"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"execution": {
|
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"iopub.execute_input": "2026-01-28T22:06:27.101365Z",
|
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"iopub.status.busy": "2026-01-28T22:06:27.101128Z",
|
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"iopub.status.idle": "2026-01-28T22:06:29.053942Z",
|
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"shell.execute_reply": "2026-01-28T22:06:29.053508Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div><style>\n",
|
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".dataframe > thead > tr,\n",
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".dataframe > tbody > tr {\n",
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" text-align: right;\n",
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" white-space: pre-wrap;\n",
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"}\n",
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"</style>\n",
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"<small>shape: (93, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>column</th><th>dtype</th><th>null_count</th><th>null_pct</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>"LMK_KEY"</td><td>"String"</td><td>0</td><td>0.0</td></tr><tr><td>"ADDRESS1"</td><td>"String"</td><td>49</td><td>0.0</td></tr><tr><td>"ADDRESS2"</td><td>"String"</td><td>14135472</td><td>49.0</td></tr><tr><td>"ADDRESS3"</td><td>"String"</td><td>26056812</td><td>90.3</td></tr><tr><td>"POSTCODE"</td><td>"String"</td><td>0</td><td>0.0</td></tr><tr><td>…</td><td>…</td><td>…</td><td>…</td></tr><tr><td>"FIXED_LIGHTING_OUTLETS_COUNT"</td><td>"Int64"</td><td>11903726</td><td>41.3</td></tr><tr><td>"LOW_ENERGY_FIXED_LIGHT_COUNT"</td><td>"Int64"</td><td>19926976</td><td>69.1</td></tr><tr><td>"UPRN"</td><td>"Int64"</td><td>646277</td><td>2.2</td></tr><tr><td>"UPRN_SOURCE"</td><td>"String"</td><td>646277</td><td>2.2</td></tr><tr><td>"REPORT_TYPE"</td><td>"Int64"</td><td>0</td><td>0.0</td></tr></tbody></table></div>"
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],
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"text/plain": [
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"shape: (93, 4)\n",
|
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"┌──────────────────────────────┬────────┬────────────┬──────────┐\n",
|
|
"│ column ┆ dtype ┆ null_count ┆ null_pct │\n",
|
|
"│ --- ┆ --- ┆ --- ┆ --- │\n",
|
|
"│ str ┆ str ┆ i64 ┆ f64 │\n",
|
|
"╞══════════════════════════════╪════════╪════════════╪══════════╡\n",
|
|
"│ LMK_KEY ┆ String ┆ 0 ┆ 0.0 │\n",
|
|
"│ ADDRESS1 ┆ String ┆ 49 ┆ 0.0 │\n",
|
|
"│ ADDRESS2 ┆ String ┆ 14135472 ┆ 49.0 │\n",
|
|
"│ ADDRESS3 ┆ String ┆ 26056812 ┆ 90.3 │\n",
|
|
"│ POSTCODE ┆ String ┆ 0 ┆ 0.0 │\n",
|
|
"│ … ┆ … ┆ … ┆ … │\n",
|
|
"│ FIXED_LIGHTING_OUTLETS_COUNT ┆ Int64 ┆ 11903726 ┆ 41.3 │\n",
|
|
"│ LOW_ENERGY_FIXED_LIGHT_COUNT ┆ Int64 ┆ 19926976 ┆ 69.1 │\n",
|
|
"│ UPRN ┆ Int64 ┆ 646277 ┆ 2.2 │\n",
|
|
"│ UPRN_SOURCE ┆ String ┆ 646277 ┆ 2.2 │\n",
|
|
"│ REPORT_TYPE ┆ Int64 ┆ 0 ┆ 0.0 │\n",
|
|
"└──────────────────────────────┴────────┴────────────┴──────────┘"
|
|
]
|
|
},
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"schema = scan().collect_schema()\n",
|
|
"\n",
|
|
"# Compute null counts in batches of columns to avoid OOM\n",
|
|
"batch_size = 10\n",
|
|
"all_nulls = {}\n",
|
|
"col_names = schema.names()\n",
|
|
"for i in range(0, len(col_names), batch_size):\n",
|
|
" batch = col_names[i : i + batch_size]\n",
|
|
" result = scan().select([pl.col(c).null_count().alias(c) for c in batch]).collect()\n",
|
|
" for c in batch:\n",
|
|
" all_nulls[c] = result[c].item()\n",
|
|
"\n",
|
|
"schema_info = pl.DataFrame(\n",
|
|
" {\n",
|
|
" \"column\": col_names,\n",
|
|
" \"dtype\": [str(d) for d in schema.dtypes()],\n",
|
|
" \"null_count\": [all_nulls[c] for c in col_names],\n",
|
|
" \"null_pct\": [round(all_nulls[c] / row_count * 100, 1) for c in col_names],\n",
|
|
" }\n",
|
|
")\n",
|
|
"schema_info"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Energy Rating Distribution"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-01-28T22:06:29.054902Z",
|
|
"iopub.status.busy": "2026-01-28T22:06:29.054824Z",
|
|
"iopub.status.idle": "2026-01-28T22:06:29.241274Z",
|
|
"shell.execute_reply": "2026-01-28T22:06:29.240893Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1200x500 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rating_order = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\"]\n",
|
|
"rating_counts = (\n",
|
|
" scan()\n",
|
|
" .filter(pl.col(\"CURRENT_ENERGY_RATING\").is_in(rating_order))\n",
|
|
" .group_by(\"CURRENT_ENERGY_RATING\")\n",
|
|
" .len()\n",
|
|
" .collect()\n",
|
|
")\n",
|
|
"rc = {r[\"CURRENT_ENERGY_RATING\"]: r[\"len\"] for r in rating_counts.iter_rows(named=True)}\n",
|
|
"\n",
|
|
"colors = [\"#1a9641\", \"#52b947\", \"#a6d96a\", \"#fee08b\", \"#f09e4a\", \"#d7191c\", \"#a50026\"]\n",
|
|
"values = [rc.get(r, 0) for r in rating_order]\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"bars = ax.bar(rating_order, values, color=colors)\n",
|
|
"ax.set_xlabel(\"Current Energy Rating\")\n",
|
|
"ax.set_ylabel(\"Number of Properties\")\n",
|
|
"ax.set_title(\"Distribution of Current Energy Ratings\")\n",
|
|
"ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x / 1e6:.1f}M\"))\n",
|
|
"for bar, v in zip(bars, values):\n",
|
|
" ax.text(\n",
|
|
" bar.get_x() + bar.get_width() / 2,\n",
|
|
" bar.get_height(),\n",
|
|
" f\"{v / 1e6:.1f}M\",\n",
|
|
" ha=\"center\",\n",
|
|
" va=\"bottom\",\n",
|
|
" fontsize=9,\n",
|
|
" )\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Property Type Breakdown"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-01-28T22:06:29.243014Z",
|
|
"iopub.status.busy": "2026-01-28T22:06:29.242929Z",
|
|
"iopub.status.idle": "2026-01-28T22:06:29.388503Z",
|
|
"shell.execute_reply": "2026-01-28T22:06:29.388068Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1200x500 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"prop_type = (\n",
|
|
" scan().group_by(\"PROPERTY_TYPE\").len().sort(\"len\", descending=True).collect()\n",
|
|
")\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"ax.barh(prop_type[\"PROPERTY_TYPE\"].to_list()[::-1], prop_type[\"len\"].to_list()[::-1])\n",
|
|
"ax.set_xlabel(\"Count\")\n",
|
|
"ax.set_title(\"Properties by Type\")\n",
|
|
"ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x / 1e6:.1f}M\"))\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## PROPERTY_TYPE and BUILT_FORM Cardinality"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"PROPERTY_TYPE — 5 distinct values:\n",
|
|
" House 17,437,884\n",
|
|
" Flat 8,236,696\n",
|
|
" Bungalow 2,448,109\n",
|
|
" Maisonette 710,695\n",
|
|
" Park home 14,577\n",
|
|
"\n",
|
|
"BUILT_FORM — 9 distinct values:\n",
|
|
" Semi-Detached 8,777,318\n",
|
|
" Mid-Terrace 7,972,697\n",
|
|
" Detached 6,428,144\n",
|
|
" End-Terrace 4,132,264\n",
|
|
" NO DATA! 548,769\n",
|
|
" Enclosed End-Terrace 450,732\n",
|
|
" Enclosed Mid-Terrace 377,198\n",
|
|
" Not Recorded 155,103\n",
|
|
" None 5,736\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for col_name in [\"PROPERTY_TYPE\", \"BUILT_FORM\"]:\n",
|
|
" counts = scan().group_by(col_name).len().sort(\"len\", descending=True).collect()\n",
|
|
" print(f\"{col_name} — {len(counts)} distinct values:\")\n",
|
|
" for row in counts.iter_rows(named=True):\n",
|
|
" print(f\" {row[col_name]!s:30s} {row['len']:>12,}\")\n",
|
|
" print()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Energy Rating by Construction Age Band"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-01-28T22:06:29.941986Z",
|
|
"iopub.status.busy": "2026-01-28T22:06:29.941893Z",
|
|
"iopub.status.idle": "2026-01-28T22:06:30.515904Z",
|
|
"shell.execute_reply": "2026-01-28T22:06:30.515522Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1200x500 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"age_eff = (\n",
|
|
" scan()\n",
|
|
" .filter(pl.col(\"CONSTRUCTION_AGE_BAND\").is_not_null())\n",
|
|
" .group_by(\"CONSTRUCTION_AGE_BAND\")\n",
|
|
" .agg(\n",
|
|
" pl.col(\"CURRENT_ENERGY_EFFICIENCY\").mean().alias(\"mean_efficiency\"),\n",
|
|
" pl.col(\"CURRENT_ENERGY_EFFICIENCY\").median().alias(\"median_efficiency\"),\n",
|
|
" pl.len().alias(\"count\"),\n",
|
|
" )\n",
|
|
" .filter(pl.col(\"count\") > 10000)\n",
|
|
" .collect()\n",
|
|
")\n",
|
|
"\n",
|
|
"# Define sort order for known age bands\n",
|
|
"age_band_order = [\n",
|
|
" \"before 1900\",\n",
|
|
" \"1900-1929\",\n",
|
|
" \"1930-1949\",\n",
|
|
" \"1950-1966\",\n",
|
|
" \"1967-1975\",\n",
|
|
" \"1976-1982\",\n",
|
|
" \"1983-1990\",\n",
|
|
" \"1991-1995\",\n",
|
|
" \"1996-2002\",\n",
|
|
" \"2003-2006\",\n",
|
|
" \"2007-2011\",\n",
|
|
" \"2007 onwards\",\n",
|
|
" \"2012 onwards\",\n",
|
|
"]\n",
|
|
"\n",
|
|
"\n",
|
|
"def age_sort_key(band):\n",
|
|
" for i, ab in enumerate(age_band_order):\n",
|
|
" if ab in str(band):\n",
|
|
" return i\n",
|
|
" return 99\n",
|
|
"\n",
|
|
"\n",
|
|
"age_eff = (\n",
|
|
" age_eff.with_columns(\n",
|
|
" pl.col(\"CONSTRUCTION_AGE_BAND\")\n",
|
|
" .map_elements(age_sort_key, return_dtype=pl.Int64)\n",
|
|
" .alias(\"sort_key\")\n",
|
|
" )\n",
|
|
" .sort(\"sort_key\")\n",
|
|
" .filter(pl.col(\"sort_key\") < 99)\n",
|
|
")\n",
|
|
"\n",
|
|
"labels = age_eff[\"CONSTRUCTION_AGE_BAND\"].to_list()\n",
|
|
"# Shorten labels\n",
|
|
"short_labels = [label.replace(\"England and Wales: \", \"\") for label in labels]\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"ax.bar(short_labels, age_eff[\"mean_efficiency\"].to_list(), label=\"Mean\", alpha=0.7)\n",
|
|
"ax.bar(short_labels, age_eff[\"median_efficiency\"].to_list(), label=\"Median\", alpha=0.5)\n",
|
|
"ax.set_xlabel(\"Construction Age Band\")\n",
|
|
"ax.set_ylabel(\"Energy Efficiency Score\")\n",
|
|
"ax.set_title(\"Energy Efficiency by Construction Age\")\n",
|
|
"ax.legend()\n",
|
|
"plt.xticks(rotation=45, ha=\"right\")\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Floor Area Distribution by Property Type"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-01-28T22:06:31.277179Z",
|
|
"iopub.status.busy": "2026-01-28T22:06:31.277103Z",
|
|
"iopub.status.idle": "2026-01-28T22:06:31.967032Z",
|
|
"shell.execute_reply": "2026-01-28T22:06:31.966642Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1997061/992856044.py:36: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
|
|
" ax.boxplot(data, labels=labels, vert=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1200x500 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"top_types = (\n",
|
|
" scan()\n",
|
|
" .group_by(\"PROPERTY_TYPE\")\n",
|
|
" .len()\n",
|
|
" .sort(\"len\", descending=True)\n",
|
|
" .head(5)\n",
|
|
" .collect()[\"PROPERTY_TYPE\"]\n",
|
|
" .to_list()\n",
|
|
")\n",
|
|
"\n",
|
|
"# Sample per property type to avoid loading all data\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"data = []\n",
|
|
"labels = []\n",
|
|
"for pt in top_types:\n",
|
|
" vals = (\n",
|
|
" scan()\n",
|
|
" .filter(\n",
|
|
" (pl.col(\"PROPERTY_TYPE\") == pt)\n",
|
|
" & pl.col(\"TOTAL_FLOOR_AREA\").is_not_null()\n",
|
|
" & (pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False) > 0)\n",
|
|
" )\n",
|
|
" .select(pl.col(\"TOTAL_FLOOR_AREA\").cast(pl.Float64, strict=False))\n",
|
|
" .collect()\n",
|
|
" .to_series()\n",
|
|
" .drop_nulls()\n",
|
|
" )\n",
|
|
" p95 = vals.quantile(0.95)\n",
|
|
" vals = vals.filter(vals < p95)\n",
|
|
" # Sample if too large\n",
|
|
" if len(vals) > 100_000:\n",
|
|
" vals = vals.sample(n=100_000, seed=42)\n",
|
|
" data.append(vals.to_list())\n",
|
|
" labels.append(pt)\n",
|
|
"\n",
|
|
"ax.boxplot(data, labels=labels, vert=True)\n",
|
|
"ax.set_ylabel(\"Total Floor Area (m²)\")\n",
|
|
"ax.set_title(\"Floor Area Distribution by Property Type (95th pctl)\")\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Lodgement Trends Over Time"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-01-28T22:06:31.967996Z",
|
|
"iopub.status.busy": "2026-01-28T22:06:31.967918Z",
|
|
"iopub.status.idle": "2026-01-28T22:06:32.076565Z",
|
|
"shell.execute_reply": "2026-01-28T22:06:32.076202Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1200x500 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"lodgements = (\n",
|
|
" scan()\n",
|
|
" .filter(pl.col(\"LODGEMENT_DATE\").is_not_null())\n",
|
|
" .with_columns(pl.col(\"LODGEMENT_DATE\").dt.year().alias(\"year\"))\n",
|
|
" .filter(pl.col(\"year\").is_between(2008, 2025))\n",
|
|
" .group_by(\"year\")\n",
|
|
" .len()\n",
|
|
" .sort(\"year\")\n",
|
|
" .collect()\n",
|
|
")\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"ax.bar(lodgements[\"year\"].to_list(), lodgements[\"len\"].to_list())\n",
|
|
"ax.set_xlabel(\"Year\")\n",
|
|
"ax.set_ylabel(\"Certificates Lodged\")\n",
|
|
"ax.set_title(\"EPC Lodgements by Year\")\n",
|
|
"ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x / 1e6:.1f}M\"))\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Main Fuel Type Distribution"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-01-28T22:06:32.398389Z",
|
|
"iopub.status.busy": "2026-01-28T22:06:32.398313Z",
|
|
"iopub.status.idle": "2026-01-28T22:06:32.909646Z",
|
|
"shell.execute_reply": "2026-01-28T22:06:32.909308Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 1200x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fuel = (\n",
|
|
" scan()\n",
|
|
" .filter(pl.col(\"MAIN_FUEL\").is_not_null() & (pl.col(\"MAIN_FUEL\") != \"\"))\n",
|
|
" .group_by(\"MAIN_FUEL\")\n",
|
|
" .len()\n",
|
|
" .sort(\"len\", descending=True)\n",
|
|
" .head(10)\n",
|
|
" .collect()\n",
|
|
")\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots(figsize=(12, 6))\n",
|
|
"ax.barh(fuel[\"MAIN_FUEL\"].to_list()[::-1], fuel[\"len\"].to_list()[::-1])\n",
|
|
"ax.set_xlabel(\"Count\")\n",
|
|
"ax.set_title(\"Top 10 Main Fuel Types\")\n",
|
|
"ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f\"{x / 1e6:.1f}M\"))\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "property-map",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|