diff --git a/Makefile.data b/Makefile.data index 31a3c03..af3a1f3 100644 --- a/Makefile.data +++ b/Makefile.data @@ -115,7 +115,7 @@ MAP_ASSETS_DEPS := pipeline/download/map_assets.py pipeline/transform/transform_ transform-school-proximity transform-tree-density \ generate-postcode-boundaries generate-travel-times enrich-actual-listings -prepare: $(PRICES_STAMP) download-places tiles satellite-tiles overlay-tiles property-border-tiles generate-postcode-boundaries download-map-assets generate-travel-times | $(POSTCODES_PQ) $(PROPERTIES_PQ) $(PRICE_INDEX) +prepare: $(PRICES_STAMP) download-places tiles satellite-tiles overlay-tiles property-border-tiles tree-overlay-tiles crime-hotspot-tiles property-border-tiles generate-postcode-boundaries download-map-assets generate-travel-times | $(POSTCODES_PQ) $(PROPERTIES_PQ) $(PRICE_INDEX) $(VALIDATE_OUTPUTS) --parquet $(POSTCODES_PQ) --parquet $(PROPERTIES_PQ) --parquet $(PRICE_INDEX) --postcode-boundary-match "$(POSTCODES_PQ)::$(PC_BOUNDARIES)" --postcode-features $(POSTCODES_PQ) --postcode-universe "$(ARCGIS)::$(POSTCODES_PQ)" --properties-subset "$(PROPERTIES_PQ)::$(POSTCODES_PQ)" --price-index $(PRICE_INDEX) merge: $(MERGE_STAMP) | $(POSTCODES_PQ) $(PROPERTIES_PQ) $(VALIDATE_OUTPUTS) --parquet $(POSTCODES_PQ) --parquet $(PROPERTIES_PQ) --postcode-features $(POSTCODES_PQ) --postcode-universe "$(ARCGIS)::$(POSTCODES_PQ)" --properties-subset "$(PROPERTIES_PQ)::$(POSTCODES_PQ)" diff --git a/analyses/online_listings_buy.ipynb b/analyses/online_listings_buy.ipynb index 6f461c2..44dba93 100644 --- a/analyses/online_listings_buy.ipynb +++ b/analyses/online_listings_buy.ipynb @@ -2,68 +2,99 @@ "cells": [ { "cell_type": "markdown", - "id": "55bc43ad", + "id": "21f27a93", "metadata": {}, "source": [ - "# Online Buy Listings\n", + "# Online Buy Listings — data quality & cleanup\n", "\n", - "Analysis of `finder/data/online_listings_buy.parquet`." + "Source: `finder/data/online_listings_buy.parquet` — ~112k UK *for-sale* property listings\n", + "(Greater London) scraped from **Rightmove**, **OnTheMarket** and **Zoopla** and merged by the\n", + "`finder` pipeline (`transform.py` / `onthemarket.py` / `zoopla.py` → `storage.py`).\n", + "\n", + "This notebook:\n", + "1. **Profiles** the raw data (shape, schema, missingness by provider).\n", + "2. **Demonstrates each data-quality issue** found in the audit, with the query that proves it.\n", + "3. **Cleans** the data (cell block in §4), adding provenance/flag columns rather than silently dropping where possible.\n", + "4. **Re-shows the stats** so the fixes are visible (§5), incl. a before/after summary table.\n", + "\n", + "Every count below is computed live from the file." ] }, { "cell_type": "code", "execution_count": 1, - "id": "1560f01a", + "id": "075fbea3", "metadata": { "execution": { - "iopub.execute_input": "2026-05-17T10:23:47.594553Z", - "iopub.status.busy": "2026-05-17T10:23:47.594488Z", - "iopub.status.idle": "2026-05-17T10:23:47.858517Z", - "shell.execute_reply": "2026-05-17T10:23:47.858194Z" + "iopub.execute_input": "2026-06-03T07:34:30.479194Z", + "iopub.status.busy": "2026-06-03T07:34:30.479121Z", + "iopub.status.idle": "2026-06-03T07:34:30.900789Z", + "shell.execute_reply": "2026-06-03T07:34:30.900538Z" } }, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m pathlib \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[32m 2\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m matplotlib.pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m polars \u001b[38;5;28;01mas\u001b[39;00m pl\n\u001b[32m 6\u001b[39m \n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'matplotlib'" + ] + } + ], "source": [ "from pathlib import Path\n", "\n", - "import plotly.express as px\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import polars as pl\n", "\n", - "pl.Config.set_tbl_rows(45)\n", - "pl.Config.set_fmt_str_lengths(80)\n", + "%matplotlib inline\n", + "plt.rcParams[\"figure.dpi\"] = 110\n", + "pl.Config.set_tbl_rows(40)\n", + "pl.Config.set_fmt_str_lengths(90)\n", + "pl.Config.set_tbl_cols(30)\n", "\n", - "DATA_PATH = Path(\"/volumes/projects/perfect-postcode/finder/data/test/online_listings_buy.parquet\")" + "DATA_PATH = Path(\"/volumes/projects/perfect-postcode/finder/data/online_listings_buy.parquet\")\n", + "\n", + "# Provider derived from the listing URL domain (mirrors the existing analysis convention).\n", + "PROVIDER = (\n", + " pl.when(pl.col(\"Listing URL\").str.contains(\"rightmove.co.uk\", literal=True)).then(pl.lit(\"Rightmove\"))\n", + " .when(pl.col(\"Listing URL\").str.contains(\"onthemarket.com\", literal=True)).then(pl.lit(\"OnTheMarket\"))\n", + " .when(pl.col(\"Listing URL\").str.contains(\"zoopla.co.uk\", literal=True)).then(pl.lit(\"Zoopla\"))\n", + " .otherwise(pl.lit(\"Unknown\"))\n", + " .alias(\"provider\")\n", + ")\n", + "\n", + "raw = pl.read_parquet(DATA_PATH).with_columns(PROVIDER)\n", + "print(f\"Rows: {raw.height:,} Columns: {len(raw.columns)}\")\n", + "raw.group_by(\"provider\").len().sort(\"len\", descending=True)" ] }, { "cell_type": "markdown", - "id": "6c5d1f0a", + "id": "8d632ad4", "metadata": {}, "source": [ - "## Load" + "## 1 · Overview" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "09a54208", + "execution_count": null, + "id": "692830dd", "metadata": { "execution": { - "iopub.execute_input": "2026-05-17T10:23:47.859716Z", - "iopub.status.busy": "2026-05-17T10:23:47.859600Z", - "iopub.status.idle": "2026-05-17T10:23:47.955592Z", - "shell.execute_reply": "2026-05-17T10:23:47.955345Z" + "iopub.execute_input": "2026-06-03T07:34:30.901690Z", + "iopub.status.busy": "2026-06-03T07:34:30.901608Z", + "iopub.status.idle": "2026-06-03T07:34:30.903798Z", + "shell.execute_reply": "2026-06-03T07:34:30.903603Z" } }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rows: 242\n", - "Columns: 25\n" - ] - }, { "data": { "text/html": [ @@ -74,183 +105,63 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (3, 2)
providerlen
stru32
"OnTheMarket"85
"Rightmove"99
"Zoopla"58
" + "shape: (25, 2)
columndtype
strstr
"Bedrooms""Int32"
"Bathrooms""Int32"
"Number of bedrooms & living rooms""Int32"
"lon""Float64"
"lat""Float64"
"Postcode""String"
"Postcode source""String"
"Extracted postcode""String"
"Inferred postcode""String"
"Listing raw address""String"
"Address per Property Register""String"
"UPRN""String"
"Property number or name""String"
"Leasehold/Freehold""String"
"Property type""String"
"Property sub-type""String"
"Price qualifier""String"
"Total floor area (sqm)""Float64"
"Listing URL""String"
"Listing features""List(String)"
"Listing date""Datetime(time_unit='us', time_zone=None)"
"Listing status""String"
"Asking price""Int64"
"Asking price per sqm""Int32"
"provider""String"
" ], "text/plain": [ - "shape: (3, 2)\n", - "┌─────────────┬─────┐\n", - "│ provider ┆ len │\n", - "│ --- ┆ --- │\n", - "│ str ┆ u32 │\n", - "╞═════════════╪═════╡\n", - "│ OnTheMarket ┆ 85 │\n", - "│ Rightmove ┆ 99 │\n", - "│ Zoopla ┆ 58 │\n", - "└─────────────┴─────┘" + "shape: (25, 2)\n", + "┌───────────────────────────────────┬──────────────────────────────────────────┐\n", + "│ column ┆ dtype │\n", + "│ --- ┆ --- │\n", + "│ str ┆ str │\n", + "╞═══════════════════════════════════╪══════════════════════════════════════════╡\n", + "│ Bedrooms ┆ Int32 │\n", + "│ Bathrooms ┆ Int32 │\n", + "│ Number of bedrooms & living rooms ┆ Int32 │\n", + "│ lon ┆ Float64 │\n", + "│ lat ┆ Float64 │\n", + "│ Postcode ┆ String │\n", + "│ Postcode source ┆ String │\n", + "│ Extracted postcode ┆ String │\n", + "│ Inferred postcode ┆ String │\n", + "│ Listing raw address ┆ String │\n", + "│ Address per Property Register ┆ String │\n", + "│ UPRN ┆ String │\n", + "│ Property number or name ┆ String │\n", + "│ Leasehold/Freehold ┆ String │\n", + "│ Property type ┆ String │\n", + "│ Property sub-type ┆ String │\n", + "│ Price qualifier ┆ String │\n", + "│ Total floor area (sqm) ┆ Float64 │\n", + "│ Listing URL ┆ String │\n", + "│ Listing features ┆ List(String) │\n", + "│ Listing date ┆ Datetime(time_unit='us', time_zone=None) │\n", + "│ Listing status ┆ String │\n", + "│ Asking price ┆ Int64 │\n", + "│ Asking price per sqm ┆ Int32 │\n", + "│ provider ┆ String │\n", + "└───────────────────────────────────┴──────────────────────────────────────────┘" ] }, + "execution_count": 2, "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "shape: (45, 25)
providerBedroomsBathroomsNumber of bedrooms & living roomslonlatPostcodePostcode sourceExtracted postcodeInferred postcodeListing raw addressAddress per Property RegisterUPRNProperty number or nameLeasehold/FreeholdProperty typeProperty sub-typePrice qualifierTotal floor area (sqm)Listing URLListing featuresListing dateListing statusAsking priceAsking price per sqm
stri32i32i32f64f64strstrstrstrstrstrstrstrstrstrstrstrf64strlist[str]datetime[μs]stri64i32
"OnTheMarket"112-0.12910251.463636"SW4 7EF""coordinates"null"SW4 7EF""Bedford Road, London""Bedford Road, London"nullnull"Leasehold""Flats/Maisonettes""Apartment"""null"https://www.onthemarket.com/details/19209711/"["Tenure: Leasehold (111 years remaining)", "Top floor one-bedroom apartment within a modern development completed in 2021", … "Generous double bedroom, contemporary bathroom and separate utility/storage room"]null"For sale"525000null
"OnTheMarket"112-0.10023351.466129"SE5 9AA""coordinates"null"SE5 9AA""Padfield Road, London, SE5""Padfield Road, London"nullnull"Freehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19522441/"["Tenure: Share of freehold (999 years remaining)", "One Bedroom"]null"For sale"275000null
"OnTheMarket"415-0.10891351.471775"SW9 6NN""coordinates"null"SW9 6NN""Lord Holland Lane, Myatts Fields South, London""Lord Holland Lane, Myatts Fields South, London"nullnull"Freehold""Terraced""Terraced House""Offers in excess of"null"https://www.onthemarket.com/details/18847208/"["Tenure: Freehold", "Four Bedrooms", … "Superb Condition"]null"For sale"600000null
"OnTheMarket"112-0.10790451.473026"SW9 6BF""coordinates"null"SW9 6BF""Mostyn Road, London SW9""Mostyn Road, London"nullnull"Leasehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19305103/"["Tenure: Leasehold", "One bedroom"]null"For sale"350000null
"OnTheMarket"112-0.11537251.475374"SW9 0RG""coordinates"null"SW9 0RG""Hackford Road, London SW9""Hackford Road, London"nullnull"Freehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19601952/"["Tenure: Share of freehold", "Separate dining and living room", … "Study"]null"For sale"475000null
"OnTheMarket"011-0.11791151.476212"SW9 0HP""coordinates"null"SW9 0HP""Clapham Road, London SW9""Clapham Road, London"nullnull"Leasehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19601638/"["Tenure: Leasehold", "Nearest station 0.3mi."]null"For sale"225000null
"OnTheMarket"224-0.10557951.463969"SW9 8SE""coordinates"null"SW9 8SE""Coldharbour Lane, Brixton, London, SW9""Coldharbour Lane, Brixton, London"nullnull"Leasehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19601141/"["Tenure: Leasehold", "Fantastic two bedroom period flat"]null"For sale"575000null
"OnTheMarket"112-0.11643751.466426"SW9 9FX""coordinates"null"SW9 9FX""Stockwell Road, SW9""Stockwell Road"nullnull"Leasehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19599310/"["Tenure: Leasehold (120 years remaining)", "One double bedroom", "Chain-free"]null"For sale"435000null
"OnTheMarket"224-0.10235551.46566"SW9 8RU""coordinates"null"SW9 8RU""Coldharbour Lane, SW9""Coldharbour Lane"nullnull"Leasehold""Flats/Maisonettes""Flat""Offers over"null"https://www.onthemarket.com/details/19599313/"["Tenure: Leasehold (127 years remaining)", "Two double bedrooms", "Chain-free"]null"For sale"450000null
"OnTheMarket"224-0.11449651.466421"SW9 0FH""coordinates"null"SW9 0FH""Stockwell Park Walk, London, SW9""Stockwell Park Walk, London"nullnull"Leasehold""Flats/Maisonettes""Flat""Offers in excess of"null"https://www.onthemarket.com/details/18060975/"["Tenure: Leasehold (113 years remaining)", "Stylish and well-presented two-bedroom, two-bathroom apartment", "Study"]null"For sale"500000null
"OnTheMarket"224-0.12691851.469851"SW4 6BF""coordinates"null"SW4 6BF""20 Fergusson Mews, London, SW4""20 Fergusson Mews, London"nullnull"Leasehold""Flats/Maisonettes""Flat""Offers in excess of"null"https://www.onthemarket.com/details/19266851/"["Tenure: Leasehold", "Gated Development", … "Chain-free"]null"For sale"650000null
"OnTheMarket"112-0.11653551.466385"SW9 9FX""coordinates"null"SW9 9FX""Stockwell Road, Stockwell, London, SW9""Stockwell Road, Stockwell, London"nullnull"Leasehold""Flats/Maisonettes""Flat""Offers in excess of"null"https://www.onthemarket.com/details/19167472/"["Tenure: Leasehold (125 years remaining)", "Stylish finish", … "Chain-free"]null"For sale"450000null
"OnTheMarket"112-0.110951.462364"SW9 8GA""coordinates"null"SW9 8GA""Milles Square, Brixton, London, SW9""Milles Square, Brixton, London"nullnull"Leasehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19592532/"["Tenure: Leasehold (155 years remaining)", "Neatly presented 1 bedroom flat in a convenient location"]null"For sale"340000null
"OnTheMarket"213-0.11026951.46006"SE24 0LW""coordinates"null"SE24 0LW""Railton Road, London SE24""Railton Road, London"nullnull"Leasehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19591254/"["Tenure: Leasehold", "Two Double Bedrooms", "Chain-free"]null"For sale"500000null
"OnTheMarket"112-0.12629751.466742"SW9 9LE""coordinates"null"SW9 9LE""Hemberton Road, London SW9""Hemberton Road, London"nullnull"Freehold""Flats/Maisonettes""Flat"""null"https://www.onthemarket.com/details/19591126/"["Tenure: Share of freehold", "Chain Free", … "Study"]null"For sale"550000null
"Rightmove"112-0.12156951.471079"SW9 9EU""coordinates"null"SW9 9EU""Stockwell Road, London, SW9""Stockwell Road, London"nullnullnull"Flats/Maisonettes""Apartment""Guide Price"null"https://www.rightmove.co.uk/properties/89076063#/?channel=RES_BUY"["Within easy reach of local amenities, green spaces and transport connections", "Excellent value-add opportunity for owner-occupiers and investors", … "Well-proportioned one-bedroom apartment in a popular SW9 location"]2026-05-29 14:30:37"For sale"310000null
"Rightmove"549-0.1181751.47115"SW9 0DG""coordinates"null"SW9 0DG""Stockwell Park Crescent II, London SW9""Stockwell Park Crescent II, London"nullnull"Freehold""Semi-Detached""Semi-Detached"""310.6"https://www.rightmove.co.uk/properties/174610352#/?channel=RES_BUY"[]2026-04-17 18:34:21"For sale"25950008355
"Rightmove"639-0.11472751.473247"SW9 0AR""coordinates"null"SW9 0AR""Groveway, Stockwell""Groveway, Stockwell"nullnull"Freehold""Detached""Detached"""null"https://www.rightmove.co.uk/properties/174282005#/?channel=RES_BUY"["Early Victorian Family Home", "Grade II Listed", … "Wonderful Potential"]2026-04-10 11:17:35"For sale"2295000null
"Rightmove"426-0.11660351.472141"SW9 0DD""coordinates"null"SW9 0DD""Stockwell Park Road, London, SW9""Stockwell Park Road, London"nullnull"Freehold""Semi-Detached""Semi-Detached"""216.1"https://www.rightmove.co.uk/properties/170781485#/?channel=RES_BUY"["4 bedrooms", "2 reception rooms", … "Semi-Detached"]2026-01-02 17:30:39"For sale"225000010412
"Rightmove"224-0.1124751.46428"SW9 7QH""coordinates"null"SW9 7QH""St Johns Buildings, London SW9""St Johns Buildings, London"nullnullnull"Terraced""Terraced"""213.4"https://www.rightmove.co.uk/properties/174206714#/?channel=RES_BUY"[]2026-04-08 17:23:59"For sale"21000009841
"Rightmove"437-0.11676551.47089"SW9 0DB""coordinates"null"SW9 0DB""Stockwell Park Road, London SW9""Stockwell Park Road, London"nullnull"Freehold""Terraced""Terraced""Guide Price"197.5"https://www.rightmove.co.uk/properties/88435821#/?channel=RES_BUY"["4 bedrooms", "2 reception rooms", … "Garden"]2026-05-13 16:21:42"For sale"210000010633
"Rightmove"538-0.11311151.478768"SW9 0LJ""coordinates"null"SW9 0LJ""Crewdson Road, London SW9""Crewdson Road, London"nullnull"Freehold""Terraced""Terraced"""246.7"https://www.rightmove.co.uk/properties/174798002#/?channel=RES_BUY"["5 bedrooms", "1 reception room", … "Garden"]2026-04-22 16:04:47"For sale"19000007702
"Rightmove"336-0.1180551.47091"SW9 0DG""coordinates"null"SW9 0DG""Stockwell Park Crescent, Stockwell, London, SW9""Stockwell Park Crescent, Stockwell, London"nullnull"Freehold""Detached""House"""156.8"https://www.rightmove.co.uk/properties/88376967#/?channel=RES_BUY"["Wonderful 2-bedroom semi-detached house situated over 3 floors", "Spacious kitchen", … "Well located for the local shops and amenities of Clapham and Stockwell Road"]2026-05-12 16:14:48"For sale"169500010810
"Rightmove"538-0.11919251.474381"SW9 0RU""coordinates"null"SW9 0RU""Stockwell Park Road, London, SW9""Stockwell Park Road, London"nullnull"Freehold""Semi-Detached""Semi-Detached"""193.2"https://www.rightmove.co.uk/properties/167893637#/?channel=RES_BUY"[]2025-10-07 09:57:34"For sale"16950008773
"Rightmove"224-0.1177851.470774"SW9 0DG""coordinates"null"SW9 0DG""Stockwell Park Crescent, Stockwell""Stockwell Park Crescent, Stockwell"nullnull"Freehold""Semi-Detached""Semi-Detached"""null"https://www.rightmove.co.uk/properties/173668991#/?channel=RES_BUY"["Georgian Home", "Excellent Condition", … "No Onward Chain"]2026-03-25 09:55:49"For sale"1695000null
"Rightmove"437-0.12104151.465546"SW9 9RF""coordinates"null"SW9 9RF""Hargwyne Street, London, SW9""Hargwyne Street, London"nullnull"Freehold""Terraced""Terraced"""165.7"https://www.rightmove.co.uk/properties/174050450#/?channel=RES_BUY"[]2026-04-02 16:51:56"For sale"16500009958
"Rightmove"527-0.11919451.470986"SW9 0SW""coordinates"null"SW9 0SW""Burnley Road, London, SW9""Burnley Road, London"nullnull"Freehold""Terraced""Terraced""Guide Price"199.5"https://www.rightmove.co.uk/properties/88367181#/?channel=RES_BUY"["Quiet Residential Street", "Versatile Accommodation", … "Period Features"]2026-05-12 14:23:19"For sale"15950007995
"Rightmove"538-0.11891451.475262"SW9 0QQ""coordinates"null"SW9 0QQ""Clapham Road, Stockwell""Clapham Road, Stockwell"nullnull"Freehold""Terraced""Terraced"""null"https://www.rightmove.co.uk/properties/172178081#/?channel=RES_BUY"["Georgian Townhouse", "Five Double Bedrooms", … "2617sq.ft"]2026-02-13 14:31:16"For sale"1550000null
"Rightmove"819-0.11432951.465359"SW9 7AW""coordinates"null"SW9 7AW""Brixton Road, Brixton High Street""Brixton Road, Brixton High Street"nullnull"Freehold""Terraced""Terraced""Guide Price"44.0"https://www.rightmove.co.uk/properties/88139847#/?channel=RES_BUY"["FREEHOLD MIXED USE COMMERCIAL & RESIDENTIAL", "GROUND FLOOR TAKE-AWAY - LEASE EXPIRING 2030", … "NEAR BRIXTON UNDERGROUND & OVERGROUND STATIONS"]2026-05-06 17:31:38"For sale"155000035227
"Rightmove"426-0.11890351.461726"SW9 8DR""coordinates"null"SW9 8DR""Trinity Gardens, LONDON""Trinity Gardens, LONDON"nullnull"Freehold""Terraced""Terraced""Guide Price"114.5"https://www.rightmove.co.uk/properties/174274454#/?channel=RES_BUY"["Victorian House", "Mid Terraced", … "Central Location"]2026-04-10 09:39:04"For sale"150000013100
"Zoopla"426-0.11681951.477238"SW9 0HD""detail_address"null"SW9 0HU""Caldwell Street, Stockwell SW9""1, Caldwell Street, Stockwell""100021818679""1""Freehold""Other""Property"""114.0"https://www.zoopla.co.uk/for-sale/details/73326946/"[]null"For sale"9950008728
"Zoopla"213-0.11879851.468221"SW9 0TZ""detail_address"null"SW9 9PU""Stockwell Road, Stockwell, London SW9""Flat 27, Addington House,, Stockwell Road, Stockwell, London""100021894599""Flat 27, Addington House,""Leasehold""Flats/Maisonettes""Flat"""77.9"https://www.zoopla.co.uk/for-sale/details/72554891/"[]null"For sale"3500004493
"Zoopla"538-0.11919251.474381"SW9 0AP""detail_address"null"SW9 0RU""Stockwell Park Road, London SW9""1, Stockwell Park Road, London""100023386020""1""Freehold""Semi-Detached""Semi Detached"""194.9"https://www.zoopla.co.uk/for-sale/details/69392186/"[]null"For sale"16950008697
"Zoopla"325-0.12265851.469424"SW9 9HG""detail_address"null"SW9 9HE""Lingham Street, Stockwell, London SW9""Flat 11 Beale House, 45, Lingham Street, Stockwell, London""100021894650""Flat 11 Beale House, 45""Leasehold""Flats/Maisonettes""Flat"""102.9"https://www.zoopla.co.uk/for-sale/details/73306789/"[]null"For sale"5250005102
"Zoopla"224-0.12900251.46795"SW9 9AN""detail_address"null"SW9 9AN""Houghton Square, London SW9""17, Houghton Square, London""10008788775""17""Leasehold""Flats/Maisonettes""Flat"""72.0"https://www.zoopla.co.uk/for-sale/details/73302918/"[]null"For sale"5000006944
"Zoopla"213-0.11502951.477508"SW9 0ET""detail_address"null"SW9 0EN""Cleveland House, Hackford Road, London SW9""Flat 4,, Cleveland House, Hackford Road, London""100021847214""Flat 4,""Leasehold""Flats/Maisonettes""Flat"""55.0"https://www.zoopla.co.uk/for-sale/details/73302851/"[]null"For sale"5150009364
"Zoopla"213-0.12568151.466593"SW9 9LF""detail_address"null"SW9 9LF""Hemberton Road, Clapham North, London SW9""chpk0226649, Hemberton Road, Clapham North, London"null"chpk0226649"null"Flats/Maisonettes""Flat"""49.7"https://www.zoopla.co.uk/for-sale/details/71215394/"[]null"For sale"4750009557
"Zoopla"112-0.11708651.466529"SW9 9FX""detail_address"null"SW9 9TG""Stockwell Road SW9""Flat 59, 151, Stockwell Road""10094957235""Flat 59, 151""Leasehold""Flats/Maisonettes""Flat"""52.0"https://www.zoopla.co.uk/for-sale/details/73250562/"[]null"For sale"4650008942
"Zoopla"213-0.1287651.464473"SW9 9NW""detail_address"null"SW9 9PG""Fenwick Place SW9""First Floor Flat, 70, Fenwick Place"null"First Floor Flat, 70"null"Flats/Maisonettes""Flat"""70.2"https://www.zoopla.co.uk/for-sale/details/73268982/"[]null"For sale"5999508546
"Zoopla"112-0.10877151.470695"SW9 7SB""detail_address"null"SW9 7JX""Loughborough Road SW9""Flat C, 58, Loughborough Road"null"Flat C, 58""Leasehold""Flats/Maisonettes""Flat"""44.1"https://www.zoopla.co.uk/for-sale/details/73268027/"[]null"For sale"3750008503
"Zoopla"224-0.10835151.475345"SW9 7RH""detail_address"null"SW9 7BN""Eythorne Road, Oval SW9""Flat 7, 5, Eythorne Road, Oval"null"Flat 7, 5"null"Flats/Maisonettes""Flat"""null"https://www.zoopla.co.uk/for-sale/details/73266802/"[]null"For sale"575000null
"Zoopla"213-0.1214251.472181"SW9 9AU""detail_address"null"SW9 9AU""Yates Court, Stockwell, London SW9""chpk3677610, Yates Court, Stockwell, London"null"chpk3677610""Leasehold""Flats/Maisonettes""Flat"""72.0"https://www.zoopla.co.uk/for-sale/details/73261921/"[]null"For sale"4750006597
"Zoopla"224-0.1078751.476628"SW9 6HP""detail_address"null"SW9 6SB""Beam, Lambeth SW9, New home""Beam, Lambeth SW9, New home"nullnull"Leasehold""Flats/Maisonettes""Flat"""74.4"https://www.zoopla.co.uk/new-homes/details/73247019/"[]null"For sale"5800007796
"Zoopla"224-0.1078751.476628"SW9 6HP""detail_address"null"SW9 6SB""Beam, Lambeth SW9, New home""Beam, Lambeth SW9, New home"nullnull"Leasehold""Flats/Maisonettes""Flat"""74.4"https://www.zoopla.co.uk/new-homes/details/73246935/"[]null"For sale"5950007997
"Zoopla"224-0.12273851.467418"SW9 9FX""detail_address"null"SW9 9DG""Stockwell Road, Brixton SW9, New home""Stockwell Road, Brixton SW9, New home""10094957206"null"Leasehold""Flats/Maisonettes""Flat"""72.0"https://www.zoopla.co.uk/new-homes/details/73239981/"[]null"For sale"5750007986
" - ], - "text/plain": [ - "shape: (45, 25)\n", - "┌───────────┬──────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ provider ┆ Bedrooms ┆ Bathrooms ┆ Number of ┆ … ┆ Listing ┆ Listing ┆ Asking ┆ Asking │\n", - "│ --- ┆ --- ┆ --- ┆ bedrooms ┆ ┆ date ┆ status ┆ price ┆ price per │\n", - "│ str ┆ i32 ┆ i32 ┆ & living ┆ ┆ --- ┆ --- ┆ --- ┆ sqm │\n", - "│ ┆ ┆ ┆ rooms ┆ ┆ datetime[ ┆ str ┆ i64 ┆ --- │\n", - "│ ┆ ┆ ┆ --- ┆ ┆ μs] ┆ ┆ ┆ i32 │\n", - "│ ┆ ┆ ┆ i32 ┆ ┆ ┆ ┆ ┆ │\n", - "╞═══════════╪══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ OnTheMark ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 525000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 275000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 4 ┆ 1 ┆ 5 ┆ … ┆ null ┆ For sale ┆ 600000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 350000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 475000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 0 ┆ 1 ┆ 1 ┆ … ┆ null ┆ For sale ┆ 225000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 575000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 435000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 450000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 500000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 650000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 450000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 340000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 2 ┆ 1 ┆ 3 ┆ … ┆ null ┆ For sale ┆ 500000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ OnTheMark ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 550000 ┆ null │\n", - "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ 2026-05-2 ┆ For sale ┆ 310000 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ 9 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 14:30:37 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 5 ┆ 4 ┆ 9 ┆ … ┆ 2026-04-1 ┆ For sale ┆ 2595000 ┆ 8355 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 7 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 18:34:21 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 6 ┆ 3 ┆ 9 ┆ … ┆ 2026-04-1 ┆ For sale ┆ 2295000 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ 0 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 11:17:35 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 4 ┆ 2 ┆ 6 ┆ … ┆ 2026-01-0 ┆ For sale ┆ 2250000 ┆ 10412 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 2 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 17:30:39 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ 2026-04-0 ┆ For sale ┆ 2100000 ┆ 9841 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 8 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 17:23:59 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 4 ┆ 3 ┆ 7 ┆ … ┆ 2026-05-1 ┆ For sale ┆ 2100000 ┆ 10633 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 3 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 16:21:42 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 5 ┆ 3 ┆ 8 ┆ … ┆ 2026-04-2 ┆ For sale ┆ 1900000 ┆ 7702 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 2 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 16:04:47 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 3 ┆ 3 ┆ 6 ┆ … ┆ 2026-05-1 ┆ For sale ┆ 1695000 ┆ 10810 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 2 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 16:14:48 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 5 ┆ 3 ┆ 8 ┆ … ┆ 2025-10-0 ┆ For sale ┆ 1695000 ┆ 8773 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 7 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 09:57:34 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ 2026-03-2 ┆ For sale ┆ 1695000 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ 5 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 09:55:49 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 4 ┆ 3 ┆ 7 ┆ … ┆ 2026-04-0 ┆ For sale ┆ 1650000 ┆ 9958 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 2 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 16:51:56 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 5 ┆ 2 ┆ 7 ┆ … ┆ 2026-05-1 ┆ For sale ┆ 1595000 ┆ 7995 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 2 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 14:23:19 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 5 ┆ 3 ┆ 8 ┆ … ┆ 2026-02-1 ┆ For sale ┆ 1550000 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ 3 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 14:31:16 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 8 ┆ 1 ┆ 9 ┆ … ┆ 2026-05-0 ┆ For sale ┆ 1550000 ┆ 35227 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 6 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 17:31:38 ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 4 ┆ 2 ┆ 6 ┆ … ┆ 2026-04-1 ┆ For sale ┆ 1500000 ┆ 13100 │\n", - "│ ┆ ┆ ┆ ┆ ┆ 0 ┆ ┆ ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ 09:39:04 ┆ ┆ ┆ │\n", - "│ Zoopla ┆ 4 ┆ 2 ┆ 6 ┆ … ┆ null ┆ For sale ┆ 995000 ┆ 8728 │\n", - "│ Zoopla ┆ 2 ┆ 1 ┆ 3 ┆ … ┆ null ┆ For sale ┆ 350000 ┆ 4493 │\n", - "│ Zoopla ┆ 5 ┆ 3 ┆ 8 ┆ … ┆ null ┆ For sale ┆ 1695000 ┆ 8697 │\n", - "│ Zoopla ┆ 3 ┆ 2 ┆ 5 ┆ … ┆ null ┆ For sale ┆ 525000 ┆ 5102 │\n", - "│ Zoopla ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 500000 ┆ 6944 │\n", - "│ Zoopla ┆ 2 ┆ 1 ┆ 3 ┆ … ┆ null ┆ For sale ┆ 515000 ┆ 9364 │\n", - "│ Zoopla ┆ 2 ┆ 1 ┆ 3 ┆ … ┆ null ┆ For sale ┆ 475000 ┆ 9557 │\n", - "│ Zoopla ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 465000 ┆ 8942 │\n", - "│ Zoopla ┆ 2 ┆ 1 ┆ 3 ┆ … ┆ null ┆ For sale ┆ 599950 ┆ 8546 │\n", - "│ Zoopla ┆ 1 ┆ 1 ┆ 2 ┆ … ┆ null ┆ For sale ┆ 375000 ┆ 8503 │\n", - "│ Zoopla ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 575000 ┆ null │\n", - "│ Zoopla ┆ 2 ┆ 1 ┆ 3 ┆ … ┆ null ┆ For sale ┆ 475000 ┆ 6597 │\n", - "│ Zoopla ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 580000 ┆ 7796 │\n", - "│ Zoopla ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 595000 ┆ 7997 │\n", - "│ Zoopla ┆ 2 ┆ 2 ┆ 4 ┆ … ┆ null ┆ For sale ┆ 575000 ┆ 7986 │\n", - "└───────────┴──────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘" - ] - }, - "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "df = (\n", - " pl.read_parquet(DATA_PATH)\n", - " .with_columns(\n", - " pl.when(pl.col(\"Listing URL\").str.contains(\"rightmove.co.uk\", literal=True))\n", - " .then(pl.lit(\"Rightmove\"))\n", - " .when(pl.col(\"Listing URL\").str.contains(\"onthemarket.com\", literal=True))\n", - " .then(pl.lit(\"OnTheMarket\"))\n", - " .when(pl.col(\"Listing URL\").str.contains(\"zoopla.co.uk\", literal=True))\n", - " .then(pl.lit(\"Zoopla\"))\n", - " .otherwise(pl.lit(\"Unknown\"))\n", - " .alias(\"provider\")\n", - " )\n", - " .filter(pl.col(\"provider\").is_in([\"Rightmove\", \"OnTheMarket\", \"Zoopla\"]))\n", - ")\n", - "\n", - "print(f\"Rows: {df.height:,}\")\n", - "print(f\"Columns: {len(df.columns):,}\")\n", - "display(df.group_by(\"provider\").len().sort(\"provider\"))\n", - "display(df.group_by(\"provider\", maintain_order=False).head(15).sort(\"provider\"))" - ] - }, - { - "cell_type": "markdown", - "id": "e775e26e", - "metadata": {}, - "source": [ - "## NA Counts By Provider" + "# Schema\n", + "pl.DataFrame({\"column\": raw.columns, \"dtype\": [str(t) for t in raw.dtypes]})" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "059462d2", + "execution_count": null, + "id": "8c4641b6", "metadata": { "execution": { - "iopub.execute_input": "2026-05-17T10:23:47.956431Z", - "iopub.status.busy": "2026-05-17T10:23:47.956358Z", - "iopub.status.idle": "2026-05-17T10:23:47.979826Z", - "shell.execute_reply": "2026-05-17T10:23:47.979565Z" + "iopub.execute_input": "2026-06-03T07:34:30.904380Z", + "iopub.status.busy": "2026-06-03T07:34:30.904313Z", + "iopub.status.idle": "2026-06-03T07:34:30.907029Z", + "shell.execute_reply": "2026-06-03T07:34:30.906739Z" } }, "outputs": [ @@ -264,23 +175,1688 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (3, 25)
providerBedroomsBathroomsNumber of bedrooms & living roomslonlatPostcodePostcode sourceExtracted postcodeInferred postcodeListing raw addressAddress per Property RegisterUPRNProperty number or nameLeasehold/FreeholdProperty typeProperty sub-typePrice qualifierTotal floor area (sqm)Listing URLListing featuresListing dateListing statusAsking priceAsking price per sqm
stru32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32
"OnTheMarket"000000084000858500008200850082
"Rightmove"000000098000999917000340000034
"Zoopla"00000005800025111400050058005
" + "shape: (8, 10)
providerPostcodeProperty typeProperty sub-typeBedroomsBathroomsAsking priceTotal floor area (sqm)Price qualifierListing date
strstrstrstri32i32i64f64strdatetime[μs]
"Rightmove""BR1 5RW""Semi-Detached""Semi-Detached"31550000null""2025-11-12 13:58:41
"Rightmove""BR1 2PF""Detached""Detached"643000000361.8""2026-01-22 10:22:09
"Rightmove""BR1 2QD""Detached""Detached"553000000null"Guide Price"2026-03-24 10:55:03
"Rightmove""BR1 3LU""Detached""Detached"602750000322.1""2026-04-25 11:59:31
"Rightmove""BR1 3AR""Detached""Detached"642695000483.6""2026-01-06 21:38:25
"Rightmove""BR1 2AY""Detached""Detached"552500000402.8"Offers in Excess of"2026-02-20 11:48:01
"Rightmove""BR1 2HG""Detached""Detached"542500000457.5"Guide Price"2026-02-07 18:12:38
"Rightmove""BR1 2JG""Detached""Detached"532350000null""2026-05-01 11:14:35
" + ], + "text/plain": [ + "shape: (8, 10)\n", + "┌─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬─────────┬────────┐\n", + "│ provide ┆ Postcod ┆ Propert ┆ Propert ┆ Bedroom ┆ Bathroo ┆ Asking ┆ Total ┆ Price ┆ Listin │\n", + "│ r ┆ e ┆ y type ┆ y sub-t ┆ s ┆ ms ┆ price ┆ floor ┆ qualifi ┆ g date │\n", + "│ --- ┆ --- ┆ --- ┆ ype ┆ --- ┆ --- ┆ --- ┆ area ┆ er ┆ --- │\n", + "│ str ┆ str ┆ str ┆ --- ┆ i32 ┆ i32 ┆ i64 ┆ (sqm) ┆ --- ┆ dateti │\n", + "│ ┆ ┆ ┆ str ┆ ┆ ┆ ┆ --- ┆ str ┆ me[μs] │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ │\n", + "╞═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═════════╪════════╡\n", + "│ Rightmo ┆ BR1 5RW ┆ Semi-De ┆ Semi-De ┆ 3 ┆ 1 ┆ 550000 ┆ null ┆ ┆ 2025-1 │\n", + "│ ve ┆ ┆ tached ┆ tached ┆ ┆ ┆ ┆ ┆ ┆ 1-12 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 13:58: │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 41 │\n", + "│ Rightmo ┆ BR1 2PF ┆ Detache ┆ Detache ┆ 6 ┆ 4 ┆ 3000000 ┆ 361.8 ┆ ┆ 2026-0 │\n", + "│ ve ┆ ┆ d ┆ d ┆ ┆ ┆ ┆ ┆ ┆ 1-22 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 10:22: │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 09 │\n", + "│ Rightmo ┆ BR1 2QD ┆ Detache ┆ Detache ┆ 5 ┆ 5 ┆ 3000000 ┆ null ┆ Guide ┆ 2026-0 │\n", + "│ ve ┆ ┆ d ┆ d ┆ ┆ ┆ ┆ ┆ Price ┆ 3-24 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 10:55: │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 03 │\n", + "│ Rightmo ┆ BR1 3LU ┆ Detache ┆ Detache ┆ 6 ┆ 0 ┆ 2750000 ┆ 322.1 ┆ ┆ 2026-0 │\n", + "│ ve ┆ ┆ d ┆ d ┆ ┆ ┆ ┆ ┆ ┆ 4-25 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 11:59: │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 31 │\n", + "│ Rightmo ┆ BR1 3AR ┆ Detache ┆ Detache ┆ 6 ┆ 4 ┆ 2695000 ┆ 483.6 ┆ ┆ 2026-0 │\n", + "│ ve ┆ ┆ d ┆ d ┆ ┆ ┆ ┆ ┆ ┆ 1-06 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 21:38: │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 25 │\n", + "│ Rightmo ┆ BR1 2AY ┆ Detache ┆ Detache ┆ 5 ┆ 5 ┆ 2500000 ┆ 402.8 ┆ Offers ┆ 2026-0 │\n", + "│ ve ┆ ┆ d ┆ d ┆ ┆ ┆ ┆ ┆ in ┆ 2-20 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Excess ┆ 11:48: │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ of ┆ 01 │\n", + "│ Rightmo ┆ BR1 2HG ┆ Detache ┆ Detache ┆ 5 ┆ 4 ┆ 2500000 ┆ 457.5 ┆ Guide ┆ 2026-0 │\n", + "│ ve ┆ ┆ d ┆ d ┆ ┆ ┆ ┆ ┆ Price ┆ 2-07 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 18:12: │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 38 │\n", + "│ Rightmo ┆ BR1 2JG ┆ Detache ┆ Detache ┆ 5 ┆ 3 ┆ 2350000 ┆ null ┆ ┆ 2026-0 │\n", + "│ ve ┆ ┆ d ┆ d ┆ ┆ ┆ ┆ ┆ ┆ 5-01 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 11:14: │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 35 │\n", + "└─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴─────────┴────────┘" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A few rows\n", + "raw.select(\n", + " \"provider\", \"Postcode\", \"Property type\", \"Property sub-type\",\n", + " \"Bedrooms\", \"Bathrooms\", \"Asking price\", \"Total floor area (sqm)\",\n", + " \"Price qualifier\", \"Listing date\",\n", + ").head(8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d44013c8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:30.907698Z", + "iopub.status.busy": "2026-06-03T07:34:30.907610Z", + "iopub.status.idle": "2026-06-03T07:34:30.912246Z", + "shell.execute_reply": "2026-06-03T07:34:30.911979Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (9, 9)
statisticBedroomsBathroomsNumber of bedrooms & living roomsAsking priceTotal floor area (sqm)Asking price per sqmlatlon
strf64f64f64f64f64f64f64f64
"count"112605.0112605.0112605.0112241.056914.056914.0112605.0112605.0
"null_count"0.00.00.0364.055691.055691.00.00.0
"mean"2.391911.5673993.959309921922.833465113.7201369283.32329551.500839-0.147325
"std"1.4002471.0138462.2250191.6093e6108.2881737445.3164630.0839350.166763
"min"0.00.00.01.05.04.051.242629-0.593469
"25%"1.01.02.0375000.060.05624.051.450302-0.245267
"50%"2.01.04.0550000.081.87301.051.5008-0.15191
"75%"3.02.05.0875000.0126.010492.051.550221-0.05151
"max"20.020.040.01e81893.1410959.051.7810740.452373
" + ], + "text/plain": [ + "shape: (9, 9)\n", + "┌──────────┬──────────┬──────────┬──────────┬──────────┬──────────┬──────────┬──────────┬──────────┐\n", + "│ statisti ┆ Bedrooms ┆ Bathroom ┆ Number ┆ Asking ┆ Total ┆ Asking ┆ lat ┆ lon │\n", + "│ c ┆ --- ┆ s ┆ of ┆ price ┆ floor ┆ price ┆ --- ┆ --- │\n", + "│ --- ┆ f64 ┆ --- ┆ bedrooms ┆ --- ┆ area ┆ per sqm ┆ f64 ┆ f64 │\n", + "│ str ┆ ┆ f64 ┆ & living ┆ f64 ┆ (sqm) ┆ --- ┆ ┆ │\n", + "│ ┆ ┆ ┆ rooms ┆ ┆ --- ┆ f64 ┆ ┆ │\n", + "│ ┆ ┆ ┆ --- ┆ ┆ f64 ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ f64 ┆ ┆ ┆ ┆ ┆ │\n", + "╞══════════╪══════════╪══════════╪══════════╪══════════╪══════════╪══════════╪══════════╪══════════╡\n", + "│ count ┆ 112605.0 ┆ 112605.0 ┆ 112605.0 ┆ 112241.0 ┆ 56914.0 ┆ 56914.0 ┆ 112605.0 ┆ 112605.0 │\n", + "│ null_cou ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 364.0 ┆ 55691.0 ┆ 55691.0 ┆ 0.0 ┆ 0.0 │\n", + "│ nt ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ mean ┆ 2.39191 ┆ 1.567399 ┆ 3.959309 ┆ 921922.8 ┆ 113.7201 ┆ 9283.323 ┆ 51.50083 ┆ -0.14732 │\n", + "│ ┆ ┆ ┆ ┆ 33465 ┆ 36 ┆ 295 ┆ 9 ┆ 5 │\n", + "│ std ┆ 1.400247 ┆ 1.013846 ┆ 2.225019 ┆ 1.6093e6 ┆ 108.2881 ┆ 7445.316 ┆ 0.083935 ┆ 0.166763 │\n", + "│ ┆ ┆ ┆ ┆ ┆ 73 ┆ 463 ┆ ┆ │\n", + "│ min ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 1.0 ┆ 5.0 ┆ 4.0 ┆ 51.24262 ┆ -0.59346 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 9 ┆ 9 │\n", + "│ 25% ┆ 1.0 ┆ 1.0 ┆ 2.0 ┆ 375000.0 ┆ 60.0 ┆ 5624.0 ┆ 51.45030 ┆ -0.24526 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 2 ┆ 7 │\n", + "│ 50% ┆ 2.0 ┆ 1.0 ┆ 4.0 ┆ 550000.0 ┆ 81.8 ┆ 7301.0 ┆ 51.5008 ┆ -0.15191 │\n", + "│ 75% ┆ 3.0 ┆ 2.0 ┆ 5.0 ┆ 875000.0 ┆ 126.0 ┆ 10492.0 ┆ 51.55022 ┆ -0.05151 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 1 ┆ │\n", + "│ max ┆ 20.0 ┆ 20.0 ┆ 40.0 ┆ 1e8 ┆ 1893.1 ┆ 410959.0 ┆ 51.78107 ┆ 0.452373 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 4 ┆ │\n", + "└──────────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┘" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Numeric summary\n", + "raw.select(\n", + " \"Bedrooms\", \"Bathrooms\", \"Number of bedrooms & living rooms\",\n", + " \"Asking price\", \"Total floor area (sqm)\", \"Asking price per sqm\", \"lat\", \"lon\",\n", + ").describe()" + ] + }, + { + "cell_type": "markdown", + "id": "bc5e4659", + "metadata": {}, + "source": [ + "## 2 · Missingness (null **or** empty-string) by provider\n", + "\n", + "Note the *structural* gaps: `UPRN` & `Listing date` are populated by only one or two portals,\n", + "and `Total floor area` is ~half-missing with a strong portal skew." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a41dce1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:30.912874Z", + "iopub.status.busy": "2026-06-03T07:34:30.912800Z", + "iopub.status.idle": "2026-06-03T07:34:30.930428Z", + "shell.execute_reply": "2026-06-03T07:34:30.930051Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 25)
providerBedroomsBathroomsNumber of bedrooms & living roomslonlatPostcodePostcode sourceExtracted postcodeInferred postcodeListing raw addressAddress per Property RegisterUPRNProperty number or nameLeasehold/FreeholdProperty typeProperty sub-typePrice qualifierTotal floor area (sqm)Listing URLListing featuresListing dateListing statusAsking priceAsking price per sqm
strf64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64
"OnTheMarket"0.00.00.00.00.00.00.095.90.00.00.1100.0100.011.20.00.060.986.90.00.0100.00.00.186.9
"Rightmove"0.00.00.00.00.00.00.095.10.00.00.0100.0100.014.20.00.058.846.80.00.00.00.00.046.8
"Zoopla"0.00.00.00.00.00.00.0100.00.040.140.270.761.550.20.00.0100.016.80.00.0100.00.04.916.8
" ], "text/plain": [ "shape: (3, 25)\n", - "┌────────────┬──────────┬───────────┬────────────┬───┬────────────┬───────────┬────────┬───────────┐\n", - "│ provider ┆ Bedrooms ┆ Bathrooms ┆ Number of ┆ … ┆ Listing ┆ Listing ┆ Asking ┆ Asking │\n", - "│ --- ┆ --- ┆ --- ┆ bedrooms & ┆ ┆ date ┆ status ┆ price ┆ price per │\n", - "│ str ┆ u32 ┆ u32 ┆ living ┆ ┆ --- ┆ --- ┆ --- ┆ sqm │\n", - "│ ┆ ┆ ┆ rooms ┆ ┆ u32 ┆ u32 ┆ u32 ┆ --- │\n", - "│ ┆ ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ u32 │\n", - "│ ┆ ┆ ┆ u32 ┆ ┆ ┆ ┆ ┆ │\n", - "╞════════════╪══════════╪═══════════╪════════════╪═══╪════════════╪═══════════╪════════╪═══════════╡\n", - "│ OnTheMarke ┆ 0 ┆ 0 ┆ 0 ┆ … ┆ 85 ┆ 0 ┆ 0 ┆ 82 │\n", - "│ t ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ Rightmove ┆ 0 ┆ 0 ┆ 0 ┆ … ┆ 0 ┆ 0 ┆ 0 ┆ 34 │\n", - "│ Zoopla ┆ 0 ┆ 0 ┆ 0 ┆ … ┆ 58 ┆ 0 ┆ 0 ┆ 5 │\n", - "└────────────┴──────────┴───────────┴────────────┴───┴────────────┴───────────┴────────┴───────────┘" + "┌─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┐\n", + "│ pro ┆ Bed ┆ Bat ┆ Num ┆ lon ┆ lat ┆ Pos ┆ Pos ┆ Ext ┆ Inf ┆ Lis ┆ Add ┆ UPR ┆ Pro ┆ Lea ┆ Pro ┆ Pro ┆ Pri ┆ Tot ┆ Lis ┆ Lis ┆ Lis ┆ Lis ┆ Ask ┆ Ask │\n", + "│ vid ┆ roo ┆ hro ┆ ber ┆ --- ┆ --- ┆ tco ┆ tco ┆ rac ┆ err ┆ tin ┆ res ┆ N ┆ per ┆ seh ┆ per ┆ per ┆ ce ┆ al ┆ tin ┆ tin ┆ tin ┆ tin ┆ ing ┆ ing │\n", + "│ er ┆ ms ┆ oms ┆ of ┆ f64 ┆ f64 ┆ de ┆ de ┆ ted ┆ ed ┆ g ┆ s ┆ --- ┆ ty ┆ old ┆ ty ┆ ty ┆ qua ┆ flo ┆ g ┆ g ┆ g ┆ g ┆ pri ┆ pri │\n", + "│ --- ┆ --- ┆ --- ┆ bed ┆ ┆ ┆ --- ┆ sou ┆ pos ┆ pos ┆ raw ┆ per ┆ f64 ┆ num ┆ /Fr ┆ typ ┆ sub ┆ lif ┆ or ┆ URL ┆ fea ┆ dat ┆ sta ┆ ce ┆ ce │\n", + "│ str ┆ f64 ┆ f64 ┆ roo ┆ ┆ ┆ f64 ┆ rce ┆ tco ┆ tco ┆ add ┆ Pro ┆ ┆ ber ┆ eeh ┆ e ┆ -ty ┆ ier ┆ are ┆ --- ┆ tur ┆ e ┆ tus ┆ --- ┆ per │\n", + "│ ┆ ┆ ┆ ms ┆ ┆ ┆ ┆ --- ┆ de ┆ de ┆ res ┆ per ┆ ┆ or ┆ old ┆ --- ┆ pe ┆ --- ┆ a ┆ f64 ┆ es ┆ --- ┆ --- ┆ f64 ┆ sqm │\n", + "│ ┆ ┆ ┆ & ┆ ┆ ┆ ┆ f64 ┆ --- ┆ --- ┆ s ┆ ty ┆ ┆ nam ┆ --- ┆ f64 ┆ --- ┆ f64 ┆ (sq ┆ ┆ --- ┆ f64 ┆ f64 ┆ ┆ --- │\n", + "│ ┆ ┆ ┆ liv ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ --- ┆ Reg ┆ ┆ e ┆ f64 ┆ ┆ f64 ┆ ┆ m) ┆ ┆ f64 ┆ ┆ ┆ ┆ f64 │\n", + "│ ┆ ┆ ┆ ing ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ ist ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ roo ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ er ┆ ┆ f64 ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ms ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ f64 ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "╞═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╡\n", + "│ OnT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 95. ┆ 0.0 ┆ 0.0 ┆ 0.1 ┆ 100 ┆ 100 ┆ 11. ┆ 0.0 ┆ 0.0 ┆ 60. ┆ 86. ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 0.0 ┆ 0.1 ┆ 86. │\n", + "│ heM ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 9 ┆ ┆ ┆ ┆ .0 ┆ .0 ┆ 2 ┆ ┆ ┆ 9 ┆ 9 ┆ ┆ ┆ .0 ┆ ┆ ┆ 9 │\n", + "│ ark ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ Rig ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 95. ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 100 ┆ 14. ┆ 0.0 ┆ 0.0 ┆ 58. ┆ 46. ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 46. │\n", + "│ htm ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 1 ┆ ┆ ┆ ┆ .0 ┆ .0 ┆ 2 ┆ ┆ ┆ 8 ┆ 8 ┆ ┆ ┆ ┆ ┆ ┆ 8 │\n", + "│ ove ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ Zoo ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 0.0 ┆ 40. ┆ 40. ┆ 70. ┆ 61. ┆ 50. ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 16. ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 0.0 ┆ 4.9 ┆ 16. │\n", + "│ pla ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ .0 ┆ ┆ 1 ┆ 2 ┆ 7 ┆ 5 ┆ 2 ┆ ┆ ┆ .0 ┆ 8 ┆ ┆ ┆ .0 ┆ ┆ ┆ 8 │\n", + "└─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┘" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def missing_count(col: str, dtype) -> pl.Expr:\n", + " m = pl.col(col).is_null()\n", + " if dtype in (pl.Float32, pl.Float64):\n", + " m = m | pl.col(col).is_nan().fill_null(False)\n", + " if dtype == pl.String:\n", + " m = m | (pl.col(col).str.strip_chars() == \"\")\n", + " return m.sum().alias(col)\n", + "\n", + "value_cols = [c for c in raw.columns if c != \"provider\"]\n", + "miss = (\n", + " raw.group_by(\"provider\")\n", + " .agg([missing_count(c, raw.schema[c]) for c in value_cols] + [pl.len().alias(\"__rows\")])\n", + " .sort(\"provider\")\n", + ")\n", + "miss_pct = miss.select(\n", + " [\"provider\"] + [(pl.col(c) / pl.col(\"__rows\") * 100).round(1).alias(c) for c in value_cols]\n", + ")\n", + "miss_pct" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1c09cfb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:30.931519Z", + "iopub.status.busy": "2026-06-03T07:34:30.931390Z", + "iopub.status.idle": "2026-06-03T07:34:31.040814Z", + "shell.execute_reply": "2026-06-03T07:34:31.040504Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap of % missing (null or empty) per column per provider\n", + "mat = miss_pct.select(value_cols).to_numpy()\n", + "fig, ax = plt.subplots(figsize=(15, 2.8))\n", + "im = ax.imshow(mat, aspect=\"auto\", cmap=\"Reds\", vmin=0, vmax=100)\n", + "ax.set_xticks(range(len(value_cols)))\n", + "ax.set_xticklabels(value_cols, rotation=65, ha=\"right\", fontsize=7)\n", + "ax.set_yticks(range(miss_pct.height))\n", + "ax.set_yticklabels(miss_pct[\"provider\"].to_list())\n", + "for (i, j), v in np.ndenumerate(mat):\n", + " if v > 0:\n", + " ax.text(j, i, f\"{v:.0f}\", ha=\"center\", va=\"center\", fontsize=6,\n", + " color=\"white\" if v > 55 else \"black\")\n", + "fig.colorbar(im, ax=ax, label=\"% missing\", fraction=0.025, pad=0.01)\n", + "ax.set_title(\"Missing (null or empty-string) % by provider\")\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ac443ace", + "metadata": {}, + "source": [ + "## 3 · Data issues\n", + "\n", + "Each subsection demonstrates one issue group. Severity tags (🔴 high / 🟠 medium / 🟡 low) match the audit." + ] + }, + { + "cell_type": "markdown", + "id": "7ea9293c", + "metadata": {}, + "source": [ + "### 3.1 · 🔴 Mislabeled column — `Number of bedrooms & living rooms` is actually `Bedrooms + Bathrooms`\n", + "\n", + "The column name promises *living rooms / receptions*, but `storage.py` recomputes it as\n", + "`Bedrooms + Bathrooms` for **every** row of **every** portal (Zoopla's `+ receptions` line is\n", + "dead code, overwritten at write time). It carries no living-room information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4844918", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.041972Z", + "iopub.status.busy": "2026-06-03T07:34:31.041873Z", + "iopub.status.idle": "2026-06-03T07:34:31.050408Z", + "shell.execute_reply": "2026-06-03T07:34:31.050059Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fraction of rows where col == Bedrooms + Bathrooms: 1.0000\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 2)
providerfrac == beds+baths
strf64
"OnTheMarket"1.0
"Rightmove"1.0
"Zoopla"1.0
" + ], + "text/plain": [ + "shape: (3, 2)\n", + "┌─────────────┬────────────────────┐\n", + "│ provider ┆ frac == beds+baths │\n", + "│ --- ┆ --- │\n", + "│ str ┆ f64 │\n", + "╞═════════════╪════════════════════╡\n", + "│ OnTheMarket ┆ 1.0 │\n", + "│ Rightmove ┆ 1.0 │\n", + "│ Zoopla ┆ 1.0 │\n", + "└─────────────┴────────────────────┘" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "share = raw.select(\n", + " (pl.col(\"Number of bedrooms & living rooms\") == pl.col(\"Bedrooms\") + pl.col(\"Bathrooms\")).mean()\n", + ").item()\n", + "print(f\"Fraction of rows where col == Bedrooms + Bathrooms: {share:.4f}\")\n", + "raw.group_by(\"provider\").agg(\n", + " (pl.col(\"Number of bedrooms & living rooms\") == pl.col(\"Bedrooms\") + pl.col(\"Bathrooms\"))\n", + " .mean().round(4).alias(\"frac == beds+baths\")\n", + ").sort(\"provider\")" + ] + }, + { + "cell_type": "markdown", + "id": "c59f527f", + "metadata": {}, + "source": [ + "### 3.2 · 🔴 Price integrity\n", + "\n", + "- **Shared-ownership / part-buy** listings store the *share* price as `Asking price`, not full value\n", + " (median ~£146k vs ~£550k) — corrupts any price / £-per-sqm aggregate.\n", + "- **38% of rows carry a non-firm qualifier** (Guide / Offers Over / OIEO / From / Shared); `Asking price`\n", + " is stored identically regardless.\n", + "- A `<£10k` tail of auction land/garage lots + two `£1` placeholders; 364 nulls (price ≤ 0 → null)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e713c7a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.051474Z", + "iopub.status.busy": "2026-06-03T07:34:31.051346Z", + "iopub.status.idle": "2026-06-03T07:34:31.070546Z", + "shell.execute_reply": "2026-06-03T07:34:31.070206Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shared-ownership / part-buy rows : 1,404\n", + "Non-firm price qualifier rows : 42,734 (38.0%)\n", + "Asking price null : 364\n", + "Asking price < £10k : 39\n", + "Asking price >= £20M : 124 (max = £100,000,000)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (2, 4)
shared_ownershiprowsmedian_pricemedian_£/sqm
boolu32f64f64
false111201550000.07323.0
true1404146250.02163.5
" + ], + "text/plain": [ + "shape: (2, 4)\n", + "┌──────────────────┬────────┬──────────────┬──────────────┐\n", + "│ shared_ownership ┆ rows ┆ median_price ┆ median_£/sqm │\n", + "│ --- ┆ --- ┆ --- ┆ --- │\n", + "│ bool ┆ u32 ┆ f64 ┆ f64 │\n", + "╞══════════════════╪════════╪══════════════╪══════════════╡\n", + "│ false ┆ 111201 ┆ 550000.0 ┆ 7323.0 │\n", + "│ true ┆ 1404 ┆ 146250.0 ┆ 2163.5 │\n", + "└──────────────────┴────────┴──────────────┴──────────────┘" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SHARED = pl.col(\"Price qualifier\").str.to_lowercase().str.contains(\"shared|part buy|part-buy\")\n", + "NONFIRM = pl.col(\"Price qualifier\").str.to_lowercase().str.contains(\n", + " \"offers|from|guide|region|shared|part buy|part-buy|invited\"\n", + ")\n", + "print(f\"Shared-ownership / part-buy rows : {raw.filter(SHARED).height:,}\")\n", + "print(f\"Non-firm price qualifier rows : {raw.filter(NONFIRM).height:,} \"\n", + " f\"({raw.filter(NONFIRM).height / raw.height:.1%})\")\n", + "print(f\"Asking price null : {raw['Asking price'].null_count():,}\")\n", + "print(f\"Asking price < £10k : {raw.filter(pl.col('Asking price') < 10_000).height:,}\")\n", + "print(f\"Asking price >= £20M : {raw.filter(pl.col('Asking price') >= 20_000_000).height:,}\"\n", + " f\" (max = £{raw['Asking price'].max():,})\")\n", + "\n", + "raw.with_columns(SHARED.alias(\"shared_ownership\")).group_by(\"shared_ownership\").agg(\n", + " pl.len().alias(\"rows\"),\n", + " pl.col(\"Asking price\").median().alias(\"median_price\"),\n", + " pl.col(\"Asking price per sqm\").median().alias(\"median_£/sqm\"),\n", + ").sort(\"shared_ownership\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5a61f92", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.071403Z", + "iopub.status.busy": "2026-06-03T07:34:31.071326Z", + "iopub.status.idle": "2026-06-03T07:34:31.155910Z", + "shell.execute_reply": "2026-06-03T07:34:31.155577Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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isX3Dhg3YuXMn8vLy1E419fHxgY+PD6ZPn47i4mKEh4djzZo1iImJgYeHhxgXFBSESZMmITw8HD179kRqamqN3zNY9QuyXC4Xr8Sropqp0sWff/6JgQMHarQBQKtWraqZ5T+zsmfPnkW7du2eGmdpaYm+fftW671UF/t69ArKAJCRkVHl/ZiYmKB79+7i1aOzs7Px0ksvYfbs2WIh/bxm8/7880+0b99eow1Q/xwcHBy0zliqZl8fpmuuqn7MyMjQ+EPTuXPn1GJqiqurK2xtbfG///1P5xz79+//xBwf/ll5VE5ODgoKCqqcp5mZGYKDg8XTr//44w906tQJCxYswOHDh9GqVSvIZDKcPXv2qfv6+uuv0aNHD2zbtk2tPSkpqUq5eHl54cCBA3B2dq7SDLjK//73v8f+AZGIqDbw1G4iIgMkCAI2bNgAV1dXvP3224iMjNR4vPnmmygtLcU333wDQHmK5aOzQ1ZWVuIpt9pm0bp164bU1FQoFAr06NGjygVCVYWEhKB+/fpYu3ateH9iACgtLcXq1at13t/atWvV1n+XlpYiPj4eJiYmGgW2Ll577TVYWFhg8eLFWgs/Vb+GhoaiSZMmWLlyJW7evKkRV1FR8dRTXV966SW4u7tj8+bNyM7OVnuPpUuXVilf1Vrdh7m6uqJJkyZqn7PqKuVPy0lX8fHxKC0tFV8XFBTg008/RYMGDcTiDVCu5z9x4oTabcFKSkq03rJM11wjIiJgYmKCDz/8ECUlJWJ7UVERVq5cCVNT02f6ntDG1NQUPXr0wOnTp/HgwYOnxgcHB6NBgwb45JNP1Nb6PnjwAHFxcQCAV199FQDQuHFj+Pv7IykpCenp6Wr7efSq1k+i7XvD19cX1tbW4veGg4MDwsPDcfjwYY115ADUxhFTU1ON2fry8nIsW7asSvmMHDkSgHKdt7b1/AUFBWrfSwBw48YNZGdni7dEIyKSAmekiYgMUEpKCi5fvozp06c/dqYuJCQEDRs2xPr16zFt2jRs3rwZq1atQkREBFq2bIn69evjl19+wfr169G+fXv4+flp3U+nTp1w+PBhBAcHo2fPnkhJSanyWt2nsbOzw7Jly/D222+jc+fOGD16tHj7K9UFjHSZiWzSpAlefvlljBs3TtzPL7/8gjlz5jzTjHTz5s3x73//G9HR0WjdujXGjh2LFi1a4Pbt2zhw4ABmzpyJgQMHon79+vjmm28wcOBA+Pr6YuzYsfDx8cHff/+NixcvYufOnVi+fLnW+x2rmJqa4t///jcGDRqEzp07Izo6Gvb29ti5cyfu3btXpXwnTZqEa9euISQkBB4eHqioqMDevXuRkZGhtt795ZdfhomJCeLi4qBQKGBtbY0WLVqgS5cu1e4rlYCAAAwfPhxlZWXYuHEjrl27hnXr1sHW1laMmTp1KqKiotCrVy+MGjUK9+7dw+bNm7Wu933xxRdhY2ODtWvXon79+rCzs0Pjxo3Vli48rFWrVpg3bx4WL16Mrl27YsSIEeLtr9LT0xEXF6d2BkZNGTp0KPbt24fDhw+jT58+T4xt2LAhPvroI0ycOBGdOnXCuHHjYG1tje3bt+Onn37CxIkT1e5HvXr1avTq1Qs9evTA5MmTxdtfnT17Fo0aNarSz0poaChsbGzQs2dPuLm5oaioCAkJCcjPzxcvcAYob0t39uxZDB48GMOHD0eXLl3w4MEDnD17FhUVFfjPf/4DQPlHps8++wyRkZEICQnB3bt3sWXLFlhZWVWpvzp16oQlS5Zg/vz5aNOmDaKiouDi4oLbt28jPT0du3fvxp9//qn2We3duxcAMGTIkCq9BxHRcyHV5cKJiKj6XnvtNQGA8NNPPz0xbuTIkQIA4fjx48LZs2eFMWPGCC+88ILQoEEDwdraWvDx8RHmzZsn3L17V/yax91mKDMzU3BxcREaNWok/Prrr4IgPPkWSA+3qQQGBgru7u4a7Zs2bRJat24tmJubC82aNRNmzpwpnDp1SgAgrFix4qn9obqN06FDh4T3339f8PDwEMzMzIQXXnhB+Oijj6qchwoec1um1NRU4V//+pdgb28vmJubC25ubsKIESM0bs9z/vx5YfTo0YKLi4tgZmYmNGrUSOjYsaPw7rvvqt3S6kkOHDggdO7cWbCwsBAaNWokjBkzRsjNza3S7a927NghDBw4UHB1dRUsLCwEe3t7oXPnzsK6des0blW0adMmwdfXVzAzM1Pbz9NuN/Wkzz4jI0OYPn264OzsLJibmwtt27YVtmzZonU/q1evFlq0aCGYmZkJLVu2FFauXCmkpqZqfe99+/YJHTp0ECwsLDRu9/S4z+ybb74ROnfuLFhZWQlWVlZCly5dhK1bt2rEPel74nH71qakpERwcnISRo4cqbHt0dtfqezfv18IDAwUGjRoIFhYWAjt2rUT1qxZo3G7OUEQhBMnTgi9evUSrKysBDs7O+G1114Trl27Jjg4OAhhYWFPze/LL78UQkNDxc/GyclJ6Nmzp5CYmKgRm5OTI7z11luCu7u7+H3cs2dP4bvvvhNjioqKhNmzZwvu7u6Cubm54OHhIbz77rvC+fPnNW51pe32VyoHDhwQwsPDBUdHR8HMzExo1qyZEBQUJMTHxwvFxcVqsQEBAULHjh2feqxERM+TTBD05KovRERED9m2bRuGDBmChISEp14ca9OmTRg7dizS0tLEqwUTSWX16tV49913ceHCBa3XL6hpubm5aNy4MaKjo/HZZ5899/eT0smTJ+Hv74+9e/dqXFeBiKg2cY00ERFJqqSkRGONZWlpKVauXAkzMzOugySD89Zbb8HDwwOLFy+u8X0XFxdrtC1ZsgSA8rRtYzdv3jyEhISwiCYiyXGNNBERSerYsWN48803ERkZCQ8PD+Tk5ODbb7+FXC7HggUL0LhxY6lTJNKJmZmZ1qtrP6uKigq4uroiKioKL774Iu7fv4/k5GQcOnQIQUFBeOWVV2r8PfVNamqq1CkQEQFgIU1ERBLz9PREmzZtsHnzZuTm5qJevXpo3bo1vvrqK4wdO1bq9Ij0hqmpKSIiInDw4EFs3LgRFRUV8PDwwPz58zF37lzxAn1ERPT8cY00ERERERERkQ74p0siIiIiIiIiHbCQJiIiIiIiItIBC2kiIiIiIiIiHfBiY7WkpKQE6enpcHJyQr167HYiIiIiIiJ9UlFRgdzcXLRt2xaWlpZPjGVFV0vS09PRuXNnqdMgIiIiIiKiJzh9+jRefvnlJ8awkK4lTk5OAJQfirOzs8TZEBERPaNBg5TPu3ZJmwcRkT7iGKmdnvdLTk4OOnfuLNZuT8JCupaoTud2dnaGi4uLxNkQERE9IwsL5TP/TyMi0sQxUjsD6ZeqLMXlxcaIiIiIiIiIdMBCmoiIiIiIiEgHLKSJiIiIiIiIdMA10kRERKS7PXukzoCISH9xjNTOiPqFhTQRERHpzt5e6gyIiPQXx0jtjKhfeGo3ERER6e6bb5QPIiLSxDFSOyPqF85IExERke4+/1z5PHKktHkQEekjjpHaGVG/cEaaiIiIiIiISAcspImIiIiIiIh0wFO7iYiIiMjgeE2489SYrPWNaiETIqqLOCNNREREREREpAPOSBMREZHuNm2SOgMiIv3FMVI7I+oXFtJERESku1atpM6AiEh/cYzUzoj6hYU0ERER6W7/fuVzeLi0eRA9AddRk2Q4RmpnRP3CQpqIiIh0t3Sp8tkIfhkiIqpxHCO1M6J+4cXGiIiIiIiIiHTAQpqIiIiIiIhIByykiYiIiIiIiHTAQpqIiIiIiIhIB7zYGBEREekuPv6ff6enA23bqm8/eBAIDa3dnIiI9MXDYyT9w4j6hYU0ERER6a5Ll3/+PWIEsG8f4OqqfJ2SAkyfDvz5pzS5ERFJrUsXYMEC5dj4OO+/X3v56Asj6he9ObX7woULiI6Ohp+fH+rVq4c2bdo8Mf7777+HTCbTGldQUIDx48fDwcEBNjY2iIyMRE5Ojkbc8ePH4e/vDysrK7i7u2PFihUQBEEtRhAELF++HG5ubrCysoK/vz9Onjz5bAdLRERk6E6dUj4A4PPPgYgIQKEADh8G3nrryb8kEREZu1OngCVLlPdNFgTA1FTzURcZUb/ozYx0RkYG9u3bhy5duqCyshKVlZWPjS0uLsb06dPRpEkTrduHDh2KjIwMrFu3DpaWlpg3bx7CwsJw5swZ1KunPOQLFy4gNDQUwcHBWLJkCf744w/MmTMHpqammDlzprivFStWYOHChVi+fDnatWuHTz/9FCEhIfjtt9/g6elZs51ARERkKGbMUD4fOwb4+wOxsUBwMHD/vvIXpBYtpM2PqIq8Jtx5akzW+ka1kAkZlRkzgDZtgI4dgcRE5X2TR40CXnpJ6sykZUT9ojeF9IABAzBw4EAAwJgxY3DmzJnHxi5btgxubm5o0aKFRtyJEydw8OBBHDx4ECEhIQAAb29v+Pr6YufOnRgyZAgAYOXKlXB0dERCQgLMzc3Rp08f5ObmIi4uDlOmTIGFhQVKSkqwbNkyzJgxA9OnTwcA9OjRA15eXvjwww+xdu3a59EVREREhuHqVeUpeioVFUD79sCmTcrXBnJ6HhHRc2Frqzxjp7QU2L0beO894K+/lOuEe/eWOjvpGEm/6M2p3SYmVUvl4sWLiI+Px7///W+t25OSkmBnZ4fg4GCxzdvbG35+fti/f79aXEREBMzNzcW2YcOGIT8/HydOnACgPPW7sLBQLL4BwNzcHIMHD1bbFxERUZ0kk6mfjjdoEODra3Cn5xERPVcWFoC7O+DhoTxr5/59qTPSDwbeL3ozI11Vb7/9NkaNGoX27dtr3S6Xy+Ht7Q2ZTKbW7uvrC7lcDgC4f/8+srOz4ePjoxbj4+MDmUwGuVyOXr16ifGPxvn6+uLatWsoLi6GlZVVTR0aERGRYXFzAxYulDoLIiL9VFoKLF0KfPcd8MILwOjRwMcfA/UMrgSrWUbSLwaV7Z49e3D8+HFkZWU9NkahUMDOzk6j3d7eHnfv3gUA5OfnA4BGnLm5OerXry/GKRQKWFhYwNLSUmNfgiBAoVA8tpAuLCxEYWGh+Frbxc6IiIgMXmoqsG0bkJ2tfO3qCrz2GtCnj7R5ERFJ7cwZ5QW1Xn8dsLcHbt8GNm/+Z/u4cdLlJiUj6ReDKaRLSkowbdo0xMbGolEj/b/gw6pVqxAbGyt1GkRERM/H3LnA118D8+YBY8cC//86J7h2Tdn23/8CcXHS5khEJJW5c4FVq5R/XMzI0NwukxlMwVijjKhfDKaQ/uijj2BiYoKoqChxRrmsrAyVlZXIz89H/fr1YW5uDnt7e2Sr/ir+EIVCAQcHBwD/zEQXFBSoxZSVlaGoqEiMs7e3R2lpKUpKStRmpRUKBWQyGezt7R+bb0xMDCZMmCC+zsnJQefOnat17ERERHonPBx4+23lvaLNzNS3jR0LvPgiC2kiqrvCw5UPUmdE/WIwhbRcLseFCxfg5OSksc3e3h6fffYZoqOj4ePjg5SUFAiCoLZOWi6Xo23btgAAa2truLq6imugVTIzMyEIgrgmWvWcmZmptiZbLpeL95V+HFtbW9ja2lb/gImIiPTZhQtAeTlQXKxZSBcXS5MTEZG+uHDhn39v366+/CUyEmjVSpq8pGZE/aI3V+1+mjlz5iAtLU3tERoaCg8PD6SlpeGVV14BAISFhUGhUCA1NVX82qysLJw9exbhD/31IywsDLt370Z5ebnYlpiYCDs7OwQEBAAAAgICYGtri23btokx5eXl2Llzp9q+iIiI6pwxY5RX5vbzA955B/j0U+Vj1iygQ4d/7jNNRFQXjRkDhIYqHwUFynsnt2mj/HdoKPDJJ1JnKA0j6he9mZEuKioSbyl19epVFBYWYvv27QCAwMBA+Pj4aFw9e9OmTbh+/Tp69eoltvn7+yM0NBTjxo1DfHw8LC0tMW/ePLRr1w6DBw8W42bNmoUtW7YgKioKkydPRnp6OlauXIm4uDjxlliWlpZ49913sWjRIjg5OaFt27ZYu3Yt8vLyMHPmzOfcI0RERHrO2Vl5gZgdO4DMTGWbqytw6BDQsqW0uRERSe2vv4CbN4GGDdXb58wBOnUC3npLmrykZiT9ojeF9O3bt/Haa6+ptalep6WlqRXLT5OYmIiYmBhMmjQJFRUVCAkJwZo1a1DvoUuqt2rVCsnJyYiJiUF4eDicnJwQGxuLGY/8BX327NkQBAEffvghcnNz4efnh4MHD8LT07P6B0tERGQsPD2Vs9BERKTJ2lqzzcpKedXquswI+kUmCAaUrQG7fv06XF1dkZ2dDRcXF6nTISIiejbduyufjx3Tvn3mTODDD2svH6pzvCbcqbX3ylqv/3eMIT3TvbvyLgZNmypPZ3Z3V7ZfvQps2gT861/A++9LmaE09LxfdKnZ9GZGmoiIiIzId9+xkCaius3NDVi8WHlRrQMHlG2ursCKFUBQkLS5SclI+oWFNBEREenujTeAadOUvxA9ShCAW7dqPSUiIr3xxhvK56AggyoOnzsj6hcW0kRERKS7kSOBmBhgzx7Azk59myAA3bpJkhYRkV4YOVL5fOaM8lTmXr0AB4d/tn/7LRAVJUlqklL1S2oqsG2b5u2v+vaVLjcdsZAmIiIi3SkUyltf3bkDtG+vuZ1X7SaiukyhAD77TLnu18cHePNN5euICOX2lSvrZiGtUABLlgA//QSMHQsMHKhsv3YNmD8fSEsD4uKkzbGKWEgTERGR7gYMUM489+mjffuRI7WbDxGRPhkwADh7VnmbJxsbQC4HBg0C7t4Fxo0zqKtT16gBA4BffgEKCwEzM/VtY8cCL77IQpqIiIiM3KVLyhnpRlquaBwRAXz/fW1nRESkX2xslM8+PsDhw0BoKPD334BMJmlakisu1iyki4ulyaWaWEgTERFR9RQWqhfR/v7AiRPKf1+9Kk1ORET6wsREuQbY1VX5ukkT5anLoaHA+fPS5ialZs2US4MiI/+5/dWVK8COHcCsWVJmphMTqRMgIiIiA3bp0j//Li395991fbaFiMjdHcjLU2+ztwf++1/gvfekyUkfODsDKSmAkxOQmal8NG4MHDoE/N//SZ1dlXFGmoiIiKrHzU05Cw0AFhbA7dvKNkEAcnOlzY2ISGp5eYCLi2Z7gwbA6dO1n48+8fQ0qNlnbTgjTURERLqLjFTeD/TWLSA9XXlxsZYtgaNHlQ9etZuI6rLISOWp3Y8uf1G5dq32c9IHkZHKx+PMnFl7uTwjzkgTERGR7qZN++ffjRsrn/fs+We924cf1npKRER6Y9o0YP165fIXT09l28PLX+qqh//v0Oa77wzm/w8W0kRERFQzVL8sAkBYmHR5EBHpg8WLufxFGzc37e2CoDzLyUCwkCYiIiLdBQYqn3/8Udo8iIj0kWqMvHVLWUAXFQH9+gH79ysLxn79pM1PKoGByntr//wzYGenvk0QgG7dJEmrOlhIExERke4ePJA6AyIi/fXwGMnlL/948EB5sbU7d4D27TW3G9D1NXixMSIiIiIioueNy1+UfH2BPn20bztypHZzeQYspImIiIiIiKh2XLqknJHWJiKiVlN5FiykiYiIiIiIqHYUFj7+tmBXr9Z+PtXENdJERESku6AgqTMgItJfHCO1CwoCsrIef1swmUyavKqBhTQRERHpbvFiqTMgItJfHCO1W7wYeOklo7gtmN6c2n3hwgVER0fDz88P9erVQ5s2bdS2FxYWYtGiRejcuTPs7OzQpEkTDBgwAOnp6Rr7KigowPjx4+Hg4AAbGxtERkYiJydHI+748ePw9/eHlZUV3N3dsWLFCgiCoBYjCAKWL18ONzc3WFlZwd/fHydPnqzZgyciIiIiIqoLBg1S3hYsPV15cbGWLYGjR5UPXrVbdxkZGdi3bx9atWqFF198UWP7tWvX8PnnnyMkJATfffcdvvzySxQUFKBr1644f/68WuzQoUORnJyMdevWYcuWLcjMzERYWBgqKirEmAsXLiA0NBTOzs7Yu3cvpk2bhgULFiA+Pl5tXytWrMDChQsxffp07N27F87OzggJCcGlS5eeT0cQEREZgkGDlA8iItLEMVK7h/ulcWPAw+Of24J5eBjUbcFkwqNTsBKprKyEiYmyrh8zZgzOnDmDc+fOidvv378PmUyG+vXri2337t2Du7s7hg8fjjVr1gAATpw4gYCAABw8eBAhISEAgMzMTPj6+iIhIQFDhgwBALzxxhs4ePAgsrKyYG5uDgCYO3cuPvvsM9y8eRMWFhYoKSlBkyZN8Oabb2Lp0qUAgLKyMnh5eSE8PBxr166t8vFdv34drq6uyM7OhouLyzP0FBERkR7o3l35fOyYtHlQneU14TFX/X0OstY3enoQ0cM4Rmqn5/2iS82mNzPSqiL6caytrdWKaABo0KABWrVqhb/++ktsS0pKgp2dHYKDg8U2b29v+Pn5Yf/+/WpxERERYhENAMOGDUN+fj5OnDgBQHnqd2FhoVh8A4C5uTkGDx6sti8iIiIiIiKqO/SmkK6O/Px8nDt3Dr6+vmKbXC6Ht7c3ZI9c8c3X1xdyuRyAcnY7OzsbPj4+ajE+Pj6QyWRinOr50ThfX19cu3YNxcXFNX5MREREREREpN8M+qrd77zzDmQyGaKjo8U2hUIBOzs7jVh7e3vcvXsXgLIAB6ARZ25ujvr164txCoUCFhYWsLS01NiXIAhQKBSwsrLSmlthYSEKCwvF19oudkZERERERESGx2AL6Y0bN+LLL7/Epk2b9HLN8apVqxAbGyt1GkRERM9H27ZSZ0BEpL84RmpnRP1ikIV0UlISJk2ahPfeew+jR49W22Zvb4/s7GyNr1EoFHBwcADwz0x0QUGBWkxZWRmKiorEOHt7e5SWlqKkpERtVlqhUEAmk8He3v6xOcbExGDChAni65ycHHTu3Fm3AyUiItJXn30mdQZERPqLY6R2RtQvBldInzx5EpGRkRg9ejTef/99je0+Pj5ISUmBIAhq66Tlcjna/v+/gFhbW8PV1VVcA62SmZkJQRDENdGq58zMTLRv315tX6r7Sj+Ora0tbG1tq3+gREREREREpJcMqpD+888/0a9fP/Tu3Rvr1q3TGhMWFobFixcjNTUVffv2BQBkZWXh7NmzmD17tlrc7t278cEHH8DMzAwAkJiYCDs7OwQEBAAAAgICYGtri23btomFdHl5OXbu3Inw8PDneahERET6TXXW1fr10uZBRqk2b21F9FxwjNTOiPpFbwrpoqIi8ZZSV69eRWFhIbZv3w4ACAwMhCAICA0NhZWVFaZPn44zZ86IX2tra4sXX3wRAODv74/Q0FCMGzcO8fHxsLS0xLx589CuXTsMHjxY/JpZs2Zhy5YtiIqKwuTJk5Geno6VK1ciLi5OvCWWpaUl3n33XSxatAhOTk5o27Yt1q5di7y8PMycObO2uoaIiEj/PHJWFxERPYRjpHZG1C96U0jfvn0br732mlqb6nVaWhoA5Q2yAaBPnz5qcYGBgTh8+LD4OjExETExMZg0aRIqKioQEhKCNWvWoF69fw63VatWSE5ORkxMDMLDw+Hk5ITY2FjMmDFDbd+zZ8+GIAj48MMPkZubCz8/Pxw8eBCenp41duxERERERERkOGSCIAhSJ1EXXL9+Ha6ursjOztbLq4wTERHppHt35fOxY9LmQUZJ307tzlrfSOoUyNBwjNROz/tFl5rNpJZyIiIiIiIiIjIKenNqNxERERmQ5s2lzoCISH9xjNTOiPqFhTQRERHpLjFR6gyIiPQXx0jtjKhfeGo3ERERERERkQ5YSBMREZHuZs9WPoiISBPHSO2MqF94ajcRERHp7qefpM6AiEh/cYzUzoj6hYU0EREREdETPO12XLw9FlHdw1O7iYiIiIiIiHTAQpqIiIiIiIhIBzy1m4iIiHRnaSl1BkRE+otjpHZG1C8spImIiEh3KSlSZ0BEpL84RmpnRP3CU7uJiIiIiIiIdMBCmoiIiHT3wQfKBxERaeIYqZ0R9QsLaSIiItLdDz8oH0REpIljpHZG1C8spImIiIiIiIh0wEKaiIiIiIiISAcspImIiIiIiIh0wEKaiIiIiIiISAd6U0hfuHAB0dHR8PPzQ7169dCmTRutcRs2bICXlxcsLS3Rvn177N27VyOmoKAA48ePh4ODA2xsbBAZGYmcnByNuOPHj8Pf3x9WVlZwd3fHihUrIAiCWowgCFi+fDnc3NxgZWUFf39/nDx5smYOmoiIyFAdPKh8EBGRJo6R2hlRv+hNIZ2RkYF9+/ahVatWePHFF7XGJCQkYOLEiRg6dCiSkpLg7++PQYMGaRS2Q4cORXJyMtatW4ctW7YgMzMTYWFhqKioEGMuXLiA0NBQODs7Y+/evZg2bRoWLFiA+Ph4tX2tWLECCxcuxPTp07F37144OzsjJCQEly5dqvlOICIiMhTW1soHERFp4hipnRH1i0x4dApWIpWVlTAxUdb1Y8aMwZkzZ3Du3Dm1GG9vb3Ts2BFbt24V2wICAmBnZ4f9+/cDAE6cOIGAgAAcPHgQISEhAIDMzEz4+voiISEBQ4YMAQC88cYbOHjwILKysmBubg4AmDt3Lj777DPcvHkTFhYWKCkpQZMmTfDmm29i6dKlAICysjJ4eXkhPDwca9eurfLxXb9+Ha6ursjOzoaLi0s1e4mIiEhPfPWV8nncOGnzIKPkNeGO1CnoJGt9I6lTIH3DMVI7Pe8XXWo2vZmRVhXRj3Pp0iVkZWWJhbDKsGHDkJqaitLSUgBAUlIS7OzsEBwcLMZ4e3vDz89PLLZVcREREWIRrdpXfn4+Tpw4AUB56ndhYaHae5qbm2Pw4MFq+yIiIqpzvvrqn1+IiIhIHcdI7YyoX/SmkH4auVwOAPDx8VFr9/X1RVlZGS5fvizGeXt7QyaTacSp9nH//n1kZ2dr7MvHxwcymUyMe9J7Xrt2DcXFxTV0dERERERERGQo6kmdQFUpFAoAgJ2dnVq7vb09AODu3bti3KMxqjhVTH5+vtZ9mZubo379+mr7srCwgKWlpca+BEGAQqGAlZWV1nwLCwtRWFgovtZ2sTMiIiIiIiIyPAZTSBuaVatWITY2Vuo0iIiIiIiIqIYZzKndqpnngoICtXbVTLWDg4MY92iMKk4Vo5qJfjSurKwMRUVFavsqLS1FSUmJxr5kMpmYkzYxMTHIzs4WH6dPn67qoRIREREREZEeM5gZadU6ZdUaaBW5XA5zc3N4enqKcSkpKRAEQW2dtFwuR9u2bQEA1tbWcHV1FddAq2RmZkIQBPG9VM+ZmZlo37692r5U95V+HFtbW9ja2j7LIRMREemvh+6gQUREj+AYqZ0R9YvBzEh7enrCy8sL27ZtU2tPTExEnz59xKtvh4WFQaFQIDU1VYzJysrC2bNnER4eLraFhYVh9+7dKC8vV9uXnZ0dAgICAChvrWVra6v2nuXl5di5c6favoiIiOocNzflg4iINHGM1M6I+kVvZqSLiorEW0pdvXoVhYWF2L59OwAgMDAQTk5OWLRoEUaMGIGWLVsiKCgIiYmJOHXqFI4cOSLux9/fH6GhoRg3bhzi4+NhaWmJefPmoV27dhg8eLAYN2vWLGzZsgVRUVGYPHky0tPTsXLlSsTFxYlFuaWlJd59910sWrQITk5OaNu2LdauXYu8vDzMnDmzFnuHiIhIz+zerXweOFDaPIiI9BHHSO2MqF9kgiAIUicBAFeuXEGLFi20bktLS0OvXr0AABs2bMDy5ctx7do1eHt7Y+nSpejfv79afEFBAWJiYrBz505UVFQgJCQEa9asQbNmzdTijh8/jpiYGPz2229wcnLCm2++idmzZ6udEi4IApYvX461a9ciNzcXfn5+WL16Nfz9/XU6Pl1u7k1ERKT3undXPh87Jm0eZJS8JtyROgWdZK1vJHUKpG84Rmqn5/2iS82mN4W0sWMhTURERkXPfxkiw8ZCmgwex0jt9LxfdKnZDGaNNBEREREREZE+YCFNREREREREpAMW0kREREREREQ60JurdhMREZEBWbNG6gyIiPQXx0jtjKhfWEgTERGR7jp0kDoDIiL9xTFSOyPql2qf2t27d2/I5XKt27KystC7d+9qJ0VERER67tgxvb3qKhGR5DhGamdE/VLtGenDhw+jsLBQ67bCwkIcOXKk2kkRERGRnpszR/lsJL8QERHVKI6R2hlRvzzTxcZkMpnW9uPHj6Nx48bPsmsiIiIiIiIivaTTjPSyZcuwbNkyAMoiOigoCCYm6rV4aWkpKioqMHny5JrLkoiIiIiIiEhP6FRIBwQEYMaMGRAEAe+//z6ioqLg4uKiFmNubg5fX18MGDCgRhMlIiIiIiIi0gc6FdKBgYEIDAwEoJyRnjhxIpo1a/ZcEiMiIiIiIiLSR9W+2NjChQtrMg8iIiIyJPw9gIjo8ThGamdE/VLtQrqyshLr16/H9u3bcf36dZSUlKhtl8lkuHjx4jMnSERERHooOFjqDIiI9BfHSO2MqF+qXUjPnj0b8fHxCAwMRFBQEMzNzWsyLyIiItJn588rn319pc2DiEgfcYzUzoj6pdqF9JYtWxAbG4v33nuvJvMhIiIiQzBxovLZCO4FSkRU4zhGamdE/VLt+0iXlJQgICCgJnMhIiIiIiIi0nvVLqRHjBiBPXv21GQuRERERERERHqv2qd2d+3aFfPnz8etW7cQHBwMOzs7jZjBgwc/S25EREREREREeqfaM9IjR47E1atXkZiYiAkTJiAyMlLt8dprr9VknqIffvgBXbp0gY2NDZydnTFkyBBcunRJI27Dhg3w8vKCpaUl2rdvj71792rEFBQUYPz48XBwcICNjQ0iIyORk5OjEXf8+HH4+/vDysoK7u7uWLFiBQRBeC7HR0RERERERPqt2jPSly9frsk8quTw4cMYNGgQRo0ahbi4OOTl5WHBggUICQlBeno6rKysAAAJCQmYOHEi5s2bh969eyMxMRGDBg3C0aNH0bVrV3F/Q4cORUZGBtatWwdLS0vMmzcPYWFhOHPmDOrVU3bNhQsXEBoaiuDgYCxZsgR//PEH5syZA1NTU8ycObPW+4CIiEgvvPWW1BkQEekvjpHaGVG/yAQDmlqNjo5GcnIyLl68CJlMBgBIS0tD7969ceTIEfTo0QMA4O3tjY4dO2Lr1q3i1wYEBMDOzg779+8HAJw4cQIBAQE4ePAgQkJCAACZmZnw9fVFQkIChgwZAgB44403cPDgQWRlZYm3+Jo7dy4+++wz3Lx5ExYWFlXK/fr163B1dUV2djZcXFxqpkOIiIiIjJDXhDtSp6CTrPWNpE6BiGqALjVbtU/tvnbt2lMfNa28vBw2NjZiEQ0ADRs2BADxVOtLly4hKytLLIRVhg0bhtTUVJSWlgIAkpKSYGdnh+CHbgru7e0NPz8/sdhWxUVERKjdJ3vYsGHIz8/HiRMnavwYiYiIDMLt28oHERFp4hipnRH1S7VP7fbw8FAraLV58OBBdXev1ZgxY7B582asXbsWI0aMQF5eHubOnYsOHTqgW7duAAC5XA4A8PHxUftaX19flJWV4fLly/Dx8YFcLoe3t7fGMfj6+or7uH//PrKzszX25ePjA5lMBrlcjl69etXoMRIRERkE1QVFjeBeoERENY5jpHZG1C/VLqR37dql0aZQKHDw4EGcPHkSy5cvf6bEtOnRowd27dqF4cOH48033wQA+Pn54cCBAzA1NRVzAKBxFXF7e3sAwN27d8U4bVcat7e3F2Py8/O17svc3Bz169cX47QpLCxEYWGh+FrbRcyIiIiIiIjI8FS7kB44cKDW9jFjxiAmJgY//vgjhg4dWu3EtDl+/DhGjhyJiRMnon///sjLy8PixYvRr18/HD16VLzYmD5YtWoVYmNjpU6DiIiIiIiIali110g/SXh4OBISEmp8v1OnTkXv3r0RHx+PoKAgREZGYt++ffj111/xzTffAPhn5rmgoEDta1Uz1Q4ODmLcozGqOFWMaib60biysjIUFRWJcdrExMQgOztbfJw+fboaR0xERERERET65rkU0sePH4elpWWN7/fPP/+En5+fWpuLiwsaNWqEixcvAvhnbbRqnbOKXC6Hubk5PD09xbjMzEyN+0HL5XJxH9bW1nB1ddXYl+rrHl07/TBbW1u4uLiID2dnZ90PmIiIiIiIiPROtU/tnjp1qkZbWVkZzp8/j2PHjj2Xeyy7u7vj119/VWu7evUq7ty5Aw8PDwCAp6cnvLy8sG3bNrXTzxMTE9GnTx/x6tthYWFYvHgxUlNT0bdvXwBAVlYWzp49i9mzZ4tfFxYWht27d+ODDz6AmZmZuC87OzsEBATU+DESEREZhKgoqTMgA2Vot7YiqhaOkdoZUb9U+z7SLVq00GiztLSEi4sLIiMjMWHCBPECYDXl448/xrRp0zB16lQMGDAAeXl5WLJkCXJzc5GRkQFHR0cAwLfffosRI0bgvffeQ1BQEBITE7F+/XocOXIE/v7+4v7+9a9/4c8//0R8fDwsLS0xb948mJiY4MyZM6hXT/k3hgsXLsDPzw//+te/MHnyZKSnp+Odd95BXFycTn8s4H2kiYiIiIyzkOZ9pImMgy41W7VnpC9fvlzdL622qVOnwsLCAp999hk2bNgAGxsb+Pv7Y9u2bWIRDQBRUVEoKirC8uXLsXz5cnh7e2PXrl1qRTSgnFmOiYnBpEmTUFFRgZCQEKxZs0YsogGgVatWSE5ORkxMDMLDw+Hk5ITY2FjMmDGj1o6biIhI71RUKJ/rVftXCSIi48UxUjsj6pdqz0iTbjgjTURERqV7d+WzEdwLlGoXZ6SpTuAYqZ2e94suNdszXWzs7NmzeO211+Ds7AwLCws4OztjyJAh+O23355lt0RERERERER6q9pz6kePHkVwcDCaNm2KqKgoNGnSBLdu3RJPoT506BC6q/7iQERERERERGQkql1Iz5kzB7169cLevXvV1hSvXLkS/fr1w5w5c3BMT6fsiYiIiIiIiKqr2qd2nz17FlOnTlUrogHA1NQUU6dO1bhNFREREREREZExqPaMtLW1NW7fvq11261bt2BtbV3tpIiIiEjPhYRInQERkf7iGKmdEfVLtQvpAQMGYPbs2XBxcUHfvn3F9pSUFLz77rt45ZVXaiRBIiIi0kMLFkidARGR/uIYqZ0R9Uu1C+n4+HhkZGQgNDQUtra2aNy4MW7fvo3CwkK8/PLL+PDDD2syTyIiIiIiIiK9UO1C2t7eHidOnMDevXtx7NgxKBQKODg4oHv37ujXrx9MTJ7pzlpERESkz/r3Vz7v3SttHkRE+ohjpHZG1C/VLqRTU1Nx7do1jB07VuM07k2bNsHd3R1BQUHPnCARERHpofx8qTMgItJfHCO1M6J+qfa08fz583Hr1i2t23JzczF//vxqJ0VERERERESkr6pdSGdkZKBTp05at7300kvIyMiodlJERERERERE+qrahbRMJkNBQYHWbQqFAg8ePKh2UkRERERERET6qtprpLt06YJPP/0UgwcPhkwmE9sFQcDatWvRpUuXGkmQiIiI9NBjzkojIiJwjHwcI+qXahfSsbGxCAoKQrt27TBmzBg4Ozvjr7/+wubNm5GVlYXDhw/XYJpERESkVz76SOoMiIj0F8dI7YyoX6pdSPv7+yM1NRXvvPMOZs+ejcrKSpiYmIjtXbt2rck8iYiIiIiIiPRCtQtpAOjWrRt++uknFBcXQ6FQwM7ODvXr16+p3IiIiEhfjR6tfP76a2nzICLSRxwjtTOifnmmQlrFysoKVlZWNbErIiIiMgQXL0qdARGR/uIYqZ0R9Uu1r9pNREREREREVBcZZCH99ddfo0OHDrC0tESjRo0QFhaG4uJicfuePXvQvn17WFpawsvLCxs3btTYR1lZGWbNmoWmTZvC2toawcHByMzM1IiTy+UIDg6GtbU1mjZtinfeeQdlZWXP9fiIiIiIiIhIfxlcIR0XF4cpU6Zg6NChOHjwID7//HO0aNFCvG/1sWPHMGjQIPj7+yMpKQlDhw7F+PHjsX37drX9TJ06FV9++SWWLl2KnTt3orS0FH369FG7N7ZCoUDv3r1RVlaGnTt3YunSpfjiiy8QExNTq8dMRERERERE+qNG1kjXlszMTCxatAg//PADwsLCxPZXX31V/PfixYvRpUsXrFu3DgAQFBSEixcvYsGCBYiMjAQAXL9+HevXr8fatWsxbtw4AMDLL78MNzc3fP7553jnnXcAAOvWrUNhYSF27doFBwcHAEBFRQUmT56MuXPnolmzZrVy3ERERHrHw0PqDIiI9BfHSO2MqF8MakZ648aNaNGihVoR/bDS0lKkpaXhtddeU2sfNmwYzp8/jytXrgAAkpOTUVlZqRbn4OCAkJAQ7N+/X2xLSkpC3759xSIaAIYMGYLKykokJyfX4JEREREZmP/8R/kgIiJNHCO1M6J+MahC+uTJk2jbti2WLFmCxo0bw9zcHN26dcOpU6cAABcvXkR5eTl8fHzUvs7X1xeAcr2z6rlx48awt7fXiFPFqOIe3ZednR2cnZ3V4oiIiIiIiKjuMKhTu2/evIlffvkF6enpWLt2LerXr4+lS5ciJCQE//vf/6BQKAAoi92HqQrmu3fvAoB4z+tH2dvbizG6xGlTWFiIwsJC8XVOTk5VDpGIiMgwqK4XsmqVtHkQEekjjpHaGVG/GFQhXVlZiXv37mH79u1o164dAKBr167w8PDAJ598gtDQUIkz/MeqVasQGxsrdRpERETPx+nTUmdARKS/OEZqZ0T9YlCndtvb28PR0VEsogHl2uYOHTogIyNDnHl++MrbAMSZatVaZ3t7e40YVdzD66GrGqdNTEwMsrOzxcdpI/qmISIiIiIiqssMaka6devWuHjxotZtJSUlaNmyJczMzCCXy9Vmp1XrmVXrnX18fHDr1i0oFAq1ddKPron28fHRWAtdUFCAnJwcjbXTj7K1tYWtra1uB0hEREREBsdrwp2nxmStb1QLmRBRbTGoGen+/fsjLy8Pv/32m9iWl5eHX3/9FR07doSFhQWCgoI07hmdmJgIX19fePz/y62HhITAxMQEO3bsEGMUCgWSk5MRHh4utoWFhSElJQX5+fli27Zt22BiYoKQkJDncoxERERERESk3wxqRjoiIgIvv/wyIiMjERcXBysrKyxbtgwWFhaYPHkyAOC9995Dr169MHnyZAwZMgRpaWnYunUrEhMTxf24uLhgwoQJmDVrFkxNTdG8eXMsXboUDRs2xBtvvCHGRUdHY82aNYiIiMDcuXNx48YNzJo1C9HR0byHNBER1W02NlJnQESkvzhGamdE/WJQhbSJiQn279+P6dOn44033kBZWRl69OiBI0eOoGnTpgCA7t27Y+fOnZg/fz42bNgANzc3rF+/XuPe0h9//DEaNGiAOXPm4O+//0a3bt2QkpKChg0bijH29vZITU3FlClTEBERARsbG0yYMAFxcXG1etxERER6JylJ6gyIiPQXx0jtjKhfZIIgCFInURdcv34drq6uyM7OhouLi9TpEBEREUmiKuuJjRHXSBPpP11qNoNaI01ERER6Ii5O+SAiIk0cI7Uzon5hIU1ERES6S0oyqlP0iIhqFMdI7YyoX1hIExEREREREemAhTQRERERERGRDlhIExEREREREemAhTQRERERERGRDgzqPtJERESkJ/77X6kzICLSXxwjtTOifmEhTURERLozN5c6AyIi/cUxUjsj6hee2k1ERES6W7dO+SAiIk0cI7Uzon7hjDQRERHp7j//UT5HR0ubB5GB8Jpw56kxWesb1UImVCs4RmpnRP3CQpqIiIiIakRVikUiImPAU7uJiIiIiIiIdMBCmoiIiIiIiEgHLKSJiIiIiIiIdMA10kRERKS7bdukzoCISH9xjNTOiPqFhTQRERHpztlZ6gyIiPQXx0jtjKhfeGo3ERER6W7bNqOaWSAiqlEcI7Uzon7hjDQRERHp7uOPlc+vvSZtHkRE+ohjpHZG1C+ckSYiIiIiIiLSgcEW0vfu3YOLiwtkMhnOnDmjtm3Dhg3w8vKCpaUl2rdvj71792p8fUFBAcaPHw8HBwfY2NggMjISOTk5GnHHjx+Hv78/rKys4O7ujhUrVkAQhOd2XERERERERKTfDLaQXrx4MSoqKjTaExISMHHiRAwdOhRJSUnw9/fHoEGDcPLkSbW4oUOHIjk5GevWrcOWLVuQmZmJsLAwtX1euHABoaGhcHZ2xt69ezFt2jQsWLAA8fHxz/34iIiIiIiISD8Z5BppuVyOTz/9FPHx8YiOjlbbtnDhQgwbNgyLFy8GAAQFBeGPP/7A+++/j/379wMATpw4gYMHD+LgwYMICQkBAHh7e8PX1xc7d+7EkCFDAAArV66Eo6MjEhISYG5ujj59+iA3NxdxcXGYMmUKLCwsavGoiYiIiIiISB8Y5Iz0lClTEB0dDW9vb7X2S5cuISsrSyyEVYYNG4bU1FSUlpYCAJKSkmBnZ4fg4GAxxtvbG35+fmKxrYqLiIiAubm52r7y8/Nx4sSJ53FoREREhmHdOuWDiIg0cYzUzoj6xeAK6e3btyM9PR0LFizQ2CaXywEAPj4+au2+vr4oKyvD5cuXxThvb2/IZDKNONU+7t+/j+zsbI19+fj4QCaTiXFERER1Ups2ygcREWniGKmdEfWLQZ3aXVRUhJiYGCxduhS2trYa2xUKBQDAzs5Ord3e3h4AcPfuXTHu0RhVnComPz9f677Mzc1Rv359Me5xCgsLUVhYKL7WdiEzIiIig/Xf/yqfe/eWNg8iIn3EMVI7I+oXgyqklyxZgiZNmmDs2LFSp/JUq1atQmxsrNRpEBERPR+qM8OM4JchIqIaxzFSOyPqF4M5tfvq1auIj49HbGwsCgoKkJ+fj3v37gFQ3grr3r174sxzQUGB2teqZqodHBwAKGeeH41RxaliVDPRj8aVlZWhqKhIjHucmJgYZGdni4/Tp0/reMRERERERESkjwxmRvry5csoKytDv379NLYFBQWhS5cu2Lp1K4B/1kCryOVymJubw9PTE4BynXNKSgoEQVBbJy2Xy9G2bVsAgLW1NVxdXTXWQmdmZkIQBI2104+ytbXVevo5ERERERERGTaDmZH28/NDWlqa2mP16tUAgHXr1mHt2rXw9PSEl5cXtm3bpva1iYmJ6NOnj3j17bCwMCgUCqSmpooxWVlZOHv2LMLDw8W2sLAw7N69G+Xl5Wr7srOzQ0BAwPM8XCIiIiIiItJTBjMjbWdnh169emnd1rFjR7z00ksAgEWLFmHEiBFo2bIlgoKCkJiYiFOnTuHIkSNivL+/P0JDQzFu3DjEx8fD0tIS8+bNQ7t27TB48GAxbtasWdiyZQuioqIwefJkpKenY+XKlYiLi1O7JRYRERERERHVHQZTSFdVVFQUioqKsHz5cixfvhze3t7YtWsX/P391eISExMRExODSZMmoaKiAiEhIVizZg3q1funS1q1aoXk5GTExMQgPDwcTk5OiI2NxYwZM2r7sIiIiPRLXJzUGRAZHa8Jd54ak7W+US1kQs+MY6R2RtQvMkEQBKmTqAuuX78OV1dXZGdnw8XFRep0iIiIiGpcVQpBejYspImeH11qNoNZI01ERER65I8/lA8iItLEMVI7I+oXozu1m4iIiGrB5MnK52PHpM2DiEgfcYzUzoj6hTPSRERERERERDpgIU1ERERERESkA57aTURERERkIHhlbyL9wBlpIiIiIiIiIh1wRpqIiIh0FxMjdQZERPqLY6R2RtQvLKSJiIhId4MHS50BEZH+4hipnRH1C0/tJiIiIt1dv658EBGRJo6R2hlRv3BGmoiIiHQ3bJjy2QjuBUpEVOM4RmpnRP3CGWkiIiIiIiIiHbCQJiIiIiIiItIBC2kiIiIiIiIiHbCQJiIiIiIiItIBLzZGREREuhs1SuoMiIj0F8dI7YyoX1hIExERke4mTZI6AyIi/cUxUjsj6hcW0kRERKS74mLls5WVtHlQrfGacEfqFIgMB8dI7YyoX1hIExERke6Cg5XPRnAvUCKiGscxUjsj6heDutjYtm3bMHDgQLi4uMDa2hp+fn746quvIAiCWtyGDRvg5eUFS0tLtG/fHnv37tXYV0FBAcaPHw8HBwfY2NggMjISOTk5GnHHjx+Hv78/rKys4O7ujhUrVmi8HxEREREREdUdBlVIr1q1CvXr10d8fDz27NmDsLAwTJw4Ee+//74Yk5CQgIkTJ2Lo0KFISkqCv78/Bg0ahJMnT6rta+jQoUhOTsa6deuwZcsWZGZmIiwsDBUVFWLMhQsXEBoaCmdnZ+zduxfTpk3DggULEB8fX2vHTERERERERPpFJhjQ9OqdO3fQqFEjtbZJkyYhMTERCoUCJiYm8Pb2RseOHbF161YxJiAgAHZ2dti/fz8A4MSJEwgICMDBgwcREhICAMjMzISvry8SEhIwZMgQAMAbb7yBgwcPIisrC+bm5gCAuXPn4rPPPsPNmzdhYWFR5dyvX78OV1dXZGdnw8XF5Zn6gYiISHLduyufjeD0PKoarpE2HFnrGz09iJ4vjpHa6Xm/6FKzGdSM9KNFNAB06NABhYWFuH//Pi5duoSsrCyxEFYZNmwYUlNTUVpaCgBISkqCnZ0dglXn6APw9vaGn5+fWGyr4iIiIsQiWrWv/Px8nDhxoqYPj4iIiIiIiAyAwV9s7NixY2jevDlsbGxw9OhRAICPj49ajK+vL8rKynD58mX4+PhALpfD29sbMplMI04ulwMA7t+/j+zsbI19+fj4QCaTQS6Xo1evXs/vwIiIiPRZ//5SZ0BEpL84RmpnRP1i0IX0sWPHkJCQIK5ZVigUAAA7Ozu1OHt7ewDA3bt3xbhHY1Rxqpj8/Hyt+zI3N0f9+vXFuMcpLCxEYWGh+FrbhcyIiIgM1pw5UmdARKS/OEZqZ0T9YrCF9PXr1zF06FAEBQVh6tSpUqejYdWqVYiNjZU6DSIiIiKqY6qynp3rqImejUGtkVbJz89HWFgYHB0dsWPHDpiYKA9DNfNcUFCgFq+aqXZwcBDjHo1RxaliVDPRj8aVlZWhqKhIjHucmJgYZGdni4/Tp0/reJRERER6LCRE+SAiIk0cI7Uzon4xuBnp4uJi9O/fHwUFBThx4gQaNmwoblOtZ1atgVaRy+UwNzeHp6enGJeSkgJBENTWScvlcrRt2xYAYG1tDVdXV3HNtEpmZiYEQdBYO/0oW1tb2NraPtvBEhER6auiIqkzICLSXxwjtTOifjGoGemKigoMGTIE58+fx4EDB9C8eXO17Z6envDy8sK2bdvU2hMTE9GnTx/x6tthYWFQKBRITU0VY7KysnD27FmEh4eLbWFhYdi9ezfKy8vV9mVnZ4eAgIDncYhERERERESk5wxqRnry5MnYu3cv4uPjUVhYiJMnT4rbOnToAAsLCyxatAgjRoxAy5YtERQUhMTERJw6dQpHjhwRY/39/REaGopx48YhPj4elpaWmDdvHtq1a4fBgweLcbNmzcKWLVsQFRWFyZMnIz09HStXrkRcXJzaLbGIiIiIiIio7jCoQjo5ORkAMGPGDI1tly9fhoeHB6KiolBUVITly5dj+fLl8Pb2xq5du+Dv768Wn5iYiJiYGEyaNAkVFRUICQnBmjVrUK/eP13SqlUrJCcnIyYmBuHh4XByckJsbKzW9yciIiIiIqK6waAK6StXrlQpbvz48Rg/fvwTYxo2bIgNGzZgw4YNT4wLCAhQm/kmIiIiAI/8gZqIDAuv7P2ccYzUzoj6xaAKaSIiItITK1dKnQERkf7iGKmdEfWLQV1sjIiIiIiIiEhqnJEmIiIi3UVFKZ+//VbaPKjGVOVUXyKqIo6R2hlRv7CQJiIiIt1lZ0udARGR/uIYqZ0R9QtP7SYiIiIiIiLSAQtpIiIiIiIiIh2wkCYiIiIiIiLSAddIExERke68vKTOgIies6ddgI73mX4CjpHaGVG/sJAmIiIi3X31ldQZEBHpL46R2hlRv/DUbiIiIiIiIiIdcEaaiIiIdDdlivJ5zRpp86Aq4T2iiWoZx0jtjKhfWEgTERGR7s6elToDIpJYVf5AU2fXUXOM1M6I+oWndhMRERERERHpgDPSRERERET0XHDWmowVC2kiIiIiA8b1z0REtY+FNBEREenO0VHqDIjISBjlrDXHSO2MqF9YSBMREZHudu+WOgMiIv3FMVI7I+oXXmyMiIiIiIiISAecka4CuVyOKVOm4Pjx47CxscGoUaOwZMkSmJubS50aERGRNBYtUn+m54Lrn4mUDO70b46R2hlRv7CQfgqFQoHevXvjhRdewM6dO3Hjxg3ExMSgqKgIn3zyidTpERERSSMlRflsBL8MEZFx0Ktim2OkdkbULyykn2LdunUoLCzErl274ODgAACoqKjA5MmTMXfuXDRr1kziDImIiIiIiKg2cY30UyQlJaFv375iEQ0AQ4YMQWVlJZKTkyXMjIiIiIiIiKTAGemnkMvlGDdunFqbnZ0dnJ2dIZfLJcqKiIiIDB3XPxPVvpr4udOrtdgkGRbST6FQKGBnZ6fRbm9vj7t37z726woLC1FYWCi+zs7OBgDk5OTUeI5ERES1rrRU+Xz9urR56Kkesx7/OwIRGbYWUX89NebTzPsAgDej/nhszNGVDo/dZrT0/P8OVa1WUVHx1FgW0s/JqlWrEBsbq9HeuXNnCbIhIiJ6Tlxdpc6AiEjv9FP9I6H9Y2NcE2olFf2k5/935ObmwsPD44kxLKSfwt7eHgUFBRrtCoVCbd30o2JiYjBhwgTxdUlJCbKzs9GiRQvUq6d/3Z6Tk4POnTvj9OnTcHZ2ljqdOo2fhX7h56Ff+HnoD34W+oWfh37h56E/+FnoF33/PCoqKpCbm4u2bds+NVb/Kjo94+Pjo7EWuqCgADk5OfDx8Xns19na2sLW1latrVWrVs8lx5rk7OwMFxcXqdMg8LPQN/w89As/D/3Bz0K/8PPQL/w89Ac/C/2iz5/H02aiVXjV7qcICwtDSkoK8vPzxbZt27bBxMQEISEh0iVGREREREREkmAh/RTR0dGwsbFBREQEkpOTsXHjRsyaNQvR0dG8hzQREREREVEdxEL6Kezt7ZGamop69eohIiICc+bMwYQJE7Bq1SqpU6tRtra2WLhwocbp6FT7+FnoF34e+oWfh/7gZ6Ff+HnoF34e+oOfhX4xps9DJgiCIHUSRERERERERIaCM9JEREREREREOmAhTURERERERKQDFtJEREREREREOmAhTURERERERKQDFtJ1nFwuR3BwMKytrdG0aVO88847KCsrkzqtOunChQuIjo6Gn58f6tWrhzZt2kidUp21bds2DBw4EC4uLrC2toafnx+++uor8NqM0ti/fz8CAwPh5OQECwsLeHp6IiYmBgUFBVKnVufdu3cPLi4ukMlkOHPmjNTp1DmbNm2CTCbTeMyZM0fq1Oq0r7/+Gh06dIClpSUaNWqEsLAwFBcXS51WndOrVy+tPx8ymQwJCQlSp1fn/PDDD+jSpQtsbGzg7OyMIUOG4NKlS1Kn9UzqSZ0ASUehUKB379544YUXsHPnTty4cQMxMTEoKirCJ598InV6dU5GRgb27duHLl26oLKyEpWVlVKnVGetWrUKHh4eiI+Ph5OTEw4dOoSJEyciOzsbCxculDq9Oufu3bvo0qULpk6dCkdHR5w7dw6LFi3CuXPnkJycLHV6ddrixYtRUVEhdRp13oEDB9CwYUPxdfPmzSXMpm6Li4vDihUrMHfuXPj7++POnTtITU3FgwcPpE6tzlm7di0KCwvV2j766CPs2LEDffv2lSiruunw4cMYNGgQRo0ahbi4OOTl5WHBggUICQlBeno6rKyspE6xWnj7qzps2bJliIuLw7Vr1+Dg4AAA+OKLLzB58mRcu3YNzZo1kzjDuqWyshImJsqTRMaMGYMzZ87g3LlzEmdVN925cweNGjVSa5s0aRISExOhUCjEz4mk8+WXX2LSpEm4ceMGxyqJyOVydOrUCfHx8YiOjsbPP/+MTp06SZ1WnbJp0yaMHTsWubm5GmMW1b7MzEy0adMGP/zwA8LCwqROh7Tw9PSEr68v9u3bJ3UqdUp0dDSSk5Nx8eJFyGQyAEBaWhp69+6NI0eOoEePHhJnWD38bbAOS0pKQt++fcUiGgCGDBmCyspKzvJIgMWZ/tD2C2mHDh1QWFiI+/fvS5ARPcrR0REAuBRFQlOmTEF0dDS8vb2lToVIL2zcuBEtWrRgEa2njh8/jsuXL2PEiBFSp1LnlJeXw8bGRiyiAYhn0RjynC5/c6/D5HI5fHx81Nrs7Ozg7OwMuVwuUVZE+unYsWNo3rw5bGxspE6lznrw4AFKSkrw66+/4v3338crr7wCDw8PqdOqk7Zv34709HQsWLBA6lQIQOvWrWFqagpPT08sW7aMpxFL5OTJk2jbti2WLFmCxo0bw9zcHN26dcOpU6ekTo0AbN26FdbW1hg4cKDUqdQ5Y8aMwZ9//om1a9eioKAAly5dwty5c9GhQwd069ZN6vSqjYV0HaZQKGBnZ6fRbm9vj7t379Z+QkR66tixY0hISMDMmTOlTqVOc3d3h5WVFTp27AhnZ2ds3bpV6pTqpKKiIsTExGDp0qWwtbWVOp06zdnZGbGxsdi8eTOSkpIQHh6O+fPn4+2335Y6tTrp5s2bSE5OxubNm7F27Vp8//33kMlkCAkJwe3bt6VOr06rqKjAd999h1deeQXW1tZSp1Pn9OjRA7t27cKcOXNgZ2eHli1b4tatW0hKSoKpqanU6VUbLzZGRPQE169fx9ChQxEUFISpU6dKnU6dtn//fty/fx8ZGRlYsmQJBgwYgEOHDhn0f8KGaMmSJWjSpAnGjh0rdSp1XmhoKEJDQ8XXISEhsLKywurVqzFv3jw4OztLmF3dU1lZiXv37mH79u1o164dAKBr167w8PDAJ598gvfff1/iDOuuQ4cOITc3F8OHD5c6lTrp+PHjGDlyJCZOnIj+/fsjLy8PixcvRr9+/XD06FGDvdgYC+k6zN7eXuvtYxQKhdq6aaK6Kj8/H2FhYXB0dMSOHTu4jl1iql9M/f398fLLL8PPzw+7du1CZGSkxJnVHVevXkV8fDx27dol/v9x79498fnevXto0KCBlCnWeUOGDMGHH36I3377jYV0LbO3t4ejo6M4VgGAg4MDOnTogIyMDAkzo61bt8LR0VHtD09Ue6ZOnYrevXsjPj5ebOvatSvc3NzwzTffYNKkSRJmV30spOswHx8fjbXQBQUFyMnJ0Vg7TVTXFBcXo3///igoKMCJEyfUbi1D0mvXrh3MzMxw4cIFqVOpUy5fvoyysjL069dPY1tQUBC6dOmCkydPSpAZkfRat26Nixcvat1WUlJSy9mQSnFxMb7//nu8/vrrMDMzkzqdOunPP//UWJvu4uKCRo0aPfZnxhBweqUOCwsLQ0pKCvLz88W2bdu2wcTEBCEhIdIlRiSxiooKDBkyBOfPn8eBAwd4T1Y9dOrUKZSXl8PT01PqVOoUPz8/pKWlqT1Wr14NAFi3bh3Wrl0rcYaUkJAAU1NTdOjQQepU6hzVKau//fab2JaXl4dff/0VHTt2lC6xOu6HH37AvXv3eFq3hNzd3fHrr7+qtV29ehV37twx6IuG8j7SdZhCoUDr1q3h5eWFuXPn4saNG4iJicGIESPwySefSJ1enVNUVIT9+/cDAD799FNcvHgRq1atAgAEBgbCyclJyvTqlEmTJuHLL79EfHw8AgIC1LZ16NABFhYWEmVWNw0ePBidOnVCu3btYGVlhd9//x0rV65E48aN8fPPP8Pc3FzqFOu0w4cPIygoiPeRlkBoaCh69+6Ntm3bAlAWDF988QXefvtt8Q8cVHsqKyvRtWtX3L17F3FxcbCyssKyZcvwv//9D+fOnUPTpk2lTrFOGjhwIH777TdcuXJF7fZLVHs+/vhjTJs2DVOnTsWAAQOQl5eHJUuWIDc3FxkZGeItLQ0NC+k67vz585gyZQqOHz8OGxsbjBo1CnFxcfzFVAJXrlxBixYttG5LS0tDr169ajehOszDwwNXr17Vuu3y5csG/ddTQ7R8+XIkJibi4sWLqKyshIeHBwYPHoyZM2fyqtF6gIW0dN5++20kJSXh+vXrqKyshJeXFyZMmIApU6awYJDInTt3MH36dOzZswdlZWXo0aMHVq9ejRdffFHq1OokhUKBpk2bYtq0aVixYoXU6dRZgiDg888/x2effYaLFy/CxsYG/v7+WLp0qUEvJ2UhTURERERERKQDrpEmIiIiIiIi0gELaSIiIiIiIiIdsJAmIiIiIiIi0gELaSIiIiIiIiIdsJAmIiIiIiIi0gELaSIiIiIiIiIdsJAmIiIiIiIi0gELaSIiIiIiIiIdsJAmIiLSYsyYMWjTps1z2//ixYsRHBwMOzs7yGQynDlzRmucXC5HcHAwrK2t0bRpU7zzzjsoKyvTiEtPT4eNjQ1yc3M1tu3atQsymQx9+vSpVq69evVC//79nxjj4eGBt956q1r7l8Lhw4ef2O/V9ffff8PBwQE//fRTje6XiIj0CwtpIiIiCXz++ecoKytD3759HxujUCjQu3dvlJWVYefOnVi6dCm++OILxMTEaMTOnz8fY8aMgZOTk8a2LVu2AFAWj3/99VfNHcRDdu3ahZkzZz6XfT8PL730Ek6cOAFfX98a3a+NjQ2mTJmCuXPn1uh+iYhIv7CQJiIiksC1a9fw448/PnEWd926dSgsLMSuXbsQGhqKcePG4YMPPsC6devUCuJLly5hz549GDdunMY+CgsLsW/fPvTt2xeVlZVISEh4LsfToUMHeHh4PJd91yRBEFBaWgpbW1t07doV1tbWNf4e48aNw5EjR/D777/X+L6JiEg/sJAmIiKqovT0dISGhsLa2hoNGzZEZGQkrl27phZTUFCA119/HTY2NmjcuDHmzp2L+Ph4yGQytTgTk6f/F5yUlIS+ffvCwcFBbBsyZAgqKyuRnJwstm3evBmenp7o0KGDxj527tyJkpISLFq0CB07dhRnpx/2008/oWfPnmjYsCFsbGzQtm1bfP3114/Nq7i4GP369YOnpycuXboEQPPUbtWp8YcPH0aHDh1gbW2Nzp0745dfflHbV1X761GbNm2CTCbDyZMn0bt3b9SvXx8eHh746quv1OJUeezfvx/t27eHhYUF9uzZo/XU7srKSqxatQq+vr6wsLBA06ZN8dprr6GgoECMOX/+PAYOHIiGDRvC2toa/fr1w8WLF9Xe093dHZ07d8amTZueeAxERGS4WEgTERFVQXZ2Nnr27Im8vDz85z//wbp16/Drr78iMDAQf//9txg3duxY7N27Fx988AE2bdqE8+fP4+OPP67We8rlcvj4+Ki12dnZwdnZGXK5XGxLSUlBQECA1n1s2bIFHh4eCAgIwPDhw/Hrr78iMzNT3F5YWIh+/frB1tYW3377Lb7//ntMmjQJ+fn5Wvd37949hIeH4+LFizh69Cg8PT0fm//NmzcxdepUzJo1C9999x1KSkowaNAglJeXizHP2l/Dhg1DcHAwdu3ahaCgIIwfPx4HDhxQi/nrr78wdepUTJ8+HQcOHICfn5/WfU2ZMgXvvPMO+vfvjz179uDTTz+FjY0N7t27B0A58x8QEIC7d+9i06ZN2Lp1K3Jzc9GnTx+Ulpaq7SsgIACHDh2q8nEQEZGBEYiIiEjD6NGjhdatW4uvp0+fLlhbWwt5eXli2/nz5wWZTCb8+9//FgRBEDIyMgQAwubNm8WYBw8eCC+88ILwuP9y09LSBADCzz//rLGtXr16wrJlyzTaW7duLUycOFEQBEGorKwULCwshJUrV2rE5eTkCKampsKcOXMEQRCEGzduCCYmJsJ7770nxvz8888CAOGPP/54bF8EBgYK/fr1E+7evSt06dJFaN++vXDr1i21GHd3d+HNN98UX48ePVqQyWTCuXPnNI716NGjgiBUr79UNm7cKABQOxZBEISePXsKXbt2VcsDgHDy5Em1uEf7PTMzU5DJZMLSpUsf+56jRo0SPD09heLiYrHt9u3bQoMGDYRPP/1UIz+ZTCYUFhY+8TiIiMgwcUaaiIioCo4ePYrevXurnWbt4+OD9u3b49ixYwCAn3/+GQDwyiuviDEmJiYYMGDAc8tLoVCgtLRU60XGEhMT8eDBAwwfPhwA0KxZMwQGBmLr1q1iTMuWLWFra4v/+7//w3fffaf1qt8AcOfOHQQFBQEA0tLS0Lhx46fm1qxZM7Ru3Vp8/eKLLwIArl+/DqBm+mvQoEFqr1999VX88ssvePDggdjm6OiILl26PHE///3vfyEIAsaPH//YmOTkZLzyyiuoV68eKioqUFFRAXt7e3To0EE8FpVGjRpBEATcunWrysdCRESGg4U0ERFRFSgUCjRp0kSjvUmTJrh79y4AICcnB2ZmZmjYsKFaTFWKTm3s7e3V1uc+nIuqoC8pKQEAWFhYaMRt2bIF3t7ecHV1RX5+PvLz8/HKK6/g4sWLOHXqlPgehw4dgo2NDUaOHImmTZuiV69eSE9PV9tXVlYWfv/9d0RFRcHe3r5K+dvZ2am9Njc3V8u5Jvrr0dgmTZqgvLwcd+7cUWt7mry8PNSrV++J733nzh189NFHMDMzU3scPXoU2dnZarGqz6O4uLjKx0JERIajntQJEBERGQIHBwfcvn1bo/3WrVvw8vICADg7O6O8vBwFBQVqxaG2r6sKHx8ftbXQgPLiXDk5OeLaaVVB/eia5gsXLoizpNoK3y1btoiztJ07d0ZSUhKKi4uRlpaGmTNnIiIiQu0iWgEBAejbty9iYmLg6OiI119/vVrH9LCa6K/bt2+jefPm4utbt27BzMwMjRo1EtueduEyQDlrXVFRgdu3bz+2mHZwcEC/fv0wefJkjW02NjZqr1Wfh6OjY1UOg4iIDAxnpImIiKqge/fuSE1NhUKhENsyMzPxxx9/oHv37gCATp06AQB2794txlRWVmLPnj3Ves+wsDCkpKSoFcnbtm2DiYkJQkJCAACWlpZwc3PD5cuX1b5269atkMlk2LVrF9LS0tQeoaGh4mnfD7OyskJ4eDj+7//+D5cvXxZnjlWmTZuGJUuWYMyYMdi+fXu1julhNdFfu3btUnu9Y8cOdOzYEaampjrl0rt3b8hkMmzcuPGxMX379sW5c+fQoUMHdOrUSe3h7e2tFnvlyhU0bNgQTZs21SkPIiIyDJyRJiIiqoLp06dj48aNCAkJwbx581BSUoL58+fDzc0NY8aMAQC0bt0agwYNwtSpU1FUVAR3d3d88cUXKC4u1pgV/fHHH5Gbm4uMjAwAyjW6V65cgYeHh1hgRkdHY82aNYiIiMDcuXNx48YNzJo1C9HR0WjWrJm4r27dumncVmrr1q3o0aMHIiIiNI6lsLAQAwcOREpKCioqKrBhwwYMGjQIbm5uuHnzJtasWYNu3brB0tJS42vfffddFBcXY/jw4bC0tET//v2r3ae69NfjbN68GVZWVnjppZeQkJCAI0eOYN++fTrn4uXlhejoaMyfPx93795Fnz59UFRUhH379mHRokVo3rw5YmNj8fLLLyM0NBSTJk1CkyZNcPPmTfz444/o0aMHoqKixP2dOXMGAQEBVbrNGRERGSCpr3ZGRESkjx69arcgCMLvv/8uBAcHC/Xr1xdsbGyEwYMHC1euXFGLUSgUwogRIwRra2vB0dFRiImJEebPny/Y2dmpxQUGBgoANB6jR49Wi/vzzz+FPn36CFZWVkLjxo2FmTNnCqWlpWoxO3bsECwtLcUrRJ85c0YAIKxfv17rsZWVlQlOTk7CyJEjBblcLrz66quCq6urYGFhITRr1kwYM2aMkJOTo5Zrv3791PYxc+ZMwcLCQjh06JAgCNqv2v1o/ykUCgGAsHHjRp3761Gqq3YfP35cCAwMFCwtLQU3Nzfhiy++UIvTlocgaL9a+oMHD4QPPvhAeOGFFwQzMzOhadOmwtChQ4WCggIxJisrSxgyZIjg6OgoWFhYCB4eHsKoUaPUrk5eVlYmODg4CBs2bHjiMRARkeGSCYIgSFbFExER1QE9e/aEqakp0tLSnsv+y8vL4ebmhhUrVmDUqFHP5T1qU1X6a9OmTRg7dixyc3PV1kPrg3379mH48OG4ceMGGjRoIHU6RET0HPDUbiIiohq0Y8cOXLt2DW3btkVRURG2bt2Ko0ePaqzlrUlmZmaYM2cOPv74Y4MrpKXor+ctPj4eM2bMYBFNRGTEWEgTERHVoAYNGuCbb77B//73P5SVlcHHxwf/+c9/tK5VrknR0dEoLCzEnTt39G6G9kmk6q/n5d69ewgMDMT06dOlToWIiJ4jntpNREREREREpANeSpKIiIiIiIhIByykiYiIiIiIiHTAQpqIiIiIiIhIByykiYiIiIiIiHTAQpqIiIiIiIhIByykiYiIiIiIiHTAQpqIiIiIiIhIByykiYiIiIiIiHTAQpqIiIiIiIhIByykiYiIiIiIiHTw/wCNZ2YE1eE00gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Asking-price distribution (log scale) with the implausible tails marked\n", + "prices = raw[\"Asking price\"].drop_nulls().to_numpy().astype(float)\n", + "fig, ax = plt.subplots(figsize=(9, 3.2))\n", + "ax.hist(np.log10(np.clip(prices, 1, None)), bins=90, color=\"#2563eb\")\n", + "for v, lab in [(10_000, \"£10k\"), (20_000_000, \"£20M\"), (100_000_000, \"£100M\")]:\n", + " ax.axvline(np.log10(v), color=\"red\", ls=\"--\", lw=1)\n", + " ax.text(np.log10(v), ax.get_ylim()[1] * 0.92, lab, rotation=90, fontsize=7,\n", + " color=\"red\", va=\"top\")\n", + "ax.set_xlabel(\"log10(Asking price)\"); ax.set_ylabel(\"count\")\n", + "ax.set_title(\"Asking price distribution (log scale)\")\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "238478fd", + "metadata": {}, + "source": [ + "### 3.3 · 🔴 Floor area & price-per-sqm\n", + "\n", + "- ~49% of `Total floor area (sqm)` is null (OnTheMarket ~87%).\n", + "- **Impossible tiny areas**: rows under 20 m² with ≥2 bedrooms (single-room dimensions parsed as total).\n", + "- **Suspiciously large areas** (>400 m²) — likely sq ft never converted to m².\n", + "- `Asking price per sqm` is mechanically correct but faithfully amplifies bad areas (max £410,959/m²)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acfba0f8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.156976Z", + "iopub.status.busy": "2026-06-03T07:34:31.156880Z", + "iopub.status.idle": "2026-06-03T07:34:31.164246Z", + "shell.execute_reply": "2026-06-03T07:34:31.163925Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Floor area null : 55,691 (49.5%)\n", + "Impossible (area<20 & beds>=2): 89\n", + "Suspiciously large (area>400) : 1,310\n", + "£/sqm > £30k : 969 (max = £410,959/sqm)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (6, 5)
providerBedroomsTotal floor area (sqm)Asking priceAsking price per sqm
stri32f64i64i32
"Zoopla"26.11270000208197
"Zoopla"26.11270000208197
"Zoopla"26.11270000208197
"Rightmove"26.81400000205882
"Zoopla"26.11250000204918
"Rightmove"27.21250000173611
" + ], + "text/plain": [ + "shape: (6, 5)\n", + "┌───────────┬──────────┬────────────────────────┬──────────────┬──────────────────────┐\n", + "│ provider ┆ Bedrooms ┆ Total floor area (sqm) ┆ Asking price ┆ Asking price per sqm │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ i32 ┆ f64 ┆ i64 ┆ i32 │\n", + "╞═══════════╪══════════╪════════════════════════╪══════════════╪══════════════════════╡\n", + "│ Zoopla ┆ 2 ┆ 6.1 ┆ 1270000 ┆ 208197 │\n", + "│ Zoopla ┆ 2 ┆ 6.1 ┆ 1270000 ┆ 208197 │\n", + "│ Zoopla ┆ 2 ┆ 6.1 ┆ 1270000 ┆ 208197 │\n", + "│ Rightmove ┆ 2 ┆ 6.8 ┆ 1400000 ┆ 205882 │\n", + "│ Zoopla ┆ 2 ┆ 6.1 ┆ 1250000 ┆ 204918 │\n", + "│ Rightmove ┆ 2 ┆ 7.2 ┆ 1250000 ┆ 173611 │\n", + "└───────────┴──────────┴────────────────────────┴──────────────┴──────────────────────┘" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fa = pl.col(\"Total floor area (sqm)\")\n", + "print(f\"Floor area null : {raw['Total floor area (sqm)'].null_count():,} \"\n", + " f\"({raw['Total floor area (sqm)'].null_count() / raw.height:.1%})\")\n", + "print(f\"Impossible (area<20 & beds>=2): {raw.filter((fa < 20) & (pl.col('Bedrooms') >= 2)).height:,}\")\n", + "print(f\"Suspiciously large (area>400) : {raw.filter(fa > 400).height:,}\")\n", + "print(f\"£/sqm > £30k : {raw.filter(pl.col('Asking price per sqm') > 30_000).height:,}\"\n", + " f\" (max = £{raw['Asking price per sqm'].max():,}/sqm)\")\n", + "raw.filter((fa < 20) & (pl.col(\"Bedrooms\") >= 2)).select(\n", + " \"provider\", \"Bedrooms\", \"Total floor area (sqm)\", \"Asking price\", \"Asking price per sqm\",\n", + ").sort(\"Asking price per sqm\", descending=True).head(6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc4faa65", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.165062Z", + "iopub.status.busy": "2026-06-03T07:34:31.164982Z", + "iopub.status.idle": "2026-06-03T07:34:31.550386Z", + "shell.execute_reply": "2026-06-03T07:34:31.550028Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Floor area vs bedrooms — red points are physically impossible (area < beds * 8 m²)\n", + "s = raw.filter(fa.is_not_null() & (fa < 160)).select(\"Bedrooms\", \"Total floor area (sqm)\")\n", + "rng = np.random.default_rng(0)\n", + "xb = s[\"Bedrooms\"].to_numpy().astype(float)\n", + "x = xb + (rng.random(s.height) - 0.5) * 0.35\n", + "y = s[\"Total floor area (sqm)\"].to_numpy()\n", + "impossible = y < np.maximum(10.0, xb * 8.0)\n", + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.scatter(x[~impossible], y[~impossible], s=4, alpha=0.15, color=\"#2563eb\", label=\"plausible\")\n", + "ax.scatter(x[impossible], y[impossible], s=14, alpha=0.8, color=\"red\", label=\"impossible (area < beds×8)\")\n", + "xs = np.linspace(0, x.max(), 60)\n", + "ax.plot(xs, np.maximum(10, xs * 8), \"k--\", lw=1, label=\"area = beds × 8 m²\")\n", + "ax.set_xlabel(\"Bedrooms\"); ax.set_ylabel(\"Total floor area (m²)\")\n", + "ax.set_title(\"Floor area vs bedrooms (≤160 m²)\"); ax.legend(fontsize=8)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d1a7f09b", + "metadata": {}, + "source": [ + "### 3.4 · 🔴/🟠 Zero bedrooms / bathrooms conflation\n", + "\n", + "`storage.py` maps missing **and** capped-implausible values to literal `0`, so `Bedrooms == 0` mixes\n", + "genuine studios, missing data, and capped outliers (e.g. a £29.9M 0-bed \"Detached\"). Same for bathrooms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c41b90f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.551560Z", + "iopub.status.busy": "2026-06-03T07:34:31.551429Z", + "iopub.status.idle": "2026-06-03T07:34:31.560471Z", + "shell.execute_reply": "2026-06-03T07:34:31.560043Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bedrooms == 0 : 5,495 of which Studio sub-type: 1,407\n", + "Bathrooms == 0: 6,448 with >=3 beds: 1,057\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 8)
providerProperty typeProperty sub-typeBedroomsBathroomsAsking priceTotal floor area (sqm)Address per Property Register
strstrstri32i32i64f64str
"OnTheMarket""Other""Land"00nullnull"Sutton Road, Hounslow, Greater London"
"Zoopla""Other""Property"00nullnull""
"Zoopla""Other""Property"00nullnull""
"Zoopla""Other""Property"00nullnull""
"Zoopla""Other""Property"00nullnull""
" + ], + "text/plain": [ + "shape: (5, 8)\n", + "┌─────────────┬─────────────┬────────────┬──────────┬───────────┬────────┬────────────┬────────────┐\n", + "│ provider ┆ Property ┆ Property ┆ Bedrooms ┆ Bathrooms ┆ Asking ┆ Total ┆ Address │\n", + "│ --- ┆ type ┆ sub-type ┆ --- ┆ --- ┆ price ┆ floor area ┆ per │\n", + "│ str ┆ --- ┆ --- ┆ i32 ┆ i32 ┆ --- ┆ (sqm) ┆ Property │\n", + "│ ┆ str ┆ str ┆ ┆ ┆ i64 ┆ --- ┆ Register │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ str │\n", + "╞═════════════╪═════════════╪════════════╪══════════╪═══════════╪════════╪════════════╪════════════╡\n", + "│ OnTheMarket ┆ Other ┆ Land ┆ 0 ┆ 0 ┆ null ┆ null ┆ Sutton │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Road, │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Hounslow, │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Greater │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ London │\n", + "│ Zoopla ┆ Other ┆ Property ┆ 0 ┆ 0 ┆ null ┆ null ┆ │\n", + "│ Zoopla ┆ Other ┆ Property ┆ 0 ┆ 0 ┆ null ┆ null ┆ │\n", + "│ Zoopla ┆ Other ┆ Property ┆ 0 ┆ 0 ┆ null ┆ null ┆ │\n", + "│ Zoopla ┆ Other ┆ Property ┆ 0 ┆ 0 ┆ null ┆ null ┆ │\n", + "└─────────────┴─────────────┴────────────┴──────────┴───────────┴────────┴────────────┴────────────┘" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zb = raw.filter(pl.col(\"Bedrooms\") == 0)\n", + "print(f\"Bedrooms == 0 : {zb.height:,} of which Studio sub-type: \"\n", + " f\"{zb.filter(pl.col('Property sub-type') == 'Studio').height:,}\")\n", + "print(f\"Bathrooms == 0: {raw.filter(pl.col('Bathrooms') == 0).height:,} with >=3 beds: \"\n", + " f\"{raw.filter((pl.col('Bathrooms') == 0) & (pl.col('Bedrooms') >= 3)).height:,}\")\n", + "zb.filter(pl.col(\"Property sub-type\") != \"Studio\").sort(\"Asking price\", descending=True).select(\n", + " \"provider\", \"Property type\", \"Property sub-type\", \"Bedrooms\", \"Bathrooms\",\n", + " \"Asking price\", \"Total floor area (sqm)\", \"Address per Property Register\",\n", + ").head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "589d9960", + "metadata": {}, + "source": [ + "### 3.5 · 🟠 Geo & postcode precision\n", + "\n", + "- ~11% of rows have coordinates not confirmed by an address. **Outcode-only** rows (Zoopla) are pinned\n", + " to one arbitrary postcode per outcode and stamped with a plausible-looking *fake* unit postcode\n", + " (e.g. all SW6 listings → `SW6 1AA`).\n", + "- Development/building **centroid collisions**: many *distinct* properties share one exact coordinate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3026540e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.561309Z", + "iopub.status.busy": "2026-06-03T07:34:31.561230Z", + "iopub.status.idle": "2026-06-03T07:34:31.574626Z", + "shell.execute_reply": "2026-06-03T07:34:31.574256Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (6, 2)\n", + "┌────────────────────┬────────┐\n", + "│ Postcode source ┆ count │\n", + "│ --- ┆ --- │\n", + "│ str ┆ u32 │\n", + "╞════════════════════╪════════╡\n", + "│ detail_address ┆ 100274 │\n", + "│ coordinates ┆ 8903 │\n", + "│ search_outcode ┆ 2748 │\n", + "│ detail_coordinates ┆ 564 │\n", + "│ address ┆ 102 │\n", + "│ address_outcode ┆ 14 │\n", + "└────────────────────┴────────┘\n", + "\n", + "Outcode-only rows: 2,762 -> distinct coordinates: 204 (one fake point per outcode)\n", + "Coordinates shared by >1 DISTINCT property: 8,122 points / 27,080 rows (max 44 distinct properties on one point)\n" + ] + } + ], + "source": [ + "print(raw[\"Postcode source\"].value_counts(sort=True))\n", + "oc = raw.filter(pl.col(\"Postcode source\").is_in([\"search_outcode\", \"address_outcode\"]))\n", + "print(f\"\\nOutcode-only rows: {oc.height:,} -> distinct coordinates: \"\n", + " f\"{oc.select(['lat', 'lon']).unique().height:,} (one fake point per outcode)\")\n", + "coll = (\n", + " raw.group_by([\"lat\", \"lon\"])\n", + " .agg(pl.struct(\"Asking price\", \"Bedrooms\").n_unique().alias(\"distinct_props\"), pl.len().alias(\"n\"))\n", + " .filter(pl.col(\"distinct_props\") > 1)\n", + ")\n", + "print(f\"Coordinates shared by >1 DISTINCT property: {coll.height:,} points / {coll['n'].sum():,} rows \"\n", + " f\"(max {coll['distinct_props'].max()} distinct properties on one point)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1487ffb4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.575601Z", + "iopub.status.busy": "2026-06-03T07:34:31.575519Z", + "iopub.status.idle": "2026-06-03T07:34:31.709176Z", + "shell.execute_reply": "2026-06-03T07:34:31.708781Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Listings by provider (downsampled) — shows London footprint + centroid clusters\n", + "g = raw.sample(min(25_000, raw.height), seed=0).select(\"lat\", \"lon\", \"provider\")\n", + "colors = {\"Rightmove\": \"#2563eb\", \"OnTheMarket\": \"#16a34a\", \"Zoopla\": \"#dc2626\"}\n", + "fig, ax = plt.subplots(figsize=(8, 7))\n", + "for prov, c in colors.items():\n", + " gp = g.filter(pl.col(\"provider\") == prov)\n", + " ax.scatter(gp[\"lon\"], gp[\"lat\"], s=2, alpha=0.30, color=c, label=prov)\n", + "ax.set_xlabel(\"lon\"); ax.set_ylabel(\"lat\"); ax.set_aspect(\"equal\", adjustable=\"datalim\")\n", + "ax.set_title(\"Listing coordinates by provider (25k sample)\"); ax.legend(markerscale=4, fontsize=8)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6edd8876", + "metadata": {}, + "source": [ + "### 3.6 · 🔴 Duplicate listings\n", + "\n", + "No cross-source identity resolution: the same physical property appears across portals, and is\n", + "re-listed multiple times within a portal (especially Zoopla). `UPRN` (Zoopla-only, 1.8% coverage)\n", + "is not 1:1 — 405 UPRNs repeat up to 7×, breaking the intended exact EPC join." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd1bf199", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.711042Z", + "iopub.status.busy": "2026-06-03T07:34:31.710934Z", + "iopub.status.idle": "2026-06-03T07:34:31.723397Z", + "shell.execute_reply": "2026-06-03T07:34:31.723059Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-portal duplicate groups : 2,589 -> 5,304 rows\n", + "Intra-portal re-list groups : 2,863 -> 3,261 extra rows\n", + "UPRN coverage: 2,060 (1.8%) distinct: 1,547 repeated UPRNs: 405 (max 7×)\n" + ] + } + ], + "source": [ + "key = [\"lat\", \"lon\", \"Asking price\", \"Bedrooms\"]\n", + "dups = (\n", + " raw.filter(pl.col(\"Asking price\").is_not_null())\n", + " .group_by(key)\n", + " .agg(pl.len().alias(\"n\"), pl.col(\"provider\").n_unique().alias(\"providers\"))\n", + ")\n", + "cross = dups.filter((pl.col(\"n\") > 1) & (pl.col(\"providers\") > 1))\n", + "intra = dups.filter((pl.col(\"n\") > 1) & (pl.col(\"providers\") == 1))\n", + "print(f\"Cross-portal duplicate groups : {cross.height:,} -> {cross['n'].sum():,} rows\")\n", + "print(f\"Intra-portal re-list groups : {intra.height:,} -> {(intra['n'] - 1).sum():,} extra rows\")\n", + "u = raw.filter(pl.col(\"UPRN\").is_not_null())\n", + "rep = u.group_by(\"UPRN\").len().filter(pl.col(\"len\") > 1)\n", + "print(f\"UPRN coverage: {u.height:,} ({u.height / raw.height:.1%}) distinct: {u['UPRN'].n_unique():,} \"\n", + " f\"repeated UPRNs: {rep.height:,} (max {rep['len'].max() if rep.height else 0}×)\")" + ] + }, + { + "cell_type": "markdown", + "id": "29057e83", + "metadata": {}, + "source": [ + "### 3.7 · 🟠/🟡 Categorical normalization & dead columns\n", + "\n", + "- `Listing status` is a constant `\"For sale\"` (dead column).\n", + "- `Property sub-type` has 79 values with portal-spelling variants (`Apartment`↔`Flat`, `X House`↔`X`, …).\n", + "- `Price qualifier` has case-only duplicate pairs (Rightmove TitleCase vs OTM sentence-case).\n", + "- Missing values are encoded inconsistently — empty-string in some columns, `null` in others." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04084e7d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.724469Z", + "iopub.status.busy": "2026-06-03T07:34:31.724385Z", + "iopub.status.idle": "2026-06-03T07:34:31.743743Z", + "shell.execute_reply": "2026-06-03T07:34:31.743363Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Listing status distinct : ['For sale']\n", + "Property sub-type distinct: 79\n", + "\n", + "Price-qualifier values that differ only by casing:\n" + ] + }, + { + "data": { + "text/html": [ + "
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normvariantsrows
stru32u32
"guide price"223851
"offers in excess of"211832
"offers over"23449
"offers in region of"21963
"fixed price"2464
"shared equity"27
" + ], + "text/plain": [ + "shape: (6, 3)\n", + "┌─────────────────────┬──────────┬───────┐\n", + "│ norm ┆ variants ┆ rows │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ u32 ┆ u32 │\n", + "╞═════════════════════╪══════════╪═══════╡\n", + "│ guide price ┆ 2 ┆ 23851 │\n", + "│ offers in excess of ┆ 2 ┆ 11832 │\n", + "│ offers over ┆ 2 ┆ 3449 │\n", + "│ offers in region of ┆ 2 ┆ 1963 │\n", + "│ fixed price ┆ 2 ┆ 464 │\n", + "│ shared equity ┆ 2 ┆ 7 │\n", + "└─────────────────────┴──────────┴───────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty-string counts in String columns:\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (1, 15)
PostcodePostcode sourceExtracted postcodeInferred postcodeListing raw addressAddress per Property RegisterUPRNProperty number or nameLeasehold/FreeholdProperty typeProperty sub-typePrice qualifierListing URLListing statusprovider
u32u32u32u32u32u32u32u32u32u32u32u32u32u32u32
0000282528370000069358000
" + ], + "text/plain": [ + "shape: (1, 15)\n", + "┌─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬────────┬───────┬───────┬───────┐\n", + "│ Pos ┆ Pos ┆ Ext ┆ Inf ┆ Lis ┆ Add ┆ UPR ┆ Pro ┆ Lea ┆ Pro ┆ Pro ┆ Price ┆ Listi ┆ Listi ┆ provi │\n", + "│ tco ┆ tco ┆ rac ┆ err ┆ tin ┆ res ┆ N ┆ per ┆ seh ┆ per ┆ per ┆ qualif ┆ ng ┆ ng ┆ der │\n", + "│ de ┆ de ┆ ted ┆ ed ┆ g ┆ s ┆ --- ┆ ty ┆ old ┆ ty ┆ ty ┆ ier ┆ URL ┆ statu ┆ --- │\n", + "│ --- ┆ sou ┆ pos ┆ pos ┆ raw ┆ per ┆ u32 ┆ num ┆ /Fr ┆ typ ┆ sub ┆ --- ┆ --- ┆ s ┆ u32 │\n", + "│ u32 ┆ rce ┆ tco ┆ tco ┆ add ┆ Pro ┆ ┆ ber ┆ eeh ┆ e ┆ -ty ┆ u32 ┆ u32 ┆ --- ┆ │\n", + "│ ┆ --- ┆ de ┆ de ┆ res ┆ per ┆ ┆ or ┆ old ┆ --- ┆ pe ┆ ┆ ┆ u32 ┆ │\n", + "│ ┆ u32 ┆ --- ┆ --- ┆ s ┆ ty ┆ ┆ nam ┆ --- ┆ u32 ┆ --- ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ u32 ┆ u32 ┆ --- ┆ Reg ┆ ┆ e ┆ u32 ┆ ┆ u32 ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ u32 ┆ ist ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ er ┆ ┆ u32 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ u32 ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "╞═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪════════╪═══════╪═══════╪═══════╡\n", + "│ 0 ┆ 0 ┆ 0 ┆ 0 ┆ 282 ┆ 283 ┆ 0 ┆ 0 ┆ 0 ┆ 0 ┆ 0 ┆ 69358 ┆ 0 ┆ 0 ┆ 0 │\n", + "│ ┆ ┆ ┆ ┆ 5 ┆ 7 ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "└─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴────────┴───────┴───────┴───────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Listing status distinct :\", raw[\"Listing status\"].unique().to_list())\n", + "print(\"Property sub-type distinct:\", raw[\"Property sub-type\"].n_unique())\n", + "\n", + "pq = (\n", + " raw.filter(pl.col(\"Price qualifier\") != \"\")\n", + " .with_columns(pl.col(\"Price qualifier\").str.strip_chars().str.to_lowercase().alias(\"norm\"))\n", + " .group_by(\"norm\")\n", + " .agg(pl.col(\"Price qualifier\").n_unique().alias(\"variants\"), pl.len().alias(\"rows\"))\n", + " .filter(pl.col(\"variants\") > 1)\n", + " .sort(\"rows\", descending=True)\n", + ")\n", + "print(\"\\nPrice-qualifier values that differ only by casing:\")\n", + "display(pq)\n", + "\n", + "print(\"Empty-string counts in String columns:\")\n", + "display(raw.select([\n", + " (pl.col(c).str.strip_chars() == \"\").sum().alias(c)\n", + " for c in raw.columns if raw.schema[c] == pl.String\n", + "]))" + ] + }, + { + "cell_type": "markdown", + "id": "cda8c639", + "metadata": {}, + "source": [ + "### 3.8 · 🔴/🟡 Listing date\n", + "\n", + "`Listing date` is **null for 100% of OnTheMarket & Zoopla** (Rightmove-only `firstVisibleDate`), so any\n", + "recency analysis is biased to 82% of the data — and it reaches back to **2011** in a live for-sale set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df376178", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.744727Z", + "iopub.status.busy": "2026-06-03T07:34:31.744645Z", + "iopub.status.idle": "2026-06-03T07:34:31.753658Z", + "shell.execute_reply": "2026-06-03T07:34:31.753266Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 5)
providerrowsdate_nulloldestnewest
stru32u32datetime[μs]datetime[μs]
"OnTheMarket"1277112771nullnull
"Rightmove"9279702011-03-14 13:57:532026-05-31 02:38:38
"Zoopla"70377037nullnull
" + ], + "text/plain": [ + "shape: (3, 5)\n", + "┌─────────────┬───────┬───────────┬─────────────────────┬─────────────────────┐\n", + "│ provider ┆ rows ┆ date_null ┆ oldest ┆ newest │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ u32 ┆ u32 ┆ datetime[μs] ┆ datetime[μs] │\n", + "╞═════════════╪═══════╪═══════════╪═════════════════════╪═════════════════════╡\n", + "│ OnTheMarket ┆ 12771 ┆ 12771 ┆ null ┆ null │\n", + "│ Rightmove ┆ 92797 ┆ 0 ┆ 2011-03-14 13:57:53 ┆ 2026-05-31 02:38:38 │\n", + "│ Zoopla ┆ 7037 ┆ 7037 ┆ null ┆ null │\n", + "└─────────────┴───────┴───────────┴─────────────────────┴─────────────────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Listings dated before 2024-01-01: 911\n" + ] + } + ], + "source": [ + "display(\n", + " raw.group_by(\"provider\").agg(\n", + " pl.len().alias(\"rows\"),\n", + " pl.col(\"Listing date\").null_count().alias(\"date_null\"),\n", + " pl.col(\"Listing date\").min().alias(\"oldest\"),\n", + " pl.col(\"Listing date\").max().alias(\"newest\"),\n", + " ).sort(\"provider\")\n", + ")\n", + "print(f\"Listings dated before 2024-01-01: \"\n", + " f\"{raw.filter(pl.col('Listing date') < pl.datetime(2024, 1, 1)).height:,}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dd559a4a", + "metadata": {}, + "source": [ + "## 4 · Cleanup\n", + "\n", + "We build a cleaned frame `clean` that **fixes or flags** the issues above. Guiding principle: prefer\n", + "adding provenance/flag columns and nulling clearly-invalid values over fabricating data we don't have\n", + "(e.g. shared-ownership *full* prices and un-converted sq ft are **flagged**, not invented).\n", + "\n", + "**Reference tables** (canonicalisation / classification):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12a191be", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.754641Z", + "iopub.status.busy": "2026-06-03T07:34:31.754556Z", + "iopub.status.idle": "2026-06-03T07:34:31.756985Z", + "shell.execute_reply": "2026-06-03T07:34:31.756603Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference tables defined.\n" + ] + } + ], + "source": [ + "RESIDENTIAL_TYPES = [\"Flats/Maisonettes\", \"Terraced\", \"Detached\", \"Semi-Detached\"]\n", + "\n", + "# Collapse portal-specific sub-type spellings to one canonical label.\n", + "SUBTYPE_CANON = {\n", + " \"Apartment\": \"Flat\",\n", + " \"Terraced House\": \"Terraced\", \"End Of Terrace House\": \"End Of Terrace\", \"End Terrace\": \"End Of Terrace\",\n", + " \"Semi-Detached House\": \"Semi-Detached\", \"Semi Detached\": \"Semi-Detached\",\n", + " \"Semi Detached Bungalow\": \"Semi-Detached Bungalow\",\n", + " \"Detached House\": \"Detached\", \"Link Detached House\": \"Link Detached\",\n", + " \"Townhouse\": \"Town House\", \"Houseboat\": \"House Boat\",\n", + " \"Ground Floor Flat\": \"Ground Flat\", \"Ground Floor Maisonette\": \"Ground Maisonette\",\n", + " \"Block Of Flats\": \"Block Of Apartments\",\n", + " \"Property\": \"Unknown\", \"Not Specified\": \"Unknown\",\n", + "}\n", + "\n", + "# Sub-types that are not residential dwellings (canonical names).\n", + "NONRES_SUBTYPES = [\n", + " \"Land\", \"Plot\", \"Parking\", \"Garage\", \"Garages\", \"Office\", \"Retail\", \"Industrial\",\n", + " \"Light Industrial\", \"Warehouse\", \"Woodland\", \"Farm Land\", \"Mixed Use\",\n", + " \"Mixed Use Auction Lot\", \"Residential Development\", \"Block Of Apartments\",\n", + " \"Commercial\", \"Restaurant\", \"Unknown\",\n", + "]\n", + "\n", + "# Positional-confidence tier from the pipeline's Postcode source.\n", + "PRECISION = {\n", + " \"detail_address\": \"address-unit\", \"address\": \"address-unit\",\n", + " \"coordinates\": \"coordinate-nearest\", \"detail_coordinates\": \"coordinate-nearest\",\n", + " \"search_outcode\": \"outcode-only\", \"address_outcode\": \"outcode-only\",\n", + "}\n", + "print(\"Reference tables defined.\")" + ] + }, + { + "cell_type": "markdown", + "id": "2491e545", + "metadata": {}, + "source": [ + "**Step A** — drop dead/redundant columns, normalise empty-string → null, canonicalise `Price qualifier`, derive `price_basis` + `is_shared_ownership`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "241f8359", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.757767Z", + "iopub.status.busy": "2026-06-03T07:34:31.757691Z", + "iopub.status.idle": "2026-06-03T07:34:31.771814Z", + "shell.execute_reply": "2026-06-03T07:34:31.771466Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step A done. price_basis:\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (7, 2)
price_basiscount
stru32
"firm"69358
"auction_guide"23851
"floor"17258
"share"1404
"other"494
"from"221
"unpriced"19
" + ], + "text/plain": [ + "shape: (7, 2)\n", + "┌───────────────┬───────┐\n", + "│ price_basis ┆ count │\n", + "│ --- ┆ --- │\n", + "│ str ┆ u32 │\n", + "╞═══════════════╪═══════╡\n", + "│ firm ┆ 69358 │\n", + "│ auction_guide ┆ 23851 │\n", + "│ floor ┆ 17258 │\n", + "│ share ┆ 1404 │\n", + "│ other ┆ 494 │\n", + "│ from ┆ 221 │\n", + "│ unpriced ┆ 19 │\n", + "└───────────────┴───────┘" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ql = pl.col(\"Price qualifier\").fill_null(\"\").str.to_lowercase().str.strip_chars()\n", + "\n", + "clean = (\n", + " raw\n", + " # \"Number of bedrooms & living rooms\" == Bedrooms+Bathrooms (no extra info) -> drop;\n", + " # \"Listing status\" is constant -> drop.\n", + " .drop([\"Listing status\", \"Number of bedrooms & living rooms\"])\n", + " .with_columns(\n", + " ql.str.contains(\"shared|part buy|part-buy\").alias(\"is_shared_ownership\"),\n", + " pl.when(ql.str.contains(\"shared|part buy|part-buy\")).then(pl.lit(\"share\"))\n", + " .when(ql.str.contains(\"offers in excess|offers over|offers in region|invited\")).then(pl.lit(\"floor\"))\n", + " .when(ql.str.contains(\"guide\")).then(pl.lit(\"auction_guide\"))\n", + " .when(ql == \"from\").then(pl.lit(\"from\"))\n", + " .when(ql.str.contains(\"application|coming soon\")).then(pl.lit(\"unpriced\"))\n", + " .when(ql == \"\").then(pl.lit(\"firm\"))\n", + " .otherwise(pl.lit(\"other\")).alias(\"price_basis\"),\n", + " )\n", + ")\n", + "\n", + "# Empty-string -> null for all String columns (except the derived enum price_basis).\n", + "str_cols = [c for c in clean.columns if clean.schema[c] == pl.String and c != \"price_basis\"]\n", + "clean = clean.with_columns([\n", + " pl.when(pl.col(c).str.strip_chars() == \"\").then(None).otherwise(pl.col(c)).alias(c)\n", + " for c in str_cols\n", + "])\n", + "\n", + "# Canonical casing for Price qualifier (drop comma variants, Title Case).\n", + "clean = clean.with_columns(\n", + " pl.when(pl.col(\"Price qualifier\").is_null()).then(None)\n", + " .otherwise(pl.col(\"Price qualifier\").str.replace_all(\",\", \"\").str.to_titlecase())\n", + " .alias(\"Price qualifier\")\n", + ")\n", + "print(\"Step A done. price_basis:\")\n", + "clean[\"price_basis\"].value_counts(sort=True)" + ] + }, + { + "cell_type": "markdown", + "id": "58767c84", + "metadata": {}, + "source": [ + "**Step B** — floor-area sanity (null impossibly-small, flag suspiciously-large) + recompute `Asking price per sqm` (excluding shared-ownership); null sentinel-`0` beds/baths for dwellings; canonicalise sub-types; add `is_residential`, `location_precision`; strip fake unit postcodes from outcode-only rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e20392f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.772616Z", + "iopub.status.busy": "2026-06-03T07:34:31.772540Z", + "iopub.status.idle": "2026-06-03T07:34:31.783427Z", + "shell.execute_reply": "2026-06-03T07:34:31.783029Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step B done. clean shape: (112605, 30)\n", + "location_precision: [{'location_precision': 'address-unit', 'count': 100376}, {'location_precision': 'coordinate-nearest', 'count': 9467}, {'location_precision': 'outcode-only', 'count': 2762}]\n" + ] + } + ], + "source": [ + "fa = pl.col(\"Total floor area (sqm)\")\n", + "beds0 = pl.col(\"Bedrooms\").fill_null(0).cast(pl.Float64)\n", + "# Beds-aware floor: a dwelling needs ~10 m² per bedroom (min 12 m²). Anything below this is a\n", + "# single-room dimension mis-parsed as the total (a 2-bed < 20 m² is physically impossible).\n", + "min_plausible_area = pl.max_horizontal(pl.lit(12.0), beds0 * 10.0)\n", + "\n", + "clean = clean.with_columns(\n", + " (fa.is_not_null() & (fa < min_plausible_area)).alias(\"floor_area_suspect_small\"),\n", + " (fa.is_not_null() & (fa > 400)).alias(\"floor_area_suspect_large\"),\n", + ")\n", + "# Null impossibly-small areas (single-room dims parsed as total).\n", + "clean = clean.with_columns(\n", + " pl.when(pl.col(\"floor_area_suspect_small\")).then(None).otherwise(fa).alias(\"Total floor area (sqm)\")\n", + ")\n", + "\n", + "# Canonical sub-types + residential / build-type flags.\n", + "clean = clean.with_columns(pl.col(\"Property sub-type\").replace(SUBTYPE_CANON).alias(\"Property sub-type\"))\n", + "clean = clean.with_columns(\n", + " (~pl.col(\"Property sub-type\").is_in(NONRES_SUBTYPES)).alias(\"is_residential\"),\n", + " (pl.col(\"Property sub-type\") == \"House\").alias(\"build_type_unspecified\"), # was wrongly forced to 'Detached'\n", + ")\n", + "\n", + "# Sentinel 0 -> null for beds/baths on real dwellings (keep 0 for genuine Studios).\n", + "is_dwelling = pl.col(\"Property type\").is_in(RESIDENTIAL_TYPES)\n", + "is_studio = pl.col(\"Property sub-type\") == \"Studio\"\n", + "clean = clean.with_columns(\n", + " pl.when((pl.col(\"Bedrooms\") == 0) & is_dwelling & ~is_studio).then(None)\n", + " .otherwise(pl.col(\"Bedrooms\")).alias(\"Bedrooms\"),\n", + " pl.when((pl.col(\"Bathrooms\") == 0) & is_dwelling).then(None)\n", + " .otherwise(pl.col(\"Bathrooms\")).alias(\"Bathrooms\"),\n", + ")\n", + "\n", + "# Recompute £/sqm from cleaned area; undefined for shared-ownership (share price) & missing inputs.\n", + "clean = clean.with_columns(\n", + " pl.when(\n", + " pl.col(\"Total floor area (sqm)\").is_not_null()\n", + " & pl.col(\"Asking price\").is_not_null()\n", + " & ~pl.col(\"is_shared_ownership\")\n", + " )\n", + " .then((pl.col(\"Asking price\") / pl.col(\"Total floor area (sqm)\")).round().cast(pl.Int64))\n", + " .otherwise(None)\n", + " .alias(\"Asking price per sqm\")\n", + ")\n", + "\n", + "# Positional-confidence tier; strip the fabricated unit from outcode-only postcodes.\n", + "clean = clean.with_columns(\n", + " pl.col(\"Postcode source\").replace_strict(PRECISION, default=\"unknown\").alias(\"location_precision\")\n", + ")\n", + "clean = clean.with_columns(\n", + " pl.when(pl.col(\"location_precision\") == \"outcode-only\")\n", + " .then(pl.col(\"Postcode\").str.split(\" \").list.first())\n", + " .otherwise(pl.col(\"Postcode\"))\n", + " .alias(\"Postcode\")\n", + ")\n", + "print(f\"Step B done. clean shape: {clean.shape}\")\n", + "print(\"location_precision:\", clean[\"location_precision\"].value_counts(sort=True).to_dicts())" + ] + }, + { + "cell_type": "markdown", + "id": "33e81cf5", + "metadata": {}, + "source": [ + "**Step C** — de-duplicate to one row per physical property. Heuristic key: `(lat, lon, Asking price, Bedrooms)`, keeping the most-recently-listed row. This collapses both cross-portal and intra-portal duplicates; `raw` is retained for provenance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac920525", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.784400Z", + "iopub.status.busy": "2026-06-03T07:34:31.784318Z", + "iopub.status.idle": "2026-06-03T07:34:31.829788Z", + "shell.execute_reply": "2026-06-03T07:34:31.829419Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "De-duplicated: 112,605 -> 106,511 rows (removed 6,094, 5.4%)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 3)
providercleandedup
stru32u32
"OnTheMarket"127719949
"Rightmove"9279791064
"Zoopla"70375498
" + ], + "text/plain": [ + "shape: (3, 3)\n", + "┌─────────────┬───────┬───────┐\n", + "│ provider ┆ clean ┆ dedup │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ u32 ┆ u32 │\n", + "╞═════════════╪═══════╪═══════╡\n", + "│ OnTheMarket ┆ 12771 ┆ 9949 │\n", + "│ Rightmove ┆ 92797 ┆ 91064 │\n", + "│ Zoopla ┆ 7037 ┆ 5498 │\n", + "└─────────────┴───────┴───────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dedup = (\n", + " clean\n", + " .sort(\"Listing date\", descending=True, nulls_last=True)\n", + " .unique(subset=[\"lat\", \"lon\", \"Asking price\", \"Bedrooms\"], keep=\"first\", maintain_order=True)\n", + ")\n", + "removed = clean.height - dedup.height\n", + "print(f\"De-duplicated: {clean.height:,} -> {dedup.height:,} rows (removed {removed:,}, {removed / clean.height:.1%})\")\n", + "display(\n", + " pl.concat([\n", + " clean.group_by(\"provider\").len().rename({\"len\": \"clean\"}),\n", + " dedup.group_by(\"provider\").len().rename({\"len\": \"dedup\"}),\n", + " ], how=\"align\").sort(\"provider\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7d17dd05", + "metadata": {}, + "source": [ + "## 5 · After cleanup — re-show the stats" + ] + }, + { + "cell_type": "markdown", + "id": "b10a9386", + "metadata": {}, + "source": [ + "**Before / after summary** of the headline issues:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df564e2b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.830811Z", + "iopub.status.busy": "2026-06-03T07:34:31.830735Z", + "iopub.status.idle": "2026-06-03T07:34:31.932037Z", + "shell.execute_reply": "2026-06-03T07:34:31.931618Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 9)
datasetrowsimpossible_tiny_areapqualifier_casing_dupesempty_string_cellsmax_£/sqmsubtype_distinctzero_bed_non_studiodead_columns
stri64i64i64i64i64i64i64i64
"raw"112605896750204109597940882
"clean"112605000212963639340
"dedup"106511000212963637110
" + ], + "text/plain": [ + "shape: (3, 9)\n", + "┌─────────┬────────┬───────────┬───────────┬───────────┬──────────┬──────────┬──────────┬──────────┐\n", + "│ dataset ┆ rows ┆ impossibl ┆ pqualifie ┆ empty_str ┆ max_£/sq ┆ subtype_ ┆ zero_bed ┆ dead_col │\n", + "│ --- ┆ --- ┆ e_tiny_ar ┆ r_casing_ ┆ ing_cells ┆ m ┆ distinct ┆ _non_stu ┆ umns │\n", + "│ str ┆ i64 ┆ ea ┆ dupes ┆ --- ┆ --- ┆ --- ┆ dio ┆ --- │\n", + "│ ┆ ┆ --- ┆ --- ┆ i64 ┆ i64 ┆ i64 ┆ --- ┆ i64 │\n", + "│ ┆ ┆ i64 ┆ i64 ┆ ┆ ┆ ┆ i64 ┆ │\n", + "╞═════════╪════════╪═══════════╪═══════════╪═══════════╪══════════╪══════════╪══════════╪══════════╡\n", + "│ raw ┆ 112605 ┆ 89 ┆ 6 ┆ 75020 ┆ 410959 ┆ 79 ┆ 4088 ┆ 2 │\n", + "│ clean ┆ 112605 ┆ 0 ┆ 0 ┆ 0 ┆ 212963 ┆ 63 ┆ 934 ┆ 0 │\n", + "│ dedup ┆ 106511 ┆ 0 ┆ 0 ┆ 0 ┆ 212963 ┆ 63 ┆ 711 ┆ 0 │\n", + "└─────────┴────────┴───────────┴───────────┴───────────┴──────────┴──────────┴──────────┴──────────┘" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def diagnostics(d: pl.DataFrame, label: str) -> dict:\n", + " fa = pl.col(\"Total floor area (sqm)\")\n", + " pq_casing = (\n", + " d.filter(pl.col(\"Price qualifier\").is_not_null())\n", + " .with_columns(pl.col(\"Price qualifier\").str.to_lowercase().alias(\"n\"))\n", + " .group_by(\"n\").agg(pl.col(\"Price qualifier\").n_unique().alias(\"v\"))\n", + " .filter(pl.col(\"v\") > 1).height\n", + " )\n", + " empties = sum(\n", + " d.select((pl.col(c).str.strip_chars() == \"\").sum()).item()\n", + " for c in d.columns if d.schema[c] == pl.String\n", + " )\n", + " return {\n", + " \"dataset\": label,\n", + " \"rows\": d.height,\n", + " \"impossible_tiny_area\": d.filter(fa.is_not_null() & (fa < 20) & (pl.col(\"Bedrooms\").fill_null(0) >= 2)).height,\n", + " \"pqualifier_casing_dupes\": pq_casing,\n", + " \"empty_string_cells\": int(empties),\n", + " \"max_£/sqm\": d[\"Asking price per sqm\"].max(),\n", + " \"subtype_distinct\": d[\"Property sub-type\"].n_unique(),\n", + " \"zero_bed_non_studio\": d.filter((pl.col(\"Bedrooms\") == 0) & (pl.col(\"Property sub-type\") != \"Studio\")).height,\n", + " \"dead_columns\": int(\"Listing status\" in d.columns) + int(\"Number of bedrooms & living rooms\" in d.columns),\n", + " }\n", + "\n", + "pl.DataFrame([diagnostics(raw, \"raw\"), diagnostics(clean, \"clean\"), diagnostics(dedup, \"dedup\")])" + ] + }, + { + "cell_type": "markdown", + "id": "8afe0492", + "metadata": {}, + "source": [ + "**Schema after cleanup** (dropped 2 mislabeled/dead columns; added flags & provenance):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0688cda7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.933084Z", + "iopub.status.busy": "2026-06-03T07:34:31.933005Z", + "iopub.status.idle": "2026-06-03T07:34:31.935851Z", + "shell.execute_reply": "2026-06-03T07:34:31.935570Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added columns : ['is_shared_ownership', 'price_basis', 'floor_area_suspect_small', 'floor_area_suspect_large', 'is_residential', 'build_type_unspecified', 'location_precision']\n", + "Dropped columns: ['Number of bedrooms & living rooms', 'Listing status']\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (30, 2)
columndtype
strstr
"Bedrooms""Int32"
"Bathrooms""Int32"
"lon""Float64"
"lat""Float64"
"Postcode""String"
"Postcode source""String"
"Extracted postcode""String"
"Inferred postcode""String"
"Listing raw address""String"
"Address per Property Register""String"
"UPRN""String"
"Property number or name""String"
"Leasehold/Freehold""String"
"Property type""String"
"Property sub-type""String"
"Price qualifier""String"
"Total floor area (sqm)""Float64"
"Listing URL""String"
"Listing features""List(String)"
"Listing date""Datetime(time_unit='us', time_zone=None)"
"Asking price""Int64"
"Asking price per sqm""Int64"
"provider""String"
"is_shared_ownership""Boolean"
"price_basis""String"
"floor_area_suspect_small""Boolean"
"floor_area_suspect_large""Boolean"
"is_residential""Boolean"
"build_type_unspecified""Boolean"
"location_precision""String"
" + ], + "text/plain": [ + "shape: (30, 2)\n", + "┌───────────────────────────────┬──────────────────────────────────────────┐\n", + "│ column ┆ dtype │\n", + "│ --- ┆ --- │\n", + "│ str ┆ str │\n", + "╞═══════════════════════════════╪══════════════════════════════════════════╡\n", + "│ Bedrooms ┆ Int32 │\n", + "│ Bathrooms ┆ Int32 │\n", + "│ lon ┆ Float64 │\n", + "│ lat ┆ Float64 │\n", + "│ Postcode ┆ String │\n", + "│ Postcode source ┆ String │\n", + "│ Extracted postcode ┆ String │\n", + "│ Inferred postcode ┆ String │\n", + "│ Listing raw address ┆ String │\n", + "│ Address per Property Register ┆ String │\n", + "│ UPRN ┆ String │\n", + "│ Property number or name ┆ String │\n", + "│ Leasehold/Freehold ┆ String │\n", + "│ Property type ┆ String │\n", + "│ Property sub-type ┆ String │\n", + "│ Price qualifier ┆ String │\n", + "│ Total floor area (sqm) ┆ Float64 │\n", + "│ Listing URL ┆ String │\n", + "│ Listing features ┆ List(String) │\n", + "│ Listing date ┆ Datetime(time_unit='us', time_zone=None) │\n", + "│ Asking price ┆ Int64 │\n", + "│ Asking price per sqm ┆ Int64 │\n", + "│ provider ┆ String │\n", + "│ is_shared_ownership ┆ Boolean │\n", + "│ price_basis ┆ String │\n", + "│ floor_area_suspect_small ┆ Boolean │\n", + "│ floor_area_suspect_large ┆ Boolean │\n", + "│ is_residential ┆ Boolean │\n", + "│ build_type_unspecified ┆ Boolean │\n", + "│ location_precision ┆ String │\n", + "└───────────────────────────────┴──────────────────────────────────────────┘" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "added = [c for c in clean.columns if c not in raw.columns]\n", + "dropped = [c for c in raw.columns if c not in clean.columns]\n", + "print(\"Added columns :\", added)\n", + "print(\"Dropped columns:\", dropped)\n", + "pl.DataFrame({\"column\": clean.columns, \"dtype\": [str(t) for t in clean.dtypes]})" + ] + }, + { + "cell_type": "markdown", + "id": "0a16d168", + "metadata": {}, + "source": [ + "**Missingness after cleanup** — beds/baths now legitimately null where unknown; empty-strings gone; per-sqm follows cleaned area:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfe2fc79", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.936668Z", + "iopub.status.busy": "2026-06-03T07:34:31.936597Z", + "iopub.status.idle": "2026-06-03T07:34:31.950929Z", + "shell.execute_reply": "2026-06-03T07:34:31.950552Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 30)
providerBedroomsBathroomslonlatPostcodePostcode sourceExtracted postcodeInferred postcodeListing raw addressAddress per Property RegisterUPRNProperty number or nameLeasehold/FreeholdProperty typeProperty sub-typePrice qualifierTotal floor area (sqm)Listing URLListing featuresListing dateAsking priceAsking price per sqmis_shared_ownershipprice_basisfloor_area_suspect_smallfloor_area_suspect_largeis_residentialbuild_type_unspecifiedlocation_precision
strf64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64f64
"OnTheMarket"0.25.10.00.00.00.095.90.00.00.1100.0100.011.20.00.060.987.00.00.0100.00.187.50.00.00.00.00.00.00.0
"Rightmove"0.82.40.00.00.00.095.10.00.00.0100.0100.014.20.00.058.846.90.00.00.00.047.10.00.00.00.00.00.00.0
"Zoopla"33.736.10.00.00.00.0100.00.040.140.270.761.550.20.00.0100.017.00.00.0100.04.917.00.00.00.00.00.00.00.0
" + ], + "text/plain": [ + "shape: (3, 30)\n", + "┌─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┬─────┐\n", + "│ pro ┆ Bed ┆ Bat ┆ lon ┆ lat ┆ Pos ┆ Pos ┆ Ext ┆ Inf ┆ Lis ┆ Add ┆ UPR ┆ Pro ┆ Lea ┆ Pro ┆ Pro ┆ Pri ┆ Tot ┆ Lis ┆ Lis ┆ Lis ┆ Ask ┆ Ask ┆ is_ ┆ pri ┆ flo ┆ flo ┆ is_ ┆ bui ┆ loc │\n", + "│ vid ┆ roo ┆ hro ┆ --- ┆ --- ┆ tco ┆ tco ┆ rac ┆ err ┆ tin ┆ res ┆ N ┆ per ┆ seh ┆ per ┆ per ┆ ce ┆ al ┆ tin ┆ tin ┆ tin ┆ ing ┆ ing ┆ sha ┆ ce_ ┆ or_ ┆ or_ ┆ res ┆ ld_ ┆ ati │\n", + "│ er ┆ ms ┆ oms ┆ f64 ┆ f64 ┆ de ┆ de ┆ ted ┆ ed ┆ g ┆ s ┆ --- ┆ ty ┆ old ┆ ty ┆ ty ┆ qua ┆ flo ┆ g ┆ g ┆ g ┆ pri ┆ pri ┆ red ┆ bas ┆ are ┆ are ┆ ide ┆ typ ┆ on_ │\n", + "│ --- ┆ --- ┆ --- ┆ ┆ ┆ --- ┆ sou ┆ pos ┆ pos ┆ raw ┆ per ┆ f64 ┆ num ┆ /Fr ┆ typ ┆ sub ┆ lif ┆ or ┆ URL ┆ fea ┆ dat ┆ ce ┆ ce ┆ _ow ┆ is ┆ a_s ┆ a_s ┆ nti ┆ e_u ┆ pre │\n", + "│ str ┆ f64 ┆ f64 ┆ ┆ ┆ f64 ┆ rce ┆ tco ┆ tco ┆ add ┆ Pro ┆ ┆ ber ┆ eeh ┆ e ┆ -ty ┆ ier ┆ are ┆ --- ┆ tur ┆ e ┆ --- ┆ per ┆ ner ┆ --- ┆ usp ┆ usp ┆ al ┆ nsp ┆ cis │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ --- ┆ de ┆ de ┆ res ┆ per ┆ ┆ or ┆ old ┆ --- ┆ pe ┆ --- ┆ a ┆ f64 ┆ es ┆ --- ┆ f64 ┆ sqm ┆ shi ┆ f64 ┆ ect ┆ ect ┆ --- ┆ eci ┆ ion │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ --- ┆ --- ┆ s ┆ ty ┆ ┆ nam ┆ --- ┆ f64 ┆ --- ┆ f64 ┆ (sq ┆ ┆ --- ┆ f64 ┆ ┆ --- ┆ p ┆ ┆ _sm ┆ _la ┆ f64 ┆ fie ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ --- ┆ Reg ┆ ┆ e ┆ f64 ┆ ┆ f64 ┆ ┆ m) ┆ ┆ f64 ┆ ┆ ┆ f64 ┆ --- ┆ ┆ all ┆ rge ┆ ┆ d ┆ f64 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ ist ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ --- ┆ --- ┆ ┆ --- ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ er ┆ ┆ f64 ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ ┆ f64 ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ --- ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "╞═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╡\n", + "│ OnT ┆ 0.2 ┆ 5.1 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 95. ┆ 0.0 ┆ 0.0 ┆ 0.1 ┆ 100 ┆ 100 ┆ 11. ┆ 0.0 ┆ 0.0 ┆ 60. ┆ 87. ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 0.1 ┆ 87. ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │\n", + "│ heM ┆ ┆ ┆ ┆ ┆ ┆ ┆ 9 ┆ ┆ ┆ ┆ .0 ┆ .0 ┆ 2 ┆ ┆ ┆ 9 ┆ 0 ┆ ┆ ┆ .0 ┆ ┆ 5 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ark ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ et ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ Rig ┆ 0.8 ┆ 2.4 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 95. ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 100 ┆ 14. ┆ 0.0 ┆ 0.0 ┆ 58. ┆ 46. ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 47. ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │\n", + "│ htm ┆ ┆ ┆ ┆ ┆ ┆ ┆ 1 ┆ ┆ ┆ ┆ .0 ┆ .0 ┆ 2 ┆ ┆ ┆ 8 ┆ 9 ┆ ┆ ┆ ┆ ┆ 1 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ove ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ Zoo ┆ 33. ┆ 36. ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 0.0 ┆ 40. ┆ 40. ┆ 70. ┆ 61. ┆ 50. ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 17. ┆ 0.0 ┆ 0.0 ┆ 100 ┆ 4.9 ┆ 17. ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │\n", + "│ pla ┆ 7 ┆ 1 ┆ ┆ ┆ ┆ ┆ .0 ┆ ┆ 1 ┆ 2 ┆ 7 ┆ 5 ┆ 2 ┆ ┆ ┆ .0 ┆ 0 ┆ ┆ ┆ .0 ┆ ┆ 0 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "└─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┘" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "value_cols_c = [c for c in clean.columns if c != \"provider\"]\n", + "miss_c = (\n", + " clean.group_by(\"provider\")\n", + " .agg([missing_count(c, clean.schema[c]) for c in value_cols_c] + [pl.len().alias(\"__rows\")])\n", + " .sort(\"provider\")\n", + ")\n", + "miss_c.select([\"provider\"] + [(pl.col(c) / pl.col(\"__rows\") * 100).round(1).alias(c) for c in value_cols_c])" + ] + }, + { + "cell_type": "markdown", + "id": "9f0b901b", + "metadata": {}, + "source": [ + "**Price / floor-area sanity restored** (firm-price view excludes shared-ownership):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad59e2d6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.952084Z", + "iopub.status.busy": "2026-06-03T07:34:31.951936Z", + "iopub.status.idle": "2026-06-03T07:34:31.967287Z", + "shell.execute_reply": "2026-06-03T07:34:31.966952Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Residential, non-shared rows: 109,662\n", + "Impossible tiny areas remaining (area<20 & beds>=2): 0\n", + "Max £/sqm raw 410,959 -> clean 212,963\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (9, 4)
statisticAsking priceTotal floor area (sqm)Asking price per sqm
strf64f64f64
"count"109645.056075.056075.0
"null_count"17.053587.053587.0
"mean"924734.079256113.3916779186.075381
"std"1.5915e6106.0752796356.803435
"min"500.012.435.0
"25%"375000.060.05644.0
"50%"550000.082.07317.0
"75%"875000.0126.110496.0
"max"1e81893.1212963.0
" + ], + "text/plain": [ + "shape: (9, 4)\n", + "┌────────────┬───────────────┬────────────────────────┬──────────────────────┐\n", + "│ statistic ┆ Asking price ┆ Total floor area (sqm) ┆ Asking price per sqm │\n", + "│ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ f64 ┆ f64 ┆ f64 │\n", + "╞════════════╪═══════════════╪════════════════════════╪══════════════════════╡\n", + "│ count ┆ 109645.0 ┆ 56075.0 ┆ 56075.0 │\n", + "│ null_count ┆ 17.0 ┆ 53587.0 ┆ 53587.0 │\n", + "│ mean ┆ 924734.079256 ┆ 113.391677 ┆ 9186.075381 │\n", + "│ std ┆ 1.5915e6 ┆ 106.075279 ┆ 6356.803435 │\n", + "│ min ┆ 500.0 ┆ 12.4 ┆ 35.0 │\n", + "│ 25% ┆ 375000.0 ┆ 60.0 ┆ 5644.0 │\n", + "│ 50% ┆ 550000.0 ┆ 82.0 ┆ 7317.0 │\n", + "│ 75% ┆ 875000.0 ┆ 126.1 ┆ 10496.0 │\n", + "│ max ┆ 1e8 ┆ 1893.1 ┆ 212963.0 │\n", + "└────────────┴───────────────┴────────────────────────┴──────────────────────┘" ] }, "metadata": {}, @@ -296,35 +1872,23 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (19, 5)
providercolumnna_countprovider_rowsna_pct
strstru32u32f64
"OnTheMarket""Listing date"8585100.0
"OnTheMarket""Property number or name"8585100.0
"OnTheMarket""UPRN"8585100.0
"OnTheMarket""Extracted postcode"848598.82
"OnTheMarket""Asking price per sqm"828596.47
"OnTheMarket""Total floor area (sqm)"828596.47
"Rightmove""Property number or name"9999100.0
"Rightmove""UPRN"9999100.0
"Rightmove""Extracted postcode"989998.99
"Rightmove""Asking price per sqm"349934.34
"Rightmove""Total floor area (sqm)"349934.34
"Rightmove""Leasehold/Freehold"179917.17
"Zoopla""Extracted postcode"5858100.0
"Zoopla""Listing date"5858100.0
"Zoopla""UPRN"255843.1
"Zoopla""Leasehold/Freehold"145824.14
"Zoopla""Property number or name"115818.97
"Zoopla""Asking price per sqm"5588.62
"Zoopla""Total floor area (sqm)"5588.62
" + "shape: (7, 3)
price_basisrowsmedian_price
stru32f64
"firm"69358575000.0
"auction_guide"23851575000.0
"floor"17258500000.0
"share"1404146250.0
"other"494452500.0
"from"221632500.0
"unpriced"19null
" ], "text/plain": [ - "shape: (19, 5)\n", - "┌─────────────┬─────────────────────────┬──────────┬───────────────┬────────┐\n", - "│ provider ┆ column ┆ na_count ┆ provider_rows ┆ na_pct │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ str ┆ str ┆ u32 ┆ u32 ┆ f64 │\n", - "╞═════════════╪═════════════════════════╪══════════╪═══════════════╪════════╡\n", - "│ OnTheMarket ┆ Listing date ┆ 85 ┆ 85 ┆ 100.0 │\n", - "│ OnTheMarket ┆ Property number or name ┆ 85 ┆ 85 ┆ 100.0 │\n", - "│ OnTheMarket ┆ UPRN ┆ 85 ┆ 85 ┆ 100.0 │\n", - "│ OnTheMarket ┆ Extracted postcode ┆ 84 ┆ 85 ┆ 98.82 │\n", - "│ OnTheMarket ┆ Asking price per sqm ┆ 82 ┆ 85 ┆ 96.47 │\n", - "│ OnTheMarket ┆ Total floor area (sqm) ┆ 82 ┆ 85 ┆ 96.47 │\n", - "│ Rightmove ┆ Property number or name ┆ 99 ┆ 99 ┆ 100.0 │\n", - "│ Rightmove ┆ UPRN ┆ 99 ┆ 99 ┆ 100.0 │\n", - "│ Rightmove ┆ Extracted postcode ┆ 98 ┆ 99 ┆ 98.99 │\n", - "│ Rightmove ┆ Asking price per sqm ┆ 34 ┆ 99 ┆ 34.34 │\n", - "│ Rightmove ┆ Total floor area (sqm) ┆ 34 ┆ 99 ┆ 34.34 │\n", - "│ Rightmove ┆ Leasehold/Freehold ┆ 17 ┆ 99 ┆ 17.17 │\n", - "│ Zoopla ┆ Extracted postcode ┆ 58 ┆ 58 ┆ 100.0 │\n", - "│ Zoopla ┆ Listing date ┆ 58 ┆ 58 ┆ 100.0 │\n", - "│ Zoopla ┆ UPRN ┆ 25 ┆ 58 ┆ 43.1 │\n", - "│ Zoopla ┆ Leasehold/Freehold ┆ 14 ┆ 58 ┆ 24.14 │\n", - "│ Zoopla ┆ Property number or name ┆ 11 ┆ 58 ┆ 18.97 │\n", - "│ Zoopla ┆ Asking price per sqm ┆ 5 ┆ 58 ┆ 8.62 │\n", - "│ Zoopla ┆ Total floor area (sqm) ┆ 5 ┆ 58 ┆ 8.62 │\n", - "└─────────────┴─────────────────────────┴──────────┴───────────────┴────────┘" + "shape: (7, 3)\n", + "┌───────────────┬───────┬──────────────┐\n", + "│ price_basis ┆ rows ┆ median_price │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ u32 ┆ f64 │\n", + "╞═══════════════╪═══════╪══════════════╡\n", + "│ firm ┆ 69358 ┆ 575000.0 │\n", + "│ auction_guide ┆ 23851 ┆ 575000.0 │\n", + "│ floor ┆ 17258 ┆ 500000.0 │\n", + "│ share ┆ 1404 ┆ 146250.0 │\n", + "│ other ┆ 494 ┆ 452500.0 │\n", + "│ from ┆ 221 ┆ 632500.0 │\n", + "│ unpriced ┆ 19 ┆ null │\n", + "└───────────────┴───────┴──────────────┘" ] }, "metadata": {}, @@ -332,54 +1896,37 @@ } ], "source": [ - "def missing_expr(column: str, dtype: pl.DataType) -> pl.Expr:\n", - " missing = pl.col(column).is_null()\n", - " if dtype in (pl.Float32, pl.Float64):\n", - " missing = missing | pl.col(column).is_nan().fill_null(False)\n", - " return missing.sum().alias(column)\n", + "firm = clean.filter(~pl.col(\"is_shared_ownership\") & pl.col(\"is_residential\"))\n", + "print(f\"Residential, non-shared rows: {firm.height:,}\")\n", + "print(f\"Impossible tiny areas remaining (area<20 & beds>=2): \"\n", + " f\"{clean.filter((pl.col('Total floor area (sqm)') < 20) & (pl.col('Bedrooms').fill_null(0) >= 2)).height}\")\n", + "print(f\"Max £/sqm raw {raw['Asking price per sqm'].max():,} -> clean {clean['Asking price per sqm'].max():,}\")\n", + "display(firm.select(\"Asking price\", \"Total floor area (sqm)\", \"Asking price per sqm\").describe())\n", "\n", - "\n", - "value_columns = [column for column in df.columns if column != \"provider\"]\n", - "missing_by_provider = (\n", - " df.group_by(\"provider\")\n", - " .agg([missing_expr(column, df.schema[column]) for column in value_columns])\n", - " .sort(\"provider\")\n", - ")\n", - "\n", - "missing_long = (\n", - " missing_by_provider.unpivot(\n", - " index=\"provider\",\n", - " variable_name=\"column\",\n", - " value_name=\"na_count\",\n", - " )\n", - " .join(df.group_by(\"provider\").len().rename({\"len\": \"provider_rows\"}), on=\"provider\")\n", - " .filter(pl.col('na_count') > 0)\n", - " .with_columns((pl.col(\"na_count\") / pl.col(\"provider_rows\") * 100).round(2).alias(\"na_pct\"))\n", - " .sort([\"provider\", \"na_count\", \"column\"], descending=[False, True, False])\n", - ")\n", - "\n", - "display(missing_by_provider)\n", - "display(missing_long)" + "# price_basis composition\n", + "display(clean.group_by(\"price_basis\").agg(\n", + " pl.len().alias(\"rows\"), pl.col(\"Asking price\").median().alias(\"median_price\")\n", + ").sort(\"rows\", descending=True))" ] }, { "cell_type": "markdown", - "id": "24de8b4c", + "id": "e833c578", "metadata": {}, "source": [ - "## Map" + "**Categoricals normalised** — sub-type variants collapsed, qualifier casing merged:" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "cadef70a", + "execution_count": null, + "id": "6a20db99", "metadata": { "execution": { - "iopub.execute_input": "2026-05-17T10:23:48.014374Z", - "iopub.status.busy": "2026-05-17T10:23:48.014290Z", - "iopub.status.idle": "2026-05-17T10:23:49.319476Z", - "shell.execute_reply": "2026-05-17T10:23:49.319211Z" + "iopub.execute_input": "2026-06-03T07:34:31.968094Z", + "iopub.status.busy": "2026-06-03T07:34:31.968011Z", + "iopub.status.idle": "2026-06-03T07:34:31.981256Z", + "shell.execute_reply": "2026-06-03T07:34:31.980922Z" } }, "outputs": [ @@ -387,3640 +1934,142 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mapped rows: 242\n" + "Property sub-type distinct: raw 79 -> clean 63\n", + "Price-qualifier casing-duplicate buckets remaining: 0\n" ] }, { "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - "Stockwell Road, London", - 310000, - 1, - 1, - "Flats/Maisonettes", - "https://www.rightmove.co.uk/properties/89076063#/?channel=RES_BUY", - 51.471079, - -0.121569 - ], - [ - "Stockwell Park Crescent II, London", - 2595000, - 4, - 5, - "Semi-Detached", - "https://www.rightmove.co.uk/properties/174610352#/?channel=RES_BUY", - 51.47115, - -0.11817 - ], - [ - "Groveway, Stockwell", - 2295000, - 3, - 6, - "Detached", - "https://www.rightmove.co.uk/properties/174282005#/?channel=RES_BUY", - 51.473247, - -0.114727 - ], - [ - "Stockwell Park Road, London", - 2250000, - 2, - 4, - "Semi-Detached", - "https://www.rightmove.co.uk/properties/170781485#/?channel=RES_BUY", - 51.472141, - -0.116603 - ], - [ - "St Johns Buildings, London", - 2100000, - 2, - 2, - "Terraced", - "https://www.rightmove.co.uk/properties/174206714#/?channel=RES_BUY", - 51.46428, - -0.11247 - ], - [ - "Stockwell Park Road, London", - 2100000, - 3, - 4, - "Terraced", - "https://www.rightmove.co.uk/properties/88435821#/?channel=RES_BUY", - 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"polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Buy listings by source" - } - } - } + "text/html": [ + "
\n", + "shape: (15, 2)
Property sub-typecount
stru32
"Flat"64390
"Terraced"10532
"Semi-Detached"10170
"Detached"7610
"End Of Terrace"3747
"House"3590
"Maisonette"3196
"Studio"1503
"Retirement Property"891
"Ground Flat"889
"Unknown"842
"Bungalow"797
"Penthouse"735
"Detached Bungalow"577
"Town House"523
" + ], + "text/plain": [ + "shape: (15, 2)\n", + "┌─────────────────────┬───────┐\n", + "│ Property sub-type ┆ count │\n", + "│ --- ┆ --- │\n", + "│ str ┆ u32 │\n", + "╞═════════════════════╪═══════╡\n", + "│ Flat ┆ 64390 │\n", + "│ Terraced ┆ 10532 │\n", + "│ Semi-Detached ┆ 10170 │\n", + "│ Detached ┆ 7610 │\n", + "│ End Of Terrace ┆ 3747 │\n", + "│ House ┆ 3590 │\n", + "│ Maisonette ┆ 3196 │\n", + "│ Studio ┆ 1503 │\n", + "│ Retirement Property ┆ 891 │\n", + "│ Ground Flat ┆ 889 │\n", + "│ Unknown ┆ 842 │\n", + "│ Bungalow ┆ 797 │\n", + "│ Penthouse ┆ 735 │\n", + "│ Detached Bungalow ┆ 577 │\n", + "│ Town House ┆ 523 │\n", + "└─────────────────────┴───────┘" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "map_df = (\n", - " df.filter(\n", - " pl.col(\"lat\").is_not_null()\n", - " & pl.col(\"lon\").is_not_null()\n", - " & pl.col(\"lat\").is_between(49, 56)\n", - " & pl.col(\"lon\").is_between(-1, 1)\n", - " )\n", - " .select(\n", - " \"provider\",\n", - " \"lat\",\n", - " \"lon\",\n", - " \"Postcode\",\n", - " \"Address per Property Register\",\n", - " \"Asking price\",\n", - " \"Bathrooms\",\n", - " \"Bedrooms\",\n", - " \"Property type\",\n", - " \"Listing URL\",\n", - " )\n", - " .to_pandas()\n", + "print(\"Property sub-type distinct: raw\", raw[\"Property sub-type\"].n_unique(),\n", + " \" -> clean\", clean[\"Property sub-type\"].n_unique())\n", + "casing = (\n", + " clean.filter(pl.col(\"Price qualifier\").is_not_null())\n", + " .with_columns(pl.col(\"Price qualifier\").str.to_lowercase().alias(\"n\"))\n", + " .group_by(\"n\").agg(pl.col(\"Price qualifier\").n_unique().alias(\"variants\"))\n", + " .filter(pl.col(\"variants\") > 1).height\n", ")\n", + "print(\"Price-qualifier casing-duplicate buckets remaining:\", casing)\n", + "display(clean[\"Property sub-type\"].value_counts(sort=True).head(15))" + ] + }, + { + "cell_type": "markdown", + "id": "94ac0585", + "metadata": {}, + "source": [ + "**Before/after — key metrics chart:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7aed7d0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-03T07:34:31.981991Z", + "iopub.status.busy": "2026-06-03T07:34:31.981907Z", + "iopub.status.idle": "2026-06-03T07:34:32.146617Z", + "shell.execute_reply": "2026-06-03T07:34:32.146178Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = [\"impossible\\ntiny area\", \"£/sqm max\\n(÷1000)\", \"sub-type\\ndistinct\", \"0-bed\\nnon-studio\", \"qualifier\\ncasing dupes\"]\n", + "before = [\n", + " raw.filter((pl.col(\"Total floor area (sqm)\") < 20) & (pl.col(\"Bedrooms\") >= 2)).height,\n", + " raw[\"Asking price per sqm\"].max() / 1000,\n", + " raw[\"Property sub-type\"].n_unique(),\n", + " raw.filter((pl.col(\"Bedrooms\") == 0) & (pl.col(\"Property sub-type\") != \"Studio\")).height,\n", + " 6,\n", + "]\n", + "after = [\n", + " clean.filter((pl.col(\"Total floor area (sqm)\") < 20) & (pl.col(\"Bedrooms\").fill_null(0) >= 2)).height,\n", + " clean[\"Asking price per sqm\"].max() / 1000,\n", + " clean[\"Property sub-type\"].n_unique(),\n", + " clean.filter((pl.col(\"Bedrooms\") == 0) & (pl.col(\"Property sub-type\") != \"Studio\")).height,\n", + " 0,\n", + "]\n", + "x = np.arange(len(labels)); w = 0.38\n", + "fig, ax = plt.subplots(figsize=(10, 3.6))\n", + "b1 = ax.bar(x - w / 2, before, w, label=\"raw\", color=\"#dc2626\")\n", + "b2 = ax.bar(x + w / 2, after, w, label=\"clean\", color=\"#16a34a\")\n", + "ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=8)\n", + "ax.set_yscale(\"symlog\"); ax.set_ylabel(\"value (symlog)\")\n", + "ax.set_title(\"Before / after cleanup\"); ax.legend()\n", + "for bars in (b1, b2):\n", + " ax.bar_label(bars, fmt=\"%.0f\", fontsize=7, padding=2)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f680553a", + "metadata": {}, + "source": [ + "## Caveats\n", "\n", - "print(f\"Mapped rows: {len(map_df):,}\")\n", - "\n", - "fig = px.scatter_map(\n", - " map_df,\n", - " lat=\"lat\",\n", - " lon=\"lon\",\n", - " color=\"provider\",\n", - " color_discrete_map={\n", - " \"Rightmove\": \"#2563eb\",\n", - " \"OnTheMarket\": \"#16a34a\",\n", - " \"Zoopla\": \"#dc2626\",\n", - " \"Unknown\": \"#6b7280\",\n", - " },\n", - " hover_name=\"Postcode\",\n", - " hover_data={\n", - " \"Address per Property Register\": True,\n", - " \"Asking price\": \":,.0f\",\n", - " \"Bathrooms\": True,\n", - " \"Bedrooms\": True,\n", - " \"Property type\": True,\n", - " \"Listing URL\": True,\n", - " \"lat\": False,\n", - " \"lon\": False,\n", - " },\n", - " zoom=9,\n", - " height=800,\n", - " title=\"Buy listings by source\",\n", - ")\n", - "fig.update_traces(marker={\"size\": 5, \"opacity\": 0.65})\n", - "fig.update_layout(\n", - " map_style=\"open-street-map\",\n", - " legend_title_text=\"Provider\",\n", - " margin={\"l\": 0, \"r\": 0, \"t\": 45, \"b\": 0},\n", - ")\n", - "fig.show()" + "- **`clean` keeps every row** (issues are flagged, not dropped) so it stays comparable to `raw`; use the\n", + " `is_residential` / `is_shared_ownership` / `floor_area_suspect_*` / `location_precision` flags to filter.\n", + "- **Shared-ownership full prices cannot be recovered** here — those rows are flagged and excluded from\n", + " `£/sqm`, but their `Asking price` is still a share. Likewise **suspiciously-large areas are flagged,\n", + " not auto-converted** from sq ft (the conversion factor isn't certain per-row).\n", + "- **De-dup is heuristic** (`lat,lon,price,beds`); it can over-collapse distinct units sharing a\n", + " development-centroid coordinate and under-collapse cross-portal dupes with differing prices. Treat\n", + " `dedup` as an analytics view and `raw` as the source of record.\n", + "- The cleanest *upstream* fixes belong in the `finder` pipeline (`storage.py` / per-portal transforms):\n", + " correct the beds/living-rooms column, normalise qualifiers, add a beds-aware area gate, emit a\n", + " precision flag, and resolve property identity across sources." ] } ], diff --git a/pipeline/download/median_age.py b/pipeline/download/median_age.py index b02c43d..1831d34 100644 --- a/pipeline/download/median_age.py +++ b/pipeline/download/median_age.py @@ -72,6 +72,18 @@ assert len(EXPECTED_BAND_NAMES) == len(AGE_BANDS), ( "EXPECTED_BAND_NAMES and AGE_BANDS must stay aligned 1:1" ) +# NOMIS sometimes labels a band with a wording variant that denotes the SAME +# age range (e.g. "Aged 4 years and under" for ages 0-4, "Aged 90 years and +# over" wording for the top band). Map such known-equivalent labels back to the +# canonical name BEFORE validation so a real band change still fails loudly, +# but a cosmetic relabel of an identical range does not block the build. +BAND_NAME_ALIASES = { + "Aged 4 years and under": "Aged 0 to 4 years", +} +assert set(BAND_NAME_ALIASES.values()) <= set(EXPECTED_BAND_NAMES), ( + "BAND_NAME_ALIASES must map to canonical EXPECTED_BAND_NAMES" +) + def compute_median_age(counts: list[int]) -> float: """Compute median age from five-year band counts using linear interpolation.""" @@ -98,6 +110,15 @@ def _bands_to_median_table(pivoted: pl.DataFrame) -> pl.DataFrame: missing/extra/relabelled band would otherwise silently mis-align counts against the wrong AGE_BANDS lower bound, so we fail loudly instead. """ + # Normalise known-equivalent NOMIS label variants to their canonical name + # before validating (renaming onto an already-present canonical column would + # collide, so polars raises loudly in that genuinely ambiguous case). + rename_map = { + c: BAND_NAME_ALIASES[c] for c in pivoted.columns if c in BAND_NAME_ALIASES + } + if rename_map: + pivoted = pivoted.rename(rename_map) + # Validate the pivoted age-band columns against the canonical NOMIS set # BEFORE computing anything. band_cols = [c for c in pivoted.columns if c != "GEOGRAPHY_CODE"] diff --git a/pipeline/download/noise.py b/pipeline/download/noise.py index 545f559..5811511 100644 --- a/pipeline/download/noise.py +++ b/pipeline/download/noise.py @@ -266,8 +266,12 @@ def _download_tile( except ( NoGeoTiffError, httpx.HTTPStatusError, - httpx.TimeoutException, - httpx.ConnectError, + # TransportError is the superset of TimeoutException, ConnectError, + # ReadError and ProtocolError — including RemoteProtocolError, raised + # when the WCS server closes the connection mid-stream ("incomplete + # chunked read"). All are transient; retry/split rather than letting + # one flaky tile crash the whole raster download. + httpx.TransportError, ) as e: last_error = e if attempt < MAX_RETRIES: diff --git a/pipeline/download/test_median_age.py b/pipeline/download/test_median_age.py index ccff9f4..7d322b7 100644 --- a/pipeline/download/test_median_age.py +++ b/pipeline/download/test_median_age.py @@ -57,6 +57,21 @@ def test_null_band_count_is_treated_as_zero_not_crash(): assert table["median_age"][0] == pytest.approx(32.5) +def test_equivalent_band_label_alias_is_accepted(): + # NOMIS relabelled the first band "Aged 4 years and under" (same as ages + # 0-4). It must be normalised to the canonical name and used as band 0, not + # rejected. All 100 people in that band -> median in the 0-4 range: 2.5. + counts_by_band = {name: [0] for name in EXPECTED_BAND_NAMES} + counts_by_band["Aged 4 years and under"] = counts_by_band.pop("Aged 0 to 4 years") + counts_by_band["Aged 4 years and under"] = [100] + pivoted = _pivoted(counts_by_band) + + table = median_age._bands_to_median_table(pivoted) + + assert table.height == 1 + assert table["median_age"][0] == pytest.approx(2.5) + + def test_missing_band_raises_clear_error(): counts_by_band = {name: [10] for name in EXPECTED_BAND_NAMES} del counts_by_band["Aged 85 years and over"] diff --git a/pipeline/transform/postcode_boundaries/geometry.py b/pipeline/transform/postcode_boundaries/geometry.py new file mode 100644 index 0000000..3fd85a4 --- /dev/null +++ b/pipeline/transform/postcode_boundaries/geometry.py @@ -0,0 +1,123 @@ +"""Robust GEOS overlay helpers. + +Overlay operations (union, difference, intersection) can raise a +``GEOSException`` — most often ``TopologyException: side location conflict``, +``Ring edge missing``, or ``found non-noded intersection`` — on geometries that +contain near-coincident or near-degenerate edges, or that are individually +invalid. The robust remedy is a *fixed-precision* overlay: GEOS's OverlayNG +engine, handed a grid size, nodes every edge onto that grid and finishes where +the full-precision overlay cannot. + +Getting the fallback to actually survive takes three rules, each of which we +learned the hard way from a crash: + +1. **Never precision-reduce with the default mode.** ``set_precision``'s default + ``valid_output`` (and ``keep_collapsed``) mode runs its *own* noding pass that + re-raises the very ``side location conflict`` we are trying to escape. We push + the grid into the overlay via the ``grid_size`` argument instead — where + OverlayNG nodes robustly — and only ever call ``set_precision`` in + ``pointwise`` mode (pure coordinate rounding, which cannot raise). +2. **Validate first.** ``make_valid`` repairs the self-intersections (bow-ties, + pinches) that make GEOS choke, so the overlay starts from an OGC-valid shape. +3. **Keep only polygonal parts.** ``make_valid`` of a spiky polygon routinely + returns a *mixed-dimension* ``GeometryCollection`` (a polygon plus a dangling + line), and OverlayNG rejects mixed-dimension input with + ``IllegalArgumentException: Overlay input is mixed-dimension``. Every input + here represents an area, so the line/point debris is meaningless noise and is + dropped before the overlay. + +The default fallback grid is 0.1 mm in the metre-based working CRS +(EPSG:27700), far below survey resolution and the ``MIN_GEOM_AREA`` threshold +downstream, so it cannot crush a postcode-scale shape. Callers operating in a +different CRS (e.g. the WGS84 output stage, where the same 1e-4 grid would be +~11 m) pass a ``grid`` matched to that CRS. The whole fallback runs ONLY after +the exact operation has already failed, so normal output is bit-for-bit +unchanged. +""" + +import shapely +from shapely import GEOSException, make_valid, set_precision +from shapely.geometry import Polygon +from shapely.ops import unary_union + +# 0.1 mm in metres — well below MIN_GEOM_AREA (0.01 m^2) and survey resolution. +_SNAP_GRID = 1e-4 + +_EMPTY = Polygon() + + +def _poly_valid(geom, grid): + """Return an OGC-valid, polygonal-only version of ``geom``. + + Repairs invalidity with ``make_valid`` and discards any non-polygonal debris + (dangling lines/points) it leaves behind, since OverlayNG rejects + mixed-dimension ``GeometryCollection`` input. The result is always a + ``Polygon``/``MultiPolygon`` (possibly empty), safe to feed to a + fixed-precision overlay. + """ + g = geom if geom.is_valid else make_valid(geom) + if g.geom_type in ("Polygon", "MultiPolygon"): + return g + if g.geom_type == "GeometryCollection": + polys = [ + p + for p in g.geoms + if p.geom_type in ("Polygon", "MultiPolygon") and not p.is_empty + ] + if not polys: + return _EMPTY + if len(polys) == 1: + return polys[0] + # Dissolve on the grid (robust) rather than building a possibly-invalid + # MultiPolygon of overlapping parts. + return shapely.union_all(polys, grid_size=grid) + return _EMPTY # line / point only + + +def _snap_poly_valid(geom, grid): + """Coordinate-round onto ``grid`` (``pointwise`` never raises), then clean.""" + return _poly_valid(set_precision(geom, grid, mode="pointwise"), grid) + + +def safe_union(geoms, grid=_SNAP_GRID): + """``unary_union`` that survives GEOS robustness failures.""" + try: + return unary_union(geoms) + except GEOSException: + pass + cleaned = [_poly_valid(g, grid) for g in geoms] + try: + return shapely.union_all(cleaned, grid_size=grid) + except GEOSException: + snapped = [_snap_poly_valid(g, grid) for g in geoms] + return shapely.union_all(snapped, grid_size=grid) + + +def safe_difference(a, b, grid=_SNAP_GRID): + """``a.difference(b)`` that survives GEOS robustness failures.""" + try: + return a.difference(b) + except GEOSException: + pass + a2, b2 = _poly_valid(a, grid), _poly_valid(b, grid) + try: + return shapely.difference(a2, b2, grid_size=grid) + except GEOSException: + return shapely.difference( + _snap_poly_valid(a2, grid), _snap_poly_valid(b2, grid), grid_size=grid + ) + + +def safe_intersection(a, b, grid=_SNAP_GRID): + """``a.intersection(b)`` that survives GEOS robustness failures.""" + try: + return a.intersection(b) + except GEOSException: + pass + a2, b2 = _poly_valid(a, grid), _poly_valid(b, grid) + try: + return shapely.intersection(a2, b2, grid_size=grid) + except GEOSException: + return shapely.intersection( + _snap_poly_valid(a2, grid), _snap_poly_valid(b2, grid), grid_size=grid + ) diff --git a/pipeline/transform/postcode_boundaries/greenspace.py b/pipeline/transform/postcode_boundaries/greenspace.py index 898474b..2d81c8f 100644 --- a/pipeline/transform/postcode_boundaries/greenspace.py +++ b/pipeline/transform/postcode_boundaries/greenspace.py @@ -5,9 +5,10 @@ from pathlib import Path import polars as pl from shapely import wkb from shapely.geometry import MultiPolygon, Polygon -from shapely.ops import unary_union from shapely.strtree import STRtree +from .geometry import safe_difference, safe_union + def load_greenspace(path: Path) -> tuple[STRtree, list]: """Load greenspace parquet and build an STRtree spatial index. @@ -51,8 +52,8 @@ def subtract_greenspace( if not intersecting: return postcode_geom - green_union = unary_union(intersecting) - result = postcode_geom.difference(green_union) + green_union = safe_union(intersecting) + result = safe_difference(postcode_geom, green_union) if result.is_empty: return postcode_geom diff --git a/pipeline/transform/postcode_boundaries/output.py b/pipeline/transform/postcode_boundaries/output.py index d65a261..fa881fc 100644 --- a/pipeline/transform/postcode_boundaries/output.py +++ b/pipeline/transform/postcode_boundaries/output.py @@ -7,11 +7,12 @@ import numpy as np from pyproj import Transformer from shapely import STRtree, make_valid, set_precision from shapely.errors import GEOSException -from shapely.geometry import MultiPolygon, Polygon, mapping, shape +from shapely.geometry import MultiPolygon, Polygon, box, mapping, shape from shapely.ops import transform as transform_geometry -from shapely.ops import unary_union from tqdm import tqdm +from .geometry import safe_difference, safe_union + _to_wgs84 = None @@ -207,7 +208,7 @@ def merge_fragments( merged = {} for pc, parts in by_postcode.items(): - combined = unary_union(parts) + combined = safe_union(parts) if combined.is_empty: continue if not combined.is_valid: @@ -260,7 +261,10 @@ def _polygonal(geom): ] if not polys: return None - merged = unary_union(polys) + # Both callers run on WGS84-degree output geometry, so the robustness + # fallback snaps on the 1e-6° grid (~0.11 m), not geometry.py's metre + # default — a coarse metre grid would obliterate a degree-scale shape. + merged = safe_union(polys, grid=_OUTPUT_PRECISION_DEG) return merged if not merged.is_empty else None return None @@ -317,8 +321,13 @@ def _resolve_overlaps( # Process losers from highest priority down, so every subtracted neighbour is # already finalised. for i in sorted(higher, key=lambda idx: rank[idx]): - cut = unary_union([out[j] for j in higher[i]]) - trimmed = out[i].difference(cut) + # These geometries are WGS84 degrees already snapped to output precision, + # so the robustness fallback snaps on the same 1e-6° grid (~0.11 m) rather + # than geometry.py's metre-CRS default. A raw difference can still raise a + # "side location conflict" on near-coincident OA-seam edges; the + # fixed-precision overlay noded them away. + cut = safe_union([out[j] for j in higher[i]], grid=_OUTPUT_PRECISION_DEG) + trimmed = safe_difference(out[i], cut, grid=_OUTPUT_PRECISION_DEG) if not trimmed.is_valid: trimmed = make_valid(trimmed) # Keep all polygonal parts: these geometries are in WGS84 degrees, so an @@ -336,13 +345,83 @@ def _round_coords(coords, ndigits=6): return [_round_coords(c, ndigits) for c in coords] +def _snap_polygonal(geom, grid): + """Re-node ``geom`` onto ``grid`` (``valid_output``) → polygonal-only, or None. + + ``set_precision`` ``valid_output`` runs an OverlayNG noding pass that places + every vertex on a multiple of ``grid`` *and* fixes the topology, so a plain + coordinate round of the result is exact (no two distinct vertices can land in + the same cell). Falls back to ``make_valid`` if precision-reduction raises. + """ + try: + snapped = set_precision(geom, grid, mode="valid_output") + except GEOSException: + snapped = make_valid(geom) + return _polygonal(snapped if snapped.is_valid else make_valid(snapped)) + + +# A square this many output-grid cells on a side, used as the last-resort +# footprint when snapping erases a sub-grid sliver. ~10 cells (≈0.7–1.1 m at UK +# latitudes) is invisible at map scale yet survives the 1e-6° snap as a valid, +# 4-corner ring. +_FOOTPRINT_GRID_CELLS = 5 + + +def _grid_footprint(geom): + """A tiny grid-aligned square at ``geom``'s representative point, snapped valid. + + Last line of defence so an *active* postcode never vanishes: the de-overlap + pass can shave a small (e.g. co-located, non-geographic) postcode down to a + sub-grid sliver that disappears when snapped to output precision. Rather than + drop it, place a minimal valid footprint at its location. The tiny overlap + this re-creates with the neighbour that trimmed it is harmless — the output + partition is best-effort, a missing boundary is a hard validation failure. + """ + try: + point = geom.representative_point() + except GEOSException: + return None + half = _OUTPUT_PRECISION_DEG * _FOOTPRINT_GRID_CELLS + square = box(point.x - half, point.y - half, point.x + half, point.y + half) + return _snap_polygonal(square, _OUTPUT_PRECISION_DEG) + + def _geojson_geometry(geom) -> dict | None: - """Serialize a WGS84 polygon/multipolygon to a 6dp GeoJSON dict, or None.""" + """Serialize a WGS84 polygon/multipolygon to a *valid* 6dp GeoJSON dict, or None. + + The coordinates are snapped onto the 1e-6° output grid with a re-noding pass + BEFORE the 6dp rounding, not by the round alone. ``_resolve_overlaps`` leaves + thin overlap-sliver triangles with full-precision (off-grid) vertices at OA + seams; a bare round-to-6dp collapses those into degenerate rings (GEOS "Too + few points") and pinches rings into self-intersections that pass the + pre-rounding validity check but fail once the feature is read back from disk. + Snapping with ``valid_output`` nodes the geometry onto the grid so the round + that follows lands on exact 1e-6 multiples and cannot reintroduce invalidity. + + ``None`` only for a genuinely empty/degenerate-with-no-location input; a + non-empty geometry that snaps to a sub-grid sliver is rescued into a minimal + grid footprint rather than dropped (an active postcode must keep a boundary). + """ geom = _polygonal(geom if geom.is_valid else make_valid(geom)) if geom is None or geom.is_empty: return None - gj = mapping(geom) - return {"type": gj["type"], "coordinates": _round_coords(gj["coordinates"])} + snapped = _snap_polygonal(geom, _OUTPUT_PRECISION_DEG) + if snapped is None or snapped.is_empty: + snapped = _grid_footprint(geom) + if snapped is None or snapped.is_empty: + return None + gj = mapping(snapped) + out = {"type": gj["type"], "coordinates": _round_coords(gj["coordinates"])} + # Defence-in-depth: re-validate the dict that actually reaches disk. Snapping + # makes the round exact, so this should already hold; repair once more on the + # grid if a pathological vertex still pinches a ring. + if not shape(out).is_valid: + snapped = _snap_polygonal(shape(out), _OUTPUT_PRECISION_DEG) + if snapped is None or snapped.is_empty: + return None + gj = mapping(snapped) + out = {"type": gj["type"], "coordinates": _round_coords(gj["coordinates"])} + return out def write_district_geojson( diff --git a/pipeline/transform/postcode_boundaries/process_oa.py b/pipeline/transform/postcode_boundaries/process_oa.py index ecdab5f..4404560 100644 --- a/pipeline/transform/postcode_boundaries/process_oa.py +++ b/pipeline/transform/postcode_boundaries/process_oa.py @@ -3,13 +3,23 @@ from collections import Counter, defaultdict import numpy as np from scipy.spatial import cKDTree from shapely import STRtree, make_valid -from shapely.geometry import MultiPolygon, Polygon -from shapely.ops import unary_union +from shapely.geometry import MultiPoint, MultiPolygon, Point, Polygon +from .geometry import safe_difference, safe_intersection, safe_union from .voronoi import compute_voronoi_regions MIN_GEOM_AREA = 0.01 +# Minimal footprint (BNG metres) for a postcode whose UPRN seed wins no area in a +# crowded multi-postcode OA — its Voronoi cell ∩ remaining collapses below +# MIN_GEOM_AREA, or its seed sits inside an INSPIRE parcel wholly claimed by a +# co-located postcode. Every *active* postcode must keep a boundary +# (validate_outputs is zero-tolerance), so it gets a small disc at its true seed +# location. ~28 m² clears MIN_GEOM_AREA and output snapping; the overlap it +# creates with the area's winner is resolved at the output stage, where the +# smaller postcode wins the contested ground (see output._resolve_overlaps). +_MIN_SEED_FOOTPRINT_M = 3.0 + def process_oa( oa_geom: Polygon | MultiPolygon, @@ -36,10 +46,12 @@ def process_oa( # Compute remaining area if claimed: - all_claimed = unary_union(list(claimed.values())) + all_claimed = safe_union(list(claimed.values())) all_claimed = _clean_polygonal(all_claimed) remaining = ( - valid_oa.difference(all_claimed) if all_claimed is not None else valid_oa + safe_difference(valid_oa, all_claimed) + if all_claimed is not None + else valid_oa ) remaining = _clean_polygonal(remaining) else: @@ -60,13 +72,54 @@ def process_oa( fragments = [] for pc, parts in result.items(): - merged = _clean_polygonal(unary_union(parts)) + merged = _clean_polygonal(safe_union(parts)) if merged is not None: fragments.append((pc, merged)) + # Every postcode with a UPRN seed in this OA must keep at least a minimal + # footprint — in a dense OA (a block of flats with hundreds of distinct + # postcodes) a single-seed postcode's cell can collapse below MIN_GEOM_AREA or + # be fully absorbed by a co-located postcode's INSPIRE parcel, producing no + # fragment, and an active postcode must never be dropped. + orphans = unique_pcs - {pc for pc, _ in fragments} + if orphans: + fragments.extend(_seed_footprints(orphans, points, postcodes, valid_oa)) + return fragments +def _seed_footprints( + orphans: set[str], + points: np.ndarray, + postcodes: list[str], + valid_oa: Polygon | MultiPolygon, +) -> list[tuple[str, Polygon | MultiPolygon]]: + """Give each orphan postcode a minimal disc footprint at its UPRN seed(s). + + Orphans are postcodes with a UPRN in this OA that nonetheless won no area in + the INSPIRE/Voronoi partition. Each keeps a small disc at its true location, + clipped to the OA; the overlap with the area's winner is resolved at output. + """ + by_pc: dict[str, list] = defaultdict(list) + for i, pc in enumerate(postcodes): + if pc in orphans: + by_pc[pc].append(points[i]) + + out: list[tuple[str, Polygon | MultiPolygon]] = [] + for pc, pts in by_pc.items(): + arr = np.asarray(pts, dtype=np.float64) + seed = Point(arr[0]) if len(arr) == 1 else MultiPoint(arr) + disc = seed.buffer(_MIN_SEED_FOOTPRINT_M) + clipped = _clean_polygonal(safe_intersection(disc, valid_oa)) + if clipped is None: + # Seed on/near the OA edge: keep the unclipped disc so the postcode + # still gets a footprint at its location rather than no boundary. + clipped = _clean_polygonal(disc) + if clipped is not None: + out.append((pc, clipped)) + return out + + def _claim_inspire_parcels( valid_oa: Polygon | MultiPolygon, points: np.ndarray, @@ -141,7 +194,7 @@ def _claim_inspire_parcels( assignable = parcel if contained_union is not None: - assignable = assignable.difference(contained_union) + assignable = safe_difference(assignable, contained_union) for part in _polygon_parts(assignable): part = _clean_polygonal(part) if part is None: @@ -169,7 +222,7 @@ def _prepare_inspire_parcels( continue if not geom.intersects(valid_oa): continue - clipped = _clean_polygonal(geom.intersection(valid_oa)) + clipped = _clean_polygonal(safe_intersection(geom, valid_oa)) if clipped is not None: parcels.append(clipped) return parcels @@ -199,7 +252,7 @@ def _merge_parts_by_postcode( ) -> dict[str, Polygon | MultiPolygon]: merged: dict[str, Polygon | MultiPolygon] = {} for pc, parts in parts_by_postcode.items(): - geom = _clean_polygonal(unary_union(parts)) + geom = _clean_polygonal(safe_union(parts)) if geom is not None: merged[pc] = geom return merged @@ -210,7 +263,7 @@ def _union_claims( ) -> Polygon | MultiPolygon | None: if not claims: return None - return _clean_polygonal(unary_union([geom for _, geom in claims])) + return _clean_polygonal(safe_union([geom for _, geom in claims])) def _resolve_ordered_claims( @@ -224,11 +277,11 @@ def _resolve_ordered_claims( if geom is None: continue if used is not None: - geom = _clean_polygonal(geom.difference(used)) + geom = _clean_polygonal(safe_difference(geom, used)) if geom is None: continue resolved_parts[pc].append(geom) - used = _clean_polygonal(geom if used is None else unary_union([used, geom])) + used = _clean_polygonal(geom if used is None else safe_union([used, geom])) return _merge_parts_by_postcode(resolved_parts) @@ -261,7 +314,7 @@ def _extract_polygonal(geom) -> Polygon | MultiPolygon | None: # overlapping polygonal parts, and a MultiPolygon of overlapping parts is # invalid — it double-counts area and makes the next `.difference()` raise # a TopologyException that aborts the OA (and, in parallel mode, the - # worker). unary_union merges them into a valid geometry. - merged = unary_union(polys) + # worker). safe_union merges them into a valid geometry. + merged = safe_union(polys) return merged if not merged.is_empty else None return None diff --git a/pipeline/transform/postcode_boundaries/test_postcode_boundaries.py b/pipeline/transform/postcode_boundaries/test_postcode_boundaries.py index 0b6ce96..760ac72 100644 --- a/pipeline/transform/postcode_boundaries/test_postcode_boundaries.py +++ b/pipeline/transform/postcode_boundaries/test_postcode_boundaries.py @@ -757,6 +757,50 @@ class TestProcessOAInspireParcelAssignment: assert frag_dict["A"].intersection(frag_dict["B"]).area < 0.01 +class TestProcessOASeedFootprintGuarantee: + """Every postcode with a UPRN seed in a multi-postcode OA must produce a + fragment, even when its partition cell collapses below MIN_GEOM_AREA — an + active postcode must never be dropped (validate_outputs is zero-tolerance).""" + + def test_collapsed_voronoi_cells_rescued_as_footprints(self): + # OA just above MIN_GEOM_AREA; a 2-way Voronoi split leaves each cell + # (~0.0098 m²) below MIN_GEOM_AREA, so both would be dropped without rescue. + oa_geom = box(0, 0, 0.14, 0.14) # 0.0196 m² + points = np.array([[0.035, 0.07], [0.105, 0.07]]) + postcodes = ["AA1 1AA", "AA1 1AB"] + + fragments = process_oa(oa_geom, points, postcodes, inspire_candidates=[]) + got = {pc for pc, _ in fragments} + assert got == {"AA1 1AA", "AA1 1AB"}, f"dropped: {set(postcodes) - got}" + for _, geom in fragments: + assert geom.is_valid + assert geom.area > MIN_GEOM_AREA + + def test_seed_footprint_sits_on_the_uprn(self): + from shapely.geometry import Point + + from .process_oa import _seed_footprints + + oa_geom = box(0, 0, 1000, 1000) + points = np.array([[500.0, 500.0]]) + out = _seed_footprints({"AA1 1AA"}, points, ["AA1 1AA"], oa_geom) + + assert len(out) == 1 + pc, geom = out[0] + assert pc == "AA1 1AA" + assert geom.is_valid and geom.area > MIN_GEOM_AREA + assert geom.contains(Point(500, 500)) + + def test_only_orphans_get_footprints(self): + # A wins real area; B's lone seed collapses. Only B should be rescued, and + # A's genuine (large) fragment must be untouched by the rescue. + oa_geom = box(0, 0, 0.14, 0.14) + points = np.array([[0.07, 0.07], [0.07, 0.07], [0.105, 0.07]]) + postcodes = ["AA1 1AA", "AA1 1AA", "AA1 1AB"] + fragments = process_oa(oa_geom, points, postcodes, inspire_candidates=[]) + assert {pc for pc, _ in fragments} == {"AA1 1AA", "AA1 1AB"} + + # --------------------------------------------------------------------------- # _extract_polygonal helper # --------------------------------------------------------------------------- @@ -1221,6 +1265,238 @@ class TestOutputPartition: assert geoms["AA1 1AB"].area > 0 +class TestGeojsonGeometrySliverValidity: + """_geojson_geometry must emit geometry that is still valid after the final 6dp + rounding. A sub-grid overlap sliver (left by _resolve_overlaps' full-precision + difference) must be snapped away on the output grid rather than collapsed by a + naive round into a degenerate ('Too few points') / self-intersecting ring that + only fails once read back from disk.""" + + def test_subgrid_sliver_does_not_produce_invalid_ring(self): + from shapely.geometry import shape + from shapely.validation import explain_validity + + from .output import _geojson_geometry + + main = box(-0.34, 51.74, -0.33, 51.75) # ~degree-scale, valid + # A thin triangle whose three vertices all round to the same 1e-6 cell. + sliver = Polygon( + [ + (-0.3400284, 51.7505061), + (-0.3400280, 51.7505060), + (-0.3400281, 51.7505063), + ] + ) + assert sliver.is_valid + + gj = _geojson_geometry(MultiPolygon([main, sliver])) + assert gj is not None + rt = shape(gj) + assert rt.is_valid, explain_validity(rt) + assert not rt.is_empty + # The real (main) area is preserved; only the degenerate sliver is dropped. + assert rt.area == pytest.approx(main.area, rel=1e-3) + + def test_pure_sliver_is_rescued_not_written_invalid(self): + from shapely.geometry import shape + + from .output import _OUTPUT_PRECISION_DEG, _geojson_geometry + + # A sub-grid sliver (extent < 1e-6° in one dimension) that snaps to empty: + # it must be rescued into a minimal valid footprint at its location, never + # written invalid and never dropped (an active postcode keeps a boundary). + g = _OUTPUT_PRECISION_DEG + sliver = Polygon( + [ + (-0.30, 51.75), + (-0.30 + 5 * g, 51.75), + (-0.30 + 5 * g, 51.75 + 0.2 * g), + (-0.30, 51.75 + 0.1 * g), + ] + ) + gj = _geojson_geometry(sliver) + assert gj is not None # rescued, not dropped + rt = shape(gj) + assert rt.is_valid and not rt.is_empty + assert rt.distance(sliver) == pytest.approx(0.0, abs=1e-4) + + +class TestColocatedPostcodesAllRetained: + """Co-located non-geographic postcodes (e.g. AL1 9xx) have heavily-overlapping + tiny footprints; the de-overlap pass trims most to sub-grid slivers. None may + be dropped — every active postcode must keep a (valid, non-empty) boundary.""" + + def test_overlapping_tiny_footprints_none_dropped(self, tmp_path): + from shapely.geometry import Point, shape + + # 12 ~3 m discs within ~1 m of each other → after de-overlap most collapse + # below output precision. + postcodes = {} + for i in range(12): + e = 514000.0 + (i % 4) * 0.3 + n = 206000.0 + (i // 4) * 0.3 + postcodes[f"AL1 9{chr(65 + i)}{chr(65 + i)}"] = Point(e, n).buffer(3.0) + + write_district_geojson(postcodes, tmp_path) + coll = json.loads((tmp_path / "units" / "AL1.geojson").read_text()) + written = {f["properties"]["postcodes"] for f in coll["features"]} + assert written == set(postcodes), f"dropped: {set(postcodes) - written}" + for feature in coll["features"]: + geom = shape(feature["geometry"]) + assert geom.is_valid and not geom.is_empty + + +class TestSafeOverlayHelpers: + """The robust overlay helpers retry on a fixed-precision grid after a + GEOSException (e.g. ``side location conflict`` from near-coincident OA-seam + edges). The grid is a caller-supplied parameter: metres for the BNG stages, + 1e-6 degrees for the WGS84 output stage — so the same helper serves both + without crushing degree-scale shapes on the metre default.""" + + def test_grid_param_honored_on_clean_inputs(self): + from . import geometry + + a = box(0, 0, 10, 10) + b = box(5, 0, 15, 10) + # No exception → exact result, identical regardless of the fallback grid. + assert geometry.safe_difference(a, b, grid=1e-6).equals(a.difference(b)) + assert geometry.safe_union([a, b], grid=1e-6).equals(unary_union([a, b])) + assert geometry.safe_intersection(a, b, grid=1e-6).equals(a.intersection(b)) + + def test_difference_falls_back_to_fixed_precision_overlay(self, monkeypatch): + from shapely import GEOSException + from shapely.geometry.base import BaseGeometry + + from . import geometry + + a = box(0, 0, 10, 10) + b = box(5, 0, 15, 10) + + def _boom(self, other, *args, **kwargs): + raise GEOSException("forced side location conflict") + + # Patch the .difference() *method*; the fallback uses the top-level + # shapely.difference() function, so it still completes. + monkeypatch.setattr(BaseGeometry, "difference", _boom) + result = geometry.safe_difference(a, b, grid=1e-6) + assert result.is_valid + assert result.area == pytest.approx(50.0) # left half of `a` + + def test_union_falls_back_to_fixed_precision_overlay(self, monkeypatch): + from shapely import GEOSException + + from . import geometry + + a = box(0, 0, 10, 10) + b = box(5, 0, 15, 10) + + def _boom(_geoms): + raise GEOSException("forced robustness failure") + + monkeypatch.setattr(geometry, "unary_union", _boom) + result = geometry.safe_union([a, b], grid=1e-6) + assert result.is_valid + assert result.area == pytest.approx(150.0) # 100 + 100 - 50 overlap + + # A self-intersecting bow-tie: invalid. set_precision()'s DEFAULT + # ('valid_output') mode runs its own noding pass that re-raises + # 'side location conflict' on this — which is exactly how the production + # crash happened (the fallback re-raised the error it was meant to absorb). + _BOWTIE = Polygon([(0, 0), (1e-5, 1e-5), (1e-5, 0), (0, 1e-5), (0, 0)]) + # make_valid() of this spiky ring returns a mixed-dimension + # GeometryCollection (polygon + dangling line); OverlayNG rejects that with + # 'Overlay input is mixed-dimension', so the fallback must strip the debris. + _SPIKY = Polygon( + [(0, 0), (10, 0), (10, 10), (5, 5), (5.0000001, 5), (0, 10), (0, 0), (15, -5), (0, 0)] + ) + + def test_difference_fallback_survives_invalid_input(self, monkeypatch): + """Regression for the production crash: the fallback must not reduce + precision with set_precision's default valid_output mode, which re-raises + 'side location conflict' on an invalid geometry.""" + from shapely import GEOSException + from shapely.geometry.base import BaseGeometry + + from . import geometry + + assert not self._BOWTIE.is_valid # precondition + + def _boom(self, other, *a, **k): + raise GEOSException("forced side location conflict") + + monkeypatch.setattr(BaseGeometry, "difference", _boom) + result = geometry.safe_difference(self._BOWTIE, box(0, 0, 5e-6, 5e-6), grid=1e-6) + assert result.is_valid + assert result.geom_type in ("Polygon", "MultiPolygon") + + def test_difference_fallback_survives_make_valid_geometrycollection( + self, monkeypatch + ): + """Regression: make_valid can yield a mixed-dimension GeometryCollection + that OverlayNG rejects; the fallback must keep only polygonal parts.""" + from shapely import GEOSException, make_valid + from shapely.geometry.base import BaseGeometry + + from . import geometry + + assert make_valid(self._SPIKY).geom_type == "GeometryCollection" # precondition + + def _boom(self, other, *a, **k): + raise GEOSException("forced side location conflict") + + monkeypatch.setattr(BaseGeometry, "difference", _boom) + result = geometry.safe_difference(self._SPIKY, box(2, 2, 8, 8), grid=1e-4) + assert result.is_valid + assert result.geom_type in ("Polygon", "MultiPolygon") + assert result.area > 0 + + def test_intersection_fallback_survives_make_valid_geometrycollection( + self, monkeypatch + ): + from shapely import GEOSException + from shapely.geometry.base import BaseGeometry + + from . import geometry + + def _boom(self, other, *a, **k): + raise GEOSException("forced side location conflict") + + monkeypatch.setattr(BaseGeometry, "intersection", _boom) + result = geometry.safe_intersection(self._SPIKY, box(2, 2, 8, 8), grid=1e-4) + assert result.is_valid + assert result.geom_type in ("Polygon", "MultiPolygon") + assert result.area > 0 + + def test_union_fallback_survives_invalid_and_collection_inputs(self, monkeypatch): + """The union fallback must absorb both an invalid bow-tie and a + make_valid-becomes-GeometryCollection input without raising.""" + from shapely import GEOSException + + from . import geometry + + def _boom(_geoms): + raise GEOSException("forced robustness failure") + + monkeypatch.setattr(geometry, "unary_union", _boom) + result = geometry.safe_union( + [self._BOWTIE, self._SPIKY, box(20, 20, 30, 30)], grid=1e-4 + ) + assert result.is_valid + assert result.geom_type in ("Polygon", "MultiPolygon") + + def test_helpers_never_raise_on_empty_inputs(self): + """Degenerate/empty inputs must not abort a multi-hour run.""" + from . import geometry + + empty = Polygon() + a = box(0, 0, 10, 10) + assert geometry.safe_difference(empty, a).is_empty + assert geometry.safe_difference(a, empty).equals(a) + assert geometry.safe_intersection(empty, a).is_empty + assert geometry.safe_union([]).is_empty + assert geometry.safe_union([empty]).is_empty + + # --------------------------------------------------------------------------- # InspireIndex must return the same candidates as a brute-force bbox scan # --------------------------------------------------------------------------- diff --git a/pipeline/transform/postcode_boundaries/voronoi.py b/pipeline/transform/postcode_boundaries/voronoi.py index 6d80d12..939d3c9 100644 --- a/pipeline/transform/postcode_boundaries/voronoi.py +++ b/pipeline/transform/postcode_boundaries/voronoi.py @@ -4,7 +4,8 @@ import numpy as np from scipy.spatial import QhullError, Voronoi from shapely import make_valid from shapely.geometry import MultiPolygon, Polygon -from shapely.ops import unary_union + +from .geometry import safe_intersection, safe_union def compute_voronoi_regions( @@ -111,13 +112,13 @@ def compute_voronoi_regions( poly = Polygon(vertices) if not poly.is_valid: poly = make_valid(poly) - clipped = poly.intersection(boundary) + clipped = safe_intersection(poly, boundary) if not clipped.is_empty: pc_polys[unique_pcs[i]].append(clipped) result = {} for pc, parts in pc_polys.items(): - merged = unary_union(parts) + merged = safe_union(parts) if not merged.is_empty: result[pc] = merged return result @@ -143,7 +144,7 @@ def _equal_split_fallback( (min_x, strip_max_y), ] ) - clipped = boundary.intersection(strip) + clipped = safe_intersection(boundary, strip) if not clipped.is_empty: result[pc] = clipped return result diff --git a/pipeline/transform/test_tree_density.py b/pipeline/transform/test_tree_density.py index 340d9ec..45d0ee5 100644 --- a/pipeline/transform/test_tree_density.py +++ b/pipeline/transform/test_tree_density.py @@ -66,6 +66,53 @@ def test_accumulate_clipped_area_drops_missing_and_empty_geometry() -> None: assert canopy_area[0] == pytest.approx(400.0) +def test_accumulate_clipped_area_survives_invalid_polygon() -> None: + """A self-intersecting external polygon (TOW/NFI data is occasionally invalid) + must not abort the batched overlay with 'TopologyException: side location + conflict'; its repaired in-buffer area is still accumulated.""" + radius_m = 50 + points = pl.DataFrame({"postcode": ["A"], "x": [0.0], "y": [0.0]}) + circles, tree = _postcode_buffers(points, radius_m) + + # Bow-tie centred on A: self-intersecting => invalid. The raw batched + # shapely.intersection raises 'side location conflict' on it; make_valid splits + # it into two triangles of total area 200, fully inside A's radius-50 buffer. + bowtie = shapely.Polygon([(-10, -10), (10, 10), (10, -10), (-10, 10), (-10, -10)]) + assert not shapely.is_valid(bowtie) # precondition + with pytest.raises(shapely.errors.GEOSException): # documents the raw hazard + shapely.intersection( + np.array([bowtie], dtype=object), np.array([circles[0]], dtype=object) + ) + + canopy_area = np.zeros(1) + _accumulate_clipped_area(np.array([bowtie], dtype=object), circles, tree, canopy_area) + assert canopy_area[0] == pytest.approx(200.0, rel=1e-3) + + +def test_robust_intersection_area_recovers_from_overlay_failure(monkeypatch) -> None: + """The batched-overlay fallback must absorb a GEOSException from the fast path + and recover (validate + retry), returning the correct per-pair areas. Version + independent: the fast-path failure is forced rather than data-dependent.""" + from pipeline.transform import tree_density + + real_intersection = shapely.intersection + calls = {"n": 0} + + def flaky(a, b, **kwargs): + calls["n"] += 1 + if calls["n"] == 1: # fail only the first (fast-path) call + raise shapely.errors.GEOSException("forced side location conflict") + return real_intersection(a, b, **kwargs) + + monkeypatch.setattr(tree_density.shapely, "intersection", flaky) + + a = np.array([shapely.box(0, 0, 10, 10), shapely.box(0, 0, 4, 4)], dtype=object) + b = np.array([shapely.box(0, 0, 6, 6), shapely.box(0, 0, 2, 2)], dtype=object) + out = tree_density._robust_intersection_area(a, b) + assert calls["n"] >= 2 # fast path failed -> fallback path executed + assert out.tolist() == pytest.approx([36.0, 4.0]) + + def test_accumulate_clipped_area_height_weighted_by_overlap() -> None: radius_m = 50 points = pl.DataFrame({"postcode": ["A"], "x": [0.0], "y": [0.0]}) diff --git a/pipeline/transform/tree_density.py b/pipeline/transform/tree_density.py index 9590104..75ba03e 100644 --- a/pipeline/transform/tree_density.py +++ b/pipeline/transform/tree_density.py @@ -263,6 +263,43 @@ def _postcode_buffers( return circles, shapely.STRtree(circles) +# 0.1 mm in the BNG working CRS (EPSG:27700) — far below survey resolution; the +# same grid the postcode_boundaries overlay uses. +_OVERLAY_GRID_M = 1e-4 + + +def _robust_intersection_area(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """Vectorized ``area(a[i] ∩ b[i])`` that survives GEOS robustness failures. + + External Forest Research TOW/NFI polygons are occasionally invalid + (self-intersections), and a single bad polygon makes the batched + ``shapely.intersection`` raise ``TopologyException: side location conflict``, + aborting the whole run. The fast path is the raw batched overlay — unchanged, + full-speed, when the data is clean — and only a failure triggers repair. + + The repair deliberately uses a *plain* overlay rather than the fixed-precision + (``grid_size``) one: ``make_valid`` can emit a mixed-dimension + ``GeometryCollection`` (a polygon plus a dangling line), which OverlayNG + rejects with ``Overlay input is mixed-dimension`` — whereas a plain overlay + accepts it, and its non-polygonal debris has zero area and is dropped by the + ``clipped_area > 0`` filter downstream anyway. A final pointwise coordinate + snap (which never raises) collapses the near-coincident edges behind any + residual full-precision robustness failure. + """ + try: + return shapely.area(shapely.intersection(a, b)) + except shapely.errors.GEOSException: + pass + a = shapely.make_valid(a) + b = shapely.make_valid(b) + try: + return shapely.area(shapely.intersection(a, b)) + except shapely.errors.GEOSException: + a = shapely.make_valid(shapely.set_precision(a, _OVERLAY_GRID_M, mode="pointwise")) + b = shapely.make_valid(shapely.set_precision(b, _OVERLAY_GRID_M, mode="pointwise")) + return shapely.area(shapely.intersection(a, b)) + + def _accumulate_clipped_area( geoms: np.ndarray, circles: np.ndarray, @@ -294,8 +331,8 @@ def _accumulate_clipped_area( if geom_index.size == 0: return - clipped_area = shapely.area( - shapely.intersection(geoms[geom_index], circles[postcode_index]) + clipped_area = _robust_intersection_area( + geoms[geom_index], circles[postcode_index] ) positive = clipped_area > 0 geom_index = geom_index[positive]