Fmt
This commit is contained in:
parent
479ef92236
commit
c38d654ac7
44 changed files with 2526 additions and 701 deletions
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@ -52,6 +52,7 @@
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"buy = pl.read_parquet(f\"{DATA}/online_listings_buy.parquet\")\n",
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"rent = pl.read_parquet(f\"{DATA}/online_listings_rent.parquet\")\n",
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"\n",
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"\n",
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"def tag_source(df: pl.DataFrame) -> pl.DataFrame:\n",
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" return df.with_columns(\n",
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" pl.when(pl.col(\"Listing URL\").str.contains(\"rightmove\"))\n",
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@ -62,6 +63,7 @@
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" .alias(\"source\")\n",
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" )\n",
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"\n",
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"\n",
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"buy = tag_source(buy)\n",
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"rent = tag_source(rent)\n",
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"\n",
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@ -122,7 +124,7 @@
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" print(f\"\\n=== {label} ===\")\n",
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" for row in counts.iter_rows():\n",
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" src, cnt = row\n",
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" print(f\" {src}: {cnt:,} ({cnt/len(df)*100:.1f}%)\")\n",
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" print(f\" {src}: {cnt:,} ({cnt / len(df) * 100:.1f}%)\")\n",
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"\n",
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"# Known dedup count from scraper logs\n",
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"CROSS_DEDUP_BUY = 2_220\n",
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@ -132,7 +134,7 @@
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"print(f\"Home.co.uk scraped (before dedup): {hk_buy_total:,}\")\n",
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"print(f\"Home.co.uk unique (after dedup): {hk_buy_unique:,}\")\n",
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"print(f\"Cross-source duplicates removed: {CROSS_DEDUP_BUY:,}\")\n",
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"print(f\"Overlap rate: {CROSS_DEDUP_BUY/hk_buy_total*100:.1f}%\")"
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"print(f\"Overlap rate: {CROSS_DEDUP_BUY / hk_buy_total * 100:.1f}%\")"
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]
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},
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{
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@ -987,23 +989,29 @@
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"# Venn-style summary\n",
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"rm_buy = len(buy.filter(pl.col(\"source\") == \"Rightmove\"))\n",
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"\n",
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"fig = go.Figure(go.Sankey(\n",
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" node=dict(\n",
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" label=[\n",
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" f\"Rightmove\\n{rm_buy:,}\",\n",
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" f\"Home.co.uk\\n{hk_buy_total:,} scraped\",\n",
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" f\"Merged BUY\\n{len(buy):,}\",\n",
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" f\"Deduped\\n{CROSS_DEDUP_BUY:,}\",\n",
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" ],\n",
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" color=[\"#2563eb\", \"#10b981\", \"#6366f1\", \"#ef4444\"],\n",
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" ),\n",
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" link=dict(\n",
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" source=[0, 1, 1],\n",
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" target=[2, 2, 3],\n",
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" value=[rm_buy, hk_buy_unique, CROSS_DEDUP_BUY],\n",
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" color=[\"rgba(37,99,235,0.3)\", \"rgba(16,185,129,0.3)\", \"rgba(239,68,68,0.3)\"],\n",
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" ),\n",
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"))\n",
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"fig = go.Figure(\n",
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" go.Sankey(\n",
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" node=dict(\n",
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" label=[\n",
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" f\"Rightmove\\n{rm_buy:,}\",\n",
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" f\"Home.co.uk\\n{hk_buy_total:,} scraped\",\n",
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" f\"Merged BUY\\n{len(buy):,}\",\n",
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" f\"Deduped\\n{CROSS_DEDUP_BUY:,}\",\n",
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" ],\n",
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" color=[\"#2563eb\", \"#10b981\", \"#6366f1\", \"#ef4444\"],\n",
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" ),\n",
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" link=dict(\n",
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" source=[0, 1, 1],\n",
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" target=[2, 2, 3],\n",
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" value=[rm_buy, hk_buy_unique, CROSS_DEDUP_BUY],\n",
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" color=[\n",
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" \"rgba(37,99,235,0.3)\",\n",
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" \"rgba(16,185,129,0.3)\",\n",
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" \"rgba(239,68,68,0.3)\",\n",
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" ],\n",
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" ),\n",
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" )\n",
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")\n",
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"fig.update_layout(title=\"BUY Channel: Source Contribution Flow\", height=350)\n",
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"fig.show()"
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]
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@ -1106,8 +1114,11 @@
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"oc_comparison = (\n",
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" hk_by_oc.join(rm_by_oc, on=\"outcode\", how=\"left\")\n",
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" .with_columns(\n",
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" (pl.col(\"hk_count\") / (pl.col(\"hk_count\") + pl.col(\"rm_count\").fill_null(0)) * 100)\n",
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" .alias(\"hk_pct_of_total\")\n",
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" (\n",
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" pl.col(\"hk_count\")\n",
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" / (pl.col(\"hk_count\") + pl.col(\"rm_count\").fill_null(0))\n",
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" * 100\n",
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" ).alias(\"hk_pct_of_total\")\n",
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" )\n",
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" .sort(\"hk_count\", descending=True)\n",
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")\n",
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@ -2215,18 +2226,28 @@
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"source": [
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"# Bar chart: home.co.uk vs Rightmove counts per outcode\n",
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"fig = go.Figure()\n",
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"fig.add_trace(go.Bar(\n",
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" x=oc_comparison[\"outcode\"], y=oc_comparison[\"rm_count\"],\n",
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" name=\"Rightmove\", marker_color=\"#2563eb\",\n",
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"))\n",
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"fig.add_trace(go.Bar(\n",
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" x=oc_comparison[\"outcode\"], y=oc_comparison[\"hk_count\"],\n",
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" name=\"Home.co.uk\", marker_color=\"#10b981\",\n",
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"))\n",
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"fig.add_trace(\n",
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" go.Bar(\n",
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" x=oc_comparison[\"outcode\"],\n",
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" y=oc_comparison[\"rm_count\"],\n",
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" name=\"Rightmove\",\n",
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" marker_color=\"#2563eb\",\n",
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" )\n",
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")\n",
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"fig.add_trace(\n",
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" go.Bar(\n",
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" x=oc_comparison[\"outcode\"],\n",
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" y=oc_comparison[\"hk_count\"],\n",
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" name=\"Home.co.uk\",\n",
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" marker_color=\"#10b981\",\n",
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" )\n",
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")\n",
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"fig.update_layout(\n",
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" barmode=\"group\", height=400,\n",
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" barmode=\"group\",\n",
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" height=400,\n",
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" title=\"Listings per Outcode: Rightmove vs Home.co.uk (outcodes with HK coverage)\",\n",
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" xaxis_title=\"Outcode\", yaxis_title=\"Listings\",\n",
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" xaxis_title=\"Outcode\",\n",
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" yaxis_title=\"Listings\",\n",
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")\n",
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"fig.show()"
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]
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@ -3121,10 +3142,14 @@
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"sample = covered.sample(n=min(30_000, len(covered)), seed=42)\n",
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"\n",
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"fig = px.scatter_map(\n",
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" sample.to_pandas(), lat=\"lat\", lon=\"lon\",\n",
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" sample.to_pandas(),\n",
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" lat=\"lat\",\n",
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" lon=\"lon\",\n",
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" color=\"source\",\n",
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" color_discrete_map={\"Rightmove\": \"#2563eb\", \"Home.co.uk\": \"#10b981\"},\n",
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" zoom=7, opacity=0.4, size_max=4,\n",
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" zoom=7,\n",
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" opacity=0.4,\n",
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" size_max=4,\n",
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" title=\"Listing Locations in Covered Outcodes (by source)\",\n",
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")\n",
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"fig.update_layout(height=600)\n",
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@ -3188,15 +3213,41 @@
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"# For covered outcodes, compare home.co.uk listings against Rightmove\n",
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"# to find near-matches (same postcode, same beds, price within 5%)\n",
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"\n",
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"hk = buy_oc.filter(pl.col(\"source\") == \"Home.co.uk\").select(\n",
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" \"Postcode\", \"Bedrooms\", \"Asking price\", \"Property type\", \"Address per Property Register\"\n",
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").rename({\"Asking price\": \"hk_price\", \"Property type\": \"hk_type\", \"Address per Property Register\": \"hk_addr\"})\n",
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"hk = (\n",
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" buy_oc.filter(pl.col(\"source\") == \"Home.co.uk\")\n",
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" .select(\n",
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" \"Postcode\",\n",
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" \"Bedrooms\",\n",
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" \"Asking price\",\n",
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" \"Property type\",\n",
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" \"Address per Property Register\",\n",
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" )\n",
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" .rename(\n",
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" {\n",
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" \"Asking price\": \"hk_price\",\n",
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" \"Property type\": \"hk_type\",\n",
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" \"Address per Property Register\": \"hk_addr\",\n",
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" }\n",
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" )\n",
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")\n",
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"\n",
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"rm = buy_oc.filter(\n",
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" pl.col(\"source\") == \"Rightmove\"\n",
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").select(\n",
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" \"Postcode\", \"Bedrooms\", \"Asking price\", \"Property type\", \"Address per Property Register\"\n",
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").rename({\"Asking price\": \"rm_price\", \"Property type\": \"rm_type\", \"Address per Property Register\": \"rm_addr\"})\n",
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"rm = (\n",
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" buy_oc.filter(pl.col(\"source\") == \"Rightmove\")\n",
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" .select(\n",
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" \"Postcode\",\n",
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" \"Bedrooms\",\n",
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" \"Asking price\",\n",
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" \"Property type\",\n",
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" \"Address per Property Register\",\n",
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" )\n",
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" .rename(\n",
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" {\n",
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" \"Asking price\": \"rm_price\",\n",
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" \"Property type\": \"rm_type\",\n",
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" \"Address per Property Register\": \"rm_addr\",\n",
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" }\n",
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" )\n",
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")\n",
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"\n",
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"# Join on postcode + bedrooms\n",
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"joined = hk.join(rm, on=[\"Postcode\", \"Bedrooms\"], how=\"inner\")\n",
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@ -3213,16 +3264,24 @@
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"exact = joined.filter(pl.col(\"hk_price\") == pl.col(\"rm_price\"))\n",
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"\n",
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"print(f\"Home.co.uk listings (unique, in file): {len(hk):,}\")\n",
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"print(f\"Rightmove listings in covered outcodes: {len(rm.filter(pl.col('Postcode').is_in(hk['Postcode']))):,}\")\n",
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"print(\n",
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" f\"Rightmove listings in covered outcodes: {len(rm.filter(pl.col('Postcode').is_in(hk['Postcode']))):,}\"\n",
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")\n",
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"print()\n",
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"print(f\"Joined on (postcode, bedrooms): {len(joined):,} candidate pairs\")\n",
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"print(f\" Exact price match: {len(exact):,} pairs (likely same property, different beds or already deduped)\")\n",
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"print(f\" Price within 5%: {len(near):,} pairs (probable duplicates with price rounding)\")\n",
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"print(\n",
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" f\" Exact price match: {len(exact):,} pairs (likely same property, different beds or already deduped)\"\n",
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")\n",
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"print(\n",
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" f\" Price within 5%: {len(near):,} pairs (probable duplicates with price rounding)\"\n",
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")\n",
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"print()\n",
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"# Unique hk listings that have at least one near-match\n",
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"hk_with_near = near.select(\"hk_price\", \"hk_addr\", \"Postcode\").unique()\n",
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"print(f\"Home.co.uk listings with a near-match in RM: ~{len(hk_with_near):,}\")\n",
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"print(f\"Estimated additional overlap: ~{len(hk_with_near)/len(hk)*100:.1f}% of unique HK listings\")"
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"print(\n",
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" f\"Estimated additional overlap: ~{len(hk_with_near) / len(hk) * 100:.1f}% of unique HK listings\"\n",
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")"
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]
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},
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{
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@ -4178,9 +4237,13 @@
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")\n",
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"\n",
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"fig = px.histogram(\n",
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" clipped.to_pandas(), x=\"Asking price\", color=\"source\", nbins=80,\n",
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" clipped.to_pandas(),\n",
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" x=\"Asking price\",\n",
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" color=\"source\",\n",
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" nbins=80,\n",
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" color_discrete_map={\"Rightmove\": \"#2563eb\", \"Home.co.uk\": \"#10b981\"},\n",
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" barmode=\"overlay\", histnorm=\"probability density\",\n",
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" barmode=\"overlay\",\n",
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" histnorm=\"probability density\",\n",
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" title=\"Price Distribution by Source (normalised, £50k–£2M)\",\n",
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")\n",
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"fig.update_traces(opacity=0.6)\n",
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@ -5095,10 +5158,7 @@
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],
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"source": [
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"# Property type distribution by source\n",
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"type_by_src = (\n",
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" buy.group_by(\"source\", \"Property type\")\n",
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" .agg(pl.len().alias(\"count\"))\n",
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")\n",
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"type_by_src = buy.group_by(\"source\", \"Property type\").agg(pl.len().alias(\"count\"))\n",
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"# Normalise within each source\n",
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"totals = type_by_src.group_by(\"source\").agg(pl.col(\"count\").sum().alias(\"total\"))\n",
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"type_by_src = type_by_src.join(totals, on=\"source\").with_columns(\n",
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@ -5107,7 +5167,10 @@
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"\n",
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"fig = px.bar(\n",
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" type_by_src.sort(\"Property type\").to_pandas(),\n",
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" x=\"Property type\", y=\"pct\", color=\"source\", barmode=\"group\",\n",
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" x=\"Property type\",\n",
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" y=\"pct\",\n",
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" color=\"source\",\n",
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" barmode=\"group\",\n",
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" color_discrete_map={\"Rightmove\": \"#2563eb\", \"Home.co.uk\": \"#10b981\"},\n",
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" title=\"Property Type Distribution by Source (%)\",\n",
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")\n",
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@ -5186,7 +5249,9 @@
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"# Property sub-type comparison — top home.co.uk sub-types\n",
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"hk_subtypes = (\n",
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" buy.filter(pl.col(\"source\") == \"Home.co.uk\")[\"Property sub-type\"]\n",
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" .value_counts().sort(\"count\", descending=True).head(20)\n",
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" .value_counts()\n",
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" .sort(\"count\", descending=True)\n",
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" .head(20)\n",
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")\n",
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"print(\"Top 20 Home.co.uk property sub-types:\")\n",
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"hk_subtypes"
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@ -5263,9 +5328,16 @@
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"source": [
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"# Field completeness by source\n",
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"fields = [\n",
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" \"Bedrooms\", \"Bathrooms\", \"Postcode\", \"Address per Property Register\",\n",
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" \"Leasehold/Freehold\", \"Property type\", \"Total floor area (sqm)\",\n",
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" \"Listing date\", \"Asking price\", \"Price qualifier\",\n",
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" \"Bedrooms\",\n",
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" \"Bathrooms\",\n",
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" \"Postcode\",\n",
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" \"Address per Property Register\",\n",
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" \"Leasehold/Freehold\",\n",
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" \"Property type\",\n",
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" \"Total floor area (sqm)\",\n",
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" \"Listing date\",\n",
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" \"Asking price\",\n",
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" \"Price qualifier\",\n",
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"]\n",
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"\n",
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"rows = []\n",
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@ -5276,17 +5348,19 @@
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" non_null = n - subset[f].null_count()\n",
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" # Also count empty strings as missing for string fields\n",
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" if subset[f].dtype == pl.Utf8:\n",
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" non_null = len(subset.filter(\n",
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" pl.col(f).is_not_null() & (pl.col(f).str.len_chars() > 0)\n",
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" ))\n",
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" non_null = len(\n",
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" subset.filter(pl.col(f).is_not_null() & (pl.col(f).str.len_chars() > 0))\n",
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" )\n",
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" rows.append({\"source\": src, \"field\": f, \"pct_available\": non_null / n * 100})\n",
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"\n",
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"completeness = pl.DataFrame(rows)\n",
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"pivot = completeness.pivot(on=\"source\", index=\"field\", values=\"pct_available\")\n",
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"pivot = pivot.with_columns([\n",
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" pl.col(\"Rightmove\").round(1),\n",
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" pl.col(\"Home.co.uk\").round(1),\n",
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"])\n",
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"pivot = pivot.with_columns(\n",
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" [\n",
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" pl.col(\"Rightmove\").round(1),\n",
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" pl.col(\"Home.co.uk\").round(1),\n",
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" ]\n",
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")\n",
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"print(\"Field completeness (% non-null/non-empty):\")\n",
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"pivot"
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]
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@ -6198,19 +6272,26 @@
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"# Bedroom distribution comparison\n",
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"fig = make_subplots(rows=1, cols=2, subplot_titles=(\"Rightmove\", \"Home.co.uk\"))\n",
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"for i, src in enumerate([\"Rightmove\", \"Home.co.uk\"], 1):\n",
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" beds = buy.filter(\n",
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" (pl.col(\"source\") == src) & (pl.col(\"Bedrooms\") <= 8)\n",
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" )[\"Bedrooms\"].value_counts().sort(\"Bedrooms\")\n",
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" beds = (\n",
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" buy.filter((pl.col(\"source\") == src) & (pl.col(\"Bedrooms\") <= 8))[\"Bedrooms\"]\n",
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" .value_counts()\n",
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" .sort(\"Bedrooms\")\n",
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" )\n",
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" # Normalise\n",
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" total = beds[\"count\"].sum()\n",
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" fig.add_trace(\n",
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" go.Bar(\n",
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" x=beds[\"Bedrooms\"], y=beds[\"count\"] / total * 100,\n",
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" x=beds[\"Bedrooms\"],\n",
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" y=beds[\"count\"] / total * 100,\n",
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" name=src,\n",
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" marker_color=\"#2563eb\" if src == \"Rightmove\" else \"#10b981\",\n",
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" ), row=1, col=i,\n",
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" ),\n",
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" row=1,\n",
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" col=i,\n",
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" )\n",
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"fig.update_layout(height=350, title=\"Bedroom Distribution by Source (%)\", showlegend=False)\n",
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"fig.update_layout(\n",
|
||||
" height=350, title=\"Bedroom Distribution by Source (%)\", showlegend=False\n",
|
||||
")\n",
|
||||
"fig.update_yaxes(title_text=\"%\", row=1, col=1)\n",
|
||||
"fig.show()"
|
||||
]
|
||||
|
|
@ -6287,17 +6368,23 @@
|
|||
"\n",
|
||||
"comparison_rows = []\n",
|
||||
"for ptype in [\"Detached\", \"Semi-Detached\", \"Terraced\", \"Flats/Maisonettes\", \"Other\"]:\n",
|
||||
" rm_p = rm_covered.filter(pl.col(\"Property type\") == ptype)[\"Asking price\"].drop_nulls()\n",
|
||||
" rm_p = rm_covered.filter(pl.col(\"Property type\") == ptype)[\n",
|
||||
" \"Asking price\"\n",
|
||||
" ].drop_nulls()\n",
|
||||
" hk_p = hk_only.filter(pl.col(\"Property type\") == ptype)[\"Asking price\"].drop_nulls()\n",
|
||||
" if len(rm_p) > 0 and len(hk_p) > 0:\n",
|
||||
" comparison_rows.append({\n",
|
||||
" \"Property type\": ptype,\n",
|
||||
" \"RM count\": len(rm_p),\n",
|
||||
" \"RM median £\": int(rm_p.median()),\n",
|
||||
" \"HK count\": len(hk_p),\n",
|
||||
" \"HK median £\": int(hk_p.median()),\n",
|
||||
" \"HK premium %\": round((hk_p.median() - rm_p.median()) / rm_p.median() * 100, 1),\n",
|
||||
" })\n",
|
||||
" comparison_rows.append(\n",
|
||||
" {\n",
|
||||
" \"Property type\": ptype,\n",
|
||||
" \"RM count\": len(rm_p),\n",
|
||||
" \"RM median £\": int(rm_p.median()),\n",
|
||||
" \"HK count\": len(hk_p),\n",
|
||||
" \"HK median £\": int(hk_p.median()),\n",
|
||||
" \"HK premium %\": round(\n",
|
||||
" (hk_p.median() - rm_p.median()) / rm_p.median() * 100, 1\n",
|
||||
" ),\n",
|
||||
" }\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"comp = pl.DataFrame(comparison_rows)\n",
|
||||
"print(\"Price comparison in covered outcodes (Home.co.uk unique listings vs Rightmove):\")\n",
|
||||
|
|
@ -7245,9 +7332,13 @@
|
|||
"# Listing age histogram comparison\n",
|
||||
"age_plot = with_age.filter(pl.col(\"days_on_market\") <= 730) # cap at 2 years\n",
|
||||
"fig = px.histogram(\n",
|
||||
" age_plot.to_pandas(), x=\"days_on_market\", color=\"source\", nbins=60,\n",
|
||||
" age_plot.to_pandas(),\n",
|
||||
" x=\"days_on_market\",\n",
|
||||
" color=\"source\",\n",
|
||||
" nbins=60,\n",
|
||||
" color_discrete_map={\"Rightmove\": \"#2563eb\", \"Home.co.uk\": \"#10b981\"},\n",
|
||||
" barmode=\"overlay\", histnorm=\"probability density\",\n",
|
||||
" barmode=\"overlay\",\n",
|
||||
" histnorm=\"probability density\",\n",
|
||||
" title=\"Days on Market Distribution by Source (normalised, capped at 2 years)\",\n",
|
||||
")\n",
|
||||
"fig.update_traces(opacity=0.6)\n",
|
||||
|
|
@ -7330,7 +7421,9 @@
|
|||
"print(f\" Projected home.co.uk total: ~{projected_hk:,}\")\n",
|
||||
"print(f\" Projected cross-dedup: ~{projected_dedup:,}\")\n",
|
||||
"print(f\" Projected unique additions: ~{projected_unique:,}\")\n",
|
||||
"print(f\" Projected merged dataset: ~{rm_buy + projected_unique:,} ({projected_unique/rm_buy*100:.1f}% increase)\")\n",
|
||||
"print(\n",
|
||||
" f\" Projected merged dataset: ~{rm_buy + projected_unique:,} ({projected_unique / rm_buy * 100:.1f}% increase)\"\n",
|
||||
")\n",
|
||||
"print()\n",
|
||||
"print(\"⚠️ These are rough estimates — the covered outcodes may not be representative\")"
|
||||
]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue