vibes

analyses/rightmove_buy.ipynb

@@ -48,7 +48,6 @@
     "import polars as pl\n",
     "import plotly.express as px\n",
     "import plotly.graph_objects as go\n",
-    "from plotly.subplots import make_subplots\n",
     "\n",
     "pl.Config.set_tbl_rows(20)\n",
     "pl.Config.set_fmt_str_lengths(80)\n",
@@ -265,7 +264,7 @@
    ],
    "source": [
     "price = clean[\"price\"]\n",
-    "print(f\"Price stats:\")\n",
+    "print(\"Price stats:\")\n",
     "print(f\"  Min: £{price.min():,}\")\n",
     "print(f\"  P5:  £{price.quantile(0.05):,.0f}\")\n",
     "print(f\"  P25: £{price.quantile(0.25):,.0f}\")\n",
@@ -590891,7 +590890,7 @@
     ")\n",
     "\n",
     "age = with_age[\"days_on_market\"].drop_nulls()\n",
-    "print(f\"Days on market stats:\")\n",
+    "print(\"Days on market stats:\")\n",
     "print(f\"  Median: {age.median():.0f} days\")\n",
     "print(f\"  Mean:   {age.mean():.0f} days\")\n",
     "print(f\"  P25:    {age.quantile(0.25):.0f} days\")\n",
@@ -594786,20 +594785,20 @@
     }
    ],
    "source": [
-    "print(f\"=== Rightmove Buy Listings Summary ===\")\n",
+    "print(\"=== Rightmove Buy Listings Summary ===\")\n",
     "print(f\"Total listings: {len(clean):,}\")\n",
     "print(f\"Outcodes covered: {clean['outcode'].n_unique():,}\")\n",
-    "print(f\"\")\n",
+    "print(\"\")\n",
     "print(f\"Price: median £{clean['price'].median():,.0f}, mean £{clean['price'].mean():,.0f}\")\n",
     "print(f\"Bedrooms: median {clean['bedrooms'].median():.0f}, mean {clean['bedrooms'].mean():.1f}\")\n",
-    "print(f\"\")\n",
+    "print(\"\")\n",
     "print(f\"Tenure known: {(len(clean) - clean['tenure'].null_count())/len(clean)*100:.1f}%\")\n",
     "print(f\"  Freehold: {len(clean.filter(pl.col('tenure') == 'Freehold')):,}\")\n",
     "print(f\"  Leasehold: {len(clean.filter(pl.col('tenure') == 'Leasehold')):,}\")\n",
-    "print(f\"\")\n",
+    "print(\"\")\n",
     "print(f\"Floorspace available: {clean['floorspace_sqm'].drop_nulls().len()/len(clean)*100:.1f}%\")\n",
-    "print(f\"\")\n",
-    "print(f\"Property types:\")\n",
+    "print(\"\")\n",
+    "print(\"Property types:\")\n",
     "for row in clean['property_type'].value_counts().sort('count', descending=True).iter_rows():\n",
     "    print(f\"  {row[0]}: {row[1]:,} ({row[1]/len(clean)*100:.1f}%)\")"
    ]