docs: Complete Phase 6 notebooks and Phase 7 documentation review

Phase 6 - Jupyter Notebooks (15 total):
- Overview tab: livability_choropleth, top_bottom_10_bar, income_safety_scatter
- Housing tab: affordability_choropleth, rent_trend_line, tenure_breakdown_bar
- Safety tab: crime_rate_choropleth, crime_breakdown_bar, crime_trend_line
- Demographics tab: income_choropleth, age_distribution, population_density_bar
- Amenities tab: amenity_index_choropleth, amenity_radar, transit_accessibility_bar

Phase 7 - Documentation:
- Updated CLAUDE.md with Sprint 9 completion status
- Added notebooks directory to application structure
- Expanded figures directory listing

Closes #71, #72, #73, #74, #75, #76, #77

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

notebooks/housing/tenure_breakdown_bar.ipynb

@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Housing Tenure Breakdown Bar Chart\n",
+    "\n",
+    "Shows the distribution of owner-occupied vs renter-occupied dwellings across neighbourhoods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Data Reference\n",
+    "\n",
+    "### Source Tables\n",
+    "\n",
+    "| Table | Grain | Key Columns |\n",
+    "|-------|-------|-------------|\n",
+    "| `mart_neighbourhood_housing` | neighbourhood × year | pct_owner_occupied, pct_renter_occupied, income_quintile |\n",
+    "\n",
+    "### SQL Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine\n",
+    "import os\n",
+    "\n",
+    "engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
+    "\n",
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "    neighbourhood_name,\n",
+    "    pct_owner_occupied,\n",
+    "    pct_renter_occupied,\n",
+    "    income_quintile,\n",
+    "    total_rental_units,\n",
+    "    average_dwelling_value\n",
+    "FROM mart_neighbourhood_housing\n",
+    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_housing)\n",
+    "  AND pct_owner_occupied IS NOT NULL\n",
+    "ORDER BY pct_renter_occupied DESC\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query, engine)\n",
+    "print(f\"Loaded {len(df)} neighbourhoods with tenure data\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transformation Steps\n",
+    "\n",
+    "1. Filter to most recent year with tenure data\n",
+    "2. Melt owner/renter columns for stacked bar\n",
+    "3. Sort by renter percentage (highest first)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare for stacked bar\n",
+    "df_stacked = df.melt(\n",
+    "    id_vars=['neighbourhood_name', 'income_quintile'],\n",
+    "    value_vars=['pct_owner_occupied', 'pct_renter_occupied'],\n",
+    "    var_name='tenure_type',\n",
+    "    value_name='percentage'\n",
+    ")\n",
+    "\n",
+    "df_stacked['tenure_type'] = df_stacked['tenure_type'].map({\n",
+    "    'pct_owner_occupied': 'Owner',\n",
+    "    'pct_renter_occupied': 'Renter'\n",
+    "})\n",
+    "\n",
+    "data = df_stacked.to_dict('records')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Highest Renter Neighbourhoods:\")\n",
+    "df[['neighbourhood_name', 'pct_renter_occupied', 'pct_owner_occupied', 'income_quintile']].head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data Visualization\n",
+    "\n",
+    "### Figure Factory\n",
+    "\n",
+    "Uses `create_stacked_bar` from `portfolio_app.figures.bar_charts`.\n",
+    "\n",
+    "**Key Parameters:**\n",
+    "- `x_column`: 'neighbourhood_name'\n",
+    "- `value_column`: 'percentage'\n",
+    "- `category_column`: 'tenure_type'\n",
+    "- `show_percentages`: True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, '../..')\n",
+    "\n",
+    "from portfolio_app.figures.bar_charts import create_stacked_bar\n",
+    "\n",
+    "# Show top 20 by renter percentage\n",
+    "top_20_names = df.head(20)['neighbourhood_name'].tolist()\n",
+    "data_filtered = [d for d in data if d['neighbourhood_name'] in top_20_names]\n",
+    "\n",
+    "fig = create_stacked_bar(\n",
+    "    data=data_filtered,\n",
+    "    x_column='neighbourhood_name',\n",
+    "    value_column='percentage',\n",
+    "    category_column='tenure_type',\n",
+    "    title='Housing Tenure Mix - Top 20 Renter Neighbourhoods',\n",
+    "    color_map={'Owner': '#4CAF50', 'Renter': '#2196F3'},\n",
+    "    show_percentages=True,\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### City-Wide Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# City-wide averages\n",
+    "print(f\"City Average Owner-Occupied: {df['pct_owner_occupied'].mean():.1f}%\")\n",
+    "print(f\"City Average Renter-Occupied: {df['pct_renter_occupied'].mean():.1f}%\")\n",
+    "\n",
+    "# By income quintile\n",
+    "print(\"\\nTenure by Income Quintile:\")\n",
+    "df.groupby('income_quintile')[['pct_owner_occupied', 'pct_renter_occupied']].mean().round(1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}