personal-portfolio/notebooks/housing/tenure_breakdown_bar.ipynb

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   "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 \u00d7 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",
    "from dotenv import load_dotenv\n",
    "import os\n",
    "\n",
    "# Load .env from project root\n",
    "load_dotenv('../../.env')\n",
    "\n",
    "engine = create_engine(os.environ['DATABASE_URL'])\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 public_marts.mart_neighbourhood_housing\n",
    "WHERE year = (SELECT MAX(year) FROM public_marts.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)"
   ]
  }
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