personal-portfolio/notebooks/toronto/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 × year | pct_owner_occupied, pct_renter_occupied, income_quintile |\n",
    "\n",
    "### SQL Query"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "from dotenv import load_dotenv\n",
    "from sqlalchemy import create_engine\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",
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   "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\", \"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[\n",
    "    [\n",
    "        \"neighbourhood_name\",\n",
    "        \"pct_renter_occupied\",\n",
    "        \"pct_owner_occupied\",\n",
    "        \"income_quintile\",\n",
    "    ]\n",
    "].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Visualization\n",
    "\n",
    "### Figure Factory\n",
    "\n",
    "Uses `create_stacked_bar` from `portfolio_app.figures.toronto.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",
    "\n",
    "sys.path.insert(0, \"../..\")\n",
    "\n",
    "from portfolio_app.figures.toronto.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\")[\n",
    "    [\"pct_owner_occupied\", \"pct_renter_occupied\"]\n",
    "].mean().round(1)"
   ]
  }
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