personal-portfolio/notebooks/toronto/housing/rent_trend_line.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Rent Trend Line Chart\n",
    "\n",
    "Shows 5-year rental price trends across Toronto 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 | year, avg_rent_2bed, rent_yoy_change_pct |\n",
    "\n",
    "### SQL Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "# City-wide average rent by year\n",
    "query = \"\"\"\n",
    "SELECT\n",
    "    year,\n",
    "    AVG(avg_rent_bachelor) as avg_rent_bachelor,\n",
    "    AVG(avg_rent_1bed) as avg_rent_1bed,\n",
    "    AVG(avg_rent_2bed) as avg_rent_2bed,\n",
    "    AVG(avg_rent_3bed) as avg_rent_3bed,\n",
    "    AVG(rent_yoy_change_pct) as avg_yoy_change\n",
    "FROM public_marts.mart_neighbourhood_housing\n",
    "WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.mart_neighbourhood_housing)\n",
    "GROUP BY year\n",
    "ORDER BY year\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(query, engine)\n",
    "print(f\"Loaded {len(df)} years of rent data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Steps\n",
    "\n",
    "1. Aggregate rent by year (city-wide average)\n",
    "2. Convert year to datetime for proper x-axis\n",
    "3. Reshape for multi-line chart by bedroom type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create date column from year\n",
    "df[\"date\"] = pd.to_datetime(df[\"year\"].astype(str) + \"-01-01\")\n",
    "\n",
    "# Melt for multi-line chart\n",
    "df_melted = df.melt(\n",
    "    id_vars=[\"year\", \"date\"],\n",
    "    value_vars=[\"avg_rent_bachelor\", \"avg_rent_1bed\", \"avg_rent_2bed\", \"avg_rent_3bed\"],\n",
    "    var_name=\"bedroom_type\",\n",
    "    value_name=\"avg_rent\",\n",
    ")\n",
    "\n",
    "# Clean labels\n",
    "df_melted[\"bedroom_type\"] = df_melted[\"bedroom_type\"].map(\n",
    "    {\n",
    "        \"avg_rent_bachelor\": \"Bachelor\",\n",
    "        \"avg_rent_1bed\": \"1 Bedroom\",\n",
    "        \"avg_rent_2bed\": \"2 Bedroom\",\n",
    "        \"avg_rent_3bed\": \"3 Bedroom\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\n",
    "    [\n",
    "        \"year\",\n",
    "        \"avg_rent_bachelor\",\n",
    "        \"avg_rent_1bed\",\n",
    "        \"avg_rent_2bed\",\n",
    "        \"avg_rent_3bed\",\n",
    "        \"avg_yoy_change\",\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Visualization\n",
    "\n",
    "### Figure Factory\n",
    "\n",
    "Uses `create_price_time_series` from `portfolio_app.figures.toronto.time_series`.\n",
    "\n",
    "**Key Parameters:**\n",
    "- `date_column`: 'date'\n",
    "- `price_column`: 'avg_rent'\n",
    "- `group_column`: 'bedroom_type' (for multi-line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.insert(0, \"../..\")\n",
    "\n",
    "from portfolio_app.figures.toronto.time_series import create_price_time_series\n",
    "\n",
    "data = df_melted.to_dict(\"records\")\n",
    "\n",
    "fig = create_price_time_series(\n",
    "    data=data,\n",
    "    date_column=\"date\",\n",
    "    price_column=\"avg_rent\",\n",
    "    group_column=\"bedroom_type\",\n",
    "    title=\"Toronto Average Rent Trend (5 Years)\",\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### YoY Change Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show year-over-year changes\n",
    "print(\"Year-over-Year Rent Change (%)\")\n",
    "df[[\"year\", \"avg_yoy_change\"]].dropna()"
   ]
  }
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