personal-portfolio/notebooks/demographics/population_density_bar.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Population Density Bar Chart\n",
    "\n",
    "Shows population density (people per sq km) 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_demographics` | neighbourhood × year | population_density, population, land_area_sqkm |\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",
    "    population_density,\n",
    "    population,\n",
    "    land_area_sqkm,\n",
    "    median_household_income,\n",
    "    pct_renter_occupied\n",
    "FROM mart_neighbourhood_demographics\n",
    "WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_demographics)\n",
    "  AND population_density IS NOT NULL\n",
    "ORDER BY population_density DESC\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(query, engine)\n",
    "print(f\"Loaded {len(df)} neighbourhoods\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Steps\n",
    "\n",
    "1. Sort by population density\n",
    "2. Select top 20 most dense neighbourhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.head(20).to_dict('records')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['neighbourhood_name', 'population_density', 'population', 'land_area_sqkm']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Visualization\n",
    "\n",
    "### Figure Factory\n",
    "\n",
    "Uses `create_horizontal_bar` from `portfolio_app.figures.bar_charts`."
   ]
  },
  {
   "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_horizontal_bar\n",
    "\n",
    "fig = create_horizontal_bar(\n",
    "    data=data,\n",
    "    name_column='neighbourhood_name',\n",
    "    value_column='population_density',\n",
    "    title='Top 20 Most Dense Neighbourhoods',\n",
    "    color='#9C27B0',\n",
    "    value_format=',.0f',\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Density Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"City-wide Statistics:\")\n",
    "print(f\"  Total Population: {df['population'].sum():,.0f}\")\n",
    "print(f\"  Total Area: {df['land_area_sqkm'].sum():,.1f} sq km\")\n",
    "print(f\"  Average Density: {df['population_density'].mean():,.0f} per sq km\")\n",
    "print(f\"  Max Density: {df['population_density'].max():,.0f} per sq km\")\n",
    "print(f\"  Min Density: {df['population_density'].min():,.0f} per sq km\")"
   ]
  }
 ],
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