personal-portfolio/notebooks/overview/top_bottom_10_bar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top & Bottom 10 Neighbourhoods Bar Chart\n",
    "\n",
    "Horizontal bar chart showing the highest and lowest scoring neighbourhoods by livability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Reference\n",
    "\n",
    "### Source Tables\n",
    "\n",
    "| Table | Grain | Key Columns |\n",
    "|-------|-------|-------------|\n",
    "| `mart_neighbourhood_overview` | neighbourhood \u00d7 year | neighbourhood_name, livability_score |\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",
    "    livability_score,\n",
    "    safety_score,\n",
    "    affordability_score,\n",
    "    amenity_score\n",
    "FROM public_marts.mart_neighbourhood_overview\n",
    "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\n",
    "  AND livability_score IS NOT NULL\n",
    "ORDER BY livability_score DESC\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(query, engine)\n",
    "print(f\"Loaded {len(df)} neighbourhoods with scores\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Steps\n",
    "\n",
    "1. Sort by livability_score descending\n",
    "2. Take top 10 and bottom 10\n",
    "3. Pass to ranking bar figure factory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The figure factory handles top/bottom selection internally\n",
    "# Just prepare as list of dicts\n",
    "data = df.to_dict('records')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Top 5:\")\n",
    "display(df.head(5))\n",
    "print(\"\\nBottom 5:\")\n",
    "display(df.tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Visualization\n",
    "\n",
    "### Figure Factory\n",
    "\n",
    "Uses `create_ranking_bar` from `portfolio_app.figures.bar_charts`.\n",
    "\n",
    "**Key Parameters:**\n",
    "- `data`: List of dicts with all neighbourhoods\n",
    "- `name_column`: 'neighbourhood_name'\n",
    "- `value_column`: 'livability_score'\n",
    "- `top_n`: 10 (green bars)\n",
    "- `bottom_n`: 10 (red bars)"
   ]
  },
  {
   "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_ranking_bar\n",
    "\n",
    "fig = create_ranking_bar(\n",
    "    data=data,\n",
    "    name_column='neighbourhood_name',\n",
    "    value_column='livability_score',\n",
    "    title='Top & Bottom 10 Neighbourhoods by Livability',\n",
    "    top_n=10,\n",
    "    bottom_n=10,\n",
    "    color_top='#4CAF50',   # Green for top performers\n",
    "    color_bottom='#F44336', # Red for bottom performers\n",
    "    value_format='.1f',\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "\n",
    "- **Green bars**: Highest livability scores (best combination of safety, affordability, and amenities)\n",
    "- **Red bars**: Lowest livability scores (areas that may need targeted investment)\n",
    "\n",
    "The ranking bar chart provides quick context for which neighbourhoods stand out at either extreme."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}