lmiranda
92763a17c4
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Fixes Pylance type error - create_engine() expects str, not str | None. Using direct access raises KeyError if not set, which is correct behavior. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
124 lines
3.9 KiB
Plaintext
124 lines
3.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Amenity Radar Chart\n",
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"\n",
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"Spider/radar chart comparing amenity categories for selected neighbourhoods."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Data Reference\n",
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"\n",
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"### Source Tables\n",
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"\n",
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"| Table | Grain | Key Columns |\n",
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"|-------|-------|-------------|\n",
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"| `mart_neighbourhood_amenities` | neighbourhood × year | parks_index, schools_index, transit_index |\n",
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"\n",
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"### SQL Query"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "import pandas as pd\nfrom sqlalchemy import create_engine\nfrom dotenv import load_dotenv\nimport os\n\n# Load .env from project root\nload_dotenv('../../.env')\n\nengine = create_engine(os.environ['DATABASE_URL'])\n\nquery = \"\"\"\nSELECT\n neighbourhood_name,\n parks_index,\n schools_index,\n transit_index,\n amenity_index,\n amenity_tier\nFROM public_marts.mart_neighbourhood_amenities\nWHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\nORDER BY amenity_index DESC\n\"\"\"\n\ndf = pd.read_sql(query, engine)\nprint(f\"Loaded {len(df)} neighbourhoods\")"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Transformation Steps\n",
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"\n",
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"1. Select top 5 and bottom 5 neighbourhoods by amenity index\n",
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"2. Reshape for radar chart format"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Select representative neighbourhoods\n",
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"top_5 = df.head(5)\n",
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"bottom_5 = df.tail(5)\n",
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"\n",
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"# Prepare radar data\n",
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"categories = ['Parks', 'Schools', 'Transit']\n",
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"index_columns = ['parks_index', 'schools_index', 'transit_index']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Sample Output"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Top 5 Amenity-Rich Neighbourhoods:\")\n",
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"display(top_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])\n",
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"print(\"\\nBottom 5 Underserved Neighbourhoods:\")\n",
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"display(bottom_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Data Visualization\n",
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"\n",
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"### Figure Factory\n",
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"\n",
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"Uses `create_radar` from `portfolio_app.figures.radar`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "import sys\nsys.path.insert(0, '../..')\n\nfrom portfolio_app.figures.radar import create_comparison_radar\n\n# Compare top neighbourhood vs city average (100)\ntop_hood = top_5.iloc[0]\nmetrics = ['parks_index', 'schools_index', 'transit_index']\n\nfig = create_comparison_radar(\n selected_data=top_hood.to_dict(),\n average_data={'parks_index': 100, 'schools_index': 100, 'transit_index': 100},\n metrics=metrics,\n selected_name=top_hood['neighbourhood_name'],\n average_name='City Average',\n title=f\"Amenity Profile: {top_hood['neighbourhood_name']} vs City Average\",\n)\n\nfig.show()"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Index Interpretation\n",
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"\n",
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"| Value | Meaning |\n",
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"|-------|--------|\n",
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"| < 100 | Below city average |\n",
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"| = 100 | City average |\n",
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"| > 100 | Above city average |"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.11.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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