docs: Complete Phase 6 notebooks and Phase 7 documentation review
Phase 6 - Jupyter Notebooks (15 total): - Overview tab: livability_choropleth, top_bottom_10_bar, income_safety_scatter - Housing tab: affordability_choropleth, rent_trend_line, tenure_breakdown_bar - Safety tab: crime_rate_choropleth, crime_breakdown_bar, crime_trend_line - Demographics tab: income_choropleth, age_distribution, population_density_bar - Amenities tab: amenity_index_choropleth, amenity_radar, transit_accessibility_bar Phase 7 - Documentation: - Updated CLAUDE.md with Sprint 9 completion status - Added notebooks directory to application structure - Expanded figures directory listing Closes #71, #72, #73, #74, #75, #76, #77 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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notebooks/demographics/age_distribution.ipynb
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notebooks/demographics/age_distribution.ipynb
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{
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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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"# Age Distribution Analysis\n",
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"\n",
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"Compares median age and age index across Toronto 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_demographics` | neighbourhood × year | median_age, age_index, city_avg_age |\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": [
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"import pandas as pd\n",
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"from sqlalchemy import create_engine\n",
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"import os\n",
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"\n",
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"engine = create_engine(os.environ.get('DATABASE_URL', 'postgresql://portfolio:portfolio@localhost:5432/portfolio'))\n",
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"\n",
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"query = \"\"\"\n",
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"SELECT\n",
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" neighbourhood_name,\n",
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" median_age,\n",
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" age_index,\n",
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" city_avg_age,\n",
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" population,\n",
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" income_quintile,\n",
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" pct_renter_occupied\n",
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"FROM mart_neighbourhood_demographics\n",
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"WHERE year = (SELECT MAX(year) FROM mart_neighbourhood_demographics)\n",
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" AND median_age IS NOT NULL\n",
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"ORDER BY median_age DESC\n",
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"\"\"\"\n",
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"\n",
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"df = pd.read_sql(query, engine)\n",
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"print(f\"Loaded {len(df)} neighbourhoods with age data\")"
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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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"### Transformation Steps\n",
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"\n",
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"1. Filter to most recent census year\n",
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"2. Calculate deviation from city average\n",
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"3. Classify as younger/older than average"
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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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"city_avg = df['city_avg_age'].iloc[0]\n",
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"df['age_category'] = df['median_age'].apply(\n",
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" lambda x: 'Younger' if x < city_avg else 'Older'\n",
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")\n",
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"df['age_deviation'] = df['median_age'] - city_avg\n",
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"\n",
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"data = df.to_dict('records')"
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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(f\"City Average Age: {city_avg:.1f}\")\n",
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"print(\"\\nYoungest Neighbourhoods:\")\n",
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"display(df.tail(5)[['neighbourhood_name', 'median_age', 'age_index', 'pct_renter_occupied']])\n",
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"print(\"\\nOldest Neighbourhoods:\")\n",
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"display(df.head(5)[['neighbourhood_name', 'median_age', 'age_index', 'pct_renter_occupied']])"
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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_ranking_bar` from `portfolio_app.figures.bar_charts`."
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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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"import sys\n",
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"sys.path.insert(0, '../..')\n",
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"\n",
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"from portfolio_app.figures.bar_charts import create_ranking_bar\n",
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"\n",
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"fig = create_ranking_bar(\n",
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" data=data,\n",
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" name_column='neighbourhood_name',\n",
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" value_column='median_age',\n",
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" title='Youngest & Oldest Neighbourhoods (Median Age)',\n",
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" top_n=10,\n",
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" bottom_n=10,\n",
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" color_top='#FF9800', # Orange for older\n",
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" color_bottom='#2196F3', # Blue for younger\n",
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" value_format='.1f',\n",
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")\n",
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"\n",
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"fig.show()"
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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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"### Age vs Income Correlation"
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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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"# Age by income quintile\n",
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"print(\"Median Age by Income Quintile:\")\n",
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"df.groupby('income_quintile')['median_age'].mean().round(1)"
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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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