personal-portfolio/notebooks

Toronto Neighbourhood Dashboard - Notebooks

Documentation notebooks for the Toronto Neighbourhood Dashboard visualizations. Each notebook documents how data is queried, transformed, and visualized using the figure factory pattern.

Directory Structure

notebooks/
├── README.md              # This file
├── overview/              # Overview tab visualizations
├── housing/               # Housing tab visualizations
├── safety/                # Safety tab visualizations
├── demographics/          # Demographics tab visualizations
└── amenities/             # Amenities tab visualizations

Notebook Template

Each notebook follows a standard two-section structure:

Section 1: Data Reference

Documents the data pipeline:

• Source Tables: List of dbt marts/tables used
• SQL Query: The exact query to fetch data
• Transformation Steps: Any pandas/python transformations
• Sample Output: First 10 rows of the result

Section 2: Data Visualization

Documents the figure creation:

• Figure Factory: Import from portfolio_app.figures
• Parameters: Key configuration options
• Rendered Output: The actual visualization

Available Figure Factories

Factory	Module	Use Case
create_choropleth	figures.choropleth	Map visualizations
create_ranking_bar	figures.bar_charts	Top/bottom N rankings
create_stacked_bar	figures.bar_charts	Category breakdowns
create_scatter	figures.scatter	Correlation plots
create_radar	figures.radar	Multi-metric comparisons
create_age_pyramid	figures.demographics	Age distributions
create_time_series	figures.time_series	Trend lines

Usage

1. Start Jupyter from project root:

   jupyter notebook notebooks/
   

2. Ensure database is running:

   make docker-up
   

3. Each notebook is self-contained - run all cells top to bottom.

Notebook Naming Convention

{metric}_{chart_type}.ipynb

Examples:

• livability_choropleth.ipynb
• crime_trend_line.ipynb
• age_pyramid.ipynb