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- Rename dbt project from toronto_housing to portfolio - Restructure dbt models into domain subdirectories: - shared/ for cross-domain dimensions (dim_time) - staging/toronto/, intermediate/toronto/, marts/toronto/ - Update SQLAlchemy models for raw_toronto schema - Add explicit cross-schema FK relationships for FactRentals - Namespace figure factories under figures/toronto/ - Namespace notebooks under notebooks/toronto/ - Update Makefile with domain-specific targets and env loading - Update all documentation for multi-dashboard structure This enables adding new dashboard projects (e.g., /football, /energy) without structural conflicts or naming collisions. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Dashboard Documentation Notebooks
Documentation notebooks organized by dashboard project. Each notebook documents how data is queried, transformed, and visualized using the figure factory pattern.
Directory Structure
notebooks/
├── README.md # This file
└── toronto/ # Toronto Neighbourhood Dashboard
├── 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
-
Start Jupyter from project root:
jupyter notebook notebooks/ -
Ensure database is running:
make docker-up -
Each notebook is self-contained - run all cells top to bottom.
Notebook Naming Convention
{metric}_{chart_type}.ipynb
Examples:
livability_choropleth.ipynbcrime_trend_line.ipynbage_pyramid.ipynb