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62d1a52eed
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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>
71 lines
2.1 KiB
Markdown
71 lines
2.1 KiB
Markdown
# Dashboard Documentation Notebooks
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Documentation notebooks organized by dashboard project. Each notebook documents how data is queried, transformed, and visualized using the figure factory pattern.
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## Directory Structure
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```
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notebooks/
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├── README.md # This file
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└── toronto/ # Toronto Neighbourhood Dashboard
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├── overview/ # Overview tab visualizations
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├── housing/ # Housing tab visualizations
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├── safety/ # Safety tab visualizations
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├── demographics/ # Demographics tab visualizations
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└── amenities/ # Amenities tab visualizations
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```
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## Notebook Template
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Each notebook follows a standard two-section structure:
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### Section 1: Data Reference
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Documents the data pipeline:
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- **Source Tables**: List of dbt marts/tables used
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- **SQL Query**: The exact query to fetch data
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- **Transformation Steps**: Any pandas/python transformations
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- **Sample Output**: First 10 rows of the result
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### Section 2: Data Visualization
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Documents the figure creation:
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- **Figure Factory**: Import from `portfolio_app.figures`
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- **Parameters**: Key configuration options
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- **Rendered Output**: The actual visualization
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## Available Figure Factories
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| Factory | Module | Use Case |
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|---------|--------|----------|
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| `create_choropleth` | `figures.choropleth` | Map visualizations |
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| `create_ranking_bar` | `figures.bar_charts` | Top/bottom N rankings |
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| `create_stacked_bar` | `figures.bar_charts` | Category breakdowns |
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| `create_scatter` | `figures.scatter` | Correlation plots |
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| `create_radar` | `figures.radar` | Multi-metric comparisons |
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| `create_age_pyramid` | `figures.demographics` | Age distributions |
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| `create_time_series` | `figures.time_series` | Trend lines |
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## Usage
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1. Start Jupyter from project root:
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```bash
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jupyter notebook notebooks/
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```
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2. Ensure database is running:
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```bash
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make docker-up
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```
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3. Each notebook is self-contained - run all cells top to bottom.
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## Notebook Naming Convention
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`{metric}_{chart_type}.ipynb`
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Examples:
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- `livability_choropleth.ipynb`
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- `crime_trend_line.ipynb`
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- `age_pyramid.ipynb`
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