lmiranda
1eba95d4d1
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>
70 lines
2.1 KiB
Markdown
70 lines
2.1 KiB
Markdown
# Toronto Neighbourhood Dashboard - Notebooks
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Documentation notebooks for the Toronto Neighbourhood Dashboard visualizations. 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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├── 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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