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lmiranda 92763a17c4

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Details

fix: Use os.environ[] instead of .get() for DATABASE_URL

Fixes Pylance type error - create_engine() expects str, not str | None.
Using direct access raises KeyError if not set, which is correct behavior.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

2026-01-18 23:03:23 -05:00

amenities

fix: Use os.environ[] instead of .get() for DATABASE_URL

2026-01-18 23:03:23 -05:00

demographics

fix: Use os.environ[] instead of .get() for DATABASE_URL

2026-01-18 23:03:23 -05:00

housing

fix: Use os.environ[] instead of .get() for DATABASE_URL

2026-01-18 23:03:23 -05:00

overview

fix: Use os.environ[] instead of .get() for DATABASE_URL

2026-01-18 23:03:23 -05:00

safety

fix: Use os.environ[] instead of .get() for DATABASE_URL

2026-01-18 23:03:23 -05:00

.gitkeep

feat: project bootstrap and structure

2026-01-11 13:49:28 -05:00

README.md

docs: Complete Phase 6 notebooks and Phase 7 documentation review

2026-01-17 12:10:46 -05:00

README.md

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

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.ipynb
crime_trend_line.ipynb
age_pyramid.ipynb