| Name | Description |
|---|---|
| Overture Explorer | Inspect and explore Overture data and schema |
| Overture Documentation | Learn how to access and work with Overture data and schema |
Install uv, a fast Python environment manager:
# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows: see https://docs.astral.sh/uv/getting-started/installation/
Clone the repo and start JupyterLab:
git clone https://github.com/OvertureMaps/workshop.git
cd workshop
uv sync
uv run jupyter lab
uv sync installs all dependencies into a project-local .venv/ based on the locked versions in uv.lock. First run takes a minute; subsequent runs are instant. You don’t need to activate the venv manually — uv run handles it.
If you prefer pip and already have a Python environment:
git clone https://github.com/OvertureMaps/workshop.git
cd workshop
pip install -r requirements.txt
jupyter lab
The requirements.txt is generated from pyproject.toml and uv.lock, so versions match the uv setup.
Note: GitHub Codespaces support is being updated for the new setup. For now, please use one of the local setup paths above.
When launching DuckDB, specify a database name like duckdb workshop.dbb so you can save tables and views that persist across sessions.
To attach Overture’s hosted DuckDB database (experimental):
LOAD spatial;
ATTACH 'https://labs.overturemaps.org/data/latest.ddb' as overture;
-- Now you can just reference `overture.place` for type=place features
SELECT count(1) from overture.place;