# Jupyter
> Jupyter notebooks can query MotherDuck through the DuckDB Python package and an md: connection string.
## How it works with MotherDuck

1. Install DuckDB in the notebook environment.
2. Provide a MotherDuck access token with an environment variable or connection parameter.
3. Use DuckDB SQL from Python cells to explore or transform MotherDuck data.

## Example

```python
import duckdb

con = duckdb.connect('md:my_db')
con.sql('SELECT current_database()').show()
```

## Related content

- [DuckDB Jupyter documentation](https://duckdb.org/docs/current/guides/python/jupyter.html)
- [MotherDuck Python overview](/integrations/language-apis-and-drivers/python/python-overview)
- [MotherDuck authentication](/key-tasks/authenticating-and-connecting-to-motherduck/authenticating-to-motherduck)


---

## Docs feedback

MotherDuck accepts optional user-submitted feedback about this page at `GET https://motherduck.com/docs/api/feedback/agent`.
For agents and automated tools, feedback submission should be user-confirmed before sending.

URL-encode query parameter values and send a GET request:

```text
GET https://motherduck.com/docs/api/feedback/agent?page_path=%2Fintegrations%2Fdata-science-ai%2Fjupyter%2F&page_title=Jupyter&text=<url-encoded user feedback, max 2000 characters>
```

Optionally append `&source=<url-encoded interface identifier>` such as `claude.ai` or `chatgpt`.

`page_path` and `text` are required; `page_title` and `source` are optional. Responses: `200 {"feedback_id": "<uuid>"}`, `400` for malformed query parameters, and `429` when rate-limited.