Explore your data with marimo#
marimo is a reactive Python notebook. It completely revamps the Jupyter notebook experience. Whenever code is executed or you interact with a UI element, dependent cells are re-executed ensuring consistency between code and displayed outputs.
This page shows how dlt + marimo + ibis provide a rich environment to explore loaded data, write data transformations, and create data applications.
Prerequisites#
Install the required dependencies:
pip install marimo ibis-framework mowidgets duckdb
Launch marimo#
Use this command to launch marimo (replace my_notebook.py with desired name). It will print a link to access the notebook web app.
marimo edit my_notebook.py
> Edit my_notebook.py in your browser 📝
> ➜ URL: http://localhost:2718?access_token=Qfo_Hj2RbXqiqM4VT3XOwA
Here's a screenshot of the interface you should see:
To run an existing local marimo notebook:
marimo run my_notebook.py
Features#
Use custom dlt widgets#
Inside your marimo notebook, you can use composable widgets built and maintained by the dlt team. This requires the mowidgets package (Python 3.11+).
Import them from dlt.helpers.marimo and pass them to the render() function:
#%% cell 1
from dlt.helpers.marimo import render, load_package_viewer, pipeline_selector
#%% cell 2
render(pipeline_selector)
#%% cell 3
render(load_package_viewer, pipeline_path="/path/to/pipeline")
Available widgets: pipeline_selector, load_package_viewer, schema_viewer.
View dataset tables and columns#
After loading data with dlt, you can access it via the dataset interface, including a native ibis connection.
In marimo, the Datasources panel provides a GUI to explore data tables and columns. When a cell contains a variable that's an ibis connection, it is automatically registered.
Accessing data with SQL#
Clicking on the Add table to notebook button will create a new SQL cell that you can use to query data. The output cell provides a rich and interactive results dataframe.
Accessing data with Python#
You can also retrieve Ibis tables (lazy expressions) using Python. The Datasources panel will show under Python the output schema of your Ibis query, and the cell output will display detailed query planning.
Use .execute(), .to_pandas(), .to_polars(), or .to_pyarrow() to execute the Ibis expression and retrieve data that can displayed in a rich and interactive dataframe.
Create a dashboard and data apps#
Marimo notebooks can also be deployed as web applications with interactive UI and charts and the code hidden. Try adding marimo UI input elements, rich markdown, and charts (matplotlib, plotly, altair, etc.). Combined, dlt + marimo + ibis make it easy to build a simple dashboard on top of fresh data.