marimo

Yes, you can build interactive dashboards directly within reactive Python notebooks using built-in UI elements like sliders. These elements update visualizations in real-time without requiring explicit callback functions, streamlining the data exploration process.

You can execute SQL queries and manipulate Polars DataFrames seamlessly within reactive Python notebooks. This integrated data handling allows rapid querying and transformation, with results automatically updating across connected notebook cells during exploration.

You can version control Python notebooks by saving them as pure `.py` files. This plain text format eliminates JSON overhead, making reactive notebooks fully diffable and version-control friendly for collaborative reproducible research.

marimo supports deploying reactive Python notebooks as interactive web apps, runnable scripts, or static HTML/WASM exports. This allows you to transition data analysis from local exploration to shareable deployment without modifying core logic.

The best way to handle notebook state management is using reactive execution, which automatically tracks cell dependencies and re-runs affected code. This eliminates hidden state issues common in traditional notebooks, ensuring consistent data analysis.