# AI use cases for Marimo (marimo-team/marimo)

Repo: [marimo-team/marimo](https://github.com/marimo-team/marimo) | Use cases: 3 | Page: https://dedupedai.com/use-cases/marimo-team-marimo/

marimo is a reactive Python notebook: run a cell or move a UI element and marimo automatically runs the cells that depend on it, so code, outputs and state stay consistent with no hidden state. Notebooks are stored as plain .py files, run as scripts, deploy as web apps or slides, and support SQL against dataframes and databases, interactive dataframes, built-in package management, AI assistants and conversion from Jupyter.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Switch from Jupyter to a reactive, git-friendly notebook: https://dedupedai.com/use-cases/marimo-team-marimo/replace-jupyter-with-a-reactive-notebook/
- Turn an analysis notebook into an interactive web app: https://dedupedai.com/use-cases/marimo-team-marimo/turn-a-notebook-into-a-shareable-web-app/
- Query dataframes and databases with SQL in Python notebooks: https://dedupedai.com/use-cases/marimo-team-marimo/query-dataframes-with-sql-in-a-notebook/

## How to switch from Jupyter to a reactive, git-friendly notebook with Marimo

Repo: [marimo-team/marimo](https://github.com/marimo-team/marimo) | Difficulty: Beginner | Time: 30 minutes | Basis: From the README

Convert existing Jupyter notebooks to Marimo so they rerun dependent cells automatically and diff cleanly in git.

**Who it is for:** A data scientist tired of out-of-order cells and unreadable notebook diffs.

**The problem:** Jupyter notebooks hide state, break when cells run out of order and store as noisy JSON.

**The outcome:** Notebooks saved as pure Python that stay consistent and review well in pull requests.

### Steps

1. Install with `pip install marimo` and try the tutorial with `marimo tutorial intro`
2. Convert a notebook with `marimo convert your_notebook.ipynb > your_notebook.py`
3. Open it with Marimo edit and fix any cells that redefine the same variable
4. Commit the .py file and review changes like normal code
5. Run it top to bottom as a script with `python your_notebook.py`

### Prompt to paste into your agent

```text
Using marimo-team/marimo, convert <notebook.ipynb> with marimo convert, fix any multiple-definition errors so it runs reactively, and confirm `python your_notebook.py` executes it end to end.
```

### Caveat

This card is written from the marimo-team/marimo README, not from a test run by Deduped. Before you start, make sure you have Python and pip or conda. Check the repo for newer instructions before you rely on these steps.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/marimo-team-marimo/replace-jupyter-with-a-reactive-notebook/

## How to turn an analysis notebook into an interactive web app with Marimo

Repo: [marimo-team/marimo](https://github.com/marimo-team/marimo) | Difficulty: Beginner | Time: 45 minutes | Basis: From the README

Add sliders and dropdowns to a Marimo notebook and serve it as an app with the code hidden.

**Who it is for:** An analyst who wants teammates to explore results without touching Python.

**The problem:** Sharing notebook results usually means static screenshots or rebuilding the analysis in Streamlit.

**The outcome:** A running web app where UI elements drive the analysis and the code stays hidden and uneditable.

### Steps

1. Install with `pip install marimo`
2. Create or edit a notebook with Marimo edit and bind sliders, dropdowns or tables to Python values; dependent cells rerun automatically
3. Run it as an app with `marimo run your_notebook.py`
4. Use the slides layout if you want to present it
5. Share a cloud copy through molab when teammates cannot run it locally

### Prompt to paste into your agent

```text
With marimo-team/marimo, add a slider and a dropdown to <notebook.py> that filter <dataset> and update the chart, then serve it with `marimo run your_notebook.py` and give me the local URL.
```

### Caveat

This card is written from the marimo-team/marimo README, not from a test run by Deduped. Before you start, make sure you have Python and pip or conda. Check the repo for newer instructions before you rely on these steps.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/marimo-team-marimo/turn-a-notebook-into-a-shareable-web-app/

## How to query dataframes and databases with SQL in Python notebooks with Marimo

Repo: [marimo-team/marimo](https://github.com/marimo-team/marimo) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Mix SQL cells that depend on Python values with Python cells, and page through results in interactive dataframes.

**Who it is for:** An analyst who thinks in SQL but needs Python for the rest of the analysis.

**The problem:** Switching between a SQL client and a notebook breaks the flow and loses reproducibility.

**The outcome:** A notebook where SQL results come back as Python dataframes and the whole thing is still a .py file.

### Steps

1. Install the extras that unlock SQL cells with `pip install "marimo[recommended]"`
2. Open a notebook with Marimo edit and add a SQL cell that references a Python variable
3. Query dataframes, databases, CSVs or warehouses with the built-in SQL engine
4. Page through, search, filter and sort the result in the interactive dataframe viewer
5. Run pytest on the notebook if you want tests around the analysis

### Prompt to paste into your agent

```text
Using marimo-team/marimo, create a notebook that loads <CSV or database>, adds a SQL cell filtered by a Python dropdown value, and shows the result in an interactive dataframe.
```

### Caveat

This card is written from the marimo-team/marimo README, not from a test run by Deduped. Before you start, make sure you have Python and pip or conda. Check the repo for newer instructions before you rely on these steps.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/marimo-team-marimo/query-dataframes-with-sql-in-a-notebook/