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Marimo is an open-source reactive Python notebook: install it in a project environment, load data in a cell, and build analysis, controls, SQL queries, and visualizations that respond to the variables they depend on. Because a Marimo notebook is a Python file, you can also execute it as a script or run it as an interactive app.
What Marimo is—and what makes it different
Marimo describes itself as a reactive Python notebook. Unlike a workflow where you must keep track of cells’ execution order yourself, Marimo analyzes variable definitions and references to build a dependency graph. When an input changes, dependent cells run automatically or are marked stale, depending on the execution mode.
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Notebooks are stored as pure Python files. Marimo also supports interactive UI elements, SQL, package management, script execution, and running notebooks as apps. These are documented capabilities, not guarantees of performance for a particular dataset or environment.
Install Marimo and start a notebook
Use the installation guide for the current installation options and requirements. Install Marimo in the project environment where you intend to work; the exact package manager and dependencies depend on your setup. The installation documentation also describes sandbox options for a self-contained trial.
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- Install Marimo in your chosen Python environment, following the current installation instructions.
- Launch the introductory tutorial using the option shown in the getting-started documentation. This gives you a guided place to learn the notebook interface and reactive workflow.
- Create a notebook for your analysis, then add cells for data loading, transformations, and outputs.
- Save the notebook as a Python file. This keeps the analysis in a source-code format that can also be executed as a script.
Build an analysis with reactive cells
Load data and make dependencies explicit
Start by reading the data in one cell and assigning it to a clearly named variable. In later cells, use that variable to filter, summarize, or visualize the data. Marimo infers the relationship from the names each cell defines and references; you do not need to arrange cells in the order they must execute.
For example, one cell might load a dataframe called sales, another might define a filtered dataframe from sales, and a third might plot the filtered result. If you change the filtering rule and rerun its cell, dependent cells can update from the new value.
Know the limit of automatic tracking
Marimo documents that it does not track mutations to variables or assignments to attributes. If you alter an object in place, do not assume every cell that uses it will rerun. Prefer explicit assignments and transformations that make the data flow visible. For expensive or side-effecting work, consider lazy execution: dependent cells can be marked stale rather than run immediately. The behavior is based on dependency relationships, not simply on where a cell appears in the notebook.
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Explore data with interactive controls
Marimo’s documented interactive features include dataframes and UI elements such as sliders, dropdowns, and file uploads. Put a control’s value into an analysis cell, then use the resulting value in a summary or chart. Because the analysis cell depends on the control value, changing that value can update the dependent result.
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- Load a dataset with a category column and a numeric measure.
- Add a dropdown containing the categories you want readers to inspect.
- In a separate cell, filter the dataframe using the selected category.
- Build a summary or plot from the filtered dataframe.
- Change the dropdown selection and inspect the dependent output.
This pattern is useful for exploring a date range, a category, or another parameter without manually rewriting the filtering expression each time. Marimo documents native controls and broader widget integration; behavior can vary across third-party widgets and Python objects, so do not assume every widget package works identically.
Query data with SQL, then continue in Python
Marimo SQL cells can query Python dataframes and databases such as SQLite or PostgreSQL. The SQL documentation says query results are returned as Python dataframes that later cells can use. The broader feature page also names DuckDB and MySQL among supported backends. SQL support requires additional dependencies; connecting to a database also requires the setup and credentials appropriate for that source.
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A practical workflow is to use SQL for filtering or aggregation near the data source, then pass the resulting dataframe to Python cells for further analysis and visualization. Consult the SQL guide for configuration details. Database performance depends on the data, query, and environment; the documented support does not promise a particular speed or a setup-free connection to every database.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run the notebook as an app or share an export
Serve an app locally
To serve a notebook as an app, run marimo run notebook.py from the environment where Marimo and the notebook’s dependencies are available, replacing notebook.py with your file’s name. The app guide says code is hidden by default in the app view and that layouts can be customized.
This command serves the notebook as an app; it does not, by itself, publish a secure public service. Hosting, network exposure, and access control depend on how and where you deploy it.
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Export an interactive HTML version
Marimo also documents WebAssembly HTML exports that run Python in the browser and preserve interactivity. Use the static deployment guide for the current export process and constraints. A browser-based export and a hosted app are different sharing paths: choose according to the runtime and access model your readers need.
Consider cloud collaboration separately
Marimo Cloud is described as offering on-demand cloud resources for experimentation, collaboration, sharing, and deployment. Its current prices, plan limits, and availability are not established here, so check the service directly before choosing it.
Quick Recap
Choose the right Marimo workflow
- For a first exploration: use a notebook with explicit data-loading and transformation cells, then add a native control when you want to inspect how results change.
- For expensive or side-effecting steps: consider lazy execution, and keep in-place mutations from obscuring the dependency graph.
- For database-backed analysis: configure the extra SQL dependencies and the connection details for your selected backend before building dependent Python cells.
- For sharing: use
marimo runfor an app workflow or an interactive WebAssembly export when a browser-running HTML artifact fits your needs; deployment security depends on the hosting setup.
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