The Tool Desk
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What Marimo is and what you can do with it
Marimo is an open-source reactive notebook for Python. Unlike a notebook format stored as a separate document, a Marimo notebook is a Python file: you can work with it interactively, execute it as a script, or run it as an app. Its documented features include native UI elements, interactive dataframes, SQL support, package management, and browser-based options. These are capabilities described by Marimo, not independent performance benchmarks. See the Marimo documentation overview.
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Install Marimo and start a notebook
Use an environment appropriate to your project so Marimo and your analysis dependencies are installed together. The installation guide provides the current installation options and sandbox approaches for trying Marimo without first configuring a project environment: Marimo installation guide.
- Install Marimo using an option in the installation guide that fits your Python environment.
- Launch the introductory tutorial as documented in the getting-started guide to learn the editor and notebook basics.
- Create a notebook and load a dataset in one cell. Keep the file path or other source details explicit so the analysis can be run again.
- Add analysis and visualization cells that refer to variables defined by the data-loading cell. Marimo uses those references to determine which cells depend on the data.
The exact package-manager commands and optional dependencies depend on the environment and features you choose; follow the current installation instructions rather than assuming every SQL or database integration is included in a minimal install.
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How reactive Python notebook cells work
Marimo statically analyzes variable definitions and references in cells to build a dependency graph. If one cell defines a dataframe and another uses it, the second depends on the first. When an input changes, Marimo can automatically run dependent cells; in lazy execution mode, it can mark them stale instead. This means execution follows the relationships between variables, not simply the cells’ visual order. The reactivity guide explains the model.
Make dependencies explicit
For example, a data-loading cell can define df, a filtering cell can define filtered from df, and a chart cell can use filtered. Changing the filtering logic gives Marimo a visible dependency chain to evaluate. Prefer explicit assignments and transformations over hidden state changes.
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Know the mutation limitation
Marimo documents that it does not track mutations to variables or assignments to object attributes. If you modify an object in place, do not assume every cell that uses it will rerun. When you need reliable reactive updates, create a new value through an explicit assignment instead. Lazy execution is also useful when cells are expensive or have side effects, because dependent work can be left stale until needed.
Explore data with interactive controls
Marimo’s documented UI elements include sliders, dropdowns, and file uploads, and its feature overview describes interactive dataframes. A control becomes useful when its value is referenced by downstream analysis: for instance, a dropdown can select a category, a summary cell can filter the dataframe by that selection, and a plot can visualize the resulting subset. Updating the control then updates dependent cells according to Marimo’s reactive model. See Marimo’s interactive guide.
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- Load a dataframe in a cell.
- Add a native control for a meaningful analysis choice, such as a category or date range.
- Use the control’s value in a filtering or aggregation cell.
- Build a table, summary, or visualization from that result.
This pattern applies to Marimo’s documented controls; behavior can vary for third-party widgets and arbitrary Python objects, so do not assume every widget integration behaves identically.
Query data with SQL in the same analysis
Marimo SQL cells can query Python dataframes and databases such as SQLite or PostgreSQL, returning results as Python dataframes for later cells. Its broader feature documentation also lists DuckDB and MySQL. SQL support requires additional dependencies, and connecting to an external database still requires the relevant driver, connection details, and credentials. Check the SQL guide for setup details.
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A practical division of labor is to use SQL for filtering or aggregation near the data source, then use Python cells to continue analysis or make plots from the returned dataframe. The documented backend list does not by itself guarantee a working connection in a particular environment or any specific query speed.
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 it as an app
From a terminal in the environment containing Marimo, run marimo run notebook.py, replacing notebook.py with the path to your file. The app guide says code is hidden by default in this view and documents options for customizing the layout. This command serves an app; it does not by itself configure secure public hosting, deployment, or access control. Those depend on the hosting environment and its settings. See the app and deployment guide.
Export interactive HTML
Marimo also documents WebAssembly-based HTML exports that run Python in the browser and preserve interactivity. This is a different sharing route from serving a live app: consider where computation should run and what the recipient needs before choosing an export or hosted deployment. Consult the export documentation for the current workflow: Marimo export guide.
Consider cloud collaboration separately
Marimo describes Marimo Cloud as offering on-demand cloud resources for experimentation, collaboration, sharing, and deployment. Availability, prices, plan limits, and terms can change; consult Marimo Cloud for current details before choosing it.
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