Project Title

marimo — A Next-Gen Reactive Notebook for Data Transformation, Model Training, and SQL Execution

Overview

Marimo is a cutting-edge reactive Python notebook designed for data scientists and developers who require a reproducible, Git-friendly, and deployable solution for data transformation, model training, and SQL execution. It stands out with its ability to automatically run dependent cells upon changes, ensuring code and outputs remain consistent, and its support for pure Python storage with first-class SQL integration.

Key Features

Reactivity: Automatically runs dependent cells or marks them as stale upon changes.
Interactive UI: Bind sliders, tables, plots, and more to Python without callbacks.
Git-Friendly Storage: Notebooks stored as .py files for easy version control.
SQL Support: First-class support for querying dataframes, databases, and more with SQL.
AI-Native: Generate cells with AI tailored for data work.
Executable as Scripts: Execute notebooks as Python scripts with CLI arguments.

Use Cases

Data Scientists: Use marimo for data analysis, transformation, and visualization in a reproducible manner.
Machine Learning Engineers: Train models and deploy them as interactive web apps or scripts.
Database Administrators: Execute SQL queries and manage data workflows within a Pythonic environment.

Advantages

Integrated Tools: Replaces multiple tools like Jupyter, Streamlit, and Jupytext with a single, unified interface.
Modern Development: Supports modern development practices with package management and testing capabilities.
Interoperability: Easily share and deploy notebooks as web apps, scripts, or slides.

Limitations / Considerations

Learning Curve: Users unfamiliar with reactive programming may need time to adapt.
Limited Documentation: As a newer project, comprehensive documentation might still be in development.

Jupyter Notebook: A widely-used interactive computing environment, but marimo offers a more modern and integrated approach.
Streamlit: Focuses on creating web apps for machine learning, while marimo provides a broader range of functionalities.
Binder: Used for creating reproducible, shareable environments, marimo extends this concept with a reactive and interactive interface.

Basic Information

GitHub: https://github.com/marimo-team/marimo
Stars: 15,791
License: Unknown
Last Commit: 2025-09-08

📊 Project Information

Project Name: marimo
GitHub URL: https://github.com/marimo-team/marimo
Programming Language: Python
⭐ Stars: 15,791
🍴 Forks: 678
📅 Created: 2023-08-14
🔄 Last Updated: 2025-09-08

🏷️ Project Topics

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📚 Documentation

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marimo

Project Description