marimo-notebook

Run Python notebooks with reactive cell execution on dependencies.

76|10|Updated Feb 5, 2026

Search Tags:#data analysis #Python notebooks #reactive computing #interactive data visualization #Marimo

Ready to run

npx skills add https://github.com/pymc-labs/python-analytics-skills --skill marimo-notebook-pymc-labs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: marimo-notebook
Source: https://github.com/pymc-labs/python-analytics-skills/tree/main/skills/marimo-notebook
Command: npx skills add https://github.com/pymc-labs/python-analytics-skills --skill marimo-notebook-pymc-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires marimo, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies creating, running, and analyzing data within Python notebooks, eliminating the need for complex environments and providing an intuitive user experience.

Core Features & Use Cases

Reactive Notebooks: Cells auto-execute on dependencies for live results.
Data Visualization: Integrated plotting libraries (Matplotlib, Plotly, etc.) for interactive graphs.
Code Organization: Structure notebooks with modules and functions for clarity and reusability.
Use Case: If you need to explore and visualize data interactively without setting up a complex environment, Marimo Notebooks provide an efficient solution.

Quick Start

Start by opening the 'data_analysis_template.py' notebook, upload your data file, and execute the cells to visualize and manipulate your dataset.

Frequently Asked Questions about marimo-notebook

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create reactive Python notebooks for interactive data analysis?▼

Reactive Python notebooks automatically re-execute dependent cells when inputs change, enabling live data analysis. You can build them by opening a template, uploading your dataset, and running cells to interactively visualize and manipulate data.

What is reactive computing in Python notebooks and when is it needed?▼

Reactive computing in Python notebooks automatically re-runs cells when their dependencies update, ensuring live results. You need it for exploratory data analysis and interactive data visualization where variables frequently change and immediate feedback is required.

Can I use Matplotlib and Plotly for interactive data visualization in Python notebooks?▼

Yes, you can use integrated plotting libraries like Matplotlib and Plotly for interactive data visualization in Python notebooks. These reactive notebooks support live graphs that update dynamically as your data analysis cells re-execute.

Do I need to install Marimo and other data science packages to run reactive notebooks?▼

Yes, you need to install Marimo and various data science Python packages to run reactive notebooks. These dependencies provide the reactive computing environment and data analysis libraries required to execute and visualize your interactive notebooks.

How do I structure Python notebooks with modules and functions for data analysis?▼

You structure Python notebooks by organizing code with modules and functions for clarity and reusability. This approach allows you to build complex data analyses iteratively, keeping reactive notebook cells maintainable and logically separated.

Reactive notebooks vs traditional Python notebooks: which approach suits exploratory data analysis?▼

Reactive notebooks are best for exploratory data analysis because they automatically re-execute dependent cells, eliminating hidden state issues found in traditional Python notebooks. This ensures live, consistent results during interactive data visualization and modeling.