#WhatIsMarimo

1. What is marimo?

marimo is an open-source, reactive Python notebook framework designed to provide lightweight, reproducible, and interactive notebooks—ideal for both rapid experimentation and building polished data apps.

Unlike traditional notebooks like Jupyter, marimo offers a reactive execution model—meaning cells automatically recompute when their dependencies change, similar to modern reactive frontend frameworks like React or Vue.

Developed for developers, data scientists, and educators, marimo supports a code-as-configuration paradigm, integrates well with version control (no hidden outputs), and produces cleaner, more reproducible notebooks with .py files as the primary format.

✅ Key characteristics:

• Text-based (no hidden outputs like Jupyter)
• Reactive computation model
• Web-based UI served via CLI
• Interactive inputs (sliders, dropdowns, buttons)
• App export and publishing options

#MarimoUseCases

2. Major Use Cases of marimo

marimo is ideal for scenarios where transparency, reproducibility, and interactivity are critical. Some major use cases include:

📊 2.1. Data Exploration and Visualization

• marimo allows users to build rich, interactive dashboards using Python, matplotlib, plotly, or altair.
• Developers can quickly test models or visualize data with dynamic inputs.

👩‍🏫 2.2. Educational Notebooks

• Great for instructors teaching data science or Python.
• Reactive cells allow students to explore “what-if” scenarios by tweaking inputs.

⚗️ 2.3. Scientific Computing & Prototyping

• Use marimo to test algorithms, build experiments, or run mathematical simulations.
• Supports NumPy, SciPy, SymPy, and Pandas natively.

🚀 2.4. Rapid Development of Data Apps

• Export a marimo notebook as a standalone web app.
• Great for demos, sharing models, or building MVPs.

📦 2.5. Clean Alternative to Jupyter

• Perfect for developers who prefer using version-controlled, code-first environments.
• Avoids cluttered .ipynb JSON files by using clean Python .py scripts.

#MarimoArchitecture

3. How marimo Works – Architecture Overview

marimo is designed with a modern architecture that aligns with best practices in reactive programming and frontend-backend separation. It consists of the following main components:

⚙️ 3.1. Core Components

🧠 A. Reactive Engine

• All Python code in a marimo notebook is reactive.
• If one variable or function changes, all dependent cells automatically re-evaluate.
• Based on a Directed Acyclic Graph (DAG) dependency system.

🌐 B. Web-based Frontend

• Renders UI controls, markdown, and outputs.
• Updates outputs in real time as inputs change.

🔁 C. marimo Inputs API

• Provides interactive components like:
  • mo.ui.slider()
  • mo.ui.select()
  • mo.ui.button()
• Inputs are reactive and bind to Python variables.

📄 D. File-based Notebook System

• Notebooks are simple .py files written in structured Python.
• Each cell is marked with # cell or # markdown.

📡 E. marimo CLI Server

• Launches the interactive notebook in a browser.
• Hot-reloading, shareable links, and export features supported.

#MarimoWorkflow

4. Basic Workflow of marimo

Here’s how marimo works under the hood when you launch and use a notebook:

🧩 Step-by-Step Workflow

This flow makes it incredibly efficient for data apps or interactive exploration.

#MarimoGettingStarted

5. Step-by-Step Getting Started Guide for marimo

Let’s walk through a simple guide to start using marimo in minutes.

✅ Step 1: Install marimo

Use pip to install:

pip install marimo

✅ Step 2: Create Your First Notebook File

# filename: hello_marimo.py

# cell
import marimo
mo = marimo.App()

# cell
name = mo.ui.text(label="Your name").value

# cell
f"👋 Hello, {name or 'world'}!"

# cell
mo.stop()

✅ Step 3: Run the Notebook

marimo run hello_marimo.py

It launches a web-based notebook at http://localhost:XXXX where you can interact with the inputs and see live updates.

✅ Step 4: Add Visualization and Logic

# cell
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
freq = mo.ui.slider(1, 10, label="Frequency").value

# cell
plt.plot(x, np.sin(freq * x))
plt.title(f"Sine Wave (freq = {freq})")
plt.show()

✅ Step 5: Export as Web App

marimo export hello_marimo.py --out app.html

This allows you to host it on a website or share as a static interactive page.