Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • GetResponse Reviews & Ratings
    4.3 out of 5 stars 1,801 Ratings
    Company Website for GetResponse
  • DbVisualizer Reviews & Ratings
    4.8 out of 5 stars 593 Ratings
    Company Website for DbVisualizer
  • Okyline Reviews & Ratings
    5.0 out of 5 stars 2 Ratings
    Company Website for Okyline
  • MaintainX Reviews & Ratings
    4.8 out of 5 stars 2,692 Ratings
    Company Website for MaintainX
  • Apify Reviews & Ratings
    4.8 out of 5 stars 1,859 Ratings
    Company Website for Apify
  • Google Cloud Run Reviews & Ratings
    4.5 out of 5 stars 352 Ratings
    Company Website for Google Cloud Run
  • Maintainly Reviews & Ratings
    4.3 out of 5 stars 73 Ratings
    Company Website for Maintainly
  • Anaconda Reviews & Ratings
    4.4 out of 5 stars 626 Ratings
    Company Website for Anaconda
  • Daylight Reviews & Ratings
    4.8 out of 5 stars 15 Ratings
    Company Website for Daylight
  • Google Cloud BigQuery Reviews & Ratings
    4.5 out of 5 stars 2,037 Ratings
    Company Website for Google Cloud BigQuery

What is marimo?

Introducing a cutting-edge reactive notebook tailored for Python, enabling users to perform repeatable experiments, execute scripts effortlessly, launch applications, and manage versions via git. 🚀 All-in-one solution: it effectively replaces tools like Jupyter, Streamlit, Jupytext, ipywidgets, and Papermill, among others. ⚡️ Adaptive: upon executing a cell, Marimo instantly processes all related cells or marks them as outdated. 🖐️ Interactive: effortlessly link sliders, tables, and graphs to your Python code without requiring callbacks. 🔬 Consistent: it eliminates hidden states, ensures deterministic execution, and incorporates built-in package management for reliability. 🏃 Versatile: can be run as a standard Python script, enabling adjustments through CLI arguments. 🛜 User-friendly: has the capability to morph into an interactive web application or presentation and operates seamlessly in the browser via WASM. 🛢️ Data-focused: proficiently queries dataframes and databases using SQL, while allowing easy filtering and searching through dataframes. 🐍 git-friendly: saves notebooks as .py files, simplifying version control processes. ⌨️ Modern editing: equipped with features like GitHub Copilot, AI assistants, vim keybindings, a variable explorer, and numerous other enhancements to optimize your workflow. With these advanced features, this notebook transforms your Python programming experience, fostering a more productive and collaborative coding atmosphere, making it easier to share insights and results with others.

What is JupyterHub?

JupyterHub is a powerful tool that enables the creation of a multi-user environment, allowing for the spawning, management, and proxying of multiple instances of Jupyter notebook servers. Created by Project Jupyter, it is specifically tailored to support numerous users at once. This platform serves a wide array of functions, making it suitable for educational settings, corporate data science teams, collaborative scientific research endeavors, or groups that utilize high-performance computing resources. However, it's essential to highlight that JupyterHub does not officially support Windows operating systems. While some users may attempt to run JupyterHub on Windows using compatible Spawners and Authenticators, the default settings are not optimized for such an environment. Additionally, any issues encountered on Windows will not receive support, and the testing framework is not designed to work on Windows platforms. Minor patches that could potentially address basic compatibility issues on Windows are infrequent and not guaranteed. Consequently, for those using Windows, it is recommended to operate JupyterHub within a Docker container or a Linux virtual machine, as this ensures better performance and compatibility. This strategy not only improves functionality but also streamlines the installation process, making it easier for Windows users to access the benefits of JupyterHub. Ultimately, adopting this method can lead to a more seamless user experience.

Media

No images available

Media

Integrations Supported

Python

Integrations Supported

Azure Marketplace
Cleanlab
Coiled
DataOps.live
JetBrains DataSpell
Jupyter Notebook
JupyterLab
NeevCloud
OpenHexa
Quantinuum Nexus
Timbr.ai
Train in Data
Vast.ai
Wizata

API Availability

API Availability

Has API

Pricing Information

$0
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac
Linux

Supported Platforms

Linux

Customer Service / Support

Not specified

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

marimo

Date Founded

2022

Company Location

United States

Company Website

marimo.io

Company Facts

Organization Name

JupyterHub

Date Founded

2014

Company Website

github.com/jupyterhub/jupyterhub

Categories and Features

Categories and Features

Operating Systems

Not specified

Popular Alternatives

Popular Alternatives

Positron Reviews & Ratings

Positron

Posit PBC
JupyterLab Reviews & Ratings

JupyterLab

Jupyter
Jupyter Notebook Reviews & Ratings

Jupyter Notebook

Project Jupyter
5.0 out of 5 stars