Marimo vs Mineo
Overview
Share your Stack
Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
CLI (Node.js)
Manual
Detailed Comparison
It is the platform to explore your data, develop and deploy your Python supercharged Notebooks and track the quality of your data using Machine learning. | It is a reactive notebook for Python. It allows you to rapidly experiment with data and models, code with confidence in your notebook’s correctness, and productionize notebooks as pipelines or interactive web apps. |
Develop, share and deploy your supercharged Python notebooks on a scalable platform;
Automate and streamline your workflows with Python-based data pipelines that leverage the power of notebooks;
Establish processes to measure and track the quality of your data over the time using machine learning | Python-first design;
Rapidly experiment with code and models;
Bind UI elements to Python values;
Pick-up-and-play design, with depth for power users;
Built for collaboration |
Statistics | |
GitHub Stars - | GitHub Stars 16.8K |
GitHub Forks - | GitHub Forks 753 |
Stacks 0 | Stacks 1 |
Followers 1 | Followers 2 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Mineo, Marimo?
Jupyter
The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.
Apache Zeppelin
A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.
Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
NumPy
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
Deepnote
Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore and analyze it with real-time collaboration and versioning, and easily share and present the polished assets to end users.
PyXLL
Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!
SciPy
Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
Dataform
Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.
PySpark
It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
Anaconda
A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
