Mineo vs mljar Mercury
Overview
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Detailed Comparison
It is the easiest way to turn your Python Notebooks into interactive web applications and publish to the cloud. It is dual-licensed. The main features are available in the open-source version. It is perfect for quick demos, educational purposes, sharing notebooks with friends. | 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. |
You define interactive widgets for your notebook with the YAML header;
Your users can change the widgets values, execute the notebook and save result (as PDF or HTML file);
You can add authentication to your notebooks, so only logged users will see the notebook;
You can hide your code to not scare your (non-coding) collaborators;
Easily deploy to any server;
You can schedule the notebook for automatic execution in selected time intervals;
| 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 |
Statistics | |
GitHub Stars 4.3K | GitHub Stars - |
GitHub Forks 272 | GitHub Forks - |
Stacks 1 | Stacks 0 |
Followers 3 | Followers 1 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to mljar Mercury, Mineo?
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.
