Zupiter
Zupiter is an open-source data science platform that allows users to write and execute Python code for data analysis. It provides a Jupyter notebook-style interface with support for Python kernels, version control, data visualization, and collaboration features.
opensource
python
data-analysis
jupyter-notebook
version-control
data-visualization
collaboration
Zupiter: Open-Source Data Science Platform
Write and execute Python code for data analysis, with Jupyter notebook-style interface, Python kernels, version control, data visualization, and collaboration features.
What is Zupiter?
Zupiter is an open-source data science platform built as an alternative to Jupyter Notebook. It provides a web-based interactive computing environment that allows users to create live code, equations, visualizations and prose.
Some key features of Zupiter include:
- Supports Python kernels for running Python code
- Built-in version control and rollback using Git
- Interactive widgets for plots, maps, media and more
- Collaboration tools like comments, tags and presenter mode
- Import from various data sources like CSV, Excel, databases etc
- Export to HTML, PDF or Python scripts
- Pluggable authentication and access controls
- Docker container or VM deployment
Zupiter allows individuals and teams to do end-to-end data science workflows encompassing data access, modeling, visualization and reporting. Its version control capabilities make it easier to track experiments and collaborate. With support for Python-based libraries like NumPy, Pandas, Matplotlib, Zupiter provides a flexible platform for analyzing data and building machine learning models.
Zupiter Features
Features
- Jupyter notebook-style interface for writing and running Python code
- Built-in Python kernels for data analysis and machine learning
- Version control integration
- Visualization and charting capabilities
- Collaboration features like sharing and commenting on notebooks
Pricing
- Open Source
Pros
Open source and free to use
Familiar Jupyter interface for Python data science workflows
Integrated version control for tracking changes
Support for visualizing and charting data
Collaboration features make it easy to share work
Cons
Lacks some advanced features of paid platforms like JupyterHub
Visualizations may be more limited compared to dedicated data viz tools
Collaboration requires manual sharing of notebooks
Official Links
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