Abstract
Datapane is an open-source Data Science project. Build and share data reports in 100% Python. Datapane makes it simple to build interactive reports in seconds using Python. It is built using Python. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
Datapane makes it simple to build interactive reports in seconds using Python.
Datapane reports are interactive and can also contain pages, tabs, drop downs, and more. Once created, reports can be exported as HTML, shared as standalone files, or embedded into your own application, where your viewers can interact with your data and visualizations.
We've made the difficult decision to stop driving this project and therefore we will no longer actively respond to issues or pull requests. If you would like to take over maintaining this project independently, please let us know so we can add a link to your forked project here.
2. Objective
Build and share data reports in 100% Python
This project demonstrates how Python can be applied to a real-world Data Science problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/datapane/datapane.git
cd datapaneFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Turn the analysis into an interactive dashboard
- Automate data refresh with a scheduled job
- Add a predictive model on top of the analysis
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by datapane and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.
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