Panel: The powerful data exploration & web app framework for Python
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Updated
Jun 29, 2024 - Python
Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics. Its practitioners use statistics and data science to convey the meaning behind data in ethical and accurate ways.
Panel: The powerful data exploration & web app framework for Python
A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
A ninja python package that unifies the Google Earth Engine ecosystem.
A data dashboard template
Support for jupyter notebook templates in jupyterlab
A declarative library for Python designed to create interactive multi-scale visualizations of genomics and epigenomics data.
These examples on Interactive Data Visualization in the web browser using Flask RESTful API and D3.js are compiled with some modifications from the book "Data Visualization with Python and JavaScript: Scrape, Clean, Explore, and Transform Your Data" by Kyran Dale, published by O'Reilly Media in 2023.
Data science tools for exploration, visualization, and model iteration.
Circular visualization in Python (Circos Plot, Chord Diagram, Radar Chart)
Highcharts meets Python in your jupyter notebook
Automated Database Structure Discovery
Animate timeseries data with Grafana.
A multi-page streamlit app for geospatial
🐍 📊 📈 Build complex dashboards without any front-end code. Use your own endpoints. JSON config only. Ready to go.
📊📈 Serves up Pandas dataframes via the Django REST Framework for use in client-side (i.e. d3.js) visualizations and offline analysis (e.g. Excel)
Graphic Server Protocol (GSP) : Matplotlib implementation
Cellxgene Gateway allows you to use the Cellxgene Server provided by the Chan Zuckerberg Institute (https://github.com/chanzuckerberg/cellxgene) with multiple datasets.
Created by Charles Joseph Minard