Data Visualization
The Data Visualization environments are designed for various data visualization tools and libraries. These include environments for matplotlib, seaborn, plotly, hvplot, datashader, dash, and more. Each environment is equipped with the necessary tools for efficient data visualization tasks, providing a streamlined experience for creating interactive, publication-quality graphs and web analytic applications.
| Environment | Description | Quickstart |
|---|---|---|
| datavisualization | Environment equipped with tools for data visualization like matplotlib, seaborn, plotly, hvplot, datashader, and dash. |  |
| hvplot | Environment equipped with the tools and SDKs for working with hvPlot, a high-level plotting API for the PyData ecosystem built on HoloViews. | |
| datashader | Environment equipped with the tools and SDKs for working with Datashader, a data rasterization pipeline for automating the process of creating meaningful representations of large amounts of data. | |
| dash | Environment equipped with the tools and SDKs for working with Dash, a productive Python framework for building web analytic applications. | |
| plotly | Environment for Plotly, a graphing library makes interactive, publication-quality graphs. | |
| cuxfilter | Environment equipped with the tools and SDKs for working with cuXfilter, a RAPIDS framework to connect web-based visualization with GPU-accelerated crossfiltering. | |
| bokeh | Environment equipped with the tools and SDKs for working with Bokeh, an interactive visualization library for modern web browsers. | |
| seaborn | Environment for Seaborn, a Python data visualization library based on Matplotlib. | |
| matplotlib | Environment for Matplotlib, a plotting library for the Python programming language and its numerical mathematics extension NumPy. | |