James A. Bednar

This tutorial will show you how to use the Pandas, Dask, or Xarray APIs you already know to interactively explore and visualize your data, even if the data is gigabyte or petabyte sized or is in non-columnar scientific formats such as multidimensional arrays, networks, or unstructured grids. As soon as you have something you like, you can then share a live app as HTML+WASM or backed by a live...

This tutorial will show you how to use the Pandas, Dask, or Xarray APIs you already know to interactively explore and visualize your data even if it is big, streaming, or multidimensional. Then just replace your expression arguments with widgets to get an instant web app that you can share as HTML+WASM or backed by a live Python server.

Each new SciPy brings even more tools for data visualization and for building data-rich scientific applications and dashboards. This BoF brings together maintainers of Python tools for data visualization and building apps to help make sense of this complex landscape for users and to highlight new developments, trends, and opportunities.

This tutorial will show you how to use the Pandas or Xarray APIs you already know to interactively explore and visualize your data even if it is big, streaming, or multidimensional. Then just replace your expression arguments with widgets to get a web app that you can share as HTML+WASM or backed by a live Python server.

In this tutorial, you will see how to use Panel (https://panel.pyviz.org) to add widgets and layouts to the output from just about any plotting program, and then how to share the result as a fully standalone application.