SciPy 2023

James A. Bednar

Jim Bednar is the Director of Custom Services at Anaconda, Inc. Dr. Bednar holds a Ph.D. in Computer Science from the University of Texas, along with degrees in Electrical Engineering and Philosophy. He has published more than 50 papers and books about the visual system, software development, and reproducible science. Dr. Bednar manages the HoloViz project, a collection of open-source Python tools that includes Panel, hvPlot, Datashader, HoloViews, GeoViews, Param, Lumen, and Colorcet. Dr. Bednar was a Lecturer and Reader in Computational Neuroscience at the University of Edinburgh from 2004-2015, and previously worked in hardware engineering and data acquisition at National Instruments.

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hvPlot and Panel: Visualize all your data easily, from notebooks to dashboards
Sophia Yang, James A. Bednar

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. These tools let you focus on your data rather than the API, and let you build linked, interactive drill-down exploratory apps without having to run a web-technology software development project, which you can then share without becoming an operations specialist.

Classroom 105
[BoF Room 103] Python Visualization and App Tools
Sophia Yang, Bane Sullivan, Kushal Kolar, Juan Nunez-Iglesias, Elliott Sales de Andrade, Jon Mease, Nathan Jessurun, Hadley Wickham, James A. Bednar

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. Join us and stay ahead of the curve!

Birds of a Feather (BoF)
Classroom 103
SOSA: The Scalable Open-Source Analysis Stack
Martin Durant, James A. Bednar

Are you held back by your scientific domain’s legacy tools for reading, processing, plotting, analyzing, modeling, or exploring data? Consider building on dask, xarray, numba, hvplot, jupyter, and other modern domain-independent libraries from the SOSA stack for scalable open-source analysis. SOSA can run nearly any scientific data-processing workflow at machine-code speeds, from laptops to petaflop-scale supercomputers. We'll show how these libraries support distributed computation all the way from data files up to a rendered visualization, making it as simple to work with remote cloud clusters as on your own local machine.

General Track