2026-07-14 –, Viz
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 Python server, by simply replacing your expression arguments with widgets so that users can explore it on their own. 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.
Installation instructions: https://holoviz.org/tutorial/Setup.html
Python offers many powerful visualization tools (listed on pyviz.org), each with their own strengths and advantages. Few people have the time and interest to learn all the different APIs required to use these different tools, but a de-facto standard API for data plotting has emerged in the Pandas .plot() API, now supported by many different plotting packages.
In this tutorial, you will learn how to use hvPlot, a high-level interactive plotting library that exposes the power of Bokeh, Matplotlib, Plotly, Datashader, HoloViews, GeoViews, and Cartopy using the same .plot API you may already know from using Pandas, Dask, or Xarray's plotting interface. We'll also show you how to turn nearly any expression you can write with that API into a web app with plots and tables by simply substituting widgets for any parameters you want users to be able to change, easily creating reactive expression pipelines. Thanks to the HoloViz tools on which hvPlot is built, the resulting apps can easily handle big data (up to billions of rows on an ordinary laptop or petabytes on a distributed cluster), remote data (either in Jupyter or in standalone apps), streaming data, geographical data (building on the geoscience software stack), and multidimensional data (using Xarray).
hvPlot's high-level interface should be sufficient for nearly all of the common data-exploration and data-analysis tasks you want to do with Pandas, Dask, or Xarray, but in keeping with the HoloViz philosophy of "shortcuts rather than dead ends", we'll also show you how and when to drop down to lower-level APIs when you need to, such as when building more complex apps using Panel, doing complex graphical data calculations using Datashader, or integrating plotting and interactivity into your own libraries using Param and HoloViews.
We'll also provide guidance on how to use AI effectively with the HoloViz ecosystem, including a brief preview of our new Lumen.HoloViz.org tool for natural-languge data exploration along with advice for AI code generation.
With the techniques you learn in the hands-on exercises in this tutorial, you'll get the tools and know-how to effectively explore, analyze and visualize simple or complex, small or large, and static or dynamic data easily, concisely, and reproducibly. The resulting visualizations and apps can be shared as static images, simple HTML documents with limited interactivity, HTML+WASM documents with full Python-backed interactivity, or as Python apps deployed on a remote server. We expect participants to have previously used some sort of plotting tool and to be comfortable with Python and at least one array-based Python library (Numpy, Pandas, Xarray, CuPy, cuDF, Dask, etc.).
Experience plotting data with any tool (Python or otherwise) should be sufficient background for understanding and appreciating the approach used here.
Some experience with pandas, xarray, or numpy is helpful.
Jim Bednar is the Senior Director of Professional 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 founded 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.