The user guide provides a detailed introduction to the API and features of hvPlot. In the Introduction you will learn how to activate the plotting API and start using it. Next you will learn to use the API for tabular data and get an overview of the types of plots you can generate and how to customize them; including how to customize interactivity using widgets. Next is an overview on how to display and save plots in the notebook, on the commandline, and from a script. Another section will introduce you to generating subplots from your data.
Once the basics are covered you can learn how to use the plotting API for specific types of data including streaming data, gridded data network graphs, geographic data, and timeseries data. These sections are not meant to be read in a particular order; you should take a look at any that seem relevant to your data.
Lastly the statistical plots section will take you through a number of specialized plot types modelled on the pandas.plotting module and the pandas API section mimics the pandas visualization docs but using pandas.options.plotting.backend to do the plotting in HoloViews rather than Matplotlib.
Introduction Introduction to hvPlot and how to start using it.
Plotting Overview of plotting your data with hvPlot.
Customization Listing of available options to customize plots.
Widgets Adding and customizing interactivity using Panel widgets.
Viewing Displaying and saving plots in the notebook, at the command prompt, or in scripts.
Subplots How to generate subplots and grids.
Streaming How to use hvPlot for streaming plots with the streamz library.
Gridded Data How to use hvPlot for plotting XArray-based gridded data.
Network Graphs How to use hvPlot for plotting NetworkX graphs.
Geographic Data Using GeoViews, Cartopy, GeoPandas and spatialpandas to plot data in geographic coordinate systems.
Timeseries Data Using hvPlot when working with timeseries data.
Statistical Plots A number of statistical plot types modeled on the pandas.plotting module.
Pandas API How to use pandas.plot directly by switching out the plotting backend.