hvPlot allows you to generate a number of different types of plot quickly from a standard API, returning HoloViews objects as discussed in the previous notebook. Each initial plot will make some aspects of the data clear, and using the automatic interactive pan, zoom, and hover tools you can find additional trends and outliers at different spatial locations and spatial scales within each plot.
Beyond what you can discover from each plot individually, how do you understand how the various plots relate to each other? For instance, imagine you have a data frame with columns u, v, w, z, and have separate plots of u vs. v, u vs. w, and w vs. z. If you see a few outliers or a clump of unusual datapoints in your u vs. v plot, how can you find out the properties of those points in the w vs. z or other plots? Are those unusual u vs. v points typically high w, uniformly distributed along w, or some other pattern?
To help understand multicolumnar and multidimensional datasets like this, scientists will often build complex multi-pane dashboards with custom functionality. HoloViz (and specifically Panel) tools are great for such dashboards, but here we can actually use the fact that hvPlot returns HoloViews objects to get quite sophisticated interlinking (linked brushing) “for free”, without needing to build any dashboard. HoloViews objects store metadata about what dimensions they cover, and we can use this metadata programmatically to let the user see how any data points in any plot relate across different plots.
To see how this works, let us get back to the example we were working on at the end of the last notebook:
import holoviews as hv
import pandas as pd
import hvplot.pandas # noqa
import colorcet as cc