Statistical Plots

In addition to the plots available via the plot interface, hvPlot makes a number of more sophisticated, statistical plots available that are modelled on pandas.plotting. To explore these, we will load the iris and stocks datasets from Bokeh:

import pandas as pd
import hvplot.pandas  # noqa

from bokeh.sampledata import iris, stocks 

iris = iris.flowers

Scatter Matrix

When working with multi-dimensional data, it is often difficult to understand the relationship between all the different variables. A scatter_matrix makes it possible to visualize all of the pairwise relationships in a compact format. hvplot.scatter_matrix is closely modelled on pandas.plotting.scatter_matrix:

hvplot.scatter_matrix(iris, c="species")

Compared to a static Seaborn/Matplotlib-based plot, here it is easy to explore the data interactively thanks to Bokeh’s linked zooming, linked panning, and linked brushing (using the box_select and lasso_select tools).

Parallel Coordinates

Parallel coordinate plots provide another way of visualizing multi-variate data. hvplot.parallel_coordinates provides a simple API to create such a plot, modelled on the API of pandas.plotting.parallel_coordinates():

hvplot.parallel_coordinates(iris, "species")

The plot quickly clarifies the relationship between different variables, highlighting the difference of the “setosa” species in the petal width and length dimensions.

Andrews Curves

Another similar approach is to visualize the dimensions using Andrews curves, which are constructed by generating a Fourier series from the features of each observation, visualizing the aggregate differences between classes. The hvplot.andrews_curves() function provides a simple API to generate Andrews curves from a datafrom, closely matching the API of pandas.plotting.andrews_curves():

hvplot.andrews_curves(iris, "species")