import numpy as np import holoviews as hv hv.extension('matplotlib')
elements where the area below the line is filled. Like
elements are used to display the development of quantitative values over an interval or time period.
Elements may also be stacked to display multiple data series in a cumulative fashion over the value dimension.
The data of an
Element should be tabular with one key dimension representing the samples over the interval or the timeseries and one or two value dimensions. A single value dimension will fill the area between the curve and the x-axis, while two value dimensions will fill the area between the curves. See the
Columnar Data Tutorial
for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays.
Area under the curve ¶
By default the Area Element draws just the area under the curve, i.e. the region between the curve and the origin.
xs = np.linspace(0, np.pi*4, 40) hv.Area((xs, np.sin(xs)))
Area between curves ¶
When supplied a second value dimension the area is defined as the area between two curves.
X = np.linspace(0,3,200) Y = X**2 + 3 Y2 = np.exp(X) + 2 Y3 = np.cos(X) hv.Area((X, Y, Y2), vdims=['y', 'y2']) * hv.Area((X, Y, Y3), vdims=['y', 'y3'])
Stacked areas ¶
Areas are also useful to visualize multiple variables changing over time, but in order to be able to compare them the areas need to be stacked. To do this, use the
classmethod to stack multiple
elements in an (Nd)Overlay.
In this example we will generate a set of 5 arrays representing percentages and create an
of them. Then we simply call the
method with this overlay to get a stacked area chart.
values = np.random.rand(5, 20) percentages = (values/values.sum(axis=0)).T*100 overlay = hv.Overlay([hv.Area(percentages[:, i], vdims=[hv.Dimension('value', unit='%')]) for i in range(5)]) overlay + hv.Area.stack(overlay)