Errorbars

Title
ErrorBars Element
Dependencies
Matplotlib
Backends
Matplotlib
Bokeh
In [1]:
import numpy as np
import holoviews as hv
hv.extension('matplotlib')

ErrorBars provide a visual indicator for the variability of the plotted data on a graph. They are usually applied on top of other plots such as scatter, curve or bar plots to indicate the variability in each sample.

ErrorBars may be used to represent symmetric error or assymetric error. An ErrorBars Element must have one key dimensions representing the samples along the x-axis and two or three value dimensions representing the value of the sample and positive and negative error values associated with that sample. See the Columnar Data Tutorial for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays.

Symmetric error

By default the ErrorBars Element accepts x- and y-coordinates along with a symmetric error value:

In [2]:
np.random.seed(7)
errors = [(0.1*i, np.sin(0.1*i), np.random.rand()/2) for i in np.linspace(0, 100, 11)]
hv.Curve(errors) * hv.ErrorBars(errors)
Out[2]:

Assymetric error

ErrorBars is a set of x-/y-coordinates with associated error values. Error values may be either symmetric or asymmetric, and thus can be supplied as an Nx3 or Nx4 array (or any of the alternative constructors Chart Elements allow).

In [3]:
errors = [(0.1*i, np.sin(0.1*i), np.random.rand()/2, np.random.rand()/4) for i in np.linspace(0, 100, 11)]
hv.Curve(errors) * hv.ErrorBars(errors, vdims=['y', 'yerrneg', 'yerrpos'])
Out[3]:

Download this notebook from GitHub (right-click to download).