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Plot yerr/xerr as shaded region rather than error bars

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How do you shade a plot?

Make sure you are on a layout tab. Select the border of the viewport that you want to modify. Right-click to display a shortcut menu and click Properties. In the Properties palette, under Misc., click Shade Plot, and then from the drop-down menu, select an option for plotting.

What function is used to make an error bar plot?

plt. errorbar() method is used to plot error bars and we pass the argument x, y, and xerr and set the value of xerr = 0.9. Then we use plt. show() method to display the error bar plotted graph.


Ignoring the smooth interpolation between points in your example graph (that would require doing some manual interpolation, or just have a higher resolution of your data), you can use pyplot.fill_between():

from matplotlib import pyplot as plt
import numpy as np

x = np.linspace(0, 30, 30)
y = np.sin(x/6*np.pi)
error = np.random.normal(0.1, 0.02, size=y.shape)
y += np.random.normal(0, 0.1, size=y.shape)

plt.plot(x, y, 'k-')
plt.fill_between(x, y-error, y+error)
plt.show()

enter image description here

See also the matplotlib examples.


This is basically the same answer provided by Evert, but extended to show-off some cool options of fill_between

enter image description here

from matplotlib import pyplot as pl
import numpy as np

pl.clf()
pl.hold(1)

x = np.linspace(0, 30, 100)
y = np.sin(x) * 0.5
pl.plot(x, y, '-k')


x = np.linspace(0, 30, 30)
y = np.sin(x/6*np.pi)
error = np.random.normal(0.1, 0.02, size=y.shape) +.1
y += np.random.normal(0, 0.1, size=y.shape)

pl.plot(x, y, 'k', color='#CC4F1B')
pl.fill_between(x, y-error, y+error,
    alpha=0.5, edgecolor='#CC4F1B', facecolor='#FF9848')

y = np.cos(x/6*np.pi)    
error = np.random.rand(len(y)) * 0.5
y += np.random.normal(0, 0.1, size=y.shape)
pl.plot(x, y, 'k', color='#1B2ACC')
pl.fill_between(x, y-error, y+error,
    alpha=0.2, edgecolor='#1B2ACC', facecolor='#089FFF',
    linewidth=4, linestyle='dashdot', antialiased=True)



y = np.cos(x/6*np.pi)  + np.sin(x/3*np.pi)  
error = np.random.rand(len(y)) * 0.5
y += np.random.normal(0, 0.1, size=y.shape)
pl.plot(x, y, 'k', color='#3F7F4C')
pl.fill_between(x, y-error, y+error,
    alpha=1, edgecolor='#3F7F4C', facecolor='#7EFF99',
    linewidth=0)



pl.show()