Python - matplotlib axes limits approximate ticker location

Question

When no axes limits are specified, matplotlib chooses default values as nice, round numbers below and above the minimum and maximum values in the list to be plotted.

Sometimes I have outliers in my data and I don't want them included when the axes are selected. I can detect the outliers, but I don't want to actually delete them, just have them be beyond the area of the plot. I have tried setting the axes to be the minimum and maximum value in the list not including the outliers, but that means that those values lie exactly on the axes, and the bounds of the plot do not line up with ticker points.

Is there a way to specify that the axes limits should be in a certain range, but let matplotlib choose an appropriate point?

For example, the following code produces a nice plot with the y-axis limits automatically set to (0.140,0.165):

from matplotlib import pyplot as plt
plt.plot([0.144490353418, 0.142921640661, 0.144511781706, 0.143587888773, 0.146009766101, 0.147241517391, 0.147224266382, 0.151530932135, 0.158778411784, 0.160337332636])
plt.show()

Plot from the first code example.

After introducing an outlier in the data and setting the limits manually, the y-axis limits are set to slightly below 0.145 and slightly above 0.160 - not nearly as neat and tidy.

from matplotlib import pyplot as plt
plt.plot([0.144490353418, 0.142921640661, 0.144511781706, 0.143587888773, 500000, 0.146009766101, 0.147241517391, 0.147224266382, 0.151530932135, 0.158778411784, 0.160337332636])
plt.ylim(0.142921640661, 0.160337332636)
plt.show()

Plot from the second code example.

Is there any way to tell matplotlib to either ignore the outlier value when setting the limits, or set the axes to 'below 0.142921640661' and 'above 0.160337332636', but let it decide an appropriate location? I can't simply round the numbers up and down, as all my datasets occur on a different scale of magnitude.

unutbu · Accepted Answer · 2013-05-14 13:17:45Z

1

You could make your data a masked array:

from matplotlib import pyplot as plt
import numpy as np

data = [0.144490353418, 0.142921640661, 0.144511781706, 0.143587888773, 500000, 0.146009766101, 0.147241517391, 0.147224266382, 0.151530932135, 0.158778411784, 0.160337332636]
data = np.ma.array(data, mask=False)
data.mask = data>0.16
plt.plot(data)
plt.show()

enter image description here

answered May 14, 2013 at 13:17

unutbu

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2 Comments

thornate Over a year ago

It's not an ideal solution, as I want to still show that the outliers exist. I'd prefer it to be plotted like my second example, but with better axes limits.

thornate Over a year ago

Also, now that I look at it, the y-axis limits haven't been set right. Ideally they should be 0.140 to 0.165.

thornate · Accepted Answer · 2013-05-15 05:52:47Z

unutbu actually gave me an idea that solves the problem. It's not the most efficient solution, so if anyone has any other ideas, I'm all ears.

EDIT: I was originally masking the data like unutbu said, but that doesn't actually set the axes right. I have to remove the outliers from the data.

After removing the outliers from the data, the remaining values can be plotted and the y-axis limits obtained. Then the data with the outliers can be plotted again, but setting the limits from the first plot.

from matplotlib import pyplot as plt

data = [0.144490353418, 0.142921640661, 0.144511781706, 0.143587888773, 500000, 0.146009766101, 0.147241517391, 0.147224266382, 0.151530932135, 0.158778411784, 0.160337332636]
cleanedData = remove_outliers(data) #Function defined by me elsewhere.
plt.plot(cleanedData)

ymin, ymax = plt.ylim()
plt.clf()
plt.plot(data)
plt.ylim(ymin,ymax)
plt.show()

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