Condense Y-Ticks to be useful

Trying to plot a set of points where a couple of the points have extreme Y values. Is there a way to showcase those values on a graph without losing the value of the other points. For example:

[(1, 10), (2, 33), (3, 100000), (4, 17), (5, 45), (6, 8), (7, 950000)]

I'm picturing a graph with Y ticks 0-50 and then similar sized gap between 50-100000 to showcase the extreme point.

Not sure whether matplotlib can do it automatically, but there are several things you try:

• Use a log-scale y-axis, i.e. ax.set_yscale('log'). This will to some extent decrease the difference between regular points and outlier points, but you will still see all regular data lying at the bottom of the graph.

• Manually change the y-value of the outliers to some not-that-extreme value. You can later show those points are of some value by manually setting display-content of y-axis using plt.yticks(actual_data_array, what_to_display_array)

• Generate a regular (high enough) plot, then do image processing later to cut the middle part of the image. There might be a better way, but one way to do that would be plt.savefig to save the plot, matplotlib.image.imread to read the plot, and finally process it using plt.imshow(np.concatenate((image_data[:100], image_data[-100:]), 0))

Here is an example of the second method I was talking about:

import numpy as np
import matplotlib.pyplot as plt

a = np.array(((1, 10), (2, 33), (3, 100000), (4, 17), (5, 45), (6, 8), (7, 950000)), 'f')

# scale the array so that matplotlib can plot it "uniformly"
a[a[:,1]>99999,1] = a[a[:,1]>99999,1] / 20000 + 55

plt.plot(*a.T)

# do the displaying trick 
plt.yticks(np.r_[np.linspace(0, 50, 5),
                 np.linspace(100000/20000+55, 950000/20000+55, 5)],
           np.r_[np.linspace(0, 50, 5, dtype='i'),
                 np.linspace(100000, 950000, 5, dtype='i')])

plt.grid()

It looks like this: