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copied!<p>As you have noticed, <code>xscale</code> and <code>yscale</code> does not support a simple linear re-scaling (unfortunately). As an alternative to Hooked's answer, instead of messing with the data, you can trick the labels like so:</p> <pre><code>ticks = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x*scale)) ax.xaxis.set_major_formatter(ticks) </code></pre> <p>A complete example showing both x and y scaling:</p> <pre><code>import numpy as np import pylab as plt import matplotlib.ticker as ticker # Generate data x = np.linspace(0, 1e-9) y = 1e3*np.sin(2*np.pi*x/1e-9) # one period, 1k amplitude # setup figures fig = plt.figure() ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) # plot two identical plots ax1.plot(x, y) ax2.plot(x, y) # Change only ax2 scale_x = 1e-9 scale_y = 1e3 ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_x)) ax2.xaxis.set_major_formatter(ticks_x) ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y)) ax2.yaxis.set_major_formatter(ticks_y) ax1.set_xlabel("meters") ax1.set_ylabel('volt') ax2.set_xlabel("nanometers") ax2.set_ylabel('kilovolt') plt.show() </code></pre> <p>And finally I have the credits for a picture:</p> <p><a href="https://i.stack.imgur.com/IdZsD.png" rel="noreferrer"><img src="https://i.stack.imgur.com/IdZsD.png" alt="Left: ax1 no scaling, right: ax2 y axis scaled to kilo and x axis scaled to nano"></a></p> <p>Note that, if you have <code>text.usetex: true</code> as I have, you may want to enclose the labels in <code>$</code>, like so: <code>'${0:g}$'</code>.</p>

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