Changing plot scale by a factor in matplotlib

As you have noticed, xscale and yscale 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:

ticks = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x*scale))
ax.xaxis.set_major_formatter(ticks)

A complete example showing both x and y scaling:

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() 

And finally I have the credits for a picture:

Left: ax1 no scaling, right: ax2 y axis scaled to kilo and x axis scaled to nano

Note that, if you have text.usetex: true as I have, you may want to enclose the labels in $, like so: '${0:g}$'.

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