I finally found some time to do some experiments in order to understand the difference between them. Here's what I discovered:

• `log` only allows positive values, and lets you choose how to handle negative ones (`mask` or `clip`).
• `symlog` means symmetrical log , and allows positive and negative values.
• `symlog` allows to set a range around zero within the plot will be linear instead of logarithmic.

I think everything will get a lot easier to understand with graphics and examples, so let's try them:

``````import numpy
from matplotlib import pyplot

# Enable interactive mode
pyplot.ion()

# Draw the grid lines
pyplot.grid(True)

# Numbers from -50 to 50, with 0.1 as step
xdomain = numpy.arange(-50,50, 0.1)

# Plots a simple linear function 'f(x) = x'
pyplot.plot(xdomain, xdomain)
# Plots 'sin(x)'
pyplot.plot(xdomain, numpy.sin(xdomain))

# 'linear' is the default mode, so this next line is redundant:
pyplot.xscale('linear')``````

``````# How to treat negative values?
# 'mask' will treat negative values as invalid
# 'mask' is the default, so the next two lines are equivalent
pyplot.xscale('log')

``````# 'clip' will map all negative values a very small positive one
pyplot.xscale('log', nonposx='clip')``````

``````# 'symlog' scaling, however, handles negative values nicely
pyplot.xscale('symlog')``````

``````# And you can even set a linear range around zero
pyplot.xscale('symlog', linthreshx=20)``````

Just for completeness, I've used the following code to save each figure:

``````# Default dpi is 80
pyplot.savefig('matplotlib_xscale_linear.png', dpi=50, bbox_inches='tight')``````

Remember you can change the figure size using:

``````fig = pyplot.gcf()
fig.set_size_inches([4., 3.])
# Default size: [8., 6.]``````