# Fix Python – What is the difference between ‘log’ and ‘symlog’?

## Question

Asked By – Denilson Sá Maia

In matplotlib, I can set the axis scaling using either `pyplot.xscale()` or `Axes.set_xscale()`. Both functions accept three different scales: `'linear'` | `'log'` | `'symlog'`.

What is the difference between `'log'` and `'symlog'`? In a simple test I did, they both looked exactly the same.

I know the documentation says they accept different parameters, but I still don’t understand the difference between them. Can someone please explain it? The answer will be the best if it has some sample code and graphics! (also: where does the name ‘symlog’ come from?)

Now we will see solution for issue: What is the difference between ‘log’ and ‘symlog’?

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.]
``````