What is the difference between 'log' and 'symlog'?

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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?)

logarithm scale matplotlib python
3 Answers

Here's an example of behaviour when symlog is necessary:

Initial plot, not scaled. Notice how many dots cluster at x~0

    ax = sns.scatterplot(x= 'Score', y ='Total Amount Deposited', data = df, hue = 'Predicted Category')

[Non scaled '

Log scaled plot. Everything collapsed.

    ax = sns.scatterplot(x= 'Score', y ='Total Amount Deposited', data = df, hue = 'Predicted Category')

    ax.set_xscale('log')
    ax.set_yscale('log')
    ax.set(xlabel='Score, log', ylabel='Total Amount Deposited, log')

Log scale '

Why did it collapse? Because of some values on the x-axis being very close or equal to 0.

Symlog scaled plot. Everything is as it should be.

    ax = sns.scatterplot(x= 'Score', y ='Total Amount Deposited', data = df, hue = 'Predicted Category')

    ax.set_xscale('symlog')
    ax.set_yscale('symlog')
    ax.set(xlabel='Score, symlog', ylabel='Total Amount Deposited, symlog')

Symlog scale

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