What is the difference between 'log' and 'symlog'?
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What is the difference between
'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?)
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symlog is like log but allows you to define a range of values near zero within which the plot is linear, to avoid having the plot go to infinity around zero.
In a log graph, you can never have a zero value, and if you have a value that approaches zero, it will spike down way off the bottom off your graph (infinitely downward) because when you take "log(approaching zero)" you get "approaching negative infinity".
symlog would help you out in situations where you want to have a log graph, but when the value may sometimes go down towards, or to, zero, but you still want to be able to show that on the graph in a meaningful way. If you need symlog, you'd know.
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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')
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')
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')
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- Well... I read that, but I still don't know when I should use one or another. I was expecting some kind of graphical example so I could actually see what is the problem that symlog tries to solve.