What is the correct way to replace matplotlib label labels with computed values?

Question

I have a figure with a log axis

and I would like to move the axis labels with the value logs, not the values ​​themselves

How i did it with

plt.axes().set_xticklabels([math.log10(x) for x in plt.axes().get_xticks()]) 

but I wonder if there is a less complicated way to do this.

What is the correct idiom for systematically rewriting ticks in matplotlib areas with values ​​computed from the original tick values?

Answer 1

Take a look at Formatter classes. If you do not put text on your ticks, you will almost never directly use set_xticklabels or set_yticklabels . This completely cancels your label labels from your data. If you adjust the presentation limits, the tick marks will remain the same.

In your case, there is a formatter for this:

 fig, ax = plt.subplots() ax.loglog(np.logspace(0, 5), np.logspace(0, 5)**2) ax.xaxis.set_major_formatter(matplotlib.ticker.LogFormatterExponent()) 

matplotlib.ticker.LogFormatterExponent doc

In general, you can use FuncFormatter . For an example of using FuncFomatter see matplotlib: change yaxis tag labels , one of many examples floating around SO.

A brief example of what you want, lifting up exactly from JoeKington in the comments:

 ax.xaxis.set_major_formatter( FuncFormatter(lambda x, pos: '{:0.1f}'.format(log10(x))))