Matplotlib Log Scale Tick Label Number Formatting

Matplotlib log scale tick label number formatting

Sure, just change the formatter.

For example, if we have this plot:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.axis([1, 10000, 1, 100000])
ax.loglog()

plt.show()

You could set the tick labels manually, but then the tick locations and labels would be fixed when you zoom/pan/etc. Therefore, it's best to change the formatter. By default, a logarithmic scale uses a LogFormatter, which will format the values in scientific notation. To change the formatter to the default for linear axes (ScalarFormatter) use e.g.

from matplotlib.ticker import ScalarFormatter
for axis in [ax.xaxis, ax.yaxis]:
    axis.set_major_formatter(ScalarFormatter())

Matplotlib log scale formatting

Found in this thread

import matplotlib.pyplot as pl
from matplotlib.ticker import ScalarFormatter

fig = pl.figure()
ax = fig.add_subplot(111)

ax.set_xscale('log')
ax.set_xticks([1,2,5,10])
ax.get_xaxis().set_major_formatter(ScalarFormatter())
ax.set_xlim([1., 10.])

Matplotlib: Display xticks as integers in log-log scale

One way to achieve that is to play with xticks and yticks. Here is an example:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 200)
y = 0.1 * (x / x[0])**(np.log10(1e-07/0.1) / np.log10(x[-1]/x[0]))

plt.figure()
plt.plot(x, y)
plt.xscale("log")
plt.yscale("log")
plt.xticks([1, 2, 5, 10, 20, 50, 100, 200], [1, 2, 5, 10, 20, 50, 100, 200])
plt.yticks([0.01, 1e-04, 1e-06], [0.01, "1e-04", 1e-06])
plt.minorticks_off()
plt.show()

How to change log-scale tick labels in matplotlib

The values shown are the minor ticks, to disable them, you can state:

ax.minorticks_off()

This will result in the tick label for 7.5 dissapearing as well.

What you probably want, is the following solution:

from matplotlib.ticker import StrMethodFormatter, NullFormatter
ax.xaxis.set_major_formatter(StrMethodFormatter('{x:.1f}'))
ax.xaxis.set_minor_formatter(NullFormatter())

Specify format of floats for tick labels

See the relevant documentation in general and specifically

from matplotlib.ticker import FormatStrFormatter

fig, ax = plt.subplots()

ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))

Formatting custom labels on a logscale axis

ScalarFormatter automatically determines a useful number of significant digits to show. This is most often desired. However, ScalarFormatter is not optimized to be used on log scales. Hence it will fail in this case.

Because here, we don't need much of the capabilities of ScalarFormatter anyways, we may opt for a StrMethodFormatter instead. For three digits, use e.g.

matplotlib.ticker.StrMethodFormatter("{x:1.3f}")

Matplotlib logscale ticks format

I think you should set formatter on both minor and major axis.

Please try this:

 ax.yaxis.set_minor_formatter(ticker.FuncFormatter(lambda y, _: '{:g}'.format(y))))

set ticks with logarithmic scale

import matplotlib
from matplotlib import pyplot as plt
fig1, ax1 = plt.subplots()
ax1.plot([10, 100, 1000], [1,2,3])
ax1.set_xscale('log')
ax1.set_xticks([20, 200, 500])
ax1.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())

or

ax1.get_xaxis().get_major_formatter().labelOnlyBase = False
plt.show()

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