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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallTo change tick label font size and color on an existing Axes, call ax.tick_params(axis='both', labelsize=12, labelcolor='navy'). That one call sets the size and text color of both axes’ tick labels. The rest of this guide covers how to narrow the change to one axis or to minor ticks, how to set the same styling as a default for every plot, and why styling occasionally disappears after a figure is redrawn.
Set tick label size and color on one Axes
The method is Axes.tick_params. It accepts keyword arguments that control the tick marks and the tick labels, and it only affects the Axes you call it on. The example below assumes you already have a figure and an Axes named ax:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [2, 4, 3])
ax.tick_params(axis='both', labelsize=12, labelcolor='navy')
plt.show()Three keywords do most of the work:
| Keyword | What it changes | Example value |
|---|---|---|
labelsize |
Font size of tick labels, given in points or as a named size such as 'large' |
labelsize=12 |
labelcolor |
Text color of tick labels only; tick marks keep their color | labelcolor='navy' |
colors |
Tick marks and tick labels together, using one shared color | colors='navy' |
Use colors when you want the tick marks and labels to match. Use labelcolor when the labels should differ from the marks. Passing both in one call is allowed, but the more specific labelcolor is the one you will see on the labels, so avoid passing both unless you have checked the result.
Limit the change to one axis or tick class
Two keywords narrow the scope. axis selects the x-axis ('x'), the y-axis ('y'), or both ('both', the default). which selects major ticks ('major', the default), minor ticks ('minor'), or both classes ('both').
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ax.tick_params(axis='x', which='major', labelsize=10, labelcolor='darkgreen')
ax.tick_params(axis='y', which='minor', labelsize=8, labelcolor='gray')The first line changes only the x-axis major labels. The second changes only the y-axis minor labels. If your minor ticks appear unchanged, the most common cause is that the call did not include which='minor' or which='both'; minor ticks exist only when the Axes has them enabled, for example through a minor locator.
Any property you do not pass keeps its existing value. A later call therefore layers on top of earlier styling. If you want to clear previous tick settings for that Axes before applying new ones, pass reset=True together with the properties you want.
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Use the pyplot wrapper for the current Axes
If you are working in a script that uses pyplot’s state-based interface, plt.tick_params(...) is the equivalent call. It takes the same keyword arguments and applies them to the current Axes:
plt.tick_params(axis='x', labelsize=10, labelcolor='darkgreen')In an object-oriented script with several subplots, prefer ax.tick_params, because the pyplot wrapper always targets whichever Axes is current, which is easy to get wrong when there are several.
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Set the same styling as a default for every plot
When you want the same tick label styling on every figure in a project, set the defaults through rcParams instead of repeating the call. Matplotlib exposes separate keys for the x and y tick groups:
import matplotlib as mpl
mpl.rcParams.update({
'xtick.labelsize': 12,
'xtick.labelcolor': 'navy',
'ytick.labelsize': 12,
'ytick.labelcolor': 'navy',
})Grouped settings through matplotlib.rc are another supported way to apply the same values. Choose between the two approaches by how far the change should reach:
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- One figure or one Axes: use
ax.tick_params. It is explicit and easy to find in the code that produces the plot. - Every figure in a script or project: set
rcParamsonce near the top of the script or in a shared style setup. Defaults apply to figures created after the change. - Return to stock behavior: call
matplotlib.rcdefaults(), or select the default style, to restore the built-in values.
Why tick label styling sometimes disappears
Matplotlib tick and tick-label objects are not permanent. Plotting calls, panning, zooming, and other changes can create, delete, or rebuild them. Styling you applied to a specific label object may then be lost, even though the code ran without an error.
Two practices cause most of these problems:
- Editing current tick-label objects directly. Changes made to objects returned from the current tick list may not survive a redraw. Use
tick_paramsinstead, because it describes the styling rather than attaching it to a single object. - Using
set_ticklabelsto restyle labels. Matplotlib discourages this unless the tick positions are fixed first. When you need custom label text at fixed positions, set the positions and the labels together, for exampleax.set_xticks([0, 1, 2], ['low', 'mid', 'high']), and then applytick_paramsfor the size and color.
Use xticks from pyplot to set positions and labels, not to style them. Styling belongs in tick_params or rcParams.
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Troubleshooting checklist
- Labels changed back after zooming or a later plot call: move the
tick_paramscall after the last plotting call on that Axes, or switch from direct label edits totick_params. - Only one axis changed: check
axis=. The default is'both', so a call that setsaxis='x'leaves the y-axis alone. - Minor ticks look unchanged: add
which='minor'orwhich='both', and confirm the Axes actually has minor ticks. - A plot ignores your
rcParamschange: set the defaults before creating the figure. Existing figures keep the settings they were built with. - A keyword is rejected: confirm your version. The parameter names above match the Matplotlib pyplot reference current at the time of writing, which lists version 3.11.2. Run
import matplotlib; print(matplotlib.__version__)to check your installed release, and consult theAxes.tick_paramsreference for that version if it differs.
In short, use ax.tick_params with labelsize and labelcolor for individual plots, add axis and which to narrow the scope, and use rcParams only when the same styling should apply everywhere.
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