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To change the font size and color of tick labels on an existing Matplotlib Axes, call ax.tick_params() with labelsize and labelcolor. For example, ax.tick_params(axis='both', labelsize=12, labelcolor='navy') sets both axes’ tick labels to 12 points in navy. The same method also covers tick marks, minor ticks, and default styling for every plot you make later.
Set font size and color on an existing Axes
Most tick styling is done on an Axes object, the plotting area that holds the lines, bars, and axis ticks. Pass the properties you want to change to tick_params, and Matplotlib updates only those properties. Everything else keeps its current value.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 1, 9])
ax.tick_params(axis='both', labelsize=12, labelcolor='navy')
plt.show()The two properties do different jobs:
| Argument | What it changes | Accepted values | Example |
|---|---|---|---|
labelsize |
Font size of the tick labels | A number in points, or a named size such as 'small', 'medium', or 'large' |
labelsize=10 or labelsize='large' |
labelcolor |
Text color of the tick labels only | Any Matplotlib color: a name, a hex string, or an RGB tuple | labelcolor='darkgreen' |
colors |
Tick marks and tick labels together | Any Matplotlib color | colors='navy' |
Use labelcolor when you want readable text in one color while keeping the tick marks in their default color. Use colors when a single shade should apply to the whole axis, including the small tick lines.
Limit the change to one axis or to minor ticks
tick_params has two selector arguments. Leaving them out applies the change to every axis and only to major ticks, so it is worth setting them explicitly when you want a narrower effect.
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axisselects which axis is affected. The default is'both'; use'x'or'y'for one axis.whichselects the tick class. The default is'major'; use'minor'for minor ticks or'both'for both classes.
To make only the x-axis major tick labels smaller and green:
ax.tick_params(axis='x', which='major', labelsize=10, labelcolor='darkgreen')Minor tick labels are often hidden by default, so changing their size or color has no visible effect until minor ticks are turned on, for example with ax.minorticks_on(). If a change does not appear, check the which value first.
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Use the pyplot wrapper
If you work with the pyplot state-based interface and do not keep a reference to the Axes, plt.tick_params() accepts the same arguments and applies them to the current Axes. It is convenient in short scripts. In code with several subplots, an explicit ax.tick_params() call is clearer, because it is obvious which Axes is changed.
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plt.plot([1, 2, 3], [4, 1, 9])
plt.tick_params(axis='y', labelsize=11, labelcolor='firebrick')Set the same style as a default for future plots
If every chart in a project should use the same tick font size and color, set the defaults in rcParams instead of repeating tick_params calls. The relevant keys are xtick.labelsize, xtick.labelcolor, ytick.labelsize, and ytick.labelcolor.
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import matplotlib as mpl
mpl.rcParams.update({
'xtick.labelsize': 12,
'xtick.labelcolor': 'navy',
'ytick.labelsize': 12,
'ytick.labelcolor': 'navy',
})The grouped rc function does the same thing in one line:
import matplotlib as mpl
mpl.rc('xtick', labelsize=12, labelcolor='navy')
mpl.rc('ytick', labelsize=12, labelcolor='navy')Defaults apply to figures created after the setting is made. Figures that already exist keep their current styling, so set the defaults at the top of the script or in a style file. To undo your changes, call matplotlib.rcdefaults(), or select the built-in default style with plt.style.use('default').
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Why ticks are not safe to edit directly
A common alternative is to loop over ax.get_xticklabels() and set fontsize or color on each label object. This often works at first, but Matplotlib tick and label objects are not permanent. Plotting more data, panning, zooming, or other operations can create, delete, or rebuild them, and the direct changes can disappear. Matplotlib’s documentation describes this behavior, and it is the reason tick_params is the recommended route for ordinary styling.
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The same caution applies to set_ticklabels(). It is discouraged unless the tick positions have already been fixed. When you need custom text at specific positions, set the positions and the labels in one call:
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ax.set_xticks([0, 1, 2], labels=['Low', 'Medium', 'High'])
ax.tick_params(axis='x', labelsize=12, labelcolor='navy')Set the positions and labels first, then style them with tick_params. This keeps the text attached to the correct ticks even if the plot is redrawn.
Choose the right approach
| Approach | Scope | Can select x/y and major/minor? | Survives later plotting or zooming? | Best use |
|---|---|---|---|---|
ax.tick_params() |
One Axes | Yes | Yes, for the properties you set | Styling a specific chart |
plt.tick_params() |
Current Axes | Yes | Yes, for the properties you set | Short pyplot scripts |
rcParams or rc |
All figures created afterward | Only through the x/y tick groups | Applies at creation time | Project-wide or team-wide defaults |
| Editing label objects directly | Objects present at that moment | Manually | Not reliably | Avoid for ordinary styling |
Troubleshooting when the change does not appear
- The styled Axes is not the one you are viewing. In multi-panel figures, confirm that the variable you styled is the same
axyou plotted on. - A later call reset the styling. Code that redraws the axis, such as a new
set_xticks()call or a plotting function that rebuilds the ticks, can overwrite earlier changes. Move thetick_paramscall to after the last plotting or tick-setting call. - The wrong tick class was targeted. Minor tick labels need
which='minor'orwhich='both', and minor ticks must be enabled first. - An rcParams change had no effect. Defaults only apply to figures created after the change. Set them before calling
plt.subplots(), or pass the styling explicitly withtick_params. - The behavior differs from the documentation you are reading. Check your installed version with
python -c "import matplotlib; print(matplotlib.__version__)". The pyplot reference current at the time of writing lists Matplotlib 3.11.2, and older releases may differ in small details.
Using the same tick_params call across Matplotlib releases is the most dependable approach, since its arguments have been stable across versions. Confirm the exact behavior against the reference for your installed release if you rely on an edge case.
Use ax.tick_params() for a single chart, rcParams for defaults across a project, and avoid editing tick-label objects directly. That combination covers almost every case where you need to change tick font size and color.
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