For fixed custom x-axis labels in Matplotlib, pair each label with its intended position using ax.set_xticks(positions, labels). The older ax.set_xticklabels(labels) method is discouraged in the Matplotlib 3.11.2 documentation because labels can become misaligned if tick positions change.
Set custom labels and positions together
For a final plot with a known set of categories, pass the tick positions and labels in the same call. This makes their pairing explicit:
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import matplotlib.pyplot as plt
values = [12, 18, 9]
positions = [0, 1, 2]
labels = ["North", "Central", "South"]
fig, ax = plt.subplots()
ax.bar(positions, values)
ax.set_xticks(positions, labels)
ax.set_xlabel("Region")
fig.tight_layout()
plt.show()set_xticks establishes the locations and, when labels are supplied, assigns the corresponding text. Matplotlib’s current stable 3.11.2 Axes API documents this approach; see Axes.set_xticks.
When you need to keep set_xticklabels
If you are updating existing code that calls set_xticklabels, set the tick positions first and provide exactly one label for each position:
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positions = [0, 1, 2]
labels = ["North", "Central", "South"]
ax.set_xticks(positions)
ax.set_xticklabels(labels)Matplotlib’s 3.11.2 Axis.set_ticklabels API marks the method as discouraged because label assignment depends on tick positions. It applies labels through a FixedFormatter, which chooses text by tick index rather than by tick value. If the locator moves or replaces the ticks, the text may no longer describe the intended positions. A fixed formatter should be paired with a fixed locator, which is why setting the ticks first matters.
Choose fixed labels or a formatter
Use fixed positions and labels when the plot should show a deliberate set of categories. Fixed tick settings are appropriate for a specific finished plot, but they do not automatically adapt when a viewer interacts with the Axes or changes its limits.
If each label should be calculated from its tick value, use a formatter instead. For example, FuncFormatter receives a tick value and its position, then returns the text to display:
from matplotlib.ticker import FuncFormatter
ax.xaxis.set_major_formatter(
FuncFormatter(lambda x, pos: f"${x:,.0f}")
)This rule remains connected to the values as the locator chooses ticks. For date axes or specialized scales, use the corresponding date- or scale-aware locator and formatter rather than a hard-coded list. Matplotlib’s ticker API describes the available formatter and locator families; its Axis ticks guide explains how they work together.
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Fix common label problems
- Labels appear shifted or change after plotting: use
ax.set_xticks(positions, labels), or establish a fixed locator before callingset_xticklabels. - The label and position counts differ: make the sequences the same length, with one label for each tick location.
- Labels should describe numeric values, not category slots: use a value-aware formatter such as
FuncFormatterrather than a fixed list indexed by position. - Ticks should respond to pan, zoom, or changing limits: let an automatic locator choose tick locations and use a formatter to generate their labels.
- You only want to change tick appearance: prefer
set_tick_paramsfor tick styling. Keyword arguments toset_xticklabelsaffect current tick objects and may not persist when ticks are regenerated.
These recommendations reflect Matplotlib’s stable 3.11.2 documentation, reviewed on October 7, 2026.
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