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Use Axes.set_xticks() to choose x-axis tick positions and Axes.set_xlim() to control the visible range. Generate the positions yourself, then set the limits afterward: setting ticks can expand the view to keep every requested tick visible.
Set an x-axis tick interval and range
set_xticks() accepts tick positions; it does not accept a start, stop, and interval. Generate the positions first, pass them to set_xticks(), and then set the visible bounds with set_xlim().
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(x, y)
start, stop, step = 0, 10, 2
ticks = np.arange(start, stop + step, step)
ax.set_xticks(ticks)
ax.set_xlim(start, stop)
plt.show()Here, ticks are placed every two units from 0 through 10, and the displayed x-axis range is 0 to 10. Replace start, stop, and step with values appropriate to your data. With np.arange(), check the final generated value, particularly when using a non-integer step, to ensure it matches the endpoint you intend.
Why set the range after the ticks?
Matplotlib may expand the view limits to make all positions passed to set_xticks() visible. If you need an exact x-axis range, call set_xlim() after set_xticks(). For example, if your tick positions extend beyond the desired bounds, the final set_xlim(start, stop) keeps the displayed range at those bounds.
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Set custom tick labels
Pass a label for each tick position when you want custom text:
ax.set_xticks([0, 2, 4, 6], labels=["zero", "two", "four", "six"])The labels must correspond one-for-one with the tick positions. If you omit them, Matplotlib uses the axis formatter to determine the labels.
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Set minor ticks instead of major ticks
By default, set_xticks() sets major tick positions. To set minor ticks, use its minor=True argument:
ax.set_xticks(ticks, minor=True)When tick positions do not get labels
A formatter can choose not to label every tick position. For example, Matplotlib’s logarithmic formatters label decades by default, so arbitrary positions on a logarithmic axis may not receive labels. If you need labels at particular positions, provide them with set_xticks(positions, labels=...) or configure an explicit formatter. The official Matplotlib 3.11.1 Axes.set_xticks reference documents the tick-position and label arguments, the minor-tick option, and the effect on view limits; check the API reference for your installed release if you use a different version.
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