Set the visible horizontal range of a Matplotlib heatmap with ax.set_xlim(left, right). For example, ax.set_xlim(2, 8) displays the plot between x-coordinates 2 and 8. In pyplot-style code, use plt.xlim(2, 8) instead.
Set the x-axis limits
The object-oriented approach targets a specific Axes, which is especially useful for figures with multiple subplots:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
im = ax.imshow(data, cmap="viridis")
ax.set_xlim(2, 8)
plt.show()Replace data with your two-dimensional array and adjust the two arguments to the desired left and right x-coordinates. Matplotlib documents imshow for displaying the image and axis limits as view limits.
If your code uses pyplot’s current-Axes interface, set the same range with:
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plt.xlim(2, 8)plt.xlim applies to the current Axes. If you have multiple panels, make sure the intended Axes is current; ax.set_xlim avoids that ambiguity by naming the target directly. See the pyplot summary.
Understand what the limits control
The values passed to set_xlim are data coordinates, not automatically column numbers. The limits control the displayed view: a range narrower than the image’s horizontal bounds crops the visible image, while a wider range can leave empty space beside it. Explicitly setting view limits disables autoscaling for that axis, so Matplotlib keeps the chosen range until you change it or restore autoscaling. The autoscaling guide explains the distinction between data limits and view limits.
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Map image columns to x-coordinates
Default coordinates without extent
When imshow is called without an extent, pixel centers lie at integer x-coordinates. For an image with numcols columns, the horizontal image boundaries are -0.5 and numcols - 0.5. For example, a 10-column image has pixel centers from 0 through 9 and edges from -0.5 through 9.5. To show the first five complete columns, use ax.set_xlim(-0.5, 4.5); using ax.set_xlim(0, 4) instead places the view boundaries through the centers of the edge pixels.
Meaningful x-coordinates with extent
Use extent when columns represent values such as time, distance, or another data coordinate rather than pixel indices. Its order is (left, right, bottom, top); the first two values define the image’s horizontal boundaries. For example:
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im = ax.imshow(data, extent=(10, 20, 0, 5), aspect="auto")
ax.set_xlim(12, 18)Here, the image spans x-coordinates 10 to 20, and the limits show only the 12-to-18 portion. The coordinate mapping and extent behavior are described in the origin and extent tutorial and the imshow API.
Check orientation and reversed limits
origin='upper' or origin='lower' controls whether the first array row appears at the top or bottom. It primarily affects vertical placement; it does not change how set_xlim(left, right) is called. If you intentionally use descending extent values or reversed x-limits, the horizontal direction can reverse. Check the plotted result when combining those settings. Matplotlib’s origin and extent tutorial describes how extent and fixed limits affect image orientation.
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Choose the right interface
| Approach | Use it when | Example |
|---|---|---|
| Axes method | You want to set limits on a specific subplot or are writing reusable plotting code. | ax.set_xlim(2, 8) |
| Pyplot function | You are working with pyplot’s current Axes and know it is the intended one. | plt.xlim(2, 8) |
Both set the displayed x-range. The practical difference is that the Axes method names the target, whereas pyplot operates on whichever Axes is current.
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