Use ax.set_xlim(left, right) to control the horizontal range shown in a Matplotlib heatmap. The values are x-axis data coordinates, so whether they represent column positions or meaningful measurements depends on the image’s coordinate mapping.
Set the heatmap’s x-axis limits
For an Axes-based plot, call set_xlim on the Axes that contains the heatmap:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
im = ax.imshow(data, cmap="viridis")
ax.set_xlim(2, 8)This displays the horizontal interval from x = 2 to x = 8. You can also set the range through pyplot’s current-Axes interface:
plt.xlim(2, 8)plt.xlim acts on whichever Axes is current. In figures with multiple panels, ax.set_xlim makes the target subplot explicit. See the pyplot summary for the current-Axes behavior.
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Understand what the x values refer to
The limits are data coordinates, not inherently column numbers. For imshow, the image’s extent determines its bounding box in data coordinates. Its tuple order is (left, right, bottom, top). For example:
im = ax.imshow(data, extent=(10, 20, 0, 5), origin="lower")
ax.set_xlim(12, 16)Here the image spans x = 10 to x = 20, and the limits show only the section from 12 to 16. A narrower range crops the visible image; a wider range can leave blank space beside it. The imshow API documents the extent and origin parameters.
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Default coordinates without an extent
When you omit extent, columns are positioned using pixel-index coordinates: pixel centers lie at integer x values, while the image edges extend half a unit beyond the first and last centers. A 10-column image therefore has centers from 0 through 9 and edges from −0.5 through 9.5. To show complete columns 2 through 7, for example, use ax.set_xlim(1.5, 7.5); using ax.set_xlim(2, 8) instead places the view boundaries at those coordinates, cutting through the pixels centered at the endpoints.
If array columns correspond to real measurements, dates, or other coordinates, set extent to map the image to those values, then choose limits in the same coordinate system.
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Know what setting limits changes
set_xlim sets the displayed view limits. Matplotlib distinguishes view limits from data limits, which describe the plotted data. Explicitly setting view limits disables autoscaling on that axis, so later plotting does not automatically expand the x range. Matplotlib explains this distinction in its axis autoscaling guide.
If you want Matplotlib to resume choosing the horizontal view range automatically, use ax.autoscale(enable=True, axis="x"). For more detail on image coordinate mapping and fixed limits, see the origin and extent tutorial.
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Handle reversed limits and image orientation
To display the x direction in reverse, pass the larger limit first, such as ax.set_xlim(8, 2). Descending extents and reversed limits can interact with image orientation, so inspect the plotted result when intentionally reversing direction. origin="upper" or origin="lower" controls whether the first array row appears at the top or bottom; it concerns vertical placement and does not change the basic x-limit call.
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