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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsUse plt.errorbar(x, y, yerr=...) to add vertical error bars, xerr=... for horizontal bars, or both for uncertainty in both dimensions. Supply nonnegative error magnitudes: a scalar or one-dimensional array gives symmetric bars, while a two-row array gives separate lower and upper magnitudes.
Plot basic vertical error bars
This example adds symmetric vertical intervals to three data points. The value at each position in yerr is the distance above and below its corresponding y value.
import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [2.0, 2.8, 4.2]
yerr = [0.2, 0.35, 0.25]
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr=yerr, fmt='o', capsize=3)
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()The equivalent pyplot call is plt.errorbar(x, y, yerr=yerr, fmt='o', capsize=3). The x and y arguments give the point locations. Use yerr for vertical intervals and xerr for horizontal intervals.
Choose the error input shape
The same input-shape rules apply to xerr and yerr. An error value is a magnitude, not a signed offset, so all supplied values must be zero or greater.
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| Input | Meaning |
|---|---|
| Scalar | One symmetric error magnitude applied to every point. |
Array with shape (N,) |
One symmetric error magnitude for each of the N points. |
Array with shape (2, N) |
Different lower and upper magnitudes for each point. Row 0 contains lower magnitudes; row 1 contains upper magnitudes. |
Represent asymmetric errors
Put the lower magnitudes in the first row and upper magnitudes in the second. Do not make the lower row negative to indicate direction.
lower_errors = [0.15, 0.25, 0.10]
upper_errors = [0.30, 0.20, 0.40]
yerr = [lower_errors, upper_errors]
ax.errorbar(x, y, yerr=yerr, fmt='o', capsize=3)Add horizontal bars or omit the data markers
Pass xerr for horizontal intervals. To show both dimensions, pass xerr and yerr in the same call. Set fmt='none' when the plot already contains the data points or line and you want only the error bars.
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ax.errorbar(x, y, xerr=0.1, yerr=yerr, fmt='none', capsize=3)With a format such as 'o', the call draws markers along with the intervals; the format string controls the data marker and line style.
Style the intervals and reduce overlap
ecolorsets the error-line color. If omitted, the data line color is used.elinewidthandelinestyleset the error-line width and style.capsizesets cap length in points. Its default followsrcParams['errorbar.capsize'], documented as0.0; set it explicitly if you want visible caps.capthicksets cap thickness, but legacymewormarkeredgewidthsettings override it for backward compatibility.barsabove=Truedraws error bars above the plot symbols; by default they are below.errorevery=Ndraws bars at everyNth point. Useerrorevery=(start, N)to choose a starting index and then draw everyNth bar. This thins the error bars, not the data series, and can help where bars overlap.
Mark one-sided limits
For censored or one-sided bounds, use lolims, uplims, xlolims, or xuplims. These flags tell Matplotlib that a plotted value represents a limit rather than a two-sided interval, and the plot uses caret indicators. The naming can be counterintuitive: lolims=True means the plotted y value is a lower limit of the true value, so an upward-pointing indicator is drawn.
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If an axis is inverted, set its limits before calling errorbar() so the limit indicators are drawn in the correct direction.
Interpret the quantity you plot
errorbar() draws the magnitudes you provide; it does not calculate them or determine whether they represent standard deviation, standard error, a confidence interval, or another quantity. State the quantity and how it was calculated in the surrounding text or legend so readers can interpret the bars accurately.
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Use the returned container and check version-specific behavior
The call returns an ErrorbarContainer containing the data line (Line2D), cap lines (Line2D objects), and error-bar line collections (LineCollection). This gives you access to the plotted components for later inspection or styling.
For polar plots, Matplotlib 3.7 introduced rendering of caps and error lines in polar coordinates. If a plot behaves differently from expected, check the documentation for the Matplotlib version installed in your environment. The Matplotlib 3.11.0 errorbar API reference documents the arguments, return value, and version detail.
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