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Call ax.plot(x, y) separately for each line. Each call can contain a different number of points; only the x and y values within a single call need to match. This lets you plot independently sampled series on the same axes without trimming or padding them.
Plot unequal-length lines with separate calls
Give each series its own x and y data, then call plot once per series. For example:
import matplotlib.pyplot as plt
x1 = [0, 1, 2, 3]
y1 = [1, 3, 2, 4]
x2 = [0, 1, 2, 3, 4, 5]
y2 = [2, 1, 3, 2, 4, 3]
fig, ax = plt.subplots()
ax.plot(x1, y1, marker="o", label="Series A")
ax.plot(x2, y2, marker="s", label="Series B")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.legend()
plt.show()The first line has four points and the second has six. Each x/y pair still has equal length because each x value and y value describe one coordinate. Matplotlib’s plot API calls repeated plotting the most straightforward way to draw multiple datasets; its quick-start guide also demonstrates successive Axes.plot calls.
Choose input that matches your data
| Approach | When it fits | Shape and styling behavior |
|---|---|---|
Separate ax.plot(x, y) calls |
Independent series, especially when they have different lengths or sampling points. | Each call gets its own x/y pair and can have independent labels and styling. |
| Grouped arguments in one call | Several datasets when a compact call is convenient. | Use groups such as ax.plot(x1, y1, "-", x2, y2, "--"). Each x/y pair must still match. Keyword style properties apply to all lines unless formatting is supplied per group. |
| Two-dimensional arrays | Datasets that share a compatible rectangular shape. | If both x and y are 2D, their shapes must match. If only one is 2D with shape (N, m), the other must have length N and is reused for the m datasets. This is usually not a natural fit for unrelated series of unequal lengths. |
For independent irregular-length series, separate calls make the data structure explicit. Avoid forcing them into a rectangular array unless padding has a real meaning in your dataset.
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Use implicit x coordinates when appropriate
If the horizontal coordinate is simply each observation’s position, pass only y values: ax.plot(y). Matplotlib uses indices from zero through len(y) - 1. Separate calls assign indices independently, so lines of different lengths can still be plotted together.
Represent missing observations deliberately
If the series have their own x coordinates and observation counts, plot each pair as-is; padding is not required. A different case is a series on a shared grid with intentionally missing observations. There, use NaN or a masked value at the missing position if the chart should show a break. Removing a point instead connects the remaining points, which can imply continuity across the interval. The Matplotlib masked and NaN values example illustrates these behaviors.
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Make each line easy to identify
Set a label for each plotted series and call ax.legend(). Matplotlib cycles through default line styles, but explicit markers, colors, or line styles make distinctions stable and can help when color alone is insufficient:
ax.plot(x1, y1, color="tab:blue", marker="o", label="Series A")
ax.plot(x2, y2, color="tab:orange", linestyle="--", marker="s", label="Series B")
ax.legend()The plot API accepts format strings such as "bo" as well as named properties such as color, marker, and linestyle. Use LineCollection when you need to batch-render many line segments; it has a different input and styling workflow and does not resolve mismatched x/y shapes. See Matplotlib’s LineCollection example.
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Check these common errors
- x and y lengths differ within a series: confirm that both arrays describe the same observations before passing them to one
plotcall. - Unequal series forced into a 2D array: use separate calls unless the data genuinely share dimensions or padding has a defined meaning.
- The line crosses a missing interval: deleting the missing point connects its neighbors; use
NaNor a masked value to display a gap. - Lines are hard to distinguish: label them and add markers or explicit line styles, rather than relying on color alone.
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