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For two measurements that share an x variable but need independent y ranges, create a second Axes with ax.twinx(). If the right-hand scale is a conversion of the same measurement—for example, radians to degrees—use ax.secondary_yaxis() with forward and inverse conversion functions. When both series use the same unit and a comparable range, keep them on one y-axis.

Choose one y-axis, two independent axes, or a converted axis

Situation Use Why
Both series measure comparable values in the same unit and range One Axes A shared y-scale makes direct comparison clear; a second scale is unnecessary.
Two independent measurements share x but have different units or ranges Axes.twinx() It creates a second y-axis with its own scale while sharing the original x-axis.
The right axis expresses the same quantity in another unit Axes.secondary_yaxis() An explicit conversion links the alternate scale to the original rather than treating it as an unrelated measurement.

Matplotlib’s stable “Plots with different scales” example documents twinx() for independent scales, while its “Secondary Axis” example documents the conversion-based approach. The stable gallery result identified Matplotlib 3.11.2 at research time; consult the documentation for the version you use: Plots with different scales and Secondary Axis.

Plot independent quantities with twinx()

Call twinx() on the original Axes, then draw each series on the Axes whose y-scale describes it. The second Axes shares the x-axis and places its independent y-axis on the right.

import matplotlib.pyplot as plt

fig, ax1 = plt.subplots()
ax1.plot(x, y1, color="tab:red")
ax1.set_xlabel("time (s)")
ax1.set_ylabel("quantity 1", color="tab:red")
ax1.tick_params(axis="y", labelcolor="tab:red")

ax2 = ax1.twinx()
ax2.plot(x, y2, color="tab:blue")
ax2.set_ylabel("quantity 2", color="tab:blue")
ax2.tick_params(axis="y", labelcolor="tab:blue")

fig.tight_layout()
plt.show()

Replace x, y1, and y2 with your data arrays. Give both y-axes meaningful labels, including units, and use distinct line colors that match their axis labels and tick labels. tight_layout() helps keep the right-side label from being clipped.

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What the two scales mean

The left and right axes are independent: each series is positioned according to its own y-scale. The twinned axis inherits the original Axes’ x-axis autoscale setting, but its y scale is separate. The Matplotlib API reference describes the shared-x behavior and notes that LinearLocator can be used when you need tick marks on the two y-axes to align: Axes.twinx API reference.

Show a converted unit with secondary_yaxis()

For a second display of the same underlying quantity, provide a forward conversion and its inverse. For example, if the primary axis is in radians and the secondary axis is in degrees, the functions should convert in both directions:

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import numpy as np

forward = lambda radians: np.degrees(radians)
inverse = lambda degrees: np.radians(degrees)

secax = ax.secondary_yaxis(
    "right",
    functions=(forward, inverse),
)
secax.set_ylabel("angle (degrees)")

Here, ax is the existing primary Axes, whose y-axis is in radians. The conversion functions must accept NumPy arrays. The API also accepts an invertible Transform instead of a pair of functions. Secondary-axis limits are derived from the parent Axes; setting limits on the secondary axis does not change them. See Matplotlib’s Secondary Axis example for the documented pattern.

Keep a dual-scale chart interpretable

  • Label both quantities and units directly on their respective axes.
  • Match each series’ color to its axis label and tick labels so readers can identify the scale to use.
  • Do not imply that two independently scaled measurements are directly comparable just because their lines share a chart. Explain what each series measures and which axis maps to it.
  • If two scales make the relationship hard to read, use separate, vertically aligned subplots with a shared x-axis instead. That is a design alternative, not a guarantee that one layout is universally clearer.

Independent scaling can make the lines appear to track each other even when their values are not directly comparable. Describe the measurement relationship rather than asking readers to infer it from the line shapes.

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Align tick marks when that comparison matters

If corresponding tick positions on the left and right axes need to line up, Matplotlib’s Axes.twinx() reference points to using a locator such as LinearLocator. Use alignment only when it helps interpret the chart; the axes remain independent scales, not one shared measurement.

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