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Plot two independent y-values with twinx()
Axes.twinx() creates a second Axes that shares the original x-axis but has its own y-scale and right-side y-ticks. Use it when the two plotted series are independent measurements, such as temperature and rainfall. The following example assumes x, y_left, and y_right are already defined as compatible sequences.
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import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(x, y_left, color="tab:red", label="Left quantity")
ax1.set_ylabel("Left quantity (unit)", color="tab:red")
ax1.tick_params(axis="y", labelcolor="tab:red")
ax2.plot(x, y_right, color="tab:blue", label="Right quantity")
ax2.set_ylabel("Right quantity (unit)", color="tab:blue")
ax2.tick_params(axis="y", labelcolor="tab:blue")
fig.tight_layout()
plt.show()Replace each label’s quantity and unit with the meaning of your data. Matching the line, axis-label, and tick-label colors makes it easier to see which scale belongs to which series. fig.tight_layout() helps keep the right-side label from being clipped.
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Show both series in one legend
Each Axes owns its own plotted artists, so collect handles and labels from both before creating a combined legend:
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lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc="best")Place this after plotting both series and before plt.show(). The labels come from the label arguments in the plot calls.
Use secondary_yaxis() for a converted scale
If both scales express the same underlying quantity in different units—for example, Celsius and Fahrenheit—use ax.secondary_yaxis("right", functions=(forward, inverse)). Here, forward converts values from the parent axis’s units to the secondary units, and inverse converts them back. Both functions must accept NumPy arrays. Plot the data on the parent Axes; the secondary axis is for displaying the transformed scale, not for holding a separate data series.
def celsius_to_fahrenheit(c):
return c * 9 / 5 + 32
def fahrenheit_to_celsius(f):
return (f - 32) * 5 / 9
fig, ax = plt.subplots()
ax.plot(x, temperature_c)
ax.set_ylabel("Temperature (°C)")
secax = ax.secondary_yaxis(
"right",
functions=(celsius_to_fahrenheit, fahrenheit_to_celsius),
)
secax.set_ylabel("Temperature (°F)")For exact requirements and behavior, see Matplotlib’s secondary_yaxis API documentation.
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| Need | Use | Why |
|---|---|---|
| Two independent series sharing an x-axis | ax1.twinx() |
Creates another Axes with its own y-scale. |
| A second y-scale that converts the same quantity | ax.secondary_yaxis() |
Derives its displayed scale from the parent through conversion functions. |
Matplotlib’s twinx API documentation describes the second Axes as sharing the x-axis. Its y-scale remains independent, so the two axes’ limits and tick values are not automatically synchronized.
Make the comparison readable and avoid misleading alignment
- Label both y-axes with the quantity and unit; do not rely on color alone.
- Use distinct, consistent colors for each series and its corresponding y-axis labels and ticks.
- Remember that independent scales can make unrelated trends look visually aligned. If that could mislead readers, consider separate panels instead of two y-axes.
- If matching tick-mark positions is important, Matplotlib’s twinx documentation points to
LinearLocator. Aligned positions do not make the scales equivalent.
Know the overlay and interaction behavior
The two Axes occupy the same plotting area. Matplotlib notes that, when picking artists on twin Axes, pick events are called only for artists in the top-most Axes. This matters when building interactive charts that rely on picking; consult the Axes.twinx documentation for the API’s behavior.
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