Choose the Matplotlib API based on what the second scale means: use secondary_yaxis() for a reversible conversion of the same quantity, such as Celsius to Fahrenheit; use twinx() for a separate quantity that shares the same x-axis. The distinction determines where you plot the data and how the right-hand axis gets its limits.
Choose the right kind of second y-axis
| What the second axis represents | Use | Where to plot the data | How the second scale behaves |
|---|---|---|---|
| The same quantity expressed in different units, such as temperature in °C and °F | Axes.secondary_yaxis() |
Plot on the parent Axes; the secondary axis is for displaying the transformed scale | Its limits derive from the parent axis through the conversion |
| A different quantity measured alongside the first, such as two unrelated measurements over time | Axes.twinx() |
Plot the second series on the new Axes returned by twinx() |
Each y-axis has its own scale and can be configured independently |
Matplotlib’s “Plots with different scales” example uses two Axes that share an x-axis. For a unit conversion, however, prefer a secondary axis: it makes the relationship between the scales explicit rather than treating them as unrelated measurements.
Show a converted scale with secondary_yaxis()
For Celsius data with Fahrenheit ticks on the right, provide a forward conversion from the parent axis scale and an inverse conversion back to it. The pair must be in that order:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
# Plot the data on the parent Axes in Celsius.
ax.plot(x, temperature_c, color="tab:red")
ax.set_xlabel("Time")
ax.set_ylabel("Temperature (°C)", color="tab:red")
ax.tick_params(axis="y", labelcolor="tab:red")
def celsius_to_fahrenheit(c):
return c * 1.8 + 32
def fahrenheit_to_celsius(f):
return (f - 32) / 1.8
secax = ax.secondary_yaxis(
"right",
functions=(celsius_to_fahrenheit, fahrenheit_to_celsius),
)
secax.set_ylabel("Temperature (°F)", color="tab:blue")
secax.tick_params(axis="y", labelcolor="tab:blue")
fig.tight_layout()
plt.show()Replace x and temperature_c with your own arrays. The functions must accept NumPy arrays, not just individual scalar values. Matplotlib’s secondary-axis example shows the same Celsius/Fahrenheit conversion pattern.
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Control the parent axis, not the secondary one
The secondary axis derives its limits from the parent Axes through the supplied transformation. Set the visible data range on ax, for example with ax.set_ylim(...). Setting limits on the secondary axis does not control the parent range. The secondary-axis API documentation describes it as a display axis rather than an Axes for plotting data.
Make custom conversions valid across the visible range
If your mapping is nonlinear or custom, both directions need to work not only for the values in the dataset but throughout the full visible axis range, including margins around the plotted values. Matplotlib’s secondary-axis example specifically cautions that the mapping should extend beyond the nominal plotted data so those margins can be handled.
Plot independent quantities with twinx()
When the second series is a different measurement—not a conversion of the first—create a twin Axes and plot that series on the returned object:
fig, ax1 = plt.subplots()
ax1.plot(x, series_left, color="tab:red")
ax1.set_xlabel("Time")
ax1.set_ylabel("Quantity A", color="tab:red")
ax1.tick_params(axis="y", labelcolor="tab:red")
ax2 = ax1.twinx()
ax2.plot(x, series_right, color="tab:blue")
ax2.set_ylabel("Quantity B", color="tab:blue")
ax2.tick_params(axis="y", labelcolor="tab:blue")
fig.tight_layout()twinx() creates another Axes that shares the x-axis while keeping an independent y-axis on the opposite side. Configure each y-axis on its own Axes: use ax1 for the first series and its scale, and ax2 for the second. The official Axes.twinx API documents this shared-x, independent-y arrangement.
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Label and lay out both axes clearly
- Give each axis a descriptive label with units, such as
Temperature (°C)andTemperature (°F), or the names and units of two independent quantities. - Color an axis label and its tick labels to match its line when that helps readers connect the scale to the data series.
- Call
fig.tight_layout()to help keep the right-side label from being clipped at the edge of the figure.
These choices are illustrated in Matplotlib’s two-scales example. For version-specific details, consult the API documentation matching the Matplotlib version installed in your environment; the cited stable API pages may reflect a different release.
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