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Use Axes.twinx() to plot two independent data series against a shared x-axis with separate left and right y-scales. If the two axes represent the same quantity in different units, use Axes.secondary_yaxis() with a conversion function and its inverse instead.
Choose the right kind of second y-axis
| What you need | Use | How the axes relate |
|---|---|---|
| Two independent series that share an x-axis | Axes.twinx() |
Each Axes owns its plotted series and has an independent y-scale. The second y-axis appears on the right by default. Matplotlib twinx API |
| One quantity shown in two convertible units, such as Celsius and Fahrenheit | Axes.secondary_yaxis() |
The secondary scale is derived from the parent axis using a forward conversion and its inverse. Matplotlib secondary_yaxis API |
For example, temperature and rainfall are independent measurements, so they can use separate Axes created with twinx(). A temperature scale in Celsius and another in Fahrenheit are two representations of the same data, so a secondary axis is the better fit.
Plot two independent series with twinx()
Create the first Axes normally, plot the first series on it, then call ax1.twinx() and plot the second series on the returned Axes. The two Axes share the x-axis, while each controls its own y-axis limits, ticks, and formatting.
import matplotlib.pyplot as plt
# x, y1, and y2 are your data arrays.
fig, ax1 = plt.subplots()
ax1.plot(x, y1, color="tab:red")
ax1.set_xlabel("Time")
ax1.set_ylabel("Series 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("Series 2", color="tab:blue")
ax2.tick_params(axis="y", labelcolor="tab:blue")
fig.tight_layout()
plt.show()Replace x, y1, and y2 with arrays of matching data. Keep each y-axis label and tick color aligned with its plotted series so readers can tell which scale to use. tight_layout() can help keep the right-side label inside the figure. This pattern follows Matplotlib’s two-scales example.
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Use secondary_yaxis() for convertible units
When a secondary scale is a conversion of the main scale, attach it to the original Axes and pass a pair of functions: one converting from the parent scale to the secondary scale, and the inverse converting back. Both functions must accept NumPy arrays.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot(x, temperature_c)
ax.set_ylabel("Temperature (°C)")
c_to_f = lambda c: c * 9 / 5 + 32
f_to_c = lambda f: (f - 32) * 5 / 9
secax = ax.secondary_yaxis("right", functions=(c_to_f, f_to_c))
secax.set_ylabel("Temperature (°F)")
plt.show()The example assumes x and temperature_c are defined. The secondary axis derives its limits from the parent through these functions; setting limits directly on the secondary axis does not control the view. The API documentation labels secondary_yaxis() experimental, so check compatibility against the Matplotlib version installed in your environment. The linked stable documentation identifies itself as Matplotlib 3.11.2; that is the documentation version, not a claim about your local installation. API details
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What to know about twinned Axes
twinx()creates a new Axes with an invisible x-axis and an independent y-axis opposite the original; the twin inherits the original Axes’ x-axis autoscaling setting. Matplotlib API- With twinned Axes, pick events are called only for artists in the top-most Axes. This matters if your plot uses interactive picking. Matplotlib API
- For three or more y-axes, Matplotlib’s gallery creates additional twinned Axes and moves their right spines outward. Multiple scales can make a chart harder to interpret; add another axis only when readers need that extra series. Multiple y-axis example
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