diff --git a/tutorials/intermediate/legend_guide.py b/tutorials/intermediate/legend_guide.py
index 596d6df93b55..14dbcbc55ff3 100644
--- a/tutorials/intermediate/legend_guide.py
+++ b/tutorials/intermediate/legend_guide.py
@@ -137,6 +137,51 @@
plt.show()
+###############################################################################
+# If you have multiple plots side-by-side each with a common legend and wish to
+# instead have one legend that represents all subplots, you can do this:
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Make a mosaic of subplots and use the top one for the legend.
+fig, axes = plt.subplot_mosaic(
+ (("legend", "legend", "legend"), # Top row has one column.
+ ("f", "df_deta", "d2f_deta2")), # Bottom row has three columns.
+ height_ratios=(0.1, 0.9), # Size the dummy plot appropriately.
+ num="distributions") # I like to name my figures. :)
+fig.set_constrained_layout(True)
+
+eta = np.linspace(0, 1)
+cap_lambda_vals = np.array([-24, -12, 0, 12, 24])
+for cap_lambda in cap_lambda_vals:
+ # Some data
+ f = 2 * eta - 2 * eta**3 + eta**4 + cap_lambda * eta / 6 * (1 - eta)**3
+ df_deta = 2 - 6 * eta**2 + 4 * eta**3 + cap_lambda / \
+ 6 * (1 - 6 * eta + 9 * eta**2 - 4 * eta**3)
+ d2f_deta2 = -12 * eta + 12 * eta**2 + \
+ cap_lambda / 6 * (-6 + 18 * eta - 12 * eta**2)
+
+ for axis in axes:
+ # Make the dummy plot blank.
+ if axis == "legend":
+ axes[axis].axis("off")
+ continue
+
+ # Plot data
+ axes["f"].plot(f, eta, label=f"$\Lambda = {cap_lambda}$")
+ axes["df_deta"].plot(df_deta, eta, label=f"$\Lambda = {cap_lambda}$")
+ axes["d2f_deta2"].plot(d2f_deta2, eta, label=f"$\Lambda = {cap_lambda}$")
+
+# Add a legend to the dummy plot, using the handles from the plot you want to
+# create the legend for.
+axes["legend"].legend(
+ handles=axes["f"].get_legend_handles_labels()[0],
+ loc="center", ncol=len(cap_lambda_vals),
+ mode="expand", borderaxespad=0)
+
+plt.show() # Ta-daa! (They look better if you squish em down a bit)
+
###############################################################################
# Multiple legends on the same Axes
# =================================
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