diff --git a/examples/gallery/3d_plots/scatter3d.py b/examples/gallery/3d_plots/scatter3d.py
index 0c70581fe32..d094d25f43b 100644
--- a/examples/gallery/3d_plots/scatter3d.py
+++ b/examples/gallery/3d_plots/scatter3d.py
@@ -17,7 +17,7 @@
 import pandas as pd
 import pygmt
 
-# Load sample iris data, and convert 'species' column to categorical dtype
+# Load sample iris data and convert 'species' column to categorical dtype
 df = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")
 df.species = df.species.astype(dtype="category")
 
@@ -36,9 +36,12 @@
 
 # Define a colormap to be used for three categories, define the range of the
 # new discrete CPT using series=(lowest_value, highest_value, interval),
-# use color_model="+c" to write the discrete color palette "cubhelix" in
-# categorical format
-pygmt.makecpt(cmap="cubhelix", color_model="+c", series=(0, 3, 1))
+# use color_model="+cSetosa,Versicolor,Virginica" to write the discrete color palette
+# "cubhelix" in categorical format and add the species names as annotations for the
+# colorbar
+pygmt.makecpt(
+    cmap="cubhelix", color_model="+cSetosa,Versicolor,Virginica", series=(0, 2, 1)
+)
 
 fig.plot3d(
     # Use petal width, sepal length and petal length as x, y and z data input,
@@ -58,14 +61,18 @@
     region=region,
     # Set frame parameters
     frame=[
-        "WsNeZ3",  # z axis label positioned on 3rd corner
-        'xafg+l"Petal Width"',
-        'yafg+l"Sepal Length"',
-        'zafg+l"Petal Length"',
+        'WsNeZ3+t"Iris flower data set"',  # z axis label positioned on 3rd corner, add title
+        'xafg+l"Petal Width (cm)"',
+        'yafg+l"Sepal Length (cm)"',
+        'zafg+l"Petal Length (cm)"',
     ],
     # Set perspective to azimuth NorthWest (315°), at elevation 25°
     perspective=[315, 25],
     # Vertical exaggeration factor
     zscale=1.5,
 )
+
+# Add colorbar legend
+fig.colorbar(xshift=3.1)
+
 fig.show()
diff --git a/examples/gallery/lines/line_custom_cpt.py b/examples/gallery/lines/line_custom_cpt.py
index bb42c9e54e3..756aef38b7a 100644
--- a/examples/gallery/lines/line_custom_cpt.py
+++ b/examples/gallery/lines/line_custom_cpt.py
@@ -21,13 +21,16 @@
 fig = pygmt.Figure()
 fig.basemap(frame=["WSne", "af"], region=[20, 30, -10, 10])
 
-# Create a custom CPT with the batlow CPT and 10 discrete z-values (colors)
-pygmt.makecpt(cmap="batlow", series=[0, 10, 1])
+# Create a custom CPT with the batlow CPT and 10 discrete z-values (colors),
+# use color_model="+c0-9" to write the color palette in categorical format and
+# add labels (0) to (9) for the colorbar legend
+pygmt.makecpt(cmap="batlow", series=[0, 9, 1], color_model="+c0-9")
 
 # Plot 10 lines and set a different z-value for each line
 for zvalue in range(0, 10):
     y = zvalue * np.sin(x)
     fig.plot(x=x, y=y, cmap=True, zvalue=zvalue, pen="thick,+z,-")
+
 # Color bar to show the custom CPT and the associated z-values
 fig.colorbar()
 fig.show()
diff --git a/examples/gallery/symbols/points_categorical.py b/examples/gallery/symbols/points_categorical.py
index 3abf829e7ff..de5ad85a36c 100644
--- a/examples/gallery/symbols/points_categorical.py
+++ b/examples/gallery/symbols/points_categorical.py
@@ -44,9 +44,10 @@
 
 # Define a colormap to be used for three categories, define the range of the
 # new discrete CPT using series=(lowest_value, highest_value, interval),
-# use color_model="+c" to write the discrete color palette "inferno" in
-# categorical format
-pygmt.makecpt(cmap="inferno", series=(0, 3, 1), color_model="+c")
+# use color_model="+cAdelie,Chinstrap,Gentoo" to write the discrete color palette
+# "inferno" in categorical format and add the species names as annotations for the
+# colorbar
+pygmt.makecpt(cmap="inferno", series=(0, 2, 1), color_model="+cAdelie,Chinstrap,Gentoo")
 
 fig.plot(
     # Use bill length and bill depth as x and y data input, respectively
@@ -66,7 +67,7 @@
     transparency=40,
 )
 
-# A colorbar displaying the different penguin species types will be added
-# once GMT 6.2.0 is released.
+# Add colorbar legend
+fig.colorbar()
 
 fig.show()