diff --git a/examples/gallery/3d_plots/scatter3d.py b/examples/gallery/3d_plots/scatter3d.py
index a573005cc88..568c2de7cbd 100644
--- a/examples/gallery/3d_plots/scatter3d.py
+++ b/examples/gallery/3d_plots/scatter3d.py
@@ -5,7 +5,7 @@
 The :meth:`pygmt.Figure.plot3d` method can be used to plot symbols in 3D.
 In the example below, we show how the
 `Iris flower dataset <https://en.wikipedia.org/wiki/Iris_flower_data_set>`__
-can be visualized using a perspective 3-dimensional plot. The ``region``
+can be visualized using a perspective 3D plot. The ``region``
 parameter has to include the :math:`x`, :math:`y`, :math:`z` axis limits in the
 form of (xmin, xmax, ymin, ymax, zmin, zmax), which can be done automatically
 using :meth:`pygmt.info`. To plot the z-axis frame, set ``frame`` as a
@@ -19,35 +19,53 @@
 
 # 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")
+df.species = df.species.astype(dtype="category")
 
 # Use pygmt.info to get region bounds (xmin, xmax, ymin, ymax, zmin, zmax)
-# The below example will return a numpy array like [0., 3., 4., 8., 1., 7.]
+# The below example will return a numpy array like [0.0, 3.0, 4.0, 8.0, 1.0, 7.0]
 region = pygmt.info(
     table=df[["petal_width", "sepal_length", "petal_length"]],  # x, y, z columns
-    per_column=True,  # report output as a numpy array
-    spacing="1/2/0.5",  # rounds x, y and z intervals by 1, 2 and 0.5 respectively
+    per_column=True,  # report the min/max values per column as a numpy array
+    # round the min/max values of the first three columns to the nearest multiple
+    # of 1, 2 and 0.5, respectively
+    spacing=(1, 2, 0.5),
 )
 
-# Make our 3D scatter plot, coloring each of the 3 species differently
+# Make a 3D scatter plot, coloring each of the 3 species differently
 fig = pygmt.Figure()
+
+# 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))
+
 fig.plot3d(
+    # Use petal width, sepal length and petal length as x, y and z data input,
+    # respectively
     x=df.petal_width,
     y=df.sepal_length,
     z=df.petal_length,
-    sizes=0.1 * df.sepal_width,  # Vary each symbol size according to a data column
-    color=df.species.cat.codes.astype(int),  # Points colored by categorical number code
-    cmap=True,  # Use colormap created by makecpt
-    region=region,  # (xmin, xmax, ymin, ymax, zmin, zmax)
+    # Vary each symbol size according to another feature (sepal width, scaled by 0.1)
+    sizes=0.1 * df.sepal_width,
+    # Use 3D cubes ("u") as symbols, with size in centimeter units ("c")
+    style="uc",
+    # Points colored by categorical number code
+    color=df.species.cat.codes.astype(int),
+    # Use colormap created by makecpt
+    cmap=True,
+    # Set map dimensions (xmin, xmax, ymin, ymax, zmin, zmax)
+    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"',
     ],
-    style="uc",  # 3D cUbe, with size in centimeter units
-    perspective=[315, 25],  # Azimuth NorthWest (315°), at elevation 25°
-    zscale=1.5,  # Vertical exaggeration factor
+    # Set perspective to azimuth NorthWest (315°), at elevation 25°
+    perspective=[315, 25],
+    # Vertical exaggeration factor
+    zscale=1.5,
 )
 fig.show()