Add a gallery example to show coloring of points by categories (#1006)

Provide gallery example to show coloring of points by categories.
Uses the Palmer Penguins example dataset loaded into a pandas
DataFrame, with the categorical dtype column converted to int type.

Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com>
Co-authored-by: Dongdong Tian <seisman.info@gmail.com>
Co-authored-by: Yao Jiayuan <coreman.seism@gmail.com>

examples/gallery/symbols/points_categorical.py

@@ -0,0 +1,72 @@
+"""
+Color points by categories
+---------------------------
+The :meth:`pygmt.Figure.plot` method can be used to plot symbols which are
+color-coded by categories. In the example below, we show how the
+`Palmer Penguins dataset <https://github.com/allisonhorst/palmerpenguins>`__
+can be visualized. Here, we can pass the individual categories included in
+the "species" column directly to the ``color`` parameter via
+``color=df.species.cat.codes.astype(int)``. Additionally, we have to set
+``cmap=True``. A desired colormap can be selected via the :meth:`pygmt.makecpt`
+method.
+"""
+
+import pandas as pd
+import pygmt
+
+# Load sample penguins data and convert 'species' column to categorical dtype
+df = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/penguins.csv")
+df.species = df.species.astype(dtype="category")
+
+# Use pygmt.info to get region bounds (xmin, xmax, ymin, ymax)
+# The below example will return a numpy array like [30.0, 60.0, 12.0, 22.0]
+region = pygmt.info(
+    table=df[["bill_length_mm", "bill_depth_mm"]],  # x and y columns
+    per_column=True,  # report the min/max values per column as a numpy array
+    # round the min/max values of the first two columns to the nearest multiple
+    # of 3 and 2, respectively
+    spacing=(3, 2),
+)
+
+# Make a 2D categorical scatter plot, coloring each of the 3 species differently
+fig = pygmt.Figure()
+
+# Generate a basemap of 10 cm x 10 cm size
+fig.basemap(
+    region=region,
+    projection="X10c/10c",
+    frame=[
+        'xafg+l"Bill length (mm)"',
+        'yafg+l"Bill depth (mm)"',
+        'WSen+t"Penguin size at Palmer Station"',
+    ],
+)
+
+# 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")
+
+fig.plot(
+    # Use bill length and bill depth as x and y data input, respectively
+    x=df.bill_length_mm,
+    y=df.bill_depth_mm,
+    # Vary each symbol size according to another feature (body mass, scaled by 7.5*10e-5)
+    sizes=df.body_mass_g * 7.5e-5,
+    # Points colored by categorical number code
+    color=df.species.cat.codes.astype(int),
+    # Use colormap created by makecpt
+    cmap=True,
+    # Do not clip symbols that fall close to the map bounds
+    no_clip=True,
+    # Use circles as symbols with size in centimeter units
+    style="cc",
+    # Set transparency level for all symbols to deal with overplotting
+    transparency=40,
+)
+
+# A colorbar displaying the different penguin species types will be added
+# once GMT 6.2.0 is released.
+
+fig.show()