Note
Click here to download the full example code
Color points by categories¶
The 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
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 pygmt.makecpt
method.
![points categorical](../../_images/sphx_glr_points_categorical_001.png)
Out:
<IPython.core.display.Image object>
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()
Total running time of the script: ( 0 minutes 1.746 seconds)