3D Scatter plots

The pygmt.Figure.plot3d method can be used to plot symbols in 3D. In the example below, we show how the Iris flower dataset can be visualized using a perspective 3D plot. The region parameter has to include the \(x\), \(y\), \(z\) axis limits in the form of (xmin, xmax, ymin, ymax, zmin, zmax), which can be done automatically using pygmt.info. To plot the z-axis frame, set frame as a minimum to something like frame=["WsNeZ", "zaf"]. Use perspective to control the azimuth and elevation angle of the view, and zscale to adjust the vertical exaggeration factor.

Out:

<IPython.core.display.Image object>

import pandas as pd
import pygmt

# Load sample iris data
df = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")
# Convert 'species' column to categorical dtype
# By default, pandas sorts the individual categories in an alphabetical order.
# For a non-alphabetical order, you have to manually adjust the list of
# categories. For handling and manipulating categorical data in pandas,
# have a look at:
# https://pandas.pydata.org/docs/user_guide/categorical.html
df.species = df.species.astype(dtype="category")
# Make a list of the individual categories of the 'species' column
# ['setosa', 'versicolor', 'virginica']
# They are (corresponding to the categorical number code) by default in
# alphabetical order and later used for the colorbar labels
labels = list(df.species.cat.categories)

# Use pygmt.info to get region bounds (xmin, xmax, ymin, ymax, zmin, zmax)
# The below example will return a numpy array [0.0, 3.0, 4.0, 8.0, 1.0, 7.0]
region = pygmt.info(
    data=df[["petal_width", "sepal_length", "petal_length"]],  # x, y, z columns
    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 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="+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",
    # Use the minimum and maximum of the categorical number code
    # to set the lowest_value and the highest_value of the CPT
    series=(df.species.cat.codes.min(), df.species.cat.codes.max(), 1),
    # convert ['setosa', 'versicolor', 'virginica'] to
    # 'setosa,versicolor,virginica'
    color_model="+c" + ",".join(labels),
)

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,
    # Vary each symbol size according to another feature (sepal width, scaled
    # by 0.1)
    size=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+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()