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 3-dimensional 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.



<IPython.core.display.Image object>

import pandas as pd
import pygmt

# 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")

# 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.]
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

# Make our 3D scatter plot, coloring each of the 3 species differently
fig = pygmt.Figure()
pygmt.makecpt(cmap="cubhelix", color_model="+c", series=(0, 3, 1))
    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)
        "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

Total running time of the script: ( 0 minutes 1.634 seconds)

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