How To Color Points Conditionally in Plotly 3D Scatter Plot

In this tutorial, you’ll learn how to color points in a 3D scatter plot based on conditions using various methods.

These methods will help you show additional information through color.

 

 

To get started, import the necessary libraries and create some sample data:

import plotly.graph_objects as go
import numpy as np
np.random.seed(42)
n_points = 1000
x = np.random.randn(n_points)
y = np.random.randn(n_points)
z = np.random.randn(n_points)

This code creates the environment and random 3D data points for our examples.

 

Color by Distance

To color points based on their distance from a specific point, calculate the distances and use them as color values:

# Calculate distances from the origin (0, 0, 0)
distances = np.sqrt(x**2 + y**2 + z**2)

# Create the 3D scatter plot
fig = go.Figure(data=[go.Scatter3d(
    x=x, y=y, z=z,
    mode='markers',
    marker=dict(
        size=5,
        color=distances,
        colorscale='Viridis',
        colorbar=dict(title='Distance from Origin')
    )
)])
fig.update_layout(title='3D Scatter Plot: Color by Distance from Origin')
fig.show()

Output:

This code creates a 3D scatter plot where points are colored based on their distance from the origin (0, 0, 0).

The ‘Viridis’ colorscale is used, with darker colors representing points closer to the origin and brighter colors for points farther away.

 

Color by Density

To color points based on their density, you can use a kernel density estimation:

from scipy.stats import gaussian_kde

# Calculate point density
xyz = np.vstack([x, y, z])
kde = gaussian_kde(xyz)
density = kde(xyz)
fig = go.Figure(data=[go.Scatter3d(
    x=x, y=y, z=z,
    mode='markers',
    marker=dict(
        size=5,
        color=density,
        colorscale='Plasma',
        colorbar=dict(title='Point Density')
    )
)])
fig.update_layout(title='3D Scatter Plot: Color by Density')
fig.show()

Output:

This code uses Gaussian kernel density estimation to calculate the density of points in 3D space.

The resulting plot shows areas of high density in brighter colors and areas of low density in darker colors using the ‘Plasma’ colorscale.

 

Color by Categorical data

To color points based on categorical data, assign a color to each category:

categories = np.random.choice(['A', 'B', 'C'], n_points)

# Create a color map
color_map = {'A': 'red', 'B': 'green', 'C': 'blue'}
colors = [color_map[cat] for cat in categories]
fig = go.Figure(data=[go.Scatter3d(
    x=x, y=y, z=z,
    mode='markers',
    marker=dict(
        size=5,
        color=colors,
    ),
    text=categories,
    hoverinfo='text'
)])
fig.update_layout(title='3D Scatter Plot: Color by Category')
fig.show()

Output:

This code assigns random categories to each point and colors them accordingly.

The resulting plot shows distinct colors for each category so you can identify different groups of data points.

 

Color by Calculated metrics

To color points based on a calculated metric, perform the calculation and use the results for coloring:

# Calculate a metric (e.g., distance from a plane)
plane_normal = np.array([1, 1, 1])
plane_point = np.array([0, 0, 0])
points = np.column_stack((x, y, z))
distances_from_plane = np.abs(np.dot(points - plane_point, plane_normal)) / np.linalg.norm(plane_normal)
fig = go.Figure(data=[go.Scatter3d(
    x=x, y=y, z=z,
    mode='markers',
    marker=dict(
        size=5,
        color=distances_from_plane,
        colorscale='Turbo',
        colorbar=dict(title='Distance from Plane')
    )
)])
fig.update_layout(title='3D Scatter Plot: Color by Distance from Plane')
fig.show()

Output:

This code calculates the distance of each point from a plane defined by the normal vector [1, 1, 1] passing through the origin.

The resulting plot colors points based on their distance from this plane, using the ‘Turbo’ colorscale.