How to plot scatter points in a 3D figure with a colorbar in Matplotlib?

To create 3D scatter plots with colorbars in Matplotlib, we use the scatter() method on a 3D axes object along with colorbar() to display a color scale. The colorbar helps visualize how point colors relate to data values.

Basic 3D Scatter Plot with Colorbar

Here's how to create a 3D scatter plot where point colors are mapped to one of the coordinate values ?

import numpy as np
import matplotlib.pyplot as plt

# Set figure size
plt.rcParams["figure.figsize"] = [10, 8]
plt.rcParams["figure.autolayout"] = True

# Create figure and 3D axis
fig = plt.figure()
ax = fig.add_subplot(projection="3d")

# Generate random data points
xs = np.random.rand(100)
ys = np.random.rand(100) 
zs = np.random.rand(100)

# Create scatter plot with colors based on x-values
scatter = ax.scatter(xs, ys, zs, c=xs, cmap="viridis", s=50)

# Add colorbar
plt.colorbar(scatter, ax=ax, shrink=0.8)

# Set labels
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis') 
ax.set_zlabel('Z axis')
ax.set_title('3D Scatter Plot with Colorbar')

plt.show()

Using Different Color Mappings

You can map colors to any data values, not just coordinates ?

import numpy as np
import matplotlib.pyplot as plt

# Create figure
fig = plt.figure(figsize=(12, 5))

# First subplot: Color by distance from origin
ax1 = fig.add_subplot(121, projection="3d")
xs = np.random.rand(100) * 2 - 1
ys = np.random.rand(100) * 2 - 1
zs = np.random.rand(100) * 2 - 1

# Calculate distance from origin
distances = np.sqrt(xs**2 + ys**2 + zs**2)
scatter1 = ax1.scatter(xs, ys, zs, c=distances, cmap="plasma", s=60)
plt.colorbar(scatter1, ax=ax1, shrink=0.8)
ax1.set_title('Color by Distance from Origin')

# Second subplot: Color by sum of coordinates
ax2 = fig.add_subplot(122, projection="3d")
coord_sum = xs + ys + zs
scatter2 = ax2.scatter(xs, ys, zs, c=coord_sum, cmap="coolwarm", s=60)
plt.colorbar(scatter2, ax=ax2, shrink=0.8)
ax2.set_title('Color by Sum of Coordinates')

plt.tight_layout()
plt.show()

Customizing the Colorbar

You can customize colorbar appearance, including position, size, and labels ?

import numpy as np
import matplotlib.pyplot as plt

# Generate sample data
np.random.seed(42)
n_points = 150
xs = np.random.normal(0, 1, n_points)
ys = np.random.normal(0, 1, n_points)
zs = np.random.normal(0, 1, n_points)

# Create temperature-like values
temperatures = 20 + 10 * np.sin(xs) + 5 * np.cos(ys) + 2 * zs

# Create plot
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(projection="3d")

scatter = ax.scatter(xs, ys, zs, c=temperatures, 
                    cmap="coolwarm", s=80, alpha=0.8)

# Customize colorbar
cbar = plt.colorbar(scatter, ax=ax, shrink=0.6, pad=0.1)
cbar.set_label('Temperature (°C)', rotation=270, labelpad=20)
cbar.ax.tick_params(labelsize=10)

# Set plot properties
ax.set_xlabel('X Position')
ax.set_ylabel('Y Position')
ax.set_zlabel('Z Position')
ax.set_title('3D Temperature Visualization', fontsize=14)

plt.show()

Key Parameters

Conclusion

Use ax.scatter() with the c parameter to map colors to data values, then add plt.colorbar() for the color scale. Customize the colorbar with shrink, pad, and label parameters for better visualization.