Create 3D plot with Colorbar in Python using Matplotlib

This tutorial will guide you through the process of creating 3D plots with colorbars using the Python Matplotlib library.

You’ll learn how to generate 3D scatter plots, add colorbars to represent additional dimensions of data, and customize various aspects of your visualizations.

We’ll cover topics ranging from using custom colormaps to normalizing color scales, and much more.

 

 

Create a 3d Scatter Plot

To create a 3D scatter plot with a colorbar, you’ll use Matplotlib Axes3D and scatter function:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
c = np.random.rand(100)
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, c=c, cmap='viridis')
plt.show()

Output:

The color of each point is determined by the c values and mapped to the ‘viridis’ colormap.

 

Add a Colorbar to 3D Plot

Create colorbar object

To add a colorbar to your 3D plot, use the colorbar function:

fig = plt.figure(figsize=(12, 9))
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, c=c, cmap='viridis')
cbar = plt.colorbar(scatter)
cbar.set_label('Color Value')
plt.show()

Output:

The colorbar is now added to the right side of the plot, showing the range of color values.

Mapping colors to Z values

You can map colors directly to the Z values for better visualization:

scatter = ax.scatter(x, y, z, c=z, cmap='viridis')
cbar = plt.colorbar(scatter)
cbar.set_label('Z Value')
plt.show()

Output:

Now the colors of the points correspond to their Z-axis positions, providing a clearer representation of depth.

 

Customize Colorbar Appearance

You can customize the colorbar size, aspect ratio, and label font sizes for better readability:

cbar = plt.colorbar(scatter, shrink=0.8, aspect=20)
cbar.set_label('Z Value', fontsize=14)
cbar.ax.tick_params(labelsize=12)
plt.show()

Output:

 

Using Predefined Colormaps

Matplotlib offers various predefined colormaps. Here’s how to use them:

colormaps = ['viridis', 'plasma', 'coolwarm']
fig, axes = plt.subplots(1, 3, figsize=(18, 6), subplot_kw={'projection': '3d'})
for ax, cmap in zip(axes, colormaps):
    scatter = ax.scatter(x, y, z, c=z, cmap=cmap)
    plt.colorbar(scatter, ax=ax, label='Z Value')
    ax.set_title(f'Colormap: {cmap}')
plt.tight_layout()
plt.show()

Output:

This code creates three subplots, each using a different colormap to visualize the same data.

 

Normalize Color Scales

To normalize color scales, use Matplotlib’s normalization classes:

from matplotlib.colors import Normalize, LogNorm, SymLogNorm
fig, axes = plt.subplots(1, 3, figsize=(18, 6), subplot_kw={'projection': '3d'})
norms = [Normalize(), LogNorm(), SymLogNorm(linthresh=0.1)]
titles = ['Linear', 'Logarithmic', 'Symmetric Log']
for ax, norm, title in zip(axes, norms, titles):
    scatter = ax.scatter(x, y, z, c=z, cmap='viridis', norm=norm)
    plt.colorbar(scatter, ax=ax, label='Z Value')
    ax.set_title(f'{title} Normalization')
plt.tight_layout()
plt.show()

Output:

This code shows linear, logarithmic, and symmetric logarithmic color scaling.

 

Discrete Color Levels

To create discrete color levels, use the BoundaryNorm class:

from matplotlib.colors import BoundaryNorm
levels = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
norm = BoundaryNorm(levels, ncolors=len(levels)-1)
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, c=z, cmap='viridis', norm=norm)
cbar = plt.colorbar(scatter, label='Z Value', ticks=levels)
plt.show()

Output:

This plot uses discrete color levels which makes it easier to categorize data points into specific ranges.

 

Highlight Specific Regions or Thresholds

To highlight specific regions, create a custom colormap with distinct colors:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.colors as mcolors
from matplotlib.colors import LinearSegmentedColormap
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
c = np.random.rand(100)

# Define the custom colormap
threshold = 0.5
colors = ['#1E88E5', '#FFC107']
n_bins = 2
cmap = LinearSegmentedColormap.from_list('custom', colors, N=n_bins)
norm = mcolors.BoundaryNorm(boundaries=[0, threshold, 1], ncolors=n_bins)

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, c=z, cmap=cmap, norm=norm)
cbar = plt.colorbar(scatter, label='Z Value')
cbar.set_ticks([0.25, 0.75])
cbar.set_ticklabels(['Below threshold', 'Above threshold'])
plt.show()

Output:

This plot highlights values above and below a specific threshold using distinct colors.

 

Using Multiple Colorbars for Different Data Aspects

To use multiple colorbars, create separate scatter plots for each aspect:

fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
scatter1 = ax.scatter(x, y, z, c=z, cmap='viridis')
scatter2 = ax.scatter(x, y, z, c=c, cmap='plasma')
cbar1 = plt.colorbar(scatter1, label='Z Value', pad=0.1)
cbar2 = plt.colorbar(scatter2, label='C Value', pad=0.15)
plt.tight_layout()
plt.show()

Output:

This plot uses two colorbars to represent different aspects of the data simultaneously.