3D scatterplots in Python Matplotlib with hue colormap and legend

3D scatter plots with hue colormaps allow you to visualize four dimensions of data simultaneously: x, y, z coordinates and a color dimension. In this tutorial, we'll create 3D scatter plots using Matplotlib with Seaborn color palettes and legends.

Basic 3D Scatter Plot with Hue Colormap

Let's create a 3D scatter plot where points are colored based on their x-coordinate values ?

import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

# Set figure size
plt.rcParams["figure.figsize"] = [8.00, 6.00]
plt.rcParams["figure.autolayout"] = True

# Generate random data points
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)

# Create 3D plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# Create colormap using Seaborn's husl palette
cmap = ListedColormap(sns.color_palette("husl", 256).as_hex())

# Create scatter plot with color based on x values
sc = ax.scatter(x, y, z, s=40, c=x, marker='o', cmap=cmap, alpha=0.8)

# Add colorbar legend
plt.colorbar(sc, ax=ax, shrink=0.5, aspect=10)

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

plt.show()

Using Different Color Variables

You can color points based on any variable, not just coordinates. Here's an example using a calculated distance from origin ?

import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

# Generate data
np.random.seed(42)
x = np.random.rand(150) * 10
y = np.random.rand(150) * 10
z = np.random.rand(150) * 10

# Calculate distance from origin as color variable
distances = np.sqrt(x**2 + y**2 + z**2)

# Create 3D plot
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

# Use viridis colormap
sc = ax.scatter(x, y, z, s=50, c=distances, marker='o', cmap='viridis', alpha=0.7)

# Add colorbar with label
cbar = plt.colorbar(sc, ax=ax, shrink=0.6, aspect=15)
cbar.set_label('Distance from Origin', rotation=270, labelpad=20)

# Customize plot
ax.set_xlabel('X Coordinate')
ax.set_ylabel('Y Coordinate')
ax.set_zlabel('Z Coordinate')
ax.set_title('3D Scatter Plot Colored by Distance')

plt.show()

Categorical Color Mapping with Legend

For categorical data, you can create discrete color groups with a custom legend ?

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

# Generate data
np.random.seed(123)
n_points = 120
x = np.random.randn(n_points)
y = np.random.randn(n_points)
z = np.random.randn(n_points)

# Create categorical groups
categories = np.random.choice(['Group A', 'Group B', 'Group C'], n_points)
category_codes = np.array([0 if cat == 'Group A' else 1 if cat == 'Group B' else 2 for cat in categories])

# Create custom colormap
colors = ['red', 'green', 'blue']
cmap = ListedColormap(colors)

# Create 3D plot
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

# Scatter plot with categorical colors
sc = ax.scatter(x, y, z, s=60, c=category_codes, cmap=cmap, alpha=0.8)

# Create custom legend
handles = [plt.scatter([], [], c=colors[i], s=60, label=f'Group {chr(65+i)}') 
           for i in range(3)]
ax.legend(handles=handles, loc='upper right')

# Labels and title
ax.set_xlabel('X Values')
ax.set_ylabel('Y Values')
ax.set_zlabel('Z Values')
ax.set_title('3D Scatter Plot with Categorical Colors')

plt.show()

Key Parameters

Conclusion

3D scatter plots with hue colormaps effectively visualize multi-dimensional data by mapping colors to data values. Use colorbar() for continuous variables and custom legends for categorical data to make your plots informative and visually appealing.