How to color a Matplotlib scatterplot using a continuous value?

To color a matplotlib scatterplot using continuous values, we can map a third variable to the color of each point. This creates a visual representation where color intensity or hue represents the magnitude of the continuous variable.

Basic Scatterplot with Continuous Coloring

Here's how to create a scatter plot where colors represent continuous values ?

import numpy as np
import matplotlib.pyplot as plt

# Set figure size and layout
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Generate random data points
x, y, z = np.random.rand(3, 50)

# Create figure and subplots
f, ax = plt.subplots()

# Create scatter plot with continuous coloring
points = ax.scatter(x, y, c=z, s=50, cmap="plasma")

# Add colorbar to show the color scale
f.colorbar(points)

plt.show()

Understanding the Parameters

The key parameters for continuous coloring are ?

• c=z − Maps the z values to colors

• cmap="plasma" − Specifies the colormap to use

• s=50 − Sets the size of scatter points

• colorbar() − Adds a legend showing the color scale

Using Different Colormaps

You can experiment with different colormaps for various visual effects ?

import numpy as np
import matplotlib.pyplot as plt

# Generate sample data
np.random.seed(42)
x = np.random.randn(100)
y = np.random.randn(100)
colors = x * y  # Continuous values based on x*y

fig, axes = plt.subplots(1, 3, figsize=(12, 4))

# Different colormaps
colormaps = ['viridis', 'coolwarm', 'plasma']
titles = ['Viridis', 'Coolwarm', 'Plasma']

for ax, cmap, title in zip(axes, colormaps, titles):
    scatter = ax.scatter(x, y, c=colors, cmap=cmap, alpha=0.7)
    ax.set_title(f'{title} Colormap')
    plt.colorbar(scatter, ax=ax)

plt.tight_layout()
plt.show()

Customizing Color Ranges

Control the color mapping by setting specific value ranges ?

import numpy as np
import matplotlib.pyplot as plt

# Create sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)
colors = np.cos(x)

plt.figure(figsize=(8, 5))

# Use vmin and vmax to control color range
scatter = plt.scatter(x, y, c=colors, cmap='RdYlBu', 
                     vmin=-0.5, vmax=0.5, s=60)

plt.colorbar(scatter, label='Color Values')
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Scatter Plot with Custom Color Range')
plt.show()

Popular Colormaps

Conclusion

Use the c parameter in scatter() to map continuous values to colors. Choose appropriate colormaps based on your data type and add a colorbar for reference. This technique effectively visualizes three-dimensional relationships in 2D plots.