Color Mapping¶

Map continuous data values to colors using colormaps, enabling visualization of three-dimensional relationships in 2D scatter plots.

Basic Color Mapping¶

Use the c parameter with numeric values to map data to colors.

1. Color by Third Variable¶

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)  # Third variable for color

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z)
plt.colorbar(scatter)
plt.show()

2. Color by Computed Value¶

x = np.random.rand(100) * 10
y = np.random.rand(100) * 10
distance = np.sqrt(x**2 + y**2)

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=distance)
plt.colorbar(scatter, label='Distance from Origin')
plt.show()

3. Color by Category Index¶

categories = np.random.randint(0, 5, 100)

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=categories, cmap='Set1')
plt.colorbar(scatter, label='Category')
plt.show()

Colormap Selection¶

The cmap parameter selects the color scheme.

1. Sequential Colormaps¶

fig, axes = plt.subplots(1, 3, figsize=(12, 4))
cmaps = ['viridis', 'plasma', 'Blues']

for ax, cmap in zip(axes, cmaps):
    scatter = ax.scatter(x, y, c=z, cmap=cmap)
    ax.set_title(cmap)
    plt.colorbar(scatter, ax=ax)

plt.tight_layout()
plt.show()

2. Diverging Colormaps¶

z_centered = np.random.randn(100)

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z_centered, cmap='RdBu', vmin=-3, vmax=3)
plt.colorbar(scatter)
plt.show()

3. Qualitative Colormaps¶

# For categorical data
categories = np.random.randint(0, 8, 100)

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=categories, cmap='Set2')
plt.colorbar(scatter, ticks=range(8))
plt.show()

Value Range¶

Control the mapping between data values and colors.

1. Auto Range (Default)¶

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z)  # Maps min(z) to max(z)
plt.colorbar(scatter)
plt.show()

2. Fixed Range¶

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z, vmin=0, vmax=1)
plt.colorbar(scatter)
plt.show()

3. Centered at Zero¶

z_centered = np.random.randn(100) * 2
max_abs = np.abs(z_centered).max()

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z_centered, cmap='RdBu', 
                     vmin=-max_abs, vmax=max_abs)
plt.colorbar(scatter)
plt.show()

Normalization¶

Transform data before color mapping.

1. Linear Normalization (Default)¶

from matplotlib.colors import Normalize

norm = Normalize(vmin=0, vmax=1)
scatter = ax.scatter(x, y, c=z, norm=norm)

2. Logarithmic Normalization¶

from matplotlib.colors import LogNorm

z_log = np.random.rand(100) * 1000 + 1

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z_log, norm=LogNorm(vmin=1, vmax=1000))
plt.colorbar(scatter, label='Log Scale')
plt.show()

3. Power Normalization¶

from matplotlib.colors import PowerNorm

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z, norm=PowerNorm(gamma=0.5))
plt.colorbar(scatter)
plt.show()

Discrete Colors¶

Map continuous data to discrete color bins.

1. BoundaryNorm¶

from matplotlib.colors import BoundaryNorm

bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
norm = BoundaryNorm(bounds, plt.cm.viridis.N)

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z, cmap='viridis', norm=norm)
plt.colorbar(scatter, boundaries=bounds, ticks=bounds)
plt.show()

2. Fixed Number of Bins¶

n_bins = 5
cmap = plt.cm.get_cmap('viridis', n_bins)

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z, cmap=cmap)
plt.colorbar(scatter)
plt.show()

3. Custom Bin Edges¶

bins = [0, 0.1, 0.3, 0.7, 0.9, 1.0]
norm = BoundaryNorm(bins, plt.cm.RdYlGn.N)

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z, cmap='RdYlGn', norm=norm)
plt.colorbar(scatter, ticks=bins)
plt.show()

Color and Size Combined¶

Encode two additional dimensions using both color and size.

1. Four-Dimensional Visualization¶

np.random.seed(42)
x = np.random.rand(50)
y = np.random.rand(50)
colors = np.random.rand(50)
sizes = np.random.rand(50) * 500

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap='viridis')
plt.colorbar(scatter, label='Color Variable')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Size and Color Encoding')
plt.show()

2. Bubble Chart¶

# Population, GDP, Life Expectancy example
np.random.seed(42)
gdp = np.random.rand(30) * 50000
life_exp = 50 + np.random.rand(30) * 35
population = np.random.rand(30) * 1000

fig, ax = plt.subplots(figsize=(10, 6))
scatter = ax.scatter(gdp, life_exp, c=population, s=population, 
                     alpha=0.6, cmap='YlOrRd')
plt.colorbar(scatter, label='Population (millions)')
ax.set_xlabel('GDP per Capita ($)')
ax.set_ylabel('Life Expectancy (years)')
plt.show()

3. Legend for Sizes¶

from matplotlib.lines import Line2D

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap='viridis')
plt.colorbar(scatter, label='Color')

# Size legend
size_legend = [100, 300, 500]
handles = [Line2D([0], [0], marker='o', color='w', 
                  markerfacecolor='gray', markersize=np.sqrt(s)/2, 
                  label=str(s)) for s in size_legend]
ax.legend(handles=handles, title='Size', loc='upper right')
plt.show()

Colorbar Customization¶

Customize the colorbar for scatter plots.

1. Label and Ticks¶

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z, cmap='viridis')
cbar = plt.colorbar(scatter)
cbar.set_label('Value', fontsize=12)
cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
plt.show()

2. Shrink and Position¶

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z, cmap='viridis')
plt.colorbar(scatter, shrink=0.8, pad=0.02)
plt.show()

3. Horizontal Colorbar¶

fig, ax = plt.subplots()
scatter = ax.scatter(x, y, c=z, cmap='viridis')
plt.colorbar(scatter, orientation='horizontal', pad=0.1)
plt.show()

Practical Example¶

Create a complete color-mapped scatter plot.

1. Generate Realistic Data¶

np.random.seed(42)
n = 200
x = np.random.randn(n)
y = 0.5 * x + np.random.randn(n) * 0.5
magnitude = np.sqrt(x**2 + y**2)

2. Create Visualization¶

fig, ax = plt.subplots(figsize=(8, 6))

scatter = ax.scatter(x, y, 
                     c=magnitude, 
                     s=50, 
                     cmap='plasma',
                     alpha=0.7,
                     edgecolors='white',
                     linewidths=0.5)

ax.set_xlabel('X Variable', fontsize=12)
ax.set_ylabel('Y Variable', fontsize=12)
ax.set_title('Scatter Plot with Color Mapping', fontsize=14)
ax.grid(True, alpha=0.3)
ax.set_aspect('equal')

3. Add Colorbar¶

cbar = plt.colorbar(scatter, shrink=0.8)
cbar.set_label('Distance from Origin', fontsize=11)

plt.tight_layout()
plt.show()