How to display the matrix value and colormap in Matplotlib?

To display matrix values with a colormap in Matplotlib, you can use matshow() to create a color-coded visualization and overlay text annotations. This technique is useful for visualizing correlation matrices, confusion matrices, or any 2D data arrays.

Basic Matrix Display with Values

Here's how to create a matrix visualization with both colors and text values ?

import numpy as np
import matplotlib.pyplot as plt

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

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

# Create a sample matrix
min_val, max_val = 0, 5
matrix = np.random.randint(0, 5, size=(max_val, max_val))

# Display matrix as color-coded image
ax.matshow(matrix, cmap='ocean')

# Add text annotations for each cell
for i in range(max_val):
    for j in range(max_val):
        value = matrix[j, i]
        ax.text(i, j, str(value), va='center', ha='center', color='white')

plt.title('Matrix with Values and Colormap')
plt.show()

Customizing the Visualization

You can enhance the matrix display with better formatting and different colormaps ?

import numpy as np
import matplotlib.pyplot as plt

# Create a correlation-like matrix
data = np.array([[1.0, 0.8, 0.3, 0.1],
                 [0.8, 1.0, 0.5, 0.2],
                 [0.3, 0.5, 1.0, 0.7],
                 [0.1, 0.2, 0.7, 1.0]])

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

# Display with different colormap
im = ax.matshow(data, cmap='coolwarm', vmin=-1, vmax=1)

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

# Add text annotations with formatting
for i in range(data.shape[0]):
    for j in range(data.shape[1]):
        value = data[i, j]
        ax.text(j, i, f'{value:.2f}', 
                va='center', ha='center', 
                color='black', fontsize=12, fontweight='bold')

# Customize appearance
ax.set_title('Correlation Matrix', pad=20)
ax.set_xticks(range(4))
ax.set_yticks(range(4))
ax.set_xticklabels(['A', 'B', 'C', 'D'])
ax.set_yticklabels(['A', 'B', 'C', 'D'])

plt.tight_layout()
plt.show()

Key Parameters

Common Colormaps

Different colormaps work better for different types of data ?

import numpy as np
import matplotlib.pyplot as plt

# Sample matrix
matrix = np.random.rand(4, 4)

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

for idx, cmap in enumerate(colormaps):
    ax = axes[idx]
    im = ax.matshow(matrix, cmap=cmap)
    
    # Add values
    for i in range(4):
        for j in range(4):
            ax.text(j, i, f'{matrix[i,j]:.2f}', 
                   va='center', ha='center', color='white')
    
    ax.set_title(f'Colormap: {cmap}')
    plt.colorbar(im, ax=ax, shrink=0.6)

plt.tight_layout()
plt.show()

Conclusion

Use matshow() to display matrices as color-coded images and text() to overlay values. Choose appropriate colormaps and text colors for better readability and visual appeal.