How to plot data into imshow() with custom colormap in Matplotlib?

To plot data into imshow() with custom colormap in Matplotlib, we can create our own colormap from a list of colors and apply it to display 2D data as an image.

Creating a Custom Colormap

We use ListedColormap to generate a colormap object from a list of colors ?

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

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

# Create random data
data = np.random.rand(5, 5)

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

# Display data with custom colormap
plt.imshow(data, cmap=cmap)
plt.colorbar()
plt.title('Custom Colormap Example')
plt.show()

Advanced Custom Colormap

You can create more sophisticated colormaps using hex colors or RGB tuples ?

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

# Create sample data
data = np.random.rand(8, 8)

# Custom colormap with hex colors
colors = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#FF00FF']
custom_cmap = ListedColormap(colors)

plt.figure(figsize=(10, 4))

# Plot 1: Basic colormap
plt.subplot(1, 2, 1)
plt.imshow(data, cmap='viridis')
plt.title('Default Viridis Colormap')
plt.colorbar()

# Plot 2: Custom colormap
plt.subplot(1, 2, 2)
plt.imshow(data, cmap=custom_cmap)
plt.title('Custom Colormap')
plt.colorbar()

plt.tight_layout()
plt.show()

Colormap with Specific Value Ranges

You can control how values map to colors using vmin and vmax parameters ?

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

# Create structured data
data = np.array([[1, 2, 3, 4],
                 [2, 3, 4, 1],
                 [3, 4, 1, 2],
                 [4, 1, 2, 3]])

# Custom colormap for discrete values
colors = ['lightblue', 'yellow', 'orange', 'red']
discrete_cmap = ListedColormap(colors)

plt.imshow(data, cmap=discrete_cmap, vmin=1, vmax=4)
plt.colorbar(label='Value')
plt.title('Discrete Custom Colormap')

# Add text annotations
for i in range(4):
    for j in range(4):
        plt.text(j, i, str(data[i, j]), ha='center', va='center', 
                color='black', fontsize=12, fontweight='bold')

plt.show()

Key Parameters

Conclusion

Use ListedColormap to create custom colormaps from color lists. Combine with imshow() to visualize 2D data with personalized color schemes that enhance data interpretation.