How to give Matplolib imshow plot colorbars a label?

To add a label to a matplotlib imshow() plot colorbar, you can use the set_label() method on the colorbar object. This helps viewers understand what the color scale represents in your visualization.

Steps to Add Colorbar Labels

Here's the process for adding colorbar labels:

• Set the figure size and adjust the padding between and around the subplots.

• Create sample data using NumPy.

• Use imshow() method to display the data as an image on a 2D regular raster.

• Create a colorbar for the image using colorbar().

• Set colorbar label using set_label() method.

• Display the figure using show() method.

Basic Example

Here's how to add a simple label to your colorbar −

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 sample data
data = np.random.rand(5, 5)

# Display data as image
im = plt.imshow(data, cmap="copper")

# Create colorbar and add label
cbar = plt.colorbar(im)
cbar.set_label("Random Values")

plt.show()

Displays a heatmap with a colorbar labeled "Random Values"

Customizing Colorbar Labels

You can customize the label appearance with additional parameters −

import numpy as np
import matplotlib.pyplot as plt

# Create temperature data
temperature = np.random.uniform(20, 40, (8, 8))

# Create the plot
fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(temperature, cmap="coolwarm")

# Add customized colorbar label
cbar = plt.colorbar(im, ax=ax)
cbar.set_label("Temperature (°C)", rotation=270, labelpad=20, fontsize=12)

# Add title and show
ax.set_title("Temperature Distribution")
plt.show()

Displays a temperature heatmap with a rotated colorbar label "Temperature (°C)"

Multiple Subplots with Colorbars

When working with subplots, you can add individual colorbar labels −

import numpy as np
import matplotlib.pyplot as plt

# Create data
data1 = np.random.rand(6, 6)
data2 = np.random.rand(6, 6) * 100

# Create subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# First subplot
im1 = ax1.imshow(data1, cmap="viridis")
cbar1 = plt.colorbar(im1, ax=ax1)
cbar1.set_label("Probability")
ax1.set_title("Dataset 1")

# Second subplot
im2 = ax2.imshow(data2, cmap="plasma")
cbar2 = plt.colorbar(im2, ax=ax2)
cbar2.set_label("Count")
ax2.set_title("Dataset 2")

plt.tight_layout()
plt.show()

Shows two side-by-side heatmaps, each with its own labeled colorbar

Parameters for set_label()

Conclusion

Use cbar.set_label() to add descriptive labels to your colorbar. Customize with parameters like rotation, labelpad, and fontsize for better readability. This makes your data visualizations more informative and professional.