Changing Colorbar Range in Matplotlib

Matplotlib is a popular library for creating static, animated, and interactive visualizations in Python. One common task in data visualization is adjusting the range of colors displayed in a colorbar. In this article, we will explore how to change the colorbar range in Matplotlib.

Example 1: Basic Colorbar Creation

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis')
plt.colorbar()
plt.show()

Output:

In this example, we create a basic heatmap using random data and display a colorbar using the default settings.

Example 2: Changing Colorbar Limits

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis')
cbar = plt.colorbar()
cbar.set_clim(0, 1)
plt.show()

By using the set_clim method of the colorbar object, we can change the limits of the colorbar to display only a specific range of values.

Example 3: Adjusting Colorbar Range with vmin and vmax

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis', vmin=0.2, vmax=0.8)
plt.colorbar()
plt.show()

Output:

Specifying vmin and vmax arguments in the imshow function allows us to set the minimum and maximum values for the colorbar range.

Example 4: Normalizing Colorbar Range

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

# Create a dummy heatmap
data = np.random.rand(10, 10)
norm = Normalize(vmin=0, vmax=1)
plt.imshow(data, cmap='viridis', norm=norm)
plt.colorbar()
plt.show()

Output:

Using the Normalize class from matplotlib.colors, we can normalize the colorbar range to a specific set of values.

Example 5: Logarithmic Colorbar Scale

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis', norm=LogNorm(vmin=0.1, vmax=1))
plt.colorbar()
plt.show()

If you want to use a logarithmic scale for the colorbar, you can utilize LogNorm from matplotlib.colors to set the range in a logarithmic manner.

Example 6: Discretizing Colorbar Range

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
levels = np.linspace(0, 1, 6)
plt.imshow(data, cmap='viridis', levels=levels)
plt.colorbar()
plt.show()

By specifying custom levels using np.linspace, we can discretize the colorbar range into specific intervals.

Example 7: Setting Tick Locations on Colorbar

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis')
cbar = plt.colorbar()
cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
plt.show()

Output:

You can set custom tick locations on the colorbar using the set_ticks method of the colorbar object.

Example 8: Formatting Colorbar Ticks

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis')
cbar = plt.colorbar()
cbar.set_ticks([0, 0.5, 1])
cbar.set_ticklabels(['Low', 'Mid', 'High'])
plt.show()

Output:

Formatting the colorbar tick labels is possible by providing custom labels using the set_ticklabels method.

Example 9: Reversing Colorbar Range

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis')
cbar = plt.colorbar()
cbar.ax.invert_yaxis()
plt.show()

Output:

To reverse the colorbar range, you can use the invert_yaxis method of the colorbar axis.

Example 10: Adding Colorbar Title

import matplotlib.pyplot as plt
import numpy as np

# Create a dummy heatmap
data = np.random.rand(10, 10)
plt.imshow(data, cmap='viridis')
cbar = plt.colorbar()
cbar.set_label('Intensity')
plt.show()

Output:

To provide a title to the colorbar, you can use the set_label method of the colorbar object.