Matplotlib Choosing Colormaps

Matplotlib Choosing Colormaps

Choosing the right colormap for your Matplotlib plots is essential for effectively communicating the underlying data. The choice of colormap can enhance the readability of the plot, highlight important data trends, and make your visualizations more intuitive and engaging. This article will guide you through the process of selecting appropriate colormaps for different types of data and visualization needs. We will provide a comprehensive overview, along with 10 detailed examples of how to apply and customize colormaps in Matplotlib.

Understanding Colormaps

A colormap in Matplotlib is a range of colors used to map data values to colors in a plot. Colormaps are crucial in data visualization as they can influence interpretation and perception of the data. Matplotlib provides a wide variety of built-in colormaps, and also allows for the creation of custom colormaps.

Types of Colormaps

1. Sequential colormaps: These are suitable for representing data that has ordering. They usually follow a light-to-dark color scheme to represent low-to-high data values.

2. Diverging colormaps: These are used for data where both the low and high values are interesting. They typically have two distinct colors at the ends, with a neutral color in the middle.

3. Categorical colormaps: These are best for nominal data, where data points belong to distinct groups without any inherent order.

4. Qualitative colormaps: Similar to categorical, these are used for discrete data that does not have ordering or relationships.

Choosing the Right Colormap

The choice of colormap depends on the type of data you are visualizing:

• Use sequential colormaps for continuous data with a clear progression from low to high.
• Use diverging colormaps for data where the middle point is significant, such as changes around zero.
• Use categorical or qualitative colormaps for discrete data groups.

Examples of Applying Colormaps in Matplotlib

Below are examples demonstrating how to apply and customize colormaps in Matplotlib. Each example is a standalone code snippet that can be run independently.

Example 1: Applying a Sequential Colormap

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randn(10000)
y = np.random.randn(10000)

plt.hexbin(x, y, gridsize=30, cmap='Blues')
plt.colorbar()
plt.title("Hexbin plot with 'Blues' colormap - how2matplotlib.com")
plt.show()

Output:

Example 2: Applying a Diverging Colormap

import matplotlib.pyplot as plt
import numpy as np

data = np.random.randn(10, 10)

plt.imshow(data, cmap='coolwarm')
plt.colorbar()
plt.title("Heatmap with 'coolwarm' colormap - how2matplotlib.com")
plt.show()

Output:

Example 3: Custom Sequential Colormap

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

colors = ["#f0e442", "#f0e442", "#0072b2"]
n_bins = [3, 6, 10, 100]  # Discretizes the interpolation into bins
cmap_name = "custom1"

cm = LinearSegmentedColormap.from_list(cmap_name, colors, N=5)

data = np.random.randn(1000, 1000)
plt.imshow(data, cmap=cm)
plt.colorbar()
plt.title("Custom Sequential Colormap - how2matplotlib.com")
plt.show()

Output:

Example 4: Applying a Categorical Colormap

import matplotlib.pyplot as plt
import numpy as np

categories = np.random.randint(0, 5, 100)
colors = plt.cm.tab10(categories)

plt.scatter(np.random.randn(100), np.random.randn(100), c=colors)
plt.title("Scatter plot with 'tab10' categorical colormap - how2matplotlib.com")
plt.show()

Output:

Example 5: Modifying Colormap Transparency

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randn(1000)
y = np.random.randn(1000)

plt.hexbin(x, y, gridsize=30, cmap='Purples', alpha=0.6)
plt.colorbar()
plt.title("Hexbin plot with modified transparency - how2matplotlib.com")
plt.show()

Output:

Example 6: Creating a Custom Diverging Colormap

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

colors = ["#d73027", "#ffffff", "#1a9850"]  # Red, White, Green
cmap_name = "custom_diverging"

cm = LinearSegmentedColormap.from_list(cmap_name, colors)

data = np.random.randn(10, 10)
plt.imshow(data, cmap=cm)
plt.colorbar()
plt.title("Custom Diverging Colormap - how2matplotlib.com")
plt.show()

Output:

Example 7: Using a Qualitative Colormap

import matplotlib.pyplot as plt
import numpy as np

data = np.random.randint(1, 10, size=(10, 10))
plt.matshow(data, cmap='Set3')
plt.colorbar()
plt.title("Matshow with 'Set3' Qualitative Colormap - how2matplotlib.com")
plt.show()

Output:

Example 8: Light to Dark Sequential Colormap

import matplotlib.pyplot as plt
import numpy as np

data = np.random.randn(100, 100)
plt.imshow(data, cmap='Greys')
plt.colorbar()
plt.title("Light to Dark Sequential Colormap - how2matplotlib.com")
plt.show()

Output:

Example 9: High Contrast Diverging Colormap

import matplotlib.pyplot as plt
import numpy as np

data = np.random.randn(10, 10)
plt.imshow(data, cmap='Spectral')
plt.colorbar()
plt.title("High Contrast Diverging Colormap - how2matplotlib.com")
plt.show()

Output:

Example 10: Customizing Colormap Range

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

data = np.random.randn(100, 100)
norm = Normalize(vmin=-2, vmax=2)

plt.imshow(data, cmap='viridis', norm=norm)
plt.colorbar()
plt.title("Customizing Colormap Range - how2matplotlib.com")
plt.show()

Output:

Conclusion

Choosing the right colormap is crucial for creating effective and visually appealing plots in Matplotlib. By understanding the types of colormaps available and their appropriate use cases, you can enhance the readability and interpretability of your visualizations. The examples provided in this article demonstrate various ways to apply and customize colormaps in Matplotlib, offering a practical guide to improving your data visualization skills. Remember, the goal is to choose a colormap that best represents your data and makes your plot intuitive and informative for your audience.