How to increase the size of the annotations of a seaborn heatmap in Python?

How to Increase the Size of the Annotations of a Seaborn Heatmap in Python

Seaborn heatmaps are powerful tools for visualizing data relationships using color intensity. When annotations are added to display the exact values in each cell, they can sometimes appear too small. To improve readability, you can customize their font size easily with a few tweaks.

1. Use annot_kws to Control Font Size

The most straightforward way to enlarge the annotation text is by using the annot_kws parameter inside sns.heatmap(). This parameter accepts a dictionary where you can define the font size and style.

Example:

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Sample data
data = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

# Create a heatmap with enlarged annotation font
sns.heatmap(data, annot=True, annot_kws={"size": 14})
plt.title("Heatmap with Larger Annotation Text")
plt.show()

2. Apply Global Font Scaling with sns.set()

If you want to scale all fonts across the plot, including annotations, axis labels, and tick labels, you can use Seaborn’s global font scaling option. This is helpful for presentations or larger displays.

Example:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Generate random data
matrix = np.random.rand(4, 4)

# Increase all font sizes globally
sns.set(font_scale=1.5)

# Create heatmap
sns.heatmap(matrix, annot=True, cmap='Blues')
plt.title("Heatmap with Global Font Scaling")
plt.show()

3. Combine Both Methods for More Control

You can also combine both approaches—using sns.set() to scale fonts overall and annot_kws to precisely control annotation size, weight, or color. This gives you full flexibility in how your heatmap appears.

Example:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Create sample data
data = np.random.rand(6, 6)

# Set global font scaling
sns.set(font_scale=1.3)

# Create heatmap with customized annotation style
sns.heatmap(
    data,
    annot=True,
    fmt=".2f",
    cmap="YlOrRd",
    annot_kws={"size": 12, "weight": "bold", "color": "black"},
    linewidths=0.5
)
plt.title("Heatmap with Custom Annotation Style")
plt.show()

Conclusion

Adjusting annotation size in a Seaborn heatmap is simple and effective. Use annot_kws for precise annotation styling or apply global font scaling with sns.set() for consistent typography throughout your visualization. Combining both methods can help create clean, professional, and readable heatmaps.