Seaborn provides various color palettes to enhance your data visualizations. Diverging color palettes are particularly useful when you want to represent data that varies positively and negatively from a central point. Here’s how to use a diverging color palette in Seaborn:
- Import Seaborn: First, ensure you have Seaborn imported into your Python script:
import seaborn as sns import matplotlib.pyplot as plt
- Choose a Diverging Color Palette: Seaborn offers several predefined diverging color palettes, such as
'coolwarm','RdBu_r','PuOr', and more. You can choose one that suits your data and visualization. For example:
palette = 'coolwarm'
- Create Your Plot: When creating your Seaborn plot, specify the chosen diverging color palette using the
paletteparameter:
data = sns.load_dataset('flights')
pivot_data = data.pivot('month', 'year', 'passengers')
plt.figure(figsize=(10, 6))
sns.heatmap(pivot_data, annot=True, fmt='d', cmap=palette)
plt.title('Diverging Color Palette - Heatmap by Pythoneo.com')
plt.show()
In this example, we use the ‘coolwarm’ palette for a heatmap visualization, which emphasizes positive and negative values with distinct colors.
- Customize Your Plot: Customize your plot as needed, adding labels, adjusting color intensity, or specifying other formatting options.
Diverging color palettes are especially helpful when visualizing data that has a clear central point.