Diverging Color Palettes in Seaborn

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:

  1. Import Seaborn: First, ensure you have Seaborn imported into your Python script:
import seaborn as sns
import matplotlib.pyplot as plt
  1. 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'
  1. Create Your Plot: When creating your Seaborn plot, specify the chosen diverging color palette using the palette parameter:
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')

In this example, we use the ‘coolwarm’ palette for a heatmap visualization, which emphasizes positive and negative values with distinct colors.

  1. Customize Your Plot: Customize your plot as needed, adding labels, adjusting color intensity, or specifying other formatting options.
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Diverging color palettes are especially helpful when visualizing data that has a clear central point.