Seaborn Basics
Seaborn is a high‑level statistical visualization library built on Matplotlib. It provides attractive default styles and easy‑to‑use functions for complex plots like heatmaps, pair plots, and regression lines.
Installing and Importing Seaborn
pip install seabornimport seaborn as sns
import matplotlib.pyplot as pltHeatmap (Correlation Matrix)
Great for visualizing correlations between features.
import numpy as np
import pandas as pd
# Sample data
df = pd.DataFrame(np.random.rand(10, 4), columns=['A','B','C','D'])
corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Feature Correlation Heatmap')
plt.show()Pairplot (Scatter Matrix)
Shows pairwise relationships in a dataset. Very useful for initial EDA.
# Load built‑in dataset
iris = sns.load_dataset('iris')
sns.pairplot(iris, hue='species')
plt.show()Regression Plot (Scatter with Trend Line)
tips = sns.load_dataset('tips')
sns.regplot(x='total_bill', y='tip', data=tips)
plt.title('Tip vs Total Bill with Regression Line')
plt.show()Why Seaborn for AI?
Seaborn makes it easy to visualize high‑dimensional data, check for correlations (heatmap), and quickly explore feature relationships (pairplot). It saves time compared to raw Matplotlib.
Two Minute Drill
- Heatmap:
sns.heatmap(corr, annot=True)– correlation matrix. - Pairplot:
sns.pairplot(data, hue='category')– all pairwise plots. - Regression plot:
sns.regplot(x='col1', y='col2', data=df). - Seaborn works seamlessly with Pandas DataFrames.
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