Seaborn Visualization

Introduction

Seaborn is a statistical visualization library built on matplotlib that provides a high-level interface.

Distribution Plots

import seaborn as sns
import matplotlib.pyplot as plt

# Histogram with KDE
sns.histplot(data, kde=True)

# Box plot
sns.boxplot(x="category", y="value", data=df)

# Violin plot
sns.violinplot(x="category", y="value", data=df)

Relationship Plots

# Scatter with regression
sns.regplot(x="feature", y="target", data=df)

# Line plot with confidence interval
sns.lineplot(x="time", y="value", data=df)

# Pair plot
sns.pairplot(df, hue="category")

Categorical Plots

# Bar plot
sns.barplot(x="category", y="value", data=df)

# Count plot
sns.countplot(x="category", data=df)

# Heatmap
sns.heatmap(correlation_matrix, annot=True)

Practice Problems

1. Create distribution of ages in dataset
2. Visualize correlation matrix as heatmap
3. Build pair plot for feature comparison
4. Compare categories with box plot
5. Add regression line to scatter plot