Seaborn Advanced

Introduction

Advanced Seaborn visualizations for statistical analysis.

Categorical Plots

import seaborn as sns

# Strip plot
sns.stripplot(x="category", y="value", data=df, jitter=True)

# Swarm plot (no overlap)
sns.swarmplot(x="category", y="value", data=df)

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

Regression Plots

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

# With confidence intervals
sns.lmplot(x="feature", y="target", data=df, ci=99)

# Polynomial regression
sns.regplot(x="x", y="y", data=df, order=2)

FacetGrid

g = sns.FacetGrid(df, col="category", row="region")
g.map(plt.hist, "value")
g.add_legend()

Practice Problems

1. Create faceted plots by multiple variables
2. Customize regression plots
3. Build pair plots with custom colors
4. Use catplot for complex categoricals
5. Create joint distribution plots