Introduction

Seaborn is a Python data visualization library built on top of Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

It emphasizes statistical relationships and data distribution, making it ideal for exploratory analysis and publication-quality charts with minimal code.

Installation

# Using pip
pip install seaborn

# Using conda
conda install seaborn

Install matplotlib and pandas alongside Seaborn for full functionality.

Basic Plots

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

# Scatter plot
sns.scatterplot(x="total_bill", y="tip", data=tips)
plt.show()

# Histogram
sns.histplot(tips["total_bill"], bins=20)
plt.show()

Use hue and style to add grouping and improve interpretability.

Categorical Plots

# Boxplot
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

# Violin plot
sns.violinplot(x="day", y="total_bill", data=tips)
plt.show()

# Bar plot
sns.barplot(x="day", y="total_bill", data=tips)
plt.show()

Box and violin plots summarize distributions. Bar plots show aggregates with confidence intervals by default.

Regression Plots

# Linear regression
sns.lmplot(x="total_bill", y="tip", data=tips)
plt.show()

# Residual plot
sns.residplot(x="total_bill", y="tip", data=tips)
plt.show()

Regression plots help diagnose trends and variance; residual plots expose model misfit and non-linearity.

Matrix Plots

flights = sns.load_dataset("flights").pivot("month","year","passengers")

# Heatmap
sns.heatmap(flights, annot=True, fmt="d", cmap="YlGnBu")
plt.show()

Heatmaps are excellent for correlation matrices and time-series grids.

Pair & Joint Plots

# Pair plot
sns.pairplot(tips, hue="sex")
plt.show()

# Joint plot
sns.jointplot(x="total_bill", y="tip", data=tips, kind="scatter")
plt.show()

Pair plots show pairwise relationships; joint plots combine scatter and marginal distributions.

Customization

# Themes
sns.set_style("whitegrid")

# Palette
sns.set_palette("pastel")

# Figure size
plt.figure(figsize=(8,5))

sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Use sns.set_theme() and sns.color_palette() for consistent styling across plots.

Advanced Topics

• FacetGrid for multi-plot grids
• Relational plots: sns.relplot
• Time series visualization
• Integration with Matplotlib for complex plots

# FacetGrid example
g = sns.FacetGrid(tips, col="sex", row="smoker")
g.map(sns.scatterplot, "total_bill", "tip")
plt.show()

Facet grids scale comparisons across categories, and relational plots handle long-form data elegantly.