Seaborn for Statistical Visualization

Introduction

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

Why Seaborn?

• Statistical plots: Built-in support for statistical visualizations
• Beautiful defaults: Attractive color palettes and styles
• DataFrame integration: Works directly with Pandas DataFrames

Installation and Setup

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

sns.set_theme(style="darkgrid")
tips = pd.read_csv('tips.csv')

Distribution Plots

# Distribution plot
sns.histplot(tips['total_bill'], kde=True)

# Multiple distributions
sns.displot(data=tips, x='total_bill', hue='sex', kind='kde')

# Box plot
sns.boxplot(x='day', y='total_bill', data=tips)

Relational Plots

# Scatter plot with regression line
sns.regplot(x='total_bill', y='tip', data=tips)

# Scatter with hue
sns.scatterplot(x='total_bill', y='tip', hue='day', data=tips)

# Line plot with confidence intervals
sns.lineplot(x='size', y='tip', data=tips)

Categorical Plots

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

# Count plot
sns.countplot(x='day', data=tips)

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

# Strip plot
sns.stripplot(x='day', y='total_bill', data=tips)

Multi-Plot Grids

g = sns.FacetGrid(tips, col='time', row='smoker')
g.map_dataframe(sns.scatterplot, x='total_bill', y='tip')
g.add_legend()

g = sns.PairGrid(tips.select_dtypes(include=[np.number]))
g.map_diag(sns.histplot)
g.map_offdiag(sns.scatterplot)

Heatmaps

# Correlation heatmap
corr = tips.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)

# Pivot table heatmap
pivot = tips.pivot_table(values='tip', index='day', columns='size')
sns.heatmap(pivot, annot=True)

Customization

sns.set_style("whitegrid")
sns.set_palette("husl")
sns.set_context("notebook", font_scale=1.2)

plt.figure(figsize=(10, 6))
sns.boxplot(x='day', y='total_bill', data=tips, palette='Set2')

Key Takeaways

1. Seaborn simplifies statistical visualization
2. Use FacetGrid for complex multi-panel plots
3. Customizable themes and palettes for professional plots