What are the most important Seaborn plot types for exploratory data analysis?

Seaborn divides its plots into relational (relationship between variables), distributional (distribution of a single variable), and categorical (comparison across categories). Knowing when to use each makes EDA far more efficient.

import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset('tips')

# --- Relational ---
# Scatter with colour encoding
sns.scatterplot(data=tips, x='total_bill', y='tip',
                hue='smoker', size='size', palette='Set1')

# Regression line + scatter
sns.regplot(data=tips, x='total_bill', y='tip', ci=95)

# --- Distributional ---
# Histogram + KDE
sns.histplot(data=tips, x='total_bill', hue='sex', kde=True, bins=20)

# KDE only
sns.kdeplot(data=tips, x='total_bill', hue='sex', fill=True)

# ECDF — empirical cumulative distribution
sns.ecdfplot(data=tips, x='total_bill', hue='day')

# --- Categorical ---
# Box plot
sns.boxplot(data=tips, x='day', y='total_bill', hue='smoker', palette='pastel')

# Violin — box + KDE combined
sns.violinplot(data=tips, x='day', y='tip', inner='quartile')

# Bar chart with error bars (95% CI by default)
sns.barplot(data=tips, x='day', y='tip', estimator='mean', errorbar='ci')

# Strip plot — all individual points
sns.stripplot(data=tips, x='day', y='tip', jitter=True, alpha=0.4)

# --- Multi-variable overview ---
# Pair plot — scatter matrix of all numeric column pairs
sns.pairplot(tips, hue='sex', diag_kind='kde')

# Heatmap — great for correlation matrices
corr = tips.select_dtypes('number').corr()
sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm', vmin=-1, vmax=1)

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