How do you compare distributions across categories using Seaborn categorical plots?

Comparing how a numeric variable's distribution differs across groups is one of the most common analytical tasks. Seaborn's categorical plot family gives you progressively more information from left to right: bar (mean only) → box (five-number summary) → violin (full distribution shape) → strip/swarm (individual points).

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

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Bar plot — mean + 95% CI error bars
sns.barplot(data=tips, x='day', y='tip', hue='sex',
            palette='Set2', ax=axes[0, 0])
axes[0, 0].set_title('Mean Tip by Day and Sex')

# Box plot — median, IQR, whiskers, outlier dots
sns.boxplot(data=tips, x='day', y='total_bill', hue='smoker',
            palette='pastel', ax=axes[0, 1])
axes[0, 1].set_title('Total Bill Distribution by Day and Smoker')

# Violin plot — box + KDE combined
sns.violinplot(data=tips, x='day', y='tip',
               inner='quartile',   # show quartile lines inside
               palette='muted', ax=axes[1, 0])
axes[1, 0].set_title('Tip Violin by Day')

# Strip + box overlay — all points + summary
sns.boxplot(data=tips, x='time', y='tip', color='lightblue',
            ax=axes[1, 1], width=0.4)
sns.stripplot(data=tips, x='time', y='tip', color='navy',
              alpha=0.4, jitter=True, ax=axes[1, 1])
axes[1, 1].set_title('Tip by Time — Box + All Points')

plt.tight_layout(); plt.show()

# Figure-level catplot for easy faceting
sns.catplot(data=tips, x='day', y='tip', hue='sex',
            col='time', kind='violin', height=5, aspect=0.8)

When to use each: bar plots are fine for comparing means but hide distributional information. Box plots add spread and outliers. Violin plots reveal multi-modality (two bumps indicating two groups within a category). Strip/swarm overlays add individual points, essential for small datasets where a box plot can be misleading with n < 30.

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