What is Seaborn and how does it differ from Matplotlib?

Seaborn is a high-level statistical visualisation library built on top of Matplotlib. Where Matplotlib gives you full control over every pixel, Seaborn provides opinionated, attractive defaults and plot types designed specifically for statistical exploration — with far less boilerplate code.

import seaborn as sns
import matplotlib.pyplot as plt

# Load a built-in example dataset
tips = sns.load_dataset('tips')

# Seaborn: one line to create a scatter with regression line and hue
sns.regplot(data=tips, x='total_bill', y='tip')

# Matplotlib equivalent would require:
# 1. Compute regression manually
# 2. Plot scatter
# 3. Plot fitted line
# 4. Shade confidence interval — ~15 lines total

# Themes and contexts
sns.set_theme(style='whitegrid', context='notebook', palette='muted')
# styles: darkgrid, whitegrid, dark, white, ticks
# contexts: paper, notebook, talk, poster (scale font/line sizes)

Seaborn plots return Matplotlib Axes objects, so all standard Matplotlib customisation still applies after the Seaborn call: ax = sns.scatterplot(...); ax.set_title('My Title'). Seaborn does not replace Matplotlib — it is a complement that handles the tedious parts of statistical plotting.

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