Seaborn is a high-level library built on top of Matplotlib that makes it easier to create beautiful, informative visualizationsespecially for statistical graphics.

Why Use Seaborn?
Simplifies complex plots with less code
Built-in themes and color palettes
Excellent for visualizing relationships and distributions
Itegrates smoothly with Pandas DataFrames
Great for correlation, regression, and categorical data

Core Features
1. Quick Visualizations
python
import seaborn as sns
import pandas as pd

df = sns.load_dataset('tips')
sns.histplot(df['total_bill'])
sns.boxplot(x='day', y='total_bill', data=df)
sns.scatterplot(x='total_bill', y='tip', data=df)


2. Heatmaps & Correlation
python
corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')


3. Categorical Plots
python
sns.catplot(x='day', y='tip', hue='s*x', kind='bar', data=df)


4. Regression Plots
python
sns.lmplot(x='total_bill', y='tip', data=df)


5. Styling & Themes
python
sns.set_style('whitegrid')
sns.set_palette('pastel')


Common Functions
- sns.histplot(): Histograms
- sns.boxplot(): Box plots
- sns.heatmap(): Heatmaps
- sns.scatterplot(): Scatter plots
- sns.pairplot(): Plot pairwise relationships
- sns.lmplot(): Linear regression plots

Real-World Use Cases
Quick visual summary of data
Statistical analysis visuals
Exploratory data analysis
Feature relationships and insights

Summary
Ideal For: Statistical and categorical plotting
Strength: Simplicity + beauty
Bonus: Less code, more elegance