> ## Documentation Index
> Fetch the complete documentation index at: https://python4ai.codewithsiva.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Seaborn

> Statistical data visualization built on top of Matplotlib

## Introduction to Seaborn

**Seaborn** is a statistical data visualization library built on top of Matplotlib. It integrates closely with Pandas DataFrames and provides a high-level interface for drawing attractive, informative, and modern statistical graphics.

### Why use Seaborn?

* Beautiful default themes and color palettes.
* Simplifies complex plots (like heatmaps, distribution plots, and linear regressions) into a single line of code.
* Automatically handles Pandas DataFrames.

***

## Creating a Seaborn Plot

Here's how to create a simple scatter plot with a regression line:

```python theme={null}
import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a scatter plot with regression line
sns.lmplot(data=tips, x="total_bill", y="tip", hue="smoker")

# Add a title and show
plt.title("Tips vs Total Bill by Smoker Status")
plt.show()
```

***

## Key Plot Types

### 1. Distribution Plots

Visualize how your data is distributed:

```python theme={null}
sns.histplot(data=tips, x="total_bill", kde=True)
```

### 2. Categorical Plots

Compare different categories (like box plots or bar plots):

```python theme={null}
sns.boxplot(data=tips, x="day", y="total_bill")
```

### 3. Correlation Heatmaps

Perfect for finding relationships between numerical columns:

```python theme={null}
# Select only numerical columns
numerical_tips = tips.select_dtypes(include=["number"])
sns.heatmap(numerical_tips.corr(), annot=True, cmap="coolwarm")
```

***

## Summary

By combining **NumPy**, **Pandas**, **Matplotlib**, and **Seaborn**, you have a full stack of tools to load, process, analyze, and visualize data in Python.