> ## 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.

# Matplotlib

> Creating static, animated, and interactive visualizations in Python

## Introduction to Matplotlib

**Matplotlib** is the standard plotting library for Python. It allows you to create high-quality, customizable visualizations such as line plots, scatter plots, bar charts, histograms, and more.

### Why use Matplotlib?

* Full control over every element in a chart (titles, axes, colors, labels).
* Exportable to many file formats (PNG, PDF, SVG).
* Integrates seamlessly with NumPy and Pandas.

***

## Creating Your First Plot

The most common way to use Matplotlib is via the `pyplot` module:

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

# Data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Create a line plot
plt.plot(x, y, label="Double Value", color="green", marker="o")

# Add labels and title
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.title("Simple Line Plot")
plt.legend()

# Display the plot
plt.show()
```

***

## Common Plot Types

* **`plt.scatter(x, y)`**: Creates a scatter plot (points).
* **`plt.bar(x, height)`**: Creates a vertical bar chart.
* **`plt.hist(data)`**: Creates a histogram to see data distribution.

***

## Next Steps

While Matplotlib is powerful, it can require a lot of code to make plots look modern. Let's look at **Seaborn**, which builds on Matplotlib to provide beautiful, statistical charts with less code.