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# Styling and Themes

Matplotlib comes with several **built-in styles** that let you quickly change the overall appearance of your plots.

You can also customize individual elements to match your preferred visual style, brand identity, or presentation theme.

<br/>

## Using Built-in Styles

Use `plt.style.use("style_name")` to apply a visual theme across all plots in your session.

```python title="Applying a Built-in Style"
import matplotlib.pyplot as plt

plt.style.use("ggplot")  # Apply the ggplot theme

x = [1, 2, 3, 4]
y = [10, 20, 15, 25]

plt.plot(x, y)
plt.title("Styled Plot with ggplot")
plt.show()
```

Popular style names include:

* `"ggplot"`
* `"seaborn"`
* `"bmh"`
* `"dark_background"`
* `"fivethirtyeight"`

<br/>

## Listing Available Styles

To see what's available, run:

```python title="List Available Styles"
print(plt.style.available)
```

<br/>

## Customizing Individual Elements

You can override specific elements, even when using a theme:

```python title="Customize Colors and Line Width"
plt.plot(x, y, color="purple", linewidth=3)
```

This approach helps maintain a consistent overall look while highlighting specific data or adjusting key visual elements.

Matplotlib provides a convenient way to change the appearance of your plots by using built-in styles. By using plt.style.use('style_name'), you can apply a theme globally, affecting all subsequent plots. This feature simplifies the process of ensuring a consistent look across multiple visualizations.

### Matplotlib allows you to apply a built-in style globally using plt.style.use('style_name').