Summary  
This chapter explains how to customize plot appearance in seaborn by setting global styles, defining color palettes, and adjusting context settings.  

General domain of usage  
Data visualization

### Setting the style for the plot

To set the style use: 
```python
sns.set_style('style_name')
```

Styles available: `white`, `dark`, `whitegrid`, `darkgrid`, `ticks` etc.

### Changing colors of the main elements of the plot

``seaborn`` has different palette options. 
Below there are a few of them. 

Palettes: `RdBu`, `PRGn`, `RdBu_r`, `PRGn_r` (different colors in the palette), `Greys`, `Blues`, `PuRd`, `GnBu` (gradient colors).



You can also create your **unique palette** by creating a list variable. To set the palette use:
 
```python
sns.set_palette(palette_name)
```

### Context setting

To set the context use:

```python
sns.set_context('context_name')
```

Contexts available: `paper`, `notebook`, `talk`, `poster` etc.

Together we will make the first dive into visualization based on a seaborn library. We will learn how to build simple plots and how to customize them. You will cope faster with tasks if you know something about pandas. Do not hesitate, and let's go!

In this section, we will learn about the seaborn library and build our first plot! We will also learn about the friendship between the pandas and the Seaborn.

We continue learning plots with Seaborn.\n\nYou will learn some more new plots and even build 3-variable plot!

Plots seem so boring without customization...\nIn this section, we will learn how to make our plots perfectly styled!!

Decoration and...Decoration!

Setting the style for the plot

Changing colors of the main elements of the plot

Context setting

Oplossing