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# Grouping and Hue for Comparisons in Seaborn

One of Seaborn’s most powerful features is its ability to easily compare **subgroups** within a dataset using the `hue` parameter.  
By adding `hue`, you can split data into categories and display them with different colors — making comparisons more intuitive.

<br/>

## Why Hue is Useful
- **Adds another layer of information** without creating separate plots.
- Helps identify **patterns** and **differences** between categories.
- Works across most Seaborn functions like `barplot`, `scatterplot`, and `lineplot`.

<br/>

## Example – Comparing Categories in a Scatterplot

```python title="Scatterplot with Hue"
import seaborn as sns
import matplotlib.pyplot as plt

# Sample dataset
tips = sns.load_dataset("tips")

# Scatterplot with hue for gender
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="sex")

plt.title("Total Bill vs Tip by Gender")
plt.show()
```

* `hue="sex"` automatically assigns different colors for male and female groups.
* Seaborn adds a **legend** by default to indicate which color corresponds to which group.

<br/>

## Pro Tip

When using `hue`, you can also adjust **palette** for custom color schemes:

```python title="Custom Color Palette"
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="sex", palette="Set2")
```

<br/>

In the next Jupyter notebook, you’ll explore how to use `hue` in different chart types and experiment with your own datasets.

The hue parameter in Seaborn is a powerful tool for distinguishing subgroups within your dataset using colors. By automatically assigning different colors to these subgroups, it adds an additional layer of information to your visualization, facilitating the comparison of patterns and differences between these categories.

### How to use the hue parameter to visualize subgroups within your data in Seaborn?