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# Grouping and `Hue` for Comparisons in Seaborn

One of Seaborn’s most powerful features is the ability to **compare subgroups** within a dataset using the `hue` parameter.

Adding `hue` lets you separate data into categories and automatically color them — making comparisons clear and visually engaging.

<br/>

## Why Hue is Useful

- Adds a new layer of information without needing multiple plots.
- Highlights category-level patterns and differences.
- Works seamlessly across functions like `barplot`, `scatterplot`, and `lineplot`.

<br/>

## Comparing Categories in a Scatterplot

You can apply `hue` in scatterplots to visually compare categories within your data.

```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 distinct colors for male and female groups.
* A *legend* is added by default to show which color represents each group.

<br/>

## Pro Tip

You can also customize the **palette** to control your color scheme when using `hue`:

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

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?

Grouping and Hue for Comparisons in Seaborn