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# Distribution Plots (histplot, kdeplot)

Visualizing **data distributions** helps you understand how your data is spread, detect patterns, and identify potential outliers.

Seaborn provides two main tools for this:

- `histplot()` – shows the frequency distribution of a dataset.
- `kdeplot()` – shows the probability density function (smoothed distribution curve).

<br/>

## Using `histplot()`

The `histplot()` function creates a histogram that shows how many data points fall into each range (bin).

```python title="Basic Histogram"
import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.histplot(data=tips, x="total_bill")
plt.title("Distribution of Total Bills")
plt.show()
```

Key points:

* `x` specifies the variable to plot.
* The plot is divided into *bins* (intervals) along the X-axis.
* The height of each bar shows how many observations fall into that bin.

<br/>

## Using `kdeplot()`

The `kdeplot()` function displays a smooth curve representing the estimated probability density of the data.

```python title="Basic KDE Plot"
sns.kdeplot(data=tips, x="total_bill")
plt.title("KDE of Total Bills")
plt.show()
```

Key points:

* KDE = Kernel Density Estimate (a smoothed version of the histogram).
* Good for showing trends in continuous data.
* Can be combined with `histplot()` for more context.

<br/>

## Combining Histogram and KDE

You can combine both in a single `histplot()` by setting `kde=True`:

```python title="Histogram with KDE Overlay"
sns.histplot(data=tips, x="total_bill", kde=True)
plt.title("Total Bill Distribution with KDE")
plt.show()
```

<br/>

In the next Jupyter Notebook, you will experiment with:

* Changing bin sizes in histograms.
* Adding hue categories to compare groups.
* Styling KDE plots for clarity.

The `kdeplot()` function in Seaborn is used to display a smooth curve that represents the estimated probability density of the data, essentially smoothing out the histogram to show trends more clearly. It's great for visualizing the distribution of continuous data and can be overlaid on a histogram for additional context.

### What function would you use to visualize a smoothed version of a histogram in Seaborn?

Distribution Plots (histplot, kdeplot)

Using histplot()

Using kdeplot()

Combining Histogram and KDE

What function would you use to visualize a smoothed version of a histogram in Seaborn?

Using `histplot()`

Using `kdeplot()`