## Introduction

A KDE Plot is an excellent tool to start with. Unlike bar charts or line graphs, KDE Plots provide a smooth estimate of data distribution, making them ideal for exploring the shape of your dataset. In this article, we'll use a sample dataset to show you step-by-step how to create your own KDE Plot.

## What is KDE

KDE stands for Kernel Density Estimate. It's a way to show how data points are spread out. Imagine you have a bunch of points along a line. A KDE plot is like drawing a small hill over each point and then adding all these hills together to make one smooth curve.

You should install Seaborn.

`pip install seaborn`

The below code is the Python code snippet.

```
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
# Sample data: Generating random data for demonstration
data = np.random.normal(size=100)
# Create a KDE plot
sns.kdeplot(data, shade=True)
# Adding titles and labels
plt.title('Sample KDE Plot')
plt.xlabel('Value')
plt.ylabel('Density')
# Display the plot
plt.show()
```

**Output**

### Explanation

- We import Seaborn, NumPy for data generation, and Matplotlib for additional plot customization.
- We generate a sample dataset (
`data`

) using NumPy's`random.normal`

, which creates random data points following a normal distribution. - We create a KDE plot of this data using
`sns.kdeplot`

. The`shade=True`

argument fills the area under the KDE curve. - We add a title and labels for the x and y-axes using Matplotlib's
`plt.title`

,`plt.xlabel`

, and`plt.ylabel`

. - Finally, we display the plot with
`plt.show()`

.

## Conclusion

KDE Plots are particularly useful for seeing the shape of the data, such as whether it's skewed, has multiple peaks, or is normally distributed. This curve shows where most of your data is. If the curve is high in a certain area, it means you have a lot of data points there. It's like a smoother version of a histogram, which uses bars to show how many data points fall into different ranges.