# Unpacking the Power of Stacked Density Plots in Seaborn: A Comprehensive Guide on Visualizing Diamond Prices # Unpacking the Power of Stacked Density Plots in Seaborn: A Comprehensive Guide on Visualizing Diamond Prices

## Introduction

The field of data visualization has seen tremendous growth over the years, with Python libraries like Matplotlib and Seaborn leading the charge. One of the fascinating types of plots you can create with Seaborn is a stacked density plot, also known as a “filled” Kernel Density Estimate (KDE) plot. These plots are especially useful for visualizing the distribution of a numerical variable across different categories.

In this article, we’ll dive deep into creating a stacked density plot using Seaborn to analyze the price distribution of diamonds across different cuts. The diamonds dataset from the `plotnine` package will be our data source.

## What is a Stacked Density Plot?

A stacked density plot is a variation of the classic KDE plot, where multiple KDE plots are stacked on top of each other. This creates a clear visual representation of how the density of one category compares to others. It allows us to understand the distribution of a numerical variable across different categories more comprehensively.

## Code Explanation

### Importing Libraries and Dataset

We start by importing the necessary libraries and dataset. Here, we use Seaborn for visualization, Matplotlib for additional customization, and the diamonds dataset from `plotnine` as our data.

``````
import seaborn as sns
import matplotlib.pyplot as plt
from plotnine.data import diamonds # dataset

``````

### Setting the Theme

We set the theme of the plot to “whitegrid” to make it more readable.

``````sns.set(style="whitegrid")
``````

### Creating the Stacked Density Plot

We create the stacked density plot using the `sns.kdeplot` function. We specify the `data`, the `x` variable, and the `hue` to categorize the data. The `common_norm=False` parameter ensures that each density plot is independent, and `multiple=”fill”` fills the area under each density curve.

``sns.kdeplot(data=diamonds, x="price", hue="cut", common_norm=False, multiple="fill", alpha=1)``

### Displaying the Plot

Finally, we display the plot using Matplotlib’s `plt.show()` method.

``plt.show()``

## End-to-End Code Example

Here’s the complete code snippet for creating a stacked density plot of diamond prices across different cuts:

``````# libraries
import seaborn as sns
import matplotlib.pyplot as plt
from plotnine.data import diamonds # dataset

# set seaborn whitegrid theme
sns.set(style="whitegrid")

# stacked density plot
sns.kdeplot(data=diamonds, x="price", hue="cut", common_norm=False, multiple="fill", alpha=1)

# show the graph
plt.show()
``````

## Elaborated Prompts for Further Exploration

1. How can you modify the stacked density plot to include more variables?
2. Can you replace the `cut` variable with `color` or `clarity` to see different distributions?
3. How do you add legends and labels to the plot for better interpretation?
4. What are the other themes in Seaborn that you can experiment with?
5. How can you change the color palette of the stacked density plot?
6. What is the significance of setting `common_norm=False`, and what changes if you set it to `True`?
7. How would you annotate specific points or ranges on the plot?
8. Can you adjust the opacity level of each layer in the stacked density plot?
9. How can you display vertical lines to indicate the mean or median price for each cut?
10. Is it possible to create a horizontal stacked density plot? How would you do it?
11. How can you save this stacked density plot as a high-resolution image?
12. What are the limitations or drawbacks of using a stacked density plot?
13. Can you create a stacked density plot with smoothed edges?
14. How would you customize the x-axis and y-axis limits for better visualization?
15. Are there real-world scenarios where a stacked density plot would provide exceptional insights?

## Conclusion

Stacked density plots offer a rich, layered view of data distributions across multiple categories. By using Seaborn’s simple yet powerful functionalities, you can create these intricate visualizations to make better sense of your data. Whether you’re a data scientist, a business analyst, or someone who’s just curious about data, mastering stacked density plots can be a valuable addition to your data visualization toolkit.

## Find more … …

Mastering Data Visualization with Seaborn: Using FacetGrid and KDE Plots to Analyze Diamond Prices

How to use stacking of Machine Learning Algorithms in R

End-to-End Machine Learning: stacking in R