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

## Introduction

Data visualization is a crucial aspect of data analysis and machine learning. It helps to understand the underlying patterns, correlations, and trends in data, which may not be apparent in raw numbers. One of the most popular Python libraries for data visualization is Seaborn. Built on top of Matplotlib, Seaborn provides a high-level, easy-to-use interface for creating complex plots.

In this article, we will focus on how to use Seaborn’s `FacetGrid` to create small multiples of Kernel Density Estimation (KDE) plots. Specifically, we will explore how the price of diamonds varies with different cuts using the diamonds dataset from the `plotnine` package.

## What is FacetGrid?

FacetGrid is a feature in Seaborn that allows you to create a grid of subplots based on one or more categorical variables. This is particularly useful for comparing different subsets of your data at once. Instead of having a single plot with multiple lines or colors representing different categories, you get multiple smaller plots (facets), each focusing on a single category.

## What is KDE Plot?

Kernel Density Estimation (KDE) is a non-parametric way of estimating the probability density function of a random variable. KDE plots are used to visualize the distribution of data points over a continuous interval. They are smoother than histograms and offer a better understanding of the data distribution.

## Combining FacetGrid and KDE Plots

The power of FacetGrid comes into full view when you combine it with different kinds of plots. In our example, we will combine FacetGrid with KDE plots to see how the distribution of diamond prices varies with the cut of the diamond.

## Code Explanation

### Importing Libraries

Firstly, we import the necessary libraries: Seaborn for data visualization, Matplotlib for plot customization, and `plotnine.data` for the diamonds dataset.

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

### Setting Up Seaborn

We set the Seaborn theme to “whitegrid,” which provides a grid to make it easier to read the plots.

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

``````

### Creating the FacetGrid

We create a FacetGrid object, specifying that we want to create facets based on the ‘cut’ of the diamonds. We also set the hue to ‘cut’ and limit the number of facets in a single row using `col_wrap`.

``````
g = sns.FacetGrid(diamonds, col='cut', hue='cut', col_wrap=3)

``````

We add KDE plots to the FacetGrid by using the `map` method. Here, we specify various options such as filling the area under the curve (`fill=True`) and making each KDE independent (`common_norm=False`).

``````
g = g.map(sns.kdeplot,"price", cut=0, fill=True, common_norm=False, alpha=1, legend=False)

``````

### Customizing Titles

We use `set_titles` to customize the title of each facet to display the name of the diamond cut.

``````
g = g.set_titles("{col_name}")

``````

### Displaying the Plot

Finally, we display the entire grid of plots using Matplotlib’s `show` method.

``````
plt.show()

``````

## End-to-End Code Example

Here is the full code for creating small multiples of KDE plots to analyze diamond prices based on their cut:

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

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

# using small multiple
# create a grid
g = sns.FacetGrid(diamonds, col='cut', hue='cut', col_wrap=3)

# draw density plots
g = g.map(sns.kdeplot,"price", cut=0, fill=True, common_norm=False, alpha=1, legend=False)

# control the title of each facet
g = g.set_titles("{col_name}")

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

## Elaborated Prompts for Further Exploration

1. How can you modify the code to include multiple variables in the FacetGrid?
2. Can you customize the FacetGrid to display facets based on both ‘cut’ and ‘color’ of diamonds?
3. How would you add a legend to the plot?
4. What other Seaborn themes can you try instead of “whitegrid”?
5. How can you customize the color palette for the KDE plots?
6. What happens if you set `common_norm=True` in the `map` method?
7. How can you add axis labels to each facet?
8. How to add global titles and subtitles to the FacetGrid?
9. Can you change the size and aspect ratio of each facet?
10. How would you save the entire FacetGrid as an image file?
11. What are the limitations of using FacetGrid for visualizations?
12. How can you plot histograms instead of KDE plots on the FacetGrid?
13. Can you add multiple types of plots (like KDE and scatter plots) to the same FacetGrid?
14. How would you sort the facets based on the median price of diamonds for each cut?
15. What are some real-world applications where using FacetGrid would be beneficial?

## Conclusion

Seaborn’s FacetGrid combined with KDE plots provides a powerful tool for visualizing and understanding complex multi-dimensional data. By categorizing the data into smaller, focused plots, it allows for easier comparison and deeper insights. With customization options, you can adapt the visualization to meet your specific needs.

## Find more … …

A Deep Dive into Seaborn’s Kernel Density Estimation Plots: Visualize Data Distributions

Mastering Multi-Distribution KDE Plots in Seaborn: A Complete Guide to Overlapping Density Plots

Fine-Tuning Your Data Visualizations with Seaborn’s KDE Bandwidth Parameter: A Comprehensive Guide