5 Best Ways to Display a Kernel Density Estimation Plot with Seaborn’s Joinplot in Python

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πŸ’‘ Problem Formulation: Data scientists and analysts often need to visualize the relationship between two data sets, along with their individual distribution characteristics. Seaborn’s Joinplot is a perfect tool for this, combining scatter plots or regression plots with kernel density estimation plots (KDE). This article focuses on displaying KDE using joinplot in Python, where the input is a pandas DataFrame and the desired output is a statistical visualization.

Method 1: Basic Joinplot with Kernel Density Estimation

This method involves creating a basic joinplot that shows the relationship between two variables with their respective kernel density estimations on the axes. Seaborn’s joinplot function is highly customizable but can also be used with minimal arguments for quick insights.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Load an example dataset
iris = sns.load_dataset('iris')

# Create a joinplot
sns.jointplot(data=iris, x='sepal_width', y='sepal_length', kind='kde')

# Display the plot
plt.show()

The output is a plot with ‘sepal_width’ distributions on the x-axis, ‘sepal_length’ distributions on the y-axis, and a two-dimensional KDE in the center.

This code snippet loads the iris dataset, uses joinplot() to create a KDE for ‘sepal_width’ and ‘sepal_length’, and then displays the resulting plot. It is an easy method to get a quick visualization of the data.

Method 2: Joinplot with Enhanced Bandwidth

The kernel density estimation process is sensitive to the bandwidth chosen. This method adjusts the bandwidth parameter to fine-tune the smoothness of the KDE. It demonstrates how to change the bandwidth for more or less detailed views.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Load an example dataset
iris = sns.load_dataset('iris')

# Create a joinplot with adjusted bandwidth
sns.jointplot(data=iris, x='sepal_width', y='sepal_length', kind='kde', bw_adjust=0.5)

# Display the plot
plt.show()

The output is similar to the first method but the KDE appears smoother or rougher depending on the bandwidth adjustment.

The bw_adjust parameter controls the smoothness of the KDE curves. The jointplot() function is versatile, letting users explore data relationships at different levels of detail.

Method 3: Adding Color to the KDE

Color can enhance the KDE’s visual appeal and highlight densities more effectively. Seaborn’s palette options can be combined with joinplot to create colorful visualizations.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Load an example dataset
iris = sns.load_dataset('iris')

# Create a colorized joinplot
sns.jointplot(data=iris, x='sepal_width', y='sepal_length', kind='kde', cmap='coolwarm')

# Display the plot
plt.show()

The output is a colorful visual representation where the intensity of color corresponds to the density of points.

Here, we utilize the cmap parameter to apply a color map to the KDE plot. This enriches the plot and can make patterns within the data more discernable.

Method 4: Overlaying KDE with Scatter Plot

Overlaying a KDE with a scatter plot can display both the joint distribution and the individual data points. This method leverages Seaborn’s simultaneous plotting capabilities.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Load an example dataset
iris = sns.load_dataset('iris')

# Create a joinplot with both scatter and KDE
sns.jointplot(data=iris, x='sepal_width', y='sepal_length', kind='kde')
plt.scatter(iris['sepal_width'], iris['sepal_length'], alpha=0.4)

# Display the plot
plt.show()

The output shows a scatter plot on top of the KDE, making it easy to see individual data point distributions in relation to the overall density.

The KDE is generated first with jointplot(), and then scatter points are overlaid using plt.scatter(), with transparency set by the alpha parameter.

Bonus One-Liner Method 5: JointGrid Customization

Seaborn’s JointGrid gives you the power to customize joinplots even more, for seasoned users who want fine-tuned control over every aspect of their statistical visualization.

Here’s an example:

from seaborn.axisgrid import JointGrid
import seaborn as sns
import matplotlib.pyplot as plt

# Load an example dataset
iris = sns.load_dataset('iris')

# Create a custom JointGrid
g = JointGrid(data=iris, x='sepal_width', y='sepal_length')
g = g.plot_joint(sns.kdeplot)
g = g.plot_marginals(sns.kdeplot, shade=True)

# Display the plot
plt.show()

The output is a highly customizable joinplot where both the central and marginal plots can be individually styled.

This snippet uses Seaborn’s JointGrid for complete customization of the joined KDE plot, applying kdeplot for both central and marginal views.

Summary/Discussion

  • Method 1: Basic Joinplot with Kernel Density Estimation. Quick and simple visualization. Limited customization options.
  • Method 2: Joinplot with Enhanced Bandwidth. Allows detailed analysis through bandwidth adjustment. Could potentially misrepresent data if bandwidth isn’t chosen carefully.
  • Method 3: Adding Color to the KDE. Introduces visual appeal and clarity. Color choices are crucial and may affect interpretability.
  • Method 4: Overlaying KDE with Scatter Plot. Provides dual insights into individual points and overall density. May become cluttered with large datasets.
  • Bonus Method 5: JointGrid Customization. Offers ultimate control and customization. Requires more lines of code and a deeper understanding of Seaborn’s API.