5 Best Ways to Use Seaborn Library for Kernel Density Estimations in Python

Rate this post

π‘ Problem Formulation: Data visualization is a critical component in data analysis, and Kernel Density Estimation (KDE) is a powerful tool for visualizing probability distributions of a dataset. The challenge lies in efficiently creating KDE plots that are both informative and visually appealing. Using the Seaborn library in Python can simplify this process. This article demonstrates how to use Seaborn to display KDEs, with an emphasis on practical examples starting from a dataset input to produce clear, polished KDE visualizations as output.

Method 1: Basic KDE Plot

Seaborn simplifies the process of creating a kernel density estimation with its sns.kdeplot function. This method plots the density of a univariate distribution, giving an overview of the distribution’s shape. The function takes in data points and returns a smoothed continuous representation of the probability density function.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = [1, 2, 3, 4, 5, 5, 6, 7]

# Create KDE plot
sns.kdeplot(data)
plt.show()

In this example, the KDE of the sample data is displayed as a smooth curve, depicting the probability density across the range of values.

Method 2: Two-Dimensional KDE Plot

For multidimensional data, Seaborn can plot two-dimensional KDEs using the same sns.kdeplot function. This extends the visualization capabilities to explore the joint distribution between two variables, showing the density of data points in a two-dimensional space.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Generate sample data
x = np.random.normal(size=100)
y = np.random.normal(size=100)

# Create 2D KDE plot
sns.kdeplot(x, y)
plt.show()

The output is a contour plot that represents regions of different density levels in a two-dimensional space. Darker regions indicate higher density.

The bw_adjust parameter in the sns.kdeplot function allows fine-tuning of the KDE’s smoothness. Lower bw_adjust values lead to a bumpier KDE, while higher values result in a smoother KDE. Adjusting the bandwidth is essential for appropriately capturing the data’s underlying structure.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = [1, 1.5, 2, 2.5, 3, 4, 5, 5.5]

# Create KDE plot with adjusted bandwidth
plt.show()

The output is a KDE plot with a specified smoothness degree. The lower bandwidth value chosen for this plot reveals individual peaks more clearly.

Method 4: Overlaying with Histogram

Combining a KDE plot with a histogram can provide a more detailed view of the data’s distribution. Seaborn’s sns.histplot function allows overlaying a histogram with a KDE plot, using the kde=True parameter to add the KDE on top of the histogram.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = [1, 2, 2, 3, 4, 5, 6, 7, 7, 7]

# Create overlaid Histogram and KDE plot
sns.histplot(data, kde=True)
plt.show()

The output pairs a histogram with a KDE plot, providing a bin-based view alongside the smooth density estimation, which aids in understanding the distribution’s shape and spread.

Bonus One-Liner Method 5: KDE Plot with Shading

The shade=True parameter in sns.kdeplot quickly adds a visual emphasis to the KDE by shading the area under the curve, making the density distribution even more evident for presentations.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = [3, 3, 4, 5, 6, 6, 6, 7, 8, 9]

# Create a shaded KDE plot