Seaborn Kdeplot – A Comprehensive Guide
Last Updated : 20 Dec, 2023
Kernel Density Estimate (KDE) Plot is a powerful tool for estimating the probability density function of continuous or non-parametric data. KDE plot is implemented through the kdeplot function in Seaborn. This article explores the syntax and usage of kdeplot in Python, focusing on one-dimensional and bivariate scenarios for efficient data visualization.
What is KDE plot?
Kernel Density Estimate (KDE) Plot allows to estimate the probability density function of the continuous or non-parametric from our data set curve in one or more dimensions it means we can create plot a single graph for multiple samples which helps in more efficient data visualization.
In order to use the Seaborn module, we need to install the module using the below command:
!pip install seaborn
Syntax: seaborn.kdeplot(x=None, *, y=None, vertical=False, palette=None, **kwargs)
Parameters:
x, y : vectors or keys in data
vertical : boolean (True or False)
data : pandas.DataFrame, numpy.ndarray, mapping, or sequence
How to visualize KDE Plot using Seaborn?
We learn the usage of some parameters through some specific examples:
Importing Libraries
First import the corresponding library
Python3 import pandas as pd import seaborn as sb import numpy as np from matplotlib import pyplot as plt %matplotlib inline
Draw a simple one-dimensional kde image:
Let's see the Kde of our variable x-axis and y-axis, so let pass the x variable into the kdeplot() methods.
Python3 # data x and y axis for seaborn x= np.random.randn(200) y = np.random.randn(200) # Kde for x var sns.kdeplot(x)
Output:
Then after check for y-axis.
Python3 Output:
Use Shade to fill the area covered by curve:
We can highlight the plot using shade to the area covered by the curve. If True, shadow processing is performed in the area below the kde curve, and color controls the color of the curve and shadow.
Python3 sns.kdeplot(x, shade = True)
Output:
You can change the Shade color with color attributes:
Python3 sns.kdeplot(x, shade = True , color = "Green")
Output:
Use Vertical to draw indicates whether to draw on the X axis or on the Y axis
Python3 sns.kdeplot(x, vertical = True)
Output:
Bivariate Kdeplot for two variables:
Simple pass the two variables into the seaborn.kdeplot() methods.
Python3 Output:
Shade the area covered by a curve with shade attributes:
Python3 sns.kdeplot(x,y, shade = True)
Output:
Now you can change the color with cmap attributes:
Python3 sns.kdeplot(x,y, cmap = "winter_r")
Output:
Use of Cbar: If True, add a colorbar to annotate the color mapping in a bivariate plot. Note: Does not currently support plots with a hue variable well.
Python3 sns.kdeplot(x, y, shade=True, cbar=True)
Output:
KDE Plot of Iris Dataset
Let see the example with Iris Dataset which is plot distributions for each column of a wide-form dataset:
Iris data set consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray
Loading the iris dataset for Kdeplot:
Python3 iris = sns.load_dataset('iris') iris Output:
Bivariate Kdeplot for two variables of iris:
Once we have species set then if we want to simply calculate the petal_length and petal_width then Simple pass the two variables(Setosa and virginica ) into the seaborn.kdeplot() methods.
Python3 setosa = iris.loc[iris.species=="setosa"] virginica = iris.loc[iris.species == "virginica"] sns.kdeplot(setosa.petal_length, setosa.petal_width)
Output:
See another example if we want to calculate another variable attribute which is sepal_width and sepal_length.
Python3 sns.kdeplot(setosa.sepal_width, setosa.sepal_length)
Output:
If we pass the two separate Kdeplot with different variable:
Python3 sns.kdeplot(setosa.petal_length, setosa.petal_width) sns.kdeplot(virginica.petal_length, virginica.petal_width)
Output:
Conclusion
In summary, kdeplot in Seaborn offers a versatile approach to visualize probability density functions, aiding in the exploration of one or more dimensions in datasets. Whether shading areas, adjusting colors, or applying it to real-world datasets like Iris, kdeplot stands as a valuable tool for data scientists and analysts.
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