Seaborn is a library that helps in visualizing data. It comes with customized themes and a high level interface.
Kernel Density Estimation, also known as KDE is a method in which the probability density function of a continuous random variable can be estimated. This method is used for the analysis of the non-parametric values. While using ‘jointplot’, if the argument ‘kind’ is set to ‘kde’, it plots the kernel density estimation plot.
Let us understand how the ‘jointplot’ function works to plot a kernel density estimation in python.
import pandas as pd import seaborn as sb from matplotlib import pyplot as plt my_df = sb.load_dataset('iris') sb.jointplot(x = 'petal_length',y = 'petal_width',data = my_df,kind = 'kde') plt.show()
The required packages are imported.
The input data is ‘iris_data’ which is loaded from the scikit learn library.
This data is stored in a dataframe.
The ‘load_dataset’ function is used to load the iris data.
This data is visualized using the ‘jointplot’ function.
Here, the ‘x’ and ‘y’ axis values are supplied as parameters.
Here, the ‘kind’ parameter is specified as ‘kde’ so that the plot understands to print kernel density estimation.
This kernel density estimation data is displayed on the console.