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Python Scikit-learn: Create a joinplot using “kde” to describe individual distributions on the same plot between Sepal length and Sepal width

Python Machine learning Iris Visualization: Exercise-9 with Solution

Write a Python program to create a joinplot using “kde” to describe individual distributions on the same plot between Sepal length and Sepal width.

Note: The kernel density estimation (kde) procedure visualize a bivariate distribution. In seaborn, this kind of plot is shown with a contour plot and is available as a style in jointplot().

Sample Solution:

Python Code:

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
iris = pd.read_csv("iris.csv")
fig=sns.jointplot(x='SepalLengthCm', y='SepalWidthCm', kind="kde", color='cyan', data=iris)  
plt.show()

Output:

Python Machine learning Output: Iris Visualization: Exercise-9
 

Python Code Editor:


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Next: Write a Python program to create a joinplot and add regression and kernel density fits using “reg” to describe individual distributions on the same plot between Sepal length and Sepal width.

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