Python Scikit-learn: Create a joinplot using “kde” to describe individual distributions on the same plot between Sepal length and Sepal width and use ‘+’ sign as marker
Python Machine learning Iris Visualization: Exercise-12 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 and use ‘+’ sign as marker.
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().
import pandas as pd import seaborn as sns import matplotlib.pyplot as plt iris = pd.read_csv("iris.csv") g = sns.jointplot(x="SepalLengthCm", y="SepalWidthCm", data=iris, kind="kde", color="m") g.plot_joint(plt.scatter, c="w", s=40, linewidth=1, marker="+") g.ax_joint.collections.set_alpha(0) g.set_axis_labels("$SepalLength(Cm)$", "$SepalWidth(Cm)$") plt.show()
Python Code Editor:
Have another way to solve this solution? Contribute your code (and comments) through Disqus.Write a Python program to create a pairplot of the iris data set and check which flower species seems to be the most separable.
What is the difficulty level of this exercise?
- New Content published on w3resource:
- Scala Programming Exercises, Practice, Solution
- Python Itertools exercises
- Python Numpy exercises
- Python GeoPy Package exercises
- Python Pandas exercises
- Python nltk exercises
- Python BeautifulSoup exercises
- Form Template
- Composer - PHP Package Manager
- PHPUnit - PHP Testing
- Laravel - PHP Framework