Donut Chart

Using the iris dataset from scikit-learn, create a donut chart showcasing the distribution of samples across species. Customize the width of the ring and provide appropriate labels.

Example Output:

A donut chart is a modified version of a pie chart. After computing the counts for each species, use the pie function from Matplotlib to plot a pie chart and modify its appearance to look like a donut by setting the wedgeprops parameter.

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import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
def donut_chart():
iris = load_iris()
species_counts = [sum(iris.target == target) for target in range(3)]
plt.pie(species_counts, labels=iris.target_names, startangle=90, wedgeprops={'width': 0.3})
plt.gca().set_aspect('equal')
plt.title("Donut Chart of Iris Species Distribution")
plt.show()
# Example usage
donut_chart()
import matplotlib.pyplot as plt from sklearn.datasets import load_iris def donut_chart(): iris = load_iris() species_counts = [sum(iris.target == target) for target in range(3)] plt.pie(species_counts, labels=iris.target_names, startangle=90, wedgeprops={'width': 0.3}) plt.gca().set_aspect('equal') plt.title("Donut Chart of Iris Species Distribution") plt.show() # Example usage donut_chart()
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

def donut_chart():
    iris = load_iris()
    species_counts = [sum(iris.target == target) for target in range(3)]
    
    plt.pie(species_counts, labels=iris.target_names, startangle=90, wedgeprops={'width': 0.3})
    plt.gca().set_aspect('equal')
    plt.title("Donut Chart of Iris Species Distribution")
    plt.show()

# Example usage
donut_chart()

 

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