Using the iris dataset from scikit-learn, create box plots for each measurement (sepal length, sepal width, etc.) by species. Customize the outliers with a different symbol and color.
Example Output:
Use the boxplot function from Matplotlib, and customize the appearance of the outliers with the flierprops parameter. Remember, you need to prepare separate data for each species, and consider using a loop to iterate through species for plotting.
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
def customized_box_plots():
iris = load_iris()
labels = iris.feature_names
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 5), sharey=True)
colors = ['red', 'blue', 'green']
for target, color, ax in zip(range(3), colors, axes):
data = [iris.data[iris.target == target][:, feature] for feature in range(4)]
ax.boxplot(data, flierprops=dict(markerfacecolor=color, marker='D'))
ax.set_title(iris.target_names[target])
ax.set_xticklabels(labels, rotation=45, fontsize=8)
plt.suptitle("Customized Box Plots for Iris Dataset")
plt.show()
# Example usage
customized_box_plots()
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