How to Use One-Way ANOVA in Python with the Iris Dataset

This tutorial demonstrates how to perform a one-way ANOVA in Python using the sklearn.feature_selection.f_classif function from the scikit-learn library. We’ll work with the iris dataset included in scikit-learn for an example.

Importing Libraries:

from sklearn.datasets import load_iris
from sklearn.feature_selection import f_classif
import pandas as pd

Loading the Iris Dataset:

iris = load_iris()
x = iris.data  # Features
y = iris.target  # Target variable (species)

One-Way ANOVA with scikit-learn:

# Perform ANOVA
f_statistic, p_value = f_classif(x, y)

# Print results
print("F-statistic:", f_statistic)
print("p-value:", p_value)

#F-statistic: [ 119.26450218   49.16004009 1180.16118225  960.0071468 ]
#p-value: [1.66966919e-31 4.49201713e-17 2.85677661e-91 4.16944584e-85]

Explanation:

• f_classif takes two arguments: x (the features) and y (the target variable).
• It returns two values: f_statistic which represents the overall difference between groups and p_value which tests the null hypothesis (no difference between groups).
• Smaller p-values (<0.05) indicate statistically significant differences between groups.

Interpreting Results:

In this example, the f_statistic and p_value will depend on the specific dataset and features used. However, a small p-value indicates that at least one of the three iris species has a significantly different distribution of petal and sepal measurements compared to the others.

Additional Notes:

• This is a basic example. You can further analyze the results using post-hoc tests like Tukey’s HSD to identify specific groups that differ.
• Remember to check the assumptions of normality and homogeneity of variance before performing ANOVA.
• Consider visualizing the data using boxplots or violin plots to get a better understanding of the distributions within each group.

Bonus: Visualizing with Pandas:

# Create pandas DataFrame
df = pd.DataFrame(x, columns=iris.feature_names)
df['species'] = iris.target_names[y]

# Boxplots for each feature
df.groupby('species').boxplot(rot=-90, layout=(1, 3))