Analyzing multiple groups in Python – ANOVA and Kruskal–Wallis test

So far, we have made comparisons between two groups based on variables such as TG. What if we have three or more categories to compare simultaneously? We can use ANOVA to perform this task.

This comparison can be used to compare multiple groups in the data. For example, for BMI, we can have underweight, normal, and overweight subjects and compare their lipid levels simultaneously.

Here is how we can try this:

import pandas as pd
import scipy.stats as stats
# Load the data
data = pd.read_csv(r'C:\Users\KORISNIK\Downloads\Dataset of Diabetes .csv')
# Filter data for rows with 'Y'(diabetic) or 'N'(non-diabetic) values in 'CLASS' column
filtered_data = data[data['CLASS'].isin(['Y', 'N'])]

Now, we need to create a new column called weight_class, which will contain the categories of weight class based on BMI. Those subjects with...