How can we determine if the conditional means in Python pandas are distinct?

edit

To determine if the conditional means in Python pandas are distinct, we can use the ANOVA (analysis of variance) test. This test compares the means of multiple groups and determines if there is a significant difference between them.

Here is an example code to perform an ANOVA test on the conditional means:

import pandas as pd from scipy.stats import f_oneway # create a pandas DataFrame with the data df = pd.DataFrame({'group': ['A', 'A', 'B', 'B', 'C', 'C'], 'value': [10, 12, 8, 9, 14, 16]}) # group the data by group and calculate the mean group_means = df.groupby('group').mean() # perform the ANOVA test f, p = f_oneway(group_means.loc['A'], group_means.loc['B'], group_means.loc['C']) # print the result if p < 0.05: print('The conditional means are significantly different.') else: print('The conditional means are not significantly different.') 

In this example, we have a DataFrame with three groups (A, B, and C) and their corresponding values. We group the data by group and calculate the mean for each group. Then, we use the f_oneway() function from the scipy.stats module to perform the ANOVA test. The function returns two values: f (the F-statistic) and p (the p-value). If the p-value is less than 0.05, we can conclude that the means are significantly different. Otherwise, we cannot reject the null hypothesis that the means are equal (i.e., they are not significantly different).