Friedman test for paired data and Post-Hoc tests

test_stat,p_value = stats.friedmanchisquare(method_A,method_B, method_C)
print("p value:%.4f" % p_value)

Note: Since the data is not normal, the nonparametric version of the posthoc test is used.
data = np.array([method_A, method_B, method_C])
posthoc_df=sp.posthoc_wilcoxon(data, p_adjust="holm")
# posthoc_df = sp.posthoc_nemenyi_friedman(data.T) ## another option for the posthoc test

group_names= ["Method A", "Method B","Method C"]
posthoc_df.columns= group_names
posthoc_df.index= group_names
posthoc_df.style.applymap(lambda x: "background-color:violet" if x<0.05 else "background-color: white")