main.py

from scipy.stats import friedmanchisquare
import pandas as pd
import scikit_posthocs as sp

clonalg_avg_values = [
    2.38261E-01,
    1.25806E-01,
    -1.00000E+08,
    2.14973E-01,
    -3.99073E+02,
    6.47681E+02,
    3.49423E-01,
    5.67716E-02,
    9.36057E+01,
    2.13307E+01
]
abc_avg_values = [
    7.54952E-15,
    4.05812E-06,
    -1.00000E+08,
    6.48575E-09,
    -3.99111E+02,
    3.14285E-04,
    6.08654E-02,
    1.88505E-47,
    6.29153E-01,
    4.05030E-01
]
ba_avg_values = [
    1.71720E+01,
    2.18870E+00,
    -9.99822E+07,
    8.81781E+01,
    -2.68344E+02,
    8.13859E+02,
    9.55534E+01,
    7.27770E-29,
    4.64709E+01,
    1.65901E-28
]
cs_avg_values = [
    2.93731E-02,
    9.87446E-02,
    -1.00000E+08,
    3.81305E-02,
    -3.99111E+02,
    1.80594E+01,
    9.94959E-02,
    5.70226E-15,
    2.62479E+01,
    4.11928E-08
]
de_avg_values = [
    6.01602E-05,
    1.81164E+00,
    -1.00000E+08,
    4.94388E-01,
    -3.99111E+02,
    1.70148E-04,
    9.94959E-02,
    1.87801E-11,
    6.21226E+01,
    1.57644E-05
]
pso_avg_values = [
    1.63869E-13,
    5.52171E-10,
    -9.99940E+07,
    1.17524E+02,
    -2.80175E+02,
    1.48139E-41,
    3.67820E+02,
    1.28913E-43,
    2.65496E+01,
    2.17610E-37
]

if __name__ == "__main__":
    stat, p = friedmanchisquare(clonalg_avg_values, abc_avg_values, ba_avg_values, cs_avg_values, de_avg_values, pso_avg_values)
    print('Statistics=%.3f, p=%.3f' % (stat, p))

    alpha = 0.05
    if p < alpha:
        print('Different distributions (reject H0)')
    else:
        print('Same distribution (fail to reject H0)')

    data = pd.DataFrame({
        "Treatment1": clonalg_avg_values,
        "Treatment2": abc_avg_values,
        "Treatment3": ba_avg_values,
        "Treatment4": cs_avg_values,
        "Treatment5": de_avg_values,
        "Treatment6": pso_avg_values
    })

    result = sp.posthoc_nemenyi_friedman(data.transpose())
    print(result)