mean_var_std.py

import numpy as np

def calculate(list):

    if len(list) != 9:
        raise ValueError("List must contain nine numbers.")

    matrix = np.reshape(list, (3, 3))

    calculations = {
        'mean': [matrix.mean(axis=0).tolist(), matrix.mean(axis=1).tolist(), matrix.flatten().mean()],
        'variance': [matrix.var(axis=0).tolist(), matrix.var(axis=1).tolist(), matrix.flatten().var()],
        'standard deviation': [matrix.std(axis=0).tolist(), matrix.std(axis=1).tolist(), matrix.flatten().std()],
        'max': [matrix.max(axis=0).tolist(), matrix.max(axis=1).tolist(), matrix.flatten().max()],
        'min': [matrix.min(axis=0).tolist(), matrix.min(axis=1).tolist(), matrix.flatten().min()],
        'sum': [matrix.sum(axis=0).tolist(), matrix.sum(axis=1).tolist(), matrix.sum().item()]
    }


    for key, value in calculations.items():
        for i in range(len(value)):
            value[i] = value[i].tolist() if isinstance(value[i], np.ndarray) else value[i]

    return calculations