获取 AttributeError: 'builtin_function_or_method' object 没有属性 'tolist'

Question

import numpy as np

calculations = {}


def calculate(list):
    try:
        len(list)
        array_1 = np.array(list)
    except len(list) < 9:
        print("List must contain nine numbers")
    new_array = array_1.reshape(3, 3)
    calculations['mean'] = [(new_array.mean(axis=0).tolist()), (new_array.mean(axis=1).tolist()),
                            (new_array.mean.tolist())]

    calculations['variance'] = [(new_array.var(axis=0).tolist()), (new_array.var(axis=1).tolist()),
                                (new_array.var().tolist())]

    calculations['standard deviation'] = [(new_array.std(axis=0).tolist()), (new_array.std(axis=1).tolist()),
                                          (new_array.std().tolist())]

    calculations['max'] = [(new_array.max(axis=0).tolist()), (new_array.max(axis=1).tolist()),
                           (new_array.max().tolist())]

    calculations['min'] = [(new_array.min(axis=0).tolist()), (new_array.min(axis=1).tolist()),
                           (new_array.min().tolist())]

    calculations['sum'] = [(new_array.sum(axis=0).tolist()), (new_array.sum(axis=1).tolist()),
                           (new_array.sum().tolist())]


    return calculations

I'm getting an error

AttributeError: 'builtin_function_or_method' object has no attribute 'tolist'

Answer 1

You have a mistake in your code over here

calculations['mean'] = [(new_array.mean(axis=0).tolist()), (new_array.mean(axis=1).tolist()),
                            (new_array.mean.tolist())]

The new_array.mean.tolist() is causing the issue as, new_array.mean will refer to a function, but not call it. So tolist() will be considered as the atribute of the function. Change it to new_array.mean().tolist()) - All is well!

--UPDATE--

This solution works well after the fix. Eg.

print(calculate([3,2,4,5,6,7,2,1,3]))

produced an output

{
    "mean": [
        [
            3.3333333333333337,
            3.0,
            4.666666666666667
        ],
        [
            3.0,
            6.0,
            2.0
        ],
        3.6666666666666667
    ],
    "variance": [
        [
            1.5555555555555554,
            4.666666666666667,
            2.888888888888889
        ],
        [
            0.6666666666666666,
            0.6666666666666666,
            0.6666666666666666
        ],
        3.5555555555555555
    ],
    "standard deviation": [
        [
            1.247219128924647,
            2.160246899469287,
            1.699673171197595
        ],
        [
            0.816496580927726,
            0.816496580927726,
            0.816496580927726
        ],
        1.8856180831641268
    ],
    "max": [
        [
            5,
            6,
            7
        ],
        [
            4,
            7,
            3
        ],
        7
    ],
    "min": [
        [
            2,
            1,
            3
        ],
        [
            2,
            5,
            1
        ],
        1
    ],
    "sum": [
        [
            10,
            9,
            14
        ],
        [
            9,
            18,
            6
        ],
        33
    ]
}