import numpy as np

def calculate(list):
  if(len(list)==9):
    re=np.reshape(list,(3,3))
    calculations={'mean':[np.mean(re,axis=0).tolist(),np.mean(re,axis=1).tolist(),np.mean(re.flatten())],
                  'variance':[np.var(re,axis=0).tolist(),np.var(re,axis=1).tolist(),np.var(re.flatten())],
                  'standard deviation':[np.std(re,axis=0).tolist(),np.std(re,axis=1).tolist(),np.std(re.flatten())],
                  'max':[np.max(re,axis=0).tolist(),np.max(re,axis=1).tolist(),np.max(re.flatten())],
                  'min':[np.min(re,axis=0).tolist(),np.min(re,axis=1).tolist(),np.min(re.flatten())],
                  'sum':[np.sum(re,axis=0).tolist(),np.sum(re,axis=1).tolist(),np.sum(re.flatten())]
    }
    return calculations
  else:
      raise ValueError("List must contain nine numbers.")
3bfe1656e2dc710e67bd96901a4b72f6ca32a986
<mask token>

<mask token>


def calculate(list):
    if len(list) == 9:
        re = np.reshape(list, (3, 3))
        calculations = {'mean': [np.mean(re, axis=0).tolist(), np.mean(re,
            axis=1).tolist(), np.mean(re.flatten())], 'variance': [np.var(
            re, axis=0).tolist(), np.var(re, axis=1).tolist(), np.var(re.
            flatten())], 'standard deviation': [np.std(re, axis=0).tolist(),
            np.std(re, axis=1).tolist(), np.std(re.flatten())], 'max': [np.
            max(re, axis=0).tolist(), np.max(re, axis=1).tolist(), np.max(
            re.flatten())], 'min': [np.min(re, axis=0).tolist(), np.min(re,
            axis=1).tolist(), np.min(re.flatten())], 'sum': [np.sum(re,
            axis=0).tolist(), np.sum(re, axis=1).tolist(), np.sum(re.
            flatten())]}
        return calculations
    else:
        raise ValueError('List must contain nine numbers.')

import numpy as np


def calculate(list):
    if len(list) == 9:
        re = np.reshape(list, (3, 3))
        calculations = {'mean': [np.mean(re, axis=0).tolist(), np.mean(re,
            axis=1).tolist(), np.mean(re.flatten())], 'variance': [np.var(
            re, axis=0).tolist(), np.var(re, axis=1).tolist(), np.var(re.
            flatten())], 'standard deviation': [np.std(re, axis=0).tolist(),
            np.std(re, axis=1).tolist(), np.std(re.flatten())], 'max': [np.
            max(re, axis=0).tolist(), np.max(re, axis=1).tolist(), np.max(
            re.flatten())], 'min': [np.min(re, axis=0).tolist(), np.min(re,
            axis=1).tolist(), np.min(re.flatten())], 'sum': [np.sum(re,
            axis=0).tolist(), np.sum(re, axis=1).tolist(), np.sum(re.
            flatten())]}
        return calculations
    else:
        raise ValueError('List must contain nine numbers.')

import numpy as np

def calculate(list):
  if(len(list)==9):
    re=np.reshape(list,(3,3))
    calculations={'mean':[np.mean(re,axis=0).tolist(),np.mean(re,axis=1).tolist(),np.mean(re.flatten())],
                  'variance':[np.var(re,axis=0).tolist(),np.var(re,axis=1).tolist(),np.var(re.flatten())],
                  'standard deviation':[np.std(re,axis=0).tolist(),np.std(re,axis=1).tolist(),np.std(re.flatten())],
                  'max':[np.max(re,axis=0).tolist(),np.max(re,axis=1).tolist(),np.max(re.flatten())],
                  'min':[np.min(re,axis=0).tolist(),np.min(re,axis=1).tolist(),np.min(re.flatten())],
                  'sum':[np.sum(re,axis=0).tolist(),np.sum(re,axis=1).tolist(),np.sum(re.flatten())]
    }
    return calculations
  else:
      raise ValueError("List must contain nine numbers.")