code: import numpy as np
  
  def calculate(list):
      if len(list) != 9:
          raise ValueError('List must contain nine numbers.')
      input_array = np.array([[list[0], list[1], list[2]], [list[3], list[4], list[5]], [list[6], list[7], list[8]]])
      calculations = dict()
      print(input_array)
  
      # calc mean
      c_mean = np.mean(input_array, axis=0) # axis 0 is column
      r_mean = np.mean(input_array, axis=1)
      f_mean = np.mean(input_array)
      calculations['mean'] = [c_mean.tolist(), r_mean.tolist(), f_mean]
  
      # variance
      c_var = np.var(input_array, axis=0)
      r_var = np.var(input_array, axis=1)
      f_var = np.var(input_array)
      calculations['variance'] = [c_var.tolist(), r_var.tolist(), f_var]
  
      # standard dev
      c_std = np.std(input_array, axis=0)
      r_std = np.std(input_array, axis=1)
      f_std = np.std(input_array)
      calculations['standard deviation'] = [c_std.tolist(), r_std.tolist(), f_std]
  
      # max
      c_max = np.amax(input_array, axis=0)
      r_max = np.amax(input_array, axis=1)
      f_max = np.amax(input_array)
      calculations['max'] = [c_max.tolist(), r_max.tolist(), f_max]
  
      # min
      c_min = np.amin(input_array, axis=0)
      r_min = np.amin(input_array, axis=1)
      f_min = np.amin(input_array)
      calculations['min'] = [c_min.tolist(), r_min.tolist(), f_min]
  
      # sum
      c_sum = np.sum(input_array, axis=0)
      r_sum = np.sum(input_array, axis=1)
      f_sum = np.sum(input_array)
      calculations['sum'] = [c_sum.tolist(), r_sum.tolist(), f_sum]
  
      return calculations
  
  # this code below is for testing the function, and what the dict should look like when outputting data
  # test calculations
  print(calculate([0,1,2,3,4,5,6,7,8]))
  # should return:
  '''
  {
    'mean': [[3.0, 4.0, 5.0], [1.0, 4.0, 7.0], 4.0], 
    'variance': [[6.0, 6.0, 6.0], [0.6666666666666666, 0.6666666666666666, 0.6666666666666666], 6.666666666666667], 
    'standard deviation': [[2.449489742783178, 2.449489742783178, 2.449489742783178], [0.816496580927726, 0.816496580927726, 0.816496580927726], 2.581988897471611],
    'max': [[6, 7, 8], [2, 5, 8], 8],
    'min': [[0, 1, 2], [0, 3, 6], 0],
    'sum': [[9, 12, 15], [3, 12, 21], 36]
  }'''
apis: numpy.sum numpy.var numpy.amax numpy.amin numpy.array numpy.std numpy.mean