mean_var_std.py

import numpy as np

def calculate(list):
  if(len(list) !=9):
      raise ValueError("List must contain nine numbers.")
  ls=np.array(list)
  print(ls)
  mean_rows = ([ls[[0,1,2]].mean(),ls[[3,4,5]].mean(),ls[[6,7,8]].mean()])
  mean_columns=([ls[[0,3,6]].mean(),ls[[1,4,7]].mean(),ls[[2,5,8]].mean()])
  var_rows = ([ls[[0,1,2]].var(),ls[[3,4,5]].var(),ls[[6,7,8]].var()])
  var_columns=([ls[[0,3,6]].var(),ls[[1,4,7]].var(),ls[[2,5,8]].var()])
  std_rows = ([ls[[0,1,2]].std(),ls[[3,4,5]].std(),ls[[6,7,8]].std()])
  std_columns=([ls[[0,3,6]].std(),ls[[1,4,7]].std(),ls[[2,5,8]].std()])
  max_rows = ([ls[[0,1,2]].max(),ls[[3,4,5]].max(),ls[[6,7,8]].max()])
  max_columns=([ls[[0,3,6]].max(),ls[[1,4,7]].max(),ls[[2,5,8]].max()])
  min_rows = ([ls[[0,1,2]].min(),ls[[3,4,5]].min(),ls[[6,7,8]].min()])
  min_columns=([ls[[0,3,6]].min(),ls[[1,4,7]].min(),ls[[2,5,8]].min()])
  sum_rows = ([ls[[0,1,2]].sum(),ls[[3,4,5]].sum(),ls[[6,7,8]].sum()])
  sum_columns=([ls[[0,3,6]].sum(),ls[[1,4,7]].sum(),ls[[2,5,8]].sum()])
  

  return {
    'mean': [mean_columns,mean_rows , ls.mean()],
    'variance': [var_columns, var_rows, ls.var()],
    'standard deviation': [std_columns, std_rows, ls.std()],
    'max': [max_columns, max_rows, ls.max()],
    'min': [min_columns, min_columns, ls.min()],
    'sum': [sum_columns, sum_rows, ls.sum()]
  }


  return calculations