Questions on formatting and input validation

Python
1

So I believe that I am 90% done with this challenge but I have ran into 2 problems:

  1. My formatting for the calculation output is off. For some reason the output include the word array in my calculation output for each calculation that I do. I have tried to research this problem to the best of my ability so I am not sure if I am describing it correctly because I didn’t get any usual information.
  2. My input validation isn’t properly working. I believe I have it set up according to the documentation that I found to meet the requirements of the challenge but I don’t know why it isn’t properly working.

If anyone can teach me what I am doing wrong that would be greatly appreciated.

import numpy as np

def calculate(list):


  #0.5 Input validation for list  
  while True:
     try:
        #1. Transform list into a 3x3 matrix
        arr = np.asarray(list)
        matx = np.reshape(arr, (3,3))

        break
     except ValueError(len(list) < 9):
         print("List must contain nine numbers.")
    
  

  #2. Create Dict and compute stats for matrix
  calculations = {
    'Mean': [[np.mean(matx,axis=0)], [np.mean(matx, axis=1)], np.mean(matx)],
    'Variance': [[np.var(matx,axis=0)], [np.var(matx, axis=1)], np.var(matx)],
    'Std Deviation': [[np.std(matx,axis=0)], [np.std(matx, axis=1)], np.std(matx)],
    'Max': [[np.max(matx,axis=0)], [np.max(matx, axis=1)], np.max(matx)],  
    'Min': [[np.min(matx,axis=0)], [np.min(matx, axis=1)], np.min(matx)],  
    'Sum': [[np.sum(matx,axis=0)], [np.sum(matx, axis=1)], np.sum(matx)]  
  }
  print(calculations)
  return calculations

Console Output Log

 python main.py
{'Mean': [[array([3., 4., 5.])], [array([1., 4., 7.])], 4.0], 'Variance': [[array([6., 6., 6.])], [array([0.66666667, 0.66666667, 0.66666667])], 6.666666666666667], 'Std Deviation': [[array([2.44948974, 2.44948974, 2.44948974])], [array([0.81649658, 0.81649658, 0.81649658])], 2.581988897471611], 'Max': [[array([6, 7, 8])], [array([2, 5, 8])], 8], 'Min': [[array([0, 1, 2])], [array([0, 3, 6])], 0], 'Sum': [[array([ 9, 12, 15])], [array([ 3, 12, 21])], 36]}
{'Mean': [[array([3., 4., 5.])], [array([1., 4., 7.])], 4.0], 'Variance': [[array([6., 6., 6.])], [array([0.66666667, 0.66666667, 0.66666667])], 6.666666666666667], 'Std Deviation': [[array([2.44948974, 2.44948974, 2.44948974])], [array([0.81649658, 0.81649658, 0.81649658])], 2.581988897471611], 'Max': [[array([6, 7, 8])], [array([2, 5, 8])], 8], 'Min': [[array([0, 1, 2])], [array([0, 3, 6])], 0], 'Sum': [[array([ 9, 12, 15])], [array([ 3, 12, 21])], 36]}
{'Mean': [[array([3.66666667, 5.        , 3.        ])], [array([3.33333333, 4.        , 4.33333333])], 3.888888888888889], 'Variance': [[array([9.55555556, 0.66666667, 8.66666667])], [array([ 3.55555556, 10.66666667,  6.22222222])], 6.987654320987654], 'Std Deviation': [[array([3.09120617, 0.81649658, 2.94392029])], [array([1.88561808, 3.26598632, 2.49443826])], 2.6434171674156266], 'Max': [[array([8, 6, 7])], [array([6, 8, 7])], 8], 'Min': [[array([1, 4, 0])], [array([2, 0, 1])], 0], 'Sum': [[array([11, 15,  9])], [array([10, 12, 13])], 35]}
E{'Mean': [[array([4.66666667, 4.33333333, 2.66666667])], [array([5.        , 3.        , 3.66666667])], 3.888888888888889], 'Variance': [[array([ 9.55555556, 11.55555556,  4.22222222])], [array([10.66666667,  0.        , 14.88888889])], 9.209876543209875], 'Std Deviation': [[array([3.09120617, 3.39934634, 2.05480467])], [array([3.26598632, 0.        , 3.8586123 ])], 3.0347778408328137], 'Max': [[array([9, 9, 5])], [array([9, 3, 9])], 9], 'Min': [[array([2, 1, 0])], [array([1, 3, 0])], 0], 'Sum': [[array([14, 13,  8])], [array([15,  9, 11])], 35]}
EE
======================================================================
ERROR: test_calculate (test_module.UnitTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/runner/boilerplate-mean-variance-standard-deviation-calculator/test_module.py", line 10, in test_calculate
    self.assertAlmostEqual(actual, expected, "Expected different output when calling 'calculate()' with '[2,6,2,8,4,0,1,5,7]'")
  File "/usr/lib/python3.8/unittest/case.py", line 943, in assertAlmostEqual
    diff = abs(first - second)
TypeError: unsupported operand type(s) for -: 'dict' and 'dict'

======================================================================
ERROR: test_calculate2 (test_module.UnitTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/runner/boilerplate-mean-variance-standard-deviation-calculator/test_module.py", line 15, in test_calculate2
    self.assertAlmostEqual(actual, expected, "Expected different output when calling 'calculate()' with '[9,1,5,3,3,3,2,9,0]'")
  File "/usr/lib/python3.8/unittest/case.py", line 943, in assertAlmostEqual
    diff = abs(first - second)
TypeError: unsupported operand type(s) for -: 'dict' and 'dict'

======================================================================
ERROR: test_calculate_with_few_digits (test_module.UnitTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/runner/boilerplate-mean-variance-standard-deviation-calculator/mean_var_std.py", line 16, in calculate
    matx = np.reshape(arr, (3,3))
  File "<__array_function__ internals>", line 5, in reshape
  File "/opt/virtualenvs/python3/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 299, in reshape
    return _wrapfunc(a, 'reshape', newshape, order=order)
  File "/opt/virtualenvs/python3/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 58, in _wrapfunc
    return bound(*args, **kwds)
ValueError: cannot reshape array of size 7 into shape (3,3)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/runner/boilerplate-mean-variance-standard-deviation-calculator/test_module.py", line 18, in test_calculate_with_few_digits
    self.assertRaisesRegex(ValueError, "List must contain nine numbers.", mean_var_std.calculate, [2,6,2,8,4,0,1,])
  File "/usr/lib/python3.8/unittest/case.py", line 1357, in assertRaisesRegex
    return context.handle('assertRaisesRegex', args, kwargs)
  File "/usr/lib/python3.8/unittest/case.py", line 202, in handle
    callable_obj(*args, **kwargs)
  File "/home/runner/boilerplate-mean-variance-standard-deviation-calculator/mean_var_std.py", line 19, in calculate
    except ValueError(len(list) < 9):
TypeError: catching classes that do not inherit from BaseException is not allowed

----------------------------------------------------------------------
Ran 3 tests in 0.008s

FAILED (errors=3)

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Challenge: Mean-Variance-Standard Deviation Calculator

Link to the challenge:

2

  1. There’s array in the output, because those are numpy’s arrays and their string representation looks like that.

  2. I found this very interesting, issue is that with except ValueError(len(list) < 9): function is trying to catch not simply error - ValueError class, but an instance of it that’s made with ValueError(len(list) < 9) expression.

4

Okay then thank you for the insight. I thought numerical strings could only be created with quotation marks…? How should I have created my numpy calc so that it doesn’t go that? How should I have set up my input validation statement so that it doesn’t look for the instance of the error?

5

For the first one, there’s needed one more step to change type from numpy’s array to normal python list. Regarding the exception - for the line catching exception - one with except, use just ValueError, without anything in parenthesis. Then only in the indented block, if that’s needed, instantiate with specific description.

6

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