Tell us what’s happening:
Describe your issue in detail here.
I wrote my code, yet when I want to test my code with the given test cases they all give errors. But the solution is correct since when I take the given test cases and paste them to main, they give expected results too.
Your code so far
import numpy as np
def calculate(list):
try:
matrix = np.array(list).reshape(3, 3)
calculations = {}
# First we handle mean
axis1 = np.mean(matrix, axis=0)
axis2 = np.mean(matrix, axis=1)
flattened = np.mean([axis1, axis2])
calculations['mean'] = [axis1.tolist(), axis2.tolist(), flattened.tolist()]
#We do variation now
axis1 = np.var(matrix, axis=0)
axis2 = np.var(matrix, axis=1)
flattened = np.var([axis1, axis2])
calculations['variance'] = [axis1.tolist(), axis2.tolist(), flattened.tolist()]
#We do standart deviation
axis1 = np.std(matrix, axis=0)
axis2 = np.std(matrix, axis=1)
flattened = np.std([axis1, axis2])
calculations['standard deviation'] = [axis1.tolist(), axis2.tolist(), flattened.tolist()]
#We do max element
axis1 = np.max(matrix, axis=0)
axis2 = np.max(matrix, axis=1)
flattened = np.max([axis1, axis2])
calculations['max'] = [axis1.tolist(), axis2.tolist(), flattened.tolist()]
#We do min element
axis1 = np.min(matrix, axis=0)
axis2 = np.min(matrix, axis=1)
flattened = np.min([axis1, axis2])
calculations['min'] = [axis1.tolist(), axis2.tolist(), flattened.tolist()]
#We do sum element
axis1 = np.sum(matrix, axis=0)
axis2 = np.sum(matrix, axis=1)
flattened = np.sum([axis1, axis2])
calculations['sum'] = [axis1.tolist(), axis2.tolist(), flattened.tolist()]
except ValueError:
print("List must contain nine numbers.")
return calculations
Given test cases
import unittest
import mean_var_std
# the test case
class UnitTests(unittest.TestCase):
def test_calculate(self):
actual = mean_var_std.calculate([2,6,2,8,4,0,1,5,7])
expected = {'mean': [[3.6666666666666665, 5.0, 3.0], [3.3333333333333335, 4.0, 4.333333333333333], 3.888888888888889], 'variance': [[9.555555555555557, 0.6666666666666666, 8.666666666666666], [3.555555555555556, 10.666666666666666, 6.222222222222221], 6.987654320987654], 'standard deviation': [[3.091206165165235, 0.816496580927726, 2.943920288775949], [1.8856180831641267, 3.265986323710904, 2.494438257849294], 2.6434171674156266], 'max': [[8, 6, 7], [6, 8, 7], 8], 'min': [[1, 4, 0], [2, 0, 1], 0], 'sum': [[11, 15, 9], [10, 12, 13], 35]}
self.assertAlmostEqual(actual, expected, "Expected different output when calling 'calculate()' with '[2,6,2,8,4,0,1,5,7]'")
def test_calculate2(self):
actual = mean_var_std.calculate([9,1,5,3,3,3,2,9,0])
expected = {'mean': [[4.666666666666667, 4.333333333333333, 2.6666666666666665], [5.0, 3.0, 3.6666666666666665], 3.888888888888889], 'variance': [[9.555555555555555, 11.555555555555557, 4.222222222222222], [10.666666666666666, 0.0, 14.888888888888891], 9.209876543209875], 'standard deviation': [[3.0912061651652345, 3.39934634239519, 2.0548046676563256], [3.265986323710904, 0.0, 3.8586123009300755], 3.0347778408328137], 'max': [[9, 9, 5], [9, 3, 9], 9], 'min': [[2, 1, 0], [1, 3, 0], 0], 'sum': [[14, 13, 8], [15, 9, 11], 35]}
self.assertAlmostEqual(actual, expected, "Expected different output when calling 'calculate()' with '[9,1,5,3,3,3,2,9,0]'")
def test_calculate_with_few_digits(self):
self.assertRaisesRegex(ValueError, "List must contain nine numbers.", mean_var_std.calculate, [2,6,2,8,4,0,1,])
if __name__ == "__main__":
unittest.main()
Challenge: Data Analysis with Python Projects - Mean-Variance-Standard Deviation Calculator
Link to the challenge:
