I’m not sure what the issue is here, I’m getting 2 errors on the test_module but the code looks pretty correct to me.

## ERROR: test_calculate (test_module.UnitTests)

Traceback (most recent call last):

File “/Users/peteraugerinos/Desktop/明けの明星 /Coding/PyCerts/PyDataAn/problems/problem1/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 “/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/unittest/case.py”, line 870, in assertAlmostEqual

if first == second:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

##
======================================================================

ERROR: test_calculate2 (test_module.UnitTests)

Traceback (most recent call last):

File “/Users/peteraugerinos/Desktop/明けの明星 /Coding/PyCerts/PyDataAn/problems/problem1/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 “/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/unittest/case.py”, line 870, in assertAlmostEqual

if first == second:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

```
def calculate(list):
if len(list) != 9:
raise ValueError("List must contain nine numbers.")
data = np.array(list)
data = data.reshape((3,3))
calculations = {}
mean = [np.mean(data, axis=0).astype('float32'), np.mean(data, axis=1).astype('float32'), np.mean(data)]
variance = [np.var(data, axis=0).astype('float32'), np.var(data, axis=1).astype('float32'), np.var(data)]
standard_deviation = [np.std(data, axis=0).astype('float32'), np.std(data, axis=1).astype('float32'), np.std(data)]
maximum = [np.max(data, axis=0), np.max(data, axis=1), np.max(data)]
minimum = [np.min(data, axis=0), np.min(data, axis=1), np.min(data)]
total = [np.sum(data, axis=0), np.sum(data, axis=1), np.sum(data)]
calculations = {'mean' : mean,
'variance' : variance,
'standard_deviation' : standard_deviation,
'maximum' : maximum,
'minimum' : minimum,
'total' : total}
return calculations
```