I want to make some unit-tests for my app, and I need to compare two arrays. Since `array.__eq__` returns a new array (so `TestCase.assertEqual` fails), what is the best way to assert for equality?

Currently I’m using

``````self.assertTrue((arr1 == arr2).all())
``````

but I don’t really like it

check out the assert functions in `numpy.testing`, e.g.

`assert_array_equal`

for floating point arrays equality test might fail and `assert_almost_equal` is more reliable.

update

A few versions ago numpy obtained `assert_allclose` which is now my favorite since it allows us to specify both absolute and relative error and doesn’t require decimal rounding as the closeness criterion.

I think `(arr1 == arr2).all()` looks pretty nice. But you could use:

``````numpy.allclose(arr1, arr2)
``````

but it’s not quite the same.

An alternative, almost the same as your example is:

``````numpy.alltrue(arr1 == arr2)
``````

Note that scipy.array is actually a reference numpy.array. That makes it easier to find the documentation.

I find that using
`self.assertEqual(arr1.tolist(), arr2.tolist())`
is the easiest way of comparing arrays with unittest.

I agree it’s not the prettiest solution and it’s probably not the fastest but it’s probably more uniform with the rest of your test cases, you get all the unittest error description and it’s really simple to implement.

Since Python 3.2 you can use `assertSequenceEqual(array1.tolist(), array2.tolist())`.

This has the added value of showing you the exact items in which the arrays differ.

``````self.assertTrue(np.array_equal(x, y, equal_nan=True))
``````

`equal_nan = True` if you want to `np.nan == np.nan` returns `True`

or you can use numpy.allclose to compare with torelance.

In my tests I use this:

``````numpy.testing.assert_array_equal(arr1, arr2)
``````

Use numpy

``````numpy.array_equal(a, b)
``````

`np.linalg.norm(arr1 - arr2) < 1e-6`