Time for action – asserting arrays almost equal

Let's form arrays with the values from the previous Time for action section by adding a 0 to each array:

1. Call the function with lower precision:

   print("Decimal 8", np.testing.assert_array_almost_equal([0, 0.123456789], [0, 0.123456780], decimal=8))

   The result is as follows:

   Decimal 8 None
   

2. Call the function with higher precision:

   print("Decimal 9", np.testing.assert_array_almost_equal([0, 0.123456789], [0, 0.123456780], decimal=9))

   The test raises an AssertionError:

   Decimal 9
   Traceback (most recent call last):
     …
    assert_array_compare
       raise AssertionError(msg)
   AssertionError:
   Arrays are not almost equal
   
   (mismatch 50.0%)
    x: array([ 0.        ,  0.12345679])
    y: array([ 0.        ,  0.12345678])
   

What just happened?

We compared two arrays with the NumPy array_almost_equal() function.

Have a go hero – comparing arrays with different shapes

Use the NumPy array_almost_equal() function to compare two arrays...