Time for action – comparing arrays

Let's compare two arrays with the functions we just mentioned. We will reuse the arrays from the previous Time for action tutorial and add a NaN to them:

1. Call the array_allclose function:

   print "Pass", np.testing.assert_allclose([0, 0.123456789, np.nan], [0, 0.123456780, np.nan], rtol=1e-7, atol=0)

   The result is:

   Pass None

2. Call the array_equal function:

   print "Fail", np.testing.assert_array_equal([0, 0.123456789, np.nan], [0, 0.123456780, np.nan])

   An exception is thrown:

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

What just happened?

We compared two arrays with the array_allclose function and the array_equal function.