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 with different shapes.

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