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# numpy.testing.assert_equal

testing.assert_equal(actual, desired, err_msg='', verbose=True, *, strict=False)

  Raises an AssertionError if two objects are not equal.

  Given two objects (scalars, lists, tuples, dictionaries or numpy arrays), check that all elements of these objects are equal. An exception is raised at the first conflicting values.

  This function handles NaN comparisons as if NaN was a “normal” number. That is, AssertionError is not raised if both objects have NaNs in the same positions. This is in contrast to the IEEE standard on NaNs, which says that NaN compared to anything must return False.

  Parameters:
    actual array_like

      The object to check.

    desired array_like

      The expected object.

    err_msg str, optional

      The error message to be printed in case of failure.

    verbose bool, optional

      If True, the conflicting values are appended to the error message.

    strict bool, optional

      If True and either of the actual and desired arguments is an array, raise an `AssertionError` when either the shape or the data type of the arguments does not match. If neither argument is an array, this parameter has no effect.

      New in version 2.0.0.

  Raises:
    AssertionError

      If actual and desired are not equal.

  See also

  `assert_allclose`
  `assert_array_almost_equal_nulp`
  `assert_array_max_ulp`

  Notes

  When one of actual and desired is a scalar and the other is array_like, the function checks that each element of the array_like is equal to the scalar. Note that empty arrays are therefore considered equal to scalars. This behaviour can be disabled by setting `strict==True`.

  Examples

      >>> np.testing.assert_equal([4, 5], [4, 6])
      Traceback (most recent call last):
          ...
      AssertionError:
      Items are not equal:
      item=1
       ACTUAL: 5
       DESIRED: 6

  The following comparison does not raise an exception. There are NaNs in the inputs, but they are in the same positions.

      >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])

  As mentioned in the Notes section, `assert_equal` has special handling for scalars when one of the arguments is an array. Here, the test checks that each value in x is 3:

      >>> x = np.full((2, 5), fill_value=3)
      >>> np.testing.assert_equal(x, 3)

  Use strict to raise an AssertionError when comparing a scalar with an array of a different shape:

      >>> np.testing.assert_equal(x, 3, strict=True)
      Traceback (most recent call last):
          ...
      AssertionError:
      Arrays are not equal

      (shapes (2, 5), () mismatch)
       ACTUAL: array([[3, 3, 3, 3, 3],
             [3, 3, 3, 3, 3]])
       DESIRED: array(3)

  The strict parameter also ensures that the array data types match:

      >>> x = np.array([2, 2, 2])
      >>> y = np.array([2., 2., 2.], dtype=np.float32)
      >>> np.testing.assert_equal(x, y, strict=True)
      Traceback (most recent call last):
          ...
      AssertionError:
      Arrays are not equal

      (dtypes int64, float32 mismatch)
       ACTUAL: array([2, 2, 2])
       DESIRED: array([2., 2., 2.], dtype=float32)