Function assert_equal

numpy/testing/_private/utils.py:260–468

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

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

def 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.

        .. versionadded:: 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