testing.assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True)[source]#

Raises an AssertionError if two items are not equal up to desired precision.


It is recommended to use one of assert_allclose, assert_array_almost_equal_nulp or assert_array_max_ulp instead of this function for more consistent floating point comparisons.

The test verifies that the elements of actual and desired satisfy.

abs(desired-actual) < float64(1.5 * 10**(-decimal))

That is a looser test than originally documented, but agrees with what the actual implementation in assert_array_almost_equal did up to rounding vagaries. An exception is raised at conflicting values. For ndarrays this delegates to assert_array_almost_equal


The object to check.


The expected object.

decimalint, optional

Desired precision, default is 7.

err_msgstr, optional

The error message to be printed in case of failure.

verbosebool, optional

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


If actual and desired are not equal up to specified precision.

See also


Compare two array_like objects for equality with desired relative and/or absolute precision.

assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal


>>> from numpy.testing import assert_almost_equal
>>> assert_almost_equal(2.3333333333333, 2.33333334)
>>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
Traceback (most recent call last):
Arrays are not almost equal to 10 decimals
 ACTUAL: 2.3333333333333
 DESIRED: 2.33333334
>>> assert_almost_equal(np.array([1.0,2.3333333333333]),
...                     np.array([1.0,2.33333334]), decimal=9)
Traceback (most recent call last):
Arrays are not almost equal to 9 decimals

Mismatched elements: 1 / 2 (50%)
Max absolute difference: 6.66669964e-09
Max relative difference: 2.85715698e-09
 x: array([1.         , 2.333333333])
 y: array([1.        , 2.33333334])