Python | Numpy np.assert_approx_equal() method Last Updated : 30 Jan, 2020 Comments Improve Suggest changes Like Article Like Report With the help of np.assert_approx_equal() method, we can get the assertion error if two items are not equal up to significant digits by using np.assert_approx_equal() method. Syntax : np.assert_approx_equal(actual, desired, significant) Return : Return the assertion error if two values are not equal. Example #1 : In this example we can see that by using np.assert_approx_equal() method, we are able to get the assertion error if two values are not equal up to a significant digit by using this method. Python3 1=1 # import numpy and assert_approx_equal import numpy as np import numpy.testing as npt # using np.assert_approx_equal() method gfg = npt.assert_approx_equal(1.2222222222, 1.2222222222, significant = 5) print(gfg) Output : Nope Example #2 : Python3 1=1 # import numpy and assert_approx_equal import numpy as np import numpy.testing as npt # using np.assert_approx_equal() method gfg = npt.assert_approx_equal(1.2222222222, 1.23422222, significant = 5) print(gfg) Output : AssertionError: Items are not equal to 5 significant digits: ACTUAL: 1.2222222222 DESIRED: 1.23422222 Comment More infoAdvertise with us Next Article Python | Numpy np.assert_approx_equal() method J Jitender_1998 Follow Improve Article Tags : Python Python-numpy Python numpy-Testing Practice Tags : python Similar Reads Python | Numpy np.assert_array_equal() method With the help of np.assert_array_equal() method, we can get the assertion error if two array like objects are not equal by using np.assert_array_equal() method. Syntax : np.assert_array_equal(x, y) Return : Return the assertion error if two objects are not equal. Example #1 : In this example we can 1 min read Python | Numpy np.assert_equal() method With the help of np.assert_equal() method, we can get the assertion error when two objects are not equal by using np.assert_equal() method. Syntax : np.assert_equal(actual, desired) Return : Return assertion error if two object are unequal. Example #1 : In this example we can see that by using np.as 1 min read Python | Numpy np.assert_almost_equal() method With the help of np.assert_almost_equal() method, we can get the assertion error if two items are not equal up to desired precision value by using np.assert_almost_equal() method. Syntax : np.assert_almost_equal(actual, desired, decimal) Return : Return the assertion error if two values are not equa 1 min read Python | Numpy np.assert_array_almost_equal() method With the help of np.assert_array_almost_equal() method, we can get the assertion error if two array objects are not equal up to desired precision value by using np.assert_array_almost_equal() method. Syntax : np.assert_array_almost_equal(actual, desired, decimal) Return : Return the assertion error 1 min read Python | Numpy np.assert_string_equal() method With the help of np.assert_string_equal() method, we can get the assertion error if two string are not equal by using np.assert_string_equal() method. Syntax : np.assert_string_equal(actual, desired) Return : Return assertion error if two strings are unequal. Example #1 : In this example we can see 1 min read Python | Numpy np.assert_array_less() method With the help of np.assert_array_less() method, we can get the assertion error if two array like objects are not ordered by less than by using np.assert_array_less() method. Syntax : np.assert_array_less(x, y) Return : Return assertion error if two array objects are unequal. Example #1 : In this exa 1 min read Python | numpy.assert_allclose() method With the help of numpy.assert_allclose() method, we can get the assertion errors when two array objects are not equal upto the mark by using numpy.assert_allclose(). Syntax : numpy.assert_allclose(actual_array, desired_array) Return : Return the Assertion error if two array objects are not equal. Ex 1 min read numpy.array_equal() in Python numpy.array_equal(arr1, arr2) : This logical function that checks if two arrays have the same shape and elements. Parameters : arr1 : [array_like]Input array or object whose elements, we need to test. arr2 : [array_like]Input array or object whose elements, we need to test. Return : True, if both ar 1 min read numpy.greater_equal() in Python The numpy.greater_equal() checks whether x1 >= x2 or not. Syntax : numpy.greater_equal(x1, x2[, out]) Parameters : x1, x2 : [array_like]Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape out : [ndarray, boolean]Array of bools, or a single bool if x1 and x2 are sca 2 min read numpy.array_equiv() in Python numpy.array_equiv(arr1, arr2) : This logical function that checks if two arrays have the same elements and shape consistent. Shape consistent means either they are having the same shape, or one input array can be broadcasted to create the same shape as the other one. Parameters : arr1 : [array_like] 2 min read Like