Python | Numpy np.assert_almost_equal() method Last Updated : 30 Jan, 2020 Comments Improve Suggest changes Like Article Like Report 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 equal. Example #1 : In this example we can see that by using np.assert_almost_equal() method, we are able to get the assertion error if two values are not equal up to a precision value by using this method. Python3 1=1 # import numpy and assert_almost_equal import numpy as np import numpy.testing as npt # using np.assert_almost_equal() method gfg = npt.assert_almost_equal(1.2222222222, 1.2222222222, decimal = 5) print(gfg) Output : Nope Example #2 : Python3 1=1 # import numpy and assert_almost_equal import numpy as np import numpy.testing as npt # using np.assert_almost_equal() method gfg = npt.assert_almost_equal(1.2222222222, 1.2223422222, decimal = 5) print(gfg) Output : AssertionError: Arrays are not almost equal to 5 decimals ACTUAL: 1.2222222222 DESIRED: 1.2223422222 Comment More infoAdvertise with us Next Article Python | Numpy np.assert_almost_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_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_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_approx_equal() method 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 1 min read 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_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.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 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 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 Python | numpy.isin() method With the help of numpy.isin() method, we can see that one array having values are checked in a different numpy array having different elements with different sizes. Syntax : numpy.isin(target_array, list) Return : Return boolean array having same size as of target_array. Example #1 : In this example 1 min read Like