![]() It should be of the right type, C-contiguous and same dtype as that of dot(a,b). ",np.ma.dot(arr1, arr2)) Output Array1.The Numpy’s dot function returns the dot product of two arrays. # To return the dot product of two masked arrays, use the ma.dot() method in Python Numpy # Creating another 3x3 array with int elements using the numpy.arange() method # Get the number of elements of the Array # Creating a 3x3 array with int elements using the numpy.arange() method ",np.ma.dot(arr1, arr2)) Example import numpy as np To return the dot product of two masked arrays, use the ma.dot() method in Python Numpy − print(" Get the number of elements of the Array − print("Ĭreate masked array 1 − arr1 = ma.array(arr1)Ĭreate Array 2, another 3x3 array with int elements using the numpy.arange() method − arr2 = np.arange(9).reshape((3,3))Ĭreate masked array2 − arr2 = ma.array(arr2) Get the dimensions of the Array − print(" StepsĪt first, import the required library − import numpy as npĬreate Array 1, a 3x3 array with int elements using the numpy.arange() method − arr1 = np.arange(9).reshape((3,3)) Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). The output parameter suggests that it must have the exact kind that would be returned if it was not used. Propagating the mask means that if a masked value appears in a row or column, the whole row or column is considered masked. The strict parameter sets whether masked data are propagated (True) or set to 0 (False) for the computation. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. The strict and out are in different position than in the method version. This function is the equivalent of numpy.dot that takes masked values into account. To return the dot product of two masked arrays, use the ma.dot() method in Python Numpy. ![]()
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