To evaluate a Hermite series at points x, use the hermite.hermval() method in Python Numpy. The 1st parameter, x, if x is a list or tuple, it is converted to an ndarray, otherwise it is left unchanged and treated as a scalar. In either case, x or its elements must support addition and multiplication with themselves and with the elements of c.The 2nd parameter, C, an array of coefficients ordered so that the coefficients for terms of degree n are contained in c[n]. If c is multidimensional the remaining indices enumerate multiple polynomials. In the two dimensional case the coefficients ... Read More

To add one Hermite series to another, use the polynomial.hermite.hermadd() method in Python Numpy. The method returns an array representing the Hermite series of their sum. Returns the sum of two Hermite series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1, 2, 3] represents the series P_0 + 2*P_1 + 3*P_2. The parameters c1 and c2 are 1-D arrays of Hermite series coefficients ordered from low to high.StepsAt first, import the required library −import numpy as np from numpy.polynomial import hermite as HCreate 1-D arrays of Hermite series coefficients −c1 ... Read More

To return the cumulative product of array elements over a given axis treating NaNs as one, use the nancumprod() method. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones. Ones are returned for slices that are all-NaN or empty. The method returns a new array holding the result is returned unless out is specified, in which case it is returned. Cumulative works like, 5, 5*10, 5*10*15, 5*10*15*20The 1st parameter is the input array. The 2nd parameter is the Axis along which the cumulative product is computed. By default the input is flattened. ... Read More

To return a boolean array which is True where the string element in array ends with suffix, use the numpy.char.endswith() method in Python Numpy. The first parameter is the input array. The second parameter is the suffix. The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_StepsAt first, import the required libraries −import numpy as npCreate a One-Dimensional array of strings −arr = np.array(['KATIE', 'JOHN', 'KATE', 'AmY', 'BRADley'])Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype)Get the dimensions of the Array −print("Array Dimensions...", arr.ndim)Get the shape of the Array −print("Our Array Shape...", arr.shape)Get the ... Read More

To get the Inner product of two arrays, use the numpy.inner() method in Python. Ordinary inner product of vectors for 1-D arrays, in higher dimensions a sum product over the last axes. The parameters are 1 and b, two vectors. If a and b are nonscalar, their last dimensions must match.StepsAt first, import the required libraries −import numpy as npCreating two numpy One-Dimensional array using the array() method −arr1 = np.array([5, 10, 15]) arr2 = np.array([20, 25, 30])Display the arrays −print("Array1...", arr1) print("Array2...", arr2)Check the Dimensions of both the arrays −print("Dimensions of Array1...", arr1.ndim) print("Dimensions of Array2...", arr2.ndim)Check the Shape ... Read More

To return the dot product of One-Dimensional vectors, use the numpy.vdot() method in Python. The vdot(a, b) function handles complex numbers differently than dot(a, b). If the first argument is complex the complex conjugate of the first argument is used for the calculation of the dot product. The vdot handles multidimensional arrays differently than dot: it does not perform a matrix product, but flattens input arguments to 1-D vectors first. Consequently, it should only be used for vectors.The method returns the dot product of a and b. Can be an int, float, or complex depending on the types of a ... Read More

To return the dot product of two multi-dimensional vectors, use the numpy.vdot() method in Python. The vdot(a, b) function handles complex numbers differently than dot(a, b). If the first argument is complex the complex conjugate of the first argument is used for the calculation of the dot product. The vdot handles multidimensional arrays differently than dot: it does not perform a matrix product, but flattens input arguments to 1-D vectors first. Consequently, it should only be used for vectors.The method returns the dot product of a and b. Can be an int, float, or complex depending on the types of ... Read More

To return the dot product of two vectors, use the numpy.vdot() method in Python. The vdot(a, b) function handles complex numbers differently than dot(a, b). If the first argument is complex the complex conjugate of the first argument is used for the calculation of the dot product. The vdot handles multidimensional arrays differently than dot: it does not perform a matrix product, but flattens input arguments to 1-D vectors first. Consequently, it should only be used for vectors.The method returns the dot product of a and b. Can be an int, float, or complex depending on the types of a ... Read More

To compute the inverse hyperbolic tangent with arctanh, use the numpy.emath.arctanh() method in Python. Returns the “principal value” of arctanh(x). For real x such that abs(x) < 1, this is a real number. If abs(x) > 1, or if x is complex, the result is complex. Finally, x = 1 returns``inf`` and x=-1 returns -inf.The method returns the inverse hyperbolic tangent(s) of the x value(s). If x was a scalar so is out, otherwise an array is returned. The 1st parameter is the value(s) whose arctanh is (are) required.StepsAt first, import the required libraries −import numpy as npCreate a numpy ... Read More

To compute the inverse sine with scimath, use the numpy.emath.arcsin() method in Python. Returns the “principal value” of the inverse sine of x. For real x such that abs(x)