numpy.conj() in Python Last Updated : 14 Aug, 2024 Comments Improve Suggest changes Like Article Like Report The numpy.conj() function helps the user to conjugate any complex number. The conjugate of a complex number is obtained by changing the sign of its imaginary part. If the complex number is 2+5j then its conjugate is 2-5j. Syntax:numpy.conj(x[, out] = ufunc 'conjugate') Parameters :x [array_like]: Input value. out [ndarray, optional] : Output array with same dimensions as Input array, placed with result. Return : x : ndarray. The complex conjugate of x, with same dtype as y.Code #1 : Python # Python3 code demonstrate conj() function #importing numpy import numpy as np in_complx1 = 2+4j out_complx1 = np.conj(in_complx1) print ("Output conjugated complex number of 2+4j : ", out_complx1) in_complx2 =5-8j out_complx2 = np.conj(in_complx2) print ("Output conjugated complex number of 5-8j: ", out_complx2) Output : Output conjugated complex number of 2+4j : (2-4j)Output conjugated complex number of 5-8j: (5+8j)Code #2 : Python # Python3 code demonstrate conj() function # importing numpy import numpy as np in_array = np.eye(2) + 3j * np.eye(2) print ("Input array : ", in_array) out_array = np.conjugate(in_array) print ("Output conjugated array : ", out_array) Output : Input array : [[ 1.+3.j 0.+0.j] [ 0.+0.j 1.+3.j]]Output conjugated array : [[ 1.-3.j 0.-0.j] [ 0.-0.j 1.-3.j]] Comment More infoAdvertise with us Next Article numpy.conj() in Python J jana_sayantan Follow Improve Article Tags : Python Python-numpy Python numpy-Mathematical Function Practice Tags : python Similar Reads numpy.any() in Python The numpy.any() function tests whether any array elements along the mentioned axis evaluate to True. Syntax :Â numpy.any(a, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c) Parameters :Â array :[array_like]Input array or object whose elements, we need to test. axis : 3 min read numpy.inner() in python numpy.inner(arr1, arr2): Computes the inner product of two arrays. Parameters : arr1, arr2 : array to be evaluated. Return: Inner product of the two arrays. Code #1 : Python3 1== # Python Program illustrating # numpy.inner() method import numpy as geek # Scalars product = geek.inner(5, 4) print( 1 min read numpy.isnan() in Python The numpy.isnan() function tests element-wise whether it is NaN or not and returns the result as a boolean array. Syntax :Â numpy.isnan(array [, out]) Parameters :Â array : [array_like]Input array or object whose elements, we need to test for infinity out : [ndarray, optional]Output array placed wit 2 min read numpy.i0() function | Python numpy.i0() function is the modified Bessel function of the first kind, order 0. it's usually denoted by I0. Syntax : numpy.i0(x) Parameters : x : [array_like, dtype float or complex] Argument of the Bessel function. Return : [ndarray, shape = x.shape, dtype = x.dtype] The modified Bessel function ev 1 min read Python | Numpy matrix.conjugate() With the help of Numpy matrix.conjugate() method, we are able to find the conjugate of a given matrix having one or more than one dimension. Syntax : matrix.conjugate()Return : Return conjugate of given matrixExample #1 : In this example we can see that matrix.conjugate() method is used to conjugate 1 min read Like