numpy.conj() in Python Last Updated : 11 Jul, 2025 Summarize Comments Improve Suggest changes Share 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 Python | Numpy matrix.conjugate() 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 NumPy Array in Python NumPy (Numerical Python) is a powerful library for numerical computations in Python. It is commonly referred to multidimensional container that holds the same data type. It is the core data structure of the NumPy library and is optimized for numerical and scientific computation in Python. Table of C 2 min read Like