numpy.frompyfunc() in Python Last Updated : 29 Jun, 2021 Comments Improve Suggest changes Like Article Like Report numpy.frompyfunc(func, nin, nout) function allows to create an arbitrary Python function as Numpy ufunc (universal function). Parameters: func: [A python function object ] An arbitrary python function nin: [int] Number of input arguments to that function. nout: [int] Number of objects returned by that function.Return: A Numpy universal function object. For example, abs_value = numpy.frompyfunc(abs, 1, 1) will create a ufunc that will return the absolute values of array elements. Code #1: Python3 # Python code to demonstrate the # use of numpy.frompyfunc import numpy as np # create an array of numbers a = np.array([34, 67, 89, 15, 33, 27]) # python str function as ufunc string_generator = np.frompyfunc(str, 1, 1) print("Original array-", a) print("After conversion to string-", string_generator(a)) Output: Original array- [34 67 89 15 33 27] After conversion to string- ['34' '67' '89' '15' '33' '27'] Code #2: Python3 # Python code to demonstrate # user-defined function as ufunc import numpy as np # create an array of numbers a = np.array([345, 122, 454, 232, 334, 56, 66]) # user-defined function to check # whether a no. is palindrome or not def fun(x): s = str(x) return s[::-1]== s # 'check_palindrome' as universal function check_palindrome = np.frompyfunc(fun, 1, 1) print("Original array-", a) print("Checking of number as palindrome-", check_palindrome(a)) Output: Original array- [345 122 454 232 334 56 66] Checking of number as palindrome- [False False True True False False True] Note: This custom ufunc created using frompyfunc always accept a ndarray as an input argument and also return a ndarray object as output. Comment More infoAdvertise with us Next Article numpy.frompyfunc() in Python T Tanvi_Garg Follow Improve Article Tags : Python Python-numpy Python numpy-Logic Functions Practice Tags : python Similar Reads numpy.fromiter() function – Python NumPy's fromiter() function is a handy tool for creating a NumPy array from an iterable object. This iterable can be any Python object that provides elements one at a time. The function is especially useful when you need to convert data from a custom data source, like a file or generator, into a Num 2 min read numpy.fromfunction() function – Python numpy.fromfunction() function construct an array by executing a function over each coordinate and the resulting array, therefore, has a value fn(x, y, z) at coordinate (x, y, z). Syntax : numpy.fromfunction(function, shape, dtype) Parameters : function : [callable] The function is called with N para 2 min read numpy.fromstring() function – Python numpy.fromstring() function create a new one-dimensional array initialized from text data in a string. Syntax : numpy.fromstring(string, dtype = float, count = -1, sep = ' ') Parameters : string : [str] A string that contained the data. dtype : [data-type, optional] Data-type of the array. Default d 1 min read numpy.frombuffer() function – Python numpy.frombuffer() function interpret a buffer as a 1-dimensional array. Syntax : numpy.frombuffer(buffer, dtype = float, count = -1, offset = 0) Parameters : buffer : [buffer_like] An object that exposes the buffer interface. dtype : [data-type, optional] Data-type of the returned array, default da 1 min read numpy.matrix.A() function - Python numpy.matrix.A() function return self as an ndarray object. Syntax : numpy.matrix.A() Parameters : None Return : [ndarray] Return self as an ndarray. Code #1 : Python3 # Python program explaining # numpy.matrix.A() function # importing numpy as geek import numpy as geek mat = geek.matrix(geek.arange 1 min read Like