numpy.isposinf() in Python Last Updated : 08 Mar, 2024 Comments Improve Suggest changes Like Article Like Report The numpy.isposinf() function tests element-wise whether it is positive infinity or not and returns the result as a boolean array. Syntax : numpy.isposinf(array, y = None) Parameters: array : [array_like]Input array or object whose elements, we need to test for infinity. y : [array_like]A boolean array with the same shape and type as x to store the result. Return: boolean array containing the result. For scalar input, the result is a new boolean with value True if the input is positive or negative infinity; otherwise the value is False. For array input, the result is a boolean array with the same shape as the input and the values are True where the corresponding element of the input is positive or negative infinity; elsewhere the values are False. Code 1: Python # Python Program illustrating # numpy.isposinf() method import numpy as geek print("Positive : ", geek.isposinf(1), "\n") print("Positive : ", geek.isposinf(0), "\n") # not a number print("Positive : ", geek.isposinf(geek.nan), "\n") # infinity print("Positive : ", geek.isposinf(geek.inf), "\n") print("Positive : ", geek.isposinf(geek.NINF), "\n") x = geek.array([-geek.inf, 0., geek.inf]) y = geek.array([2, 2, 2]) print("Checking for positivity : ", geek.isposinf(x, y)) Output : Positive : False Positive : False Positive : False Positive : True Positive : False Checking for positivity : [0 0 1] Code 2 : Python # Python Program illustrating # numpy.isposinf() method import numpy as geek # Returns True/False value for each element b = geek.arange(18).reshape(3, 6) print("\n",b) print("\nIs Positive Infinity : \n", geek.isposinf(b)) # geek.inf means Infinity # geek.NINF means negative infinity b = [[geek.inf], [geek.NINF]] print("\nIs Positive Infinity : \n", geek.isposinf(b)) Output : [[ 0 1 2 3 4 5] [ 6 7 8 9 10 11] [12 13 14 15 16 17]] Is Positive Infinity : [[False False False False False False] [False False False False False False] [False False False False False False]] Is Positive Infinity : [[ True] [False]] Note : These codes won't run on online IDE's. So please, run them on your systems to explore the working. Comment More infoAdvertise with us Next Article numpy.isposinf() in Python M Mohit Gupta Improve Article Tags : Python Python-numpy Python numpy-Logic Functions Practice Tags : python Similar Reads numpy.isinf() in Python numpy.isinf() test element-wise whether a value is positive or negative infinity. It returns a Boolean array with True where the input is either +inf or -inf and False otherwise. Example:Pythonimport numpy as np a = np.array([1, np.inf, -np.inf, 0, np.nan]) res = np.isinf(a) print(res)Output[False T 2 min read numpy.isneginf() in Python The numpy.isneginf() function tests element-wise whether it is negative infinity or not, and returns the result as a boolean array. Syntax :  numpy.isneginf(array, y = None) Parameters : array : [array_like]Input array or object whose elements, we need to test for infinity. y : [array_like]A boole 2 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 : numpy.isnan(array [, out]) Parameters : 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.isfinite() in Python The numpy.isfinite() function tests element-wise whether it is finite or not(not infinity or not Not a Number) and return the result as a boolean array. Syntax : numpy.isfinite(array [, out]) Parameters : array : [array_like]Input array or object whose elements, we need to test for infinity out : 2 min read numpy.isscalar() in Python In this article, we will elucidate the `numpy.isscalar()` function through a well-documented code example and comprehensive explanation. Python numpy.isscalar() Syntax Syntax : numpy.isscalar(element) Parameters: element: The input element to be checked for scalar properties.Return Type: bool: Retur 3 min read Like