numpy.isreal() in Python Last Updated : 21 Jun, 2025 Comments Improve Suggest changes Like Article Like Report numpy.isreal() tests element-wise whether each value in the input array is a real number (i.e., not complex). It returns a Boolean result as a boolean array. Example: Python import numpy as np a = np.array([1+0j, 2+3j, 5, 4.5, 7j]) res = np.isreal(a) print(res) Output[ True False True True False] Explanation: np.isreal() returns True for real numbers (even if written with +0j) and False for complex numbers with a non-zero imaginary part like 2+3j or 7j.Syntaxnumpy.isreal(x)]Parameters: x is the input array or scalar (can be complex).Returns: A Boolean array of the same shape as x.Returns True for elements that are real.Returns False for elements that have a non-zero imaginary part.ExamplesExample 1: In this example, we use np.isreal() with a purely real-valued array. Python import numpy as np a = np.array([1, 2.5, 0, -3]) res = np.isreal(a) print(res) Output[ True True True True] Explanation: All elements in the array are real numbers, so np.isreal() returns True for each one.Example 2: In this example, we use a 2D array with both real and complex values to check element-wise real status. Python import numpy as np a = np.array([[1+0j, 3+4j], [5, 0-2j]]) res = np.isreal(a) print(res) Output[[ True False] [ True False]] Explanation:1+0j and 5 are real, so they return True.3+4j and 0-2j have imaginary parts, so they return False.Example 3: In this example, we filter out only real values from an array that includes complex numbers. Python import numpy as np a = np.array([2+0j, 3+1j, 4, 5-2j]) res = a[np.isreal(a)] print(res) Output[2.+0.j 4.+0.j] Explanation: We use the Boolean mask returned by np.isreal() to extract only the real values from the array. Complex numbers are excluded. Comment More infoAdvertise with us Next Article numpy.isreal() in Python M Mohit Gupta_OMG Improve Article Tags : Python Python-numpy Python numpy-Logic Functions Practice Tags : python Similar Reads numpy.isrealobj() in Python numpy.isrealobj(array) : This logical function helps to checks if the array has no complex type or array has a complex number. Even if imaginary part is equal to zero, it is not considered to be a Real Object. Parameters : array : [array_like]Input array or object whose elements, we need to test. Re 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.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 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 Like