The numpy.logical_or() method is used to calculate truth values between x1 and x2 element-wise. The logical OR returns true, when at least one input is true. In mathematical terms, it is represented with v. Here, we have a truth table for the OR operation between p and q.
numpy.logical_or(x1,x2,/,out=None,*,where=True,casting='same_kind',order='K',dtype=None,subok=True)
x1, x2: These are array-like objects that are used to apply logical OR between input values. If x1 and x2 dimensions are not alike, they are broadcasted to a common shape.out: These are locations in memory used to store results. It can be an Ndarray or tuple of Ndarray or None.where: When a location becomes true, the out array is set to ufunc.casting: Its default value is 'same_kind', meaning object casting will be the same as float32 or float64.**kwargs: These are more argument values as keywords.This method returns a single boolean value when x1 and x2 are boolean values. When x1 & x2 are boolean Ndarray, it returns a broadcasted boolean Ndarray.
In the code snippet, we're going to evaluate logical OR between two scalar values, two boolean arrays, and two logical conditions in lines 4, 6 and 11, respectively.
# import numpy library in programimport numpy as np# invoking logical or on two boolean valuesprint("x1 v x2 between boolean values: ", np.logical_or(1, 0))# invoking logical or on two boolean arraysprint("x1 v x2 between boolean arrays: ", np.logical_or(np.array([True, True, False, False, True]), np.array([True, False, True, False, False])))# creating a numpy array from 0 to 7x = np.arange(7)# print logical_or() resultsprint("x1 v x2 between conditions: ", np.logical_or(x < 1, x > 3))