numpy.logical_or#

numpy.logical_or(x1x2/out=None*where=Truecasting='same_kind'order='K'dtype=Nonesubok=True[signature]) = <ufunc 'logical_or'>#

Compute the truth value of x1 OR x2 element-wise.

Parameters:

x1x2array_like: Logical OR is applied to the elements of x1 and x2. If x1.shape != x2.shapethey must be broadcastable to a common shape (which becomes the shape of the output).
outndarrayNoneor tuple of ndarray and Noneoptional: A location into which the result is stored. If providedit must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
wherearray_likeoptional: This condition is broadcast over the input. At locations where the condition is Truethe out array will be set to the ufunc result. Elsewherethe out array will retain its original value. Note that if an uninitialized out array is created via the default out=Nonelocations within it where the condition is False will remain uninitialized.
**kwargs: For other keyword-only argumentssee the ufunc docs.

Returns:

yndarray or bool: Boolean result of the logical OR operation applied to the elements of x1 and x2; the shape is determined by broadcasting. This is a scalar if both x1 and x2 are scalars.

See also

Examples

>>> import numpy as np
>>> np.logical_or(True, False)
True
>>> np.logical_or([True, False], [False, False])
array([ TrueFalse])

>>> x = np.arange(5)
>>> np.logical_or(x < 1, x > 3)
array([ TrueFalseFalseFalse True])

The | operator can be used as a shorthand for np.logical_or on boolean ndarrays.

>>> a = np.array([True, False])
>>> b = np.array([False, False])
>>> a | b
array([ TrueFalse])