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Fonction logical_or - module numpy

Signature de la fonction logical_or

Description

logical_or.__doc__

logical_or(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

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

Parameters
----------
x1, x2 : array_like
    Logical OR is applied to the elements of `x1` and `x2`.
    If ``x1.shape != x2.shape``, they must be broadcastable to a common
    shape (which becomes the shape of the output).
out : ndarray, None, or tuple of ndarray and None, optional
    A location into which the result is stored. If provided, it 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.
where : array_like, optional
    This condition is broadcast over the input. At locations where the
    condition is True, the `out` array will be set to the ufunc result.
    Elsewhere, the `out` array will retain its original value.
    Note that if an uninitialized `out` array is created via the default
    ``out=None``, locations within it where the condition is False will
    remain uninitialized.
**kwargs
    For other keyword-only arguments, see the
    :ref:`ufunc docs <ufuncs.kwargs>`.

Returns
-------
y : ndarray 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
--------
logical_and, logical_not, logical_xor
bitwise_or

Examples
--------
>>> np.logical_or(True, False)
True
>>> np.logical_or([True, False], [False, False])
array([ True, False])

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