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

Computes the bit-wise OR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator | .

Parameters

x1, x2 : array_like

Only integer and boolean types are handled. 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 :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`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 ufunc docs <ufuncs.kwargs> .

Returns

out : ndarray or scalar

Result. This is a scalar if both x1 and :None:None:`x2` are scalars.

Compute the bit-wise OR of two arrays element-wise.

See Also

binary_repr

Return the binary representation of the input number as a string.

bitwise_and

bitwise_xor

logical_or

Examples

The number 13 has the binary representation 00001101 . Likewise, 16 is represented by 00010000 . The bit-wise OR of 13 and 16 is then 000111011 , or 29:

This example is valid syntax, but we were not able to check execution

>>> np.bitwise_or(13, 16)
29

This example is valid syntax, but we were not able to check execution

>>> np.binary_repr(29)
'11101'

This example is valid syntax, but we were not able to check execution

>>> np.bitwise_or(32, 2)
34

This example is valid syntax, but we were not able to check execution

>>> np.bitwise_or([33, 4], 1)
array([33,  5])

This example is valid syntax, but we were not able to check execution

>>> np.bitwise_or([33, 4], [1, 2])
array([33,  6])

This example is valid syntax, but we were not able to check execution

>>> np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))
array([  6,   5, 255])

This example is valid syntax, but we were not able to check execution

>>> np.array([2, 5, 255]) | np.array([4, 4, 4])
array([  6,   5, 255])

This example is valid syntax, but we were not able to check execution

>>> np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32),
...               np.array([4, 4, 4, 2147483647], dtype=np.int32))
array([         6,          5,        255, 2147483647])

This example is valid syntax, but we were not able to check execution

>>> np.bitwise_or([True, True], [False, True])
array([ True,  True])

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

This example is valid syntax, but we were not able to check execution

>>> x1 = np.array([2, 5, 255])
... x2 = np.array([4, 4, 4])
... x1 | x2
array([  6,   5, 255])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ma.core.bitwise_and numpy.ma.core.logical_or numpy.ma.core.bitwise_xor

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