`numpy:bitwise_or`

source: /numpy/__init__.py

Summary

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

Extended Summary

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 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 ufunc docs <ufuncs.kwargs>.

Returns

out : ndarray or scalar: Result. This is a scalar if both x1 and x2 are scalars.

Examples

import numpy as np

✓

The number 13 has the binary representation ``00001101``. Likewise, 16 is represented by ``00010000``. The bit-wise OR of 13 and 16 is then ``00011101``, or 29:

np.bitwise_or(13, 16)

✗

np.binary_repr(29)

✓

np.bitwise_or(32, 2)

✗

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

✓

np.bitwise_or(np.array([2, 5, 255]), np.array([4, 4, 4]))
np.array([2, 5, 255]) | np.array([4, 4, 4])
np.bitwise_or(np.array([2, 5, 255, 2147483647], dtype=np.int32),
              np.array([4, 4, 4, 2147483647], dtype=np.int32))
np.bitwise_or([True, True], [False, True])

✓

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

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

✓

Aliases