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# numpy.bitwise_or

`numpy.``bitwise_or`(x1, x2[, out])

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

  When calculating the bit-wise OR between two elements, `x` and `y`, each element is first converted to its binary representation (which works just like the decimal system, only now we’re using 2 instead of 10):

  where `W` is the bit-width of the type (i.e., 8 for a byte or uint8), and each and is either 0 or 1. For example, 13 is represented as `00001101`, which translates to .

  The bit-wise operator is the result of

  where is the OR operator, which yields one whenever either or is 1.

  Parameters: | x1, x2 : array_like Only integer types are handled (including booleans).
  Returns: | out : array_like Result.

  See also

  `bitwise_and`, `bitwise_xor`, `logical_or`

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

  Examples

  We’ve seen that 13 is represented by `00001101`. Similary, 16 is represented by `00010000`. The bit-wise OR of 13 and 16 is therefore `000111011`, or 29:

      >>> np.bitwise_or(13, 16)
      29
      >>> np.binary_repr(29)
      '11101'

      >>> np.bitwise_or(32, 2)
      34
      >>> np.bitwise_or([33, 4], 1)
      array([33,  5])
      >>> np.bitwise_or([33, 4], [1, 2])
      array([33,  6])

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

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