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# numpy.right_shift

numpy.right_shift(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'right_shift'>

  Shift the bits of an integer to the right.

  Bits are shifted to the right x2. Because the internal representation of numbers is in binary format, this operation is equivalent to dividing x1 by `2**x2`.

  Parameters:
    x1 array_like, int

      Input values.

    x2 array_like, int

      Number of bits to remove at the right of x1. 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.

  Returns:
    out ndarray, int

      Return x1 with bits shifted x2 times to the right. This is a scalar if both x1 and x2 are scalars.

  See also

  `left_shift`

    Shift the bits of an integer to the left.

  `binary_repr`

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

  Examples

      >>> import numpy as np
      >>> np.binary_repr(10)
      '1010'
      >>> np.right_shift(10, 1)
      5
      >>> np.binary_repr(5)
      '101'

      >>> np.right_shift(10, [1,2,3])
      array([5, 2, 1])

  The `>>` operator can be used as a shorthand for `np.right_shift` on ndarrays.

      >>> x1 = 10
      >>> x2 = np.array([1,2,3])
      >>> x1 >> x2
      array([5, 2, 1])