numpy.left_shift(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'left_shift'>
Shift the bits of an integer to the left.
Bits are shifted to the left by appending
x2 0s at the right of
x1. Since the internal representation of numbers is in binary format, this operation is equivalent to multiplying
x1 : array_like of integer type
x2 : array_like of integer type
Number of zeros to append to
x1. Has to be non-negative. 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.
For other keyword-only arguments, see the ufunc docs.
out : array of integer type
x1 with bits shifted
x2 times to the left. This is a scalar if both
x2 are scalars.
- Shift the bits of an integer to the right.
- Return the binary representation of the input number as a string.
>>> np.left_shift(5, 2)
>>> np.left_shift(5, [1,2,3])
array([10, 20, 40])