- 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 by
- x1array_like of integer type
- x2array_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).
- outndarray, 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.
- wherearray_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.
- outarray of integer type
Return x1 with bits shifted x2 times to the left. This is a scalar if both x1 and x2 are scalars.
>>> np.binary_repr(5) '101' >>> np.left_shift(5, 2) 20 >>> np.binary_repr(20) '10100'
>>> np.left_shift(5, [1,2,3]) array([10, 20, 40])
Note that the dtype of the second argument may change the dtype of the result and can lead to unexpected results in some cases (see Casting Rules):
>>> a = np.left_shift(np.uint8(255), 1) # Expect 254 >>> print(a, type(a)) # Unexpected result due to upcasting 510 <class 'numpy.int64'> >>> b = np.left_shift(np.uint8(255), np.uint8(1)) >>> print(b, type(b)) 254 <class 'numpy.uint8'>
<<operator can be used as a shorthand for
>>> x1 = 5 >>> x2 = np.array([1, 2, 3]) >>> x1 << x2 array([10, 20, 40])