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Fonction left_shift - module numpy

Signature de la fonction left_shift

Description

left_shift.__doc__

left_shift(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

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 ``2**x2``.

Parameters
----------
x1 : array_like of integer type
    Input values.
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.
**kwargs
    For other keyword-only arguments, see the
    :ref:`ufunc docs <ufuncs.kwargs>`.

Returns
-------
out : array of integer type
    Return `x1` with bits shifted `x2` times to the left.
    This is a scalar if both `x1` and `x2` are scalars.

See Also
--------
right_shift : Shift the bits of an integer to the right.
binary_repr : Return the binary representation of the input number
    as a string.

Examples
--------
>>> 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
:ref:`Casting Rules <ufuncs.casting>`):

>>> 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'>

The ``<<`` operator can be used as a shorthand for ``np.left_shift`` on
ndarrays.

>>> x1 = 5
>>> x2 = np.array([1, 2, 3])
>>> x1 << x2
array([10, 20, 40])