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Fonction bitwise_and - module numpy

Signature de la fonction bitwise_and

Description

bitwise_and.__doc__

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

Compute the bit-wise AND of two arrays element-wise.

Computes the bit-wise AND of the underlying binary representation of
the integers in the input arrays. This ufunc implements the C/Python
operator ``&``.

Parameters
----------
x1, x2 : array_like
    Only integer and boolean types are handled.
    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 : ndarray or scalar
    Result.
    This is a scalar if both `x1` and `x2` are scalars.

See Also
--------
logical_and
bitwise_or
bitwise_xor
binary_repr :
    Return the binary representation of the input number as a string.

Examples
--------
The number 13 is represented by ``00001101``.  Likewise, 17 is
represented by ``00010001``.  The bit-wise AND of 13 and 17 is
therefore ``000000001``, or 1:

>>> np.bitwise_and(13, 17)
1

>>> np.bitwise_and(14, 13)
12
>>> np.binary_repr(12)
'1100'
>>> np.bitwise_and([14,3], 13)
array([12,  1])

>>> np.bitwise_and([11,7], [4,25])
array([0, 1])
>>> np.bitwise_and(np.array([2,5,255]), np.array([3,14,16]))
array([ 2,  4, 16])
>>> np.bitwise_and([True, True], [False, True])
array([False,  True])

The ``&`` operator can be used as a shorthand for ``np.bitwise_and`` on
ndarrays.

>>> x1 = np.array([2, 5, 255])
>>> x2 = np.array([3, 14, 16])
>>> x1 & x2
array([ 2,  4, 16])