dask.array.bitwise_and
dask.array.bitwise_and¶
- dask.array.bitwise_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'bitwise_and'>¶
This docstring was copied from numpy.bitwise_and.
Some inconsistencies with the Dask version may exist.
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, x2array_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).- 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.- **kwargs
For other keyword-only arguments, see the ufunc docs.
- Returns
- outndarray 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 by00010001
. The bit-wise AND of 13 and 17 is therefore000000001
, 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 fornp.bitwise_and
on ndarrays.>>> x1 = np.array([2, 5, 255]) >>> x2 = np.array([3, 14, 16]) >>> x1 & x2 array([ 2, 4, 16])