bitwise_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
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 &
.
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).
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.
This condition is broadcast over the input. At locations where the condition is True, the :None:None:`out`
array will be set to the ufunc result. Elsewhere, the :None:None:`out`
array will retain its original value. Note that if an uninitialized :None:None:`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 <ufuncs.kwargs>
.
Result. This is a scalar if both :None:None:`x1`
and :None:None:`x2`
are scalars.
This docstring was copied from numpy.bitwise_and.
binary_repr
Return the binary representation of the input number as a string.
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) # doctest: +SKIP 1This example is valid syntax, but we were not able to check execution
>>> np.bitwise_and(14, 13) # doctest: +SKIP 12This example is valid syntax, but we were not able to check execution
>>> np.binary_repr(12) # doctest: +SKIP '1100'This example is valid syntax, but we were not able to check execution
>>> np.bitwise_and([14,3], 13) # doctest: +SKIP array([12, 1])This example is valid syntax, but we were not able to check execution
>>> np.bitwise_and([11,7], [4,25]) # doctest: +SKIP array([0, 1])This example is valid syntax, but we were not able to check execution
>>> np.bitwise_and(np.array([2,5,255]), np.array([3,14,16])) # doctest: +SKIP array([ 2, 4, 16])This example is valid syntax, but we were not able to check execution
>>> np.bitwise_and([True, True], [False, True]) # doctest: +SKIP array([False, True])
The &
operator can be used as a shorthand for np.bitwise_and
on ndarrays.
>>> x1 = np.array([2, 5, 255]) # doctest: +SKIPSee :
... x2 = np.array([3, 14, 16]) # doctest: +SKIP
... x1 & x2 # doctest: +SKIP array([ 2, 4, 16])
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.ufunc.bitwise_or
dask.array.ufunc.bitwise_xor
dask.array.ufunc.invert
dask.array.ufunc.logical_and
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